MSRAction3D_Dataset_Reading_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MSRAction3D\n",
    "\n",
    "* 567 actions\n",
    "* 10  subjects\n",
    "* 20  3D joints\n",
    "* 20  action class\n",
    "\n",
    "\n",
    "1. high arm wave\n",
    "2. horizontal arm wave\n",
    "3. hammer\n",
    "4. hand catch\n",
    "5. forward punch\n",
    "6. high throw\n",
    "7. draw x\n",
    "8. draw tick\n",
    "9. draw circle\n",
    "10. hand clap\n",
    "11. two hand wave\n",
    "12. side-boxing\n",
    "13. bend\n",
    "14. forward kick\n",
    "15. side kick\n",
    "16. jogging\n",
    "17. tennis swing\n",
    "18. tennis serve\n",
    "19. golf swing\n",
    "20. pick up & throw\n",
    "\n",
    "https://www.uow.edu.au/~wanqing/#Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import join\n",
    "import numpy as np\n",
    "import bunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def full_fname2_str(data_dir, fname, sep_char):\n",
    "    fnametostr = ''.join(fname).replace(data_dir, '')\n",
    "    ind = int(fnametostr.index(sep_char))\n",
    "    label = int(fnametostr[ind + 1:ind + 3])\n",
    "    return label\n",
    "\n",
    "def frame_normalizer(frame, frame_size):\n",
    "    assert frame.shape[0] == frame_size\n",
    "    frame = frame.reshape(frame_size // 3, 3)\n",
    "    spine_mid = frame[1]\n",
    "    new_frame = []\n",
    "    j = 0\n",
    "    for joint in frame:\n",
    "        new_frame.append(joint - spine_mid)\n",
    "        j += 1\n",
    "    new_frame = np.asarray(new_frame)\n",
    "    return (list(new_frame.flatten()))\n",
    "\n",
    "\n",
    "def read(data_dir):\n",
    "    print('Loading MSR 3D Data, data directory %s' % data_dir)\n",
    "    data, labels, lens, subjects = [], [], [], []\n",
    "    filenames = []\n",
    "    documents = [join(data_dir, d)\n",
    "                 for d in sorted(listdir(data_dir))]\n",
    "    filenames.extend(documents)\n",
    "    filenames = np.array(filenames)\n",
    "\n",
    "    for file in filenames:\n",
    "        #print(\"file\",file)\n",
    "        action = np.loadtxt(file)[:, :3].flatten()\n",
    "        #print(\"action\",action)\n",
    "\n",
    "        labels.append(full_fname2_str(data_dir, file, 'a'))\n",
    "        frame_size = len(action) // 60  # 20 iskeleton num x,y,z 3D points\n",
    "        lens.append(frame_size)\n",
    "        action = np.asarray(action).reshape(frame_size, 60)\n",
    "        #print(\"action shape\",action.shape)\n",
    "        \n",
    "        new_act = []\n",
    "        for frame in action:\n",
    "            new_act.append(frame)\n",
    "\n",
    "        data.append(new_act)\n",
    "        subjects.append(full_fname2_str(data_dir, file, 's'))\n",
    "        \n",
    "    data = np.asarray(data)\n",
    "    labels = np.asarray(labels) -1\n",
    "    lens = np.asarray(lens)\n",
    "    \n",
    "    subjects = np.asarray(subjects)\n",
    "    print(\"All files read!\")\n",
    "    print('initial shapes [data label len]: %s %s %s' % (data.shape, labels.shape, lens.shape))\n",
    "    return data,labels,lens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading MSR 3D Data, data directory dataset/MSRAction3D/MSRAction3DSkeleton(20joints)/\n",
      "All files read!\n",
      "initial shapes [data label len]: (567,) (567,) (567,)\n"
     ]
    }
   ],
   "source": [
    "MSR_data_dir = 'dataset/MSRAction3D/MSRAction3DSkeleton(20joints)/'\n",
    "\n",
