binder_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from LPICS.ics import CheatSheet as cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peak power\t\t\t1000 TW\n",
      "Peak intensity\t\t\t3.53e+19 W/cm^2\n",
      "Field amplitude (normalized)\t4.064\n",
      "Laser energy\t\t\t15.97 J\n",
      "\n",
      "Density for transverse match:\t2.545e+17 cm^-3\n",
      "Density for longitudinal match:\t7.744e+18 cm^-3\n",
      "Density for critical power:\t3.035e+16 cm^-3\n"
     ]
    }
   ],
   "source": [
    "lpi = cs(Power=1e15, R_fwhm=50, tau_fwhm=15)\n",
    "\n",
    "print(f\"Peak power\\t\\t\\t{lpi.prm['Power']*1e-12:0.4g} TW\" )\n",
    "print(f\"Peak intensity\\t\\t\\t{lpi.prm['Intensity']:0.4g} W/cm^2\" )\n",
    "print(f\"Field amplitude (normalized)\\t{lpi.prm['a0']:0.4g}\" )\n",
    "print(f\"Laser energy\\t\\t\\t{lpi.prm['Energy']:0.4g} J\")\n",
    "\n",
    "print(f\"\\nDensity for transverse match:\\t{lpi.match_density('WLu'):0.4g} cm^-3\")\n",
    "print(f\"Density for longitudinal match:\\t{lpi.match_density('longitudinal'):0.4g} cm^-3\")\n",
    "print(f\"Density for critical power:\\t{lpi.match_density('critPower'):0.4g} cm^-3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_perp = []\n",
    "n_Pc = []\n",
    "\n",
    "R_scan = np.arange(80,125,0.5)\n",
    "\n",
    "for R_fwhm in R_scan:\n",
    "    lpi = cs(Power=1e15, R_fwhm=R_fwhm, tau_fwhm=15)\n",
    "    n_perp.append(lpi.match_density('WLu'))\n",
    "    n_Pc.append(lpi.match_density('critPower'))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(R_scan,n_perp, label='transverse match')\n",
    "plt.plot(R_scan,n_Pc, label='critical power')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r'Laser radius ($\\mu$m)')\n",
    "plt.ylabel(r'Plasma density (cm$^{-3}$)');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}