{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the packages we'll need\n",
    "\n",
    "%matplotlib notebook\n",
    "from matplotlib.pyplot import *\n",
    "import string\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "from pyobjcryst import loadCrystal\n",
    "from diffpy.srfit.pdf import PDFGenerator, PDFParser\n",
    "from diffpy.srfit.fitbase import Profile\n",
    "from diffpy.srfit.fitbase import FitContribution, FitRecipe\n",
    "from diffpy.srfit.fitbase import FitResults, initializeRecipe\n",
    "from diffpy.Structure import loadStructure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define functions for optimization and plotting that we will use later\n",
    "\n",
    "def scipyOptimize(recipe):\n",
    "    from scipy.optimize.minpack import leastsq\n",
    "    print \"Fit using scipy's LM optimizer\"\n",
    "    leastsq(recipe.residual, recipe.getValues())\n",
    "    return\n",
    "\n",
    "def plotRecipe(recipe):\n",
    "    r = recipe.pdf.profile.x\n",
    "    g = recipe.pdf.profile.y\n",
    "    gcalc = recipe.pdf.evaluate()\n",
    "    diffzero = -0.8 * max(g) * np.ones_like(g)\n",
    "    diff = g - gcalc + diffzero\n",
    "    plot(r,g,'bo',label=\"G(r) Data\")\n",
    "    plot(r, gcalc,'r-',label=\"G(r) Fit\")\n",
    "    plot(r,diff,'g-',label=\"G(r) diff\")\n",
    "    plot(r, diffzero,'k-')\n",
    "    xlabel(\"$r (\\AA)$\")\n",
    "    ylabel(\"$G (\\AA^{-2})$\")\n",
    "    legend(loc=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the data and make it a PDFprofile. Define the range of the data that will be \n",
    "# used in the fit.\n",
    "\n",
    "grdata =  \"E05.gr\"\n",
    "pdfprofile = Profile()\n",
    "pdfparser = PDFParser()\n",
    "pdfparser.parseFile(grdata)\n",
    "pdfprofile.loadParsedData(pdfparser)\n",
    "pdfprofile.setCalculationRange(xmin = 1.5, xmax = 30)\n",
    "\n",
    "# Setup the PDFgenerator that calculates the PDF from the model\n",
    "\n",
    "pdfgenerator = PDFGenerator(\"G\")\n",
    "pdfgenerator.setQmax(22.0)\n",
    "pdfgenerator.setQmin(0.5)\n",
    "pdfgenerator._calc.evaluatortype = 'OPTIMIZED'\n",
    "\n",
    "# Load structure from the CIF file.\n",
    "\n",
    "ciffile = \"newocc.cif\"\n",
    "structure = loadCrystal(ciffile)\n",
    "pdfgenerator.setStructure(structure)\n",
    "\n",
    "# Add the profile and generator the the PDFcontribution\n",
    "\n",
    "pdfcontribution = FitContribution(\"pdf\")\n",
    "# IMPORTANT - setup profile with xname=\"r\", because\n",
    "# `sphericalCF` and `npwave` functions expect it later on.\n",
    "pdfcontribution.setProfile(pdfprofile, xname=\"r\")\n",
    "pdfcontribution.addProfileGenerator(pdfgenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setup correction scaling due to finite spherical shape.  This adds\n",
    "# the psize parameter from the spherical characteristic function.\n",
    "\n",
    "from diffpy.srfit.pdf.characteristicfunctions import sphericalCF\n",
    "pdfcontribution.registerFunction(sphericalCF, name = \"f\")\n",
    "\n",
    "pdfcontribution.psize = 90;\n",
    "\n",
    "pdfcontribution.setEquation('f * G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the recipe to do the fit and add it to the PDFcontribution\n",
    "\n",
    "recipe = FitRecipe()\n",
    "recipe.addContribution(pdfcontribution)\n",
    "\n",
    "# Avoid too much output during fitting \n",
    "recipe.clearFitHooks()\n",
    "\n",
