plotting_rays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 31,
=======
   "execution_count": 2,
>>>>>>> origin/master
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from foxsisim.module import Module\n",
    "from foxsisim.detector import Detector\n",
    "from foxsisim.source import Source\n",
    "from foxsisim.plotting import scatterHist"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 32,
=======
   "execution_count": 3,
>>>>>>> origin/master
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module: passing  10  rays\n",
      "0 ray bounce number 1\n",
<<<<<<< HEAD
      "[ 0.01258108  0.03260358  0.99938918]\n",
      "0 ray bounce number 2\n",
      "None\n",
      "0 ray killed by reflect\n",
      "1 ray bounce number 1\n",
      "[ 0.02748012  0.02441707  0.9993241 ]\n",
      "1 ray bounce number 2\n",
      "None\n",
      "1 ray killed by reflect\n",
      "2 ray bounce number 1\n",
      "[ 0.01753165  0.03446943  0.99925197]\n",
=======
      "[-0.00874618  0.01078123  0.99990363]\n",
      "0 ray bounce number 2\n",
      "[-0.01338297  0.01649691  0.99977435]\n",
      "1 ray bounce number 1\n",
      "[-0.01244049 -0.00439597  0.99991295]\n",
      "1 ray bounce number 2\n",
      "[-0.01905484 -0.00673321  0.99979577]\n",
      "2 ray bounce number 1\n",
      "[ 0.01501175  0.00255384  0.99988406]\n",
>>>>>>> origin/master
      "2 ray bounce number 2\n",
      "None\n",
      "2 ray killed by reflect\n",
      "3 ray bounce number 1\n",
<<<<<<< HEAD
      "[-0.03309958 -0.00323761  0.99944681]\n",
=======
      "[ 0.01458583  0.00390257  0.99988601]\n",
>>>>>>> origin/master
      "3 ray bounce number 2\n",
      "None\n",
      "3 ray killed by reflect\n",
      "4 ray bounce number 1\n",
<<<<<<< HEAD
      "[ 0.00842379  0.00749328  0.99993644]\n",
      "4 ray bounce number 2\n",
      "None\n",
      "4 ray killed by reflect\n",
      "5 ray bounce number 1\n",
      "[ 0.03305313 -0.00324818  0.99944832]\n",
      "5 ray bounce number 2\n",
      "None\n",
      "5 ray killed by reflect\n",
      "6 ray bounce number 1\n",
      "[ 0.03423857  0.0071114   0.99938839]\n",
      "6 ray bounce number 2\n",
      "None\n",
      "6 ray killed by reflect\n",
      "7 ray bounce number 1\n",
      "[-0.03109142  0.00572731  0.99950014]\n",
=======
      "[ 0.01497693  0.00229719  0.9998852 ]\n",
      "5 ray bounce number 1\n",
      "[-0.00165491  0.01230677  0.9999229 ]\n",
      "6 ray bounce number 1\n",
      "[ 0.01329926  0.00566793  0.9998955 ]\n",
      "7 ray bounce number 1\n",
      "[-0.01303842 -0.00465978  0.99990414]\n",
>>>>>>> origin/master
      "7 ray bounce number 2\n",
      "None\n",
      "7 ray killed by reflect\n",
      "8 ray bounce number 1\n",
<<<<<<< HEAD
      "[-0.00922275 -0.00797624  0.99992566]\n",
      "8 ray bounce number 2\n",
      "None\n",
      "8 ray killed by reflect\n",
      "9 ray bounce number 1\n",
      "[-0.00541445 -0.01003695  0.99993497]\n",
      "9 ray bounce number 2\n",
      "[-0.0112904 -0.0209294  0.9997172]\n"
=======
      "[-0.00774489 -0.01215719  0.9998961 ]\n",
      "9 ray bounce number 1\n",
      "[-0.01235114  0.00243306  0.99992076]\n",
      "9 ray bounce number 2\n",
      "[-0.01864914  0.00367371  0.99981934]\n"
>>>>>>> origin/master
     ]
    },
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e92048>"
=======
      "image/png": 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DgzyPi4gaAr1xqPoTCnAsZSJya3DoYK/GNV651KfjghVLmYjcWvPUGjyzeSF+aPtPt+Ne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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151cdf28>"
>>>>>>> origin/master
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "module = Module()\n",
    "detector = Detector()\n",
    "\n",
    "source_distance = -2e3\n",
    "offaxis_angle_arcmin = 0.0\n",
    "aux=source_distance * np.sin(np.deg2rad(offaxis_angle_arcmin/60.0))\n",
    "\n",
    "source = Source(type='point',\n",
    "                center=[ 0.0 , aux , source_distance ],\n",
    "                color=[0,1,1])\n",
    "rays = source.generateRays(module.targetFront, 10)\n",
    "module.passRays(rays, robust=True)\n",
    "detector.catchRays(rays)\n",
    "scatterHist(rays)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 3,
=======
   "execution_count": 4,
>>>>>>> origin/master
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ray number: 0\n",
      "ray 0 bounces: 2\n",
<<<<<<< HEAD
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([  3.10063797,   3.41160003,  32.79777658]), array([  3.10063797,   3.41160003,  32.79777658])]\n",
      "-----------------------\n",
      "ray number: 1\n",
      "ray 1 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([ -4.03108351,   1.43861497,  16.56677082]), array([ -3.77741149,   1.34808438,  42.58649559]), array([ -8.76188011e-13,   3.03845837e-12,   2.30000000e+02])]\n",
      "-----------------------\n",
      "ray number: 2\n",
      "ray 2 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([ -4.05295266,  -3.06432827,  33.6216149 ]), array([ -3.6754904 ,  -2.77893923,  45.84831221])]\n",
      "-----------------------\n",
      "ray number: 3\n",
      "ray 3 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([ -2.05715093,  -4.66046043,  32.93448833]), array([ -2.05715093,  -4.66046043,  32.93448833])]\n",
      "-----------------------\n",
      "ray number: 4\n",
      "ray 4 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([ -3.47439181,   1.97144063,  30.3504202 ]), array([ -3.14114302,   1.78234848,  43.1183799 ])]\n",
      "-----------------------\n",
      "ray number: 5\n",
      "ray 5 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([  2.97490858,   3.03596284,  22.25689427]), array([  2.97490858,   3.03596284,  22.25689427])]\n",
      "-----------------------\n",
      "ray number: 6\n",
      "ray 6 bounces: 3\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([-1.13403547, -4.90475037,  7.79487655]), array([ -1.02073341,  -4.41471428,  49.98360033]), array([ -1.02073341,  -4.41471428,  49.98360033])]\n",
      "-----------------------\n",
      "ray number: 7\n",
      "ray 7 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([  3.58421539,   1.77455214,  30.03637725]), array([  3.58421539,   1.77455214,  30.03637725])]\n",
      "-----------------------\n",
      "ray number: 8\n",
      "ray 8 bounces: 2\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([  3.01154753,  -3.19106537,  32.48274098]), array([  2.72710515,  -2.88966743,  44.94322014])]\n",
      "-----------------------\n",
      "ray number: 9\n",
      "ray 9 bounces: 3\n",
      "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]), array([-4.08741823,  0.36848742,  8.92809819]), array([ -3.6978708 ,   0.33336909,  49.06175882]), array([ -3.6978708 ,   0.33336909,  49.06175882])]\n",
=======
      "[array([    0.,    -0., -2000.]), array([  2.95530805,  -3.64294718,  24.49598144]), array([  2.80005893,  -3.4515748 ,  42.24477984]), array([   0.28676869,   -0.35349384,  230.        ])]\n",
      "-----------------------\n",
      "ray number: 1\n",
      "ray 1 bounces: 2\n",
      "[array([    0.,    -0., -2000.]), array([  4.20633852,   1.48635002,  24.15737296]), array([  3.97225973,   1.40363603,  42.97161379]), array([  4.07735081e-01,   1.44077097e-01,   2.30000000e+02])]\n",
      "-----------------------\n",
      "ray number: 2\n",
      "ray 2 bounces: 2\n",
      "[array([    0.,    -0., -2000.]), array([ -5.18142693,  -0.88147996,  13.54313058]), array([ -5.18142693,  -0.88147996,  13.54313058])]\n",
      "-----------------------\n",
      "ray number: 3\n",
      "ray 3 bounces: 2\n",
      "[array([    0.,    -0., -2000.]), array([-5.15219205, -1.37851445,  1.16052094]), array([ -4.73486352,  -1.26685452,  29.76917038])]\n",
      "-----------------------\n",
      "ray number: 4\n",
      "ray 4 bounces: 1\n",
      "[array([    0.,    -0., -2000.]), array([-5.23959669, -0.80365776,  6.37998457])]\n",
      "-----------------------\n",
      "ray number: 5\n",
      "ray 5 bounces: 1\n",
      "[array([    0.,    -0., -2000.]), array([  0.57479024,  -4.27443541,  10.20315081])]\n",
      "-----------------------\n",
      "ray number: 6\n",
      "ray 6 bounces: 1\n",
      "[array([    0.,    -0., -2000.]), array([ -4.61529171,  -1.96696398,  10.6733458 ])]\n",
      "-----------------------\n",
      "ray number: 7\n",
      "ray 7 bounces: 2\n",
      "[array([    0.,    -0., -2000.]), array([  4.43691592,   1.585701  ,  20.887921  ]), array([  4.43691592,   1.585701  ,  20.887921  ])]\n",
      "-----------------------\n",
      "ray number: 8\n",
      "ray 8 bounces: 1\n",
      "[array([    0.,    -0., -2000.]), array([ 2.70916662,  4.25259004,  6.4369676 ])]\n",
      "-----------------------\n",
      "ray number: 9\n",
      "ray 9 bounces: 2\n",
      "[array([    0.,    -0., -2000.]), array([  4.13522522,  -0.81460125,  29.10701776]), array([  4.09895619,  -0.80745658,  32.04327768]), array([  4.06565962e-01,  -8.00897466e-02,   2.30000000e+02])]\n",
>>>>>>> origin/master
      "-----------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for ray in rays:\n",
    "    print(\"ray number: %i\" % (ray.num))\n",
    "    print(\"ray %i bounces: %i\" % (ray.num, ray.bounces) )\n",
    "    print(ray.hist)\n",
    "    print(\"-----------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raypath = np.array([ray.hist for ray in rays])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  0.00000000e+00,  -0.00000000e+00,  -1.00000000e+15]),\n",
       " array([  3.10063797,   3.41160003,  32.79777658]),\n",
       " array([  3.10063797,   3.41160003,  32.79777658])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raypath[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            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",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var 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",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\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",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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wgjA00fyRzVJ/Ni9Njs7t7mQs3xyDN6VdjY03Okuvhi5GIe4soKFppUQoCsaBGBlRTyVIIjOkaQiHWxMC0ee+7PkVElElkgkUe1nIAhkSaGUNdgZ9gFRo+1oP16Ler1sGUMKRRJ+MfHEupAVDzszBOIQMAIYh5OFMgSGggCJHxLHObZ1v74L1vfjzF40lvPzspBzuuGU8EKy5HwQv0gP+qn3EYoLEQBJhf7coK/MOsVzNOKz/jpLGkLyEJ4bKSSOo3NEly8IoGxt2jJdtmVZGvnzMOPLFJoydjfLnwSbbEIr2NEQyEfACGA+LnbXEBgqAvCozMxgbNSc34X5CbFaTiZRULny9fw2GSMD9/kI/bLfFZZPy8275eWysX9pxZhf8SbJszaJRDq1Sb4arP3zkOyvLflHgSnbWoVEwo6GQAwCRgBjULIwhsAAEUBXL7tOFxe3JV2f0t271NFibm4h6e7VBrbzqPUT8E9xyqHBzScV1I9Ere31BjWfmj7TSnc3khSUeQpbh8gEVkCgPVsyUSF+lMujPDVSSJTsaAhkETACmEXErg2BISHAMX6bDr9EC1nPj8umYBwgxvmhu1e72YrUVTkkRUUh69/v7o7u/V3h+qmlY2pDX48Uovswu+ixykynZVf9RUBxr2LLUKduW+Ip5fIoMZQUpsPgnUL60o2djiMx7dcQmCQEjABOkjUtL2OJAIgayR8zQO8WxtJhw3gm6e4dxiuLRjFNIqFfetkZjENcTGYgD7Ph1LTLiISOQVTvYQLzpqdTSICSAZUloex3EAjk2RLpyruB8gg7huUS5+E1wqZt6QMUkEKElXIB+WHaMqbRSCGws22yEBhGazJZCFpuDIEGCHQv5CzEKmzI4PHD0i7aKMUmKMRFum9j4xSHYzcwZx9Txyrd0SS26VSgZ7rhTj9vdqW4EQ8SQJktLWmH5CE8908T1dJEQmU2023yYgvG/cyXYM8lYjDTHWnClrQbj9AiPMc17Jm2Je6F9gzPET+7VqEQRMTCOymzj9Nx8Mw2Q2D0ETACOPo2Mg0nEAF6/NhNi0YlO4EC2V5Y2JqQv1GAoEl39Cjo360DZ43KEyWnJA084nl4DhKBe2kiMTokIFEOCk7JRjumyafYk3bjEZCE5wJRNVIoi1eLHMxahwzqIN3IKAujUx4kj/ZrCGQRMAKYRcSuDYE+IpBH/ECs0NVLAoIJFOiOYpdUc3WSFqq2GOql3dGy7Ay8kpO0aYOtRIJ5V9IQkofwfNRJ4SRZKi4vYk+1JWJVI4WIUUbyQ9kcV4jZ+/ROkgQinHkKgaZto4XAZNXgo4WtaWMIdBDIG+cH4sfZvgiI7qSFBVnPb3n5jhYJYEeNSidoLOGVFPIjUcuXnSkTj0aQRDRsOMviDP9ZO6SQ+QCpn9QFi2lb5nUQx+ppFpNC6EtCz2N4T/Ij3j45R3hcaxlJn8tzIYb5pBAEcVLLAzGy4ygjYARwlK1juk0EAt3j/LCQM9bz0y9gLCws+okgdcb5FUMUNkzFobqfgPitr696crrsH2ojO0rd0d1aD+6O4qpEttxTSN0w9jD8Vi7iy64yGdaOg0JAsa9qz7jPFqp85IieQploIoQy9BQaKRyU3S0djGK1zRAwBPqCQF53b3acX5FHjWMDQwLWREkQlHRDlC8tzyuJkOiOjomfLzX2rpLN2BijEk6xySMRSvTVnqGXCfgiJ0oIca4yRyWX06OHYt8fe6p8YGqkcHpK1mjl1AjgaNnDtBlzBIQorSfr9HGtvjyPGsbPgfzJEhN5mZaGR4hB3vN270HH7Ofb2B29soLuaJCU+gQNhLZB9HYzOyBpbOQ135x1DAVIAHkM74mCEs9IoaAx/N9+2BO5olweJadKChlGvreMPwacfWzdx4KV/dZFwAhgXeQsniGQQQAkZ31dunZBnjDwO8+jRmKVid6nSxCIYuIGUhfrlWxHQegCnaZz00ZeMVBiTTLIIzAKz0miEVd3lTmdmA4z14p9PXtC97IZyCofIblOISaZyGQT3BVvMWcfGykUTOw3BgEjgDEoWRhDoASBcILH+ro0BOvrIFZ3JGPpEBUVuXzBQ7/tWyIyeAQC0J8t/vNt2rj1R5PplqqNvOLcjBT2r8xMt6Xicp9nT8QUm5LQ85g8wVOcdLY8Uth56E80DdyFt1CWptH7HEJAYsjrUIqdTzsCRgCnvQRY/hsh0D3BQyrytTVMoJCtaJwfn+cdtSLPexp/T7teoRfJKSahLCXeSUgKZx/HS64aEmmnG7mqEqYpvNq/HikUrNITTlTmpCGpGPU7Z0rMsySsd8qCf1rXKqRQUwDhk6vQpuG5kEIhhrjP4QQSxkihYjndZ0YAp9v+lvuaCORP8FjtePwgVsb5bcn8W6+ZYO1oSrzQ2NT5fBtIZFsbG6625E2THG3g1SBKSMAIwp3I8Ksn/mlCGhBXd5XJ8HYcJAKCv9oTaReTQmoms48lrNqSf/BCm4bn5aQQcqT7OB2HadpxEhEwAjiJVrU89Q2BLPFDQvw0WrhwM8gflk1pumkD30zS6upqMtZPSALI6cLmJJR041OeyqbboTyQPR0gAtpYqx03NjDrGLYiOSAxhGLhub9KTMpwclSZCF91Uz2qxqwePlG+erQRjyH4p3GUuoXED8+Y97Q9kTXpPsaZ2BPH0KbhOeSgjsGO+ywPqgPi2rhCoDmJmxHASbSq5al1BOAFI/mj8PX1bFcqKsu5TS9gugJnnPhj0/iSEgkku6RnZ+c2xyKiGyh2a0eX2NQsXFMEYC8QAymPkMZyIPdJGvJJBElAEYGAPNsGjYC+g5gFjE1sSlsmd3B3c+c1jgiLX8jQPSSC4TlkQDZ23MdENtybmVlIyKCUq1m/TuiKW1xchGDbxhSBKq3AmGbR1DYEmiGAcX4h+UPFiC94LC8f7Iyjwzi/xcVtPly41Ef9dJt2u8JjgK+JSIMAPWYSj+Ti4mHJEjV1NJNGpE7MMA4WmcaAdYxPIwEJn9t5PxBAQy47vDmYiAQSwR1lVsaFadqwDbqP0fivelut+B3HItuZLRW7fp6FRJDeOdizfZsqKUQ5ACFcT/7cohx897vfcc95zu/0M6MmewAImAdwACBbEuOJAEkfyRgIS3bJlHpdqVXwqNaw5umI1LD0zCh8uzf89B30SnNA9TrgmW39RUAbeCUVSspR7sIduvBa9BLb0aMk96bnVzEbTJ7j6oF2bdptW8q/9dZbzPs3GMP3NRUjgH2F14SPIwJZ4oc8dC+Zkt+VSrIojWWT3EsDkyZIxfLQcGc/3wbCh25q/HNnxV0sIeZJXCOUlSSkQuJCF9mQv6w8XMPLhBBsfOTYjv6QO02blKEqOVacNW4VUij2M9tVwbzfYZvblBqiV2HV7d9/wO/73Y4dO/igteP73/9e94lPfDypr0488V7uhS98qR+yojTl5ptvcq9+9cvdLbfc4o46are74ILXuyOOOKK19KdNkHUBT5vFLb+5CJCkzM5upLp70V25vHzId/niaxgYBzPboytVGs5Y4parTMWbquOSjyk6oqsXk1BY+WsjXlF4g+BIE8QZXeVMH13kW7Zs9xhjPNGCl47KXcmGJgcWWKULUmPaWbsIoAzJXtZ9HKY5ybZL/p2EmR3L8zibZt/LdU/IznO/8iuPd3/yJ29w//Vf/+kuueRi9/nP/3/uRz/6UecdrwvIN77x7+7yyy9zf/EXf+Xe8573+xULVt2HPnRpStyFF+51Z575BPfe937APeYxZ7g3v/mNqed2UQ0BpdbV4lloQ2CiEFhfB8Fb890ah/l8zSWVWfbTaLKeHz7DlK0Y86Boq6EolgNvmizrot+a5VdG4nTM07v7Xh1Z2QkylCqfv1P8IHtjA/9DMdYMR/4nRb7D3V92rnHurxJoIEv3OromwuynEgKKs9hSPbscAztI22l5qpQJC5xCIGtTPBS7cvbxrDvllFPc97//fXfTTTclcd/97ks6Mk444ec9Ifzr2mOMd+7c5c4770X+DyK+ouTc3e9+gvvxj3/UkQ9CeM01X3avfe3e5N4ZZ5zpLrroQv8nc80Pb2G56wS3kwgEjABGgGRBJhcB1hu+pzQhFCAu8KiBWHGrMs5PK1HGrncskyOetZWUjsWLTQ+2cYRu2bUGFxYW/T1MImD3bzEmmm/Vm95DJYAkF5ATnosNdUKDyFCZxenak7YQEG8hpVW3HWymu9mOSPKo7wXv9Peo6WGiyVOf+rRkf9e7/tJPBLnB3eMeJ7jrr78u2RcXtyRDTvgN9Kp6HXfcXR12bBhj+KEPfcD98R+/KrnGz+23/9Rt27a9Q/ZA+rZv3+Fuu+3WpDu4E9BOohEwAhgNlQWcJASy4/zEm+RHMCXED6TC04hZrOW36D1+9EoNH4HusYijoSMa+u4xiLrWIPQGxkoI4rFUEqCNkcohAeQRctNEs9dnteI1GZeQUn5HQdvqtgvtSEKvhBDkUGWOTj77j/Vo5XVpacmdcMKJ7jd/87daz/oPf/gD96IXPd894Qlnu/vd7/4d+fi8Zt5Wl3DmyZq2e0YAp83iU55f9N6S/BEKWZaE3Rwc57fFh6vfraAEhanUO1JOtks19vNt2ludX3lW0Yq6ZOMAv3B2b721BrNSy6+VBMSQQuQ9L/9Y0gTkXmSozPK07WkzBBTnGNshrbT95M+axpXn4XUz/eJiDzq9OK36Fyqd33379rm73OX41pP71reu9+TvD93Tnvbb7uyzn5ySj8keBw8e6HgZ0fV78OBBt2vX4alwdhGPgBHAeKws5BgjkEf80CW5srKceK6Yteafb0tXlJRb/ShyQLpArrD8jGwzyRc8oKc2pGXS29KnOw3o1t3dK8S5W7f+6UHNNE1NS0grCQSPjIFrEn94mxAv3P2VMmhGsmMfEFCcs7ZDYrQbj7xHRUDkYcfQdqGnkOHaOEKHadry84tZwBiz1+Z26623uhe84Fx3/vkvcQ9/+OldojEbGB7BT3/6cofxfziedNIDOl3CXRHsRk8EjAD2hMgCjDsCHOfHthykILueHzxqIISyoKo2QlXzzjSk0aoaOwzPild0xZMqYxFDSTgXr0n2bux1Gg/Bb9WTP8w6lq2JbpTRHDOVxDMhFln9Sabh/cuSCuIuEowUEsm4oxK5uPBloVSW2k8IPWLBTkreRU5oS5Z5xNVdZUqM8ftVLIapez+Wgbn00vclHr13v/sv3Lve9Y7kz9dDHnJasuTLaac93J166sP8JJEXu9e//gL3N3/zHnf44Ue4V7ziNcOEYezTNgI49ia0DBQhwK5ekjI0Ht3j1OYTjxomKaytYexYmgAUyS6+37yCli5VfMVDtkF0qTKtXsdhdPf20qn+c/3GqRALEggeITk8l5SypHD8SUV9BAcdU7HG7HESQAzVwHtHW/EI7cJzf4XLgBDiXGXimW0xCOzfv8+vA7gzJmh0mGc96xyHvWzbs+cYv/TL28qC2LMKCBgBrACWBR0PBLLED1rnE5fFzji/thuBOh63vC5p6C5L0+CsyZa0fLUEkECDPLMrGnjNz8d/XYQyaikwgEhi/zR5jyWF004q1Cs3AEPlJkESp/ZTnUgAeYSA8HxcSGH99zcXsso3FVtElS7gdglgZZUsQmMEjAA2htAEjAoCIBkkf9QpS6pIXOBVyyN92nBQQt1jfIWNNLNd0nNzi/4elqJJV7xVtcnLYxUZopusM0hsipeciZEcj0uMtHphgGlvPdojhfW0tFhxCOSVcb2n7w/LrxJAlAGWg/B8XEhhHD7NQhGftBR8DaQfXwJJp2JX/UbACGC/ETb5A0Egb5xfeoKC/+bE/GIyjk4bh1A1bSjCu1XP82XnS0GDlN8lLQuhCgHMr4DzJbZ7V5acWU7GRkIy8gZv5Cgti9NujntLE/tqWalCKuwzab3xjQ2huMfGkPIroevZL+vVhw5V3vd4TUc/5IED+BSceQBH31LlGhoBLMfHno4wAvDubWwsJ4RkZmYx0RSVMokL/93HTFDQLsr2CFdZA5Htko5d1qWuOao0mFmvqXgh0djBa1p3TURtdOvmYRTjKQHQ/CnW9CqF6xLynuRGSAXi6q4yRzHHk6WTYl1mv7TNBAHYFPUPrtR2OFeZErLdX9WzXbnVpC0vL/s/g1LnVotpoUcJASOAo2QN0yUaAXT1oqFdWVndJCXoMsXMVPVYVZs8IRWrVOjRalQOCHIlnsn4z7eVEcnKCpREQDrZrmh4TdGohTN+S0T0fNRvfHsqMIAASgBYpkgAua4kCUXCHrxGvBblspNMcFdlShj77R8CirWSLSX1fG/D9LP2wzPE1V1lhvGqnLOsVInTZljFok2pJmu4CBgBHC7+lnpFBMJxfuvrUimhcl5ePpRM9IA4eKmku3dYxRt6pSvsInIF72TzxqEMxLiKW8jzUkKqIS1cDxHPZEvnafNmzUM3RjUFjVG0tHdISQUJRPaoWTNSqFgM44zvqNgBduLMY2iTZzfeE23ljw/KvO6UKSFG9Rf5sG1SERhWCzmpeFq++oQAiR/Ed3fXYiydLAnBmalVK1cN37zCg35S4UPWzKZnUslV/CfmmpMkxSrfMHW/MJIvze5WQUDLHOwsWzcppPcwTSgQ2kjhJmhDOsTZL7RbeM46Qgkh6gqVOaRMRSSL7t+FhYWIkBZk1BEwAjjqFjL9vDdKQCCZyfOmxYzzGxyUQtxArtbWljrktOo4PyWSbWiOxkc3YJieJFP8hZF2GyXRo928ab7G/UyxJimc2/TKkjzwiJyG55Jz9VDJc9hZZUoY++0fAoo17QeiJ2Ve7RXaLTwfD1JoS8D0r/wMWrIRwEEjbulFI8AlXULiB08fxqNppSriFhZk5my08IKAWbkFwUpvU8bKChdzLiZXpYI6D9FIaIPSuR11ko4H3bpnHi8kXebaeEUJtkADQkDs0m3HYkJBxThRAXF175+d0zpSi/4eh5EmchSfruKtcVhHFNuQpJGkkChu+LrPp85Kkbf7flTdMQN4+/YdfU/REug/AkYA+4+xpVARgSzxQ3Qhfvhur3T1cpyfkqyKiXQF1wqu61HkDVTqMl5OK+9mnknoxAo/UomCYGg0gB2+K4yJKNhkkowuhl0QtXNbG63OreiTwTdY0aoNLGCbGIisdJkV+6Ds8QsZzBruaZlEWQgJIc7b1I2pTuZRcWySP8VbbajvF+3FY5iS2HZwNuzObz8+Axfm0M4Hh4ARwMFhbSn1QAB/akn+GDRv1iwneKASXdn8rCsqT61UGTv+qH+ouyu8GClZcoU48EqCAI7CBv2WlzmZYybRrWgx7MHrq43g4NOenBSl/OMTaSD4KMes3kkkeESew3N/hcvASzj6pDBRGEpPzKb1l74P3aQez5j34djw9ttvt0WgJ6TUsYaYkOxYNsYVgfrj/MIKsUnupdKVhjBeDhrblRV4JoVcpRthrcjjJeaFZIWf96z4HhoP6sVGo55Hsq18iK4YGwlCWhXr4pzakyIEWB7D50oqSCBYvngtoeNJIeOHqdh5GwjAfuF7MjMz76+JN+3FI1IMz/tD7A8cOOB27tzVRvZMxpARMAI4ZANMe/L0+NEDJ6QlPc6vbNYs4kl9iIqvDaICOb036JmeRKFfGgm7WXtLKg6RrfyLQ3Y/yfNILi5u8x7Wugs5d6dR5w7XasyPi+5uwV+IS34ou9sMgSwpJOZKHkISEZ7zXcN7Fu7N9LHYsQhI/abvhtZ3TW2oMnvrgi5gGwPYG6dxCGEEcBysNIE6zsxseDKCsUeauSxpiZs1CwHNx8nFVoCoaLsnUcz7SRRbGnVBKwrNzvK6zIEPunuHRf4EMxl3KN2TyCOIqNxL51g8qdklTmLtk5bV6wrkJiiAvYJP6HPFVrGoQigUFiPwisVgz5raUP53wf7hrnlQ+c7t27fPuoAVmrE+MwI41uYbR+XhOTvgFZ9xW7duTzKQ7UbFs4UFTE6Yr0CqxHPUBiJo/MIKjzJjCaqS2nZ00saYmnQfESbvKx4g0ZgoEyOjW6rcaZKf7DqDsO3i4lavK7qB8Sktephs4kIR/tXvNy93Wv5jSSG1zCfweKoyGXZ8j4PNSz17qo6xNuS72G0X+eMmk4UwC7hfXcCQfc45z3R7977Z3fnOd04pctVVV7pXv/plbs+eY5L797jHz7uXvOQVqTB2UQ0BI4DV8LLQDRDAOD/5pwkh4i3IIy0Yp6aVV3mCCKcyy8P2foqKsruyzfOqYYJH8SQKqXCb66UVd5nu7Faldy3sMgfJGsYGYpftIoce4omcSwggrrP2m5lZCEghGyTahNeIybIEjHSPLTeJAPuphIBiq+VSCDzKGMsZntFOtJskk/Xq4q7KlDDVf1WX6nGrxWjyJ6paSv0LrXgrbpqv0G5p22HprfPPP9/dcsst7phjjnHbtu10X/nKl90JJ5zYmjfw61//mnvjG1/nbrjhu7kAXHvt193Tn/677ilPeWruc7tZHQEjgNUxsxgVEQjH+WkF5NzS0kEvSSoa+fTYom8Q6o1R00qsonJBcHi6QtIGmc0IaroSDZKKOlXPW37wrOc0rss8X1b5XWksQmzKwgsh1bUaZSY0voiyHNXgSxnRBgppiX3zGijeE41ER8RFORIZYZmTUPbbFgLAVogdJMJDFE5SACkM7ROeiwb9IYUi237jEND3Q985fd/EM48/bhj7d8MNNyQ7JH/+859LEjjuuOPdr//6b7izznpSXIIFoT7+8Q97kvlH7jWveWVuiOuuu9a3GUvuk5/8B09Cj3Xnnfcid/TRe3LD2s04BIwAxuFkoWogEBI/Rkc3qm4yNg3LuqCCqbdppVUvfhgLsqSRWluLn4gSSsC5VqjZJ3WvoZNuVYhpLxKpUpufZbt7Q0K6urq5Xs9mMlUxkvBqa2mgIEzspUfe03LWTTLSeG6qZIeWEFDb6juthCK0F22Vtke3vfLeqXScllQfcTFa/vutKGyo79hMspzVJZe82/3gBz9wf/Znb3ZbthyWjAW8/vrr3Pe+9133v//3ZxsTQO3Ozbftrl2Hu1/6pce6k08+xX3kIx90F1zwMq/LO/oNxUTLNwI40eYdTuZAOkj+qIGQA10uBfdlPb/47l7KCo9KcPIrjTBs7PnyMsbMSZdWSGJi47cXTir80PPW7V2LnYDSHj7Z/HUT0u4vn6idsrHrXSvJ0EZRGyzkFTu7JXmdTQtjEHHPFkLOItP2dZbAQ35TUti2jqMrLymkQ1cPk8iOO+44d/vt+93znvdMd5/73Dex4Y9//CN35JFH9l2/l770lZ00zjrrV93FF/+5O3jwgO+OlrHknYd2Eo1Avf62aPEWcJoQQCOPcX7Y2eCjkseyKMvLB4M16aTRrjbJowjJbpJUFLLsPvQk6ZMjSMwWP2HhsNreSSUkZSnHPQOBXl4+1JnQAWIK3RYWtvbwOLaDj2jZ3RCBkMK2GO+HDd29W7Zs89gVEftuGSK7+S9Ihuyz/jjndyzCjf+48ERpl7CmBF3gKVz1tl/xO45Yn5CTUzSkncUgIGUtJiTCpG2FCV9l9oKtQOjFXiG5H7y9quUTeZ2kLfwUHGx45zsf69/5rX3N4vLysvvrv353VxoYOmRbfQQMvfrYWcwAgfyFnFc3iYE0+lyEGERG//0HQhqd1iMW0IOTKJi8TKJosqxLuw2EjENc2lSv27tGvQd5RKMLYs8u/eF6SotzjgYKHr5wA9mTjfdZdnDkOTxUCMX4chR5Ett+20cg3160S3hk2rgn3f1mL2LS9pHviciVZWB2tp1IqbzFxUV3+eUfdz/3c3d3D33oae6yyz7m7n3v+3ji2c434EsTn+CHRgAn2LiDyBq7eunxQ5okVOJJk5mf8KZxHTpU8lJZN9ewSYOcXdaF2uBfZRO5ikXCICi20rHbIyneNXSb19Otvi5hekKYVzoeP2QKtu2NWboRqQRG64GhC/CAp1A6QfQPCe5zR8LheUgK1aMY4oMYtrWLgOCbLj/w1optkBbtifMieyGM7PXtBdnTtOXnF5NBdu7sJwFUW+/d+1p32mm/6E499WF+zN/r/SzhN/iu37f6Luej3Mte9qppMkZf8qpI90X85Au98cbb89+SCc96HvHLmwTA7/aGcGCMHRZTRvdlUxc+JhdgiQJ4F7E0S8wm3isdj4gGASQGxLUNvSAHa+8hb8hj1S1LTKEf5NSZKANis7SEdRedX3dxR1VVOuHvuGN/cg5dhCyBkMaOP5Q/BcAE3lWsA7i6uurxpucGrxA/pdf/bycreUA3cf4oGOZRCQV0LHrVlVzQ2xiSDHocpYuzA2nfTpgeur9DPfqVILrNpUtWut77lU4oN5tmNXtBUtpmMTjJH1rkVWY7h/r081zzWlxe+5G+ppu268Mffqr7wheuHkjZ6ke+Qpl79uyaag5kHsCwNNh5TwTg3SL5Y2BUvtk138q8QpAhW1GDyue9jyqrd9h8PRcT8ogGIPvd3N4Sy0NU9XLm6YcUMJ6tDvmTuB2wy5UteUpPLoJAx1Ht7i3JQuVHSggUP+RdNpLB4kkm6ZmslZO3CNEIiH3i7EW7QXh4jnKNe/TqZmXi2TC3RLlhKtCVtuLd9chujBECRgDHyFjDUlUavg1PlEBG8G9QNMH97Dp5HOdXXkGIgE572ihjm8oUemeEtMR9vq1NvZCpuIpbcOweLwlPQ+zaeY0gLIicZ982Zm4XJDfyt7VMs8zNbXpDSSZ4RFbCc8maeB4RV3aVJ8/tt10EFF/aS+oCSYX24RF3Seg3Q2yorcRmCDuNm+I3jbmf5DwbAZxk67aUt5mZde/hw8SNuWTmKYgBuinR9YpzbPBQheP84pJur0ItIpPZ7lTRs9e6g8300oanNwr5+sl4SZDrzV7S3oIiQsBWsbplx3FSPAh+rAzGSR+bYZuWNfwrwSLdQMo7gXyGO3TltegtZTZNMpphK3Kn47deOVJ81Wasw9Q+tBOPWUS5YoDIUJnZcON+3Y0xhm3MccbfuGfP9E/WSDAYDIFCBPCuk1yhohTih/FzMn6rTndgmxWmykpXVui2lG5pGVuGf/Dln2/zIejaLESj2gNtWLrjZfVD2uw2zwndfavSHTRUaXyKokOvlZVwfORsghvGbcbKKJI9LfelHIUEg7OOsRwNSQXtwWtBR0khuyPbL5fTYofYfOp7H9qMdgmPoUSp/3An3d0/2aTwwIEDbvt2W3cvLAnjfG4ewHG2Xp91Bx/CLhUcjrIWnSQLQgVPWv2B5mUEqW7WIDPbLT1K3ZbV9JPGhAS8LiYx8XrpJeUgRlJ+GG1ku5/j2SDy2J3yYO+EE0607IcEA+fYcJxOgpFkfwR+siQeKunkIXkvxU7JEzzFSWfrDylkup1kBn4iS8DUn0w2cIUtwVIEjACWwmMPSQxCJOLG+YUx0udlZCAdMuaKJInr+YXd0piFW/37wto4x6SfFya/os52q8p6g9X1y0ux7F4v8tbdDV2GGxq6/PyV6WDP0gjoO6BYarkDxtg5Jo3XKiNLMFSehsm7p0/trDoCsJWUf0zMwiY2o314TJ7gKU46W4zNOoFTJ2k5qUcDudAyiiVgduzo5xIwA8mQJbKJgBFAKwqFCGDiRDjODwHxlYfQk1EYufSBVCja4JUGLn0IcoNNui7RTQmvpXRbVp85qxVdIqjmD3ViA1DUrRqvX1sNAORoHqvphXht6VET2DGMVqWMK2GjjeInmWjXMeOOIVhjqLLYLI15LClM2wwyRu2ThN3vO74CsmOHeQDHsKjmqmwEMBcWuwkEQBBQmYFQ4Vy2dGW3ebPmobuCqSIIuvETZNSN6w5qYxovMUvc4mNmQwpG1C/UsUp3dJ08ZDWRa+iDgesgx7Br/W5yyshPJ/5ue3mLT3McQzYjGBh7CNvr3l/ckY5tzWwG/NRecj46mN5+++3mARwdczTWxAhgYwgnV8Ds7IKf8TWbzPBdWjroM9qMsBGppo2QEBjxTlImjuKdbNIISVyQnLY2kr8qiya3lXaenGrdvXkSmt2D7co2PG9aPsrkT8Kz3gSDf9aQW+CtmAv8KOeTMsmkyfsOfOps1dPM2kzfA9qHR+gTnof64Q850h6epxAewP5+BSTMr533GwEjgP1GeKzlz3gCKEVEPEfIDCqn6hVgHgxaCeY9zb/XTWDmghnJ7egVNpj5WhTf5ddQGKJ+dzQleG16kCYNWX6G7vxw9ja9peWxsk9hf9tGDYGQYKi3Hu8uyQSP0BznNskESFTb2iv7+idH6yx9z2krHqmlEnsl8kII+0kKVVfnMAZw+3brAqZFxv1oBHDcLTgw/VHRaDdis2S10ouV0z1eTZdN4WfKUIGGlVWsbIZrEhdpy7IzXPJDpC4uHtZAp+o4MS88Qi82LCR/VbqhKaf5UfMCPYCV6IX7+qx5OtMtgbYGClKeFVt9RmJBQsFrxY6eJrFNL48T4g96G0aa/c2j1j+hzWAjknV4bUNbhef+SdfC1SwD1fXWspKOi2VgzAOYxmScr4wAjrP1hqJ784pXK7reslARZYlVN4FBhdlbVixcRZVfXnyE7f7KyEKyFE1e+Cr3dExilVgalt5SYoNJJ1gLsc4kHtjMZ7XxBiK/vHyoRM7wu7lKlBvrR/rekWDUnWSC+L1I4VhDNULK01Ygc+HMY6iIFzJvV/XbJIWQimVgjjvuZzQBOxtrBIwAjrX5Bqc8CUAVctRLuzJCkU+s5pPFkrUhkxTa657WyraX7nhOTxY9a+FXRrDkC4lXjKzyMNWYF7BDd6/ooJJBnOuQP5VQ7wz6ZHXBEjjoLtcGjLLplfJPkmwL2YjzRFGGHWMRkHcpXe7lHc8SC0jkPZGu9mH55DE2dQtXBwGt/9RuYjNIo434HvFaU2pCCm0ZGMVxEs6MAE6CFfuYB1Ty4onSyqa95PIbDHquOJYpJFb5aUO35t3TsR43IVjLgZev+aLYxfkiEcoPEd4l0QL54wbSB/IFLLWR4NP+H7O2RIqYrLO+DnvxKy3II7vOWc5YNtINWJPGq/+5nYwUqpNC5htlDMQDNpyUSSbM27CPfC/y9egmhfHeXUjsfq/4/qXTtWVg8vEf17t4S20zBHoioOSIFUPPKIUBtLJCxaPy0HisrNyRdBHiHOEWFrYme9yaeSqrMPHSB6zs8uUI8VtxmBGNr41gw6LYIDTZb+S2iVepypsPQbTQtUryB7ygV9tev9BeZXoh3MrKUseWQgrwZwJdh2XVzpx/jv+l3NHtpWTClxh/DZKBcVEgkCC32Nf8PhyS65WY+I12Qzdkt32y2YeNiu0TW4ayUkfnmvXEIDTKr4tiUs7abGZmwUfDe5V9pyAt+15x3CH+qK1veuvRBdyfWcAglr/1W7/ufvSjH0GZ1HbzzTe5c899jvvN33yK+4M/OMfddtttqed2UR+Bspq4vlSLOYEISKUX8LWGedRKVIjV8iaxEq8QiMviIohV70/NoaJrc8vLY5pgbSRL40A/GVOXl36beBU3AlmiBSxAmmXySZuvd14eu1GHPvA4Zkny4uLW7sAld6TxErKYJh29Gy94E40UloDbwqPQPiT3QtRDgsGEsuRiXEl78XvInI76Uew260m8EPlyUii5ufHGH7onPvFM96QnPdG/14fc5z73GXfllV9sjYh9/etfc+ec80x3ww3fzYXvwgv3ujPPfIJ773s/4B7zmDPcm9/8xtxwdrM6Am22ENVTtxhjgwAqDtnaqQQpDmRheflgMtED8jE2TD1XTHMz6R6H9jwLmsciryQI1uxszOujsnqo3/VYMe965AkOxvnBG3mg440MSXMYg1jLv/zwSbvnGNMHDy52pAUvpJLkGKzK9anWeAF39RQKKRQvYb9xKM/FJD+ViSHw8ArBiPc41SXtZe/IJCPdZt7y3yt9XxcXt7g73elO7uabb3bXX3+9+9CHLnUveMG57nGPe7R78pOf4P7P/7mikTof//iH3fnn/5E76qiju+Ssrq66a675snv0ox+bPDvjjDN9el/wdR49lF1R7EYFBPB3zTZDIBqBtkgWvWzsskSjAW9aXFdvVt1qRDEbm9dhYyKerJUOMUUYEKxsVy/jZo+QxTxmn9W5hj7ULzuuDpjNzwM7rbTrpFE3TjdWxWMi28QE+goman8tnyCA4Y7QHBiPc2wghAiD+DarFYi0vWXtA/mCeWgbnGPjvc2r5LbYJrSR2ljC2W8/EOA7NeuOPHK3e897/sYTwJvcc5/7HPeoRz3Wfec733bf+Ma17oc//IEnhd9wD3nIqbWVeMlLXrEZl+VARd1++0/dtm3bfb0rM6BxxDqEt912qyeMuzWgndVCwAhgLdimJxLaR/EgsUJolndU3vJ1DH3ZQV5iunqLUu6HhwteSTY08EouLAxnBm2YZ2LH8YdoXIldGK77XGzXhHwVYSxk9I4OViDIMu6wnfLSnZfyOyTJQhgkLO0oBAMkUMteSAoFH+itu8orT9eeAoE4m7dDCgVx2HY6bBSHbXvlMHxHROru3Uf7MXo/ds9+9nOTP5vAXmYF929haEwWy9uG9Wc3T5dxvmcEcJytN0DdtZLNfyF7qYLKAt299PgxPAjM/Dy6ippsUjlKA15fDrp7uUnDMpuQGX4Nhc+qHCGn2Ya88du9mHgi8qp4I5ulnx9busaX/eBwGbPZzIObn0Zbd7XsAkt81xo6A0d6THFOO4Xn/m5yG/Ekrj+ZEsKBnA5uy5JCfW9oDx6hU2KUTeUwnpD24XGSbBTmdXD2KEoJdiH5gs36vSj0EUcc4Q4ePJBMQkG66Po9ePCg27Xr8CIV7X4FBFgDVohiQacZAa2Y41FIT6DAOD8MQBaXvjbO8fKKQ9arLJEneCXle8ciHaQU4/zqkz80Rm1skid6TTmurp6XrR4+kgvJD/6Ry9jDgx3yB11kTKTYtDzX1KEtfMpTK3+Kbt+YwfDQGX8O8mcd13knyvWyp6gXZOd4QvgquGebLdqneOZxm4i2W2e1qdnkyZqfn3f3u9/93ac/fXmSORxPOukBnS7hycvxYHOUfZMGm7qlNjYI1Kn04CVaXg6XdcE4P5mhOjtLAkBCUB+KOroxtewkFN6fnV1o5Okp6jKl/F5HkAoso6JbuCROtde2CT6avpyh+5le3CYTdrJyY7sPu+O1dwc46QQGkg0cszOPB0s42sthvyQ1f4djNBP7oN4Iy3+efSAtbaO6k0yo1/SSfNbT8Ib3286a1t69r3VXXPHPCfznnfdi98lPXuae9rSnuMsu+5j7wz98Ic1ix4YI4O2xzRCIRiCmEkCYuM+3sYstOvnSgDG6UQBmrILIwDuJjV2YIF3SFdy0stPKjGnGHJGH7KflEA/EmQOhY+S0GUZ04qw7GXMVN/ZQtWiThKrU/p6pzmpLKWMoG+EOPXgtOklbiXgaV57Yb5sIiI3SGNezkdhKbd6mluMoq7v+Q1fs9u3b+5aZSy/9aEf2i1/8ss75nj3H+KVf3ta5tpP2EDAC2B6WEymJf/q0YuyuGJjxPPKCLlSQBY3P0DwWy2OI3sd0A1AWHjqmySkmUmB2r6w3CD2Z5zI5sc+qyMqSUnT38nNpxfjFalI9nNhzbdMTKXYCUZY1BuMxz0tZPaR5T/t9D7rXK3dih3Te4wkHupARlzv+eKRl9TvnkyU/H7tmNgJCkAsvo8gfDRvl5xXaDmqzz8ANCunBpWMEcHBYj3lKUgEVEZr8pUkWk/F+eRlvs1LVNrS4Uc8np4OYsVqsE3HJI6VcEgdf91CCwRj1jiInLm6WjMJeiE+iHCdlOkJlCYfiDNuHs45xnS4P2QkM7b0XwycMo2T9chvRLrQNjvR4e4slk0w0N7Bve3ZSufln1Cn/6SDv7t9/wO3Y0b8Zv4PMi6UlCBgBtJJQEYF0hYTuUvGowbshHg14/GSiR1kjREKZlldRmc3glJUfG+QUOrK7F7rB64dj90ZZzfSKaSDQkJA4kxhkl1GBnCLS3a170Z0yO6TjQCcZ57e8+UDW9AMh5PIz6RjVr5Qg5cUF7vH65kkY9j21PWyHvMi7IZMYSAhZvnDkOcgGtEcc3VUentkW4lUXDcVUy5qWS9qEqwKofSS97pnHKq+uRqMaT/HZt2+fJ4A7R1VR06sGAkYAa4A2jVHCCo4VZZooVF0omShmK1fer3NMy4Ke2e5erOeHyQthfuqkFBuHWGXDZz1sQkqHt5gz9OMyPdQ5JKMbGySE2ZzYdSwCUub0T4fgTLLBI6SF5yEpHLVuydicj0c4rRNIeuY8IactcCQhRH54X/I2ecQd+Utv+/eDAJoHMI3KeF8ZARxv+/Vde6nYmAwqRlnPb21tebNyxLIudRZKlko2LZ/pVDtqxS3xUGkLmQFpkYosJDNl0mO6k8vi93oG3eqQUmmIeknPf94rT1XW9GuiR75203tXyi3JhuAg+JJc8IhnOM92SyIud3+mhkYE21pAgDaSiWEQCLxB4mkbHvEsPPdXuOzYR+xU30aIP9yt34s+Dzd305m6EcDptHvNXCc1micwsjwJZ87md6XGJiEyY0OXhUPjyS5VVtjVPWtS0UrlXZZar2fpCjvUTRoK34xEfTUjLadXqvnP82VAJ3r9GE8mxDRbAoeyuo/Qoz17d8sf/zskHMyJEEJcATfs9ELxGs9k0/GEuDacN2Fp/ZC1UYJ2UmHQJjwmT/yP2kLqFbwHsd5cjQtpw9zMAzhM9PuTthHA/uA6UVLRCMFrFW4ySaF+V2r9f8KhFjwXggPSh0kT2CC/6lIllCbHZhWvOmNASvOXnIkhzionrV2dK2l8JGY3Ue7lxc0nkXX0sDjxCOh7Qvyz3ZJlZANfjkE83VVevA6xIfspO1aHYYWTvNNGnvJ1Xjbah0doiPPR9eaq7lKPElPxAO7ipR0nAAEjgBNgxH5mYWNjzX8hQ0gV05E16dopOmFlQ/lVjoi/vo6GTrcmXqz2GjFpDPDporW1g5vKyYSKemMQ0Wg033KLVfMAAEAASURBVIBX2AWN/DYjys11MgnVEMiSDcSW9whlhJNMKDMkHginZFCIYbqRZ6zxOCrhGjV9tR5RHbWuo01ivbmjkbsDBw64o4++82goY1q0gkA7rXgrqpiQ0URAVt2HtwoVGLtWm+qqFWR9SVkvFiRt2bLNe/9E5/qS2aDWlwDdZBPiFtfdm5eeNCAdh0JekMh7sJ187k50qkKU2/BEQgbzAV3YILZRFiIhmNhgJIWCL8seqneSjexRociSwir2oA1V2qSfKaGrmlPFlTLivbmYSd7ETlV1zQuPWcA/+7N3z3tk98YUASOAY2q4QamNSmtxcZuf6DGbfNatLQJI70OdBgQ6rKxgWRddeoZympM/Vs71EM5290JK/Hdy66XZO5YQPtqu+rjIMAWRFd6per6yckcyVpPxpGGjXB751I71EJBv6fI9gwx5R4BvuCdPNu/hnCQd74HuSl6SIEP+mZwyIrim65y0neglBOS0m8AvZF9tBHv1007oAt6505aBGXLhbzV5I4CtwjmZwkiqtHJpswKOl4WKsWjpmaWlAwn4CKN6VrdHXU9Xnm5IHdjFjPXrrWk8TpSVp1P9sZvpRoppxB6hC3Zs6h1l7DBv8Ayi0WPDJt7cJjZlKtN+FAzTdhSbAP9wB1K8FtTEdJy4IDKmyyZhGRVM+vVLO4ltSADzvLnQIM9OsA9thToobfN4vdPxDhzYb+sAxoM3FiGNAI6FmUZLSTbkTbSqWilxtirTlk/MLSYEq4ke3XFZ6cVX+N26LSSkD56upltVnJheVie5P5PMPGaYOsdNDlcpKr2ijARCPDe36McioluLDRi7LRmK96UBzHZ/IVRdbJiCNJ56NY1ngiHLvEekY2DizyPQIRkRpMQmihriNreJyrMzRaCanWAzfZ+qvzuI373Zp+C6MRn3O0YAx92CA9S/X5V7WcNB8kCvETxq/ExaOutoxPIrrnS4uKtOO1gSXLqilzoerVA36D2Mrbt7fDYhfVi6p1/2K8on7Jr12CIsJp2Ih48kDl/MAF6wHzwX2HGet/vbm1u2YRt0/qjHJB0VwyJSSDsh19n3bfjj1CbJFmV56W0n2IZ1EN8jlVjn3ZEvgdhC0Iri+J8ZARx/Gw48B+olaJp0MWlDGuFsVRAGmbSQv/SMTjBAZaeNV30Ns42bSsojNpxJy4q5dq+LJpM664W56LTqMZM1GhGZkzx6xU0l1POiGJcwanaCDibBwCvZTRrCWDjnOCa1oejPRoxHhA3P/VWiGuK10f0F+bYBAZZpea+ALbAm9jiSaCRP8BQnyaY2oV1CeQxlxzYQSNsJEuMnmaTtlK8NZgG3PQbws5/9jHvXu96R9AY89rG/7J7xjGemEr/qqivdq1/9MrdnzzHJ/Xvc4+fdS17yilQYu6iPgBHA+thNTUxUDiA0WsG0k3XI04ZEGnxckzywIYmbQYv4GGcmutbVsFces12r0hW9pQQbbQzr6SS4lMUlXuJF8/Qn82UWwbhMQnvPkBZIqJA92EI9ttCzWBexX54mYpM0DiIH2IY7YuO6uPurl30hwbbeCNAmYgcSQDQnoT1wjo33JFzW+4QQ42OXdDmE7v3f6qdJO4U6lr87tBlibCTrqn7xi190u3cf7VcQWHLbt7fnAbzllp+4t73tLe6d73xvIvf883/fXX31le7kk0/pqHvttV93T3/677qnPOWpnXt20h4CRgDbw3IKJElFVNyI14OApA0EAV4/HLHJbFV8u1e/n9o7hbAC6x26KEQ2j3ldq/ld0ZRIrHhd71jmSYSOoZcUlT09kWFqKqM+Nr0aaOiCWdmYnS0Nvnogu+NCj/qNGvKWbdjUXpANosG84shzf6anyX3E69bPP7KtMgLlNqEdwqMmkSWFcTZpVoY09VE9SxXW1pTM2gmC5f2hbUjonfvCF/7ZXXDBBUnaWAniWc/6LXfiifdy97znvd197nNf99/+28/W1uvqq7/kHvCAk92uXYcnMs4440z3T//0jykCeN111ybE85Of/Ad3zDHHuvPOe5Ffi3BP7TQtYhoBI4BpPOwqCoF2KiZURGiQhVytJOPFJPnqCyZTVpT6pYHSjQoqxuw4NpIsqUhLhfmH7WAVyoFO9PrxPrt783VK56mXxlWfZ8dpgrCDHMP7N6hN84285nVTsnELNcK97nFrCKHywvB2XgUBxVDLX5pohDYJz1EnICXE013lVdGirbCJQm0JGzk5gq3YSbAHCZxJyNhTn/o/3Ne+9jV37bX/7r75zeuT/WMf+3CSh717/9SdeurDauXnpptuTJE5eBlvvPHGlCyQw1/6pccmenzkIx/0ZPRl7s/+7B2pMHZRHwEjgPWxm7qYbVfA0hg47zXS2bJx3b150LPyalZRh96y6t29eXo1vcd8iZw8sgVCin/nvTap2HuFin/eTY5B3KHLXC6BQvlpW4cybcNGDeFY3qSLOCwnOA+vEVaJBxrCtst+md7j+UzKaS/dszZB+GqkEOWcHqq0zXqlbc+rIjDjx/ztcs95znMTG51++mnu3e9+n/vGN/492UHg7nrX46sK7YTX97Fzy9cd6XL00pe+svPwrLN+1V188Z+7gwcPuG3btnfu20l9BIwA1sduamJqo00y0rzilfFgrMjZ3RtHZPKAV+KW97TKPc0jiWk4ji1WUkgYUNGF17EywnCQge5e7LJV95KG8uqes9KmB5LjDusT97qaVI9HGwi5QxkGmcCO83D3l51rnIOk4BdlA7uQbcrDE9vqIyA4ynsHKSxjagPaBk+1zsC5hKVdJpWoKzZAYHCbpovxf1u2bHV3v/s9kv3xjz+rsRroyv3qV7/SkfOTn9zsPYIy2QM3l5eX3d/93d+6pz3tGZ0wOMG4a9vaQUBqsnZkmZQJR0BJVtIa1sotKmwQq+Vl/b4wXmj5WkaT4sjKqpluq6vZ7wpvGYEveaBRlPGRAB1kC5+8w7EaCamPjRAfaZxXVpYS+4H8gRzDdtLlSxvUKhqbkZroWD1d4Ic8zMzAa4kZ5gteCBoYjDtFeQzzBN1AQDCbGV3H2DGxhUTE3x6ZLdS7n0q1by+xSdouYhPYJVtH0CYYNwx7rIy4Xfppi7Zkd9t0//59rU4AgaYPfOCD3b/8y5fdrbfe6v/YrrpPfeoT7qEPPbWTicXFRXf55R93X/ziF5J7l132MXfve9/H131YRsq2NhAwKt0GilMjQxoV8YZUyzSIX3YsHRpeIRFVJnmUp1tHN0jMerRwr/l3hYFXd2UK2TEbsAFm3Op4IhG3Gklkaulj6JWhTuXjDtPxx+lK8FICFeZd7EkvFGyr9pWyh3jhPk45H11dtQyHS5uQoNMOtAWvJT9puwiBVHlV86zlomrM6uGZn+ox247Rj8/A7d692z33uee65z//97xTYMU97GGnJ/veva91p532i8nYwgsueL174xvf4Lt+3+qOPPIo97KXvartrE21PCOAU23+upmvVjFlyRWXKZExdjprtK42iKcVejXdQLLS3xUWUiq6NKvs4TGVxgc6xcsSspxe0w/5g6dN8ykaDuIXGGnXs/fB+DF+seMO8/RD/tSbnBditO4p5rRhSEJgW+7QOzxP54NEUuWln9tVVQTESxjGEoxpAx4RgudC3rNjPBGi3C6IP72bfAWkvSVgiOTppz/KYQ+3F7/4ZZ3Le/h1/97xjnd3ru2kXQSMALaL50RLCytIacTZIOZnO49ccTHn/BjN77KR7SUJ4bIeSXq0lpYO+uhtVPjAp5qcbrI8t7ksTjvjm2LsRuzyMMKzhYWtPRpLSig+YrA3ylOsvYolDeeJvAvp8i95gb3DPdQPXcf+qU0wCUFp9TxrFy1ftEm+99bskmcGLd/2FZA8fMb/nhHA8bdh33MgXiwm05vU5BEHkquQRNIrppU006hz1MqqV+xukjXvSU0/vissmgC/Xh4vYAAvG7tXgRO8bOj2XV4GIR3slocR1vmDXmkbVtEr3kZVpI5KWMFF8yjlGsRDF6YWXUlGNq9wmeo2boKxyJyOX8W6KL9aVhk23nsr9R7iMW5RKv2+P+j0kwKZypR9BzgFx8RcGAGcGFMOJiOoULVh666Y8pdOySdXSoq6K5yquYmRxa7MvK9UhOlBllT+0Ks7j2HYuPPi/AHLsgWUoXM7G/JRrAfT6Cai8iUPENGlJfFgMawdyxGQd0XDYIKJvjuwBXeECc9R/mCvcPdXWsgRoWDrbeOCiFNzW3BMv9exdpGJJhx7KDLi7FIH3tGxpRHAOvYb/ThGAEffRiOpISrMsD3Krk8XN2FBKlAhW02zWSwLupKYMpV8jySfQlY7n5Ury1sWs/Kxdc0aAyW1zGP3ERhhhq+QkfSXPKSBBDHpjmd34hHIkg/iKpgD3OyusrOksC3i4Vf4cJdcstW/I86de66uyakpj9pZ+4Wwt13CP2LhOd4J1Be6t2WXUUL9wIH9bseO9scAjlIep1EXI4DTaPUGeUblFpIANGBh1yUqQo7zi68I26zQ07LyujLju3vTsurCpo28SMB1evxh2QLKxcS2nj7Ik8hkfBmrubQ51rD5JA/KzR7DPwzZZ9N6re+I2kTKC+wU7kCI14KWvIeIR4+U3I/5Xfcc5sorF/w6a1vcRz6yxQ8zQLfzhnvqU5f8WmxIZ7q3rF3UE48mEwQwtEV4DkII7GAX3VUeno3uFtZVoc7wAO7adeToKm6a1ULACGAt2KYvEiq1sAGH9wqVRegxqroYcFjBNEU0Kwu6ra4uJZ4/yMZzjKmLWUQUYaUSb6xVlwB6IlnRVsWsS2D0DTRGScvUiQEd0kQUEzywGDfWw0P4vC0tIy9E73ttyOidyriGEOzT+Et5AW7hjhziOjvGUO4jTmhHlOmvfW3OffjDW9xHP7rF/fCH6eWXduzA+4y4dba0vnUkjGocvqvQT/BU3MrtAtvIJvUJMFKyHtqG4Ub1CAJ4l7scP6rqmV41ETACWBO4aY3GSgvEgf+K47p78xCTRiOsYPNCVbkn3aorCfljvPLuXobqPrapl3jZlpPxfkipOmbamHRrGn8HDRG4XdYzCmIsk04mtyGPR2n0Qsp7p7bRsolygT3dLSn3sDCyc//3/857L99h7kMf2ur+4z/yq/z733/FfeADP/WL/Y5e3kdZo952oX2QC5wrWc92HSME61ec66Z213uDPbMxgIPFe1Cp5dcGg0rd0hkrBNDogGBhE/LX7HNkhU6mWqiwkhTPH0RgTJ18oUIWf40XS1nxMfJCMn8ytk4nUNATmV/ZpyXFhEnHKL+CDeG1DWcbY1kXYFW2taNHGlclMWUp27M8BNQexJSzW0EwNtyNN866j31sq/f2HeauuWaxI2LnznW3fz883NLli+N//+9L7u1v3+cXPu8Es5OaCHTbBXUliB82kkGSdV7L0yREajwh4+nzYZ3ZMjDDQr6/6RoB7C++EyEdFZh0XeqizfBgNV+YWBovrSDrwYX44ULFqIRJsupIJHGTCruOBInDeh+zfLFxAWxgV2dDPrWBqSPBefKHQf7SsNT1jNZLWWOJl/Zg50+E93t0dKJuGnpSzkjU+peffftm3Sc+sehJ31b3hS8s+j9rkubu3evuXvda8d2/8/6zW1jEG+UIfwRm3FOectC96U0/9feEFIotqixDMzokpX/INpOs7yzLAMk6CSCPSCc8Z7r43CDiyq7y+LwfR+oqsjEJZOfOXf1IyGQOEQEjgEMEf5ySxng6bKh8QETQZdheRVSvEYEeHFMXYrm4uK2hblL5kcCFsmPOSUhJ/FBxwxMZM/4wRn7VMPDWsrseDUz5bOPe0usSUcTDRu+jpJRt8DD7GoR50A2eaDNuv3d4Pv9P/wTSt8V95jOLfqkeKbvbt6+7X/7lJXfSSStJ1+/nPy/uvfvcZ8Vdd918Qv5+93cPuAsuuN2XB+Q6bQcxldoA9mjvfW8H5VHTp0quRHexFePJ+0E78Iin4bm/CsigvCf+V/+1UlzNI9Lq3qQL2GYBdyMz3neMAI63/QaiPSqXuTlMDhDyBzLIxryJAk0qrewYNpAa3MPWRG6T/ACT7Jp+kAfi14z8oaHIr5jL9IU+WYI8uEknac2ghxJiWWJmbQ1kD91hbOCYR16LDCEjHDwvjeawbJzO1fCvvvKVeffMZ4pnZmFhwz32sXe4s88+5E4+ecW99a07/bdTdySewOOPX/PPlt0734nlXmbc+ecf9Psh/640W5tw0Ai0Ue/U1zlN2OrLyY8pZVrT2NjgjBz2GPC94FHlZElh2++HjQFUrCfpzAjgJFmzj3kBcYCnQL03bKzbSRQVe0ylhXDhsjOIw+7eO+7Y344yHSnxeZRuzfRSKujqBV4x+eokmXOCP/dCgnIeFtzKEmTxFIj3r74+aJziMYFqIHgYc0hyjnskoevrGFIgjZsQQRB4pIF7bOSYHsdN+Ud4mvGC1M+TyBvX31NOWXWPf/yS+8VfXHZnnrnsu+lW3fvet8U9+tG73S23zLqtWzfc8553wK/htuFe9Srx4FxwwX73rGfpen+CXUg8iHlog/A8Dy2Q+bh3OC/2aN8jHoPTMiS6MzM6Plfuh7agbrwnOkp9AZvq3uQdMQ/g4Gw/yJSMAA4S7YlISxqKqoSkOOuQx0qsOBQqvqx3rWgMW9OGqEpFibRA8nQMoq7pR7IslXZx3uKfACfBvygO0sojyFkPXFH8tu4LLqteFxk6ALloyDY21noQYnQ10uMBoseyEXoKIS2vwUM8NnhIrxwrSBn3DX/K3vGOfUk2vvrVefeSl+xyX/3qQnJ9xhlLnvQdSLqHQf5mZzfchRfud7/+62qTvPwrboqf2IGY8xjGxj2ZdSw2oC2mww4hEv08F9uoXZBWsW3SdpJXiXYRGWrrrNbpNJaXl/0kIZsllEVp3K+NAI67BQesv1YYqFyab5CnFVi60qH0rDereAwb4rejF9JW8kFN0kd2rzIcPVuKUX5+0lJiriRfqMDLOE2ZPnjWdIv1RHZ7Q/Gt5S2bRFmXwRCiUG4vxTLGCzKdXsKbb55xb3jD9sTzB8/o3e626l7zmn3uEY9Yc69+9TZ38cXbPP4b7m1v2+ce9zh4XatvYod0eZZyL7OO0xJhU7WzeWvT6LR9lbUN6yP9kwR78D0rf0c0XNtamrxRRMAI4ChaZQx00kqmHWUhL0tucC/Pm1U0nk4JCiq7dGNVTcvyuNKt2XtNv2x+qukQHzpfHyztgn/7srWrSz6+sFeeN7TIXtQtfWRDlb4bXpU3ePQUIgZkqTwQ6HS50Gd4Mm4bPt32V3+11e3du8399Kez7rDDNtzzn7/Pd+/u98MiZt0LXnC4+9u/3Zrcf+c7b3enn84xZe3kFHYQcgccUdZY3nAdYwe8Z7KLTf2lbY0RUCy1HtP6utw2mriMz8U7E9Yj+rz52Wc/+xn3rne9w9fxq3586i+7ZzzjmSmhN998k/8D83I/lOEWd9RRu/2Epde7I444IhXGLpohwDe2mRSLPfEISOOJbGql0kamtbJSaSQSS0sHE0KBJ/CuYXZvOZkQ3VRXlVnlTMkSKkvdSEihFyc0YPwhlsMpX0cvLUclVj1LyxF9VvzMzzx9il7ttIyqGhSFh5d2eflQQtgRBvbasqXcXopzkdS4+yhDss/6I2anowsU/23hNQQOYZlF/omBzDhGtzRItDaSPsiIb1ddNe/OOOMI99KX7kjI3xOfuOSXfrnV/f7vH0zW87vmmgX3/vdv8Z/vWvefe/tp6+QvD57qdgBJhKcQXcfY1Q6jaYuwHOUh0I977aRZzTbIx4b75Ccvc495zCPd05/+G+7YY4/1i4l/0F1//XUJYWua01tu+Yn3SL/F/fmf/4V773sv9cMWvuKuvvrKlNgLL9zrx7U+wT//gNfjDPfmN78x9dwumiNgHsDmGE6VBG202Yi2k31W+PHdvWXpNtWtm0h264VuzUVPNoqIFvTrllOmddEzVN5ZUltdnzYaEsgAafI52xQnJHS5Q9SBh3xOTrtsi/LVz/vAjPgjHZYvIX/aPSnXWl4EZ8YV24qsfmobL/vHP55xr33tdvfBD25NIp1wwqp7/esPuFNPFe8ey8kDH7jqZwHvdz//86vu3vcO8xufVrWQmwUiE6ncDsA9u6uA8q7j/PQ0tp31QiBrG4TXmccz7sgj75SM+/uv//rPRNSb3vT/JsfFxS3uAQ94oB9f+jr/5Zh6S8NcffWXvIyT/R+UwxOZZ5xxpl/O6B/97PVTkmt4Ba+55su+rO/tPL/oogt9PbPm/1wOt25JFJqQHyOAE2LIwWVDKl5tUJumrPLCL1Sg8SaRiG2AEY4NYFOtJL7MbKzSDZ2frhKM/Ofxd7sJl86CjpHSJj4YVxh+C7poUk5Wr/bKTlZy8bWWIY45hfcJZQ8kLyQhkMFrhEGjiHC6q6zk8UB+8I1eLOHypjdt81/ymPWzetd9F+9B9zu/c4d/T/JVOPvs8ske+bH6e1exk/ceqWl5YLcx3xfaQXRScp7ESuKpPAnTn1/q0x/p+VKHkSY0gV2Q9qw75ZRT/ddkPpEQsb179/o/GQ931177dfftb3/TXXXVld7zjE8H1iOAN910ozv66D1IMNl27z7af73mRl6622//qdu2bXuH7IH0Ia3bbrs16Q7uBLSTRggYAWwEn0VujoBUdOFs0e7JFNVS0QalWjyGZqOCbkF0r0qFKGvXQTc+Z/iiY2y4ovjZ+yBc6Hpm/urh1LxhAS6YFchu8OJJOdkcjNp10YxjYIRdCKCcK25KRAZDCq+4YsF39W533/ymVNdPfvIdfn2/A27PHtVp1JCtoo++J+rZkTJOO/AIqWGe8S6oDYS8+F+6p6soYWFzERDitdPd6U5H+XUjX5yEWVpaSt7/nTt35saJuck6LAyLdWa5ra+Hdubd/o1H1BSm68wI4HTZu3Zu6TkKK1e8xOF1VeGYLYqdW3MioRUIZdY5guBgYyXVXK86WmgcYs9lZYbVzUo85HNyoh/XYIwrB+3YR5Fp90zzQD3jP9mVJSIqq76O3//+rB8Ev917YWT5jV/4hVX3utftdw96UPMZ3fW1ysbMb6izoapeC360g76LMmYwTBPn4TXCIp7ubdiiqv7jH16xz34HGMvBNF0SBt4/jPvj9pOf3Ow9gsfwMpnscfDggaR9wCQUdP0ePHiw02XcCWgnjRAoG8DUSLBFnlwEmlaoIBLoOlxeVu+afCc3PXO1KoL6xz/dIMTKoV7SrSmxFha2+i62ZnqROMXqwXCIt7q64hs0