CallableBond_HullWhite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from openpyxl import load_workbook\n",
    "import re\n",
    "import QuantLib as ql\n",
    "from scipy.interpolate import interp2d\n",
    "from collections import namedtuple, OrderedDict\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'C:/Users/rstrepparava/Documents/Projects_2018/AU_Teaching/HW_CallableBonds/CallableBond.xlsx'\n",
    "wb = load_workbook(filename=file, read_only=False, data_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_workbook_range(range_string, ws):\n",
    "    col_start, col_end = re.findall(\"[A-Z]+\", range_string)\n",
    "\n",
    "    data_rows = []\n",
    "    for row in ws[range_string]:\n",
    "        data_rows.append([cell.value for cell in row])\n",
    "\n",
    "    return pd.DataFrame(data_rows[1:], columns=data_rows[0])\n",
    "    #from openpyxl.utils import get_column_interval\n",
    "    #return pd.DataFrame(data_rows, columns=get_column_interval(col_start, col_end))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_date(date):\n",
    "    return(ql.Date(date.day, date.month, date.year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_curves(*curves):\n",
    "    %matplotlib notebook\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_xlim(0,15)\n",
    "    ax.set_xticks([0,5,10,15])\n",
    "    times = np.linspace(0.0, 15.0, 400)\n",
    "    for curve, style in curves:\n",
    "        rates = [ curve.zeroRate(t, ql.Continuous).rate() for t in times ]\n",
    "        plt.plot(times, rates, style)\n",
    "        plt.xlabel('tenor (Yrs)')\n",
    "        plt.ylabel('rate')\n",
    "\n",
    "\n",
    "def plot_curve(curve):\n",
    "    plot_curves((curve,'-'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'1Mo': 1/12, '3Mo': 3/12, '6Mo': 6/12, '9Mo': 9/12, \n",
    "   '1Yr': 1, '2Yr': 2, '3Yr': 3, '4Yr': 4, '5Yr': 5,\n",
    "   '6Yr': 6, '7Yr': 7, '8Yr': 8, '9Yr': 9, '10Yr': 10,\n",
    "   '12Yr': 12, '15Yr': 15, '20Yr': 20, '25Yr': 25, '30Yr': 30}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_surface(surf_df):\n",
    "    %matplotlib notebook\n",
    "    ## DataFrame from 1D-arrays\n",
    "    surf = surf_df.stack().reset_index()\n",
    "    x = surf.iloc[:, 0]\n",
    "    y = surf.iloc[:, 1]\n",
    "    z = surf.iloc[:, 2]\n",
    "    df = pd.DataFrame({'x': x, 'y': y, 'z': z}, index=range(len(x)))\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    # Plot using `.trisurf()`:\n",
    "    ax.plot_trisurf(df.x, df.y, df.z, cmap=cm.coolwarm, edgecolor='none', linewidth=0.2)\n",
    "    ax.set_xlabel('Option tenor')\n",
    "    ax.set_ylabel('Swap tenor')\n",
    "    ax.set_zlabel('ATM Normal Volatility')\n",
    "    #cmap='viridis'\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interp(surf_df):\n",
    "    surf = surf_df.stack().reset_index()\n",
    "    x = surf.iloc[:, 0]\n",
    "    y = surf.iloc[:, 1]\n",
    "    z = surf.iloc[:, 2]\n",
    "    return interp2d(x, y, z, kind='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_swaption_helpers(data, index, term_structure, engine):\n",
    "    '''Implement function missing from guide\n",
    "    '''\n",
    "    # based on\n",
    "    # http://khandrikacm.blogspot.ch/2014/03/european-style-interest-rate-swaption.html\n",
    "    nominal = 1.0\n",
    "    fixed_leg_tenor = ql.Period(6, ql.Months)\n",
    "    fixed_leg_daycounter = ql.Thirty360()\n",
    "    floating_leg_daycounter = ql.Actual360()\n",
    "    \n",
    "    swaptions = [ql.SwaptionHelper(ql.Period(swap.start, ql.Years),\n",
    "                                   ql.Period(swap.length, ql.Years),\n",
    "                                   ql.QuoteHandle(ql.SimpleQuote(swap.volatility)),\n",
    "                                   #/\\\n",
    "                                   #term_structure.forwardRate(swap.start, swap.start+swap.length, \n",
    "                                   #                           ql.Continuous).rate())),\n",
    "                                   index, \n",
    "                                   fixed_leg_tenor,\n",
    "                                   fixed_leg_daycounter, \n",
    "                                   floating_leg_daycounter,\n",
    "                                   term_structure,\n",
    "                                   ql.CalibrationHelper.RelativePriceError) for swap in data]\n",
    "                                   #ql.nullDouble(),\n",
    "                                   #nominal,\n",
    "                                   #voltype,\n",
    "                                   #displacement) for swap in data]\n",
    "    for swap in swaptions:\n",
    "        swap.setPricingEngine(engine)\n",
    "    return swaptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calibration_report(swaptions, data):\n",
    "    cal_report = OrderedDict()\n",
    "    for ix, swap in enumerate(swaptions):\n",
    "        swaprep = OrderedDict()\n",
    "        swaprep['Model Price'] = swap.modelValue()\n",
    "        swaprep['Market Price'] = swap.marketValue()\n",
    "        swaprep['Abs Error Price'] = swap.marketValue()-swap.modelValue()\n",
    "        swaprep['Rel Error Price'] = (swap.modelValue()-swap.marketValue())/swap.marketValue()\n",
    "        swaprep['Implied Vol'] = swap.impliedVolatility(swap.modelValue(),\n",
    "                                                        0.01, 1000, 0.001, 0.9)\n",
    "        swaprep['Market Vol'] = data[ix].volatility\n",
    "        swaprep['Abs Error Vol'] = swaprep['Implied Vol'] - swaprep['Market Vol']\n",
