cantera-jupyter-test/reactors/stirred_reactor.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Continuous Reactor Example \n",
    "### Simulation of a CSTR/PSR/WSR \n",
    "\n",
    "In this example we will illustrate how Cantera can be used to simulate a Continuously Stirred Tank Reactor (CSTR), also interchangeably referred to as a Perfectly Stirred Reactor or a Well Stirred Reactor, a Jet Stirred Reactor or a Longwell Reactor (there may well be more \"aliases\"). A cartoon of such a reactor is shown below\n",
    "\n",
    "<img src=\"images/stirredReactorCartoon.png\" alt=\"Cartoon of a Stirred Reactor\" style=\"width: 300px;\"/>\n",
    "\n",
    "As the figure illustrates, this is an open system (unlike a Batch Reactor which is isolated). P, V and T are the reactor's pressure, volume and temperature respectively. The mass flow rate at which reactants come in is the same as that of the products which exit; and these stay in the reactor for a characteristic time $\\tau$, called the *residence time*. This is a key quantity in sizing the reactor and is defined as follows:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\tau = \\frac{m}{\\dot{m}}\n",
    "\\end{equation*}\n",
    "\n",
    "where $m$ is the mass of the gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Cantera version: 2.5.0a2\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import cantera as ct\n",
    "\n",
    "print(\"Running Cantera version: {}\".format(ct.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules and set plotting defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.style.use('seaborn-pastel')\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 18\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "plt.rcParams['figure.autolayout'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the gas\n",
    "In this example, we will work with $nC\n",
    "_{7}H_{16}$/$O_{2}$/$He$ mixtures, for which experimental data can be found in the paper by [Zhang et al.](http://dx.doi.org/10.1016/j.combustflame.2015.08.001). We will use the same mechanism reported in the paper. It consists of 1268 species and 5336 reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "**** WARNING ****\n",
      "For species OHV, discontinuity in h/RT detected at Tmid = 1000\n",
      "\tValue computed using low-temperature polynomial:  53.6206\n",
      "\tValue computed using high-temperature polynomial: 53.5842\n",
      "\n",
      "\n",
      "**** WARNING ****\n",
      "For species CHV, discontinuity in h/RT detected at Tmid = 1000\n",
      "\tValue computed using low-temperature polynomial:  107.505\n",
      "\tValue computed using high-temperature polynomial: 107.348\n"
     ]
    }
   ],
   "source": [
    "gas = ct.Solution('data/galway.cti')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define initial conditions\n",
    "#### Inlet conditions for the gas and reactor parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inlet gas conditions\n",
    "reactorTemperature = 925  # Kelvin\n",
    "reactorPressure = 1.046138*ct.one_atm  # in atm. This equals 1.06 bars\n",
    "concentrations = {'NC7H16': 0.005, 'O2': 0.0275, 'HE': 0.9675}\n",
    "gas.TPX = reactorTemperature, reactorPressure, concentrations \n",
    "\n",
    "# Reactor parameters\n",
    "residenceTime = 2  # s\n",
    "reactorVolume = 30.5*(1e-2)**3  # m3\n",
    "\n",
    "# Instrument parameters\n",
    "\n",
    "# This is the \"conductance\" of the pressure valve and will determine its efficiency in \n",
    "# holding the reactor pressure to the desired conditions. \n",
    "pressureValveCoefficient = 0.01\n",
    "\n",
    "# This parameter will allow you to decide if the valve's conductance is acceptable. If there\n",
    "# is a pressure rise in the reactor beyond this tolerance, you will get a warning\n",
    "maxPressureRiseAllowed = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation termination criterion\n",
    "maxSimulationTime = 50  # seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reactor arrangement\n",
    "\n",
    "We showed a cartoon of the reactor in the first figure in this notebook; but to actually simulate that, we need a few peripherals. A mass-flow controller upstream of the stirred reactor will allow us to flow gases in, and in-turn, a \"reservoir\" which simulates a gas tank is required to supply gases to the mass flow controller. Downstream of the reactor, we install a pressure regulator which allows the reactor pressure to stay within. Downstream of the regulator we will need another reservoir which acts like a \"sink\" or capture tank to capture all exhaust gases (even our simulations are environmentally friendly !). This arrangment is illustrated below\n",
    "\n",
    "<img src=\"images/stirredReactorCanteraSimulation.png\" alt=\"Cartoon of a Stirred Reactor\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize the stirred reactor and connect all peripherals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuelAirMixtureTank = ct.Reservoir(gas)\n",
    "exhaust = ct.Reservoir(gas)\n",
    "\n",
    "stirredReactor = ct.IdealGasReactor(gas, energy='off', volume=reactorVolume)\n",
    "\n",
    "massFlowController = ct.MassFlowController(upstream=fuelAirMixtureTank,\n",
    "                                           downstream=stirredReactor,\n",
    "                                           mdot=stirredReactor.mass/residenceTime)\n",
    "\n",
    "pressureRegulator = ct.Valve(upstream=stirredReactor,\n",
    "                             downstream=exhaust,\n",
    "                             K=pressureValveCoefficient)\n",
    "\n",
    "reactorNetwork = ct.ReactorNet([stirredReactor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now compile a list of all variables for which we will store data\n",
    "columnNames = [stirredReactor.component_name(item) for item in range(stirredReactor.n_vars)]\n",
    "columnNames = ['pressure'] + columnNames\n",
    "\n",
    "# Use the above list to create a DataFrame\n",
    "timeHistory = pd.DataFrame(columns=columnNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation Took 8.79s to compute, with 1153 steps\n"
     ]
    }
   ],
   "source": [
    "# Start the stopwatch\n",
    "tic = time.time()\n",
    "\n",
    "# Set simulation start time to zero\n",
    "t = 0\n",
    "counter = 1\n",
    "while t < maxSimulationTime:\n",
    "    t = reactorNetwork.step()\n",
    "\n",
    "    # We will store only every 10th value. Remember, we have 1200+ species, so there will be\n",
    "    # 1200 columns for us to work with\n",
    "    if(counter%10 == 0):\n",
    "        # Extract the state of the reactor\n",
    "        state = np.hstack([stirredReactor.thermo.P, stirredReactor.mass, \n",
    "                   stirredReactor.volume, stirredReactor.T, stirredReactor.thermo.X])\n",
    "        \n",
    "        # Update the dataframe\n",
    "        timeHistory.loc[t] = state\n",
    "    \n",
    "    counter += 1\n",
    "\n",
    "# Stop the stopwatch\n",
    "toc = time.time()\n",
    "\n",
    "print('Simulation Took {:3.2f}s to compute, with {} steps'.format(toc-tic, counter))\n",
    "\n",
    "# We now check to see if the pressure rise during the simulation, a.k.a the pressure valve\n",
    "# was okay\n",
    "pressureDifferential = timeHistory['pressure'].max()-timeHistory['pressure'].min()\n",
    "if(abs(pressureDifferential/reactorPressure) > maxPressureRiseAllowed):\n",
    "    print(\"WARNING: Non-trivial pressure rise in the reactor. Adjust K value in valve\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a test, we plot the mole fraction of $CO$ and see if the simulation has converged. If not, go back and adjust max. number of steps and/or simulation time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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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9ReaoFtbW5t8+OGH0tjYKOrb8OHDpbq6WiZPnizFxcVBu+N6vkWLFsnnn39uxdHV1SWjRo2SCRMmyLrrruvaZiEDKQAWQi85Y00LUXIiJRIIBEsAbQXLm9nSRQB9pSvfRBscgbhoq76tVF6sGx0cmP/MVJTJytSaxVJZ1hn43EwYfwImfVEAjH+OYxXBggUL5O6775Y333xTVq5cuZbvWgjcfffd5YADDgik8Nbc3Gz5M2fOHGlvb1/LnyFDhsiUKVPkoIMOkoqKCsesV6xYIf/+97/l008/tf7R/69z5tpmm20m559/vmO7vQe89dZb8uCDD8onn3zSp6311ltP9ttvP9l1110LnsuJAQqATmglt69pIUpu5EQGAX8JoC1/+WI93QTQV7rzT/T+EYiLtuY0VMr8lnL/QAxgmReBQ8GeiElN+qIAmIg0xyOIF154QW6++eY+C229I1h//fXljDPOkDFj/Htu/Z133pFrrrlGli1bZgSoQlF/Jk6caOyrHe6880557bXXRHfkZXX/eD+t0AKg2r799tvl8ccft+XXzjvvLCeeeKKUlASzlZ0CoK20JL6TaSFKPAAChIBPBNCWT2AxCwGO2PMNQMA3AnFYuzq7MvLYl2MlKxnfOJgM8yKwiRC/74uASV8UAPluAiGgO/4uvfTSHsWwcePGyeabby5Dhw6Vuro6eeONN6SjoyPvzzrrrCMXXnihq513pqDmzp0r5513Xo9ipO4+3GabbWTkyJGyePFiyx/dwZdr+vOLL75YqqqqTOblzDPPlC+++MLYr9AC4F133SWPPPJIj3k22WQT2WCDDaSoqMjy4b333uvBXXcBnnzyyUbfvOhAAdALivG3YVqI4h8hEUAgHAJoKxzuzJoOAugrHXkmyuAJxEFbSzuK5amF/m1EsUOdF4HtUKJPbwImfVEA5JvxnYAeeT311FOltXX1ZaaZTEYOO+ww2WuvvXrc97d06VK58sor5f3338/7pDvWdKyXTYuMp5xyinVPXq7ts88+csghh/Q4dqz+3njjjfLyyy/n++kdhVqUNLW+CoB6nHjSpElWQS7XCikAaoFSi6q5pvZPP/102XLLLXu499lnn8lll13WI95jjjlGZsyYYQqj4N9TACwYYSIMmBaiRARJEBAIgQDaCgE6U6aGAPpKTaoJNGACcdBWU3uJPLuoOmAyPafLSFb25UXgUHMQx8lN+qIAGMesxsznW265RZ588sm819///vflwAMP7DMKLc6dddZZMn/+fOv3Wiy85JJLRI8Ee9UeffRR64hurk2bNk2OP/74Ps3rQxoXXXRRj6Ldz372M9lhhx0GdOdXv/qV5bvuxNN/NtxwQxk7dqw1RuPPNbcFQD36q0XGefPm5Tnpjka111fTuxe1f2fn6stkdbfjtddeK6WlpV5h7dMOBUBf8cbGuGkhik0gOAqBiBFAWxFLCO4kigD6SlQ6CSZCBOKgrSjsANSUzaitl+Ela9+bH6F04krECJj0RQEwYglLmjtLliyRE044If/gh35wustvoJd1//nPf8oFF1yQR6HFNi26edH04ZHjjjsuf++fPuxx3XXXWceQ+2t6j5/uGMzd5afFyO4775z65UUB8NVXX5XLL788P7U+VHLSSScN6Mp9991nPRSSa0ceeaTsueeeTt131J8CoCNcie1sWogSGziBQcBnAmjLZ8CYTzUB9JXq9BO8jwTioK2OVRl57CvdvBHeHYCagu2qmmW9oatP0dEgYIeASV8UAO1QpI9rArNmzZKbbropP/6HP/yh7L///kZ7p512Wn4XoD5Yceutt8rgwYON40wd9OEP3dGXa3vssYccffTRpmHWGB2ba7p7TsXjpnlRALz66qvlH//4R3763/72t9Yuw4FaU1OTVYzVXY3aJk+eLL/+9a/dhGB7DAVA26gS3dG0ECU6eIKDgI8E0JaPcDGdegLoK/WfAAB8IhAHbc1dWiFvNY/0iYB9s0OLV8oetfWSCbcOad9heoZOwKQvCoChpyjZDujxXX0AxGnh7N5775WHHnooP87OsVs7JLWQ+MQTT+S7nnvuubLFFlsYhz7zzDPyxz/+Md/v8MMPF7030E0rtACoRTUtWuYeKNFHSW644QZbrmi8H330kdVX/+Wgxdnhw4fbGuumEwVAN9SSN8a0ECUvYiKCQDAE0FYwnJklnQTQVzrzTtT+E4i6trJZkUe/HCsrs4P8h2FjBl4DtgGJLnkCJn1RAORj8ZXAEUccIS0tLdYcI0aMkJtvvtnWfG+99Zb14m6u6YMhaqvQ1v1xDhXHbbfdZmtn4VdffWU9sJFr22+/vXWnnptWaAFQH/XQexJzzclDKXr3od6BmGsag8biV6MA6BfZeNk1LUTxigZvIRAdAmgrOrnAk+QRQF/JyykRRYNA1LW1oKVMZjdURQOWiPAacGRSEQtHTPqiABiLNMbTST1y+pOf/CTv/NZbby2//OUvbQWjdwcee+yx+b7f+MY35JxzzrE1tr9OevRVXx/OPYQxfvx46z5CO03v/9Ox+kiJttraWrnqqqvsDF2rT6EFwJdeekmuueaavF19vXjmzJm2fNFjw3p8ONecjLU1Qa9OFADdUEveGNNClLyIiQgCwRBAW8FwZpZ0EkBf6cw7UftPIOraerFulNS3FX71lFckeQ3YK5LpsGPSFwXAdHwHoUTZ+zGP6dOn9yjqDeSUFtz0vkB9tENbdXW1XH/99QXFUVdXJyeffHLehtOioj4EsnDhQmt8UVGR3HHHHQM+ZtKfs4UWAHs/5vHTn/5UdtllF1tsPv74Y9EXinNt6tSpcuKJJ9oa66YTBUA31JI3xrQQJS9iIoJAMATQVjCcmSWdBNBXOvNO1P4TiLK2Orsy1vHfsB//6J0FXgP2/7tMygwmfVEATEqmIxjHCy+8YL2wm2sHHXSQfO9737Ptqb5qW19fny+43XPPPbbH9tXxww8/lPPOOy//q912263HDkWTcX0w4/33389303v39P49p63QAqDO++yzz+an1ReTN910U1tuLF682HoIJNe23HLLHgVBW0YcdKIA6ABWgruaFqIEh05oEPCVANryFS/GU04AfaX8AyB83whEWVtLO4rlqYVjfIvdreFpYxtkVFmn2+GMSxEBk74oAKboYwg61CeffFJuueWW/LQ/+tGPZO+997btRvf7+nSQ3l9XWlpqe3zvjoXeK3jZZZfJ66+/njerx4f1GLHTVmgBUOedPXt2flr1a+LEibbcWL58uRx11FH5vhtttFGPV5HtGGlsbDR2GzlypLVLUguADQ0Nxv50SDYBXYjGjFn9hykt6udeok521EQHAf8JoC3/GTNDegmgr/Tmnsj9JRBlbTW1l8jTC5xv8PCXmMi2VUtk0vA2v6fBfgIImPSlJyv17+lJbpmsnielBU5AH5vQol2u6cu1e+yxh20/9L7ATz/9NN9fi4mFvFirRbPud/7tv//+1jFju6134U0fKdlggw3sDs/3K7QAqPNqMTPXnBQi9Q7DQw89ND92woQJ8vvf/95RDN3972+g292RjhyhMwQgAAEIQAACEIAABCAAAY8INK4Quftdj4x5aKZ4kMj3NhcZM9RDo5iCQEIJUAAMKbEPPPCA3H///fnZ9UEQPXZrt+lxXT22m2t/+MMfZPTo0XaHr9Wv95FkPY6sx5LtNj3OrDZyTY8ET5482e7wfL9CC4B65FfvV8y1a6+9VnQrr52mO68OPvjgfFcdp+OdNAqATmjRFwIQgAAEIAABCEAAAhCIA4HPmkT+38fR9LSqXOQH3xDJZKLpH15BICoEKACGlAl2APYNvtACYNg7ADkCHJKgYjytaSt6jEPDdQiESgBthYqfyRNOAH0lPMGEFxqBKGtrdt1I+arFmxeAB0mXdMkgTzlPHdskY8o7PLWJsWQRMOmLI8DJynekouEOQH8KgGHfAejkI+MRECe0ktvXdBltciMnMgj4SwBt+csX6+kmgL7SnX+i949AVLWlLwA/9uVYyYo3W+y2HLFEvmgZIks7SzyDOb6iVXasbvbMHoaSR8CkLx4BSV7OIxNR7yO3uvPtwAMPtO2f368AT5s2TY4//njb/kT1FWAnR5F5Bdh2uunoIQHTQuThVJiCQKoIoK1UpZtgAyaAvgIGznSpIRBVbXn9AvD0cfXSlc3Ic4uqPNsJmJGs7DthkZQM4omD1AjGYaAmfVEAdAiU7vYJ6D11el9drk2fPl2OPfZYWwb03RZ9oGPlypVWf92qev3119sa21+nuro6Ofnkk/O//sY3viHnnHOObZs//elPZdGiRVZ/fTnnjjvukOLiYtvjcx0LPQJ83333yYMPPpifV/3aZZddbPnx0UcfybnnnpvvO3XqVDnxxBNtjXXTiR2Abqglb4xpIUpexEQEgWAIoK1gODNLOgmgr3Tmnaj9JxBVbekLwM8uqvYMwLSxDTKqrFM+W1YhbzaN9MzujNp6GV6y+u/INAj0JmDSFwVAvhnfCDQ3N8txxx2Xt++k4LZkyZIexcKtt95a9FXgQpo+gHH44YeLvoSrrba2Vq666ipbJrUgqa/ndnZ2Wv3Hjx/f40VhW0b+06nQAuBLL70k11xzTX7KQw45RGbOnGnLhX/84x9y9dVXuxpra4JenSgAuqGWvDGmhSh5ERMRBIIhgLaC4cws6SSAvtKZd6L2n0BUteX1DsBcoc6vwqL/mWKGOBIw6YsCYByzGiOfjzjiCGlpabE8HjFihNx88822vH/zzTflkksuyffda6+9RG0V2s4880z54osvLDMqjttuu00GDzZf9PrVV1/J6aefnp9+++23F7XlphVaAPz888/l5z//eX7qnXbaSU477TRbrtx5552ij7PkmsagsfjVKAD6RTZedk0LUbyiwVsIRIcA2opOLvAkeQTQV/JySkTRIBBVbXl5B2D3o7p+FRajkU28iBoBk74oAEYtYwnzR4t4WszLNd25NnbsWGOU9957rzz00EP5fj/72c9khx12MI4zdbj11lvliSeeyHf71a9+JVtuuaVpmDz99NNy44035vvpTsJ99tnHOK6vDoUWALWodvTRR8uKFSss81VVVXLDDTfY8kXj/fjj1W/b678cbrrpJhk+fLitsW46UQB0Qy15Y0wLUfIiJiIIBEMAbQXDmVnSSQB9pTPvRO0/gShra05DpcxvKS8YQvfHOvwqLBbsJAYSScCkLwqAiUx7dIKaNWuWVWTKNbvHVU899VRZsGCBNaykpES0cGdnp54p8nfffVcuvPDCfLcZM2bIMcccYxomF110kbzzzjv5ftdee62oeNy0QguAOqcWUvUocK6pfxtttNGA7jQ1NckJJ5wgehRa2+TJk0UfEPGzUQD0k258bJsWovhEgqcQiBYBtBWtfOBNsgigr2Tlk2iiQyDK2qpvK5UX60YXDGtKzWKpHrz62iltfhQWC3YSA4kkYNIXBcBEpj06Qeldflp0yj3moR/clVdeOeDjGb0fD9Gdf7oD0IumfvzkJz+RpUuXWuYqKiqsx0WGDBnSr3l9+OOUU04RvQdQ2/rrry+XXnqpa3e8KAC++uqrcvnll+d9mDJliuiryQO13o+HHHnkkbLnnnu6jsPOQAqAdiglv49pIUo+ASKEgD8E0JY/XLEKASWAvvgOIOAPgShrS/+6N2thtSztLHEd/PCSTpk+rkEymTUmvCosblX5tWw4bEUP264dZWAiCZj0RQEwkWmPVlC9j91qAezAAw/s00l9oOMXv/iF6J172jKZjHUXoBbd+mr19fU9Cl92XgvWO/D0LrxcmzZtmhx//PF92tfdcrq77r333sv/vtDjyF4UALUYqff3zZs3L8/pvPPOk80226zPOHQ3pfbPPWJSWVkpuouxtLTU14+FAqCveGNj3LQQxSYQHIVAxAigrYglBHcSRQB9JSqdBBMhAlHXVnN7iTxfVyWrsoMcUyvKdMnUmkapLFv9cGSueVFYzNnSAuN2VUvWmsOxswxIJKkql5AAACAASURBVAGTvigAJjLt0QpKj57qIxWtra35YtVhhx0m+rCHfqC5prvydHfg+++/n//ZzjvvLHocuL/mpgCoRUbd0dfY2Jg3q/f56fHk4uLi/M/UX7337+WXX87/bOONN+5xhNgNaS8KgDrvG2+80WMnou5i1IdKet9p+Nlnn8lll13WI1499qzHn/1uFAD9JhwP+6aFKB5R4CUEokcAbUUvJ3iUHALoKzm5JJJoEYiDtupay2R2Q6WjIqAW/3aqbpaa8vY+gRdSWOxt0DRXtDKON0ESMOmLAmCQ2UjxXPoQiB6bzR2jVRTjxo2TLbbYQoYOHSp6zFYLWlqcy7V11lnHKrbpMV0vC4Bqa+7cuaI75trb1/wLWnfFbbPNNjJy5EhZvHix5U/uoQ0doz+/+OKLrUc3TO2DDz6QCy64oM9uuTv4cr/sXgTtPkB36OmOxoHaXXfdJY888kiPLptssolsuOGGVnFVXzzW3Yvdue+6665y8sknm0Lw5PcUAD3BGHsjpoUo9gESAARCIoC2QgLPtKkggL5SkWaCDIFAXLSlBbvXG0faOg5sd1eem8Jifynqb7dhCCllyggRMOmLAmCEkpV0V1544QW5+eabexTd+ot54sSJ1r1/Y8aMGRCLmx2AOYNvv/22dQx22bJlRvTqxxlnnNHvUeTeBnQXY6EPbFx33XXG+LWYePvtt8vf/vY3YwzaQXdU6p2Mfh/9zTlDAdBWWhLfybQQJR4AAULAJwJoyyewmIUAdwDyDUDANwJxWrs6VmXkn83D5LMVQ9fikZGs1Fa0yQbDVsjosg7b9/I5KSyaktDXfYOmMfw+2QRM+qIAmOz8Ry46vYtOd63pjkAtDvVuugtv9913lwMOOGDAh0Jy4wopAKqN5uZmy59XXnmlz8KkHqvVBzYOOuigAXcihlUAzM371ltvyQMPPCD/+te/+sz5uuuuK/vtt58VS5CNAmCQtKM7l2khiq7neAaBaBNAW9HOD97FmwD6inf+8D66BKKuLb2vr6G9VOYuGyILWgZLVrq95iEiJZlVstWopVJb3ialRasfiXTadI5Plw2Rd5tHOB26Vv/eLw4XbBADsSZg0hcFwFinN77O6667jz/+2LqXTu/a0+O1ustu00037XEvYFARtrW1iR7bVX+WL18uI0aMkNGjR8vkyZOlpMT9K1BB+Z+bR49S6/FmvXdRdweOGjVKtPin/4TRKACGQT16c5oWouh5jEcQiAcBtBWPPOFlPAmgr3jmDa+jTyDK2nKyO8/usd/+MjKnoVLmt5QXnLDxFa2yY3VzwXYwkAwCJn1RAExGnokCApEkQAEwkmkJ3CnTQhS4Q0wIgYQQQFsJSSRhRJIA+opkWnAqAQSiqi039/O5fYyjsysjj305dq3dhW7Sq0eR952wSEoGuduN6GZOxkSXgElfFACjmzs8g0DsCVAAjH0KPQnAtBB5MglGIJBCAmgrhUkn5MAIoK/AUDNRyghEUVuFvNDr5jGOpR3F8tTCge+6d/JZzKitl+ElK50MoW9CCZj0RQEwoYknLAhEgQAFwChkIXwfTAtR+B7iAQTiSQBtxTNveB0PAugrHnnCy/gRiJq29D6+WQurbb322x9tp49xNLWXyLOLqj1L3nZVzbLe0FbP7GEovgRM+qIAGN/c4jkEIk+AAmDkUxSIg6aFKBAnmAQCCSSAthKYVEKKDAH0FZlU4EjCCERNW/VtpfJi3eiCKTt5jMPrHYCDpEt2HtMsNeXtBceBgXgTMOmLAmC884v3EIg0AQqAkU5PYM6ZFqLAHGEiCCSMANpKWEIJJ1IE0Fek0oEzCSIQNW2F8RiHl3cA5j4NLQJ+e2yjVJZ1JuhrIRSnBEz6ogDolCj9IQAB2wQoANpGleiOpoUo0cETHAR8JIC2fISL6dQTQF+p/wQA4BOBKGnLy0Kc08c4vCo8dk9TRdFK+c74eslknCdPWbSuLJKV2YwUZ7JSXryKh0WcYwx9hElfFABDTxEOQCC5BCgAJje3TiIzLURObNEXAhBYQwBt8TVAwD8C6Ms/tlhON4Eoacvro7hOHuPw6uhx769pm1HNMmmY+T5ALfi1dBbJ4vZSWdA6WBraynq9SpyVMYPbpbaiTUaXdUhF8Sprqt5Fwt4/Kx7UJSu7Bq1VSOyvwDhQ4dFuUdJuP/XVSd+BlOqVHa//bWDSFwVAr4ljDwIQyBOgAMjHoARMCxGUIAABdwTQljtujIKAHQLoyw4l+kDAOYEoacvrxzimjW2QUTaP4Hrx+Ehf9PUo8Mx1F621C7B3wa++rUxEnGwVzP5nuu5j+vtZzz6Di7qkbdWgXvNlpb+fa8FRLTe1l/YoSuouSy1IThq2QkaXdsjijlKZu2yILGgZ3G+/6rIOy++Gdnt9B9o9qTnzwo5z1dgfYdIXBUD7LOkJAQg4JEAB0CGwhHY3LUQJDZuwIOA7AbTlO2ImSDEB9JXi5BO6rwSipK0wdwAq5Ob2Enm+rkpWZbU45l2rHdwiG49YYRlc0lFi7fBzXvDzzh8/LA2SrHTZKGAOKdY7ETOyYmWx0Q19zXm7qiV93qOouXq9caSt16IHsmN0osAOJn1RACwQMMMhAIH+CVAA5OtQAqaFCEoQgIA7AmjLHTdGQcAOAfRlhxJ9IOCcQJS0FeYdgDlyda1l8nJ9pXSJt0VA55lhhBIoynTJTtU9X1TWHM1uqHRUqO3LThCETfqiABhEFpgDAiklQAEwpYnvFbZpIYISBCDgjgDacseNURCwQwB92aFEHwg4JxA1bXn1GMf4ilbZsbrZORAR+WJZubzeVOlqLIO8J6DFu6k1q19ULmSXZnc73nvZt0WTvigABpUJ5oFACglQAExh0vsI2bQQQQkCEHBHAG2548YoCNghgL7sUKIPBJwTiJq2vHqMY0rNYqkevPq+OafN66PITuen/9oE9Bjv7mMb5OlF1baO/fbHUO1MH9fg6mVmN3kx6YsCoBuqjIEABGwRoABoC1PiO5kWosQDIEAI+EQAbfkEFrMQ4PoKvgEI+EYgamuXF49xFFrk0aPIj3451uGjHL6lCMP/IbBV5dfybvOIgnkUUhx2OrlJXxQAnRKlPwQgYJsABUDbqBLd0bQQJTp4goOAjwTQlo9wMZ16Augr9Z8AAHwiEEVtReGY54t1o6S+bbBP1DHrhsDgolXStqrIzdAeYwo5Hu50cpO+KAA6JUp/CEDANgEKgLZRJbqjaSFKdPAEBwEfCaAtH+FiOvUE0FfqPwEA+EQgqtoK+6GHBS360ESVT9QxGyaBjGRl3wmLpGRQ1nc3TPqiAOh7CpgAAuklQAEwvbnvHrlpIYISBCDgjgDacseNURCwQwB92aFEHwg4JxBlbelOwNcbR9q6802P/W5XtcR6KMKLpkeRH5k3VlbxGrAXOCNnY0ZtvQwvWem7XyZ9UQD0PQVMAIH0EqAAmN7cUwAk9xDwn4DpD3n+e8AMEEguAfSV3NwSWbgEoq4tLcQtbi+Vl+tHycrsoB6wdCdXbUWbbDBshYwu6/D8YYd/fV0h7y4ZGW6CmN0XAtPGNsgoj4rFAzlo0hcFQF/Si1EIQEAJUADkO1ACpoUIShCAgDsCaMsdN0ZBwA4B9GWHEn0g4JxAXLT12JdjpaNrTQHwm6OWyDpDWn09xskuQOffU1xGsAMwuExlslmVEg0CEAiaAAXAoIlHc764/EEvmvTwCgL9E0BbfB0Q8I8A+vKPLZbTTSAO2lrZlZFHvhzXI1FBFXDmLq2Qt5rZBRgNlWgZKVOwK9wBWDBCRwYoADrCRWcIeEeAAqB3LONsKQ5/0IszX3xPLwG0ld7cE7n/BNCX/4yZIZ0E4qCtZZ1F8uSCmh4J2n/CQikO4BEH3br09/ljpGVVcTo/kAhFzSvAEUqGA1coADqARVcIeEmAAqCXNONrKw5/0IsvXTxPMwG0lebsE7vfBNCX34Sxn1YCcdBWXWupvFQ/Op+ikkFdst+ERYGlTB8jeb5utKzKFr77LDCnEzjRVpVfy7vNIwqObErNYqke3FGwHTsGTPriDkA7FOkDAQi4IkAB0BW2xA0yLUSJC5iAIBAQAbQVEGimSSUB9JXKtBN0AATioK3Pl5fLG42VeRr64u+M2oYA6KyZoq61TGY3VMqqXg+RBOpEiifTnO8+tkGeXlRt61Xo/lCpnenjGjx/MKa/+Uz6ogCY4o+a0CHgNwEKgH4Tjod900IUjyjwEgLRI4C2opcTPEoOAfSVnFwSSbQIxEFbHy4ZKh98PTwPbmx5m3xrTFPgIHUn4GuLR8qylSWBz53mCYsyXTK1plEqyzpl9W7MKleF2O52guJp0hcFwKAywTwQSCEBCoApTHofIZsWIihBAALuCKAtd9wYBQE7BNCXHUr0gYBzAnHQ1huNI+Tz5UPywa0/dIV8s+pr58F6MELvBPxsebm83TRSsh48SOGBS4k2oUW7naqbpaa8PR+nm92YfdkJApxJXxQAg8gCc0AgpQQoAKY08b3CNi1EUIIABNwRQFvuuDEKAnYIoC87lOgDAecE4qCtl+pGSV3b4Hxwm49cKpuOWO48WA9HuClCeTh95EwNkqx02SiIDinutF7yXbHS/KiKHtfdrmqJtfOvd9OdgK83jrR1HHggO36DNOmLAqDfGcA+BFJMgAJgipPfLXTTQgQlCEDAHQG05Y4boyBghwD6skOJPhBwTiAO2npqQc9737arapb1hrY6D9bjEU6KUM6nzkp1WbsML10pyzqLpaGtrNeOw+x/THZ/mKS/n/XsM7ioS9pWDbIKcWtaVvr7+eiy1Q9mNLaX9vAhI1mprWiTDYatkKrSDmnsKJV/LxsiC1oG99svZ2txu72+mQHeXdHdmF7YcZ4b+yNM+qIAaJ8lPSEAAYcEKAA6BJbQ7qaFKKFhExYEfCeAtnxHzAQpJoC+Upx8QveVQNS11dmVkb9+WSOrRAtWq9uuNYtlTECvuJrgdy9CzW/RXYpuXwrOypjB7VZBTYtkFcWrpGRQrqAnohxaVxXJyq6MFA/KSnnRKss108+KM12yMjuoxzi125e9gX6uc/U3pjcju/2c2DTlwcmcJlte/t6kLwqAXtLGFgQg0IMABUA+CCVgWoigBAEIuCOAttxxYxQE7BBAX3Yo0QcCzglEUVtaVGtoL5W5fewm0whrBrfJxiOWS3VZR2Cvudohq0WolpVFsritVBa0Dpb6trIBCoIDF/zszEef6BMw6YsCYPRziIcQiC0BCoCxTZ2njpsWIk8nwxgEUkQAbaUo2YQaOAH0FThyJkwJgahpy8mx2jDvdrPzeXTflda9f24HX/cdfnbs0Sd+BEz6ogAYv5ziMQRiQ4ACYGxS5aujpoXI18kxDoEEE0BbCU4uoYVOAH2FngIcSCiBKGnLzcMaYb3umtDPgbA8JmDSFwVAj4FjDgIQWEOAAiBfgxIwLURQggAE3BFAW+64MQoCdgigLzuU6AMB5wSioi3d+fd8XZWsyq65689uNFoEnFrT2OdrsXZt0A8CfhAw6YsCoB/UsQkBCFgEKADyIVAA5BuAgH8ETH/I829mLEMg+QTQV/JzTIThEIiCtvTOv1kLe77065SGHgeePq4hUncCOo2B/skjYNIXBcDk5ZyIIBAZAhQAI5OKUB0xLUShOsfkEIgxAbQV4+TheuQJoK/IpwgHY0ogCtqqbyuVF+tGF0xwSs1iqY7I68AFB4OBRBAw6YsCYCLSTBAQiCYBCoDRzEvQXpkWoqD9YT4IJIUA2kpKJokjigTQVxSzgk9JIBAFbc1pqJT5LeUF4xxf0So7VjcXbAcDEPCKgElfFAC9Io0dCEBgLQIUAPkolIBpIYISBCDgjgDacseNURCwQwB92aFEHwg4JxC2tvSl3Me+HCtZyTh3vteIjGRl3wmLhNd1C0aJAY8ImPRFAdAj0JiBAATWJkABkK+CAiDfAAT8I2D6Q55/M2MZAskngL6Sn2MiDIdA2Npa2lEsTy0c41nwM2rrZXjJSs/sYQgChRAw6YsCYCF0GQsBCAxIgAIgHwgFQL4BCPhHwPSHPP9mxjIEkk8AfSU/x0QYDoGwtdXUXiLPLqr2LPhpYxtkVFmnZ/YwBIFCCJj0RQGwELqMhQAEKADyDRgJmBYiowE6QAACfRJAW3wYEPCPAPryjy2W000gbG2xAzDd31/SozfpiwJg0r8A4oNAiATYARgi/AhNbVqIIuQqrkAgVgTQVqzShbMxI4C+YpYw3I0NgbC1xR2AsflUcNQFAZO+KAC6gMoQCEDAHgEKgPY4Jb2XaSFKevzEBwG/CKAtv8hiFwI8YMU3AAG/CERh7eIVYL+yi92wCZj0RQEw7AwxPwQSTIACYIKT6yA000LkwBRdIQCBbgTQFp8DBPwjgL78Y4vldBOIgrbq20rlxbrRBSdiSs1iqR7cUbAdDEDAKwImfVEA9Io0diAAgbUIUADko1ACpoUIShCAgDsCaMsdN0ZBwA4B9GWHEn0g4JxAFLSVzYrMWlgtSztLnAfwnxHDSzpl+rgGyWRcm2AgBDwnYNIXBUDPkWMQAhDIEaAAyLdAAZBvAAL+ETD9Ic+/mbEMgeQTQF/JzzERhkMgKtpqbi+R5+uqZFV2kGMQRZkumVrTKJW8/uuYHQP8JWDSFwVAf/ljHQKpJkABMNXpzwdvWoigBAEIuCOAttxxYxQE7BBAX3Yo0QcCzglESVt1rWUyu6HSURFQi387VTdLTXm78+AZAQGfCZj0RQHQ5wRgHgJpJkABMM3ZXxO7aSGCEgQg4I4A2nLHjVEQsEMAfdmhRB8IOCcQNW3pTsDXG0faOg6sx363q1rCzj/naWdEQARM+qIAGFAimAYCaSRAATCNWV87ZtNCBCUIQMAdAbTljhujIGCHAPqyQ4k+EHBOIIra0jsBF7eXykt1VdIlPS/1y0hWaivaZINhK2R0WQd3/jlPOSMCJGDSFwXAAJPBVBBIGwEKgGnLeN/xmhYiKEEAAu4IoC133BgFATsE0JcdSvSBgHMCUdWWFgH/Mm+cZLsVAP9rdJN11LdkUNZ5oIyAQAgETPqiABhCUpgSAmkhQAEwLZkeOE7TQgQlCEDAHQG05Y4boyBghwD6skOJPhBwTiCq2mpfNUj+31djewS05/hFUlHc5TxIRkAgJAImfVEADCkxTAuBNBCgAJiGLJtjNC1EZgv0gAAE+iKAtvguIOAfAfTlH1ssp5tAVLX1dUexzFo4pkdyvrvuAhnU80RwupNH9JEnYNIXBcDIpxAHIRBfAhQA45s7Lz03LURezoUtCKSJANpKU7aJNWgC6Cto4syXFgJR1VZda6m8VD86n4bSQatk3wl1aUkLcSaEgElfFAATkmjCgEAUCVAAjGJWgvfJtBAF7xEzQiAZBNBWMvJIFNEkgL6imRe8ij+BqGpr3vJyea2xMg9YX/ydUdsQf+BEkCoCJn1RAEzV50CwEAiWAAXAYHlHdTbTQhRVv/ELAlEngLainiH8izMB9BXn7OF7lAlEVVsfLhkiH3w9Io+uqqxdvj22Mcoo8Q0CaxEw6YsCIB8NBCDgGwEKgL6hjZVh00IUq2BwFgIRIoC2IpQMXEkcAfSVuJQSUEQIRElb+vJvQ3upzF02ROa3DBbp9gKwSFbGV7TJpGErpLqsQzLcBRiRLwg3BiJg0hcFQL4fCEDANwIUAH1DGyvDpoUoVsHgLAQiRABtRSgZuJI4AugrcSkloIgQiIq2mttL5PXGkbK0s8RIRo8Db1e1RCrLOo196QCBMAmY9EUBMMzsMDcEEk6AAmDCE2wzPNNCZNMM3SAAgV4E0BafBAT8I4C+/GOL5XQTiIK26lrLZHZDpazKDrKdjKJMl+xU3Sw15e22x9ARAkETMOmLAmDQGWE+CKSIAAXAFCV7gFBNCxGUIAABdwTQljtujIKAHQLoyw4l+kDAOYGwtaU7/56vq3JU/MtFqUXAqTWN7AR0nnZGBETApC8KgAElgmkgkEYCFADTmPW1YzYtRFCCAATcEUBb7rgxCgJ2CKAvO5ToAwHnBMLUlt75N2thta1jv/1FpseBp49r4E5A56lnRAAETPqiABhAEpgCAmklQAEwrZnvGbdpIYISBCDgjgDacseNURCwQwB92aFEHwg4JxCmturbSuXFutHOne41YkrNYqke3FGwHQxAwGsCJn1RAPSaOPYgAIE8AQqAfAxKwLQQQQkCEHBHAG2548YoCNghgL7sUKIPBJwTCFNbcxoqZX5LuXOne40YX9EqO1Y3F2wHAxDwmoBJXxQAvSaOPQhAgAIg30APAqaFCFwQgIA7AmjLHTdGQcAOAfRlhxJ9IOCcQFja6uzKyGNfjpWsZJw73WtERrKy74RFUjIoW7AtDEDASwImfVEA9JI2tiAAgR4E2AHIB6EETAsRlCAAAXcE0JY7boyCgB0C6MsOJfpAwDmBsLS1tKNYnlo4xrnD/YyYUVsvw0tWemYPQxDwgoBJXxQAvaCMDQhAoE8CFAD5MCgA8g1AwD8Cpj/k+TczliGQfALoK/k5JsJwCISlrab2Enl2UbVnQU8b2yCjyjo9s4chCHhBwKQvCoBeUMYGBCBAAZBvoF8CpoUIdBCAgDsCaMsdN0ZBwA4B9GWHEn0g4JxAWNr6uqNYZrED0HnCGBErAiZ9UQCMVTpxFgLxIsAOwHjlyy9vTQuRX/NiFwJJJ4C2kp5h4guTAPoKkz5zJ5lAWNpa0FImsxuqPEHLHYCeYMSIDwRM+qIA6AN0TEIAAqsJUADkS1ACpoUIShCAgDsCaMsdN0ZBwA4B9GWHEn0g4JxAWNry6gVgjZhXgJ3nnRHBEDDpiwJgMHlgFgikkgAFwFSmfa2gTQsRlCAAAXcE0JY7boyCgB0C6MsOJfpAwDmBMLTl5QvAGvHO1Y0yrqLdefCMgIDPBEz6ogDocwIwD4E0E6AAmObsr4ndtBBBCQIQcEcAbbnjxigI2CGAvuxQog8EnBMIQ1tevwA8fVy9jCjlBWDn2WeE3wRM+qIA6HcGsA+BFBOgAJji5HcL3bQQQQkCEHBHAG2548YoCNghgL7sUKIPBJwTCENbvADsPE+MiCcBk74oAMYzr3gNgVgQoAAYizT57qRpIfLdASaAQEIJoK2EJpawIkEAfUUiDTiRQAJhaMvrHYAzautleAk7ABP4ecY+JJO+KADGPsUEAIHoEqAAGN3cBOmZaSEK0hfmgkCSCKCtJGWTWKJGAH1FLSP4kxQCYWjLyzsAeQE4KV9iMuMw6YsCYDLzTlQQiAQBCoCRSEPoTpgWotAdxAEIxJQA2opp4nA7FgTQVyzShJMxJBCWtubUV8r81vKCifECcMEIMeAjAZO+KAD6CB/TEEg7AQqAaf8CVsdvWoigBAEIuCOAttxxYxQE7BBAX3Yo0QcCzgmEpa1Plg6R95pHOHe414itKr+WjYavKNgOBiDgBwGTvigA+kEdmxCAgEWAAiAfAgVAvgEI+EfA9Ic8/2bGMgSSTwB9JT/HRBgOgbC0Nbu+UhZ4sAOwtrxVdhrTHA48ZoWAgYBJXxQA+YQgAAHfCFAA9A1trAybFqJYBYOzEIgQAbQVoWTgSuIIoK/EpZSAIkIgDG1xB2BEko8bvhMw6YsCoO8pYAIIpJcABcD05r575KaFCEoQgIA7AmjLHTdGQcAOAfRlhxJ9IOCcQBja4hVg53liRDwJmPRFATCeecVrCMSCAAXAWKTJdydNC5HvDjABBBJKAG0lNLGEFQkC6CsSacCJBBIIQ1tN7SXy7KJqz2hOG9sgo8o6PbOHIQh4RcCkLwqAXpHGDgQgsBYBCoB8FErAtBBBCQIQcEcAbbnjxigI2CGAvuxQog8EnBMIQ1vsAHSeJ0bEk4BJXxQA45lXvIZALAhQAIxFmnx30rQQ+e4AE0AgoQTQVkITS1iRIIC+IpEGnEgggTC0xR2ACfyQCKlPAiZ9UQDkw4EABHwjQAHQN7SxMmxaiGIVDM5CIEIE0FaEkoEriSOAvhKXUgKKCIGwtDWnoVLmt5QXTGF8RavsWM0rwAWDxIAvBEz6ogDoC3aMQgACSoACIN+BEjAtRFCCAATcEUBb7rgxCgJ2CKAvO5ToAwHnBMLSVn1bqbxYN9q5w71GTKlZLNWDOwq2gwEI+EHApC8KgH5QxyYEIGARoADIh0ABkG8AAv4RMP0hz7+ZsQyB5BNAX8nPMRGGQyAsbWWzIrMWVsvSzhLXgQ8v6ZTp4xokk3FtgoEQ8JWASV8UAH3Fj3EIpJsABcB05z8XvWkhghIEIOCOANpyx41RELBDAH3ZoUQfCDgnEKa2mttL5Pm6KlmVHeTY8aJMl0ytaZRKXv91zI4BwREw6YsCYHC5YCYIpI4ABcDUpbzPgE0LEZQgAAF3BNCWO26MgoAdAujLDiX6QMA5gbC1VddaJrMbKh0VAbX4t1N1s9SUtzsPmBEQCJCASV8UAANMRhBTLV++XNra2qSsrEyGDRsWxJTMAYF+CVAA5ONQAqaFCEoQgIA7AmjLHTdGQcAOAfRlhxJ9IOCcQBS0pTsBX28caes4sB773a5qCTv/nKeaESEQMOmLAmAISfFyyrlz58pzzz0n77//vixYsEC6urry5ktKSqS2tlY23nhj2XbbbWWrrbaSoqIiL6fHFgQGJEABkA+EAiDfAAT8I2D6Q55/M2MZAskngL6Sn2MiDIdAVLSldwJ+snSI/HPJiLVAZCQrtRVtssGwFTK6rIM7/8L5VJjVBQGTvigAuoAahSFLliyRG264Qd5++23b7owaNUr23ntvmT59ugwePNj2ODpCwC0BCoBuySVrnGkhSla0RAOB4AigreBYM1P6CKCv9OWciIMhECVtzVteLq81VuYDH1K0UnauaZLyolVSMigbDBBmgYCHBEz6ogDoIeygTOmuv0svvVS0COimDRkyRI488kjZdddd3QxnDARs8eC74AAAIABJREFUE6AAaBtVojuaFqJEB09wEPCRANryES6mU08AfaX+EwCATwSipK1/LR0i7zav2QFYXdYuU8Y2+hQ5ZiHgPwGTvigA+p8DT2dobm6Ws88+W/R/uzdN5KRJk2T48OFSXFwsK1askKamJvnss89k2bJlffrwX//1X3LsscfK0KFDPfURYxDIEaAAyLegBEwLEZQgAAF3BNCWO26MgoAdAujLDiX6QMA5gShp6/3mYfLR0jX35o+vaJUdq3v+Pdt5hIyAQHgETPqiABheblzNfMUVV8grr7ySH7v99tvLgQceKBMnTuzXnt4NOHv2bHnxxRdl4cKFPfrpHYHnnHOOjB492pU/DILAQAQoAPJ9UADkG4CAfwRMf8jzb2YsQyD5BNBX8nNMhOEQiJK23mwcIZ8tH5IHMWnoCtmm6utwwDArBDwgYNIXBUAPIAdlYt68eXLmmWfmp/vhD38o++23n+3ps9mszJkzR/785z/L/Pnz8+Oqq6vlN7/5jVRWrrn/wLZROkJgAAIUAPk8KADyDUDAPwKmP+T5NzOWIZB8Augr+TkmwnAIRElbsxsqZUFLeR7EpiOWyeYj+z49Fw4tZoWAMwImfVEAdMYz1N5auHvggQcsH3beeWc55ZRTXPmzcuVKefDBB+Whhx7Kj9fjw1oE1OPDNAh4RYACoFck423HtBDFOzq8h0B4BNBWeOyZOfkE0Ffyc0yE4RCIkraeXVglTR1leRBbjPxaNhmxIhwwzAoBDwiY9EUB0APIQZk4//zz5cMPP7Smu/LKK0WP7xbSXnvtNbnqqqtEC4LaZs6cKT/4wQ8KMclYCPQgQAGQD0IJmBYiKEEAAu4IoC133BgFATsE0JcdSvSBgHMCYWsrmxVpaC+VucuGyPyWwSKS6RZEVsZXtMmkYSukuqxDMt1/5TxURkAgcAImfVEADDwl7ic84YQTpLGx0bqv7/rrr3dvqNtIPRKsxURtuvtPC4J6JJgGAS8IUAD0gmL8bZgWovhHSAQQCIcA2gqHO7OmgwD6SkeeiTJ4AmFqq7m9RF5vHClLO0uMgQ8v6ZTtqpZIZVmnsS8dIBAVAiZ9UQCMSqZs+HHYYYdJR0eHbLrppvLrX//axgh7XW666SZ5+umnrc577723HH744fYG0gsCBgIUAPlElIBpIYISBCDgjgDacseNURCwQwB92aFEHwg4JxCWtupay0Tv/FuVHWTb6aJMl+xU3Sw15e22x9ARAmESMOmLAmCY2XE4tz76ocd1N998czn33HMdju6/+9KlS0V3F3Z2dsrIkSPlxhtv9Mw2htJNgAJguvOfi960EEEJAhBwRwBtuePGKAjYIYC+7FCiDwScEwhDW7rz7/m6KkfFv1xkWgScWtPITkDnqWZECARM+qIAGEJS3E553HHHyZIlS2SdddaR3//+927N9DnuiiuukFdeecX6ndrWOWgQKJQABcBCCSZjvGkhSkaURAGB4AmgreCZM2N6CKCv9OSaSIMlELS29M6/WQurbR377Y+EHgeePq6BOwGD/VSYzQUBk74oALqAGtaQ3CMgmtTbb79dSktLPXPlySeflFtvvdWyp68L6yvDNAgUSoACYKEEkzHetBAlI0qigEDwBNBW8MyZMT0E0Fd6ck2kwRIIWlv1baXyYt3ogoOcUrNYqgd3FGwHAxDwk4BJXxQA/aTvse27775bHnnkEV+KdO+++65cdNFFlm29a3Cfffbx2HvMpZEABcA0Zn3tmE0LEZQgAAF3BNCWO26MgoAdAujLDiX6QMA5gaC1NaehUua3lDt3tNeI8RWtsmN1c8F2MAABPwmY9EUB0E/6Htv+5JNP5Fe/+pVldeLEiXLppZd6NsNHH30k5513nmXvoIMOkgMOOMAz2xhKLwEKgOnNfffITQsRlCAAAXcE0JY7boyCgB0C6MsOJfpAwDmBILXV2ZWRx74cK1nJOHe014iMZGXfCYukZFC2YFsYgIBfBEz6ogDoF3mf7J555pkyb948y/qPfvQj2WuvvTyZ6fXXX5ff/e53lq0jjzxSvvOd73hiFyPpJkABMN35z0VvWoigBAEIuCOAttxxYxQE7BBAX3Yo0QcCzgkEqa2lHcXy1MIxzp3sZ8SM2noZXrLSM3sYgoDXBEz6ogDoNXGf7XUv1BUXF8sZZ5wh3/zmNwue9f7775cHH3zQsnPqqafKTjvtVLBNDECAAiDfgBIwLURQggAE3BFAW+64MQoCdgigLzuU6AMB5wSC1FZTe4k8u6jauZP9jJg2tkFGlXV6Zg9DEPCagElfFAC9Jh6Ave4v9moRUHfsTZ8+3fXMXV1dVtGvrq7OsnH99dfL6NGFX5Tq2iEGJoYABcDEpLKgQEwLUUHGGQyBFBNAWylOPqH7TgB9+Y6YCVJKIEhtsQMwpR9ZisM26YsCYAw/jpaWFjnnnHNkwYIFee+33XZbOeqoo1wV7rrv/hs3bpxcddVVMaSCy1EkQAEwilkJ3ifTQhS8R8wIgWQQQFvJyCNRRJMA+opmXvAq/gSC1BZ3AMb/eyECZwRM+qIA6IxnZHovXrxYzj//fGloaMj7pLsBp0yZYt0LOGHCBKOvWpy577778i8L64Cjjz5aZsyYYRxLBwjYIUAB0A6l5PcxLUTJJ0CEEPCHANryhytWIaAE0BffAQT8IRC0tngF2J88YjWaBEz6ogAYzbzZ8mrJkiXWS8Bz585dq//48eOtuwHXX3990f8/dOhQKSkpEd09qEd9P/zwQ3n++eeluXnNU+ZaNFR7RUVFtuanEwRMBCgAmgil4/emhSgdFIgSAt4TQFveM8UiBHIE0BffAgT8IRC0turbSuXFusKvt5pSs1iqB3f4AwWrEPCIgElfFAA9Ah2WGb2/Tx/v+Mtf/iJabHHbhg8fLhdddJGMGePdK0lufWFccghQAExOLguJxLQQFWKbsRBIMwG0lebsE7vfBNCX34Sxn1YCQWsrmxWZtbBalnaWuEY+vKRTpo9rkEzGtQkGQiAQAiZ9UQAMJA3+TzJv3jz585//LK+99ppk9d9yDtp6661nvSasH0NQbfny5fLRRx9JU1OTtSuxsrLSmn/jjTe2jlwE3dra2qxdkY2NjaK+aUG0urpaJk+eLHq02oumsX766adWzO3t7TJq1Cipra2VDTbYwAvzlt9qv76+3mKayWRkyJAhMnbsWGuO8vJyT+ZxYoQCoBNaye1rWoiSGzmRQcBfAmjLX75YTzcB9JXu/BO9fwTC0FZze4k8X1clq7LO/55ZlOmSqTWNUsnrv/59FFj2jIBJXxQAPUMdDUN6vPdvf/ubvPLKK1ahaaCmu/323Xdf2W233Twrcpko6MMld999t7z55puycuXKtbprIXD33XeXAw44IBCf9Ai0+jNnzhyrKNe7aQFN71U86KCDpKKiwhRen7/Xoty9994r7733Xp/FWRXhnnvuaf2jRTun7d1335XHHntM9H/7K/7qsW59KGb//feXjTbayOkUrvtTAHSNLlEDTQtRooIlGAgESABtBQibqVJHAH2lLuUEHBCBsLRV11omsxsqHRUBtfi3U3Wz1JSv/ffEgHAxDQQcETDpiwKgI5zx6qzFtn//+9/WnX+6O0yb3gVYVVUlm2yyibX7LMj2wgsvyM0339xnoa23H3p3oe5K9PNI8jvvvCPXXHONLFu2zIhBhaL+TJw40di3e4dHHnnEKv7ZOZ695ZZbymmnnWblyE7T49/K8+mnn7bT3eqjBcbvfve7cvDBB9seU0hHCoCF0EvOWNNClJxIiQQCwRJAW8HyZrZ0EUBf6co30QZHIExt6U7A1xtH2joOrMd+t6taws6/4D4NZvKAgElfFAA9gBy0iS+//FKGDRsmI0eODHpq1/Ppjj99YKT7DrVx48bJ5ptvbhW8tEj5xhtvSEfHmotV11lnHbnwwgtd77wbyFl9OOW8887rUYzU3YfbbLONxVVfWVZ/VqxYkTejP7/44outAqqd9tRTT1kFuu5NC4ibbrqplJWVyfz58+Wtt97qURxUHuecc46t3Y/XXXedaFG1e1Mft9hiC6twqgVCfSVadx4uXbq0Rz8tAv7gBz+wE0ZBfSgAFoQvMYNNC1FiAiUQCARMAG0FDJzpUkUAfaUq3QQbIIGwtaW3ZX22rELeal7779IZyUptRZtsMGyFjC7r4M6/AL8LpvKGgElfFAC94RyYFb2j7tRTT7UKZVrs+da3viWHH354YPO7mUiP2arPra2t1nDdhXbYYYfJXnvt1eO+Py1SXXnllfL+++/np9l5552tsV42ZXfKKadY9/3l2j777COHHHJIj8Kb+nvjjTfKyy+/nO+ndxRqUdLUPv/8czn77LPzxT19gfn444+XXXbZpcdQLXxedtllokXdXJs5c6bly0BNC6qXXHJJvosy1WPK++2331rFw87OTnnooYesx2JyTfvreN1p6WejAOgn3fjYNi1E8YkETyEQLQJoK1r5wJtkEUBfycon0USHQBS0tai1TP5Rv2ZTR+mgVTJ1bKOUF62SkkHO7tOPDlk8gYBY9RV9A0DbokWLrE1B3RsFwJh9Jddee6289NJLltdxebn3lltukSeffDJP+vvf/74ceOCBfZLX4txZZ51l7Y7T5keh6tFHH5U777wzP/+0adOs4lxfTQWjryPrLrpc+9nPfiY77LDDgF+O7hTU3X25dtJJJ1l3CfbVtPCpx4u//vpr69elpaWiedYdif213/72t/L222/nf22naHjHHXdYdwXm2vTp0+XYY4/1VQEUAH3FGxvjpoUoNoHgKAQiRgBtRSwhuJMoAugrUekkmAgRiIK25q0ol9cWr/m71rCSTtmjtiFClHAFAu4ImPRFAdAd11BG6V11P/nJT/KPZ2jRyFSICsXRbpMuWbJETjjhhLzP+sHpLr+BXtb95z//KRdccEHeisaoRTcvmj48ctxxx+Xv/dOHPfQo7UD37mnlXHcM5o4v6645Pc7cX/vss8+sImau6UvCv/71rwd0/5lnnpE//vGP+T66I7G/nZ3qx6GHHiq6s0+bPvBx6623Go9K68vAP/7xj/O7EvW/DOgdiH42CoB+0o2PbdNCFJ9