TestCC_incudeEOF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TestCC_includeEOF - 反応物と生成物の生成エンタルピーの差から発熱量を計算する\n",
    "TestCC　では、燃料ガスの低位発熱量(Lower Heating Value of fuel)をパラメータ HH に設定しました。このモデルに含まれる　CombustionChamberモデルは、以下のように設定することによって、反応物と生成物の生成エンタルピー(enthalpy of formation)の差から発熱量を計算することができるようになります。\n",
    "\n",
    "* すべての Media （物性モデル) について、excludeEnthalpyOfFormation = false にする。\n",
    "* HH = 0 にする。\n",
    "\n",
    "TestCC にこのような改造を施したモデル TestCC_includeEOF を作成し、シミュレーションを実行します。\n",
    "\n",
    "環境変数 MODELICAPATH に MSL のパスと ThermoPower ライブラリのパスを設定します。\n",
    "\n",
    "Windows の場合は、\n",
    "```\n",
    "import os\n",
    "os.environ['MODELICAPATH'] = 'C:\\\\JModelica.org-2.14\\\\install\\\\ThirdParty\\\\MSL;C:\\\\Users\\\\ユーザー名\\\\jmodelica\\\\lib\n",
    "```\n",
    "のように設定します。\n",
    "\n",
    "以下は Mac の場合です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['MODELICAPATH'] = '/usr/local/jmodelica/ThirdParty/MSL:/home/jmodelica/jmodelica/lib'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TestCC のソースコードを改編して TestCC_includeEOF を作成します。主な改編点は次のようになります。\n",
    "\n",
    "* 全体モデル TestCC_include の replaceable package として Air、Fuel、Exhaust を宣言し、各コンポーネントはこれらを redeclare するようにしました。\n",
    "* Air, Fuel, Exhaust の宣言で、 excludeEnthalpyOfFormation = false を設定しました。\n",
    "* CombustionChamber1 のパラメータで、HH=0 としました。\n",
    "\n",
    "テキストベースで作成したので、コンポーネントの位置などに関する情報は記述してません。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting TestCC_includeEOF.mo\n"
     ]
    }
   ],
   "source": [
    "%%writefile TestCC_includeEOF.mo\n",
    "model TestCC_includeEOF\n",
    "   import SI = Modelica.SIunits;\n",
    "     \n",
    "  replaceable package Air = ThermoPower.Media.Air(excludeEnthalpyOfFormation = false);\n",
    "  replaceable package Fuel = ThermoPower.Media.NaturalGas(excludeEnthalpyOfFormation = false);\n",
    "  replaceable package Exhaust = ThermoPower.Media.FlueGas(excludeEnthalpyOfFormation = false);\n",
    "   \n",
    "  ThermoPower.Gas.SourceMassFlow Wcompressor(redeclare package Medium = Air, w0=158, T=616.95);\n",
    "  \n",
    "  ThermoPower.Gas.CombustionChamber CombustionChamber1(\n",
    "      redeclare package Air = Air, redeclare package Fuel = Fuel, redeclare package Exhaust = Exhaust,\n",
    "     initOpt=ThermoPower.Choices.Init.Options.steadyState, HH= 0.0, pstart=11.2e5,  V=0.1, S=0.1);\n",
    "  \n",
    "  ThermoPower.Gas.SourceMassFlow Wfuel(redeclare package Medium = Fuel, use_in_w0=true);  \n",
    "  ThermoPower.Gas.PressDrop PressDrop1(redeclare package Medium = Exhaust,\n",
    "      FFtype=ThermoPower.Choices.PressDrop.FFtypes.OpPoint,\n",
    "      rhonom=3.3, wnom=158.9, pstart=11.2e5, dpnom=0.426e5);  \n",
    "  ThermoPower.Gas.SensT SensT1(redeclare package Medium = Exhaust);  \n",
    "  Modelica.Blocks.Sources.Step Step1(startTime=0.5, height=-0.3, offset=3.1);  \n",
    "  ThermoPower.Gas.ValveLin ValveLin1(redeclare package Medium = Exhaust, Kv=161.1/9.77e5);\n",
    "  ThermoPower.Gas.SinkPressure SinkP1(redeclare package Medium = Exhaust);\n",
    "  Modelica.Blocks.Sources.Constant Constant1(k=1);\n",
    "  inner ThermoPower.System system;\n",
    "\n",
    "equation\n",
    "  connect(Wfuel.flange, CombustionChamber1.inf);\n",
    "  connect(Wcompressor.flange, CombustionChamber1.ina);\n",
    "  connect(CombustionChamber1.out, PressDrop1.inlet);\n",
    "  connect(PressDrop1.outlet, SensT1.inlet);\n",
    "  connect(Step1.y, Wfuel.in_w0);\n",
    "  connect(ValveLin1.outlet, SinkP1.flange);\n",
    "  connect(SensT1.outlet, ValveLin1.inlet);\n",
    "  connect(Constant1.y, ValveLin1.cmd);\n",
    "end TestCC_includeEOF;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "モデルをコンパイルして FMU を作成します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from pymodelica import compile_fmu\n",
    "fmu = compile_fmu('TestCC_includeEOF', 'TestCC_includeEOF.mo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FMU をロードしてシミュレーションを実行します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Run Statistics: --- \n",
      "\n",
      " Number of steps                                 : 87\n",
      " Number of function evaluations                  : 107\n",
      " Number of Jacobian evaluations                  : 3\n",
      " Number of function eval. due to Jacobian eval.  : 21\n",
      " Number of error test failures                   : 0\n",
      " Number of nonlinear iterations                  : 99\n",
      " Number of nonlinear convergence failures        : 0\n",
      " Number of state function evaluations            : 89\n",
      " Number of time events                           : 1\n",
      "\n",
      "Solver options:\n",
      "\n",
      " Solver                   : CVode\n",
      " Linear multistep method  : BDF\n",
      " Nonlinear solver         : Newton\n",
      " Linear solver type       : DENSE\n",
      " Maximal order            : 5\n",
      " Tolerances (absolute)    : [  1.00000000e+00   5.00000000e-04   1.00000000e-07   1.00000000e-07\n",
      "   1.00000000e-07   1.00000000e-07   1.00000000e-03]\n",
      " Tolerances (relative)    : 0.0001\n",
      "\n",
      "Simulation interval    : 0.0 - 5.0 seconds.\n",
      "Elapsed simulation time: 0.0161700248718 seconds.\n"
     ]
    }
   ],
   "source": [
    "from pyfmi import load_fmu\n",
    "model = load_fmu(fmu)\n",
    "res = model.simulate(final_time=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シミュレーション結果から、燃焼室温度、燃料ガス温度、コンプレッサー空気温度を抽出します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = res['time']\n",
    "Tc = res['CombustionChamber1.fluegas.T']\n",
    "Tf = res['Wfuel.gas.T']\n",
    "Ta = res['Wcompressor.gas.T']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比較するために、DyMat を使って TestCC の燃焼室温度も抽出します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import DyMat\n",
    "d = DyMat.DyMatFile('ThermoPower_Test_GasComponents_TestCC_result.mat')\n",
    "t1 = d.abscissa('CombustionChamber1.fluegas.T', valuesOnly=True)\n",
    "Tc1 = d.data('CombustionChamber1.fluegas.T')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TestCC の燃焼室温度、 TestCC_includeEOF の燃焼室温度、燃料ガス温度、コンプレッサ空気温度をプロットします。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    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",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "                        'ui-button-icon-only');\n",
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       "        icon_img.addClass(image);\n",
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       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
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       "\n",
       "        button.append(icon_img);\n",
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       "\n",
       "        nav_element.append(button);\n",
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       "\n",
       "    var fmt_picker = $('<select/>');\n",
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       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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vhQvQABYvClnRAABBBBAAAEEEEAAAQQQQGACAXYB5oawAAEg90GvBAgAeyXPdRFAAAEEEEAAAQQQQAABBAaMAAHggBnqfjtKAMh90CsBAsBeyXNdBBBAAAEEEEAAAQQQQACBASNAADhghpoAkKEupQABYCmHhUYhgAACCCCAAAIIIIAAAgiEJEAAGNJott4XZgC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZScJAEs5LDQKAQQQQAABBBBAAAEEEEAgJAECwJBGs/W+EAC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZScJAEs5LDQKAQQQQAABBBBAAAEEEEAgJAECwJBGs/W+EAC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZScJAEs5LDQKAQQQQAABBBBAAAEEEEAgJAECwJBGs/W+EAC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZScJAEs5LDQKAQQQQAABBBBAAAEEEEAgJAECwJBGs/W+EAC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZScJAEs5LDQKAQQQQAABBBBAAAEEEEAgJAECwJBGs/W+EAC2bseR7QkQALbnx9EIIIAAAggggAACCCCAAAIINBUgAGxKNCAqEAAOiGEuZSf7AsDdz7xZ08z4lVI2ciA1aoYpJ9GWy82lr8405UDqNn1FAAEEEEAAAQQQQAABBIIWIAAMengzd44AMDMVFQsW6AsA59j9Ag2ZZqaCT8/pWhGYaapJdMcBa2rKSYe0cjjHIIAAAggggAACCCCAAAIIlEyAALBkA9Kj5hAA9giey4oAsKQ3waU7r6iVFyCQLenw0CwEEEAAAQQQQAABBBBAIJcAAWAurmArEwAGOyY/4vcAACAASURBVLSl7xgBYEmH6NwdltPai/JIdkmHh2YhgAACCCCAAAIIIIAAArkECABzcQVbmQAw2KEtfcf6AsB9z/urpp1p1tI3ONQGnn/PixN07eztl9M3FyMADHW86RcCCCCAAAIIIIAAAggMLAECwIE13o16SwDIfdArAXYB7pV8zXXXGHa7Xnzn476vnrHtslr/awSyJRkemoEAAggggAACCCCAAAIItCVAANgWXzAHEwAGM5SV6wgBYEmGbK3j/6bn3/qorzWnbbOMNlxitpK0jmYggAACCCCAAAIIIIAAAgi0I0AA2I5eOMcSAIYzllXrCQFgSUbsmyfcoX+/OaqvNb/7/tf17aVmL0nraAYCCCCAAAIIIIAAAggggEA7AgSA7eiFcywBYDhjWbWeEACWZMTWP/FOjXj9v32tOWmrpbXJ0nOUpHU0AwEEEEAAAQQQQAABBBBAoB0BAsB29MI5lgAwnLGsWk8IAEsyYhudfJf+9eqHfa05Ycul9N1lPDwUBBBAAAEEEEAAAQQQQACBqgsQAFZ9BItpPwFgMY6cJb8AAWB+s44csfEpd+ufr3zQd+7jNl9SWy43V0euxUkRQAABBBBAAAEEEEAAAQS6K0AA2F3vsl6NALCsIxN+uwgASzLG3zntHj3y8vt9rfn1d5fQVivMXZLW0QwEEEAAAQQQQAABBBBAAIF2BAgA29EL51gCwHDGsmo9IQAsyYhtcca9eujF9/pa88tNv6ZtV5qnJK2jGQgggAACCCCAAAIIIIAAAu0IEAC2oxfOsQSA4Yxl1XpCAFiSEfv+Wffrvuff6WvNkd9eTDuuMm9JWkczEEAAAQQQQAABBBBAAAEE2hEgAGxHL5xjCQDDGcuq9YQAsCQjtt25D+iuf7/d15rDNlpUOw+dryStoxkIIIAAAggggAACCCCAAALtCBAAtqMXzrEEgOGMZdV6QgBYkhHb6fwHdfvTb/W15uANFtFuq89fktbRDAQQQAABBBBAAAEEEEAAgXYECADb0QvnWALAcMayaj0hACzJiO1y0d91y5Nv9LXmp+supL3WWrAkraMZCCCAAAIIIIAAAggggAAC7QgQALajF86xBIDhjGXVekIAWJIR2+OShzX88df7WrP32gtqv28uVJLW0QwEEEAAAQQQQAABBBBAAIF2BAgA29EL51gCwHDGsmo9IQAsyYjtc/kjuvbRV/ta48d//RgwBQEEEEAAAQQQQAABBBBAoPoCBIDVH8MiekAAWIQi52hFgACwFbUOHHPAVY/pqodf6TvzD1edV4d/a7EOXIlTIoAAAggggAACCCCAAAIIdFuAALDb4uW8HgFgOcdlILSKALAko3zYNY/r9/e/3NeabVeaW7/cdImStI5mIIAAAggggAACCCCAAAIItCNAANiOXjjHEgCGM5ZV6wkBYElG7BfXP6lz736hrzVbLDunhm2xVElaRzMQQAABBBBAAAEEEEAAAQTaESAAbEcvnGMJAMMZy6r1hACwJCM27KYROvX25/pa860lZ9MpWy9TktbRDAQQQAABBBBAAAEEEEAAgXYECADb0QvnWALAcMayaj0hACzJiJ16+7MadtPTfa1Ze5FZdO6Oy5ekdTQDAQQQQAABBBBAAAEEEECgHQECwHb0wjmWADCcsaxaTwgASzJiF9zzgo687sm+1qw47wy64kffKEnraAYCCCCAAAIIIIAAAggggEA7AgSA7eiFcywBYDhjWbWeEACWZMT+8PAr2v+qx/pas+hs0+jGfYaWpHU0AwEEEEAAAQQQQAABBBBAoB0BAsB29MI5lgAwnLGsWk8IAEsyYrc+9YZ+eOHf+1oz27ST6b5D1i5J62gGAggggAACCCCAAAIIIIBAOwIEgO3ohXMsAWA4Y1m1nhAAlmTEHh35vjY99Z6+1kw80SA9/YsNNHgwLw8lGSKagQACCCCAAAIIIIAAAgi0LEAA2DJdUAfyCT/bcM4iaYX4j3dH8J8Z40MvlLRjhtNMJmk9SevE51lQ0tSS/ivJOzDcJOlMSa9lOJerTCFpT0lbSFpA0iSSRkq6QdLJkl7OeJ65Je0taSNJ/vunkp6VdKWk0yR9nPE8easRAOYV61D9Nz/8n1Y4+tYJzn7vwWtp9ukm79AVOS0CCCCAAAIIIIAAAggggEC3BAgAuyVd7usQAGYbn7H9VMsSAC4p6e448Ovvig4Dd47Dt/7qzR8HfQs3qPSBpK0lDW/SPYd+l0iatkE9B5MbSno+G1OuWgSAubg6V3ns2LFa4sibNerT0X0XOXO7ZbXe4rN27qKcGQEEEEAAAQQQQAABBBBAoCsCBIBdYS79RQgAsw1ROgD0LLunJK0bH5olAFxV0l1xfT9reb0kL7r2jqSZJX03Dv4mkvSFpG9LurFB06aS9JCkReLvny3pckmfSFpT0iGSXMcz97yV6z8bnGcpSffGMwlHSTpG0u2SPO1rK0m7xMeNiGc8uk6RhQCwSM02z7X12ffr3ud8O44rgwZJ00w2sfwU8Jix4/49eNAgOSwcX2fcy4e/5jourj8orpf+WnSsb+4xrjtWEw0epIl8Ukmjx4yNvu6v+fFjHz/GXxs7Vr5c+pzpH0RevNocdA5HAAEEEEAAAQQQQACBASHw2Qdv6ckTtk36OpekVwZEx+nkBAJ8hs52QxwVh24O3t6Q9FVJL8SHZgkAV5a0jySf58kGl9xE0p+cvUh6TpIfEa438/BISUfE5zhQ0rCa8zn0u1PSkDjQW6vB9Rz2reH8RdJqku6rqXeApOPir/l6P89GlbkWAWBmqs5XvOCeF3TkdY1uzc5fnysggAACCCCAAAIIIIAAAgh0RmD0h2/rP6f3rVxGANgZ5tKflQCwtSHKGwBmvcrVkjaLKy8j6ZGaAyeW9Kak6eJZiF+TNKbOyc+Q9KP468tJerimjtcwfDD+mtcd3K3OOQZLekLSopLek/QVSZ9n7UiGegSAGZC6VeXjz0Zr/RPv0svvdmrJx271hOsggAACCCCAAAIIIIAAAgikBQgAuR8sQADY2n3QqQDQm3qcEjfJm3s4EEyXb0q6Of7CwZKObdD8lVIz+o6W9LOaer+SdGj8Ndd9oMF5fA0/GuziR55vaY2r7lEEgAViFnGql9/5WAf/8Z8TPApcxHk5BwIIIIAAAggggAACCCCAQO8ECAB7Z1+mKxMAtjYanQoA95N0fNwkzwT8Y03z/Bju4fHX/Kjv/Q2a78d/35c0Zfw48Oo19fyI8FBJH8WzCcfv/jBhRV/D6wS6+NrJo8etqU14FAFgEYodOMe7H32mV9//RB99Ojp6Bj1Z+89r8g3SoGhNPpdkjT//vXbtv2TNwHH1xkZr+nltP9cbPHjQuLUA4xN47b8hEw3S6C/GrQXo66TXCHQbvBZgvGRg9FuL2mfjkxey/nbr6QAVp0QAAQQQQAABBBBAAAEESi/w5muvapNVvTdpVHgEuPQj1pkGEgC25tqpAPBaSRvHTVq8znqBV0naPP7+9HHI16gHj0nyT/hbkmapqeSvzSTJdZbuh8DXeDf+vq+9ZWtcdY8iACwQk1MhgAACCCCAAAIIIIAAAgggUE+AXYC5LyxAANjafdCJANC78nqtPu8E/C9JXt+vtnjG34rxzD3v9Ntf8U7DG8UVJpP0aerv3jHY5QZJ32pyHu/+65mEvrZnBGYtDvj6K7PGG6to5MiRmnPOZtWzXpZ6CCCAAAIIIIAAAggggAACCCCQCBAAci9YgACwtfug6ABwUkl3S/KGHS7eEfjPdZrmYHCxeCdiB2j9lStSM/Y82++duPLM8UYi/qfrbNXkPN712DMIvSHIEjm4Mj+NSQCYQ5WqCCCAAAIIIIAAAggggAACCOQQIADMgRVwVQLA1ga36ADwbEk7x025UFLf/tw1zXtO0nySRkqau0nTL5K0XVwn/Yy///5y/PWLJW3f5Dyu62N87QVycBEA5sCiKgIIIIAAAggggAACCCCAAAKdECAA7IRq9c5JANjamBUZAB4iyTv1uvgRYG/Y4c056pUqzQBs9kwvjwC3du9xFAIIIIAAAggggAACCCCAAAKZBQgAM1MFXZEAsLXhLSoA/JGkM+ImPB3vzOsNOhqVKq0B2EyWTUCaCfF9BBBAAAEEEEAAAQQQQAABBNoUIABsEzCQwwkAWxvIIgLA