    "        swaprep['Rel Error Vol'] = (swaprep['Implied Vol'] - swaprep['Market Vol'])/swaprep['Market Vol']\n",
    "        cal_report[(data[ix].start, data[ix].length,\n",
    "                    data[ix].volatility)] = swaprep\n",
    "    cal_report = pd.DataFrame(cal_report).T\n",
    "    print(\"Cumulative Error : %15.5f\" % np.sqrt((cal_report['Rel Error Vol']**2).sum())) \n",
    "    return cal_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup dates and calendars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "issue      = ql.Date(10, ql.September, 2015)\n",
    "valuation  = ql.Date(31, ql.March, 2017)\n",
    "maturity   = ql.Date(10, ql.September, 2045)\n",
    "first_call = ql.Date(10, ql.September, 2020)\n",
    "cpn_freq   = 1\n",
    "call_freq  = 1\n",
    "dayCounterCurve = ql.Actual360()\n",
    "dayCounterBond  = ql.Thirty360(ql.Thirty360.BondBasis)\n",
    "bizdayconv = ql.ModifiedFollowing\n",
    "calendar   = ql.UnitedStates(ql.UnitedStates.GovernmentBond)\n",
    "ql.Settings.instance().evaluationDate = valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Setup Curve and Volatility Surface "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
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       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
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       "    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",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "        var icon_img = $('<span/>');\n",
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       "        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",
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       "\n",
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       "        fmt_picker.append(option)\n",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "}\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",
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       "}\n",
       "\n",
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       "\n",
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999630089984\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ws = wb['Market Data']\n",
    "\n",
    "discount_df = load_workbook_range('B19:C44', ws)\n",
    "discount_df.set_index(keys='DATE', drop=True, inplace=True)\n",
    "discount_df.dropna(inplace=True)\n",
    "\n",
    "YC = ql.YieldTermStructureHandle(ql.DiscountCurve(discount_df.index.map(lambda x: convert_date(x)),\n",
    "                                                  discount_df['DISCOUNT FACTOR'].values,\n",
    "                                                  dayCounterCurve))\n",
    "plot_curve(YC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999630089984\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VOLSF = load_workbook_range('I2:U21', ws)\n",
    "VOLSF.set_index(keys='VOL.MATRIX', drop=True, inplace=True)\n",
    "VOLSF.dropna(inplace=True)\n",
    "VOLSF = VOLSF / 10000 # Normal volatiltiy in NumeriX in bps\n",
    "VOLSF.columns = VOLSF.columns.map(lambda x: int(x[:-2]))\n",
    "VOLSF.index = VOLSF.index.map(lambda x: d[x])\n",
    "\n",
    "# this version of Quantlib does not handle Bachelier normal volatility, hence\n",
    "# we will use the crude approximation sigmaATM_LN = sigmaATM_N / F valid only when T -> 0\n",
    "\n",
    "for t in VOLSF.index:\n",
    "    for T in VOLSF.columns:\n",
    "        VOLSF.loc[t, T] = VOLSF.loc[t, T] / YC.forwardRate(t, t+T, ql.Continuous, \n",
    "                                                           ql.Annual, True).rate()\n",
    "\n",
    "plot_surface(VOLSF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Setup calibrating instruments (Swaptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = ql.USDLibor(ql.Period(3, ql.Months),YC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=20,15 m=228 fp=0.000001 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n"
     ]
    }
   ],
   "source": [
    "CalibrationData = namedtuple(\"CalibrationData\", \"start, length, volatility\")\n",
    "f = interp(VOLSF)\n",
    "data = [CalibrationData(3, 25, f(3,25)[0]),\n",
    "        CalibrationData(4, 24, f(4,24)[0]),\n",
    "        CalibrationData(5, 23, f(5,23)[0]),\n",
    "        CalibrationData(6, 22, f(6,22)[0]),\n",
    "        CalibrationData(7, 21, f(7,21)[0]),\n",
    "        CalibrationData(8, 20, f(8,20)[0]),\n",
    "        CalibrationData(9, 19, f(9,19)[0]),\n",
    "        CalibrationData(10, 18, f(10,18)[0]),\n",
    "        CalibrationData(11, 17, f(11,17)[0]),\n",
    "        CalibrationData(12, 16, f(12,16)[0]),\n",
    "        CalibrationData(13, 15, f(13,15)[0]),\n",
    "        CalibrationData(14, 14, f(14,14)[0]),\n",
    "        CalibrationData(15, 13, f(15,13)[0]),\n",
    "        CalibrationData(16, 12, f(16,12)[0]),\n",
    "        CalibrationData(17, 11, f(17,11)[0]),\n",
    "        CalibrationData(18, 10, f(18,10)[0]),\n",
    "        CalibrationData(19, 9, f(19,9)[0]),\n",
    "        CalibrationData(20, 8, f(20,8)[0]),\n",
    "        CalibrationData(21, 7, f(21,7)[0]),\n",
    "        CalibrationData(22, 6, f(22,6)[0]),\n",
    "        CalibrationData(23, 5, f(23,5)[0]),\n",
    "        CalibrationData(24, 4, f(24,4)[0]),\n",
    "        CalibrationData(25, 3, f(25,3)[0]),\n",
    "        CalibrationData(26, 2, f(26,2)[0]),\n",
    "        CalibrationData(27, 1, f(27,1)[0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Hull-White Calibration of Reversion Speed (lambda1) and  Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda1 = 0.02025, sigma = 0.00812\n",
