Stationary Bootstrap Italian Swap Example.ipynb

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   "source": [
    "# Example of the stationary bootstrap algorithm in practice"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a782c67e",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a6b055",
   "metadata": {},
   "source": [
    "## Goal\n",
    "\n",
    "The goal of this workbook is to demonstrate the use of the stationary bootstrap algorithm. In this example, 3 new samples are generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8fe8c460",
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   "source": [
    "nSample = 3 # Number of generated samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e065d54",
   "metadata": {},
   "source": [
    "EURO denominated 6M interest-rate-swap rate obtained from https://www.teleborsa.it/Quotazioni/Tassi/Eurirs for date 12/11/2021 and interpolated using the Smith & Wilson algorithm for missing durations."
   ]
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   "cell_type": "code",
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   "id": "ce1daaa1",
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   "outputs": [],
   "source": [
    "swap = pd.DataFrame({'Swap rate': [-0.00497999999999998,\n",
    "-0.00336999999999998,\n",
    "-0.00219000000000003,\n",
    "-0.00137999999999994,\n",
    "-0.000839999999999952,\n",
    "-0.000349999999999961,\n",
    "0.000140000000000029,\n",
    "0.000669999999999948,\n",
    "0.00116999999999989,\n",
    "0.00165999999999999,\n",
    "0.00208999999999993,\n",
    "0.00245999999999991,\n",
    "0.00277690952371801,\n",
    "0.00303541131903184,\n",
    "0.00323000000000007,\n",
    "0.00336225372994803,\n",
    "0.00345212702349018,\n",
    "0.00351682745133952,\n",
    "0.00356726077755898,\n",
    "0.00360999999999989,\n",
    "0.00364393467821089,\n",
    "0.00365001038370538,\n",
    "0.00360991273768496,\n",
    "0.00350996855727703,\n",
    "0.00333999999999990,\n",
    "0.00310968998405592,\n",
    "0.00288888447388769,\n",
    "0.00274107227669318,\n",
    "0.00271077735186531,\n",
    "0.00283000000000011]})"
   ]
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Swap rate</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>-0.00498</td>\n",
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       "      <td>-0.00138</td>\n",
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       "      <th>4</th>\n",
       "      <td>-0.00084</td>\n",
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      "text/plain": [
       "   Swap rate\n",
       "0   -0.00498\n",
       "1   -0.00337\n",
       "2   -0.00219\n",
       "3   -0.00138\n",
       "4   -0.00084"
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    "swap.head()"
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   "cell_type": "code",
   "execution_count": 198,
   "id": "2926967d",
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   "source": [
    "sampleLen = swap.size; # Length of the sample will be the same as the input. "
   ]
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  {
   "cell_type": "markdown",
   "id": "9df42aa7",
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   "source": [
    "The swap rates cann ot be stacked in a sampling algorithm therefore, they are used to   Vector with the 1-year forward curve. Convert rates into 1-year forward curve"
   ]
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   "cell_type": "markdown",
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    "Note that you loose 1 degree of freedom when converting from rates to 1-year-forward rates using the formula\n",
    "\n",
    "$ fwd(t) = \\frac{(1 + r_{SWAP}(0, t+1))^{t+1}}{(1 + r_{SWAP}(0, t))^{t}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "baace934",
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   "outputs": [],
   "source": [
    "forwards = np.zeros((sampleLen-1,1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "869c62ec",
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   "source": [
    "for iTime in range(0,sampleLen-1): \n",
    "    forwards[iTime][0] = np.power(1 + swap[\"Swap rate\"][iTime+1],iTime+1) / np.power(1+swap[\"Swap rate\"][iTime], iTime) - 1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "930b2cab",
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   "outputs": [],
   "source": [
    "forwards = pd.DataFrame(forwards, columns={'Forward rate'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "2b641380",
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       "      <th>Forward rate</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.003370</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.000242</td>\n",
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       "      <td>0.000782</td>\n",
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       "      <td>0.001612</td>\n",
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       "   Forward rate\n",
       "0     -0.003370\n",
       "1     -0.001009\n",
       "2      0.000242\n",
       "3      0.000782\n",
       "4      0.001612"
      ]
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     "execution_count": 202,
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    "forwards.head()"
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   "source": [
    "Calculated using the method presented in 2004 paper by Politis & White."
