BAXTER_LSM_Z_CONTROLLER_INDIVIDUALS.ipynb

{
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    "# LSM CONTROLLER PARA MANTER O BAXTER TOCANDO A MESA\n",
    "\n",
    "# A LSM FOI CRIADA A PARTIR DOS DADOS OBTIDOS COM UM PID PROJETADO COM O MESMO OBJETIVO\n",
    "\n",
    "\n",
    "# EXPERIMENTOS REALIZADOS: \n",
    "# 0-99 no caderno (superfície mole), sobre a mesa, sem inclinação.\n",
    "# 100-199 Quadro branco\n",
    "# 200-249 Quadro branco inclinado no lado oposto ao robô (8mm) \n",
    "# 250-299 Quadro branco inclinado no lado direito do robô (8mm) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "font = {'family' : 'serif',\n",
    "        'weight' : 'medium',\n",
    "        'size'   : 12}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "import numpy\n",
    "import rospy\n",
    "import baxter_interface\n",
    "\n",
    "\n",
    "import save_load_file as slf\n",
    "import sys\n",
    "from BEE import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "working_frequency = 100.0\n",
    "\n",
    "\n",
    "#simulation type\n",
    "#platform = \"gazeboBaxter\"\n",
    "platform = \"realBaxter\"\n",
    "\n",
    "trial_number = 0\n",
    "total_trials = 100\n",
    "\n",
    "##--data filenames--\n",
    "input_filename = \"BaxterArm_VREP_simulation_data/square/XY_movement_square_+0.5cm\"\n",
    "#input_filename = \"BaxterArm_VREP_simulation_data/square/XY_movement_square_AJUSTABLE_0.25cm\"\n",
    "#input_filename = \"XY_movement_square_HIGHTABLE3_free\"\n",
    "\n",
    "input_folder = \"PIDs_DATA_FOR_THE_LSM\"\n",
    "data_folder = \"PIDs_LSM\"\n",
    "sim_type = \"with_PID_0.5cm\"\n",
    "\n",
    "##\n",
    "limb = \"left\"\n",
    "movtimeout = 1.0 #seconds\n",
    "armspeed = 0.3 #percentage of arm speed\n",
    "#[S0, S1, E1, W1]: [-0.60048157 -0.75280726  2.09665728  0.24717198] : 0.0\n",
    "#[S0, S1, E1, W1]: [-0.60068953 -0.76690102  2.10270405  0.25267634] : +0.5cm\n",
    "#[S0, S1, E1, W1]: [-0.60048157 -0.75280726  2.09665728  0.24717198] : +1cm\n",
    "#[S0, S1, E1, W1]: [-0.60131329 -0.80917645  2.11977959  0.27020329] : +2cm\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Total time spent during the movement (in seconds)\n",
    "MT = 2.0\n",
    "\n",
    "# Simulation time step (in seconds)\n",
    "time_step = 2/1000.0\n",
    "total_steps = int(MT/time_step)\n",
    "\n",
    "# S0, S1, E1 and W1 are the Baxter's joint names\n",
    "# vel_x and vel_y are the velocities from accelerometer data \n",
    "# Four joints + 2 velocities => Six input variables\n",
    "Number_of_input_variables=2 # defines how the liquid is going to be divided to receive the inputs\n",
    "\n",
    "#\n",
    "# Number of neurons at each input layer (defines the INPUT resolution of the system)\n",
    "#\n",
    "# >>>>>>>>> It MUST be smaller or equal to (NofN/Number_of_input_variables) <<<<<<<<<<<\n",
    "#\n",
    "Ninput = 100\n",
    "\n",
    "\n",
    "#\n",
    "# Set points of the PID controller, they are just used here to compare results\n",
    "#\n",
    "force_sensor_setpoint = -2.0\n",
    "range_sensor_setpoint = 15.7\n",
    "\n",
    "#LIQUID CONFIGURATION (MUSTBE THE SAME AS THE TRAINING)\n",
    "\n",
    "#\n",
    "# Liquid's shape\n",
    "#\n",
    "net_shape = numpy.array([8,5,5])\n",
    "\n",
    "number_of_liquids = 5\n",
    "liquids_used = [1,2,3,4] #liquid nº 0 is not used here because it was a bad one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "range_sensor_limits = numpy.array([15. , 18.])\n",
    "IR_range = range_sensor_limits[1]-range_sensor_limits[0]\n",
    "range_delta = (range_sensor_limits[0]+IR_range/2)\n",
    "\n",
    "force_sensor_limits = numpy.array([-10. , 2.])\n",
