py: compare runs

compare_runs.ipynb

{
 "cells": [
  {
   "metadata": {
    "trusted": true,
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   "cell_type": "code",
   "source": "from IPython.display import HTML\n\nHTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": "<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>",
      "text/plain": "<IPython.core.display.HTML object>"
     },
     "metadata": {},
     "execution_count": 1
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    "run_control": {
     "marked": false,
     "old_gutter_background": "rgb(247, 247, 247)"
    }
   },
   "cell_type": "code",
   "source": "%matplotlib nbagg\n%load_ext autoreload\n%autoreload 1\n\nimport sys\nsys.path.append(\"/home/smutch/projects/meraxes/paper_plots/\")",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/home/smutch/pyenv/lib/python2.7/site-packages/matplotlib/__init__.py:866: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n  warnings.warn(self.msg_depr % (key, alt_key))\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom IPython.display import clear_output, display_html, display_latex\nfrom dragons import meraxes, plotutils, nbody, munge\nfrom astropy.utils.console import ProgressBar\nimport pandas as pd\nimport seaborn as sns\nfrom aesthetics import colors\nfrom collections import OrderedDict\nimport tools\nimport smf_z5\nfrom astropy import cosmology, log\n\nsns.set(font_scale=1.3, rc={'lines.linewidth':3})\npd.set_option('mode.use_inf_as_null', True)\n\nclear_output()",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Recalibrated *no supernova feedback* runs\n\nThe *RECALIBRATED* runs below have had their star formation efficiencies recalibrated to **approximately** (I'll do a more accurate recalibration of the paper) reproduce the z=5 total stellar mass density of the original *fiducial* model.  These are just 128^3 runs for testing."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
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   "cell_type": "code",
   "source": "models = OrderedDict()\nmodels['fiducial'] = {\n    'fname': '/home/smutch/data_agama/meraxes/paper_runs/128/fiducial/output/meraxes.hdf5',\n    'plotkw': {\n        'label': 'fiducial',\n        'dashes': [],\n        'color': colors[0],\n    },\n}\nmodels['no_sn_fb'] = {\n    'fname': '/home/smutch/data_agama/meraxes/paper_runs/128/no_sn_fb/output/meraxes.hdf5',\n    'plotkw': {\n        'label': 'no SN feedback',\n        'dashes': [],\n        'color': '0.5',\n    },\n}\nmodels['no_feedback'] = {\n    'fname': '/home/smutch/data_agama/meraxes/paper_runs/128/no_reion_no_sn_fb/output/meraxes.hdf5',\n    'plotkw': {\n        'label': 'no feedback',\n        'dashes': [],\n        'color': colors[4],\n    },\n}\nmodels['no_sn_fb-recalib'] = {\n    'fname': 'output/meraxes.hdf5',\n    'plotkw': {\n        'label': 'no SN feedback (RECALIBRATED)',\n        'dashes': [8,3],\n        'color': '0.5',\n    },\n}\nmodels['no_fb-recalib'] = {\n    'fname': './no_reion_no_sn_fb/output/meraxes.hdf5',\n    'plotkw': {\n        'label': 'no feedback (RECALIBRATED)',\n        'dashes': [8,3],\n        'color': colors[4],\n    },\n}",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "scrolled": true
   },
   "cell_type": "code",
   "source": "meraxes.set_little_h(models.values()[0]['fname'])\nfor model in models.itervalues():\n    model['params'] = meraxes.read_input_params(model['fname'])\n    model['gals'] = meraxes.read_gals(model['fname'], snapshot=-1, props=[\"StellarMass\", \"GhostFlag\", \"FOFMvir\",\n                                                                          \"CentralGal\", \"HotGas\", \"ColdGas\", \"EjectedGas\"], pandas=True)\n\nclear_output()",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "for name, model in models.iteritems():\n    total_stellar = model['gals'].StellarMass.sum() * 1e10\n    if name == \"fiducial\" or name == \"no_sn_fb-recalib\":\n        label = r\"$\\textbf{\" + model['plotkw']['label'] + r\"}$\"\n    else:\n        label = model['plotkw']['label'] \n    display_latex(label + r\" $\\sum M_*$ = %.2e$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = %.2f\\times 10^{-2}\\right)$\"\n                  % (total_stellar, model['params']['SfEfficiency']*100), raw=True)",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/latex": "$\\textbf{fiducial}$ $\\sum M_*$ = 2.47e+13$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = 3.00\\times 10^{-2}\\right)$"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/latex": "no SN feedback $\\sum M_*$ = 1.25e+14$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = 3.00\\times 10^{-2}\\right)$"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/latex": "no feedback $\\sum M_*$ = 2.18e+14$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = 3.00\\times 10^{-2}\\right)$"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/latex": "$\\textbf{no SN feedback (RECALIBRATED)}$ $\\sum M_*$ = 2.74e+13$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = 0.13\\times 10^{-2}\\right)$"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/latex": "no feedback (RECALIBRATED) $\\sum M_*$ = 3.18e+13$\\,{\\rm M_{\\odot}}\\ \\left(\\alpha_{\\rm SF} = 0.13\\times 10^{-2}\\right)$"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "nbins = 50\nfor name, model in models.iteritems():\n    gals = model['gals']\n    sm = np.log10(1e10 * gals.StellarMass).dropna()\n\n    # generate mass function\n    mf = munge.mass_function(sm,\n                             model[\"params\"][\"Volume\"], nbins,\n                             range=(5, 12),\n                             poisson_uncert=True)\n\n    model['smf'] = mf",
   "execution_count": 7,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Here are the resulting galaxy stellar mass functions:"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "scrolled": false
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots(1, 1)\n\nfor name, model in models.iteritems():\n    kwargs = model['plotkw']\n    mf = model['smf']\n    ax.semilogy(mf[:,0], mf[:,1], nonposy='mask', alpha=0.8, lw=4, **kwargs)\n\nax.text(0.95, 0.95, \"z=5\", horizontalalignment=\"right\",\n        verticalalignment=\"top\", transform=ax.transAxes)\n\nsmf_z5.plot_obs(ax, logy=False, hubble=models.values()[0]['params']['Hubble_h'])\n# handles, _ = ax.get_legend_handles_labels()\n# handles = handles[-3:]\nleg = ax.legend(loc=3)\n\nax.set_xlim(5, 11)\nax.set_ylim(1e-5, 5)\nax.set_ylabel(r\"$\\phi [\\rm Mpc^{-3} dex^{-1}]$\")\nax.set_xlabel(r\"$\\log_{10}(M_{*} / {\\rm M_{\\odot}})$\")\n\nplt.setp(ax.get_yticklines(), visible=False)\nax.set_yticks([], minor=True)\nplt.setp(ax.get_xticklines(), visible=False)\n\ntools.pdf_plot(\"plots/smf_multi_model.pdf\")\nplt.show()",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": "<a href=\"#\" onclick=\"window.open('http://localhost:8881/files/plots/smf_multi_model.pdf', 'NewWin');\">PDF version</a>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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\">"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "So by reducing the efficiency of star formation, the relative effect of reionisation feedback is reduced.  This can be seen by the small difference between the *recalibrated* dashed lines when compared to the corresponding difference between the original $\\alpha_{\\rm SF}$ solid lines.\n\nI understand this as being that star formation in low mass haloes in the original model runs is, to some extent, cold gas supply limited.  Therefore, by switching on reionisation (solid gold -> solid grey), there is a smaller supply of cold gas to these galaxies and hence star formation (an ionising photon production) is reduced.  For the *recalibrated* models, star formation in low mass haloes is probably no longer gas supply limited.  The star formation efficiency is so low that not all of the supplied gas can be turned into stars in many of the *no feedback (RECALIBRATED)* runs.  By turning on reionisation feedback (dashed gold -> dashed grey), less gas will make it into the haloes but what does make it in will cool rapidly and still provide the galaxy with more gas than it can use.  **Therefore, this shows that our model truly does predict that reionisation is not self-regulated.**\n\nImportantly, I tested varying several other galaxy formation model parameters (merger star formation efficiencies etc.) and, without supernova feedback, there is no freedom in the current model to reproduce the slope of the galaxy stellar mass function observations without employing supernova feedback.  Of course, we will need to do an MCMC analysis to really prove this, but this anecdotaly agrees with the findings of my MCMC paper for the $z<1$ Universe.  \n\nOne could argue that by cranking up the star formation efficiency, instead of lowering it, the effect of reionisation could be enhanced.  However, such a model would not reproduce either the observed stellar mass density (let alone the slope of the stellar mass function) without strong supernova feedback that would again swamp the effects of reionisation.  An alternative argument might be that $f_{\\rm esc}$ could be increased to boost the efficiency of reionisation.  This would prevent us from matching the $\\tau_{\\rm e}$ constraints though, unless (perhaps) there was a strong mass/redshift dependency, but my inuition is that anything strong enough to allow as to match the slope of stellar mass function would be incompatible with what reionisation constraints are available."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## xHI history\n\nJust a quick check to make sure everything looks ok..."
  },
  {
   "metadata": {
    "scrolled": false,
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "log.setLevel('WARNING')\nfig, ax = plt.subplots(1, 1)\nweighting = \"volume\"\nxH = {}\nz_HI_50pc = {}\nfor model, aes in models.iteritems():\n    fname = aes['fname']\n    if model == \"fiducial\":\n        # read in the model run parameters and set up the cosmology\n        run_params = meraxes.read_input_params(fname)\n        cosmo = cosmology.FlatLambdaCDM(H0=run_params['Hubble_h']*100.,\n                                        Om0=run_params['OmegaM'],\n                                        Ob0=run_params['OmegaM']\n                                        * run_params['BaryonFrac'])\n        meraxes.set_little_h(run_params[\"Hubble_h\"])\n        run_params = meraxes.read_input_params(fname)\n\n    outputs = meraxes.io.read_snaplist(fname)\n    xH[model] = np.ones([outputs[0].max()+1, 3])\n    xH[model][:, 1] = 0\n    sel = np.s_[outputs[0].min():]\n    xH[model][sel, 0] = outputs[1]\n    xH[model][sel, 2] = outputs[2] / 1000.\n\n    if weighting == \"volume\":\n        xH[model][sel, 1] = meraxes.read_global_xH(fname, outputs[0], quiet=True)\n\n    elif weighting == \"mass\":\n        for ii, snap in tqdm(enumerate(outputs[0]), total=xH[model].shape[0]):\n            try:\n                xh_grid = meraxes.read_grid(fname, snap, \"xH\", quiet=True)\n                density = meraxes.read_grid(fname, snap, \"deltax\", quiet=True) + 1.0\n                density /= density.sum()\n                xH[model][sel, 1][ii] = np.average(xh_grid, weights=density)\n            except UnboundLocalError:\n                pass\n\n    sel = ~np.isnan(xH[model][:, 1])\n\n    # calculate the redshift of xH=0.5\n    z_HI_50pc[model] = np.interp(0.5, xH[model][sel][:, 1][::-1], xH[model][sel][:, 0][::-1])\n    print \"model: %s  :: z_{x_HI=0.5} = %.2f\" % (model, z_HI_50pc[model])\n\n\nfor model, aes in models.iteritems():\n    kwargs = aes['plotkw']\n    l, = ax.plot(xH[model][:, 0], xH[model][:, 1], **kwargs)\n\nax.legend(loc=3)\n\nax.set_ylim([1e-2, 1])\nax.set_xlim([15, 5])\nax.set_xlabel(\"redshift\")\nax.set_ylabel(r\"$\\left< x_{\\rm HI}\\right>$\")\n\nplt.setp(ax.get_yticklines(), visible=False)\nplt.setp(ax.get_xticklines(), visible=False)\n\ntools.pdf_plot(\"plots/xH_fraction.pdf\")\nplt.show()",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "text": "model: fiducial  :: z_{x_HI=0.5} = 7.91\nmodel: no_sn_fb  :: z_{x_HI=0.5} = 11.29\nmodel: no_feedback  :: z_{x_HI=0.5} = 11.38\nmodel: no_sn_fb-recalib  :: z_{x_HI=0.5} = 7.87\nmodel: no_fb-recalib  :: z_{x_HI=0.5} = 7.91\n",
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": "<a href=\"#\" onclick=\"window.open('http://localhost:8881/files/plots/xH_fraction.pdf', 'NewWin');\">PDF version</a>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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    },
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     "output_type": "display_data",
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      "text/plain": "<IPython.core.display.HTML object>",
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\">"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true
   },
   "cell_type": "markdown",