      "Cumulative Error :         0.48570\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Abs Error Price</th>\n",
       "      <th>Abs Error Vol</th>\n",
       "      <th>Implied Vol</th>\n",
       "      <th>Market Price</th>\n",
       "      <th>Market Vol</th>\n",
       "      <th>Model Price</th>\n",
       "      <th>Rel Error Price</th>\n",
       "      <th>Rel Error Vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>25</th>\n",
       "      <th>0.282551</th>\n",
       "      <td>0.014768</td>\n",
       "      <td>-0.045598</td>\n",
       "      <td>0.236953</td>\n",
       "      <td>0.091513</td>\n",
       "      <td>0.282551</td>\n",
       "      <td>0.076745</td>\n",
       "      <td>-0.161375</td>\n",
       "      <td>-0.161381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>24</th>\n",
       "      <th>0.278306</th>\n",
       "      <td>0.015245</td>\n",
       "      <td>-0.042906</td>\n",
       "      <td>0.235400</td>\n",
       "      <td>0.098890</td>\n",
       "      <td>0.278306</td>\n",
       "      <td>0.083645</td>\n",
       "      <td>-0.154163</td>\n",
       "      <td>-0.154170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>23</th>\n",
       "      <th>0.273153</th>\n",
       "      <td>0.014622</td>\n",
       "      <td>-0.038863</td>\n",
       "      <td>0.234289</td>\n",
       "      <td>0.102774</td>\n",
       "      <td>0.273153</td>\n",
       "      <td>0.088153</td>\n",
       "      <td>-0.142269</td>\n",
       "      <td>-0.142277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>22</th>\n",
       "      <th>0.267091</th>\n",
       "      <td>0.013090</td>\n",
       "      <td>-0.033653</td>\n",
       "      <td>0.233438</td>\n",
       "      <td>0.103898</td>\n",
       "      <td>0.267091</td>\n",
       "      <td>0.090807</td>\n",
       "      <td>-0.125992</td>\n",
       "      <td>-0.126000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>21</th>\n",
       "      <th>0.261380</th>\n",
       "      <td>0.011277</td>\n",
       "      <td>-0.028549</td>\n",
       "      <td>0.232832</td>\n",
       "      <td>0.103256</td>\n",
       "      <td>0.261380</td>\n",
       "      <td>0.091979</td>\n",
       "      <td>-0.109214</td>\n",
       "      <td>-0.109222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>20</th>\n",
       "      <th>0.256527</th>\n",
       "      <td>0.009492</td>\n",
       "      <td>-0.023964</td>\n",
       "      <td>0.232563</td>\n",
       "      <td>0.101618</td>\n",
       "      <td>0.256527</td>\n",
       "      <td>0.092126</td>\n",
       "      <td>-0.093410</td>\n",
       "      <td>-0.093418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>19</th>\n",
       "      <th>0.251593</th>\n",
       "      <td>0.007507</td>\n",
       "      <td>-0.019126</td>\n",
       "      <td>0.232467</td>\n",
       "      <td>0.098753</td>\n",
       "      <td>0.251593</td>\n",
       "      <td>0.091246</td>\n",
       "      <td>-0.076014</td>\n",
       "      <td>-0.076021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>18</th>\n",
       "      <th>0.248827</th>\n",
       "      <td>0.006185</td>\n",
       "      <td>-0.016075</td>\n",
       "      <td>0.232753</td>\n",
       "      <td>0.095751</td>\n",
       "      <td>0.248827</td>\n",
       "      <td>0.089566</td>\n",
       "      <td>-0.064596</td>\n",
       "      <td>-0.064603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>17</th>\n",
       "      <th>0.246877</th>\n",
       "      <td>0.005093</td>\n",
       "      <td>-0.013629</td>\n",
       "      <td>0.233248</td>\n",
       "      <td>0.092272</td>\n",
       "      <td>0.246877</td>\n",
       "      <td>0.087179</td>\n",
       "      <td>-0.055198</td>\n",
       "      <td>-0.055204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>16</th>\n",
       "      <th>0.244520</th>\n",
       "      <td>0.003754</td>\n",
       "      <td>-0.010434</td>\n",
       "      <td>0.234086</td>\n",
       "      <td>0.087982</td>\n",
       "      <td>0.244520</td>\n",
       "      <td>0.084228</td>\n",
       "      <td>-0.042668</td>\n",
       "      <td>-0.042673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>15</th>\n",
       "      <th>0.241597</th>\n",
       "      <td>0.002327</td>\n",
       "      <td>-0.006758</td>\n",
       "      <td>0.234839</td>\n",
       "      <td>0.083208</td>\n",
       "      <td>0.241597</td>\n",
       "      <td>0.080880</td>\n",
       "      <td>-0.027968</td>\n",
       "      <td>-0.027972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>14</th>\n",
       "      <th>0.239121</th>\n",
       "      <td>0.001106</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>0.234121</td>\n",
       "      <td>0.078214</td>\n",
       "      <td>0.239121</td>\n",
       "      <td>0.077108</td>\n",
       "      <td>-0.014144</td>\n",
       "      <td>-0.020910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>13</th>\n",
       "      <th>0.237300</th>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237300</td>\n",
       "      <td>0.073092</td>\n",
       "      <td>0.237300</td>\n",
       "      <td>0.072938</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>12</th>\n",
       "      <th>0.235449</th>\n",
       "      <td>-0.000672</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.240449</td>\n",
       "      <td>0.067778</td>\n",
       "      <td>0.235449</td>\n",
       "      <td>0.068450</td>\n",
       "      <td>0.009920</td>\n",