    "# Add the scale factor.\n",
    "recipe.addVar(pdfgenerator.scale, 0.1)\n",
    "\n",
    "# Instrumental parameters from standard refinement:\n",
    "recipe.addVar(pdfgenerator.qbroad, 0.0399, fixed=True)\n",
    "recipe.addVar(pdfgenerator.qdamp, 0.035, fixed=True)\n",
    "\n",
    "# Add the delta2 parameter, and make sure it cannot take unphysical values\n",
    "recipe.addVar(pdfgenerator.delta2, 2)\n",
    "\n",
    "# Add the psize variable for diameter of the spherical nanoparticle.\n",
    "recipe.addVar(pdfcontribution.psize,30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['xyz', 'xyz_mo1', 'xyz_mo']\n",
      "['xyz', 'xyz_mo1', 'xyz_mo']\n",
      "['xyz', 'xyz_mo1', 'xyz_mo']\n",
      "['xyz', 'xyz_o1', 'xyz_o']\n",
      "['xyz', 'xyz_o1', 'xyz_o']\n",
      "['xyz', 'xyz_o1', 'xyz_o']\n",
      "['xyz', 'xyz_o2', 'xyz_o']\n",
      "['xyz', 'xyz_o2', 'xyz_o']\n",
      "['xyz', 'xyz_o2', 'xyz_o']\n",
      "['xyz', 'xyz_mo2', 'xyz_mo']\n",
      "['xyz', 'xyz_mo2', 'xyz_mo']\n",
      "['xyz', 'xyz_mo2', 'xyz_mo']\n"
     ]
    }
   ],
   "source": [
    "# Add the structural paramters using space group constaints.\n",
    "# Ignore the ADP-s which will be set later.\n",
    "\n",
    "phase = pdfgenerator.phase\n",
    "sgpars = phase.sgpars\n",
    "for par in sgpars.latpars:\n",
    "    recipe.addVar(par, tag='cell')\n",
    "for par in sgpars.xyzpars:\n",
    "    lclabel = par.par.obj.GetName().lower()\n",
    "    lcsymbol = lclabel.rstrip(string.digits)\n",
    "    # name this variable as x_fe1, y_fe1, etc.\n",
    "    name=\"{}_{}\".format(par.par.name, lclabel)\n",
    "    # tag this variable with (\"xyz\", \"xyz_fe\", \"xyz_fe1\")\n",
    "    tags = ['xyz', 'xyz_' + lclabel, 'xyz_' + lcsymbol]\n",
    "    recipe.addVar(par, name=name, tags=tags)\n",
    "    print tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       68.48648397\n",
      "Contributions  68.48648397\n",
      "Restraints     0.00000000\n",
      "Chi2           68.48648397\n",
      "Reduced Chi2   0.02420873\n",
      "Rw             0.77244026\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Biso_Mo1  5.00000000e-03 +/- 4.84781853e+17\n",
      "a         5.69000000e+00 +/- 6.73267848e+00\n",
      "b         4.85600000e+00 +/- 2.63242448e+00\n",
      "beta      2.11097573e+00 +/- 6.01003173e-01\n",
      "c         5.62900000e+00 +/- 1.43789380e+00\n",
      "delta2    2.00000000e+00 +/- 1.50557310e+02\n",
      "occ_mo1   1.00000000e+00 +/- 1.02066433e+00\n",
      "occ_mo2   2.00000000e-01 +/- 6.26335211e+00\n",
      "psize     3.00000000e+01 +/- 4.76893978e+01\n",
      "scale     1.00000000e-01 +/- 2.95525187e-02\n",
      "x_mo1     2.32900000e-01 +/- 7.16753492e-01\n",
      "x_mo2     2.32900000e-01 +/- 8.79968558e+00\n",
      "x_o1      1.12700000e-01 +/- 1.57019851e+01\n",
      "x_o2      3.90300000e-01 +/- 1.47892139e+01\n",
      "y_mo1     -8.00000000e-03 +/- 7.21034117e-01\n",
      "y_mo2     4.92000000e-01 +/- 4.51415495e+00\n",
      "y_o1      2.16400000e-01 +/- 3.77048233e-02\n",
      "y_o2      6.96600000e-01 +/- 1.24590414e+01\n",
      "z_mo1     1.69000000e-02 +/- 4.52140511e-01\n",
      "z_mo2     1.69000000e-02 +/- 2.87195911e+00\n",
      "z_o1      2.33900000e-01 +/- 1.18795495e+01\n",
      "z_o2      2.99000000e-01 +/- 1.24278448e+01\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "Biso_Mo      5.00000000e-03\n",
      "Biso_oxygen  8.00000000e-02\n",