    "force_range = force_sensor_limits[1]-force_sensor_limits[0]\n",
    "force_delta = (force_sensor_limits[0]+force_range/2)\n",
    "\n",
    "input_values_normalized=numpy.empty((Ninput,Number_of_input_variables))\n",
    "for ji in range(Number_of_input_variables):\n",
    "        input_values_normalized[:,ji]=numpy.linspace(-1,1,Ninput)\n",
    "    \n",
    "def generates_input_spikes(range_sensor,force_sensor):\n",
    "    temp_IR=(range_sensor-range_delta)/(IR_range/2.)\n",
    "    temp_Force=(force_sensor-force_delta)/(force_range/2.)\n",
    "    \n",
    "    normalized_IR=abs(input_values_normalized[:,0]-temp_IR).argmin()\n",
    "    normalized_Force=abs(input_values_normalized[:,1]-temp_Force).argmin()\n",
    "\n",
    "    return normalized_IR,normalized_Force"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liquid 1: [6414 8475 7022 1657 8307 5933]\n",
      "Liquid 2: [5772 3395 5210 3482  288  759]\n",
      "Liquid 3: [7898 1411 6211 2244  858 8653]\n",
      "Liquid 4: [6489 9401 4603 5848 2598 5854]\n"
     ]
    }
   ],
   "source": [
    "liqseeds=numpy.load(data_folder+\"/\"+\"Contoller_liqseeds.npy\") \n",
    "for i in liquids_used:    \n",
    "    print \"Liquid %d:\" % i, liqseeds[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_joints=numpy.zeros((0,4)) #creates an empty array with the right shape...\n",
    "temp=numpy.load(\"./\"+input_filename+\".npy\")\n",
    "original_joints=numpy.concatenate((original_joints,temp[:1000]))\n",
    "\n",
    "joints=numpy.copy(original_joints)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(joints)\n",
    "plt.title(\"Original joint curves\")\n",
    "plt.ylabel(\"angle (rad)\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sensor_msgs.msg import Range\n",
    "\n",
    "class BaxterRangeSensor():\n",
    "    def __init__(self):\n",
    "        self.distance = 0\n",
    "        sensor_name = \"/robot/range/left_hand_range/state\"\n",
    "        self.__left_sensor  = rospy.Subscriber(sensor_name,Range, callback=self.__sensorCallback,queue_size=1)\n",
    "\n",
    "    def __sensorCallback(self,msg,):\n",
    "        self.distance = msg.range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rospy.init_node(\"arm_mov\", anonymous=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#initialize arms and settings\n",
    "\n",
    "arm = baxter_interface.Limb(limb)\n",
    "arm.set_command_timeout(movtimeout)\n",
    "arm.set_joint_position_speed(armspeed)\n",
    "\n",
    "#head = baxter_interface.Head()\n",
    "#head.set_pan(-0.3, speed=0.30, timeout=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "range_sensor = BaxterRangeSensor()\n",
    "print range_sensor.distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# joints = numpy.copy(original_joints) + numpy.array([0.0, -0.001409, 0.00057,0.00057])*40\n",
    "# print joints.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "command:  {'left_w0': 0.0, 'left_w1': 0.2526763379573822, 'left_w2': 0.0, 'left_e0': 0.0, 'left_e1': 2.1027040481567383, 'left_s0': -0.60068953037261963, 'left_s1': -0.76690101623535156}\n",
      "read: {'left_w0': 0.002684466378799474, 'left_w1': 0.2527233348041219, 'left_w2': -0.0019174759848567672, 'left_e0': 0.007286408742455715, 'left_e1': 2.1030876601909023, 'left_s0': -0.6063059064117098, 'left_s1': -0.7654564131548215} {'position': Point(x=0.505857714418683, y=0.34347856040177294, z=-0.15979155117838298), 'orientation': Quaternion(x=-0.0915113492392514, y=0.9957679066206815, z=0.0025986401477495554, w=-0.008074414781264768)}\n"
     ]
    }
   ],
   "source": [
    "#moves the arm to the initial position\n",
    "joint_names = ['left_s0','left_s1','left_e1','left_w1','left_w0','left_w2','left_e0']\n",
    "joint_values = numpy.insert(joints[0],4,[0.0,0.0,0.0])\n",
