Lecture 15 - Linear Regression.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ----- make nice figures -----\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 100\n",
    "from cycler import cycler\n",
    "COLORS = ['#F00D2C', '#242482', '#0071BE', '#4E8F00', '#553C67', '#DA5319']\n",
    "default_cycler = cycler(color=COLORS)\n",
    "plt.rc('axes', prop_cycle=default_cycler) \n",
    "# -----------------------------\n",
    "\n",
    "# This will be used to make the 3d plots interactive\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples of the linear model\n",
    "\n",
    "For linear regression, we use the linear model\n",
    "$$ y(x) = \\theta_1 \\varphi_1(x) + \\theta_2 \\varphi_2(x) + ... + \\theta_n \\varphi_n(x),$$\n",
    "\n",
    "The functions $\\varphi_j(x)$ are called **basis functions**, and they have to be specified before we can\n",
    "run linear regression. Choosing the best basis functions for your given data is a kind of art. Below, we'll explore a few simple examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1\n",
    "\n",
    "Here, our choice of basis functions are:\n",
    "* $ \\mathbf x = (x_1, x_2)$\n",
    "* $ \\varphi_1(x_1, x_2) = x_1$\n",
    "* $ \\varphi_2(x_1, x_2) = x_2$\n",
    "\n",
    "This results in the model:\n",
    "$$ y = \\theta_1 x_1 + \\theta_2 x_2 $$\n",
    "\n",
    "Let's plot this for a choice of the parameters $\\theta = (\\theta_1, \\theta_2)^T$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_1 = -1\n",
    "theta_2 = 0.5\n",
    "\n",
    "# We'll plot the model function in the interval [-1, 1] for both input variables\n",
    "x1 = np.linspace(-1, 1, 10)\n",
    "x2 = np.linspace(-1, 1, 10)\n",
    "\n",
    "# Form all pairs of points (x_1, x_2)\n",
    "X1, X2 = np.meshgrid(x1, x2)\n",
    "\n",
    "# Evaluate the function at all the point\n",
    "y = theta_1*X1 + theta_2*X2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have 2 inputs and one output, to plot $y$ versus $x_1$ and $x_2$, we'll make 3D surface plot. To plot the surface, we need to use `matplotlib`'s 3D functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "    this.root.append(canvas_div);\n",
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       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "    nav_element.append(status_bar);\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
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       "        break;\n",
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       "        cursor = 'move';\n",
       "        break;\n",
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       "\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",
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       "}\n",
       "\n",
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       "    fig.image_mode = msg['mode'];\n",
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       "\n",
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       "    // Called whenever the canvas gets updated.\n",
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       "}\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",
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       "\n",
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       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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IECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIHDt2DEJAAAAwzD/riujTxQMwq4SIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAzm2fmAAAQUUlEQVQgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQIBDjtwPxQmOq1gAAAABJRU5ErkJggg==\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we do this, the `matplotlib` axis `ax` can make 3D plots. We'll use the `plot_surface` function. See the documentation of `Axis3D` to see what other kinds of 3D plots you can make.\n",
    "\n",
    "https://matplotlib.org/3.1.1/api/_as_gen/mpl_toolkits.mplot3d.axes3d.Axes3D.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x2')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make the surface plot\n",
    "ax.plot_surface(X1, X2, y)\n",
    "ax.set_xlabel('x1')\n",
    "ax.set_ylabel('x2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2\n",
    "\n",
