Eval_2D_triangulation_and_3D_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example triangulation and 2D/3D evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook serves as an example to demonstrate: (1) single-sample evaluation of CNN 2D predictions, and (2) how to triangulate from 2 cameras to 3D space for 3D evaluation. It requires the generated .h5 files (accumulated frames and labels) from this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras.backend as K\n",
    "from keras.models import load_model\n",
    "import os\n",
    "from os.path import join\n",
    "import h5py\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change directories and parameters in the next cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# home directory of the repository\n",
    "homedir = ''\n",
    "# Folder where .h5 files are generated, 346x260/\n",
    "datadir = ''\n",
    "\n",
    "# Selected recording\n",
    "subj, sess, mov = 16, 2, 1\n",
    "# Which image of the selected recording to use\n",
    "imgidx = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_mat_dir = join(homedir, 'P_matrices/')\n",
    "\n",
    "# constant parameters\n",
    "H = 260; W = 344; num_joints = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decay_heatmap(heatmap, sigma2=4):\n",
    "    heatmap = cv2.GaussianBlur(heatmap,(0,0),sigma2)\n",
    "    heatmap /= np.max(heatmap) # keep the max to 1\n",
    "    return heatmap\n",
    "\n",
    "def mse2D(y_true, y_pred):\n",
    "    mean_over_ch = K.mean(K.square(y_pred - y_true), axis=-1)\n",
    "    mean_over_w = K.mean(mean_over_ch, axis=-1)\n",
    "    mean_over_h = K.mean(mean_over_w, axis=-1)\n",
    "    return mean_over_h\n",
    "\n",
    "def get_2Dcoords_and_heatmaps_label(vicon_xyz, ch_idx):\n",
    "    \" From 3D label, get 2D label coordinates and heatmaps for selected camera \"\n",
    "    if ch_idx==1: P_mat_cam = np.load(join(P_mat_dir,'P1.npy'))\n",
    "    elif ch_idx==3: P_mat_cam = np.load(join(P_mat_dir,'P2.npy'))\n",
    "    elif ch_idx==2: P_mat_cam = np.load(join(P_mat_dir,'P3.npy'))\n",
    "    elif ch_idx==0: P_mat_cam = np.load(join(P_mat_dir,'P4.npy'))\n",
    "\n",
    "    # use homogeneous coordinates representation to project 3d XYZ coordinates to 2d UV pixel coordinates.\n",
    "    vicon_xyz_homog = np.concatenate([vicon_xyz, np.ones([1,13])], axis=0)\n",
    "    coord_pix_homog = np.matmul(P_mat_cam, vicon_xyz_homog)\n",
    "    coord_pix_homog_norm = coord_pix_homog / coord_pix_homog[-1]\n",
    "    u = coord_pix_homog_norm[0]\n",
    "    v = H - coord_pix_homog_norm[1] # flip v coordinate to match the image direction\n",
    "\n",
    "    # mask is used to make sure that pixel positions are in frame range.\n",
    "    mask = np.ones(u.shape).astype(np.float32)\n",
    "    mask[np.isnan(u)] = 0; mask[np.isnan(v)] = 0\n",
    "    mask[u>W] = 0; mask[u<=0] = 0; mask[v>H] = 0; mask[v<=0] = 0\n",
    "\n",
    "    # pixel coordinates\n",
    "    u = u.astype(np.int32)\n",
    "    v = v.astype(np.int32)\n",
    "    \n",
    "    # initialize, fill and smooth the heatmaps\n",
    "    label_heatmaps = np.zeros((H, W, num_joints))\n",
    "    for fmidx, zipd in enumerate(zip(v, u, mask)):\n",
    "        if zipd[2]==1: # write joint position only when projection within frame boundaries\n",