    "limb_joints = dict(zip(joint_names, joint_values))\n",
    "print \"command: \",limb_joints\n",
    "arm.move_to_joint_positions(limb_joints, timeout=5.0, threshold=0.003)\n",
    "\n",
    "joint_angles = arm.joint_angles()\n",
    "joint_endpoint = arm.endpoint_pose()\n",
    "print \"read:\" ,joint_angles,joint_endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here it's selected WHICH liquid to use:\n",
    "# (remember, there are multiple liquids => number_of_liquids)\n",
    "# lsm_i goes from 0 to (number_of_liquids-1)\n",
    "lsm_i = 4\n",
    "\n",
    "RANDOM_0,RANDOM_1,RANDOM_2,RANDOM_3,RANDOM_4,_ = liqseeds[lsm_i] # the \"_\" ignores the input noise seed\n",
    "RANDOM_1,RANDOM_2,RANDOM_3\n",
    "\n",
    "#\n",
    "# Loads the coeficients for the readout function\n",
    "#\n",
    "filename = \"./\"+data_folder+\"/\"+\"sensors_LSM_\"+str(lsm_i)+\".gzpickle\"\n",
    "[c_sensors,r_sensors] = slf.load_from_file(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#\n",
    "# SETUP - everything that should change between liquids is expressed below...\n",
    "#\n",
    "\n",
    "\n",
    "\n",
    "RANDOM_input=liqseeds[lsm_i][5] # input gaussian noise\n",
    "\n",
    "input_noise_std = 1 # Standard deviation of the input gaussian noise\n",
    "\n",
    "membrane_time_constant = 30E-3\n",
    "\n",
    "# Neuron parameters\n",
    "taum=0.03 # membrane time constant\n",
    "cm=3e-08 # membrane capacitance\n",
    "taue=0.003 # excitatory synapse time constant\n",
    "taui=0.006 # inhibitory synapse time constant\n",
    "\n",
    "\n",
    "# The \"current_rand\" noise source is the problematic one. Because it has a mean value different from\n",
    "# zero, it generates a new liquid every run as the neuron models change according to the offset current \"current_rand\".\n",
    "\n",
    "# Maass noisy sources\n",
    "# membrane_rand=[0.0135, 0.015] # mean value 13.75E-3, uniform distribution, max value equal to threshold (15mV).\n",
    "# current_rand=[14.975e-09, 15.025e-09] # mean value 15E-9, uniform distribution.\n",
    "# noisy_current_rand=2e-10 # normal distribution, mean zero.\n",
    "\n",
    "\n",
    "# Parameters paper Joshi/Maass\n",
    "membrane_rand=[13.5E-3,14.9E-3] # mean value 13.7E-3, uniform distribution, max value below threshold (15mV).\n",
    "current_rand=[13.5E-09, 14.5E-09] # mean value 14E-9, uniform distribution.\n",
    "noisy_current_rand=1E-9 # normal distribution, mean zero.\n",
    "\n",
    "factor=2*10.0\n",
    "\n",
    "SpkLiq_vresets_rand=[13.8E-3,13.8E-3*(1+1/factor)] # the membrane reset values are drawn from a uniform distribution\n",
    "\n",
    "\n",
    "lbd_value = 1.2 \n",
    "\n",
    "# Controls if the neurons are connected to each other (basically ignores the distance based connection probability)\n",
    "disconnected = False\n",
    "\n",
    "parameter_list=\\\n",
    "[[[0.1  ,  0.32 ,  0.144,  0.06 ,  -47.  ,  0.8],\\\n",
    "  [0.4  ,  0.25 ,  0.7  ,  0.02 ,  -47  ,  0.8]],\\\n",
    " [[0.2  ,  0.05 ,  0.125,  1.2  ,  150  ,  0.8],\\\n",
    "  [0.3  ,  0.5  ,  1.1  ,  0.05 ,  70  ,  1.5]]]\n",
    "\n",
    "# parameter_list = [] # Maass parameters change only the gains (nA): 1.2 (EE), 1.6 (EI), -3.0 (IE), -2.8 (II)\n",
    "                    # instead of -47,-47,150,70\n",
    "\n",
    "input_gain=105. # gain used for the input neurons\n",
    "w_SD = 3.0 # input neurons standard deviation\n",
    "\n",
    "\n",
    "#\n",
    "# END OF THE SETUP - everything that should change between liquids is expressed above...\n",
    "#\n",
    "\n",
    "\n",
    "#\n",
    "# This first initialization of the simulator is only used to generate the liquid structure's data\n",
    "#\n",