   "source": "# Baryon distributions\n\nIt appears that reionisation feedback has even less effect if the star formation efficiency is lowered to the value required to reproduce the z=5 stellar mass density of the *fiducial* model.\n\nWe can check this by comparing the distribution of baryon mass between the different reservoirs in each model.  Below are plots of the mean FOF group mass fraction in the form of different baryonic types as a function of $M_{\\rm vir}$."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "# group by FOF groups and calculate the means of all of the properties\nbin_width = 0.1  # dex\nprops = ['FOFMvir', 'StellarMass', 'ColdGas', 'HotGas', 'EjectedGas']\nfor model in models.itervalues():\n    gals = model['gals']\n    groups = gals.pivot_table(index='CentralGal', aggfunc='sum')\n    for p in props[1:]:\n        groups[p] /= groups.FOFMvir\n    ind = (np.log10(groups.FOFMvir * 1e10) / bin_width).values.astype(int)\n    model['baryons'] = groups[props].groupby(ind).aggregate('mean').reset_index().drop('index', 1)",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "fofmvir = pd.concat([model['baryons'].FOFMvir for model in models.values()], axis=1, join='inner').mean(axis=1)",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false,
    "scrolled": false
   },
   "cell_type": "code",
   "source": "# for p in props[1:]:\n#     with sns.axes_style('ticks'):\n#         fig, ax = plt.subplots(1, 1)\n#         plt.stackplot(np.log10(fofmvir*1e10),\n#                       [model['baryons'].loc[fofmvir.index][p] for model in models.itervalues()],\n#                       #baseline='weighted_wiggle',\n#                       baseline='zero',\n#                       labels=[model['plotkw']['label'] for model in models.itervalues()],\n#                       alpha=0.7)\n#         ax.legend(loc=1, markerfirst=False)\n#         ax.text(0.5, 0.98, p, horizontalalignment='center', verticalalignment='top', transform=ax.transAxes)\n#         sns.axlabel(r\"$\\log_{10}(M_{\\rm vir} / {\\rm M_{\\odot}})$\",\n#             r\"$\\left< m/M_{\\rm vir}\\right>$\")\n#         plt.show()",
   "execution_count": 12,
   "outputs": []
  },
  {
   "metadata": {
    "code_folding": [],
    "trusted": true,
    "collapsed": false,
    "scrolled": false
   },
   "cell_type": "code",
   "source": "for p in props[1:]:\n    fig, ax = plt.subplots(1, 1)\n    for model in models.values():\n        plt.semilogy(np.log10(fofmvir*1e10), model['baryons'].loc[fofmvir.index][p],\n                  label=model['plotkw']['label'],\n                  alpha=0.7)\n    ax.legend(loc=3)\n    ax.set_title(p)\n    sns.axlabel(r\"$\\log_{10}(M_{\\rm vir} / {\\rm M_{\\odot}})$\",\n        r\"$\\left< m/M_{\\rm vir}\\right>$\")\n    plt.show()",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\">"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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rpK5WM92YyJRMm6WsSB69ku968YEMGN35xuSEWACWH7+oPeXGkcAy88nmQCWp83YbuVtNyaA5Wu/kpCcCWBJmCEjIZgAZgRXyVysZ7sxkSgZN8tYED37pd51YwKYsbvzDRwBLG8f0PtLjSOA5emf7Jdst1JDQO8+yQSw1DyuzOThCGCZGYzrAJafwdYk1vOXEUcAy9YtuQ5gmZqOy8BwGZicXZcJYM4QFnwCPRMJ1q3g7qTZgmw7zaAs6ERst4LCrdliTACZAObsTEwAc4aw4BPwC7vgkGuyoJ7txhFATVykKJPo2S/1rhtvARflkdHPokwAy8+Wen+p8RnA8vNJJoDlaTO2W3nbjQlg+dqvJCRnAlgSZshICCaAGcFVMhfr2W5MJErGzTIWRM9+qXfdmABm7O58QzICTADLzx/0/lLjCGD5+SQTwPK0GdutvO3GBLB87VcSkjMBLAkzZCQEE8CM4CqZi/VsNyYSJeNmGQuiZ7/Uu25MADN2d76BI4Dl7QN6f6lxBLA8/ZP9ku1Wagjo3SeZAJaax5WZPBwBLDODcR3A8jPYmsR6/jLiCGDZuiXXASxT03EZGC4Dk7PrMgHMGcKCT6BnIsG6FdydNFuQbacZlAWdiO1WULg1W4wJIBPAnJ2JCWDOEBZ8An5hFxxyTRbUs904AqiJixRlEj37pd514y3gojwy+lmUCWD52VLvLzU+A1h+PskEsDxtxnYrb7sxASxf+5WE5EwAS8IMGQnBBDAjuErmYj3bjYlEybhZxoLo2S/1rhsTwIzdnW9IRoAJYPn5g95fahwBLD+fZAJYnjZju5W33ZgAlq/9SkJyJoAlYYaMhGACmBFcJXOxnu3GRKJk3CxjQfTsl3rXjQlgxu7ON3AEsLx9QO8vNY4Alqd/sl+y3UoNAb37JBPAUvO4MpOHI4BlZjCuA1h+BluTWM9fRhwBLFu35DqAZWo6LgPDZWBydl0mgDlDWPAJ9EwkWLeCu5NmC7LtNIOyoBOx3QoKt2aLMQFkApizMzEBzBnCgk/AL+yCQ67Jgnq2G0cANXGRokyiZ7/Uu268BVyUR0Y/izIBLD9b6v2lxmcAy88nmQCWp83YbuVtNyaA5Wu/kpCcCWBJmCEjIZgAZgRXyVysZ7sxkSgZN8tYED37pd51YwKYsbvzDckIMAEsP3/Q+0uNI4Dl55NMAMvTZmy38rYbE8DytV9JSM4EsCTMkJEQTAAzgqtkLtaz3ZhIlIybZSyInv1S77oxAczY3fkGjgCWtw/o/aXGEcDy9E/2S7ZbqSGgd59kAlhqHldm8nAEsMwMxnUAy89gaxLr+cuII4Bl65ZcB7BMTcdlYLgMTM6uywQwZwgLPoGeiQTrVnB30mxBtp1mUBZ0IrZbQeHWbDEmgEwAc3YmJoA5Q1jwCfiFXXDINVlQz3bjCKAmLlKUSfTsl3rXjbeAi/LI6GdRJoDlZ0u9v9T4DGD5+SQTwPK0GdutvO3GBLB87VcSkjMBLAkzZCQEE8CM4CqZi/VsNyYSJeNmGQuiZ7/Uu25MADN2d74hGQEmgOXnD3p/qXEEsPx8kglgedqM7VbedmMCWL72KwnJmQCWhBkyEoIJYEZwlczFerYbE4mScbOMBdGzX+pdNyaAGbs738ARwPL2Ab2/1DgCWJ7+yX7Jdis1BPTuk0wAS83jykwejgCWmcG4DmD5GWxNYj1/GXEEsGzdkusAlqnpuAwMl4HJ2XWZAOYMYcEn0DORYN0K7k6aLci20wzKgk7Ediso3JotxgSQCWDOzsQEMGcICz4Bv7ALDrkmC+rZbhwB1MRFijKJnv1S77rxFnBRHhn9LMoEsPxsqfeXGp8BLD+fZAJYnjZju5W33ZgAlq/9SkJyJoAlYYaMhGACmBFcJXOxnu3GRKJk3CxjQfTsl3rXjQlgxu7ONyQjwASw/PxB7y81jgCWn08yASxPm7HdyttuTADL134lITkTwJIwQ0ZCMAHMCK6SuVjPdmMiUTJulrEgevZLvevGBDBjd+cbOAJY3j6g95caRwDL0z/ZL9lupYaA3n2SCWCpeVyZycMRwDIzGNcBLD+DrUms5y8jjgCWrVtyHcAyNR2XgeEyMBtc1+v1HlcU5TuKovx1b2/vX6fj10wA00GptK7RM5Fg3UrL1zKRhm2XCVqlcy3brXRskYkkTACZACb8pampye50Or+vKEovgB4mgABvJWbyOimNa/nLqDTskI0UbLtsUCv+PWy34tsgGwmYADIBTPYbsbOz0xgOh39XUZQ5JoBMALN5qRT7Hv4yKrYFsl+fbZc9dsW8k+1WTPSzX5sJIBPALd7j8Xj+oJQIYHBlGKHVcVgd+2Cy1WXv7RneyS+1DAErkcvZbiViiCzEYNtlAVoJ3MJ2KwEjZCECE8AyJoDt7e2doih+BYBXkiR7sv07Ozud4XD4bwA8CMAK4HVBEH7D5/Nd9Xq9v6Ioyq8KgkDH9wT6V5blv+jt7f0OzVFKBDASWsTc0E+gQFHVs1d44ai+AYJoyMLdM7uFX2qZ4VUqV7PdSsUSmcvBtsscs1K4g+1WClbIXAYmgGVKAD0ez0cA/LkgCGcURfnAZgLo9Xq/qihKC4CPCYKwqCjKXwK4Q5Kko8Aam9rGX0qJAK4uSFiafmeDpEazG6660zBZazL3+Azu4JdaBmCV0KVstxIyRoaisO0yBKxELme7lYghMhSDCWCZEkCv1/txURSfi0ajDwP4u2QCeOjQoRpZlsdlWX6wr6/vRfKJAwcOVBiNxilBEB71+XzP7eQnRAAFQZj3+Xx/lY4/5TMLeGnqTawuXtkihiAIsFcehb3qCARBTEfMjK8plZeaosiIBGcRDsxAjq5CkcPqjyyHYv+tRCEaLBANttiP0QrRYIUoWiCIJgiiMfavEP9X4LINGXtDadxQKj6ZLzT0rB/rli+vye+8ercb1wHMr//kdXaPx/PJzQTQ4/HQtu/TJpOpoqurazkugMfj6QLwFUmS/jiVUB0dHacA/C9BEOigXRTAjMlkerirq2t8JyWIAM7MLEGJ7dJqOmaHn0Y4MAvIMgSDEVDkDfPTmcDKxntVkqP1oAe/psaFfOm2nbyKHEU4MIlQYBrh1UlEgjOg32kyBAGi0Q6jyQl3ZS0CIRMMRieMlmoYTA5Nlij2JMWyWyH01rNuhJ+e9WPdCvGEaL+G3u1WW+sWtEetfGYsa+VTEcCOjg7a9v2SJEnmZDN4PB7aLn67t7f3N7U0DxFALeeLz0WRr/53v4yVwUFEAwG4rAdRddMNmJu6tIEIVjfeiOrGE/kQoaBzkr5Ls32YGT2LaNhf0LXpm9dZtR81jTfCZHEXdm1ejRFgBBgBRqAoCFASQFEWLpFFy1r5bSKAHwXw5RQE8GVFUd7KBwHMR5QsElrAVM+3EJqcUF1FvGqBre4A3A/diaWZNxENLam/p+3Nmn3vU7dBtRyF/Msv6B/Dysy7iATnt1WBopwGk2ttW9cEUTRDMJggQIQcDarbw3I0ADkSUP/dcQiA2WREKBzZeCJUEGFzH4C9qhMG44a8Ii2hzetchbRbXhVJMbmedVOf5SJF3QthR9atEChrv4be7cYRQO19pmAzpiKAXq/3fkVRnhEEodLn88VYUiy7t0cQhC/6fL4/1VLAfJ0BDCwPYLbnx4jMz8UIYI8NgizA0twC10O3Y2FSPd6oDnvlEThrbtBSrYKckyPCtzz7LkL+sS2yE6E1WesSP0ZLVdrnHSmaGD8rqMiRtbOCYZUoRiMrkMPLMJuCWF6YRTQFWRQEA2wVHbC5PWW3Naz3Mzt6LU4eJ4B61Y/9UtPXc8Em07vd+AxgwVxJ+4VSEcBjx45VBYPB8bWEj+fXyF8tADrLd68kSS9rKUm+CODyzHks+J5H1L8CISRC7KNqNrFh3tsM0+kahAKx6CARlurW98FgtGmmWr4ffMpwXp4+myhxExfcaHLBUXMDzPZm5Cs6n6xbJOyHf64LgaUrSLWbT/KY7I0w2xpgttXn5bylZkbjPsdaQlnwufL9zBVcoaQFWbdiop/92nq3GxPA7H2jaHfu37+/3mQyUebDhwF8HkAHCeP3+2eHh4dXPR7PF9d+95FQKLRiNpspo/ewJEm3ai10vgjg/NgLWPK9CSUShrBogDhiRXKmibGlFsJRERBjWcAUsXLV3qyZevl88CmzmTKckwdF/Gjr1ebuSDvSl62yqXSLhpewMnsRweXBLaQ0vg5lXBstNTBZa9UyPEZLraakO1t9ku/Lp920kC+XOfSsW+wPOe6+k4t/FOtetluxkM9tXbIbE8DcMCzK3R6P5yqA1hSLf0qSpC83Nzfb7HY7kb4nABBRfM5gMHymu7t7615jjhrkiwBO9z8Jf3+PKp0wZULVkYexcvECIrMzCYmFwxYY91eq3xxETqpb3guDyZmjRrHb8/VSCywPYmni1QTJIrltFV51G1s0bMjb0USPVJPspBttS6/MXUBoZXRbIpg8p8HoUAmh1dUGk60xb1HLdMHIl93SXT+f1+lZt3w+c/m0Sbpz69l2rFu6XlBa1zEBLNM6gKXkRvkggJTIMOn7OkJjo6qq4pAFDR/4FQhmC2a+9yTC09Pq7xWzDMOtDhgqKtT/t7r2w73ntCbw5OOlFvSPYnH8DOiMnvqFBwGu+tthdabi8pqokXKSdHSj84Kh1QmE/WNqK75oZPfMZNoidtacBJ1XLNZIR7diyZbrunrWjQlgrt5RvPv17Jd6140jgMV7bnSxcj4IICVFzPh+gPBMjOgZrrrQ9KufU6NLcmAVU09+C5GZWCRQ2afA3FlP6cAqoapqeQ+M5hghzGVo/eCHViexMPaCWrg5Plx1t8LmPpiLmFndm6ludDYwGl5UiWCE6hMGZtRkklSDbGBx7VNb9hUjkzhT3bICsEg36Vk3JoBFcioNltWzX+pdNyaAGjwA1/MU+SCA/vluzPmeQXRpKZb5O78fez78sQTMgcEBzHxPbV0MxSTDcMoJQ0Wsfp3F0YKKhjtzNomWDz4Rpvmx50AZufHhrL1R7W1cjKGFbtHIaowMBqcRWLq2pfRMLJOYtrYPF2xrm0lEMbxJuzW18EvtpNF2JtZNWzwLNZve7cYEsFCepNN18kEAFydexaL0KuRgEMKKAS7Xzai8nxqcxAZFpKaf/BZCoyOxX7QApqN7Egkh1C84trsvrJ0PNMDmOgir+0DaVtDqwaeafLNDP1FLsMSHo/oYHFXUlnldn/DkBAID1xAcuIbwzAwEUYx1PzEaIBiN6n8bHA6Yamthqq2DsaYWxspK9bpMh1a6xdeV5TBW57vhn+/ZEOGkz9eTW9rVbO18D611y7e8mcyvZ90IBz3rx7pl4umlc63e7cYEsHR8rSwlyQcBnBn8Mfy9F9SzcsKsEdWH3wPHseMb8AkODWH6u9+OEUKDAsNpBwyVO3exqGy6Xy1lks7Q6sFfnjmnEqP4sFcegqP6hJrRHLhyBYGrVxAYGIC8uvsZu81yEzEkIki1Ea37D8Dc0JAWIdRKt83y0DnBldkLCC5d25JAQsk5jurjsDha85ooki/d0vGZfF+jZ92YAObbe/I3v579Uu+6MQHM33NxXcysNQGkvreT0lcRHB5S8RNHzdjz0MdhbmzagKcaBfzOtxEaGY79vhEwnViPAqYCnzJWq1seS6uWnRYPPm2Tzg7+MBEVs9ib4G64G0oohNmnfqJG+7Qcos0G6779Khm0tLZCtKTujqKFbjvJHQnOgYgvJZFsHiZLtXo+0Gxv0FL1xFz51i0vQqc5qZ51YwKYphOU4GV69ku968YEsAQfqHISSWsCSOflpn3fQXhqMkYA+21o+tSvQzRtLZESHBlWt4JpKFBgP30Ylo6WtfZm1KKYkheWQV1F4iPdeoFaPPhU6Nm/4EusXd38KBAyYPaH309kMifbWjCZYGlpVSN6an3DaBRKJAIlGoESDiOysIDw9BSii4u7u4jBAEvTXljb2mBpOxDbLialCrTVRgQ9vDqO5dnzINknz/UAACAASURBVEK4eVAkliKCVEJGy6GF3bSUR8u59KxbofxSS3tkMpeebce6ZeIJpXMt2Y0JYOnYoywl0ZoAUpHkOd9TiCzMA4oA81gDGj7x6W2xoShgPFpIZWIafvnTEK3rXUOIiCyMPQfKwo2PdLaCc32p0Xbo7OCP1qN/jhbYhHbM/OgHkFfWM2gNTidsHi+s+9rUKCdt6+426GwkZUgTGQyNjKhbyEpo/YxhqvuJAFooOthKBLMZe5pqMD29lFxbe7dls/qc8A8uX1O3hlOVkrE49sJRdRxGS2VW82++KVe7aSJEnibRs25MAPPkNAWYVs9+qXfdmAAW4AHR8xJaE8Cl6Xew4HtJPRMnBEU4hGOofuzntoUwODqC6W9/M/G569ZTcJ++fcP11OVidujfEmSMzqNVN79nx63gXB980oPavalfbBBgkw9j8ZmX1IhefBDhq37v+2Cw23NyESUaRWh0FIGr/Qhc60dkfn7H+ShxxN3WArm2AZa9LTA3NqZFPHMRkrb2Vxd74Z+/vCEhJo6PxdkKe9URGM25EcFc7ZaLjvm+V8+6MQHMt/fkb349+6XedWMCmL/n4rqYWWsCODfyLJalt0GkRlgworLlXhCp22lMf+87CA7GtnkFsxnVj74XppoaiE5nYtuTtmJpSzY+dtsKzuXBj0X/6OzfWsHnRSNCL49saGVn6/Ci6qGHNSdeFHGLzM0icO0agteugggy5JgciSEAZrMRoVBE3S6niKnr1tNwHL8hrSSSXBxbjoawuuCDf6FnQ1mc+Jx0TtJWeRgma11WySK52C0XvQpxr551YwJYCA/Kzxp69ku968YEMD/PxHUzq5YEUE3s6PsGVgeuqPiJEybU3PYEbAd2Lt8SGh/D1Df/dQvmdKbOWFkFY3U17J1HERB8CAWmEtfttBWcy4O/NPUWVhf71HXkQBCRMzMQQuvlWly33ArX6duzIjiZOhZtFweHBhEgMjgwgOjKslodJ5kAxuc01e1B5X33w9zQmOkyGV9P5XGo3uPqQu+W0jE0GfUapvZ4ZvvejHDKxW4ZK1HgG/SsGxPAAjuThsvp2S/1rhsTQA0fhOtxKi0JICVsTPm+CSJ0KgEcsKDhw/8eRtfO5V3o2unvf3fnrFqDATUfehxLy2+ktRWc7YMfDa9gdojO/sWibtFri5C71kq8iCKqHngI9sNHiuIqsejgHEIjQzDOTmLW1wc5ENgoiyDA0XkU7tvvgGi1bZGT5gishrHqD2N1JQT/SgjhcBSVVXbsaXLBaMys1h9FS2lbOLDYn5IIUk1He8VhWFxtar/n3Ua2dttt3lL4XM+6MQEsBQ/LTgY9+6XedWMCmJ3P811rCGhJAIPLQ5iVfoTw7Kw6u/FqBRp/9bNpRYCoa8jcz36K0NiYmjGbapj27IHz4RuxPPdu4mObux2uulu2XJ7tg7809SYokYUGkavImdlE9M916jTcp24ruu+QbjU1Tlw8N4T+c9ewPDAEU2gFZkRgEegnDJPVBGPHESi1TQhEBKz6Q1hdCSPgD0OWKcN66xANAuqb3GhsrkBdowsGw+6ELT4LRQQpGkjnJmU5tGVyaitHnUWs7oMQRdO2GGZrt6IbJQ0B9KwbE8A0HKBEL9GzX+pdNyaAJfpQlYtYWhLAldmLmJeeQ3R5GUJEgM3vQe0Hn8gICopQUZYtRbrCc7NqVJCSI+LDefo0lL0rG7aC3fV3wOps3bBONg8+RTBnh36ciP5Fri5Aubyqzkvn7Oo/+elta/NlpGQOFxM+E6OLGO6fw+TEUqxkjiIjsrgQSx7ZTO4EQLTbYXC61pJVYqVkdhsGo6iSwdaD1aiqsadF4mlOapdHBHp1oSdl1rAomkHnN20VHoiG9WzvuDzZ2G03XUrlcz3rxgSwVLwsczn07Jd6140JYOb+znckIaAlAVwYfwlL0huQQyEIywa4qk+j8u57c8Kbsm4n//VriMzOxOYRRVQ/8ThWVt8GtTCL/cqEquZHQdnBuRAJamEXrzmoBAIIJ0X/3LfdATr7V6xBxG9ybAm9XRNYXAikPAOoRMJqGzrZv01XEoMBBqcDBpsdVHSaenfZ7CaV3PmXt0bt4rq6K61oPViDptaKtLeIaQs9uDygnhOMhBa2wKb2GnYfhK3iEAwmR052K5ZNMl1Xz19GTAAz9YbSuV7Pfql33ZgAls5zVJaSaEkAZ659HytXutRsWWHahOrj74XjSGfOuIQmxjH1rW8ksmGpn6775+7E4tTriblNlhpU7n0wcc4s0wc/uDKMhfEzifmiV+Yh98TO19FZunqqT2jeWsw6Z+XSmGB5KYiLbw9jbnqN2CUlgdhsJtTvdSMYiMTO9dFW7+yiWnRaCfhhRhRmIby2Nby+RWw1C3Dta4b9wAGY97XBHxIwNryAsaGFbcmgySxib1sVWvdXw+neGr1LpQoR15B/VD0nGA5MpyCCIizOfbBXHobRXMH9ZNPwh1K9JNNnrlT1SCUX61ZO1lqXVe92YwJYnn5ZMlJrRQCpPMiU72uxsiVEmobN2PPYJ2Hes0cTXRdfexVLb72RmMt1yykI7cbEeT36gEiEs+aEek0mD74cDap1BuVobLtXCYQRfnFa3cam4b7jTrhu2nrOUBPFdpiEyNNVaRpS1wTkaNK5PQFq0kbz/ko0t1VBpK4jSUOWZYRDUQgBPwK+bvi7u3buPkKRwPYOUJTTUFGBxfkARgfnMXxtTp0n1aissalrNzZXwmROL3EkvDqlEsGgfzTlnBZHMxzVnWjcu68gRa7zbb/N82fik4WWTYv19Kwf66aFhxR+Dr3bjQlg4X1KVytqRQCpU8eM73tqdwuVAPbbsffTn9OsTh7VFZz6xtfWW7CJImqfeAIr4fMbthgrG++B2d6UEQFcnHwNgaX1vr6U9UvZv6oeNjvqf/lTKVvZ5dMRlhcDuPD2MOZnYqQ0Piw2I9oP78HJW1oxN7eSVicQNQo3Mgx/T496npKKdKccBgOcx25Q6zbSmcdoRMbo0DwG+mZUUphqqIkje93Y21qJmj3OtBJHIsH5GBFcHlRbACYPotyVdfshWDpgtNTkE+KCz63nLyMCU8/6sW4Ff1w0WVDvdmMCqImbXL+TaEUAqVDzvO8ZNRkBsgDzVDMaPv4JTYENTU1i6htfT2wFG6trUP3B92J+/GeJEiSUWEDnAY0mG2prXbtGkjZv/RpCNqw+0692/6BRcdfdcJ68SVM9dpqMondXpRn0Xt4a9Wtrr4HnaD1MJkNauqVaR+3xOzEe6zpytT9lT2O1sPQtp2KFpQ0G0D3zs36VCI4PL26bRWwwCqjd41QziOsaXLDZd94yp6Qb/3wPAkvrJWQI9XiNQ5O9EY6qTrWotB6Gnr+MmACWr4fq2S/1rhsTwPJ97kpCcq0I4PLMOcx3Pw85sAohIMJhOoHqR96juY6Lb76OpddfS8zruOEkLCebQcWb48Nsq0dl0z2oq6vckQBu3voVRBPkt/yIjMcSTkSHA/WfoOjf9mVLtFKQavEN9c/iau80gqvr7eZofofLjGM3N6O6NpYsoeVLLbK4iOW338RK16UNnU5oHeo/7L79TlgPtieygEPBiHpWcOTaHOZnN0YnN2PhqrCoRJB+qmrtW7aq49fLkVXQHxDUag5yJEEA47FBs0oEj6nFpct5aGm3UsRBz/qxbqXocbvLpHe7MQHc3Qf4ih0Q0IoALoy/jMXLZ6DIspoBXNnyIFw33aw59upW8Le+gfDkRGLuykfeg2jlDALLg4nfWRxNOND5KGZnV7fdJt289WuJtGD56XUiWXH3vXCeOKm5DskTUlHma30zGLwyg0h4a8u3/R218HTWg8qyxEc+XmqUPbzw8kspi3Gbm/aqkVBzfcMGLGibenhgHiMDc1tI62bQjCYRtfXOBCG02raS6lgtQR+igasIrq5u2hwGLI69KhE0WqryapN8TZ4Pu+VL1mzm1bN+rFs2HlH8e/RuNyaAxfexspZAKwI40/9jrPTHCjQLc0bUnfoorK378oJNeHZG3QqOF4ymlnE1TzyB5dW3EQ0vxWSgs2S1bTC5bwWErUkKwZURUNma+DBb67H6zFXIi7GzfwaHE/Wf/JRmZxg3A0ERP+nShBr1S1WY2VVhxdEbm1C1FvVLvj+fL7XA4AAWX34p5dawzXtITRQxujd2dqEt4sX5VbVMzdT4UiwymLrW9Dppr7GrBacbmt0btopJt6pKE4b6z2J13pco9ZOsP9V8tFcdVbOGy2nk026lgIOe9WPdSsHDMpdB73ZjApi5T/AdSQhoRQAnLn0FwfEBdWZx0oTG938WBvt6fTetQV/tlTD7bz9OTGusqkL1B/8dFmdeAZ0ti58lg7EW7vq7IYgxEki1AynKtDrfk1RH0AxxyIXVd7vWCcp9D8Bx7LjWYqvzEUm6+M6I2pVj86ius+OAt06NlFF9vlQj3y81iuL6uy+rW+1q7+GkIRiMcJw4CdfNt2xbFJtK0kxPLGFyfAnTE8sIB1NnEsenpe3hhuYKlRBSXcL42c1oJKjairaHqcD0BjkgwOJsKSsimG+75cVZM5hUz/qxbhk4Qgldqne7MQEsIWcrR1G0IIBU9Hf83BdAkTkahmk39n70c3mHg7Ysl8++k1jHeuAgKh65Hwtjz0MOL68nE9ga4N5zG1aXrqwRv41Fj63CASz96NXEPLTlWfvzH0q7+0W6itL5ue7zYxgZmN94iwC168YBby2qanYnzYV6qcnhkIov/Wxuz0e1EakwNpFkwWjcFoJ4AsnU+LJKfBd2OjcoANV1DhzqbIDdZYbZEpuXtoapoDS1mlOUjWSSknVUIljZCaOlMl1TFOW6QtmtKMppfDa1WDpst66ebce6lZq3pScP2Y0JYHpY8VXbIKAFAYxG/Jg4+4+ghAIaluVm1L//k3nHnCJV0999Ui1xEh+UtGA/0YmF0edgEFYRCkXU3UgiCptLjtA9Vsc+rDzVndj6JTKz5xd+EcZKbc+ZjY8soOvsqFqwOXnU7HGg82RT2oWV6d5Cv7Cptd/i66+qUcHNByoN7gq4b78Dtg5PWoQ5GAhjfGRRTSSZnVrZulW8VuQ6HI6oZWWaWirVbWKj0YAoJYvMX0ZgsS/Rri8ZS4ujBQ7aGi5RIlhou+X9Ady0gJ71Y90K7U3arKd3uzEB1