HcMVN+mEEvTY1GZC1BVX2AuTPObn4z2iqs34nAE3EG6sYYgJJjK5BMfsBBNggz8OnNQAu4Go8Q+OYudvlm7eweIuuugw97CHHZmQv8MPX/fLvOz3g/JvGzHyV5qNVh+KHUBK2GThPM8OSDbPFqM+wQR6521KxPKetn+vu5ziO8A7dtT39uXp+MAHPtj9y7982X+b+tZkUsmnPvUJ99CHntoJOj8/7+53v/u7T3/68uQejied9IBOl3AnoJ00QsA8gI3gm9bIqJRkfFwVYgFCw8kL/NeOxhUeN63gm2AqlSU9ZlUkZdfQk7j4BF6TV6R+5Q3CBU8bvZHQB2PsQLgGucFm6aVdqMfiINUYmbS0vKttheCj4Qx3qMxrUT/7jWPcVXkS5rOfXXAvf/kO953vgHBuuN/4jTvcH/3RAbd7d3fDLDGyv7HhsvHG71qwUzsgB8W2IBGXfNbzEqbT6i9io2PHffv21x7rV4TR7t273XOfe65ftuj3fD234v/snJ7se/e+1p122i/68YYPc+ed92I/wekC9zd/8x53+OFHuFe84jVF4ux+TQSatG41k7Rok4NAfCUFQoNxfiCA2Nitiutw/F872MTrBYK1sqLj2UBIQbRAvtSjWE8rjV9Fn+61D6GT4Na8AZIGMi4/JOtKQpG+fE4uTkI6lOKRvj/4q+Y4hjqXExEQD9ofR56LBBKR7353zr3ylTvdpz4l3b0nnbSSzO693/1Gqbs3zPVonmdtoeUduBfbQv40olzoniXno5nj/muFz8A1Ge9XpOHppz/KL0/0qNTjF7/4ZZ3rPXuO8Uu/vK1zbSftI2AEsH1MJ14iKsZYLxsq4HAWLSpYLJ+CLkSRw+7NdMNYB8QqFTb06vZsbUl5/GLzWKxrNaKR9UJykgfwC7+sUJxe2ZN4XbI2A67oBoceIIXaqJalV/asua3LpI/CMymLeE+AO0kcuo2Rd+7OHTq04ddC2+7XRNvh7rhjxg+2X/cTPvYln2vDoHiUwSrlehTyPhgd4sqzYofw0n0s5Zc24BFah+f+qjOWkN5DPJ++DV3A/SCA04fk6OXYCODo2WRkNZLGCOpJ5VtGBPCMHiSpWGVxYHjXtFIOZbWX7TK9kAr1omdLxh/qmn4av50KvxeRRHoYd8hZtfD4cQmcNCrt6JOWmb7KktDYpV3SUuyqG4HsjGPnx/QteK/fDnfDDXP+D9GG++3fPuBe+MJ9fgB82s5KRPDeyaLX3fLtTiwCJOcMn37fgX12Z0guFi52mDRbKA7pPx3oAt69+84EwY4ThIARwAky5qCyol156YaK6We7e4sJDWPgmC8rDNH7XIhpUThUcKE3Eg2BTPCAZ0a3NEHV+/XP8vMGfUD6wkknebNq29BHbZafC8FmyXtFxVslNstOfinHN19yeDcdv418hdLH5fw735n1y7jscJ/7nIyjfNCD0N27393rXsAeZZFdlSw3OPLcnyWn8GaRiKQbbP9ggjfFoa1MajnU8ilkiLjTA4gUeU9SF1vQDuJhVHltaKg6tSGtjgzzANZBbTziGAEcDzuNmJZSKUnlp6plCRYaqLC7V0PqWZuVpZKcdCOhRAtdqfIs1rOFuHV1LIuXJckYEwmvH4hXf7cYbKQrvEz/Jjpmy43IGn5D1yRPxXEVb7+KhR/TtM39r/91mJ8BP+PX8Vv3Ez4OuCc9acnbHRKIgf4hSRMRyKK8kJSAFCIudj73p7bVRkDKvthDbUDSTTsQa16LTdQWYpN67xFl185CaxExBnDHjnoLPremhAnqCwJGAPsC67QI1Uoq23XI8Wu9K7+wkm2KG2WpnDyiBS9b7w+cQ5bmTyXWOyORxDE99hAkOT32sCgFaYiKnva6342NTIBJT8wpI6FKsHulZc9DBEB4P/7xrX5Nv53uBz+Y85OMNtxznnPQz3I85MdWlZexkIhAppYBxKOnMHmCpzjZ3GTZExn3Jrbv/S4ybp2jpFEn5vjEwXJAms+0LWgP5Abnagv5w4N4uodyEGO0Ns0j9OrXJJDRyvN0amMEcDrtXivXqMhQ/7HyQgWYJVhx3b2avNanWmHq07pn3Z9wQ+XLcXXUv0w69JKKG3qlK8SyeN3PEFfyJmMPsbSLXFclyd2y691B+nkkFF7IGGyYn3qpayzogT0uTY03TmfXXz/nu3sPd1dcIbN7Tztt2X/f9ID/Ri8nP1XLjWKFciXeYilPKFPYQ88grjWddjxTXqRtCQJpW+BWlYXDEZ4eRalfVB6eDWOTeimb8v79+8wDmAVlQq6NAE6IIQebDamwQGhAJGTDN2kXk1m01SoykbXJiRplg+miQcQi09IwFk0+6ZUU9AJBEdLbK3Sv53GTPPKlKEnOf17lLrx+y8uHfL6EKGCdQ3hEiV25LLFVeZjeT6kDyg82JSbJVXJv3H/27ZtxF164zf3lX2L29Iw79tg1d8EFB9zjHrfssW43d2I7vkdoxLGTXBR7CeWdQzzsQibjyoEPPlVbPjHKgyC0BZ+nCTrtg6chWQ/fA9iDaYpdKWsYR+kC3jWMpC3NPiNgBLDPAE+ieJIHHuM9WWVosMIrC1P+jISPelX1RpZLr/5UK35f1fvJHthixx7mp1Yfo25s8ifA5KebvluXrIc6pGWEjSIaQmBFYjJes16Rrw9+cIt7zWu2u5tumnWLixvu3HP3+/2AX0xXx/alEe3HFWcd53kJQ7x5LmREybjg35sQIr5tZQhkSSHfA+3CJ4a0RSgNXfnDfRdsDGBoj8k6NwI4Wfbsa27Ec6NjxpDY4uJhyaLOdRMOGxhUjOF1rEzES3dpgmg1m8gAPYSksHKO1UbCsWucsfApscXFupM8xAsg+lBi/BGeNllLUOK0Q9jj00dI4KHeYnhlUfXM+XvwAgLj8fdUff3rc34Nvx3u6qvlay2PfOSyH/e3z93tbph8JDb0J0PZikkISQe9UbwWNaXMkYCMkpdwuHg2MaLWcfqHQEghsecRqYTn+HNEW/CIHoq2sUjLO3TokDvssMOaZNnijigCRgBH1DCjqNbqavrTZPCwYdxY8w0VDiq66puMq1vyFSMbMJEx6E+mUfM8MopnZZMrGLftI3TBV1a4tAvk0yvaLK14W+XhITqga3R1s/HqvWCyNoR1PVXNclwW+7bbZtyf/Mk29573bPVEd8Ydf/yaJ34H3GMeA+KXLpdlcgb5TEkDG/uY8Wt52A9S68lNq5ugc2iNkG4t/2lCCESypFBtWxWv4ve6vsyqOlj4QSJgBHCQaI95WrOzGMi+4knffPKptLaygz+wVb1bWXIDUoHuVXzCrU1vC9KJ3bJkFJ42IV/dlXaszHS4eF2QrqwvKHFEFzYqaan9usrigXKDrvBejQlsyU3xJ4bZo4QchqfKOzXd+963xa/ht93dcsus27p1wz3veQfcOefAYxLqxdyM9rGbhLC8ZTHndZifQXVVUqcw7cGc9yq3/dFCZx7Lu0DseUSq4TnrUhB7vEdC8Jvo3iQutLNtdBEwAji6thk5zeDtm5ubS7rz2lUOlVTcjFBUgrJ4cveafqpTG42EVJwqs/gMOoWeNnrZgBc8kyBCTbYqFTDSkwknkiZ0gPcRBCnsgq2jT6wegsdyJz3igTSXl2UsZGz6mqbao3dDmOepyhtLWL+cfPWr8+4lL9nuvvpV6e4944ylZJLH8cePpscvFu8wXD3sRULWK4W7Ki9MZbTPpayNho6Cn74H0Kr8XdB6J2uPWFtgiEZs2NFAybSogoARwCpoWdgEAVYI7VeO5Q0yx9WRUIHcFK3pB92oZx2z6bCaYp2QRhEZzabdPlbpXEE+12LkE1n2ht9cHgwxEa+fLnUTTnqh3ZpiIdhqQ6jyYCvszCuvBRGQYHpENu/IoeIv5Pz93y8m5O9ud1vzEz72u0c+crDe1d4qKz69w8aH6Mae+abXlpjzqLLrkhCVMB1nWp57k+ZueySF3ANF/Hu9Cygn3Imvlp0DBzB5aTsf2HHCEDACOGEGHWx2WNk0SxWVmDTO+XJQIYafcEOFVbymHyqvNvSSSrBIrzwymj/OTyvT/NzF3w0bhjAWdEHXN8dByreN00u7KKENY9Y7z9MjayN6/UDS+71JI4hUiHWv8WyqUZ0Zx9/97qy75JJt7sgj191nP3ur2yJL/KnQqTyr01UJe4W7v2qzoE6hHRS/2Hchr67EN48lfj8XgT548ID/8/QK/y3sG5JJJq961evcXe5yXJfVnv3s33Z33HEo6X3Cwxe+8KXunve8d1c4u1EdASOA1TGb+hhayUgXRHjdBJw8YkGvFp/1msGK9qOItNXTLV1BQo/0jOMyMooGrV6q6Vj5QrK6wA6c/ZyOH16l8xM+qXueHW8Yev3SMvPzkQ7TzpWUSU2P5Uf+HMArQhxw5DnLDsdOSfxs+UY37zHHrLkf/3guGft37LH0srSj+7hLyWKP/Aj+xDp71Bybl1CxaOssa4/0uxDagiluuEsvfZ/7u7/7O/czP/MzfmzrVnfllV/036q+t9u163AGany85JKL/YLo93RveMOF7itf+bJfIP0V7u1vf2dKLiaK3Xjjj92HP/yJ1H27aAcBI4Dt4DgVUtLECo2jNpzNANCGmnKyY9niPUqiFyq5bMNN2THHvLjSvakzjnuR0XQ69bHKI5HVdRGM0zZMa9j7Km0nYBwucF3WJZ8nOw/jvHBt3NO06G3m+Ch4KLONYJrQ5ZGSU05ZdR/96Jz70pfm3VlnYTzqdG+Kbz4O8lzLj5IQknG+H7SFyJHyGhJyhstPx+72RkBtlbUHx+fK/Ztvvtlhx/aCF5ybHI877nj3iEc8yj372c9Nrpv8XHHFP7uLLro4EXH/+z/Q/5m6JSF7e/Yc0xH7ne98O/ls5/Off4677bbb/Lt2tt9/tfPcTpohwIEbzaRY7KlDQElJ8wpZKyT9hNvS0sHO5Al4tZquN1jXQGiohOjc0fmCBsgo9JEuX61E89OQ582IFyVTl6WaulBO8yO8fmIjaTRgo4WFrUll3Vz6oCTIYslYo3FmBv+FuZNwUA+UcRAVkMZVXx5W3YMetJQ8vPLKhU3vVnI5Aj/N38dBZALvvOyC/cwMJtMAfxDyLP6KveqGdwFdlYPKb6/3XDVr72xwaUodzPTm3FOe8j/896s/4We0P897AX/WPeQhpybev+9977t+/OsH/B8/jv2sn9ubbrrJHX30no6Ao47a7QngjZ1rnKAL+uSTH+ze+Ma3uLe85W3uQx+6NPEWpgLZRW0EzANYG7ppj4jKApVwW92cvolNFixeSSp2oFvtM2ViD1Rk0iY0bRikMkQjA6JDb2dx96akX/zbRB/qstGCLk30kNyB/HFGMWcZgxT32vRPQ6+Qw3kujSDS1nGLQjCAWXp/8IOFAF51FapQkELYSHeV5W9P6NY2+UqTEI94h9xlvYQAFPdkk2DAXolje/g3f1+oZ/xxGGlCu3S66O7duXNn0vX7x3/8qsQe3//+9/yf30X/Z09mv8fk6XOf+4x761v/NBVUvHzp9BBgdlbqOgaGZxA7tsMPP8J/SvGJ7otf/ELnHsPZsR4CRgDr4WaxOgh0v8SdRxVPQCywxXf3FiegjUdxmPInki/OXK3avUnZbTREnNwhMjd8JVk8+5nptn0Enhsb7DYVbMJZxtXSa6/MVEu3eugiUnLiieu+QVp3110377umZtwRRyBPmi8lhNXTtBiCgL47ISGn54lEgZjjyPIJ8qhkHMRcZRm6VRCAB27Hjh1JFGB43HF3rRI9CfuIRzzadxs/uiver/3aWe4nP7m54wXEedj9iwhf/vJVnmwuuvve935JfEx4w1JktrWDQO+/7e2kY1ImBAH+KWeF2oRoIS48SfQmASJ42Jp197JhqAc4dMKM4/DTaSA6w+jeFF1Wku5e5qZuVyvtRTlVjpxlrAR9xs983e4r4oWpa1iBI3Y0QiefLJ6/q6/Gqs/ZrksQEnip6KkCgUZ4LJg8yK7LKpYep7DZbnvgH777xJ/d9uhZAP6CfZN6a5xQaqorCOD27TubismN/5CHnOYuu+xjyTNMAtm2bZvbvfvoVNjbbrvVXXzxW319vOoOHNjvLr/8H9zDH/6IVBi7qI+AEcD62E15zLCyrQ4FSYV8rULiY/kSEMAmZEW7GekZiNcN3r7l5UMp8ofxYW0QnaoNDvHBAtPhhk/cNcEHsmJ1EQK67DHR8ZiIL2Pm6ti/OE7TPEGvQW8PfrB4o666CjbBMighKSEhDLXKkhIjhCE6dc5RbmQH/ljzsmwsIfAXQsixnEbIy1EHAdy1a1d5oJpPn/nM57hvf/ub7ulP/zX3trdd5F7+8lcnkq677hvuRS96fnL+6Ec/1t3nPv+Pe8Yznur+5//8LfeEJ5zt7n3vX6iZokXLImBdwFlE7DoKgbpEi6RCvX7iTYF3SWVGqVAQSEgGPZUFgVK3szqhMQcZXVtbbkGnYtKTUmLzAroAG/VAyjIz8ok7IW+DIEskoOx+xnhMEJwsIc3LQ697VWzTS9Ywn5MAfulL6fFQYh+WQxzZNQlSCBKS3SUXggvCY5f/5oOwtaQ+Ob8h/sgV3inZiLt6ZcUWm0+TYGn8NyMO6QBdBrMpRviDp+mKB1C6gNvWBF3Lr33tn3SJPfHEe/pva7+5c/+cc/7AT0b5g861nbSHgBHA9rCcMklSSXTq1ojcg+SBQLCy4TIq8LzhWRVZvZNjpV8eEumG38zlJA8QH/8VpMab1qW99QEO0EVJ10LHI9rCpDufF9isXI8sAUVjwLUFgZVtisB977uafP/3X/913h3084R8D1bJBk+VdrjIOwBbhDui81pIio1lK4E08pESGhKbXguFwwYkiZoIbKay9H77Z0h/NLb9+/d1xgCOhkamRZsIaI3UplSTNfEIaEXYu7ICoUHXqnytApVoehmVKrJ6AauyykOGOqHRxcSKxcVtHcLF2CSrvK5+7E2UkQaIHzCCXll8JE02XtU1iI2R7QIHQQcm8P6lt942T4efzCs/GdKddBL+QMy4a65JewF75RjlFHZOdxsD52zXMcmIjWXrhWmV573xz75vsAP+pHIs4XSM5exnF3AVe1nY/iBgBLA/uE6s1KyXrowg4Rm6MmPX9CuTVRXQIlndOkkXa/ckj2wDUFWDuPDwqmGMHbvEiybBqCcxTm5eKJWRJnDApDcBTXcN5cmPv5dOPxuvyHbZcKNwrd3AWZJcTTshJCEprDeWrVqq4xs69o9ebA678Yc9sefVA1lSPkljOdP53b//gPcA9mcSSKxtLFz/EGhWa/VPL5M88gikK4qsut3dmZjgkf4+rcYpl6XhYs6KZVXRqYgsxWiQHyZNeki61tfDpW9iFlKGnOI85qddfFcwwXeERT92gbfdwBZrMN5PlACCsB1qNTNiA7U1bSTdxOiiZJnCkedUAR4q/L+X+JNgT80/89jfIzCTLnhgCyzpLyHe2aPoI68ScNe9Hv5qe5Hcz99s+ZG0rAu4n5gPX7YRwOHbYCw1YIWWrZRxjXF+HC+Gbi5ZLw5dW/lbm2QrT5botNzxskF38fgV68SGc5MX5SsecZc4MSh0AemTcYdyl2PssmEZR45oDJouvK0yIAveWXoeY+yU1qf6VZg/wUG60YB1+Ky65OHFeOADV/3wgQ2/XtmCxxPLGPVPF8UIdhQyAhyF/IVH6IBrHcTa37GEgyQqyNtwtjT+okM5/kqqsvgjtsobTn5iUkUXMBaDtm0yEehjdTWZgFmusghIJafEBt9FlXvx3iRpQJqSLdEsLatokkd85auVeDbnVa4Fn/WEHMPrhq3KVzSqpNUrLNKXWcZV7SSSpdHrlUr5c453ZChpIDtX/mQ8SMWOHRvuF35h1f3bvy24r3993t3vfoOdKCPlWLHCGDXZQBBhX5bf8NzfTW4jDOJK/Ph3wkeZuk0xDrOexR/P8klhGn8JR+zlqPjTZmFKwzk/6Gc3bdu2fTiJW6p9R4A+7b4nZAlMBgIkaVpZ+flyfnV2meSBNetkQsWWLd0TKnoj0F7FFzvJo7dOTUNIw4FGQdfTEw8kFryG563aVh8jEjdZyqV44ks1feJDM33EgH1C8oFyoxs8g+Fge3g+w+cachTOHvxgIX1XXjlK/6dj1sWDDWRyia2L115JQt3YPcGHk3tCIokyrTaQMg97sKzz2J5uvSWF+kndPjtbtY7qnYqFGA0EzLKjYYcx1EIqCniTQGzYoKNrtSqxCclkWw099KGnrc6XPNrSCeRYNqnMMaMW5Lh7Zm15EQj1KQ+Z/1S65LVBqfdFkXTjkJ9S/l0pJzpGTpYA2uobSpAm7Hnd8dAX+IFgYaD9aA62T48D9KqO2JZPSIA5qv/QpsRbSGFvvLU8jViWW1SneR6z+JcvVs36AlnAV0sGVeab57NF0E3UgBAYpb+sA8qyJdMGAiRXlMU1/eoTFTREzSoh6BQuVAySVTzxhJr35wgiG46xQyrNPnFXT08QYYw3DO0Fkl6VgNZLHZ6+bhwgC8MD1vxCi3guZQYD7qVrXMggywPKBBtFnGsZEacgSYwQmfrlD1rV20gA8UUQ6KTjUOvJ63csxUhJt/zxIr7Zo2gkeANn7mqLfus8afJZ5pkvwR9XKOss77imLXCO9wm/gynz+m4iTdsmEQEjgJNo1T7mCYRieTlNKNogNmg0pXJDDSeNeWw2UFFlyRZkgOg026BH9UYO3rZwwWvqgDF/TTdtKMolIRz1YEg0Ok0q9arEBqQT5FO8w0L6ZOyhb+LWpWGbm5NOiLW1EGdOCmE5KFu4N2wsUYYQR3clO0Sh/ePu3Rvu535u1f3Hf8y7b31rzp1wAols+2nFSqya73xCIjaSd4D24b1QE3TRww60W/isH+csF/2QPRyZai+UdZZp1hfEnDbgc9G1X2X+0KGD7rDD8J1r2yYVAal9JzV3lq/WEVhfX0l5k5BAG8SGpE9IYLzaIDlYZ5CzWenZqkpW8lJUGax480LpPZArLHYdLni9sNBWBRrf6PEzbvSG4rN2W7ZsT8YlQdtYEqk5y56V4yE4dC9sDa8f7byyIjaD10+8k5y8gPKUJhIkrd3jqtroxszmrd41xwF+6UuT8Z8ahCTEu7zbEpix23h8xm7Ws/QgY6VtIEMlUL5ADMP6AO8jSGHWBuhCrjp+VuXu24evgNgMYA/sxG5GACfWtP3J2MzMou8+XEgIBVNoTigoCcdycsGQ4okUsoU4aKzkSx5bkiBViSTlpo9SGfaShfyDgAoRlQkBIDviGW3nFVMymtYwvIIe8LB1TzbBWDvkRSv3MF78ee/4IHNFC1vD6wcySj1gQ4TXrl//xH9vGKqCBMI7GB6pJ/IpBAUNJMLjO8VFCyd3N47axVa1caQG3Ud2A2e/C9wdcnzvZEmhdEUyP2HZIOZtj92MqxuoUbvHMH/tSo6VpmU+ZoIPsBJCGDd+thtbLAJtS8DEWmc8w03G39XxxH4stUYlhHF1sqFS7K446mQMcnsRLcgVsiVdrEwnXEcPz2XjkaGaHItlgcSEY+z6v7RLvi70+rH7qJ/jHzsQB5CSfNITC0LO9R9B/ITsbXhSN+dJ3WzSPR0SP4rCPYwNlE08gQgPeewupo1lfo3igTJEcon4Eg7Pwz15gp/NDYPsGU+OIofP446nnCIezEkmgN1IAC9ssNP8Jt64Jt7squQ1nsEu+EVc/DkSGXUwh5Rp3/LLPFAh5r1sQDskRknBiTUAd+zYkbrX9sVll33M/eu/XuNe+tJX5or+4Aff7z7ykb9P6oSnPvVp7glP+JXccHazHgJGAOvhZrE8AmhvpTJH5SEVeVNg2LjnycmOKYM3aWFhMSEHDN9mQwJZeWQHaUFPkB2OaUP+SXiKdECcomfUv/go+Gb1yeoB+STEWVkJP0pudlf22bBVrsUu+V8TIflDgyTlZX2T/EnDBMIsXkHBFESRO+JI/tiIocwJGUQ+hRDKuEboC2wQXs6JtZZLPhPPSIgBzvW6DiG8613X3bHHrrnvfW/Off/7s+4ud1GdE4UG8KP5G0BiOUlo2SbmZWM3gTeJPuyGOLqrrJyEBnpLy8VAkwUa+sJGJ61xYm2A/IVlFWuVrjms/4evgGzf3h8CuLy87C655OKE3J1++iNz8/etb13vPv7xj7q//Mv3JgTw937vd/w6m/d3xx//M7nh7WZ1BNrpn6qersWYCASkksmSkjpZ04qrOzYaNnjZuHgwwspyM+jaLC7CbTWIWTnSzXmoQ/7gbePSLvn5YGXcnbcmd7r1WEi6wTkOslt2W3pIo5i2CwgXu+EXEzIGr6Qsg0MiB8/tsn+GBgcEbiHZgRl2ePrEc7mYkFguJC5jTFnWIHN1k3zjizOQByKB7mV2G4u3ENfcoCvT0a5LPOeYKvVGCRmEjtKFFrMmIdrq6fQCEuH8o2AO0i5d9elxbOG7SyKSxbzOOLZ8XcbhbrauaUPneBsgtQ13wQWvcI9//C+7iy76U/9n5gZ36aXvd9/4xr/7OljH6TbV6ytf+XLyPp5zzrmFoq644p/dIx7xKF+3bvGLUW/z5492n/3sPxaGtwfVETAPYHXMpj4GCB8aPFQsQv6EEDQDRhrrbAXImay8H7fcDGS1pxPzBR1AYLSbs9jbxjg4AivBKbxb91zIVFoP7W6NkdqGLlm7kKyhTOR7/TA5QGxCrx/CFm3yDMQQIeY8MQSG0pUsHkKe4wjSgF02egmFUArJYNpQIcw/02FcCQc9wx1PeS3ekjyPFSaCfPjDzmFB6LPPxqLo07IV2zGLgNqcceK9hCpL3gGVpU/srDcCiltoA5K7WXfSSfd3X/va19yPfvSjRNhb3vKm5Li4uOh+5Vee7H7/9/+wdyI9QpxyykP9H6aHussv/4fCkDfffJM78cR7dZ7v3r3bE9FrO9d20hwBI4DNMZx6CWxc2wECDS0aaYytW068PbhGo84uVlyXbUq4IIuVXFmM/GchP8l2c8YR0azc+vqw0gYusvC24BQSr2xqbV9TB8jFTGdsYpetifeOxA/HmRlppIEbPHaywUM378NyeYvN25EHpA9PEheNFkJIIihdxxAlBBGeQhFMQoiyIGRRHoi8sAu5rNsYeGPfFNohhJIGSOWDHoTzHQ7jAKFbiJeEst88BAQnfU+1PiHeOGY3jN3EPXpuJb5hnsWp6vWs//PyZHfWWU9y73znJe4///O/3J3vfKz/zOG/+SWOrnff/vY3Kwn83Oc+49761j9NxTnmmGPc29/+ztS9vAvpPUg/wbtsW3sIGAFsD8splKSV9v/P3pXA3TXc7ZGVJGKLBLXvO40gkcQnpXa+1FpaPlTEUkptpbagiKXWWkLQFrVECSWtLS2JXZQQW6zV2BJ79oRvnnne/505273n3HPu8r7vzO9379lm+c8zc+b/nP9seTMvDTca/wULMLbOWlBkTJv4qZwW5AIxAEGp7DvZBwOjm1EaoyxE1MabXx5RirC8wVUnh0gUp1DlWfJR0hYfLvkU8gfcScAx1i+b1U/iTXtEfWCdCFv5SAZt97MlhzZuhOUsYxIJlhGey+SSIImTipRssVp77flqySW/U2++2Ul9/vlCtfTSNjWSR/e6LZxXV48q5dy+58DcLVuQbyHgEkvwOs4yKz79MT0CsJ7PnTtXbbTRJmr//Q8wARcsWKA/4LJ9vKHbFr9qXO/efdSMGdNLQadPn6569+5duvYn+RHwdDo/hu02BttQF6cIQDKE/HH9Ouwp3LlF0WeFOp9csCTBCfkD4cm36HV18gAT6XaGPNXKYcsLsaR3IEKyvqGEAg6QA+QJ+FiyJQtQY2we8hsc6yfha3FE/vCDhREWWliMO3YMThKy6X6vZWZdI8EHWWV5d+zIsYSyDI2Lm5BCkMfwuDaku/nm80wSzz6LNQ+Rf1vm9dvWy+ayrZy5ZUBSCNsFflBhQs6RW+CNcpSxhMGt1Fgn4a+yc/0G068cNr8PN0/5Y0sbg5tPzAJ2l4Hp1AnLLdVPrv79B6p//vNRNWfOHDMpZfz4R9WAAQPTZsX7S4GAJ4ApQPJeyiPgNpTlfcY/RXi79Ae7ztJM8oiPDdaxfI0Uu1lna4LAcWWIj2sMgkxkj7uaMMgbcJk3T9Y6ZG5BikG8qo0zCbOk+yCf7vqG4g8ESKx+wItdvrD62W57ECLIWm2Xr6RV