    "\n",
    "# Resets the simulator\n",
    "BEE_free()\n",
    "\n",
    "tstp = time_step\n",
    "\n",
    "seeds=numpy.array(numpy.random.randint(0,10000,5),dtype=numpy.uint32)\n",
    "# // RANDOM-0: Membrane initial potentials\n",
    "# // RANDOM-1: Noisy offset currents / resets\n",
    "# // RANDOM-2: Selection of the inhibitory and excitatory neurons\n",
    "# // RANDOM-3: Internal connections of the liquid\n",
    "# // RANDOM-4: Noisy corrents\n",
    "seeds[1] = RANDOM_1\n",
    "seeds[2] = RANDOM_2\n",
    "seeds[3] = RANDOM_3\n",
    "\n",
    "input_noise_seed = RANDOM_input\n",
    "\n",
    "if not BEE_initialized():\n",
    "    initialize_sim(my_net_shape = net_shape, \\\n",
    "                   my_lbd_value = lbd_value, \\\n",
    "                   my_seeds=seeds, \\\n",
    "                   SpkLiq_step=time_step,\\\n",
    "                   SpkLiq_threads_N = 5,\\\n",
    "                   SpkLiq_vresets = SpkLiq_vresets_rand,\\\n",
    "                   SpkLiq_membrane_rand=membrane_rand,\\\n",
    "                   SpkLiq_current_rand=current_rand,\\\n",
    "                   SpkLiq_taum=taum, SpkLiq_cm=cm, SpkLiq_taue=taue, SpkLiq_taui=taui, \\\n",
    "                   SpkLiq_noisy_current_rand=noisy_current_rand)\n",
    "    \n",
    "\n",
    "if parameter_list!=[]:\n",
    "    change_parameters(parameter_list)\n",
    "\n",
    "if BEE_initialized() and (not BEE_connected()):\n",
    "    generate_connections()\n",
    "\n",
    "#\n",
    "# Cancels the connections (if disconnected==True), but keeps the parameters\n",
    "#\n",
    "if disconnected:\n",
    "    BEE_writes_SpkLiq_inh_connections(0)\n",
    "    BEE_writes_SpkLiq_exc_connections(0)\n",
    "\n",
    "if BEE_initialized() and (not BEE_connected()):\n",
    "    process_connections()\n",
    "\n",
    "NofN,NoINHN,NoEXCN,NoINHC,NoEXCC=output_stats(stats=0)\n",
    "\n",
    "# Normally the BEE simulator does NOT updates neurons that have no connections to other neurons.\n",
    "# The neurons without connections are marked in a special array that can be read from the\n",
    "# Python function output_connected(NofN).\n",
    "# Every time the simulator goes through one step it checks if the neurons is marked as connected before\n",
    "# updating its state variables.\n",
    "\n",
    "# Disconnects all the neurons (nothing will generate spikes anymore)\n",
    "# control_connected(numpy.zeros(NofN,dtype=numpy.int32))\n",
    "\n",
    "# Forces the simulator to calculate the values for all neurons (even the unconnected ones)\n",
    "# This is important during the tests without connections\n",
    "control_connected(numpy.ones(NofN,dtype=numpy.int32))\n",
    "\n",
    "\n",
    "#\n",
    "# Input converter\n",
    "# \n",
    "\n",
    "# This information is used to define the neurons receiving inputs (excitatory neurons):\n",
    "inhibitory_index_L = output_inh_indices(NoINHN)\n",
    "inhibitory_index_L.shape\n",
    "\n",
    "inhibitory_index_L=[] # input signals through all neurons (not only excitatory ones)\n",
    "\n",
    "#\n",
    "# Input pattern generation\n",
    "# \n",
    "\n",
    "Number_of_neurons_lsm = NofN\n",
    "Number_of_input_layers = Number_of_input_variables\n",
    "Number_of_neurons_inputs = Ninput\n",
    "\n",
    "\n",
    "\n",
    "# \n",
    "# 2D Gaussian generation\n",
    "#\n",
    "\n",
    "# DON'T FORGET I HAVE THE 3D and the flat version of the input code!!!!!!!!\n",
    "# See: BEE_Simulator_ArmControl_v1_CONNECTION_PATTERN_VISUALIZATION_2D.ipynb\n",
    "\n",
    "#\n",
    "# Generates the connection pattern used. In the case below it's a gaussian \n",
    "# distributed one based on the connection weights.\n",
    "#\n",
    "\n",