   ]
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  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "a3cd222f",
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   "outputs": [],
   "source": [
    "m = 3.8299"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "Generate Stationary bootstraped samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b8ad3921",
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   "source": [
    "def StationaryBootstrap(data: np.ndarray, m, sampleLength)-> np.ndarray:\n",
    "    \"\"\"\n",
    "    Returns a bootstraped sample of the time-series \"data\" of length \"sampleLength. \n",
    "    The algorithm used is stationary bootstrap from 1994 Politis & Romano.\n",
    "    \n",
    "    Args:     \n",
    "        data ... ndarray array. A single vector of numbers containing the time-series.\n",
    "        m    ... floating number. Parameter to stationary bootstrap indicating the average length of each block in the sample.\n",
    "        sampleLength ... integer. Length of the bootstrapped sample returned as output.\n",
    "     \n",
    "    Returns:     \n",
    "        sample ... ndarray array containing the final bootstraped sample.\n",
    "      \n",
    "    Example of use:\n",
    "    >>> import numpy as np\n",
    "    >>> data = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "    >>> m = 4\n",
    "    >>> sampleLength = 12\n",
    "    >>> StationaryBootstrap(data, m, sampleLength)\n",
    "    Out[0]:  array([[9.],\n",
    "                    [3.],\n",
    "                    [4.],\n",
    "                    [5.],\n",
    "                    [6.],\n",
    "                    [7.],\n",
    "                    [8.],\n",
    "                    [7.],\n",
    "                    [2.],\n",
    "                    [3.],\n",
    "                    [4.],\n",
    "                    [2.]])\n",
    "\n",
    "    Original paper about stationary bootstrap:\n",
    "    Dimitris N. Politis & Joseph P. Romano (1994) The Stationary Bootstrap, Journal of the American Statistical \n",
    "        Association, 89:428, 1303-1313, DOI: 10.1080/01621459.1994.10476870    \n",
    "\n",
    "    Implemented by Gregor Fabjan from Qnity Consultants on 12/11/2021.\n",
    "\n",
    "    \"\"\"\n",
    "    accept = 1/m  \n",
    "    lenData = data.shape[0]\n",
    "\n",
    "    sampleIndex = np.random.randint(0,high =lenData,size=1);\n",
    "    sample = np.zeros((sampleLength,1))\n",
    "    for iSample in range(sampleLength):\n",
    "        if np.random.uniform(0,1,1)>=accept:\n",
    "            sampleIndex += 1\n",
    "            if sampleIndex >= lenData:\n",
    "                sampleIndex=0        \n",
    "        else:\n",
    "            sampleIndex = np.random.randint(0,high = lenData,size=1)\n",
    "\n",
    "        sample[iSample,0] = data[sampleIndex]\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c27a447",
   "metadata": {},
   "source": [
    "In this step, the samples are generated by repeatedly calling th StationaryBootstrap function. These samples are then saved into the Samples array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "304550da",
   "metadata": {},
   "outputs": [],
   "source": [
    "for iSample in range(0, nSample):\n",
    "    if iSample == 0:\n",
    "        Samples = np.array(StationaryBootstrap(forwards.values,m,sampleLen))\n",
    "    else:\n",
    "        Samples = np.append(Samples, np.array(StationaryBootstrap(forwards.values,m,sampleLen)),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "93654210",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "          0         1         2\n",
       "0 -0.002402  0.004801  0.006168\n",
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       "2 -0.001094  0.004425  0.001314\n",
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    "Samples = pd.DataFrame(Samples)\n",
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       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = Samples.copy().to_numpy()\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(plot_data) \n",
    "ax.set_title('Forward rate samples')\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}