70v6vaTBkl6StKqkV5o052pJm8V12AW4tbHjKAQQQAABBBBAAAEEEEAAAQQGjAAB4IAZ6n47SgDY2n3QbgC4saQ/SBoi6bV45p/X2GtWfi7p8LiSd+T1jMB6xed9P9699874seJ0PX9taPyo8XSSRjc4j69xb/w9X/uIZg3M8X1mAObAoioCCCCAAAIIIIAAAggggAACrQgQALaiFt4xBICtjWk7AeDakm6Q5J1/vTOv1/zz2n5ZyrqSboorHizp2AYHrSTpvvh7x0g6tKae1xz02oMurvtAg/P4Gj7eZT1JN2dpZMY6BIAZoaiGAAIIIIAAAggggAACCCCAQKsCBICtyoV1HAFga+PZagC4chyiTSnpQ0lrxRt/ZG3FJJLelDStpKckLS6p3m67XlfQ6wu6rCDpoZoL+GtJ6HempN3qNMCPJj8hadF4NuEskj7P2tAM9QgAMyBRBQEEEEAAAQQQQAABBBBAAIF2BAgA29EL51gCwNbGspUAcGlJt0vyI7fe5dcz6u5p4fLpx4APlDSs5hx+bNeP+Pox4DskrdHgGsljwH78d7XUjMGk+gGSjov/cZSkI1toa3+HEAAWDMrpEEAAAQQQQAABBBBAAAEEEKgVIADknrAAAWC2+8AbdCyQqjpTKnhziHdOzWkuqPn3/PFaep5F57KvpL82ubRn+vlPbZla0t8lLRR/4yxJl0v6RNKa8eO+U8X/9ozDRxtc5+txADm5pFGS/FiwA0r/eytJu8bHPSNpOUn/zUaVuRYBYGYqKiKAAAIIIIAAAggggAACCCDQmgABYGtuoR1FAJhtRB3o7ZCtalSr1nVHSefnON5V+5t15zByuKQFG5zTjxdvI+n6Jtf8drwT8TQN6jn820jSsznbnqU6AWAWJeoggAACCCCAAAIIIIAAAggg0IYAAWAbeAEdSgCYbTDLFgC61V5HcE9JW8SzE70+4Mg4GDxJ0kvZuqZ5JO0TB30O5T6LA7+rJJ0i6eOM58lbjQAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRgkmogAAggggAACCCCAAAIIIFBtAQLAao9fUa0nACxKkvPkFSAAzCtGfQQQQAABBBBAAAEEEEAAAQRyChAA5gQLtDoBYKADW4FuEQBWYJBoIgIIIIAAAggggAACCCCAQLUFCACrPX5FtZ4AsChJzpNXgAAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRgkmogAAggggAACCCCAAAIIIFBtAQLAao9fUa0nACxKkvPkFSAAzCtGfQQQQAABBBBAAAEEEEAAAQRyChAA5gQLtDoBYKADW4FuEQBWYJBoIgIIIIAAAggggAACCCCAQLUFCACrPX5FtZ4AsChJzpNXgAAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRgkmogAAggggAACCCCAAAIIIFBtAQLAao9fUa0nACxKkvPkFSAAzCtGfQQQQAABBBBAAAEEEEAAAQRyChAA5gQLtDoBYKADW4FuEQBWYJBoIgIIIIAAAggggAACCCCAQLUFCACrPX5FtZ4AsChJzpNXgAAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRgkmogAAggggAACCCCAAAIIIFBtAQLAao9fUa0nACxKkvPkFSAAzCtGfQQQQAABBBBAAAEEEEAAAQRyChAA5gQLtDoBYKADW4FuEQBWYJBoIgIIIIAAAggggAACCCCAQLUFCACrPX5FtZ4AsChJzpNXgAAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRgkmogAAggggAACCCCAAAIIIFBtAQLAao9fUa0nACxKkvPkFSAAzCtGfQQQQAABBBBAAAEEEEAAAQRyChAA5gQLtDoBYKADW4FuEQBWYJBoIgIIIIAAAggggAACCCCAQLUFCACrPX5FtZ4AsChJzpNXgAAwrxj1EUAAAQQQQAABBBBAAAEEEMgpQACYEyzQ6gSAgQ5sBbpFAFiBQaKJCCCAAAIIIIAAAggggAAC1RYgAKz2+BXVegLAoiQ5T14BAsC8YtRHAAEEEEAAAQQQQAABBBBAIKcAAWBOsECrEwAGOrAV6BYBYAUGiSYigAACCCCAAAIIIIAAAghUW4AAsNrjV1TrCQCLkuQ8eQUIAPOKUR8BBBBAAAEEEEAAAQQQQACBnAIEgDnBAq1OABjowFagWwSAFRikPE387LPPNGrUKH300Ufy38eMGZPncOoigAACCCCAAAIIIFAqgcGDB2uSSSbRlFNOqammmir6OwWBKgoQAFZx1IpvMwFg8aacMZsAAWA2p9LXGjt2rN5+++3oDwUBBBBAAAEEEEAAgVAFZp55Zs0444waNIiP0aGOcaj9IgAMdWTz9YtXrnxe1C5OgACwOMuenunVV1/VBx98MEEb/KZoookm6mm7uDgCCCCAAAIIIIAAAu0IfPHFF/Ivu9Nl2mmn1eyzz97OaTkWga4LEAB2nbyUFyQALOWwDIhGEQAGMMz/+9//9MILL/T1xL8RnWaaaTTppJPym9EAxpcuIIAAAggggAACA1nA4d+nn36qDz/8UO+8804fxXzzzRe936UgUBUBAsCqjFRn20kA2Flfzt5YgAAwgLvj9ddf13vvvRf1ZJZZZokeiaAggAACCCCAAAIIIBCagAPAN998M+rW9NNPr1lnnTW0LtKfgAUIAAMe3BxdIwDMgUXVQgUIAAvl7M3Jnn/++ei3oi4LLbQQj/32Zhi4KgIIIIAAAggggECHBfw48DPPPBNdxbP/PAuQgkBVBAgAqzJSnW0nAWBnfTl7YwECwADujn//+98aPXq0hgwZogUXXDCAHtEFBBBAAAEEEEAAAQTqCzgAdBDIe1/ukKoJEABWbcQ6014CwM64ctbmAgSAzY1KX4MAsPRDRAMRQAABBBBAAAEEChLgvW9BkJym6wIEgF0nL+UFCQBLOSwDolEEgAEMM2+CAhhEuoAAAggggAACCCCQSYD3vpmYqFRCAQLAEg5KD5pEANgDdC4ZCRAABnAj8CYogEGkCwgggAACCCCAAAKZBHjvm4mJSiUUIAAs4aD0oEkEgD1A55IEgKHcA7wJCmUk6QcCCCCAAAIIIIBAMwHe+zYT4vtlFSAALOvIdLddBIDd9eZq4wWYARjA3cCboAAGkS4ggAACCCCAAAIIZBLgvW8mJiqVUIAAsISD0oMmEQD2AJ1LRgIEgAHcCLwJCmAQW+jCBRdcoJ122ik68oUXXtBXv/rVFs7CIQgggAACnRZYY401dMcdd2j11VfX3/72t05fjvMjELwA732DH+JgO0gAGOzQ5uoYAWAuLioXKEAAWCBmr07Fm6DuyvvD25prrpnrovvss49OPPHEXMc0q0wA2EyouO+/+eabuuKKK3TrrbfqiSee0Ntvv62PP/5Y00wzjeacc04ts8wyWnvttbXxxhtr6qmnLu7CJTyTg+aXXnpJ88wzj1588cWmLXSdeeedN6q3ww47yPdtbenEOZs2rMQVXnvtNS266KL64IMPolY2C43ef/99PfTQQ3rwwQf7/utzZDm2HQZfz+N5++236z//+Y/GjBmjr3zlK1pkkUWin4fttttOM888czuXyHzsX//6V/3+97/X3XffLfd9yJAhUVuWXHLJvrZMNdVUmc+XVMxy/9ae9Jvf/Kbcnj322EOnnnpq9G0CwNz0HIBAvwK89+UGqaoAAWBVR67YdhMAFuvJ2bILEABmtyptTd4EdXdoCAC7653lakceeaSOOuqoqOrYsWOzHJKpzmeffSaf++STT9ZHH33U9JjJJ59cu+66qw4//HDNOOOMTetXsUInwrpOnLOKtkmbN998c/3hD3/o60KzANABa6MwttmxrTh9+umn2muvvXTuuef2+/P2pz/9SZtuumkrl8h8zHvvvRfNhL722mv7PeaRRx7R0ksvnfm8ScW8AeB///tfzTTTTPJrx4033qj1118/OhUBYG56DkCgXwHe+3KDVFWAALCqI1dsuwkAi/XkbNkFCACzW5W2Jm+Cujs06QBw9913j2Z5NCv+QDjrrLM2q5br+8wAHM/ViQDw3XffjcKLu+66K7rQZJNNpi233FJrrbVW9Li1Z/+5jgMCz/YZPny4Pvzww6juZZddpq222irXeFalcifCuk6csyqete287rrropmks8wyizzz1KVZiJf4ua5nvS2//PK6/vrrMx2b18nBln8uHG65DB06VNtvv300Y9Gz7jw79LHHHtNVV12l4447rqMBoGdIeqbhww8/HLVlo402in7uFlhgAX3xxRdRWzwz8uqrr5ZduxEA+lpbbLGFppxySr3zzjuadNJJo7YRAOa906iPQP8CvPflDqmqAAFgVUeu2HYTABbrydmyCxAAZrcqbU3eBHV3aNIB4BFHHBHNEOtFIQAcr150AOhHGddbb70o2HPxLJ7zzjtPs802W8Oh9mOYv/vd73TMMcdEdQkAx1FlmUFFADjOatSoUVpsscU0cuRIXXTRRVGw5tIsAPzNb34TPWbt4G/uueeOjhk0aNxby2bH5n3t+r//+z/94he/iA7zdffff/+Gp/j888818cQT571E5vr2ufjii6Pg0Y//fu9736t7rGcFOxB0vbwly/2bPqcfc/fYfec739Ef//jHvm8RAOaVpz4C/Qvw3pc7pKoCBIBVHbli200AWKwnZ8suQACY3aq0NXkT1N2hIQDsrneWqxUdAP72t7/VfvvtF13aQaBnU2UND5566qnoceHlllsuS9MrV6cTYV0nzlk5WEl77713FCJ7jdHbbrutrRCvEwHg888/H63v52Bvxx131Pnnn98zZq/159mHLv759y9jOlHyBIDJGoheI9SPR//gBz8gAOzEoHBOBCTx3pfboKoCBIBVHbli200AWKwnZ8suQACY3aq0NXkT1N2haTcA9GyUDTbYQDfddJMGDx4c7Qy56qqr1u3ESSedpJ/85CfR9w499FD96le/6qtXOwPQM3/OOeecaFF+h1B+VG/++eePZsXsu+++mmKKKepewx9a3Sc/wnrffffp6aefjjYf8CNsDmbWWWedKJhIZhbVO0kS4DTa5CE5xqHBhRde2HADCc/S8YweP0Lrxwj9iK3b7Y0EfH0/7ufHDz1LyiVt0N9dkGeXZLt5NtWrr74aPfbr0KO/mX957j6fy+ui2fvxxx/XG2+8ER3uxz1XWmmlaC2zZM2wRuf1TENvLOBQcsSIEdGssemmmy4yWnjhhbXuuutqs802i87ZidKJsK4T5+xE3zt5Tm+o8Y1vfCMKmv/5z39GY9lOiNfOsY36efDBB+vYY4+N2vXcc8/1be7SqovX7/O9fMMNN0Qf5r1+ntfO9ExGv5Z897vfbXhqz7D1xjze2MObfrSywUeWducJAO+9916tssoqkY/b5Mexk5JlBuCll14a9Xv06NHR64Ffk6effvoJmul7Y9iwYVFA7EeM/XO+2mqrRb+w8GZEzV5js/SZOghUQYD3vlUYJdpYT4AAkPvCAgSA3Ae9EiAA7JV8gdflTVCBmBlO1W4A6Eu8/vrrWmKJJaLdZB1+OOzymnLp8uSTT2rZZZfV//73v2g2mT9cph+nS4df3pnWIZ0/FNYrK6ywQvQ9h3q1JT17rlH3vsEddAAAIABJREFUHcL5ETs/1lavFBEAOsjacMMN+9bca9QWh1teZ8ulEwFgsgabz98s0Mxwu/RVcQg533zzNT1k2223jWZW1Ztx6GDXgazDyf6KZ5F5k4ZOlE6EdZ04Zyf63qlzOvDxz7hfB7yBzM9//vPoUu2EeO0c26if/oWCQ2wHdA4sXfwLBN+PnhXodU69EU6W4nBrm222kQPtRsVr+l1++eVfCvcc0k877bTRa6PX27vyyiujU9jRuxG7727LJJNMkqUp/dbJEwAecsgh+vWvf60VV1xR999//wTnbRYAnnLKKdFruH9B5F2E/YuC2tdr//Jkl112iaxri//fcPbZZ0c7Mvf3S5a2QTgBAiUR4L1vSQaCZuQWIADMTRbkAQSAQQ5rJTpFAFiJYeq/kbwJ6u4gFhEAusV//vOftckmm0SNd+jjmW9J8Qdch3YOBBy+eQfLhRZaaIKOpsOvlVdeOfrAud1220UbVfjD78svvxwtwu9ZfS6eveP16WrLYYcdFq1Z53DPM5AcUnnmm9chc+h42mmnRbPM/LV//OMf0WL/taWIAPCnP/2pjj/++OjU3/rWt6JwwLP+fN233norsvCsN8+q8QYDLg4P/EbKbTz99NOjr3lmXW3xbKqsa5Gl2+EZRvYsojz77LNafPHFo0eK/QHfsxhnmGGGaJbjM888E82E+te//hVdyuusJbsap6/tkMgbHrgvDgI8k9RjnYQwDmW8e+yPf/xjAsAiBq1L53Bo5PDIAZvDfN/zLu2EeO0cW6/b/hlMZpU6qPI6gL5Pvd6dZ/K5OLT2a9GBBx4YbcjRqNxyyy3RvesZv37t8GZKDs38SxAHeP658y8cXDwLML0jsr/mjT38+uhywgkn6Pvf/37k59eFZLduG/pRar++uU2tljwB4Ne+9rXoZ/iXv/ylfvazn01wyf4CQAe+ySPM/gWHZwLWhpd+5NnrOfpn3SGrZ3V7trA3Gfn73/8evbZ70xi/xvj/F/PMM0/DnaFbteA4BMokwHvfMo0GbckjQACYRyvcugSA4Y5t2XtGAFj2EcrQvixvgsaMGav3Pv4sw9nCqTL9FJNo8ODiX15b2QW4UQD1ox/9SGeddVaEnt459oADDogW2Hc588wzteuuu35pYGpnvzlAdJCYLp9++mk0s8ihgh+t88zD2pll/oA7xxxzNAzI/EbFj6P5g3ltUJlcq4gA0GGfQ8fNN9+8L+Crdzc6MHNwli5FrgHoR2gdULj4Z8s7ihZRHEx4l+BGjxN75o/XDPO4euaPvT3LKSmeeeWAyKW/GX4+j4PR2kcHi+iDz5GM9eyzzx49xt6seHaYQ0+XRjMqO3HOZu3y95NQJkvdRnXanSXqcXVw9Mknn0Sevv+S0k6I186x9frqpQrs5XLQQQdFs3D9GHCj4oDK4Vxt8c+B72M//u6+eqZbveUJPJsted3zZjx+/D8pnuHmR11djj766Og6nk1dr3iZBf9iIVlKIe9YZw0A0/UeffRRLbXUUhNcql4A6J9Vt+vkk0+O6u68887R673bXFu8g7F/CeJg0LP8akNNh3/+BY7vJxcCwLwjTf2qCWR571u1PtHegSFAADgwxrlZL4v/hNrsinwfgXECBIAB3AlZ3gS9M+pTLfvLcTuaDpTy8GHraMapJi28u+kAMOvJG61B9/HHH0frNnndPa/j5g94/lDtD7v+cLjxxhvr2muvrXuZdABYb5ZMcpA/UO62227RP33+JZdcMmuz++olaxF6ho7DpSRcSCoUEQD6g60fbfO1PMMoTykyAPR4eAaNi/uaDuHSbfI6iQ4s6xX3pXbGZpb+ONz0LCvPjHLA4tlASUnWF2tnHLO0oVmdZKyb1av3/WYBYJHnzHKuMgSAng3qgMtrdfpx13RpJ8Rr59h6dg7qkjX5POvMv1xwCOWZZ34k2I/j3njjjfIMWq9/5+JZuclrT3JOP+rqGaqeoffSSy/1u1alZwV6VqtnAyczAn2e9CY9SVs8a9ivAw5T/bPpWYOe9ezQ3RZeZ9CzDvOWrAGgQ3m/bs0111zR7OvaUhsA+nFlB/7JzG//0scztusVz+52uOfSKFj199KzygkA84409asmkOW9b9X6RHsHhgAB4MAY52a9JABsJsT3OyVAANgp2S6eN8ubIALA4gakyADQrfIjnf5w5/DLm4H4Q7GDJT/a6QXfvblDvZIOAP1ht9GC+X48zB/QXfwh3pto9Ff8gdmLyzucdAjp4seIk9k4Dihr17IrIgD0B1Z/cHao4Bl4jTYtqdf2IgPABRdcUH5c18Uf0ieaaKK6XA4k/Mh1vZLlw7fH27OgvPGBA7+kOPz1bJ70WnD+XnoNwf5CgOLu9PpnCikAtGnyyGirbp5p6Rm0rRQ/PutQ1MG6N3SpnR3aTojXzrH1+lJ7v3t9Uj+WmjyunBzj/x99/etfj1z92uXXs/S6gEng6Vmhf/nLX/plS2ZC+/UmPdvQj9j65yMp3/72t3XNNdd8aeZc+rFZB4N+Pa395UWzccsaACYzh/fYY4/ocf7akg4A3W8vLeD1Rl0cojqsbFS8+ZMfZXapN7swOc6vI14iwa/fWV6DmvWd7yNQZoEs733L3H7aNnAFCAAH7tine04AyH3QKwECwF7JF3jdLG+CCACLAy9qDcB0i5I1wNJf82ya/naETQeAXncq2Rm3tqfpR0cdONQLrfwh3Y8c+wOp/95feeCBB/rW30rqFREAej0xryvm4llwXtzfYZhD0UYhaHL9IgPA9AxAzySq3ZwluWYrAaBDPz/y7Vk/nmXotR4bFa+L5rUN08W7fd51113RlzzeniHoYMGPaOcJTNv5aejEhh2dOGc7fezGsX5k1etp+r+NHuluJ8Rr59h6/feMVP9MJsUhVvJod2399BIGnpXmgC4pDkz72/ij3rV9b6eDWr9W+RpJcXjqZRbqFbc52TTIAaA3X8pTsgSADvFnmmmm6Oe50et2EgD69cU7Ft95551RYOlZkvWWeEi30WscenaoZzvaodEvJXyMNwm69dZbCQDzDDJ1KymQ5b1vJTtGo4MXIAAMfogzdZAAMBMTlTogQADYAdRunzLLmyACwOJGpRMBoBd2dxjgzSBcvBaU18Dqr6QDwEaPGPv49AdY7y6brJ2VnNsfWL3unmf8ZSnuvxejT5ciAkCHY35c0G1MZh76Gg4yvLC9Zzh6do1nuNSWIgPAZIaSr+GZgMm6e1ls+guy/HivZwl5xmeW4nGyRbp4XUAHGsnGLsn3vCmIZ5E6KPBxtbOyslwva51OhHWdOGfW/vSq3vbbbx8FwV6j06F6vXXf2gnx2jm2nonXJ0x+IeFH3B16Ndpl1480++fIJT2T1T/jre7Mm35NSC9rMO+88/ate1ev3eecc060YY7LueeeGz12m6dkCQCTcNRrd3r2nYO62lLvcfNGswVrj7W7/T1DtNkO4ElYyAzAPKNM3SoKZHnvW8V+0ebwBQgAwx/jLD0kAMyiRJ1OCBAAdkK1y+fM8iaITUCKG5ROBIDDhw+fYNfMRRZZJNpxN/3oXG0PiggA/WHVm1x4Ro5npXj9Ls/qcejlte+SD+u33XZb3yL8XoA+2QwgaVMRAWByrieffDLaEMXX9OPL6VlybqNn3iW7JyfHFBkApncBvvLKKyeY9dTsLuovyPLMy2QdMz+G7SDC6zF6tqMDuySwSTZD6W9zCc/w+eMf/yhvzGCvdDjisfP91MoahM365+93IqzrxDmz9KVXjwA7xEkeG/aOuX5ktl5xmOPiXw54hqyLAy+vjdesFB0A+j5zEO/SaJ27pE1e09SvYS7e6OiMM86I/u51ApPXND8Cm36Mt1l//AhvUtKvl0OHDo1m0zUq6eCy2aO29c6RJQB06O6NSfxz7WUW6pUkALShlxawkWfyXXLJJdH6j/0VAsBmdwffH4gCWd77DkQX+lx+AQLA8o9RN1pIANgNZa5RT4AAMID7gjdB3R3EogPAt956K3oszevB+XFTr8Hn0mx2SBEBoD+Y+1FTl5tvvrlv1k6tqNcY9CxBl3oBoNfocpjikMuPGTcqyeN4WWeneGfUe+65R5deeml0Xq9x5QDB64Gl10srMgD0Y9DefMXFIZ1nDWUtjYIsj6kfEfQMqK233jr60N+oTD311Bo1alTDHXNrj3OI6xlXfrTYoamLdwxNNjLJ2vas9ToR1nXinFn606tNQNKhUpZ2putk3XW46ADQ965nuPm/3gHas1EblXRYuOeee8obfyTF5/BsY8+GzbKLdL1reJkC3zMuXjPUrxGNSjosHDZsWPRLjjylWQDo2dter9Wv4/3NMEyvAejXM//b/+/2ruz+hUfy+lqvbTwCnGfEqDtQBHjvO1BGOrx+EgCGN6at9IgAsBU1jilCgACwCMUen4M3Qd0dgKIDQIdNDp38CGAS5CS7gfrDa6OdK4sIAL0bpz+czzDDDNGja43KIYccIq9T6FIvAFxqqaWiBfa9E2eysH29c3nG2+OPP97S+lTpnT8ddiWP9fk6Rx11VLQDqEt6Nlwrd4Z3N/UsK+9k6rXHHGx6ll6W0ijI8mO/ftTTpXZNtPR50zOnsgY96eM9M9Lnd/Hj5N7QpOjSibCuE+fM0m8CwCxK4+v40X/PtvPMNa+P6TCvXkmH6EcffbT8+pEUr+npwM7HerObVteuTDYN8pIAr7/+esOOJLvzuoKDt2RWZdaeNwsAk925Hbj6NaPeEgW+Vu0uwA5Q/TUvM+AQ8Kqrrmq4QVN60xMH+w746xU2Ack6qtQLQYD3viGM4sDsAwHgwBz32l4TAHIf9EqAALBX8gVelzdBBWJmOFWRAWB6Bp4XtT/uuOOix3EdlHknYH+YdGBWbxOMIgJAz/5zG/xh3LPU6q1D5tk6DpKStafqBYB+9O3aa6+NZgb5jU29nTafeOKJvgX4s84ATA+HA0YHjS61ocKxxx7bt4umHzOstwZXhqHtq3LCCSdo//33j/7tUNN9q2dTe85GQZbXePNGHS4Odxs98ufdfU888cSoXisB4Mknn6x99tknOt7BhNcFLLp0IqzrxDmL7ncvztfOLL52jm3U1/T95Vmsns1ar+y0007y65OLN61x6JcUv8YddNBB0T/99/RmHnmM99tvP/mXAi4OFD0TsF5Zc8015ddsF7+mzjmn3/ZkL80CwOSXI34s+/7772944toAMGmPv+6NmryOp9cSTGYfp0/kNT+T/vW3A7jD/2R5hFZeY7OrUBOB3gvw3rf3Y0ALWhMgAGzNLbSjCABDG9Hq9IcAsDpj1bClvAnq7iAWFQB6hpbX/nLA5mDrwQcf7FtzzyGbd3P042X+QHfNNdd8qZNFBIDpoOuKK66Q1+VKF88o8WO9fkQtKfUCwPTsvHrBgDcM8I6+Dz30UHSa2g+n3iDj7rvvjnYLrRce+pj0zp9uz1ZbbdXXJj8e7MDMpb8dkbPeKe63NzFwX138odybsvQ3E9Dj6PX3PBuptn+eXekQ17MTN9xwQ91www1fasr1118fzQDytV1qA8BHH300+nqj2T8+t+8Vz76yoWcYpR+TTgcZnsmVhCJZTZJ6nQjrOnHOvP0qY/12Qry8x6Zf1xqFz3403fe4Z+75HnewXTvjzefxa5fvY6/b5+A+/TPtXzT4HN792EG918xrNMvZY+Jwz+fyDtjp8vLLL0c7/zrwX3bZZaP1MGtnJKZ36t5oo43kn7G8pVkA6D76Ncc7mB922GENT18vAHRl98M/j76O11z12p5ua21JZlm7jl+XagNPP4LsXzI4TKz3Gpu339RHoOwCvPct+wjRvkYCBIDcGxYgAOQ+6JUAAWCv5Au8Lm+CCsTMcKr0B2XPoPNafc2K161L7ybrdbT8Ac6bXHgDCP83WWA/OZdnxjj0cql95NVfKyIA9JsQbwLix17dxp/85CfRh3evRegPtX58zo+vrrLKKn3rbNULAP3h0+fxh3v3x7NivJmIP/i7bw4aHUh5MwM/wlYbkCUfsh0Eebdfz6ZxHT8a58fqHGp5N08Hop7BM2LEiAk+7PsxuuRxV68t9rOf/SwKv5Lgwef1ufIUBxQO/pIdd/2oomfurbXWWlHbbOQ1Ct12z7ZzgOpgxMX99Dpo6eKZhEnwZxtvjuANP3yM11j0eHotRc8AtWdtCJOM9/LLLx8Fpcsss0y09pjvJT+m7B2Db7nlluiS9TYjIADMM/rlqJs1xHM4nATEScs9C8/FIdnBBx88QYe83pw31EmXLAGg6/s+92O0Dpy9GYjPvcIKK0RBnHcU9y8D/HPhnzef068dtcVLHTj082YYnlm72WabRX+S10j/zPt1x+GgA0S/Du21115fOo/X9PMmKi6LLbZY9HcHcn482UGaZzc7PPTPql+HWnkkvr8AMP09+yczlOvdPY0CQNf1moYOAf1fh6L+hU+y43JyLv+CxHX8GujXas+AdB3Xd9+8wYl/+eD/j7gtfs3z6wIFgVAFeO8b6siG3y8CwPDHOEsPCQCzKFGnEwIEgJ1Q7fI5eRPUXfD0B+WsV/YHw/QHdAdUfozV5aSTTtLee+/9pVN591t/sH7ssceisMvHO2RLShEBoM/l4GjnnXeOPljWKw69vN6eg0GXegGgv+41rBwMJDPY0udyKOhZeg7AvFtmowCwmad3TnUYWG/XVLfTu/bWK/4gnGwa0Owa6e97DDyrx+skOtRoVvzB3MGL1yT0ph/p4scP/SikZ/zUKw4DHaB4hqCDgEYBYLM2+Bp+ZNnrOqbLU089FYUkLg5ZHTq2UjoxW68T52ylb2U7JmsAmN4EJ0sf6v08ZA0Aff5TTz01CqDSO3Snr9tot+50HW9Ys8022/S7fl9S368Z22+/fd2u+ZcNXgKg0dqfnrXrQK3Vx+HTIZ93+/XrZVKS9QWb7Yrs+v0FgP6+x8QBn18n/Hrpx3k9Czld7ODXYof+tcWB6+mnnx6t0XjxxRdHuzD7Z56CQKgCvPcNdWTD7xcBYPhjnKWHBIBZlKjTCQECwE6odvmcvAnqLni7AWB6Jodnq/3lL39p+NirZ+F58wjPrvGsOB+bzGQrKgC0nmeweTaNH7fzDDSHVw4tHWb5seB0nxsFgD6PH2P2ZiFup2fh+PFAz5jzbEbPTPEH6HoBoD+8e6aPZ7A5GPBjbN4V2Y8cTjfddNGxnvW26667yrvk1iv+UOz187yOljfT8GPHSajZagCYXMdt8dp9t956azQz0rMDHQh6ZpFn4Xk2nh9R9C7H0047bcMb0o8CO6xwQOeQzx/0HX55xp7X7pt++umjf9cLAB22eJxs5HXV/AbS7fIsKoccboMfi3YQWm+9wvPOO08//OEPo7bVrsuW5yeoE2FdJ86Zp09lrVvWANBe/jlwEOj70bN7vTGIZ7B6VppnEqcfP2/k68fmHaj50Vz/osM/H753/bi8Z9E6EPPMQM9i7K94lq7DL9/Xnj3on6uFFloomsHrjY76+5lsNvZeqiG5fu3O7H79dv+b7djuazQLAF3Hu5u7nn+2/csEu/j1M138Oum1E/067NchW3mWpQNZ/z8iWY+12ZqEzfrN9xEouwDvfcs+QrSvkQABIPeGBQgAuQ96JUAA2Cv5Aq/Lm6ACMTkVAoEKJOGrN0VwyEpBAIHmAukNfDxz2zvyuvgXDP5FiYP5/nZrb36FYmt4lriDxG233TaaCUhBIFQB3vuGOrLh94sAMPwxztJDAsAsStTphAABYCdUu3xO3gR1GZzLIVBBgXnnnTdar7C/GZwV7BZNRqCjAumZsw7UHKy5+BF6r6Xo5Rk8E8+zDntdvMmSl41w8bIFe+65Z6+bxPUR6JgA7307RsuJOyxAANhh4IqcngCwIgMVYDMJAAMYVN4EBTCIdAGBDgokbzaHDh0arRFGQQCB5gJemsCP5Ppnxo9je/1Ob0LkcvPNN0eP5Hs902TDleZnbK+GNztKrwObPpsfn/ZO636U2huD+Ge+dh3S9q7O0QiUS4D3vuUaD1qTXYAAMLtVyDUJAEMe3XL3jQCw3OOTqXW8CcrERCUEEEAAAQT6FfCjvV4z9NVXX43WFfRmHC7tbJxTFLln8frPd77zHS255JLR2obvvfdetHbraaedFq1/6PKLX/wi2ryIgkDIArz3DXl0w+4bAWDY45u1dwSAWaWoV7QAAWDRoj04H2+CeoDOJRFAAAEEghPwjsEO2NJlwQUXjDZCmn322Xva32SznP4a4Q1JvDtxvY2Aetp4Lo5AwQK89y0YlNN1TYAAsGvUpb4QAWCphyfoxhEABjC8vAkKYBDpAgIIIIBAzwWSANC7jftxW++q612NveN3r8sdd9yh6667Tv6vZ/t57UHvCu+dyFddddVol/SVV165183k+gh0RYD3vl1h5iIdECAA7ABqBU9JAJht0GaR5NWN/Wf5+M+M8aEXStox22n6aq0vadf4fDNLekvSg5LOkvSXjOcaIumHkraRtKikqST9R9JfJZ0s6cmM53E/9pa0qaSvxjtDvyDpmvg872Q8T95qBIB5xUpYnzdBJRwUmoQAAggggAACCCDQEQHe+3aElZN2QYAAsAvIFbgEAWC2QRrbT7U8AaC9z4jDv0andAi4m6T+runQ7gZJKzY4yaeS9pB0XpPuOcy8VtJsDeq9KmkTSX/PxpSrFgFgLq5yVuZNUDnHhVYhgAACCCCAAAIIFC/Ae9/iTTljdwQIALvjXParEABmG6F0GDdS0lOS1o0PzRMA/krSofFxj0g6TtJzkuaXdKCkr8ffc71GqyhPJOk2SavFdf8o6WxJ78aBoI/zjMUvJG0k6aYGXZxD0sOSviJptKQTJF0f1/2WpP0keZbhG5KWjWcXZtPKVosAMJtTqWvxJqjUw0PjEEAAAQQQQAABBAoU4L1vgZicqqsCBIBd5S7txQgAsw3NUZIeiv84EPOjsn5M1iVrALhAHBw6VPOMOgd4n6QuP4WkOyQtFwdyi8ThYG0L/bjx+fEXT5O0Z00FX8fBnheN+bekxeLz1Z7nAkk7xF/cUtJVNRW2kHRl/DVf7wfZqDLXIgDMTFXeirwJKu/Y0DIEEEAAAQQQQACBYgV471usJ2frngABYPesy3wlAsDWRqeVAPDU+LFcX/Ebku6vc+mVJN0Xf/0UST+uU+dfcaj3niSHaB/XqXOwpGPir28u6Q81dTzrz+sFejahZwh6TcJ6xesRrhfPJvSMQYefRRUCwKIke3ge3gT1EJ9LI4AAAggggAACCHRVgPe+XeXmYgUKEAAWiFnhUxEAtjZ4eQNAO/vRYYdoI+JNOxpd2d9fWNIrkuauWQtwQUnPxAd6LcHdG5xkVkmvxd+7NN4oJF11l3jDEX9tK0lXNDiPv3dZ/D1vWuJHjYsqBIBFSfbwPLwJ6iE+l0YAAQQQQAABBBDoqgDvfbvKzcUKFCAALBCzwqciAGxt8PIGgPOlHuc9M97ko9GV/X2HbS4+LnnU2P/2Y7jnxt/7vqTL+2n+05IWkvSypHlq6l0kabv4a94A5PUG5/H3vBGIi49JHhluTW3CowgAi1Ds8Tl4E9TjAeDyCCCAAAIIIIAAAl0T4L1v16i5UMECBIAFg1b0dASArQ1c3gDQm3EkG2zsK+nEfi7r73tDDhcfNzxVd5ikn8b/9oYhj/ZzHu/uu3E8g3BqSR+l6no9Q681+IGk6ZoQuI7XE/QxK7TGVfcoAsACMXt1Kt4E9Uqe6yKAAAIIIIAAAgh0W4D3vt0W53pFCRAAFiVZ7fMQALY2fnkDwN0knR5fyptrXN3PZb1mX7Ihh4/zjMCkeMbf9+J/zCzp7X7O4zUEkw1CvKGIZwQmxTP+vA6g1xP8WhOCJyQtHs8S9IzArMUBX3/Fjyk7VNTIkSM155zNqme9LPW6KcCboG5qcy0EEEAAAQQQQACBXgrw3reX+ly7HQECwHb0wjmWALC1scwbAB4g6bj4UhtI8uYajYq/n8z682y/41MVb5C0YfzvySX9r5/zHCvpwPj7nu3nnYGT4tmA3nX4AUneeKS/4jqe+TdKkmcSZi1js1YkAMwqVb56vAkq35jQIgQQQAABBBBAAIHOCPDetzOunLXzAgSAnTeuwhUIAFsbpbwB4OGSfh5fam1Jt/Vz2bUk3Rp/38f9MlXXX/f3XbyD75h+zuPr+XiXoZLuTtX9QtJgSXdJWq0JwZ3x8T5mSA4uAsAcWFWtypugqo4c7UYAAQQQQAABBBDIK8B737xi1C+LAAFgWUait+0gAGzNP28AOBBnADZ7ppdHgFu790p1FG+CSjUcNAYBBBBAAAEEEECggwK89+0gLqfuqAABYEd5K3NyAsDWhipvADgQ1wBsJssmIM2EKvB93gRVYJBoIgIIIIAAAggggEAhArz3LYSRk/RAgACwB+glvCQBYGuDkjcA/Jak6+JLtbML8G8k7R+fp51dgP8uaVl2AW5t8DlqvABvgrgbEEAAAQQQQAABBAaKAO99B8pIh9dPAsDwxrSVHhEAtqIm5Q0A55P0XHwp7+rrGYGNir+/a/xNH/dCquIPJJ0b//v7krwrcKPiXX8XkvSypHlqKl0kabv4a97Z17sC1yv+3qvxN3zMDq1x1T2KGYAFYvbqVLwJ6pV8b697wQUXaKeddooa8cILL+irX/VLIgWBzgoMGjTuLcsRRxyhI488srMXa+HsZfu5KLtXC8RdO8SvaS+99JJ22GEHeVx7VV588UXNO++80eXPP/987bjjjr1qSqmu+8QTT+jXv/617rjjDr3xxhv6/PPPo/Y98sgjWnrppXO19eKLL9bZZ5+txx9/XB988IHGjh2rpZZaSo8++miu81C5ucAaa6wRjdnqq6+uv/3tb80PKGkN3vuWdGBoVlMBAsCmRAOiAgFga8OcNwC08yuSZpc0QtKi/Vz2KUmLSPqPpLkkpTfTcKDnYM/lDEm7NziP19d7Lf7eZZJDBJGsAAAgAElEQVS2rqnngNFBo8tWkq5ocB5/z8e7/EjSWa1x1T2KALBAzF6dijdB3ZX3G+Y111wz10X32WcfnXjiibmOaVa5bEFHs/ZW+ftvvvmmrrjiCt16663yh963335bH3/8saaZZhrNOeecWmaZZbT22mtr44031tRT59movXoqZQ+0yvZzUXavMt+BBIDlHZ2HH35YQ4cO1SeffPKlRuYNAA888EANGzbsS+chAOzM+BMAdsaVsyKQVYAAMKtU2PUIAFsb37wBoK9yWiqw+4ak++tceiVJ98Vfd/0969R5Mg4Q340Dwo/r1DlY0jHx17eUdFVNHQeEDhi9E/BNktZvwPAXSevFuw3P0c9MwVYUCQBbUSvZMQSA3R0QAsDueme5mmeiHXXUUVFVzxwpqnz22WfRLLeTTz5ZH330UdPTTj755Np11111+OGHa8YZZ2xav4oVyh5oEQCW+67y7LkLL7xQ88wzjzyzrr9CAFjesVx33XV1yy23RL8E8SzA5ZZbTn79c1lggQU02WSTZWr8yJEjo9mVX3zxhVZaaSUddthhmmuuuTR48ODofPPPP3+m8wz0Snl+rggAB/rdQv97LUAA2OsRKMf1CQBbG4dWAkDP3vuXpCGSvAbfapLSv770u5c7JS0nabSkxST9u07z0o8Bnyppr5o6fsfyD0nTxI8dezahz1db0o8BbyHp6poK/tqV8dculFT0cycEgK3de6U6igCwu8ORDgB333137bHHHk0bMNNMM2nWWZ35F1fKFnQU17P8Z+pEAPjuu+9q00031V133RU1yB9ot9xyS6211lrR49b+4Os6DjH++te/avjw4frwww+jupdddpm22sqTtyndFijbz0XZA9Nuj0+eoIIAsNujk+16ftR3qqmmkn9B8tOf/rTu7L1sZ5Iuuuii6BFvF8+uXnzxxbMeSr2UQJ6fq1DgeO8bykgOvH4QAA68Ma/XYwLAbPfBqv7FYqrqTJKSZwbukXROzWkaLRjjWXmenefyiKRj45DOod1Bkryxh4vrHdqgaRNJukPSKvH3/yDpbEnvSVpB0uGSZoln7XnzkRsbnMePFz8saeY4IDxe0vVxXR/nzUYcVr4laZn4EeZsWtlqEQBmcyp1Ld4EdXd40gFgL9dBK1vQ0d1RmPBqRQeAY8aM0XrrrRcFey7rr7++zjvvPM02m5dkrV/ef/99/e53v9MxxxwT1SUA7M0dUbafCwLACe+DPEEFAWBvfoaaXfW1117T7LN7NR3prLPO0i677NLskIbf9+vloYeOe6vtQHHiiSdu+VwD+cA8P1ehOPHeN5SRHHj9IAAceGNer8cEgNnuAwd6eTbAaOTqR24d1nkWX6PiTT68Rt+Yfuo4gBwuafkGdT6LZwb6Wv2VFSVdI6nR9CBvDrKppAeyMeWqRQCYi6uclXkT1N1xIQDsrneWqxUdAP72t7/VfvvtF13aQeD111+vIUP8u5jm5amnnooeF/YjcZTuCxAAdt88zxXzBBUEgHlku1e3yE1Rin7t7p5Cua6U5+eqXC1vvTW8923djiN7K0AA2Fv/slydADDbSBQVACZX2zAO+RzgOcx7W9JD8cYcjWbs1bbUnwj9q09v8OFNRaaMd+y9VdJJ8ePGWXrn6+8TB33JVp7eefhaSd454J0sJ2mhDgFgC2hlO4Q3Qd0dkXYDQK9Rt8EGG+imm26K1jnybnyrruoJzl8uJ510kn7yk59E3/AsiV/96ld9lWqDjrnnnlvnnHNOtFumQyjPpvD6Sd/73ve07777aooppqh7Dc92c5/8COt9992np59+OtqFccopp4wedV1nnXW09957y+dvVLJ+UG/2IcXrQHk3SD9C+9hjj0WP2LrdM888c3R9b7Thx3IXW8yrMyjqa7ITcn93QZ5dku3mNaleffXV6LHf559/vt+Zf3nuPp/rT3/6U+Tt3S69c6bLLLPMEq1/5b54tmF/xTMNTz311CiUHDFihEaNGqXpppsuMlp44YXltbk222yz6JydKP3NaCvqnkza7XvTm69cffXVeuihh/TWW29F94M3Xll22WWjR7J9f6bD2WYBYFH3atLGSy65JNq91PerH430+e3vnzmPS9YZgA8++GB0Hr8e+N7z64TXQvM979eABRdcsJDhbPU67Y5tOujpryPpNTxrx8r3+/HHHx+tPedZaPZdeeWVddBBB0U/P42K6/rn7rbbbovGyb6jR4+Wl2ZwUL/11ltriy22iF6P65VmgVdtiOWfUf8SwfetdzGeZJJJtOSSS0Yz5bbZZpsvXeLPf/6zNtlkk+jrl19+efSa3V/Zf//9dcIJJ0Ttddt8n+Qtfp3z/y+uuuqq6NFbv+bPMMMM0YZG9vCfWo8sY5h1Vnwytnlft6+77rpoDcn7778/ej3wo8gLLbRQ5LfXXntF/65Xau9fz+Y+/fTTI+9nn31W77zzzgQ7m9fee//4xz+ie+/OO++MNoGaY445ok2f/P/l/2fvPKAlqaq2/TozMGQGBgSJkkFAwjAgWYKAJIkCgoDkDJIzgoJK+siSFMmIRImigCBBkkRJv2TJMOQMM/96a6ouNT1V3dW3q7vCffZaLpzbp0549qnqU2/vc7bnUWR33XVX4Pt77rlHTh7lchtssEFwtmFagqhOvoOz+MR9i99XWc8AvOOOO3TGGWcEx2C89tprwfehvxvXWGMNObGZv3OSLL5GuvXWW+X2Lr300qCuRx55JPjO8ve5fbb//vsH864/xtq3P9S4pgwEEADL4IXi+4AAWLwPBmoPEABr4HkWQb11YqcCoHvrxfSCCy4YvEj4RcMvpT5TLm6PP/54IHJ8+umnwUuqXyzi26PiLzR+gbNI5xfcJFt88cWDzyzqNVqWFwiLLhdccIHWXXfdxPrzEFX8UrD66qv3nbmX5lWLK36xtnVDAPQLpl/sbD6bym3kYRYhZ5999pZVbbbZZjrnnHMSIw4t7FrwsoDRzLwV2S/D3bCsAmAnc9L9trDh+fbQQw81HUb0ghkV6pUAaAHJ27wvv9wngIxvFt8tVEU+TxNGXI/vXYsRaeb73qJvJ1stO22n0+dNlueMx58mAPqe/OlPfxpk3260wYMHy0JsknDmHxUswFlkaWY/+MEPdMUVVyQKSO0IgBb5XdczzzyT2JzFIP/AERet3UcLIr6vHXF8443O/ZZs9qNFJYtLrcqm1WFR0j9C+XmSZv5R6uqrrx5HnMniw24JgP4etChpITfNvC35uuuu08ILLzxekfj89Y8Jvpcany3xvse/0yzCb7PNNsGPao1m8dGivc/4PfbYY+WMxkmJqCysulySQJmFa9p3cJZrG++rVgKg7xU/k/zMSbMpp5wyEI891xstvkbyMRo+EuOiiy5KrMoJYyww9ueMZNa+TR9pfFhiAgiAJXZOD7uGANhD2DQ1DgEEwBpMCBZBvXViHgKgexyP+rDo48i3yPyiYdHOwqAX/g8++GAQ5RC3+AuNo2AcEeEXZEdFeTH94osv6uijjw6i+mz+pd3nLTWaIxO8QLfYsuSSSwaChX/pd3ZGi46nnXZa8Iu9/+YoiPnmc7DzuJaHAOjD5B1hYVtzzTWDSBm/FLtdR3qYhaPepptuuuDFw+ZIGy+k3MdIQHFkXaM5Mi7r2VLxfjj6zDzzMEeZ+IB7v7T7pclRjI58cJTj008/Hbxs/ec/zlElHXrooX1ZjeNtWwh+4IEHgrH4BdYv8fa1X9gsHji6y4LUrrvuWrgA2MmcdGSkX5gjodOJVyzGzjvvvEFEncVUC9qeBx6vX2gj65UAaMannHJK0Kznl1/8HeXlSCr3y9F8FvAtNtjShBGPy4kQbPan573vdY/TAsUJJ5zQNy/8zFhrrbX6NR07bafT540FK//PzxsLSxZrHAXdaAsssEDfn6LnyiKLLBKIVY7acvSb7wOLLL7eGWgtDvkHFN9jjVFJFsyGDh0azBHz9Q8vLvPBBx8E0b32U/SM3HzzzYPoskZrRwAcOXJkcI86G7jFPgsljnr67W9/G9znNs8dZxaPmyPJ/Hx21J0FOke5JpnZOQra1p/nk5/lCy20UDB2m+vaaqutAn/4vvKctlBl8/eBxRkLrLbIh5FQ6b/96le/6ote9L8deZwl+tgs/D3X7Nkdf25b3HUUmc399zzwd5Gfn47i8/z0nPAz1bwtksYtPn99n/p7wt+Xrjf6vvQ88RyxRXPPYqJ/jLNQ5e8Gzx/PHX9n+kcxm+9Z/zC13nrrBZGo9q/77h/47GdH19scqer52midfAf3575qJQD6WXbMMWOPWHfEn/vt57GPtvAzyHPE0c4W1v2dY3/ELb5G8veA1xGeZ76/nP3bz3d/31mstfmHFIvi7Rpr33aJUb4sBBAAy+KJYvuBAFgs/4HcOgJgDbyfaRHk6IdPRtVgtG0MYeKppZQtXW3UMl7R/mQBThOgtt9+++AQdVs8c+w+++wTRBPYvG3GL5ON1hj9ZgHRQmLcPvvss+Bl2dFYw4cPDyIPG8+y88utX5bSBDIvVPxS8/LLLwf1x4XKqK08BECLfRYd/dIcCXxJfvILX+OWoTzPkfIWWkdu2Xxv+cUvD/PLk7MEpyUS8curX8TtV0dqmrfFg8j8wu6oMluzCD/XY2F0qqmmyqPb49WRNQLQF/Z3TlqMvuoqH42rQDjxC2mSmalFhPhYeyEAWmCwKGXhNS2yJ57d1H1PEgAtXnq+2yxEOcqo0SxuecudBU/fZ56TWc+jjOrKo528njetjgGIjz++TdRi6s033zzOPeGyjvyLnnveFuut13Hz/eBovGb3sX1zxBFHBKKrj0Bo3G7djgDoth3ttMkmm4zTD4tGyy67bPBDhkU+i7sWk+L3t/vo/vqohygxRuN88LZJizB+nvsZYdGqHYt/t1h4+uUvfzkeLwtj5mqzQOds93FrxaOd/mR5dlso8o9CNkfjWVCz+BQ33z/R96R/tLE4GrfG+fv73/8+eN6mWXzuWcTyd0LjMRpux99VFkj9rF5hhRWCdiPB1HU7utPRlP6Brpvfwe3cV80EQAujFj39bLMYbwHYW+3j5ghVP5Ncxj9Uertz3OJrJP/dIvFBBx003jzzcRc33XRT8DyzqJy2pTjNR5nWvu1MRspCoEcEEAB7BLrkzSAAltxBNe4eAmANnJtpEfTRW9IxY8WDAWP7PCNN+vXZPHmNu3Fxm6XetDPovJ3NAoJfOr3I9suhX1b9kuMXQW97c8RHksVfaBx5kLYV0QLiDjvsEFTh+h390K5FZxE6ysbiUiQCRfXkIQD6hc5RBW7L24/asSwvkVnrsz8ccWnzWOMiXLwOR3pZsEwyj6UxYjNL+xY3HT3jl0Zvc3ZUSWSOolh66bGJ5/vrxyx9aFUmqwDY3znpc94cHen5b7EjEgJb9Sv6vBcC4E477dQXcXr//fcHkX5J5i3tN9ww9kjhJAEwiui04Ontp2nm6Lfo3EsLEd4G3o7l0U5ez5t2hIq4CJM25z1PHC1nAaEVxzRmvt8cBeaILf/w4uiyuLUSvOLPHwtVPkYgyRwttcQSzvsmeQ41brH0c99CrwXIKFowXk90ply0ndvPynbMPwh5nH6ueT5ZyI6LVVFd/qHCkeA+F8/losjk6PNWPNrpU5Znd3Qf+Ucqfz+mnXnoyGpvObWg5Aj4+I8t8fnriGKLyc0sPvccAZgU+R5fC1gc9PdB0nl2PtIhEhv7++xu9R3czn3VTACMP9scGZt2tqYj0H2GpM3z2pGvkcW5RFHQjWsGl3UEb3Tmrdc50dEbWedPprVv1sooB4EeEkAA7CHsEjeFAFhi59S8awiANXBwpkUQAmBuns5TAHSnvF3MW60sfjlSwNu//CLhFzW/oKX9Kh5/obH4Z8ElySxQRItzn58UbR9LA+KXP7/4WZyMzjLyi0AUXeEXsMaz7PIQAL01yC9tadEWzRyY5SUy6wTwy7e3Etr8op30guzPvP3LkTJJ5rH4JbmZ2d/eCuXIIAsQkVkE8Iv+IYccEkQlRRY/Q9BRTo52KsKyCoD9nZMeVyTA+F5bfvnl2xpmLwRAi7t+7jqCy/domlnEdXIJW6MA6OitaJtnPPo3rS4/ByxQeU54bmS1vNrJ63nTjlARPVdacbbo5igxb0VsdWako5YcCe37zvdgZL6Xfa3/G23Jjj5rJXjFnz/N5r3rc1SVBTXPIf/wEzfPA59zZ3PkVWNyKB+R4G2oNve1cetlqzkR/xHBx0M4GjDN4kKQxdW4mNaKR6t+xD9v9ez2M9g/wvj7yEJgtG00qQ1vEY7OgWyMwozP31bRf647mnvN5pR/sHFUn63ZjxU+OiP6kaBb38Ht3FfNBMDo2ZYk/MaZx8Xso446SgcccEDfx/E1kufsnnvumTgl/DyL1jdOnBIlPMs6fzKtfbNWRjkI9JAAAmAPYZe4KQTAEjun5l1DAKyBgzMtghAAc/N0XmcAxjvkc4HiC2h/5sihZhlh4y80fqGMIoQaBxrfOuoX2yTRyqKjI18cueL/38y83cfbfuKWhwDoc++i7WiOgrNwYjHML8Gttga1eolsx/nxCEBH+TUmZ4nq6o8AaMHBW769NdZRhkmHykf1e9udt9/FbbnllutLkmJ/O0LQL3OO0kjL8tzO2LOUzSoA9ndOen6araN9LNK0u8Wx2wKgt+SadbRl22JCmlnQthhsaxQA42e5ZeEelUmKHGt2fV7t5PW8aUeoiJ4rfhZE578ljdVni/me8g8TSck37CtvabWv/Pz65JNPUpH5DLjozLaoUCvBK/78sc+bZeV1JJgjwnwfeS7Ft7I6Qs9n8VlYcrnGuRWJh35G+Yejds3PHh87YfM5f36epJnPQrSvbN6mGU/20IpHO/1q9ex2JKSP0LAdfvjhwfmoaebvLs8Zm7dQeyt1ZI1JQBwV28yyzD2LydEPRBZTLaomWS++g9u5r9IEQM8/n7lrc0Z6n3OYZv4u81EV/q9F62jLuMvH10gWbC3cJlmcX7s/bLi+TGvfdiYjZSHQIwIIgD0CXfJmEABL7qAadw8BsAbOzbQIQgDMzdPdEAC9EPYWo2jbl88C85lGzayV0BFd2+plzUKjzyFLyq6Z1H5SVFYeAqBfJLxV2S/H8SyKflF28gxHOFr8cBKQRmv1EtmO86NtZL7GkYDRuXtZ6og4JEUA+qXe5wtmfXH3C51ZxM3RXBZDoqQF0WcWyxxF6nPHfF30Epelz+2WySoApm17d3vN5qQFGJ8xZT87Uqtda3VfdDpX3acoGiotsU7U5/gLdaMAGD+zrJ0xJs2LZtfn1U4rrlmfN+0IFZ36yn2yyOZnR7QVuxVriyPOLB23Vs/Q+PPH7TUTrf1DT5QIwnOp8XnmKChv93S2WH8eZW6PR1x567Cfhe1a/Icmb7WPhLWkeuLbM51kI55huRWPdvrV6tnts/P8bLOdfvrpfQJmUhvx+83fJfHM2vH5m+W5nnXuNXseZr0n8vgObue+ShMA23m2eWx+Dvoa/1AZv7/ia6TGLO2NfsvCL20+ZVr7tjMZKQuBHhFAAOwR6JI3gwBYcgfVuHsIgDVwbqZFEElAcvN0NwRAR5z4UO3InO3U24Ymnnji1H7n8ULurb4+eN5nQvmF09vLnKXWope3XUXRKT6XytF4tqQFfdaXpSwvKT5vyVvh3Ka3L8ej5NxHR4d5u1XcWr1EtuP8eBZgRx1FWziz1NFMAIwi21xPlHnT5zE62tGCXfQiFCVDcdZW+zjJfH6Vz4xzFI95xQVT+87zqT9nEGYZY68EQG+Bf/XVV7N0aZwyre6LTueq++QoLZvFHG9/S7NmAqDFjCi5gqNnsp7N6YQnjRlOm0HKq51WXKM+tBKHsjwDoro69ZXr8XZpJyGweTv5zjvvHJy76vnl56sTctii6FqX8TM+bq3GFH/+2OeNCSridVk0dmIbW5IA6IRNUXIQM/dzwOa5Yl/6WeE52JiYIcuNEhcAvf242TPCInyUEbcsAmBaQqxo7FkFwGY/TrQ797IIWM3mT17fwe3cV1kEwFbPNnPyPeRjLBAAs9x9lIHA1wQQAJkNJoAAyDwoigACYFHkc2w3kwCYY3sDvaq8BcA333wzeOHzQtrbTX0Gn63VVr88Xsjj4kDjNq+4n+NZRJMEQG+980tV0vlZ8XospvlctCxn5Pk6b9W78847g6ya3r7ss/L80u5tfvEzqfIUAL0NOjqMPGkbXrP5nyYA2qfTTDNN4napxvomn3xyffjhh8GLf5oAGL/GL5A++N7b+yya2pzFMUpkkvf92m0BML4F2ByaiSlJY2t1X3Q6Vx3hFQnzreZHsy3Azh7qLKK2pKyxefktr3ZacY3620osa0eo6FQAtDBusdZCm48SsGAeCX6NfP0MtvjWqQDYyRbgqE/e0u+tylFfPOf8vPMPNY7y9Xzpj7EF+GcBtjIJgHl9B7dzX+WxBdhnM/oohFZbgIkA7M+dyjV1J4AAWHcPZxsfAmA2TpTKnwACYP5Me14jAmBvkectAFpssujkF9NIyHHEhc2RXFEURuMo83gh33XXXXXKKacEmQstJKVZfNta0oLeB6U7GUKzDJiu21FOjz76aGYBMN4fHxIeHSbuF1lnIYzMZ0NZBLTFo+H6MzMcRTLbbLMFUTZ+wfHLoqP0sliaAOhtv9GZU3/5y1+01lprJVbnqBxHf9qyCoDxihwZ6fpt3k7uhCZ5W7cFwLifu5EEJI+5mjUJSFw4b9wC7G2IkX/a3dbbjk/zaieP54377bPFXFeWHwE6FQD9TLPwbjvppJPk512SWWj2M9BiRqcCYKskIJHQmJQEJOqbs6v6+eZ7zf7zNthNN900+Lg/WaCjettJAuJIyegM0qokAWkmdmedvxGrrHOv0wjAvL6D27mv8kgCct999/WdBdwsCQgCYDtPa8oOFAIIgAPF083HiQDIPCiKAAJgUeRzbBcBMEeYGarKUwCM//ofHSLuKA8LZc4E7POhLJglJcHI+kLTLCIn2lbmc6YcpZYUHeOzAS1U+CXQlrSg95ZWJxtwtI0XNtFLURxnfGtblpf/RldYYIyyXja+cHhLnbfW2VqdwZXBxUGG3SgTrUVNjy0tciheX5oA6GgeR/XYGrfTxa93dt8TTjgh+FN/BECLHLvvvntwvV/2o7Ozsow5a5luC4AWLi2CWshtllkzrb+t7os85mo8Q6q36i+yyCKJ3fG2/iihRKMA6At8tqW3cHtbp8Vfb//uhuXRTiuuUb9bRQBGz5wsW7yzijBp0U+OqnY7Nm9/3W+//RLx+sy9KANppwKgf9Dx8yLJ4hnZm0V4W5B0xJ//6y3MPvPTPw6ZhxNKJD1fs8wb/7hhHv6O8Zx4+OGHE7OcO/mOI2WdpTUpG2wrH2fpS1QmS/S2k0j4jDmfdero77QkKz6+wpHsTszh7894lHjW+Rv1K+vc61QAzOs7uJ37qpkAGH+2ed5F31uNPnUyGf8QZ/P5lCNHjuwrwhmA7dwBlB2IBBAAB6LXxx8zAiDzoCgCCIBFkc+xXQTAHGFmqCovAdBCh4UDC2wWtryIjrY7WmRbeeWV5eQgaSJI1heaZi9rcaHrT3/6U9+WxAiDt9x6S6bP5IssSQCMR235PDNnBYybXyh9hqCjBmyNAqATZNxxxx1BZFzay62zFFsktbk/G2+8cV8T3h4cnZXVLPtsBvcGRTxuJwOJkgH4pd7JFJpFAtqPPn/P2w0bx+dIJIu4FrX8MuvMiI127bXXBmcDum1bowD40EMPBX/39t4kiwQzR5OaoROGxF+A4/MgSeTIyqbbAqD74ezGPuPQ5syakd8b+/jRRx8FZ0T6XLzIWt0Xnc5Vt2PhxPeumTuy08+EKFlD1A/fB5tttllfv5IEQM/j6F4ZMWJEIHKkZby2eOOssN523G6SlzzaacU1GmgrccjZPs3CgrqFKG95T7OsIkyaAOjn5/Dhw4N2fN9YiG/cUu5n0oorrhiIbbZOBUDXkfQsdf0+Z9Bb8z12/7fZuY+OAHQkoJ85FuI8FotlZteJ+V7ys9RmcdH+iJvntHn6mWpzFGB0VmVWH7fTvywCoJ+X/iHG5u9F/7vRj85Yu/XWWwdlvLXePohb1vkbXZN17nUqAOb1HdzOfdVMAPQPjr5XPN8crfrPf/4zOA84bhZZ/T3m76rFF188uK/ihgDYzh1A2YFIAAFwIHp9/DEjADIPiiKAAFgU+RzbRQDMEWaGquKLW78YZcnG6DPD4tlkvdVsqaWWCpJc+GXe/3VERtziL2qNW15dLusLTbMXci9CnATE4oL76CgYv2D5LEILaSeffHKQtXbppZcOzuKzJQmAPsfQ9TiK0OPxlmFHY/jlyGPzS44FKWc69otvo0AW9dEvXc7YucQSSwRlhgwZEmzFtajll2G/lMw000xyBsu44BLf5uhMuwcddFAgfkUvZ67XdbVjfum28Bdl3PV2YGfCtFjgvpmRzyh03x1t5xfON954I2jC43RkV9z8AhsJf2bjCApHfPkabxu0Px11Y7HCPBsFwMjfjrSwUBolMvBc8jZlZwz29kCbhcQrr7xynParJAA6csvji6JOzdyihCMD7VOPxfehoyl9pqRfaCNrdV90OlejdqKte/63++XoMr8wv/fee/JWRN+zHoPnvy1JAPTfI/HK/9/bVT0vLEJZCLTA6Ygnv4RbELVQbjHdyXDatU7bacU16k8rAdCRbBbXbRY/zdEiXXSv+jkSWVYRptn5Z7vssoucNddmscJRtm7DfnJ0pgUu8/QWYP8o06kAaEHYzzj70dnV/Zxw9LKjlB3lafOYHa3bzOJRwy5nPr7P/ezpxDx/LPA4ktC27rrrBqKyo7ddv4+EiJKgOILYc88RdXFr5eN2+pdFAHR9FvV8X9ncf0do+zn7zjvvBM8BC4AWL+1H825MlJN1/rY79zoVAPP6Dm7nvmomAATr5k0AACAASURBVHr8++67r4455pgAhb+T/GyLfqz0d7Hnrr93LMJ6njb+KIUA2M4dQNmBSAABcCB6ffwxIwAyD4oigABYFPkc20UAzBFmhqrii9sMxYMijvCLIrj8bwtUUfZQbz/bbbfdxqvKkU1+YXW0kcUuXx9/Oc76QtPqZc3C0TbbbBOIa0lm0cvRKBYGbWln+vjlzAfURxFs8bosCjqixALYueeemyoAtuLplzq/gCRtuXQ/nbU3ybIc+p50nX1w8MEHBy/FFvtamUVUn8XkMwmjs8eia7wlzYkInCQgySwGOgLMkRUvvPBCqgDYqg9uw1sQ/SIctyeeeCLYzmezyGrRsT/WiwhA98sChaNfvXW8mTXOxyz3RSdzNeqLX4AdhRpFKjb20edIOluzX6BtaQKg75cDDzxQxx13XOK9E6/XzwELmM2yg6ex6rSdLFzddqvnjZ8z/kHB59olWfwMzzwEQAt9Fjziz994u75PLJYfeuihQZKQTgVAz1tHO/uZk2SObrVgleUHiejMVNdj0dSRV3mYfeSzZf1DSprZRz5PtPE5ksXH7fQxqwDoox0sGDf+sBFvyyKmv2OSoqSzzt+ovqxzr1MB0O3l8R3czn3VSgB0XRapozMgk/zpqEB/3/oHt0ZDAGznDqDsQCSAADgQvT7+mBEAmQdFEUAALIp8ju0iAOYIM0NVnQqA3urql0wvsr14vvHGG1O3vToKzxElfvlxVJyvjV4cs77QtHoh95AdweZf/B3l5wg0i1cWLS1mOfIi64Le25h91pb76Rdvn2Ho6C1HMzrCMS1Sxy/9jtpwBJsz2fol2lFg3jY3bNiw4FpHvW233XapWwYtyPj8PEeEOdLGkS6RqNlfATCaDu6LX9ot6Ngnjg60IOjoHp+p5Ugvb+9zluPG7VLxKeWtwI4EskBnkc/CqF80HbHns/u8lTXtxdNipP1kRo7K8QLS/XI2Rm8TdB8sSFkITTqvML5FztdbKOyP9UoAdN8sWl1wwQVB5I/P2jM/M7cQbHHcY11hhRXGGW/W+6K/c7WRmfvnaD/PX89BR2g5qmrvvfcO/JlFIHCdjj6LMjn7nnU0raNOLQxb0PCzwvU22zKbxZ/9bScr1yzPG9+b3tptMd8Rjo50jIS/vAVAM/H2fEchW7Dw96WfoT5Hzmc0+r5zVHEzUaTVmBpFLEeleZutxSrf5z67zs9TP7+iZB5ZfGVG0bmFjcceZLm+WRk/T3ysge8ti+yebxb7/OOK+2ixLe3c01Y82ulbVgEwqtNzxnPRArKfwxbFnVDFz1BHe6ZFx2adv0UIgHl9B2e9r1oJgBEDf0+cccYZwfeNv2uGDh0a/KDhH6m8WyDtuIKs6wW3k/X5mDSnWPu2c6dRtkwEEADL5I3i+oIAWBz7gd4yAmANZgCLoBo4kSFAoMsEIvHVgplFVgwCEMiHQLsiVtZWff6qBWYLyT4KwQIMBgEIjCXA2peZUFUCCIBV9Vy+/UYAzJcntWUngACYnVVpS7IIKq1r6BgESkPAW1IduZO2hbs0HaUjEKgYgW4IgI6g9jmmjjR2ZJvPY8UgAIGvCbD2ZTZUlQACYFU9l2+/EQDz5Ult2QkgAGZnVdqSLIJK6xo6BoFSEIgWm8suu6xuv/32UvSJTkCgLgS6IQD6jFifFWtzZtYFFligLrgYBwRyIcDaNxeMVFIAAQTAAqCXsEkEwBI6ZYB0CQGwBo5mEVQDJzIECEAAAhCoJIE8BECf5ekIXWdkt0jvLLeO/nP2cJ97h0EAAuMSYO3LjKgqAQTAqnou334jAObLk9qyE0AAzM6qtCVZBJXWNXQMAhCAAARqTiAPATCeWCPC5YQ3DzzwwDjZ32uOkuFBIDMB1r6ZUVGwZAQQAEvmkIK6gwBYEHiaFQJgDSYBi6AaOJEhQAACEIBAJQnkLQA6q/eSSy6pI488MsiAjkEAAuMTYO3LrKgqAQTAqnou334jAObLk9qyE0AAzM6qtCVZBJXWNXQMAhCAAAQgAAEIQCBnAqx9cwZKdT0jgADYM9SlbggBsNTuqXXnEABr4F4WQTVwIkOAAAQgAAEIQAACEMhEgLVvJkwUKiEBBMASOqWALiEAFgCdJgMCCIA1mAgsgmrgRIYAAQhAAAIQgAAEIJCJAGvfTJgoVEICCIAldEoBXUIALAA6TSIA1mUOsAiqiycZBwQgAAEIQAACEIBAKwKsfVsR4vOyEkAALKtnetsvBMDe8qa1rwkQAViD2cAiqAZOZAgQgAAEIAABCEAAApkIsPbNhIlCJSSAAFhCpxTQJQTAAqDTZEAAAbAGE4FFUA2cyBAgAAEIQAACEIAABDIRYO2bCROFSkgAAbCETimgSwiABUCnSQTAuswBFkF18STjgAAEIAABCEAAAhBoRYC1bytCfF5WAgiAZfVMb/uFANhb3rT2NQEiAGswG1gE1cCJDAECEIAABCAAAQhAIBMB1r6ZMFGohAQQAEvolAK6hABYAHSaDAggANZgIrAIqoETGQIEIAABCEAAAhCAQCYCrH0zYaJQCQkgAJbQKQV0CQGwAOg0iQBYlznAIqgunmQcEIAABCAAAQhAAAKtCLD2bUWIz8tKAAGwrJ7pbb8QAHvLm9a+JkAEYA1mA4ugGjiRIUAAAhCAAAQgAAEIZCLA2jcTJgqVkAACYAmdUkCXEAALgE6TAQEEwBpMBBZBNXAiQ4AABCAAAQhAAAIQyESAtW8mTBQqIQEEwBI6pYAuIQAWAJ0mEQDrMgdYBNXFk4wDAhCAAAQgAAEIQKAVAda+rQjxeVkJIACW1TO97RcCYG9509rXBIgArMFsYBFUAycyBAhAAAIQgAAEIACBTARY+2bCRKESEkAALKFTCugSAmAB0GkyIIAAWIOJwCKoBk4saAibbbaZLrzwQs0xxxz673//W1AvaBYCEIAABCAAAQhkJ8DaNzsrSpaLAAJgufxRVG8QAIsiT7sIgDWYAyyCeuvEf/zjH1phhRUyN3rOOedoyy23zFy+lwW7IQA+9thjuvzyy3Xrrbfq2Wef1dtvv63Ro0dr2LBhmnPOOTVixAitueaaAcPBgwf3cri0BQEIQAACEIBADQiw9q2BEwfoEBAAB6jjG4ZdlAB4RQ/wj5G0fg/aoYn+EUAA7B+3Ul3FIqi37kAATOb9yiuvaLfddtMVV1yhMWP86G9uM8wwg/bbbz/tvPPOCIGtYPE5BCAAAQhAAAJ9BFj7MhmqSgABsKqey7ffRQmAoyW1fkvr/1g9LtdPiEf/GXb7SgTAbhPuQf0sgnoAOdZEXADccccdtdNOOzXtwEwzzRREv5XR8ooAfOCBB7TWWmvp1VdfDYZpcW+jjTbSMssso+mnn15Dhw7VG2+8occff1x//etfdfPNNwdRgbY333xT00wzTRnx0CcIQAACEIAABEpIgLVvCZ1ClzIRQADMhKn2hYoWAJ+W9GnOlCeSNA8CYM5U868OATB/pj2vkUVQb5HHBcDDDjtMv/jFL3rbgRxby0MAdOTfoosuqtdffz3o2YEHHqhDDjlEE03kr4Fk85w94ogjdMEFFyAA5uhPqoIABCAAAQgMBAKsfQeCl+s5RgTAevq13VEVLQAuKOnxdjvdovz8kh5FAMyZav7VIQDmz7TnNbII6i1yBMBxea+xxhq6/vrrgz8eddRROuCAAzI75KqrrtJKK62kySefPPM1FIQABCAAAQhAYGATYO07sP1f5dEjAFbZe/n1HQEwP5bU1B4BBMD2eJWyNIug3rqlUwEwa9Td2WefrW233TYY3EsvvSRvJU6yL774Queee26QeOOhhx7SqFGjAkFt/vnn1/rrr6/tttsuNRova1/SCD/44INB9J9t5MiR+te//qVBgwbl4pC7775b1157re6880498cQTeuedd4JxzDzzzFp++eWD8wbnnXfepm099dRTOvnkk4OEJC+88ILMytuNp5122iAZyaqrrqof/ehHwRZlDAIQgAAEIACBahBg7VsNP9HL8QkgADIrTAABkHlQFAEEwKLI59gui6AcYWaoqkwCoH2/9tpr68knn0zt+TzzzBMIac7A22idCoC77767TjrppKDa888/X64vD4uLn2n1DRkyRKeccoq23377xCKXXHKJtthiC33++edNu2RxsZWQmMeYqAMCEIAABCAAgXwIsPbNhyO19J4AAmDvmZexxaIEwClDGO93IRmIxzRFWP97ZYROnwICCIA1mAgsgnrrxLIIgC+//LIWWWSR4Ay9KaaYIhDCVlxxRU033XR69913g2Qbjn77+OOPA/Hv3//+93hbbTsVABdeeGE9/PDDgQPeeustDR8+PBdnnH766TrooIO0zjrrBIlE5pprLk066aTyeYNOOGLR8e233w6iDW+55ZYgIjBuLucxf/LJJ0ESEmcaXmKJJYLov08//VTPPPOMbr/99iBj8R133IEAmIvXqAQCEIAABCDQGwKsfXvDmVbyJ4AAmD/TKtZYlABYRVb0OV8CCID58iyktiyLoNFjRuvdz94tpH9FNTps6DAN+kY+21HjY2gnC/A3v/lN+X9xyyq6tdoC/MMf/lA33nijZp11VrlP3/72t8dDff/99wfimEVAJytx0pL+9CXJh87iO+GEE+qrr74K+vD888/n5movjiwmTjzxxIl1ejvwsssuq//85z/6/ve/H2zxjduZZ57ZFxnozMPzzTdfYj0WCL/xjW80TViS26CoCAIQgAAEIACBXAhkWfvm0hCVQCBnAgiAOQOtaHV1EgA9ljEV9cNA7DYCYA28nmURNOrTUVr+T+NGSdVg6E2HcNtGt2nqiabOfZhxAbBV5UlZgvMQAH3Wn6P/bNddd51WX3311K7stddeOv744zXLLLME5+DFLWtfkir3WYNRxN9iiy2m++67L7UPjla0aJdkU089tWaYYYZWKMf73GcebrDBBoGA574MGzasr4wzDJu9z/p744032q6bCyAAAQhAAAIQKC+BLGvf8vaeng1kAgiAA9n7X4+9bALgjyRd3Q/XONTmPEn5HALVjw5wSdsEEADbRla+C7IsghAA8/NbGQTAww8/PIjoc7KP9957LxDB0uzqq68OttLavDX2W9/6Vl/RTgRAi4lR1GFSFF68P1tuuWWQqCTJtt56aznasZl99NFHwVZn/3fMmLG/MT377LNBAg/bbbfdpuWWW66virPOOitIfmLz+YfOVIxBAAIQgAAEIFAPAlnWvvUYKaOoGwEEwLp5tH/jKZsA+LEkh5P8o43hWPy7SNKGkga3cR1FiyWAAFgs/1xaz7IIQgDMBXVQSRnOAFxrrbUCYatd8zmAUeSgr+1EAIxHADoD8L333pvanf4IgBb8jjvuuOCcvv/+9799wl9SI44GXG+99fo+8rU+A/D9998PxFGfjbjmmmsGIuFCCy2kwYP5mmp37lAeAhCAAAQgUBYCWda+Zekr/YBAnAACIPPBBMomAI6W5MQgK0l6IIOL/CZ1saQNwu2/vFllgFaSIgiAJXFEJ93IsghCAOyE8LjXlkEAdEKLZoJb2mid8GLppZfu+7gTATB+BqAjAZ977rnMkC3oObGHLSkC0NuJV1tttWBrbxa74IILtOmmm45T1H7y3xz1GLcpp5xSK6+8srbaaqumW6eztEsZCEAAAhCAAAR6TyDL2rf3vaJFCLQmgADYmtFAKFE2AfBBSQtJelOS91Q91cQJFvsulbRuWOZUSbsOBKfVZIwIgDVwZJZFEElA8nN0GQRAn7nnbLiOcrvyyiszD2722WfXJJNMkosA6EriWYCdldfn+WWxZgLgZ599pnnmmSc4r3CCCSbQ7rvvLkc8zj333Jpqqqk0dOjQoImnn346KGc7//zzg2jGRnOSD0cHXn/99UHWX59FGDefnXjZZZelJhvJMhbKQAACEIAABCDQWwJZ1r697RGtQSAbAQTAbJzqXqpsAuC0ku6UNKeklyQ5XOR/CU4YIukySWuHn50safe6O6tm40MArIFDWQT11omdCoCbb755IFjNNttswTl2aXbiiSdqjz32CD5+6aWXNNNMvl3H2qqrrqqbbropOAPw3Xff1aBB/ct23EkEoPthce6kk04K+nTRRRdpk002yeSMZgKgtzZb8LP94Q9/0M9+9rPEOu+55x5973vfCz5LEwAbL3zmmWeCpCknn3xysK3Ytvfee+uYY47J1G8KQQACEIAABCBQPAHWvsX7gB70jwACYP+41e2qsgmA5jtrKAI6NePTkpYNIwIj9hNIukJSdLL6/0naq26OGQDjQQCsgZNZBPXWiZ0KgDvvvLNOO+20IGttWmZcj2innXbS7373u2BwjQLg/vvvr9/+9rfBZ43betuh0akA6DMFR4wYETS55JJL6s4772yakCTqWzMB0OPy+GxO+hGPWIyP7dRTT9Uuu+wS/CmrABhdb9F0/vnnD7YHJ2VHbochZSEAAQhAAAIQ6C0B1r695U1r+RFAAMyPZZVrKqMAaJ7fcXJFSd7T9ZCk70v6QJL3X3nP2Woh9OMk7VNlBwzgviMA1sD5LIJ668ROBcBjjz1W++wz9pFpIWyOOeYYbwCffvppECH42muvBZ81CoB33323llpqqeCztddeW8702x/rVAB0m95Ge8MNNwTNO2nHnnvu2bIrzQTAI488UgcffHBQx1tvvaXhw4ePV99XX30VCI8PP/xw8Fm7AmDE7ZprrgkERguNGAQgAAEIQAAC1SDA2rcafqKX4xNAAGRWmEBZBUD3bXFJf5c0qaR/hmf9/UnSyqHrjpY0NlQDqyIBBMAqeq2hzyyCeuvETgVAJ7hYfHE/WqUdd9wxiAaM25gxY7Tddtvp7LPP7vtzowDoD1ZaaSXdcsstQZkjjjhChxxySCoIJ+hw0pCNNtponDJ5CIA+V8+ZhZ151xl3DzvsMB1wwAGacMIJU/sTZ9CYBOTSSy/t62eaoLjXXnvp+OOP76u/UQC0IOk+TT/99Il9cOSlIwBfffXV4L+PPfZYbycRrUEAAhCAAAQg0G8CrH37jY4LCyaAAFiwA0rSfJkFwOA9U9K1kvw25wjAKUJuv5F0YEkY0o3+EUAA7B+3Ul3FIqi37uhUAHRvLQBaBLM5G62FOGendWILb/t1wgpvqXWkny1JAPQCwslAXn/99aCMy/u8vAUWWEATTTSRnJTjkUceCaLzLBRuuOGGuuSSS8aBlYcA6AotLjoSMeqLzyvceOONg4zDFuEmnnhivffee8H4/v73v+uqq66Sk33Ydt11175zBP3vDz/8UM4q7P4PGTJEO+ywQ3AmoCMBPdfPPPNM3XrrrUHd3nJsaxQAPa4///nP+sEPfqBVVlklYOIEJe+//74effTR4AzAp54am9/qlFNOkbdlYxCAAAQgAAEIVIMAa99q+Ilejk8AAZBZYQJlFwDdx/UkOfLPWX9tv5J0KO6rPAEEwMq7UIEo8uWXXwZiyVxzzVWDEZV7CHkIgI8//riWX375YItrku27776BL7fddtvg4yQB0H9//vnntcEGGwQZgVuZ67J4Fre8BEDX6QWNxTxvR3YUYyuzMOiz/iy+ee7GzVl711tvvT6RsLEuRz86AnChhZywPlkAvPDCC1t1QbvttptOOOGETOcWtqyMAhCAAAQgAAEI9IQAa9+eYKaRLhBAAOwC1ApWWZQA+N02WW0labcw868FwDR7pM16KV4cAQTA4tjn1jKLoNxQZqooDwHQDXkB4PPuHKHnrahOCuKIPotoq622WrAFuJUA6Hostl155ZXy1llnxnUUngXhqaaaKhARfVago/OWWWaZ8caXpwAYVe6ow8svvzyI0vPWY0fyjR49OujPrLPOqpEjRwZZjD3GRuEv3kFH6v3mN78J6rFQ6uvnm2++IFrSUZPOoBwJ3o0RgE7ycfPNN+tvf/ub7r///uAsRW9RdnszzzxzwMRsHTWJQQACEIAABCBQLQKsfavlL3r7NQEEQGaDCRQlAI72u2POLnB944Zy5NwA1eVKAAEwV5zFVMYiqBjutAoBCEAAAhCAAAQg0HsCrH17z5wW8yGAAJgPx6rXUqQAmDc7C4DRNuG866a+/AkgAObPtOc1sgjqOXIahAAEIAABCEAAAhAoiABr34LA02zHBBAAO0ZYiwqKEgB/1CV6V3epXqrNnwACYP5Me14ji6CeI6dBCEAAAhCAAAQgAIGCCLD2LQg8zXZMAAGwY4S1qKAoAbAW8BhERwQQADvCV46LWQSVww/0AgIQgAAEIAABCECg+wRY+3afMS10hwACYHe4Vq1WBMCqeaw+/UUArIEvWQTVwIkMAQIQgAAEIAABCEAgEwHWvpkwUaiEBBAAS+iUArqEAFgAdJoMCCAA1mAisAiqgRMZAgQgAAEIQAACEIBAJgKsfTNholAJCSAAltApBXQJAbAA6DSJAFiXOcAiqC6eZBwQgAAEIAABCEAAAq0IsPZtRYjPy0oAAbCsnultv4oSAHcLh3m+pHdyHvLUkjYL6zwp57qpLj8CRADmx7KwmlgEFYaehiEAAQhAAAIQgAAEekyAtW+PgdNcbgQQAHNDWemKihIAR0saI2lBSY/nTHB+SY9KchtDcq6b6vIjgACYH8vCamIRVBh6GoYABCAAAQhAAAIQ6DEB1r49Bk5zuRFAAMwNZaUrqrMAaIFxcKW9U+/OIwDWwL8sgmrgRIYAAQhAAAIQgAAEIJCJAGvfTJgoVEICCIAldEoBXSpaAFxf0rM5j3sOSZeHEYYIgDnDzbE6BMAcYRZVFYugosjTLgQgAAEIQAACEIBArwmw9u01cdrLiwACYF4kq11P0QJgt+h5XEQAdotuPvUiAObDsdBaWAQVip/GIQABCEAAAhCAAAR6SIC1bw9h01SuBBAAc8VZ2cqKFAC7DQ0BsNuEO6sfAbAzfqW4mkVQKdxAJyAAAQhAAAIQgAAEekCAtW8PINNEVwggAHYFa+UqLUoA/FGPSF3do3Zopn0CCIDtMyvdFSyCSucSOgQBCEAAAhCAAAQg0CUCrH27BJZqu04AAbDriCvRQFECYCXg0MmuEkAA7Cre3lTOIqg3nGkFAhCAAAQgAAEIQKB4Aqx9i/cBPegfAQTA/nGr21UIgHXzaHXGgwBYHV+l9pRFUA2cyBAgAAEIQAACEIAABDIRYO2bCROFSkgAAbCETimgSwiABUCnyYAAAmANJgKLoBo4kSFAAAIQgAAEIAABCGQiwNo3EyYKlZAAAmAJnVJAlxAAC4BOkwiAdZkDLILq4knGAQEIQAACEIAABCDQigBr31aE+LysBBAAy+qZ3vYLAbC3vGntawJEANZgNrAIqoETGQIEIAABCEAAAhCAQCYCrH0zYaJQCQkgAJbQKQV0CQGwAOg0GRBAAKzBRGARVAMnMgQIQAACEIAABCAAgUwEWPtmwkShEhJAACyhUwroEgJgAdBpEgGwLnOARVBdPMk4IFAtAr/4xS90+OGHB50eM2ZMtTpPbyEAAQhAoLIEWPtW1nUDvuMIgAN+CgQAEACZB0URIAKwKPI5tssiKEeY/ajqiy++0OWXX64bbrhB9957r9544w29//77mnLKKTXrrLNq8cUX1/rrr68VV1xRgwYN6kcLXAKB7hK44IIL9NOf/nTsguQb39Bzzz0XzN1WhgDYihCfQwACEIBANwiw9u0GVersBQEEwF5QLn8bCIDl91Fde4gAWAPPsggqzolXX3219txzTz377LMtOzH33HPr+OOP1xprrNGyLAUg0EsCq6yyiv72t7/1NfnLX/5SBx98cMsuIAC2REQBCEAAAhDoAgHWvl2ASpU9IYAA2BPMpW8EAbD0LqptBxEAa+BaFkHFOPHXv/61DjrooL6tjyuvvLJ+9KMf6Tvf+Y6GDRumUaNG6amnntI111wTiCujR4/WQgstpIceeqiYDtMqBBIIvPLKK5p55pmD+TnZZJPpww8/lMVqz10MAhCAAAQgUEYCrH3L6BX6lIUAAmAWSvUvgwBYfx+XdYQIgGX1TBv9YhHUBqycip5//vnafPPNg9qmnXZa/elPf9IKK6yQWvujjz6qPfbYQ2+//TYCYE4+oJp8CBx99NHab7/9NGTIEJ166qnafvvtg4rvvvtufe9738unEWqBAAQgAAEI5EiAtW+OMKmqpwQQAHuKu7SNIQCW1jW17xgCYA1czCKot050xJQjpD766CNNMskkuu+++4Kov1bmCKuLLrpIm222WauifA6BnhFYYIEF9J///Eerr766rrzySk0//fR65513tOOOO+q0007rWT9oCAIQgAAEIJCVAGvfrKQoVzYCCIBl80gx/SmzALiIpJ9JWlLS9JImlrS0pPjeoJGS5pL0gaRrikFIq/0kgADYT3BluoxFUG+9sc8+++jYY48NGj3mmGO0995759KBO+64Q2eccYb++c9/6rXXXtNEE02k2WabLTgzcPfddw8iDZPsH//4R1/04a233qrll19ef/jDH3TOOefoiSeekJOUzDvvvNp11137Ej24ns8//zwo98c//lFPP/10UO673/1u0NaPf/zjxLaef/75oE8217/lllvqz3/+c9DvRx55RB988EGQPGKdddYJosqmmmqqxHoaz4577733dNJJJ+mKK64IElD431H98Qo+/vhjnXnmmfLZi48//nggVHm79cILL6xNNtkkiMocPHhwqj9uueUWnXXWWfrXv/4VMHbCi29+85uB6LXMMssEIpiTtTTau+++G0THXXvttXryySeDbbJu1z6ZZ5555DP0nOjFdSWZuZ144om66aab9OKLL+qrr77SjDPOGLS1yy67aMEFF0zts/toO+yww2RuHsPvfve7vjG4HtffH3vggQe02GKLBZdanDbD7bbbLmA09dRT69VXX9WEE06YWnWrMwC//e1v64UXXtAWW2wRzDO3d/LJJ+u2226ThXTPQbIHm9AB0wAAIABJREFU98dzXAMBCEBgYBNg7Tuw/V/l0SMAVtl7+fW9jAKgV/ynStoqHGbUxzGS/KbyeGz4flv6u6SvJM0p6YX80FBTlwkgAHYZcC+qZxHUC8pj27BYMd100+nNN9/UpJNOGogYU0wxRUcdcGTgbrvtFghMaeaMwhbafvCDH4xXJC4AWmCywOJzB5PM7ViIsnBmke72229PLHfkkUfqwAMPHO+zRgHQYqVFxCT71re+pb///e+J0ZFx4cjiowW0RhGrUQB0pOW6666rl19+OZWTMy7/5S9/CXzUaE7W8n//939NfTV8+HC99dZb45SxiOrzHe3rZmbuFvMa7bzzzgtEtc8++yzxcguWTrpxwAEHJH4eFwAt0h511FHjlLPg2l8B0GKvhVef/ff6668HEa2eExaRbc5uvd5666UOux0B0NuJLUJ/+eWX49SHANjR44OLIQABCAxIAqx9B6TbazFoBMBauLHjQZRRALxYkkNA3LcnJd0haRu//yYIgAZgQXAeSftJGhsaU26zwPlTSRtKWkjS1JK+kOQ3yzslnSnpXxmGsJqk7SQt7qPAJL0p6d7w+hszXO8iQyRtLWlTSfNJmizsh0XVkxrE1oxVZi6GAJgZVXkLsgjqnW+8VdJbJm2rrbaabrjhho4b33fffYNIQpuj6xw5t+iiiwZbjC1mnXLKKUF0niOx7r333iCRSNziAuASSyyhe+65R5tuuql+8pOfBJFtFtgs1ERJHZyQxGLV9ddfr2233TYQ1Sx8OTnJIYccEghdFqUefvhhzT///OO0FRcAR44cGWx/tuj285//XHPNNZfeeOMNnXvuucGZiLaZZpop2F7aKJLGhSNHHVpk22GHHbT22msHUYOe0xa2llzSweeSz1D0/zcTR9l5e+qyyy4b9NttmpOjEC0umYGFyQkmmKCv747cW2uttYJ/uz1fP99888nCqqMNHdVnLj73rlFgdIScI9dcn3n98Ic/DLhauDUr+8RCmcWtRgHwuuuuC9q1yGWRba+99grERJ+3d9ddd8mJZCLB0dtt3a9GiwRA99tRlo4WNG/Pw08++STwm4Xdds2sZphhhkDMduSk/WZzXz0PHbnnpDZXXXVVatVZBUBvkff8c7IRR8yOGDEiiIK0n/bff/92u055CEAAAhAY4ARY+w7wCVDh4SMAVth5OXa9bALgDyVdF4p9cUFvdBMB8JeSDpJ0vaQ1c2TTjapmDseXvudqbKsOFdkrHPN472SSTg/Fv7Q+WkTcIeX66JrhYV+WSKnEISM7SUoOsemcDgJg5wwLryHLImjM6NH66t13C+9rLzsweNgwfWPQoFyb9DZJi2s2R8g5Uq4Ts7Dl7asWkyzoWBDx1tK43XjjjcE2YJex2GaBL25xAdB/P+GEE4JtvHHzdldvVX3//feDbasWnbzd1lGAcbPAtMgiiwRtRdGC8c/jAqD/7i2z3o5rQStujmg79NBDgz9Z8IkEzqhMXDgaNGiQPMak6EaXtyBlRu6bxU9HFU4zzTTjYY9zOvvss7X11v5dZaxZ4HLiFouKjz32WCDGJZkzN3vra2TPPvus5phjjuCfaRF+UR+9TTi+5dmirYU0C4puz771OOJmkc3CprfaOvrO/24cWyQA+rqVVlpJFhWHDh2a2P92/mjR1AKfzZGjcf6e2xYnLXq6bxZakyyrAOhrLVw6urBxfrfTZ8pCAAIQgAAETCDL2hdSECgjAQTAMnql930qmwB4mSTv+fF/4wdBNRMA15bkMAEfRDR77xFmbtFvqf8Ooxh90SOSjg/PNJxc0jKh6DdpWOO+PuYroXa/9Uf74x6UdLSkZyT5TdHX+OxEm8sdnNI7H1R1i6Tlws+vkHSWpFGSLAj6Oh8o5a3Va0j6a+ZRZi+IAJidVWlLZlkEfTlqlP7fUj6+c+DYXHfdqSExMSePkXu7ZCSueSttfyKv4v3YaaedgvPcbM2yrjryzKKWzRFnjr6LrDEC0OfbJZnP64uivDbeeGNdfLEDvcc3b/+0UGMh8N//9uPya4sLgBahLJA5iqzRLCBarLPYZlHMIlJctIoLR1tttZV+//vfp7onHr3nqERHwqXZRhttpEsvvVRLL720fKZiZN5i7Ag/Rzta+MxqjtJzXbZWbTfW6X64PzaLaWmRbhdeeGFfYhhn5PUZk3GLBEALpc8884x8rl4etsEGGwSRi96q7cWo64/M5ytG0Z/NhM92BEDPKUdtYhCAAAQgAIFOCWRZ+3baBtdDoBsEEAC7QbV6dZZNAPyfpG9JcmhI/CCpZgKgTxH31tePwy2sZfXC+qGw6f7dLclvIxbY4jYi/Mz7x94JRbj4oUU+5/CJcOvu/aGA90msgkkk3SbJTHzdvKE42MhkS5+jH/7RqRZ3bijgdh6Q5APG/p8kpxkd9/CkzikjAHbOsPAasiyCEADzcZMj/g4+eKym3xhl1p8WnE3Y/vMWSW+VTTOLft7aavMZcPHz4uICoM+422OPPRKrOf7444MtqDZne22M/osusqhp0cfbYx3VFre4AOitrY4iSzMnSonELAtp0XZel48LRzfffHNi4o2o3kj8dASjt+o2M5+j6G243i7thCFRQhAnofBZfI5kcwRlFNXXymdOSDL77GN/0/K2WzPMalEyDQt4jsBMSxDiRBj+zFuRLVT+9a/j/tYTCYAWz9LObMzap6icz4C08OdzCX024nHHHTdeFd6G/uCDDyZGnUaFswqA3vrr5CcYBCAAAQhAIA8CWda+ebRDHRDImwACYN5Eq1lf2QTATyVZ/FrUQQ8xpM0EQJe1GPa5pIlK7Aa/vf087J+jFtOyFjtEZN2wnLcKPxYbk0/q97Zcmw+oSgq3+V4oIrrMKZJ2TWDit32LehYZLcRZPG00H4706/CPG/hM9pzZIgDmDLSI6rIsghAA8/FMnhGAFl+c6df2s5/9LDWZhj/3dlInHfF/fbafo8YiiwuA3h7qbblJ5mQd0bZYi40WHZPMW3e9hdcRYT6nLW5xAfCII44IzgxMs3gyCWfutZAXWVw48hl0SVt6o7KORPQ5d+2azwaMMidbZPTZezYz99bXVVddNYhIm3NO/9aSbsstt1ywfddmZs72+/3vf19OauFtu2m21FJLBVGd3gbsSMlmtsIKK8h+dDRl4xmEkQC48847B+dB5mGOOnX0qc1RnmbcaHHB2MKrBdhGyyoAegu7IzkxCEAAAhCAQB4Esqx982iHOiCQNwEEwLyJVrO+sgmAToE4VRjZ5oQYkTUTAKMtw69KmrHEbvDbUxRp55P800JuvO1373AcjuRzJJ7NvnopHKNDUZy0I838ud+YHFE5S8NZgHNJejq80GcJjn/y+9gPp5dkpraLwkQheeJFAMyTZkF1ZVkEIQDm45w8zwB0VJijsGzeHuptos3MZX1NY/KRuAB46623BuJUkv3xj38MhEabI9vStpI2E3XiAuDpp5+u7bffPrXLTvow77wOgJZ+85vfBMlNIou34WQUUaReUmUzzjhjywy8Sde5rz7zLzJHBzoi0Ykz4ub611xzzSABR2OCFZezILfhhhsGYl7cfD6eoxo32WQTeXt1JOZGZTx2M7BQ2HhtY39dxyWXXBLU0di/SAB05KmF2TzM/fZW8WaRp9627cg9i8Bp511mFQA322yz4AxGDAIQgAAEIJAHgSxr3zzaoQ4I5E0AATBvotWsr2wCoLfyehvsnpJOjCFtJgCeJ2kzSc5cu0qJ3eBUidGYskQAOuuxT+R/PxyT94L5rD/bGWGSj7Th+nNnCLb5uudiBbeSFB16tYmkS5owe0rS3JK8f+rrt9l8ICMA5sOx0FqyLIJIApKPi/LMAhwXAL2l11t7m5kzz77++uulEQCdddfbXNPMUWPOtGtrJgA6yUeWcfssPouOWc0Ra/FMwL7OUYEWcX0e4J133hlsu43MQpuFrl/96leJTTiK0OcH3nbbbfIZefF+e0uxsyp7S3dkkQBosc1boJuZz2R05uRmAuBhhx0WbJ3u1Py8iPczS32zzDKLLKjGE5L4uqwCoLdgW4DGIAABCEAAAnkQyLL2zaMd6oBA3gQQAPMmWs36yiYAHhEmoLDw5O2v0blzaQKgz9FzMgufIO4Dpk4osRumlfTf8Fw9Rzcun3AGoPdCeVvvhJJ8Sv5PYuNxMo5oH5O3Ejcbqz+PDozydc6QHFk8wtDtNdvfdrUki5V+S3aiko9y5IsAmCPMoqpiEdQ78hZ9fF6bs+h6S+4rr7yiKabwMZ3tWztbgB0l5+2mrbYA9zICMK8twK0EQCejsOBm0coRdXmZE5V4a7FFPUcHRucdXnXVVX3ZcdPaevvtt4NsxN7afMst/vpTkOHXZ+ZFlvcW4LwEQEcS9id7tcfprcpxQwDMazZSDwQgAAEItEOAtW87tChbJgIIgGXyRnF9KZsAaJHMUW7OhOsz57xnzKJTowBowc+fWeSyMPW2JKcnzFOg6oZXfLafD9CaWJLf1izieTvuZJKc7tEipsdjUe6Hkl6LdWIHSWNTdkobxhKKJPXTZ/b9OfzA1zkiMDJH/I1NDymZt7ddp1l827L307XzBmyBr5l5i/F9LvDSSy9ppplaFe+GO6izUwIsgjol2N713kbqBBc2J09wEoX+WtYkIPfdd1+QjMHWLAlILwXAVklAzGbvvceepNAsCUgrAdDbR6MzDxu39faXe+N1PgdvxAgHvmu8MxZbteHzBKNkKE8//bTmmssnPCiIjjzrrLOCqLlmSUAs6vqswlZJQPIQAM3aZxK+8MILQTbleDKZtHH63EgnVPE253POifJWjS2NANhqdvA5BCAAAQh0gwBr325Qpc5eEEAA7AXl8rdRNgHQxH4cRr/5/zsNpNMSbhxGoXnrqgUyhwJYvHL/LQ46Si0e5VZm8j793m/t3orbyP91Sb+VdGaCmLmPpKPDgVkcvLHJIP15xMNvwfE0i9dJik7qtxDpxCtp5r7sG34YP48wC9/me+tiNSAAZsFZzjIsgnrrF58JZ+HOooijAO+///6+s+6a9cQRZ95+akErMidicEIGm8+J83lxSeaz9hxtZnNG4JEjR/YVK+oMwKFDhwZnCUbnGMb77bE6Iu7RRx/VVFNNJZ8n5/KRtRKO4nVdeuml2mijsb+XmJej9bphU089tZwdNykTb7P24olh4kJnvN8+39HnPCbZxRdfHIiOtqOPProvc3JUNtp2m4cAGJ8rFrGjrNDNxrfBBhvo8ssv1+STTx4ImfHEJ6386HMmLTayBbgbM5Y6IQABCAxcAqx9B67vqz5yBMCqezCf/pdRAPTI1pd0tqQpGxJY9L2XhP/nQ0lbSLoyHxxdr8UZjg+T5JSU30xpzVFxh0uyUBc3p7z0FmnbSuHW57QOryjp5vBDXxc/WMp/9+e2waGAmlaP24tSbXq79R1tEEIAbANWVYuyCOq95xwJtdVW/v1AwZZgiz3LL+8TBZLNW1h33313OeNtPKOtBTILZRbMFlxwwSDb7JRT+pH7td10001BZl8nY3AU4D333DPO50UJgO6EowCvvPLK8ZJ4OErxoIMOCvppkSmKmIw63ko4ig/Q4zabJ554Ioimc1RdlM04ifZjjz0WCJPuW2Q+X2/ttdfWxBP795bxzSJuJKpabI3OGox8ZR8lmSPqHAF4zTXXBH2zOBwJoo7sswDmbeIWz+zbxiQj/uHFoq/LWFizWNaYETlPAdBzNoria5YIJj5WJydxkhLbBRdcoE033bTv41Z+RADs/bOJFiEAAQgMBAKsfQeCl+s5RgTAevq13VGVVQD0OKZ20EUY3ec3oCGxwTmD7l/CpBpvtDvogsp7W7Oj8pYLz/5zVJ73ND0raSJJS0g6VNIyoejpc/ziiVCqFgHYak8vW4ALmoh5NssiKE+a2etyRtZDD/XjYqw5csxikBNfDBs2TKNGjZK3hF533XW68cYbAwHPAlBcAPR1++67r445xseCSrPPPnuQLXeRRRYJIgwtLDnCzGLShBNOGIh/jWJUUQLgYostFkQ/LrHEEvr5z38ebH11ko1zzz03yGhr87ECFuQaRc1WwlGjFyyU+ky9Dz/0703SqquuGkTNRYk+3K7P37v22muD7caNoqOFKG+xtX+WW265IILT0Zs+y++OO+7QySefHPjL2YjNONoOHGVOtjhoQXHRRReVk7HYHxbQLKY5oYhtnXXWCcTQuNn3vs5C4WSTTRZE96200koaMmRI0E8nR3HfbaeddlqQibjR8hIAPZ/c9w8++CAYn32XxVzeIvenn346XnRkKz8iAGYhTBkIQAACEGiXAGvfdolRviwEEADL4oli+1FmATBOxmf+WRB0xNooSV8Ui61frfvgLp/xZ9tS0rkJtVjkvCnc4uytzU7S8UhYrmpnALaCRBKQVoQq8DmLoOKc5AQSFpt8Nl0rczKL448/PhBR4ubov1133TUQgNLMApqjDBuvdfmiBECLX86Im5bd1ZFwTpTxne/4xIVxrZVwlMThkUcekbejer63ssMPP3wccTYSoppd5wy8zmy8+eab9xWLBMBW7S2zzDK6+uqr5W3EjWZB1FGFTvqSZBYdLSannceXlwDocxSj7efNtiQn9TE659B9ffHFFzXDDDMExVr5EQGw1czhcwhAAAIQ6A8B1r79ocY1ZSCAAFgGLxTfh7IJgN8NkVjk+1/xeHLrgTk72Ybf0Jz0Y54mNTsZSLTV1klCHAloW1PSNeH/7yQLcFyIJAtwbi4emBWxCCrW759//rkuu+wy3XDDDXKyDkd0OWrK2YEtgHiL5/rrrx9kUI3EnKQee4uoBSj/9/XXXw/OzHNEoLf/7rHHHkGiiCQrUgB0YgifYefzCR2l5wi9WWedNYiG85l3Pv8vyVoJR2kedTZkn6PoSLsHHngg2FJtAXX48OFBNKCFuHXXXTeI1IubRStH6vl/3o7ts+x83p+33c4555xBVJ6j75wgI272rSP1fJ394kWbfeN+OCrO7Wy88cbBGYWDBvk3smSzQHzCCSfI27ndF/fZItqKK64YiL/e4pxmeQmAjpp0+7Z4spIsd895550XnONni59T2MqPCIBZ6FIGAhCAAATaJcDat11ilC8LAQTAsnii2H6UTQCMsv062u2sYtHk2rq3u74a1vinMKlJWgPeDvxJ+KETfTihh232MEOy/7+z+ppRmvnz7WLXPRcr6MPDnEzF5sOVxu6XSzZn/Z1b0ouSZs2ViEQEYM5Ai6iORVAR1AdmmxayIpHMEYAWADEIQAACEIAABCDQSwKsfXtJm7byJIAAmCfN6tZVNgHwA0mThOfhZTskqBrsp5H0ZtjVyyVt0KTbznL8fvj5tT7rPvz/9pWjIr3/6UlJ8zWp4wlJ80p6WdLMDYlULOhZ2LOdLmn8g5/GfhYXLS+WNDZVZH6GAJgfy8JqYhFUGPoB1zAC4IBzOQOGAAQgAAEIlI4Aa9/SuYQOZSSAAJgRVM2LlU0AtHBlgcqJMu6sEXvvz3pH0hSSXgmj6b5MGV98q+/JknaLlfNBXZFgt6SkfyXU8T1Jd4d/d/mdE8o8HgqI3mptgfDjhDL7S/p1+PcfS/pzzv5AAMwZaBHVsQgqgvrAbBMBcGD6nVFDAAIQgAAEykSAtW+ZvEFf2iGAANgOrfqWLZsAeLyk3SUdIumommG/KNxy62H9QtLhCePzoVU+/y86uX7VMClIVNTiqDMgO1mIIyQtlEbbhV1mYkm3S1pMkgVG15N0an18G/CpknZp6Msckv4dCpbPhNGEaYJlf92EANhfciW6jkVQiZxR864gANbcwQwPAhCAAAQgUAECrH0r4CS6mEgAAZCJYQJlEwBnCbPefiVp8diZd3XwlrfkPhBucfZ4nNDDmYCfleRz/xy5t4ckM7DdLGnlhIE7Ks/RebYHJf025GTRbr8wc7A/c7kDU8A5m/JtkpxwxOZtyT5z0VGK5m4B9puSfCajIxJv6IIDEAC7ALXXVbII6jXxgdseAuDA9T0jhwAEIAABCJSFAGvfsniCfrRLAAGwXWL1LF82AdCUV5TkRBljwkg5nz9nYaoOZkHP4/GZgM3slvCcwKRxezuxxTpH8aWZk3w4CYgFvDRzH66XNDKlwOdhZGC3krEgANZgRrMIqoETKzIEBMCKOIpuQgACEIAABGpMgLVvjZ1b86EhANbcwRmHVzYB0NtObdNKmjGWvMLJLCyGOTIwzSwYjsg47iKLDZe0dZjdd35Jw8Ltuq9Juk+Stwr/pSFxR1J/Vw9FPgt4FvPeCq93BuCsEXveSrxtmODDSUUmDc8odPThieF2426xQgDsFtke1ssiqIewaQoCEIAABCAAAQhAoFACrH0LxU/jHRBAAOwAXo0uLZsA6Ig1C3n96Zev89ZWrBoEEACr4aemvWQRVAMnMgQIQAACEIAABCAAgUwEWPtmwkShEhJAACyhUwroUn+Etm5286oMkW/N2l+3m52j7lwJIADmirOYylgEFcOdViEAAQhAAAIQgAAEek+AtW/vmdNiPgQQAPPhWPVayiYAVp0n/c9OAAEwO6vSlmQRVFrX0DEIQAACEIAABCAAgZwJsPbNGSjV9YwAAmDPUJe6IQTAUrun1p1DAKyBe1kE1cCJDAECEIAABCAAAQhAIBMB1r6ZMFGohAQQAEvolAK6hABYAHSaDAggANZgIrAIqoETGQIEIAABCEAAAhCAQCYCrH0zYaJQCQkgAJbQKQV0CQGwAOg0iQBYlznw7LPP6rPPPtM3vvENzTPPPMF/MQhAAAIQgAAEIAABCNSNwJgxY/TUU0/J/x06dKhmn332ug2R8dSYAAJgjZ3bxtDK9ra+dht9Tyr6lw6v5/LeESACsHesu9aSv0g++OCDoP5ZZplFk046adfaomIIQAACEIAABCAAAQgUReCjjz7Siy++GDQ/+eSTa6aZ/DqDQaAaBBAAq+GnbveybALg6A6yAI+RNKTbwKg/NwIIgLmhLK6i999/Xy+//HLQgckmmyxYCBEFWJw/aBkCEIAABCAAAQhAIH8CjvqzgPLhhx8Glc8444yaYoop8m+IGiHQJQIIgF0CW7FqyygA9hehBcDB/b2Y63pOAAGw58jzb3D06NF6+umng60QkQg49dRTa5JJJkEIzB83NUIAAhCAAAQgAAEI9JCA17gff/yxRo0a1Sf++cfuueeeW4MGDephT2gKAp0RQADsjF9dri6bALhQBrDeYziPpE0krSzpLkk/l/S5pIczXE+RchBAACyHHzruhbcAOwowEgFdoRdGgwejx3cMlwogAAEIQAACEIAABAoj8NVXX423xnX0n7cAYxCoEgEEwCp5q3t9LZsA2O5Id5B0mqRrJXV6fmC7bVO+MwIIgJ3xK9XVSSJgqTpIZyAAAQhAAAIQgAAEINABAf/AjfjXAUAuLZQAAmCh+EvTeNUFQIO8UNLGkraRdE5pyNKRVgQQAFsRqtjn3g7sc1F8LuDnn38u/2KKQQACEIAABCAAAQhAoKoEvKNlwgknDM7783nXbPutqifpNwIgc8AE6iAAriHpGkl3SloWt1aGAAJgZVxFRyEAAQhAAAIQgAAEIAABCECgqgQQAKvquXz7XQcBcGFJ/5b0jqTh+eKhti4SQADsIlyqhgAEIAABCEAAAhCAAAQgAAEImAACIPPABOogAK4engH4iSQnCMGqQQABsBp+opcQgAAEIAABCEAAAhCAAAQgUGECCIAVdl6OXa+6AOjc63+VtJKkxyUtkCMbquouAQTA7vKldghAAAIQgAAEIAABCEAAAhCAABGAzIGAQNkEwCky+MWi31SSFpO0u6SlJI2RdISkwzNcT5FyEEAALIcf6AUEIAABCEAAAhCAAAQgAAEI1JgAEYA1dm4bQyubANiftKEew8OSlpH0URtjp2ixBBAAi+VP6xCAAAQgAAEIQAACEIAABCAwAAggAA4AJ2cYYtkEwNEZ+hwv8rGk8yQdIOm9Nq+leLEEEACL5U/rEIAABCAAAQhAAAIQgAAEIDAACCAADgAnZxhi2QRAb+ltZRYJP5D0nKT7JFkExKpHAAGwej6jxxCAAAQgAAEIQAACEIAABCBQMQIIgBVzWJe6WzYBsEvDpNoSEkAALKFT6BIEIAABCEAAAhCAAAQgAAEI1IsAAmC9/Nnf0SAA9pcc13VKAAGwU4JcDwEIQAACEIAABCAAAQhAAAIQaEEAAZApYgJlEwCPD93yG0lvZHTRcEkHhZmA98p4DcWKJ4AAWLwP6AEEIAABCEAAAhCAAAQgAAEI1JwAAmDNHZxxeGUTAH2+3xhJC0p6POMY5pD0/8LrBme8hmLFE0AALN4H9AACEIAABCAAAQhAAAIQgAAEak4AAbDmDs44PATAjKAoljsBBMDckVIhBCAAAQhAAAIQgAAEIAABCEBgXAIIgMwIE6iDAPgdSY9J+kzSxLi1MgQQACvjKjoKAQhAAAIQgAAEIAABCEAAAlUlgABYVc/l2+86CIBbSDpH0kuSZs