7RFy0QpJYgfsQAgpE5SZbf7gl5NL5pkPEIZD+buTS2SSia0DKCOUBX8d9Lgm1plnnsEuNLCWWL9RciKLJreviQ7VlqcbzmKebR3IdGviuSnV+9x+MNQr5aT2ux7LwITz+Prrr6mTTjrW3F533fXUrrsOVcOGHagOPfQAtcsuu6u11lonHMRf50DAdwHnAK+9BoVhBzyoCBIiFj9phBAnyFYRcUucacsJ/kUeNwyIA7pE6ukgB2Y+kzRoKqHHzuFevXABFrDEIk04krmuZiY2ZCJBQheqvtJ+ixzrZxLM8RcnD63ItgxlTUEkA/8ggPYn13hKQgcihzpAyx+JoA6G0PjT+cf/93ocICyA3RX2BYZf+pE4QAjhX3761JzLc33VtMuhQNbmdnw3LOFGuVqs5Rx5cM895kmlOnMm1gGs7U4gO+20q8JPHEgDzJ53AABAAElEQVTfhRdeJpdqzz33Mb/SDX9SKAKeABYKZ/uMjA1ttrwjDBdQljFtHIzPbjXbiGeLVXxnD48uTG7hZq1FUChu16vEXu0xDU4gIXELS4NgkYxBedXWhcmnOwYTxB96VdY/LK3Z3CISSA+6X4sgqtXkEviRnBInEFcQ+HLy4Fl0colY5WT8IEmuJYT4ABJCiDyjzi2i1yn7Tu+f+r2eRdlJd1spfS654JhDubJ1QcgI612UnCAEiCvjx1W5vOC5d0SAOAXbAuIumIePDEfSbvFur3j6reDafsl7Atj2y7hmOaxGEaEBBsGQcX4QDgQDcXFmKRV3HqFBUugqx0V57ILOkEMIDyajFONKAiVGF4cLZz0LeSFBoHJKjKbCg/JyQAaXlIM8QQYQHenuBbkDWRZrWThBIWCWHLF7NOyv6GvIHrVCdjZWu6xpoQ6QEDIk4gYxQ96Yd3uNe+IQrnPnDqpv3/lq4sQuatKkjmrgQNYh4CEO8cEvnRyTJ5dwTJuE9hYri0T2M+IumANLlqUl3tJmyH2mgfGbloTXo05bGbPnstoQwTTDYwCrjdWHa14EPAFs3rJpBZKxwWAjWlncOCub7OQh4+3SxlU+NZGrvC+kCcIjSjy8tEtJR5suo/JxlXtaKR7gAkIsGMBiReIVbJDLpZH3WTmrH8gfZMQ4PxAZdPvaMZtikZLlVKT7NEiMLCGE1azYfAnplLqTxuqXBS8hDZJ3hEVaSNf+cI3fQt0NPNcQwKee6qQJIPGR7mMJi6OGVDshHBZH3IWT/JAAuoTEPYc/4FkspkaAdvAnZStZtZgDYxSQlE8Yc4QQ3Enui6nXkh7ir5eLT3PmzJk13wquXjn06cQj4AlgPC7+biYE4hsQiQKNqrtwMRpOztB0q18tFFi8XGF5oNhpbQsvcUCZjJFAMlPVMT4eyIHuSulOjcclnGB8nsK+4q4t77JxQIZKVj+QHISF30pWNvixpEi6UUmMbPcpu0NJioBNdRaVNPLE4VDEPSj7uG5j4LPllu5MYOImQwlcIizdxsgHHA723LUS2nrJ5/Af9zPRmD9arKTrWCNsC996arVnfJ9qIb7FCR80SIdDVDihx8Ucqcs1P3YsEa++TtciT9XG6buAq0Wu9YRzNXDrkdpL2lAEWvRVKqUi3b2i2MJWNsmI1U9oVPM524hH44laurqkGLOWX6awJGFrKCZ5SFd42C+uy+Upzn/8vaDiDGNBEswuZ9fqh7hA6tKMrYOcUWIUJIVQnLCUBa2Idjwd4wjKGs4P6hPlofIt2uoXTi/NNcgfcNpss/m6Tn2vdwTponbcsZe69dYv1TLLYKKHxUHiY16Zd040AvHAU9Y5sRIivxYXi008IZTw9ZpcYuWRfLWNo7z3/GCRPElbxjKCH9ZBe02fLEdg0zqJON5PLP3iXdtFwJdu2y3bOuSMDb9tEG2SuOfOIk22skkYiUuu8xwlLmnAddOsFbO7swgIAwgXlW58WlC4xToQgKDVD2nImMNi00qODeQOy8vIDh1xY/2AF7IPeStZ/ZJTInEljuwmY11hlzLSkGueiyJFOHYVW4uZLVP4FYsa0sZHBfLQKAd8XHl69uykNt54oXrxxU7q5Zc7q7ffXlTvpoD16CwBlLzzHoiakDVaQ5Fv1M10VkKLTdBiBTzlHcBRzlGuCGN/xdd1HX3hzspfeNQZIrRYEXdYB6UeE2MXa5xL2TY77pIfgmHzmQEc77VVIeAJYKsqruYS1vIj2zCjIQSxQLeiOKy7BiWdrkGxcUn4rEcrFxrc+EknsLilkydr6lH/kg6U/rx5s7VMJDpJ1tBoDPYOFY29znImYWF907AYV4TVL4sMxILLqEg4yEVCJGPq5Bo+RHmSGDEPFB4kKX29ktSKO0IW1HUQQDiR5667uqoPPiDhxf3XX8dkEC7hk846yvzT+idkmITYWgmBARU2CD2dHMViFSTdQXICv+If7wliEELIcFJvGbf/L4eA1GvxI+8aMS5HxBECeAv2UiZBMibx1uYoadrY+U5G71sf/qwtIOAJYFsoxYblgY2UkInoZAY7i7SSiK6yQePjXlcKG30ucnFpF1HQnFyRtDNENBZRsLYxj/NT+Z6ER5crHIhC/JjDcnHlUwggWK6VqpZWv3K5iHuGso4SIxk/SFIIRSo4unGgbIUU5aszbqyVz1HXiSeVJD8osCjxInobuEX0FlaWAL7xhj13Y2a+Ua58zvwxnygvpMF8CwYS2l2TkFZCkED5sBBfiM+mYeuPpENyAvlF0cs5P1AqWwklnKToj4KArYvR8o3iTrwlLI8oc5zxwyf4rPZX2Aaua9eutU/Ip9BQBDwBbCj8bSVxTvLINpkhLu9oLPMrFSEKQvzQiIJwQUnX20EGIX5IO5s11ErrWjXt3cpnwCJskQUBxVZucNWO9auccvU+SFrYpQv5Wa+i9UKshnGWMuTRKuHqZQmHFDylbiENWiEtydt113nq9NO7lYK+8Ua67mnKC4WPoNjOTr8NOv+STznHO8L0xTpaSsqckIxiEWpihoM9l4+rMCFEUPjHTwiJXOMZ4sA/8omwxbyriLH5ncUqj6xSvhKHlImdbWwAbnmMc5ZvmIjDQy3qdkvC5sBdQGq7CLSbnj9vDAL114iNyadPtUAEWvRKoBES8ldNt6aIBsXHuNH4VdfoknBxFibizSsPZXMbZt6p9I/GHZgErW7cxq1S2PLP08sC4sAZvlQksPoBHygPdhuSXBB3kgoZFwj8gV25MZLl5cz/lGVp12KkPJYYCjFKaynLqzSRHteGZBlwhxha/dzcrrji93otwAV6HUA2r1OmpCOAbhxyDpnD1lGQNORZ8i9+5Yg6JwQYR7GQ2neLxB/+UU8tLvLOlRvTJuRQUsOxsdYqV5Jiz9O/a9Wka3G39eP776W+S1lABvnZVMKk0MZl/WQ/kzSV4i4gPbJH4UO0KgQ8AWxVxdU8wpLg2HF+UDTZuzXD+UEDBFKCL9zws/LXcYRLZCofstxTCkHFWc5f8Fl4dq0lXkF/2a7SywIsglY/WkChJDAGEeQBE3SEJABzkAaEg4O8lXbPyCZ7Nt+QAxhirCIc5AT5c5UczoUYpbeUsTsN8QkpcuNMkpJ4LjSY0g8m7nCrt6Qwu+02r0QAv/yyg5o+fRHVqxfxTQqT5j7lRT5IAiUMsMA7Q1IoVkM8FSuh221sx2BKmdOKauWTdCR+8ZfVWpUGX0nDH4EA3nOUA2eGJ+MOP7a88hFCG4+O1DguAeMJoODRVo+eALbVkq1hvqCY586do1OwDUd+8ucKbON17yadgyyA0EhjCbLgWt6SwqW/n04epB9eUw8zfKGUYc1q4Vfpk63CJ9IKWv3sotILF4r1hgQxLvoOHTprsmUtEnF+ankvavXDZJ2olS0sA4iGEEI8Q1nw51rK7L1ot7HMOibRlvgRB8kxsUtLjnfddb4aMUJiUQrdwL16yZpy9n7WM8gTN/HEJVo230IEITvCgQwKISSx5kdA2tnGkNbWjTTWKtZ5YCpdx0g3iDFizebyhs+WWmN8M48WKxd3tEdJP0prcUc8/Nm4KufI7wJSGaO24MMTwLZQinXPAxqU742lSLrfihABDRQbrnSxQdG5hAvKrHPnRY0lpAgCmKXBDFv9ZGkXxOESr3Q5K+crnoySqLhb7AXHPXKSwPdmyRlLSIUQ2vS++26+IRiwkAk5yIKDjSnbWRpikyVGyEy5s02wYDhLVmyXOIYTdGmxHFaWZNVVv1PrrrvQzACGb5kJXDlksg+UW5ouaOYhShhstzHJA+LDT5zk3bWQ8n1kneOwAZJrYst2IGitChMTxI57lnjms1aJtG31SKzL5c5iT194d6KEEM/kvvizZBCkkPHwWfgfBLB7dz8GMIxLW7v2BLCtlWgd8gMF0aVLN9OAYP9eWhaKSBgNFBVMudiELID8iXMJFxvEyvFI2DRHxBnXYEKBBi1u6Wc+p0lX/MSlLc/CMmASAPBAmOhYP+LiKn5098LhHu+HrUVUFkIKKykPkSvtEcSEhJ3KL2lsXdr44vwRP+QDT+MmWCDvYiG0ZIVxoftUuljj60FcmnvsMU+ddx4n22Bf4F/8Is5X5Xus79m6oN1YJe8dO5IM4xnzaq2jwbxL/oEXust5lPA2rCUruCfpSNq4R4cjrZD2Wp5BlrTExIZpidgfNALlcQdm4R9hY/EA+yiu9egCfuCB+9RLL72oTj31TAoU+h8+/GA1Z87sUo/EiSeeqtZbb4OQL3+ZBwFPAPOg147D0jpkAbCNvb2X9YzKGaGiDZLEJePXhHRCMaP7GfJEXXI8Ub9Jd+IbSORXup4ZEhY3WIii6wuygU6KP/t9UbbxMtjZzsAKBBDjxeBA7tidyeu47kyWIwigJQcoD6blWouCFsJq8og4g92Z0Rm12dFJHwIyh7uNXXlsTCIn77DuS/4Rh2GV1nvL2e67WwKIRaGrccQd4zOJfVyZVRNvXN6RlnwEyIcAd2yxKch7JvLgY4AfBrTeIw44HC0uwCdoieU7Dr/uDyERFv8IY8PZuPCs3i6+fOstRdr0LFZWbpaLi7UBWUcpR8S+UD3++L/U1KlT9UftPLXoooumTTKTv3nz5qkbbrhW3Xvv3WqbbX4UG3bBggXq008/Uffc82Dsc3+zGASqa5WKSdvH0gYQsI1NEZlhg9WiQwIRogEDeZHZxlAO9VjaBbqd8qChpHxREmrH2QWEDl2IcgzdruoyLEPU6keLFuWnRc92ZybP8GV5hicJkAAKKYTSEKIgwiNcmBjJs7gj4qq11S8u3aR7KBuX/CEvJPMofxJpS4pIjiWupLyvscZ3qk+f79Qnn3RQ778f94EiMcQfo+Mha7fjCfOA+h0kanF5j0oLcmon1tj3BbON4Zv1x6bB98g8MZ6TiAnuk/jSSogQcPZd5HWt/pFO23DAXtov5Mi2RcijWHyVuu66a9WHH34IL8Y988yTasMNNza/bbbZVi2+eP5u4UmTnjcfB0ceeYyaMuUVSSpwfOedqebD4thjj1RffvmlGjp0D/3bK+DHX+RHwBPA/Bi28xjYmNsGpQg4gg0vFCG6WamAMRbLdnHGpcbGjk8gl3sd57/8PeSP8iCu6kioVXjl06r0lLKUkwHEKqvVr1yqojigP2B9ggMOKAv7o4LnrF1RJmIZspYyCRskWvW1+pkMhP7CZBT1y514QjLIQGnzzjCLqF12matuvHExPfsai0MvovcEDtbtkCjmkvWs/CzouHBF3rPlDmIHIhfc7o5kAnnhx4UlEbSIynCBLFvZifzIP985HEkA5R2kH0yoQj0DWcU7wfcr33vOmBv5z3xTglrnxcYPy628sx3VBRdcqMaPf0w9+eRE9e677+oPl/fMD921Tz75hDr//EtyQ9S//1YKv3Hj/pYYF8Ygbr75lur443+jZs2aqY4+erhaeeVV9fJK/RLD+AfZEfAEMDtmPkQsAmis8znbKDEeNIjuWnp4jkkeQkTKp0ayVN5P+qcgCZAlLQl1YzYf3+6Nqs+JsVhBXSIM0gdlCcyQHo5Q2mmsflnFQTmEu06RniWEUNphYqDVtLaqCX5IsxZj/bLkhRi5W7lVJqNp8y5dpwcc8K0hgJDrqaeU2nln7LGc3G0cJaNcbgZhGuGiGNkleVjm0XLHfTvxieWOskceQAxB2OBHiBvrLuusxUby665JKERFkBByyOv0YwklvD+yHIgDymillVZRBx54sHrnnXfV3nv/TE9kWldNnvyyevPN19VWWw3OBNj48Y+oK6+8NBCmT58+6pprbgzci7sA0ROyt8QSS6pdd/1fTUAnlO7FhfH3siPgCWB2zHwIjQDab+ikWigmKpDo0i7YRSNtepDN6Jic3UXSQGKyCxzST09CTRDnjwTOuZHqlHjIArEIEuz+Ltrql0oox5NV2sHuQ8glpBDeXfIn1+giFHKQtmydpKs+hSxpZtRWSiAp74gf+V9nnYV6Qg4+ZBZRd97ZVe2ww5cmSoYT6yhxcwk7nnPtQz6rJEctnocxilpGhcgGyx31VfKvSz1QByBnOO+dOnXQWOHd4PuBOsFwtN7DP+q8a6nCNf3Ds7xXNg59U/tnOPoVWfHEu0oIwALXs2dPY3WD5W2XXXavFCTyfMiQ7RR+1bjnn3/WjKneZJNNTXC8S41cnqqaPLSGMI1rXVoDOl7GFAigkUVjK41wiiAJXtjQo7tpge7ynWPiBDnAtmWc6MG0EoKHbotcodsZLqGQrXJB13NnM/s5nQXSTah6WdDwYfFmsfohVhBQKGMoTcgIZYuJHiSKIM6wVKI8QCK6GLkFW1eqWp2LgoeM+AUdsBA8QJIWaGvRPC3zXPND9zbzlL8+BdPllYsRyxaLOgOj6OSduPCV7kneYd1EvKi3q63GevT++zYNyIF8sjufeRdrLayrWI8Rdb8RLgmjuAlOrnw275hl3dnknbPzOXZR8hPOO1cS4EQXcD10G2PGMX6cgSwWQ6ZmPxhAoIFpZ/0A9QxDFIAZ6xfLFwQR+KNLHWmwax0y4JfOSXzpfLcFX9wJJP94v2qx+PLLL9S1116p24QFZlcSdBdvvfWQaqPz4RIQCLfOCd78bY9APALm49w8StuYxsdDpWA+/UsNc7X75gZTyC4XZAGJgnIWByKFWb75XHpZIIM71g/KlfqKVhEQQxDALDN888meLTTLM7kLGs9BXO2PCplWHihsuPhxhHyW/V8wRZpwIPKVSE32VKIh/vznb1W3bt+r5ZfHMywMbvOOcnQ/MuCD3cfAjhZC6TZFHai1KxojkkIQMzt+FPlFvqXskSc5N3Doa5fkER9ZRBt1QpY3QriglRBxweE+w7lH86TlvrQ1wNT+6oExpCjval/O5dNXqh7LwIRleP311/RwievUhRdeprbbbgfT7XzQQfuZurLnnvuqDTbYMBzEX+dEoPE1LWcGGh3800+/Tq/VGy1sgeljKA9+XAB5jlGkXbpUt2wAGn93LT2I2bUr1hms3gLCLc8WGuthFosdrDJifYQckAHyidUN97I6KKO5c2eaYIsuWnl7JShHykAlBWsKyDDyBFmQHypI4gOZxXoEZQb/JA1ZJS3GP2QEeaUSTke04Je/IDEISyT5zkKKJN4woW8sRiT4wIqOs2hxbe8Fc8+8oytTuo6Lbb7FIimpsh6RuMm9oo9SRyTfcYTYTZPjRlHvmXcbXkif69ueiz9LCgV368eeSfzyIWItt9ZP8Wcsd6SJMq6PbYa4gFwH0xw6dDd1881/0ZOWlik+o00UY+/ePYt9iZoob2lEqU8tSyOJ99OqEDAf2Fpi+8WcnQej8XGtXAKAKDi5znO0DX/5WODPtfpBBnTfQT4OaM+eP0nRYkQl5V6LHxzDeMCfO94QMkFJQFHbWZc2Bjy3CtLer9cZ5K+WjCKvxIWkluWW1lJkCVEYW8FUSBW6V4vq7q0W1zREC3KHSRGvkaoQk2IspCw3dzKMnehRbR7ThpPykklFsq9zsB7Z2PChE7QSipUUetx2FWv4zPuEkMifpAM/dO7kErzb8sPTMDnEMAvGT6IkcTCm1v0f367NnDlT9ehR+WO1defdS+8JoK8DhSCARjaLi1q5OhnyAstXMS59I00rJnYVYR7crmc8g8uYPRMmyx8UXnCpG1r9oLg4QN5a/tBFGGcpAUHAeLq20nUIZSu7T7AMkkiRVdjASz4ghNhIOdTDoiVpxR1FHpQ1HOSETJac2FDMB7tOhRQJIUQ8OEd9ZVxCCBknPwRoxYqL26aCeh221uI9rLz3shtH0eeQKWjRpgUOdZ4YkKzxPL7sxULM95bvNYkjLc3EV0gdc2DbMGIr7QGPtn1zyWDbI4TEYu7cuboXpmvRRevjazIEPAFssgJpfeKkJ1rIGxrZoKUNg/C5ewVJTTEIaB7Q4mzDLXfkCFnY9UySB8UHWeK7BpPjkfjKHyFQNA7I4FpBoZhcqx8wkbF+VObwLwrKKvw4xRhnKbGKr7y0aZ9CfqQd7F6tzYLFlD2JFIEcULkHxxEyJzLWD36IY9ocFuMPGOWZdezmHRIhH6hPlhSRCAkpipa9EOPSi2HIoy03vIeNm3gieaKFTwhy0Fqb9DEgdZ/lj7BCiJlnu5Ud8p7GSijjFWUMMK3SfH+Ju/sum6IwlkXpOsZ7aXFG3rzzCDQjAp4ANmOptCKZbEMXJTfhbNDKxdm9eAbrB6xtEocc3cY1HEf6azbAbJyDoaAoxOImaQkJtTIwTPg6GFP6K+gDygKcKJvIAAUG5+IhVj8SFoSlzEHLSHSsH5UgLSUk1CRokgbSQZ7ESsZjdcoKaYFASNzlLFpIt2jHfJAQIu4oRjbFuK5DsRIVVcY2NXsWlakYokWZrYUU6aAu44jyKFf2mCnLMuM72wzd4pDHJciVrLVxZS95R1ySP06oseXh1nsQQ0sI5Z0kJsRTwqGbmc8FZ2Btf/CHayGe+srpMkYaEh4+K7vq3sfK8Zbz0Yg0y8njn9UDAU8A64FyO0gj2GAGM4xnWOZDulPRCGN8XdLkDKPLglHkuGKDLhFAMbgTTiADZIFM5Vy5/JULZ5+hgYVyxp2wFTSIh2v1M75DyhEyi0ULz13nKsZo12GSlSz7WDKQV2s9InlNKk9Xvlqdo3yC1iPkKdx1WMlKRkWfTVkn5wgyuQS5XLklx5LuCWUG0YB/WM5AQiz5t+dBosLY4Q/kJV23McMU80+5UJfsLN9qLJGs98g832PEy/eNFnRLCIUcUn6GkzGk6JKXIRcY92f9ID6bhiVLTAf+4Nla5nndEgGepiKE1j9irI+Lpjl//nz9Ye6pQX3wb2wqvpQbi3+rTV0ax0oZKDe+LjlstFFK9hv/hArRPkNDDVlAROnQ1dqlRBKsz/CZbezDT6q5BrnDOD0qpGKsfuXkoNIKWsmABdKXH5QVCJ21YAS7zlwLBnFsnNUvLq/Ih2s94kQYO44tqeuwnJXMtRSF61KcDOF7zUCQ3bKX+k+iF5QW+MXv3FEsIQ6mClJUO4LMMpN6zJTD9Z7XQn6t9c6Vk3UJFlO2STjYc3c4QZR84r2K/kQWHBHGhuOTxv9jEWg/AaTx5VAPCTwBrAfKbTgNVzmiYZRrKJWwpS15fB0BkrC4cuPKAx/igaIH8SPJwbIknVqsfsWSu3JyitJYsEB2FLFWP3T3Ai8Z68f8h0lNstWvXLrhZ8CYOActJcAIMuAHpRXXdSZySZyVuunEX62OLNvgEjiVrEfMPwlx1EqG/CPvYVIgREK6AgXDaM4oU2Nm1Eal4R3IFLREcqJHy1OTXyl/lr3UA/ogZtZKxvqTlFq6+6hnLmmvR11yyx5S2rJ2635QfliVIat8ENhuYxOD8czhGrbNIj62bUE6wDX4k/C4T8s07tCxDhaBs8SY5UgC2LhFoLPI6v3mQ8ATwHz4+dAaATRUbOTYCAYtbdi5At29adfSQsOJRjGvYwMctMQEt1BLk0LL0B/ttXqZSDxteHeWcbVj/dLInsaPKCuxkomygtITYoq8kxjaGKXMQRxwznjs81qfQc4gqamOIFP2yhZSEmJrJbKEwHabRkkN6ry1RNYak7j4g/UfFmdYvUn+6R9lh48iYMD3F/mwv3KEmMTYtRAzzuR/lBtkkrGswB8y1bv+QEKWPdoudvsanmZEZ3123wW3/jOcJcTYyg5+6R87GSES+74zb2FCKH7gL0oAuXMJ/LB+0VJImXG3eGfl4y4gfgmY4jFuvhg9AWy+MmmFEqHxYMPObcjYoFVjaYMyYkOMhtE2StlBoQzScGPtN1ggsysaymCVQ3pJoBDcGc8ICfKHn5ArHN3dPFyrSC3HjCXlQpQVCA54QphASDgqPBm3JcrUKsXsOEvM5Y9MNzzrOExqysdR7inkpuwkSVTqJMC0kqFe8hp1i8oeMfIdkLg5ucklWvKkPkfIHRwTmbzkjCsR85+WENuQQoh5FAztc5xBpiJIezDWfFcoQ8pEwha2REp9gz/+kggxyTDINcYRoj6w7rA9s+e2l8S2b7ImIT4wKIfNVZAcphtLaENXPgunp5TvAq6MWlvx4QlgWynJBuQDpIiEjY2IXcMvu6XNik9FKnHb++nORMmQSDEM9hIWC0e6WOJ8RRvKOF9yD8QpbkcRKMg0Vr9KXZmSTq2OwDGJQOCZVYhQjPFKEcrQEgMS6TzyStkibbh6zF4lGRTlTund/IMUUmkH6wdIhc07cMiff6Ze+R/4uB8SXPi6OkskCSFkjyPErAfIv9QHkY7hbPmz7ObJY02SarNUUCmBFCfBj5v42dnMRxpCTAzkg8CWPSdYkRBCKNYTtgH6Sr87grE+bXFIT+pLuH7BU8mjDg9/9oe48rpvvvlajwH0XcB5cWwN4T0BbA2l1MQyohF1GyQ07LR+VNcQoQFjQ2gbubTZJ+maa5SRhEFDnIf8ZW1Q0aC7Vj+kjy5wKGQoSXSPw+KHdpp5DSrrRlj9BCs5gtSAwEi5ciC8JRCQW3ZuQBgSIksCSNBoEQ5OLLGzjaG0smAbVNaNnXUs+aeyD3YLEw8hxXgmz4VE8pg1/4i3kkM5AKdg92qxa/uxzCQvlIjlLxYyyTvqg5t/kZ5bFWYpewlZ1JE4ueM0g+sNlkuHZY+2LUyIhQCSsIUJMcrb/SCKdhu77R3aB7Ees2seMkFuvpNxR/iAH/yLfGyDs2L97bfYBcQTQCDZ1p0ngG29hGuUPzRGLtFBMhjLg5m19XZhWdB4gohi4kfWxi9Jdja+SU95nwTUrnMIIgw5KAO7S6mcbdepG2PzdRuKshZl5Eprz5OUIrtMSQyguIBh1tmmCJNkibQS1PeM+XBnQgcJBJ7zZ0kR84/lTqysqKf2l40Q21h4Fpapnh8SLP84KxnIqJNhIyrqAC2BNu+0kBb1roaxca+JE2bhk3DltURSZpQdUklefgflTywsHjb/IG62TeAHq9uFbK2EJHfMEfMQJoN4hns2nXRWQpJFhPZdwEChfThPANtHOdcglxzPg4jRkOGLlzPk8ibFhkga6EqxJZEuWqHQsLKhrxRP8nPbMCb5QRouGQYe3M2D3b2Qhd2VkAfjxqRbJxgjrYNCCvIRgmDMla8qWf0qx2B9iFKMTiyBEnQJoZwzrFWItDABV9cSmacr00qX74w4le/KJCFCvQlbiaITa6SeQirmn3mX8zTS4h0gTvSdl9SkSbOcH+Sf9dySEFiR4XCf7yQtZkXkv5ws7rMgTvFdvq7/as9Z/kFCDFImdV/yTCzQFljHckddQP0J9obQ4syPC1vHbPtk2zq0eW4bIySR6QQJYbR9/Pbbb9Tii9fGAvjRR9PUBRecq7766kv9cdxRHXXUr1Tfvv0sAC1nY8bcru69925drxeq/fY7QO2++08ifvyN/Ah4Apgfw3YZAxoqkBwcoXxg2cnNtTSS/JIGpNGGyQU6nnTZxaXZyLkhqjuvJI/MeJbG17X6yUQP5AUTKr7/PrgDAyQCfniO8GGFwEZeCCGtJNXlIjkU0g1a2NJZ/ZJjjD4RZQYspTvezS/zHc2/G1OjSU0Up3STKpAHm39OrMG95PzjKYlTpfLPIxNSqYVLK1Oa/IMAud2mxDG71Gllyh5zuhBS/vJBhFAgg999BytysJ3Du2Ct5G7+8V4KKWQYBJXwOFp84C/48YE2JvqDJOIWqmnTPtbWv5nqm2++Ub16LScPCj1eeumFapdddlPbb7+Tev/999