    "            label_heatmaps[zipd[0], zipd[1], fmidx] = 1\n",
    "            label_heatmaps[:,:,fmidx] = decay_heatmap(label_heatmaps[:,:,fmidx])\n",
    "    \n",
    "    return np.stack((v,u), axis=-1), mask, label_heatmaps\n",
    "\n",
    "def predict_CNN_extract_skeleton_2d(ch_idx, imgidx, verbose=False):\n",
    "    \"\"\" Predict with CNN, extract predicted 2D coordinates, \n",
    "    and return image, 2D label and 2D prediction for imgidx sample \"\"\"\n",
    "    # Cropping rightmost pixels\n",
    "    x_selected = x_h5['DVS'][imgidx:imgidx+1,:,:344, ch_idx]\n",
    "    pred = trained_model.predict(np.expand_dims(x_selected, axis=-1))\n",
    "    y_2d, gt_mask, y_heatmaps = get_2Dcoords_and_heatmaps_label(y_h5['XYZ'][imgidx], ch_idx)\n",
    "    p_coords_max = np.zeros(y_2d.shape)\n",
    "    confidence = np.zeros(y_2d.shape[0]) # confidence is not used in this example.\n",
    "    \n",
    "    for j_idx in range(y_2d.shape[0]):\n",
    "        pred_j_map = pred[0,:,:,j_idx]\n",
    "        # Predicted max value for each heatmap. Keep only the first one if more are present.\n",
    "        p_coords_max_tmp = np.argwhere(pred_j_map == np.max(pred_j_map))\n",
    "        p_coords_max[j_idx] = p_coords_max_tmp[0]\n",
    "        # Confidence of the joint\n",
    "        confidence[j_idx] = np.max(pred_j_map)\n",
    "        if verbose:\n",
    "            print('j{} GT: {} -- PRED: {} (mask: {})'.format(j_idx, y_2d[j_idx], p_coords_max[j_idx], gt_mask[j_idx]))\n",
    "    y_2d_float = y_2d.astype(np.float)\n",
    "    # where mask is 0, set gt back to NaN\n",
    "    y_2d_float[gt_mask==0] = np.nan\n",
    "    dist_2d = np.linalg.norm((y_2d_float - p_coords_max), axis=-1)\n",
    "    mpjpe = np.nanmean(dist_2d)\n",
    "    print('sample mpjpe: {}'.format(mpjpe))\n",
    "    return x_selected[0], y_2d_float, p_coords_max\n",
    "\n",
    "def plot_2d(dvs_frame, sample_gt, sample_pred):\n",
    "    \" To plot image and 2D ground truth and prediction \"\n",
    "    plt.figure()\n",
    "    plt.imshow(dvs_frame, cmap='gray')\n",
    "    plt.plot(sample_gt[:,1], sample_gt[:,0], '.', c='red', label='gt')\n",
    "    plt.plot(sample_pred[:,1], sample_pred[:,0], '.', c='blue', label='pred')\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "# For 3D triangulation and plot:\n",
    "\n",
    "def project_uv_xyz_cam(uv, M):\n",
    "    # adapted from: https://www.cc.gatech.edu/~hays/compvision/proj3/\n",
    "    N = len(uv)\n",
    "    uv_homog = np.hstack((uv, np.ones((N, 1))))\n",
    "    M_inv= np.linalg.pinv(M)\n",
    "    xyz = np.dot(M_inv, uv_homog.T).T\n",
    "    x = xyz[:, 0] / xyz[:, 3]; y = xyz[:, 1] / xyz[:, 3]; z = xyz[:, 2] / xyz[:, 3]\n",
    "    return x,y,z\n",
    "\n",
    "def find_intersection(P0,P1):\n",
    "    # from: https://stackoverflow.com/questions/52088966/nearest-intersection-point-to-many-lines-in-python/52089867\n",
    "    \"\"\"P0 and P1 are NxD arrays defining N lines.\n",
    "    D is the dimension of the space. This function \n",
    "    returns the least squares intersection of the N\n",
    "    lines from the system given by eq. 13 in \n",
    "    http://cal.cs.illinois.edu/~johannes/research/LS_line_intersect.pdf.\"\"\"\n",
    "\n",
    "    # generate all line direction vectors \n",
    "    n = (P1-P0) / np.linalg.norm(P1-P0,axis=1)[:,np.newaxis] # normalized\n",