      "qbroad       3.99000000e-02\n",
      "qdamp        3.50000000e-02\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(x_o1, Biso_Mo1)      -1.0000\n",
      "corr(x_o1, z_o1)          1.0000\n",
      "corr(x_o2, Biso_Mo1)      1.0000\n",
      "corr(y_o2, Biso_Mo1)      1.0000\n",
      "corr(x_o2, z_o2)          1.0000\n",
      "corr(z_o2, Biso_Mo1)      1.0000\n",
      "corr(z_o1, Biso_Mo1)      -1.0000\n",
      "corr(y_o2, z_o2)          1.0000\n",
      "corr(x_o2, y_o2)          1.0000\n",
      "corr(x_o1, y_o2)          -1.0000\n",
      "corr(x_o1, x_o2)          -1.0000\n",
      "corr(x_o1, z_o2)          -1.0000\n",
      "corr(z_o1, y_o2)          -1.0000\n",
      "corr(z_o1, x_o2)          -1.0000\n",
      "corr(z_o1, z_o2)          -1.0000\n",
      "corr(x_mo2, Biso_Mo1)     -1.0000\n",
      "corr(a, Biso_Mo1)         1.0000\n",
      "corr(x_o1, x_mo2)         1.0000\n",
      "corr(a, y_o2)             1.0000\n",
      "corr(a, z_o2)             1.0000\n",
      "corr(a, x_o2)             1.0000\n",
      "corr(a, x_o1)             -1.0000\n",
      "corr(z_o2, x_mo2)         -1.0000\n",
      "corr(z_o1, x_mo2)         1.0000\n",
      "corr(y_o2, x_mo2)         -1.0000\n",
      "corr(x_o2, x_mo2)         -1.0000\n",
      "corr(a, z_o1)             -1.0000\n",
      "corr(a, x_mo2)            -1.0000\n",
      "corr(a, b)                -1.0000\n",
      "corr(b, Biso_Mo1)         -1.0000\n",
      "corr(b, z_o2)             -1.0000\n",
      "corr(x_mo1, x_o1)         -1.0000\n",
      "corr(x_mo1, z_o1)         -1.0000\n",
      "corr(x_mo1, Biso_Mo1)     1.0000\n",
      "corr(x_mo1, y_o2)         1.0000\n",
      "corr(b, y_o2)             -1.0000\n",
      "corr(b, x_o2)             -1.0000\n",
      "corr(b, x_o1)             1.0000\n",
      "corr(x_mo1, x_mo2)        -1.0000\n",
      "corr(y_mo2, Biso_Mo1)     1.0000\n",
      "corr(b, x_mo2)            1.0000\n",
      "corr(b, z_o1)             1.0000\n",
      "corr(x_o2, y_mo2)         1.0000\n",
      "corr(y_mo1, z_o2)         1.0000\n",
      "corr(x_o1, y_mo2)         -1.0000\n",
      "corr(y_mo1, Biso_Mo1)     1.0000\n",
      "corr(z_o1, y_mo2)         -1.0000\n",
      "corr(z_o2, y_mo2)         1.0000\n",
      "corr(x_mo1, x_o2)         1.0000\n",
      "corr(x_mo1, z_o2)         1.0000\n",
      "corr(y_mo1, x_o1)         -1.0000\n",
      "corr(y_mo1, z_o1)         -1.0000\n",
      "corr(y_mo1, x_o2)         1.0000\n",
      "corr(x_mo2, y_mo2)        -1.0000\n",
      "corr(y_o2, y_mo2)         1.0000\n",
      "corr(y_mo1, y_o2)         1.0000\n",
      "corr(y_mo1, x_mo2)        -1.0000\n",
      "corr(a, x_mo1)            1.0000\n",
      "corr(y_mo1, y_mo2)        1.0000\n",
      "corr(a, y_mo2)            1.0000\n",
      "corr(a, y_mo1)            1.0000\n",
      "corr(beta, x_o2)          1.0000\n",
      "corr(beta, Biso_Mo1)      1.0000\n",
      "corr(beta, y_o2)          1.0000\n",
      "corr(beta, z_o2)          1.0000\n",
      "corr(beta, x_o1)          -1.0000\n",
      "corr(a, beta)             1.0000\n",
      "corr(beta, z_o1)          -1.0000\n",
      "corr(b, y_mo2)            -1.0000\n",
      "corr(b, x_mo1)            -1.0000\n",
      "corr(b, y_mo1)            -1.0000\n",
      "corr(x_mo1, y_mo2)        1.0000\n",
      "corr(beta, y_mo1)         1.0000\n",
      "corr(beta, y_mo2)         1.0000\n",
      "corr(beta, x_mo2)         -1.0000\n",