       "      <td>0.021236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>11</th>\n",
       "      <th>0.234116</th>\n",
       "      <td>-0.001298</td>\n",
       "      <td>0.009073</td>\n",
       "      <td>0.243188</td>\n",
       "      <td>0.062416</td>\n",
       "      <td>0.234116</td>\n",
       "      <td>0.063714</td>\n",
       "      <td>0.020792</td>\n",
       "      <td>0.038753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>10</th>\n",
       "      <th>0.233536</th>\n",
       "      <td>-0.001687</td>\n",
       "      <td>0.007555</td>\n",
       "      <td>0.241091</td>\n",
       "      <td>0.057045</td>\n",
       "      <td>0.233536</td>\n",
       "      <td>0.058732</td>\n",
       "      <td>0.029580</td>\n",
       "      <td>0.032353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>9</th>\n",
       "      <th>0.232825</th>\n",
       "      <td>-0.002064</td>\n",
       "      <td>0.010288</td>\n",
       "      <td>0.243113</td>\n",
       "      <td>0.051474</td>\n",
       "      <td>0.232825</td>\n",
       "      <td>0.053538</td>\n",
       "      <td>0.040099</td>\n",
       "      <td>0.044188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>8</th>\n",
       "      <th>0.232316</th>\n",
       "      <td>-0.002348</td>\n",
       "      <td>0.013239</td>\n",
       "      <td>0.245556</td>\n",
       "      <td>0.045791</td>\n",
       "      <td>0.232316</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>0.051287</td>\n",
       "      <td>0.056988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>7</th>\n",
       "      <th>0.232120</th>\n",
       "      <td>-0.002293</td>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.246903</td>\n",
       "      <td>0.040303</td>\n",
       "      <td>0.232120</td>\n",
       "      <td>0.042596</td>\n",
       "      <td>0.056893</td>\n",
       "      <td>0.063684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>6</th>\n",
       "      <th>0.230622</th>\n",
       "      <td>-0.002363</td>\n",
       "      <td>0.017831</td>\n",
       "      <td>0.248453</td>\n",
       "      <td>0.034503</td>\n",
       "      <td>0.230622</td>\n",
       "      <td>0.036866</td>\n",
       "      <td>0.068499</td>\n",
       "      <td>0.077318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>5</th>\n",
       "      <th>0.227629</th>\n",
       "      <td>-0.002497</td>\n",
       "      <td>0.022732</td>\n",
       "      <td>0.250361</td>\n",
       "      <td>0.028496</td>\n",
       "      <td>0.227629</td>\n",
       "      <td>0.030993</td>\n",
       "      <td>0.087611</td>\n",
       "      <td>0.099865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>4</th>\n",
       "      <th>0.226547</th>\n",
       "      <td>-0.002287</td>\n",
       "      <td>0.026236</td>\n",
       "      <td>0.252783</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>0.226547</td>\n",
       "      <td>0.025034</td>\n",
       "      <td>0.100547</td>\n",
       "      <td>0.115810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <th>3</th>\n",
       "      <th>0.224808</th>\n",
       "      <td>-0.002025</td>\n",
       "      <td>0.031409</td>\n",
       "      <td>0.256217</td>\n",
       "      <td>0.016911</td>\n",
       "      <td>0.224808</td>\n",
       "      <td>0.018937</td>\n",
       "      <td>0.119751</td>\n",
       "      <td>0.139715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <th>2</th>\n",
       "      <th>0.221766</th>\n",
       "      <td>-0.001543</td>\n",
       "      <td>0.036073</td>\n",
       "      <td>0.257839</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>0.221766</td>\n",
       "      <td>0.012734</td>\n",
       "      <td>0.137866</td>\n",
       "      <td>0.162662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <th>1</th>\n",
       "      <th>0.223392</th>\n",
       "      <td>-0.000758</td>\n",
       "      <td>0.035796</td>\n",
       "      <td>0.259188</td>\n",
       "      <td>0.005636</td>\n",
       "      <td>0.223392</td>\n",
       "      <td>0.006394</td>\n",
       "      <td>0.134571</td>\n",
       "      <td>0.160239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Abs Error Price  Abs Error Vol  Implied Vol  Market Price  \\\n",
       "3  25 0.282551         0.014768      -0.045598     0.236953      0.091513   \n",
       "4  24 0.278306         0.015245      -0.042906     0.235400      0.098890   \n",
       "5  23 0.273153         0.014622      -0.038863     0.234289      0.102774   \n",
       "6  22 0.267091         0.013090      -0.033653     0.233438      0.103898   \n",
       "7  21 0.261380         0.011277      -0.028549     0.232832      0.103256   \n",
       "8  20 0.256527         0.009492      -0.023964     0.232563      0.101618   \n",
       "9  19 0.251593         0.007507      -0.019126     0.232467      0.098753   \n",
       "10 18 0.248827         0.006185      -0.016075     0.232753      0.095751   \n",
       "11 17 0.246877         0.005093      -0.013629     0.233248      0.092272   \n",
       "12 16 0.244520         0.003754      -0.010434     0.234086      0.087982   \n",
       "13 15 0.241597         0.002327      -0.006758     0.234839      0.083208   \n",
       "14 14 0.239121         0.001106      -0.005000     0.234121      0.078214   \n",
       "15 13 0.237300         0.000154       0.000000     0.237300      0.073092   \n",
       "16 12 0.235449        -0.000672       0.005000     0.240449      0.067778   \n",
       "17 11 0.234116        -0.001298       0.009073     0.243188      0.062416   \n",