    "    # generate the array of all projectors \n",
    "    projs = np.eye(n.shape[1]) - n[:,:,np.newaxis]*n[:,np.newaxis]  # I - n*n.T\n",
    "    # see fig. 1 \n",
    "\n",
    "    # generate R matrix and q vector\n",
    "    R = projs.sum(axis=0)\n",
    "    q = (np.matmul(projs,P0[:,:,np.newaxis])).sum(axis=0)\n",
    "    # solve the least squares problem for the \n",
    "    # intersection point p: Rp = q\n",
    "    p = np.linalg.lstsq(R,q, rcond=None)[0]\n",
    "    return p.T\n",
    "\n",
    "def skeleton(x,y,z):\n",
    "    \" Definition of skeleton edges \"\n",
    "    # rename joints to identify name and axis\n",
    "    x_head, x_shoulderR, x_shoulderL, x_elbowR, x_elbowL, x_hipR, x_hipL, \\\n",
    "    x_handR, x_handL, x_kneeR, x_kneeL, x_footR, x_footL = x[0], x[1], x[2], x[3], \\\n",
    "    x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12]\n",
    "    y_head, y_shoulderR, y_shoulderL, y_elbowR, y_elbowL, y_hipR, y_hipL, \\\n",
    "    y_handR, y_handL, y_kneeR, y_kneeL, y_footR, y_footL = y[0], y[1], y[2], y[3], \\\n",
    "    y[4], y[5], y[6], y[7], y[8], y[9], y[10], y[11], y[12]\n",
    "    z_head, z_shoulderR, z_shoulderL, z_elbowR, z_elbowL, z_hipR, z_hipL, \\\n",
    "    z_handR, z_handL, z_kneeR, z_kneeL, z_footR, z_footL = z[0], z[1], z[2], z[3],\\\n",
    "    z[4], z[5], z[6], z[7], z[8], z[9], z[10], z[11], z[12]\n",
    "    #definition of the lines of the skeleton graph\n",
    "    skeleton=np.zeros((14,3,2))\n",
    "    skeleton[0,:,:]=[[x_head,x_shoulderR],[y_head,y_shoulderR],[z_head,z_shoulderR]]\n",
    "    skeleton[1,:,:]=[[x_head,x_shoulderL],[y_head,y_shoulderL],[z_head,z_shoulderL]]\n",
    "    skeleton[2,:,:]=[[x_elbowR,x_shoulderR],[y_elbowR,y_shoulderR],[z_elbowR,z_shoulderR]]\n",
    "    skeleton[3,:,:]=[[x_elbowL,x_shoulderL],[y_elbowL,y_shoulderL],[z_elbowL,z_shoulderL]]\n",
    "    skeleton[4,:,:]=[[x_elbowR,x_handR],[y_elbowR,y_handR],[z_elbowR,z_handR]]\n",
    "    skeleton[5,:,:]=[[x_elbowL,x_handL],[y_elbowL,y_handL],[z_elbowL,z_handL]]\n",
    "    skeleton[6,:,:]=[[x_hipR,x_shoulderR],[y_hipR,y_shoulderR],[z_hipR,z_shoulderR]]\n",
    "    skeleton[7,:,:]=[[x_hipL,x_shoulderL],[y_hipL,y_shoulderL],[z_hipL,z_shoulderL]]\n",
    "    skeleton[8,:,:]=[[x_hipR,x_kneeR],[y_hipR,y_kneeR],[z_hipR,z_kneeR]]\n",
    "    skeleton[9,:,:]=[[x_hipL,x_kneeL],[y_hipL,y_kneeL],[z_hipL,z_kneeL]]\n",
    "    skeleton[10,:,:]=[[x_footR,x_kneeR],[y_footR,y_kneeR],[z_footR,z_kneeR]]\n",
    "    skeleton[11,:,:]=[[x_footL,x_kneeL],[y_footL,y_kneeL],[z_footL,z_kneeL]]\n",
    "    skeleton[12,:,:]=[[x_shoulderR,x_shoulderL],[y_shoulderR,y_shoulderL],[z_shoulderR,z_shoulderL]]\n",
    "    skeleton[13,:,:]=[[x_hipR,x_hipL],[y_hipR,y_hipL],[z_hipR,z_hipL]]\n",
    "    return skeleton\n",
    "\n",
    "def plotSingle3Dframe(ax, y_true_pred, c='red', limits=[[-500,500],[-500,500],[0,1500]], plot_lines=True):\n",
    "    \" 3D plot of single frame. Can be both label or prediction \"\n",
    "    x = y_true_pred[:, 0]; y = y_true_pred[:, 1]; z = y_true_pred[:, 2]\n",
    "    ax.scatter(x, y, z, zdir='z', s=20, c=c, marker='o', depthshade=True)\n",
    "    #plot skeleton\n",
    "    lines_skeleton=skeleton(x,y,z)\n",
    "    if plot_lines:\n",
    "        for l in range(len(lines_skeleton)):\n",