      "corr(beta, x_mo1)         1.0000\n",
      "corr(x_mo1, y_mo1)        1.0000\n",
      "corr(x_mo1, z_mo1)        1.0000\n",
      "corr(b, beta)             -0.9999\n",
      "corr(x_mo2, z_mo2)        0.9999\n",
      "corr(z_mo1, x_mo2)        -0.9999\n",
      "corr(z_mo1, z_o1)         -0.9999\n",
      "corr(c, beta)             0.9999\n",
      "corr(z_mo1, x_o1)         -0.9999\n",
      "corr(x_o1, z_mo2)         0.9999\n",
      "corr(z_o1, z_mo2)         0.9999\n",
      "corr(z_mo1, Biso_Mo1)     0.9999\n",
      "corr(z_mo2, Biso_Mo1)     -0.9999\n",
      "corr(z_mo1, y_o2)         0.9999\n",
      "corr(z_mo1, z_mo2)        -0.9999\n",
      "corr(x_mo1, z_mo2)        -0.9999\n",
      "corr(z_o2, z_mo2)         -0.9999\n",
      "corr(y_o2, z_mo2)         -0.9999\n",
      "corr(z_mo1, z_o2)         0.9999\n",
      "corr(b, z_mo1)            -0.9999\n",
      "corr(a, z_mo1)            0.9999\n",
      "corr(z_mo1, x_o2)         0.9999\n",
      "corr(z_mo1, y_mo2)        0.9999\n",
      "corr(y_mo2, z_mo2)        -0.9999\n",
      "corr(x_o2, z_mo2)         -0.9999\n",
      "corr(b, z_mo2)            0.9999\n",
      "corr(a, z_mo2)            -0.9999\n",
      "corr(beta, z_mo1)         0.9999\n",
      "corr(b, c)                -0.9999\n",
      "corr(y_mo1, z_mo2)        -0.9999\n",
      "corr(a, c)                0.9999\n",
      "corr(c, x_o1)             -0.9999\n",
      "corr(c, y_mo1)            0.9999\n",
      "corr(c, x_o2)             0.9999\n",
      "corr(c, z_o2)             0.9999\n",
      "corr(y_mo1, z_mo1)        0.9999\n",
      "corr(c, Biso_Mo1)         0.9999\n",
      "corr(c, z_o1)             -0.9999\n",
      "corr(c, y_o2)             0.9999\n",
      "corr(c, y_mo2)            0.9999\n",
      "corr(c, x_mo2)            -0.9999\n",
      "corr(c, x_mo1)            0.9999\n",
      "corr(z_o2, occ_mo2)       0.9999\n",
      "corr(z_o1, occ_mo2)       -0.9999\n",
      "corr(Biso_Mo1, occ_mo2)   0.9999\n",
      "corr(x_o1, occ_mo2)       -0.9999\n",
      "corr(y_o2, occ_mo2)       0.9999\n",
      "corr(x_o2, occ_mo2)       0.9999\n",
      "corr(a, occ_mo2)          0.9999\n",
      "corr(x_mo2, occ_mo2)      -0.9999\n",
      "corr(x_mo1, occ_mo2)      0.9999\n",
      "corr(beta, z_mo2)         -0.9999\n",
      "corr(b, occ_mo2)          -0.9999\n",
      "corr(c, z_mo1)            0.9999\n",
      "corr(y_mo1, occ_mo2)      0.9998\n",
      "corr(beta, occ_mo2)       0.9998\n",
      "corr(y_mo2, occ_mo2)      0.9998\n",
      "corr(z_mo1, occ_mo2)      0.9998\n",
      "corr(z_mo2, occ_mo2)      -0.9998\n",
      "corr(c, z_mo2)            -0.9998\n",
      "corr(c, occ_mo2)          0.9998\n",
      "corr(psize, z_mo2)        0.9958\n",
      "corr(psize, b)            0.9957\n",
      "corr(psize, y_mo2)        -0.9957\n",
      "corr(psize, x_mo2)        0.9957\n",
      "corr(psize, z_o2)         -0.9957\n",
      "corr(psize, x_o2)         -0.9957\n",
      "corr(psize, Biso_Mo1)     -0.9957\n",
      "corr(psize, y_o2)         -0.9956\n",
      "corr(psize, c)            -0.9956\n",
      "corr(psize, x_mo1)        -0.9956\n",
      "corr(psize, x_o1)         0.9956\n",
      "corr(psize, a)            -0.9956\n",
      "corr(psize, z_o1)         0.9956\n",
      "corr(psize, z_mo1)        -0.9956\n",
      "corr(psize, y_mo1)        -0.9956\n",
      "corr(psize, occ_mo2)      -0.9956\n",
      "corr(psize, beta)         -0.9955\n",