    "dataset = read(MSR_data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data,labels,lens = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 60)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(data[0]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flatten Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480\n",
      "('start_a', 450)\n",
      "('end_a', 480)\n"
     ]
    }
   ],
   "source": [
    "action = 16 #jogging\n",
    "\n",
    "subjects_num = 10 #9\n",
    "move_num = 3\n",
    "\n",
    "action_total = action*subjects_num*move_num\n",
    "print(action_total)\n",
    "\n",
    "start_a = (action-1)*subjects_num*move_num\n",
    "end_a = start_a + (subjects_num*move_num)\n",
    "\n",
    "print(\"start_a\",start_a)\n",
    "print(\"end_a\",end_a)\n",
    "\n",
    "action_data = data[start_a : end_a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(frame,20joints*3)\n",
      "(50, 60)\n",
      "(48, 60)\n",
      "(37, 60)\n",
      "(32, 60)\n",
      "(34, 60)\n",
      "(46, 60)\n",
      "(42, 60)\n",
      "(45, 60)\n",
      "(50, 60)\n",
      "(26, 60)\n",
      "(33, 60)\n",
      "(30, 60)\n",
      "(47, 60)\n",
      "(38, 60)\n",
      "(42, 60)\n",
      "(45, 60)\n",
      "(45, 60)\n",
      "(45, 60)\n",
      "(44, 60)\n",
      "(44, 60)\n",
      "(46, 60)\n",
      "(36, 60)\n",
      "(37, 60)\n",
      "(38, 60)\n",
      "(31, 60)\n",
      "(35, 60)\n",
      "(30, 60)\n",
      "(43, 60)\n",
      "(42, 60)\n",
      "(51, 60)\n",
      "('total_frames_in_action', 1212)\n"
     ]
    }
   ],
   "source": [
    "total_frames_in_action = 0\n",
    "\n",
    "print(\"(frame,20joints*3)\")\n",
    "for a in action_data:\n",
    "    selected_data = np.asarray(a)\n",
    "    print(selected_data.shape)\n",
    "    \n",
    "    total_frames_in_action+=selected_data.shape[0]\n",
    "    \n",
    "print(\"total_frames_in_action\",total_frames_in_action)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Action 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mpl_toolkits.mplot3d.axes3d as p3\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from IPython.display import HTML\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadData(action, subject, instance):\n",
    "    ins = np.loadtxt(basePath + 'a%02i_s%02i_e%02i_skeleton.txt'%(action, subject, instance))\n",
    "    print(\"ins.shape[0]\",ins.shape[0])\n",
    "    ins = ins.reshape((ins.shape[0]//20, 20, 4))\n",
    "    return ins\n",
    "\n",
    "def animate(i):\n",
    "    graph._offsets3d = (data[i,:,0], data[i,:,2], data[i,:,1])\n",
    "    return graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ins.shape[0]', 860)\n",
      "(43, 20, 4)\n"
     ]
    }
   ],
   "source": [
    "basePath = 'dataset/MSRAction3D/MSRAction3DSkeleton(20joints)/'\n",
    "\n",
    "action = 18 #tennis serve\n",
    "subject = 1\n",
    "instance = 1\n",
    "\n",
    "data = loadData(action, subject, instance)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = p3.Axes3D(fig)\n",
    "\n",
    "# Setting the axes properties\n",
    "ax.set_xlim3d([0.0, 300.0])\n",
    "ax.set_xlabel('X')\n",
    "\n",
    "ax.set_ylim3d([0.0, 1400.0])\n",
    "ax.set_ylabel('Z')\n",
    "\n",
    "ax.set_zlim3d([300.0, 0.0])\n",
    "ax.set_zlabel('Y')\n",
    "\n",
    "graph = ax.scatter([], [], [])\n",
    "\n",
    "\n",
    "anim = FuncAnimation(fig, animate, frames=data.shape[0], interval=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save an animation as a video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "anim.save('action_a{}s{}i{}_animation.mp4'.format(action, subject, instance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}