    "Here we consider a different set of basis functions. It's important to note that basis functions can be whatever you want/need them to be, if you think the function represents your data somehow. Below, we picked basis functions that are polynomials in one or both input variables. We also picked a constant function $\\varphi_3(x) = 1$. This allows us to have an \"intercept\" term.\n",
    "\n",
    "* $ \\varphi_1(x_1, x_2) = x^2_2$\n",
    "* $ \\varphi_2(x_1, x_2) = x^2_1 + x_2$\n",
    "* $ \\varphi_3(x_1, x_2) = 1$\n",
    "\n",
    "This results in the model function\n",
    "\n",
    "$$ y = \\theta_1 x^2_2 + \\theta_2(x^2_1 + x_2) + \\theta_3$$\n",
    "\n",
    "We see that without the constant term $\\varphi_3(x) = 1$, we would not have the parameter $\\theta_3$ in the above. This parameter is an example of an \"intercept\" term. The model predicts that at $x_1 = x_2 = 0$, $y = \\theta_3$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x2')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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IECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIECAAAECBAgQIECAAAECgYBAH6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0fIECAAAECBAgQIHDt2DEJAAAAwzD/riujTxQMwq4SIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAzm2fmAAAQUUlEQVQgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQICDQ+wABAgQIECBAgAABAgQIECBAgAABAgQIEBgEBPoB3SQBAgQIECBAgAABAgQIECBAgAABAgQIEBDofYAAAQIECBAgQIAAAQIECBAgQIAAAQIECAwCAv2AbpIAAQIECBAgQIAAAQIECBAgQIAAAQIECAj0PkCAAAECBAgQIECAAAECBAgQIECAAAECBAYBgX5AN0mAAAECBAgQIECAAAECBAgQIECAAAECBAR6HyBAgAABAgQIECBAgAABAgQIECBAgAABAoOAQD+gmyRAgAABAgQIECBAgAABAgQIECBAgAABAgK9DxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUFAoB/QTRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAYHeBwgQIECAAAECBAgQIECAAAECBAgQIECAwCAg0A/oJgkQIECAAAECBAgQIECAAAECBAgQIECAgEDvAwQIECBAgAABAgQIECBAgAABAgQIECBAYBAQ6Ad0kwQIECBAgAABAgQIECBAgAABAgQIECBAQKD3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAgI9AO6SQIECBAgQIAAAQIECBAgQIAAAQIECBAgIND7AAECBAgQIECAAAECBAgQIECAAAECBAgQGAQE+gHdJAECBAgQIECAAAECBAgQIECAAAECBAgQEOh9gAABAgQIECBAgAABAgQIECBAgAABAgQIDAIC/YBukgABAgQIECBAgAABAgQIECBAgAABAgQICPQ+QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgGBfkA3SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEBHofIECAAAECBAgQIECAAAECBAgQIECAAAECg4BAP6CbJECAAAECBAgQIECAAAECBAgQIECAAAECAr0PECBAgAABAgQIECBAgAABAgQIECBAgACBQUCgH9BNEiBAgAABAgQIECBAgAABAgQIECBAgAABgd4HCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAICDQD+gmCRAgQIAAAQIECBAgQIAAAQIECBAgQICAQO8DBAgQIECAAAECBAgQIECAAAECBAgQIEBgEBDoB3STBAgQIECAAAECBAgQIECAAAECBAgQIEBAoPcBAgQIECBAgAABAgQIECBAgAABAgQIECAwCAj0A7pJAgQIECBAgAABAgQIECBAgAABAgQIECAg0PsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYBAT6Ad0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ6H2AAAECBAgQIECAAAECBAgQIECAAAECBAgMAgL9gG6SAAECBAgQIECAAAECBAgQIECAAAECBAgI9D5AgAABAgQIECBAgAABAgQIECBAgAABAgQGAYF+QDdJgAABAgQIECBAgAABAgQIECBAgAABAgQEeh8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQKDgEA/oJskQIAAAQIECBAgQIAAAQIECBAgQIAAAQICvQ8QIECAAAECBAgQIECAAAECBAgQIECAAIFBQKAf0E0SIECAAAECBAgQIECAAAECBAgQIECAAAGB3gcIECBAgAABAgQIECBAgAABAgQIECBAgMAgINAP6CYJECBAgAABAgQIECBAgAABAgQIECBAgIBA7wMECBAgQIAAAQIECBAgQIAAAQIECBAgQGAQEOgHdJMECBAgQIAAAQIECBAgQIAAAQIECBAgQECg9wECBAgQIECAAAECBAgQIECAAAECBAgQIDAICPQDukkCBAgQIECAAAECBAgQIECAAAECBAgQIBDjtwPxQmOq1gAAAABJRU5ErkJggg==\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_1 = 2\n",
    "theta_2 = -1\n",
    "theta_3 = 10\n",
    "\n",
    "# Evaluate the function\n",
    "y = theta_1*X2**2 + theta_2*(X1**2 + X2) + theta_3\n",
    "\n",
    "# Plot the function\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.plot_surface(X1, X2, y)\n",
    "ax.set_xlabel('x1')\n",
    "ax.set_ylabel('x2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Least Squares Examples\n",
    "\n",
    "Given some data points $\\left\\{(\\textbf x_i, y_i)\\right\\}_{i=1}^m$, and a linear model \n",
    "$$y = f(\\textbf x,  \\theta)$$\n",
    "$$ = \\theta_1 \\varphi_2(\\textbf x) + \\theta_2 \\varphi_2(\\textbf x) + ... + \\theta_n \\varphi_n(\\textbf x),$$\n",
    "**linear least squares regression** attempts to find the value of parameters $\\theta$ that minimize the cost function\n",
    "\n",
    "$$J(\\theta) = \\sum_{i=1}^m \\left[ y_i - f(\\textbf x_i, \\theta) \\right]^2. $$\n",