      "corr(delta2, y_mo1)       -0.9879\n",
      "corr(delta2, z_o2)        -0.9878\n",
      "corr(delta2, x_o2)        -0.9878\n",
      "corr(delta2, x_mo2)       0.9878\n",
      "corr(delta2, a)           -0.9878\n",
      "corr(delta2, b)           0.9878\n",
      "corr(delta2, Biso_Mo1)    -0.9878\n",
      "corr(delta2, z_o1)        0.9877\n",
      "corr(delta2, y_mo2)       -0.9877\n",
      "corr(delta2, x_o1)        0.9877\n",
      "corr(delta2, c)           -0.9877\n",
      "corr(delta2, z_mo2)       0.9877\n",
      "corr(delta2, beta)        -0.9877\n",
      "corr(delta2, x_mo1)       -0.9876\n",
      "corr(delta2, y_o2)        -0.9876\n",
      "corr(delta2, z_mo1)       -0.9875\n",
      "corr(delta2, occ_mo2)     -0.9872\n",
      "corr(delta2, psize)       0.9857\n",
      "corr(occ_mo1, occ_mo2)    0.9414\n",
      "corr(x_mo1, occ_mo1)      0.9381\n",
      "corr(z_o2, occ_mo1)       0.9381\n",
      "corr(b, occ_mo1)          -0.9381\n",
      "corr(z_o1, occ_mo1)       -0.9381\n",
      "corr(x_mo2, occ_mo1)      -0.9381\n",
      "corr(y_o2, occ_mo1)       0.9381\n",
      "corr(a, occ_mo1)          0.9381\n",
      "corr(z_mo1, occ_mo1)      0.9381\n",
      "corr(x_o2, occ_mo1)       0.9381\n",
      "corr(x_o1, occ_mo1)       -0.9381\n",
      "corr(Biso_Mo1, occ_mo1)   0.9381\n",
      "corr(beta, occ_mo1)       0.9380\n",
      "corr(c, occ_mo1)          0.9380\n",
      "corr(y_mo2, occ_mo1)      0.9379\n",
      "corr(z_mo2, occ_mo1)      -0.9379\n",
      "corr(y_mo1, occ_mo1)      0.9377\n",
      "corr(psize, occ_mo1)      -0.9359\n",
      "corr(delta2, occ_mo1)     -0.9084\n",
      "corr(y_o1, y_mo2)         0.8212\n",
      "corr(x_mo1, y_o1)         0.8208\n",
      "corr(z_mo1, y_o1)         0.8206\n",
      "corr(y_o1, z_o1)          -0.8204\n",
      "corr(b, y_o1)             -0.8204\n",
      "corr(beta, y_o1)          0.8204\n",
      "corr(y_o1, y_o2)          0.8203\n",
      "corr(x_o1, y_o1)          -0.8203\n",
      "corr(y_o1, Biso_Mo1)      0.8203\n",
      "corr(y_o1, z_o2)          0.8203\n",
      "corr(y_o1, x_o2)          0.8202\n",
      "corr(c, y_o1)             0.8202\n",
      "corr(y_mo1, y_o1)         0.8199\n",
      "corr(y_o1, x_mo2)         -0.8199\n",
      "corr(y_o1, occ_mo2)       0.8198\n",
      "corr(a, y_o1)             0.8198\n",
      "corr(y_o1, z_mo2)         -0.8197\n",
      "corr(psize, y_o1)         -0.8193\n",
      "corr(delta2, y_o1)        -0.7984\n",
      "corr(y_o1, occ_mo1)       0.7560\n",
      "corr(scale, occ_mo1)      -0.2908\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Constrain isotropic displacement parameters per each iron site:\n",
    "initial_Biso_Mo = 0.005\n",
    "initial_occ_Mo1 = 1\n",
    "initial_occ_Mo2 = 0.2\n",
    "\n",
    "#tfe = ['adp_Mo', 'adp']\n",
    "recipe.addVar(phase.Mo1.Biso, name=\"Biso_Mo1\", value=initial_Biso_Mo, tags=['tfe'])\n",
    "#recipe.addVar(phase.Mo2.Biso, name=\"Biso_Mo2\", value=initial_Biso_Mo, tags=['adp_Mo'])\n",
    "\n",
    "recipe.addVar(phase.Mo1.occ, name=\"occ_mo1\", value=initial_occ_Mo1)\n",
    "recipe.addVar(phase.Mo2.occ, name=\"occ_mo2\", value=initial_occ_Mo2)\n",
    "\n",
    "# Use the same isotropic displacement parameters for all oxygens\n",
    "initial_Biso_O = 0.08\n",
    "recipe.newVar(name='Biso_oxygen', value=initial_Biso_O, fixed='true', tags=['adp_o'])\n",
    "oxypars = [s for s in phase.scatterers if s.element.startswith('O')]\n",