I8BQC0SKAtqKVD7xJFgH0lax8Ek10CERBW58uHSLvNI/IQxld1m7tAKRBIO4ETPqiABijDOsuOi30aNO74s4999yCvNeC0NVXXy2jR4+WSZMmiR5vnTBhQkE2ew+eNWuW3HTTTfkf//CHP7ReojU1fQwjtwtQj89q3IMHDzYNM/5eH/7QHX25tscee8jRRx9tHKdjdGyu6Q49FU9fTV8Vfvjhh/O/0iPMepR5oKY7H7UwmbtzUO8ZvOGGG/ocojsGu/u83nrrye9+9ztjDNrhzDPPlC+++MLqq/cQ6q5APxsFQD/pxse2aSGKTyR4CoFoEUBb0coH3iSLAPpKVj6JJjoEoqCtD5YMlQ+/Hp6HUlveKjuNaY4OJDyBgEsCJn1RAHQJNoxhWqzL3UenBbIdd9yxYDdefPFFawectvLycqtYp0dQvWp6z5zeV5drAxXOus+pL+fqvXW5ZufYrR2ftZD4xBNP5LtqEVUfzTC13jv0dHee7tLrq+nOzNydfrrT8bbbbrPFVNloPnJNdxn2dUef7qrsfnTX7r2Eavd//ud/5JNPPrGmoABoyjq/94qAaSHyah7sQCBtBNBW2jJOvEESQF9B0mauNBGIgrbebhou/142NI994tAVsm3V6uuYaBCIMwGTvigAxii7WvRbsGCB6I64P/3pT7aKSnbC01dnP/30U6vriSee2O9ddXZs9e5zxBFHiO401DZixIi1XsXtz6ben6f36OWaPhiitgpt3XfAqTi0OGdnZ+FXX30lp59+en767bff3tpN17stX75cjjrqqPyPN9poox47DgfyX3d46n2Juabxaty9m95LqL9ra2uzfjVq1Kgex4cHmkN3GeqjLNqc7Bx0y50dgG7JJWucaSFKVrREA4HgCKCt4FgzU/oIoK/05ZyIgyEQBW292jBSvmypyAe88fDlsmXl0mAAMAsEfCRg0hcFQB/he21a72/TAtO4cePkqquu8sy87irU3YXa9L493cHmRWtqarLuLMy1rbfeWn75y1/aMt17l9s3vvEN0UJlIU0LZ/r6cO7uvPHjx1v3Edppeu+ejtWjutpqa2v7zMHHH38sv/rVr/ImZ8yYIcccc4ydKaydebpDL9cGeqTj8ssvl1dffTXf9ze/+Y1ssskmA87z4YcfynnnnZfvc8ABB8jBBx9syze3nSgAuiWXrHGmhShZ0RINBIIjgLaCY81M6SOAvtKXcyIOhkAUtPVS3Sipa1tzvdQWI5fKJiOWBwOAWSDgIwGTvigA+gjfa9M/+MEPrGecN9tssx6FnELnUZtapNLioj6GkbtnsFC7vR/zcPLqrBbc9L5AfbRDW3V1tVx//fUFuVRXVycnn3xy3obToqI+BLJw4UJrvD68offn9X7MpPdRYc3Zd7/7XVt+9y6Yap7PP//8Psfqjk0tiOYeJtG7G7XvsGHD+uyvLwxr8U93kGrTflr81Jek/WwUAP2kGx/bpoUoPpHgKQSiRQBtRSsfeJMsAugrWfkkmugQiIK2nlk4Wpo71lx79c1RS2T9YatPrdEgEGcCJn1RAIxRdn/0ox9Zxz433XRT46uyTsO64oor5JVXXrGG6W5AfSG20PbCCy/k7xdUWwcddJB873vfs232pJNOkvr6equ/Ftzuuece22P76th7B9xuu+3WY4eiybi+5Pv+++/nu+kjHfpYR/f2wAMPyP3335//kcYwZcoUk2nr91qI1aKnFs20qTj1XsD+2iOPPCJ33XVX/td6FHjmzJmyzTbbWA+7aHFw8eLF8sYbb1iPkmgRUJve8fiLX/zC1t2HthwfoBMFwEIJJmO8aSFKRpREAYHgCaCt4JkzY3oIoK/05JpIgyUQBW39ff4YWbGyOB/4jtVNMr5i9fVKNAjEmYBJXxQAY5RdvZ9PCzpanMsd2fXK/ccff1xuv/12y5zebbfddtsVbLr3nXZawNx7771t2+1+X58OuvPOOwu697DQewUvu+wyef311/P+6w46PUbcvamPjz76aP5HTh8vUUatra3WeDt3Jr700ktW3nLFPRPcyZMnix4lX3fddU1d+/19Y2OjcezIkSOtoq0WABsaGoz96ZBsAroQjRkzxgpSi/pa7KZBAAKFE0BbhTPEAgT6I4C++DYg4A+BKGjr4S/GSGfXoHyA3x7bKNXlnf4EjFUIBEjApC89Wal/T09yy2Rz5yRjHuWFF14o7733nmQyGeuxCD2u61WbM2dO/j48LRDtscceBZvWQpgWxHLt6KOPdmRX7wvMPU6iNjTmQo6szp49u8edf/vvv7+1485u04Kf2sg1faRkgw026DFcfdTCZ65pDHr3od2mR7FzxTy7r/TqIyv/+7//K88//3y/0+g3oznVXZiFfjff//73jeH0tTvSOIgOEIAABCAAAQhAAAIQgAAEfCTQlRW5fk7PCQ75hkjVmjdBfJwd0xCAgN8EElMAvO++++Shhx6yeB177LGy++67e8bugw8+yB8rPuSQQ0SLY4W23sdh9UEQPXZrt+mddXpsN9f+8Ic/WEdb3bbeR5L1OLIWxOy26667TtRGrumRYN1R171p4evZZ5/N/+jcc891dNT2+OOPl9wOOy3aac4Has8995zVx86uPLWjLx7rMWH9R//rgJtGAdANNcZAAAIQgAAEIAABCEAAAmETaO0UuWXNoS7LnaO2FRmy5krAsF1kfghAoAACiSkA6iuxuRdm9Riw7khzW8TpzVMf7NCXZLV5VQBkB6BYrx77sQNQN7XefPPNMmvWrHwqdTvvPvvsI/q4Sfc7AN955x3561//2uMo7vbbby+nn366q+2/doqNHAEu4N9YCRxq2oqewJAJCQKBEEBbgWBmkpQSQF8pTTxh+04gbG0t7SiSJ+ZX94jzgImLpCjje+hMAAHfCZj0xRFg31Pg7QRatJk/f75lVF+XPfjggz2ZQO+Syz04ocdQ9cXeQht3AIr4dQegPupx991351OkRT/9NsrLy/tMm94r+Pvf/17efffd/O/1+9FXiv1sPALiJ9342DZdRhufSPAUAtEigLailQ+8SRYB9JWsfBJNdAiEra3FbaXyfN2aU2VFmS6Zue6i6ADCEwgUQMCkLx4BKQBuGENffvnl/AMgekRUj9V++9vfLtgVvbvuqaeesuycddZZ8s1vfrNgm72P3OrR0QMPPNC2Xb9fAZ42bZrokVu7LYhXgHX3Ze6BhP5eAV66dKnld2fn6otqdbfdVVddJRUVA19coXcFnnrqqbJkyRJrnF7+qceqKysr7SJw3I8CoGNkiRxgWogSGTRBQSAAAmgrAMhMkVoC6Cu1qSdwnwmEra0FLWUyu6EqH2VF0UrZc516n6PGPASCIWDSFwXAYPLg6Sy//e1vRY91atMioBbVnBTWejvT1tYm+sLw8uXLPX1gRI8VX3DBBfnpdFeh3l1op+kRV32gY+XKlVZ33ap6/fXX2xnab5+6ujo5+eST87/XXXPnnHOObZs//elPZdGi1f91SItnd9xxhxQXr3k+Xn/+zDPPyB//+Me8Td1hpzvt7LSmpiaroJtrm222mZx//vlrDf373/8uf/rTn/I/d1JY7X0v4+GHH24dG/arUQD0i2y87JoWonhFg7cQiA4BtBWdXOBJ8gigr+TllIiiQSBsbX2+vFzeaFyzAWJkaYfsPm5xNODgBQQKJGDSFwXAAgGHMVx3gGnhqr5+zX+p2HjjjUVf2V1vvfUcu3TrrbfmX66dNGmS6Ou2XrTm5mY57rjj8qacFNx0l1r3YqHeo6f36RXSdGedFrw6OjosM7W1tdbOOTtNC5KHHnpoftfd+PHje7wonLOh9zT+z//8T96kk6Jn77EzZswQPY7du11zzTWiR7Zzra/HSPqLSR976V5U/Na3viWnnHKKHQSu+lAAdIUtcYNMC1HiAiYgCAREAG0FBJppUkkAfaUy7QQdAIGwtfXJ0iHyXvOIfKRjBrfLrjWNAUTOFBDwn4BJXxQA/c+BLzPoTjR9tGPx4p7/tWLbbbeVvfbaSzbffHNrN99ATXfX3XnnnfK3v/0t3013yO2yyy6e+XzEEUeIHj3VNmLECOvhCjvtzTfflEsuuSTfVWNSW4W2M888U7744gvLjIrjtttus17GNbWvvvrKumMv1/QRDbXVu61YsUKOPPLI/I833HBD0R2bdtoTTzwhWozNNY1X4+7dLrzwwh53+V199dUybtw4O1PIggULrGPAubbVVlv1KFjaMuKgEwVAB7AS3NW0ECU4dEKDgK8E0JaveDGecgLoK+UfAOH7RiBsbf2zeZh8vHRYPr51Klrlv6qbfYsXwxAIkoBJXxQAg8yGx3PpDrsrrrhCdOdY7zZ8+HDZZpttRHf0rbvuujJs2DDrgQg97qtFww8//FCee+65/H1wOl6LVVpU9OplYbWpRTwt5uWa7l7TF4xN7d5775WHHnoo383pYxr92dcCmxback1fVd5yyy1N7sjTTz8tN954Y77fQEdnzzjjDPnyyy+tvnpU+Pbbb5fSUvO78r139l166aWy/vrrr+Wb/vyNN97I//x3v/ud7Z2fn3/+ufz85z/Pj+2vkGkEYrMDBUCboBLezbQQJTx8woOAbwTQlm9oMQwB68/DuT+z6n94z93RDBoIQKAwAmFr683GEfLZ8iH5ICYNXSHbVH1dWFCMhkBECJj0RQEwIoly64YeTX3sscfk/vvvzx9PdWNLC4Z69Hf06DUvIrmx03vMrFmz5Kabbsr/WB+5mDlzptG07lLT3WraSkpKrJ1xdnbqmQzrK7i6gy7X+jtm29vORRddlL93UX+nLyarePpq99xzj/zlL3/J/0pj2XnnnQd0TY8l63Fp3UGoraqqSm644YY+x+gdg3rXYK7ZsZ/rq0eHtdCYa06OKJvY9vV7CoBuqCVvjGkhSl7ERASBYAigrWA4M0s6CaCvdOadqP0nELa25jRUyvyW8nygm45YJpuPXOZ/4MwAgQAImPRFATCAJAQxhf6XyYcfflhefPHF/MMZduedMGGCtStszJgxdofY7qd3+Z1wwgl5n/SDu/LKK9d6PKO7wd6Ph+ywww6iOwC9aHrsWR/a0HsUtenLufq4yJAha/4rUO95lK3ek6fFVm26K0934fXXPvvsM+sl5VybPHmy6D19A7Xej4fowxy6y7Cv1ruo6mQXX+8dmZobL16R7i82CoBefLXxt2FaiOIfIRFAIBwCaCsc7syaDgLoKx15JsrgCYStrRcWVUlDe1k+8K0qv5aNhq/ehEGDQNwJmPRFATDuGe7lvxbcnnrqKXn11Vdl3rx5A0anBb99991XdttttwELcoUi6n3sdqBXa3Un3C9+8QvRO/e06T2GWrTq6yis/l4fQjnppJPyLtp5LfjRRx+17j7MtWnTpsnxxx/fZ5h63EN3/7333nv539s5jty70KY+Tpkypc85tBipx4a//nr11nM9Lqw7DCsr17xO1X1gY2OjFbMW13KMtICr9z8O1ObMmWMdGc81fcFYi5/9zVNo3nU8BUAvKMbfhmkhin+ERACBcAigrXC4M2s6CKCvdOSZKIMnELa2Zi2olq87S/KBb1fVLOsNbQ0eBDNCwAcCJn1RAPQBem+TunPM9CCHH24sW7ZM5s6dK3V1ddaONy3G6E43fYxjo402snUXnxd+NTU1yWmnnSatrav/xaosDjvsMOuBi+73DaqPujvw/fffz0+rR2e7P1rR2x83BUAtMuqOPi2k5ZruuNPjyVoUyzX1V+/9e/nll/M/09eWux8h7o+P3rV39tln54t0eoxZi4y9H1hR/3U3Ye7OQLWnR6TVl4Ga+qX3Euaa2tdHQ7SYq/cOdm8+pVCQAAAgAElEQVS66/HJJ5+0ip76/3PtO9/5jhx11FFepLhfGxQAfcUbG+OmhSg2geAoBCJGAG1FLCG4kygC6CtR6SSYCBEIW1uPf1UjravW/H1p5+pGGVfRHiFCuAIB9wRM+qIA6J6t7ZFa4NECmJ2HIGwbjVlHfQhEOeSO0ar7+nLtFltsIUOHDhU9ZqsPW2hxLtfWWWcdq9imx3T7a24KgGpLC6PnnXeetLev+Ze97oTTh1NGjhxpPZSi/uTu5NMx+nO9J1Hv57PTtOh2yy239Og6ceJE2XTTTaWsrEzmz58vb731Vr5IqB319eZzzjnHuCNT/dIHTHI7JXOTjBo1ymKqdzkq64aGBtEj1boztHtbb7315IILLrAehvGzUQD0k258bJsWovhEgqcQiBYBtBWtfOBNsgigr2Tlk2iiQyBsbT08b6ysyg7KA/n22AapKuuMDiA8gUABBEz6ogBYAFy7Qw866CDZYIMNrHvhdPddWtsLL7wgN998c4+iW38stFCmR21N9xK6LQDqvG+//bZ11FZ3Spqa+qHHdPs7itzfeL2X8b777utR5OuvrxbuTj/9dKsgaqfpzsqrr77aetHZSdNXj08++WSroOl3owDoN+F42DctRPGIAi8hED0CaCt6OcGj5BBAX8nJJZFEi0CY2lrVJfLwl7U9gOxRWyfDSlZfrUSDQNwJmPRFATCADGsBUJsWkfRYaG1tz3/pBOBCZKbQl33vuusu0R2BuTvsujunu/B23313OeCAA4y74HRcIQVAHd/c3Gz588orr/RZmNQj03p3n+ZwoJ2IAwH+17/+Jffee6+1E6/7DsjcGBXhnnvuaf3j9Ki43lGohdUnnnhC/v3vfw+Y5w033FD02O+uu+7qeB63HxAFQLfkkjXOtBAlK1qigUBwBNBWcKyZKX0E0Ff6ck7EwRAIU1tfdxTJrIU1PQLdZ52FUla0+rFHGgTiTsCkLwqAAWQ4VwDUqbSgdOaZZ4q+DJvmprvuPv74Y+sePr1rT3ejaYFUj8d2vxcwKEZtbW3ywQcfWP4sX77c2qmpx2g1T3q/nhdNd+xpMVD/V486a7FTi8FamPOi6R2Kn376qXV8uaWlxTKpRUuNQ+cYPny4F9M4skEB0BGuxHY2LUSJDZzAIOAzAbTlM2DMp5oA+kp1+gneRwJBayubFWloL5W5y4bIgpbBkpVMt+iyMr68TSYNXyHVZR2S6f4rHxlgGgJ+ETDpiwKgX+S72e1eANQf60MTJ554ougDFzQIJJkABcAkZ9d+bKaFyL4lekIAAt0JoC2+Bwj4RwB9+ccWy+kmEKS2mttL5PXGkbK026u//dEfXtIp21UtkUruA0z3Bxrz6E36ogAYQIKPOeYY6xXe7k2Peh588MHWi69BNt15lubHSIJkzVxiHfPWV6hp6SZgWojSTYfoIeCeANpyz46REDARQF8mQvweAu4IBKWtutYymd1Q2ePBD5PHRZku2am6WWrKeRXYxIrfR5OASV8UAAPIm95Tp6/H6v13vdv06dPlxz/+se/HXvWY7d///nd5/PHHrYc4aBAIggAFwCAoR38O00IU/QjwEALRJIC2opkXvEoGAfSVjDwSRfQIBKEt3fn3fF2Vo+JfjpQWAafWNLITMHqfDh7ZIGDSFwVAGxC96LJixQq57LLL5KOPPlrL3DbbbCOnnXaalJWVeTFVDxt6n91f//pXq/iXuxdOX6WlQSAIAhQAg6Ac/TlMC1H0I8BDCESTANqKZl7wKhkE0Fcy8kgU0SPgt7b0zr9ZC6ttHfvtj44eB54+roE7AaP3+eCRgYBJXxQAA/yEVq5cKX/4wx/kH//4x1qzTpo0Sc466yzrMQwv2pIlS+Sxxx6TWbNmiT5w0b1RAPSCMDbsEKAAaIdS8vuYFqLkEyBCCPhDAG35wxWrEFAC6IvvAAL+EPBbW/VtpfJi3eiCnZ9Ss1iqB3cUbAcDEAiSgElfFACDzMZ/5rrnnnvk4YcfXmvm6upqOfvss2X8+PGuvdIXYB955BF59tlnpbOzs087FABd42WgQwIUAB0CS2h300KU0LAJCwK+E0BbviNmghQTQF8pTj6h+0rAb23NaaiU+S3lBccwvqJVdqxuLtgOBiAQJAGTvigABpmNbnM988wz1l18XV1dPTyoqKiQM888UzbbbDNHni1cuFD+8pe/yEsvvWQ9vNBfGzJkiPzpT39yZJvOEHBLgAKgW3LJGmdaiJIVLdFAIDgCaCs41syUPgLoK305J+JgCPiprc6ujDz25VjJSqbgYDKSlX0nLJKSQdmCbWEAAkERMOmLAmBQmehjnnfeeUeuuOKKtY7oFhcXy/HHHy+77LKL0bt58+bJQw89JK+88spaxcTug4cNGyZ77bWX7LnnnlJeXvh/ETE6RgcICK8A8xGsJmBaiOAEAQi4I4C23HFjFATsEEBfdijRBwLOCfipraUdxfLUwjHOnepnxIzaehlestIzexiCgN8ETPqiAOh3Bgz2v/jiC7nkkkukqalprZ4HH3ywfPe73+3TwqeffmoV/t54440BZ9A7Bffdd1+ZMWOGL4+MhIyP6SNOgB2AEU9QQO6ZFqKA3GAaCCSOANpKXEoJKEIE0FeEkoEriSLgp7aa2kvk2UXVnvGaNrZBRpX1fa2WZ5NgCAIeEjDpiwKgh7DdmtLi38UXXyy6m69322233eSYY46xdtBo++CDD6zC33vvvTfgdKNHj5b99ttPdHxJSYlb1xgHgYIIUAAsCF9iBpsWosQESiAQCJgA2goYONOligD6SlW6CTZAAn5qix2AASaSqSJJwKQvCoARSZu+1KvHgfVYcO+29dZbWzv4Hn30Ufn4448H9FgTOnPmTJk6daoUFRVFJDrcSCsBCoBpzXzPuE0LEZQgAAF3BNCWO26MgoAdAujLDiX6QMA5AT+1xR2AzvPBiGQRMOmLAmCE8q0PgujDIPpAiNOmLwfrceFvfetb+d2CTm3QHwJeE6AA6DXReNozLUTxjAqvIRA+AbQVfg7wILkE0Fdyc0tk4RLwW1u8Ahxufpk9XAImfVEADDc/fc6uR3zvu+8+W55NnDjRKvztuOOOtvrTCQJBEqAAGCTt6M5lWoii6zmeQSDaBNBWtPODd/EmgL7inT+8jy4Bv7VV31YqL9aNLhjAlJrFUj24o2A7GIBAkARM+qIAGGQ2DHPpDsCXXnpJHn74YZk/f/6AvTfccEM54IADZNttt41QBLgCgZ4EKADyRSgB00IEJQhAwB0BtOWOG6MgYIcA+rJDiT4QcE7Ab21lsyKzFlbL0k739+APL+mU6eMaJJNxHh8jIBAmAZO+KACGmZ3/zL1y5Up57rnn5JFHHpH6+nqjR8XFxXLaaafJdtttZ+xLBwiESYACYJj0ozO3aSGKjqd4AoF4EUBb8coX3saLAPqKV77wNj4EgtBWc3uJPF9XJauyqx/SdNKKMl0ytaZRKnn91wk2+kaEgElfFABDTFRHR4c89dRT8thjj0lzc7MjTzSxRxxxhPz3f/+3o3F0hkCQBCgABkk7unOZFqLoeo5nEIg2AbQV7fzgXbwJoK945w/vo0sgKG3VtZbJ7IZKR0VALf7tVN0sNeXt0QWIZxAYgIBJXxQAQ/h8Wlpa5O9//7s8/vjjsmzZsn49yGQy1t1+u+++u/zf//2fzJs3b62+++67rxx66KEhRMGUEDAToABoZpSGHqaFKA0MiBECfhBAW35QxSYEVhNAX3wJEPCHQJDa0p2ArzeOtHUcWI/9ble1hJ1//qQdqwERMOmLAmBAidBptNj317/+VZ544gnRImB/TZO26667ysyZM6W2ttbq1tbWJpdffrm89957aw3baaed5KSTThI9GkyDQJQIUACMUjbC88W0EIXnGTNDIN4E0Fa884f30SaAvqKdH7yLL4GgtaV3Ai5uL5W3m0asVQjMSFZqK9pkg2Er5P+zdybgdVVV/163bca2adMmbTrxZxYKMihDGb5CpWUsMko/9AMBFWVQJgGxAsKHH4MDiCACIqAIFJFRFGgZShWKzCCjiEBpMzVNm7YZ29z/sy7e0yRNss8995xz9znn3c/DoyR7r73Wu86PXRZ7qCrp5M6/6H5WeP4fAiZ9UQAM4VPR470PPfSQPPHEE9LRMfB2Yi3gzZgxQw477DCprq7eyDMtptx4442ycOHCjX637bbbyrnnnivDhw8PISKmgIA7AhQA3XGKey/TQhT3+IkPAkERQFtBkcUuBNgByDcAgaAIFGrt+kfzSHm3ZaQTVk1pm+xWvVKKhqSDChW7EAidgElfFABDSMlXvvIV0Yc+BmrFxcUyc+ZM+eIXvyiVlZVGj+bNmyf33XffRv0mTZok3//+96WqKv9nz41O0AECLghQAHQBKQFdTAtRAhAQIgQCIYC2AsGKUQhkCKAvPgQIBEOgUNp6uWmU/HvNhs0ym49cKzuPWRVMkFiFQIEImPRFATCExMyZM6ffWcrKyjKPeMyePVtGjtzwXyPcuPTkk0/KzTffLN3d3b26jxo1Sr73ve/J5ptv7sYMfSAQKAEKgIHijYxx00IUmUBwFAKWEUBbliUEd2JFAH3FKp0EYxGBQmlrcWOlLG0tc0hsM2q1bDd64Pv4LUKGKxBwTcCkLwqArlF679i3ADhixAg5+OCD5aCDDpLy8nLPhl955RW55pprMvcD9mwlJSVy5plnyuc+9znPthkIAT8IUAD0g2L0bZgWouhHSAQQKAwBtFUY7syaDALoKxl5JsrwCRRKW8/Uj5XG9hIn4B0qV8lWFWvDB8CMEAiQgElfFAADhJ81nS0Ajh49OrPbb//99xct0vnRPvjgA7nyyitl5cqVvcxp4k866SSZNWuWH9NgAwKeCFAA9IQtdoNMC1HsAiYgCIREAG2FBJppEkkAfSUy7QQdAoFCaWvBsmpZ1VXkRLjL2Gb5fyPaQoiYKSAQHgGTvigAhpCL0047LfOwhz7wUVS04R86fk3d0NAgl19+uSxbtmwjkzrvl7/8Zb+mwg4EciJAATAnXLHtbFqIYhs4gUEgYAJoK2DAmE80AfSV6PQTfIAECqWtv3wyTlrXD3Mi26O6SSaWD/xAZ4AIMA2BwAiY9EUBMDD0GwxrEWTo0KGBzrR27Vq56qqr5J133tlonr322ktOPfVU0VeGaRAIkwAFwDBp2zuXaSGy13M8g4DdBNCW3fnBu2gTQF/Rzh/e20ugUNp68OMaWZce4oDZZ/xyqSrttBcUnkHAAwGTvigAeoBq6xB9afjaa6+V559/fiMXp06dKueee25edw7aGjd+2UuAAqC9uQnTM9NCFKYvzAWBOBFAW3HKJrHYRgB92ZYR/IkLgUJoqzstcv/HE3shnDWxQSqK1sUFK3FAIEPApC8KgDH8UG6//Xb585//vFFkkydPlgsuuECqqqpiGDUh2UiAAqCNWQnfJ9NCFL5HzAiBeBBAW/HII1HYSQB92ZkXvIo+gUJoq339EHnkk5pe8A6ZXCelQ7ujD5QIINCDgElfFABj+rloAfC3v/2tpNPpXhHqQyQ33nhjTKMmLNsIUAC0LSOF8ce0EBXGK2aFQPQJoK3o55AI7CWAvuzNDZ5Fm0AhtNXSNUzmLxvXC9zhmyyToalos8R7CPQlYNIXBcAYfzN6FPgXv/iFdHV19Ypy3rx5MY6a0GwiQAHQpmwUzhfTQlQ4z5gZAtEmgLainT+8t5sA+rI7P3gXXQKF0FZTR5E8XVftQBua6pbDN6mLLkQ8h8AABEz6ogAY80/n3XffzTwOsmbNGidSCoAxT7pF4VEAtCgZBXTFtBAV0DWmhkCkCaCtSKcP5y0ngL4sTxDuRZZAIbRV21oizzaOdZiVDV0nB09uiCxDHIfAQARM+qIAmIBvZ9myZXL55ZdLQ8On/5CjAJiApFsSIgVASxJRYDdMC1GB3WN6CESWANqKbOpwPAIE0FcEkoSLkSRQCG19tKZMXmyqdHiNKuqSmRMbI8kPpyEwGAGTvigAJuT7aWlpyRQBP/jgAwqACcm5DWFSALQhC4X3wbQQFd5DPIBANAmgrWjmDa+jQQB9RSNPeBk9AoXQ1j9bhsvrzaMcWNUlHTK9pil68PAYAgYCJn1RAEzQJ9Te3i4///nP5fzzz09Q1IRaSAIUAAtJ3565TQuRPZ7iCQSiRQBtRStfeBstAugrWvnC2+gQKIS23lo5Ut5eNdKBNLG8Tfaobo4ONDyFgEsCJn1RAHQJMi7duru7RT8KGgTCIEABMAzK9s9hWojsjwAPIWAnAbRlZ17wKh4E0Fc88kgU9hEohLZeXVEh/1o9woGx6Yi18vmxq+yDg0cQyJOASV8UAPMEzHAIQGBgAhQA+TqUgGkhghIEIOCNANryxo1REHBDAH25oUQfCOROoBDa+vvy0bJkbbnj7NYVq+Wzlatzd54RELCcgElfFAAtTyDuQSDKBCgARjl7/vluWoj8mwlLEEgWAbSVrHwTbbgE0Fe4vJktOQQKoa2/1o+R+vZSB/L2o1vkM6PWJAc6kSaGgElfFAAT8ykQKATCJ0ABMHzmNs5oWohs9BmfIBAFAmgrClnCx6gSQF9RzRx+206gENp6qrZKVnQWO2h2HrNSNh/Zajsq/INAzgRM+qIAmDNSBkAAAm4JUAB0Syre/UwLUbyjJzoIBEcAbQXHFssQQF98AxAIhkAhtPXoJ+Nk7fphTkCfH7NCNh3ZHkyAWIVAAQmY9EUBsIDJYWoIxJ0ABcC4Z9hdfKaFyJ0VekEAAn0JoC2+CQgERwB9BccWy8kmEJa20mmRxo5i+WD1cFnaqsd/Uz3Ap2VSebtsPnKtVJd0Sqrnr5KdHqKPOAGTvigARjzBuA8BmwlQALQ5O+H5ZlqIwvOEmSAQLwJoK175JBq7CKAvu/KBN/EhEIa2mjuK5MWm0dLSVWQEV1HUJbuMXSmVJV3GvnSAgO0ETPqiAGh7BvEPAhEmQAEwwsnz0XXTQuTjVJiCQKIIoK1EpZtgQyaAvkIGznSJIRC0turbSuS5xkpZnx7imunQVLfsUd0s48s6XI+hIwRsJGDSFwVAG7OGTxCICQEKgDFJZJ5hmBaiPM0zHAKJJYC2Ept6Ag+BAPoKATJTJJJAkNrSnX8L68fmVPzLJkGLgPuMb2InYCK/yvgEbdIXBcD45JpIIGAdAQqA1qWkIA6ZFqKCOMWkEIgBAbQVgyQSgrUE0Je1qcGxiBMISlt659+C2mpXx34HQqjHgWdOaOROwIh/Y0l236QvCoBJ/jqIHQIBE6AAGDDgiJg3LUQRCQM3IWAdAbRlXUpwKEYE0FeMkkkoVhEISlsN7cWyqL4q71inj18u1aWdedvBAAQKQcCkLwqAhcgKc0IgIQQoACYk0YYwTQsRlCAAAW8E0JY3boyCgBsC6MsNJfpAIHcCQWlrcWOlLG0ty92hPiMmlbfJtOrmvO1gAAKFIGDSFwXAQmSFOSGQEAIUABOSaAqAJBoCBSFg+kNeQZxiUgjEhAD6ikkiCcM6AkFoq6s7JQ8vqZG0pPKONyVpOXRKnRQNSedtCwMQCJuASV8UAMPOCPNBIEEEKAAmKNmDhGpaiKAEAQh4I4C2vHFjFATcEEBfbijRBwK5EwhCWy2dw2R+7bjcnRlgxKyJDVJRtM43exiCQFgETPqiABhWJpgHAgkkQAEwgUnvJ2TTQgQlCEDAGwG05Y0boyDghgD6ckOJPhDInUAQ2lrRUSRP1VXn7swAI2bUNMqYki7f7GEIAmERMOmLAmBYmWAeCCSQAAXABCadAiBJh0BoBEx/yAvNESaCQAwJoK8YJpWQrCAQhLbYAWhFanHCAgImfVEAtCBJuACBuBKgABjXzOYWl2khys0avSEAgSwBtMW3AIHgCKCv4NhiOdkEgtAWdwAm+5si+g0ETPqiAMjXAgEIBEaAAmBgaCNl2LQQRSoYnIWARQTQlkXJwJXYEUBfsUspAVlCICht8QqwJQnGjYISMOmLAmBB08PkEIg3AQqA8c6v2+hMC5FbO/SDAAR6E0BbfBEQCI4A+gqOLZaTTSAobTW0F8ui+qq84U4fv1yqSzvztoMBCBSCgElfFAALkRXmhEBCCFAATEiiDWGaFiIoQQAC3gigLW/cGAUBNwTQlxtK9IFA7gSC0lY6LbKgtlpauopyd+o/IyqKumTmhEZJpTybYCAECkrApC8KgAVNT+/J58yZs5E3qVRK7r77bk9e+m3PkxMMSjQBCoCJTr8TvGkhghIEIOCNANryxo1REHBDAH25oUQfCOROIEhtNXcUycL6sbI+PSRnx4amumWf8U1Syeu/ObNjgD0ETPqiAGhPrqS/gp26N2/ePE9e+m3PkxMMSjQBCoCJTj8FQNIPgYAJmP6QF/D0mIdArAmgr1inl+AKSCBobf2rpVxebR4lIu638Wnxb4/qZhlf1lFAMkwNgfwJmPRFATB/xr5Z8Ltg57c93wLFUGIIUABMTKoHDdS0EEEJAhDwRgBteePGKAi4IYC+3FCiDwRyJxCkturbSuS5xsocdwCmZafKVbJFRWvuwTACApYRMOmLAqBFCfO7YOe3PYtQ4UpECFAAjEiiAnbTtBAFPD3mIRBbAmgrtqklMAsIoC8LkoALsSQQlLY4/hvLz4WgciRg0hcFwByBBtnd74Kd3/aCjB3b8SRAATCeec01KtNClKs9+kMAAp8SQFt8CRAIjgD6Co4tlpNNIAht8QBIsr8pot9AwKQvCoAWfS2NjY39elNdXe3JS7/teXKCQYkmQAEw0el3gjctRFCCAAS8EUBb3rgxCgJuCKAvN5ToA4HcCQShrYb2YllUX5W7M31GTB+/XKpLO/O2gwEIFIqASV8UAAuVGeaFQAIIUABMQJJdhGhaiFyYoAsEINAPAbTFZwGB4Aigr+DYYjnZBILQ1uLGSlnaWpY32EnlbTKtujlvOxiAQKEImPRFAbBQmWFeCCSAAAXABCTZRYimhciFCbpAAAIUAPkGIBAqAdauUHEzWYII+K2tru6UPLykRtI5vPo7EO6UpOXQKXVSNCSdoIwQapwImPRFATBO2SYWCFhGgAKgZQkpkDumhahAbjEtBCJPAG1FPoUEYDEB9GVxcnAt0gT81taqzmGyoHacb0xmTWyQiqJ1vtnDEATCJGDSFwXAMLPBXBBIGAEKgAlL+ADhmhYiKEEAAt4IoC1v3BgFATcE0JcbSvSBQO4E/NbWv1aXy6srRufuyAAjZtQ0ypiSLt/sYQgCYRIw6YsCYJjZYC4IJIwABcCEJZwCIAmHQKgETH/IC9UZJoNAzAigr5gllHCsIeC3thbVj5GG9lLf4mMHoG8oMVQAAiZ9UQAsQFKYEgJJIUABMCmZHjxO00IEJQhAwBsBtOWNG6Mg4IYA+nJDiT4QyJ2An9rS+/8eWlIj4sP9fxoJdwDmnk9G2EXApC8KgHblC28gECsCFABjlU7PwZgWIs+GGQiBhBNAWwn/AAg/UALoK1C8GE8wAT+11dI5TOb7eP/f+NJ22Xv8igRnh9CjTsCkLwqAUc8w/kPAYgIUAC1OToiumRaiEF1hKgjEigDailU6CcYyAujLsoTgTmwI+KmtFR1F8lRdtW9sdqpcKVtUtPpmD0MQCJuASV8UAMPOCPNBIEEEKAAmKNmDhGpaiKAEAQh4I4C2vHFjFATcEEBfbijRBwK5E/BTW37vAJw5oUFGFfMCcO5ZZYQtBEz6ogBoS6bwAwIxJEABMIZJ9RCSaSHyYJIhEICAiKAtPgMIBEcAfQXHFsvJJuCntvQOwIeX1EjalzsA0/LFKXVSNCSd7AQRfaQJmPRFATDS6cV5CNhNgAKg3fkJyzvTQhSWH8wDgbgRQFtxyyjx2EQAfdmUDXyJEwG/tbW4oVKWtpXljWhSeZtMq27O2w4GIFBIAiZ9UQAsZHaYGwIxJ0ABMOYJdhmeaSFyaYZuEIBAHwJoi08CAsERQF/BscVysgn4ra33WobLG82j8oa6Q+Uq2apibd52MACBQhIw6YsCYCGzw9wQiDkBCoAxT7DL8EwLkUszdIMABCgA8g1AIDQCrF2hoWaihBHwW1vPNVTKMh92AE4sa5M9xrEDMGGfY+zCNemLAmDsUk5AELCHAAVAe3JRSE9MC1EhfWNuCESZANqKcvbw3XYC6Mv2DOFfVAn4qS0/7wBMSVoO5Q7AqH5W+P0fAiZ9UQDkU4EABAIjQAEwMLSRMmxaiCIVDM5CwCICaMuiZOBK7Aigr9illIAsIeCntvx+BXjWxAapKOIVYEs+FdzwQMCkLwqAHqAyBAIQcEeAAqA7TnHvZVqI4h4/8UEgKAJoKyiy2IUAr2zzDUAgKAJ+rl0rOorkqbpq31ydUdMoY0q6fLOHIQiETcCkLwqAYWeE+SCQIAIUABOU7EFCNS1EUIIABLwRQFveuDEKAm4IoC83lOgDgdwJ+KktdgDmzp8R8SZg0hcFwHjnn+ggUFACFAALit+ayU0LkTWO4ggEIkYAbUUsYbgbKQLoK1LpwtkIEfBTW9wBGKHE42ooBEz6ogAYShqYBALJJEABMJl57xu1aSGCEgQg4I0A2vLGjVEQcEMAfbmhRB8I5E7Ab20tbqyUpa1luTvSZ8Sk8jaZVs0rwHmDxEBBCZj0RQGwoOlhcgjEmwAFwHjn1210poXIrR36QQACvQmgLb4ICARHAH0FxxbLySbgt7Ya2otlUX1V3lCnj18u1aWdedvBAAQKScCkLwqAhcwOc0Mg5gQoAMY8wS7DMy1ELs3QDQIQ6EMAbfFJQCA4AugrOLZYTjYBv7WVTossqK2Wlq4iz2Arirpk5oRGSaU8m2AgBKwgYNIXBUAr0oQTEIgnAQqA8cxrrlGZFqJc7dEfAhD4lADa4kuAQHAE0FdwbLGcbAJBaKu5o0gW1o+V9ekhOcMdmuqWfcY3SSWv/+bMjgH2ETDpiwKgffyMQQsAACAASURBVDnDIwjEhgAFwNikMq9ATAtRXsYZDIEEE0BbCU4+oQdOAH0FjpgJEkogKG3Vt5XIc42VORUBtfi3R3WzjC/rSGg2CDtuBEz6ogAYt4wTDwQsIkAB0KJkFNAV00JUQNeYGgKRJoC2Ip0+nLecAPqyPEG4F1kCQWpLdwK+2DTa1XFgPfa7y9iV7PyL7JeE4/0RMOmLAiDfDQQgEBgBCoCBoY2UYdNCFKlgcBYCFhFAWxYlA1diRwB9xS6lBGQJgaC1pXcCNurDIA0bPwySkrRMLG+XLUaulaqSTu78s+SbwA3/CJj0RQHQP9aBWWppaZFly5ZJa2tr5i8tquTT9tlnn3yGMxYCrglQAHSNKtYdTQtRrIMnOAgESABtBQgX04kngL4S/wkAICACYWirc31KHv5kQq8Ipo9rlNEl66RoSDqgyDALgcITMOmLAmDhc9SvB++//7488cQT8tprr0lTU5OvXs6bN89XexiDwEAEKADybSgB00IEJQhAwBsBtOWNG6Mg4IYA+nJDiT4QyJ1AGNpa0zVUHls2vpdzh02plWEU/3JPGCMiRcCkLwqAlqWzublZbrrpJnn55ZcD84wCYGBoMdyHAAVAPgkKgHwDEAiOgOkPecHNjGUIxJ8A+op/jomwMATC0NaKjiJ5qq7aCXCIpOXwTWo58luYlDNriARM+qIAGGIyTFPV1dXJRRddJKtWrTJ1zev3FADzwsfgHAhQAMwBVoy7mhaiGIdOaBAIlADaChQvxhNOAH0l/AMg/MAIhKGturYS+VvDWCeG0qHr5ZDJ9YHFhGEI2ELApC8KgJZkqqOjQ8455xxpbGwM3CMKgIEjZoL/EKAAyKegBEwLEZQgAAFvBNCWN26MgoAbAujLDSX6QCB3AmFoa8naMvn78krHuZFFXbL/xOD/PTt3GoyAgL8ETPqiAOgvb8/W7r33XvnDH/6w0fgxY8bIHnvsIVtuuaXU1NRIeXm5DB061PM8OrC6esN26LwMMRgCBgIUAPlEKADyDUAgOAKmP+QFNzOWIRB/Augr/jkmwsIQCENb/1pdLq+uGO0EOLakQ/at8fde/cLQY1YIDE7ApC8KgJZ8QSeffHKvo7+pVErmzJkjX/ziF/Mu+FkSIm4kkAAFwAQmvZ+QTQsRlCAAAW8E0JY3boyCgBsC6MsNJfpAIHcCYWjr7ZUj5K1VFY5zE8raZc9xK3J3lhEQiBgBk74oAFqQ0A8//FDOP//8Xp6ceOKJcuCBB1rgHS5AwDsBCoDe2cVppGkhilOsxAKBMAmgrTBpM1fSCKCvpGWceMMiEIa2Xl9RIf9cPcIJ6f8Nb5VdqlaGFSLzQKBgBEz6ogBYsNRsmHjRokVy3XXXOT+YMmWK/OQnP7HAM1yAQH4EKADmxy8uo00LUVziJA4IhE0AbYVNnPmSRAB9JSnbxBomgTC09eLy0fLR2nInrC1HrpEdx7SEGSZzQaAgBEz6ogBYkLT0nvSRRx6R3/72t84PjzrqKDnmmGMs8AwXIJAfAQqA+fGLy2jTQhSXOIkDAmETQFthE2e+JBFAX0nKNrGGSSAMbT3bUCm1bWVOWFNHtci2o9eEGSZzQaAgBEz6ogBYkLT0nvT++++Xu+++2/nhaaedJtOnT7fAM1yAQH4EKADmxy8uo00LUVziJA4IhE0AbYVNnPmSRAB9JSnbxBomgTC0tbBurCzvKHHC2rFypWxZ0RpmmMwFgYIQMOmLAmBB0tJ70scff1xuueUW54dnnnlm5uVfGgSiToACYNQz6I//poXIn1mwAoHkEUBbycs5EYdHAH2Fx5qZkkUgDG3NX1YtLV1FDthdq5plk+FtyQJNtIkkYNIXBUALPos333xTLr30UseTk046SQ444AALPMMFCORHgAJgfvziMtq0EMUlTuKAQNgE0FbYxJkvSQTQV5KyTaxhEghDW498Ml7a1w91wtprXJPUlHWEGSZzQaAgBEz6ogBYkLT0nrSzs1O+9rWvif6vtn322UdOPfVUCzzDBQjkR4ACYH784jLatBDFJU7igEDYBNBW2MSZL0kE0FeSsk2sYRIIQ1sPfFwj69NDnLBm1DTKmJKuMMNkLggUhIBJXxQAC5KWjSf95S9/KQsXLsz8oqKiQm644QYZNmyYJd7hBgS8EaAA6I1b3EaZFqK4xUs8EAiLANoKizTzJJEA+kpi1ok5DAJBa2t9t8gDSyb2CmX/ifUysmh9GOExBwQKSsCkLwqABU3Phsnr6+vlnHPOka6uT//LxFe+8hX54he/aIl3uAEBbwQoAHrjFrdRpoUobvESDwTCIoC2wiLNPEkkgL6SmHViDoNA0NpqWzdE/ry0plcosyfXScnQ7jDCYw4IFJSASV8UAAuant6TP/roo3Lrrbdmfqi7/77//e/LdtttZ5GHuAKB3AhQAMyNV1x7mxaiuMZNXBAImgDaCpow9pNMAH0lOfvEHiSBoLXV0jlM5teO6xXCEZsskyGpIKPCNgTsIGDSFwVAO/LkeHHnnXfKgw8+mPn7oqIiOe6443gQxLIc4Y57AhQA3bOKc0/TQhTn2IkNAkESQFtB0sV20gmgr6R/AcQfFIGgtbW8vVgW1lc57g9Ldcthm9QFFQ52IWAVAZO+KABala5PnVmwYIHcdtttznFgTdKMGTNk6tSpMmHCBBkxYoRoYmkQsJ0ABUDbMxSOf6aFKBwvmAUC8SOAtuKXUyKyhwD6sicXeBIvAkFr6+M1pfJC0xgHWtmQdXLwlIZ4QSQaCAxAwKQvCoCWfDpz5swJxZNUKiV33313KHMxCQQoAPINKAHTQgQlCEDAGwG05Y0boyDghgD6ckOJPhDInUAQ2kqnRRo7iuWD1cNlaWupiPQ875uWSeXtsvnItVJd0ikpjgLnnjRGRIaASV8UAC1JZVgFQA133rx5lkSNG3EnQAEw7hl2F59pIXJnhV4QgEBfAmiLbwICwRFAX8GxxXKyCfitreaOInmxabS0dBUZwVYUdckuY1dKZcmnD2/SIBA3AiZ9UQC0JOMUAC1JBG74SoACoK84I2vMtBBFNjAch0CBCaCtAieA6WNNAH3FOr0EV0ACfmqrvq1EnmuslPVp99djDU11yx7VzTK+rKOAFJgaAsEQMOmLAmAw3HO2mrQC4Jo1a+Sdd96RFStWSGtrq1RWVop+jFtvvXVB7jdsb2+Xt99+W5qamkR9q6iokOrqatl2220zLzL70TTW999/PxNzR0eHjBkzRiZOnChbbLGFH+YdG93d3fLvf/9blixZIitXrhT9+9LS0kw8U6ZMkZqaGl/nG8wYBcDQUFs9kWkhstp5nIOAxQTQlsXJwbXIE0BfkU8hAVhKwC9t6c6/hfVjcyr+ZZFoEXCf8U3sBLT0G8Et7wRM+qIA6J2tryP/8Ic/+GpvMGNf+tKXQpur70TLli0Tfen45ZdflnXr1m3khxYC99tvPznyyCN9K7wNFmxzc3PGn8WLF2eKcn3b8OHDZfr06aIF2vLyck/ctOin9y6+8cYbktYLKvo0FeFBBx2U+UvvaPTaWlpa5IEHHpCFCxfK6tWrBzQzcuRI2XHHHeWkk07KPCgTZKMAGCTd6Ng2LUTRiQRPIWAXAbRlVz7wJl4E0Fe88kk09hDwQ1v6r1QLaqtdHfsdKHI9DjxzQiN3AtrzaeCJDwRM+qIA6ANkTLgj8Mwzz8jNN9/cb6Gtr4XNNttMzjnnHBk3bpw74x56vfbaa3LttdcOWizLmlWhqD+bbrppTjM9+OCDmeKfFsJM7bOf/aycddZZnopyzz77rNx0002Z3ZRu29VXXy2TJk1y291TPwqAnrDFbpBpIYpdwAQEgZAIoK2QQDNNIgmgr0SmnaBDIOCHthrai2VRfVXe3k4fv1yqSzvztoMBCNhCwKQvCoC2ZCrmfuiOvyuvvLLXDrgJEybIdtttlyl41dfXy0svvSSdnRv+ATx58mS57LLLPO+8GwzpBx98IBdffHGvYqTuPtx5551l9OjRsnz58ow/a9eudczozy+//HIZO3asq2zNnz8/U/Ds2bSAuM0220hJSYksXbpUXnnllV7FQeUxd+7cnHY/PvTQQ3LHHXf0mkeP/G6//faZAqruXNQ4Pvnkk8wR5La2tkxfCoCu0kgnHwiYFiIfpsAEBBJJAG0lMu0EHRIB9BUSaKZJHAE/tLW4sVKWtpblzW5SeZtMq27O2w4GIGALAZO+KADakqkY+6HHbM8880yn8KTHXI877jg5+OCDe933p0dYtSj15ptvOjT23HPPzFg/mxYZzzjjjMx9f9k2e/Zs+fKXv9yr8KaFshtvvFF0d1226R2FWpQ0tQ8//FAuuOACp7hXVFQkp5xyiuy99969hmrh86qrrsrc15dthx9+eMYXN+2vf/1rZhdjtmlhUY8r77///lJcXLyRCT12rTsfH330UTnxxBMzdxAG2dgBGCTd6Ng2LUTRiQRPIWAXAbRlVz7wJl4E0Fe88kk09hDIV1td3Sl5eEmNpMX71UlZGilJy6FT6qRoyMbXNNlDDE8g4J6ASV8UAN2zpKdHAr/+9a/l8ccfd0Yfc8wxcvTRR/drTYtz559/fmZ3nDYtFl5xxRWiR4L9an13zM2YMSNTnOuv6QMaP/rRjzL392Xbd7/7Xdltt90GdUd3Curuvmw7/fTTM3cJ9te08KnHi1etWpX5tRbufvGLX2QeRhmsaX8tjmZ3KZaVlWV2NW6++eZ+ocrbDgXAvBHGwoBpIYpFkAQBgQIQQFsFgM6UiSGAvhKTagINmUC+2mrpHCbza/27JmrWxAapKNr4bvqQsTAdBHwhYNIXBUBfMGNkIAL6Cu2pp57qPPihH5zu8hvsZd1//OMfcumllzomtdimRTc/mu6A++Y3v+nc+6fHY6+77rpB792rq6vL7BjMPuChxUg9zjxQ0xd4tYiZbfqS8CWXXDKo+08++aT86le/cvrojsTjjz9+0DG68093AGabm8KkHwxzsUEBMBda8e1rWojiGzmRQSBYAmgrWL5YTzYB9JXs/BN9cATy1daKjiJ5qq7aNwdn1DTKmJIu3+xhCAKFJGDSFwXAQmYnAXMvWLAg8zhFtn3lK1+Rww47zBi5PoaR3QWox2dvueUW0Xvt8m16/FV39GWbHpX9+te/bjSrY3RstukOPRVPf01fFdbXeLNNd+npUebBmu581MJkdjef3jN4ww03DDhE7yg87bTTnKKkPiBy4YUXGuMIuwMFwLCJ2zmfaSGy02u8goD9BNCW/TnCw+gSQF/RzR2e200gX22xA9Du/OJdYQmY9EUBsLD5cTW7HkPVRyveffdd0d1lq1evljVr1mTu1NNjn/qIxsiRIzNHP/WOOv1fTbwNTY/v6gMgbgpnPf3Vl3Pvu+8+50d+7W7TQuJjjz3m2L3ooosyj2WYWt8dero7T3fp9df0OG/2Tj/d6Xjbbbf1ex9f37FaVFy0aJHzY91lONDR53vuuUfuvfdep++5554ru+66qymM0H9PATB05FZOaFqIrHQapyAQAQJoKwJJwsXIEkBfkU0djltOIF9tcQeg5QnGvYISMOmLAmBB0zP45Pp4hhardBedFv3ctoqKCpk1a1bmIQh9ubaQ7YQTTpDW1taMC6NGjdroVdyBfNP78/QevWzTB0PUVr5NC2UfffRRxoyKQ4tzbnYW6gu6Z599tjO9FtvUVt+mhdmTTjrJ+fFWW23Va8fhYP7rPYl6X2K2abwad39NjyTX1tZmfqXHmHXcYMeq8+XmdTwFQK/k4jXOtBDFK1qigUB4BNBWeKyZKXkE0Ffyck7E4RDwQ1u8AhxOrpglegRM+qIAaGlO//KXv8jvf/976eryfh+BPiahR24PPPDAgkS5YsUK+da3vuXMvdNOO8n3v/99V77o3YEnn3yy03fHHXeUuXPnuho7UCfdSamvD2eZTpo0KXMfoZum9//pWD2qq01fz73mmms2Gqq7NHsexdVC7De+8Q03U8h7770nP/jBD5y+M2fO7MUg+4u+RUY/2Lhy0EMnCoAeoMVwiGkhimHIhASBUAigrVAwM0lCCaCvhCaesAMn4Ie2GtqLZVF9Vd6+Th+/XKpLP/33OxoE4kDApC8KgJZlWYtTWpR66aWXfPPs85//fGb3Wtg7xPo+5jFQQau/QLXgpsVLfbRDW3V1tVx//fV5Mamvr5dvf/vbjo1cC2c9d90NHTpUfve7323EtO9R4WOPPVaOOOIIV373LZhOnTpVfvjDH2409vXXX5fLLrvM+fmRRx4p//3f/525D/DVV1+VZ555Rj788ENpamoSvT9Rd4TqTsTPfe5zmdeLwzweTgHQVepj38m0EMUeAAFCICACaCsgsJiFwH9OitTU1GRY6INw+h+SaRCAQP4E/Fi70mmRBbXV0tJV5NmhiqIumTmhUVIpzyYYCAHrCJj0RQHQopRpAUeLf88///ygXqVSqczdf3p0tb29PXMXYPaF2oEG7r777r2OsIYRthai9IXdbJszZ44cddRRrqc+/fTTpaGhIdNfC2533XWX67H9dXz77bfl4osvdn71hS98odcORZNxfcn3zTffdLrpIx36WEfPpvfy6f182aYxTJ8+3WQ683v9g6UWPbVopk3FqfcC9m1//vOfM0eXs013WeojIL/85S97+dffpLrrUXdW6svEYTQKgGFQtn8O00JkfwR4CAE7CaAtO/OCV/EggL7ikUeisI+AX9pq7iiShfVjZX0697vvh6a6ZZ/xTVLJ67/2fSB4lBcBk74oAOaF19/Bf/zjH3sVj7LWNYm6W22PPfaQLbbYQrSIo0XAbNPi37Jly+Rf//qXPPfcc5ldYP39V0otwOlusbBa3zvtvvrVr8ohhxzievqe9/XpoDvuuMPVYxoDTZDvvYJXXXWVvPjii455LdZqLno29fGhhx5yfpTr4yXKSAu62ga6M7HvAyB6xFgLj3pnpJumO0H1BeG99trLTfe8+lAAzAtfbAabFqLYBEogEAiZANoKGTjTJYoA+kpUugk2RAJ+aqu+rUSea6zMqQioxb89qptlfFlHiFEzFQTCIWDSFwXAcPJgnEXvvPvOd74jHR29/0Gk9+bpoxKaKLdNd8395je/ES149Wy6Y/Daa6/NFJbCaFoI04JYtn3961/PPEzitul9ge+//77TXR+60OOsXpsWR3ve+XfYYYdldty5bTpWbWSbPlKiBdmeTX3Uwme2aQyaQ7dNi3mrVq3KdC8pKckcM+7bbr31VtE7IrNNHwHJPrSiBUk9cqwvGysrfTxGdy3qi8r6kEm26dHg//3f/828GJ1r06PFpqaPz+iuTS0ANjY2mrrz+5gT0IVo3LhxmSj1n08co4p5wgkvNAJoKzTUTJRAAugrgUkn5FAI+K2t5o5h8vfGUa6OA+ux392qV0llyafXTNEgEDcCJn3p1Wr67+lxbqm06XysBdH/9re/lUceeaSXJ1rI0bvdvLZ58+ZlCj892+zZszOPWYTR+h6H1aOqeuzWbdPjunpsN9v0iGtVlffLXvseSdbjyLor0m3T48xqI9v0SHDfo7R6LPipp55y+lx00UWZYpzbdsopp2Tu7tOmuzw1h31b3zmyv995551Fdxxqca9v07sUf/rTn/a6W1KLf1dccYVb15x+xxxzjHFMf8ejjYPoAAEIQAACEIAABCAAAQhAIEcCeifg0haRBe+LrO7zpseQlMjmY0Q+O15kUoX+O1aOxukOAQhEikAkCoD6OEX2vjulu++++4oWg/Jtv/rVr3oVpHQnoe4CDKOxA1Ayrx77vQOw7y5DzeWYMWPkZz/7mehuwIGaHi3Wx2B67uDLtUCptikAhqEe5oAABCAAAQhAAAIQgAAEciHw8DsiH/a4FWnHGpFpU0SKh+Vihb4QgECUCVhfANSXxfSF2WzTo7q6g2qwYo7bhOjRUC0k6mMh2fbzn/9csq+aubXjpR93AEpmR56+vOu2ubkDsO89g2pbjzLrkWZT012mt99+u9Pt4IMPlhNOOME0rNfvOQKcEy46/+clRY4A8ylAwH8CpmMe/s+IRQgkhwD6Sk6uiTRcAkFq68llY6Spo9gJ6HNjV8kWFZ/er06DQBIImPTFEWALvoIXXnhBfvKTnzie7L333qI7Av1q+pLsX//6V8fcOeeck1NRyqsffY/c6s6xo48+2rW5oF8BnjFjRk67LMN4BfjLX/6ycz/aQK8A62MxfY8G6+6/yZMnG9lqsVnvmsw2r8eAjRP9pwOPgLglFe9+psto4x090UEgOAJoKzi2WIYA+uIbgEAwBILU1uNLq2X1ug3XIe1WtUKmDN+wESaYiLAKAXsImPTFIyAW5GrBggVy8803O57ojqyDDjrIN88effRR0Ycjsk0fmpg5c6Zv9gcy9I9//EMuvfRS59c658knn+xqXr22UXe16d112rRSff3117saO1Cn+vr6XoVVfVl57ty5rm1q4UwLaNr04kx9oENf1O3ZnnzySdFj19l27LHHZh7lcNNWrFghek9itk2dOlV++MMfbjT06aefFr0PMdvUlzvvvLPXy9CDzfc///M/0tn56eUY+ljHTTfd5MY9T30oAHrCFrtBpoUodgETEARCIoC2QgLNNIkkgL4SmXaCDoFAkNr605Lx0tG94YGDvcctl/FlfS4FDCFGpoBAoQiY9EUBsFCZ6THvAw88IHfddZfzkzPPPFP22GMP3zzTl2uvueYax54WpQ4//HDf7A9kqLm5Wb75zW86v86l4KavIvcsFuo9enqfXj5NXx49/vjjneLXxIkTe3EZzLYWJLVw1tXVlemmr+32fFE4O/a9996TH/zgB46pXIqefcfOmjVLtFjbt+nLyD1ZjBw5Um655RbXaDQnmhttWsDU4mFQjQJgUGSjZde0EEUrGryFgD0E0JY9ucCT+BFAX/HLKRHZQSAobelDIPd/PEHSsuGVjy/UNEplyaf//kaDQBIImPRFAdCCr+Avf/mL3HbbbY4nWqDJ5bVcUwi6K+3GG290uvm9w3Cw+XUuvYdQ26hRo3rtdBxs3Msvv9zrhVovd9X1Z//cc8+Vjz76KPMrFYdy1zsXTe2TTz7JPKCRbbvuuquorb5t7dq1cuKJJzo/3nLLLeX//u//TOYzv3/sscd6FfKUncbdt+l9jnpXYPZxa/VfX5F22772ta/J6tWrM93Lysp63Qno1obbfhQA3ZKKdz/TQhTv6IkOAsERQFvBscUyBNAX3wAEgiEQlLa6ulPy0JIJvZw+cFK9DB+2PphAsAoBCwmY9EUB0IKkPfvss6IPc2SbPuagd8H51XSH14MPPuiY06Ose+21l1/mB7VzxRVXiBbzsk1fIHbzAMndd98t9913nzMu18c0BnJKd8ppoS3bLrzwQvnsZz9rZPHEE0/0KqLqTsLZs2f3O07vWFyyZEnmd3o8Vx/dKC7ecBntQJMpm553NV555ZWy2Wab9dv9e9/7nnzwwQfO77SQ6ebRmI6OjswuyGzxcKB7Bo1AXHagAOgSVMy7mRaimIdPeBAIjADaCgwthiGQ+Q/F2T+z6hUwepKEBgEI5E8gKG2tXTdUHl06vpeDX5xSK0VD0vk7jQUIRISASV8UAC1I5DvvvCMXX3yx48mECRNcH0114/5ZZ50ly5Ytc7rqYxbbbLONm6F599H7DXveMaeFTTfHj/UYdNbnoqKizM44Nzv1TA6//vrrctlllzndBjpm29fOj370I3nttdecH+vDKiqe/poe577//vudX2kse+6556Cu6Z18uvNTdxBqGzt2bOYl6IGaFke1SJptF1xwgey8886m8KXvvYzTpk3rtbPRaCDHDhQAcwQW0+6mhSimYRMWBAIngLYCR8wECSaAvhKcfEIPlEBQ2lrZOUyeqB3n+J6StByxSa2kNpwIDjQujEPABgImfVEAtCBL+tCFHsvUo53Zpq8A62vA+ba+uwu1iPab3/wmszMtjKZ3+Z166qnOYx76wendeX0fz+jpS98i1W677Sa6A9CPpqz1oY2WlpaMOd01p4+LDB8+fEDz+l99zzjjDGfXnO7K0915A7V///vfcv755zu/3nbbbUWLroO1vo+H6O5C3ak3UKutrRUtLGZ38u2+++6iOw9NTe+C1G8i2/SexSAfhKEAaMpIMn5vWoiSQYEoIeA/AbTlP1MsQiBLAH3xLUAgGAJBaauhrVgWNVQ5TpcMWS+zp9QHEwRWIWApAZO+KABakrirrrpKXnrpJcebESNGZF6AnTJlimcP9d46LTxli11q6POf/7ycd955nm16Gdj32O0xxxwjRx99dL+mdCecHm9V37WlUqnMXYADHYVtaGiQ008/3bHl5rXghx56SO644w5nzIwZM+SUU07p1x897qG7/9544w3n926OI/c9+qw+Tp8+vd85ND9avFu1alXm93pcWHcYVlZWDor7uuuuk2eeecbpo9w+97nPDThGdzDqfYTZoqE+HqLFTz92Vg40KQVAL4qJ3xjTQhS/iIkIAuEQQFvhcGaWZBJAX8nMO1EHTyAobX2ytlSeXz5mw79PD+uSAyY1Bh8QM0DAIgImfVEAtCRZejRVC009mxYBTzvttEGLOgO5/+qrr2aKOz2Lf9pXX6h1c+edn1hWrFghegy5ra0tY1aLescdd1zmgQv9QLNNfdXdgW+++abzMz06qzvdBmpeCoBaZNQdfU1NTY5Z3XGnx5N77kxUf/XxlJ475rbeeuteR4gH8uvDDz8UPZarBTBteoxZi4x9d3Wq/7qbMHtnoPbVI9Ju7oDUsVo41Hv9tJWUlGSOEfe3c1Rj0CPF2b7aXx8rOeigg/xM9Ua2KAAGijcyxk0LUWQCwVEIWEYAbVmWENyJFQH0Fat0EoxFBILS1gery+WVFaOdSMeUdMqMmuUWRY4rEAiegElfFACDz4HrGbQAqIXAvk13dR1wwAGy4447tNwaJgAAIABJREFUZopnAzXd2aWFv/nz5/faTZjtv8MOO8jcuXNd++NnR30IRAtd2d1nalvvOtx+++1FC516zFZ3QGpxLtsmT56cKbYN9riFlwKg2tcHNPTexZ4FMd1xp/fojR49WpYvX57xJ3snn47Rn19++eWZ+/nctMcff1x+/etf9+q66aabZu5f1GLd0qVL5ZVXXnGKhNpxu+22y+RosCPSPQ2++OKL8uMf/7gX10mTJmXsVFRUZF771SPVOlfPZiqsuonPTR8KgG4oxb+PaSGKPwEihEAwBNBWMFyxCgElgL74DiAQDIGgtPXOqhHy5soKx+masnbZa9yKYILAKgQsJWDSFwVAixKnxSwt/vTdtZd1UY9qagFJCzx6Z50WkbSApUUqLfDorrOe9wj2DG3UqFGZYtq4cRsuRg07dD2uevPNN/cqug3kg8apR21N/notAOq8WizVo7ZaJDM19UN32w10FHmg8Q888IDMmzevV5FvoL5aDD377LMzBdFcmr4crFyzOyxNY/fff//M7r8w7oGkAGjKRjJ+b1qIkkGBKCHgPwG05T9TLEIgSwB98S1AIBgCQWnr9eYK+WfLhn+P2mR4q+xatTKYILAKAUsJmPRFAdCyxOnONL23b6BCnhd3tXCou90233xzL8N9HaMv+/7+978X3RGYPR7bcwLdhbfffvvJkUce6WoXXD4FQJ23ubk548/zzz/fb2FSC616d9+cOXMG3Yk4GKR//vOfmRd7dSdezx2Q2TEqQj2Kq38NtsNzsDmUg74+/MILL/TaRZkdo3Z15+FRRx0luhM0rEYBMCzSds9jWojs9h7vIGAvAbRlb27wLPoE0Ff0c0gEdhIISlsvLh8tH60td4LecuQa2XHMpw8/0iCQFAImfVEAtPBL0Acwfv7zn8vHH3+ct3e6k+473/lOZtegTU133b377ruZe/h055oer9Vddlqk6nkvYFg+a8H1rbfeyvizZs0a0R2TVVVVoi/46v19fjS9C1GLgfq/etRZi50TJ06ULbfc0g/zGRvK8u23387MoTtJ9fi0zqNx6JHgsBsFwLCJ2zmfaSGy02u8goD9BNCW/TnCw+gSQF/RzR2e200gKG0921AptW1lTvBTR7XItqPX2A0D7yDgMwGTvigA+gzcL3Pr1q0Tfa32sccek5Urc9+6rEWfAw88UPRxC7f3yfnlO3YgkCVAAZBvQQmYFiIoQQAC3gigLW/cGAUBNwTQlxtK9IFA7gSC0tbCurGyvKPEcWinMStli5GtuTvICAhEmIBJXxQALU+uFlD0eKo+DqI75vQI7UBNd5N95jOfyTwWsttuu4Vyx5vl+HCvwAQoABY4AZZMb1qILHETNyAQOQJoK3Ipw+EIEUBfEUoWrkaKQFDamr+sWlq6Npzc2q2qWaYMb4sUG5yFQL4ETPqiAJgv4ZDH66MfenxWH/7Q455lZWWZB0H0eGdxcXHI3jAdBAYnQAGQL0QJmBYiKEEAAt4IoC1v3BgFATcE0JcbSvSBQO4EgtLWI5+Ml/b1Qx2H9hrXJDVlHbk7yAgIRJiASV8UACOcXFyHgO0EKADanqFw/DMtROF4wSwQiB8BtBW/nBKRPQTQlz25wJN4EQhCW+m0yAMfT5BuSTmwZtQ0ypiSrnjBIxoIGAiY9EUBkE8IAhAIjAAFwMDQRsqwaSGKVDA4CwGLCKAti5KBK7EjgL5il1ICsoRAENpa152SB5dM6BXhARPrZUTRekuixg0IhEPApC8KgOHkgVkgkEgCFAATmfaNgjYtRFCCAAS8EUBb3rgxCgJuCKAvN5ToA4HcCQShrdZ1Q+QvS2t6OXPo5FopHprO3UFGQCDCBEz6ogAY4eTiOgRsJ0AB0PYMheOfaSEKxwtmgUD8CKCt+OWUiOwhgL7syQWexItAENpa2TlMnqgd1wNUWo7cpFZSG04Exwsi0UBgAAImfVEA5NOBAAQCI0ABMDC0kTJsWogiFQzOQsAiAmjLomTgSuwIoK/YpZSALCEQhLYa2otlUX2VE2HxkG45dEqdJRHjBgTCI2DSFwXA8HLBTBBIHAEKgIlLeb8BmxYiKEEAAt4IoC1v3BgFATcE0JcbSvSBQO4EgtDW0rWlsnj5GMeZ4cPWyYGTGnJ3jhEQiDgBk74oAAaY4Lfeeqtf61OnTt3o5wP1DcK9/uYPYh5sQoACIN+AEjAtRFCCAAS8EUBb3rgxCgJuCKAvN5ToA4HcCQShrX+vLpeXV4x2nKks7pQvTFieu3OMgEDECZj0RQEwwATPmTNnI+upVEruvvvujX7eX98gXBto/iDmwiYEKADyDVAA5BuAQHAETH/IC25mLEMg/gTQV/xzTISFIRCEtt5dNUL+sbLCCWh8abvsPX5FYQJkVggUkIBJXxQAA0zOQEW9efPmFawAqBP3N3+AGDCdYAIUABOc/B6hmxYiKEEAAt4IoC1v3BgFATcE0JcbSvSBQO4EgtDWG80j5b2WkY4zU4a3ym5VK3N3jhEQiDgBk74oAAaYYAqAAcLFdCQIUACMRJoCd9K0EAXuABNAIKYE0FZME0tYVhBAX1akASdiSCAIbb3UNEo+XDPcobXFyDWy05iWGNIjJAgMTsCkLwqAAX5BFAADhIvpSBCgABiJNAXupGkhCtwBJoBATAmgrZgmlrCsIIC+rEgDTsSQQBDaeq6xUpa1ljm0th21WqaOXh1DeoQEAQqApm8glU6n06ZOQfyeR0CCoIrNKBGgABilbAXnaxB/0AvOWyxDIDoE0FZ0coWn0SOAvqKXMzyOBoEgtPV07Vhp6ixxAGw/epV8ZtTaaADBSwj4SMCkL3YA+ggbUxCAQG8CFAD5IpSAaSGCEgQg4I0A2vLGjVEQcEMAfbmhRB8I5E7AL23pFp/GjmL5YPVwWdpaKiKpHs6kZVJ5u2w+cq1Ul3RKquevcneZERCIDAGTvigARiaVOAqB6BGgABi9nAXhsWkhCmJObEIgCQTQVhKyTIyFIoC+CkWeeeNOwA9tNXcUyYtNo6Wlq8iIq6KoS3YZu1IqS7qMfekAgagTMOmLAmDUM4z/ELCYAAVAi5MTomumhShEV5gKArEigLZilU6CsYwA+rIsIbgTGwL5aqu+rUT0zr/16SGumQxNdcse1c0yvqzD9Rg6QiCKBEz6ogAYxaziMwQiQoACYEQSFbCbpoUo4OkxD4HYEkBbsU0tgVlAAH1ZkARciCWBfLSlO/8W1o/NqfiXhahFwH3GN7ETMJZfFUFlCZj0RQGQbwUCEAiMAAXAwNBGyrBpIYpUMDgLAYsIoC2LkoErsSOAvmKXUgKyhIBXbemdfwtqq10d+x0oVD0OPHNCI3cCWvIt4Ib/BEz6ogDoP3NPFu+9915n3OjRo2XmzJme7PQ3aMGCBbJy5UrnV0cffbRvtjEEgcEIUADk+1ACpoUIShCAgDcCaMsbN0ZBwA0B9OWGEn0gkDsBr9pqaC+WRfVVuU/YZ8T08culurQzbzsYgICNBEz6ogBoSdbmzJnjeLLpppvKlVde6Ztn5513nnz00UeOvXnz5vlmG0MQoADIN2AiYFqITOP5PQQg0D8BtMWXAYHgCKCv4NhiOdkEvGprcWOlLG0tyxvepPI2mVbdnLcdDEDARgImfVEAtCRrFAAtSQRu+EqAHYC+4oysMdNCFNnAcBwCBSaAtgqcAKaPNQH0Fev0ElwBCXjRVld3Sh5eUiNpSeXteUrScuiUOikaks7bFgYgYBsBk74oAFqSMQqAliQCN3wlQAHQV5yRNWZaiCIbGI5DoMAE0FaBE8D0sSaAvmKdXoIrIAEv2mrpHCbza8f55vWsiQ1SUbTON3sYgoAtBEz6ogBoSaYoAFqSCNzwlQAFQF9xRtaYaSGKbGA4DoECE0BbBU4A08eaAPqKdXoJroAEvGhrRUeRPFVX7ZvXM2oaZUxJl2/2MAQBWwiY9EUB0JJMBVkA/O53vytLlizJRDp06FC58847LYkaN+JOgAJg3DPsLj7TQuTOCr0gAIG+BNAW3wQEgiOAvoJji+VkE/CiLXYAJvubIXr3BEz6ogDonmWgPYMsAJ566qnS1NSU8X/48OHym9/8JtBYMA6BLAEKgHwLSsC0EEEJAhDwRgBteePGKAi4IYC+3FCiDwRyJ+BFW9wBmDtnRiSTgElfFAAt+S6CKgC2trbKiSee6EQ5btw4+cUvfmFJ1LgRdwIUAOOeYXfxmRYid1boBQEI9CWAtvgmIBAcAfQVHFssJ5uAV23xCnCyvxuid0fApC8KgO44Bt4rqALgI488Ir/97W8d/3faaSe54IILAo+HCSCgBCgA8h0oAdNCBCUIQMAbAbTljRujIOCGAPpyQ4k+EMidgFdtNbQXy6L6qtwn7DNi+vjlUl3ambcdDEDARgImfVEADClry5cvH3Sm0047zfn95MmTPRfpuru7pb29Xerr6+WVV16Rp556SvRn2Xb44YfLscceG1LUTJN0AhQAk/4FfBq/aSGCEgQg4I0A2vLGjVEQcEMAfbmhRB8I5E7Aq7bSaZEFtdXS0lWU+6T/GVFR1CUzJzRKKuXZBAMhYDUBk74oAIaUvp47/EKast9prrzyStl0000L6QJzJ4gABcAEJXuQUE0LEZQgAAFvBNCWN26MgoAbAujLDSX6QCB3Avloq7mjSBbWj5X16SE5Tzw01S37jG+SSl7/zZkdA6JDwKQvCoAh5dKGAuAOO+wgc+fODSlipoEAR4D5Bj4lYFqI4AQBCHgjgLa8cWMUBNwQQF9uKNEHArkTyFdb9W0l8lxjZU5FQC3+7VHdLOPLOnJ3mBEQiBABk74oAIaUzEIXAPVY8Q9+8AOprKwMKWKmgQAFQL4BCoB8AxAIkoDpD3lBzo1tCMSdAPqKe4aJr1AE/NCW7gR8sWm0q+PAeux3l7Er2flXqIQzb6gETPqiABhSOgpVAJw4caLMmDFDDjzwQCkuLg4pWqaBwKcEOALMl6AETAsRlCAAAW8E0JY3boyCgBsC6MsNJfpAIHcCfmlL7wRcsrZUXmgas5ETKUnLxPJ22WLkWqkq6eTOv9zTxIiIEjDpiwJgSIl9+umnB53phhtucH5fVVUlX/rSlzx5NnToUCkrK5Phw4fLlClTZMSIEZ7sMAgCfhCgAOgHxejbMC1E0Y+QCCBQGAJoqzDcmTUZBNBXMvJMlOET8FNbuhPwybrqHkGkM498lA9bL0VD0uEHx4wQKDABk74oABY4Qdnpe+4Q1Ec69LEOGgSiToACYNQz6I//poXIn1mwAoHkEUBbycs5EYdHAH2Fx5qZkkXAT23VtZXI3xrGOgBLhqyX2VPqkwWUaCHQg4BJXxQALflcKABakgjc8JUABUBfcUbWmGkhimxgOA6BAhNAWwVOANPHmgD6inV6Ca6ABPzU1sdry+SF5RvuuB9Z1CX7T2wsYHRMDYHCEjDpiwJgYfPjzP7LX/7S+f96BPiYY46xxDPcgIB3AhQAvbOL00jTQhSnWIkFAmESQFth0maupBFAX0nLOPGGRcBPbb3fMlxeax614d+jSzpkn5qmsEJhHghYR8CkLwqA1qUMhyAQHwIUAOOTy3wiMS1E+dhmLASSTABtJTn7xB40AfQVNGHsJ5WAn9p6a+VIeXvVSAflxLI22WNcc1LREjcEjI8vUgDkI4EABAIjQAEwMLSRMuznH/QiFTjOQiBgAmgrYMCYTzQB9JXo9BN8gAT81NarKyrkX6s3PHq56Yi18vmxqwL0HtMQsJuASV8UAO3OH95BINIEKABGOn2+OW9aiHybCEMQSBgBtJWwhBNuqATQV6i4mSxBBPzU1t8bR8uS1nKH3tYVq+WzlasTRJNQIdCbgElfFAAt+mL+/ve/S1tbm+PRTjvtJKNGbbjTIFdXV65cKa+99pozbMSIEfL5z38+VzP0h4BnAhQAPaOL1UDTQhSrYAkGAiESQFshwmaqxBFAX4lLOQGHRMBPbS2qHyMN7aWO59uPbpHPjFoTUiRMAwH7CJj0RQHQkpzV1dXJGWec4XgzevRouf7662XYsGGePVy3bp2cdtppooVAbfox6GMjlZUbXkrybJyBEHBBgAKgC0gJ6GJaiBKAgBAhEAgBtBUIVoxCwPlzc01NTeb/65/Tu7u7IQMBCPhAwM+164naKlnZWex49bkxK2Wzka0+eIkJCESTgElfFAAtyetdd90lDzzwgOPNnDlz5Mgjj8zbu/vuu0/mzZvn2Pnv//5vOeKII/K2iwEIuCFAAdANpfj3MS1E8SdAhBAIhgDaCoYrViGgBNAX3wEEgiHgp7b+8sk4aV2/YcPMtOoVMqm8PRjHsQqBCBAw6YsCoCVJPO+88+Sjjz5yvLn22mtFk5Nva2hokG9/+9uOma222kouu+yyfM0yHgKuCFAAdIUp9p1MC1HsARAgBAIigLYCAotZCFAA5BuAQGAE/Fy7Hvy4Rtalhzi+7jN+uVSVdgbmO4YhYDsBk74oAFqQwTVr1sjXvvY1x5PJkyfLT3/6U98861lc1A/i1ltvldLSDXcl+DYRhiDQhwAFQD4JJWBaiKAEAQh4I4C2vHFjFATcEEBfbijRBwK5E/BLW91pkfs/ntjLgVkTGqSieF3uTjECAjEhYNIXBUALEv3OO+/IxRdf7HgyY8YM+da3vuWbZzfeeKM8+eSTjr0f/ehHsuWWW/pmH0MQGIgABUC+DQqAfAMQCI6A6Q95wc2MZQjEnwD6in+OibAwBPzSVvv6IfLIJ5/e05lth0yuk9Kh3NdZmMwyqw0ETPqiAGhBlp566in51a9+5Xjy1a9+VQ4++GDfPPvzn/8st99+u2NPHwaZPn26b/YxBAEKgHwDgxEwLUTQgwAEvBFAW964MQoCbgigLzeU6AOB3An4pa2WzmEyv3ZcLweO2GSZDEnl7hMjIBAXAiZ9UQC0INN/+tOf5He/+11gBbpnnnkm86JwtvldYLQAIS5YSoAdgJYmJmS3TAtRyO4wHQRiQwBtxSaVBGIhAfRlYVJwKRYE/NJWY3uxPFNf5TAZluqWwzapiwUjgoCAVwImfVEA9ErWx3F9X+o966yzZNq0ab7NsHjxYrn66qsde7wE7BtaDBkIUADkE1ECpoUIShCAgDcCaMsbN0ZBwA0B9OWGEn0gkDsBv7S1tLVUFjeOcRwoH7ZODprUkLtDjIBAjAiY9EUB0IJkP/roo5mHObLt61//usyaNcs3zxYsWCA333yzY+/444+XQw45xDf7GILAQAQoAPJtUADkG4BAcARMf8gLbmYsQyD+BNBX/HNMhIUh4Je23m8pl9eaRztBjC7qlP0mLi9MUMwKAUsImPRFAdCCRD377LPy85//3PHk8MMPl2OPPdY3z+666y554IEHHHunn366/Nd//Zdv9jEEAQqAfAODETAtRNCDAAS8EUBb3rgxCgJuCKAvN5ToA4HcCeSjrXRapLGjWD5YPVx0B6BIzwv/0jKpvF02H7lWqks6JcVdgLknhxGRJ2DSFwVAC1L81ltvySWXXOJ4oi/06ku9frW5c+fK+++/75jTv99hhx38Mo8dCAxIgB2AfBxKwLQQQQkCEPBGAG1548YoCLghgL7cUKIPBHIn4FVbzR1F8mLTaGnpKjJOWlHUJbuMXSmVJV3GvnSAQJwImPRFAdCCbHd2dsqJJ54o69aty3ijSfvZz34mEyZMyNu7ZcuWydlnny1p/c8l/7F9yy23SHl5ed62MQABEwEKgCZCyfi9aSFKBgWihID/BNCW/0yxCIEsAfTFtwCBYAh40VZ9W4k811gp69NDXDs1NNUte1Q3y/iyDtdj6AiBqBMw6YsCoCUZ/uEPfyhvv/22483uu++eKdzl27SQ+Pzzzztm/N5dmK9/jI83AQqA8c6v2+hMC5FbO/SDAAR6E0BbfBEQCI4A+gqOLZaTTSBXbenOv4X1Y3Mq/mUJaxFwn/FN7ARM9ieXqOhN+qIAaMnn0PchEHUr38c6/vSnP8nvfve7XhH+z//8jxx66KGWRI0bcSdAATDuGXYXn2khcmeFXhCAQF8CaItvAgLBEUBfwbHFcrIJ5KItPcS2oLba1bHfgajqceCZExq5EzDZn11iojfpiwKgJZ9CV1eX6OMcK1eudDxKpVJyxBFHyJe+9KXMsWC3rbu7W+65557Mwx/Zo786dtSoUXLddddJcXGxW1P0g0BeBCgA5oUvNoNNC1FsAiUQCIRMAG2FDJzpEkUAfSUq3QQbIoFctNXQXiyL6qvy9m76+OVSXdqZtx0MQMB2AiZ9UQC0KIOPP/646P18fduUKVNk9uzZsueeew5avNO7BP/2t7/JI488IkuWLNnIzkknnSQHHHCARRHjStwJUACMe4bdxWdaiNxZoRcEINCXANrim4BAcATQV3BssZxsArloa3FjpSxtLcsb2KTyNplW3Zy3HQxAwHYCJn1RALQsg9dee22miNdfGzZsmGy66aaiBcERI0ZIaWmptLe3y5o1a+Tjjz+WDz/8ULTg0l/be++95dvf/rZl0eJO3AlQAIx7ht3FZ1qI3FmhFwQgQAGQbwAC4RFg7QqPNTMli4BbbXV1p+ThJTWSllTegFKSlkOn1EnRkE8fxqRBIK4ETPqiAGhZ5vUo8GWXXSbvvPOOb55tv/32csEFF4gWEGkQCJMABcAwads7l2khstdzPIOA3QTQlt35wbtoE0Bf0c4f3ttLwK22WjqHyfzacb4FMmtig1QUrfPNHoYgYCMBk74oAFqYNS2a3HbbbaJHgvNthxxyiOjDH7ncIZjvnIyHQJYABUC+BSVgWoigBAEIeCOAtrxxYxQE3BBAX24o0QcCuRNwq60VHUXyVF117hMMMGJGTaOMKenyzR6GIGAjAZO+KADamLX/+PTqq6/KH//4R3nvvfdy9nLbbbeVo48+WnT3Hw0ChSJAAbBQ5O2a17QQ2eUt3kAgOgTQVnRyhafRI4C+opczPI4GAbfaYgdgNPKJl3YRMOmLAqBd+erXGz0O/OKLL8qbb76ZuedPX/nt2zTRm2++uUydOlV222032WqrrSIQGS7GnQAFwLhn2F18poXInRV6QQAC/a39NTU1mR/X1dX1++cDqEEAAt4IsHZ548YoCJgIuNUWdwCaSPJ7CGxMwKQvCoAR+2rWrVsnLS0tmYc/2trapKysTEaOHJn5izv+IpbMBLhLATABSXYRomkhcmGCLhCAQD8E0BafBQSCI4C+gmOL5WQTyEVbvAKc7G+F6HMnYNIXBcDcmTICAhBwSYACoEtQMe9mWohiHj7hQSAwAmgrMLQYhgD31/INQCAgArmsXQ3txbKovipvT6aPXy7VpZ1528EABGwnYNIXBUDbM4h/EIgwAQqAEU6ej66bFiIfp8IUBBJFAG0lKt0EGzIB9BUycKZLDIFctJVOiyyorZaWriLPfCqKumTmhEZJpTybYCAEIkPApC8KgJFJJY5CIHoEKABGL2dBeGxaiIKYE5sQSAIBtJWELBNjoQigr0KRZ964E8hVW/9qKZdXm0eJSO4VvKGpbtlnfJNU8vpv3D8r4vsPAZO+KADyqUAAAoERoAAYGNpIGTYtRJEKBmchYBEBtGVRMnAldgTQV+xSSkCWEMhFW80dRbKwfqysTw/x4H1adqpcJVtUtHoYyxAIRJOASV8UAKOZV7yGQCQIUACMRJoCd9K0EAXuABNAIKYE0FZME0tYVhBAX1akASdiSMCttjj+G8PkE1LgBEz6ogAYeAqYAALJJUABMLm57xm5aSGCEgQg4I0A2vLGjVEQcEMAfbmhRB8I5E7ArbZ4ACR3toyAgElfFAAj9o10dHRIa2uraGHFa6uqyv8lJa9zMy5ZBCgAJivfA0VrWoigBAEIeCOAtrxxYxQE3BBAX24o0QcCuRNwq63FjZWytLUs9wn6jJhU3ibTqpvztoMBCESBgElfFAAtzqIW+v7617/KO++8I//85z+lqakpr8KfhppKpeTuu++2OGpcixMBCoBxyqb3WEwLkXfLjIRAsgmgrWTnn+iDJYC+guWL9eQScKOtru6UPLykRtIeHv7oSzYlaTl0Sp0UDUknFzqRJ4aASV8UAC38FNrb2+XOO++Up59+WnTHn99t3rx5fpvEHgT6JUABkA9DCZgWIihBAALeCKAtb9wYBQE3BNCXG0r0gUDuBNxoq6VzmMyvHZe78QFGzJrYIBVF63yzhyEI2ErApC8KgJZl7uOPP5af/vSnUldXF5hnFAADQ4vhPgQoAPJJUADkG4BAcARMf8gLbmYsQyD+BNBX/HNMhIUh4EZbKzqK5Km6at8cnFHTKGNKunyzhyEI2ErApC8KgBZlbsWKFfK9731PVq1aFahXFAADxYvxHgQoAPI5UADkG4BAcARMf8gLbmYsQyD+BNBX/HNMhIUh4EZb7AAsTG6YNfoETPqiAGhRji+88EJ57733enk0duxYmTZtmkyYMEHuv//+zD2A2XbKKadIZ2enrFmzRhoaGjJjly5d2mt8eXm5HHnkkTJy5Ejn5/vuu69FUeNKnAlQAIxzdt3HZlqI3FuiJwQg0JMA2uJ7gEBwBNBXcGyxnGwCbrTFHYDJ/kaI3jsBk74oAHpn6+vI1157Tf7v//6vl80ZM2bI1772NSkqKsr8/LzzzpOPPvrI6dPfTj49QvynP/1JFi5c6PQbPXp0ZmfhZptt5qvPGIOAiQAFQBOhZPzetBAlgwJRQsB/AmjLf6ZYhECWAPriW4BAMATcaotXgIPhj9V4EzDpiwKgJfm//PLL5dVXX3W82WGHHWTu3Lm9vHNTAMwOeP311+Xaa6+V1atXZ36kOwEvu+wymTRpkiUR40YSCFAATEKWzTGaFiKzBXpAAAL9EUBbfBcQCI4A+gqOLZaTTcCtthrai2VRfVXesKaPXy7VpZ1528EABKJAwKQvCoAWZLG7u1u++tWvZo7zZps+BDJ58mRFVPMjAAAgAElEQVTPBUAd+MEHH8ill14qbW1tGTta/Pvxj38sQ4cOtSBqXEgCAQqASciyOUbTQmS2QA8IQIACIN8ABMIlwNoVLm9mSw4Bt9pKp0UW1FZLS9enp+G8tIqiLpk5oVFSKS+jGQOB6BEw6YsCoAU5ff/993vt9tt8881FdwT2bbnsAMyOffTRR+XWW291TJ100klywAEHWBA1LiSBAAXAJGTZHKNpITJboAcEIEABkG8AAuESYO0KlzezJYdALtr6V0u5vNo8SkRyr+ANTXXLPuObpJLXf5PzcRGpmPRFAdCCj+Rvf/tb5rhuth188MGZHYGmAuCdd95p3M2nuwtPPfVUaW5uzpjTXYW6u5AGgTAIUAAMg7L9c5gWIvsjwEMI2EkAbdmZF7yKBwH0FY88EoV9BNxqq7mjSBbWj5X16SEegkjLTpWrZIuKVg9jGQKB6BIw6YsCoAW5ffzxx+WWW25xPPnGN74hM2fO3Miz888/Xz788EPn57fffruUlpYaI/j1r38t8+fPd/pdf/31UlWV/30KxonpkHgCFAAT/wlkAJgWIihBAALeCKAtb9wYBQE3BNCXG0r0gUDuBNxoi+O/uXNlBATc/HsXBUALvpP7779f7r77bseTM844Q/bcc8+NPLvooovk3XffdX5+4403ir7wa2p9C4znnnuu7LLLLqZh/B4CeROgAJg3wlgYcPMHvVgEShAQCJkA2goZONMligD6SlS6CTZEAm60xQMgISaEqWJFwKQvCoAWpPvhhx+WO+64w/HkrLPOkmnTpm3kWd+XgvXv9b5AU1u8eLFcffXVTrcTTjhBDjroINMwfg+BvAlQAMwbYSwMmBaiWARJEBAoAAG0VQDoTJkYAugrMakm0JAJuNHW4sZKWdpalrdnk8rbZFr1p1dh0SCQBAImfVEAtOArWLBggdx8882OJ6eccorsu+++G3mm9wTqfYHZNlChsO/AZ555RvTYb7bNmTNHjjzySAsix4W4E6AAGPcMu4vPtBC5s0IvCECgLwG0xTcBgeAIoK/g2GI52QRM2urqTsnDS2ok7eHhj75kU5KWQ6fUSdGQdLKhE31iCJj0RQHQgk/h1Vdf7fXq73HHHSezZ8/eyLM//vGPcs899zg/P+SQQ+T44483RqCPhTz44INOv4HsGw3RAQI5EqAAmCOwmHY3LUQxDZuwIBA4AbQVOGImSDAB9JXg5BN6oARM2mrpHCbza8f55sOsiQ1SUbTON3sYgoDNBEz6ogBoQfYaGxvl9NNPdzzZb7/95OSTT97IsxdeeEF+8pOfOD+fMGGCXHPNNcYIzjzzTKmtrXX6nXbaaTJ9+nTjODpAIF8CFADzJRiP8aaFKB5REgUEwieAtsJnzozJIYC+kpNrIg2XgElbKzqK5Km6at+cmlHTKGNKunyzhyEI2EzApC8KgJZk76STTpK1a9dmvNlmm23kkksu2cizNWvWZAqDWlTJtu985zuy1157DRhF3wdAtOOPf/xj2WSTTSyJHDfiTIACYJyz6z4200Lk3hI9IQCBngTQFt8DBIIjgL6CY4vlZBMwaYsdgMn+Pog+PwImfVEAzI+vb6OvuuoqeemllzL2ioqK5NZbb838b992xRVXyCuvvOL8uLS0NLN7cNddd92orxb/1E53d7fzO301WF8PpkEgDAIUAMOgbP8cpoXI/gjwEAJ2EkBbduYFr+JBAH3FI49EYR8Bk7a4A9C+nOFRdAiY9EUB0JJc/uUvf5HbbrvN8Wbu3Lmyww47bOTdG2+8IZdddtlGP58yZUpm5+CIESNk9erVov3q6+s36nfsscfK4YcfbknUuBF3AhQA455hd/GZFiJ3VugFAQj0JYC2+CYgEBwB9BUcWywnm4AbbfEKcLK/EaL3TsCkLwqA3tn6OnLFihWir/9mm74C3PPve0529dVXy+LFi3Oef+LEiaI7CEtKSnIeywAIeCFAAdALtfiNMS1E8YuYiCAQDgG0FQ5nZkkmAfSVzLwTdfAE3Girob1YFtVX5e3M9PHLpbq0M287GIBAVAiY9EUB0KJMXnTRRfLuu+9mPNIinR7VLSsr28jDtra2zC7A999/37X3o0aNkosvvlgmTZrkegwdIZAvAQqA+RKMx3jTQhSPKIkCAuETQFvhM2fG5BBAX8nJNZGGS8CNttJpkQW11dLStfGVWG69rSjqkpkTGiWVcjuCfhCIPgGTvigARjTHWgS8/fbb5emnn5a0/hNykLbttttm7gmsqsr/v6JEFBduF4gABcACgbdsWtNCZJm7uAOByBBAW5FJFY5GkAD6imDScDkSBNxqq7mjSBbWj5X16SE5xzU01S37jG+SSl7/zZkdA6JNwKQvCoDRzq98/PHH8uyzz2bu/Fu+fHnm/j/dPVhZWZm5E3DPPfeU7bffPuJR4n5UCVAAjGrm/PXbtBD5OxvWIJAcAmgrObkm0vAJoK/wmTNjMgjkoq36thJ5rrEypyKgFv/2qG6W8WUdyQBKlBDoQcCkLwqAfC4QgEBgBCgABoY2UoZNC1GkgsFZCFhEAG1ZlAxciR0B9BW7lBKQJQRy1ZbuBHyxabSr48B67HeXsSvZ+WdJrnEjfAImfVEADD8nzAiBxBCgAJiYVA8aqGkhghIEIOCNANryxo1REHBDAH25oUQfCOROwIu29Mar5R36MMhYSUvvS/1SkpaJ5e2yxci1UlXSyZ1/uaeEETEiYNIXBUALkv3JJ59s9Krv4YcfLsOGDbPAO1yAgHcCFAC9s4vTSNNCFKdYiQUCYRJAW2HSZq6kEUBfScs48YZFwKu2utMi9388sZebe1Q1SXVZpxQNGfxO/LBiYx4IFJqASV8UAAudIRF59NFH5dZbb3U82XrrreV///d/LfAMFyCQHwEKgPnxi8to00IUlziJAwJhE0BbYRNnviQRQF9JyjaxhknAq7ba1g2RPy+t6eXq7Ml1UjK0O0z3mQsCVhMw6YsCoAXpu++++2TevHmOJ8ccc4wcddRRFniGCxDIjwAFwPz4xWW0aSGKS5zEAYGwCaCtsIkzX5IIoK8kZZtYwyTgVVsrO4fJE7XjerialiM3qeXIb5jJYy7rCZj0RQHQghQ++OCDcueddzqenHLKKbLvvvta4BkuQCA/AhQA8+MXl9GmhSgucRIHBMImgLbCJs58SSKAvpKUbWINk4BXbemLwH9tGOu4WjJkvcyeUh+m68wFAesJmPRFAdCCFD7xxBNy0003OZ6cccYZsueee1rgGS5AID8CFADz4xeX0aaFKC5xEgcEwiaAtsImznxJIoC+kpRtYg2TgFdtfby2TF5YXum4OrKoS/af2Bim68wFAesJmPRFAdCCFL711ltyySWXOJ6ccMIJctBBB1ngGS5AID8CFADz4xeX0aaFKC5xEgcEwiaAtsImznxJIoC+kpRtYg2TgFdt/bNluLzePMpxtaqkQ/apaQrTdeaCgPUETPqiAGhBCjs7O+XEE0+UdevWZbz5r//6Lzn99NMt8AwXIJAfAQqA+fGLy2jTQhSXOIkDAmETQFthE2e+JBFAX0nKNrGGScCrtv7RPFLebRnpuDqpvE2mVTeH6TpzQcB6AiZ9UQC0JIU/+clP5IUXXsh4M2LECLnhhhukuLjYEu9wAwLeCFAA9MYtbqNMC1Hc4iUeCIRFAG2FRZp5kkgAfSUx68QcBgGv2nqpaZR8uGa44+LmI9bKzmNXheEyc0AgMgRM+qIAaEkq33vvPbnwwgsdb44++mj50pe+ZIl3uAEBbwQoAHrjFrdRpoUobvESDwTCIoC2wiLNPEkkgL6SmHViDoOAV20921AptW1ljovbjlotU0evDsNl5oBAZAiY9EUB0KJU3nLLLfL4449nPNLEnXnmmbL77rtb5CGuQCA3AhQAc+MV196mhSiucRMXBIImgLaCJoz9JBNAX0nOPrEHScCrtp6uq5Kmjg0n5HYas1K2GNkapKvYhkDkCJj0RQHQopRqseSnP/2pvPTSS04R8JBDDsnsBCwpKbHIU1yBgDsCFADdcYp7L9NCFPf4iQ8CQRFAW0GRxS4EPv2P8TU1NRkUdXV10t3dDRYIQMAHAl619djScbJm3TDHg92rVsjk4e0+eIQJCMSHgElfFAAty7X+4eKee+6RBx980PmDRllZmey5556y3XbbyWabbSajRo0S/ZkmN6ptzZo18s4778iKFSuktbVVKisrRT/GrbfeuiBxtbe3y9tvvy1NTU2ivlVUVEh1dbVsu+22MmzYhoUmH94a6/vvv5+JuaOjQ8aMGSMTJ06ULbbYIh+zVo+lAGh1ekJzzrQQheYIE0EgZgTQVswSSjhWEUBfVqUDZ2JEwKu2Hvq4RrrSG/79d/r45VJd2hkjMoQCgfwJmPRFATB/xr5YmDNnji92TEZSqZTcfffdpm6B/X7ZsmVy5513yssvv+y8etxzMi0E7rfffnLkkUf6VngbLJjm5uaMP4sXL84U5fq24cOHy/Tp00XzU15e7omLFv2U+RtvvCHpdHojGyrCgw46KPOX5sev1tbWJmeffXamqNmzXXzxxZlichiNAmAYlO2fw7QQ2R8BHkLATgJoy8684FU8CKCveOSRKOwj4EVbHetT8qdPJvQKZtaEBqkoXmdfgHgEgQISMOmLAmABk9Nz6rAKgDrnvHnzChL1M888IzfffHO/hba+DulOx3POOUfGjRsXmK+vvfaaXHvttbJ6tfnyWBWK+rPpppvm5I/u5NTinxbCTO2zn/2snHXWWZlXoP1ov/71r507JSkA+kEUG14JmBYir3YZB4GkE0BbSf8CiD9IAugrSLrYTjIBt9rSfRONHcXywerhsqy1VNLSe6PEhLJ22bJijVSXdIqPeyiSnBpijwEBk74oAFqS5LgXAHXH35VXXtlrB9yECRMyO9G04FVfX5+5+7Czc8M27smTJ8tll13meefdYKn94IMPRHfC9dz1p7sPd955Zxk9erQsX74848/atWsdM/rzyy+/XMaOHevqq5k/f36m4NmzaQFxm222ydzpuHTpUnnllVd6FQeVx9y5c/Pe/fjuu+/KRRdd1O+OQ3YAukofnXwkYFqIfJwKUxBIFAG0lah0E2zIBNBXyMCZLjEE3GiruaNIXmwaLS1dRUYuFUVdssvYlVJZ0mXsSwcIxJ2ASV8UAC35AuJcANRjtvqisR5J1abHXI877jg5+OCDe93319LSIldffbW8+eabTlb07kMd62fTIuMZZ5zR62js7Nmz5ctf/nKvwpv6e+ONN8qzzz7rTK93FGpR0tQ+/PBDueCCC5ziXlFRkZxyyimy99579xqqhc+rrrpKlixZ4vz88MMPz/jita1bt07OO+88+eSTTzImtLCpOcg2CoBeyTLOKwHTQuTVLuMgkHQCaCvpXwDxB0kAfQVJF9tJJmDSVn1biTzXWCnre9z3Z+I1NNUte1Q3y/iyja90Mo3l9xCIEwGTvigAWpLtP/zhD6F5oq8Kh9n6HkU95phj5Oijj+7XBS3OnX/++Zndcdq0WHjFFVdkHj/xqz300ENyxx13OOZmzJiRKc711/RRlh/96EeZ+/uy7bvf/a7stttug7qjOwV1d1+2nX766Zm7BPtrWvjU48WrVq3K/Lq4uFh+8YtfZAp3Xpo+InPvvfdmhuqORn3QZOHChY4pCoBeqDImHwKmhSgf24yFQJIJoK0kZ5/YgyaAvoImjP2kEhhMW7rzb2H92JyKf1mOWgTcZ3wTOwGT+mERd4aAae2iAMiHEiiBlStXyqmnnuo8+KEfnO7yG+xl3X/84x9y6aWXOn5psU2Lbn403R33zW9+07n3Tx/2uO666wa9d6+uri6zYzD7gIcWI/U480Dt3//+d6aImW36kvAll1wyqPtPPvmk/OpXv3L66I7E448/PueQddef7v7TOLWQ+LOf/Uy0uEwBMGeUDPCRgGkh8nEqTEEgUQTQVqLSTbAhE0BfIQNnusQQGEhbeuffgtpqV8d+B4Klx4FnTmjkTsDEfE0E2peAae2iAMg3EyiBBQsWyE033eTM8ZWvfEUOO+ww45z6GEZ2F6Aen73llluktLTUOM7UQR/+0B192bb//vvL17/+ddOwzBgdm226Q0/F01/TV4UfeOAB51d6hFmPMg/WdOejFiazdw7qPYM33HCD0a+eHbRAqff+6f1/2vQYsR4nvv766ykA5kSSzn4TMC1Efs+HPQgkhQDaSkqmibMQBNBXIagzZxIIDKSthvZiWVRflTeC6eOXS3Xphnvl8zaIAQhEiIBp7aIAGKFkRtFVPb6rD4C4KZz1jE9fzr3vvvucH7k5duuGjxYSH3vsMaerFsy2335749