path: root/Tools/Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "This a notebook that inspects the results of a WarpX simulation.\n",
    "\n",
    "# Instruction\n",
    "\n",
    "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mthevenet/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# Import statements\n",
    "import os, glob\n",
    "import yt ; yt.funcs.mylog.setLevel(50)\n",
    "from IPython.display import clear_output\n",
    "import numpy as np\n",
    "from ipywidgets import interact, ToggleButtons, IntSlider\n",
    "# read_raw_data.py is located in warpx/Tools\n",
    "import read_raw_data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data in the simulation frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mthevenet/anaconda2/lib/python2.7/site-packages/yt-3.5.dev0-py2.7-macosx-10.6-x86_64.egg/yt/units/yt_array.py:1372: RuntimeWarning: invalid value encountered in divide\n",
      "  out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x11454b2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = yt.load( './plotfiles/plt00000' ) # Create a dataset object\n",
    "sl = yt.SlicePlot(ds, 2, 'Ex', aspect=.02) # Create a sliceplot object\n",
    "sl.set_xlabel(r'$x (\\mu m)$') # Set labels x\n",
    "sl.set_ylabel(r'$z (\\mu m)$') # Set labels y\n",
    "sl.annotate_particles(width=(10.e-6, 'm'), p_size=2, ptype='ions', col='black') # Plot particles species=ions\n",
    "sl.annotate_particles(width=(10.e-6, 'm'), p_size=2, ptype='electrons', col='black')\n",
    "sl.annotate_particles(width=(10.e-6, 'm'), p_size=2, ptype='beam', col='black')\n",
    "sl.annotate_grids() # Show grids\n",
    "sl.show() # Show the plot\n",
    "\n",
    "#############################\n",
    "### OTHER USEFUL COMMANDS ###\n",
    "#############################\n",
    "# # List all fields in the datasert\n",
    "# ds.field_list\n",
    "# # Get All Data from the dataset\n",
    "# # Then get some data. \".v\" converts arrays from units-aware yt arrays to numpy arrays.\n",
    "# ad = ds.all_data()\n",
    "# Bx = ad['boxlib', 'Bx'].v\n",
    "# # Get All Data from the dataset, on a given level and given dimension.\n",
    "# # Then get some data. \".v\" converts arrays from units-aware yt arrays to numpy arrays.\n",
    "# # This is similar to the 2 lines above, except that F has the proper shape.\n",
    "# all_data_level_0 = ds.covering_grid(level=0,left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)\n",
    "# Bx = all_data_level_0['boxlib', 'Bx'].v.squeeze()\n",
    "# # particle \n",
    "# # CAREFUL! For the moment, 2d WarpX simulations use (x, z) spatial coordinate \n",
    "# # but they are stored as  (particle_position_x, particle_position_y) in Yt\n",
    "# x = ad['beam', 'particle_position_x'].v\n",
    "# z = ad['beam', 'particle_position_y'].v\n",
    "# # For 2d simulations, WarpX and Yt use (ux, uz)\n",
    "# # ux/c should be the nirmalized momentum\n",
    "# ux = ad['beam', 'particle_momentum_x'].v\n",
    "# uy = ad['beam', 'particle_momentum_y'].v\n",
    "# uz = ad['beam', 'particle_momentum_z']\n",
    "# w = ad['beam', 'particle_weight'].v\n",
    "# # Set figure size\n",
    "# sl.figure_size = (9, 7)\n",
    "# # Save image\n",
    "# sl.save('./toto.pdf')\n",
    "# # This returns the domain boundaries\n",
    "# sl.bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data back-transformed to the lab frame when the simulation runs in the boosted frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For the moment, the back-transformed diagnostics must be read with \n",
    "# custom functions like this one.\n",
    "# It should be OpenPMD-compliant hdf5 files soon, making this part outdated.\n",
    "def get_particle_field(snapshot, species, field):\n",
    "    fn = snapshot + '/' + species\n",
    "    files = glob.glob(os.path.join(fn, field + '_*'))\n",
    "    files.sort()\n",
    "    all_data = np.array([])\n",
    "    for f in files:\n",
    "        data = np.fromfile(f)\n",
    "        all_data = np.concatenate((all_data, data))\n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116ad8950>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species = 'particle2'\n",
    "iteration = 3\n",
    "xmax = 256.e-6\n",
    "\n",
    "snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
    "header   = './lab_frame_data/Header'\n",
    "xmin = - xmax\n",
    "allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
    "F = allrd['Ex']\n",
    "nx, nz = F.shape\n",
    "x = np.linspace(-xmax, xmax, nx)\n",
    "z = info['z'] # Get box length in z. x is not in info\n",
    "xbo = get_particle_field(snapshot, species, 'x') # Read particle data\n",
    "ybo = get_particle_field(snapshot, species, 'y')\n",
    "zbo = get_particle_field(snapshot, species, 'z')\n",
    "uzbo = get_particle_field(snapshot, species, 'uz')\n",
    "\n",
    "plt.figure(figsize=(6, 3))\n",
    "extent = np.array([info['zmin'], info['zmax'], xmin, xmax])\n",
    "plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
    "plt.plot(zbo, xbo, 'k.', markersize=1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  },
  "widgets": {
   "state": {
    "11d243e9f5074fe1b115949d174d59de": {
     "views": [
      {
       "cell_index": 6
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}