0XD7V1kQACYBfhUjUEIAABCEAAAhCAAAQgAAEIQMAEEACZByZQtAC4W4MbTgjP8vutpNdauGioJJ//t4mkySRdJmkj3FoZAgiAlXEVHYUABCAAAQhAAAIQgAAEIACBqhJAAKyq5/Ltd9ECYJT0IxpV1B8nAslqvuZLSctKuifrRZQrnAACYOEuoAMQgAAEIAABCEAAAhCAAAQgUHcCCIB193C28ZVBAIz3NBL+svbrHUl3SXLE4B3ZhkypkhBAACyJI+gGBCAAAQhAAAIQgAAEIAABCNSXAAJgfX3bzsiyCm3t1NlO2Sljhd2XUeEW4KUkPdmkIguFn0r6vJ3GKFsqAgiApXIHnYEABCAAAQhAAAIQgAAEIACBOhJAAKyjV9sfU9ECYGOP3w0FwO9Jeqr94XBFhQggAFbIWXQVAhCAAAQgAAECHpSQAAAgAElEQVQIQAACEIAABKpJAAGwmn7Lu9dlEwDzHh/1lZcAAmB5fUPPIAABCEAAAhCAAAQgAAEIQKAmBBAAa+LIDoeBANghQC7vNwEEwH6j40IIQAACEIAABCAAAQhAAAIQgEA2AgiA2TjVvRQCYN09XN7xIQCW1zf0DAIQgAAEIAABCEAAAhCAAARqQgABsCaO7HAYZRYAF5G0jqSFJE0jaWJJzfrrxCAjOuTB5b0jgADYO9a0BAEIQAACEIAABCAAAQhAAAIDlAAC4AB1fMOwyygADpN0jqS1m/TVYp8t3n//bTBurQwBBMDKuIqOQgACEIAABCAAAQhAAAIQgEBVCSAAVtVz+fa7bAKgBbx/SFoqFPeek/Q/ScuG2YGvlTRVGBU4efi3RyW5nG3dfPFQWxcJIAB2ES5VQwACEIAABCAAAQhAAAIQgAAETAABkHlgAmUTAH8q6dxQ2NtT0omS5pdkkS8e4TdE0qaSjpPk/7+RpL/i0koRQACslLvoLAQgAAEIQAACEIAABCAAAQhUkQACYBW9ln+fyyYAOsJvdUm3SlopHG6SABiRWEDS3ZK+lLSwpBfyR0SNXSKAANglsFQLAQhAAAIQgAAEIAABCEAAAhCICCAAMhdMoGwC4MuSppe0haQLMgiALnKUpP0lnSDJUYNYNQggAFbDT/QSAhCAAAQgAAEIQAACEIAABCpMAAGwws7LsetlEwA/lTSBpOUk3RmOc25JT4ZbgH3u38cN418+jBh8StJ8ObKhqu4SQADsLl9qhwAEIAABCEAAAhCAAAQgAAEIcAYgcyAgUDYB8ENJE0taTNKDoY8cEfhKKABaDHymwXcue68kXzsFfq0MAQTAyriKjkIAAhCAAAQgAAEIQAACEIBAVQkQAVhVz+Xb77IJgE9IssjncwDjST0+kDRJmPjjkgYEm0k6L4wMnCxfPNTWRQIIgF2ES9UQgAAEIAABCEAAAhCAAAQgAAETQABkHphA2QTAiyX9WNIBko6OuehGSauECT+8Pfir8DNHCzoJyHfDiMERuLUyBBAAK+MqOgoBCEAAAhCAAAQgAAEIQAACVSWAAFhVz+Xb77IJgNtKOkPSbZJWiA11A0mXhtuAH5F0WRgRuH4YMThG0r6SjssXD7V1kQACYBfhUjUEIAABCEAAAhCAAAQgAAEIQMAEEACZByZQNgFweOyMP0fzxc/7swBoIdBiX2RR/50wxILhl7i1MgQQACvjKjoKAQhAAAIQgAAEIAABCEAAAlUlgABYVc/l2++yCYDNRjdY0h6Stgmj/tz3l8Lz/46U5AzCWHUIIABWx1f0FAIQgAAEIAABCEAAAhCAAAQqSgABsKKOy7nbVRIA40O3GOj/fZ4zD6rrHQEEwN6xpiUIQAACEIAABCAAAQhAAAIQGKAEEAAHqOMbhl02AdDJPGyjvE0dF9WaAAJgrd3L4CAAAQhAAAIQgAAEIAABCECgDAQQAMvgheL7UDYBcHR4xt8Oks4qHg896CIBBMAuwqVqCEAAAhCAAAQgAAEIQAACEICACSAAMg9MoGwC4Adhdt8lJN2Pi2pNAAGw1u5lcBCAAAQgAAEIQAACEIAABCBQBgIIgGXwQvF9KJsA+ESY4GM5Sc7si9WXAAJgfX3LyCAAAQhAAAIQgAAEIAABCECgJAQQAEviiIK7UTYB8HhJu0s6RNJRBbOh+e4SQADsLl9qhwAEIAABCEAAAhCAAAQgAAEIsAWYORAQKJsAOIukRyR9JWlxSc/gp9oSQACsrWsZGAQgAAEIQAACEIAABCAAAQiUhQARgGXxRLH9KJsAaBorSvpTmAzkF5IulvROsZhovQsEEAC7AJUqIQABCEAAAhCAAAQgAAEIQAACcQIIgMwHEyibAPjv0C3TSpoxFAH9p5dDEdCRgWk2RtII3FoZAgiAlXEVHYUABCAAAQhAAAIQgAAEIACBqhJAAKyq5/Ltd9kEwNGh6NefflkAHJwvHmrrIgEEwC7CpWoIQAACEIAABCAAAQhAAAIQgIAJIAAyD0ygP0JbN8ldFYv660876/bnIq4phAACYCHYaRQCEIAABCAAAQhAAAIQgAAEBhIBBMCB5O30sZZNAMQrA4cAAuDA8TUjhQAEIAABCEAAAhCAAAQgAIGCCCAAFgS+ZM0iAJbMIQOoOwiAA8jZDBUCEIAABCAAAQhAAAIQgAAEiiGAAFgM97K1igBYNo8MnP4gAA4cXzNSCEAAAhCAAAQgAAEIQAACECiIAAJgQeBL1mzZBUD37zuSppc0iaRbJH1UMoZ0p38EEAD7x42rIAABCEAAAhCAAAQgAAEIQAACmQkgAGZGVeuCZRUAp5F0iKTNJU0R88CCkh6P/dufbybpPUkb1tpT9RscAmD9fMqIIAABCEAAAhCAAAQgAAEIQKBkBBAAS+aQgrpTRgHQIt8Nkr7VkKV4jKRGAXAGSc9LGizpB2GEYEEoabZNAgiAbQKjOAQgAAEIQAACEIAABCAAAQhAoF0CCIDtEqtn+bIJgJOGEX4zSxol6VhJt0u6Q1KSAGivXCvph5JOlLRnPd1Uy1EhANbSrQwKAhCAAAQgAAEIQAACEIAABMpEAAGwTN4ori9lEwD3lfQbSe9K+p6kp0M0o5sIgLuG4t/dkpYuDiUtt0kAAbBNYBSHAAQgAAEIQAACEIAABCAAAQi0SwABsF1i9SxfNgHwzlD4O1zSETHkzQTAFSTdLOlNSdPV0021HBUCYC3dyqAgAAEIQAACEIAABCAAAQhAoEwEEADL5I3i+lI2AfBtScMkLR9u+43INBMAF5b0b0lfSBpaHEpabpMAAmCbwCgOAQhAAAIQgAAEIAABCEAAAhBolwACYLvE6lm+bALgp5ImkLSopIdjyJsJgEuFYuGHDRmD6+mx+owKAbA+vmQkEIAABCAAAQhAAAIQgAAEIFBSAgiAJXVMj7tVNgHwf2H23zXDTMARjmYC4E6STpH0X0lz95gfzfWfAAJg/9lxJQQgAAEIQAACEIAABCAAAQhAIBMBBMBMmGpfqGwC4NWSLP41ZvRNEwDd/wckLSTpAklb1N5j9RkgAmB9fMlIIAABCEAAAhCAAAQgAAEIQKCkBBAAS+qYHnerbALgZpLOk/SxpBGSngp5pAmAx0vaI8wQvIakG3vMj+b6TwABsP/suBICEIAABCAAAQhAAAIQgAAEIJCJAAJgJky1L1Q2AdD9uV/SIpJel7S3pCskfRSKfAtIel7SyuFny4Qeul2SswFj1SGAAFgdX9FTCEAAAhCAAAQgAAEIQAACEKgoAQTAijou526XTQD08CwM/VPSrKHo5+i/QeG4nSV4qti/3f9nJC0t6Y2c2VBddwkgAHaXL7VDAAIQgAAEIAABCEAAAhCAAASEAMgkMIEyCoDu1zBJJ0vaWNLgFFeNkfRnSTtKegd3Vo4AAmDlXEaHIQABCEAAAhCAAAQgAAEIQKBqBBAAq+ax7vS3rAJgNNpZwqQgi0n6ZigGOgrwQUnXSHq6O1iotQcEEAB7AJkmIAABCEAAAhCAAAQgAAEIQGBgE0AAHNj+j0ZfdgEQL9WXAAJgfX3LyCAAAQhAAAIQgAAEIAABCECgJAQQAEviiIK7gQBYsAMGcPMIgAPY+QwdAhCAAAQgAAEIQAACEIAABHpDAAGwN5zL3kpVBMDhkqYOYY6S5G3AWLUJIABW23/0HgIQgAAEIAABCEAAAhCAAAQqQAABsAJO6kEXyywALhUm+FhJ0nQNLJzx92+STpd0Vw840UT+BBAA82dKjRCAAAQgAAEIQAACEIAABCAAgXEIIAAyIUygjALghJLOlPTT0EVpfXQWYNt5kraX9DkurRQBBMBKuYvOQgACEIAABCAAAQhAAAIQgEAVCSAAVtFr+fe5jALgDZJWCcVJi3yO8LtX0uvh35wNeKSkpWNlbpS0Rv54qLGLBBAAuwiXqiEAAQhAAAIQgAAEIAABCEAAAiaAAMg8MIGyCYBbSTpbkoW/uyVtLempFFfNHZZdJiy/jaRzcGtlCPQJgM898ohmmnHGynScjkIAAhCAAAQgAAEIQAACEIAABKpC4H8vv6zZvvvdqLszWxOsSt/pZ34EyiYA/jOM7HtE0uIZtvV6u7CjAz2T75S0bH5oqKnLBPoEwFtmn0PTTzBBl5ujeghAAAIQgAAEIAABCEAAAhCAwMAj8NoXX2jFZ59BABx4rh9nxGUTAN+RNIWkzSVdmNE3m0o6X9J7kqbKeE3RxaaR5GjHH0maI+y3Mxu/JOl2SVeEEZDN+rmkpJ1C0XN6SWb3sKQ/SrqkjQFuLOlnoYhqfq9JshB7qqR/tVFPu0URANslRnkIQAACEIAABCAAAQhAAAIQgECbBBAA2wRW0+JlEwDflzSppMUkPZiR+SKSHpD0YSgeZryssGIbSvqdpOFNenC1pHWafH6opMMkDUopc42kH0v6tEkdE0n6s6Q1U8qMlvQLSb/sEikEwC6BpVoIQAACEIAABCAAAQhAAAIQgEBEAAGQuWACZRMAH5K0oKRVJf09o4tWkvQ3SY9KWijjNUUVc2Sjzym0cPdGKATeIWmUJEfxORpwrTCa0UJhkvmsw7PCDxzDe1Q49hkk7S5phfAzR1Bu1mSg/vwn4ee3SjpR0ish/wPDvvjjbcOzFvNmhgCYN1HqgwAEIAABCEAAAhCAAAQgAAEINBBAAGRKlFEAPCiMOPuDJAtdWcxJQ7yF1RFxv8pyQUFl5gujGoeGW2wjoS+pOz7b8POED4ZJek6S//uipBGS3oqVGyzpylBE9J+XD7cUN1blv/8j/KOjBdeV9FWskLcoO6pylnBr8eyS3s2ZG0lAcgZKdRCAAAQgAAEIQAACEIAABCAAgUYCJAFhTpRRAJxE0n2S5gnPtzuzhZscnXa6pCfCpCEfl9itjmh0tKIFO4uBceEua7f3kXR0WHiTlLP+LKw9L8li4LUxMTDexnWSVg9Fv2+nZADy2YAXhxftLem4rJ3MWK5PAHzppZc000z+JwYBCEAAAhCAAAQgAAEIQAACEIBAngT+97//aeaZnfw3MLIA5wm3QnWVbQuwE4B4K+u54TmATkbhpBYWBb1ldoyk6SSNlLSFpOUk3R8mDXm1CXefLVikzRuKlO6Dz9U7vJ+dcabjpSR5PNM2yZJ8Y7iN+jNJjubz+YiRTRaKj45EdLkfpvTFUYhvhucq3hVmZ+5ntxMvQwDMkyZ1QQACEIAABCAAAQhAAAIQgAAEEgggADItTKBsAmB8G6r7ZsGvmWUp4zqGFOzuQyQdEfZhfkmPh//fWXct0PkMQGcBbmYW5D4Kx/JXSas1KXxAeDagi6woyWf8ReZ/3xz+w+V+06Qet7OKpC8lOTrzixw5IgDmCJOqIAABCEAAAhCAAAQgAAEIQAACSQQQAJkXJlA2AdCZZ/M2C4DeDlukRVtu35Nk0c/JN/aV9N1Yp3y2nyMfvdU2HrEXFbFw+Fj4Dyfs2KPJgHym3xXh5ztLOi1W1v8+Jfy3y13VpB63s1v4eVy4zIMlAmAeFKkDAhCAAAQgAAEIQAACEIAABCDQhAACINPDBMomADqLbTfMQlaRZnHPZ+09LMlZfy3CpZlFPmdBdkbeuDni74bwDz4L8NgmdSwWbpt2EUf4OdIvMv97v/Af3krtLdRp5rP/jgk/dPuOCMxqrQ71c9Zjb+0WZwBmRUo5CEAAAhCAAAQgAAEIQAACEIBAewQQANvjVdfSZRMA68rZkX8+39Bn8vnsPWfU3T+M0vN5fguGW4Sj8/h85t6ykuIRkRtKujQEtGOY/CSNl5OMRNuMHe23a6zgqWGCFf/J5Z5sAt3tRNGDG0i6vA0Htdq+3VcVAmAbVCkKAQhAAAIQgAAEIAABCEAAAhBogwACYBuwalwUAbA3zvUZetE2ZJ9zuIykfzU0PSjM2huJgBb8LouV+amk88J/by3pD026PrukZ8LPfy9pm1hZ/3ur8N9zSHq2ST0u5/I2t39BG7gQANuARVEIQAACEIAABCAAAQhAAAIQgEA3CCAAdoNq9epEAOyNz3ym36RhU5dI2iSl2fg5fz7Db/1YuapFALIFuDdzi1YgAAEIQAACEIAABCAAAQhAAAKpBBAAmRwmgADYm3nwqiSfeWfbIhbJl9T6/yTN6KPxJM0SK1C1MwBbkSUJSCtCfA4BCEAAAhCAAAQgAAEIQAACEOiQAAJghwBrcnmZBcDlw3PwvJ118gyZfL3lNB4xVyYX3SvJCTdsK0m6pUnn7pb0vfC8wIli5RaQ9Gj4706yAO8i6eSwHrIAl2mW0BcIQAACEIAABCAAAQhAAAIQgEDOBBAAcwZa0erKKAA6g+0fwwQVWbF6HBYAo3P2sl7Xq3LnSNoybGwVSX9r0nAkFn4kabJYuQklfRyO0dl4HRGYZs76e1T44YqSbo0V9L9vDv/tcs4KnGZux/31GYbewvx5jsCIAMwRJlVBAAIQgAAEIAABCEAAAhCAAASSCCAAMi9MoGwCoLPS3hOKTZGo5y2xzpobz4ib5r1FSurWn8WSdrTK4PuWpOGSnpY0T8N4nB14SUnOHDxtE0HuRkmrhlGELvdBrB5HU7oNC4ouFyUdaUTnz98Msxc7KnGpnNkiAOYMlOogAAEIQAACEIAABCAAAQhAAAKNBBAAmRNlFAAvlbRBGHF2mKTTJb1TA1dZ0PM5gBOE0X+Oqksyb3v+R/hBY/Ze/3lfSb8NP3ciEScUaTQLa8+HkYLXS1ojoYz/buHPkX2zSbLI2mgbS7o4/KPbPSZnPyAA5gyU6iAAAQhAAAIQgAAEIAABCEAAAo0EEACZEyZQtgjAN8LoN29fPaRmLjpNkqP/bEninSPzbpe0cFhmcUn3NTCYWtKzkqaU9IKkEZLejpXxFugrJa0V/q1x+29UNL4N+C+S1pP0VayeaSQ9ECYhcfSlz2HMW4hFAKzZBGc4EIAABCAAAQhAAAIQgAAEIFA+AgiA5fNJET0qmwDoc++c+MLbXH0WXp3MW3HvD0U1R945uvGKcDvvgpL2kzRvOODfSdopZfDbh9f642ckHRkmB5lB0h6SVgivc/TeT5oA9OeO8rP5jMATJL0iyX05SNIc4Wc7SDqjC45AAOwCVKqEAAQgAAEIQAACEIAABCAAAQjECSAAMh9MoGwC4BOS5g7Pm/NZgHUzn3HoiLs5mwzsD5Isun3RpMzhYYRkmv+8xdcZkT9tUsfEki6TtHpKGZ+5+EtJv+iSExAAuwSWaiEAAQhAAAIQgAAEIAABCEAAAhEBBEDmQhkFQJ8zt6ekvSX9X01d5Gy63grssw7nCjP9euvznWGkXTxjbzMETsqxs6RlJU0XJkp5WJIzDkdn92VB6ChBZyheSNIwSa9L+qekUyQ5+Ue3DAHw/7d3J9DWXGWd8P+LAB9hHiMzyDwjCAEaCeAANqC0iAho2zQCDYICHUFE/EAUaMDGlmaQWSYRI9KMCjLIGGQQVAgzCEmIIAZsIIEQyLe27su63+u9t86tOufUrqpfrZW18r6ndu39/J46uW/+bw2bknVcAgQIECBAgAABAgQIECBQBQSAToUWA8ByG+uH6vPoblxfnKFT8xQQAM6zr6oiQIAAAQIECBAgQIAAgYYEBIANNWPEpbR2C3ChuFmS1yQ5M8nxSV7VcTvsiHymHiAgAByAZygBAgQIECBAgAABAgQIEFhFQAC4itL892kxACzqV05SngFY3kZ7Vn3j7Rkd7TinvhV3/l2bR4UCwHn0URUECBAgQIAAAQIECBAg0LCAALDh5mxxaS0GgLdL8qIk5a25h1lfCQCP2qKdqYYJCACH+RlNgAABAgQIECBAgAABAgQ6BQSAnUSL2OEwAds2QMpz/96d5Dw1/Ds9yUfqCy7KW2m7tp/q2sHnzQgIAJtphYUQIECAAAECBAgQIECAwFwFBIBz7ezh6motAHxlkjsn+UaS+yV5eZJVgr/DVW3vFgQEgC10wRoIECBAgAABAgQIECBAYNYCAsBZt3fl4loLAL+Q5PuSPCLJk1euwo5TFBAATrFr1kyAAAECBAgQIECAAAECkxIQAE6qXRtbbGsB4NeTHJ3k2CQf2FjVDtyCgACwhS5YAwECBAgQIECAAAECBAjMWkAAOOv2rlxcawHgSUmumeRW9VmAKxdix8kJCAAn1zILJkCAAAECBAgQIECAAIGpCQgAp9axzay3tQDw8Ul+Lcmjk/zOZkp21EYEBICNNMIyCBAgQIAAAQIECBAgQGC+AgLA+fb2MJW1FgBePMnfJLlIkv+Q5KOHKca+kxIQAE6qXRZLgAABAgQIECBAgAABAlMUEABOsWvrX3NrAWCp8OpJXpHkikkeV98E/Pn1l+6IIwsIAEdugOkJECBAgAABAgQIECBAYP4CAsD593iVClsLAE+viz53kgsmOaf++ttJzugoqOx7iVWKtk8TAgLAJtpgEQQIECBAgAABAgQIECAwZwEB4Jy7u3ptrQWA31196f9uzxIAHjVgvKHbFRAAbtfbbAQIECBAgAABAgQIECCwQAEB4AKbvkfJrQWAvzewLQ8dON7w7QkIALdnbSYCBAgQIECAAAECBAgQWKiAAHChjT+i7NYCQF1ZjoAAcDm9VikBAgQIECBAgAABAgQIjCQgABwJvrFpBYCNNWRByxEALqjZSiVAgAABAgQIECBAgACBcQQEgOO4tzarALC1jixnPQLA5fRapQQIECBAgAABAgQIECAwkoAAcCT4xqZtPQC8ZJKbJbl0kvMneUmSrzRmaDn9BASA/dyMIkCAAAECBAgQIECAAAECKwsIAFemmvWOrQaA10jy5CR3TLJ7jddPctKujvy3JMcn+ZckN0/ynVl3a17FCQDn1U/VECBAgAABAgQIECBAgECDAgLABpsywpJaDABvm+RVSS5wRPh3TpIjA8CLJjk1yfmS3DnJa0cwNGU/AQFgPzejCBAgQIAAAQIECBAgQIDAygICwJWpZr1jawHgJZJ8IsnFknw2ySOTvDPJyUn2CgBLc16e5K5JnpXkl2bdrXkVJwCcVz9VQ4AAAQIECBAgQIAAAQINCggAG2zKCEtqLQD87SS/keQLSW6c5EvV5LsHBIDlNuBnJvlAkpuOYGjKfgICwH5uRhEgQIAAAQIECBAgQIAAgZUFBIArU816x9YCwBLi/UCShyV5yi75gwLA45L8VZLTk5SXhtimISAAnEafrJIAAQIECBAgQIAAAQIEJiwgAJxw89a49NYCwPKG3wsnuWWS96wYAN4wyQeTnJ3kvGu0cajNCggAN+vr6AQIECBAgAABAgQIECBAIAJAJ0ERaC0APCPJ/5PkB5N8aMUA8FZJ3pbk/yYpLwWxTUNAADiNPlklAQIECBAgQIAAAQIECExYQAA44eatcemtBYD/kOQKSe5S3wS8U+pBtwAfn+TJST6a5LprtHGozQoIADfr6+gECBAgQIAAAQIECBAgQMAVgM6BfxVoLQB8WZK7JXlBkvvs6tF+AWC55fcjSa6S5DlJ7q+vkxEQAE6mVRZKgAABAgQIECBAgAABAlMVcAXgVDu33nW3FgDeKcmr6/P8bpvkXbXcvQLAEv69qAaG5+zx3MD1SjnaugUEgOsWdTwCBAgQIECAAAECBAgQIHCEgADQKVEEWgsAy5rekOTHknwjyf9IckKSjyUpId+Nk3wzye2S/HKSq9Y2vqIGgbo6HQEB4HR6ZaUECBAgQIAAAQIECBAgMFEBAeBEG7fmZbcYAF4kyZvqi0BK6HfQVtZf3ha8ExiumcfhNiggANwgrkMTIECAAAECBAgQIECAAIEiIAB0HhSBFgPAsq7zJPl/kzwoSQkE99rKG4OfVvc7SzsnJyAAnFzLLJgAAQIECBAgQIAAAQIEpiYgAJxaxzaz3lYDwJ1qL5Dk1klukuSYJEcl+eckH6xXCf7LZlgcdQsCAsAtIJuCAAECBAgQIECAAAECBJYtIABcdv93qh8zACwv9ij/3CDJSdqxOAEB4OJarmACBAgQIECAAAECBAgQ2LaAAHDb4m3ON3YAWJ7xd30BYJsnx4ZXJQDcMLDDEyBAgAABAgQIECBAgAABAaBzoAgIAJ0HYwkIAMeSNy8BAgQIECBAgAABAgQILEZAALiYVh9YqADQeTCWgABwLHnzEiBAgAABAgQIECBAgMBiBASAi2m1AFCrmxQQADbZFosiQIAAAQIECBAgQIAAgTkJCADn1M3+tbgCsL+dkcMEBIDD/IwmQIAAAQIECBAgQIAAAQKdAgLATqJF7NBCAHi9JB9dhLYidwsIAJ0PBAgQIECAAAECBAgQIEBgwwICwA0DT+TwLQSAX0jy7TV4lTcKX3UNx3GI7QgIALfjbBYCBAgQIECAAAECBAgQWLCAAHDBzd9VegsB4LrWUALAo7R1MgICwMm0ykIJECBAgAABAgQIECBAYKoCAsCpdm69615X+NZnVd9NUkK709Z0BWBZw/f3WYgxowgIAEdhNykBAgQIECBAgAABAgQILElAALikbu9fawsB4PWTnKQdixMQAC6u5QomQIAAAQIECBAgQIAAgW0LCAC3Ld7mfALANvuyhFUJAJfQZTUSIECAAAECBAgQIECAwKgCAsBR+ZuZXADYTCsWtxAB4OJarmACBAgQIECAAAECBAgQ2LaAAHDb4m3OJwBssy9LWJUAcAldViMBAgQIECBAgAABAgQIjCogAByVv5nJBYDNtGJxCxEALq7lCiZAgAABAgQIECBAgACBbQsIALct3uZ8AsA2+7KEVQkAl9BlNRIgQIAAAQIECBAgQIDAqAICwFH5m5lcANhMKxa3EAHg4lquYAIECBAgQIAAAQIECBDYtoAAcNvibc43ZgB4pUpyapKz2+Sxqg0KCAA3iOvQBAgQIECAAAECBAgQIECgCAgAnQdFYMwAUAeWLSAAXHb/VU+AAAECBKPQqoIAACAASURBVAgQIECAAAECWxAQAG4BeQJTCAAn0KSZLlEAONPGKosAAQIECBAgQIAAAQIE2hEQALbTizFXIgAcU3/ZcwsAl91/1RMgQIAAAQIECBAgQIDAFgQEgFtAnsAUAsAJNGmmSxQAzrSxyiJAgAABAgQIECBAgACBdgQEgO30YsyVCADH1F/23ALAZfdf9QQIECBAgAABAgQIECCwBQEB4BaQJzCFAHACTZrpEgWAM22ssggQIECAAAECBAgQIECgHQEBYDu9GHMlAsAx9Zc9twBw2f1XPQECBAgQIECAAAECBAhsQUAAuAXkCUwhAJxAk2a6RAHgTBurLAIECBAgQIAAAQIECBBoR0AA2E4vxlyJAHBM/WXPLQBcdv9VT4AAAQIECBAgQIAAAQJbEBAAbgF5AlMIACfQpJkuUQA408YqiwABAgQIECBAgAABAgTaERAAttOLMVciABxTf9lzCwCX3X/VEyBAgAABAgQIECBAgMAWBASAW0CewBQCwAk0aaZLFADOtLHKIkCAAAECBAgQIECAAIF2BASA7fRizJUIAMfUX/bcAsBl91/1BAgQIECAAAECBAgQILAFAQHgFpAnMIUAcAJNmukSBYAzbayyCBAgQIAAAQIECBAgQKAdAQFgO70YcyUCwDH1lz23AHDZ/Vc9AQIECBAgQIAAAQIECGxBQAC4BeQJTCEAnECTZrpEAeBMG6ssAgQIECBAgAABAgQIEGhHQADYTi/GXIkAcEz9Zc8tAFx2/1VPgAABAgQIECBAgAABAlsQEABuAXkCUwgAJ9CkmS5RADjTxiqLAAECBAgQIECAAAECBNoREAC204sxVyIAHFN/2XMLAJfdf9UTIECAAAECBAgQIECAwBYEBIBbQJ7AFALACTRppksUAM60scoiQIAAAQIECBAgQIAAgXYEBIDt9GLMlQgAx9Rf9twCwGX3X/UECBAgQIAAAQIECBAgsAUBAeAWkCcwhQBwAk2a6RIFgDNtrLIIECBAgAABAgQIECBAoB0BAWA7vRhzJQLAMfWXPbcAcNn9Vz0BAgQIECBAgAABAgQIbEFAALgF5AlMIQCcQJNmukQB4EwbqywCBAgQIECAAAECBAgQaEdAANhOL8ZciQBwTP1lzy0AXHb/VU+AAAECBAgQIECAAAECWxAQAG4BeQJTCAAn0KSZLlEAONPGKosAAQIECBAgQIAAAQIE2hEQALbTizFXIgAcU3/ZcwsAl91/1RMgQIAAAQIECBAgQIDAFgQEgFtAnsAUAsAJNGmmSxQAzrSxyiJAgAABAgQIECBAgACBdgQEgO30YsyVCADH1F/23ALAZfdf9QQIECBAgAABAgQIECCwBQEB4BaQJzCFAHACTZrpEgWAM22ssggQIECAAAECBAgQIECgHQEBYDu9GHMlAsAx9Zc9twBw2f1XPQECBAgQIECAAAECBAhsQUAAuAXkCUwhAJxAk2a6RAHgTBurLAIECBAgQIAAAQIECBBoR0AA2E4vxlyJAHBM/WXPLQBcdv9VT4AAAQIECBAgQIAAAQJbEBAAbgF5AlMIANto0pOSPGzXUm6b5K86lvbjSe6X5Ngkl0ryT0nem+TZSf5ixbLOneQXk/xckmsnuWCSU5O8KclTk5y04nH67CYA7KNmDAECBAgQIECAAAECBAgQOISAAPAQWDPeVQA4fnNvmOT9SUoYt7MdFACWnv1BDf/2W30JAe+f5JwDyrtEktcludk++3wryS8lef6GiASAG4J1WAIECBAgQIAAAQIECBAgsCMgAHQuFAEB4LjnwbmSvCfJTZN8KckxdTkHBYCPS/LIut8Hk5SrBz+d5KpJHp7kRvWzst+j9invqCRvSXJc/fzPkjwnyek1ECzjylq+k+SOSd6wASYB4AZQHZIAAQIECBAgQIAAAQIECOwWEAA6HwSA458DD0nye0k+luSVSX69IwC8WpKP1qsFy1WDJcA7c1cZ50/ytiQ3SXJ2kmvVcPDISu+V5AX1N5+R5IFH7FDm+UCSCyf5ZJLr1OOtU0wAuE5NxyJAgAABAgQIECBAgAABAnsICACdFgLAcc+BK9Rn7JXn7pUr/m6T5NEdAeDT6225Zbdb1KsHj6zi5klOrL/5tCS/vEeZH6mh3leSlCDujD32eUSSJ9Tfv2uSV6yZSwC4ZlCHI0CAAAECBAgQIECAAAECRwoIAJ0TAsBxz4HXJLlTkhcmKVfkPaYjACy3a5+c5HL1isHy0o79tnJF4TWTnJLkikc8C/DqST5RB5ZnCT5gn4NcOslp9bM/qi8KWaeYAHCdmo5FgAABAgQIECBAgAABAgT2EBAAOi0EgOOdA3dL8vL6zL1ym255g29XAHiVXbfzPqu+5GO/Csrn5Q3BZSvjPrtrx3sneV799T2S/PEBDB9Pco0kn09ypTVzCQDXDOpwBAgQIECAAAECBAgQIEDgSAEBoHNCADjOOXDR+hy/coXdfZM8ty6jKwAsL+N4bd33oUn+1wHLL58/pX5exr1+175PTvKr9dflhSEfOuA4r0ryk/UKwgsl+cYayQSAa8R0KAIECBAgQIAAAQIECBAgsJeAANB5IQAc5xx4dg3+3p3kh3bdntsVAN4/yTPrkn8myZ8esPzyzL4T6udlXLkicGcrV/z9bP3FpZJ8+YDjlGcI7rwgpFypWK4IXHUrAd9BWwlA31d2OPnkk3P5y3ftvuq09iNAgAABAgQIECBAgAABAgR2BASAzoUiUJ4rZ9ueQAn83p7kO0lunOTvd03dFQA+LMmT6v7/MclfHLDs8vnOVX/lar//uWvf1yW5Q/310Um+ecBxnpjk4fXz8mbh8mbgVbdzVt1RALiqlP0IECBAgAABAgQIECBAgMDhBASAh/Oa694CwO119rz1dtvy8o5yG+5OsLazgq4A8DeTPLbu/CNJ3nLA0n84yZvr52Xc7+zat/x++bxsRyX57gHHKfOV8WW7VZJ3HoJLAHgILLsSIECAAAECBAgQIECAAIFNCAgAN6E6vWMKALfXs52Ar7xQ4zp7PE+vKwCc2hWAXff0ugV4e+eemQgQIECAAAECBAgQIEBgoQICwIU2/oiyBYDbOQ/K8/P+Nkm5CvDOSV69x7RdAeDUngHYJeslIF1CPidAgAABAgQIECBAgAABAgMFBIADAWcyXAC4nUaWl3DcL8lnkvzGPlOWF3f8dP3st5OcVP/9NfVqwTslKf9etiFvAf7dJMfX43gL8Hb6bxYCBAgQIECAAAECBAgQIDCKgABwFPbmJhUAbqclf5jkv/Sc6vuT/EOSqyT5dD1GCRTLFYH7bTuBY/m8jPvsrh3vneR59df3SFLeCrzfVt76e40k5bblK/Vc/37DXAG4ZlCHI0CAAAECBAgQIECAAAECRwoIAJ0TRUAAuJ3zYB0BYOnVKUkum+RjScrLRPbbPpqk3HZ8apIrJNn9Qo4S6JVgr2x/kOQB+xykPKPvtPrZy5Lcc81UAsA1gzocAQIECBAgQIAAAQIECBAQADoH9hIQALZzXnQ9A7Cs9Bm7ArtbJHnPHsu/eZIT6++X/R+4xz7l9uISIJ5eA8Iz9tjnEUmeUH//bklOWDOVAHDNoA5HgAABAgQIECBAgAABAgQEgM4BAWDb58AqAWC5eu8jSc6d5P1Jjkty5q6yjk7y9iQ3SXJ2fdvwJ/coe/dtwE9P8qAj9rlqkr9JcuF623G5mrAcb52bAHCdmo5FgAABAgQIECBAgAABAgT2EHALsNOiCLgCsJ3zYJUAsKy2XJVXrs4r2weTPLGGdCW0+7Uk5cUeO/s9cp/yjkrytiS3rJ+/IslzknwlybFJfjPJMUm+m6S8fOTPN8AkANwAqkMSIECAAAECBAgQIECAAIHdAgJA54MAsK1zYNUA8Fw1rCtX8e23lZd8lLcOlwBvv+2SSV6f5Kb77HBWvTKwBIOb2ASAm1B1TAIECBAgQIAAAQIECBAgsEtAAOh0EAC2dQ6sGgDurPoONeQrAV4J876c5H1JyhuAV71ir9xKfN/6go/yTMALJPlCkjcn+f16u/GmlASAm5J1XAIECBAgQIAAAQIECBAgUAUEgE4FAaBzYEwBAeCY+uYmQIAAAQIECBAgQIAAgUUICAAX0ebOIj0DsJPIDhsSEABuCNZhCRAgQIAAAQIECBAgQIDAjoAA0LlQBASAzoOxBASAY8mblwABAgQIECBAgAABAgQWIyAAXEyrDyxUAOg8GEtAADiWvHkJECBAgAABAgQIECBAYDECAsDFtFoAqNVNCggAm2yLRREgQIAAAQIECBAgQIDAnAQEgHPqZv9aXAHY387IYQICwGF+RhMgQIAAAQIECBAgQIAAgU4BAWAn0SJ2EAAuos1NFikAbLItFkWAAAECBAgQIECAAAECcxIQAM6pm/1rEQD2tzNymIAAcJif0QQIECBAgAABAgQIECBAoFNAANhJtIgdBICLaHOTRQoAm2yLRREgQIAAAQIECBAgQIDAnAQEgHPqZv9aBID97YwcJiAAHOZnNAECBAgQIECAAAECBAgQ6BQQAHYSLWIHAeAi2txkkQLAJttiUQQIECBAgAABAgQIECAwJwEB4Jy62b8WAWB/OyOHCQgAh/kZTYAAAQIECBAgQIAAAQIEOgUEgJ1Ei9hBALiINjdZpACwybZYFAECBAgQIECAAAECBAjMSUAAOKdu9q9FANjfzshhAgLAYX5GEyBAgAABAgQIECBAgACBTgEBYCfRInYQAC6izU0WKQBssi0WRYAAAQIECBAgQIAAAQJzEhAAzqmb/WsRAPa3M3KYgABwmJ/RBAgQIECAAAECBAgQIECgU0AA2Em0iB0EgItoc5NFCgCbbItFESBAgAABAgQIECBAgMCcBASAc+pm/1oEgP3tjBwmIAAc5mc0AQIECBAgQIAAAQIECBDoFBAAdhItYgcB4CLa3GSRAsAm22JRBAgQIECAAAECBAgQIDAnAQHgnLrZvxYBYH87I4cJCACH+RlNgAABAgQIECBAgAABAgQ6BQSAnUSL2EEAuIg2N1mkALDJtlgUAQIECBAgQIAAAQIECMxJQAA4p272r0UA2N/OyGECAsBhfkYTIECAAAECBAgQIECAAIFOAQFgJ9EidhAALqLNTRYpAGyyLRZFgAABAgQIECBAgAABAnMSEADOqZv9axEA9rczcpiAAHCYn9EECBAgQIAAAQIECBAgQKBTQADYSbSIHQSAi2hzk0UKAJtsi0URIECAAAECBAgQIECAwJwEBIBz6mb/WgSA/e2MHCYgABzmZzQBAgQIECBAgAABAgQIEOgUEAB2Ei1iBwHgItrcZJECwCbbYlEECBAgQIAAAQIECBAgMCcBAeCcutm/FgFgfzsjhwkIAIf5GU2AAAECBAgQIECAAAECBDoFBICdRIvYQQC4iDY3WaQAsMm2WBQBAgQIECBAgAABAgQIzElAADinbvavRQDY387IYQICwGF+RhMgQIAAAQIECBAgQIAAgU4BAWAn0SJ2EAAuos1NFikAbLItFkWAAAECBAgQIECAAAECcxIQAM6pm/1rEQD2tzNymIAAcJif0QQIECBAgAABAgQIECBAoFNAANhJtIgdBICLaHOTRQoAm2yLRREgQIAAAQIECBAgQIDAnAQEgHPqZv9aBID97YwcJiAAHOZnNAECBAgQIECAAAECBAgQ6BQQAHYSLWIHAeAi2txkkQLAJttiUQQIECBAgAABAgQIECAwJwEB4Jy62b8WAWB/OyOHCQgAh/kZTYAAAQIECBAgQIAAAQIEOgUEgJ1Ei9hBALiINjdZpACwybZYFAECBAgQIECAAAECBAjMSUAAOKdu9q9FANjfzshhAgLAYX5GEyBAgAABAgQIECBAgACBTgEBYCfRInYQAC6izU0WKQBssi0WRYAAAQIECBAgQIAAAQJzEhAAzqmb/WsRAPa3M3KYwPcCwL/75N/lcpe/3LCjGU2AAAECBAgQIECAAAECBAj8O4FTTzk1N7j6DXZ+/wpJTsG0PAEB4PJ63krF3wsAr/mUa+Y8Fz9PK+uyDgIECBAgQIAAAQIECBAgMBuBb5/+7Xz8v39cADibjvYrRADYz82o4QICwOGGjkCAAAECBAgQIECAAAECBA4UEAA6QYqAANB5MJaAAHAsefMSIECAAAECBAgQIECAwGIEBICLafWBhQoAnQdjCQgAx5I3LwECBAgQIECAAAECBAgsRkAAuJhWCwC1ukkBLwFpsi0WRYAAAQIECBAgQIAAAQJzEvASkDl1s38trgDsb2fkMIHvBYAnn3xyLn/58ksbAQIECBAgQIAAAQIECBAgsE6BU045JVe4Qnn5779u3gK8TtwJHUsAOKFmzWypAsCZNVQ5BAgQIECAAAECBAgQINCegACwvZ6MsSIB4Bjq5iwCAkDnAQECBAgQIECAAAECBAgQ2LCAAHDDwBM5vABwIo2a4TIFgDNsqpIIECBAgAABAgQIECBAoC0BAWBb/RhrNQLAseTNKwB0DhAgQIAAAQIECBAgQIAAgQ0LCAA3DDyRwwsAJ9KoGS5TADjDpiqJAAECBAgQIECAAAECBNoSEAC21Y+xViMAHEvevAJA5wABAgQIECBAgAABAgQIENiwgABww8ATObwAcCKNmuEyBYAzbKqSCBAgQIAAAQIECBAgQKAtAQFgW/0YazUCwLHkzSsAdA4QIECAAAECBAgQIECAAIENCwgANww8kcMLACfSqBkuUwA4w6YqiQABAgQIECBAgAABAgTaEhAAttWPsVYjABxL3rwCQOcAAQIECBAgQIAAAQIECBDYsIAAcMPAEzm8AHAijZrhMgWAM2yqkggQIECAAAECBAgQIECgLQEBYFv9GGs1AsCx5M0rAHQOECBAgAABAgQIECBAgACBDQsIADcMPJHDCwAn0qgZLlMAOMOmKokAAQIECBAgQIAAAQIE2hIQALbVj7FWIwAcS968AkDnAAECBAgQIECAAAECBAgQ2LCAAHDDwBM5vABwIo2a4TIFgDNsqpIIECBAgAABAgQIECBAoC0BAWBb/RhrNQLAseTNKwB0DhAgQIAAAQIECBAgQIAAgQ0LCAA3DDyRwwsAJ9KoGS5TADjDpiqJAAECBAgQIECAAAECBNoSEAC21Y+xViMAHEvevAJA5wABAgQIECBAgAABAgQIENiwgABww8ATObwAcCKNmuEyBYAzbKqSCBAgQIAAAQIECBAgQKAtAQFgW/0YazUCwLHkzSsAdA4QIECAAAECBAgQIECAAIENCwgANww8kcMLACfSqBkuUwA4w6YqiQABAgQIECBAgAABAgTaEhAAttWPsVYjABxL3rwCQOcAAQIECBAgQIAAAQIECBDYsIAAcMPAEzm8AHAijZrhMgWAM2yqkggQIECAAAECBAgQIECgLQEBYFv9GGs1AsCx5M0rAHQOECBAgAABAgQIECBAgACBDQsIADcMPJHDCwAn0qgZLlMAOMOmKokAAQIECBAgQIAAAQIE2hIQALbVj7FWIwAcS968AkDnAAECBAgQIECAAAECBAgQ2LCAAHDDwBM5vABwIo2a4TIFgDNsqpIIECBAgAABAgQIECBAoC0BAWBb/RhrNQLAseTNKwB0DhAgQIAAAQIECBAgQIAAgQ0LCAA3DDyRwwsAJ9KoGS5TADjDpiqJAAECBAgQIECAAAECBNoSEAC21Y+xViMAHEvevAJA5wABAgQIECBAgAABAgQIENiwgABww8ATObwAcCKNmuEyBYAzbKqSCBAgQIAAAQIECBAgQKAtAQFgW/0YazUCwLHkzSsAdA4QIECAAAECBAgQIECAAIENCwgANww8kcMLACfSqBkuUwA4w6YqiQABAgQIECBAgAABAgTaEhAAttWPsVYjABxL3rwCQOcAAQIECBAgQIAAAQIECBDYsIAAcMPAEzm8AHAijZrhMgWAM2yqkggQIECAAAECBAgQIECgLQEBYFv9GGs1AsCx5M0rAHQOECBAgAABAgQIECBAgACBDQsIADcMPJHDCwAn0qgZLlMAOMOmKokAAQIECBAgQIAAAQIE2hIQALbVj7FWIwAcS968AkDnAAECBAgQIECAAAECBAgQ2LCAAHDDwBM5vABwIo2a4TIFgDNsqpIIECBAgAABAgQIECBAoC0BAWBb/RhrNQLA7cnfOMmPJ7lVkuslOSbJt5N8Icm7kzwvyTsOsZxyrPslOTbJpZL8U5L3Jnl2kr9Y8TjnTvKLSX4uybWTXDDJqUnelOSpSU5a8Th9dhMA9lEzhgABAgQIECBAgAABAgQIHEJAAHgIrBnvKgDcTnPfluS4FaZ6cZL7JDnrgH1Lz/6ghn/77VZCwPsnOeeA41wiyeuS3Gyffb6V5JeSPH+FdffZRQDYR80YAgQIECBAgAABAgQIECBwCAEB4CGwZryrAHA7zf1UkqvWq/1OqFf6fT7JUUlukeT4JJerS3lZknsesKzHJXlk/fyDSZ6U5NP1+A9PcqP6WdnvUfscp8z7ll2h5J8leU6S02sgWMaVKxS/k+SOSd6wASYB4AZQHZIAAQIECBAgQIAAAQIECOwWEAA6H4qAAHA758Frk7woyStqqHbkrJdM8q4k16gflKsF97od+GpJPpqk3Lr7/hrgnbnrYOdPUq42vEmSs5Ncq4aDR853ryQvqL/5jCQPPGKHMs8Hklw4ySeTXKceb51aAsB1ajoWAQIECBAgQIAAAQIECBDYQ0AA6LQQALZ1DtwpyWvqksrz9x68x/KeXm/LLR+VKwffs8c+N09yYv39pyX55T32+UgN9b6SpARxZ+yxzyOSPKH+/l1reLlOMQHgOjUdiwABAgQIECBAgAABAgQICACdA/sIuAKwnVOjvIDja3U55dl8JRDcvZVenVxvFf5YfWnHfqsvn18zySlJrnjEswCvnuQTdWB5luAD9jnIpZOcVj/7o/qikHVqCQDXqelYBAgQIECAAAECBAgQIEBAAOgcEAA2fw5cPMk/11WWKwF/8ogVX2XX7bzPqi/52K+o8nl5Q3DZyrjP7trx3vWNw+W37pHkjw+Q+Xi9Lbk8r/BKaxYUAK4Z1OEIECBAgAABAgQIECBAgMCRAm4Bdk4UAVcAtnMe/FSS8jKOsj05SXmhx+6tvIyjPEuwbA9N8r8OWHr5/Cn18zLu9bv2Lcf+1frr8sKQDx1wnFfVILK8TfhCSb6xRi4B4BoxHYoAAQIECBAgQIAAAQIECOwlIAB0XhQBAWAb58G56nP7jq3LuWl9ycfu1d0/yTPrb/xMkj89YOnlmX3lbcNlK+PKFYE7W7ni72frLy6V5MsHHKc8Q3DnBSHlhSLlisBVtxLwHbSVW4zfV3Y4+eSTc/nLd+2+6rT2I0CAAAECBAgQIECAAAECBHYEBIDOhSIgAGzjPDg+ye/WpbwyyV32WNbDkjyp/v5/TPIXByy9fL5z1V+52u9/7tq3PF/wDvXXRyf55gHHeeKuKxHLm4XLm4FX3cpVgyttAsCVmOxEgAABAgQIECBAgAABAgQOLSAAPDTZLAcIAMdv662TvCnJuZN8KckNknxxj2X9ZpLH1t//kSRvOWDpP5zkzfXzMu53du1bfr98Xrajknz3gOOU+cr4st0qyTsPwSUAPASWXQkQIECAAAECBAgQIECAwCYEBICbUJ3eMQWA4/bsuknekeRiSb6V5PZJ3rbPkqZ2BWDXPb1uAR733DM7AQIECBAgQIAAAQIECCxAQAC4gCavUKIAcAWkDe3y/fWKussm+U6S8ly/cvvvftvUngHYxeYlIF1CPidAgAABAgQIECBAgAABAgMFBIADAWcyXAA4TiNL6Feu/LtKknKr7L2SvKhjKXdK8pq6z5C3AJdnDZZnDpbNW4DH6b9ZCRAgQIAAAQIECBAgQIDAVgQEgFthbn4SAeD2W3TJepvvderUD0ry9BWWUcLCT9f9ylt9yxWB+23l8/vVD8u4z+7a8d5Jnld/fY8k5a3A+23lrb/XSPL5JFdaYY2H2cUVgIfRsi8BAgQIECBAgAABAgQIEOghIADsgTbDIQLA7Tb1IvXlHTeu0z4iSXnT7ipb6dUpScrVgx9Lcu0DBn00ybWSnJrkCvUqw53dS6BXgr2y/UGSB+xznPKMvtPqZy9Lcs9VFnmIfQSAh8CyKwECBAgQIECAAAECBAgQ6CMgAOyjNr8xAsDt9fT8Sd6Y5JZ1yscledQhp3/GrsDuFknes8f4myc5sf5+2f+Be+xzUg0QT68B4Rl77FPCySfU379bkhMOudau3QWAXUI+J0CAAAECBAgQIECAAAECAwUEgAMBZzJcALidRp63Pr/vdnW630/ykB5Tl6v3PpLk3Enen+S4JGfuOs7RSd6e5CZJzk5SbjP+5B7z7L4NuNx+XG5D3r1dNcnfJLlwve24XE1YjrfOTQC4Tk3HIkCAAAECBAgQIECAAAECewgIAJ0WRUAAuJ3z4BVJ7lKneksN/8rLP/bbzkryiX0+LFfllavzyvbBegtxeTZgCe1+rb7Yo3xW9nvkPsc4qj6HcOdqxLK+5yT5SpJjk/xmkmOSfDdJefnIn2+ASQC4AVSHJECAAAECBAgQIECAAAECuwUEgM4HAeD2zoGDwr69VvG5JFfeZ3nnqmFduYpvv6285KO8BKQEePtt5WUkr09y0312KCFkuTKwBIOb2ASAm1B1TAIECBAgQIAAAQIECBAgsEtAAOh0KAKuANzOebDOAHBnxXeoIV8J8EqY9+Uk70tS3gC86hV75Vbi+9YXfJSXilwgyReSvDlJuU253G68qU0AuClZxyVAgAABAgQIECBAgAABAlVAAOhUEAA6B8YUEACOqW9uAgQIECBAgAABAgQIEFiEgABwEW3uLNIVgJ1EdtiQgABwQ7AOS4AAAQIECBAgQIAAAQIEdgQEgM6FIiAAdB6MJSAAHEveOOs1bgAAIABJREFUvAQIECBAgAABAgQIECCwGAEB4GJafWChAkDnwVgCAsCx5M1LgAABAgQIECBAgAABAosREAAuptUCQK1uUkAA2GRbLIoAAQIECBAgQIAAAQIE5iQgAJxTN/vX4grA/nZGDhMQAA7zM5oAAQIECBAgQIAAAQIECHQKCAA7iRaxgwBwEW1uskgBYJNtsSgCBAgQIECAAAECBAgQmJOAAHBO3exfiwCwv52RwwQEgMP8jCZAgAABAgQIECBAgAABAp0CAsBOokXsIABcRJubLFIA2GRbLIoAAQIECBAgQIAAAQIE5iQgAJxTN/vXIgDsb2fkMAEB4DA/owkQIECAAAECBAgQIECAQKeAALCTaBE7CAAX0eYmixQANtkWiyJAgAABAgQIECBAgACBOQkIAOfUzf61CAD72xk5TEAAOMzPaAIECBAgQIAAAQIECBAg0CkgAOwkWsQOAsBFtLnJIgWATbbFoggQIECAAAECBAgQIEBgTgICwDl1s38tAsD+dkYOExAADvMzmgABAgQIECBAgAABAgQIdAoIADuJFrGDAHARbW6ySAFgk22xKAIECBAgQIAAAQIECBCYk4AAcE7d7F+LALC/nZHDBASAw/yMJkCAAAECBAgQIECAAAECnQICwE6iRewgAFxEm5ssUgDYZFssigABAgQIECBAgAABAgTmJCAAnFM3+9ciAOxvZ+QwAQHgMD+jCRAgQIAAAQIECBAgQIBAp4AAsJNoETsIABfR5iaLFAA22RaLIkCAAAECBAgQIECAAIE5CQgA59TN/rUIAPvbGTlMQAA4zM9oAgQIECBAgAABAgQIECDQKSAA7CRaxA4CwEW0uckiBYBNtsWiCBAgQIAAAQIECBAgQGBOAgLAOXWzfy0CwP52Rg4TEAAO8zOaAAECBAgQIECAAAECBAh0CggAO4kWsYMAcBFtbrJIAWCTbbEoAgQIECBAgAABAgQIEJiTgABwTt3sX4sAsL+dkcMEBIDD/IwmQIAAAQIECBAgQIAAAQKdAgLATqJF7CAAXESbmyxSANhkWyyKAAECBAgQIECAAAECBOYkIACcUzf71yIA7G9n5DABAeAwP6MJECBAgAABAgQIECBAgECngACwk2gROwgAF9HmJosUADbZFosiQIAAAQIECBAgQIAAgTkJCADn1M3+tQgA+9sZOUxAADjMz2gCBAgQIECAAAECBAgQINApIADsJFrEDgLARbS5ySIFgE22xaIIECBAgAABAgQIECBAYE4CAsA5dbN/LQLA/nZGDhMQAA7zM5oAAQIECBAgQIAAAQIECHQKCAA7iRaxgwBwEW1uskgBYJNtsSgCBAgQIECAAAECBAgQmJOAAHBO3exfiwCwv52RwwQEgMP8jCZAgAABAgQIECBAgAABAp0CAsBOokXsIABcRJubLFIA2GRbLIoAAQIECBAgQIAAAQIE5iQgAJxTN/vXIgDsb2fkMAEB4DA/owkQIECAAAECBAgQIECAQKeAALCTaBE7CAAX0eYmixQANtkWiyJAgAABAgQIECBAgACBOQkIAOfUzf61CAD72xk5TEAAOMzPaAIECBAgQIAAAQIECBAg0CkgAOwkWsQOAsBFtLnJIgWATbbFoggQIECAAAECBAgQIEBgTgICwDl1s38tAsD+dkYOExAADvMzmgABAgQIECBAgAABAgQIdAoIADuJFrGDAHARbW6ySAFgk22xKAIECBAgQIAAAQIECBCYk4AAcE7d7F+LALC/nZHDBASAw/yMJkCAAAECBAgQIECAAAECnQICwE6iRewgAFxEm5ssUgDYZFssigABAgQIECBAgAABAgTmJCAAnFM3+9ciAOxvZ+QwAQHgMD+jCRAgQIAAAQIECBAgQIBAp4AAsJNoETsIABfR5iaLFAA22RaLIkCAAAECBAgQIECAAIE5CQgA59TN/rUIAPvbGTlMQAA4zM9oAgQIECBAgAABAgQIECDQKSAA7CRaxA4CwEW0uckiBYBNtsWiCBAgQIAAAQIECBAgQGBOAgLAOXWzfy0CwP52Rg4TEAAO8zOaAAECBAgQIECAAAECBAh0CggAO4kWsYMAcBFtbrJIAWCTbbEoAgQIECBAgAABAgQIEJiTgABwTt3sX4sAsL+dkcMEBIDD/IwmQIAAAQIECBAgQIAAAQKdAgLATqJF7CAAXESbmyxSANhkWyyKAAECBAgQIECAAAECBOYkIACcUzf71yIA7G9n5DABAeAwP6MJECBAgAABAgQIECBAgECngACwk2gROwgAF9HmJosUADbZFosiQIAAAQIECBAgQIAAgTkJCADn1M3+tQgA+9sZOUxAADjMz2gCBAgQIECAAAECBAgQINApIADsJFrEDgLARbS5ySIFgE22xaIIECBAgAABAgQIECBAYE4CAsA5dbN/LQLA/nZGDhMQAA7zM5oAAQIECBAgQIAAAQIECHQKCAA7iRaxgwBwEW1uskgBYJNtsSgCBAgQIECAAAECBAgQmJOAAHBO3exfiwCwv52RwwQEgMP8jCZAgAABAgQIECBAgAABAp0CAsBOokXsIABcRJubLFIA2GRbLIoAAQIECBAgQIAAAQIE5iQgAJxTN/vXIgDsb2fkMAEB4DA/owkQIECAAAECBAgQIECAQKeAALCTaBE7CAAX0eYmixQANtkWiyJAgAABAgQIECBAgACBOQkIAOfUzf61CAD72xk5TEAAOMzPaAIECBAgQIAAAQIECBAg0CkgAOwkWsQOAsBFtLnJIgWATbbFoggQIECAAAECBAgQIEBgTgICwDl1s38tAsD+dkYOExAADvMzmgABAgQIECBAgAABAgQIdAoIADuJFrGDAHARbW6ySAFgk22xKAIECBAgQIAAAQIECBCYk4AAcE7d7F+LALC/nZHDBASAw/yMJkCAAAECBAgQIECAAAECnQICwE6iRewgAFxEm5ssUgDYZFssigABAgQIECBAgAABAgTmJCAAnFM3+9ciAOxvZ+QwAQHgMD+jCRAgQIAAAQIECBAgQIBAp4AAsJNoETsIABfR5iaLFAA22RaLIkCAAAECBAgQIECAAIE5CQgA59TN/rUIAPvbGTlMQAA4zM9oAgQIECBAgAABAgQIECDQKSAA7CRaxA4CwEW0uckiBYBNtsWiCBAgQIAAAQIECBAgQGBOAgLAOXWzfy0CwP52Rg4TEAAO8zOaAAECBAgQIECAAAECBAh0CggAO4kWsYMAcBFtbrJIAWCTbbEoAgQIECBAgAABAgQIEJiTgABwTt3sX4sAsL+dkcMEBIDD/IwmQIAAAQIECBAgQIAAAQKdAgLATqJF7CAAXESbmyxSANhkWyyKAAECBAgQIECAAAECBOYkIACcUzf71yIA7G9n5DABAeAwP6MJECBAgAABAgQIECBAgECngACwk2gROwgAF9HmJosUADbZFosiQIAAAQIECBAgQIAAgTkJCADn1M3+tQgA+9sZOUxAADjMz2gCBAgQIECAAAECBAgQINApIADsJFrEDgLARbS5ySIFgE22xaIIECBAgAABAgQIECBAYE4CAsA5dbN/LQLA/nZGDhMQAA7zM5oAAQIECBAgQIAAAQIECHQKCAA7iRaxgwBwEW1uskgBYJNtsSgCBAgQIECAAAECBAgQmJOAAHBO3exfiwCwv52RwwQEgMP8jCZAgAABAgQIECBAgAABAp0CAsBOokXsIABcRJubLFIA2GRbLIoAAQIECBAgQIAAAQIE5iQgAJxTN/vXIgDsb2fkMAEB4DA/owkQIECAAAECBAgQIECAQKeAALCTaBE7CAAX0eYmixQANtkWiyJAgAABAgQIECBAgACBOQkIAOfUzf61CAD72xk5TEAAOMzPaAIECBAgQIAAAQIECBAg0CkgAOwkWsQOAsBFtLmzyCsm+ZUkd0xS/v1bST6V5E+SPCPJGZ1HOPwOAsDDmxlBgAABAgQIECBAgAABAgQOJSAAPBTXbHcWAM62tSsXVkK/lya5yD4jPp7kDkk+s/IRV9tRALiak70IECBAgAABAgQIECBAgEBvAQFgb7pZDRQAzqqdhy7mhkneneT8Sb6e5AlJ3prk6CR3T3LfesSPJblp3efQk+wzQAC4LknHIUCAAAECBAgQIECAAAEC+wgIAJ0aRUAAuOzzoIR9t0lydpLjkpx4BMfDkjyp/t6jkzx2jVwCwDViOhQBAgQIECBAgAABAgQIENhLQADovBAALvscKFf0vbcSPCvJ/ffgOFeSDye5dpKvJPm+JN9eE5sAcE2QDkOAAAECBAgQIECAAAECBPYTEAA6NwSAyz4HHpfkkZXg5kn+eh+OR9Rbg8vHt0vyl2tiEwCuCdJhCBAgQIAAAQIECBAgQICAANA5cJCAW4CXe368PcmtknwjyUXrbcB7adyiPiewfFZuAS63Aq9jEwCuQ9ExCBAgQIAAAQIECBAgQIDAAQKuAHR6FAEB4HLPg39Kcskkf5vkBw5guFiS0+vnJyS525rIBIBrgnQYAgQIECBAgAABAgQIECCwn4AA0LkhAFzuOXC+JGfW8l+X5E4dFOUNwRdI8p4k5YrAVbYS8B20Xa4eL+9973tzmctcZpVj2ocAAQIECBAgQIAAAQIECBA4hMBpp52WY489dmfElZN87hDD7ToTAVcAzqSRhyzjUkm+VMe8PMndO8Z/Mckx9YUg119xrnNW3M9uBAgQIECAAAECBAgQIECAwHYEygtB37+dqczSkoAAsKVubG8tV0jy+Trdi5P8QsfUZd8y5tNJrrbiMgWAK0LZjQABAgQIECBAgAABAgQIbEngjklev6W5TNOQgACwoWZscSnbuAKw6xbgKyZ5V625vIX41C3WbyoCrQpcOsn76uLK38z9Y6sLtS4CWxbw3dgyuOkmIeB7MYk2WeQIAr4bI6CbsnmB7z2CK8lVk3ym+RVb4NoFBIBrJ53EAbfxDMAuiO+9BKReXXhK1wCfE1iAgO/FApqsxF4Cvhu92AyauYDvxcwbrLzeAr4bvekMnLGA78WMm7tqaQLAVaXmt18zbwEWAM7v5FJRbwE/mHvTGThzAd+NmTdYeb0EfC96sRm0AAHfjQU0WYmHFvC9ODTZ/AYIAOfX01UrenuSWyX5RpKLJjl7n4Hlrb/vrp89NsmjV52gYz//AVoTpMPMSsD3YlbtVMwaBXw31ojpULMR8L2YTSsVsmYB3401gzrcLAR8L2bRxmFFCACH+U159OOT/HotoDyD76/3KeYRSZ5QP7t9kjeuqWj/AVoTpMPMSsD3YlbtVMwaBXw31ojpULMR8L2YTSsVsmYB3401gzrcLAR8L2bRxmFFCACH+U159LG7Qr9nJbn/HsWcK8mHk1w7yVeTHJPk22sq2n+A1gTpMLMS8L2YVTsVs0YB3401YjrUbAR8L2bTSoWsWcB3Y82gDjcLAd+LWbRxWBECwGF+Ux+9cxtwuf33uCQnHlHQw5I8qf7ebyV5zBoL9h+gNWI61GwEfC9m00qFrFnAd2PNoA43CwHfi1m0UREbEPDd2ACqQ05ewPdi8i0cXoAAcLjhlI9woyTvSnJ0kq8nKbcFv7X++u5J7leL+0SSmyT52hqL9R+gNWI61GwEfC9m00qFrFnAd2PNoA43CwHfi1m0UREbEPDd2ACqQ05ewPdi8i0cXoAAcLjh1I/wE0lekuTC+xRSwr87JvnUmgv1H6A1gzrcLAR8L2bRRkVsQMB3YwOoDjl5Ad+LybdQARsS8N3YEKzDTlrA92LS7VvP4gWA63Gc+lGulOTBNegr/2E4qwZ+JyR5WpIzpl6g9RMgQIAAAQIECBAgQIAAAQIEliogAFxq59VNgAABAgQIECBAgAABAgQIECCwCAEB4CLarEgCBAgQIECAAAECBAgQIECAAIGlCggAl9p5dRMgQIAAAQIECBAgQIAAAQIECCxCQAC4iDYrkgABAgQIECBAgAABAgQIECBAYKkCAsCldl7dBAgQIECAAAECBAgQIECAAAECixAQAC6izYokQIAAAQIECBAgQIAAAQIECBBYqoAAcKmdVzcBAgQIECBAgAABAgQIECBAgMAiBASAi2izIgkQIECAAAECBAgQIECAAAECBJYqIABcaufVTYAAAQIECBAgQIAAAQIECBAgsAgBAeAi2qxIAgQIECBAgAABAgQIECBAgACBpQoIAJfa+XHrvmKSX0lyxyTl37+V5FNJ/iTJM5KcMe7yzE5gqwLHJDm2/nPTJOWfS9QVvDDJvba6GpMRaEPgxkl+PMmtklwvSfmefDvJF5K8O8nzkryjjaVaBYGtCFw4yR3qz4ibJLlckkslOTrJV5OclOT19bvxz1tZkUkItC/wpCQP27XM2yb5q/aXbYUE1iJwzopHeVuS26y4r90mLiAAnHgDJ7j8Evq9NMlF9ln7x+sfcD8zwdosmUAfgYN+OAsA+4gaM3WB8gfR41Yo4sVJ7pPkrBX2tQuBqQv8aJK/XKGILyf5+SRvWGFfuxCYs8ANk7w/ybkFgHNus9oOEBAAOj3+nYAA0EmxTYHyg7hcuXH+JF9P8oQkb61/e333JPeti/lY/Rvuso+NwNwFdv9wPjnJR5PcrhYtAJx799W3l0C5Ivyq9Wq/E+qVfp9PclSSWyQ5vl79VMa+LMk9MRJYgEAJAJ9f/9z0gSTl58VpSc6V5PJJ7prkLvV7UkLxcjX53y3ARYkE9hIo34v31O/Bl+pV5GU/VwA6X5YksPP/GM+sd9ntV/s3knx2STBLrlUAuOTub7/2EvaVy4vPrld3nHjEEsol+uVS/bI9Osljt79EMxLYusBvJXlf/eeLSa6864ewAHDr7TBhAwKvTfKiJK9I8p091nPJJO9Kco36Wbla0O3ADTTOEjYqUALwvb4Puyf9T0leWX/jz5L89EZX5OAE2hV4SJLfS1IuKijfiV+vSxUAttszK1u/wE4AWP5f4zHrP7wjTlFAADjFrk1zzeVvot9bl/6sJPffo4zyt3UfTnLtJF9J8n31mU/TrNiqCfQTEAD2czNqWQJ3SvKaWvJTkzx4WeWrlsC+AuUq8mslKbcCl2cE2ggsTeAK9ZmYF6xX/JWLD8qFBWUTAC7tbFh2vQLAZfd/z+oFgE6KbQk8Lskj62Q3T/LX+0z8iHprcPm43Aa5yvNutlWDeQhsQ0AAuA1lc0xdoPyP3ddqEa9LUgJBGwEC//bMsx+sj1q5EBACCxQofzlUfibs3EVRrnwSAC7wRFByBIBOgn8nIAB0UmxL4O31bY7lGQMXrbcB7zV3eb5TeU5g2cotwDs/sLe1TvMQGFtAADh2B8w/BYGLJ9l502n5n72fnMKirZHAhgXKHRR/X58DWILAcveFjcCSBO6W5OVJTq9Xwv5TvfVRALiks0CtOwICQOeCANA5MJpA+QFcntv0t0l+4IBVXKz+0C67lIe/lx/kNgJLEhAALqnbau0r8FNJyjPOyvbkJA/veyDjCExcoLxY7XJJfqJ+D8rjU8r2n5O8ZOK1WT6BwwiUCwzKLfCXri8WfG4d7ArAwyjad04COwHgSfVt2FesF+H8Y73g5g/ri6XmVLNaOgRcAegU2YbA+ZKcWSda5Vat8vbfC9S3d5UrAm0EliQgAFxSt9XaR6A8L7a8ROrYOrhc5VSudrIRWIrAvZK84IBif7eGgbvfMr8UG3UuV+DZNfgrdxL9UPK92x8FgMs9J5Ze+So/A/5PkvIz5V+WjrWU+gWAS+n0uHWWh1B/qS6hXJZ/947llDehHlNfCHL9cZdudgJbFxAAbp3chBMTOD5JCTjKVt7ueJeJrd9yCQwV2C8A/FB9ydp+z1keOq/xBFoVKIFfedxQeVP2jeut8DtrFQC22jXr2rRAefTWq5O8ub4Ru1xkU/6//Nb1Z8Ul6gLeluTHvHxz0+1o4/gCwDb6MPdVlLdxfb4W+eIkv9BRcNm3jPl0kqvNHUd9BI4QEAA6JQjsL1D+0PqmeitL+YulGyQpf2lkI7AkgXKr4+VrwUcnuWp9ZEq5Nb782ekhSV67JBC1LlrgvElK+F2egbnXIyEEgIs+PRZdfPlZ8dV9BMrjIv48yY3q5w9O8tRFay2keAHgQho9cpmuABy5AaaflIAAcFLtstgtClw3yTuSlGfFfivJ7ZOUv7W2ESDwbwLluX/lzafltq9fTFKe72QjMHeBnYCvXEBwnSTlqqfdmwBw7meA+voKXKU+N7OE6J9KcvW+BzJuOgICwOn0asor9QzAKXfP2rctIADctrj5piDw/UnemeSy9Ravn6m3/05h7dZIYJsC5VEr5QVqJQQpd1N8ZZuTm4vAlgWuVV8wWAKMO9fbHY9cggBwy00x3aQEytXid6wrLi+U+sKkVm+xhxYQAB6azICeAt4C3BPOsMUJCAAX13IFdwiU0K9c+Vf+prpc2VSef/YiagQI7ClwzyQvrZ/8XJI/4kRgxgLPSnK/JJ9J8hv71HnXJD9dP/vtJOWNqGV7zR5XC86YSmkE9hR4UpKH1U/Ky9Xex2neAgLAefe3perKg3lvVX/QlucRnL3P4spbf8vbu8r22CSPbqkIayGwBQEB4BaQTTEZgUvW23zLbV1le1CSp09m9RZKYPsC5UHub6zTPjLJE7a/BDMS2JpAuc39v/ScrVxZ/g89xxpGYC4C5bmZv1qLEQDOpasH1CEAXECTGynx8Ul+va7l5kn2e0PdI3b9YbU832nnD7GNlGEZBDYuIADcOLEJJiJwkSRvqW90LEsuPx+eOJG1WyaBsQR2vyH4V5L877EWYl4CWxAQAG4B2RSzFnhdkjvUCsvLpU6ddbWKiwDQSbAtgfI3CjuhX7lc//57THyuJB+ub/Eqbyw6xuvIt9Ue8zQkIABsqBmWMprA+etfAN2yruBxSR412mpMTGA6Arv/Z+62Sf5qOku3UgIbEfAMwI2wOugMBMqjVT6W5Dz1NvryRnnbzAUEgDNvcGPl7dwGXG7/PS7JiUesrzx/oDyHoGy/laT8wLYRWJqAAHBpHVfvkQLlYe7l2Uy3qx/8fpKHYCKwcIFyZd8fJ/nmAQ4PTfKU+nm5tbG80XG/R64snFP5CxIQAC6o2Ur9nsBPJPnzA34GfF/9/EZ1xPG7fn5gnLGAAHDGzW2wtPIfmHclOTrJ15OU24LfWn999/oQ37LsTyS5SZKvNViDJRFYt8APJbnaroOWZ56V53GUrXxfnnvEhOV2FxuBOQu8IsldaoHlFuAS/pWXf+y3nVV/bszZRG0ESqB3oSTl+1HeiP3p+mep8nvXT1Je+LFzxWz5TpS3Or4JGwEC/3pBwc4zxV0V64RYikD5mVGu7Cs/M8pFN+XXZyYp/59xm3o33iUqRvmZ8qNJvrUUnCXXKQBccvfHqb38bcRLklx4n+lL+Ff+0PqpcZZnVgJbFzjs82v8d3vrLTLhlgUOCvv2WsrnkpQrZ20E5ixQ/uftSisUeEqSeyf5yxX2tQuBJQgIAJfQZTUeKbDqz4wSEN4nSXn8lm0BAv5HcgFNbrDE8gfYB9egrzxstPxNdQn8TkjytCRnNLhmSyKwKQEB4KZkHXeqAgLAqXbOujcpUJ7NVK7QKFcwXTtJuX2rXL1Rbgn+YpIPJXltkj/x56hNtsGxJyggAJxg0yx5sMCtk5R/bpGkPOuvXPlXLsApd+GdnOTdSV64xyO5Bk/sAG0LCADb7o/VESBAgAABAgQIECBAgAABAgQIEBgkIAAcxGcwAQIECBAgQIAAAQIECBAgQIAAgbYFBIBt98fqCBAgQIAAAQIECBAgQIAAAQIECAwSEAAO4jOYAAECBAgQIECAAAECBAgQIECAQNsCAsC2+2N1BAgQIECAAAECBAgQIECAAAECBAYJCAAH8RlMgAABAgQIECBAgAABAgQIECBAoG0BAWDb/bE6AgQIECBAgAABAgQIECBAgAABAoMEBICD+AwmQIAAAQIECBAgQIAAAQIECBAg0LaAALDt/lgdAQIECBAgQIAAAQIECBAgQIAAgUECAsBBfAYTIECAAAECBAgQIECAAAECBAgQaFtAANh2f6yOAAECBAgQIECAAAECBAgQIECAwCABAeAgPoMJECBAgAABAgQIECBAgAABAgQItC0gAGy7P1ZHgAABAgQIECBAgAABAgQIECBAYJCAAHAQn8EECBAgQIAAAQIECBAgQIAAAQIE2hYQALbdH6sjQIAAAQIECBAgQIAAAQIECBAgMEhAADiIz2ACBAgQIECAAAECBAgQIECAAAECbQsIANvuj9URIECAAAECBAgQIECAAAECBAgQGCQgABzEZzABAgQIECBAYKMCj0ny6DrDUv7c9g9JrnSE6ueSXHmN0uVYn93jeC9Mcq81zuNQBAgQIECAAIEmBJbyB8kmsC2CAAECBAgQWITAfuHSYYsvf04TAP6bmgDwsGeP/QkQIECAAAECuwQEgE4HAgQIECBAgMB6BQSAwzx3rgB8VZJH1UOdleQTww77/xt9niTX3PU7b0hy2SSuAFwjskMRIECAAAEC7QgIANvphZUQIECAAAEC8xA4Mlw6sqqdsOkLSW5/QMkfngfHoavYCQC3GcaNMeehYQwgQIAAAQIECPQVEAD2lTOOAAECBAgQINBPYCdsWvdtrf1W096oMcK4MeZsT96KCBAgQIAAgdkKCABn21qFESBAgAABAo0KCAAPbswYYdwYczZ6eloWAQIECBAgMEcBAeAcu6omAgQIECBAoGWBwwSAXS8BOTK4unGS45Mcl+SSSU5N8uokj0/y5V0o/yHJQ5PcLMkxdb8/TfI7Sb7WgVf+/PjTSe6R5Ngkl0pyZpJPJnlNkv+d5KsDGnDYMO6Hk9w3yc2TXDrJOUm+lOQfk7wzyeuTvKVjPYedc0B5hhIgQIAAAQIEti8gANy+uRkJECBAgACBZQtsKgB8c5LnJjnvHrzlBRrvwKTWAAAIYElEQVS3rqHYryZ5UpK9/hz4N3W/r+/TohL2vTLJLQ9o4ReT3DnJX/ds82HCuKfUIPOgqf65hqEH7XOYOXuWZRgBAgQIECBAYDwBAeB49mYmQIAAAQIElimwiQDwQ0muk+RTSX43yd8nuVCSeyf5+cr80iSvSPJnSd5Tr9T7eA3HfiXJHep+T0zyiD1ac4Ek70ty7STlrbwvqFfXnZykfFauOvzvSS6R5CtJbpSkPOfwsNuqYdyd6hWH5fh/l+SZST6a5F+SXCTJtZL8WJJbJLlcxyJWnfOwtdifAAECBAgQINCEgACwiTZYBAECBAgQILAggU0EgIXv3TXwOuMIyz9J8jNJvlPDsbcm+dn6651dj6q3y5bbaMsVc+VW2rOPOE65tfdB9Rg/muT9e/TsSklOTHKZJC9J8p979HXVMO5F9fglZLxekv2uWrx4ktMFgD06YQgBAgQIECAwGwEB4GxaqRACBAgQIEBgIgKbCgDLFYDlCrgjt9skKaFf2Uo4eIV9ArH/muT5db8b1qvqdo5VnidYrvQ7X5IHJ3nqAdYPSPKMJN9OctE652Fas2oA+MYaeJZbku9ymAn22HfVOQdOYzgBAgQIECBAYBwBAeA47mYlQIAAAQIEliuwiQDwb5P8wD6k5Qq4clVf2V6V5D/ts195gcgH6mc/leT/7Nrv5+oVfeW3ytV95QUb+23XTfLh+mG5Lfgdh2z1qmHcC5P8Qq2tvMzk04ecZ/fuq845YApDCRAgQIAAAQLjCQgAx7M3MwECBAgQILBMgU0EgCckuds+nOfadbvvk5M8fJ/9rrIrRCvB2ot37fd7SR7So11lTWVth9lWDeN+JMmb6oG/WcPNN9TAsTwL8TDbqnMe5pj2JUCAAAECBAg0IyAAbKYVFkKAAAECBAgsRGATAWC5Gu5eB/idUz/7rSSP2We/Kyf5bP2s3A78h7v2Ky8QuWeP/pQ1lbUdZjtMGPfAJCXUPPqICU5N8tr6YpBydWTXdpg5u47lcwIECBAgQIBAcwICwOZaYkEECBAgQIDAzAWmGAD+cX1xSHn77w8eoj+nJPnqIfYvux42jDumhpPljb+3rG8A3pmyBJ+PT/KojjUcds5DlmR3AgQIECBAgMC4AgLAcf3NToAAAQIECCxPYIoB4NOT/FJt1WWTnLbBtg0J48rtzuVZiOWlIOXqwPISkrKV5x6W5x/utw2Zc4MUDk2AAAECBAgQWI+AAHA9jo5CgAABAgQIEFhVYIoB4H2SPKcW2Oe23lVtyn7rCuN2v9Tkj5KUF5kIAA/TCfsSIECAAAECsxEQAM6mlQohQIAAAQIEJiIwxQDwcvX5gOdJUp6pd5MkZ2/Ie10BYFne6UkuluSNSW4vANxQxxyWAAECBAgQaF5AANh8iyyQAAECBAgQmJnAFAPA0oJnJblf7cXzk/y3A0LA8ly+n0zy3B69WzUA/Nkkr05y5j5zlJDyffWzsvb7CwB7dMMQAgQIECBAYBYCAsBZtFERBAgQIECAwIQEphoAXjDJiUmuV61PSvLsJB9I8vX6vL3rJvnRJHdI8vf1SsHDtmbVALDsd5H6bL+3J/lEkm8kuUSSH0ryy0kunuQ7SW5W17nfWlad87C12J8AAQIECBAg0ISAALCJNlgEAQIECBAgsCCBqQaApUUlUHtpkh9foV9vTfLDK+x35C6rhnE7+x00xTfrlYov6ljHqnP2KMcQAgQIECBAgMD4AgLA8XtgBQQIECBAgMCyBKYcAO50qgR7P1+vtLtMkvMl+b9JPp3kvUleV5+7V66+O+y2ahh3xSQ/Vv+5TpJL1+f9nZHkU0nenOSZ9dmFXWtYdc6u4/icAAECBAgQINCkgACwybZYFAECBAgQIEBgsQJjhHFjzLnYBiucAAECBAgQ2L6AAHD75mYkQIAAAQIECBDYX2AnjHtVkkfV3c6qz/hbl1t5m/E1dx3sDUkum+SFSe61rkkchwABAgQIECDQioAAsJVOWAcBAgQIECBAgEAR2OvZfp9LcuU18pRjfXaP4wkA14jsUAQIECBAgEA7AgLAdnphJQQIECBAgAABAgJA5wABAgQIECBAYO0CAsC1kzogAQIECBAgQIAAAQIECBAgQIAAgXYEBIDt9MJKCBAgQIAAAQIECBAgQIAAAQIECKxdQAC4dlIHJECAAAECBAgQIECAAAECBAgQINCOgACwnV5YCQECBAgQIECAAAECBAgQIECAAIG1CwgA107qgAQIECBAgAABAgQIECBAgAABAgTaERAAttMLKyFAgAABAgQIECBAgAABAgQIECCwdgEB4NpJHZAAAQIECBAgQIAAAQIECBAgQIBAOwICwHZ6YSUECBAgQIAAAQIECBAgQIAAAQIE1i4gAFw7qQMSIECAAAECBAgQIECAAAECBAgQaEdAANhOL6yEAAECBAgQIECAAAECBAgQIECAwNoFBIBrJ3VAAgQIECBAgAABAgQIECBAgAABAu0ICADb6YWVECBAgAABAgQIECBAgAABAgQIEFi7gABw7aQOSIAAAQIECBAgQIAAAQIECBAgQKAdAQFgO72wEgIECBAgQIAAAQIECBAgQIAAAQJrFxAArp3UAQkQIECAAAECBAgQIECAAAECBAi0IyAAbKcXVkKAAAECBAgQIECAAAECBAgQIEBg7QICwLWTOiABAgQIECBAgAABAgQIECBAgACBdgQEgO30wkoIECBAgAABAgQIECBAgAABAgQIrF1AALh2UgckQIAAAQIECBAgQIAAAQIECBAg0I6AALCdXlgJAQIECBAgQIAAAQIECBAgQIAAgbULCADXTuqABAgQIECAAAECBAgQIECAAAECBNoR+P8AJmzdBv/UoYcAAAAASUVORK5CYII=\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Time [s]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(1)\n",
    "plt.plot(t1,Tc1,t,Tc,t,Tf, t,Ta)\n",
    "plt.legend(['Exhaust Gas, HH = 41.6e6 J/kg', 'Exhaust Gas, include enthalpy of formation', 'Fuel Gas', 'Compressor Air'],bbox_to_anchor=(0.1,0.8), loc='upper left')\n",
    "plt.ylim(0,1400)\n",
    "plt.xlim(0,5)\n",
    "plt.ylabel('Temperature [k]')\n",
    "plt.xlabel('Time [s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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    "version": 2
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JModelica.org 使い方メモ