    "\n",
    "After some calculus, we can show that the parameter values, $\\hat \\theta_\\text{LS}$, that minimize the above\n",
    "cost function satisfies the **normal equations**\n",
    "\n",
    "$$ \\Phi^T \\mathbf y = \\Phi^T\\Phi \\theta, $$\n",
    "\n",
    "where $\\Phi$ is the design matrix, with elements $\\Phi_{i,j} = \\varphi_j(\\mathbf x_i).$$ By solving this linear system, we get the optimal parameter values. Below we'll see several examples of how this is done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop making interactive plots\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.dpi']= 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the data in `data/lec_14_ex1.txt`, which has 2 columns corresponding to a 1 dimensional input variable $x$ and the output variable $y$. There are several $(x, y)$ pairs in this data file. Below, we plot this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.loadtxt('data/lec_15_ex1.txt', delimiter =',')\n",
    "x = X[:,0]\n",
    "y = X[:,1]\n",
    "\n",
    "# Plot the data\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data suggests that the output is a polynomial function of the input, so as a basis, we'll use the monomial basis: $\\left\\{1, x, x^2, x^3\\right\\}$. Once we do this, we have to evaluate each of our data points on each of the basis functions to form the design matrix $\\Phi$. Recall that the rows of $\\Phi$ correspond to each of the basis elements evaluated at a single input value $x_i$. Columns of $\\Phi$ correspond to a single basis function, evaluated at every input value $x_i$ in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 4)\n"
     ]
    }
   ],
   "source": [
    "# Evaluate basis functions for columns of the design matrix\n",
    "phi_0 = np.ones(len(x))\n",
    "phi_1 = x**1\n",
    "phi_2 = x**2\n",
    "phi_3 = x**3\n",
    "\n",
    "# Design matrix\n",
    "Phi = np.array([phi_0, phi_1, phi_2, phi_3]).T\n",
    "print(Phi.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we form the design matrix, we can form the **Moore-Penrose pseudoinverse**:\n",
    "\n",
    "$$\\Phi^+ = (\\Phi^T\\Phi)\\Phi^T.$$\n",
    "\n",
    "We'll use the matrix multiplication operator `@`, which can be used on `numpy` arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Form Moore-Penrose inverse\n",
    "Phi_plus = np.linalg.inv(Phi.T @ Phi) @ Phi.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the pseudoinverse, we can get the least squares solution\n",
    "\n",
    "$$\\hat \\theta_\\text{LS} = \\Phi^+ \\mathbf y.$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.06321874 -0.50114373 -0.12291531  0.9831122 ]\n"
     ]
    }
   ],
   "source": [
    "# Solve for least square solution\n",
    "theta_LS = Phi_plus @ y\n",
    "print(theta_LS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Along with the basis functions, the parameter values $\\hat \\theta_\\text{LS}$ yield a fully-specified model, that\n",
    "we can evaluate at any $x$ to predict a value of $y$.\n",
    "\n",
    "Below, we'll evaluate the model at 50 uniformly spaced points $x_i$ between -1 and 1. We'll also plot the data to compare the fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1266eed68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we have a model to evaluate the function at any x\n",
    "x_plot = np.linspace(-1, 1, 50)\n",
    "y_plot =  theta_LS[0] + theta_LS[1]*x_plot + theta_LS[2]*x_plot**2 + theta_LS[3]*x_plot**3\n",
    "\n",
    "# Plot the model and the data\n",
    "plt.plot(x_plot, y_plot, color = COLORS[1])\n",
    "plt.scatter(x, y)\n",
    "\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['Model', 'Data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2\n",
    "\n",
    "This example uses the Fourier basis instead of the monomial basis. Other than that, everything else is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load and plot the data\n",
    "X = np.loadtxt('data/lec_15_ex2.txt', delimiter =',')\n",
    "x = X[:,0]\n",
    "y = X[:,1]\n",
    "\n",
    "# Plot the data\n",
    "plt.scatter(x, y, s = 7)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5)\n"
     ]
    }
   ],
   "source": [
    "# Eavaluate basis functions and form design matrix\n",
