Neuroimaging in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import standard modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import NiPy modules\n",
    "\n",
    "If you've installed python via miniconda, most packages are available by running `conda install package` in the terminal. In general, if a package is available through conda that's the easiest way to install it. Sometimes packages aren't available through conda, in which case you can use the python package installer, `pip`. If you're running this tutorial on `syzygy.ca`, conda is not installed so all packages are installed through pip. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!pip install --user nipy nibabel dipy nilearn nipype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nipy\n",
    "import nibabel as nib\n",
    "import dipy\n",
    "import nilearn\n",
    "import nipype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load up some sample data\n",
    "\n",
    "First, we'll load up the MNI T1 template as \"t1img\". `nib.load()` returns an image object. The exact structure of the image object will depend on what type of file you load. Mostly you're\n",
    "\n",
    "Read more about nibabel image objects here: http://nipy.org/nibabel/nibabel_images.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img = nib.load('./MNI152_T1_2mm.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nibabel.nifti1.Nifti1Image"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t1img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "        this.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 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",
       "    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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<OrthoSlicer3D: ./MNI152_T1_2mm.nii.gz (91, 109, 91)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1img.orthoview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the image object is fast because it doesn't load the image data automatically. This lets you quickly access header information if that's all you want. To load the image data as a numpy array just call `get_data()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1data = t1img.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t1data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91, 109, 91)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -2.,    0.,    0.,   90.],\n",
       "       [   0.,    2.,    0., -126.],\n",
       "       [   0.,    0.,    2.,  -72.],\n",
       "       [   0.,    0.,    0.,    1.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1img.affine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing a slice is easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "        this.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 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",
       "    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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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": {
      "text/plain": [
       "(-0.5, 90.5, 108.5, -0.5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "f,ax = plt.subplots()\n",
    "sl=40\n",
    "t1slice = np.rot90(t1data[:,:,sl])\n",
    "t1im = ax.matshow(t1slice,cmap='gray')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maskimg = nib.load('./JHU-ICBM-labels-2mm.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maskdata = maskimg.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91, 109, 91)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maskdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "        this.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 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",
       "    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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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": [
    "f,ax = plt.subplots()\n",
    "sl=40\n",
    "t1slice = np.rot90(t1data[:,:,sl])\n",
    "t1im = ax.matshow(t1slice,cmap='gray')\n",
    "ax.axis('off')\n",
    "\n",
    "\n",
    "maskdata = np.ma.masked_equal(maskdata,0)\n",
    "maskslice = np.rot90(maskdata[:,:,sl])\n",
    "maskim = ax.matshow(maskslice,cmap='cool')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: Choose colormaps carefully! \n",
    "\n",
    "https://matplotlib.org/users/colormaps.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### t1data and maskdata are both just numpy 3D arrays, so we perform normal array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maskdata.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maskdata.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6669.9731296101163"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1data[maskdata==1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise (optional): Parse an atlas xml file\n",
    "\n",
    "* Parse the xml file using xml.etree.ElementTree\n",
    "* Determine how to access the index and text of each element\n",
    "* Loop over all the elements and place the data into a dictionary, with the label text as key and the index as value\n",
    "* Plot an ROI by calling it by it's string, ie 'Body of corpus callosum'\n",
