{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f84b965",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this example requires that ENDFtk and ACEtk be installed as well\n",
    "import ENDFtk\n",
    "import ACEtk\n",
    "import scion\n",
    "\n",
    "import matplotlib.pyplot as plot\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b44313bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the files we want to look at\n",
    "endffile = 'resources/n-001_H_001-endf80.endf'\n",
    "acefile = 'resources/n-001_H_001-endf80.ace'    # Lib80x at 293.6 K (1001.800c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "313c7184",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a few functions we need (these will be integrated into scion when from_endf(...) becomes available)\n",
    "\n",
    "# convert ENDF interpolation types into scion interpolation types\n",
    "def convert_interpolants( interpolants ) :\n",
    "    \n",
    "    conversion = []\n",
    "    for interpolant in interpolants :\n",
    "        \n",
    "        conversion.append( scion.interpolation.InterpolationType.LinearLinear \n",
    "                           if interpolant == 2 else scion.interpolation.InterpolationType.LogLog )\n",
    "    \n",
    "    return conversion\n",
    "\n",
    "# convert ENDF boundaries (1-based index) into scion boundaries (0-based index)\n",
    "def convert_boundaries( boundaries ) :\n",
    "    \n",
    "    return [ index - 1 for index in boundaries ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43b81161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number points:  153\n",
      "number interpolation regions:  2\n",
      "interpolation types:  [5, 2]\n",
      "boundaries indices:  [33, 153]\n"
     ]
    }
   ],
   "source": [
    "# open the ENDF file and retrieve the capture cross section\n",
    "tape = ENDFtk.tree.Tape.from_file( endffile )\n",
    "endf_capture = tape.materials.front().file( 3 ).section( 102 ).parse()\n",
    "\n",
    "print( 'number points: ', len( endf_capture.energies ) )\n",
    "print( 'number interpolation regions: ', len( endf_capture.boundaries ) )\n",
    "print( 'interpolation types: ', endf_capture.interpolants.to_list() )\n",
    "print( 'boundaries indices: ', endf_capture.boundaries.to_list() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c81ced4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number points:  410\n"
     ]
    }
   ],
   "source": [
    "# create an InterpolationTable from the ENDF data and linearise the data\n",
    "capture = scion.math.InterpolationTable( x = endf_capture.energies.to_list(),\n",
    "                                         y = endf_capture.cross_sections.to_list(),\n",
    "                                         boundaries = convert_boundaries( endf_capture.boundaries ),\n",
    "                                         interpolants = convert_interpolants( endf_capture.interpolants ) )\n",
    "\n",
    "linearised = capture.linearise() # default tolerance of 0.1%\n",
    "\n",
    "print( 'number points: ', len( linearised.x ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ed98855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zaid:  1001.805nc\n",
      "temperature:  8.6174e-12\n",
      "number points:  631\n",
      "energy index:  1\n"
     ]
    }
   ],
   "source": [
    "# open the ACE file and retrieve the capture cross section data\n",
    "ace = ACEtk.ContinuousEnergyTable.from_file( acefile )\n",
    "\n",
    "index = ace.reaction_number_block.index( 102 )\n",
    "ace_capture = ace.cross_section_block.cross_section_data( index )\n",
    "\n",
    "energy_index = ace_capture.energy_index\n",
    "\n",
    "print( 'zaid: ', ace.header.zaid )\n",
    "print( 'temperature: ', ace.header.temperature )\n",
    "print( 'number points: ', len( ace_capture.cross_sections ) )\n",
    "print( 'energy index: ', energy_index )\n",
    "\n",
    "ace_energies = [ energy * 1e+6 for energy in ace.principal_cross_section_block.energies[ energy_index - 1: ] ]\n",
    "ace_xs = ace_capture.cross_sections.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b138247b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } 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",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\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",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.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",
       "    canvas_div.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",
       "    fig.canvas_div.style.cursor = msg['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",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://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",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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",
       "    // 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": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the cross section data\n",
    "plot.figure()\n",
    "plot.plot( ace_energies, ace_xs, label = 'Lib80x - 0.1 K', color = 'red', linewidth = 1.5 )\n",
    "plot.plot( linearised.x, linearised.y, label = 'scion - 0.1 % tolerance - 0 K', color = 'blue', linewidth = 1.5 )\n",
    "plot.plot( endf_capture.energies, endf_capture.cross_sections, label = 'not linearised - 0 K', color = 'orange', linewidth = 1.5 )\n",
    "plot.xlabel( 'Incident energy [eV]' )\n",
    "plot.ylabel( 'Capture cross section' )\n",
    "plot.title( 'ENDF/B-VIII.0 H1 capture cross section' )\n",
    "plot.xscale( 'log' )\n",
    "plot.yscale( 'log' )\n",
    "plot.legend()\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba147c00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