       "18 10 0.233536        -0.001687       0.007555     0.241091      0.057045   \n",
       "19 9  0.232825        -0.002064       0.010288     0.243113      0.051474   \n",
       "20 8  0.232316        -0.002348       0.013239     0.245556      0.045791   \n",
       "21 7  0.232120        -0.002293       0.014782     0.246903      0.040303   \n",
       "22 6  0.230622        -0.002363       0.017831     0.248453      0.034503   \n",
       "23 5  0.227629        -0.002497       0.022732     0.250361      0.028496   \n",
       "24 4  0.226547        -0.002287       0.026236     0.252783      0.022747   \n",
       "25 3  0.224808        -0.002025       0.031409     0.256217      0.016911   \n",
       "26 2  0.221766        -0.001543       0.036073     0.257839      0.011191   \n",
       "27 1  0.223392        -0.000758       0.035796     0.259188      0.005636   \n",
       "\n",
       "                Market Vol  Model Price  Rel Error Price  Rel Error Vol  \n",
       "3  25 0.282551    0.282551     0.076745        -0.161375      -0.161381  \n",
       "4  24 0.278306    0.278306     0.083645        -0.154163      -0.154170  \n",
       "5  23 0.273153    0.273153     0.088153        -0.142269      -0.142277  \n",
       "6  22 0.267091    0.267091     0.090807        -0.125992      -0.126000  \n",
       "7  21 0.261380    0.261380     0.091979        -0.109214      -0.109222  \n",
       "8  20 0.256527    0.256527     0.092126        -0.093410      -0.093418  \n",
       "9  19 0.251593    0.251593     0.091246        -0.076014      -0.076021  \n",
       "10 18 0.248827    0.248827     0.089566        -0.064596      -0.064603  \n",
       "11 17 0.246877    0.246877     0.087179        -0.055198      -0.055204  \n",
       "12 16 0.244520    0.244520     0.084228        -0.042668      -0.042673  \n",
       "13 15 0.241597    0.241597     0.080880        -0.027968      -0.027972  \n",
       "14 14 0.239121    0.239121     0.077108        -0.014144      -0.020910  \n",
       "15 13 0.237300    0.237300     0.072938        -0.002113       0.000000  \n",
       "16 12 0.235449    0.235449     0.068450         0.009920       0.021236  \n",
       "17 11 0.234116    0.234116     0.063714         0.020792       0.038753  \n",
       "18 10 0.233536    0.233536     0.058732         0.029580       0.032353  \n",
       "19 9  0.232825    0.232825     0.053538         0.040099       0.044188  \n",
       "20 8  0.232316    0.232316     0.048139         0.051287       0.056988  \n",
       "21 7  0.232120    0.232120     0.042596         0.056893       0.063684  \n",
       "22 6  0.230622    0.230622     0.036866         0.068499       0.077318  \n",
       "23 5  0.227629    0.227629     0.030993         0.087611       0.099865  \n",
       "24 4  0.226547    0.226547     0.025034         0.100547       0.115810  \n",
       "25 3  0.224808    0.224808     0.018937         0.119751       0.139715  \n",
       "26 2  0.221766    0.221766     0.012734         0.137866       0.162662  \n",
       "27 1  0.223392    0.223392     0.006394         0.134571       0.160239  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displacement = 0.\n",
    "#voltype = ql.Normal\n",
    "# displacement = 0.1\n",
    "# voltype = ql.ShiftedLognormal\n",
    "\n",
    "model = ql.HullWhite(YC);\n",
    "engine = ql.JamshidianSwaptionEngine(model)\n",
    "swaptions = create_swaption_helpers(data, index, YC, engine)\n",
    "\n",
    "optimization_method = ql.LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8)\n",
    "end_criteria = ql.EndCriteria(10000, 100, 1e-6, 1e-8, 1e-8)\n",
    "model.calibrate(swaptions, optimization_method, end_criteria)\n",
    "\n",
    "lambda1, sigma = model.params()\n",
    "print(\"lambda1 = %6.5f, sigma = %6.5f\" % (lambda1, sigma))\n",
    "calibration_report(swaptions, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Hull-White Volatility Calibration with Fixed Reversion Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda1 = 0.02025, sigma = 0.00812\n",
      "Cumulative Error :         0.49268\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Abs Error Price</th>\n",
       "      <th>Abs Error Vol</th>\n",
       "      <th>Implied Vol</th>\n",
       "      <th>Market Price</th>\n",
       "      <th>Market Vol</th>\n",
       "      <th>Model Price</th>\n",
       "      <th>Rel Error Price</th>\n",
       "      <th>Rel Error Vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>25</th>\n",
       "      <th>0.282551</th>\n",
       "      <td>0.014320</td>\n",
       "      <td>-0.044216</td>\n",
       "      <td>0.238335</td>\n",
       "      <td>0.091513</td>\n",
       "      <td>0.282551</td>\n",
       "      <td>0.077193</td>\n",
       "      <td>-0.156484</td>\n",
       "      <td>-0.156490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>24</th>\n",
       "      <th>0.278306</th>\n",
       "      <td>0.014905</td>\n",
       "      <td>-0.041948</td>\n",
       "      <td>0.236358</td>\n",
       "      <td>0.098890</td>\n",
       "      <td>0.278306</td>\n",
       "      <td>0.083985</td>\n",
       "      <td>-0.150719</td>\n",
       "      <td>-0.150726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>23</th>\n",
       "      <th>0.273153</th>\n",
       "      <td>0.014393</td>\n",
       "      <td>-0.038255</td>\n",
       "      <td>0.234898</td>\n",
       "      <td>0.102774</td>\n",
       "      <td>0.273153</td>\n",
       "      <td>0.088381</td>\n",
       "      <td>-0.140041</td>\n",
       "      <td>-0.140049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>22</th>\n",