    "* Evaluate the mean T1 in that ROI, again calling it by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xml.etree.ElementTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e = xml.etree.ElementTree.parse('JHU-labels.xml').getroot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = e.find('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = data.findall('label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = labels[4]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.attrib['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labeldict = {}\n",
    "for label in labels:\n",
    "    labeldict[label.text] = int(label.attrib['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeldict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax = plt.subplots()\n",
    "sl=45\n",
    "t1slice = np.rot90(t1data[:,:,sl])\n",
    "t1im = ax.matshow(t1slice,cmap='gray')\n",
    "ax.axis('off')\n",
    "\n",
    "# Set mask equal to zero except for desired label\n",
    "maskdata2 = maskdata.copy()\n",
    "maskdata2[maskdata2 != labeldict['Body of corpus callosum']] = 0\n",
    "# Make mask a numpy mask so zeros aren't plotted\n",
    "maskdata2 = np.ma.masked_equal(maskdata2,0)\n",
    "# select a slice\n",
    "maskslice = np.rot90(maskdata2[:,:,sl])\n",
    "# Overlay the mask on the image\n",
    "maskim = ax.matshow(maskslice,cmap='cool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving modified images as Nifti files\n",
    "Let's make a copy of t1data and zero out voxels where maskdata==48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1data2 = t1data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1data2[maskdata==48] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the modified image\n",
    "\n",
    "Create a new nifti image object with `nib.Nifti1Image()`. It needs a image array and an affine matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1img2 = nib.Nifti1Image(t1data2,t1img.affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1img2.to_filename('t1img2.nii.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nibabel has tools for opening multiple formats including PAR/REC\n",
    "Loading a PAR/REC image with nibabel and then saving to nifti is, in my experience, more robust than dcm2nii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nib.parrec?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nib.load?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise (optional): Make a  pandas dataframe with the mean values for each ROI\n",
    "* Calculate the mean of each roi in the JHU atlas. Put in in a list or a dict\n",
    "* Create a new `pd.DataFrame()` with the list/dict as the data, the mask labels as the index, and a relevant string as the column header "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means = []\n",
    "for i in range(maskdata.max()):\n",
    "    mean = t1data[maskdata==i].mean()\n",
    "    means.append(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not very \"pythonic\". Avoid loops when you can do it in one line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means = [ t1data[maskdata==i].mean() for i in range(1,maskdata.max()+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=means,columns=['Subject 1'],index=range(1,maskdata.max()+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do this for multiple subjects and you've started building a database that you can run stats on, save to Excel, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating the affine\n",
    "The affine matrix describes the relationship between the image matrix and the \"real world\" coordinate system. Image orientation manipulation should be approached with caution, but it's often necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img.header.get_zooms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img.affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "canonical_t1img = nib.as_closest_canonical(t1img)\n",
    "canonical_t1img.affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "canonical_t1img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "canonical_t1data = canonical_t1img.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax = plt.subplots(1,2)\n",
    "sl=45\n",
    "t1im = ax[0].matshow(np.rot90(t1data[:,:,sl]),cmap='gray')\n",
    "t1im_canon = ax[1].matshow(np.rot90(canonical_t1data[:,:,sl]),cmap='gray')\n",
    "ax[0].set_axis_off()\n",
    "ax[1].set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1img.orthoview()\n",
    "canonical_t1img.orthoview()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notice the difference?\n",
    "\n",
    "Hard to spot, but the canonical oriented image is flipped left/right from the standard image. If you have a sagitally acquired image, the image will be reoriented in more dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercise: Rotate the image and the affine matrix by 90 degrees. Save and open in FSL. Are the orientation labels correct?\n",
    "\n",
    "http://nipy.org/nibabel/coordinate_systems.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1imrot = np.rot90(t1data,k=2,axes=(1,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, let's look at the image with the rotated data and original affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1imrot_nii = nib.Nifti1Image(t1imrot,t1img.affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "        this.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 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",