    "            ax.plot(lines_skeleton[l,0,:],lines_skeleton[l,1,:],lines_skeleton[l,2,:], c)\n",
    "  \n",
    "    ax.set_xlabel('X Label'); ax.set_ylabel('Y Label'); ax.set_zlabel('Z Label'); ax.set_aspect('equal')\n",
    "    #set same scale for all the axis\n",
    "    x_limits=limits[0]; y_limits=limits[1]; z_limits=limits[2]\n",
    "    x_range = np.abs(x_limits[1] - x_limits[0]); x_middle = np.mean(x_limits)\n",
    "    y_range = np.abs(y_limits[1] - y_limits[0]); y_middle = np.mean(y_limits)\n",
    "    z_range = np.abs(z_limits[1] - z_limits[0]); z_middle = np.mean(z_limits)\n",
    "    # The plot bounding box is a sphere in the sense of the infinity\n",
    "    # norm, hence I call half the max range the plot radius.\n",
    "    plot_radius = 0.5*np.max([x_range, y_range, z_range])\n",
    "    ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])\n",
    "    ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])\n",
    "    ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load camera matrices, camera centers, input and label files, and import CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_mat_cam1 = np.load(join(P_mat_dir,'P1.npy'))\n",
    "P_mat_cam2 = np.load(join(P_mat_dir,'P2.npy'))\n",
    "P_mat_cam3 = np.load(join(P_mat_dir,'P3.npy'))\n",
    "P_mat_cam4 = np.load(join(P_mat_dir,'P4.npy'))\n",
    "P_mats = [P_mat_cam1, P_mat_cam2, P_mat_cam3, P_mat_cam4]\n",
    "cameras_pos = np.load(join(P_mat_dir,'camera_positions.npy'))\n",
    "\n",
    "# Load input and 3D label .h5 files\n",
    "datafile = 'S{}_session{}_mov{}_7500events'.format(subj, sess, mov)\n",
    "path_x = join(datadir,datafile+'.h5')\n",
    "path_y = join(datadir,datafile+'_label.h5')\n",
    "x_h5 = h5py.File(path_x, 'r') \n",
    "y_h5 = h5py.File(path_y, 'r')\n",
    "\n",
    "# Import model\n",
    "modeldir=join(homedir,'DHP19/')\n",
    "trained_model=load_model(join(modeldir,'DHP_CNN.model'), custom_objects={'mse2D': mse2D})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate 2D prediction, calculate 2D MPJPE, and plot 2D results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "j0 GT: [ 45 124] -- PRED: [ 32. 132.] (mask: 1.0)\n",
      "j1 GT: [ 72 129] -- PRED: [ 68. 131.] (mask: 1.0)\n",
      "j2 GT: [ 70 158] -- PRED: [ 68. 161.] (mask: 1.0)\n",
      "j3 GT: [101 142] -- PRED: [100. 142.] (mask: 1.0)\n",
      "j4 GT: [111 169] -- PRED: [106. 171.] (mask: 1.0)\n",
      "j5 GT: [126 130] -- PRED: [118. 135.] (mask: 1.0)\n",
      "j6 GT: [129 151] -- PRED: [127. 157.] (mask: 1.0)\n",
      "j7 GT: [134 135] -- PRED: [126. 130.] (mask: 1.0)\n",
      "j8 GT: [144 156] -- PRED: [147. 159.] (mask: 1.0)\n",
      "j9 GT: [172 113] -- PRED: [168. 117.] (mask: 1.0)\n",
      "j10 GT: [178 149] -- PRED: [175. 154.] (mask: 1.0)\n",
      "j11 GT: [216 129] -- PRED: [211. 134.] (mask: 1.0)\n",
      "j12 GT: [231 144] -- PRED: [223. 153.] (mask: 1.0)\n",
      "sample mpjpe: 6.9048320282\n",
      "j0 GT: [ 36 197] -- PRED: [ 24. 186.] (mask: 1.0)\n",
      "j1 GT: [ 71 163] -- PRED: [ 67. 160.] (mask: 1.0)\n",
      "j2 GT: [ 58 193] -- PRED: [ 54. 189.] (mask: 1.0)\n",
      "j3 GT: [101 155] -- PRED: [105. 151.] (mask: 1.0)\n",
      "j4 GT: [ 92 203] -- PRED: [ 88. 199.] (mask: 1.0)\n",
      "j5 GT: [123 183] -- PRED: [118. 179.] (mask: 1.0)\n",
      "j6 GT: [114 199] -- PRED: [109. 196.] (mask: 1.0)\n",