    "phi_0 = np.ones(len(x))\n",
    "phi_1 = np.sin(x)\n",
    "phi_2 = np.sin(2*x)\n",
    "phi_3 = np.sin(3*x)\n",
    "phi_4 = np.sin(4*x)\n",
    "\n",
    "Phi = np.array([phi_0, phi_1, phi_2, phi_3, phi_4]).T\n",
    "print(Phi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.00000000e-01  2.00000000e-01  5.46437895e-17 -2.00000000e-01\n",
      "  3.12250226e-17]\n"
     ]
    }
   ],
   "source": [
    "# Form Moore-Penrose inverse\n",
    "Phi_plus = np.linalg.inv(Phi.T @ Phi) @ Phi.T\n",
    "\n",
    "# Solve for least square solution\n",
    "theta_LS = Phi_plus @ y\n",
    "\n",
    "print(theta_LS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x126f32fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plot = np.linspace(-6, 6, 100)\n",
    "\n",
    "# Evaluate y_plot using a for-loop\n",
    "y_plot = theta_LS[0]*np.ones(len(x_plot))\n",
    "for i in range(1, len(theta_LS)):\n",
    "    y_plot = y_plot + theta_LS[i]*np.sin(i*x_plot)\n",
    "\n",
    "# Plot the model and the data\n",
    "plt.plot(x_plot, y_plot, color = COLORS[1])\n",
    "plt.scatter(x, y)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['Model', 'Data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `numpy` functions for least-squares solutions\n",
    "\n",
    "In fact, using the Moore-Penrose pseuodinverse explicitly is not that great. One of the reasons is that it necessitates the explict calculation of an inverse $(\\Phi^T\\Phi)^{-1}$. In practice, you really should never calculate an inverse of a matrix. (See https://www.johndcook.com/blog/2010/01/19/dont-invert-that-matrix/). It is computationally expensive, and often numerically unstable.\n",
    "\n",
    "How, then, do we solve the normal equations\n",
    "\n",
    "$$ \\Phi^T \\mathbf y = \\Phi^T\\Phi \\theta\\text{ ?}$$ \n",
    "\n",
    "In general, there are ways of solving the system $b = A \\theta$ without inverting $A$. (In our example, $b = \\Phi^T \\mathbf y$, and $A = \\Phi^T \\Phi$.) How this is done is slightly out of the scope of this course. Instead, we'll point you to functions that solve this system the right way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.00000000e-01  2.00000000e-01  5.88785768e-17 -2.00000000e-01\n",
      "  2.50158868e-17]\n"
     ]
    }
   ],
   "source": [
    "# First is using np.linalg.solve to solve the normal equations\n",
    "theta = np.linalg.solve(Phi.T @ Phi, Phi.T @ y)\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.00000000e-01  2.00000000e-01 -8.67361738e-18 -2.00000000e-01\n",
      "  6.24500451e-17]\n"
     ]
    }
   ],
   "source": [
    "# We can additionally use np.linalg.lstsq to compute the least squares solution to the original equation\n",
    "theta = np.linalg.lstsq(Phi, y, rcond = None)[0]\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.00000000e-01  2.00000000e-01 -8.67361738e-18 -2.00000000e-01\n",
      "  6.24500451e-17]\n"
     ]
    }
   ],
   "source": [
    "# We can also use the LinearRegression class in scikit-learn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Instantiate a model\n",
    "model = LinearRegression(fit_intercept = False)\n",
    "\n",
    "# train model\n",
    "model.fit(Phi, y)\n",
    "\n",
    "# get the thet avalues\n",
    "theta = model.coef_\n",
    "print(theta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12700dd68>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict at any x value, but we have to evaluate the \n",
    "# x values at the basis functions, i.e. form the\n",
    "# design matrix for the points we wish to predict\n",
    "phi0_plot = np.ones(len(x_plot))\n",
    "phi1_plot = np.sin(x_plot)\n",
    "phi2_plot = np.sin(2*x_plot)\n",
    "phi3_plot = np.sin(3*x_plot)\n",
    "phi4_plot = np.sin(4*x_plot)\n",
    "Phi_plot =  np.array([phi0_plot, phi1_plot, phi2_plot, phi3_plot, phi4_plot]).T\n",
    "\n",
    "# Predict the response \n",
    "y_plot = model.predict(Phi_plot)\n",
    "\n",
    "# Plot the model and the data\n",
    "plt.plot(x_plot, y_plot, color = COLORS[1])\n",
    "plt.scatter(x, y)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(['Model', 'Data'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "holmespy_kernel",
   "language": "python",
   "name": "holmespy_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}