       "    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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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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       "<img 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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<OrthoSlicer3D: (91, 109, 91)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1imrot_nii.orthoview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's add the new affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cos_gamma = np.cos(np.pi)\n",
    "sin_gamma = np.sin(np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rotation_affine = np.array([[1, 0, 0, 0],\n",
    "                                [0, cos_gamma, -sin_gamma, 0],\n",
    "                                [0, sin_gamma, cos_gamma, 0],\n",
    "                                [0, 0, 0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outaff = t1img.affine.dot(rotation_affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -2.00000000e+00   0.00000000e+00   0.00000000e+00   9.00000000e+01]\n",
      " [  0.00000000e+00  -2.00000000e+00  -2.44929360e-16  -1.26000000e+02]\n",
      " [  0.00000000e+00   2.44929360e-16  -2.00000000e+00  -7.20000000e+01]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(outaff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -2.,    0.,    0.,   90.],\n",
       "       [   0.,    2.,    0., -126.],\n",
       "       [   0.,    0.,    2.,  -72.],\n",
       "       [   0.,    0.,    0.,    1.]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1img.affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1imrot_nii = nib.Nifti1Image(t1imrot,outaff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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",
       "        this.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 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",
       "    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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ortho = t1imrot_nii.orthoview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ortho.position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ortho.set_position(0,-200,-125)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coordinates are weird now, but the image is oriented correctly. We actually need to add a translation to the rotation to get our indexes back to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Tissue segmentation\n",
    "\n",
    "http://nipy.org/dipy/examples_built/tissue_classification.html#example-tissue-classification\n",
    "\n",
    "Let's run a tissue classification algorithm on our T1 image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dipy.data import fetch_tissue_data, read_tissue_data\n",
    "from dipy.segment.tissue import TissueClassifierHMRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of tissue classes\n",
    "nclass = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Smoothing factor. Good values 0-0.5\n",
    "beta = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Markov Random Field classifier. Similar algorithm to FAST and ANTS\n",
    "hmrf = TissueClassifierHMRF()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_segmentation, final_segmentation, PVE = hmrf.classify(t1data, nclass, beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_segmentation.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_segmentation.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,axs = plt.subplots(1,3)\n",
    "axs[0].matshow(np.rot90(final_segmentation[:,:,40]))\n",
    "axs[1].matshow(np.rot90(final_segmentation[:,50,:]))\n",
    "axs[2].matshow(np.rot90(final_segmentation[40,:,:]))\n",
    "\n",
    "[ax.set_axis_off() for ax in axs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PVE.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,axs = plt.subplots(1,3)\n",
    "axs[0].matshow(np.rot90(PVE[:,:,40,1]))\n",
    "axs[1].matshow(np.rot90(PVE[:,50,:,1]))\n",
    "axs[2].matshow(np.rot90(PVE[40,:,:,1]))\n",
    "\n",
    "[ax.set_axis_off() for ax in axs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nipype: Integrate imaging software into your Python code\n",
    "\n",
    "https://miykael.github.io/nipype_tutorial/\n",
    "\n",
    "Nipype is an amazing utility that allows you to link together different non-Python imaging tools within your Python analysis. It can be used simply to (for example) add FSL's eddy correction processing to you Python DTI processing; or it can be used to organize your entire processing workflow.\n",
    "\n",
    "Personally I don't use the full workflow functionality. Instead I use it to call FSL and SPM functions from my Python scripts. It's much easier to write everything in pure Python instead of switching back and forth between FSL command line tools and SPM in Matlab. Nipype has an amazing number of interfaces, including:\n",
    "* FSL\n",
    "* SPM\n",
    "* FreeSurfer\n",
    "* niftireg\n",
    "* ANTS\n",
    "* AFNI\n",
    "* Camino\n",
    "* MIPAV\n",
    "* DTK\n",
    "\n",
    "Full list here: http://nipype.readthedocs.io/en/latest/documentation.html\n",
    "\n",
    "For the Nipype interfaces to work, you just have to have the software you want to use install and certain environmental variables set so that Python knows where to look for the programs. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo $FSLDIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of these software tools are on the syzygy server. This example will only work on a computer with FSL installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nipype.interfaces.fsl as fsl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt = fsl.FLIRT()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flt.inputs.in_file = 'MNI152_T1_2mm.nii.gz'\n",
    "flt.inputs.reference = 'MNI152_T1_1mm.nii.gz'\n",
    "flt.inputs.out_file = 'MNI_T1_2mm_to_1mm.nii.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = flt.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_reg_file = results.outputs.out_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`my_reg_file` is a string describing a path to a file. We can now use this variable as the input to another nipype interface, or we can load the nifti file into Python with the tools we learned previously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: DTI processing\n",
    "http://nipy.org/dipy/examples_built/reconst_dti.html#example-reconst-dti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dipy.reconst.dti as dti\n",
    "from dipy.data import read_stanford_hardi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, gtab = read_stanford_hardi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gtab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gtab (**g**radient **tab**le) is the object that holds the gradient information. This example gives it to us automatically. If you created your dti nifti file with dcm2nii, it probably also gave you  .bvec and .bval files. In that case, you could create the gtab with\n",
    "```\n",
    "from dipy.io import read_bvals_bvecs\n",
    "bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\n",
    "from dipy.core.gradients import gradient_table\n",
    "gtab = gradient_table(bvals, bvecs)\n",
    "```\n",
    "\n",
    "If you're reading data straight from a PAR/REC file, \n",
    "```\n",
    "dtipar = nib.parrec.load(dti_par)\n",
    "bvals, bvecs = dtipar.header.get_bvals_bvecs()\n",
    "gtab = gradient_table(bvals, bvecs)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = img.get_data()\n",
    "print('data shape: {}'.format(data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,axs = plt.subplots(2,2)\n",
    "for ax, vol in zip(axs.flatten(),range(0,160,40)):\n",
    "    ax.matshow(np.rot90(data[:,:,25,vol]))\n",
    "    ax.set_axis_off()\n",
    "f.subplots_adjust(hspace=.05, wspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dipy.segment.mask import median_otsu\n",
    "\n",
    "maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)\n",
    "print('maskdata.shape {}' .format(maskdata.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,(ax1,ax2,ax3) = plt.subplots(1,3)\n",
    "ax1.matshow(np.rot90(data[:,:,40,0]))\n",
    "ax2.matshow(np.rot90(maskdata[:,:,40,0]))\n",
    "ax3.matshow(np.rot90(mask[:,:,40]))\n",
    "ax1.set_axis_off()\n",
    "ax2.set_axis_off()\n",
    "ax3.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a TensorModel object with our gradient information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenmodel = dti.TensorModel(gtab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will take at least a minute, be patient\n",
    "tenfit = tenmodel.fit(maskdata,mask=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dipy.reconst.dti import fractional_anisotropy, color_fa, lower_triangular\n",
    "\n",
    "FA = fractional_anisotropy(tenfit.evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.matshow(np.rot90(FA[:,:,40]))\n",
    "plt.gca().set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! We can save the FA as a nifti, or do some numerical analysis on it with numpy, or make publication images, or whatever. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: Add an eddy correction step to the above pipeline using a Nipype interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: fMRI analysis with ICA\n",
    "\n",
    "http://nilearn.github.io/connectivity/resting_state_networks.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get some sample data. This data sample is already pre-processed -- normally you need to motion correct, etc.\n",
    "\n",
    "from nilearn import datasets\n",
    "\n",
    "adhd_dataset = datasets.fetch_adhd(n_subjects=4)\n",
    "func_filenames = adhd_dataset.func  # list of 4D nifti files for each subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nilearn.decomposition import CanICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CanICA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "canica = CanICA(n_components=20, smoothing_fwhm=6.,\n",
    "                memory=\"nilearn_cache\", memory_level=2,\n",
    "                threshold=3., verbose=10, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will take about a minute\n",
    "canica.fit(func_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Retrieve the independent components in brain space\n",
    "components_img = canica.masker_.inverse_transform(canica.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "components_img."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.plotting import plot_prob_atlas\n",
    "\n",
    "# Plot all ICA components together\n",
    "plot_prob_atlas(components_img, title='All ICA components',anat_img='MNI152_T1_2mm.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.image import iter_img\n",
    "from nilearn.plotting import plot_stat_map, show\n",
    "\n",
    "for i, cur_img in enumerate(iter_img(components_img)):\n",
    "    plot_stat_map(cur_img, display_mode=\"z\", title=\"IC %d\" % i,\n",
    "                  cut_coords=1, colorbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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