TRRw9XY8f+3ZFdqbfeekPdf/9YNXr0LYYAHnHEIWrTTfuqlVdepVBZfGR6UqIHwSNQLQKLLIL1xNCYyVcoG6Zq42M4xhVqFwNRgnRhjUE2aJxZa7taW2IRkSoQyUDEsRfx8qDBrWT1yzLDF/EJAbSECPdABsSS4iqE/IQQ6biTBcJj/WLhKOgmyU3QSgJ5YKXBMez4kbGgEEIQjrvSNeRB+qJsi8ApKf9Iy9aFaPnjg0HWonOtfkXIVAmHSs+z4JScf/se4P1GfbD1nx9MxAAEp7KVHFhy4XW2Tc1gRYZMLvlj28WPQ1v2tOAl5d+tBwgDLODiJpfIMzynX+MTVy0/3P9OHX74Ydoy95VadNFF1XLL/Vt98cUMtdFGm6gNNtioMIvgDjvsrAYN+h8IoFZaaWXdjs9Xs2fPUt26dTf38Ddx4hNqyJBtVdeuXc29IUO2U4899rA66KBDS378STEIeAJYDI4+Fo2AbVyKgIMNthsT4ufi0hwvA2tS8uLS8cTNja/a83IE1LX6weIFmV0LGxrjuBm+8QrRKkM00GiwgwqhOkJImexkgSSZqsWn2nCQi/lkDFDW0G24F59/LbkmRFkIQRbZ6olT2vIPL9CN/OA9AAaNdKiXlpDG1/Fy8tn80xewZ31n2Uu9kLrA7lBb/gwf/CjKK1M5eat9Vk4mTqxKyj/xkPxL+jbffA8wuYQkkO1nfLcxQsvSM2xL8TH/858foB5++CE1depU9d577+rfaJMM0jjxxFML6YbddtvtRXR1661/UmuvvU6A/OHh9OmfqXXXXb/kr1evXuq116aUrv1JcQh4Algclu04JpKtIgBAYxN2ooiDVr+uukskzeLSbAjDcWa/BjmB1c/uaewSUCF+OCav68cdGOLyGJaHDTuwCHblBMcRhQkhFaJ0m4HYhdOCAglas6pbQDksb55r4EqZaPkDrrBoubJnJwQkA24cWWRMI1OW+LL6jSt/lh0UdrBOux8FJMTZxxFmlU/8Ayf3A6eopXlYbuj2tMQWaQkBwnsAHORa5GE4hLEfE8Ak3EMg/ut1JE72o6uSTJXyz3yzTQr3EiBuWImZBtoQd0KJRkbjSCdHthv77LOfwu+II4ar7bbbXs2aNUu98srL6u23p2aGafz4R9SVV14aCNenTx91zTU3mnu33HKzeuCBseqqq0YF/OCCZRu8jbx4VzwCngAWj2m7i9FyNtugVA8CCaA0UmjowlvKYVmVSg0CG1BKgbjc6yyyueHmzp2lgzKPsDwKARXyh2fAgo29XWYCDXCc1S+7HFYhEh8qOSGFiA942TFEbNiBFfJBf7Y7mePqGtewIg+UF0vO0EV3quB9loPNP+5KeMSRTAiECNJC4pYnY47+B600jV1yRqRDXmF5Rh2DC3YbipWMeFK/23KWDwKpByaCAv6AuzuEgPXJLltSQBKBKFB2svyQnj8QKH9bF4CP5N0GR5kCB7yLaeqADZn/jGVnZ41X213v5h9SMc9S5tZKHrYSs9xZ/2X4gFgJWVeYR8SHNNAl26/fFmr11desOvNDhmynu3G3iw1/8cUXqClTJmsyOFottdTSET+9e/dRM2ZML92fPn266t27d+nanxSHQONa/+Ly4GNqOAJC2vILAgIFh8YIygWkixYOLi4tE0/oq/b/kMM6TOboqLp06WYIHR6BeAj5gD8qxXktMqN7DjuQYKurYl81KgN86TN+4EJSimVnoISlTCDfghYLGxUjwgp5tXmr7xlwhdVPug0hc3J3frxsyAfKA8QDHwWSf9zDMziSQ3ZPwnpLC+58Uz7BsmWdC8rUoUWm2pGa+JzZu8QJ5YeZx1TQxIl5lPzjns0/rKdS32glRr6Qd3xMYewq6gSwCWNgU04+QxiERzyQiR842I2lvji55Y93DO9CnGMdoLysA/M0nvhAqy7/cWkk3ZP2AEc4fOAU9e4x/7Busg1gHZD449qAeaYOsPyxFI7I1FnHgXg6mO7XV155Ra8D2DMpS7nujx59ne5afkf94Q83xJI/RN6//0D1z38+qubMmWOskOPHP6oGDBiYK10fOB6BFnUb/9DfrYzAp59+7TKEygHakA/oWHyJg6DNmzfbKADsupHHoWHmjiKomoQWDTutftmq65w5M00cXbt2LxGCLLJRlvjJJq7VD3GKUiRZxZ38Vj/EkseRnFLRJ8UDJeJaCIQ4Jfkv4j4wEuKH+GplOUKZQMnxx/Oo/JJ/1mN5TiVtiaTcr+cR8gMnUdR4D1xyW0kWhMc7RKIjFqJoqHD5l6sDUZmi3fXRFGp7BzIFu6HZ5ctUUe4se/dDzZWoFu8A00Q9lzF2jemGlry774Gb9yeeeEL9+c9/Vqussqqxst16663qlFNO0zN1/9f1Vsj57NmzdbzbqWWXXVZ1797dlAuwHznyUm3xm6FuvPE6deGFl5m07r77Tr0MzBiN30I1dOhepmu6ECFCkfTu3TObUgmFb+2X7TrzRRSeJ4CWAEKRdO3arWpY0Vi5kzxAokD8oIyrcbAeouGDTNYiUjmmqBwMg3i4vAwUDqwxJKhQLCQ0lrBmUdSVJcruA3kIkgeQGVgFgqQoHHMtlKGkAZniFHU5wiFhizgiff6EFNICEo5bCFGjugwhT5Akw2KLrdPEqheWON018s7yt6QQ12GXVAfwLtWzyzcsV9w18uTO8q1EksPlT0zCMfOjQMbRoR3KUkcpEz68aHFHWwC5ssQRlijvNWRCncL7R8c8/fWvd6vLL788EH2PHj30zN+NzTjAnXbaNfCsrV14AtjWSrTO+fEEEMrqO20BnKUbuOoJIOLg3rmilBdpIW7Vf6MIAUSXbVrlGbX6YRs3dnV17izWTSpNNqrFjvXLW30hExR1Ggub+LVHwd6VorqZxm4MOA+T5GawsIVlCsss10IIecxGBiSOtMdwnUKaIH+1Ig9u2dNKFCWEIEB0fAZZ0JVZK5nSYhUmydWMs2X+3Y+iuHdAI6DLwf6S60DzkuR4S/LcuXPUiBFn6TZugVpiiaXU669PUe+++475UOrSpaueFfy4Luv6du2nLf8i/LV3AlidaaUI5H0cbQYBUQRoTLM6hAFZIclCaCgbjnWSeLPGGfVfWS7I4Vof8dUuk01oScPz2SUlgDTs1zTH+jWb1a8SeQC+tAoSsXhlSMuBHVhP5QdCjfgrWUcQZ9DqR2sWwzLdRvyHyYNY2IgBLYQgiK7FVOQkbkFCIM/yHBtBHmwdoJJPqgNuvuAH7wTKsBFW0midqp4kM/9oc2hdRdxS5u4wD5JjSw7j6kC4TlVDSF2ciziP1imMlWRep037UP3iF79QO+64i16Q+ThdniT6WAj61Vcnq549e7Zp8lcEvq09Dvm0a+35aJj87dkCCNA76U8INJpz52K8ndKLiPYwxzR/aDBButBIwVEJdyoRrTzdyYgP4xKRBsYlgpwlubDVD8RPumygBKDs3IVbw/FQEaKLFaSgMa9UUPkIlsl5Duch7lqUIYhQWAG6/pnvKBkIK59K3XNunLU6R55I6FnnIHs5Cxv8S955jPuYyGclRRooP/mgQB2iTPm6fPNiSAsprN90kIt1Qu7Yo9QBeQdq9R6Ey68edSpdHbBYgEzCwi1Ey31Sz3O3TWCdslbbiRMnqpNOOkGde+4Faptttq2nWE2VlrcANlVxeGFaIwJuY4/G0r2Oyw/8wOIHRQwHpUHSJYuT4m6cosX9LI5kDOnFOSj0+fM5IxLPXauffP3jiIa8Q4euWl4oabtkicSJeGTpFeQ9rAzFXy2OyFsWQpNFBpYjyEg664gxmOkEkH844ELX+AkxkIOEBuXH+sAPjvIkmeUJP/QHvIUI8og8xllJUWdYF8pZScPlhzooHx864oY4yBQkpJYk4xnyKzjU00rqEhpgyvKrPUmOrwNSD/hxFCwotAdoV4JWYr5PQZ+1uGL52aEpbp3Cs+uuu1bdc89f1W233aW3V1u1FiL4OFsJAr4LuJUUVPOLCbIVT7Rc2dGIc6wf/bp757r+qGjcO9nPk4xx0kDC+igubPUjeQGZ1bkyCtE2qFA+6N7BEc+ECFhigMHfshZZPuuQyBd3DCrEYqx+cenIPSGEIHgtvUiB/LtkQMLgCAzxDJNmSI5JzF0/tTwPlx9koIUtuxyWDFBiln+wDpAgYQaozRXzHbSS1rv8rDTJZ8iP+0ERtrDZOoA4sAQP34/oOwBMinkPWH6uhbT68kvOefonrANYVzM8DIQfsJCXdSBIDqXuS10glunTTeMzXH60RFLNY2Hn4447VrddnTQBfFAttpiMaU4Ts/fTFhHwBLAtlmoD8kSihITR+EUVKxqmJKtf7cQVOSwxhaLiWD9q5ySrX9oZvmzExeIFpRclA1D0LiGEAoBVURRC1vyzkbezDBFPtYQma9ph/1SGUHzIT3DyCeuBYCKzD0EI62clRXmT0LAOhAlNOD9Zr5kX1DNbB0T5g/SirOQasoiVNJgOPihs91zwWf2uooTUjhcrJ4XUgaCVtNJ7gHrAd4BdpRxbGk6HdT1+AkPYb72uIROwsl320fcPfqKkGPUBUsqXQfDjEE+AZbUO6dmx1NwjHRjDYWu3YcMOVfvuu7865JDDcqVTrXw+XPMh4Alg85VJK5UIDRcaPTRiwSyEx9glWf0Qym0A0Yi618FY019BJjbaWGPQtfotqokTXwHp8kUeSGbRyFurH+QgyUrucoIfyhskAyQCYg0ARrAOWSWAMEIINQJl8xxV0lheornWq4M8nOUbtpCKUiQGSYqQGKYv3zifVMB2HTbgWo9B+ZSdBLeclTQoM6xu83Q5ykcBjqGXKBig0Ct5N/ihgncwSmiyJAjZKX+59wDvJN8JIcXh/EMuLoDN1JN2ickiW16/lKkyISUGWYcOEHvgUI4Ux+UBbRXaWbhw+T3yyMPqzDPPUBdffLleZHmruOD+XjtFwBPAdlrwRWU7SvjMJ66JHo0lrW1smGBt4y4GySSKcpFM5pVRlCgUDbqdRcHBCsQZvujGgbwgeyB+lB2EjeMTeV2t1YjpJ42hcwkhFJ2Ml4tXAkUr6bzYSvgoIcV6dRwvBz/xilCIoMWAZeOSYrGSoi4IoZBUyx+jSrqx4+pYD1DnWddEeihq3IO8/AUtxfJRQGJUG0KId6OWFlLkNfwe4B7zKwSQ4yiFEOJ50NVvvF8w3eAV5Kt2HUT7HjBO5J/tDjFA3HCCQZAU84Mirh4gHvdD1R3vh3bs0kt/r8aPH6/GjBmrlltuBZOG//MICAKeAAoS/pgLATRwaNPYsLOLhNY2kihuU5VuMVQYP0z7aBRm9YqPjay2M7VMNgGRAPEr0uqXBTRRhGjIYR0SJYBG37VAhpUA5Na+S0lVS0hLERRwElY8yFOabmgqQuSHHwGCQXkrKUhE5e5CZKsSIS0g65mjQB5JsqjkXSWNyPCcP5cM8B1yraTATkgh6gTrU2ZxSgGCWNXHQiqJsx7QQuaOI2Q9kA8B8U1L4MKFNv/Me34MJIVKx6CFDYQ0X5e9yC8TrJC+WweAA955tgV4KphYDFAH3NUJ8P7Jx9fXX3+tjjrqSLX88iuou+++X6+E0AWReOcRCCDgCWAADn9RPQIkamjEgtY2u55e+rhJeEACQQarcSRV0mhihm99rH5ZZBUlIIQQYeOUABSB65g3IUX1U4IiA5STayFld2913dCCgShC5F8UH8mAvZZ8Qw4hhDyykriWENxPQ0glT7U6Eiu7lIqrpCVNYEAcKpHiypZiibPcERg3G1bIP3bZkd0zID/eWTiSIBKioKXY1oOsXaYm4hR/wApdqyJXmLyniCK1F9aDKClm/kkG8W6EMUACb7zxpl6CazG12mqrq6lT31TDhw/XvyPUT396QOr0vcf2h4AngO2vzGuUY5IUbLZOB2sbN2inckufLJUB/AeJT5oYpMG2ckCRdNBfwIua4K6lDeQyqgwrj/VLI0e1fkQJfPcdSA0tRoxLmLBYBeyzMBnKindaWeuBFWUHIaKVFLIhXSEBQcuIxcDNgzsG0b1fz3NiVX6iQJI8gkE8KbZEwGLCmNLUA4SpthszSd6899NgBT8kP66VFHVDupHTY5BW3kZjJW1BcHINegsWmnxLPv773/9qsneYuezWrZvCbN/ddvtftfba66k5c+ZoYsi2T/z7o0dAEBCtItf+mBGB9r4QNLtvsBXcbI0cCZtrbcsIp/EuCzh37mwnaaSJBw22O8MXChH3IA+6oKlAih/rl0a2tH6oDJMnn+C5KD0eoySZiqPYCQVIyyUOwBTWEJKVtLkrxp9gQAuhtfK6sdcCAzf+cueQD5MXcISrBVaCAcpFSHFYJhcDbSvTj2G5ldnY9e3yDcsm18Sq8qQK8e8es2JAgpxO5TWye9zNo3sexgofOngHsZbp9dePUhMnTlAffPCBG0R/+HY1kz/69u0XuO8viIBfCNrXBI9AlQigQQIpcGfqQdmJta3KaHWwdI20xM+GcYGWxbU+gvAp0x0NJYkuSypEzkwNdoE11upn81GZZDEPcbMLxTICgogfiBHJkUsEsihByIW4oAyBF13jiQPyo2mV/lnyh3wJyWH+gxjgmYyfy4oB853uP0wc2OXLrt10MaTz5dYDdwwdPwpQF6L1QGJG2A4dsL9w8XJJGmmO0e7xdMvOSNzVYSDWZZlpLd3vjJX13f0Aa46hBOEPMHcowcyZM9W//vUvtcEGG6k//OEGvZ/va2ry5JfUyy//W02b9l+Byx89AhEEsmnaSHB/oz1bANlYztbKBov80tqGhgkTLfI4jCGEpYITNrDgcrKDEgHxg+KFw3g0WPugHLDUCvbvDTpUeWs1q4V1Jphe5SviWAzJsopfCGFcN2k6MoS4yk1eqJyz2vgIkiyUeZA4FIlB2hwQKztWDO8D3gXUw0Y4iwHqlSXKQVlIfoQYgyTXQ17IBpnko6JWWFkMyr0LOte6rPhDm4EPHbYPeca2BnHOdxWs7/gAsxNQXn75JfXLXx6pjj/+ZLX77nvkS6gdhvYWwHZY6D7LxSAAZdGxI/bNxexLErGCYjbRaD2R6Khw52vyJwPsozN8ERiNJWSDRQxhpHGXiHGfS8BYJSDP6nEU6yRlQ3dhviVLUCZU4rTuxCvB8EDyICFEvkUuwSBMsuR+PY8s88okKw4DlDvrAckAr0GMLDkCCaiGDJWzztQTn3BakMslfyCkrA8WA2JqPxIsGaKFjHUpHHP110yvui7frKkm1QPiAgzYHuAaP9fhPaRF2b1b33PIFyTKdn1NSHLHHXeoq6++Sl177Y1qvfU2qK9wPrU2gYCfBNImirFxmSBhAWEgW5NjMRLFM0Aocm4nx0bbtfqBzFG5c6wfFRqXBxGZcA9+ICt/LhEIkqGiFaDIgHTdxh3Kht06xXbLJSnBSmRI5ERXK/AFMWqkg7zBmcd2yYtKcrEMga8lxawjVPyi/HGstB6jm1a4DJEOrX6NxQpyVbLcwo+8A1IXeB0kQ5YQ4sNCuk1dFNKfswzlgy1quU0fU3U+pR4gH1KdIRMskVIHJGYSZ34c8B2SD8R8GEj8lY7RMrRjbufPn69OP/009Z//fKDGjh2nllhiyUrR+ecegVgEPAGMhcXfTIuA0SMBz/GkLeClwgUb6qgnaRTF6gd/sr4gfGed4Yv4+HOJQHnrWJJsUWmT75BowApCrPJa/ZJTij6h/ElkSKykbjiQIruxPYmgWBldf7U5B0bR8Zq2C6yaVMMYIA6pB3FkSHME4ywZIsFziUM9y5DSxP9HSVY8URYM9CtkrM6ITTBA/eQP70eYEAoRFEJUuYubZVj7Lt94RJLvSt1CHuEwHARlbImw5B/H4EciSSSxKLrrHPK4E65c6/snn3xstnQbPPh/1O9+d1HDPsxuv/0W9eCD95vehnXXXV+deOKpui22dGL69M/U2Wefrj7//HO1zDK91IgR56kll/RE1VS0Jvpr7KdqEwHhRcmHQBHEyEpApYIGWhwUG2YHC/mDpaVLl27GOgXixy95NOQMg2v4lS4wNO5cvNVWecgMQkMLYhdDJtFlbBUBUichhEUFYw0xyxjniNeVT+Qsd4R/jDGCXAwLItZF/xo3VkyIAJWe4A1cRBlKWdBSAtmBA47IixCFcvmu9hniJvmkJcaWYWXSkTVN1gXsVMMxrPywEPIUxAAy4WeJQ75u+6yyxvmXugW54EBQkAd2ZcaFiN4TDFAfEdbFgMRI1xJDyLHFXrr3gXLhfZHdgPAeNq6+S65JsmwZQiaxdAMzYsA2gQRM3gfEQEIo77L7PqDd4bstKWU7ol3Bu4U0UB4sQ7ZZzz33rNpzzz30mL9fqRNOOMW0XdliL8b3a6+9qsaNe0DPPP6T+uMfb9fyLlB//etdgcgvuWSk2mWX3dUtt9yptt9+R3XZZRcFnvuL5kDAUvbmkMdL0WoRECUpJKL6jMAqQYeGlt1ZbBSh2DDWb9GSYstq9ZOY446Im12xbHDZkJNckuiQYLrdQ/z6t2PHGEc0dslHM5EGSAmFBWUOhQMHJRjeWxiyQ24qN2IgeOjgxoEg2F8+CyHTC+7jC4Vcz25olCNw0IhoTCzxYdkH6zju4ccwQRyITm3/o3XLdhfmSdnFAPGwXFgXpPxRb4LvA8mnEEYhfgjPMgSmjXWQl3We7Qk/DEuNTkA4YoBnwTaB7wOxAAYWDwbnu+BaS+Pjl8SArWvpdi3KeHbzzTdrMgXCdZtaffU1JVhDjosv3lP9+tcnqa5dOdlvzTXXVrBMigMhfPHF59W55440t3bccRd1xRWXaMwX6jrQ+PIXOf1Rj5H3IHgEikDAJW3542NjCXIHqx8aVzj5KkejjGdUPrVb1w/ppCGE6B5C40bHRh9kRcJDflE48NMMijCscCAr5BLF3ZIZc8AzWXMMN0gE2DUIUpisAIUMIXx5BWgSaokbWEmZu4pQ/DTmKESH5I8kGaTAKn/iEuwqZD2wOBQtu0tmEDetVSQrRafFeoByDJIh+TCQMsNRzkUGWiK5BFPauiBhizqG63w1dYuyyztOydz3gXmXOoHnbBeIndQDaRtseFqU5SNMLM/KLOR80kkn6MWdZ6t77x2nevTowUAN/F9xxZUUfnBffPG5tv7dqX7727PMNf6+/vor1a1bd10XSfZw7N69h/ryyy9Md3DJoz9pOAKeADa8CNqKAFTw+mM1t0ODCsdxN/xKr5XVL4uw0viXn0yAxt8lhG4KJFn1tGS5qcs5lJQ7xghdq1CGaRUzlRkad2sdE6UfVIAt5kHts5wCFLmiZMYqQvFT76OQOi4PgtTj10GkP1H8QoCENLofB3aSEeJKi3k430jPtRiBuJPApyPa4fiquabsdjwp4ghblCVe10qY1TomceQ5ol7ywyJKsvLEi7Du+4Br1gXWAXvOdkEIIcteiLStHxiCIB9hH374H3XooYfoXT2GqiOOOKbqugKZauE++miaOumkY83yM5tu2reUBD/OS5elk0a3eyVB/EkJAU8AS1D4k2oQiBK+fAyQJMCdKYjxSBz0Xw+rXxYMwgoQjb1YhJAPXrsxojubEyrEMpSHBLgxpzmHPJDLdsvFk5k0cbl+yitAKEIov6ACZBhaRIABSDNJAhRq/cmMmx85h8xprZHMTyXrGHHIMtNYZHGPlEvGkXLyQhYC78ZV5LmQLLwDcGJRjv84gI/K1jH4yuvYpmCYA1wxdZ5xxf/Hvw8oeyGFYjUX4sd4HnjgAbON28Ybb6o+++wzPdP3t+q880aqwYOHxCfUwLtvvfWGJn/HqQMOOFjtscfeAUkw2WPWrJnmYwDtHHpHsD1dz55LBPz5i8Yj4Alg48ugTUiARk8cFJR7LffLHUXZylg/+uXafjgH+UMDCuWCpOA/aAFJ7sJkXLX/R561WKbhg3x0sPh0bGn8LUHMSwKy5kbwJYYgDbWbuBCvAIUIihIMEkLJD8gfJ6DY+iTP6nmkJSv4IQLM0jrWf2sdY31gHXbrspCCtGMp601m0uSX76L9sEDe3XF1MpYSccGv5Nmeh+uCdLFKV2l1llLK1fhdPfg+oD7T4se6hUkx1kI+e/ZsvWXbRQYf4AS3wQYbqjfeeEOPtVtMbbbZ5qUuVT5t3P8XX3yhJ6EcoxefPkVtvfU2EUEwGxgWwYceGqcw/g/H06FJogAAQABJREFUH/5ws6aRPyJwO77R2Fa2DQDfnncCkeKT2f9z5szUt77XDVb3TAQQSg2za4WcYCICutxABjp3XszEKc+QZribKWsXJuKohQsq5/ixfqL0kAfmSYiilQj5JhGCwqhO+Ulskl5wDGJw5wzxW6+jyAS83HINpm+7SolHfZqqepIGwQEYoD7g3Qk7qQtiJRW8cL/eXb5h2XCNPKS1kiaFd3GQ/IX9Cg7sRqz8TkTlyjbMIZx+UdeUy1pvUYb8QPxePffcM+r22/9i9vP95JNPdZ2wFsKDDjpUdwcfXpQYueIZNepqddddf1ErrbSyKX8Q3AEDBpklXwYN2loNHDhYffrpJ9p6OULNmDHdrFN4xhnnqN69++RKtxaB2/tOIPVpVWtRck0SpyeA2G2DhTF37kzTIHTt2s0QmEpFhMaQy4mwewaNPLZ/wxFxwS2yCBpuWgRw3WxWP8jERr3yDhXwG3YIC6WXhgSkVX6SBuWyEypgiQG5pnVKfNX/GJULVj9YSS0WUalYB6TrvBZ5oFxWOdf7w8LNP+tElBACF7wfwIukqHFNOGR0x5IKmYmWXfo7wABEWN4HHOMc884PJWtho09+ILrW28Z+8Ij8wQ/EYFf0229PVYcdNkx3qR6kDjzwEDP5Y8qUV8x+vm+//ZbuZt1H9e3bT6Lyx4IQ8ASwICDbazSeAMLSBaWkNGmbZRpurM9HspJcK9AYBq1+dqwfuke5h2+8AkSszUJmgo06sIguo5KMQvSJSwIqWYWEAMSRoahcjZ9QgdymkUtIgFhJy5EAIYS6Rug6UT0ZCsoVVM7RUqrPHeDgfvDEp1p/SynrqF2mB7i7Xb7xclZ/N907gbKHxZzkEanh/WgWKynql4y9Dcv1j3/8Q51zzgj1+99fqTbffEuI7l2dEGjvBNCPAaxTRWsPyUARmA/4mK4syT8a8zirn4yv4vio783ED1E0aDzDjjNtvzONvLUGVE8AwvFXuoZsrnION+qVwic9B4YyZgrEOk75gRC5pMjm384qFMyKkitJ3rT3kQ8uHC0D/5OVM4lc0vg5m3dgkHcsZVSu5rSS0uLHMYhS/qwDnNgjEypIhGVdSrwP+YhxuHyJl7UqQ65aj9ks904QC5A+fCyG2wm0R7AgyljCcG5qfx3Fy3ZF4wMH4/4mTpygxoy5T/Xps1ztBfIpeAQcBDwBdMDwp8UgwMY4GhdIiWv1w+xe+UKXGb5osGnIYeNNKxjjAjHCM8RvG340+kIq2E1oCVFtCCFk4pg6Wihr2VVYTvkRG8Ei2lUmpIGEKloe9boTxotlnl4pCyFEucoerlIHEDeVvMXBDKfTmbP1gGmFcUC4orswi8C0slxC9NldKhhYHILLEAkOWYcQhPNSWa5wiNpcu+8EUkC74o5xlVSDyzGxbbDDSYolxpKme4ziZbuiv/rqK3XkkYerlVdeRY+nu08PfensBvXnHoG6IOAJYF1gbi+JxBMuKOtKVj8osUUWIaGC/6B1DdYguz4W0CQBsEofjS3v1Y4QppGr1iXtKj+xEEIB0uJH/EQG3sMzd6xUeuIl8VR7JF5u11e0HKuNW3AQyzHLnmQQdUm6AkmKmIoQIYQlabIfDuH6Va1cecLF45W87zHyAQtfeF1K5Jn5I0Hku4F7YRziiXE4D8S2fl2+4fTLXbvkD3igKxrOrQ+sA2grkAcbm9SHvMTYxmjP4uRieSn12mtT1OGHD9cE8Gi1zz7720ANOJs581stx6Fq5MjL1HLLBS2Qzz77tN7P97TS5I211lpHnXLKGQ2Q0idZKwQ8AawVsu0wXmngoHzFoSGcP3+OaZBxr7zVT77msUQC40iyriEtpmetIeFGXxRXERZCKNR6Wf0Eu3RH6QIkXrSSAhOSIRcTiY/YCSmsDSGE0uXs0PLlKDLlPTJP6CKNLk6dRAiRJsOhGYz/eIGfejiUE/EiS6uma5Xvg1i6KLVb/kk4CBHiUd4rGz6vXLXAD/lyPxLDeBEL2zbIB0GRxDguX5Xkuvfee/RYv4vVVVddpzbaaNO4KOp275VXJquLLvqd+s9/PohNE5NQDjzwF5qk7hf73N9s/Qh4Atj6y7DhOdBtsVakVgwqneKtfjaF6BkVOYSwjb6r/GgFoRUgCyEMN+hIpxmtRSAw7i4CIEJw8Rhkx8FEluJP0iNZRgDiVWlSUIqoM3lhfQgSQpcwSGSUd76xjjFMbYmxpOse+XHhzlotbsJOHA5iEbTvhHSjUyrBAWUHzMSh3ovFVe414ogyS9o6LU4e5Ad5AcENDyEABsnEmGEsQXYauZiEKJcdH4kJYfiAhcP+uOecc7aaMmWKuueeB9XSSy8TE0N9b91//z16Lb/faLnOjE349den6Il9c9Xf//43PT5xebP/77LL9o7162+2TgQ8AWyd5daUUrOhBen4Tu/hixnBtP4UZfXLkmkqsXyEEFYDd4xYkjUyi1xF+A0rmrD1w02DOJAI4T7ChhU/7+XvOg/L1SwztSGXS/5IBND0hbGoHTF2y0TOKZfbRZ48MUbC5D0m1wfXYiw42NSAGRxklvfcPq3fmdu1Gv3oSS+Hi4MMpeB7EYeD9Bsnz7hGWHcRe+5eRMymT/9ML/FymF7GZTP1l7/crT8g+XGWXtra+LTduWynw6lg544f/3gHPTO5v96HeIwaMeI0bbkcFfbmr1sxAp4AtuLCazbRoRzgZN9UKA3u4cuGEBM98LWdZaxfUXlkg5+NELppN4P1A/hC0VjrGpadsQPLXXmTzq3iow/GKUTIVX7ZCGFQMUOuZrEWhSd6hJfpcS2l5XEA4ZBlZ2gVKm8RSioD3Afuwa5VOzu0XLiin9n6QBxojQzuUkF5Ue9aBhEWiEPa/AAv1DGxSAJ/WuKrLwM37TAOce8FPhggg/Qg0KqI9DFhjSQxLNdLL72ofvnLo/S2aaeqXXcd6ibZ9Oennmotg0OH7qWuvfYPZou3bt26N73sXsB0CHgCmA4n76sCAmEC0AirXwURA4+TCSGUjHzx2yBQ1lA+VPzSTViM8rGpJJ+FCUNR1rVkHIQM4igWIcHFEiFRfoJZWAEm56i2T8KEAXIGu8iD6afBIZ4AyJIr6cdSht+VrCQ+KHlxV/bjgh9yJFj4eOOHm1jIojhodE33Kt8LYA08i3Lhul8PS3xcfYjDAbKJQzfvrbfeprp376G3QttUTZo0SV1//Sh1ww1/Umuvva54axXHefPmqTvuuM0sTO0KjG5t79oOAr40205ZNiwnCxbM1eRINluHMuhoJntAoEZa/bICAgUnRAZhZfyOKL4wEaKSEDKYngBklStKGGpnXYtTfMy3JYRxBAB5Iglovp1GynWRJ5VFHA7xBMBdcoXER6yEYSIEHMNd0UVasZLykua+W8eQd0v+EBrXtgs4ioO+YyzTYiFkXRBSGMYhjTziJzo+MpvFW+LJewQmLg5huRD/u+++q2666cZSUgiDPXCffHKCmj17jtp4400KJcalhGpw0qVLFzVu3P1qjTXWVFttNUg98MB9em/ijfQ2n11rkJqPslEIFPeZ1qgcNDhdvxMISN488wNhgoKDwu3UaVFdMuxSkyJylQzu1eNLXtIud6TywuBtfs3HEQaSoGAXYThOkobiCGEzEgbiQKIM3OKdtRCCBFB5xvss+m64joHIoDyLdqwrrA8gA0lYMP/EwO26b566H1ygO67uV8LO1gnBwVrFJKzFgVhUqhOs+7Xr8hW5qjkGyZ+1LEPmRx99SI0dO1ZNm/aR2Q8XfsWdfPJparfdmq8beO+9/1ddeeV1ZhmYkSPPVYMG/Y/Zz/ett97Qs4TP19vSzVZLLbWMOu20s1RbmwTS3ncC8QRQ3s4qj54AArjvtZJFo/+9nvwxWx9BAIJ7lQYtH2ELA+KovwsrGX7hgzDQ2lFOIiq92hFCKhlYValM824xVy4vWZ4h3+7YNVp5mmMf32AdK3aMWCWMgIt88MDqTUIYJUKIhyQLpLTYrlLEncVBRneSU1Fkme8GySAJUBSHch9L4TpWDSnNgkNav5ALHxioZ3Co+64F9+mnn1InnHC8nixxrvrRj7ZXWGMPS61gHOAHH7yvDj54mLGopU3P+6s9Ap4A1h7jNp2CJ4BoCDHoH5M/sIfvrLLlXdTYtbKJpHhYtJKh0stPCKlkFhhFg2xAUQa741JkrkZe0ljXID+IEBQ/CEa8ZYzLa8iuDFCkeRzSICkl0WgW6xowwNAIYhKfw6yWsfhYst8NlqW1YmWPqXIIvhu2PsThIYQQpFgIFmIuipRWlrK8D8jsfvi4dQzPRo++QY+Zu12NGnWTWnXV1ctH5p82DQLtnQD6MYBNUxVbryAffviB6tKls+ke6NSpq1H6WD9q1qxv1eKLLx7oAuRq/FhKgoPn0eBX6g4qEhlRRm53XBFKhgoMBnU7VkrSEiLEazuLUJSekAAQJ9ci4yqZIjHIGhfkTmtdY1lGx4wBAyGFtJRFd2VIGjtXTt56EplycoSfCSlFXuGExEtdEMuYXEt4qQs8Fv9uhMuyHtY11nNYPNkVb98L94MJ5zLJiGgAAzj4Z73i/Xr/o4zc99JtL2bPnm2sfgsWLNRLpYxT3bp1q7d4Pj2PQNUIeAJYNXQ+oCAwadIL6rrrrlbffPON2nLL/mqllVZS48c/qsfAfKq3GTpS7bvvT7VX2ZjdNvoyPEaUHrtei1d6IicUifsVj3SpmIsfCUGll40Qipw4ukrGvV/vcxAVkmUhMuFlVMpLJIQQWKNnHWVAAugSQtx3lxmB5ZNjKZPqRLQssQNI4yegAI1ypBQWcBCh5LXnglZT1iNiQUyqr6vliAzkrpdjnkgGkSYtpQtMHXBlCNYJtgvVfCS4cWY9L1eW6NYdNuwXao899tbHIxtKUrPmy/v3CACB6lsTj59BwHcB24rwySefqPPPH6Gef/5Zc3PNNddSq6++htpss75mJtnSSy9tCAAadvzECmJj4Fkl5R/2n+Y62JA3nmCBwPAXv+wM8lSk8k+DkesHsgEz6Y6DLGLFcv0Vcc76YLuNQRDDzq0TnFlutwtsFrIcJqVZrWusD+m6SgUPlEsaF6z/te3yTSOP+CFm88y7gHu2jtn6gPoR5wSDpI+EuDBp70Xrf/Bjcfz48XpSxKnqwgsv0W3b1mmjrYm/cvv5YhHqs88+XX3++edqmWV66fGJ56kll1yyJnK0xkjbexdwutajNZZsnWT2BJBAo8E8+OCfqalT31SLLdZNW/6O0QOht1PPPfeMmjDhcfXUUxO1JWmhGjBggJ5hNtAQwiWWWCI1Iaz2y58Kxm7PBKVBJdPYqg+5oNjcrmh0+cKREEWVXr0IocgFGeHq3RUt2OADgco/SgiNYPpPCENaIiThij5CzqRuwmrTIg7WYk4swrGVn3GNONzu+6ykNJxakddpSSnrIXEoN8FGCCGP1fckID23p8Ct/3h2xRVXqH/84+96jb+b1AorrFgkJJnjcvfzve22v5qZvG4kp5xygtpmmx+pHXbY2Szlgvb4rLN+53pp1+eeALbr4s+feU8ALYannnqiUdjHHHO8Wn75FeyDlrOvvvrSEMGJE0EInzSzbQcM2EovOzDIEMPFF++pw0tDn6z80cCnIYRBBdN4q58AElYwcRNjiEN55V8LQhjEjFY/YN1IBywgl1gj42QR5U9Zq1f+cXGXuxeWDWUipLRcuGqepakT6NSx74bsykMCDblAABvtophl/ygjFul6Eux7UvmjL0rk7bqD3377jTr66F+aJVHOO++iplgT7/zzz1a77LK72c9XlnKR8sXC1Lvuup0mfo/qOtnRfIDvvPOP1IMPPmauxV97PrZ3AujHALbn2l9w3tEolnNLLLGk2nHHXcwP/j7/fIYhhOhOOf/880yDKoQQYwl79OgRSwjRSNttqThejEoPS89grCG+4DGmiIPKQQ6aweqHPAcJVjIppdKCwrID4V2lBwx4nTypBHGkdcTMtZQ2z5g65NUlfx06dNblTEupDCMgHvDHHAsh5LE2hDCMWa2ta3F1At3lYikFBry2dULKH7LJu5GlXkj4oo5RzKrbAo9YFDue0n03ET92aBGs0LMxbNgwdcghw9TPf35QUXDkjqfcfr5ff/2VnpTSvUT2QAKxS8mXX35huoNzJ+4jaPUIeALY6ouw9WZg6aWXMV+v+IKF++yzzzQhfEL9/e//0ONWRujGqrvuLh6kLYQD9YbkW5oZdiQ9UPQLNQGitseRhJDrc7mIiDVGGnL3WT3PIbfbFZeVlFLh1YYQgkAsXDivBEdzWYrcJXHCRD46mSJMCCVTRRPCZsCMdRpExf1IIFlGfXMdLaj2g8haCWtDjt205TyKmbWuiZ9qj3w/SAgRh7QT/DAQSzqOLjmmtRRWU/pjexIm8g8++KD+QD1XXXbZ1apv337Vilj3cOguj3O0ksc98ffaGwKeALa3Em/i/C677LJq9933MD+IiUklTz75uF5Z/351+umnKVgQt9pqoBo8eJDq128Lteiii5Ya7vnz5+s1COdFumVgCYTyF4XXCCJIxVfsos5FEEJgnIeUInytHBRycExd8uxjV/mnn10bnFmbtl6QyNvu6KxEvlZ4MV5+ZAj549i1DuYdQR3kfZKhsAVd3hGQobRYpM1LIzBz6wTkhAz8sdsY9ctaS21OZsyYoV599TW17rrrqt69+6iLL75QPfvss+ruu//W6nbBwGSPWbNmGgsx2j+MwZ41a5bq2XMJm2F/1q4R8ASwXRd/c2e+T58+6ic/2dv8IOlHH03TE0r+pe66a4z6zW9+o3r16mUshCuttKJuoMfo7ZemaaJ4utpuux+bjEkjz7UHaf1wx0jRKpS+mzQrWlR8rgWrduPDqPCyWQjd/MSNQ3Sf1+s8ShayYxav/K3id4mA5EvqAo/crkyeyRHhkiYHiJ9GHd3uS9TxTp3QTU7LII4yjFPyTkJIEiTWr1p0n0cx66hlqf9yPXHvB62iwV6DUaNG6Qke/zDFiA9MkKWf/exAs8QVZtG2JutZp06d1Kab9lUPPTTODLvBEXsToyvYO48AEKid9msn+PpJII0r6LffnqouvfRC9e9/TzJCrLHGGmqLLfrr3xa6oeurlSC+b8JjpMLy2kHzQgLCPqq5Dluw3JmE1cSXNwwVP7vAoPyBS9hRSQYtY2E/tbyuF1kQEiTEB9dhF8YCft0Z2xgf1gxkALLnseK6WCTXi2CdADZpXPgdaJahBZAdmKF3AI4EGaRUKbQpN910k7YCvmKGpBgPLX+rrrq6uvnm21raFfdJ48+T9vP99NNP1HnnjVAzZkw3PShnnHGOsWw2XuLmkKC9TwJJ9yY3R1k1pRSeADamWNCdcfjhh6jXXntV70LSRQ/QPkIvQj1AjyGcoCZOnKBJ4YtmQeqBAwfrLuOBauONN2358q0tIaRCtl2EYWtMY9BiqpDNtWAJ4RUSEJYtTILSKv5wPGmugxas5MkxaeLK6kfyX44Q2jhpkWwW8oexm5AfDgth40Mjj0uDhdQbHuOtpcHyDFok88iXNyzy504QC4/3u/vuu9Xll1+qrr76et3DsKzex/ff+jfJHLHLx1VXjTLtTV45fPjmQMATwOYoh1YrhSeAjSk6NORYdxCN8kkn/VatuupqAUHw/N133zZrEE6c+IR6+eWX1GqrrWa6jDGGcMMNN26x4BRHCKlc7EzasHIJCFjni6BCjhIsyM6f7SqNili8tRRp5rFgRWXMf8diASIvQweC8daTHAdT5lWwPGtHsMJ1Atdh52KBDx4MuRDcmukdgOxBwmyXxcEY4hEjztLrmE5V11xzvV4seelwNv11G0TAE8A2WKj1zJIngPVEu/q00PhjKYcJEx7XFsInTBfPWmutZQjhQL0w9QYbbNgy+D07IRQl2Za6CJknThgQy1gU/XyEULpVkRZco7vJ3fwFCRataxCzVli4aZc7B1aNJMxS1+2RM2fjZG4m8hcszyBh/uyzT9Vhhw0zw0dOPPFUP0YurjDb6D1PANtowdYrW54A1gvpYtOBAnv99SmaDD6uSeET+hwz/9Yzi1JjYWqc01UmhO54umaZTAHZQVbSzqRtyWzigQq/OEJYTiEnClGHB8hnJYJVNBZps4V0gxas5JnRaePM60+wAGaob/Eu34dCfJzp7rI87ZAMdFu7a4JOmvS8+tWvjlGnnHK62mmn3dJF6n21GQQ8AWwzRdmYjHgC2Bjci04VA+BffXWyXnbmCWMhfOutt7RVcANtIRysSeFWaq211jFJUuGxmxRjicJjwdzuMBkrVbSsleKLKr3sM2nTpOFiEa/8o4ofYdxxiM1kJaqWMBOH4shxHPbNTZgtwUIXMMYiunUjmh8sNVP7pZmidS246PSf//wnPeHjRnXddTeqNddcOypmHe889tgjWpZR+oNtgdm27aCDDg2k/uyzT+u1UU8rTeBAe2QXgQ549RcZEPAEMANY3msUAU8Ao5i0hTuYZPLKKy8Z6yC6jN999x09kWRj02Xcr18/9be/3ae3WHpA7wxwiF4m4udmBiEUTtjVmxBS6dmJAfUiWFT4lUmQay1trlmhCw0xZfkFuwjDZVrpmvWgsuUYHw/8SIifSIF0EFcli2QleWr1nHXNHfMaJFgiP8ocH1gg2PEfCpo2OoQQJBLvTR4XJfN2vN/cuXM1eTpZffHFl3pf32v0jkOL50kqd1jsiHTYYQepG2+8xezUcfzxR5ulZzbfvH8p7ptvvsHs6rHPPvuV7vmT/Ai0dwKYb8pYfvx9DB6BpkQAa2Vtsklf8zvqqF+ZL/OXX/63uvfeu9Xo0TdosrBALbbYYkax/fe/H6lVVlnF5INEyCo7XtvdB2pJCMNWIhKs+uzjy3xBadtdKaKKP0iQORvzu4okqJYVJEpi8q9TR/ICS1h5LFBeerfeluxFrWKUzSXzUYJVS2zKxR0lWPHL4iRhgfBCCpEOrotanNp9D5C+u6XbtGkfqkMPPdRY2S6++LjcRLMcRmmfPffcM2qzzTYvLdCM7TIfffRhvfuRJYAYrgLi+ve//0316bO8+vWvT2p1C1OnxcP7qx8CngDWD2ufUitGAGsKfvDB++qf/3zUKC7MIt5rr33U5Mkv68b4OPXxxx/pbaI2a5llPFituOKKxnpTmRAGrUBUmNmACpMYWFPccU7ZYivGtyj+Dh0WiSh2pACFT4IYJEFprGLFSEgZgmMkrZWoqDQQj2BRiRAGFywPStBc1tLgGnpZ6ppgQYsf8yjviCWE1pJslqw0GPI94ZCLeAsh4nGtpWHr98SJE9XJJ5+ozjnnfLXNNtsGAW7gFSahLLts75IEWH7m008/LV3jBAtS//jHOxhSeO+9Y/SM5dPMkjQBT/7CI5ARAU8AMwLmvbdfBJ5//hkz5m/YsCPV/vsfoElWR73ryI7quONOMl/nL774vJll/MtfHqmmT5+uv+pBCLEO4SC1/PIrtBDCsHUw2C1muwTZLViJEEJpYmKAuOYiCsFuVcqWzSpWC0JIomDHrQFjkpj6WUs5Vi6IRdgqJmWKI8ZNwrIleBTRTerGn+acHxqY7EGrZZhgpYkjzg/wx+QpxAcnhFDwwIcCznnNGMLvCe66Y0vdNRER36hR16m//vVudeutd6qVV16VkTTJP+QLO3w4ue7UU88sXQ4dupe69to/mG3eunXrXrrvTzwCWRHwBDArYt5/u0Xg9NPP0URvTqmrxgWia9euqn//geZ3wgmnqDlz5igQxgl62Zk///mP6quvvjT7F4MMbrXVIN2N0yeg6KjgrKKTuMOKTgghlIZr7YA/kpig4pB46nlMIxvzkb2bVAgQccmeV8jmEoWiSEwefAULkDq9R0UpKuQRDnVDjkV1k5oIM/xBhnpYSyGSEEK9+I6u0+UJYVwWOnSwllzsfXvccceaj7V77nnQDNuIC9PIe7D+/btlNyPIgV07ll22T0mkefPmqTvuuE0dcMBBpXs4Acn1ziOQBwFfg/Kg58O2KwRA8vBL47CP6KBB/2N+8A9F9NxzT5sZxtdff7369ttv9c4l/c2yMwMHDjL7GoOckAiKldBeS5pCfDh+jHebaf28aomCkKBK3aThcXNZCGFrspa6e/milKVu5O0mlXqU5eiOqQNJDcuWJa5q/KYhhBLv5MmT1Zlnnql+8IMfqLXXXltP1vqb2nvvn6rhw3/ZFOP9RE732K/flnpc8Sg9KeULtfjii+u9iB/U+5/vVfKCnY7GjbtfrbHGmubj8YEH7tMrFGyUui0qReRPPAIhBLJ/QociaO+XfhZwe68B1eUfBPDZZ58yhPDJJyeYLuT+/QdoQjjQNPJLLbVUSelbUhjtKhLlCCLUiG5ByT2tfrXrVkX86AoUAgRMoi667Az8RGVrXdbSaD7jCWHYHz8WuNxKNXWDuC0wXc+Iu7nWuIxawJFH7OF7/PG/VrNnzy7BgXdj7bXXNRb4Aw882MymLT1skhOMLb7ppusVdiQZPHgbdcQRR6uRI881H5AYRvLWW2+oiy46X/cszFZLLbWMOu20swLjBpskG61OjPY+C9gTwJxV1hPAnAD64AaBb775Wj399JOmy/ippyYaojNgAAjhIDVgwEBzfeWVl2u/36tjjz02sSsrr9KvpjhAFOrdrZqWEAIPkkWS5+ayloZxq36WL/CQDwWQZNSTsMtSN1imdgayO6YuHG+9r6Oy2S5f5P3SS3+v3nzzdbP/91S9tdvkyS+VCOGIEeerbbf9cb1F9uk1KQKeADZpwbQWsdo6Afzoo2nqggvONWPYMOkBS6L07dsvUjxjxtxulkjB+nn77XeA2n33n0T8+BvpEfjyyy8UiCAWpp6gdyphN9xCtfTSS+uFa0epFVaQWcYyOD6/0k8vnfUZ7B7EuCSrjK2v2p+lJYRZuoxrKXUUt/hlVKqVIQshJDG0M2tBouzEovp3+ZbLcznZ8BF11FFH6fG1y6lzzx2p0HUKh8WVQQjRlm299RDVuXPnckn4Z+0IgfZOAP0YwHZU2avJ6qWXXqh22WU3tf32O6n3339PHX30cDV27N8D42nQPXH//WP1OJZbtOJYqLsvDlGbbtpXz7ZbpZokfRiNwJJLLqWwHhiwnT+fs3zXX38Do9z2339/hTGGW2010FgIt9hiS72YbY9Al7FYgaxViLBmsQKVKwgQjGaahOKOIaSFyC5QbPMBmRu37AzkIG5uV3ltuqNlaECWiRSCoXSvo64018SiZNxA8IYPH66GDRuuP0APtEWuz7CE0/rrb2h+gQf+wiPQzhHwBLCdV4BK2d9hh53NOBT4W2mllc0YldmzZwXG0WCnjCFDti0NSh4yZDv12GMPq4NC2xlVSss/DyKAdQfvvPMvhuwdffSvjVWVSlqpzz77TFsIn9CDw/+u1wQ7yxBATCYZOHArvVbYlrp8utWMEIIgsMu3WbtVbdeldPkS2fAYwvoSwjAxFdmkTIOlX+xVOULIjwR0IaM83a5j4hO2EBYrWeXYKuF2//33qwsvPF9deeW1ZuH2yjF6Hx4BjwAQ8ATQ14OyCGy77fal57fe+ic9mHqdAPnDw+nTP1Prrrt+yV+vXr3Ua69NKV37k+oQAOE+//yL1eqrr6lnNa4YiGTZZZfVhHAP88ODTz752EwoGTv2PnX66aepJZZYsmVR6kFmlwFYDKFIrUWQXcb2mtFD2Us3aXjiAMNjbb8FLbI0W/dgcN3B6GzV+i870wKUGcNpu1XRVV5sl6+kk/boEkLUgQUL7FqSKHcQQZa3lDUmgXC3ErEi14O4Urb5Rh7kzR1igN6G888/T02aNEmv8feAnkm/LLw0xD32WPm9fNFGnn326erzzz9XyyzTS3+0naet/Es2RFafqEdAEPAEUJBo58fx4x/RX9CXBlDAWnXXXHOjuXfLLTfrvW/Hxq4+z+7GQFCtLOqzqG4w1bZ1BSKGGYFpHMY97bHH3uYH/9Om/VcTwse1BfEuvfvByXrG4LKGEA4cOFATwn56fFTXEiFE+UHRUulHt+SCHNjiDV2+9Kev9KK9sGDVgwQgP+UciErW7mjKXXtCSNmSuy7L5asez4CbEHpgItumkfzJckQ4CiG0Xejwb8lg8n7G1eYjOE4y+LGB/XMPP3y46da94457TDdvtenkDQdZrr768sBevljyyd3K7ZJLRuqhNLubLeiwjMtll12kzjrrd3mT9uE9ArkQ8AQwF3xtJzC6bfGLcxdffIGaMmWyJoOj9RIES0e89O7dxyxeKg+wC0bv3nZrI7nvj/VDYIUVfqDXP9vP/JDqf/7zgSaE/9I7IdxqlslYfvnlddf+YDOGcNNNf6gHxqcjhIiLuzY0C/kLL1AMuTpmJqa1IIQgTcHZ0dXP8gXuRTrKlryrB8kdduaI7s6Bj4BaEkLEDfIHcgoHkumORXz55ZcUdts5/viTSxZw47FBf5X28sUkFOwShIkpcBjbe8UVl+i6sVDni/g2SHSfbDtHwBPAdl4BKmV/9Ojr1HvvvaP+8IcbEpcewQ4YI0eeo/bd92e60f5OjR//qFmnqlLc/nn9EEB38k9/eoD5QcFiQg8I4ejRN6qXXnpRT9hZ2aw/OHjwQLN8Bggh1iSDJWOJJZYwi+qKtNgKbMECWIJoAZIuY5Io8VX7YzkLUd7U8xJCDbF+F9B1SdfoLl+RA8dy3aquP/c8nhDaIQWWFOazEJKY2gk84XGSd955p26LrtRbod2o1ltvA1fEhp1X2sv366+/MsNmhOzh2L17D4WZ/ugO9s4j0CgEPAFsFPKtIF0spopxf+g+POqoQ81XPxTByJGXaovfDN3lcZ0efH2ZHv+3ntp116F6Bt6B5qsWe1WutdY6VecQXSQgJe7+l25kw4cfbBZElQb1xBNPbRpl4MrZrOcow1VXXc38fvazg0y5vvPOVLMG4bXXXqdgYQEhBL6YiNK9e3e9xM99ejLKYjpL3xmSz65gEACMu4PSh4tfiJnPivsnSUi2XhWXko0JmCF/2XYqYfhm6SqHNEWRZuBBTGS7OukiDncbW0JYqX5EiakdJ4mPkTPOON3Ux7Fjx5kxrkS38f+oj2Hn7uX73XfR5/DPoRXhkP7aI1A/BDwBrB/WrS6lxRZbTM/mnRgrN/avBPkTt+ee+yj88jjseXnDDdea9QS32eZHsVGhO+XTTz9R2NfTu2IQgCJfY421zO///u8Xuswf0Ws/nm22r8O6gyCAw4cfZsYQYmHq9dffoEX5o6vOKnyMIQTBUKp2hDBKEhqz7mAcIYRsHE8XVPgcn4juzPoQ5LhaAZKSdZxkXDxJ99IQwnL1Q+RD/IhLxiLiGhOchg07VA9X2Fp3o17YdMSp0l6+mOwxa9ZM866A9OGDCVtD9uy5BLLnnUegYQj4kfoNg94nHEZg0qTnTeN/5JHHhB+VrmGpQiN67LFH6mVm9tdkcUzpmT/JjwC20jrjjN8YBYUlgG6//R5tBb5b/eY3p2ul3ElvR3WR7irurw4++P909/FovU7hW/p+Zz0Iv6v+wWKDMXjSrJAQYhzcggVzdZfyXK385huSGGc1KSc9/INccbbq96aeML3mGENF+exs1Q4dgEnt8CiHVfiZyEZyjpm0nUyZkcSGfRdzjbjxniIt4FCpfsh4PwwtePnlV0wPAyR57rln9YflHroH4hgFSz/ibDaHvXxfeOF5s5cvPlCxly/W6BSHdQixLupDD40zt3D84Q8309g0R90VOf2x/SHgt4LLWeZtfSeQnPBUFXzcuL/pQdMvxHYBgySiAT3+eJCUmWZh6mOPPTF2d5KqEm/ngTCB5/LLLzazj7fffsdYNGD1e/XVyWaW8cSJT6ipU6fqzek30BbCwTrcQLXmmmu3hAtbCMPRpbOICYFht3MzzkBOnrAgOUYeaAFzLabyVI7p8BDfaY4oK7v8THAmbZrwtfIDPOIspuPGjdNDTDhZQiaXDRt2hFmIHtfN6irt5Ytei/POG2Emy2GJpjPOOEdPlGve/DQrzkXL1d53AvEEMGeN8gQwO4DjKyw5U44AhlO7887bdJfwp3pW4LHhR/66TgigS+uVV14yW9aBEL733rt6IsnGpS7j1VZbvUWS7ISQBMZa1tx14OqUvcRkosQ0/SzfWhNCxA+Ln1jWwjNpEzNVpwdhYgpLIayGn3zyiZ7kcZVe2+8FPUniy4A0q6yyqvrd7y5Sq666WuC+v/AIVItAeyeAfgxgtTXHh6sagSFDkpecqRTp888/q5cs6aJX/N/UeIUi8V0plVCr7XPgv8kmfc0Pe0WjGwyTeLAOIdYg/PDD/+gusB+aJWew9Mwqq6xiJp5ELWIkLXYMoSs3rFddDElw7zbqPEhgggsUp5GJ3a9ZJ5WksxDmIaZpZM/rB6SUYyUx3i+4xAvqznPPPacnle2udt55d1OP/v3vSQo/zFzHgsqeAOYtAR/eI0AEPAH0NaFVIYClE+6++069aPV1au7cOXortL+pk046rVXloa0LizFPm222ufkdc8zxCpN7oMBBCI877lgzqL9v376myxiTSlZccUVDCEFc0C2IsYJwQWKPMYDzDGEAaeCv/h0YtbKsFUUIgd+CBc1sMU2evf3EE0+oU045WXeVjtRDCYaYOgDr8dChe5r6MW/eXL3d5KLmvv/zCHgE8iPgCWB+DH0MNUbg9ddfKy05s912Oyhs/H7QQfuZWXV77rmvHn+2YW4JKi09M2bM7WZ2Mro799vvALMvb+5E20kEXbp0UVts0d/8jjvuJE3c5+ouvufMsjNHHXWEGfDfr18/QwiXXnpJvUvCpeqrr75Sf/rTnzQ5XFmjRGJIgmhnGYM0WTJY/E4U4eKpp2WtOkIYlBgTLxhP8H4jroid3aPZ7crHs6uv/oPCnr5/+cuYljIPSol8ePIXxMRfeQTyIlD/T+i8EjdZeD8GsMkKJKM44aVn4tYefOutN/TyE2epUaNu1laoheqIIw5R55wzUq+Vt0rG1Lz3OASw3uSzzz6lbr75Bj2r+E3jBWMId9ppZ9W//1ZmVxmSPyGCnEgRjquWhLDZLGvAI9qFHkHEzJq1JLkxzT3GImL2N11wIsrMmTP1jP5j9CLz3cz6otiz2juPQL0Q8GMA64W0T8cj0IQIuEvPTJnySqyEE/XEhiFDttUWiK7mOcYwPvbYw9oKeWisf38zGwKwEP71r2MM+UP38aGHHq5+8IMV1ZNPTtDbD15jlqTZcsv+ZgzhwIGD9O4JywS6jEHO+ANBDFsIsS0crYPVWMNAtJpxMgXzgjGBesSk/igRh7zCAQ8SRItHvdchrITdO++8bdb3+/nP/09h/clqysdktuA/rC5wzjln6O0T/2N2P8KevaiPYecXpA8j4q9bGwK+C7i1lZiXt1AEYGHCD2MJkxwGnq+77vqlx7169VKvvTaldO1P8iEAC+Crr75slOyIEeebnWUQI4g23LfffmsshBMmPG66CmG17d+/v+4yHmi2r1tqqaVaCKEQQRxBBvHjfrKIx1rC0hFChIflimSquZafQX7KWdYge9RCGJ5kk25SCdLK6qLYBWdIP/TQQ+rss89Sv//9lWrzzbfMGn1N/WMx+nXWWU+df/4leqgC9vA9Q3+I3BhIE5NV/IL0AUj8RStEwBPAVlhoXuTsCFRaeqZcjJjxGXZiaQnf99fZEejRo4e2AD5g9kuFBTDs8PxHP/qx+eEZ9lZ9+ukn9aSSJ/R4wcsM0evff4CeODBIDRgwUO+w0LOF/IUJIa8l/nKEEKSvmSdTVNrVQyyE2bauK4YQlsMO79Ill1xslgwaM+Y+1afPclIcTXNEvbriimuNPH379lOff/65IXvuun3ugvRYrmbo0D30b6+myYMXxCOQBoFoa5smlPfjEWhlCORZekYWpJUsY7Hk3r17y6U/FoBAlm2x4Hf77XcyPySNmeFPPTVRPf7442YRYZDIAQO2Ml3GAwYMUD16LJ6BEC6i/aILld2qIFLutmQFZDVXFJUsa0mR14sQulbJMHaY2HPkkYebsbN33XWfXs6pc5K4Db3/2Wef6f3P7fu9zDK9zFqjLgGEVRqWS3dB+pVXXtUvSN/QkvOJZ0XAE8CsiHn/7Q6B/v0HamJxjtp335+Zmcfjxz+qTjvtrHaHQ7NmeMkll9ITRnY1P8g4Y8YMTQgnqEcffVQvKfI7M3sU3cVYcmaLLbY0exuDSMFSZX9yHcwlLb14xj1qg0/rexVdexBbzVW3NVrRhBB4Bq2SHTVx5uLOQAkz+YcPP0wTwKPVPvvsX1/gElKL6xUgyUP3edB16BCcQAPLIH5w2Nlj113/14xZlXvB0P7KI9CcCHgC2Jzl4qVqMALu0jPrrruebuCH6gHrB5oB9+jqWWutdRosoU8+CQFMEoFCxg8OFp0nn3xcPfDAg+rMM8/QFsEemgwO1r+Bxoqz2GKLmQkoDz/8Dz3ucIjeym7NUtScACLWQK4/SNKFJWiCpKAUqOATkqva7uqRhxBicglwwphDOBA/7AktbuzYe3W370XqqquuUxttxAXc5Vkjj0m9AvvuO9Rs2SZWwBkzYPEPbtvmF6RvZMn5tItCoD4tWFHSNmE8fhmYJiyUJhbJzzBsfOF88snHpX2Mn3vuGbMECRYZnj9/vvrJT36iu/VOMkKKdZBjQKNWIRlDWEtCWG2Xb9EoQ47opJJoKtOmfaTeeecdteGGG6mlllpaT6A4R0/weVVde+1otfTSy0QDNOGdyy67WC255JJmlj8mgVx55e/VTTfdFpD0kUf+EViQ/sgjDzUL0hexJmkgIX9RUwTa+zIwngDmrF6eAOYEsJ0Fv+KKS/QkhSVKyuX666+OnWG49967q3vuebCdoVPf7M6aNUtdfPH56qGHxpmE0T38wgsvmGVmsNwMuoz79t1MYZkaEKB6E0Kk19wTUaxVUkru17/+tZ45O8lcduvWTWPZy9R17AzTq9ey4q2pj9/q8X0XXHC2+uCD93XZd1WnnnqGWn31NU039o03XqcuvPAyI//VV19uun3xgYAF6ffcc5+mzpcXLoqAJ4BRTPydDAh4ApgBLO9VjyMcamYYyuxHXGNbO7eLCTudnHLKCWqllVbWExz8DMNaVZvrr79G/fGPo9Xii/dUv/3tWZrwbW2SguJHl/GECU+oF1+cpFZYYQXTZYxxhNjTGJMXak0Iy02mqBUeaeNF3t3xfh06YK1F/JTeu/clvZvHrWry5MlmNxc3zo022sTU9biZ3q4/f+4RqBcC7Z0A2oEa9ULcp+MRaMcI+BmGzVP4gwdvo9cY/MZs7bfccsuXBMMOLyuvfID66U8PMETvvffeMUvO3HDDaE1wXlSrrLKK2bZu8OCBekzbJpoQdo0lhNZiyKjRZYzuYk4siR9DGEeuMJ6uXuMNSyAknEA+7MmMI5y7pRuuX3vtNfXMM8+aLl+MrZw06QVNol/Qe0G/oDB7HuvneQIIpLzzCDQeAd8FnLMMvAUwJ4BtNHjSDENY9x55ZEJp9ibGDv3yl8eq9ddP3s/4zjtvM8tQwJ93jUUAxAdrwE3Qi1JjvbjJk19Wa6yxhiaEg8zC1Bj71rFjxxIhRPcgiKBMkHCldwkhziuRKzdsI86RF5A/uuCWblic+7TTfqs+/vhjM9kDVlXXIW/NQmJdufx5+0bAWwDbd/n73HsEaoKAn2FYE1gbHilIzBprrGV+2L4MxAakHoTw8ssvV1OmvKpniK9txg9iDOF6662vyb7tMnYJIYjhwoXRRcaRyU6durRYChueZZNHzobmriogq7D8CaH7+ONpevu+Q/VC3duZ3TM4KSYot/gN3vVXHgGPQCMRqG4RqUZK7NP2CLRiBAYMGKSXI7nP5AAzDDFQPjw4HgsbX3vtlaa7bObMb802dVtvPaQV57rtig5ig23DfvGL4Wr06D9rq+Dz6je/Oc2Qt5EjR+oFqbfUkyAO1M9u0ETxTbNECrqMQfCwXAqWUJk3b34EIEz+wA/ECySzUY5WSchB8ofuaJf8Pf30U2qvvfZSxx13gv6dVLJsN0pen65HwCOQHgHfBZweq1ifvgs4FhZ/MwGBes4wfOyxR/TyFaMMkdxhh53NbExXLOxxfPbZp5utrjBbc8SI88zyF64ff54PAVj8sM8xuovxmzp1ql4iZUMzhnDTTTdWf/rTH/U+x8+qX/3qV2qPPfY0ibHLOJwuxgzaMYT1sKhBjqRZyCCGo0ePVnfc8Rc1atRNatVVVw8L3PBrfGhhzOapp54ZK8uYMbere++926ztud9+B6jdd/9JrD9/s+0i0N67gD0BzFm3PQHMCaAPXhMEPv98hjrssIPUjTfeone+6KHXtjta/exnB+qFj/uX0sNM4222+ZECOYSyxJp4Z531u9Jzf1I8AgsXLtTjBv+txo69Rz322MOGfPTq1UuTv730XsaD1WqrCZHisjO2yzgsSzH79oZjlWt3FjKslJ06ocuXHUazZ89WJ5xwvCaHC/UCz1eYnVUkXDMcMR7xhhuuNeQO9TuOAL711ht6jcKzNHm92ZTBEUccos45Z6TZpq4Z8uBlqA8C7Z0A+i7g+tQzn4pHoK4IgMxh7TWsOYhJCTvuuIveGu3hkgyYjfnii8+r7bbbwdzDc2yfBoLiXe0QQFl88cUXCpOEgHW/fltoMnWKXu7nK3XyySdry+AAddRRR+qlVP6iPvxwmukq7tSJXcacDSxNNpZiWajjQFcxFrGea87zdhmzy3eBiQsogPS54xGxRM7QobupjTfeRK9f+f/t3Q9UVFUewPEfUepZK1FE6lRunlDZyDQ2Tyikp9Tc9d9qW25uiWV2TAwyykKPueGmmX/Ss2ipSVFSWZt2DEWKBI6Fmuvpf8D6LyrbErXV/vk33Pu77AwDMxiJA8y87ztnDvPevHnv3s8bxzv33fv7LW92jT8tsw6t0B7SpKQUXfW5aG/sddf1M2kCW9phGDpmVxvkLAg4SYAwME662tTVMQL79lXUSGiv4wwrKirc9f/uu0PmP77WtnGoG7Vhoj2FOv5Qbwez+E+gsHCDbfzdccddcvvt46x9QkJfSUm534wHPGbDphQXb5RJk1Jk7969Jhh1rL1lfO21CXLRRRf/f0ygNgCrcxlXZenQxrurAf/rewhd4/1ct6C1wVkV46/qRlFRUZGZ6TvV5MWeJ717V8VM9J/S6R85Lq636GP9+rV1HkSHP0RHX+5+XXthS0tL3Os8QcAJAjQAnXCVqaPjBPQ/89qLZ0L7ykrv13V/XzM4ax+H9YYJ6C3Ju+++xwSYvsjrQJp15JpretlHaupDcvToUdOj9S87yzgpaYIZr3nA9Br2tA1CzWWs8QurrnXDGoTe4/1auD8LevyMjAzJy1tvxvytNuW+2KvcTbFBe1EzMhbUOHVkZKRXZp0aO/x/RRvPtRfXLe7a21lHIFgFaAAG65WlXo4W0ET2H3xQlZJLITShfUREdUJ7zXWqeYn1P0Jt9OntSE2NpreMWfwr0KpVK5+NP19n1VuUOnNcH5Mni+j4u23b3jUTSjaaCT6Z8v3335lxndfYsDPx8Qkmo0yHejUI9RapNnj0oQ081yxf3R4aqiFoqnr9NFB2cvI9Nq/v6tVr7S1TX+Vsim11hVqqT1k0847+m3At+/fvt3audf4i4AQB14ASJ9SVOiLgGIGrr9a8ttvseDMd7/fGG7nmtl28u/6ajaFHj1h3HlzNh3vVVb933xJ278iTZiWg2TU0g0la2nRZuzZfcnMLZMiQP5mZxiUyZsxoM6mnj3ntIcnJybHjCnX8nq8xhFWNvqoxhK7Gn/YK7979uf0hoJXetWuHmRk71IyV628me2Q0q8ZfQy9KXFy8FBVtkCNHjtj66m35Xr2q/3009Pi8H4FAEGAWcAOvErOAGwjI2/0moP/BPfvs02aCwHHbaJgwIdmM33rU9Bb1tbcQKyr2yqxZ6bYnpE2bMJk+/e81chL7rWAc2G8C2mP37rub7S3jzZuL7bWPi4uzPYS9eyfYMD/a+Dt06JBNz6Yhadq0qer1zc/Pl5kzZ9ofAZ06dbIha3QixY03jhTttQy0RccAaho61yzgsrJSMyt+qcyZs9BWZdWqV8xM4Vdt7/fw4TfJyJGjAq2KlLeBAk6fBUwDsIEfIBqADQTk7UElUFBw6tiDW7duMbEHp7kbmp07d5UpU6YHlUFzqoxO9tmyZZNtEOpfbfzFxMRIeflnNjfvoEGDjf9UU+QQ+eqrPfLEE/NM7LwP7dhDVz20t/iKK640t6CnmjzIl7o28xeBgBegARjwl7BpK0ADsGn9OXvzEahP7MGsrOV29jG9LU1z3V5++QV58sl/2F6vqKgoM4bwBxvSpSpt3e9sfD9NdTdixJ/lo48+tD1omu/42LGjNsPJkCHDm6bgnBUBPwg4vQHIJBA/fKg4JAJOFPCMPaj1d8Ue9Aw+XVZWYnuX8vLWSmTkhZKa+mCNcDVOdGusOufm5rhnzSYmjrXp6w4ePCibNr0t+flvyYMPPmB6+abILbfcZosUG9vThqnRmch7935tQtBc0lhF5TwIINAIAjQAGwGZUyDgBIFfij2oBjrLeMCAgTYjiY6/Sk+fJosWLXMCT5PXMSysrWjv3rhx4+2YUC1QeHi4DB063D5mz57vs4w6E7ljx0t9vsZGBBAIXAEagIF77Sg5As1KQMeX1V48Yw/qa64B+fpcB94vWbLYhqPRoNQs/hXQSSD6YEEAAQRUgDAwfA4QQOCMCGjsQc2w4Fpqxx7ULBcrVmS5Xnb/DQ3ld6gbgyduAc1PrbPU61rGj7/DhL65RcaOvdU+Sks/rWtXtiOAgA8Bvnl9oLAJAQR+vYDGHszMXGZjD5533nk29uCIETe5D6RZLtavzzG3IaNsT5T+Bx8T0y2o4su5K8uT0xbQHwrLly8xIVpWmbiG1/s8zokTJ0xqw73y2mu5Pl9nIwII/LIAPYC/bMQeCCBQDwHNpzpxYorJYTtBEhP/IhriRYMWa+zB4uK37RHS02eJzgTW1998M8/OLK3Hoeu1y48//mB7hL755huv/bVnMiXlbrnttpFy771JJkjyQa992NA8BN57b5vNRKIxCOtadu/eaTPYTJqUZCaq/NXG86trX7YjgIBvAeIA+nap91bCwNSbih0R8JvAJ598LHPnzpQvv/xCXnxxtcmRe0GNc02Z8oDtTRo4cJBoz6POWH7kkZk19mGleQnUDuTsWTptJGr2mvvvT7NjSJOTx5sfHpMlNvZqz914jsApBZweBoYewFN+PHgRAQQCQSAn5zXbGAgPj/Aqrt4ufP/9bdK//0D7moan2bz5HRsLz2tnNjSaQGHhWybLyOAajwkTxtbr/NrQS0t7WM455xyTySTMpsPbtOmder2XnRBAoEqAMYB8EhBAIOAFqrOJeM9E1mwYOss4NDTU1lP/tm59rrkN/F8TBqV9wNc9UCugOYb1cTrLtm1bTeOvhXTv3sO+vbKy0n19T+d4vAcBJwrQA+jEq06dEXCQQGWld6NQq3/WWXz9BerHQBvvS5ZkiPbu6thPvV3cp891gVodyo1AkwjwDdgk7JwUAQQaSyAsLMyOE9NeIl1+/vlns/6TDUrdWGXgPA0XKCsrNdlKJtkD6e38bt2uNBNARsldd42RYcNuNDPKr2j4STgCAg4SYBJIAy82k0AaCMjbETiDAjffPMykO1vmNQkkLS3VTALpZ9PTaW/Rxo1F8thj887gmTkUAggEmgCTQALtilFeBBBAoE6B6t+0nuFnUlMfkry8dTJ69Eg7C/i++ybXeQReQAABBJwgUP1t6YTa+qGO9AD6AZVDIhAgAjr+LClpnIl1uNCr13Hr1i0yY8Y06dAh0tZG4yJWT1YJkApSTASCWMDpPYDMAg7iDzdVQwAB/wl4xh70deY1nDsAAAaHSURBVJaSkk9MwOs7ZeTIUb5eZhsCCCDQpAJMAmlSfk6OAAKBKnCq2INap7KyEhtvUHPVaiDqffsqArWqlBsBBIJQgB7AILyoVAkBBPwvUH0713eYmfPPbyMDBgyUnj3jbKqy9PRpsmjRMv8XrAnP8PXX/5HZsx+VQ4cO2rh8Eyfe6zM7x6uvrrS5fnVG9qhRo80s3hFNWGpOjYAzBWgAOvO6U2sEEPCzwNSpf3OfYfjwm0zcusU2HI0GpQ7WZcGCOTJ48FC54YY/yuefl4umaFuzJs/m9nXVeceOf0tOzhrJzMy2IXk0+0ePHrHSseNvXbvwFwEEGkGAW8CNgMwpEEDAWQLHjh2TFSuyvCodGhrcv7k113Lfvtfbel9ySUc5fvy4HD78Uw2H4uK3TQaQftKyZUuToeU3NhtIQUF+jX1YQQAB/wvQAPS/MWdAAAGHCbRo0cJkp8gRV37adeteN4GKu9lGTzBT9Ot3g7uOL7zwvHTp0tWm4fOs8/79+6R9++qcze3bt2d8pCcQzxFoJIHg/jnaSIicBgEEnCxQHU1LYw8mJPSV+PhrJT19lsyd+5hNWda2bbhMm/ZI0CAVFr5lAm4vqFGfyMhIeeqpZ+y27OwsE29xjc8xj66MLJ5vDgmhL8LTg+cINIZA9TdXY5wtCM9BHMAgvKhUCYFmLrByZbbk5ubYsXXR0ZfL5MlT5eyzq3/Pay/bjBkPy7fffivh4e1tY1RT4jXGMm/ebCkp+Vjmz8+Qtm3beZ0yK2u5LfeYMXfa13Rd8zInJo712pcNCPhTwOlxAPnZ5c9PF8dGAAEEzrBAaemn5vbyOnn66efluedWyokTJ2T16n/WOMv8+Y+byRjDJDv7FTMh4w+ycOHcGq/7ayUzc6mUl++WxYuX+2z86Xnj4uKlqGiDHDlyxOZkLizcIL16xfurSBwXAQTqEKAHsA6Y+m6mB7C+UuyHAAJnQmDPni/lwIH90r37VfZwL72ULdrjl5x8n13XBuGQIf3NLdgNNhSLhloZNOh602NYYNfPRBl8HePw4cOm0dlfIiIipHXr1nLy5Enb0/f44wtMeQ/IM88slTlzFtq3rlr1ig2No2XTGdIEy/YlyjZ/Czi9B9DfvhwfAQQQQMBPApdddlmHLl26fNa5c+c+rlN06tQp0mz7wrWuf3U9JibmAs9tPEcAAWcLcAvY2def2iOAQIAKREdHXxoaGlpgir90x44dG13VaNWqla/v9RBzy7XStQ9/EUAAAV9fFKgggAACCDRjAdPj18PMpn3HFPHJ7du3z/YsqpmNu8+sn28eru/3UPP83F27dh3w3I/nCCDgbAH9YmBBAAEEEAgQgaioqAgza7bYFHeiafytqF3s8vLyynbt2vUy8fXONmPvPjK3fxPNPq3M85dq78s6Agg4V4BJIM699tQcAQQCUKBr166PmgkWk0zRt5uHfoefDAkJWWe2RZrH6+Z28FrTSLzYNBKfNa9daB4HTG/hrTt37txjnrMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBDMAv8DHmQ5RrjoYd8AAAAASUVORK5CYII=\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "%matplotlib notebook\n",
    "#soa =np.array( [ [0,0,1,1,-2,0], [0,0,2,1,1,0],[0,0,3,2,1,0],[0,0,4,0.5,0.7,0]]) \n",
    "soa =np.array( [ [0,0,0,1,1,1],[0,0,0,2,-2,-3] ] )\n",
    "\n",
    "X,Y,Z,U,V,W = zip(*soa)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.quiver(X,Y,Z,U,V,W,pivot=\"tail\")\n",
    "ax.set_xlim([-2,2])\n",
    "ax.set_ylim([-2,2])\n",
    "ax.set_zlim([-2,2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0,), (0,), (0,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,Y,Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(soa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "type() takes 1 or 3 arguments",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-f3cf8f8eb46e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: type() takes 1 or 3 arguments"
     ]
    }
   ],
   "source": [
    "type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.quiver?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aBr0p83B/UnizbuM0cOaBLOLoadfFeygQbcWGId5pn3Wdu93oMp5HEL2OcRKM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118913160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "soa =np.array( [ [0,0,1,1,-2,0], [0,0,2,1,1,0],[0,0,3,2,1,0],[0,0,4,0.5,0.7,0]]) \n",
    "\n",
    "X,Y,Z,U,V,W = zip(*soa)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.quiver(X,Y,Z,U,V,W)\n",
    "ax.set_xlim([-1,0.5])\n",
    "ax.set_ylim([-1,1.5])\n",
    "ax.set_zlim([-1,8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),\n",
    "                      np.arange(-0.8, 1, 0.2),\n",
    "                      np.arange(-0.8, 1, 0.8))\n",
    "\n",
    "u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)\n",
    "v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)\n",
    "w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *\n",
    "     np.sin(np.pi * z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QOuZcyh6+He/rjxM4/ycZm3TJJBF6BP/Df0YKh9DHTf+ak2XHoxB7a1yykyQFl8vdir21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\n0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U7zX36Lb9XTgO3uA3uBjZ5xGbHjzszo52gvIbkLfXasKp8iTb7EJCGMRCJKpRZqfoTe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\nc13Bsg34f92EOE2kLj6HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189211d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "ax.quiver(x, y, z, u, v, w,                 # data\n",
    "          length=0.15,                      # arrow length\n",
    "          color='Tomato'                    # arrow colour\n",
    "          )\n",
    "\n",
    "ax.set_title('3D Vector Field')             # title\n",
    "ax.view_init(elev=18, azim=30)              # camera elevation and angle\n",
    "ax.dist=8                                   # camera distance\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}