    "inputs_indices = [] #inputs_indices[input_variable][ni]\n",
    "inputs_weights = [] #inputs_weights[input_variable][ni]\n",
    "\n",
    "\n",
    "def gaussian(lamb,n,nt):\n",
    "    '''\n",
    "    Generates a gaussian centered at 'n'\n",
    "    '''\n",
    "    return input_gain*numpy.exp(-((nt-n)**2)/(2*(lamb)**2)) #No energy normalization\n",
    "\n",
    "weight_func = gaussian\n",
    "\n",
    "\n",
    "liquid_input_layer_size = int(Number_of_neurons_lsm/float(Number_of_input_layers))\n",
    "\n",
    "# Goes through the liquid to generate the proper connections, \n",
    "# but dividing the liquid into the same number of input layers.\n",
    "# Each input layer only connects to its own slice of the liquid.\n",
    "\n",
    "centre_positions=numpy.linspace(0,liquid_input_layer_size-1,num=Number_of_neurons_inputs).astype(dtype=numpy.int)\n",
    "\n",
    "for inp in range(Number_of_input_layers): #goes through the input layers\n",
    "    temp_i=[]\n",
    "    temp_w=[]        \n",
    "    for i,ic in zip(range(inp*Number_of_neurons_inputs,Number_of_neurons_inputs*(inp+1)),\\\n",
    "                    range(Number_of_neurons_inputs)): #goes through the input neurons\n",
    "        temp_i2 = []\n",
    "        temp_w2 = []\n",
    "        for j,ji in zip(range(inp*liquid_input_layer_size,liquid_input_layer_size*(inp+1)),\\\n",
    "                        range(liquid_input_layer_size)):\n",
    "            if j not in inhibitory_index_L:\n",
    "                centre_position=centre_positions[ic]\n",
    "                temp_weight=weight_func(w_SD,centre_position,ji)             \n",
    "                if temp_weight>0: #guaranties only real spikes will be injected, saving cpu time\n",
    "                    temp_w2.append(temp_weight) # in nA\n",
    "                    temp_i2.append(j) # i=>input's neuron; j=>liquid's neuron                                        \n",
    "        temp_i.append(temp_i2)\n",
    "        temp_w.append(temp_w2)\n",
    "    inputs_indices.append(temp_i)\n",
    "    inputs_weights.append(temp_w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing the simulator...\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 823 µs\n",
      "Verifies...\n",
      "('Total number of neurons:', 200)\n",
      "('Number of inhibitory neurons:', 40)\n",
      "('Number of excitatory neurons:', 160)\n",
      "('Number of inhibitory connections:', 72)\n",
      "('Number of excitatory connections:', 291)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Resets the simulator\n",
    "BEE_free()\n",
    "\n",
    "# Initializes the simulator\n",
    "tstp = time_step\n",
    "\n",
    "seeds=numpy.array(numpy.random.randint(0,10000,5),dtype=numpy.uint32)\n",
    "# // RANDOM-0: Membrane initial potentials\n",
    "# // RANDOM-1: Noisy offset currents / resets <<<<<<<\n",
    "# // RANDOM-2: Selection of the inhibitory and excitatory neurons <<<<<<<\n",
    "# // RANDOM-3: Internal connections of the liquid <<<<<<<\n",
    "# // RANDOM-4: Noisy corrents\n",
    "seeds[1] = RANDOM_1\n",
    "seeds[2] = RANDOM_2\n",
    "seeds[3] = RANDOM_3\n",
    "\n",
    "#DEBUG!\n",
    "# seeds[4] = RANDOM_4\n",
    "# seeds[0] = RANDOM_0\n",
    "\n",
    "\n",
    "print \"Initializing the simulator...\"\n",
    "if not BEE_initialized():\n",
    "    %time initialize_sim(my_net_shape = net_shape, \\\n",
    "                         my_lbd_value = lbd_value, \\\n",
    "                         my_seeds=seeds, \\\n",
    "                         SpkLiq_step=tstp,\\\n",
    "                         SpkLiq_threads_N = 5,\\\n",
    "                         SpkLiq_vresets = SpkLiq_vresets_rand,\\\n",
    "                         SpkLiq_membrane_rand=membrane_rand,\\\n",