    "for a in oxypars:\n",
    "    recipe.constrain(a.Biso, 'Biso_oxygen')\n",
    "    \n",
    "\n",
    "recipe.newVar(name='Biso_Mo', value=initial_Biso_Mo, fixed='true', tags=['adp_Mo'])\n",
    "oxypars = [s for s in phase.scatterers if s.element.startswith('Mo')]\n",
    "for a in oxypars:\n",
    "    recipe.constrain(a.Biso, 'Biso_Mo')\n",
    "    \n",
    "%matplotlib notebook\n",
    "plotRecipe(recipe)\n",
    "print FitResults(recipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n",
      "1\n",
      "Fit using scipy's LM optimizer\n",
      "2\n",
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "data": {
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       "        },\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       22.86491073\n",
      "Contributions  22.86491073\n",
      "Restraints     0.00000000\n",
      "Chi2           22.86491073\n",
      "Reduced Chi2   0.00804819\n",
      "Rw             0.44632090\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Biso_Mo  5.14529807e-01 +/- 2.36585926e-01\n",
      "a        5.69461778e+00 +/- 4.41533183e-02\n",
      "b        4.87631900e+00 +/- 2.75140011e-02\n",
      "beta     2.14707262e+00 +/- 8.72699597e-03\n",
      "c        5.73975364e+00 +/- 4.94560473e-02\n",
      "delta2   3.27644499e+00 +/- 2.12953045e+00\n",
      "scale    1.47618882e-01 +/- 2.18340400e-02\n",
      "x_mo2    2.80804069e-01 +/- 3.31382943e-02\n",
      "y_mo2    5.01244897e-01 +/- 3.75036608e-02\n",
      "z_mo2    9.97247228e-01 +/- 3.53689297e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "Biso_oxygen  8.00000000e-02\n",
      "occ_mo1      1.00000000e+00\n",
      "occ_mo2      1.99999998e-01\n",
      "psize        3.00000000e+01\n",
      "qbroad       3.99000000e-02\n",
      "qdamp        3.50000000e-02\n",
      "x_mo1        2.32900000e-01\n",
      "x_o1         1.12700000e-01\n",
      "x_o2         3.90300000e-01\n",
      "y_mo1        9.92000000e-01\n",
      "y_o1         2.16400000e-01\n",
      "y_o2         6.96600000e-01\n",
      "z_mo1        1.69000000e-02\n",
      "z_o1         2.33900000e-01\n",
      "z_o2         2.99000000e-01\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(x_mo2, z_mo2)      0.5986\n",
      "corr(scale, Biso_Mo)    0.4618\n",
      "corr(c, beta)           0.3495\n",
      "corr(scale, beta)       0.3311\n",
      "corr(a, c)              -0.3293\n",
      "corr(x_mo2, Biso_Mo)    -0.2828\n",
      "corr(delta2, Biso_Mo)   0.2773\n",
      "corr(a, z_mo2)          0.2687\n",
      "corr(x_mo2, y_mo2)      0.2542\n",
      "corr(z_mo2, Biso_Mo)    -0.2512\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Done with setting up, start the actual refinements!\n",
    "# First refine only the scale, cell and psize\n",
    "recipe.fix('all')\n",
    "recipe.free('scale')\n",
    "scipyOptimize(recipe)\n",
    "print 1\n",
    "recipe.free('cell')\n",
    "recipe.free('adp_Mo')\n",
    "recipe.free('delta2')\n",
    "scipyOptimize(recipe)\n",
    "print 2\n",
    "recipe.free('xyz_mo2')\n",
    "scipyOptimize(recipe)\n",
    "\n",
    "%matplotlib notebook\n",
    "plotRecipe(recipe)\n",
    "print FitResults(recipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n",
      "Fit using scipy's LM optimizer\n",
      "Fit using scipy's LM optimizer\n",
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       21.78250115\n",
      "Contributions  21.78250115\n",