       "      <th>0.267091</th>\n",
       "      <td>0.012981</td>\n",
       "      <td>-0.033372</td>\n",
       "      <td>0.233719</td>\n",
       "      <td>0.103898</td>\n",
       "      <td>0.267091</td>\n",
       "      <td>0.090917</td>\n",
       "      <td>-0.124937</td>\n",
       "      <td>-0.124945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>21</th>\n",
       "      <th>0.261380</th>\n",
       "      <td>0.011283</td>\n",
       "      <td>-0.028563</td>\n",
       "      <td>0.232817</td>\n",
       "      <td>0.103256</td>\n",
       "      <td>0.261380</td>\n",
       "      <td>0.091973</td>\n",
       "      <td>-0.109269</td>\n",
       "      <td>-0.109277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>20</th>\n",
       "      <th>0.256527</th>\n",
       "      <td>0.009593</td>\n",
       "      <td>-0.024219</td>\n",
       "      <td>0.232307</td>\n",
       "      <td>0.101618</td>\n",
       "      <td>0.256527</td>\n",
       "      <td>0.092025</td>\n",
       "      <td>-0.094405</td>\n",
       "      <td>-0.094413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>19</th>\n",
       "      <th>0.251593</th>\n",
       "      <td>0.007693</td>\n",
       "      <td>-0.019602</td>\n",
       "      <td>0.231991</td>\n",
       "      <td>0.098753</td>\n",
       "      <td>0.251593</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>-0.077904</td>\n",
       "      <td>-0.077911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>18</th>\n",
       "      <th>0.248827</th>\n",
       "      <td>0.006440</td>\n",
       "      <td>-0.016737</td>\n",
       "      <td>0.232091</td>\n",
       "      <td>0.095751</td>\n",
       "      <td>0.248827</td>\n",
       "      <td>0.089312</td>\n",
       "      <td>-0.067255</td>\n",
       "      <td>-0.067262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>17</th>\n",
       "      <th>0.246877</th>\n",
       "      <td>0.005401</td>\n",
       "      <td>-0.014452</td>\n",
       "      <td>0.232425</td>\n",
       "      <td>0.092272</td>\n",
       "      <td>0.246877</td>\n",
       "      <td>0.086871</td>\n",
       "      <td>-0.058533</td>\n",
       "      <td>-0.058540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>16</th>\n",
       "      <th>0.244520</th>\n",
       "      <td>0.004095</td>\n",
       "      <td>-0.011382</td>\n",
       "      <td>0.233138</td>\n",
       "      <td>0.087982</td>\n",
       "      <td>0.244520</td>\n",
       "      <td>0.083887</td>\n",
       "      <td>-0.046544</td>\n",
       "      <td>-0.046550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>15</th>\n",
       "      <th>0.241597</th>\n",
       "      <td>0.002677</td>\n",
       "      <td>-0.007774</td>\n",
       "      <td>0.233823</td>\n",
       "      <td>0.083208</td>\n",
       "      <td>0.241597</td>\n",
       "      <td>0.080531</td>\n",
       "      <td>-0.032172</td>\n",
       "      <td>-0.032177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>14</th>\n",
       "      <th>0.239121</th>\n",
       "      <td>0.001449</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>0.234121</td>\n",
       "      <td>0.078214</td>\n",
       "      <td>0.239121</td>\n",
       "      <td>0.076765</td>\n",
       "      <td>-0.018530</td>\n",
       "      <td>-0.020910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>13</th>\n",
       "      <th>0.237300</th>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237300</td>\n",
       "      <td>0.073092</td>\n",
       "      <td>0.237300</td>\n",
       "      <td>0.072613</td>\n",
       "      <td>-0.006549</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>12</th>\n",
       "      <th>0.235449</th>\n",
       "      <td>-0.000377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235449</td>\n",
       "      <td>0.067778</td>\n",
       "      <td>0.235449</td>\n",
       "      <td>0.068155</td>\n",
       "      <td>0.005561</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>11</th>\n",
       "      <th>0.234116</th>\n",
       "      <td>-0.001044</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.241415</td>\n",
       "      <td>0.062416</td>\n",
       "      <td>0.234116</td>\n",
       "      <td>0.063460</td>\n",
       "      <td>0.016728</td>\n",
       "      <td>0.031178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>10</th>\n",
       "      <th>0.233536</th>\n",
       "      <td>-0.001483</td>\n",
       "      <td>0.006636</td>\n",
       "      <td>0.240172</td>\n",
       "      <td>0.057045</td>\n",
       "      <td>0.233536</td>\n",
       "      <td>0.058528</td>\n",
       "      <td>0.026004</td>\n",
       "      <td>0.028417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>9</th>\n",
       "      <th>0.232825</th>\n",
       "      <td>-0.001916</td>\n",
       "      <td>0.009540</td>\n",
       "      <td>0.242365</td>\n",
       "      <td>0.051474</td>\n",
       "      <td>0.232825</td>\n",
       "      <td>0.053390</td>\n",
       "      <td>0.037213</td>\n",
       "      <td>0.040976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>8</th>\n",
       "      <th>0.232316</th>\n",
       "      <td>-0.002255</td>\n",
       "      <td>0.012704</td>\n",
       "      <td>0.245020</td>\n",
       "      <td>0.045791</td>\n",
       "      <td>0.232316</td>\n",
       "      <td>0.048045</td>\n",
       "      <td>0.049242</td>\n",
       "      <td>0.054682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>7</th>\n",
       "      <th>0.232120</th>\n",