    "                         SpkLiq_current_rand=current_rand,\\\n",
    "                         SpkLiq_taum=taum, SpkLiq_cm=cm, SpkLiq_taue=taue, SpkLiq_taui=taui, \\\n",
    "                         SpkLiq_noisy_current_rand=noisy_current_rand)\n",
    "\n",
    "if parameter_list!=[]:\n",
    "    change_parameters(parameter_list)\n",
    "\n",
    "generate_connections()\n",
    "\n",
    "#\n",
    "# Cancels the connections, but keeps the parameters\n",
    "#\n",
    "if disconnected:\n",
    "    BEE_writes_SpkLiq_inh_connections(0)\n",
    "    BEE_writes_SpkLiq_exc_connections(0)\n",
    "\n",
    "\n",
    "process_connections()\n",
    "\n",
    "print \"Verifies...\"\n",
    "NofN,NoINHN,NoEXCN,NoINHC,NoEXCC=output_stats()\n",
    "\n",
    "# Forces the simulator to calculate the values for all neurons (even the unconnected ones)\n",
    "# This is important during the tests without connections\n",
    "control_connected(numpy.ones(NofN,dtype=numpy.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE! 125\n",
      "DONE! 126\n",
      "DONE! 127\n",
      "DONE! 128\n",
      "DONE! 129\n",
      "DONE! 130\n",
      "DONE! 131\n",
      "DONE! 132\n",
      "DONE! 133\n",
      "DONE! 134\n",
      "DONE! 135\n",
      "DONE! 136\n",
      "DONE! 137\n",
      "DONE! 138\n",
      "DONE! 139\n",
      "DONE! 140\n",
      "DONE! 141\n",
      "DONE! 142\n",
      "DONE! 143\n",
      "DONE! 144\n",
      "DONE! 145\n",
      "DONE! 146\n",
      "DONE! 147\n",
      "DONE! 148\n",
      "DONE! 149\n"
     ]
    }
   ],
   "source": [
    "trial_number = 0\n",
    "total_trials = 150\n",
    "\n",
    "N = 10\n",
    "\n",
    "for trial_number in range(125,total_trials):\n",
    "    \n",
    "    \n",
    "    joint_values = numpy.insert(joints[0],4,[0.0,0.0,0.0])\n",
    "    limb_joints = dict(zip(joint_names, joint_values))\n",
    "    arm.move_to_joint_positions(limb_joints, timeout=10.5, threshold=0.003)\n",
    "\n",
    "    force_N = numpy.zeros(N)\n",
    "    range_N = numpy.zeros(N)\n",
    "    for i in range(N):\n",
    "        force_N[i] = numpy.clip(arm.endpoint_effort()['force'][2],-10,1)\n",
    "        range_N[i] = range_sensor.distance\n",
    "        rospy.sleep(0.01)\n",
    "    \n",
    "    angles_from_robot = []\n",
    "    position_from_robot = []\n",
    "    ir_sensor = []\n",
    "    Zforce_hist = []\n",
    "    controller_hist = []\n",
    "    lsm_output_hist = []\n",
    "    t = []\n",
    "    spikes = []\n",
    "    inputs = []\n",
    "    ti=0\n",
    "\n",
    "#     orientation_from_robot = []\n",
    "#     endpoint_velocity = []\n",
    "#     endpoint_torque = []\n",
    "#     endpoint_force = []\n",
    "\n",
    "\n",
    "    s1_delta = 0.\n",
    "    e1_delta = 0.\n",
    "    w1_delta = 0.\n",
    "\n",
    "    s1_inc = -0.001409/4\n",
    "    e1_inc = 0.00057/4\n",
    "    w1_inc = 0.00057/4\n",
    "    \n",
    "    \n",
    "    #\n",
    "    # Initializing the simulation...\n",
    "    #\n",
    "        \n",
    "    seeds=numpy.array(numpy.random.randint(0,10000,5),dtype=numpy.uint32)\n",
    "    # // RANDOM-0: Membrane initial potentials\n",
    "    # // RANDOM-1: Noisy offset currents / resets <<<<<<<<\n",
    "    # // RANDOM-2: Selection of the inhibitory and excitatory neurons <<<<<<<<\n",
    "    # // RANDOM-3: Internal connections of the liquid <<<<<<<<\n",
    "    # // RANDOM-4: Noisy corrents\n",
    "    seeds[1] = RANDOM_1\n",
    "    seeds[2] = RANDOM_2\n",
    "    seeds[3] = RANDOM_3     \n",
    "    liquid_soft_reset(seeds) # Resets the simulator\n",
    "    \n",
    "    \n",
    "    import membrane_lowpass_md\n",