MZPrttZtCCA1OFh8uxXEPWvqDjao52ofeT9BcGU1pz6+tfWtyoFAbXv/yBMTbUIzL6MlcWZlMfRzLY9sFcdg//Ny1g5fy4ha8Vd98B58kbNZKcIWPeFMVyT1lrZrc1MSRGHjjeiZX9VWsQpWaBivdTCU1NYeOUMgoOxrf7kYdpTr0YEKQq73db15ntSksGkLidxw1ECDLWha9lfjYoqm1qwe5UigotXtkQE1T9ASpQIFstumjnzLhPpWT/WrVBepO06ercbE0Bt/eW6m00LAkjZt9Pnvgk5GFTxc1tuR+Vd9xUMy+DoKKa/861EfUDanqz94M9jT3sDrlz8ASKhWGSSRoz4HQWVigmODGP6yW+tf6bx1i8ld1ALt81bvlQnjxI8dquTtx2AxX6pxRJFziSKfifLSWcxnTfeDLv3UEYJNJQNPU6t6IYXsLIUUiO3qZg7lZZp3l+tFp42GMJrRJAiglvPGZYaESy23fL9QOpZP9Yt396Tn/n1bjcmgPnxm+tmVi0IIBXznTn3PVCJFiEqoGrPo5pG0dIxxvL5d7Hw4vOJS0WbDZ5P/xIWYcDy7AU1icDqPgiTdY8aoaLzbZNf+wqiCwvqPVpv/UajMt59fRATo7GsZBoU9aPt3qbWyrSjZKl0L4WXGkU2V309WHz1FUSX13WMy0tZ1I4TJ+A4enzbZJHtdLNZzLh0fkQlzksL23QgEQXUN7vVvsQVVSICCz1YVbeGtxJByhp2VB+HweRKx5Xydk0p2C1vyhXhaEI+ddk8t55tx7oV0pO0W4vPAMaqffDIAQEtCODS5NuYu/B0jEgFRNR6n4CtoyMHqTK/VU02eP5n8F+6GLtZAGwuJ1yPvQ+mPRvr1xFRnX/x+fVr1Y4f2m39UomXs68OYGYytiVOw2w14JY796tbmLmOUnphK5EIVi5ewPK5symJIHUVoYig44Yb0mqnt1m3hblVDF2dxdjQPMKhTXUS14C0O83q9nBTiwXR1b6URFAQRFjd7WpnEeoWU4xRSnbLh/561o91y4fH5H9OvduNI4D59yFdr6AFAZy79gyW+t6M8a4lA+pv+yTMDY0Fx20DCVw7SxaBATWPf0CVh4ifv6cbS2+9gehawopKzhqbYlm/4nqx5WyFp0zft16+tiHb1Wo34da798PpsmQ77Yb7SvGlRthSaZ6ld95GZGZ6i56i3Q7XzbfCcfTYjlvD2+lGfYkpkWbo6lwseSTFEESgYW8FWtpssBiH1pJFNkYERdEEW+Vh2Cu8EMTts5c1MdSmSUrRblrqqWf9WDctPaVwc+ndbkwAC+dLulxJCwI43f0k/CM9MQI4a0TTY5+FweksCl5EAhdeeB4rl84nysAIJjNcN92ClcuXElu+ceFo67fuY78IU1XuWb/hUBSvPX8Fy4uxs5A0HG4Lbr2rLevzfimJjoCsewHn2yiEf3DgmkoEk7Oz4+sanC64bj0F++Ejap/hzSOdF/byUlAtoE0dSELb1Bik9nktbXZUV4wivHpV7WecPAxGm3oW1Oo6AIoOFmKko1sh5MjXGnrWj3XLl9fkd169240JYH79R/eza0EAJy58CcHJWCkWccqCvR/5LU2iadmCr3amOPMiQt0Xt00moLmN1TWovO9+WPY2Z7tU4j5ak7Z9k8/80XbvzXe2wWLVNtJULi+14NAQFl9/BaGxsS34Upkd1+nbtpSPyUQ3Omc5ObqoRgWp4HSqIYoCmlqNaKgbA6LjW+UwuWGvPqZmDqebvZyts2SiW7ZrFPM+PevHuhXTs7JfW+92YwKYvW/wnaCybooyPb20bb/c3UBSi0Cf/XuE52bVS40zlWj6yGd3u60AnyuInnsT4y+/uiWb1FhVDdep07C1d2hGVK/0TMF3cZ1gUHcLIn8m09YoV67Kl9NLLR4RXHztVYSnJreobqrbo7aXs+zbpxKwbHVbWQ6qRHD42ixCgdSdRxqaomjcMwqDGEv8SR4mSzUc1TfAbN94XjRXWyXfn61uWsqQz7n0rB/rlk/Pyd/cercbE8D8+c51MXOuBDAaXsHEuX9KKgLdgvr3f6Lo2NGDX1PjRP/3/03djlTJaVUVXKdu05T40bwzU8t448WrCaJpsRlx54Mdmkf+4qCW40uNiGDgSh8WX38NkbWOMclOQt1XqIi3dW9TTtvbFBWcGF3EYP8sZpOScNbXUlBX50dj/RjM5q0ZxmZbA5w1J2C05H4kYPNDUI52y+RB1rN+rFsmnlA61+rdbkwAS8fXylKSXAlgeHUKk+e+mlQE+ihqH3m86FgkP/jBsXEokTCIZGiR6JGsHNWve+XZvkSHDzpOdvqeA6iq3b2lW7YglfNLjbq3qOVjqM/w0np9xjgW1rY27HvsYayYHFlHpeNzLS8GcK1vBsPX5iBHN54BJLZeUzOHxvpJWK0p+gy79sFRRaVjtLNjOdstHV/Vs36sWzoeUHrX6N1uTABLz+fKSqJcCWBgeQDTZ78FObRWBNp6ByrvvLfoGBTiwZdlGW++dBWzU/6EvodvaMR+T21e9S+EbnlVgI4eUPmYSxex9NabkFfX8aPyPWazEYaW/eoZQVN1Tc6iUPu9gSsz6k94U9KIABk11VNoqJ+ClarDJBWWEgQDbBUe2CuPQDSYc5ZDD3bbCQQ968e65ez+RZlA73ZjAlgUt9LPorkSQP98N2bOfT9WBDoioKrhPXCeOFl0gArx4HefH8NVab3kSWNLBU6c4mSCTIxPBblXzp3D0tl3oNAfEcmt4CCoHUVcp2+H0e3OZNqU10YiUTUaSDZbXQlvuEYQIqitHsee2hlY7eKGhBDRYFFJoM1NZ0azP9NZCJ/MGaQcJtCzfqxbDo5RxFv1bjcmgEV0Lj0snSsBXJx4E/MXn1GhEFZF1B5+Qj1jt3nQ2SyqkUfRGPqJhKOoqLZrVhtv83r5fvCpJt3ZVwcTy1LZkdsfaM9L0kehdSuGX8uBVSy98w5WLrwLk6hsyN4WDEY4TpyE6+ZbMuoqsp0eFLkdHVzAlZ5Jte1c8jCIQVRXDqOmeh42hwkGw3qJGIPRDgdlDDv3Z5UxnG+fLIbdktfUs36sW7G9K7v19W43JoDZ+QXftYZArgRw9upPsXzlrRgBXDSg/vZfhrkhlklJB/97LoxjeGBuy9YbfW4wCjh970FNumMUkiSFQxG89HRv4twf6XH7/e1wVRSmw4SeX2qyfwVKzwVMvPaWGlVOHtTej5J4HJ1HU9YQzPShJv+k/sN93VNbWs6ZjCuocg+ismIZNocZJvN65M9orlBby5ntezMignq2m/r8l3B9ykx9o5Dvk1xly/V+tluuCBbnfrIbE8DiYK+bVXMlgNPd34Z/xBcjgDNGNL13vQj0zORaduwOaLmrrCp5onptWo58vtQuvTOiZpnGx/FbmtHcpn3W6HZ45FM3LW2QzVxx3cavjmDx9dfh775Mf0lsmIrK+LjvuAvW/dlF4jbLRURwcmwJ/b4pzE0nnUcEYDXPo9I9CIcjAIfTDLNlvaajyVoHZ81JmKzpnVPUs92YAGbj7aVxj579Uu+6MQEsjWeobKXIlQBOnP9nBKdGVP3FSQv2fuw/JaIivkvjuNI9tSs2+UicyNeDPzezgtee60/oVNfgVOv95buIcDKI+dJtV0MV4ILNuoWnprDwyksIDq5vt8fFMO9tRsUddyUizlqINz/rx9XeaYwPLSTxTgV26wwqXUOw2UKwOy0bSvxYnfvUGoK7ZQzr2W5MALXwvuLMoWe/1LtuTACL88zoZtVcCKCiRDF+9gvrRaDnqtD0of+YwOb1F/oTfVur6xzwHK1XvzipODK1TIufv6It1Lse9sDuyD3TMr54Ph58OjtGJV+WFmIZz6JBwN2PaCt3Oo6VD93SWbcQ16TSLV5MeuHlMylrCNo6PGoxaWNlpWYirvpDGLgyi6H+GYRD8tq8Mpz2SVQ4h2GxRGF3mmG1mdTPYhnDXtirjoD6DacaerYbE0DNXK/gE+nZL/WuGxPAgj8u+lowFwIYDS9h4uw/I7JWz82y0or6x39JBYjI0k+/dzlRf21zlE8tnvzC1QSYexpduOmOWDcILUY+HvzN3T68x+px8NAeLcTNaI586EYCROUoViMB+CN++COrECGiwuKGy+yEWAL9cqmGoL/rklpDcEPpGBLeYIDj6HG4brkVBrs9Izx3upiSlWi7nzKHKXkpRnSicDvG4HaOwGiEujWsEkGB/iiwqiRQzRjehFm+7KaZsjlOpGf9WLccnaNIt+vdbkwAi+RYelk2FwIYWp3A1LmvIeqPnZtyRI+iZq0I9NyMH689dyUB0+0PHERl9cYv5gtvD2P46lzimpOnW9DYsjGKQy2+luYDoAhi8vmr3fDX+sH3r4Tw0tNSgtC6Kiy440E6u7ieJbqbTFp9rpVuFFm7MH0Z0lwflsMrCERikc3NwyCIKgmstFSgwlKBOls19tjr4Da7NCPs8TXT0U0OhbB87h0sU+mY8KZyLmYzXDfdAseJExBN2kWUKYudfJXOCa76Y2saxBAqXENqVNAgCrGIoN2kYmIwOeGooozh9T9q0tFNKx8pxjx61o91K4ZH5b6m3u3GBDB3H7muZ8iFAAaWrmL63LdBX8gU/nBb70TlnXereNIXJWUAq1+URhEPPU5bYxuje1QW5qWfSonerbQ9TFuqRpOI2ekVXJOmMTG2pLZYs9lNapkVuiadoeWDT0Tp7VcGMEWyrI3b7stvt4+ddNRCt9XIKn42eAZDS7Hzm9kMq9GiEsF6ex1qbdVwmhxwmBywGixZE8NMdIsuL2PpzdexcrmLQs4bVDA4nHCdOgX7kaOadn+JlZCZB0WD40cYYhnDA7BaFlQfp6MM6nEGAWpLOWf1cZhsjepntbUu5NJ7Oxs7FeqeTGxXKJm0Wod10wrJws6jd7sxASysP+lutVwIoH/uMmbe/UGsCHRYQFXjehHod165honRGGGqqXfg1N0HUmJHX6bvvjGU+IySKkLBKBbmVrdcT4WWT55uTcsGWj74Y8MLOPfaehJC64FqHL1pb1py5OOiXHUbX5nATwdewEp4Y8arVrIaRAOcJrtKBily6DY54ba44DK71Kih3WjbliBmo1t4dhaLr76MQP96xDmuC/V/jmUMH8ialKbChf4oIL+gJKelBeoprMBqmUeVaxAmk1+NCDpcFjUiSMNsq4ez9iQa97YyAdTK0Qo4TzZ+WUDxclqKdcsJvqLdTHZjAlg0+PWxcC4EcHH8dcxf+pkKhOAXUdv5IdgOtqv1/579YXei9l9H5x50HKlPCRhd+9aZa5ieWE4L0FTbxKlu1OqlRmfAXnxaQnA1dv7LbDXgnkc8MJnTi0SmpVSGF2WrW2zLtwuvj70DWVmPmBFJO1Z7BA6TXSVndpMNDqMdESWCheAi5tWfBfW/ZwNz6v/nMmhL2a4SxOQfByrMblRZ3TjQ1IS5WX/GvYCDo6NYfOUMQmOjW8SztLai4u57NWktlzx5vITMle5JzM/SHy0KHLZpNWPYYAjCaBTVM4IWW2xruLbxMBSzB6LBlguEJXlvtn5ZkspsEop1KwcrbZVR73ZjAlieflkyUudCAGf7n8Jy/zsxAkhFoO/4ZZjrG7C8GFALJcfHrXfvR229c1ud/ctBvPTT3sT5uviFJosBLfurMXR1NkEmzRaDmjG821awVg/+5lI21OqtqTX3bFNZVjA0uYyxmRX4gxGsBiPwB9b+DUYQjW6sfZfAxCjCZBThdlkRjURhNIioq7DiJu8e2HfYT/4YKQAAIABJREFUHg9GQ3h+6AyuLmwsp7LP3Yz7W+6C1Zh+EWs6Kzi5OoVJ/zQm/PTv1LbnBzN2dAGwWsywwopKcwWqrVWod9Shwb4nLRmJkAWu9mPxlZcRmVuv1ajKIYpwHLsB7lOnIaqNf7UbtC79ESN1TWBBJYIyXPYJVLhGIIphmExEBC1wuq2IRAB7ZaeaNZxLazntpNdmJq2eOW2k0XYW1k1bPAs1m97txgSwUJ6k03VyIYBT3d/E6kiM6AkzJjS977Mw2B0Y7J/BpXdiURh6AB96/xEYjTv3UB24MoOus7F7qK1aW0ctmvdVqecHN28TNzS71a3gnTKGtXjwqRTIi0+tJ37U7HGAyGy2mcpEEqYWAugZmINvcB7+wMYEhrRdLLlf7hpPtFmNeOCmZhxsqtgyDUXtnrr2nBrBiw/S4VTDTThRdzRrfeJzkV4hOYyV8IqaTEJby/SzFFpe+1nCUnhFjQzvOlLoFr+nylqpEsEGxx7sdTaq28vbDTVj+HIXFl97dUvGsGi1wX3b7bBTRxGNk3hIx4mRRfi6JrCyGFQzhiucI3A5RiEICqxWo7otTAlNBqMDjpobYHHs7Mu7YlYiF2jxzJWIKlvEYN1K1TI7y6V3uzEBLE+/LBmpcyGA4+e/iNBUjLSJk1bs/dhvqWTi/JtDGBmYV39fUW3DHQ+0p6Xv4vwqKDJWUbXxjBh9qZ57fRDjw+sE5sTpFjRtyhhOXkSLB5/OJhL5jDFZ4M4H2+GuzHzrLhiO4uKVGXQPzGF2kc6L5Th2IElH9lfj7huaYDHFCHf/wjU8N3gGYTm2hU2Dtl4fbL0HTc5Yy75CDCoxsxz2YzG0pBLF2E+MKKpkMbwciyLuoNtmOek84V5XI5qdTdjrbIDNuNU2cjCIpbfewPK757Ykiphqa+G+825YW/dpDgH5MflOb9eEmjVM28GUKOK0z6o+rkYEXRaVCJosNXDUnIDZVviSQloqrsUzp6U8Ws7FummJZuHm0rvdmAAWzpd0uVK2BFCRIxg79wVE5mJlXIyz1Wj68GfU/37+Jz1YXYlFt9o8NThyQ1PO2AUDYXWbOByM9Yel7eG7H+6AxZqforvUEeLVn60nFVCrN2r5lskg4to7vICXzo9iZTV1tI8id1UuC+wWo/pjW/uhbd74iOdOU/wsHJERjsqwWM2Ym1/B9EIQQxPr2cl0j8thxsM3N2MkKuHs5IUNIhPpe6j1XvWcX6mNQCSAhdAiFGsYA5NjmF1dwNTqdNrJKpSJ3OZuxf6KVtRYqzdENsNzc1h46QUEB65tUZsSRNx33Kn5+UBaSC0fc20Ofd2Tah1Bp90Pp60PZtNKzI+TiKDFsReO6hMwmt2lZpq05NH7l61eM7jZbmm5d8ldxEkgaryARy4IZEsAI6FFTJz7EqKJItBtqH/846Bt0+d/HOsNTOPG21rR0Lx1WzIbmceG5nHu9fWM4fq9bnX+VFuyubzUiLhRF5N4X1jqVHLPo95E14d0ZJ9bCuKFcyMY3ETO1C99o4iDeytweF8VmvdQkeXM3DhZN4om0ZbyC++OIhSOkWMZYczZu2FyLaHCaYHJECOTx+uO4LbGWwpW1DkdnDZfs9luZAvaVh5bmcD4yiTG/RNqIspuu8m0Pbzf3Yq2ilY0OupVndWOIteuYuHMS4jMr9efVGVQzwceh+vW0zDYtCfHsTqCsxi+Oo+FBT8cVkoUGYTBQCWUAJPZsNZr2ASbux2OqqMQMziXmQ3WWt+TyzOntSxaz8e6aY1oYebTu904AlgYP9LtKtkSwJB/HJPvfh1yvAi0fBw1D78Po0PzeDeJpD3wvsO7JmxkAu7Z1wY2bgVvk5SRy4M/PryAs0llX3bKYt4seyQq462eSbzdM6lu9SWPploHjh+swYEmN0y7nIncCZNUui2uhPDTtwYhzfdhyXwVUTFGLGi4bBY8vP9unGo9kgnURbk2HbtRpHB0ZRzDS2MYWR7dNSuZop0dlQfhrWpHja1KLVu0cuG8WkOQtoiTByWHuG+/A/bOYzmfjUxFbqsqHTj75gD6eiYRDoThcoypZwQFcS2ybTbAqW4NW2CvOgx7xSEIYvEyzjNxgnRsl8l8pXQt61ZK1khfFr3bjQlg+r7AV6ZAIFsCGFjqx/S5J2NFoBUBbvtdqLzjLnSdG8FAXyz7kpI5KHKm5aBttDNUPHptK5jKstz9sGdLl5BsH3yK1Jz5aS/8yzECZbEZcc+jnl2TWOjaJX8I3ztzdcs5P8rOpXN5npZKTUhFKt1Glsfw6uibuDI9jrmlQCJCZpCtqF7thFl2qdFGIqB7a507Zgxraa9M58rGbsuhFQwvj+La4hCGloYRkWNkKtWos9eoRLCj8gDMEQWLb7yBlYvnt5wPpGz2invvh7k+dfmiTPWi65N1C1OLuSuz6JemEAkF1P7ClDUMIfZHQzwiaLU74ag+BquL6hgWvutMJnpmY7tM5i/mtaxbMdHPfm29240JYPa+obs7vV7vnwG4U1EUg6Iof9bb2/vkbkpmSwBX5i5h9twPQdmWQlhE1d73wHn8BF5+pheL87FEh+b9VTh6414EQlHYLAZNyA/Nu3krmNY5fvPG83nZPvhXpSl0n491MKFx/Ja9aG6r3g1GLCwH8eRL/VhaWY+80db08fYa3HakARbzzlnQuy6QdEGybnOBBbw2+pZKfuKDzgnOLwcRWXahyn8YIraek6xwmtFQ7UBDjR2NNXbsqdy+OHMmsuV6bbZ2S+guRzC8RGRwUP3Zqb3dPncLPFXtaIrYsfzqK2r5mA1DENRtYfdtd0C0WHJVbQMBjG9hRyJRDPTNoF+ahhJZVreF7bb18jVxImhzVsNVexJme+7naXNWZJsJcrVdvuTSYl7WTQsUCz+H3u3GBLDwPlWSKx46dOheWZZ/W5Kk93Z2dlaHw+GLkiTt2q4iWwK4OPYa5rueU7EQ/AbUHv0QjM1teOYHl9XWbTQ6b2zCK1dnMDW3CqfdhP2NbvWnZY9TrV+X7aCzXO+8MoDJpNZsp+7Zj5o962VBsnnwqTXdi0/5EA7FiiS7K61qv9/dyr5QZu93XurfkOhRX23H/TfuxZ6qjf2Ps9U5+b64bq/3XcCzAy9tKOqsym124bamm1FrbMT5vhlc7J9Rk0d2GnWVNjxwczPq8yBvJjpnY7ft5qdi1yPL4/DN9eLqwsC2kUGb0YqOqgM4uGgGXj+H6OLChilFmx3u07fBfqQTgiF7Ir+TblRwnEohEREU5Hk1Y9hiXk/uoWQRqiHorGqFs+ZGtddwqQ0tbce6FQ4BtlvhsNZyJbIbE0AtES3vucSmpibr6Ogo9fcSPR7PlCRJVFdi+/0w6lugKEo2vUln+3+C5f5zMQK4YET9XZ/CgmxTu3rEx74bGvHs+a29Zon8EQm80VuH5rrsvsgo2YSKTUfXiI3dacZdD3fAsEYss3mpXXpnBIP969GXzaQylXtMza/iu2f6sRpYL7NCJPex2/blRHJ3ckXSTbGF8A9v/OsGUmMxmHFT/Q04WnMY1I4tPoKhqEoCL1yZUbeptxtEdE96anH6SIOaqFKMkY3d0pEzFA3hysI1+Gb71ISS7UaNyQ3PUBiV0ghMykYMjJWVcJ26DTaPd9c/ClLNn45u60RwCkZMq0TQaFwvHWS1GeF02+GqOQJ75ZGSOh+Yjn7p2KoUr2HdStEqu8ukd7sxAdzdB667Kzwez78XBOEen8/38d2Uz4YAqu2vLn8VwbGBGAGcMqHp8V9HX/+i2huVBnXqMLW6cU6a3pFwvP+u/Witd+0mZsrPr/VO4/K7Y4nPDh6ug/dorLZdpg/+1PjSBvK6p8mFm+9o21Gu8Vk/vnemH0Sw4qOjpRKP3NoCg8YFhpMFCcth/GjoKYwvTCeircdqD+Pm+hO7dssgAjg24wfJPj7jx8Scf0uyCmUOU1FpIumFHpnaLRv5qCC2b65P/aHzg6mGeTmIDt8camdCats6U1Iihqm2Ti0kbWnLrCh4JrrR1nDsjOAkzOKo2lpOFGN/ZNA8aleRigo4a2+ExdGSFSHNBrud7slEP63Xzvd8rFu+Ec7P/Hq3GxPA/PhN3mdtb2/vFEXxKwC8kiRt2Cfs7Ox0hsPhvwHwIADqWfW6IAi/4fP5rnq93l9RFOVXBUEg7ibQv7Is/0Vvb+93SGiv1/u4oii/H4lEHurv79+4l5VCq2wIYDSyislL/4LwdIzsGUbsaPrE/4E3XryK2anYFyp16+gPRjAyFevxS7Xp1JIe/o318KgG3scf9mRFmGi+V5+7stZ6C6Az8nc+2AFXhTUjAhgORdQag/F+v9R95M6H2tUv2e0GtXD77pmroMP88UFlXR68uQWimFlZl0ycTe2zPPgCBleHEQpFVAJ4Q91R3N50SybTJK6lrOW3fZN4q3tr1jIVlb7jaGNBE0YK+cImLCmbmIhg//y1DcWyVYAUBe6JJTT2TKBqVUCFxQ2KssaHeW8zKu+9H6aamrSwz0a3xBnBnlG4bANwOtajlxTtdrktcFQ2wVV7E4yW3FsUpqXINhdlo18u6xXyXtatkGhrt5be7cYEUDtfKdhMHo/nIwD+XBCEM4qifGAzAfR6vV9VFKUFwMcEQVhUFOUvAdwhSdJRtdv8NuPQoUOPyLL8hxaL5dGLFy9uKnSW+qZsCGBwZQQzPd9PFIE2T9Sj7qOfxjPfv5zo53vohgY81TWeOHt21w1NONlRi+mFgLodSZ0x4oM+u9FTlxX+1D3klWf7ElmvlTU23HbfQZWEpVu49d03BjE6uM6Vj97UhNYD23+pU9/erz0jYTmpuPOxgzW47+TevEdiLkx14ZWxN2E2G1UC2Givx/sOPLJhyzcbIGcWAnj2nSE1Kpg8TCYDbjm0R7VdLuc205WpWC/scDSMq4uDuDJ/Tc0kjipJZyYVBVXD82iQJuEOiSoRtBrW/jgQRbhuvBnOW26FaEpdlDyuey66BVbD8F0cx+TICKorrm45H+hwWVFR54Wj+jjEuGzpgq7Rdbnop5EIeZuGdcsbtHmdWO92YwKYV/fJz+Rer/fjoig+F41GHwbwd8kE8NChQzWyLI/LsvxgX1/fiyTBgQMHKoxG45QgCI/6fL5Y5sWm0d7e7hZF8QyAByRph33XTfdlQwApA3heel4tAi1EBFj9HTDe/Rhee349i/LIqWZ8/43BxGpP3NeOvbUO9f+pPt7Xn+3F9MKq+v9EMj75qBeObbp67GaFnovj6O+JRSNpUPJJW3tNWgRwc0ZxXYMTN9/Zti2Ro6jRj18bwJWRdcJ40lOHu4435p38USHk71/5N8iQVQJolE14ouPfqa3dtBiyouBC3wxevTS2JWmEknhu72zAoX1VedWzFF7YwWgI1xYG0bdwVc0opmQSGkJURvXgHOp7J+GIGFBhcant5yjea3BXqNFAa9v2xwa00G1uekUttRQNDqPSNQCDYT2iHksUcaGq8QRsFR0FLxujhX5a+HE+5mDd8oFq/ufUu92YAObfh/K2gsfj+eRmAujxeGjb92mTyVTR1dUV2z8F4PF4ugB8RZKkP04lkMfj+TUAfwBAim8NGwyGT3V3d8cO6m0ziADOzCzt2lkh+faFsTNYvPIW5MAqhGUDnLYTWNh/My6fj53HMxpF7D3egGfPDse+OCHgP36gc0Px46HJZTz54nqrtSP7qvHwrRT0zHxQIght4frXSrDQ+qfv3Y+DHfXYSTeKqlDNv9DaGT4ionc/0rFjxw+KXP5sTS+StK3Bjcd3IIyZa5P6jtXIKr4l/VDtikGEgwjge1ofRJOjUaslEvNQUWlqX9eXRHLjH1K28J3HGtFa78wLEaQXdk2Na0e7aa7wDhMS7hemLuPidDdCcoxsiREZ9dIk6q5MwygY4DI54DQ71Cisrb0DFXfdA6Nr67lWrXSjP0KG+mfRe3kMVtM1uO1jifqBJB+VjnFV1qK2+RaY7fn/wyQOn1b6FdK+6a7FuqWLVGldp3e71da683feqLRMmVKaslY+FQHs6Oigbd8vSZK0ftgoRgBpu/jt3t7e39TSLkQAM53v2qVvYr77gloD0DBrQtPxRzFoaIKvK3Y+qbrWgUiNDe/4JtX/p9Ii/+GDx7cs8+3nenH56vpW8Kff14nmPdklhIyPLOD5p9Zb0JnNBtz/2CFU1cSijpsHqf3SM71q55L4uP2+g9i3w9bv9PwqvvC9i6BzczQcNhP+tw8cV//N5yBZv3Hph7g2HyPUNO5tO43TLTfmc1kMji/imTcHE+c4kxdra3Tj/ptbsrZXXgXPw+RUT/Ds6EW8NXIeq5FY9xDLwiqazg3DNutXSbnDbFO3hx0OF2rvvBM1p2/NqWzMbmrQ+cArPVPouXgFVmMvrOb1DHa612o1orZxP5o77oDFtnsty93W488ZAUagtBCgJIDSkqiw0pS18ttEAD8K4MspCODLiqK8lQ8CmEkEUI4GMdX7DQSGY4WHxSEL6h78MLqGFIyPLKq/a9jrRs9KEFQihcbR/dVqcsTmsegP4ctP+RKEiojiRx/Yve7edi7WfX5MraNGgxzD6bLi5O0tcLkpj2bjoHIvF99ZL1HT1FKBk6dbt/VeIn3feK4voRNd+P67DqCtITvCmsljcnlGwgvDryRuaXM345du/gBmZ5czitxmsmb8WiKfvcMLePniGCgyuHlQW7vbjzagtkKb/rml/hc7nRXsmvXh4tRlLIVX1EQR2hZuujwOcS0hiBJFKi0VcNU1ovKe+2Ddty/mk3mKblIEnGoIDvdLcFr7YTLGnrv4sNpMqKr3oKrxRhiM2tgplS/lS79s/Fbre1g3rREtzHx6txtHAAvjR3lZJRUB9Hq99yuK8owgCJU+ny9RCdbj8fQIgvBFn8/3p1oKk+kZQOoBPNP3I4QnY9E+sdeKpk99Fq+9PIyFudgXT2t7NZ67MqNm/dKgkiJHt4msvXF5Aq93rXfeePCWFnS2VcMfCGNs1o+JWT+WVyM41Fq5a7kYWq/r3KhaPoMYIG2T0gvg1L0H1P6qNJYWAujrnsTY8EIinYbavd31UMeWdnLJONOW6Dlp/Zwhnfuj9m75Hv7wKv7V9x3QuTQatNX4Yc/jaG6oRTb1G7OVlwgwbX+/2TOJQHC95mGM2Ajwtlaq5yDtWZ7jjMtVLmd26Fzg0NIIumZ6MLg0DDEQUUlg1fB67pXFYFGJYJW3U90WNlW40zqXmrWNqKtI7zQmBi/BaRtIlI1RbUQRQbsVtc0n4a47AkHIvqD1dvKVi+2ywZd1ywa14t+jd7vxGcDi+1jWEqQigMeOHasKBoPjawkfz9PkHo+nFgCxpHslSXo56wVT3JgpAfTPd2O+9zlEFuYhyAJM4w1o+MSn8ewPLyMUiJVEqT9QhZf717d2f+EhD+jsWKpBxOJfnvYlokvUMs1sNGwpWExZvR95oENtWbbTIBJIkb3ha3OJTFmqSXjspr3q78aHY1HK5HHLXW2o2yGSNzC+pNb7i4/aShs+cn97QbJinx18Eb1z62s/tv9BtFW05JVI7IRvMBxVifBZaWpLoojNasRDN7eo3V6yHeX4wl4KLaN7VgJFaoXJaTRfHIN1cT0KRxnDFY5q7LnldrQ9ch/mlkJ5jdyGQ1H094xidvwCXLbRDecDqTyl3VWF+rbTsDqbNT3HWY62S9dPWbd0kSqt6/RuNyaApeVvaUmzf//+epPJZATwYQCfB9BBN/r9/tnh4eFVj8fzxbXffSQUCq2Yzea/AnBYkqRb01ogg4syJYCLE69ise91yKurEFYMcJiPo+LhR/H0dyhHJTaczW6cG46draPSIZ95/9Eda+NRRu2PXl3vILKd+NRe7cP3te9aZ49I4IW3hjE1vpyolZdqTiKV3mP12L9DCZpAKKISVP9apw/S52MPdqA6xbZyBrCndSlFln7c/0zi2oOVbXh4330Z1ThMa6EsLiI8qH4gdReJrp2JjE9zQ0etmiiSTdmYcn5hR+QIumZ8ODd+HvYro2j0TSS2hQkbs8GM2toG1J6+F87O4xDyWCyc1qMkpyvdA1iZPQ+7df0PMvW5NIpwVbdgz75TMGlUP7CcbbfbI8C67YZQaX6ud7sxASxNv9tRKo/Hc5V2SlNc9ClJkr7c3Nxss9vtRPqeoHc1gOcMBsNnuru719teaKR3pgRwZvBH8PddVBNAhBkjKjsehNBxFC89JSUkitTacHU6VhC6qdaBD93XvqO0RNioqPLQxHrv0/gNFBFM7rSRbs1A9ezapUn0USLKpjQX0SCg9UA1Dnjrdsz4JRlefHcE7/audzO5/6ZmHNshUUQjs6hFib/p+y4WQ7FEcLPBhI96P6iWfCmllxp1FlEzhmlLPWlUV1jxnltbQdHSTEYp6ZaJ3MnXxs8JXhh6B1WXBlFNiUZrxyFEUYQIAc66JrQ88HNwH1D/9svrWFkKovdSD+TAJZhNGzuf0DGJ6sZOVDedhJhU5DobgfRgu+30Zt2y8Yji36N3uzEBLL6PlbUEmRBARY5gUvo6gsOx+n7iiBl1D3wUS6ZKvPnSegRvwmbA4lqR5HTPylFE6cXzI1gNRtVt3oZqOxpq7LCaDWrR5bmlWOYlRS5+8WEvKhwbkqS32IAe/OoqB376oy5MjMSIpUr8DlbjgGd34kfXzy4G8NX/n733gHLrus6Fv4syvffeB5jGXkWqUKJEieq2VWzJitzitPfylpM8xy95eb/fv/LyJ35JHMexE8ctLrIjWZKtZpGiKLH3Tg5nBjOc3gEMei/3X/tiAAymcFDuBTDg3V5epIh7z9n77H0OPuyzyyFVoFVafUUunro7svZf0RrHmakLuDx7PfD6PdU70VXSzv13sh1qBLZvDus4sOya783MrbeEwa6uSq6IdLjdUZJNtmj1R+8RELyu7UFf/zkUXxtBttYCAoBery+LXMJIkNXUAsWjzyIzP7xuIrHwo5kxYejmZaRLb0EiCe3Ik5mVjdK6rcgtVkR9LZxKulu8zqJssVhe4t5Ndb2JADBxtpUSM0cCAF12NTR9v4FL7SvvIhmgBJD/golJC65f8GXUsgww4PX4UAqA/TvroaiNrUXVuNqMN44EawaGA8T8G3921oCxIR08bhZVdflIjyBJ4e0TQxia8sUMUqLDZ/cp4nL1q7XN4fX+dwIFiMuzSvGJlscCX8zJeqgRSD94bpRL3FlIlCF8/+Zqzhu8GiWrbKvxfbvP6Wq4b24AA9dPoODGEGTzBdD97zByOfJ33w3lzkcgkwlfUmhscBbTwxeQlbYoPpABMnNLUNm8GxnZ5RGLnIq6C+iIQcLibiNWRIQviHqLcMGS5HHSmwgAk0QZa5WNSACg1dAHveoQ3AbKoGWQNl2Jipc+D1X3DAZu+kChV8rglifYH/dzj7av6q0LZ+0OXxzHjQWJJfu214F6765EsR5qixM/KK5tz8bqcFiN6RnuOvzWe5ix+DKOyUv0TOsTKF5Qxy1W2WJicJWXPV4vKLP7Qq86kAXuf6WjoQi7192+t3Ayyxb7urHQs2pcOvg2ZNdUkFIv5wXkKS5Axb7H0dq0KWovXLg8cokiPYMway8jPS1YC5Pel0oZ5BY1obLpLkgj6DKTyroTZQvXspLruVTXmwgAk8ve1hw3kQBA4+xZGFUn4LXbwdgkyE7biKKH9+Pq+TFMDPu+RBwSYHT+iiszXYbffYJKTsRerpHiAH/2QR8s81fLGekyvLRPiawMCpFcSrFsfGpVR1e/dAVMRHGILz/SBpJHaLqh6cHxiTOBaTaVrcPOyq0h08Yim9D8+8efUJvx0aWJwBr6/53Wkq6Fu5qKIFnGLtaCbNGu4ULZpjRj6P/gDUhuBdslcuMyDKzt9ai/ex+aS1pj7vG8Gq9mkx2D3Tfgtd+ETOazdz/J09K5biJFle1h7eE7RXeRl85fTQuJ/VzUW2LXP9rZRQ+gr7xV3KiqqipLIpGwlKkbt0kFnigSADg39j4s/Vd8CSA6GQpbHkLOps04e3QQ2llfcLne64Va4mOaukVQzBxfRIkG750OxhpSX9qHty9fvDmWQ+3aLQ0+vhQsEn3fxmpsbKVKPMKSxjaHNwfehYeu0AHkpeXiOeXTkEtCgWcssgkrQejo5A28rNLgbM8M3AtiA+kpyqLeta4CTZV5IeBircgWzToulo28vSO9FzB16F14jaEJUO4MOXSddajffA86StpAxaWFJO2skeNFzgyCYXwxikR0wKZnl6NGcS8yslf2uHPPitekQqpIsLFFvQm2tIIOLALAOAFAhUJRzTDMf2NZdh+FuQH4rcvl+uehoSFfNeQ1TOECQJb1YLbvF8EEkMk0lD74AtKrqnHk/T5YzVSomMWo3QXHvFduR2c5dnZU8LY69IX57ukRDC7oUfvUPY1cP97FFO2hRmVffnKgL1DsmIDKCw+RJ2Ye1fImTehATo8Lr/e/DYMjWKfw8aaHUZu7tNh0tLIJxPqqw1LHl+PLZArTi5XF2RwQrCnN8QGOOxBEeJxO3Dr6DnQXzsLtCb0WtudnYqarBvWKzdhYto7LAheKaH+ND01DPXoGabJg0XOaj5JXcoq7UNO6FZJFP0gCYPEO1J1QuojnuHfinovn+go1lwgABQaALS0tmyQSCfXezWFZ9of9/f2/pe8opVL5uNfr/SLDMGqv1/vNgYGBYBE8obQt0LjhAkCXYw6a3jeCCSBDmaj63H8BI5Pj4K+74fWwXEu3YYcLbJYvkH0lcBaLKGabi6vL55xvu0V9eF98SLHkejbaQ21xxw8hZFgsP33xUsHnAT1VB/LR5rL12FG5Zdmlila2WNadj3eHp404emUS+vmM7oVjUmIPxQeWFWbescH29plpDB/6DYxjQ1wG8UIyVORhtqsGnQ1bsLG0C3KpcMkibpcHgze7YTNchlTiy773k0SWg8rG3Sgs97W3W0hr1S7DsW1RtnD/xf6MAAAgAElEQVRWKfmeSXW9iTGAwticVKFQ/ALAgFQq/feenp6R5aZRKBRNAL4MoFqlUr0kDCvCjhouALQZB6DrPQC3cT4BZKYaFZ99GQ67C4ff6eWYtFD7NoYFOx8r9+UnOwWJm7s+qMVHF8cDC0P9aB/f1RDzVSJlsf78g76Qsi/U71doonZix8ZPB6apyqnAE00Pcwkgy9FaPtToWphKxlCiiD+e0y8jxYpSYs/j97XAaXMI2i1DaJ1Gqzf6MWAb6MfkkYMwaKdgdwfj8lgJA3VTCczt9dhauxXKwpYVbYQP+UwGC4ZvngTjWVqkPT27FnVtdyM9M9gLey3b5WrrJcq22gol5+eprjcRAApkd1u2bJFfvHgx9Gf4CnN1dnamdXd3+5q1rjEKFwCa1Bdg6DvqSwCxS5CTthmF+x6Gfs6KU4d9JVooaUKbJQNkEuTnpOFz+3116/gm+pJ859QwhiaD16X3b67B+uZgLbVIN/7iYtTxKvuisWnx5sB7gbi/TFkGnlU8ddurvkhl43v9+RiP6gVeHdBw3UQWFvqmoLOszDR0NRRii6IUaXL+e9bywX80Y0SiN9bthvnqFWjOHIfBrIXFFSytQ/GBU23lkLQ2YmflNtTmVoeVpBENz7QvpkdHoB47DQlC4xQZiQz5ZRtQ1bQREon0jry+j2ZNk+2dSOwy2XhfjZ9Ul00EgKtZgACfK5XKb/T19X1VgKHjPmS4AFA3/gHMfRfBsl4wekoA2YecjZswPW7ApdO+TMYprQXmogwukEtRV4D9O5ZeE/ElIBWOpkxdq92H0Re3aIt04y/2Km5oKcGeTcKWfXF4nHiDi/vzfbESz481Lh/3t3DdIpWNrzUXYhybw42LfWpcGdD4WsoxCPRwzkqXYWdnBTobisIuJC0Ej3yNGY3ePFYrTGdOYe7aJehsOtg9wStZa2EWJjorUVDTiO0Vm0GeY6HI7XJjuOcibIZuMExonKJUnoeKxrtRVF5zx17fC7Xu8Rg3GruMB198zJHqsokAkA8riXAMhUJxzOVyPXunJIEQ6FP3vQK7vwPIdBrK9r6ItMoqDKk06Lk6BRYsRmbNcJX5iv3eu6EK1AVESFpcq4/ajj3/QAsHBiPZ+EaLk7v69XexyMmS47MPKbnyL0IReVYOjR7BLX3wem1L+Qbui3w1ikS21cZKls9JB6duTKNvTBcAgP4WfoW56VzpmObq0IzhZOE9XD5i0ZtLrYb+2MfQjwxAZ9dzrQL9vxo0DcWYbitHdWEttldsQlmWcPvOqDdgtOc44JlcIrY0vQmbdj8Eh4u9I6/vw7WDZHsuFrtMNlkW85PqsokAMAEWqFAoPgKwCcAB6rnOMAzb19f3hQSwEvOU4XgA3U4D1DdfhUszX5x4JBNVv/NfIZHLcfPqJIZVWjjdHozrbfCU+LIUqf9vOJ0fYhVgcdKGv/VcuBt/uT7E8Uj8UOkGcHj0eED81eL+Fq5TuLLFuraJeH9GZ8XFfi36R+eW9HCm1oCUKOLPGE4Ef7HMGaveyFbtA/3QHz8K/dw0lzHuYX0lg1xZaRhbXw1zaQ4a8+uxs3ILCtLzY2F3xXeJj8mhQWgnzkDChPYWBjIgz9mIxvYOpMWhbqYgAi4zaKy6ixef0cwjyhbNqiX+HTELWOAs4JVUTNnBVKaNgB/Lsgz92d/ffyzxJhE5B+EAQLtpCHM978Jt9MXcpatrUP6Zl7m/Xzw1gpkJI0w2J2btLngLM7l4pD94ugtymbClU2h+yjx+7aMBqPXB0oyUuNFQmRvWddTiq9+OxiI8tLU28oWM4A2K53q179egK2CicOL+7hQASIdacXEOzl2bwIlrU4Ee0Avlp4zhu9dVgjy+a4n4+qKl+EDTpQswnjsLo00Po9MED+ur3TdXV4ip9kqw6XJ0lbRhS9lGZMjSBVkmp8OFkd7zcJhuUg8gbg7q+UyF1O2OUhRWbUWDohpSqfDngCACLhiUL90JzWc044uyRbNqiX9HBIBxBoDk+VOpVHsVCsX/YFn2oYUAUKVSPZB4k4icg3AAoFlzCfqej+F12ME4KQFkKwoffIib7OSHAzDobJgz2aFjWXjz0rkivy89rIycmSjfoOSTX37Yz4FBDlBlyPDc/S1obSyBRmNa8ToqUVe/B4YPY9g4FpD2kYa9aMxfvqD1cktypxzYHg+Lm8NzOLNCxnBnYxHu6qxYsRtMlOYk2Gt8682l00F/+BBsE2McCDQ5zVz/aHe6HJOdFdBX5SNDnoFt5ZvQUawULGPYbNRisv84XLZZMPMAkBaRZSWwu2pQ0bgJNQ1lgiWqCKYwEQDGY2kFnYPvPScosxEOLgLAOAPAlpaW+wYGBo62tLTkyeXykLL4K5WKiVCncX88HACoG/8Qpr5zdKKDMchQ2PoIctZv4Hj98J2bcNo9nAfOIGfAZqeBPDTxKJ+ycLEWe/Jys+T48qc2wEN1Cal09yJK3NXvLRweDTqLWwub8GDdfRHpPdUPtZKS3BDgvmLG8Hybvh3t5VjfUix4se6IlLTMw0LojezYcv0ajCePw+10cNfCBAQpJtefJGIrzEJRRgF2VW3nMoaFIOJDN9MD/dQlWC3BjGUOCHqlcKEOta1bUFpZJMT0go8phO4EZzrMCUTZwlyoJHtMBIBxBoB+/SuVyrdZlv2WSqU6nGQ2ETE74QDA2V5KAPElK0hm5Ch78HeQVl7BZW0efNNXA5sygC3ZcrAZMnQ1FWPvlpqIeYnlBfoCOnhuDH2jOt8wDFBWlI3H76pDbubSNlo3BrU4vKCWYDyufq0uK/5z0dXvp5WfQIYsIyLR79QDmzKGL/TOchnDdM24kChR5J4NVWioyE1aT5OQenObjNB/dBiOkWEuQWTOroNtvoagrqYAU20VcGfKUZNbhZ0VW1GaFSyZFJHx3eZhkq8gT4Keyx/CoB6EyxVsKUeveb1UIqoJ9e1bkV/o6/yyVkhI3SV6DUTZEq2B6OYXAWCCAGBLS8tOiUTyRwzDdLAs+z2r1fqztdofOBwAOHPlx3BofFl/kpEsVL/8X8HIZDCbHDh2QMX9+9isCfaCDCBNypXu2NFRHp1Vx/AWFRl+/8woblGruPlyIplyCZ7Z04KcTF/nBL3ZgXM3Z9A7qgeBRqJ4Zf0eGP4Iw0ZfyRyihxseQFN+5KVy7vQDm3RI8YGcnhdRTVkOlyhSUSRcy7RoTVRovXFFpPtVMJ44Do/ZxAHAOYee6yrCSiWYaSmFuqUUVFC6pYBKx2xBfnqwkHO0cvnfWyifw6bF7MgFmOZGQVf5C8ntzoAsaz2aOjuQlS1sj+NYZVpOtuVuFPiaJxHjCG2XiZDpTtGbmAWcIOuiQtFms/kPWZb9OwCvqlQqX1bEGqNwAKDmyNuwzd0EY5EiTVaFss+8yEmpmTHh3LFh7rppeNoEd2kWIJXgoW216GhIzFUPgcD3To1gaNoYKCdSkJOOfdtqcWNoDj3DugDw86sqHlm//bpBrt2bn+gL+KH6PVFZi3hg+5ZtbNaMY1cmoTEEE4AC61uTz8UHUjxqslC89OZ1uWC+fBHmixdAf6crYYPDwCWKUG/h0Y01sOdlQMpIuNhAKj+UKYs9oWY5+eyWWUwPnYfVOLnEa2tzFCO7eBNa2uuSPmM4XrpLhK2KsiVi1WOfU/QAJsgDqFAo/jeAT7Ms+77X6/2XW7duDcSuzsSMEA4AnP3lK4EewFld61D4wIO+L+ChOVy/MAG318t9GbvLs7lqxp+8rxm1ZYm74qFkkPdOD2Nyzgan072knIh/pSljkUDC1rYyQRff6rLhVdWvYXf7ivhS1u/zyk9wf0ZD4oEdXDW6Cr4xpMXp7hnYHaEFiikbvaOhkPNG52Yl3tMUb715TCYYT5+EtbcHXpaFwUnxgSZ4GGBGUYbZ5lJAwkAukWFj2TpsKOmMqcfw7eQjADg1eAZ2iyYkJpcSRcy2WlQ2bkJ9S+kdeX0fzRnA5zvxtks+eV9trFSXTfQArmYBAnyuVCr/G4Af9fX1hfZGEmAuoYcMBwBO/uu/gHX5Om4UPLAX2V3rub+rumcwcHMWdpcbkzobPPNFoF/e3wbyuiWSyBN46NIk+oa1SwCgHxhsby9HXhyuoA6NHMGAfiiwHA833I+m/IaolyfVD7XFSSDhLJTD5cEllRqXVepAQW//e1QYfLOiFFuUiW0tlyi9OaenoDv8IdxaDVc3UO8wwuy0wFKQibGN1XDk+n6IZMkzsbV8E9oKWyCVRF4EfTX56IraoO6FZuwC7LZQr63LlQmkrUfHlk6kZ8jCUXlcn1lNtrgyw/Nkomw8L2ichhM9gAnyAMZJv3GZJhwAqH79NTgnJ8DI5Sh/6XOQ5vi8e9fOj2N8WAeL3YUZixOeYt810h99ch3XjSORRJsjvyALP/jNdUzMmjlWCPi11xdie3sZ8uMEUGcss1yvXz81FzRgX/39MS2NeGCvvHzUGvBczywoK3xxokhWRmJbyyVSb1ztwHNnYbp4nsvmp0QRncMAi8eOGUUp1E0lXJwg9yMvPY/rMRxJaSLf/kJYtTe9Hju0Exegn1XBYQ/12tqdVahv343SypAiCzHtFz5eDlc2PuaK9xiibPFecX7mEwGgQACwtbX13nBUtFaLPy+ULRwA6LXbYR8Z5jJ/ZQUFgdfPHhuEdsYCg8UBjcsDb0EGV4Pvy090hrN8gj7jP9SmpvW4pNJwNQLb64tA2aLxIvJ4vHXrfUxZZrgp6artM22fQrY8tgQF8cBeXYMGs4O7Fg5khS94pSg/A/esr0R9eXwzhpNBb5w38NBBuHW+bHkqRk6t5YwZwERXJUxlwaSQzmIlVzpGJgnPIxepfC67GurR09BrZkLAuseThszCzWjp6oJEktgfkn6ziVS21S00eZ4QZUseXUTCiQgABQKACoWC6hf4U9eYFZTCqlSqyO9JItFwHJ4NBwCuxMaR9/tgNTuhNdqhZ1h4c9NRXpSFT+9tjQPnt58iGQ61QcMwDg5/HGB0W8UmbC3fGPPaJINsMQuxwgB8y0at5Y5fncKE2ucFXki15bm4e30lyuLUUYRv2aLVAXkDjWdOwXz5EucNJLK6bdA7DNCUZmKis5JrLUdE5WIern8AuWmrx/RGIx/1GTdpujEzfBEOh68zjp+8qECN4h4UlSXeGxiNbNHqJ97vibLFe8X5mU8EgMIBQGp6S6fOawzD/KNEIvE1wV1Ea7X480IxogWAXN29X3fD62FBX7KmdCnYLDmaq/Px+K7o49v42RrhX0fxNd/icTxeD5f4YXD4wkTJ6/cZ5SdjCrL3zyEe2JFpjWx1cMqIk8u0lqOwAGVdAe7qqkCewIkiyaY38gbqj3wE1+wst6AEBS0uC7QuA6abSzDb7LsWTpemYW/dvajPu32LxFjkczuNmOg/DrN+IiRJxOORg5VvQHNnB3Lzo0uaisxaln86Ftn4mF/IMUTZhFxd4cYWAaBAALCmpiYzKyvr9wH8KYXEsCz7fZlM9o2enp4p4dSZmJGjBYAOuwuH3+nlmJ7UWGDNlYNNl2Fjawnu2yhMt4FIVijRh9p1zU2cmDgbYPn+2rvRVsSPZzTRskWih0ifFVI2Sgy6MTiHszdnQEWlFxL1q93YUoJtbWVITxPGsS+kbJGus/951uuFtfsGly1MoR5EFB+otmlhypJgbH0VrEXZ3L9vLlsP8mJLmOWvZWOVj4D63HQvZobPwjvfJ9vPp9FShZyi9WjtrECmwEB9ubWMVbZo9ROP90TZ4rHK/M8hAkCBAKBfVZ2dnWlOp/PzDMN8FUAVwzA/AfB3fX19wZRO/vUa1xGjBYD6OStOHb7F8To6Y4KDEkBkEty9vorLtkw0JfJQo7iqX/S+Hij7UpxZiGdan+StF2siZRNar/GQzeH04ELfLK70+2JDFxKBvw3NJdwPmcz08GLfwl2TeMgWLi+Ln/PYbDCeOgHrzW7uWpj+p7XruGxhbUMxptrK4ZVLUZhRwIUxUAHzxUCQL/lcTivG+o7AbhzHwoYvTlc25owK1DbXoqW9DATa40V8yRYvfiOZR5QtktVKnmdFACgwAFygaqlCoXgBwNcAkBvnlyzL/m1/f39P8phDdJxECwCnxw24dHrUVwR6ygR3ha8G4P6d9VDUBhNFouMq9rcSeaidnjyPK+obASEeb3oYtblVsQs1P0IiZeNNiBUGiqdsJquTSxTpHVlaHFwmk2BdUzFXPsbfRSZW2eMpW7S8Oqenof/4cKDup9ll4drKOdNlGF9XBWNFHjc0AcEtZRtAWe1+IMinfOQNNGn7oBk7B5vFHgjIprqBelMtPEwtOjfVorSCv04mt1szPmWLVjdCvSfKJtTKCjuuCADjBwCxZ88e2eTk5BcYhvkmy7KUSvqaSqUiULimKVoAOKTSoOfqFFweL8Y0FnioCDSA5x5oQWWx7++JpEQdakanCf/Z+ybXdYGoLrcajzXt43UpEiUbr0IkAQD0s6DW27jWcuTJXkxULJy62mxVlsZcOmit6I31eGC+cgmms2dACSNOrwtqqxYur4sDgAuTRAoy8jkgSJ1tpBJJWGVgIrEjt9OAuYnjMM6pYbf5apESUaawwVSDvNIWtG+oRnqGr9WjULRWdBeN/KJs0axa4t8RAWAcAOB8y7cvsSz75wAqAfwnwzB/09fX15d4E4idg2gB4M2rkxhWaWFzujFltMNT4itt8sXHO3jzmMQiXaIOtYVFn4mHZ1ufBl0B80mJko1PGVYaK5GyUSzrhd5ZDE0Zl7DnTxahGMFo28slUrZodOfW6zlvoGOMPP2+BBEqIu1ivJhpLYOakkQkviIJeWm52FTWhd2tm6DX2UISOaKZe+E7rNcDy9xVGDU9MBlscLmC1/bUV9hsr0ddaydqm4oF6ySy1nQXyZqLskWyWsnzrAgABQSALS0t6QzD/B7DMP+dqiEA+AnDMH+bSvF/ZMrRAsCLp0YwM2GEyebErN0Fb2EmyFtCRaAlZJkJpkQcarNWDd7ofycgeXuRAntqd/O+EomQjXchVhgwGWSb1ds4IDgwbljSN5qAYEtNPpcsUhph+ZhkkC1SPdJVrK23B4bjx+C12wJA0EDdRLKkmOiqgrl0vkQMAxRl50GZp0BHkRJpUn7b7zltMzBrr8Ckm4HFaF8SH+iRtKNtozDZwmtRd+HqWpQt3JVKrudEACgQAGxtbf0zhmEoAziXMoAZhvl7lUo1kVzq54ebaAAgfSkcfrcHTrsHerMDWq8X3rx05Gan4QuPtvPDWIyjJOJQe3/oMIaNoxznVDz3BR6KPi+3DImQLUZ1hP16MsmmMzlwvnd22RhBEoiAIGW8hxsjmEyyha2Q+Qe5JJGTlCTii231ewQJCKorczgg6EmXIi1NxvXfTpekYUv5Rqwraect+Wn+ByuclnEY1Vdg0KphX9RJxGIrQ0HFZrR01PCaJLKWdbearkXZVluh5PxcBIACAcD5QtB0z3CAYRhfkaxlqK+v7wvJaRrhcxUNADRSzNShAW4SjcEGfYaUKwFTXZqDZ/Y0hz+5gE/G+1DT2LT4lertgEQby9bhrsqtgkgYb9kEEWKFQZNRNqPFyWUNdw/NLWkvR1nD926o4loMknfwdpSMskWqW+fUJPRHPg4kiRAQtLps0Moc6NtUDk9ZLgcA/VkbZVkl2FOzG8WZRZFOddvnqYC0wzQM7eRlGHVz8Hj8dfsBr1cGm6sZTZ2beUsSSQXdrbSgomy8mmbcBhMBoEAAsLW1Ndi+4Tbq7O/vj62pa9xMZeWJogGAg31q9F6b5gad0VthzE/nMoCVdYV4ZEddEkgV/0LQB4YPY8gQ9P59tv0ZZMp8vZH5JvHA5ntFwxvPbHPhkkqN67e0S8rH1FfkYu+WGuTepkZdquiNagdarl2F8cxpsE5HYPFsrBOTGyrQXeihzsCBf6cs4U1l67hkEamE3xqLLOuBea4H6tFLIdnCNLnDmQtJxjo0KJtQWCy2XxQBYHj7fK08JQJAgQCgQqF4WS6Xv9Hd3b20f9RasY4w+YwGAJ47PgTNtG9pJs0OWHJ8GXhb28qwex3lySSe4vllu9j7t6G0C7uqtgm2CPGUTTAhVhh4Lchmtbtw9OokVKP6ECnkcinXY7irsWhZb+BakC0SfXvMZug+PAjHqO+HD2E+ugK2VJbhdBMDvccSMhxlDN9fsxsV2eWRTBPWsx7qYDJ+BrrZoZAkEbAMDOZqSDMVaG4rR0l5zqqe2uUmTDXdLZRRlC0sE0u6h0QAKBwAfBPAHoZh3mcY5qe9vb2HAIRWjE06c4iOoUgBoMfjxaG3bnIt4OieZ9jmhCvTBwDv31yN9c0l0THC81vxPNQODH+EIcMIJ4FMIsVn258VzPvHfc8y4L3cBs/LH/Vwa0m2W5MGfHxpApYF5UlIcMoSpiQRqodJiVF+WkuyhatA8gaazp+F6Rx1vWEDMYCSggKMbajBJVBrt+D1LK3BtvLNXFeR1a7Mw+Vh4XN28zhmhk7BYtSHJIm43JmY0zchPbsMze1lqKjOi2j+VNRdKtvlnSJbaWle4jMuo9moPL0jmPDr1q0rtNvtzzMM8xKAJpZl/5Nl2Z8NDAxc4on3pBgmUgConTXj7FFfIxQvy+KW2w3Ifdc6T97diMZKX6HYRFO8DmytbQ6vqd4KiCu0908EgIm2rND57U43jl2dQs/w3BLG8nPSsFVZhrb6QsikkpQG7vbREegOvg+Z1xUSA+ipLMXVKi/Gs92+Xy7zVJdXg7219yBDxn9/X9brhmH2Kuamb8BucYQAQZOlAnpTHWqbStGxsRISSXjdROJ1niTCukXZErHqsc8pegAF8gAuVk1LS0uzRCIhIPhZCi0B8DOPx/PKrVu3xmJXY2JHiBQA9t2Yxq0etQ8AMsAtrydwsL/4kAIlEZbGEEr6eB1qB4c/wuAC79+Lbc8iSy5M7N+d8qu2pCQXGo2J11pyQtmZf9zhaSM+ujQBk8W5ZKqcLDm2KMuwrqkIFeX5a062cNfOYzbBfvRD6AeHA0kg9C75//T5clyvYqEv9XUMIspNy8G++j0oyxKmdaTLMQfD9FmY9bOwWpyBBB4qIj1naEJWQQ0276yDPG31ln/xOk/CXWs+nxNl43M14zeWCADjBAAXqlSpVFJht5dYlv2USqUS5uSKnw1FXAfw1OEB6OdsHIfp+em4YfD9nej3n+oCZUUmA8XjUFvs/Vtf2oHdVTsEFz8esgkuxAoTrGXZPF4vekf0XOkYgzmYHOEXNTdLjgd3NqCmKIPX0iiJ0tXieUl3xYVZGHr/MEyXL4F1hoJh6pE9lmHHYFcZ7Hk+z5+UkWBX1XZ0FrdFdCUbrsyULWwz9MGsvQ671Q6zyQ7vfDCP1VYMh7cVm3cpkZ1LzZ1WprVsl6utlSjbaiuUnJ+LADABANBvCtQh5OLFi8HeRMlpI6tyFYkH0OV049DbPYFf99lVObgy6euaIJdJ8AdPdwlyiK8qxDIPxONQ+2DkY9zSD3OzU+zfi23PIEseW7ZhOLLGQ7Zw+BDimVSQzetl0T+ux7neWcwZ7MFlmk+SyJBJsL29nCsdszBGUIj1jOeYC3XnsdlhvnYFlitXuALSfqIWiRqHDkONuVC3lAY6iTTm1+Huqp3ISROmjaTbZYJZfQ420zT3A5ZimYm8XilMtkYoN25DSfnKfYVTwS5XsgVRtnjuEv7mEgFgHABgW1vbLq/X+0MArSG1DQBWpVKtfnfAn74FGSkSADg9bsCl0/MZfwCyGgtwdcgX+0SB7y89rBSEx2gGFfpQm7Pr8JrqN4FryvUlHdhdLbz3j9ZCaNmiWW++3kkl2Sj54dakEeduzoD6DfuzZP118vJz0nH3uko0R5iQwNda8z3OcrrzupywXL8O8+WL8FqCWcHUUm4604nRDTUBb6BcIsO2ik1YV9IhiIeU9GE3DcKkvgy91gCnk8rV+MjhykVe2Q40Kuohm49pXrg+qWSXi/Uuysb3TojPeCIAjAMAVCgU3SzLnmEY5pderzfE4zcwMHA0PqoWbpZIAOCNSxMYveUDfFk5abAXZaJvVMf9N9VBe/qeJuEYjXBkoQ+1hT1/qbYZef+y4+D9EwFghIaQBI9zQHDCiDM3p2Gyu0OSJIi9mrIc3LehKmniZ6NdstvtOdbt5jKFTRfPU/9Jbgqb2wG1Yw5TLcWYXeANpKLR91XfhfLssmhZue17XrcdJs1FaCZVsFmDRzrLSmC2NaCifj3qWkpCOokIfZ4IImiYg4qyhblQSfaYCADjAwDn3G73xsHBwaDrK8kMIRZ2IgGAR97vg9Xsi+upaypCr8mOCbWvHmBXUzFXCDdZSMhDTWfX41XVrwPeP2p3dXf1zriJLqRscRNihYlSWTaKnZgxOnHw9BB