       "      <td>-0.002253</td>\n",
       "      <td>0.014518</td>\n",
       "      <td>0.246638</td>\n",
       "      <td>0.040303</td>\n",
       "      <td>0.232120</td>\n",
       "      <td>0.042555</td>\n",
       "      <td>0.055895</td>\n",
       "      <td>0.062546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>6</th>\n",
       "      <th>0.230622</th>\n",
       "      <td>-0.002370</td>\n",
       "      <td>0.017882</td>\n",
       "      <td>0.248504</td>\n",
       "      <td>0.034503</td>\n",
       "      <td>0.230622</td>\n",
       "      <td>0.036873</td>\n",
       "      <td>0.068689</td>\n",
       "      <td>0.077538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>5</th>\n",
       "      <th>0.227629</th>\n",
       "      <td>-0.002542</td>\n",
       "      <td>0.023161</td>\n",
       "      <td>0.250791</td>\n",
       "      <td>0.028496</td>\n",
       "      <td>0.227629</td>\n",
       "      <td>0.031038</td>\n",
       "      <td>0.089213</td>\n",
       "      <td>0.101751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>4</th>\n",
       "      <th>0.226547</th>\n",
       "      <td>-0.002362</td>\n",
       "      <td>0.027136</td>\n",
       "      <td>0.253683</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>0.226547</td>\n",
       "      <td>0.025109</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.119779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <th>3</th>\n",
       "      <th>0.224808</th>\n",
       "      <td>-0.002114</td>\n",
       "      <td>0.032864</td>\n",
       "      <td>0.257672</td>\n",
       "      <td>0.016911</td>\n",
       "      <td>0.224808</td>\n",
       "      <td>0.019026</td>\n",
       "      <td>0.125014</td>\n",
       "      <td>0.146187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <th>2</th>\n",
       "      <th>0.221766</th>\n",
       "      <td>-0.001626</td>\n",
       "      <td>0.038143</td>\n",
       "      <td>0.259909</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>0.221766</td>\n",
       "      <td>0.012817</td>\n",
       "      <td>0.145286</td>\n",
       "      <td>0.171998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <th>1</th>\n",
       "      <th>0.223392</th>\n",
       "      <td>-0.000813</td>\n",
       "      <td>0.038534</td>\n",
       "      <td>0.261926</td>\n",
       "      <td>0.005636</td>\n",
       "      <td>0.223392</td>\n",
       "      <td>0.006448</td>\n",
       "      <td>0.144177</td>\n",
       "      <td>0.172495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Abs Error Price  Abs Error Vol  Implied Vol  Market Price  \\\n",
       "3  25 0.282551         0.014320      -0.044216     0.238335      0.091513   \n",
       "4  24 0.278306         0.014905      -0.041948     0.236358      0.098890   \n",
       "5  23 0.273153         0.014393      -0.038255     0.234898      0.102774   \n",
       "6  22 0.267091         0.012981      -0.033372     0.233719      0.103898   \n",
       "7  21 0.261380         0.011283      -0.028563     0.232817      0.103256   \n",
       "8  20 0.256527         0.009593      -0.024219     0.232307      0.101618   \n",
       "9  19 0.251593         0.007693      -0.019602     0.231991      0.098753   \n",
       "10 18 0.248827         0.006440      -0.016737     0.232091      0.095751   \n",
       "11 17 0.246877         0.005401      -0.014452     0.232425      0.092272   \n",
       "12 16 0.244520         0.004095      -0.011382     0.233138      0.087982   \n",
       "13 15 0.241597         0.002677      -0.007774     0.233823      0.083208   \n",
       "14 14 0.239121         0.001449      -0.005000     0.234121      0.078214   \n",
       "15 13 0.237300         0.000479       0.000000     0.237300      0.073092   \n",
       "16 12 0.235449        -0.000377       0.000000     0.235449      0.067778   \n",
       "17 11 0.234116        -0.001044       0.007299     0.241415      0.062416   \n",
       "18 10 0.233536        -0.001483       0.006636     0.240172      0.057045   \n",
       "19 9  0.232825        -0.001916       0.009540     0.242365      0.051474   \n",
       "20 8  0.232316        -0.002255       0.012704     0.245020      0.045791   \n",
       "21 7  0.232120        -0.002253       0.014518     0.246638      0.040303   \n",
       "22 6  0.230622        -0.002370       0.017882     0.248504      0.034503   \n",
       "23 5  0.227629        -0.002542       0.023161     0.250791      0.028496   \n",
       "24 4  0.226547        -0.002362       0.027136     0.253683      0.022747   \n",
       "25 3  0.224808        -0.002114       0.032864     0.257672      0.016911   \n",
       "26 2  0.221766        -0.001626       0.038143     0.259909      0.011191   \n",
       "27 1  0.223392        -0.000813       0.038534     0.261926      0.005636   \n",
       "\n",
       "                Market Vol  Model Price  Rel Error Price  Rel Error Vol  \n",
       "3  25 0.282551    0.282551     0.077193        -0.156484      -0.156490  \n",
       "4  24 0.278306    0.278306     0.083985        -0.150719      -0.150726  \n",
       "5  23 0.273153    0.273153     0.088381        -0.140041      -0.140049  \n",
       "6  22 0.267091    0.267091     0.090917        -0.124937      -0.124945  \n",
       "7  21 0.261380    0.261380     0.091973        -0.109269      -0.109277  \n",
       "8  20 0.256527    0.256527     0.092025        -0.094405      -0.094413  \n",
       "9  19 0.251593    0.251593     0.091060        -0.077904      -0.077911  \n",
       "10 18 0.248827    0.248827     0.089312        -0.067255      -0.067262  \n",