    "    reload(sys.modules['membrane_lowpass_md']) # Makes sure the interpreter is going to reload the module\n",
    "    membrane_lowpass = membrane_lowpass_md.membrane_lowpass\n",
    "\n",
    "    m_v=membrane_lowpass(NofN,membrane_time_constant) # Initialize the membrane\n",
    "\n",
    "    \n",
    "    \n",
    "    for i in range(len(joints)):\n",
    "\n",
    "        next_joints = numpy.copy(joints[i])\n",
    "        \n",
    "        #Reads the sensor and pass on a moving average filter of size N \n",
    "        force_N = numpy.delete(numpy.append(force_N,numpy.clip(arm.endpoint_effort()['force'][2],\\\n",
    "                                                               force_sensor_limits[0],force_sensor_limits[1])),0)\n",
    "        curr_z_force = numpy.mean(force_N)\n",
    "        \n",
    "        range_N = numpy.delete(numpy.append(range_N,range_sensor.distance),0)\n",
    "        curr_z_range = numpy.clip(numpy.mean(range_N)*100,range_sensor_limits[0],range_sensor_limits[1])        \n",
    "        \n",
    "\n",
    "        ##\n",
    "        ## LSM SIMULATION\n",
    "        ##\n",
    "        \n",
    "        #generates the spikes \n",
    "        normalized_range, normalized_force = generates_input_spikes(curr_z_range,curr_z_force)\n",
    "        input_spikes = [normalized_range, normalized_force]\n",
    "        inputs.append(input_spikes)\n",
    "        \n",
    "        # \n",
    "        exc_inputs = numpy.concatenate([numpy.array(inputs_indices[input_variable][ni]) \\\n",
    "                                        for input_variable,ni in zip(range(len(input_spikes)),input_spikes)])\n",
    "        inh_inputs = []\n",
    "        exc_weights =  numpy.concatenate([numpy.array(inputs_weights[input_variable][ni]) \\\n",
    "                                          for input_variable,ni in zip(range(len(input_spikes)),input_spikes)])\n",
    "        inh_weights = []\n",
    "        \n",
    "        # Here the simulator is receiving the input spikes (and returning the output ones):\n",
    "        # r, current_t, received_spikes, OutputSpks, t = s.run_step(input_spikes) # Sends the spikes and reads the output\n",
    "        updates_sim(exc_inputs, inh_inputs, exc_weights*1E-9, inh_weights, len(exc_inputs), 0)\n",
    "\n",
    "        \n",
    "        # Appends to the output\n",
    "        t.append(ti)\n",
    "        spikes.append(reads_spikes(NofN))\n",
    "        ti+=tstp\n",
    "        \n",
    "        \n",
    "        OutputSpks = spikes[-1]\n",
    "        \n",
    "        \n",
    "        m_v.process_spikes(OutputSpks,ti) # Send the spikes to the membrane filter\n",
    "        OutputSpks_Filtered = m_v.check_values(ti) # Reads the values of the membranes\n",
    "        #OutputSpks_Filtered_save.append(OutputSpks_Filtered)        \n",
    "        Output = OutputSpks_Filtered # Using the low-pass (membrane) filtered version of the liquid's output\n",
    "\n",
    "\n",
    "        lsm_output=(Output.dot(c_sensors)+r_sensors) # Calculates the joint angles from the membrane filtered values\n",
    "        lsm_output_hist.append(lsm_output)\n",
    "        \n",
    "\n",
    "        ##Clips the gain just to be sure the robot doesn't do something crazy\n",
    "        gain = numpy.clip(lsm_output,-95,5)\n",
    "        \n",
    "        #Apply the gains to the joints\n",
    "        s1_delta = gain*s1_inc\n",
    "        e1_delta = gain*e1_inc\n",
    "        w1_delta = gain*w1_inc\n",
    "        \n",
    "        next_joints[1] = next_joints[1] + s1_delta \n",
    "        next_joints[2] = next_joints[2] + e1_delta\n",
    "        next_joints[3] = next_joints[3] + w1_delta\n",
    "        \n",
    "        \n",
    "        #Sends the joint values to the robot\n",