0xtAYQSqJsr65GDs6ypGZvjYvFsLRnXNmGrpDH8A9p+UsgK6EdQ491FlejGyugzPb1z+YxmovUmJb+SbBkqoclklM3ToBszG0niMVkDbblWhsa0BNA5XySe0M7nD0luhzIdr5U102sQxMtJYR5nutra2/kkgkF/r6+v4uzFfW1GPhAkDKojvy276AbJvuqsP71yYDWY93dVVwcU3JQkJu/MOjx6DS3eJEpSD2F9qfQY5cmNil5dZTSNkSrb87QbbZWSNuDutw6sYUrIt62Waky7CjvRydjYWQy5IjoSpcmwhXd8t5A+0eBzRuIwY7S6GvLghMSdfC1FZxQ0kn5FJfvVE+iUrGzI6dh2HmZsiVMHkD9cY6eKV1WL+1FkWl2WLtTT4XPk5jhWuTcWKH12lED2B8PIDU0uEUwzBU7M2X8TBPd1Iv4LGhOVy/MOGTnAEeeLwN33+vJ1Ba4aFtteho4LffZyy7RaiNb3AY8cu+NwPFbTuLlbi3ZlcsrEb8rlCyRcyIAC/cSbI5XB6c65nBlX7Nsj2G1zUVc17B27WXE0AFUWqWPHkAACAASURBVA8Zqe4WewPpYtjoMGK4Qoaxjgp4ZcEafRReQd5AZVGLIPGBDusMNKMnYTbMhXQSsTvyoDW2oElRi533NGFOZ1lT5YnCUWakegtnzGR5JtVlEz2AAluaQqH4EICSZdmjDMOExACqVKrPCzy94MOH6wG8fGYUU2MGjp/8wkxs2F2PH757M8DfJ+9rRm1ZjuD8hjuBUBv/47ET6J3r59igHqcvtH2Kq2cWTxJKtnjKsNJcd6JsOpMDx69OYmgq5Pelz8YkDFpq8rG5tRTlRcJnmMdiA9HozutywXj8KCw3rgemdnk90KQ5cX1dYSBBxP9hUUYBdlXtQG1uVSysLvsueQPN2svQz/bCYnLA7fZlCrNeKeZMDcgtbIFyfQWyc25fMoZ3xgQeMBq9CcwSb8OnumwiAOTNVJYfSKFQGL1e786BgYEg2hF4zngOHw4ApCD2w+/0wOnwZc01tZUivyoXr300EGD15f1tKEiig1GIjW9ymvGL3jfgZX1fDG1Frbi/9u54qoubSwjZ4i7EChPeybJRMekz3TOYmbMuuzoVxVlcq8XWmnyus0iyUSy6s/X3Q//RIXgdwdhIB9wYbMlHf5UspIsIya0obOb6bWfK+C+67rROw6Q+C5NezwFBfyM7p7sIWkMTFF31XAy0EK3sEqHTWPSWCH4jmTPVZRMBYCTWEMWzCoXiGoCvqFSqw1G8nvSvhAMAjXobThwKgr3t9zZizunG+2d8/W+J/uiT65LqS0mIjX9s/BS6tb44SDr8P6P8JPLT49/6TgjZksVQ73TZ6MfWpNaKK/1qLnN4YR9dv44oSaSzsQh0RZw3nzSRDPqLVXduoxG6g7+Fc2oqRBxrWT4uKzKglYYmzmTI0rGrcjsHBvkGY1TwwaK9DJO2H3T+kTeQvLFutxRzhkZk5NajtaOMiw3ke+546zJWvcWb30jmS3XZRAAYiTVE8axSqfwrlmVfBvDHLMv6Ul7nqb+//1gUQybVK+EAwME+NXqvTXN8S6QMHnqqA1cGtDhxbZL7t8wMGb78RKcgctndDsxYZ1GZXY40qS9DMBzie+ObnRb8ovd1LmuRiL509tbdGw4rvD/Dt2y8MxjDgKJswcUzWJy4OqDBjSGKSwvWrPM/QcCDaghubStDeWHir4f50B3r8cB09kxIuRiSl0lLw9zmFpzJUsPlCa2/X5NTycXhCvFjzGGdhHH2LEw6Pew2dyBW02YvhM5I18JFHBAsLstZs0CQD73FsOUFfTXVZRMBoKDmA8x7ALuWmYYKQa+tNL3lhGBZdrW+q+eODUEz48O+JeU5IA/g0SsTXPA6EcUmfXov1ckOnwhQZcoyQDX0ViKry4Y3B94FXb1Sh4BPND8WdqcAvjf+iYkzuK7p4VilsT+t/CQK0vPDF5jHJ/mWjUfWYh5KlG3pElKySO+IDtduaTFnXNBZZMGjDZV52N5ehsri+GWjL+aUT905JsahO3QQHmNoXKS0uRFXWzMxaPP9+PQTdeLZUbGFKyLNt0fO63HArL0Iu2EEWrUZHu/8pTDLwGipgMFcg/yiXLR2lKO0YuVuIjFvDoEG4FNvArEY9bCpLpsIAKM2DfFFWoFwPICH3uqGy+nzfCnXVaC5rRTvnBrG4IQvKaS5Oh+P72oIa0Epi/bI+ElMmqeRl5aDx5r2LQukKM7uvcEPMG4OXgcVZRTi6ZZHkR6GJ5DPjW91WfFK7+twe31emJaCRjxUvycseYV4iE/ZhOAvljFF2VZePboOHldbcO2WZsXr4Vr6gdZWzhWXjjfxrTuKBzQcOwJrT2j4tSQ7G9Yt7TiZNgmLKzRekryB99feE/YPxXDXiGTLkGowqjoKi8kMKotFLf+IvF4Z9KZamK3laFCUoG1dBVc7cK0Q33pLJrlTXTYRAApjbUx7e3tFT09PaDDKCnO1t7dXhvusMOxGP2o4APDEh/0w6uzc9e+9+1qRmZ2GH77XA4vNdxVDV1C711Xelgn68urW9uL01AW4ve7As/npufhEy+OcN3AhXZi5gvPTl5eMSQf8o40P3dZzSC/xufFPTZ7HVfWNAC/PKZ5GcWZh9Ise45t8yhYjK7y/LsoW3pKarE7OA399UAvXfLbqwjcJAO7qqoirR1Ao3dkGKEHkcEhPYZJV3tgIVVs+btiGQ0qz0A/Eu6t3oLWAv9hAv2yzM2qYtTdgNfTDZnGGAEGXO5MDghk5Ndi0sx4ZmfzXLQzPOiJ7Sii9RcaFME+numwiABTIbhQKxbep4gmA/1gpAaSlpWUfwzC/wzDMnEql+mNhWBF21HAAoN3m4krAFJZkoaAoC3qzAz95vzfA2JN3N6KxcuVkCLrCPTJ2IsSbt1CqiuwyPNH0MGQSXweECfMU3hk8sGK9LWVhM/cr/3ZXPXxtfJvbhp/3kPfPB1qb8uvxcMMDwiplldH5ki2hQqwwuShbZFqxOdwcELwyoIFzmTjBpqo83NVZEZc2c0LqzmM2Q3f4EBwjwyELxKSlw7WlA8eyZ2Be5A1szm/APTV3LflxGdkK+55eLJvbqedKxjgsU7BZXLCYHYHzyunKhsXRhPbNXSgpS/4rYSH1Fs1a8/lOqssmAkA+rWXRWAqFogTAHwJ4kGGYNx0Ox39kZGRIWZb9IsuyT7Ase0Aqlf5bb2+vr6/RGqRwAOBisW4Oz+HQ+bH5g5HBl5/sQEba8u2r+nWDODpxakngNl3/Gp3BnJrWwibsrb0Xdo8dv1K9HbjaoU4bjzTsxYnJs6DrYz9tKd+A7RWbV1xxvjb+yYmzuKYJXkE9q3gSJZnFCdU0X7IlVAgRAPK6/A6nB1dvaXC5XwO7I+hh94EXBoraAq7NXGGucDXshLZLukWw9fXCcOzoEm+gtKoSfW35uOnxJav5iQpIU7JWdc7tbyhWU8ZyshE/TtsULJrLcNj0MOh82cJ+cjjzUFS1GQ3KVt7jElfjN5LPhdZbJLzw/WyqyyYCQL4tZpnxWlpa0iUSyUsMw/wBy7J07/lduVz+n93d3b7GuGuYogGAhy6M4ebQHCd1SX4mXtynWHYFqGfur1RvBTJn6SFK5thTczfKskrwm4H3MGcP9uEkUDdrVWPMFAzw3l21HetLOznw9+uB92BzB4Pg99TuRnvR8nPzsfHJc/nL3jcC/Dfm1+ORBHv/fF/oENtSrcE9Fw+9UcLIZZUal1TqZa+GK0uy0VZXyNUS5LvncDzkI7V7rFYYTxyDtdeXlBUgiQQ2RR1OlpthRRAEE1+bStdja/nGVUNHVjKr28nGsl7YTYMwa6/DoNXBvqi9HyupRW3bvSgoSlyCzu22S7z0logtm+qyiQAwEVaVhHPu2bNHNjEx8T2GYQgRyRiG+X5fX9+PVmM1GgD4H+/3wmD21eRa31KC+zdVLzvNuelLuDhzNfAZFU4mQOcv50IA643+d0JA3cKBGvPr8HD9A4FfzzOWWbw9eCCQjEGeDbo6Xu7XPR8b/6Ox4+ib89U/pPGeV3wChRnBPqWrra1Qn/Mhm1C8xTquKFusK+h7n3oMX+ib5TKHPZ6gV8o/OtW0o8zh9rpC1Ffk8NJ3ON66sw8PQ//xYXhMoZnCbGYGVIo89OU7QgpIl2eVYm/dfaC440gpHNlYrwdWgwrqscuwGM2BAtI0l92RD3nuNrR21iAriWo3+s+2kpJcrFYNItI1S4bnw9FbMvAZDQ8kmwgAo1m5FHxHoVA8SFfVKpXqa+vXr8+22+1DKpWqgpLUbidupADQYnfhB+8Er0T376znrpeWo9f734ba6rsdb8irw/7GvUseWwzq/A9Qe7VnWp8EFXtdSEOGURwcORyItynOLMKzrU8uuWKJdeOT9/JV1a8D81AP0gdq70kKy4lVtqQQYgUmRNn41Q4li5y9OYOeEd2SfsP+maRSCWpKszlASLG8+VEClETozutycnUDzVcuUzpuyOIZijNxrkkKW1YwPEUulePe6p0RJ4hEIhsVkVaPXYVm/BpYNuiJpCQRjb4dtU3VaG4rgzwtOaqIRSIbv9Yp/GipLpsIAIW3oTU3Q0tLS7NEIvmtSqVSrsZ8pABQNaYP6QDyxcc7kLNMthuVZ/jpzVcD0++p2Y324uWva2/ph/HByMeBZ6nH7tPN+1GeXbYs+5dmr+Hs1MXAZ482Poj6vNqQZ2Pd+AeHP8KgwdfphOIQP5OAnr8r6S5W2VaziUR+LsomzOpTsgjt3d5RHaa1y7ea889clJfBXRFvaCmJ6Jo4kbpzabUwHP0YjnFfbLKfXIwXN1uyMVKZFuINrMqpwF2VW1GWVRrWgkcjm92qw6TqEGwWfeCHpNcrx+ycEqykAMquCtQ2FiY8PjAa2cJatCR4KNVlEwFgEhhZMrGgUCjeBLBDIpG82Nvbe2Q13iIFgEcuT3DdCYjyc9Lxuf1ty07RM6fCkbGTgc9+p+N5UED2SkRlVk5Pnec+vrtqJ7pK2ld8ljJyqSevvwYYXe18ouWxkIM0lo1PcYhv9L8bmH9dSTvurt652lLG7fNYZIsbk1FOJMoW5cJF8JrO5EDfqA69o/pAKMdyr8tlEg4EbmotRVbG8kleC99LtO64JJF+FYzHj8FjCSaYecFitjgNF1vT4UkPlYOSz6iANN043I6ilY2KSGvHj8ComYRtvmwWy0qg1bfAai9GflEmOjdVcdUVEkXRypYofiOZN9VlEwFgJNaQRM+2tLR0SiSSn1NtZZVKFbL7Ozs7c1wuF5WhoWtdKpB3hmGYP+7r6xtSKpWUgfwlhmEIuzH0p9fr/fv+/n4Cfhy1tbU1eL3egxkZGZuvXbtmuZ3YkQLAVw6poNHbuCE7Gorw0LZQz5t/roUetBK6plU8terq07UrUThxdgQYqT6fn55sfiQkFjCWjf/OrQOBkjVyiQwvtj8jSNP5VRdkhQdikS3aOeP1nihbvFaaKwIPrdGO4SkThqaNmNJYl+09LJNJsL6pGFuUBARXrm2XLLqjAtLGM6dhuRpaR9QqB662ZUFdFNpSkroRrS/pwJayDaAr4uUoFtlY1gPT7FmYdUMwm+xwOnwF5S22EuiM9fCyaahrLIKiqxxpiwBqPKwhFtniwV8sc6S6bCIAjMU6EvSuQqF4HsD/ZRjmOMuyn1gMAJVK5SssyxKy+gzDMEaWZf8BwG6VSkUt6eb7EIUy397e3sWyrLO3t1dFnyiVyoMA/rKvr+8CXwCQSk187+3uwJfEg9tq0dlQtGR4j9eDH9/8ZaD0y2olW6JRA/UD/Xnvr0C9gokoEYRAoJ+i3fjjpkm8M0hL5yMheI9G3oXvRCtbrPPG431Rtnis8vJz0DXx6IyJixccmTYteUgmlaCxyhcn2FCRu+R6ONl0Zx8dgf7QB6HeQAK9TSW4UOuFiwk9SumH5/6Gvcv2FI5VNgLbVt0NWHQ34HS4YTI4uOQc1ivlikebrBWQp8ug6ChHbVNhXDuJxCpb4ix29ZlTXTYRAK5uA0n3hFKpfFEikXzk8Xj2AfjXhQCwra2t2Ov1Tnu93gcHBgaOEvNNTU35MplMzTDMI319fR8tJ1Bra+sLDMM8rlKpXphPArkukUi2rVajMBIP4NCUEW+fGApM//L+NhTkLK0rthhEfbLlsRXj+WJRzsWZKzi3oFvIwnmi2fh0SFPv4Vmr74qbElBeaHsmrNZzscgR6bvRyBbpHIl6XpQtUSsfOu/MnBVne2YwNBmaYRv8gcWgqiSbA4PN1XncOZCMuvPYbNAfPgT74K0QAdn8XAytr0C3VL2ki8i++vtRk1sV8jxfstlNwzBrLsDrccFiccBqdnLzU/HoOUMjnK5cZOWkobWjDFV1BXGJD+RLtuSw3FAuUl02EQAmo9WFyZNCoXh5MQCcz+Y9KJfL87u7uwOBLAqFohvAz1Uq1f+3wvASpVL5HZZl1wNIY1n2u/39/T9ejZVIAODJ61O40DvLDZmdKccXH2tf9oA6OXkW19S+TGECUS93fBqU2ME3OTxO/LznNTg9vpZ09Xk1XJs4omg2/qBhGAeHg8kod1Vtw8ZScromF0UjW3JJsDI3omzJpalZHQHB2UDf75W4Ky3IhKKuADs3VMPjcK3YxScR0nHet+4bMBw/CtblOysCh0SXEucqXZh2BGv5U3kpKlfVVRw83/i0S4/bBov2MuzmEc4LaDY64JivHUjXwgZTDdyeTOTmp0PRVYGyylxBgSCfsiVCv7ebM9VlEwFgsllcBPwsBwBbW1vp2vcnKpUqJFBFoVDQdfGF/v7+r0QwxaqPEgDUak1hHdivfTSASa0vpJBKvzy6s37Z8SlBQz/ftYPatlElfqHozNRFUFawn55TPAWKOaSNX1yci3Bls7vteLXvLVjcvgzJHHkWXmj7VKA9nVD8RzNupLJFM0ei3hFlS9TK335ejcEG1ageg1Mm0N9XorQ0GYpy0tBSk4/qkmyuUDwlkyQDuXQ66D44AOdMaLcQaWEh+rtKcUPi+3HrJyoyTyVjKEZQCLt0Wqdh0lyEx2mEw+HmgKC/kwgliBjM1XC5s1FYnIWN22uQtcxtCx/rKoRsfPDFxxipLltJSR7Dxzqt1THWtPAreAA/DeCnywDAEyzLnhcCAIajfGo4/42fnYfH64ub2X9XA7Z1UJnBUNLZDPjehVcC//hU20NoL20NZ4qonrE6bfju+Z8GikO3lTTj6faHIxqLPARv9X6AXk3wmujR1vuxvmLlTOSIJhAfFlcghVZAZ7Kjf1QP1agOw1NGeOkOcwViwKCkIAPUgaSi2FdrsLwoS1CP1u2WmvV4oD1zDuqjx0CFm4PEwNRZiyNFBrCS4NdKTV4FHmjchaq8pWcdHyolHnSzN6CbvgKv1wOb1QmTwR4AgnZnEUzWWkikhdi9twXlt+m5zgc/4hhrawUoC3Rtccwvt2ta+OUAoFKpfIBl2UMMwxT09fUFIrEVCkUvwzA/7uvr+zs+lzBcD+DYrBlvHA0CpM8+pFi2wfw1dTdOTJ7jWJSAwee7XhA8hu7Egn69ZBCfUX4ShZn5YXsAqdvH4bHjgWWtz6Wr5AcT9iW1mn5T/VdtJJ7b1dYqmT5PRb3ZnW7cmjByNQbpjJDJpXA6Q3sRL9ZBUW4G2uoLoKwrjLrodKx6pbqBukMfwDkb6g10FOfjRCsDy6LQ5uqcCjyguAu5nnwKMIl1+iXve1wWWPU3YTMOgmstZ3XBYnIEfnBbbKUwmOrRvrEedc389iJPRbv0L3CqyyZ6AHnfivEbcDkAuG7dukKHwzE9n/DBBaQpFIoSAHRS7VGpVCf45DDcGEDqJnCm23dYpqdJ8XtPdi4LkCiDlpJAiKjY6lPN+/lkd9mxzE4LftH7eqBnL3Xt2Ft3T1j9co1OE15TvRXIWM6UZeA5xdPIkmcKzne0E6R6XIvYlipay0jsewQG1SYXzndPciVl3Mu0oFvMob83MSWSZN+mxIwQkrFeL8wXL8B47gzgCXoD3XIpriqzMFG8oFMHA9D1dr40HxtL16Epv16QuGaP2wqbvhc24wDnoTQa7LDP1w/0emXQGRtQWq1E24YqUDs/Pkg8T/hYxfiPQXoTYwDjv+4xz9jY2Fgul8upIulzAP4PAO6O1Gq1zo2Pj9sUCgUlb9C/Pe90Oi1paWnfAtCuUqm2xzz5ogHCBYBvHhvE2IzPIUmZf0/e3biEFSrN8uPuXwSA2M7KrdhUto5vlpcdj4pOU/FpIko4eaJ5HzY0KG7b39LLevHWrfcxbQnG/jzSsBfUgziZSTywk1k7K/OWynojqRfKR+BPZ3RgVm/z/V9ng1pnuy0opKthOluo1ExpfkbcPPDO2VnoDvwWbr0uoDy61tY0FOJSgxROeDinHwFAzrvJAkUZhdhZuQV1uTWC8On12GHV98FmUMFissJs8pW7IqK+wkjrwoYdSsjTVi/QvdpuSWW7THXZRAC4mnUn4ecKhYJqqSyHMj6vUql+WlNTk5mVlUWg7xkAtMM/kkqlf9DT0zPFtzjhAECP14vvvdUNigMkunt9JbYol7Zpoz69B4YPB1h8Xvk0d1DGgwwOI37Z92agRmG6NA1f3P4cYJOvmOByceYqzk1fCrDXUazEfTW74sFuTHOk+qEmegBjMo+EvbyaXbrcHgxOGrkOJFRnkGJvV6KcLDlqS3NQnJ8Bak1XnJeB3Cy5IGCLePA6nVwrOWtPsM8592OyqAjT25pw1TMOj8QdAIB+vqn+KP3QLcuiSxr+ia6GzdqLMM2NwKizYT4EGyzLwOZsRMuGu1BUkh3TxKvpLabBE/xyqssmAsAEG9hanz4cADg9Z8Wrh/sDoj73QAsqi5ceOkfGT6JH6/PCUWulF9ueEezAXm7dL89ex5mp+brXDFCSU4BH6/YhW7aUV2r39uuB34K8gET56Xl4tvXJFTsBJJOeU/1QEwFgMllb+LxEYpdWuxuqcT3Xkm613sR+DiibmLKKqU8xdSGiUBS+ydrbA/3Hh0PLxUilyNm5A4auMhwbvACTM9hmzj9/uC3louGXgLLTMg7d1DnoNDqudIyf7M4CFFbehaa26D2RkegtGv4T+U6qyyYCwERaVwrMHQ4AvNinxolrvrg+6gbw+093QioJLe1Ah9TPel4L9OftLG7DvTV3xXWFiIfjE2fQre0NXNnkSnLxdPOjSJP6qurQM/36QQ4o+nsJUyIV9RKmnsJrgVL9UBMB4FqwwqU8RmuXZpuLyyYenDJibMYcVuwggcH2+kKuVzF5CPkkugqeO/A+XLMzwWEZoKC5EfLd90LlmsGFmSuwue0h01K5mM1l67GpdB1XOoZv8nqcMKqvYHbseqCdHM3h8cjhka5Hx5b1yMhcuVXfSvxEqze+5RNivFSXTQSAQljNHTRmOADwnZND3NUNUU1ZDj51X/OSFdLYtPiV6u3Av1MWbX3e8n2ChVxe8uhRMedh02ggZqcmpwqPNjyIMfMEd+WrtQVjfYiX7RWbsKV8o5Bs8Tp2qh9qIgDk1VziNhgfdkmxg5RNTFfEaoMNc0YH7I7bZxXXludyvYrrynOQJucHeFG5GNPZMzBdPE+/GgM/KF1eBvn37oFM2Yor6hu4pukOlKDyLzTdJtxTfRdqF3UT4UsRTtssJvo/htVoCOkLanHUoqH9LpRXFUQ0FR96i2jCOD6c6rKJADCOxpSKU4UDAP/9nW7Y5ivV7+gox87OpTWxrqq7cWq+/ItMIsXnO19IWBFll9eNdwYPQOfWBWJ2KKvX6lpawLYutxr7Gx8UJKNPKHtJ9UNNBIBCWY6w4wphl1wXD4cbWoMdWqMdAxMGTKp9xegXE2XFUns66lNcX5HLxQ3GWibNMTkJ3aED8BgNIUkgGY1NKNjzAGwZEpyfvow+Xf+SWOOWgkbsqtqObHkW7wvv9TgwO3ISuplb8HiCsZQOZy688o1o6ajnCkiHQ0LoLZx54/FMqssmAsB4WFEKzxEOAPzBuzdhmS9FsFL834mJM7iu6eFWigKiP9X6REJXze6x4/2xDzBt1HJZe4uJYhTJ89dS0LSmwB/JkeqHmggAE7p1op48XnZJWcVXBzRcZ5LblZqhRJK68lzUl+eitiwHmenRZcx6XU4YTxyDS9UTkgTCpKUhb9fdyF63HmqbBscmTkNtDbaUo4WUS+XYU7MbBAb5JgLH5jkVpodOw2F3BoancjFafQtyi+rQ2lmG/MLbA8F46Y1v+cMZL9VlEwFgOFYgPrPiCoQDAMfVZlzu13CH6MaW5bPd3h/6EMPGMW6e5oIGUEP1RBJtfGm2Fz849ypsrmCsDnkCt5RtALV5EiJOJx4yp/qhJgLAeFgR/3PE2y5tDje6h+ZwY0gLgzkIgJaTjDyBZYWZnGeQSs2UF2ZG5B0k2dJ1Mxh+4y14LKEeyLSqahQ88CCkhQVc/DGFmfj7k/t/sD1Yt0cQEEjju+xzmLr1Mcx6bSBLmP7daK6C3lSL8qp8tHaWI69g+dqm8dYb/5a38oipLpsIAONpTSk4VzgAMByxX1P9JhBbt6G0C7uqtoXzmmDP+Dd+z+gwDo8eA10LU5mXdcXtayLT93YLk+qHmggABdsWgg6cKLskT5jO5ODiBoemTZhQm+H110tZQWJKHOlsLEJbXSGyMlb3DPplmx2bhf74sSXlYiCVInfbduRs3Awb4+bCYQb0VO3LR1SblH4UC1VjlPW6oZ8+C/1sP6wWZ+A62uHMgUangMebjvLqPLR2lC0BgonSm6DGOD94qssmAsB4WFEKz8EHAKQD+IfdrwS6adxTvRNdJYnto5vqG18ESWtvU6ayTfo9Xclgl1RvcFxt4QDhyIwJ+gVFlBdbDcUNNlXlcWVlyCtI18TLxQ0u1p19dAT6jz6Ex+hLjgsAvYwMZG/YiJwNG9FrGcXR8VOBz6SMBA83PCBYchydww7TEAyz52E12wJA0H8lbHP4arIuBoKpbJepLpsIANfe90BSccwHALS77fhx9y8DciUqA3jhwqb6xk+GL1ohDFnUmxCrGp8xk1V3BosTozMm7v8jM2a4XMG2b4tXhmoLUq/iwtx07v/UnaS6JBtSKbOktSTFBppOn4b56mVfpvACYuRyLjZwtC4bJ3TXgiBQIsX+hgcFyxCmidxOPYwzJ+G0G2A1O2Gz+jyCJksF9MZ6sPCV8KqoyUPnpiqudIx4nsRnj/A5C+03EQDyuaJ34Fh8AEAKfH69P1gCJp4dQFZSWbJ+GfFhYqJsfKxi/MdIZb3Raq4F+aibEWUS3xyew/js0oLOy1lFRroMLdV52NpZhdx0ZknSmHN6CoYTx+GcnFjyOiOVQdtZgxMFOt8CUS1ViRSPNe7jeqULRXQlbNKch900zF2H+4Gg05kFjb4VLrcvMSQ9U4bNO+ugaK+4bdtMofgUety1YJPRroEIALnqTCLFsgJ8AMBBwwgODn8UYONLXZ9NeJxdqm988Rd7LFafmHdT2SbXCgBcqHm92cEBwZvDr/caHAAAIABJREFUukCVgxUtY74XMMOyaKrKR315DqpLc5CzoPCyY2ICpgvn4BgZXjKMtqEYJ2vdFAwYAIHrSzqxobQTGTJ+C1n7J+euhM1DMGkuggChHwhaLW7MGephtpYTbAcjAbbtakBJZQ7336lEqbznRACYataagJ3HBwBcWAMwQ5bO1QBMNKX6xhcBYKItLPL5U9km1yIAXAiUjFYXdCY7V3iaEkrmTHau9qDDOX9dPA8AnU53SFmpgtx07oqYwCDVH6QYQufMNEwXzsN+ayDESDQlGTijTINXFuyilCaVY0NJJ9aVdoL6lwtBbqcRptnTcDnmuOEJCJoMduj1udAZ6+H2ZnI1DguKM7F+aw3SoiyXIwTvsY6ZyntOBIAiAIx1f1BrNFajocbs0Q+1sAZgaVYxnml9MvrBeHoz1Te+CAB5MpQ4DpPKNrmWAeBKJuBlWUxqLNyV8cC4AS4vG1IHcPF7MpkEezZWo6OhkEskcYyNYe6378DrcAQencsGznRlw50R2rKNwN/GsnWCVSlgWS8sc9dg1ftqtRLZLE6YjA5Y7cWwOhpgtaUhM0uOTTvrUFAUXhHpOG6fqKZK5T0nAkARAEa1KRa+xAcAfH/oMIaNo9ywTfn1XKZboinVN74IABNtYZHPn8o2mYoAMFTDLOxeBueuT2J42gSdMbQP8MJnFXUFeGBzDdLlUrjmtNC+/ZuQbGFXphx9W6owKDUsMSK6QekqbueqKGQKcDXstE7DOHsGXo+vK5LL6YFRb+McAGZrIQymGnjYHGzZVY/SitzIjTzJ3kjlPScCQBEAxrzd+ACAyVYDMNW/jFL9UBPBbczbOiED3El2abG7MKG2cDUHxzUWzBlCAWF+Thr276jnsoipcLT23bfhmpkO6IWyhD0bO3C5xI4J68wSfckkMnQUKbC+tBPUtYhP8nrssMxdh900CPIMsl4WFpMDVquLm8ZqK4be3IiurU2oqo2srzCffPIxVqrbpJgFzIeV3MFj8AEAf3TjFTg8vkr8d1fvwLqSjoSvaKpvfBEkJdzEImYglW3yTv/RNTRlxKHzY6DuJH6iGoO7uiqxWVEC1u2C7uAB2AdvhdiNrLgYzm1duCCZwrRldolNUQFpRWEzdlZu5d0j6HFbYdXfhN04CLmcgU5rgWW+owrVDtSZ6tHYthF1zcUR23qyvJDKe070AIoewJj3WawAkIAfAUA/JUMNwDv9yyhmo0jgAKl+YKcqcBf3HGC2uXDw3OiS8jJ52WlobyhEO3nTLp+F+cqlJTsso1UB+6Y2XLHewqhpaTmZbHkW7q+9R5D6gXQdLHUPQT15AxaTjYsL9JPdkYfCqp1obm9I4KkQ/dSpfp6IHsDobUN8E1S/NLYkEI1Ni1+pgjUAn1M8jeJMX8X5RFKqb/xUBRKi3hK5a2KbW9QdQIkjF3pncaZ7hs7WkAWlxJCasmx0ZNhR0HcBHp0vK9dPdC2ct3MXbMo6XFHfwC3D0JLkvPWlHdhRsQV0RcwX+fU2Mz0Lk/oiDJpBGPXBK22WlSAtpw1ldZuQX5gJiSSYxcwXD0KNk+o2KQJAoSznDhk3VgA4ZBjBgQU1AL/Y9VlQaYNEU6pvfBEAJtrCIp8/lW2SViOV5YtUtgmNhfMGmiy+0JjFlCWXYIdcg+LRm5SJEfJxRmMTCh7cB7PEhZOTZzFiHA/5nH5g7629j7cf2otlc1jGoR49DYNWh4UQ1u4owJyxFbkFeVyWcGFxFkrKcyCTSyPfDHF6I1K9xYktXqYRr4DFK+CYDSlWAJiMNQD9X0bDw334sz/779DrdfB6vfirv/p/sWfP3mXX7MUXn8GDDz6Mz3/+d2Na0+npabzwwqfw3e9+H21tq8dC/s3f/G/MzWnx93//z2HPm+qHmghuwzaFpHpQtMtQdXi8XgxPmdA9PMf9udgjSE8XyLzY7hxG7sxwSAlmaU4OCh/ej7SqanRre3F66jzc3mALO6lECuq53l6kiNkGltOb1+vCzPA5zE3dDOHb7U6HWqeEy53NzZuWLsX6bTUoq8yLmQ8hBkh1mxQ9gEJYzR00ZqwA8OTEWVzT3ORWrDSzGM8oEl8D0A8Av/71/wGDwYR//Md/WVWjfAHAVSda9IAIAEMXJNUP7FQFt/49l6ryxWqXlDXcO6LnOo/MLVNCppo1YYP6GrI8wfg7cqnm7bgLOVu3Qe804sPRo9DYQq+NH6y7F62FzZEeOyHP3062uZlxzA4fhcdtCVxH05WwVt8Mq70kME6jogTKdeVJdz0cq95iWliBXxY9gKIHMGYTixUAHhg+jCFDctUA9H8Z/cmf/BGqqmrxp3/6tVXXSQSAqy5RXB5I9QM7VQGSCADD2x7kBRydMePk9Smo9b5afH6Sup1QTl5FmVWNrAwZMtJkXOe49Jpa5O+5H5KCApyfuYwr6usBMCZlJNjf+FBMySGr7TmvxwHD9EnYTJNc3UCXy8P9qTeWc51EAF9MYH5RJjbuqEV2Tnp4ixGHp1aTLQ4sCDaFCABFABizccUKAH+leivwq5T6Wu6q2h4zT3wM8MUvvoT+/j7uF6lEIoXL5cTXv/432Lv3ITgcDvzDP/wtTpw4hoyMDLz44u/gzTd/FbgC/uEPv4ePPjqEV155PcDKn/7pH6O4uBh/8Rf/D/dvR49+jB/96HuYnJxAZWUVvvSlP8C99+7B9PQUnn32SXzvez9GR0cXzGYzvvnNb+DixfOwWq2oq6vHH/7hH2Pz5q3cOKIHMFTbqX5giwCQj90d/zH4tksCgv3jBpy6MQ2DeYHXj2VRph5E9UQ3pGA5EJiRJgV1GclQtKFg127MyHQ4PHYssAhyiQxPNu9HWVbQIxfJCoUjG/Hr6yTiu+0hsttc0Gpk0BlqYXfmc32EZXIJujZXo6ImLym8geHIFslaJdOzIgAUAWDM9hgpAKSDgPpmut1ebu5XVb+G0+MrILqtYhPaCltj5mm5AegAzMuScy2WwiF67Ctf+UPU1NTjT/7kz3HPPdsCAPAHP/g3HDz4W3zrW//Kgbp//ddv491338KLL77MxQD+6Ef/zgHAn//8V8sCwP5+FX7v9z6Pv/7rv8POnbs4IPn1r/8FfvzjXyA9PR3PPfcU/u3ffsQBwG984/+gp6cb3/rWvyEnJwc/+ckP8eqrv8Bbbx3gnhUBoAgAw7HntfBMqn/ZCgHeKU7w5rAOZ2/OwGLznaNEWRYdmoYuIN1pCVG9l5FAU9aEqdYCWIvHkZPp6x9MXUM+0fIY8tMjj8WLRG8O8xiM6jNgvb56hx63FwadDVZbJtdFxOagChAMJFIGuXkZyCvMQF5BJgqKMpFfGP/2cpHIthb22EIeRQAoAsCYbTYSAEjg752Tw6Cip0ReeDFqDNasol+gWbLMmHlaaYDGyjw8sbshLBDoB4DV1XXcFfBCAPjZzz6LvXv3BRI+rFYLHn/8Ibz00ufDAoD//M//AAKB3/729wKsHjt2BApFGxXWCfEAut1uuFwuZGb61mV0dAQ0/49+9ApaWlpFALhI2al+YAsBIgTbcBEOLOouwgVb8Ljb48XItIlrMzc8ZeRqCkrdLlRO96JUPQQJ6/vB7SePRIb++kKoOxgU5GZw/5yXlsuBwCx5ZGdwpHpzOw0wTp+A2+X7HqBUYZPRDpvVBZcrCwZzNddfePHXM3kF6Yo4nmVkIpUteg3G/00RAIoAMGariwQAWu1ufP+d7sCcTq8Lk+Zge6OqnAqkSYQtAfO7T3Ry8TGr0e0A4L599+HP/uxr2Ldvf2CY559/Go888lhYAPCrX/0KCgoKAtfBC3lZfAVMgO873/kndHdfh9VKMT8sCBT6PYSiBzBUk6l+YIsAcLWdm5yfx9Mu6Ye2xmDnwCCBQot2DgVD3SjSjoJZUJiFSrSMl6djZHMe8vN9oK8kswj3Vt+F8uyysBcyGtnIA2gz9sOq7wW1liNy2FxcEWmvl4XDmcclirg9PnDqp7qmInRurgrrR3zYAtzmwWhk42PeeIwhAkARAMZsZ5EAwMUeQKvbhlmrJri586ohmQ8IjpmxZQbgywN4//134atf/Uvs3/94YJZnn30Kjz76+IoA8Ctf+SOUlpZxoO9rX/sT5Obm4S//8utLuFwIANvbO0HAsrm5lfNClpSUYGJiHJ/+9CcCMYIiABQBoBB7JRFjpvqXbSLBO3kIDRPTMJw6BfvQLehMDq48C4FAbZ4bA9vLkF0S7BlMtzHrStrRnN8IKhlzO4pFbxwQNN2CTd8Lai1HDFGSCIUIuV0s9OZ6qDXFWFDBBu0bKkFZw/GgWGSLB3+xzCECQBEAxmI/3LuRAMD55wMxgL26fpyfvsyNQ8Wfn1d8ImZ+VhqAYgDzs33xLuHQ7TyABPYee+wJfO5zX+KGMhqNePLJfXj55S9yAPCVV36CX//6dbz++juBqQjIbdiwiQOA3/72P+LGjesciPPTe++9jdZWBfLy8gNXwBUVlXjqqUfwzW9+B1u3+pJjPvjgAP76r/+X6AFcQYmpfmAnEkSEs29ieUbUXSyrF/679tERjP/mHcxMz82DQBbmDCsGtlRCRu3mFhDFBnYUK1GdU4mC9HwuRGdxHDUfemNZD+ymIVh13T4guJAkBejuLoXNNp8dzABbdtWjvCryeMXwV8n3JB+yRTpnvJ4XAaAIAGO2tUgB4MIJqUr9NbUvK4yuHp5VPBUzP3wNcDsASFm5p0+fxD/903dRUFCI7373n/Hhhwfw/PMvcgDw+PEj+J//88/xne/4ijm/+eZr+OlPf4Rdu+7hAODQ0CC+8IUXub/ff/+DOHfuDPf897//E2RnZwcAoFLZjkcffQD/P3vXARbVsYX/LfQO0uwoArYUe++xxK6xxUSMXWOPMZbYTdSIBY0lsfeCvfeeRGM3NrAANkB6Z2HL+86su+wCyi67wnLfne/L88GdO3PmnDNzf06b3r2/YfGF9+7dQVDQDvz112XMmbMATZs252MAcwic6wc2DwCNtcMLdxxT00tpUhLCg/Yi8tlLNQjMEmUgvLoLMnxdlMgnj0Z/qBMQpGSR0rYl4eNUESKhEMbSS7IIpsTdRXpiiNbsWZlyRLwxQ2aWNTKzbCBT2KJW4ypwcFIWlP5YzdTkZsx18gCQB4AG65MhAJCugKOr4Kh5OZRD2/ItDKbHWAOoAKAqC7hJkzqYMeMXVgYmPT0dAQG/4u+//2JlYL75xh+nT59E3br1GQCkW0Pmz5+Dy5cvQiwWo3v3noiIeMMOWlUZmGvX/gElg0RFRaJUqdIYMGAoA3TkAtbMAr5w4Sx+/30pszJ++uln7P0lSxbin3+uMBBI2cb8TSDZUuf6gW2sD62x9okxx+FlZ0xu5j+WPCsLYQeOIOI21QVUXtpGTuHUMiUQ26AEkgTadQbzGrGsfWl8Ua4pSrm7ICaGbivJf15demSm073C/0KWlazunpGWhaTE7DuGqTyXW6mSsHX2haVdBV2G1bsP13WSvwlEb5XgX9DkgCEAULMGIF1S3rBkXZNhLtc3PleBBC83k9lCehPCy05vlhn8AgG/8HNX8Prsee0r2yytYfFFPSR4SvAq5Y3WNXI5J3WydEDfmp0hTxUZDQAyMCqXIjX+P6QnBDNgSi0lSYI0jfuRzcyEcHKxgbWjD2xcahg9OYTrOskDQIO30P/3AIYAwPX3t0EiU15k3rBkHXziWtVkmMn1jc8DQJNRNZ0J4bJOEhO4vD5TX9uLOw8Rvv8wBJnZFjYF5QxX/hQ1urWBwlyKeEkCEiVJiM9IRHjSS6RkvasxKADsrazR1LMxcwsbu0klCZCkvoQ0MwH0/+OioyHJUNYRpGZhKYa9gyUs7UrD3q0BBML8qzzoSqOpy03XdeTVj3cB8y5gQ/SHvVtQAJgpy8S6+9vU87ct3xJeDmUNpsdYA3B94/MA0FiaUnjjcFkneQBYeHr0vplS4hJwf8c+SF+91OoisXeGfcs2KFmhFNydrCAWCZEuTcep8AvKMl4CwNxcjKxMKRp41kH1ElWMbonTJCgzMwM3L9+HNCMOjnYvIRDKIKLC0Q5WsLF3hYNHEwiNVE+Wy3uOB4A8ADT41CkoAIxNj8PukIPq+SkBhBJBTKVxfePzANBUNE13OriskzwA1F0PPmZPdsXcmSuIvngRCplMPZVcIEKEpw9iPH3hXsIWpUrYwMPFCs8l/yEk6QkDgJmZUlbGpbKzD5qUrg+hQHnH78do6WmZ+PvcM8izEuHm/BgikdKTZG1jBnsnZziWbAaxOV0vZ1jj8p7jASAPAA3bHQZYAMOSXuB46Fn1/AOq9YWFSPcyLQYTns8AXN/4PAD82Bpk/PG5rJM8ADS+vhgyYtLrCDzasQ9Zsdl1Wmm8dEs7hJf9DKm2dFOH8rIOkUMMUmyeQSSE8t5hkRDejl5oUaZxvjUEDaGRQOB/N14jPjoOrs6PYW6mdEmzaz+d7OFe4UuIzLJrGxZkLi7vOR4A8gCwIHtC652CWgD/i3mIK6+vsbEI+BEANKXG9Y3PA0BT0jbdaOGyTvIAUDcdKMxelCX89OgZJN+5jYxMqVaSSHSJ8nhdsipkYjP2FZVbJiFKdA9ygRTmZkLY25ijqlsFtCnfHOIcMXkyuQyP4kIQmvQCJW08UMPtkwK7jMli+eJZHB7/9wrOdiGwsoxX4VJY27mgbNWOEIkLbljg8p7jASAPAA0+TwoKAP9+8y/uRiuvhXOxckZPE6oByH+MDFaLIhuA6wc2V4E7v+eKbMvkO3FmZATiz55BauRbBgQzMmXsP4nIHM+9aiPFzoW5gFOliYi1vAuZUOmOFQoFKGtfCn2qtYOTrTUI+IXEP8ONt3eQkvkugQRglkJfZ+986fhQh5RkCe79Gw6R/D5srLKtlnKBO7yqtWbxgQVpXD9P+CzggmgF/46aAwUFgCfCziI08QUbp7x9WbTzamlSXOX6xucqkODlZlLbSC9ieNnpxa5C7UzxgCl3biH52lUopMoM3CypHBkyBWJ9ayPCsQxev02GFOmIsb4DmVCips9C5sgKRiscIpAqTclFt42ZNfr4doOZyLB74OkO4WePIpD89gLMzbLnSUotA88KtVHe20VvSyPXdZIHgIW6jbg3WUEAoFwhx6aHO5EhVR4Sn7tVRz3PWibFHK5vfB4AmpS66UQMl3WStwDqpAJF3kmamIiEC+cgCQ/LpkUAeDZphEy/T/H0dTKuP32FJ9JrkAq1C0mTa9jD2QYiYe5bRsgNXNezplHWFx8Ti4iQo5DLs0vaRMf7QGReEmIzIQgoKuSAnO5ClitY+aGyFZzh5eOaa34u7zneBcy7gA3ecAUBgFFp0dj35Ih67k4V27K7Jk2pcX3j8wDQlLRNN1q4rJM8ANRNB0yhF8Xdpdy8jqS//1KS864MjLBUWTi1bgeBmRmeRsZgX8gxxKYnaJFsJhaisqcn6pWqiQexj5VlZACWLEJ3wTtY2BlliZK0GLwOPoa0VCUIVCiEiIyphizp+6+Oq9OkPEq4a8/P5T3HA0AeABq82QoCAG9E3cH1yNtsbjOhGN9V/fqjZosVZJHFbeNLJBJMmjQe9+/fw6RJ09CyZev3LrugawsImIcXL8KxbNlq3Lp1A2PGDMehQyfh5GT88j2jRg1FuXJemDBhkl7iK+ja9JqkiDpzeW08ACwipTJg2vRnTxF/6gQU0ix1GRgzlxJwavMlzFxcWK3AfcEn8DjyNSRZMojlVrCTlIenVRl0b1IREkEy9jw5pL49pIJDObQx4nWgGclhiH11BUkJ6ZDJFJDJLBARUw1yed5JITb2Fmj8hTeEwuzyNVzeczwA5AGgAdtf+WpBAOD+p0cRmfqWvW+K8X8f62N07twZ7N27C2Fhz5GengEnJyc0a9YSQ4YMh4WFJePHV191RFZWJrZt2wtbW+0SBj16dMLUqTPx2Wc1csntypWLmDLlR+zZcxhubu4flGtBDzVNAHj79k0GAA8ePMEDQIN3kW4DFFRuuo1e9L24vD6uri0z+i3ijhyCKDNdXQeQNEns7AJLrwoQly+Hp+JkXH8Qj5RYawigBFd2Nubo1qQC7ibcxMPYYLXyGdsblBJ7B6nxD0H3CEulckBgCanwM0DkxJJU0lIy8To820rp94kHKvhmu4K5KjfVN46PASz6c69YU6AvAKS4v40Pt6v/6mtcqh6qlahscjww9sa/dOkCZs/+GdOmzUG9eg1gbm6Op0+fYObMKfD2roRZs+YxHhDII2tew4aN8dNPP2vx5UMA8PjxIyCAdvbsO7fMBzha0LXxALBo1bSgcitaqnWfncvr4/La5GmpSDt3Eomh4awQdM4msrGFeUVvXJJ5IjwuOznExsoMdao64WzUcWTIMllsnjlsUcu2GepW9oS1peFXupG7OjHyIjLTItRkCQRC2JaoCSt7bzbnX2eeIjlR6SoWiYVo2tYHllbKhBQuy423APIWQN1P5/f01BcAPksIw6nw8+rRvvb7ymhxHwYvRmOAxo1rY8mSJdixYxfu3bsLR0dHjBw5Fk2btmC90tLSsGLFUly9+jcSExPg5VUBQ4eORK1adfIkIyBgPp49e4JVq9ZpPX/+/CkiIyPRoEEjNQD09x+IwMAALFwYqGXtex8A3L17B1auDIRMJmPAkoBj69btsG3bJhw7dhhRUZFwdXVD37790KFDF/WhtmnTNuzevROvX7+Eg4MjOnXqin79Bqjp2759M/bu3Y3U1FQ0b94KCoUcb968Zi5glQVw1qxfsX79n2wNlSr5YPLk6ShTRnmlH7mJ16xZidDQUJiZmaFmzdoYP/4n2Nvbs+fR0W+xePFvuH37BrOANmvWAqNGjYdYLEZOF/CmTetw6NB+rF69nq3lfY3rBzZXYzf/Hz62XJUd7TlnB0s8238UqQ8fABo3iGjuU4G1Ne67VMYjqfYNHSlmL5Fo+Uzd1SGjEiraVUKP5hUh0nDHFvR8V8ilSHr7NySpr7WGsLKvyIBgfEwGrl54rn7mWdYBn9dVnmFcP094C2BBtYp/j3FAXwB47sVlFvxLGVj25rbo7N2uUDhJxUjtzGx1LgNAANDPzw+TJ89gsWhLlwbg/PkzOHLkNKN35sypiIh4gzlz5sPZ2QUElrZs2YAdO/ahRInc2WQEpFasCMTEiVPQqlUbBnLyaiqQR7F8R48ewubNuxh4oqaPBXDfviBs3boRv/22FBUrejPANnHiWEYvgc0HD25h/PjxmDdvET7/vCZCQh7jxx/HgsBnt249cPPmdYwfPxIBAcvYc3JfL1z4KypXrqIGgKNHD0Pt2nWZW9rS0hIzZkxBdHQ0Nm3awayYnTu3Qc+eX2PAgCEMJI8bN5K9/+OPU9h6Bg/uBy+vihg3biJSU1Mwduz3aNq0OQYPHq4FAE+fPoHAwEVYuXINypYt/0F94fqBzVUQ8f/wseWq7DT3nCxDgozwMGQ8f4aMsFAoMpU1AVWNDIQvLFxx264SpGbKsBcF5Hhrc12dNSxUiOGeUhd1/EqhYXXjJAeSJTAt4SHS4v6DQsNMaWbpAnv3RvjvZizevMh2Bddt5gUXV/peAFyWGw8ACwV+cHcSfQCgXC7H/BuBSJIkM4bYmdvC2dKp0JhTzr402pVvpRMIJABIAKlbtz7MXU3WrLFjR+DgwZMMkLVv3xIBAYGoXbuemv5OndrA338AunfvlWtNUqmUgZgjRw5ALDZDlSpVGbBq0qQ5KlSoqO6vAnnVq3+KQYO+Rb16DTF06Pd6A0B//z5o3bot+vb1V489b95sSKVZmD59DqZNmwgHB2f88EN2ksXGjWvx779XsXLlWpDF8sWLMAb2VG3o0O9gYWGhBQAXLVqOOnWUPLhz5xYIFKriEMlKShZJFdj9448VuHbtb6xfvw1PngRj4MBvsXv3IXh4eLD36XdkbaQYR5UF8Isv2rLkFgKiVatWy1dXuH5gc/VjxAPAfFXbZDu8b89RvUDJ61dIuXNbq2wMgcCYNDnuO/og1rkMzMxEkFkm4K35XUjlcpawYS5zgEtGVfRoXBml3Qy7zk2TcZK0N0iO+gdyeTYwFYosIbaqgPu3Y5GVKYJMbgYrG1vUa1EVYrGIB4Amq3mGE5a7IJHhY/5fjaAPAHydHIHld9ao+eNmXQJW4oJVaC8ok/tX7a3TnAQAFy1ahLp1mzAA+ODBfQwfPoABluTkJAwc+E2uhAt6XrlyVYwe/cN7yUtOTsbNm//i7t07uH79GsLDQxlIGzZsZC6Q9+jRA3z//RCsXbsJFSp462UBbNGiIfvbWkCnM7PUKv/Wrlq1On7//Q98+21PvHz5Uivjjfq4uJRAUNBB/PTTOGbZ1IxDnD9/Ti4XsGbSCbl0u3Vrj1Wr1qNateo4ceIodu7chtevX0Eul4FAMLmHt24NwoULZ5nF8OJF5XWAORsBQKFQhKdPg9G+fWeMGDFaJ5HzAFAnNplkJ152JimWfInKT25kfUsPfozESxchz8iuDShXABblysGlTTuIrKxwLPQ0nsSG401sKjuvyBJYUlENQ1o1hKW54fGAqoXIspKRGHkF0kztEjXpqZlITsqOUbR1cEZpn2bwLO2FmJhkddx6vgwpJh34GEA+BtBgVdUHAN5++x8OPTuOdGkGlY5CGbtSoIDcwmr6WgAXL16MOnUa5wKAsbExDAzmzLil31WpUo3Fsenatm/fgtWrlyMo6BDc3T1ygTyyGj58eJ/FvvXs2fm9WcA5k0DataN4unH48suOuUihjU/u17p1G2LgwKF5kkruX3JlT5kyQ/38119nITIyQisGcN++o2qX99u3UejevQPWr98KArpkMZ04cSqLRyRL4Nq1qxnwIwB48eJ5TJ8+CRcuXM3TIksA8MGD/5i7/MKFc2zM0qXL5MvW/D5G+Q5gwh24vDZiO5fXx68NkKWlIfHieaQ/CdHaZSJ7ezh/2RGZjjY4+Ow4XsbHIi7DvqpMAAAgAElEQVQpu4izl403BjVobfBNIZqTUlxgcvQ1ZKQob6NStbjoVGW28LtGWctJ6ZWRlKIdt2hlbY7P6paBvWPhGjCMeTzxAJAHgAbrkz4A8PDzk3iZ9BoyhQzuNm5oVbaJwfPrOgDFANqb615klCyA7wOA5Aal+LYFC5agfn2ytIFZt7p0aYtBg4ahS5evtMiiv4D//HMli5erUUP7xpPQ0Ofo168X1q7dAl9fv1wAMD09nVnrevXqC0r2mDp1Rp5lYHICwMGD/Zm1b+zYCWpaCKCRhY/cGnPnTkNycirmz1+sfk7A1s7OnoE1AntRUVEIDFypfj5oUD9YW1trAcAlS1aw5A5qKjf5oUOnmKub4hAJIKrauHHfs8QPAoCUAT1gQF9s3bpbHddHQDc8PAzt2nVgLmCyFhKAnD59MiIiXmP16g0QiUQfFDn/odV1R5heP152picTXSjSV25UPzDxwnnIUrOvaxOIxXBs3hIiH29cevU3rjx/gHSJ8so5al4l3PClTyN2YQCd5cZodC5nJD9n/8llGey/zAwJ4mPTtIancjGxiWWRnELxiNlOQ2tbczT6wpudp8Wx8QCQB4AG662uADBLloUND7ZDRnfwAKhfsjY+c80/pstgAgs4QF4AcNiw7xAUdJjFrFFcWnx8PH75ZSHs7GyxceM6HDiwlyWBUMZwzjZ37gzcvXubJYF88snnDGS9evWSZe/Sv5s27WTu2LwSPf7++wpLOiHwM29egE4A8OTJY1i0aAHmzl3AANqTJyGMZnI1t2vXHmFhwfD398eUKTPRokUrltAyZcoEVkC6f/9BzFI3a9bP6kzkU6eOY9myRfDx8dOKAaxfvxGzEpqZiRmNlPxBcYOUNPLLLzNYvB+Bzv37g3Dp0nmWLbx37xG2/mHDBjDrIbmZ6b2JE8egVq26zN2rmQVM1sT+/fuA4gFVrvL3iVXfj1EB1aNIXuPy2oihXF4fvzbtLUOu4LiTJ7SvlANg88mncGjcFPdinmLX3TOQypUgkPhXytUWVmbmKGNXktWPJY+OsUOIyDJ4/2YokmJC4GCnzBomAEjlYlJS3RGX5KUFAstWdEa1GqWK5DwwdFIeAPIA0FAd0jkLODzpJY6FnlHP19OnC1ysCi8BRN+FNmlSh8UA5uUCJgCYkpKCJUt+Y1avzEwJKlXyZbF/mgkdmnNSiRbKFD579jQiI98gKyuLxdjVrduAuWGpKDS1Hj3IzZvbyjdjxmScP3+Wgau8CkHnVQeQ3Mv79u1GQkI8K53SrVtP9OjRW/2h3bJlBzZv3sDKxBAtbdp8yWhRxQ1S6RUCtenpaawMjI2NLcsW1iwDs2DBYpbd/PbtW/j5VWZg0MPDEyzhZ/4cXLx4DlZW1uja9SvmziVgR+MTCCQAPX/+bNy6dZNZFmmO778fw5JsKJmEMn5VN4EQn3/4YRQo6SSnFVWTz/yHVl9NN53+vOxMRxb6UFJQuSnkciT/exXJ/2rHAZt7eMKm+id4JhdgZ+jfyBIrLYUWZiJ4ulirzyea183aFTXdPkU5+/zDQ3RdE4G9mKhkZCSHApn3IBYLIZXKlEBU5IaXERWQEJsdK1i7cXm4eujuXdKVjo/djweAPAA0WMd0tQBeeX0V/8U8YvPZmFnj28o9dcrGNZjAAg5Q0EOtgNMV6mv82gqV3UabjMtyU1l5uJrlzGXZGbq29OfPEH/qJBSZ2aBKtWnepivwXJiFaEcpXrlbwtzBCq454u5EAiF6+dI9wsr6osZsWelRyEi4ioy0tOziMUJ73LvnicxMZXkuSysxGrf2gZm5fq5gsjamJz2DXJoCKwdfiMyMl+2sCw94AMgDQF305IN9dAWA2x/vRaIkiY3l51wJzcsoCx+bajP0UDPVdfEfWlOWzIdp47JO8nr5/62XWfHxiDt6GNK4WC1GUKZwREwqJFIZssQChJQvCXk1Wwgss7OJ6QVfp4po8RFiymnP2dvK8PzBUciyUtW0ZaQL8eKlPdIznJAltUapck74tI5uVkhV7GFa/H+QSZXrMLdyh2NJ5SUDhdV4AMgDQIN1TRcAmChJxvbHe9RztS7XHBUdP1zQ12DCDByAyx9bfm0GKkcRvc5lufEAsIiUygjTGksv5ZmZSL7+LyThociKjVXVrkKWTIE3MaksrIRagmMpeH/VCuGi53ieGM5+RzT08ukKJ8vc8deGLFG1tqjIt0iMuIQsSZx6uIS4NGRKZJDJLJCW4YRyvlXgWa7CBytbZKZFIiX2dq4SNDwANERKBX+XrwOYg3elS5e2sra2vg9gVkhIyOb8WKsLALwf8wiXX19Vb9T+Vb6Gpdgiv6GL9LmxDrUiXcR7JufXZopSyZ8mLsuNB4D5y99Ue3wMvZRLJMiMikRmxBtkvn6NhOdhiGTZucrLhuXmlijfrTVOCO6pEwu9Hb3wRblmRmWT5trkMrpS7h9IUl8paZApEBeTgne4lCWLuLg5wsquJMytS8Hc2hNCkfI7J81MRGrsHVAh6pzN3NIVdm71ITKzMSrt+Q3GWwB5C2AuHalUqdICgUDQFMBKYwHA46FnEZakrLfkYeOGrt7t89PNIn/+MQ61Il/UOwL4tZmKJPSjg8ty4wGgfrpgSr0LQy/Tnz5B6KFjiIvOLt5sTuVXqpfA7QpiyM2U8Xc9fTrDxcrZaOzJuTZy30pSwiBJeYHM9Cikp2UgKSG7ZqGlpRj2TsragJTsZmbpCqHYGpKUcEqY1KJLZGYHW5dPYW5dukji4XkAyANALYX08/OrLpPJpgsEgv8AhBkLAFL5lwypMsC3tsfnqOX+mdE26McaqDAOtY9Fe37j8mvLj0Om+ZzLcuMBoGnqnC5UFZZeSlNTcXPrPmSFPVeTZWUpQpJjGkLql4dELIK7eUnUdGyAUq42sLIwvF7gh9ZGSRyStEg8f3AfUkkEhMIsRpe9oyUsrZQJInk1odAc1k7VYOVQqVAvQshJCw8AeQCoqROCSpUqHRcKhQMUCsVgAKHGAoC7gvcjLiMBQoGQ/YVm7DgNXQ4pffsU1qGmL13G6M+vzRhcLPwxuCw3HgAWvj4Za8bC1MssqQwnd52D3aPrEMmUgEsmSkOMnRw3qpWETCSEa2pNOFk4o3vTinCyMyzUSJe1STKkuHw6GJAlwtoyHjbW8XD3EEIo0o4wo1uvrOwrwdqpqto1bCwZFGQcHgAWYwDo7e1dVSgUbqUEqJCQEGtNBahataptVlbWcgCtKEsdwFWBQDA6ODg41NfXd6BCoRgkEAgofE9A/8rl8gCBQOApEAjkwcHBq3x8fOj+L7IAbspPsXSJAaTs35D4Zyhp68EquReHpsvGLw7ryItGfm3FU3JclhsPAIunThaF3FLSsxB09C48H/8D29Q4KKCARBSPGCcr3K7sAQu5C1zSP4GdtTm+al4R5uYKRKW9hYXIHB427noxWtc9F/kqEbf+yb5Wzt1ThMrVhMhMewOpJB7m1h6wcf4E5PY1lcYDwGIKAH18fHoBWCgQCC4rFIquOQGgr6/vNoVCQTnpfQQCQZJCoVgEoGFISAhdvaEdiPBOG318fI4DUKVQlQaQoVAoRjx58uT0hxRWFwBoKgqvDx26bnx9xjSVvvzaTEUS+tHBZbkVBZDQj/uG9eay7IpibRGxqdh39jEqPb4M6/REZAnTIBOm442rHe5XcoWjxBcyQQYUVklwdpVBJFRa4/QNQdJnbXf/fYnX4dkxitVqlkTZCi6GKc5HfJsHgMUUAPr6+vYVCoXnZDJZawCrNAGgn5+fi1wuj5TL5a2ePn16kfSnQoUKDmKxOFogELQNDg4+l59OvbMAGs0FnN98pvhcn41vivR/iCZ+bcVNYkp6uSw3rq+Py7IrqrURCHz2LBKO/5yAKCMZcZnRzBr4vKQ97pfKvmXKXCyEh4uNGgRSpjBlDOvS9FlbVqYUl089QUa68vo6kViIxl94w9rWMDe0LnQWpA8PAIspAFQJ28fHxz8nAPTx8SG370kzMzOHBw8eqG/b9vHxeQBga0hIyLz8lIUHgNz+2OpzqOWnK6b2nF+bqUlEd3p42enOK1PqWdRykyYkIDpoF+ISIpDw7rKB4FKOCPawh4KIe3eNnIeLNYQCAcRCEbpUbA9X6/ytc/quLToyGdcvh6nF4+xqg7pNvYokyzc/HeEBIAcBYKVKlcjtuykkJMRcUwF8fHzIXXzjyZMn4/JTDH2ekws4NjYZOTLc9RnCJPvS5nBxsYOx1hYXF4uJE8chLCwUS5euQLVqnxh93T/8MBouLi7sPt6jRw8jIGA+zp//K9c8xljbV191Qteu3dG3L/0NYjrNGGszndVoU8LltaksgMbcc6YkRy7LzhTWlhkVhag9u/Am4RWy5EoLXLyZGZ6VLIdUu/JQCIB022ewpHIxAsBCYIVPrZvAUmQFM5EAPmUcUSLHFXMF1cn/br7Gi+fZBaOrfOoJL58SpqSOjBYluLX/v66FXKwX/x4LYG8Am/MAgFcUCsX1jwEATU6zTZCgzZs3Y8WKFTh37hxsbD5Owc9BgwbB1dUV8+bNw/79+zFz5kzcvXv3o3CjRYsW6NOnDwYPpoRxvvEc4DnAc6BoOZDyPBRh23cgTZIGsUgEM4E5XkenIMbSGW/KVkeEQyySxNnWOXO5Pdwza0AIESzNxRjW7RPY22jZTQq0oKwsGU7su4+UFGXpM5FIgNadqsLB0QqCd7GIBRr4I7xEWaAfYdhiM2SxXnxeANDX17eFQqE4LRAIHIODg5NVkvDx8XksEAg2BAcHLzCmdHgLoG7cXL/+T5w9exrbtgXp9kIBemlaAI8dO4KAgHk4d463ABaAlSb5iilYWj4mY7i8Pn5tH1NzsseWvHqJhPPnkRWvvFNYJlMgIjYNmVI5YlzK4K6PGGnm2dY5a6k7HDP8IIAAlcs5oU2dslqEFlRusdEpuHYhNFfGJd0WQuVh6F+RSFkqxtraHD7V3OHorFXM46MzjLcActAFXL16dSeJRBL5LuHjPGmRjw+zP0cCaBYSEnLFmJrF1Szgxo1rY8mSJdixYxfu3bsLR0dHjBw5Fk2bKi/sTktLw4oVS3H16t9ITEyAl1cFDB06ErVq1cnF3uXLF2PPnl2sEryZmRkWL16BqlWr4c8/V+DixfOIjY1B6dJlMWjQMDRq1ET9/rZtm3Ds2GFERUXC1dUNffv2Q4cOXdhzuhdz1arlOHnyGBu3Q4fOCAkJVruAjx9XAsDp0+dg5cpljEZyO0+dOhPOzs4oUcIOQUEHsHnzerx+/QqWllZo3LgpxoyZwGikFhr6HEuXLsTDhw9gZ2eHTp26on//QexZjx6d0KVLtgt4wYJfEBz8EL///iesrT+OhVMXvdU3ZkeXMU2lD5fXRjzm8vr4tRXeLlLI5Ui9/x+Sr12FPD0NMrkCcckSSKVyJHuUxT++cmQgmYEzAojWaeVhl6kEfj1beMPTJfv8MkRuj+5GIDQkRqeFO7pYo0GLijr1NVYnPgawmAJALy8vdzMzMypz3hPALwAqvQMlca9evUr38fHZ8O53vTIzM1PNzc0DAVQOCQnJjU4M1CZ9ASCBFVlSEhRSZZxGYTWBWAyRvb3OwbgEAP38/DB58gyUK+eFpUsDcP78GRw5oqyKM3PmVEREvMGcOfPh7OyC7ds3Y8uWDdixYx9KlHDNtSyyAJ47dxpbtyotgCtXBuLff6/h118Xwt3dg439yy8zsXbtFlSs6I19+4KwdetG/PbbUvbz7ds3MXHiWDZf/fqNcOTIQfz++1IsW7YKXl4VsWvXNmzatA7Nm7diMYAEAOfPn8N+njBhMjIzJZgwYQzc3NywYMFiZGWloFWrVvjxxylo374TIiMjMWLEQHTt2gPfftsfEokEX3/dHV9+2RH9+g1ARMRrjBw5FEOGDGcgVBMA0roPHz6AP/7YACcn413DVBDdMOTALsh8hfkOl9fGA8DC1CTjzmWqekn3CSffuI7UO7ehkGV/b6w6tceh1JtIl2ZAJpfj1dtUOKZVhZW0BNydrdGrhbf6O2HI2mQyOa5eeI7EuPR8Ge5R2h416pfLt58xO/AAsJgCQB8fn1AA2rZqpWZ8R7d3lC5d2sra2ppA31cACCieE4lEwx89ehRhTAWisfQBgAT+4o4cQkZo9lU+xqbnQ+NZelWAc4dOOoFAAoDjx49Ht259WILLrVs3MHbsCBw8eJJZyNq3b4mAgEDUrl1PPWWnTm3g7z8A3btTmUbtpgkAiQ9fftmSAbOWLb9QdxwzZjiz0g0ePBz+/n3QunVbrSSLefNmQyrNwrRpczBhwmgGPAnsvZMDunVrj9q166oBIPUnwFm2rPJgIVC4cOE8nD17GW5uDggPj4CVlY2aH7Nm/cyAH4HSS5cuYPbsn3Hs2DmYmyvjYu7duwNzcwv4+VVWA0ACr2ThXLlyHUqVovKRRdsMObCLlvL8Z+fy2ngAmL/8TbWHqeulNCEeb3dsgyJLeXOI2NkFsk6tcCTsNGQKORJSJIhPlsAm0xP2kgpo7esMz1cPmcHAoX4DuHo4ISamYImOBAIT49MhzZJBLlOAfiaro1z1r1wOM3MRSpV1hJm54VfX6aMjPAAspgBQHyF/7L76AEBZWhoi1/7xsUn64Pgeg4ZCZJ1/rAUBwEWLFqFu3SYMAD54cB/Dhw/A7t2HkJychIEDv8GePYfh5pZdWZ6eV65cFaNH//BBAEgZwZ07t4WZmTlzeykBHPtftGrVhgG4Fi0asp9VMbqq51WrVsfy5X/A3783WrT4Av7+AzUA5Ai4u7trWQAvXrymfk4AbuTIIThw4Bh8fb3w+++rcOjQAeZilsvpcJIyABkQsAw7dmxlVsigoIN58pMsgGSZJCvmqFHj0LUr/a1R9M3UP0aGcIjLa+MBoCGaUbTvFge9TLl9E4mXL6kZZd+gEV5VcMCFl3+xEJpX0amQyuQwzxKg2e0E+NjbsrqBtjVqwLtrhwIDwKKVzIdn5wEgDwAN1k99AGBxswAuXrwYdeo0zgUAKWaPwF5eALBKlWoYNWr8BwFgamoK2rZtjsDAVahRo1aeMmjXrgUDVuSCzav16dMNrVu3w3ffZWfhjh49DB4enmoAuGDBXFy4cFX9+t27dzBq1BAcO3YWd+5cw/TpMzB79jzUq9cAYrEYc+ZMZ7GCBADJpRwUtJOtMa9GADA5OZm9e+fOLWzevBP29g4G65OhAxSHj1FB18jltfEAsKBaUfTvFQe9pLjA6J3bkBWjjMmjkCC3b/zxQPIS1yJvIiktA2/j01Et5C1KRifDUmyBkvYlYGlmCd/hg5AituFkqTNXV74MTNHvoGJMgT4AkJZZlDGAYgfdAQpZAN8HAC0sLNC5cxssWLAE9euTpQ6QSqXo0qUtS+To0iW3NSxnDGDHjq3xzTf+6NWrr1r6FIfn4eHBfh482B9k7Rs7doL6+du3UXBxKQGRSAQCeyVLlsKkSdPYc0oK6dKlHQNkqhhAcgFTTKLKNXv06CEWy3jmzCUsXx6Ahw8fY+XKter3+/XrxQAkAcArVy5i+vTJOHLkDKzfWUyvX78GiSQDjRo1ZS7gTp26sTVQ7KCjozPmzQsock0uDh+jgjKJy2vjAWBBtaLo3ysueil58wYxe3apGUYhQS4dOyNRkoy/Xl/Fsxs3Ufm/N+rnVuZiuNu6waOiN2w7dgMEwqJnthEp4C2AvAXQYHXSFwAaPGEhDZAXABw27DsEBR1mIG3SpPGIj4/HL78shJ2dLTZuXIcDB/YywEUZwzlbTgC4ceNa7N+/B/PnL4Kvb2XcvHkdP/88kYHKzz6rwbJ7Fy1agLlzF6Bmzdp48iSEzTls2Ei0bdueuWgp+SIwcCVLUqEklJ07t6JJk+ZaLuA2bb7E2LE/MuBGZWK8vSth6tQZOHBgF9av34D167dCKBRh48Y1zM1NbuANG7YzQNu7d1c0btwMQ4aMQExMNChGsV+/7xjA1UwCefPmNQYM6Ithw0axzOCibMXlY1QQHnF5bTwALIhGmMY7xUkv48+eRtqD+2rGUUy4VYWKkKWmInjtSkREv4FCIGPPyQVsZWGO8o6lYNeoGWw++cw0GG4kKngAyANAg1WJqwCwSZM6LAYwLxcwAcCUlBQsWfIbSw6hDNtKlXxZ7F+FCnmn8ucEgASw1qxZhRMnjiItLRUlS5Zm2bcUA6hq27dvwb59u5GQEM/KwHTr1hM9elCdb6XFkbKAz549xayqHTt2AQExsk6qLIArVgRiwoRJWLFiGZKTE/H557UwefJ0ODjYw9JSgLFjx+PGjesMsFKmL2UTT5o0DuXLV2DlXKg8DFkRg4MfwcHBkWULq1zOPXp0Rpcu3dRJKpRgsnjxAqxbtwVly5Y3WK8KOkBx+hjpu0Yur40HgPpqg+n0L056Kc9IR9SWTZCnKzNzRXb2cPvmW8SfOM6SE2MS0xGXkYi7Prao8CoBTjI57K1s4GrnAfdv+kNka2s6jDeQEh4A8gDQQBXSLwvY4MkKcYDidKjpyxZ+bfpyzDT6c1luPAA0DR0rCBXFTS9THz5AwplT6qWaubkj620U+5lqBt6XOeBaJUtYpb1A3UdRsLQQw97MDp7VasG5XfuCsMgk3+EBIA8ADVZMrloAi9uhpo8g+bXpwy3T6ctlufEA0HT0TF9KipteksckZm8QMt+8zrVUoY0NQj9rjWvPY/HW5gaqPA2Hd7KEeVncbdxQqmsvWJb30pdFJtmfB4A8ADRYMXkAaDALC32A4nZg68Mgfm36cMu0+vKyMy156EpNcZRbVmwMqw0IuVxrmS6duiDN2RNbTgYjU5iMBPPraPXgNcylcoiFIpT2qATPb/tDaJb/ncFyhRwRqVGIz0hAabuScLTQPQlRV94b0o8HgDwANER/2Ls8ADSYhYU+QHE8sHVlEr82XTllev142ZmeTHShqLjKLfHKZaTcuqFeok216nBs0Yr9vP10CKIT0pFi/gquaXdR9ZmyfIyV2BIVG7WDQ6PGebKGLIUE+p4lhuJZQjjSpcpYQyor08OnM2zNiu6azJwE8wCQB4C67O8P9uEBoMEsLPQBiuuBrQuj+LXpwiXT7MPLzjTlkh9VxVVu8qxMxOwJQlb0W5i5uqFE9x4Qvrv16FZINC7fpYxgBZKsH6Ju6EPYxqcxVjhZOaGS/zCYvbvyk0BfVFo0niWE4lliGFKzlP1ytqoufmhSun5+7Cy05zwA5AGgwcrGA0CDWVjoAxTXA1sXRvFr04VLptmHl51pyiU/qoqz3ORZWZDGxcHMxYUVh1a1lPQsrD/6CAooIDJXwMz6Oqpde8yubBJAgJJeVWDVpSOeJYYza19KZmp+bIJIIEQfv+6wMzeNTGIeAPIAMF+lza8DDwDz45DpPS/OB3Z+3OTXlh+HTPc5LzvTlc2HKOOq3PZdeo6Xb5Nhbi6GmXUqSsSeh+sTZbYw3eCZbmeJxJIOSPC0h8TWAup7Pd+5fL0cysHT2h3nX11W3yJS2cUHzUorLw8o6sYDQB4AGqyDPAA0mIWFPgBXD2x2MAuAEiXsOHt3J1fXxsuu0I8Bo03I1T33MCwOp2+8ZAAwM1OKzz+TQHZoH8zTMnPxjgBgaikX2PtWRvlyn7CkD5FQxPqdfXEJIfHP2P8XCoTo7dsNDhZ27Of4ZAkSUiQo6053DxfuTSM8AOQBoMGHAA8ADWZhoQ/A1QObBxGFrkpGnZDXS6Oys9AG46rcJFkyrD3yEEKRkAHAmj6uSMv8Bxbnr0EskaoBnbXYCtZmVrASWzHLoNjREZYVK8HK2xtUYzApMxk7gvexUjLUfJ290aJMY4S8TMDJf19ALlegWgUXtKxZutBkpjor+buAC5Xl3JuMB4DFT6ZcPbB5AFj8dFGTYl4vi6f8uCy349fCERaVwgCgnZU5+rapiJuvbkDx4hXcozNgGRkPgUy7lIymFEX29rCq6I07rpl4KHnFHhG/Gjp9gQv/xjHwR83V0Qpff+FTqArAWwB5C6DBCscDQINZaJQBJBIJuyv4/v17mDRpGlq2bP3ecQt6YAcEzMOLF+FYtmw1uwKP7gY+dOgknJycjbIGzUFGjRrK7jimq+x0bffu3cHPP/+EAwf2QySyVsfd6Pq+qfcrqNxMfV0q+lTrCwz8HVeuXGbXEQoL2S32sXjFZdlxeW2hEUk4cf0lA4BQAN2bVURp1+wkDnlmJjLCQpHx9AkywsOgyMrKU4VkYhEuVDdHqr0FMjKlSHprB8e0yuq+7eqVg0+Z3HfIfyx95C2ASs6SxZZvBnCAB4C6M+/cuTPYu3cXwsKeIz09A05OTmjWrCWGDBkOCwtLNtBXX3VEVlYmtm3bC9sc90726NEJU6fOxGef1cg16ZUrFzFlyo/Ys+cw3NzcP0hUQQ9sTQB4+/ZNBgAPHjxhEgCQ7mb+9tueGDPmB/To0QWnT1/AqFHDYP6urAMxhMBEiRKu7L5luvvYzMyM8WnkyCH477+7EGtkAaoY6O3tgz/+2KDmJ4HMHTu2sP4Eugn81q3bAP37D4SLS4lcfO/duxsiI99g794juZ6TPLt06a6+Tznny7/+OgtxcbEICFjGHo0aRXTeg0ikjC0inSldugx69eqLli2/UL/euHFtthZN8OTo6IQaNWph6NDvGQ9ytvfROWbMCLZW0hm5XM7uoCaekjeLfjdgwBBGf9euXyI+Pk5NG42v6tO0aQtMnz6H3S3du3dXmJmZs3cFAgHjSY0atdG7d1+UL19eHb/5/fdDUL36p4xeLrSC7rnisHYur42KOW8+9QSJyRkMAH7IVUsZxZLwMKQTGAwLhSJTO1YwBmn451MnhGVIIVco4JZaC2ZyWzT5rCQ+r5R7T35s2fMWQB4AGqxjPADUjYWXLl3A7Nk/Y9q0OahXrwH7iD59+gQzZ06Bt3clzJo1jw1EoICARcOGjfHTTz9rDf4hAL4I0LoAACAASURBVHj8+BEQQDt79q98CSrogW3KAHDduj/w11+XsGHDNgYiCACOHk0AlSyUTownFIPz6NEDZiVs0eILjBw59h2w0s3aeOrUCSxYMIeBknbtOjKA/vz5M6xatQzPnj3F+vVbtcDwjRv/YsaMyShfvgLq1KkHf/+BueSpHwAcCj8/H4wc+QMDV5mZmTh0aB+WLVuMjRt3oEKFimx8AoCkTy3eFbWl30VEvMGCBXNBQHnt2s1adORHp6rzqVPHQaD0woWruXSsW7f26NXrawZG39cIAPbp043N7+Pjh6ysLLx8+QKHDu3H4cP78dtvS9C2bUuWwHPnzm2MHz8SQUGH4Ozskq9Om3qHgu45U18X0cf1tf0bEoO/775mANDCXIRBHapALPpwwoZCKkXGixdIe3gfGc+VCSDpWVl4nPEWV6t7ItNcDKssV7Sr0AL1qnoUiZh5AMgDQIMVj6sAkD6iS5YswY4du3Dv3l04OjoywEDWDGppaWlYsWIprl79G4mJCfDyqoChQ0eiVq06efI0IGA+nj17glWr1mk9f/78KSIjI9GgQSM1ACSgEBgYgIULA7Wsfe8DgLt378DKlYGQyWQMWBJwbN26HbZt24Rjxw4jKioSrq5u6Nu3Hzp06KI+sDdt2obdu3fi9euXcHBwRKdOXZllTNW2b9+MvXt3IzU1Fc2bt4JCIcebN6+ZC1hlAZw161esX/8nW0OlSj6YPHk6ypQpy4YgN/GaNSsRGhrKrG01a9bG+PE/wd7enj2Pjn6LxYt/w+3bN5g1q1mzFhg1ajyzXuV0AW/atI4BhdWr17O1aDYCdh07foFhw0ahY8fOOQBgbgvljh1bmSWWrKXUdHE3p6Wlolu3DsxS1b//IK35ySq2aNF8dO7cHX5+2W4dApoODg7w86uCzZvXMzCj2fS3AGoDQBqL5m7RogHmzv0NTZo0Y8OT7s6c+auWVZB+//jxIwwZ4o9duw7A07OkmpT86FR1zA8A9uzZB717f/PeM0VlAVy7dgt8ff20+q1atRynT5/AuXNnkZiYwQCuv39vpnc5+W3woVUEA3AdJHE1O53kli4D/th3lwFAau0blId3Kd2udaOzKf7UCSQ/fIQ3sanIUKQg3k6O69VKwsbBEkNr9oCrdW7PQWGoKA8AeQBosJ7pCwBpQ6SnZUEmfX/grMFE5TGASCyElbUZczvp0ugj6ufnh8mTZ7BYtKVLA3D+/BkcOXKavT5z5lRmVZkzZz6zUBBY2rJlA3bs2Jeni42A1IoVgZg4cQpzQeblbqRxVSCPYvmOHj2EzZt3qV2V+lgA9+0LwtatG/Hbb0tRsaI3A2wTJ45l9BLYfPDgFsaPH4958xbh889rIiTkMX78cSyzUnXr1gM3b15nFhhyP9Jzcl8vXPgrKleuogaAo0cPQ+3adZlb2tLSEjNmTEF0dDQ2bdrBrJidO7dBz55fMzchgeRx40ay93/8cQrj4eDB/eDlVRHjxk1EamoKxo79Hk2bNsfgwcO1QBkBg8DARVi5cg3Kli2fS3wqYLN790F4enrmCwA3blyLI0cO6gUASfa0vuPHz8HGJv9CruS6JavYypVrUb68Fzp3bos5cxYw66+qGQoAiWdBQTtx+PABbN68U03X+wDg3bt3mBtZEwDqQmdhAMD4+HimL+vWrYOPT3UGAEnmwcGPGA+Le+MBYPGUIMnNxcUWAVuuIzFF6dL1Lu2A9vVzn0PvW2Fqagaurt4Ci5g3UEAOiSgBySWsEdXEG+WcyqOdV8siYQ4PAHkAaLDi6QMACfzd/CscbyOSDZ63IAO4edqhZsNyOoFA+ogSQOrWrQ/7GJE1a+zYEcylSNas9u1bIiAgELVr11OT0qlTG/j7D0D37r1ykUeWGvqgHTlyAGKxGapUqcqAVZMmzdWuO00ASPFPgwZ9i3r1GqrjoPQBgP7+fdC6dVut+LJ582ZDKs1i8VjTpk2Eg4MzfvghO8mCgNG//15lH1yyWL54EcbAnqoNHfodLCwstADgokXLmXuT2p07t0CgUBWHSFZSskiqwO4ff6zAtWt/Y/36bXjyJBgDB36L3bsPwcND6QKh35G1kWIcVVa5L75oy5JbCIhWrVotT7GTlZMspidPXlRbN1Uu4AMHjqtdiCQDiuEj8E6uVwKm1Ggu+r0mKFfFr2laU8nSSjGPujTi5blzpxmAp0auU3K//vrrQoMAIMXjqWIAyQVMsX0//TQVjRo1VY+bFwAkWZL8Kf5OU6a60KkrAKQYQM24QxUPAwNXo1q16uoYwLwsgDRHhw6tMHbsWHzxRQe25wikk3v71KmLurDcpPvwANCkxfNe4lRyO3zhCa49VBaBFgoFGNyxCizNs28Oed8AlPCx98JzxMUlw+fJX7BJi4fQQgKFOAOJng4Ir1kG3X06ws2ajwEsCg3RzRxUFJQVkzn1AYCSDCnOHn5UpCtr2bEyLCzz37j0EV20aBHq1m3CPkYPHtzH8OEDGGBJTk7CwIHf5Eq4oOeVK1fF6NE/vHeNycnJuHnzX5A15vr1awgPD2UgbdiwkewdTZBH8WoUDL927SZUqOCt9SznBDljAFu0oGrzCjXYVZagUqBq1er4/fc/WMLEy5cvc32wKSg/KOggfvppHANOmnGI8+fPyeUC1kw6IZcuWb1WrVrPPvgnThzFzp3b2IdfLpcxdyW5h7duDcKFC2eZRe3ixWt58opAmVAowtOnwWjfvjNGjBj9Xp6S9XX//j3Mxao6sPNKAqH5KWmjT59vtGLVdHEBkzud1nL48Kl89Zf+0CE5kkuULKDUCBxT0sy+fUfVySCGWgBVgHb27GksCaVLl6/YXKS79EeKytpNoQHES3LxDx48TG0p1JVOXQGgLjGAlATyPgDYrl1zTJw4ES1atGN7TpXYdObMFa1knnwFYIIdeABogkLRgSSV3J6ExmDT8cfqN1rWKo1qXrljU2lPRSdm4PXbFLx8m4LXManIzJKx98RSCWq+vAZPcwnepEaCEkxiy7tAVL8W2ldsowM1xu3CWwB5C6DBGqUPACxuFsDFixejTp3GuQBgbGwMA4M5M27pd1WqVGNxbLq27du3YPXq5Qy8uLt75AJ5ZDV8+PA+i33r2bPze7OAcwLAdu0onm4cvvyyYy5SaOOT+7Vu3YYYOHBonqSS+5eyRadMmaF+TlasyMgIrRhAAjSqrNK3b6PQvXsHlgxBQJcsphMnTmXxiGQJXLt2NQN+BAAvXjyP6dMnsYSCvNzyBMoePPiPucsvXDjHxqSM17yaEgDuZcBVEwDmTALZtWvbO1f9btjbZ8fw6AIAL1++gKlTJ+LQoVMsHvRD7a+/LjOrJbnFNY8YiSSD8VuVDGIoAFTRQOCU4iPJtasCgJpJIPSHxIgRg7B06Up8+unnatJ1pVNXAGhIDOCrVy9ZgsjWrVtRvrzvOwB4CVOmTMDp05eZ5bk4Nx4AFk/pacptx5kniIpLYwsp5WqLr5pVZIlldJsHgb1X0fRfKjLeFYnOuWJPFxt0+swFCfv3ICbuNRIlSaxLlK87GnzZHx42H67eYGwO8gCQB4AG65Q+AJAmK8oYQGsbc53XS1aU9wFA+hhRvNKCBUtQv77yXkeyxnTp0haDBg1TW2JUk9Ga//xzJYuXo1Icmi009Dn69eultorkdPOmp6czax1lV5ILcurUGXmWgckJAAcP9mfWvrFjJ6inI4BGFj6xWIS5c6chOTkV8+cvVj8nYGtnZ8/AGoG9qKgoBAauVD8fNKgfrK2ttQDgkiUrWHIHNZWbnEASubopDpEAoqqNG/c9S/wgAEgZ0AMG9MXWrbvVcX0EdMPDw9CuXQfmliVrIQHI6dMnIyLiNVav3qBVZkQ17odcwJplaqiMybBhA+Dh4YnZs5VZ19R0AYAZGRno3r092rYl2sZpyZBkT9a9bt16ssSLiRPHMSCa0xJMsXpnz55mQJWasQAgxZ5SLCDVZKSWlwuY4k8vXjzHXNJKYAqd6VQt1hhJIATy1qzZnCsJhKzBFId66tRJxMWlql3A9AfQ6dOXdN63ptqRB4CmKpkP06Upt1sh0bh05436hUplHPE6OgVpGcpbQT7UPEvYoFPD8sxtnBUbg6ignXgZG8asgNQyG9ZE8+bvz6DPb/yCPOcBIA8AC6I3Wu/oCwANnrCQBsgLAA4b9h2Cgg6zmDWy8FDg+i+/LISdnS02blyHAwf2siSQvCxEc+fOwN27t1kSyCeffM5AFlk9KHuX/t20aSdzx+YV5/f331dY3BrFfs2bF6ATADx58hgWLVqAuXMXMID25EkIo5lcze3atUdYWDD8/f0xZcpMVi6EElrI2kIFpCnrkix1s2b9rM5Epo//smWLWPkOVRYwxfvVr9+IWQnNzMSMRkr+oOeUNPLLLzNYvB+Bzv37g3Dp0nmWLUw18Wj9BMbIekhuZnpv4sQxqFWrLnP3aoIysib2798HFA+ocpVrqoEqCYSsqCQbVRkYqgOYs1A1lW2h2MqpU2eps2R1AYA0n4onBPS++cafxd9R+ReSYUxMNFasWMtiGHv16sz4poqNVNEaExODr77qwEA3JYOQrInfFI+o2Wxt7ViJmdx1ALWzgOkPC0qSoPqPbdp8qY4VzQsAEn8pq5b4S8W1SQ660mlMAJjTBUx6R1Zw0tclS5ajWbOG6nucSd8ePXqYK3O+kI4Ao07DA0CjsrPQBtOUW2p6FtYeeaS+0u1DRIhEQni6WLOi0aXdbFHSxVrL0yF58xpPd65HQlo8G0bg4oQGw6cV2rrYnAKAvwquUFnOvcm4CgCbNKnDYgDzcgETyKCA/iVLfmNWr8xMCSpV8mUWH1UttpySpjgsclWSBYgKA1MNNIqxoyLC5BZU1arr0YPcvLmtfFRP7vz5swxc5VUIOq86gPRh3bdvNxIS4lnpFAIuPXr0VrtJt2zZgc2bN7AyMUQLgQiiReWSpdIrBGrT09NYOQ7KfiUrjWYZmAULFrPs5rdv37ISKAQGycJG1jaKGSSrk5WVNbp2/Yq5cwls0fgEAglAz58/G7du3WSWRZrj++/HsPg1ApeU8au6CYT4/MMPo0BJJzmtqPqWgdmwYQ0rb7Nlyy4WE0g0aSZXqGSnSmLYs+eIWj6UnU0WN/o3MzMLrq6urJg3ldgh/qxZs4qVM6GM5Lza1Kk/MusWJYMQACTe52zffTcY9F9eAJDmVSVaUFyfm5sb2rfvhK+/7qcehnR3xoxfcpWBoUxwcssTD+n/60qnrgAwZxIIvUdrJdlS9ryqDiAlQdHHh57RH09UOsnffxDKlSunLgRNz/r3/5qVBuLLwJj2d+P/CdweuPwc4ZG5kxgpMcTd2Rpl3gE+An/51QpMffoETw9sZYX/XT+vi3JtuxSqoHkAyFsADVY4rgLA/6dDzWAlMIEBqBA0WUrXr9+iBSJMgDSjkcBlnVRZJFT15ChJiuJQCUjzhaCNpkIfZSAu62XOtcUmZmDvpWfIkMjg5mT1zsJng1IlbGAmVt7Qo0+TJiZAlpQM81KlICjkaw95AMgDQH10Nc++PAA0mIWFPgAXD2yyyFIs5ejR49lVcHSbhDLzmTuNi3LTlI7m+iiBp1q1T1hNSC40Lsvu/21t5HGgq9xEhQzYjL0PeADIA0CDdYoHgAazsNAH4OqBTbX8pk2bhP3790EksuYBYKFrlmETqvQyMHAFKEN5+fI/tMoUGTZ60b7N1T2X03LL/9FVtHqmz+w8AOQBoD76wlsADeaWaQzAf4xMQw76UsFlufFAQl9tMJ3+XNZLrq+NTwIxnX1ULCnhLYDFT2xcP9S4fC8pV9fGA8Did46oKObPk+IpO94CyFsADdZcHgAazMJCH4A/sAud5UaZkMty4wGgUVSkSAbhsl5yfW28BbBItgx3JuUBYPGTJdcPNa5aybgsNx4AFr9zhLcAFl+ZqfYbDwCLtwyLnHoeABa5CPQmgMtAgl+b3upgMi/wsjMZUehFCC83vdhlMp15FzDvAjZYGXkAaDALC30A/sAudJYbZUIuy423ABpFRYpkEC7rJdfXxlsAi2TLcGdSHgAWP1ly/VDjXcDFTyd5AFg8ZcbLrXjLjQeAxVd+JkE5DwB1E0NcXCx++mkcwsJCsWTJClbk1tjthx9Gw8XFhV3HduzYYQQEzMe5c3/lmsYYAJCuMaM7bPv29dd5GWfPnsKaNauxfv1Wdj3Yx2jGWNvHoMsYY5rK2kivkpMTMWvWPGMsSz2GqazPqIt6Nxi/to/B1Y8/JtflxgPAj69DnJ6BB4C6iTcoaCfoDto9ew5/NPCjCQDzuhtYRakxDjV9ASDdAztgQF8EBq6Cn18VEH101625ubmagXS3raenJzp06IKePfuof//VVx0RExMNkSj7qiXVPb2NGjXRAiJ//30ZBw/uwb179yCVythdvY0bN4O//wBYW9toCSsjIwNdurRl4x44cILdQazZGjeujZkzf811p66qD90hXK6cl/q+4px00h3IdDd0v34D2H231CIjI9gdwGZm5uw+XFVzcSmBBg0aY9CgYbC1tX0vnVeuXEFSkkRd5Lp3766Ijn7L+tN903QHM61DxR/6Y4DuKm7WrB7EYrFWYWVVn549v8bQod/jxo1/MW7c92qZ0J3D7u4eqFOnHvr0+ZbdJ01NIpHgu+++Zr/r2NF495caQy91242F34tfW+Hz3Bgzcl1uPAA0hpb8H4/BA0DdhL9+/Z84d+40tm4N0u2FAvQyZQA4d+4MpKamYt68ALayvAAqAZibN69j2rSfMGTICHTv3ov1JcDUtetX+Prrfh/kytatG0H/TZs2DbVqNYRYbIbHjx9h6dKFkEgysHbtFi3AeeTIAezZsxtpaakMeLVu3c4gAJiTzrS0NGzatA5BQTsYwLS3t2cAsGfPzvjjjw2oXLmqer7Q0OeYPftnlCpVGnPn/qZFhyad48aNRYMGzfO85YTmOn78KHbu3Kf1PvGVACCN27Rp8/fykAAg3b975MgZRivxLDQ0FLt2bcO1a/+wmzkqVvRm7584cRSrVi1HUNAhLZ4WQG3Vr3D9Y8uHJhiiHUXzLtd1kgeARaNXnJmVqwCQrD9LlizBjh27cO/eXTg6OmLkyLFo2rQFkx193FesWIqrV/9GYmICvLwqYOjQkWpLj6aAly9fjD17doHukCTrzOLFK1C1ajX8+ecKXLx4HrGxMShduiwDIWTRUrVt2zYxV25UVCSzvvTt249Zx6iRpYc+wCdPHmPjdujQGSEhwWoXsApgTZ8+BytXLmM0ktt56tSZcHZ2Bn2MgoIOYPPm9SDrnKWlFRo3booxYyaoLWEESgg8PXz4AHZ2dujUqSv69x+kBmWaLuAFC35BcPBD/P77n7ksbUlJiejYsTUWL/4dNWvWfi8AVK17yZLf8OzZUzaWCgDm525+8+Y1+vTphilTpuObb3pr3QWcnJwMkoG//0AGsFRt0KB+TJ7p6Wm4e/c2VqxYYzAAzEkn8ZboWrduCypV8lVbAAkAVqlSTWu+CxfOYtasn3H69GVmrctJZ0ZGGh48uIfAwNUFBIAL1Pqb1wGUDQBPw97eQavLjBmTQTxes2Yz+z2BSpIp3b3ctm17o5xnXP/Y8gDQKGpSqINwXSd5AFio6sS9yfQFgOwibWkqFApZoTJDIBBBKLaBQNPv9gEKCAD6+flh8uQZzM23dGkAzp8/gyNHTrO3Zs6cioiIN5gzZz6cnV2wfftmbNmyATt27EOJEq65Rs5pAVy5MhD//nsNv/66kLnZaOxffpnJrFRkZdm3L4hZs377bSn7+fbtm5g4cSybr379Rjhy5CB+/30pli1bBS+visxKQxag5s1bsRhAAoDz589hP0+YMBmZmRJMmDAGbm5uWLBgMbKyUtCqVSv8+OMUtG/fCZGRkRgxYiC6du2Bb7/tz9x8X3/dHV9+2ZG5MCMiXmPkyKEYMmQ4A6GaLmBa9+HDB5hVy8nJOdfaaW1kATxx4oIaXH7IRb1gwVy8fPlCLwC4Y8dWEGA+evQ0A7cxMckfvAv48eOHGDZsAPbsOYKMjHS21i1bdqNcufJq+vV1Aed0iyckJGDt2tV49OgB1qzZxNyvKhdwXgCQwPy8ebNx5swVNQDUpFMiSUefPt2xdetulC2bTaeK4PwtgAUHgETHkCH9sX37XpQuXYZNOXnyDyycYdq0OUbZy1z/2Oqil0ZhZCEPwsutkBlupOn4MjB8GRiDVUkfAEjgLynyEiRpbwyetyADWFiXhL1HE51AIH38x48fj27d+jAgcevWDYwdOwIHD55kIKZ9+5YICAhE7dr11KR06tSGxZqpXJeaNGoCQOLDl1+2ZMCsZcsv1N3GjBnOrHSDBw+Hv38ftG7dVivJgsCBVJrFPrgTJoxmwJPAHjUas1u39qhdu64aAFJ/cjmXLVuO9SHQtXDhPJw9exlubg4ID4+AlVU2KCbrEwE/AqWXLl1gLsljx86pXXz37t2BubkF/PwqqwEggVeyrq1cuU7Luqa59nXr/mDjbdq0Q/3rvABgZmYmrl37G7NnT8P48T+hXbsOrD8BK4oBJAClaqr4NbKmfvrpZyzh5enTEPzxx3qdACCBTErMWbBgCRuS4vl8fHwxatR4gwCgJp1ZWVlwd/fE9OmzUb36p2zcvAAgWXOfPAnBrFlTmfxVMqX+mnTSgT1u3Ah4eXlr0akrACS91fwDSMXDXbsOokSJEiwGUOkCzm0BTE9PR+vWTbBwYSDq1WvApiRwe/kyyXVnQbZjrnd4IGEUNhb6ILzcCp3lRpmQB4A8ADRYkfQBgHJZBmLC9hs8pyEDlCjfFUKRZb5DEABctGgR6tZtwgDggwf3MXz4AOzefQjJyUkYOPAbltDh5uauHoueU1zX6NE/5BpfEwAS8Ojcua1WIgDNASjQqlUbBgBatGjIflZ9sFXPq1atzmKx/P17o0WLL5hbU9XGjBkBd3d3LQvgxYvX1M8JwI0cOQQHDhyDr68Xfv99FQ4dOsBczHK5AjKZlAHIgIBlIIsaWSGDgg7mySsCZWSZJCvmqFHjWIze+9qiRQsQFvac0a1qeSWBEGAiFy1ZHFXgj/rrEgNIADA4+BHWrt2ULwCkmL/Ondth2rTZaNKkGSOJ6Pn99yVaySAFsQBqxioSmKbYOUp2obkaNmycZxKIVCplbvPu3XsyF7vK/ZuTTjqwr1w5i19//TXPpJX8LYC6xgDmBoApKSlo1645Fi9erv6jhxKbtm7dwP4oMkbjgYQxuFj4Y/ByK3yeG2NGHgDyANBgPdIHABY3C+DixYtRp07jXACQYvYI7OUFACmuS9OKpGKwJgBMTU1B27bNWUZsjRq18pRBu3YtGLAiF2xejeLKKGnhu+8Gqx+PHj0MHh6eagBI1qMLF66qn9+9ewejRg3BsWNncefONUyfPgOzZ89jFh0CHXPmTGexggQAyaVMH3haY16NQBnF1tG7d+7cwubNO3PFjaneIwAYHh6KZctWawHAgACyRmaXqSE3+61b17F+/TatGDhdMo53797BsqzJBezu7vhBF/DevbuYS5/iHrObglk/f/55ljoZpCAAMK9YRQKn5NIODFypBoB//rlRnQRy8eI55iLfsGG72r1KdOVFJx3alL2sSadqDfkDwIK7gK9fv4YffhilpfN79uzE5s0bcOgQDwDzO0h5kJQfh0zzOdflxscAmqbeFRuq9AGAtKiijAEUmWmX1/gQk+nj/z4AaGFhgc6d2zD3Yf36ZKkDyIpDJUUokaNLl9zWsJwxgBRA/803/ujVq6+aDIrD8/DwYD8PHuwPsvaNHTtB/fzt2yhQuRAqW0Jgr2TJUpg0aRp7Tm7ELl3aMUCmigEkFzDFJKoSH44ePcSAz5kzl7B8eQAePnyMlSvXqt/v168XA5AEAK9cuYjp0yezjFBVzT4CAZQZ2qhRU2aV69SpG1sDxQ46OjqrM3xz8lVXFzC5GYkGilscMWK0ehhdACDxhkqi0HvDhw/WAoBkvfr++8EYP34iPv30c2Y9/eSTz9G7dzbvaTKSEVlDVcknxgKACxf+yly8BPreFwP4888/MTf3qlXr1FbfnHTSx8jZ2RYBAYtZzKaKzsIAgPQHD7n/6Y8WVSMX8KVL57F58y6jnFdc/9jyMYBGUZNCHYTrOskDwEJVJ+5Npi8ALC4cyAsADhv2HYKCDjOQNmnSeMTHx+OXXxbCzs4WGzeuw4EDexngoozhnC0nANy4cS3279+D+fMXwde3Mit/8vPPExmo/OyzGiy7lyxnc+cuYJmzBCBozmHDRrKsS3LRUvIFWZUoSYWSUHbu3IomTZpruYDbtPkSY8f+yIAblYnx9q6EqVNn4MCBXVi/fgMrykz19zZuXMPc3OQGJksUAVoCVFRDj0qyEDihGMV+/b5jAFcTlFF2KNX4GzZsFCsOnbNRditZuI4fP59vEsj161dZsgpl5KqKZesCAGnO3bu3s8zo0aNHo3XrjszC9/DhfQQGLoKVlRUrwE1rJCuoZmykil5K1qBEB3pGySCkA8Q7ct1qNgcHRzZezjqAOemkTFlK3pk69Ud8//1YlkX9PgAYHx+Hvn17sExvKq6tstZq0qn6GF2+fBWDB2fTaUwASHUAKQaQ1kiNMsEJwFP4AIFTzSzqyZMnMD5QprkxGtc/tjwANIaWFO4YXNdJHgAWrj5xbjauAsAmTeqwGMC8XMAEAMmqROVKKDmEMmypxAfF/lHh37xaTgBIAGvNmlWsnhrFepUsWZpl31IMoKpt374F+/btRkJCPCsD061bT/To0Zs9pvcpC5hu1yCrKhXkJSBG1kmVBXDFikBWpHjFimXs5obPP6+FyZOnw8HBHpaWAowdOx43blxngJXi7iibeNKkcShfvgKzLlEJE7IiUmwdAQLKFla5nHv06IwuXbqpk1Qohm7x4gWs3EnODFUqA0MJ7C2N3gAAGaNJREFUMosWLdepDAzN+d9/dxkQpfXklQRCPKC4SHp+/Pg5Nc/++ecK9u7difv37zO+eHiURJs27UDFjpVu7mmIiorKZT1TDTBgwDf4/PMazI1PADCvrHHiIcUo5gUANZNARCIxSpUqxZKCqEwPNVUdwNWr1+cqA6NKjKFMcIqty0mn5sfou++y6dQFADZvXp+tP+d6iIeks2vXblYngagKYtMzJycnlnXev/9Arex2sjh36PAFxoz5AfRHhjEa1z+2PAA0hpYU7hhc10keABauPnFuNq4CQK5v/ML+GFGJGyoETRnGH7PxcvuY3M0emyzUZG3dvfsgXwhaB5bzeqkDk0ywC9flxgNAE1S64kQSDwCLk7SUtBbFoaZyEy9dupJdBfexWlGs7WOtJee4prI2SpYhl3+fPt+oC5Mbgwemsj5jrMVUZcevTT8OcF0neQConz7wvXNwgAeAxU8liupQO3v2NNauXcWyfCl27GO0olrbx1iLqYIIik2lUkgzZ/5i1GXzsjMqOwttMF5uhcZqo07El4Hhy8AYrFA8ADSYhYU+AH9gFzrLjTIhl+VWVJZpowhGh0G4LDt+bToogAl24QEgDwANVkseABrMwkIfgD+wC53lRpmQy3LjAaBRVKRIBuGyXnJ9bbwLuEi2DHcm5QFg8ZMl1w+1wk5wKSwN4LLceABYWFpk/Hm4rJdcXxsPAI2/H/6vRuQBYPETN9cPNR4AFj+d5AFg8ZQZL7fiLTceABZf+ZkE5TwANAkx6EUEDwD1YpfJdOay3HggYTJqpjchXNZLrq+NB4B6qzs3X/D29m4qFAr3APhPoVAIBAJBeEhISP/8VssDwPw4ZHrPuX6o8RZA09M5XSji9VIXLpleH15upicTXSjik0D4JBC1nrwDgGNCQkK66aI8qj48ANSHW6bRlz+wTUMO+lLBZbnxFkB9tcF0+nNZL7m+Nt4CaDr7qEgp4QGgNvu5vvF5K1mRbrcCTc5lneQBYIFUwiRe4rJecn1tPAA0iS1U9ES8A4CrATwGUEIul//29OnTw/lRxlsA8+OQ6T3n+qHGg1vT0zldKOL1UhcumV4fXm6mJxNdKOJdwMXYBezt7V1VKBRuBeAbEhJirSnwqlWr2mZlZS0H0AqAJYCrAoFgdHBwcKivr+9AhUIxSCAQEHajWD+FXC4PEIvF/8hksiYhISG7fH19SyoUiqtSqbT68+fPEz+kTDwA1GWrmVYf/sA2LXnoSg2X5cZbAHXVAtPrx2W95PraeAug6e2nfCny8fHpBWChQCC4rFAouuYEgL6+vtsUCkUZAH0EAkGSQqFYBKBhSEhINQCKfCcgVOnre1IgEEx5/PjxTR4A6sKx4tOH64cabwEsPrqoSSmvl7zcTI0DXNdJHgCamsbpQI+vr29foVB4TiaTtQawShMA+vn5ucjl8ki5XN7q6dOnF2m4ChUqOIjF4miBQNA2ODj4XF5T+Pj49AZQOSQkZMa7/rczMzNrhIWFJfAAUAehFKMuXD/UeABYjJRRg1ReL3m5mRoHuK6TPAA0NY3Tgx4fHx//nADQx8eH3L4nzf7X3rnH11VVeXytfe4NoU1ALeAYK5b2nn0SQztCx7diUR7j44MPHJ4ygIqAglhRQBAQAQWR4htFBxAQcRRRZFRE3jCiyIhATPY6tzSUNgMfXkLakNybs9d8Vj2XuYY0KcntzTnp2v+1uWfvtb5r33N+d++11ykWt+3p6VlX685a2wMAVxDRl8YbYsmSJXOHh4d/CADbA4BBxBXOuZ9MZo5sAT/xxCDwJq0rTtZbdv4uX/x589pBfctOTDbFEo3bplDK5mc0dtmMy2RWadwmI5TNv/9d3G6D2bSuOVbl2vnxBGAYhrLt+wMiaqlHaK2V7eI/xXG8vDlodRQloASUgBJQAkpACWSTwKwTgOlW7mXjCMA7mPluFYDZnIhqlRJQAkpACSgBJdA8ArNOAEZR9DZmvgERX+ScG6yhtNb2IeIlzrlzm4dXR1ICSkAJKAEloASUQPYIzDoBuHjx4hePjIw8kh74uFmQW2u3A4BHAGAZEd2RvTCoRUpACSgBJaAElIASaB6BXArAnXba6aXFYrEAAPsBwNkAEAqyoaGhJ9esWfOstfaS9P/2r1Qq61taWr6WnvB9bfPQ6khKQAkoASWgBJSAEsgmgVwKQGvtKgDYcRykhxPRZfPnz996zpw5Ivo+AAAiFG8KguDo3t7e/81mGNQqJaAElIASUAJKQAk0j0AuBWDz8OhISkAJKAEloASUgBKYfQRUAM6+mKpHSkAJKAEloASUgBKYkIAKQJ0gSkAJKAEloASUgBLYwgioAJxCwK21bwGA3455r7CwlOLThxHR5VPoNjOXWGs7AeArAPA6ABC//gwAJxPR3ZkxcoqGlEqlbmPMOalvkh96LSIeW18yaIpdN/2y1Jcr5NXVY96HHVhrvwwA7wOAFwHAvd7748vlssQxF20C3+TVjjsWi8XLmHm3YrG4XU9Pz5O5cKrOyI35VyqVtgmC4FxmfjcAtAPAA8x8chzHt+XFx435Jof3CoXC+Yi4p/iGiL3e+9PjOL4u777V22+tle/d1emz4LK8+2at7QeADgAYTX2RZwKPuedk2s2J7idp7eDTAGAnAOhn5jPjOL4y0w41yDgVgA0CWSqVdjXG/BoAuono8QZ1OyPdWGtXAsCNQ0NDx7W2tnpjzBkA8JFKpTK/v79/eEaMasCg8nA1xvQBwDWtra0njIyMbMvMV0mJICKSE+W5adba/QHgPESUN9y8r/5mbK2Vk/HvN8a8Z3h4eHVLS8sJAPAxRAzzIHQn8i2t8yni73pEPKxYLG6fNwE4kX9hGP4EEeVhu1+xWHxsdHT0NGY+xhizqK+v74msT9BJ5qWU4BqoVCofXbBgwbq1a9d+EhHPDoLA9vb2PpRn32q2R1HUzsz3AcA8ADhGDiVm3S+xb5K4yaHL0/K6sDHJ921PRPyxfN/a29tvfeaZZ/ZGRFkgkJJxuX6Ob8q8UwG4KZQm/4yx1v4RAL5DRN+f/OPZ/URnZ+c87/1j3vvdy+XyrWJpFEURM/+VmXeO47g3u9ZPbJm19h0AcN26devaBwYGhuTTpVLpDcaY2733LyuXy4/lxbcoig42xtyUJMleY96HjdZa8eN4IvpB6o/Mz7WIeIpz7uKs+ziBbzIX90HEPu/9K2QVPo8CcCL/rLXfBAB5leWG1fb0HeWD3vt/LZfLsuuQ6TaBbzIHDwmC4Le1agypWHoaAN5FRPLjOdNtorjVDLfWfivdGXonAHw+LwJwkjkpAvD0vPgydhJN4tvvAOAPRHRKpiffZjJOBWADwFprjwCAjxPRqxvQ3Yx3Ya29lZkfbm1tPbZSqciyv6xC7J3652fcwCkaYK2Vm/Iv6wVgV1dXmCSJy8sDdqzrY9+HvWjRolIQBBQEwZLe3t4H6h5M/wUAa4joyCnia/pl473ru86ft+dVANb5cOgY8f48xl1dXTsnSfIX7/3icrn816YHYYoDThQ76VIK9g8PD5+EiPsi4i55WJmeLG6dnZ1v9N5fhYjd6Spg7kTTeHFLy67J3HullF9DRElLOC5vKUHj+CY/StYz86nyIwQRdwWAGABOIiIRhrO+qQCcZoiXLl1aHBwclEmznIiumWZ3mbi8u7v7n6rV6vUAsHNq0EPM/K48r/6JHwsXLty2UCgQM19tjDmxUqnMKRaL5wHAQcx8SBzHP8pEAF6AEWNvaumK5h2I+Arn3EDdQ0vyUtuISPKTctG2dAGYrv7dIg8lIjooF0FLjZwkdpKGIcX772fmA/N2XxnPt2XLlhXWrl37Z0SUrdJrUtE0WwTg1cxcrlarXyoUCh4Rv4CIh1erVbtq1apH8zIvx/mxvEMQBPKGMAcAB6xbty5ua2s7EQA+Mzo62vnggw+uzotvU7VTBeBUyaXXhWF4GCJ+johK0+wqE5fLjWxgYEC2n/57q622+tyzzz6bGGM+BQBHVCqV7v7+/r9lwtApGmGtfQ0AnC+7awCwWmLHzD9HxP2ccz+dYrczdtk4AvD1xpg7xwrAMAyvQMS5KgBnLFTPG3gikZS+7ehaAHi6Uqnsk7fc28lWAOXHWBAExyDipxDxX5xzss2Yizaeb1EUncrMuxDR+8WJ2SQAxwmKHDJbI7lyzjl54UIu2gQCUNJlLkidkBSahwFgBRGtyIVj0zBSBeA04MmlYRj+xhhzv3PuM9PsKhOXl0qlvYwxvxoaGmqX1+rVjLLWPsXMy+M4vjQThjbICGvtQgAoe+/fWC6X72pQt03rZuxNreaPMeaf+/r67q8Zks7TVc65o5tm3DQH2lJXAMMw7EJEyYm7oaOj4+hbbrmldvpymkSbd/lkArDuvnIvAPyCiE5vnnXTG2msb2mO9G3pVvaGVfdZLgDFv7sQ8TbnnBwwy0UbZ06K2BtGxKPrc6Ottb9HxDtmyzN9ouCoAJzG1E23FB9j5j3yVKZhIpfDMJRciGtbW1u3ue+++9ann5Vcib8x8yfyLAC7u7tbRkdH92Pm39ROeEVRdDQzf8l7/9JyuTwyjekwI5du5Kb2KDOfGMexvBNbmvxifwQRlzvnpGxMLtqWKAAXLVr0iiAIfs/MF8ZxLKe5c9k28sPkVkTcwzknW24bmrW2l5l/GMfxWXlxdBzfpITIZwFgMC2bJa68GADk/nlTnlfdOzs7FzDzSYVC4dM9PT3rxLE07UmE7ilEdFFe45bOP/kBcicRfTz1Q0Sh+HYmEX07L75N1U4VgFMlBwCdnZ3LvPc3ViqVeXnfGq1hWLBgwYtaWlrkpnxNkiSfbWtrGx0eHpbVzeWI2FWfVzYNdDN2qTxwEPGWQqFwnOR5iBhExAudc2fOmFFTGDjdIpQ6hlK+RoSC5FTB0NDQk3PmzJFf5QcaY/ZpaWlZOzw8fAoiHrx+/frO+lXdKQzblEsm8W0uAGwFAFKL80o57JIkyVPFYvHp2gOqKUZOY5BJ/JM0hAEikoNluWsb823dunVPtbW1ST7j48Vi8UOtra1PDA4OflS22hDxNc45KZ2S6bYx32TujY6OblNvPDPLCpnUPLwiD+V7JpmTBAC/q1Qqy40xXCwWz5HSU3kpKzWRb3Pnzj2Qmb8u/lSr1dtbWlokB1D8zEXZpel+YVQAToNgWkDyciIqTqObzF2anjyUQsKSL7c1AMgpxJNrZWEyZ/ALMCjdXpNfrbsAgOQzfo+IpM5hrlq6xbTjOEYfLvW6rLVfBIDDEVHqksnWtpxSl+T7zLdxfNtQeFb8kVxUAHjjOE6cSkTic+bbBLGT04jyQ6Sa+is+13w/Kw/+TTQv5WBZS0vLCmaW0/gGAGQ+nkFEv8p80P5/W3dj37l/qPdnrX0wT2VgJoobIv6BmSUfTl4MsBUz3xkEwSf6+vpEGGa+TXKvvMxaK3moxzHzDnIwyRhzdF9f3z2Zd6wBBqoAbABE7UIJKAEloASUgBJQAnkioAIwT9FSW5WAElACSkAJKAEl0AACKgAbAFG7UAJKQAkoASWgBJRAngioAMxTtNRWJaAElIASUAJKQAk0gIAKwAZA1C6UgBJQAkpACSgBJZAnAioA8xQttVUJKAEloASUgBJQAg0goAKwARC1CyWgBJSAElACSkAJ5ImACsA8RUttVQJKQAkoASWgBJRAAwioAGwARO1CCSgBJaAElIASUAJ5IqACME/RUluVgBJQAkpACSgBJdAAAioAGwBRu1ACSmBiAmEYvhoRnyEieUXWjLTu7u6X9PT0PDkjgzdhUGvtdkT0eBOG0iGUgBKYBQRUAM6CIKoLSiDrBKy1HgAOI6J/eGdqs+y21h6KiKudczdHUXQLM+/GzCfEcfyVsTbMnz9/6zlz5qwGgHnMvCyO49umY6e19mcA8AtmPgwR37q5xi2VSvMR8YD169d/e2BgYGg6Nuu1SkAJzH4CKgBnf4zVQyUw4wRmUgBGUXQCMz9SE59hGN6MiG8BgMe89wvK5fJIPSBr7XIAOA8AkJl3n44AXLx48YtHRkZWFovFHSuVyi8397hRFHV478+M4/jDMx50NUAJKIFME1ABmOnwqHFKYHYQmCkBGIbhboh4NhGJ4NvQUgG4HgCWMfPJcRx/vfa37u7ulmq1KtvUsuq3/3QFYBiGRxlj3uKcO7hZ46aCNyGi82fH7FEvlIAS2BwEVABuDqrapxJQAv9AoF4AdnV1vdJ7/w1mfhsAPA0At3rvTyiXy2vkoq6urjBJkq8BwJsBoC3tiJl5eb1Y2xTE1trrAOAOIjqnXgAaY1Z5759GxA+0t7cvvOeee6qpODwSEc9HRBGHf5zuFrC19k5jzBf6+vquFwHYjHE7OzsXe+9vq1QqL+vv7x/eFE76GSWgBLY8AioAt7yYq8dKoOkEagKwWCxeVa1W/8LMDxhjPmeMKSZJcpboPu/9EtmODcPwfkS8y3t/hjGmAwCuY+ZT4zi+CABYjF+4cOG2iLjNypUrH96YMx0dHXPa2toGAeDdRPTrsQJQ+gSAlYi43Dl3IQAE1lpi5msLhcJXkyRZNR0BWCqVFhljRHy+HAB8TQBu7nHFT2utCNr9iOiapgdbB1QCSiAXBFQA5iJMaqQSyDeBmgBk5vWI+F3v/ctruXfpoYuHmfnIOI6vttb2I+J/OOfO6u7ufmm1Wv0jAJxCRJenq3THIuJJAPDZWl6ftVa2O2W7V7Z2j3TOuVKp9CpjzP3e+zeVy+W7xgpA59yHrLXfBYC9Ozo6SgMDAwcCwEVBECwEgJbJBGCpVHqrMeZ0IpKVzOc1a+3nmXnbOI4lp3DD1rOsAE513DAM90XEQwCgHQBu996vKJfLz2xkbDkN/A0iOiPfM0etVwJKYHMRUAG4uchqv0pACTxHoE4A7iDbrkT0+no81loReVcR0Yooij7MzN8DgFE5iIGIlzvnPiKraKmQkpIy+wBAvwhAa+0hiLi7CKtSqdRtjPk+Eb2hVCq9QVbgEPF1zrk/jScAoyjaiZn7AOBYADgOEW93zh0l29STCcC0P7mHbliVHNustWXv/X7lcvl/xgrAFzqutfYMZp5vjDlrZGTkqWKx+F5EPMJ7/47xRKC1dg0iXuKck1VObUpACSiB5xFQAaiTQgkogc1OoCYAEfFpZr54aGho/po1a56VgdOTsg8x875xHN9grb2Pmb/lvb9mZGRksPa5MYLxdABYlQrAHyHiN51zd8pnrLU3JUkiZV/YGLOamfeWfscTgOnnfyCilJmLiBg551aNFYBRFB3HzC8hotNLpdLrgyA4uVqtHlIoFD5ORF8slUq7GGOkpIz0cRci/tx7/z0i6p7OuKl9nYj4ZeeciN7nmqwIAsBr4zg+cRzxKVvfy4no+5s9uDqAElACuSSgAjCXYVOjlUC+CNQEYEdHx5UDAwP3MHN/oVA4JUmSIC25MpeI3pSKwScQ8SPGmBur1WoyOjr6+NjDDNba5wRgFEXXJ0nysXK5vDIVTFcCwAVEdLe11jHzijiOZat3Q6vfipV/R1EUMXMPAPyQiA6V/xtHALYz85/a29t3HhwcvBgALg2CoDw6OnppHMe7p9vBP69Wq52rVq16NIqiC5n5ofEOn8hK5aaOm/pzjJSsIaIfd3Z2zvPeLyIiWTFFa+3tRCSHZZ5rkh9ZKBSe8t4vLpfL4pc2JaAElMDzCKgA1EmhBJTAZidgrU0A4PB0xe7liPg1Zt4z3db9hTHm+L6+vidSwXM3AOxaZ5Rs/f6UiA6ubQPXC0BrrazgXVS/AoiIH3TODYRhKPmCbyai/TcmAGuikZmlfl5vnQB8sL4MTBiGX5UcPgD4d+fc0lQkXiI5gKkAPIqIDly6dGlxcHBwIEmSXesPqYwVnps6bhRFn/Ter47j+Gey3Q0AhxDRXun1txHRbvUBtNa+V7a0iejtmz2wOoASUAK5JaACMLehU8OVwOwjYK09AgCOT5Lk3StXriwvWbJk7sjIyGuZ+UZm3jWO43tT4VO/AvhvzPxOIjq8q6tr5yRJ/pOIXpXSkZO9UgpGtmqn9Rq6RYsWlYIgeAAAPkFEF40VgEEQHFpb3WtkZGR7OQiCT0stwTFCbw8AeA8RSf7ic03ePBIEwYm9vb1xI+3QvpSAEphdBFQAzq54qjdKINcE0tO8exhjPsjMsiK4HQDIoZADELHknJPcNsnzO612CCT99/nMvJcxZtB7/7GaUJS/lUql7YMgOFcOedSunyokWW0cGho6SvISmyUAxdYoir7DzI8ODQ2dI2Nba0X8nV2tVveRLeeaP2EYfkZyEIno9qn6qNcpASWwZRBQAbhlxFm9VAK5IGCtlZp5kq8neW0FAFgLANcj4gVyOGOqTixbtqwwMDCwZ309wKn2NVPXpQdRDkLEgvf+XmPMqbLNXbNHVgqTJBmoF4QzZauOqwSUQPYJqADMfozUQiWgBJSAElACSkAJNJSACsCG4tTOlIASUAJKQAkoASWQfQIqALMfI7VQCSgBJaAElIASUAINJaACsKE4tTMloASUgBJQAkpACWSfgArA7MdILVQCSkAJKAEloASUQEMJqABsKE7tTAkoASWgBJSAElAC2SegAjD7MVILlYASUAJKQAkoASXQUAIqABuKUztTAkpACSgBJaAElED2CagAzH6M1EIloASUgBJQAkpACTSUgArAhuLUzpSAElACSkAJKAElkH0CKgCzHyO1UAkoASWgBJSAElACDSWgArChOLUzJaAElIASUAJKQAlkn8D/AcYhZkZY6SQPAAAAAElFTkSuQmCC\">"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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\">"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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\">"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "This all seems to agree with the above arguments.  The change in the cold gas fractions between the *no feedback (recalibrated)* and *no supernova feedback (recalibrated)* (gold and purple) runs is much smaller than that between their original versions (red and green).  This is because much of the cold gas is not being used for star formation in the former case."
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python2",
   "display_name": "Python 2",
   "language": "python"
  },
  "language_info": {
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python",
   "name": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.2",
   "file_extension": ".py",
   "codemirror_mode": {
    "version": 2,
    "name": "ipython"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}