C+O/R0d57u0uuv6XHe7J1+utPxtttuy+zGMzUtKi5atMjpprsMczn6/Oijj8pvfvObzPgpU6Zkdinq/BQATeT5fdAETAtR0PNjHwJxJYC24ppZ4rKBAPqyIQv4EEcCA2lrcWOlLG0tyzvkSeVtMq16w/3neRvEAAQiRMC0dlEAjFAyo+jqCSecIK2trRnXR40atdGruAPFpPfn6T162aYPhqitfNu5554rH330UcaMikOLc252FurR2rPPPtuZftdddxW11betWbNGTjrpJOfHW221Va8dh4P5//jjj4vel5htGq/G7aY1NTVl/NOHS/TeRD1yrK8Na6MA6IYgfYIkYFqIgpwb2xCIMwG0FefsEluhCaCvQmeA+eNKoD9tdXWn5OElNZKWVN5hpyQth06pk6Ih6bxtYQACUSNgWrsoAEYtoxHyd8WKFfKtb33L8XinnXaS73//+64i0LsDTz75ZKfvjjvuKHPnznU1dqBO+qCHvj7c1fXpE/GTJk3K3EfopunxWh2rR3W1TZw4Ua655pqNhurx2wsvvND5+axZs+Qb3/iGmynkvffekx/84AdO35kzZ/ZiMJgR3e330ksvZbrst99+mePE2UYB0BV+OgVIwLQQBTg1piEQawJoK9bpJbgCE0BfBU4A08eWQH/aaukcJvNrx/kW86yJDVJRtM43exiCQFQImNYuCoBRyWQE/ez7mEcuBS0tuOl9gfqYhbbq6urMTrZ8Wn19vXz72992TORaVNSHQGprazPjhw4dKr/73e82esyk71HhY489Vo444ghXbvctmE6dOlV++MMfGsc+++yzTjFSX/zVwuSIESMoABrJ0SEsAqaFKCw/mAcCcSOAtuKWUeKxiQD6sikb+BInAv1pa0VHkTxVV+1bmDNqGmVMyaebPmgQSBIB09pFATBJX0PIsT7zzDOZF3azbc6cOXLUUUe59uL000+XhoYGp+B21113uR7bX8e3335bLr74YudXX/jCF3rtUDQZ12O1b775ptNNH+nQxzp6tnvvvVfuuece50caw/Tp002mM7/XHYpa9Fy/fn3m71Wcei/gYE2PHOuDKatWrcp0628+dgC6wk+nAAmYFqIAp8Y0BGJNAG3FOr0EV2AC6KvACWD62BJgB2BsU0tgFhAwrV0UAENI0vnnn+/MokdH/z97bwJlV1Ht/++b5PaUpJNOd6c7nTBIAgIyiIxBSAQSkMHH8PiDgCgqwkPm4PD4BURdgIr6mEQmB/QxJOpjcgQiGIIEJMwyKYYhU88dOknP6ftf+8Z7093p7jr33HPOPafOp9ZiuVa6atfen13bSn9TdUpPksWhDf2m3ec+9zk59thjHYc+8Ht9Oujuu+929JjGSBPk+13B6667TlasWJE1r9eH9RrxwKY+Pvzww9k/yvXxEmWk3/HT5uSbiSpCPvHEE+n+e+6556DrxxknvBYA9XuDpjZ58uT0KUkVM5uamkzd+bnlBHQjmjp1y7UOFfVV7KZBAAL5E6C28meIBQiMRID6Ym1AwB8Cw9WWfgPwofemevYNwON3aOQbgP6kD6shJ2Dau/Rmpf6ebnNLpPQ+aQGbnnzLtB133DH9OqvTtnLlSunq6sp212uhUWkqhKkglmlnn322HHnkkY7d1+8Fvv3229n++kCGXnF125YvXz7om3/HH398+sSd06aCn9rINH2kZObMmYOGq48qfGaaxqDfPnTa9HuBmdN8xcXF6WvGI7WBV6yTyaT84Ac/kGnTpm3T3WsB8JRTTjGGM9zpSOMgOkAAAhCAAAQgAAEIQAACsSTwx7dE3m7NP/RZlSJH75K/HSxAAALRJBBpAfBrX/ta9tVafd110aJFkcnC0Ouw+iCIXrt12vS6rl7bzbQf//jHUlVV5XT4Nv2GXknW68gDxVmTYb3OrDYyTa8E77bbboOGDTyRpz/4xje+IXvssYfJdPbn5513nmRO2Gm+Fy9ePOxYfYxETxfW19enf66i3MknnzxsXwRAx/jpCAEIQAACEIAABCAAAQgUgMDqD0QeeD3/iU/cXWTGpPztYAECEIgmAWsEQMU/kiAUxtRwAlDSrx77cQLwnnvukYceeiiddr1W/v3vf1/0FOBwzWsBkCvAYay2cPtkOooebu/xDgLhJUBthTc3eBZ9AtRX9HNIBOEkMFJt6Z29R9dUSnvv8L/TOImmPNkrR05vkUTCSW/6QMA+Aqa9iyvAAeQ8nyvAA08ARk0A5BuAkj6ld8ABBzheZU6+Afjuu+/K5Zdfnn0sxHTK0GsB0HEwImkf9fVlWrwJmD5GG286RA8B9wSoLffsGAkBEwHqy0SIn0PAHYHRaqutOylLGyplc2pMzsbHJvplbk2LVPD6b87sGGAPAdPexSMgAeQ6rgLg0Cu3o11THS4Nfr8CfNhhh4leuXXagngF+PTTT88+kDDcK8D6eMLChQvlX//6V9rtuXPnyvnnnz9qCAiATjNMP78ImDYiv+bFLgRsJ0Bt2Z5h4iskAeqrkPSZ22YCptpq6CyW5U0VOYmAKv7Nrm6TmtJum9ERGwSMBEz1hQBoRJh/h7gKgAMfqVCK8+bNk3POOccRUH23RR/o6OvrS/fXo6oqZOXT9CTahRdemDWx9957p8U0p+2iiy7KfnNPX87RBzrGjRs3aPjjjz8ut912W/bPTjvtNDnxxBMdTdHa2ir6ncRM0wdfvvnNbw4aO1BUnTBhgtxwww3Gh1EQAB3hp5OPBEwbkY9TYxoCVhOgtqxOL8EVmAD1VeAEML21BJzUlp4EXNEy2dF1YL32u1/lek7+WbtiCCwXAqb6QgDMhabLvnEVANva2uTcc891JbitX79+kFio39HT7+nl0/T03Gc/+1nRBzS06bfzVEBz0lSQ/MxnPiO9vb3p7tOnTx/0onDGxj/+8Q+54oorsiZzET2Hjp0/f77oq8AD2+9//3v5xS9+kf4jfSRE/zM19X3gQ9hDx+kVYr9el+YKsCk78fi5aSOKBwWihID3BKgt75liEQIZAtQXawEC/hBwWlv6TcDm7iL5W3OFdG0eO8iZhKSkrqxLZk7cJFXFPXzzz59UYTWCBEz1hQAYQFLjKgAq2rPOOks6OjrSlCdNmiR33nmnI+IvvPCCfPe73832PeaYY9K28m1f/epXs68qa3HcddddUlJSYjS7evVqWbBgQbbf/vvvL2praNu0aZN8/vOfz/7xrFmz5NprrzXa1w6PPPKI/PSnP8321Xg17oFtoADoyKiDTvra8kc+8hEHPXPvggCYOzMbR5g2IhtjJiYIBEGA2gqCMnPElQD1FdfME7ffBHKtrb/UV0pLd3HWrQ+Xt8uHJ22S5JiU365iHwKRI2CqLwTAAFIaZwFQRTwV8zLtpptuktraWiP1RYsWyf3335/tl+tjGiNNoAKbCm2ZduWVV8qee+5p9OfPf/6z3H777dl+epLwuOOOG3bcZZddJqtWrUr/TK8K64m9oqIi4xzK5qmnnsr2+973vicf+tCHBo1DADRipEMICZg2ohC6jEsQiAQBaisSacLJiBKgviKaONwOPYFca+tPa6bKpr6tn106sKpVZozvCn2cOAiBQhAw1RcCYABZibMAuGTJErnjjjuylPWRixNOOMFI/ZJLLpG1a9em+yWTyfTJOCcn9UyGX3nlFbn66quz3Ya7ZjucjWuuuUZefvnl7I9uvvlm0eIZrt13333ywAMPZH+ksRx88MGjuqbXkvW6tJ4g1FZZWSm33nqrKRxHP+cbgI4w0clHAqaNyMepMQ0BqwlQW1anl+AKTID6KnACmN5aArnW1kPv10rfgFeB59Q0S3XJlk860SAAgcEETPWFABjAiomzAKjf8vvyl7+cfcxDF9z111+/zeMZA9Mw9PGQAw44QPQEoBdNHxXRhzba29vT5srKytKPi4wfP35E8/X19XLxxRdnv6Onp/L0dN5I7Z133pGvf/3r2R/vtttuoi8Ij9aGPh6ipwv1lKEXDQHQC4rYyIeAaSPKxzZjIRBnAtRWnLNP7H4ToL78Joz9uBLIpbY294s8uKpuEKoj6xpkYnJzXPERNwRGJWCqLwTAABZQnAVAxTv02u0pp5wiJ5988rDk9STcf//3f4t+c0+bPlih14iHXoXNDG5sbJQLLrgga8vJa8EPP/yw3H333dkxhx12mJx33nnD+qMPh+jpv1dffTX7cyfXkYdefVYf58yZM+wcKkbqteEPPvgg/XO9LqwnDCsqKjxZnQiAnmDESB4ETBtRHqYZCoFYE6C2Yp1+gveZAPXlM2DMx5ZALrW1qW+s/GnN4FtXn9punRTx/b/Yrh8CH52Aqb4QAANYQXEXAFtbW+XSSy+Vzs7OrKh35plnph+40AWaaSqE6enA1157LftnenVWr9CO1NwIgCoy6om+lpaWrFk9cafXk8eN2/p9CfVXv/v39NNPZ/vtsssug64Qj+TXu+++K5dffrnoIxja9BqzioyHHHLIoCHqv54mzHwzUH+oV6TVF68aAqBXJLHjloBpI3Jrl3EQiDsBaivuK4D4/SRAfflJF9txJpBLbbV2J+WJ+uosrjGSkhO2X8erv3FeQMQ+KgFTfSEABjSUiwIAACAASURBVLCA4i4AKmJ9CESFrpS+5/7vNm3aNNljjz1kwoQJotdsn3/+eVFxLtNmzJiRFtv0mu5IzY0AqLZWrlwp+vptd3d31rSeuNtnn31k8uTJ0tzcnPYn800+7aR//p3vfCf9fT4n7dFHH5Wf/OQng7ruuOOOsuuuu0pxcbGsWbNGXnzxxaxIqB31Nd6FCxeOekXaydwD+yAA5kqM/l4TMG1EXs+HPQjEhQC1FZdME2chCFBfhaDOnHEgkEttre0okeVNU7JYysb2ydEzGuOAiRgh4IqAqb4QAF1hzW0QAuAWXk8++aTceeedg0S3kUiqUKZXbadOnToqbLcCoBp96aWX0ldtN2zYYEyo+qHXdEe6ijySgQcffFAWL148SOQbqa+KoQsWLEgLol42BEAvaWLLDQHTRuTGJmMgAAFJn6Kvra1No9B/SNPPVtAgAAFvCFBf3nDECgSGEsiltlZuKJMXWydnTVQU9cjh05qBCgEIjEDAVF8IgAEsnYECoL5ku9NOOzme9V//+tcgwWz33Xd3PHa4jvpNvW984xt52chnsL7se88996RPBGauxw60p6fwjjjiCDnppJMcnYLLRwDUedva2tL+PPvss8MKk/o4iH67T3M42knE0Zj885//lEWLFok+bjLwBGRmjBbh0Ucfnf5P8+N1QwD0mij2ciVg2ohytUd/CEBgCwFqi5UAAf8IUF/+scVyvAnkUltvrJ8gr39QngVWW9olH5/aGm+ARA+BUQiY6gsBMIDlM1AADGA64xR6Iq3QTU/dvfXWW+nv8Om39vR6rZ6y0+uxA78LGJSfXV1d8vrrr6f92bhxo0yaNEmqqqpEX/DV7/d50fRbiCoG6v/qVWcVO+vq6mTWrFlemA+lDRV5GxoaQukbTgVHwLQRBecJM0HALgLUll35JJpwEaC+wpUPvLGHQC619WLrJFm5YXw2+B0nbJJ9K7c8nEiDAAS2JWCqLwTAAFYNAmAAkJkilAQQAEOZlsCdMm1EgTvEhBCwhAC1ZUkiCSOUBKivUKYFpywgkEttPdNUIWs6SrNRf7h8g+xRYf58kwWYCAECrgiY6gsB0BXW3AYhAObGi972EEAAtCeX+URi2ojysc1YCMSZALUV5+wTu98EqC+/CWM/rgSc1lZvf0KerK+U9b1FWVR7V3wgs8o3xRUdcUPASMBUXwiARoT5d0AAzJ8hFqJJAAEwmnnz2mvTRuT1fNiDQFwIUFtxyTRxFoIA9VUI6swZBwKj1VYqJdLUXZS+9qsvAKdk8PfR9RGQPSrapbq4R3z4dHoc8BOj5QRMexcCYAALoKmpKYBZnE9RXV3tvDM9IZAHAQTAPOBZNNS0EVkUKqFAIFAC1FaguJksZgSor5glnHADIzBSbbV1J2VFy2Rp7zV/f7082Sv7Va6XiuLewPxmIghEgYBp70IAjEIW8RECESWAABjRxHnstmkj8ng6zEEgNgSordikmkALQID6KgB0powFgeFqq6GzWJY3Vcjm1BjHDMYm+mV2dZvUlHY7HkNHCNhOwLR3IQDavgKIDwIFJIAAWED4IZratBGFyFVcgUCkCFBbkUoXzkaMAPUVsYThbmQIDK2tls6xsrShMifxLxOsioBza1o4CRiZ7OOo3wRMexcCoN8ZwD4EYkwAATDGyR8QumkjghIEIOCOALXljhujIOCEAPXlhBJ9IJA7gYG1tW5dvTy6ptLRtd+RZtLrwPOmNfFNwNxTwQgLCZj2LgRAC5NOSBAICwEEwLBkorB+mDaiwnrH7BCILgFqK7q5w/PwE6C+wp8jPIwmgYG19co7rbK0fkregcypaZbqkp687WAAAlEnYNq7EACjnmH8h0CICSAAhjg5Abpm2ogCdIWpIGAVAWrLqnQSTMgIUF8hSwjuWENgYG098HKXrO4oyTu26WWdclB1W952MACBqBMw7V0IgFHPMP5DIMQEEABDnJwAXTNtRAG6wlQQsIoAtWVVOgkmZASor5AlBHesIZCprZ4+kTueS0lKEnnHlpCUfGq7ekmOSeVtCwMQiDIB096FABjl7OI7BEJOAAEw5AkKyD3TRhSQG0wDAesIUFvWpZSAQkSA+gpRMnDFKgKZ2mrtELnnZe9Cm1/XKOXJPu8MYgkCESRg2rsQACOYVFyGQFQIIABGJVP++mnaiPydHesQsJcAtWVvboms8ASor8LnAA/sJJCprfoNIr/+u3cxHlbbJFOKe70ziCUIRJCAae9CAIxgUnEZAlEhgAAYlUz566dpI/J3dqxDwF4C1Ja9uSWywhOgvgqfAzywkwAnAO3MK1GFg4Bp70IADEee8AICVhJAALQyrTkHZdqIcjbIAAhAIE2A2mIhQMA/AtSXf2yxHG8CfAMw3vknen8JmPYuBEB/+WMdArEmgAAY6/RngzdtRFCCAATcEaC23HFjFAScEKC+nFCiDwRyJ8ArwLkzYwQEnBIw7V0IgE5J0g8CEMiZAAJgzsisHGDaiKwMmqAgEAABaisAyEwRWwLUV2xTT+A+ExhYW6+80ypL66fkPeOcmmapLunJ2w4GIBB1Aqa9CwEw6hnGfwiEmAACYIiTE6Brpo0oQFeYCgJWEaC2rEonwYSMAPUVsoTgjjUEBtbWunX18uiaSmnvTbqOrzzZK/OmNUki4doEAyFgDQHT3oUAaE2qCQQC4SOAABi+nBTCI9NGVAifmBMCNhCgtmzIIjGElQD1FdbM4FfUCQytrZbOsbK0oVI2p8bkHNrYRL/MrWmRCl7/zZkdA+wkYNq7EADtzDtRQSAUBBAAQ5GGgjth2ogK7iAOQCCiBKitiCYOtyNBgPqKRJpwMoIEhquths5iWd5UkZMIqOLf7Oo2qSntjiAFXIaAPwRMexcCoD/csQoBCIgIAiDLQAmYNiIoQQAC7ghQW+64MQoCTghQX04o0QcCuRMYqbbaupOyomWyo+vAeu13v8r1nPzLHT8jLCdg2rsQAC1fAIQHgUISQAAsJP3wzG3aiMLjKZ5AIFoEqK1o5Qtvo0WA+opWvvA2OgRGq61USqS5u0ieb5ksm/rGDQoqISmpK+uSmRM3SVVxD9/8i07K8TRAAqa9CwEwwGQwFQTiRgABMG4ZHz5e00YEJQhAwB0BassdN0ZBwAkB6ssJJfpAIHcCTmrr2abJsrqjLGt8h/GbZO8p7ZIck8p9QkZAIEYETPWFABijxUCoEAiaAAJg0MTDOZ9pIwqn13gFgfAToLbCnyM8jC4B6iu6ucPzcBNwUltL6yulubs4G8heFR/IzuWbwh0Y3kEgBARM9YUAGIIk4QIEbCWAAGhrZnOLy7QR5WaN3hCAQIYAtcVagIB/BKgv/9hiOd4EnNTWI2umysYBV4APqGqV7cZ3xRsc0UPAAQFTfSEAOoBIFwhAwB0BBEB33GwbZdqIbIuXeCAQFAFqKyjSzBNHAtRXHLNOzEEQcFJbD71fK32pMVl35tQ0S3VJTxDuMQcEIk3AVF8IgJFOL85DINwEEADDnZ+gvDNtREH5wTwQsI0AtWVbRoknTASorzBlA19sImCqrc6+MfKHNbWDQj6yrkEmJjfbhIFYIOALAVN9IQD6gt0/o/39/bJx40bp6dnyLyBVVVX+TYZlCORJAAEwT4CWDDdtRJaESRgQCJwAtRU4ciaMEQHqK0bJJtRACQxXW/r6b1N3kazcMF7WdpRIShKDfJpW2imzyjdJNa//BporJoseAdPehQAY8py+99578txzz8nrr78u77zzjnR0dGQ9TiQSsmjRohEj0L4qGGZaSUmJjBs3+Dn1kIePexEngAAY8QR65L5pI/JoGsxAIHYEqK3YpZyAAyRAfQUIm6liRWBobbV0jpUVLZOlvTdp5FCe7JX9KtdLRXGvsS8dIBBHAqa9CwEwpKtCxT4V91566aVRPVy8ePGIP//Rj34ky5Yty/78iCOOkHPOOSekEeOWjQQQAG3Mau4xmTai3C0yAgIQUALUFusAAv4RoL78Y4vleBMYWFsvv9Mqf22YLJsHfO/PRGdsol9mV7dJTWm3qSs/h0DsCJj2LgTAEC6JRx55RH75y19KX1+f0bvRBMBVq1bJV77ylayNsrIyueOOOySZNP/rinFiOkDAAQEEQAeQYtDFtBHFAAEhQsAXAtSWL1gxCoE0AeqLhQABfwhkaqtxo8hv/t6fk/iX8UhFwLk1LZwE9CdFWI0wAdPehQAYsuT++te/lt/85jeOvRpNAFQj11xzjbzyyitZewsWLJADDzzQsX06QiAfAgiA+dCzZ6xpI7InUiKBQLAEqK1geTNbvAhQX/HKN9EGR0Brq6amVu59WaS10/28eh143rQmSQz+XKB7g4yEgAUETHsXAmCIkvz000/LjTfeuI1HtbW1ss8++8jUqVPld7/7nbS0tGT7mATAJ554Qm677bZsf64BhyjhMXAFATAGSXYQomkjcmCCLhCAwDAEqC2WBQT8I0B9+ccWy/EmoLXVV1orD7yeP4c5Nc1SXbLlcUwaBCBgPr2OABiSVdLV1SUXXnihtLe3Zz0qLS2Vs88+Ww455JDsn33ta18TfRgk00wC4KZNm9I2Mo+BqIh48803hyRq3LCdAAKg7Rl2Fh+/RDnjRC8I5EqA2sqVGP0h4JwA9eWcFT0hkAsBra0XP6iVt1tzGTV83+llnXJQdVv+hrAAAUsImPYuBMCQJPrBBx+U++67L+uNvtj77W9/W3bYYYdBHuYqAOrgoWN+/vOfi34PkAYBvwkgAPpNOBr2TRtRNKLASwiEjwC1Fb6c4JE9BKgve3JJJOEisFnGykPv1UjKA7cSkpJPbVcvyTFeWPPAIUxAoMAETHsXAmCBE5SZ/rLLLpPVq1dnvTn33HPl8MMP38Y7NwKgXgHWq8CZdvXVV8vOO+8ckshxw2YCCIA2Z9d5bKaNyLklekIAAgMJUFusBwj4R4D68o8tluNNYGNfUh5ZU+0ZhPl1jVKeND+e6dmEGIJAiAmY9i4EwBAkr7W1Vc4777ysJ1VVVelrupq8oc2NAPjwww/LPffckzWlV40HXisOAQJcsJQAAqClic0xLNNGlKM5ukMAAv8mQG2xFCDgHwHqyz+2WI43gfW9xfLntZWeQTistkmmFPd6Zg9DEIgyAdPehQAYguyuWLFCvv/972c9mT9/fvq7fcM1NwLg448/LrfffnvW3Be+8AU56qijQhA5LthOAAHQ9gw7i8+0ETmzQi8IQGAoAWqLNQEB/whQX/6xxXK8CXACMN75J3p/CZj2LgRAf/k7sj5UoPvSl74k8+bN80wAfOaZZ+T666/P2jvttNPkhBNOcOQbnSCQDwEEwHzo2TPWtBHZEymRQCBYAtRWsLyZLV4EqK945ZtogyPANwCDY81M8SNg2rsQAEOwJoY+AHLppZfKQQcd5JkA+OSTT8ott9yStXfGGWfIf/zHf4QgclywnQACoO0ZdhafaSNyZoVeEIDAUALUFmsCAv4RoL78Y4vleBNIJMbIQ+/XSm9//hx4BTh/hliwi4Bp70IADEG+//CHP8gvfvGLrCcXXHCBHHrooZ4JgEPtj/TASAhQ4IJlBBAALUuoy3BMG5FLswyDQOwJUFuxXwIA8JEA9eUjXEzHmkBzd4ksrZ/iCYM5Nc1SXdLjiS2MQMAGAqa9CwEwBFl+6qmn0o9+ZNrnPvc5OeaYYzwTAH/84x/L0qVLs/a+8pWvyP777x+CyHHBdgIIgLZn2Fl8po3ImRV6QQACQwlQW6wJCPhHgPryjy2W403g2aYpsrqjJG8I4xL98h/b1UsikbcpDEDAGgKmvQsBMASpfvXVV+Xqq6/OejJnzhw5//zzPRMA1VZzc3PW3g9/+EOZMWNGCCLHBdsJIADanmFn8Zk2ImdW6AUBCCAAsgYgEBwB9q7gWDNTfAj09ifkt6tqJSVeqHaptACYHJOKD0AihYCBgGnvQgAMwRLq7OwUfZm3v3/LhxCmTJkiemovMcw/Z+T6CvDzzz8v1113XTbK8vJyufPOO0MQNS7EgQACYByybI7RtBGZLdADAhAYjgC1xbqAgH8EqC//2GI5vgTae8bJY+umegZgfl2jlCf7PLOHIQhEnYBp70IADEmGr7rqKnnzzTez3ixYsEAOPPDAbbzLRQDs7e2VhQsXynvvvZe1M3v2bLnkkktCEjVu2E4AAdD2DDuLz7QRObNCLwhAYCgBaos1AQH/CFBf/rHFcnwJtHYn5Yn6as8AHFbbJFOKez2zhyEIRJ2Aae9CAAxJhv/0pz/Jz3/+86w3lZWVold1S0tLB3mYiwCoL//qC8ADmwqCe+21V0iixg3bCSAA2p5hZ/GZNiJnVugFAQggALIGIBAcAfau4FgzU3wIcAIwPrkm0sIQMO1dCICFycs2s/b19clFF10kLS0t2Z/tsssucvnll0tZWVn2z5wIgJs2bUo/KvLiiy8OmmfmzJly7bXXhiRi3IgDAQTAOGTZHKNpIzJboAcEIDAcAWqLdQEB/whQX/6xxXJ8CXj5DcCEpORTfAMwvouJyIclYNq7EABDtHCWLVsmP/rRjwZ5VFFRIZ/+9Kfl4IMPlqKiIhlNAFy3bp2ojT/+8Y/S0dExyI4uhCuvvFJ23333EEWMK7YTQAC0PcPO4jNtRM6s0AsCEBhKgNpiTUDAPwLUl39ssRxvAl69Ajy9rFMOqm6LN0yih8AQAqa9CwEwZEvmrrvuSgt4Q1tJSYnsuOOO8v777w8S94499lhpbGxMf+dP/3ekpiLiiSeeGLJoccd2AgiAtmfYWXymjciZFXpBAAIIgKwBCARHgL0rONbMFC8C72wcLy+0TMo76Dk1zVJd0pO3HQxAwCYCpr0LATBk2U6lUnLjjTfK8uXLPfPs8MMPl3PPPdczexiCgFMCCIBOSdndz7QR2R090UHAPwLUln9ssQwB6os1AAHvCaRSIo+trZYNfcm8jJcne2XetCZJJPIyw2AIWEfAtHchAIY05b/73e/k3nvvFRVQ3DZN/hlnnCHHHXecWxOMg0BeBBAA88JnzWDTRmRNoAQCgYAJUFsBA2e6WBGgvmKVboINiEBjV5Esa6jKe7Z9prTJThM787aDAQjYRsC0dyEAhjjjq1atkgceeCB9GrC/vz8nT/fdd1859dRTZYcddshpHJ0h4CUBBEAvaUbXlmkjim5keA6BwhKgtgrLn9ntJkB92Z1foisMgWeaKmRNR2nek/P9v7wRYsBSAqa9CwEwAolvamqS5557Tl5//XV56623pL29fRuv9YGQWbNmyZ577in77befbL/99hGIDBdtJ4AAaHuGncVn2oicWaEXBCAwlAC1xZqAgH8EqC//2GI5ngR4ATieeSfqYAmY9i4EwGDz4clsfX19snHjxvR/KvyVl5eLPhJCg0DYCCAAhi0jhfHHtBEVxitmhUD0CVBb0c8hEYSXAPUV3tzgWTQJtPeMk8fWTfXM+fl1jVKe7PPMHoYgYAMB096FAGhDlokBAiElgAAY0sQE7JZpIwrYHaaDgDUEqC1rUkkgISRAfYUwKbgUaQKt3Ul5or7asxgOq22SKcW9ntnDEARsIGDauxAAbcgyMUAgpAQQAEOamIDdMm1EAbvDdBCwhgC1ZU0qCSSEBKivECYFlyJNgBOAkU4fzkeEgGnvQgCMSCJxEwJRJIAAGMWsee+zaSPyfkYsQiAeBKiteOSZKAtDgPoqDHdmtZcA3wC0N7dEFh4Cpr0LATA8ucITCFhHAAHQupS6Csi0EbkyyiAIQECoLRYBBPwjQH35xxbL8SXAK8DxzT2RB0PAtHchAAaTB2aBQCwJIADGMu3bBG3aiKAEAQi4I0BtuePGKAg4IUB9OaFEHwjkRqCxq0iWNVTlNmiY3nNqmqW6pCdvOxiAgG0ETHsXAqBtGSceCISIAAJgiJJRQFdMG1EBXWNqCESaALUV6fThfMgJUF8hTxDuRZJAKiWyZF21tPcmXftfnuyVedOaJJFwbYKBELCWgGnvQgD0MfWvv/66j9bdm959993dD2YkBHIggACYAyyLu5o2IotDJzQI+EqA2vIVL8ZjToD6ivkCIHzfCHzQWyRL66uktz/3KcYm+mVuTYtU8Ppv7vAYEQsCpr0LAdDHZXDqqaf6aN2d6UQiIYsWLXI3mFEQyJEAAmCOwCztbtqILA2bsCDgOwFqy3fETBBjAtRXjJNP6L4S0NrqKamVP7wlOYmAKv7Nrm6TmtJuX/3DOASiTMC0dyEA+pjdMAqAGu7ixYt9jBrTENhKAAGQ1aAETBsRlCAAAXcEqC133BgFAScEqC8nlOgDgdwJZGqrcaPIH9/sdXQdWK/97le5npN/ueNmRMwImPYuBEAfFwQCoI9wMR0JAgiAkUiT706aNiLfHWACCFhKgNqyNLGEFQoC1Fco0oATFhIYWFvr1tVLQ8c4WdZYKSKDP+qXkJTUlXXJzImbpKq4h2/+WbgWCMl7Aqa9CwHQe+ZZiwiAPsLFdCQIIABGIk2+O2naiHx3gAkgYCkBasvSxBJWKAhQX6FIA05YSGBobXX2puR3q6cNivSQqc0ypbhXkmNSFhIgJAj4R8C0dyEA+sdeeATER7iYjgQBBMBIpMl3J00bke8OMAEELCVAbVmaWMIKBQHqKxRpwAkLCQytrdauMfLndVMHRJqSE7dfJ2N45dfC7BOS3wRMexcCoN8ZwD4EYkwAATDGyR8QumkjghIEIOCOALXljhujIOCEAPXlhBJ9IJA7gaG1tXpjUpY36RXgLa1k7GY5dkZD7oYZAQEIGL+9jgDIIoEABHwjgADoG9pIGeaXqEilC2cjRIDailCycDVyBKivyKUMhyNCYGht/fODEnmpdXLW+ylFPXLYtOaIRIObEAgXAdPehQAYrnzhDQSsIoAAaFU6XQdj2ohcG2YgBGJOgNqK+QIgfF8JUF++4sV4jAkMra2XW8bLP9onZolML+uUg6rbYkyI0CHgnoBp70IAdM+WkRCAgIEAAiBLRAmYNiIoQQAC7ghQW+64MQoCTghQX04o0QcCuRMYWlvPNJTLqo6yrKGdJ26Uvaa0526YERCAgPH3LgRAFgkEIOAbAQRA39BGyjC/REUqXTgbIQLUVoSShauRI0B9RS5lOBwRAkNr6/G1FdLSXZz1fq+KD2Tn8k0RiQY3IRAuAqa9CwEwXPnCGwhYRQAB0Kp0ug7GtBG5NsxACMScALUV8wVA+L4SoL58xYvxGBMYWFur1jbIn1ZVSlf/uCyRg6paZfr4rhgTInQIuCdg2rsQAN2zDWxkf3+/rFy5Ut566y155513ZMOGDbJx40bp7OyU0tJSmTBhgkycOFF22mkn2WWXXdL/q4mnQaDQBBAAC52BcMxv2ojC4SVeQCB6BKit6OUMj6NDgPqKTq7wNFoEEokxsrmsVl6pF1nZmpKUJAYFUF3cLbtO3iDVxT2SGPyjaAWKtxAoAAHT3oUAWICkOJ2yra1NHnnkEVmyZEla9HPaysvLZf78+XLkkUfK5MlbX1RyOp5+EPCKAAKgVySjbce0EUU7OryHQOEIUFuFY8/M9hOgvuzPMREGT6CtOykrWiZLe2/SOHl5slf2q1wvFcW9xr50gAAEthAw7V0IgCFdKX/84x/lnnvukd5e9/+HV1RUJGeccYZ88pOfDGmUuGU7AQRA2zPsLD7TRuTMCr0gAIGhBKgt1gQE/CNAffnHFsvxJNDQWSzLmypkc8r5TbWxiX6ZXd0mNaXd8YRG1BDIkYBp70IAzBGo391V8Lv++uvl+eef92yqfffdVxYsWCDjxm39toJnxjEEgVEIIACyPJz8SxSUIAABdwRMf8lzZ5VREIAAexdrAALeEtCTf0sbKnMS/zIeqAg4t6aFk4DepgRrlhIw/d0QATBEiU+lUmnx79lnnx3Vq0Qikf72X0lJiXR1daW/BahjR2sHHnhgWgSkQSBIAgiAQdIO71ymjSi8nuMZBMJNgNoKd37wLtoEqK9o5w/vw0NAf01dsq7a0bXfkbzW68DzpjXxTcDwpBVPQkrAtHchAIYocf/3f/8nv/rVr7bxSJO49957y+zZs2XmzJkyffp0UREw01T8W7t2rfzrX/+S5cuXy0svvST6cMjQduqpp8pJJ50UoohxxXYCCIC2Z9hZfKaNyJkVekEAAkMJUFusCQj4R4D68o8tluNFoLGrSJY1VOUd9JyaZqku6cnbDgYgYDMB096FABiS7K9fv14uuugi6e4e/H2Dj370o/KFL3xBNFFOW2Njo/zsZz+TF198cdAQPTF40003yaRJk5yaoh8E8iKAAJgXPmsGmzYiawIlEAgETIDaChg408WKAPUVq3QTrI8EnmmqkDUdpXnPML2sUw6qbsvbDgYgYDMB096FABiS7P/yl7+U3//+94O8OfHEE+XTn/60aw8XL14s999