    "        joint_values = numpy.insert(next_joints,4,[0.0,0.0,0.0])\n",
    "        limb_joints = dict(zip(joint_names, joint_values))\n",
    "        for _ in range(2):\n",
    "            arm.set_joint_positions(limb_joints)\n",
    "            rospy.sleep(0.01)\n",
    "            \n",
    "            \n",
    "        #Save values on lists to save on file later  \n",
    "        joint_endpoint = arm.endpoint_pose()\n",
    "        angles_from_robot.append(arm.joint_angles().values())  \n",
    "        position_from_robot.append(joint_endpoint['position'][:])\n",
    "        ir_sensor.append(curr_z_range)\n",
    "        Zforce_hist.append(curr_z_force)\n",
    "        controller_hist.append([s1_delta, e1_delta, w1_delta])\n",
    "#         orientation_from_robot.append(joint_endpoint['orientation'][:])\n",
    "#         endpoint_torque.append(arm.endpoint_effort()['torque'][:])\n",
    "#         endpoint_force.append(arm.endpoint_effort()['force'][:])\n",
    "\n",
    "#         endpoint_velocity.append(arm.endpoint_velocity()['linear'][:])  \n",
    "        \n",
    "    print \"DONE!\",trial_number\n",
    "\n",
    "    joint_names = ['left_s0','left_s1','left_e1','left_w1','left_w0','left_w2','left_e0']\n",
    "    joint_values = numpy.insert(joints[0],4,[0.0,0.0,0.0])\n",
    "    limb_joints = dict(zip(joint_names, joint_values))\n",
    "    arm.move_to_joint_positions(limb_joints, timeout=3.0, threshold=0.003)\n",
    "\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_ANGLES_trial\"+str(trial_number)+\".npy\",angles_from_robot)\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_POSITION_trial\"+str(trial_number)+\".npy\",position_from_robot)\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_CONTROLLER_trial\"+str(trial_number)+\".npy\",controller_hist)\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_IRange_trial\"+str(trial_number)+\".npy\",ir_sensor)\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_Zforce_trial\"+str(trial_number)+\".npy\",Zforce_hist)\n",
    "    numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_GAINoutput_trial\"+str(trial_number)+\".npy\",lsm_output_hist)\n",
    "    \n",
    "#     numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_VELOCITY_trial\"+str(trial_number)+\".npy\",endpoint_velocity)\n",
    "#     numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_ORIENTATION_trial\"+str(trial_number)+\".npy\",orientation_from_robot)\n",
    "#     numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_TORQUE_trial\"+str(trial_number)+\".npy\",endpoint_torque)\n",
    "#     numpy.save(data_folder+\"/\"+sim_type+\"LSM_\"+str(lsm_i)+\"_FORCE_trial\"+str(trial_number)+\".npy\",endpoint_force)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(Zforce_hist)\n",
    "plt.plot(numpy.ones(1000)*force_sensor_setpoint)\n",
    "plt.title(\"Force sensor using the LSM controller\")\n",
    "plt.ylabel(\"Force (N)\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(ir_sensor)\n",
    "plt.plot(numpy.ones(1000)*range_sensor_setpoint)\n",
    "plt.title(\"Range sensor using the LSM controller\")\n",
    "plt.ylabel(\"Distance (cm)\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(lsm_output_hist)\n",
    "plt.plot(numpy.clip(lsm_output_hist,-85,5))\n",
    "plt.title(\"Closed loop gains from the PIs controllers\")\n",
    "plt.ylabel(\"Gain\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.show()\n",
    "\n",
    "# plt.figure(figsize=(10,5))\n",
    "# plt.plot(pidR_hist)\n",
    "# plt.title(\"Closed loop gains from the PIs controllers\")\n",
    "# plt.ylabel(\"Gain\")\n",
    "# plt.xlabel(\"step\")\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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