      "Restraints     0.00000000\n",
      "Chi2           21.78250115\n",
      "Reduced Chi2   0.00768071\n",
      "Rw             0.43562856\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Biso_Mo  5.43006959e-01 +/- 2.78464557e-01\n",
      "a        5.75603335e+00 +/- 5.35441506e-02\n",
      "b        4.85512881e+00 +/- 3.02164402e-02\n",
      "beta     2.14174494e+00 +/- 9.36749891e-03\n",
      "c        5.70110147e+00 +/- 5.35480804e-02\n",
      "delta2   3.91017328e+00 +/- 1.54856610e-01\n",
      "occ_mo1  1.07921352e+00 +/- 4.26245496e-01\n",
      "occ_mo2  3.35275425e-01 +/- 1.78389167e-01\n",
      "scale    1.37643289e-01 +/- 4.50552759e-02\n",
      "x_mo1    2.31756310e-01 +/- 8.75699974e-03\n",
      "x_mo2    2.92086872e-01 +/- 2.20589209e-02\n",
      "y_mo1    9.93962966e-01 +/- 1.61992308e-02\n",
      "y_mo2    5.07722190e-01 +/- 2.29841689e-02\n",
      "z_mo1    1.93316912e-02 +/- 9.60571626e-03\n",
      "z_mo2    8.42941259e-03 +/- 2.35626897e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "Biso_oxygen  8.00000000e-02\n",
      "psize        3.00000000e+01\n",
      "qbroad       3.99000000e-02\n",
      "qdamp        3.50000000e-02\n",
      "x_o1         1.12700000e-01\n",
      "x_o2         3.90300000e-01\n",
      "y_o1         2.16400000e-01\n",
      "y_o2         6.96600000e-01\n",
      "z_o1         2.33900000e-01\n",
      "z_o2         2.99000000e-01\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(scale, occ_mo1)     -0.8856\n",
      "corr(delta2, y_mo1)      0.8391\n",
      "corr(x_mo1, z_mo1)       0.7245\n",
      "corr(x_mo2, z_mo2)       0.5291\n",
      "corr(y_mo1, y_mo2)       0.5005\n",
      "corr(delta2, y_mo2)      0.4341\n",
      "corr(x_mo1, y_mo1)       0.4193\n",
      "corr(y_mo1, Biso_Mo)     0.4080\n",
      "corr(y_mo1, z_mo1)       0.4011\n",
      "corr(delta2, Biso_Mo)    0.3983\n",
      "corr(y_mo1, occ_mo2)     -0.3732\n",
      "corr(scale, occ_mo2)     -0.3712\n",
      "corr(scale, delta2)      0.3693\n",
      "corr(a, c)               -0.3633\n",
      "corr(scale, y_mo2)       0.3466\n",
      "corr(delta2, occ_mo1)    -0.3272\n",
      "corr(occ_mo1, occ_mo2)   0.3246\n",
      "corr(scale, y_mo1)       0.3229\n",
      "corr(delta2, occ_mo2)    -0.3153\n",
      "corr(y_mo2, occ_mo1)     -0.3130\n",
      "corr(z_mo1, x_mo2)       -0.2943\n",
      "corr(y_mo1, occ_mo1)     -0.2811\n",
      "corr(x_mo2, y_mo2)       0.2537\n",
      "corr(scale, Biso_Mo)     0.2502\n",
      "\n"
     ]
    }
   ],
   "source": [
    "recipe.free('occ_mo2')\n",
    "scipyOptimize(recipe)\n",
    "recipe.free('xyz_mo1')\n",
    "scipyOptimize(recipe)\n",
    "recipe.free('occ_mo1')\n",
    "\n",
    "scipyOptimize(recipe)\n",
    "scipyOptimize(recipe)\n",
    "%matplotlib notebook\n",
    "plotRecipe(recipe)\n",
    "\n",
    "print FitResults(recipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Troels/anaconda2/lib/python2.7/site-packages/scipy/optimize/minpack.py:427: RuntimeWarning: Number of calls to function has reached maxfev = 3600.\n",
      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
     ]
    },
    {
     "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>"
      ]
     },
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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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       20.67795561\n",
      "Contributions  20.67795561\n",
      "Restraints     0.00000000\n",
