self_localization/adaptive_mcl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../scripts/')\n",
    "from mcl import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResetMcl(Mcl): ###resetmcl4 （random_reset, sensor_resetting_drawは省略）\n",
    "    def __init__(self, envmap, init_pose, num, motion_noise_stds={\"nn\":0.19, \"no\":0.001, \"on\":0.13, \"oo\":0.2}, \\\n",
    "                 distance_dev_rate=0.14, direction_dev=0.05, amcl_params={\"slow\":0.001, \"fast\":0.1, \"nu\":3.0}): #amcl_paramsを追加\n",
    "        super().__init__(envmap, init_pose, num, motion_noise_stds, distance_dev_rate, direction_dev)\n",
    "        self.amcl_params = amcl_params #追加\n",
    "        self.slow_term_alpha, self.fast_term_alpha = 1.0, 1.0 #追加\n",
    "        \n",
    "    def random_reset(self):\n",
    "         for p in self.particles:\n",
    "                p.pose = np.array([np.random.uniform(-5.0, 5.0), np.random.uniform(-5.0, 5.0), np.random.uniform(-math.pi, math.pi)]).T\n",
    "                p.weight = 1/len(self.particles)\n",
    "                \n",
    "    def sensor_resetting_draw(self, particle, landmark_pos, ell_obs, phi_obs):\n",
    "            ##パーティクルの位置を決める##\n",
    "            psi = np.random.uniform(-np.pi, np.pi) #ランドマークからの方角を選ぶ\n",
    "            ell = norm(loc=ell_obs, scale=(ell_obs*self.distance_dev_rate)**2).rvs() #ランドマークからの距離を選ぶ\n",
    "            particle.pose[0] = landmark_pos[0] + ell*math.cos(psi)\n",
    "            particle.pose[1] = landmark_pos[1] + ell*math.sin(psi)\n",
    "            \n",
    "            ##パーティクルの向きを決める##\n",
    "            phi = norm(loc=phi_obs, scale=(self.direction_dev)**2).rvs() #ランドマークが見える向きを決める\n",
    "            particle.pose[2] = math.atan2(landmark_pos[1]- particle.pose[1], landmark_pos[0]- particle.pose[0]) - phi\n",
    "            \n",
    "            particle.weight = 1.0/len(self.particles)\n",
    "            \n",
    "    def sensor_resetting(self, observation):\n",
    "        nearest_obs = np.argmin([obs[0][0] for obs in observation])\n",
    "        values, landmark_id = observation[nearest_obs]\n",
    "        \n",
    "        for p in self.particles:\n",
    "            self.sensor_resetting_draw(p, self.map.landmarks[landmark_id].pos, *values)\n",
    "            \n",
    "    def adaptive_resetting(self, observation):\n",
    "        if len(observation) == 0: return #追加\n",
    "        \n",
    "        #＃センサリセットするパーティクルの数を決める##\n",
    "        alpha = sum([p.weight for p in self.particles])\n",
    "        self.slow_term_alpha += self.amcl_params[\"slow\"]*(alpha - self.slow_term_alpha)\n",
    "        self.fast_term_alpha += self.amcl_params[\"fast\"]*(alpha - self.fast_term_alpha)\n",
    "        sl_num = len(self.particles)*max([0, 1.0-self.amcl_params[\"nu\"]*self.fast_term_alpha/self.slow_term_alpha])\n",
    "        \n",
    "        self.resampling() #とりあえず普通にリサンプリング\n",
    "        \n",
    "        nearest_obs = np.argmin([obs[0][0] for obs in observation]) #距離が一番近いランドマークを選択\n",
    "        values, landmark_id = observation[nearest_obs]\n",
    "        for n in range(int(sl_num)): #n回パーティクルを選んで姿勢を変える（2回以上姿勢を変えられるパーティクルがあるけどとりあえず気にしない）\n",
    "            p = random.choices(self.particles)[0] #一つ選ぶ\n",
    "            self.sensor_resetting_draw(p, self.map.landmarks[landmark_id].pos, *values)\n",
    "        \n",
    "    def observation_update(self, observation):\n",
    "        for p in self.particles:\n",
    "            p.observation_update(observation, self.map, self.distance_dev_rate, self.direction_dev) \n",
    "    \n",
    "        self.set_ml()\n",
    "        self.adaptive_resetting(observation) #変更"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trial(animation): ###mclkidnap1test\n",
    "    time_interval = 0.1\n",
    "    world = World(300, time_interval, debug=not animation) \n",
    "\n",
    "    ## 地図を生成して3つランドマークを追加 ##\n",
    "    m = Map()\n",
    "    m.append_landmark(Landmark(-4,2))\n",
    "    m.append_landmark(Landmark(2,-3))\n",
    "    m.append_landmark(Landmark(3,3))\n",
    "    world.append(m)\n",
    "\n",
    "    ## ロボットを作る ##\n",
    "    init_pose = np.array([np.random.uniform(-5.0, 5.0), np.random.uniform(-5.0, 5.0), np.random.uniform(-math.pi, math.pi)]).T\n",
    "    robot_pose = np.array([np.random.uniform(-5.0, 5.0), np.random.uniform(-5.0, 5.0), np.random.uniform(-math.pi, math.pi)]).T\n",
    "    pf = ResetMcl(m, init_pose, 300)\n",
    "    a = EstimationAgent(time_interval, 0.2, 10.0/180*math.pi, pf)\n",
    "    r = Robot(robot_pose, sensor=Camera(m, phantom_prob=0.1), agent=a, color=\"red\")\n",
    "    world.append(r)\n",
    "\n",
    "    world.draw()\n",
    "    \n",
    "    return (pf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pf = trial(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trial_phantom(animation): ###mclkidnap1test\n",
    "    time_interval = 0.1\n",
    "    world = World(300, time_interval, debug=not animation) \n",
    "\n",
    "    ## 地図を生成して3つランドマークを追加 ##\n",
    "    m = Map()\n",
    "    m.append_landmark(Landmark(-4,2))\n",
    "    m.append_landmark(Landmark(2,-3))\n",
    "    m.append_landmark(Landmark(3,3))\n",
    "    world.append(m)\n",
    "\n",
    "    ## ロボットを作る ##\n",
    "    init_pose = np.array([np.random.uniform(-5.0, 5.0), np.random.uniform(-5.0, 5.0), np.random.uniform(-math.pi, math.pi)]).T\n",
    "    robot_pose = np.array([np.random.uniform(-5.0, 5.0), np.random.uniform(-5.0, 5.0), np.random.uniform(-math.pi, math.pi)]).T\n",
    "    pf = ResetMcl(m, init_pose, 1000)\n",
    "    a = EstimationAgent(time_interval, 0.2, 10.0/180*math.pi, pf)\n",
    "    r = Robot(robot_pose, sensor=Camera(m, phantom_prob=0.1), agent=a, color=\"red\")\n",
    "    world.append(r)\n",
    "\n",
    "    world.draw()\n",
    "    \n",
    "    return (pf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
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       "    };\n",
       "}\n",
       "\n",
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       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf = trial_phantom(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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