       "11 17 0.246877    0.246877     0.086871        -0.058533      -0.058540  \n",
       "12 16 0.244520    0.244520     0.083887        -0.046544      -0.046550  \n",
       "13 15 0.241597    0.241597     0.080531        -0.032172      -0.032177  \n",
       "14 14 0.239121    0.239121     0.076765        -0.018530      -0.020910  \n",
       "15 13 0.237300    0.237300     0.072613        -0.006549       0.000000  \n",
       "16 12 0.235449    0.235449     0.068155         0.005561       0.000000  \n",
       "17 11 0.234116    0.234116     0.063460         0.016728       0.031178  \n",
       "18 10 0.233536    0.233536     0.058528         0.026004       0.028417  \n",
       "19 9  0.232825    0.232825     0.053390         0.037213       0.040976  \n",
       "20 8  0.232316    0.232316     0.048045         0.049242       0.054682  \n",
       "21 7  0.232120    0.232120     0.042555         0.055895       0.062546  \n",
       "22 6  0.230622    0.230622     0.036873         0.068689       0.077538  \n",
       "23 5  0.227629    0.227629     0.031038         0.089213       0.101751  \n",
       "24 4  0.226547    0.226547     0.025109         0.103856       0.119779  \n",
       "25 3  0.224808    0.224808     0.019026         0.125014       0.146187  \n",
       "26 2  0.221766    0.221766     0.012817         0.145286       0.171998  \n",
       "27 1  0.223392    0.223392     0.006448         0.144177       0.172495  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constrained_model = ql.HullWhite(YC, 0.03, 0.001);\n",
    "engine = ql.JamshidianSwaptionEngine(constrained_model)\n",
    "swaptions = create_swaption_helpers(data, index, YC, engine)\n",
    "\n",
    "optimization_method = ql.LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8)\n",
    "end_criteria = ql.EndCriteria(10000, 100, 1e-6, 1e-8, 1e-8)\n",
    "constrained_model.calibrate(swaptions, optimization_method, end_criteria, ql.NoConstraint(), [], [True, False])\n",
    "\n",
    "lambda1, sigma = model.params()\n",
    "print(\"lambda1 = %6.5f, sigma = %6.5f\" % (lambda1, sigma))\n",
    "calibration_report(swaptions, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Callable Bond Pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "callability_schedule = ql.CallabilitySchedule()\n",
    "call_price = 100.0\n",
    "call_date = first_call\n",
    "for i in range(int(np.ceil((maturity - first_call)/365.25))):\n",
    "    callability_price  = ql.CallabilityPrice(call_price, ql.CallabilityPrice.Clean)\n",
    "    callability_schedule.append(ql.Callability(callability_price, ql.Callability.Call, call_date))\n",
    "    call_date = calendar.advance(call_date, 1, ql.Years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tenor = ql.Period(ql.Annual)\n",
    "schedule = ql.Schedule(issue, maturity, tenor, calendar, bizdayconv, bizdayconv, \n",
    "                       ql.DateGeneration.Backward, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "settlement_days = 3\n",
    "face_amount = 100\n",
    "coupon = 0.0425\n",
    "\n",
    "bond = ql.CallableFixedRateBond(settlement_days, face_amount, schedule, [coupon], dayCounterBond,\n",
    "                                bizdayconv, face_amount, issue, callability_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_bond(ts_handle, a, s, grid_points, bond):\n",
    "    model = ql.HullWhite(ts_handle, a, s)\n",
    "    engine = ql.TreeCallableFixedRateBondEngine(model, grid_points)\n",
    "    bond.setPricingEngine(engine)\n",
    "    return bond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bond price:  106.67001515218413\n"
     ]
    }
   ],
   "source": [
    "b = value_bond(YC, lambda1, sigma, 40, bond)\n",
    "print(\"Bond price: \",b.dirtyPrice())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. NonCallable Bond Pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bond price:  134.30324712745156\n"
     ]
    }
   ],
   "source": [
    "fixedschedule = ql.Schedule(issue, maturity, tenor, calendar, bizdayconv, bizdayconv, \n",
    "                            ql.DateGeneration.Backward, False)\n",
    "fixedbond = ql.FixedRateBond(settlement_days, face_amount, fixedschedule,  [coupon], dayCounterBond)\n",
    "bond_engine = ql.DiscountingBondEngine(YC)\n",
    "fixedbond.setPricingEngine(bond_engine)\n",
    "print(\"Bond price: \",fixedbond.dirtyPrice())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Price Compression for a Callable Bond vs. Non Callable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0.02, 0.07, 200)\n",
    "call   = np.zeros(200) \n",
    "bullet = np.zeros(200)\n",
    "\n",
    "for i, rate in enumerate(x):\n",
    "    ts = ql.FlatForward(valuation, rate, dayCounterCurve, ql.Compounded, ql.Annual)\n",
    "    ts_handle = ql.YieldTermStructureHandle(ts)    \n",
    "    c = value_bond(ts_handle, lambda1, sigma, 200, bond)\n",
    "    call[i] = c.dirtyPrice()\n",
    "    bond_engine = ql.DiscountingBondEngine(ts_handle)\n",
    "    fixedbond.setPricingEngine(bond_engine)\n",
    "    bullet[i] = fixedbond.dirtyPrice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999630089984\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'dirty price')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'callable': call, 'bullet': bullet}\n",
    "df = pd.DataFrame(data=d, index=x)\n",
    "ax = df.plot(colormap='jet', markersize=10, title='Negative Convexity of callable bonds')\n",
    "ax.set_xlabel('rate')\n",
    "ax.set_ylabel('dirty price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}