      "Chi2           20.67795561\n",
      "Reduced Chi2   0.00729639\n",
      "Rw             0.42443996\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Biso_Mo      5.35898282e-01 +/- 2.52215175e-01\n",
      "Biso_oxygen  3.89732441e-01 +/- 1.44464553e+00\n",
      "a            5.73865252e+00 +/- 6.49326734e-02\n",
      "b            4.84300268e+00 +/- 3.76759439e-02\n",
      "beta         2.13888783e+00 +/- 1.27480480e-02\n",
      "c            5.72077264e+00 +/- 7.03980646e-02\n",
      "delta2       3.91463068e+00 +/- 1.56835967e-01\n",
      "occ_mo1      1.01834598e+00 +/- 4.52450351e-01\n",
      "occ_mo2      3.09238910e-01 +/- 1.70964995e-01\n",
      "psize        2.44822838e+01 +/- 4.48502706e+00\n",
      "scale        1.63493958e-01 +/- 6.17533530e-02\n",
      "x_mo1        2.32980201e-01 +/- 1.03747901e-02\n",
      "x_mo2        2.90548376e-01 +/- 2.28748293e-02\n",
      "y_mo1        9.96139217e-01 +/- 3.05557431e-02\n",
      "y_mo2        5.12796174e-01 +/- 2.97672026e-02\n",
      "z_mo1        2.19062048e-02 +/- 1.07169920e-02\n",
      "z_mo2        7.88907114e-03 +/- 2.60129633e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "qbroad  3.99000000e-02\n",
      "qdamp   3.50000000e-02\n",
      "x_o1    1.12700000e-01\n",
      "x_o2    3.90300000e-01\n",
      "y_o1    2.16400000e-01\n",
      "y_o2    6.96600000e-01\n",
      "z_o1    2.33900000e-01\n",
      "z_o2    2.99000000e-01\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(delta2, y_mo1)          0.9425\n",
      "corr(scale, occ_mo1)         -0.8660\n",
      "corr(delta2, y_mo2)          0.8241\n",
      "corr(y_mo1, y_mo2)           0.8054\n",
      "corr(x_mo1, z_mo1)           0.7900\n",
      "corr(occ_mo1, Biso_oxygen)   -0.5142\n",
      "corr(scale, Biso_oxygen)     0.5049\n",
      "corr(a, c)                   -0.4984\n",
      "corr(x_mo2, z_mo2)           0.4921\n",
      "corr(z_mo1, x_mo2)           -0.4146\n",
      "corr(scale, occ_mo2)         -0.3862\n",
      "corr(occ_mo2, Biso_oxygen)   -0.3598\n",
      "corr(occ_mo1, occ_mo2)       0.3436\n",
      "corr(b, beta)                0.3344\n",
      "corr(x_mo1, x_mo2)           -0.3306\n",
      "corr(x_mo1, z_mo2)           -0.3194\n",
      "corr(beta, z_mo2)            0.3038\n",
      "corr(b, c)                   -0.2928\n",
      "corr(psize, beta)            0.2843\n",
      "corr(z_mo1, z_mo2)           -0.2772\n",
      "corr(scale, psize)           -0.2518\n",
      "corr(scale, x_mo1)           -0.2502\n",
      "\n"
     ]
    }
   ],
   "source": [
    "recipe.free('Biso_oxygen')\n",
    "recipe.free('psize')\n",
    "scipyOptimize(recipe)\n",
    "\n",
    "\n",
    "%matplotlib notebook\n",
    "plotRecipe(recipe)\n",
    "\n",
    "print FitResults(recipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = recipe.pdf.profile.x\n",
    "gobs = recipe.pdf.profile.y\n",
    "\n",
    "gcalc = recipe.pdf.evaluate()\n",
    "gdiff = gobs - gcalc\n",
    "\n",
    "plot_export = np.column_stack((r,gobs,gcalc,gdiff))\n",
    "np.savetxt('E05_onephase_newoccFit.txt', plot_export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def export_cifs():\n",
    "    name = 'refined_'+ciffile+'.cif'\n",
    "    with open(name, 'wb') as fp:\n",
    "        structure.CIFOutput(fp)\n",
    "    return\n",
    "\n",
    "# uncomment to really export\n",
    "export_cifs()"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