      "j7 GT: [138 158] -- PRED: [136. 156.] (mask: 1.0)\n",
      "j8 GT: [117 224] -- PRED: [112. 220.] (mask: 1.0)\n",
      "j9 GT: [171 198] -- PRED: [168. 195.] (mask: 1.0)\n",
      "j10 GT: [163 194] -- PRED: [163. 195.] (mask: 1.0)\n",
      "j11 GT: [214 187] -- PRED: [210. 184.] (mask: 1.0)\n",
      "j12 GT: [211 204] -- PRED: [206. 202.] (mask: 1.0)\n",
      "sample mpjpe: 5.79560125641\n"
     ]
    }
   ],
   "source": [
    "# gt_2d_cam  : used only to plot in 2D.\n",
    "# pred_2d_cam: used for 2D and 3D.\n",
    "img_cam2, gt_2d_cam2, pred_2d_cam2 = predict_CNN_extract_skeleton_2d(3, imgidx, verbose=True)\n",
    "img_cam3, gt_2d_cam3, pred_2d_cam3 = predict_CNN_extract_skeleton_2d(2, imgidx, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_2d(img_cam2, gt_2d_cam2, pred_2d_cam2)\n",
    "plot_2d(img_cam3, gt_2d_cam3, pred_2d_cam3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From here: triangulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# centers of the 2 used cameras\n",
    "Point0 = (np.stack((cameras_pos[1],cameras_pos[2])))\n",
    "# 3D label\n",
    "label_3d = y_h5['XYZ'][imgidx].T\n",
    "# initialize empty sample of 3D prediction\n",
    "pred_3d = np.zeros(label_3d.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adapt the 2D prediction to match the same format of 3D label after back projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_2d_cam2_ = np.zeros(pred_2d_cam2.shape)\n",
    "pred_2d_cam3_ = np.zeros(pred_2d_cam3.shape)\n",
    "\n",
    "pred_2d_cam2_[:,0] = pred_2d_cam2[:,1]\n",
    "pred_2d_cam2_[:,1] = H - pred_2d_cam2[:,0]\n",
    "\n",
    "pred_2d_cam3_[:,0] = pred_2d_cam3[:,1]\n",
    "pred_2d_cam3_[:,1] = H - pred_2d_cam3[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back project each 2D point to 3D space, and find intersection of rays using least squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_cam2_pred, y_cam2_pred, z_cam2_pred = project_uv_xyz_cam(pred_2d_cam2_, P_mat_cam2)\n",
    "x_cam3_pred, y_cam3_pred, z_cam3_pred = project_uv_xyz_cam(pred_2d_cam3_, P_mat_cam3)\n",
    "\n",
    "xyz_cam2 = np.stack((x_cam2_pred, y_cam2_pred, z_cam2_pred), axis=1)\n",
    "xyz_cam3 = np.stack((x_cam3_pred, y_cam3_pred, z_cam3_pred), axis=1)\n",
    "\n",
    "for joint_idx in range(13):\n",
    "    # coordinates for both cameras of 2nd point of triangulation line.\n",
    "    Point1 = np.stack((xyz_cam2[joint_idx,:], xyz_cam3[joint_idx,:]), axis=1).T\n",
    "    intersection = find_intersection(Point0, Point1)\n",
    "    pred_3d[joint_idx] = intersection[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot and calculate 3D MPJPE (in mm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample 3D mpjpe [mm]: 66.3753692654\n"
     ]
    }
   ],
   "source": [
    "fs=5\n",
    "fig = plt.figure(figsize=(fs,fs))\n",
    "ax = Axes3D(fig)\n",
    "plotSingle3Dframe(ax, label_3d, c='red') # gt\n",
    "plotSingle3Dframe(ax, pred_3d, c='blue') # pred\n",
    "\n",
    "mpjpe_3d_joints = np.linalg.norm((label_3d - pred_3d), axis=-1)\n",
    "mpjpe_3d_sample = np.mean(mpjpe_3d_joints)\n",
    "print('sample 3D mpjpe [mm]: {}'.format(mpjpe_3d_sample))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "checkdhp",
   "language": "python",
   "name": "checkdhp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}