//6Dxxx13nJx55pmubTIQArkQQADMhZa9fU0bkb2RExkE/CVAbfnLF+vxJkB9xTv/RO8Ngd7+hPx2Va2kZOvtNbeWE5KST21XL8kxo3/6yq19xkHABgKmvQsBMCRZvvDCC0VP7mXaJz7xCTnvvPPy9u62226TJ554ImtHE66nAGkQCIIAAmAQlMM/h2kjCn8EeAiBcBKgtsKZF7yygwD1ZUceiaKwBNp7xslj66Z65sT8ukYpT/Z5Zg9DELCNgGnvQgAMQcbr6+vl4osvznqiV3VvvfVWKSsry9u7jo6OtJCoj4Vk2o033ii1tbV528YABEwEEABNhOLxc9NGFA8KRAkB7wlQW94zxSIEMgSoL9YCBPIn0NqdlCfqq/M39G8Lh9U2yZTiXs/sYQgCthEw7V0IgCHI+HPPPSc/+MEPsp4ccsghoicCvWo333yzPPXUU1lzl112mRxwwAFemccOBEYkgADI4lACpo0IShCAgDsC1JY7boyCgBMC1JcTSvSBwOgEOAHICoFAsARMDU4pNQAAIABJREFUexcCYLD5GHa2JUuWyJ133pn92VlnnSVHH320Z5796U9/kp///OdZe1/60pdk3rx5ntnHEARGIoAAyNpAAGQNQMA/Aqa/5Pk3M5YhYD8B6sv+HBOh/wT4BqD/jJkBAgMJmPYuBMAQrJcHH3xQ7rvvvqwnl1xyicyePdszz5YvXy433HBD1t5pp50mJ5xwgmf2MQQBBEDWwGgETBsR9CAAAXcEqC133BgFAScEqC8nlOgDATMBXgE2M6IHBLwiYNq7EAC9Ip2HnT/+8Y9y1113ZS2ce+65cvjhh+dhcfDQxx9/XG6//fbsH3p9wtAzRzFkHQFOAFqXUlcBmTYiV0YZBAEIcL2eNQABHwmwd/kIF9OxItDYVSTLGqryjnlOTbNUl/TkbQcDELCZgGnvQgAMQfaffvpp0Yc5Mu3444+X008/3TPP7r33XnnooYey9i666CL5+Mc/7pl9DEFgJAIIgKwNJWDaiKAEAQi4I0BtuePGKAg4IUB9OaFEHwiYCaRSIkvWVUt7b9LceYQe5clemTetSRIJ1yYYCIFYEDDtXQiAIVgGb775plx11VVZT6ZNmzboym6+Ll566aWydu3arJlvfetbsuuuu+ZrlvEQMBJAADQiikUH00YUCwgECQEfCFBbPkDFJAT+TYD6YilAwDsCbd1JWdpQKZtTY3I2OjbRL3NrWqSC139zZseA+BEw7V0IgCFYE319ffLFL35Rurq6st7oK8D6GnC+bejpwpKSEvnZz34mY8eOzdc04yFgJIAAaEQUiw6mjSgWEAgSAj4QoLZ8gIpJCCAAsgYg4AuBhs5iWd5UkZMIqOLf7Oo2qSnt9sUnjELANgKmvxsiAIYk49ddd508//zzWW8mTJgg3/zmN2W77bZz7eHq1atFT/u1t7dnbey7777yta99zbVNBkIgFwIIgLnQsrevaSOyN3Iig4C/BKgtf/liPd4EqK9455/o/SGgJwFXtEx2dB1Yr/3uV7mek3/+pAKrlhIw7V0IgCFJ/CuvvCLXXHPNIG9UBDz//PPlYx/7WM5evvTSS3LLLbcMEv/UyBVXXCF77rlnzvYYAAE3BBAA3VCzb4xpI7IvYiKCQDAEqK1gODNLPAlQX/HMO1H7TyCRGCMNUivL3t12roSkpK6sS2ZO3CRVxT1888//dDCDZQRMexcCYIgSrgKgCoFDmwqARx11lOy9996SGOXLp6lUSlT4e+yxxwadJszY22uvvWThwoUhihhXbCeAAGh7hp3FZ9qInFmhFwQgMJQAtcWagIB/BKgv/9hiOd4EtLbaxtTKkn9t5VA2tk8+XtMqpWM3S3JMKt6AiB4CeRAw7V0IgHnA9XpoY2NjWqAbeGV34Bz6/b4dd9xRpk+fLuPHj5fi4mLp7u6WTZs2yZo1a+Tdd98d9B3BgWMnTZokV199tUydOtVrt7EHgREJIACyOJSAaSOCEgQg4I4AteWOG6Mg4IQA9eWEEn0gkDsBra13e2vludVbx9aWdsnHp7bmbowREIDAIAKmvQsBMGQLZuXKlenv9g18ECRfF1U41FeGd9ppp3xNMR4CORFAAMwJl7WdTRuRtYETGAR8JkBt+QwY87EmQH3FOv0E7yMBra1XNtTKW81bJ5k5caN8dMrW79b7OD2mIWA1AdPehQAYwvTr4x033nijvP/++3l7pycGL7roovSpQRoEgiaAABg08XDOZ9qIwuk1XkEg/ASorfDnCA+jS4D6im7u8DzcBLS2ljXVSv3GrX7uVfGB7Fy+KdyO4x0EIkDAtHchAIY0iX19ffLwww/LI488IuvXr8/Zy4qKCvnkJz8pxx13nIwbNy7n8QyAgBcEEAC9oBh9G6aNKPoREgEECkOA2ioMd2aNBwHqKx55JsrgCWht/X51rXT0bp17dnWL1JV1B+8MM0LAMgKmvQsBMOQJVwHl2WefTT8O8tZbb8natWtH9Liurk4+/OEPpx8LOeCAA2Ts2LEhjw73bCeAAGh7hp3FZ9qInFmhFwQgMJQAtcWagIB/BKgv/9hiOd4E+mWsPPBezSAI86c1SnlRX7zBED0EPCBg2rsQAD2AHKQJffRjw4YN6Yc/Ojs7pbS0NP0gSHl5uRQVFQXpCnNBwEgAAdCIKBYdTBtRLCAQJAR8IEBt+QAVkxD4NwHqi6UAAX8IbOgrkkfXVA0yfvx262Qcr//6AxyrsSJg2rsQAGO1HAgWAsESQAAMlndYZzNtRGH1G78gEHYC1FbYM4R/USZAfUU5e/geZgL1naXy18aKrIslYzfLsTMawuwyvkEgMgRMexcCYGRSiaMQiB4BBMDo5cwPj00bkR9zYhMCcSBAbcUhy8RYKALUV6HIM6/tBN7eMEFebi3PhllZ3C2fqG2xPWzig0AgBEx7FwJgIGlgEgjEkwACYDzzPjRq00YEJQhAwB0BassdN0ZBwAkB6ssJJfpAIHcCL7dNkrfbx2cHbj++Q/avyv3Ry9xnZgQE7Cdg2rsQAO1fA0QIgYIRQAAsGPpQTWzaiELlLM5AIEIEqK0IJQtXI0eA+opcynA4IgSebpwi6zpLst7uNmmD7D55Q0S8x00IhJuAae9CAAx3/vAOApEmgAAY6fR55rxpI/JsIgxBIGYEqK2YJZxwAyVAfQWKm8liRODRtVNlQ++4bMT7VbbJDhM6Y0SAUCHgHwHT3oUA6B97LEMg9gQQAGO/BNIATBsRlCAAAXcEqC133BgFAScEqC8nlOgDgdwIpFIiD66aJv2pRHbg3JpmqSrpyc0QvSEAgWEJmPYuBMAAFs7rr78ewCzOp9h9992dd6YnBPIggACYBzyLhpo2IotCJRQIBEqA2goUN5PFjAD1FbOEE24gBDr7xsgf1tQOmuuY6fVSOq4/kPmZBAK2EzDtXQiAAayAU089NYBZnE2RSCRk0aJFzjrTCwJ5EkAAzBOgJcNNG5ElYRIGBAInQG0FjpwJY0SA+opRsgk1MALNXUWytKEqO9+YREpO2G6dJLYeCAzMFyaCgI0ETHsXAmAAWQ+TAKjhLl68OIComQICIgiArAIlYNqIoAQBCLgjQG2548YoCDghQH05oUQfCORG4L2NpbKipSI7qDzZK/PrmnIzQm8IQGBEAqa9CwEwgMWDALgt5I0bN8qbb74pra2t0tHRIRUVFaKLcZdddkmLBUG3rq4ueeONN6SlpUXUt/LycqmurpbddttNxo3b+pHafPzSWN9+++10zN3d3TJlyhSpq6uTmTNn5mNWUqmUNDQ0yKpVq9L+K8+ioiKZMGGCbL/99rLjjjsWhKkGhQCYV2qtGWzaiKwJlEAgEDABaitg4EwXKwLUV6zSTbABEXh9/UR544OJ2dmmlXbJwVNbA5qdaSBgPwHT3oUAGMAaQADcCnnt2rVy7733ygsvvCB9fX3b0Fch8IgjjpCTTjrJM+FttBS3tbWl/XnmmWfSotzQNn78eJkzZ45oDsvKylytFhX99Nr1q6++mhbrhjYtwqOPPjr9n17RdtJU5Pvb3/6W5vjaa6/Jhg0bRhxWWloqc+fOleOOO06mTp3qxLxnfRAAPUMZaUOmjSjSweE8BApIgNoqIHymtp4A9WV9igmwAASea54s72/a+jvVrPJNsnfFBwXwhCkhYCcB096FABhA3ocKgHoy6+CDD06feitEU3GtEO3JJ5+UO++8c1ihbag/H/rQh+Syyy7zVbB6+eWX5aabbhpVPMv4pYWi/uhpulzaQw89lBb/VAgztT333FMuvfTS9Mm90drf//53ufbaa4cVUEcbV1JSIp///OflsMMOM7ni2c8RAD1DGWlDpo0o0sHhPAQKSIDaKiB8praeAPVlfYoJsAAE/lJfJS3dRdmZPzqlXWZO3FgAT5gSAnYSMO1dCIAB5H24E4CamL333lsOP/xw2W+//Qp2RTOA8NNT6Em1733ve4NOwE2bNk0+8pGPpAUvvcL6/PPPS0/P1ifgZ8yYIVdffbXrk3ejxbZy5Uq56qqrBomRKsjus88+MnnyZGlubk77s2nTpqwZ/fPvfOc7UllZ6QjbY489lhY8BzYVEHfddVcpLi6WNWvWyIsvvjhIHFQeCxcuHPX0o578+8EPfjDIbjKZlFmzZsn06dPT15f1dKVeCVaxsLe3d1DfL37xi3LUUUc5iiHfTgiA+RK0Y7xpI7IjSqKAQPAEqK3gmTNjfAhQX/HJNZEGR+D3q2uka/PY7IQfr2mT2pLO4BxgJghYTsC0dyEABrAATFeAVbA59NBD0yeztttuuwA8CnYKvWZ7ySWXSGfnlv9z12uuZ555phxzzDGDhM/29na5/vrr01daM01PSupYL5uKjBdffHH6e3mZptdjTz/99EHCm/p7++23y9NPP53tp98oVFHS1N599125/PLLs+KeCnTnnXeeHHLIIYOGqvB53XXXpcW6TDvhhBPSvozUMgKgctxjjz1k/vz58rGPfSz93b+hbf369fKzn/0sfcU503ScirG5nmY0xTzczxEA3VCzb4xpI7IvYiKCQDAEqK1gODNLPAlQX/HMO1H7R6CvPyEPrZo2aIIjpzfLxHFbD4D4NzuWIRAPAqa9CwEwgHWgj0s8/vjj8uyzzxqvv+qDEHoqUIUvt9+cCyCknKb4yU9+Io8++mh2zCmnnCInn3zysDZUnPv617+ePh2nTcWq7373u6JXgr1qDz/8sNx9991Zcyq8qjg3XOvv75drrrkm/f2+TPvKV74iBxxwwKju6ElBPd2XaRdccEH6W4LDNRU+9XrxBx9s+f6FCnk333zziFfEn3vuOVmyZImcdtppjkW8G2+8Uf76179mp9eTjipQ+t0QAP0mHA37po0oGlHgJQTCR4DaCl9O8MgeAtSXPbkkknAQ+KBnnCxZN/h75CfuUC9jpD8cDuIFBCwgYNq7EAADTLK+NKsizF/+8hf5xz/+MerMKgIdeOCB8olPfCJ9yiuqTU+gffnLX85+r04XnJ7yG+1lXb22+u1vfzsbsoptKrp50fRq7Lnnnpv97p+KrD/60Y9G/e5efX19+sRg5gEPFSP1BN1I7Z133kmLmJmmLwl/61vfGtV9FYhvu+22bB89kfjZz3522DEqqo0du/XovBMu+rLx+eefnz2FqeN/+tOf+i4yIwA6yY79fUwbkf0EiBAC/hCgtvzhilUIKAHqi3UAAW8JvL+xRJ5rmZI1WjZO5Njt6kUPXNAgAAFvCJj2LgRAbzjnbEVfw/3zn/8sy5Yty578GsmIvtyqQqC+5FpVVZXzXIUcoCfV7rjjjqwLZ5xxhhx//PFGl/QxjMwpQL0+q2KVPmKRb9OHP/REX6YdeeSRcvbZZxvN6hgdm2l6Qk+LZ7imrwo/+OCD2R/pFWY90Tla05OPKkxmvjmo3xm89dZbjX7l0uGHP/xh+hRqpn3zm9+U3XffPRcTOfdFAMwZmZUDTBuRlUETFAQCIEBtBQCZKWJLgPqKbeoJ3EMCqZRIU3eRrNwwXtZ06O9yiUHWZ5R1yYcmbpTq4h5JDP6Rh15gCgLxIWDauxAAC7wW9F889LGJJ554In1ldLR/AdHrsPpSrF5Z1VNxo52iK3BY2en1+q4+AJJpowlnA33Wl3Pvv//+7B85uXbrJGYVEh955JFs12984xuOTlgOPaGnp/P0lN5wTa/zZr7ppzm66667hv0+39CxykYF4UzTU4ZeXn2+5557RF8lzjQnwqQTpqP1QQDMl6Ad400bkR1REgUEgidAbQXPnBnjQ4D6ik+uidQfAm3dSVnRMlnae5PGCcqTvbJf5XqpKB78eKFxIB0gAIFBBEx7FwJgiBaMfgNOrwfrf3pCcLQ2fvz49IMSKgZ6KRJ5jeOss86Sjo6OtNlJkyZt8yruSPOpGKrf0cs0fTBEbeXbvvrVr8p7772XNqPFoeKck5OFq1evlgULFmSn33///UVtDW161fYLX/hC9o933nnnQScOR/Nfv5Oo30vMNI1X4/aqDf0Wo1ei6mj+IQB6lb1o2zFtRNGODu8hUDgC1Fbh2DOz/QSoL/tzTIT+EWjoLJblTRWyOTXG8SRjE/0yu7pNakq7HY+hIwQgMJiAae9CAAzpitFvBOoVYX29Vb8dOFrbYYcd0kKgviQ8YcKE0ETU2toq//Vf/5X156Mf/aj8v//3/xz5p98OPOecc7J99957b1m4cKGjsSN10tOV+vpwb++Wf1maPn16+nuETpp+/0/H6lVdbXV1dXLDDTdsM/Stt96SK6+8Mvvn+kLvl770JSdTpL8LecUVV2T7zps3bxADR0ZG6aSnHd98881sj2uvvVZmzZqVr9lRxyMA+oo3MsZNG1FkAsFRCISMALUVsoTgjlUEqC+r0kkwARLQk39LGypzEv8y7qkIOLemhZOAAeaLqewiYNq7EABDnu/u7m55+umn06cCB4o3w7mt103322+/tBioYluh29DHPHIRtFRw0+8F6qMd2qqrq+WWW27JK6SGhga58MILszZyFRX1IZB169alx+sjGv/7v/+7zTXsoVeF9aXeE0880ZHfQwVT/T6ffqfPi9bY2JiOPfOQiZ4g1ROBuT4mkqsvCIC5ErOzv2kjsjNqooKA/wSoLf8ZM0N8CVBf8c09kbsnoN/8W7Ku2tG135Fm0evA86Y18U1A92lgZIwJmPYuBMAILQ4Vn1RgevLJJ0VPyI3W9OENvXJbyKZ+6gu7mXbqqafKf/7nfzp26YILLhAVrjKC23333ed47HAd33jjDbnqqquyPzr88MMHnVA0GdeXfF977bVsN32kQx/rGNh+85vfyK9+9avsH2kMc+bMMZlO/1xPKKroqaKZNi1O/S6gF+3HP/5xWkTONBWJzzvvPC9Mj2oDAdB3xJGYwLQRRSIInIRACAlQWyFMCi5ZQ4D6siaVBBIggcauIlnWkP+jlXNqmqW6ZMvNKxoEIOCcgGnvQgB0zjI0PVUo0m/k6cMh+sBGRjAa6GAYBMCh37T73Oc+J8cee6xjjgO/16eD7r77bkePaYw0Qb7fFbzuuutkxYoVWfN6fVivEQ9s6uPDDz+c/aNcv7OnjDo7O9Pjc/lm4mhQX3nllfR3CDOn//Sk6P/8z/9IbW2t41y47YgA6JacXeNMG5Fd0RINBIIjQG0Fx5qZ4keA+opfzok4fwLPNFXImo7SvA1NL+uUg6rb8raDAQjEjYBp70IAjPiK0FNtN954o7S1Df4/yDAIgCqEqSCWaWeffbYceeSRjonr9wLffvvtbH+9slpeXu54/NCOy5cvH/TNv+OPPz594s5pU8FPbWSaPlIyc+bMQcOHPrShMeRyHVu/F6iPwWgrLi5OXzPOp+m6+PrXvz7oxOjJJ58sp5xyimuzLS0txrGTJ09OXy9WAbCpqcnYnw52E9CNaOrUqekg9VTvaK+d202C6CDgLQFqy1ueWIPAQALUF+sBArkR6O1PyEPvTZWUJHIbOEzvhKTk+B0aJTkmlbctDEAgTgRMe5d+Ws3vz4AVmncilTn6VGhPPJpfHwXR7wLqCUB9OGK4FgYBcOh1WH0QRK/dOm16XVcFzkzTa6xVVe6PlA+9kqzXkfVastOm15nVRqbpleDddttt0HC9Fqx5yTR9eGOPPfZwOkX6Wm5GYEskErJ48WLHY4d21AdLvv3tbw9aI7vsskv6u4J6CtBtcyIeDnc92u18jIMABCAAAQhAAAIQgAAEwk2gtUPknpe98/GMvUWmlHlnD0sQgEA8CFgjAKoYpuKSvgysj4OM1u688868Tst5sTQ4ASjpV48LcQJQT1jpNd+//e1v2VTq9wr1KvCUKVPySi8CYF74GAwBCEAAAhCAAAQgAAHrCNRvEPn1370L6//bQ6R2onf2sAQBCMSDQKQFQH0ZVh9vWLp0qdTX14+aMb0ee+ihh6ZP2c2YMaPg2eUbgCKF+gbgbbfdln4wJtMmTJiQPg3oxbrgCnDBSytyDpiOokcuIByGQEgIUFshSQRuWEmA+rIyrQTlI4H2nrHyyJpqz2Y4anqTlBdteRyRBgEIOCNg2ru4AuyMY6C9+vr65Lnnnkuf9nv11VdH/V6WJlhPmOmrrvvuu2+o7nMPvXKrJ8f0+3NOm9+vAOf6Em4QrwCffvrp2Xy7fQV46EMkJSUlcuWVV8rOO+/sFL1n/XgExDOUkTZk+hhtpIPDeQgUkAC1VUD4TG09AerL+hQToMcE9BuAv11V69k3AD+1XT3fAPQ4R5izn4Bp7+IRkBCtgXfffTct+j311FOycePGUT2rq6tLi35z5swRfXAhjO3vf/97+tRZps2bN0/OOeccR67qZxv1gQ4VQ7WpUn3LLbc4GjtSp4aGBrnwwguzP957771l4cKFjm1edNFF2VOY+uFMfaBj6Lf09NSdnr7LtNNOO01OPPFER3PoaU/9TmKm7b777unv9eXS7r//flm0aFF2SDKZTD8Cstdee+VixrO+CICeoYy0IdNGFOngcB4CBSRAbRUQPlNbT4D6sj7FBOgDAV4B9gEqJiGQAwHT3oUAmANMP7qq0KeCnwp/KgCO1vQk1+zZs9NXfPUxh7A3fYH23HPPzbqZi+C2fv36QWKhnnLU7+nl0/S7eJ/97GdFH8fQpiLqDTfc4MikCpKf+cxnpLe3N91/+vTpg14UzhjRR1muuOKKrM1cRM+hY+fPny/6KrDT9sgjj8hPf/rTbHct/gULFsgBBxzg1ITn/RAAPUcaSYOmjSiSQeE0BEJAgNoKQRJwwVoC1Je1qSUwHwk0dhXJsgb3jzZmXJtT0yzVJVt+Z6NBAALOCZj2LgRA5yw966li0ssvv5wW/VasWJE95TbSBLvuumv6tJ+Kf8XFxZ75EYShs846Szo6OtJTTZo0SfRxEifthRdekO9+97vZrsccc4yorXzbV7/6VXnvvffSZrQ47rrrLlFh1dRWr16dFtMybf/99xe1NbRt2rRJPv/5z2f/eNasWXLttdeazKd/PlTA03g1bidNr1vrCcnMg9f6gvD555+fPiFayIYAWEj64ZnbtBGFx1M8gUC0CFBb0coX3kaLAPUVrXzhbTgIpFIiS9ZVS3tv0rVD5clemTetSRIJ1yYYCIHYEjDtXQiAAS4NfcQj86CHXvccrelLrSreqPBXW1sboJfeTqUinop5mXbTTTc5ikevsep11kzL9TGNkaLQE3IqtGWafhtvzz33NAb95z//WW6//fZsPz1JeNxxxw077rLLLpNVq1alf6ZXhX/xi19IUVGRcQ5lo6dBM+173/uefOhDHzKO0+9F6ou/KrZl2tlnny1HHnmkcazfHRAA/SYcDfumjSgaUeAlBMJHgNoKX07wyB4C1Jc9uSSSYAm0dSdlaUOlbE6NyXnisYl+mVvTIhXFW25d0SAAgdwImPYuBMDceLrqrS/46mm/N954Y9Tx+j05fchDRT+9LqvJi3pbsmSJ3HHHHdkw9JGLE044wRjWJZdcImvXrk330+/YqXDn5KSeyfArr7wiV199dbab02u211xzTfrUZqbdfPPNosUzXLvvvvvkgQceyP5IYzn44INHdU2vJet1aT1BqK2yslJuvfVWUzii31n8zne+k72arAOcMjYa96ADAqAHEC0wYdqILAiRECBQEALUVkGwM2lMCFBfMUk0YfpCoKGzWJY3VeQkAqr4N7u6TWpKu33xCaMQiAMB096FABjAKjj11FNHnWX77bdPi36HHnqoTJw4MQCPgptCv+X35S9/OXvNWRfc9ddfv83jGQM9Gvp4iH7DTk8AetH0URF9aKO9vT1trqysLH11dvz48SOa15ObF198cfZ6rZ7K09N5I7V33nkn/fBGpu22226iLwiP1oY+HqKnC/WU4Wjt7bffTj+y0tXVle120kknyac//WkvUHliAwHQE4yRN2LaiCIfIAFAoEAEqK0CgWfaWBCgvmKRZoL0kYCeBFzRMtnRdWC99rtf5XpO/vmYD0zHg4Bp70IADGAdDCcAqvB0yCGHpIW/nXbaKQAvCjfF0Gu3p5xyipx88snDOqQn4f77v/9b9Jt72vRbdnqNeKSrsI2NjXLBBRdkbTl5Lfjhhx+Wu+++OztGc3DeeecN648+HKKn/1599dXsz51cRx569Vl9HOl7fCpG6rXhDz74ID2HXhfWE4YVFRUjJk2vGF911VWDXov26juJXq4UBEAvaUbXlmkjim5keA6BwhKgtgrLn9ntJkB92Z1foguGwKbeMfKntcN/zmpMQqSurFN2mrBJqop7+OZfMClhFssJmPYuBMAAFsBQAXDChAmyzz77pK+2Bt1UUDvnnHMCnVa/d3jppZdKZ2dnVtQ788wz0w9cDLzmrEKYng587bXXsv7p1Vm9QjtScyMAqsioJ/paWlqyZvXEnV6d1WvYmab+6nf/nn766eyf6evLA68Qj+SXvuh8+eWXZ7/Lp7lWkVFF34FN/dfThJlvBurP9Iq0+jJazPrtQn1lOdP0ZWi9Qqz5DVNDAAxTNgrni2kjKpxnzAyBaBOgtqKdP7wPNwHqK9z5wbtoENCrwE81VmadHSv9Mndam0ybWikTikRam+tFD1zQIAABbwiY9i4EQG84j2rFdAU4ABcGTbF48eKgp0w/BKJCV+aVWnVg2rRpsscee4gKonrN9vnnnxcV5zJtxowZabFNT0uOJoblegJQba1cuTJ9gq67e+s3JvTEnQqzkydPlubm5rQ/mW/y6Rj9c/3enn6fz0l79NFH5Sc/+cmgrjvuuKPoq876mvOaNWvkxRdfHPR4x0c+8hFZuHDhqFekf/3rX4v+N7C5+V6knsIc6SSmk/ic9EEAdELJ/j6mjch+AkQIAX8IUFv+cMUqBJQA9cU6gED+BP7ZPl5eaZuUNVRR1CPzprdmH4XU3wERAPPnjAUIZAiY9i4EwADWCgLgFshPPvmk3HnnnYNEt5Hwq1CmV22nTp06aobcnADMGHzppZfSV21NyYFxAAAgAElEQVQ3bNhgXAXqh17TdfIq70BjDz74oKjgOvCF3pEmUzF0wYIFaUF0tParX/1KfvOb3xh9NnVQ8U+vY/vZEAD9pBsd26aNKDqR4CkEwkWA2gpXPvDGLgLUl135JJrCEHihZZK8s3Hrt9a3H98hB05tRwAsTDqYNQYETHsXAmAAiwABcCtkfdn3nnvuSZ8IHE4U01N4RxxxhOhjFgOv446UpnwEQLWp12jVn2effXZYYVIfB9Fv92kORzuJONoy+uc//ymLFi1Kv9g78ARkZowW4dFHH53+z8kVXgTAAIqWKTwlYNqIPJ0MYxCIEQFqK0bJJtTACVBfgSNnQgsJLK2vlObu4mxke0xul90qOhAALcw1IYWDgGnvQgAMIE8IgNtC1lN3b731Vvo7fPqtPb1eq6fs9Hqsm+us+aZRX9J9/fXX0/5s3LhRJk2aJFVVVaIv+Hr1rUb9FqKKgfq/etVZxc66ujqZNWtWvu6HdjwnAEObmkAdM21EgTrDZBCwiAC1ZVEyCSV0BKiv0KUEhyJI4LeraqSnf2zW89nVLTJjQi8CYARzicvRIGDauxAAA8hjU1NTALM4n0JfyqVBIAgCCIBBUA7/HKaNKPwR4CEEwkmA2gpnXvDKDgLUlx15JIrCEejePEZ+t3rwC8BH1jXIpOIUAmDh0sLMlhMw7V0IgJYvAMKDQCEJIAAWkn545jZtROHxFE8gEC0C1Fa08oW30SJAfUUrX3gbPgJNXUXyZENV1rExkpLjt18n48aOQQAMX7rwyBICpr0LAdCSRBMGBMJIAAEwjFkJ3ifTRhS8R8wIATsIUFt25JEowkmA+gpnXvAqOgRWbiiTF1snZx0uT/bK/LomXtiOTgrxNIIETHsXAmAEk4rLEIgKAQTAqGTKXz9NG5G/s2MdAvYSoLbszS2RFZ4A9VX4HOBBtAm83Foub2+YkA1iRlmnHFjdhgAY7bTifcgJmPYuBMCQJxD3IBBlAgiAUc6ed76bNiLvZsISBOJFgNqKV76JNlgC1FewvJnNLgK9/Ql5qmGKtPZsfQF4t0ntsvvkjQiAdqWaaEJGwLR3IQCGLGG4AwGbCCAA2pRN97GYNiL3lhkJgXgToLbinX+i95cA9eUvX6zbRyCVEmnqLpKVG8bL2o4SSUliUJBTinrkIxXtUlPaJ9OmbXkcpL6+Xvr7++2DQUQQKBAB096FAFigxDAtBOJAAAEwDlk2x2jaiMwW6AEBCAxHgNpiXUDAPwLUl39ssWwfgbbupKxomSztvUljcPotwKN3TcrUCQiARlh0gECOBEx7FwJgjkDpDgEIOCeAAOiclc09TRuRzbETGwT8JEBt+UkX23EnQH3FfQUQv1MCDZ3FsrypQjanxjgdIskxIsd8WKSoixOAjqHREQIOCJj2LgRABxDpAgEIuCOAAOiOm22jTBuRbfESDwSCIkBtBUWaeeJIgPqKY9aJOVcCevJvaUNlTuJfZg4VAefWNsukZE+u09IfAhAYgYBp70IAZOlAAAK+EUAA9A1tpAybNqJIBYOzEAgRAWorRMnAFesIUF/WpZSAPCag3/xbsq7a0bXfkabW68DzpjVJYvDnAj32FHMQiA8B096FABiftUCkEAicAAJg4MhDOaFpIwql0zgFgQgQoLYikCRcjCwB6iuyqcPxgAg0dhXJsoaqvGebU9Ms1SWcAswbJAYgIGJ8ZRsBkGUCAQj4RgAB0De0kTLML1GRShfORogAtRWhZOFq5AhQX5FLGQ4HTOCZpgpZ01Ga96zTyzrloOq2vO1gAAIQQADUNZBIpfSAMg0CEAiaAAJg0MTDOR+/RIUzL3gVfQLUVvRzSAThJUB9hTc3eFZ4Ar39CfntqlpJSf53dxOSkk9tVy/JMfzKXvjM4kHUCZj2Lk4ARj3D+A+BEBNAAAxxcgJ0zbQRBegKU0HAKgLUllXpJJiQEaC+QpYQ3AkVgfaecfLYuqme+TS/rlHKk32e2cMQBOJKwLR3IQDGdWUQNwQCIIAAGADkCExh2ogiEAIuQiCUBKitUKYFpywhQH1ZkkjC8IVAa3dSnqiv9sz2YbVNMqW41zN7GIJAXAmY9i4EwLiuDOKGQAAEEAADgByBKUwbUQRCwEUIhJIAtRXKtOCUJQSoL0sSSRi+EOAEoC9YMQqBvAmY9i4EwLwRYwACEBiJAAIga0MJmDYiKEEAAu4IUFvuuDEKAk4IUF9OKNEnrgT4BmBcM0/cYSdg2rsQAMOeQfyDQIQJIABGOHkeum7aiDycClMQiBUBaitW6SbYgAlQXwEDZ7rIEeAV4MilDIdjQMC0dyEAxmARECIECkUAAbBQ5MM1r2kjCpe3eAOB6BCgtqKTKzyNHgHqK3o5w+NgCbz1wXj5+/pJeU86p6ZZqkt68raDAQhAwHzzCgGQVQIBCPhGAAHQN7SRMswvUZFKF85GiAC1FaFk4WrkCFBfkUsZDgdIoK07KX+pr5R+GZPXrOXJXpk3rUkSibzMMBgCEPg3AdPehQDIUoEABHwjgADoG9pIGTZtRJEKBmchECIC1FaIkoEr1hGgvqxLKQF5RCCVElmyrlrae5N5WUyOEZlb2yyTkpz+ywskgyEwgIBp70IAZLlAAAK+EUAA9A1tpAybNqJIBYOzEAgRAWorRMnAFesIUF/WpZSAPCLQ2FUkyxqq8rb28e1F6sbWS39/f962MAABCGwhYNq7EABZKRCAgG8EEAB9Qxspw6aNKFLB4CwEQkSA2gpRMnDFOgLUl3UpJSCPCDxZXylN3cV5W5tVKbJPOQJg3iAxAIEBBEx7FwIgywUCEPCNAAKgb2gjZdi0EUUqGJyFQIgIUFshSgauWEeA+rIupQTkAYGezQn57epaEcn/o31q4fgdGmSsbPbAM0xAAAJKwLR3IQCyTiAAAd8IIAD6hjZShk0bUaSCwVkIhIgAtRWiZOCKdQSoL+tSSkAeEHhnY6m80FLhgaUtJo6a3iQTxvV6Zg9DEIg7AdPehQAY9xVC/BDwkQACoI9wI2TatBFFKBRchUCoCFBboUoHzlhGgPqyLKGE4wmBJ+unSFN3iSe21MgRdS0yOdntmT0MQSDuBEx7FwJg3FcI8UPARwIIgD7CjZBp00YUoVBwFQKhIkBthSodOGMZAerLsoQSTt4EevsT8vAqb67/ZpzhBGDeacEABAYRMO1dCIAsGAhAwDcCCIC+oY2UYdNGFKlgcBYCISJAbYUoGbhiHQHqy7qUElCeBNp7xslj66bmaWXrcL4B6BlKDEEgS8C0dyEAslggAAHfCCAA+oY2UoZNG1GkgsFZCISIALUVomTginUEqC/rUkpAeRJo6UrKXxqq87SydfiMcpHZlbwC7BlQDEGAR0DSayCRSqVSrAYIQCB4AgiAwTMP44z8EhXGrOCTDQSoLRuySAxhJUB9hTUz+FUoAl4/ADJvpkhFPwJgofLJvHYSMO1dnAC0M+9EBYFQEEAADEUaCu6EaSMquIM4AIGIEqC2Ipo43I4EAeorEmnCyQAJPN1YIes6Sz2b8Zz9RNpaEAA9A4ohCHACML0GOAFIKUCgQAQQAAsEPmTT8ktUyBKCO9YQoLasSSWBhJAA9RXCpOBSwQjoAyC/XVUrKdEv9+Xfpk8UOWkPkfp6BMD8aWIBAlsJmPYuTgCyWiAAAd8IIAD6hjZShk0bUaSCwVkIhIgAtRWiZOCKdQSoL+tSSkB5EPD6ARC9/rvbVATAPFLCUAgMS8C0dyEAsnAgAAHfCCAA+oY2UoZNG1GkgsFZCISIALUVomTginUEqC/rUkpAeRBo7U7KE/XePQBy8kdEppUjAOaREoZCAAFwhDXAFWCKAwIFIoAAWCDwIZuWX6JClhDcsYYAtWVNKgkkhASorxAmBZcKRsDrE4Bn7C0ypQwBsGAJZWJrCZj2Lk4AWpt6AoNA4QkgABY+B2HwwLQRhcFHfIBAFAlQW1HMGj5HhQD1FZVM4WcQBLz8BmBCUnLO/gkpGocAGETumCNeBEx7FwJgvNYD0UIgUAIIgIHiDu1kpo0otI7jGARCToDaCnmCcC/SBKivSKcP530g8ExThazpyP8V4BnjO+XEvbbY4REQHxKFyVgTMO1dCICxXh4EDwF/CSAA+ss3KtZNG1FU4sBPCISNALUVtozgj00EqC+bskksXhB464Px8vf1k/I2Nbe2Vfb60BQEwLxJYgAC2xIw7V0IgKwaCEDANwIIgL6hjZRh00YUqWBwFgIhIkBthSgZuGIdAerLupQSUB4E2rqT8pf6SumXMXlYESlP9sqR01tk2rRaBMC8SDIYAsMTMO1dCICsHAhAwDcCCIC+oY2UYdNGFKlgcBYCISJAbYUoGbhiHQHqy7qUEpBLAqmUyJJ11dLem3RpYcuwsYl+mVvTIpWlm6W2FgEwL5gMhsAIBEx7FwIgSwcCEPCNAAKgb2gjZdi0EUUqGJyFQIgIUFshSgauWEeA+rIupQTkkkBjV5Esa6hyOXrrsD0nfyC7TNok1FbeKDEAgREJmOoLAZDFAwEI+EYAAdA3tJEybNqIIhUMzkIgRASorRAlA1esI0B9WZdSAnJJwKvHP6aXdcpB1W0IgC7zwDAIOCFg2rsQAJ1QpA8EIOCKAAKgK2zWDTJtRNYFTEAQCIgAtRUQaKaJJQHqK5ZpJ+ghBHr7E/LbVbWSkkTebBKSkk9tVy/F4xJcAc6bJgYgMDwB096FAMjKgQAEfCOAAOgb2kgZNm1EkQoGZyEQIgLUVoiSgSvWEaC+rEspAbkg0N4zTh5bN9XFyOGHzK9rlMnF/QiAnhHFEAQGEzDtXQiArBgIQMA3AgiAvqGNlGHTRhSpYHAWAiEiQG2FKBm4Yh0B6su6lBKQCwKt3Ul5or7axcjhhxxW2yRVPALiGU8MQWAoAdPehQDImoEABHwjgADoG9pIGTZtRJEKBmchECIC1FaIkoEr1hGgvqxLKQG5IMAJQBfQGAKBAhIw7V0IgAVMDlNDwHYCCIC2Z9hZfKaNyJkVekEAArn+Ky/EIAAB9wTYu9yzY6Q9BPgGoD25JJJ4EDDtXQiA8VgHRAmBghBAACwI9tBNatqIQucwDkEgIgSorYgkCjcjSYD6imTacNoHArwC7ANUTELAJwKmvQsB0CfwmIUABEQQAFkFSsC0EUEJAhBwR4DacseNURBwQoD6ckKJPnEg0NhVJMsaqvIOdU5Ns1SX9PD3wrxJYgACIxMw7V0IgKweCEDANwIIgL6hjZRh00YUqWBwFgIhIkBthSgZuGIdAerLupQSkEsCqZTIknXV0t6bdGlBpDzZK/OmNUkiwT8Mu4bIQAg4IGDauxAAHUCkCwQg4I4AAqA7braNMm1EtsVLPBAIigC1FRRp5okjAeorjlkn5pEItHUnZWlDpWxOjckZ0thEv8ytaZGK4t70WGorZ4QMgIBjAqb6QgB0jJKOEIBArgQQAHMlZmd/00ZkZ9REBQH/CVBb/jNmhvgSoL7im3siH55AQ2exLG+qyEkEVPFvdnWb1JR2Z41SW6wwCPhHwFRfCID+sccyBGJPAAEw9kuAf+llCUDARwKmv+T5ODWmIWA9AerL+hQToAsCehLw6aYp0rV5rHG0Xvvdr3J99uRfZgC1ZURHBwi4JmCqLwRA12gZCAEImAggAJoIxePnpo0oHhSIEgLeE6C2vGeKRQggUrAGIDA6gWcaK2RNZ+mwnRKSkrqyLpk5cZNUFfekv/k3tLF3scIg4B8BU30hAPrHHssQiD0BBMDYL4E0ANNGBCUIQMAdAWrLHTdGQcAJAerLCSX6xI2APgjy+9U10t2/9QTgbpPapba0W8aNSUnp2M2SHJMaFQu1FbdVQ7xBEjDVFwJgkNlgLgjEjAACYMwSPkK4po0IShCAgDsC1JY7boyCgBMC1JcTSvSJG4H2nnHy2Lqpg8I+qq5BJiQ3O0ZBbTlGRUcI5EzAVF8IgDkjZQAEIOCUAAKgU1J29zNtRHZHT3QQ8I8AteUfWyxDgPpiDUBgK4He/oR09o2VdzeVyj/bJ2Z/UDq2T46e3jjsVd+R+FFbrCwI+EfAVF8IgP6xxzIEYk8AATD2SyANwLQRQQkCEHBHgNpyx41REHBCgPpyQok+NhPQ675N3UWycsN4WdtRIinZ9oN+U0u65JCprQiANi8EYosUAdPehQAYqXTiLASiRQABMFr58stb00bk17zYhYDtBKgt2zNMfIUkQH0Vkj5zF5qAvva7omWytPcmja6M9NrvSAOpLSNSOkDANQFTfSEAukbLQAhAwEQAAdBEKB4/N21E8aBAlBDwngC15T1TLEIgQ4D6Yi3ElUBDZ7Esb6qQzakxjhGMTfTL7Oo2qSntNo6htoyI6AAB1wRM9YUA6BotAyEAARMBBEAToXj83LQRxYMCUULAewLUlvdMsQgBBEDWQJwJ6Mm/pQ2VOYl/GV4qAs6taZGK4t5REbJ3xXmFEbvfBEz1hQDodwawD4EYE0AAjHHyB4Ru2oigBAEIuCNAbbnjxigIOCFAfTmhRB+bCOg3/5asq3Z07XekuPU68LxpTaN+E5DasmnVEEvYCJjqCwEwbBnDHwhYRAAB0KJk5hGKaSPKwzRDIRBrAtRWrNNP8D4ToL58Boz50BFo7CqSZQ1Vefs1p6ZZqkt6RrRDbeWNGAMQcF1fCIAsHghAwDcCCIC+oY2UYf6iF6l04WyECFBbEUoWrkaOAPUVuZThcJ4EnmmqkDUdpXlaEZle1ikHVbe5FijydgADEIgxAdPehQAY48VB6BDwmwACoN+Eo2HftBFFIwq8hED4CFBb4csJHtlDgPqyJ5dEYibQ25+Q366qlZQkzJ0NPRKSkk9tVy/JMalhe1JbeSPGAARcC+wIgCweCEDANwIIgL6hjZRh/qIXqXThbIQIUFsRShauRo4A9RW5lOFwHgTae8bJY+um5mFh8ND5dY1SnuxDAPSMKIYg4IyAae9CAHTGkV4QgIALAgiALqBZOMS0EVkYMiFBIBAC1FYgmJkkpgSor5gmPqZht3Yn5Yn6as+iP6y2SaaM8BowteUZZgxBYBsCpvpCAGTRQAACvhFAAPQNbaQMmzaiSAWDsxAIEQFqK0TJwBXrCFBf1qWUgEYhwAlAlgcE7CBg2rsQAO3IM1FAIJQEEABDmZbAnTJtRIE7xIQQsIQAtWVJIgkjlASor1CmBad8IsA3AH0Ci1kIBEzAtHchAAacEKaDQJwIIADGKdsjx2raiKAEAQi4I0BtuePGKAg4IUB9OaFEH5sI8AqwTdkklrgSMO1dCIBxXRnEDYEACCAABgA5AlOYNqIIhICLEAglAWorlGnBKUsIUF+WJJIwHBNo7CqSZQ1VjvuP1HFOTbNUl/SMaIfayhsxBiDgur4QAFk8EICAbwQQAH1DGynD/EUvUunC2QgRoLYilCxcjRwB6ityKcPhPAmkUiJL1lVLe2/StaXyZK/Mm9YkicTIJqgt13gZCAEjAVN9IQAaEdIBAhBwSwAB0C05u8aZNiK7oiUaCARHgNoKjjUzxY8A9RW/nBOxSFt3UpY2VMrm1JiccYxN9MvcmhapGOH134xBaitntAyAgGMCpvpCAHSMko4QgECuBBAAcyVmZ3/TRmRn1EQFAf8JUFv+M2aG+BKgvuKb+7hH3tBZLMubKnISAVX8m13dJjWl3UZ81JYRER0g4JqAqb4QAF2jZSAEIGAigABoIhSPn5s2onhQIEoIeE+A2vKeKRYhkCFAfbEW4kpAXwRWEXBFy2RHIqBe+92vcr3x5B+1FdcVRdxBEjDtXQiAQWaDuSAQMwIIgDFL+AjhmjYiKEEAAu4IUFvuuDEKAk4IUF9OKNHHFgL6/b+m7iJZuWG8rO0okZSM8hE/EUlISurKumTmxE1SVdwz6jf/hjKitmxZNcQRRgKm+kIADGPW8AkClhBAALQkkXmGYdqI8jTPcAjElgC1FdvUE3gABKivACAzRSgI6Hf/9LSfk8c/xo/tkz0q2tNXfZNjUq78p7ZcYWMQBBwRMNUXAqAjjHSCAATcEEAAdEPNvjGmjci+iIkIAsEQoLaC4cws8SRAfcUz73GL2u/v/Q3Hk9qK2yoj3iAJmOoLATDIbDAXBGJGAAEwZgkfIVzTRgQlCEDAHQFqyx03RkHACQHqywkl+kSZQBAv/iIARnmF4HsUCZj2LgTAKGYVnyEQEQIIgBFJlM9umjYin6fHPASsJUBtWZtaAgsBAeorBEnABd8I6Df/lqyrdnTtdyQn9PGPedOacvr+n9qitnxLK4YhYKwvBEAWCQQg4BsBBEDf0EbKMH/Ri1S6cDZCBKitCCULVyNHgPqKXMpwOAcCjV1FsqyhKocRw3edU9Ms1SU9OdmhtnLCRWcI5ETAVF8IgDnhpDMEIJALAQTAXGjZ29e0EdkbOZFBwF8C1Ja/fLEebwLUV7zzb3v0zzRVyJqO0rzDnF7WKQdVt+Vkh9rKCRedIZATAVN9IQDmhJPOEIBALgQQAHOhZW9f00Zkb+REBgF/CVBb/vLFerwJUF/xzr/N0ff2J+S3q2olJYm8w0xISj61XX1OLwJTW3ljxwAERiRgqi8EQBYPBCDgGwEEQN/QRsqwaSOKVDA4C4EQEaC2QpQMXLGOAPVlXUoJ6N8E2nvGyWPrpnrGY35do5Qn+xzbo7Yco6IjBHImYKovBMCckTIAAhBwSgAB0Ckpu/uZNiK7oyc6CPhHgNryjy2WIUB9sQZsJfDehlJZ0VrhWXiH1TbJlOJex/aoLceo6AiBnAmY6gsBMGekDIAABJwSQAB0SsrufqaNyO7oiQ4C/hGgtvxji2UIUF+sARsJNHQWy9ONFdIvYzwLjxOAnqHEEATyJmDauxAA80aMAQhAYCQCCICsDSVg2oigBAEIuCNAbbnjxigIOCFAfTmhRJ8oEWjrTsrShkrZnPJO/OMbgFFaAfgaBwKmvQsBMA6rgBghUCACCIAFAh+yaU0bUcjcxR0IRIYAtRWZVOFoBAlQXxFMGi6PSCCVElmyrlrae5OeUuIVYE9xYgwCeRMw7V0IgHkjxgAEIDASAQRA1oYSMG1EUIIABNwRoLbccWMUBJwQoL6cUKJPVAg0dhXJsoYqz92dU9Ms1SU9OdmltnLCRWcI5ETAVF8IgDnhpDMEIJALAQTAXGjZ29e0EdkbOZFBwF8C1Ja/fLEebwLUV7zzb1v0zzRVyJqOUk/DKk/2yrxpTZJI5GaW2sqNF70hkAsBU30hAOZCk74QgEBOBBAAc8JlbWfTRmRt4AQGAZ8JUFs+A8Z8rAlQX7FOv1XB9/Yn5OFVtSKSo1I3CoWxiX6ZW9MiFTm8/psxR21ZtbwIJmQETPWFABiyhOEOBGwigABoUzbdx2LaiNxbZiQE4k2A2op3/oneXwLUl798sR4cgdWbSuTZ5imeTaji3+zqNqkp7XZlk9pyhY1BEHBEwFRfCICOMNIJAhBwQwAB0A01+8aYNiL7IiYiCARDgNoKhjOzxJMA9RXPvNsWtT7+8ciaqbJp8zjPQtuvsk12mNDp2h615RodAyFgJGCqLwRAI0I6QAACbgkgALolZ9c400ZkV7REA4HgCFBbwbFmpvgRoL7il3MbI/bj8Y/5dY1SnuxzjYvaco2OgRAwEjDVFwKgESEdIAABtwQQAN2Ss2ucaSOyK1qigUBwBKit4FgzU/wIUF/xy7mNET9ZXylN3cWehZaQlHxqu3pJjkm5tkltuUbHQAgYCZjqCwHQiJAOEICAWwIIgG7J2TXOtBHZFS3RQCA4AtRWcKyZKX4EqK/45dy2iNdsKpFnmis8ffxjelmnHFTdlhcqaisvfAyGwKgETPWFAMgCggAEfCOAAOgb2kgZNm1EkQoGZyEQIgLUVoiSgSvWEaC+rEtprAJq607KX+orpV/GeBr3nJpmqS7pycsmtZUXPgZDAAHQsAYSqZR+/pQGAQgETQABMGji4ZyPv+iFMy94FX0C1Fb0c0gE4SVAfYU3N3g2OoH+fpFH1k6VDg8f/tAZx4/rk6PqGiWRyC8D1FZ+/BgNgdEImOqLE4CsHwhAwDcCCIC+oY2UYdNGFKlgcBYCISJAbYUoGbhiHQHqy7qUxiIgPfn3TFOF5+KfwjuwqlVmjO/KmyO1lTdCDEBgRAKm+kIAZPFAAAK+EUAA9A1tpAybNqJIBYOzEAgRAWorRMnAFesIUF/WpdT6gBo6i2V5U4VsTnl77XcLuJT8R56Pf2QSQG1ZvxQJsIAETPWFAFjA5DA1BGwngABoe4adxWfaiJxZoRcEIDCUALXFmoCAfwSoL//YYtl7Anryb2lDpU/in4gXj38gAHqfdyxCINe/GyIAsmYgAAHfCCAA+oY2Uob5JSpS6cLZCBGgtiKULFyNHAHqK3Ipi63D+rX7Jeuqpb036RsDLx7/QAD0LT0YhkCWgGnvQgBksUAAAr4RQAD0DW2kDJs2okgFg7MQCBEBaitEycAV6whQX9al1NqAVraXyYttk32LrzzZK/OmNeX9+AcCoG8pwjAEEAAHrAFeAaYgIFAgAgiABQIfsmn5JSpkCcEdawhQW9akkkBCSID6CmFScGkbAvUdxfLXpikikufTvCOwHZvol7k1LVJR3OsZfWrLM5QYgsA2BEz1xQlAFg0EIOAbAQRA39BGyrBpI4pUMDgLgRARoLZClAxcsY4A9WVdSq0L6L2NpbKiRU/++SP+6cMfB1W3yvSybk/ZUVue4sQYBAYRMNUXAiALBgIQ8I0AAqBvaCNl2LQRRSoYnIVAiAhQWyFKBq5YR4D6si6l1gSk3/x7qXWirNw4wUfxT6S6pEvm1LR6zo3a8hwpBiGQJWCqLwRAFgsEIOAbAQRA39BGyrBpI4pUMDgLgRARoLZClAxcsY4A9WVdSq0IqKkrKc80TpGe1Fjf4/Hy4Y+BzlJbvqeOCWJMwFRfCIAxXhyFDH3jxo3y5tOKaEEAACAASURBVJtvSmtrq3R0dEhFRYXoYtxll11EF23QraurS9544w1paWkR9a28vFyqq6tlt912k3Hjxnnijsb69ttvp2Pu7u6WKVOmSF1dncycOdMT+2rk/fffl1WrVqXnUI6VlZWy4447Sm1trWdz5GIIATAXWvb2NW1E9kZOZBDwlwC15S9frMebAPUV7/yHKXo98dfUXSSvr58oLd1Fvp76y8Tt9cMfCIBhWlH4YjMB096FAGhz9kMY29q1a+Xee++VF154Qfr6+rbxUIXAI444Qk466STPhLfRMLS1tf3/7d0HuBXVtcDxdS+9SO+giBik2IPEIMEgqM8SY8ESFVsU1IgUCyoqiqiABQRBBDFGUdHnM8oziRAsUVRirCggRkUUlN57ufd9a8zMO+dwzp05Z8qZ8t/f5/deuDN71v7t2c51sYsRz9y5c42kXGapVauWdO/eXc4991ypWbNmQaKa9Js+fbp89tlnUq5f8Iyig/Ckk04y/ikpKWwPj7feektmzJhhJACzFU2s9u7dWw4//PCC2lDoTSQAC5WL1312H6J4tZbWIBCcAGMrOGuelDwBxlfy+jyMLdYZfx+uqS9bdnszIcFJG/04+IMEoBN5rkHAvYDdt4sEoHtjanAooEmqKVOmZE20ZVbRpk0bue6666RJkyYOa8//sk8//VTGjRsnmzZtsr1ZB4rGo7Pp8ikvv/yykfzTRJhdOeSQQ2TQoEFSu7bu5+Gs7Ny5UyZMmCDvvfee7Q2aXDz55JPloosuKjjRaPuQjAtIAOYrFs/r7T5E8Ww1rULAfwHGlv/GPCG5Aoyv5PZ9sVtejBl/Zps1+ffLxuukaQ1vD/5INWVsFfsN4/lxFrAbXyQA49z7IWqbzvgbNWpU2gy45s2bS6dOnYyE14oVK+TDDz8UTWiZpVWrVjJixIiCZ95V1PxvvvlGhg0blpaM1NmHRxxxhNSrV09Wr15txLNlyxarGv3ze++911hW66T8/e9/NxKeqUUTiO3bt5dq1arJsmXL5OOPP05LDqrH0KFDHc9+fOihh+Sdd96xHqFJvkMPPVRat25tzLD8+uuvZdGiRWkx6OzK8847z0kTXF9DAtA1YSwqsPsQxaKRNAKBIggwtoqAziMTI8D4SkxXh6Khu8pKZNvuSrJuZxVZuGGfQGf8mQA1K++Woxutk/rVdvlqwtjylZfKEy5gN75IACb8BQmi+brMduDAgbJt2zbjcZqk6tOnjzEbLXW/v40bN8qYMWNk/vz5Vlhdu3Y17vWyaJJxwIABxn5/Zjn11FPl/PPPT0u8abyPPvqovPvuu9Z1upRWk5J25dtvv5Wbb77ZSu5VqVJFrrrqKunWrVvarZr4HD16tLFvn1lOP/10Ixa78uqrr8rjjz9uXaaJySFDhuw1S3HevHny4IMPGnstmuWWW24JZDkwCUC7XkzGz+0+RMlQoJUIeC/A2PLelBoRMAUYX7wLXgiYib3d5SVSuaRcKpeWye6yUtH/XV4msn5XFVm2tYas3lFVyqWwrYC8iLNmpd1yYouVEsRW7IwtL3qMOhDILmA3vkgA8ub4LvDYY4/JrFmzrOecc845xn502Yom5zSJpbPjtGiycOTIkaJLgr0qulfetGnTrOp69OhhJOeylbKyMrn77ruN/fvMcv3110uXLl0qDEdnCursPrNcc801xl6C2YomPnV58YYNG4wfV61aVcaPH28cjJKr6KEl/fv3t+7RBON9991nHCqSrSxYsEDuvPNOawamzkTUGZmF7jnotC+ilADM/AWtRuU9UqV07z0bnbY9SdfZ2dl9iIplZRd30HGFJZ6g4wj6eXb9GrZ4UuPNjK1W1XLZt0VT45Lly5eLfrO8Kn46+Fm32f64PMNpfwbR3lyx+PFsN3W6uVfbaN5fVlJJmjVuKLWriqxdbT++vHqumShy+3tIofHY3Zf6c9Ffk/6Ts8pMbqX+LNf/n3lPoXWYVrliz/bn2tc64069C41P49Vnp9alf6bLaFdtrybfbamZJbGXguZ0gPt8nd97/mWGH9bfC31mpnoEAhGwG18kAAPphuQ+ZP369XL11VdbB37oC6ez/Co6Wffzzz+X4cOHW2iabNOkmxdFl8X269fP2vdPD/Z4+OGHK9x3T/+jSmcMmgd4aDJSk2e5yuLFi40kpln0JGFNvlVUXn/9dZk0aZJ1ic5I1L36cpVXXnlFnnzySevHmlDVxGpFRdup+zCa5cYbb5TOnTt7wZqzjrAnAM09Vr7ZVEt+2Fo97W9eS6RcWtTcLgfss0UaV9spBZ7P4qtvMSvPx65SpVLrJGqvkxT5GuQTdxB9HpZ4go4j6OfZvSdhiyc1XrvY2jYokUOaiVTaulzKdTqJi2L3LDf/TvSzbrPJcXmG0y4Mor25YvHj2W7qdHOvtrHi+0Va1twubfbZvNfvA/4+N//fQwqNx+6+5jW2S8PqO2XN9qryw7bqFZxCW0hyK/OewuqoXqlMtu8pzYitXHL9+U/vtpMZd3bxmX9ZnFpXIW1wOvK9vU5/3z2myVpf9/zLjNguQeFtC6kNgWQJ2I0vEoDJeh8Cb+3s2bNl8uTJ1nMvuOAC+e1vf2sbhx6GYc4C1NltU6dOlerV9RcOd0UP/tAZfWY54YQT5PLLL7etVO/Re82iM/R08GQreqrwSy+9ZP1IlzDrUuaKis581MSkueegLud95JFHct5y6623ypdffmn8XAf5xIkTpUGDBhU+Q6/X+8yiMxJ1ZqKfJcwJwHU7qsgHa+rJxl1VbAnqVNklnRuu931PFNtAQnJBvnZdGm+QDq0bGdEXMwGYb9x+93lY4gk6jqCfZzdswhZParxBxubns/ys2/SKyzPs3tcg25srFj+s3dTp5l5tY6H3F3qfX31YaDz53Of0/eS66Agc0WCdHLDPT9s0BVXsEhRBxcFzEIijgN34IgEYx14PUZt0+a4eAGKWihJnqWHrybkvvvii9UdOlt06abYmEmfOnGldevvtt8vBBx9se2vmDD2dnaez9LIVXc5r7umnMx2feOIJY1mvXVGbt99+27pMZxlmW/qsS4X79u1rzUh0MsPQrFSXOpt7H+rhK3pISaVKlexCK/jnYU0ArthWTd5bVV/2lOvfFDsrQZyK5iyS4l5VqN2p7Utlv3rFSwAWGrdfJ+GFJZ6g4wj6eXajJWzxpMYbZGx+PsvPuk2vuDzD7n0Nsr25YvHD2k2dGqeb73mhz25fd7N8saF2wb9HFPrcXN+kQusrpB1O31OuC7/APpV3yfEtVgW+ysUuQRF+OSJEILwCduOLBGB4+y4WkV1yySXW4RN169bd61TcXI3U/fN0Hz2z6IEhWpfbcsMNN8iSJUuManRwaHLOyczCpUuXyuDBg63HH3XUUaJ1ZZbNmzfLZZddZv3xz372s7QZhxXFr/sk6n6JZtH2arszy/vvvy/333+/9cc6o1JnVjopuvz6vffesy7VA0h0P0C/ShgTgPo33f9Y0TCvX9pNn6D3SPGrXwqt141dlVKRMzuJlG2230ep0Phy3ecmbj/6PCzxBB1H0M+ze4/CFk9qvEHG5uez/Kzb9IrLM+ze1yDbG+S/S930X6mUGXsZ79E93PIs+u/2IxtskI/W1i3o94H0TeOcP9ztc7N9k9wYFtoO5y3myrAKlJaUy6+bri7K6ha7BEVYzYgLgSgI2I0vEoBR6MWIxrh27Vq58sorregPP/xw0dNnnRTdO1BnuZnlsMMOk6FDhzq5Nec1ujm6nj68a9dPR9u3bNnS2I/QSdH9//ReXaqrRQ/bGDt27F63Llq0SG677Tbrz48//ni54oornDzCWNKbukS3V69eaQZmJTozUmdImsXJEmPzWl2arEuUzaJ7Gx5zzDGO4ivkorAlAHWPm9k/Nna07DdXe3U5cK/mwf9taSH+Xt7jhV3DGiI9mrrfpyyfdnkRt5d9HpZ4go4j6OfZvSNhiyc13iBj8/NZftZtesXlGXbva5DtzRWLH9Ze1OnULtt1pVKuKUQ3VRR0r9vnpn6Tim1YEAA3hUCgXI5pvFaa1dxRlFjsEhRFCYqHIhATAbvxRQIwJh0dxmZkHuaRK6GVLXZNuOmsNj20Q0vjxo1lwoQJrpq5YsUK4+Rcs+SbVNRk2Y8//mjcrstmn3rqqb0OM8lcKvy73/1OzjjjDEdxZyZMO3bsKHfcccde92Ye5jFixAhp166do2fMmTNHxo0bZ13r5PAQRxXnuChsCcCV26vK2yt+2ovOTenedLU0rv5TMjgpxSu7Y5utlUbVtgfG5lXcXvV5WOIJOo6gn2f3goUtntR4g4zNz2f5WbfpFZdn2L2vQbY3Vyx+WHtVp1O/OF1nfpMwjFOvBtWWcmNf69a1g933L7V1dgmKoCR4DgJxFLAbXyQA49jrIWmTnjirySqznHvuuXLWWWc5jk4PqFi5cqVxvSbcnn32Wcf3Zrtw4cKFMmzYMOtHxx13XNoMRbvK9STf+fPnW5fpIR16WEdqeeGFF+T555+3/kjboIdtOCk6Q1GTnpo006KDU/cFzCx6QrImV82SLY5cz1uwYEFaUjFfAyftSL0mbAnAuavqy7KtNfJtxl7Xt6y5TY5uvM51PVGqwCu7VrW2yS8aBWfnVdxe9XlY4gk6jqCfZze2whZParxBxubns/ys2/SKyzPs3tcg25srFj+svarTqV+crjO/SRjGqVeDaEu5HFZvgxxYd2sQD8v5DLsERVGD4+EIRFzAbnyRAIx4B4c5/Mw97S6++GI55ZRTHIecul+f3jRt2jRHh2nkeoDbfQV1v7wPPvjAql6XD+sy4tSiMc6YMcP6o3wPL1Gjbdt++hu5XHsm6jLqr776ynrGH//4R6lVq5Yj18WLF8uQIUOsa/V0Yl1CnE8xDxGp6J569eoZSVtNAK5atSqf6n27dldZiby8pImUe7Lcp1yaVN8ppcGvHPLNp6KKy8pFdJaBRMwubHGHJZ6g4wj6eXaDJGzxpMYbZGx+PsvPuk2vuDzD7n0Nsr25YvHDWp/l3XfFqWKcriuXRtV2yuodXn2b42RDW7ILlMsRDTfKgXWKN/PPjEsTFE2aNDH+p0720EkQFAQQ8EbAbnzpyko/DwH1phXuaikp1/WklMAFNBGmCTGzXH755XLCCSc4jiMz0aUHZNSpU8fx/ZkX6uEXqXv+5XN4htaVeYCGHlLStm3btMdojJr4NIu2Qfc+dFp0v0A95VdLtWrVjGXGmUUPI9FDScyST2I08zCTI488Um666San4RnXnXPOObbX5zMr0bYyjy5Yu1Xk6U89qoxqEEAAAQQQQAABBBCIgECNKiK/OUik6T4RCJYQEUAAAZcCJABdAhZ6e+ZyWD0QRJecOi26XFeX7Zpl4sSJ0qhR4fu3ZS5J1uXIuizZacnce0+XBHfo0CHtdk18vfHGG9af3X777XLwwQc7fYRcddVVYs6w05Ptnnvuub3u1X0MdT9Ds+iBIJrpd1Iy90HU2DTGfEpUE4DLN4n89/+vnM6nyVyLAAIIIIAAAggggECkBKqUinRrLdKpqUhJQlatRKqDCBYBBHwRIAHoC6t9pcwAFOPU47jNAIzqEuCNOyvJzGWN7V9crkAAAQQQQAABBBBAIJIC5VKv6i7pVG+zNK+5M5SJP7slipFkJ2gEQiJgN75YAhySjopjGOwBKBLHPQDzeVfDdAiI7gH4v98382wPwI51N0qlhPxt6p5ykQUbdPm9Fw0ul6DswhZ3WOIJOo6gn2f376iwxZMab5Cx+fksP+s2veLyDLv3Ncj25orFD2t9lnffFaeKcbquXA6qs1EWbfTq2xwnm+S2pVblXfLzBhukcY2doUawO6Qg1METHAIhF7AbXxwCEvIOjHJ4mUtudelo7969HTfJ71OAe/ToYSy5dVqCOAX4/PPPtzbCdXoKcD5LozkFmFOAnb7vmdd5ddIgpwCH4x30qj+dno4c9PPs3vOwxZMab5Cx+fksP+s2veLyDLv3Ncj25orFD2uv6nTqF6frOAU4Tr3pti0/HQjTsd4m4/9GYamvXYLCrQj3I5BkAbvxRQIwyW+Hz23//PPPZfjw4dZTevXqJX379nX0VD235YILLpDdu3cb1+tU1QkTJji6N9dFmfvfHXbYYTJ06FDHdV577bWyfPly43o9OUcP6KhcuXLa/a+//rpMmjTJ+rPf/e53csYZZzh6xtq1a0X3STRLx44d5Y477tjr3sy9CEeMGCHt2rVz9Iy3335bxo8fb12rCVkne/o5qjzLRWGaAajh6YmDb68ofB9Js4ndm66WxtXD/berhfZZrvu8sju22VppVG271+HlrM+ruL3q87DEE3QcQT/P7gULWzyp8QYZm5/P8rNu0ysuz7B7X4Nsr9/fgNR/l3rVf0794nSd6YhhnHo1/7ZEZcZfZsvsEhT5S3AHAgiYAnbjiwQg74pvAuvWrZN+/fpZ9eeTcFu/fn1aslD30dP99NwUPWL+oosukp07f0rctGjRQsaOHeuoSk1IXnjhhbJr1y7j+pYtW6adKGxW8uWXX8qtt95q1ZlP0jPz3uOPP170VODM8uc//1meffZZ648HDBggxxxzjKN2vPTSS/LMM89Y1w4cOFC6du3q6N5CLgpbAlDPA5/9Y2PZuKtKIc0x7qlTZZf0ar4qEn/DWnAjs9zohV3DGiI9mi6X8vIyL0OrsC4v4vayz8MST9BxBP08uxcsbPGkxhtkbH4+y8+6Ta+4PMPufQ2yvbli8cPaizqd2mW7rlTKpcyTrS3yi8Ltc1O/ScU2zK/lXO2FQImUS4ua26XtPlsiM+Mvs912CQovnKgDgaQK2I0vEoBJfTMCavcll1wiW7duNZ5Wt25dmTJliqMnf/TRRzJy5Ejr2pNPPlm0LrflhhtukCVLlhjV6OB44oknpHr16rbVLl26VAYPHmxdd9RRR4nWlVm2bNkil156qfXHBx54oNxzzz229esFM2fOlKlTp1rXanu13Znl/fffl/vvv9/649NOO81ITjopDz74oMydO9e6dPTo0bL//vs7ubWga8KWANRGrNtRRf6xoqHsKXd2cnJqwyuVlMmxTddI/Wo/JYKTVtzY6Ul0Z3YSKdu83FrmHpSfm7j96POwxBN0HEE/z+79Cls8qfEGGZufz/KzbtMrLs+we1+DbG+uWPywdlNnqabvSkpkT3n++9Pqv9uPbLBBPlpbt6DfB0TKC9oX1+1zs32T3BgW2g6n7yvXeSOgM/061Nks9avvkhqV9kiVUn3/olvsEhTRbRmRI1B8AbvxRQKw+H0U6wg0iafJPLOMGzdOmjVrZtvm6dOny4svvmhdl+9hGrkeoAk2TbSZ5bbbbpNDDjnENp7XXntNHn30Ues6nUl46qmnZr3vuuuuk++//974mS4V/tOf/iRVq1a1fYbazJkzx7pu1KhR0qZNm73u27BhgzE7Umclamnfvn3aUuuKHqR7Hpqn+NauXdtIyGqMfpUwJgC1rSu2VZP3VtXP65d+/aX7l43XSdMaO/ziikS9hdqd2r5U9qsnxjJ6nY0bdCk0br/6PCzxBB1H0M+ze8/CFk9qvEHG5uez/Kzb9IrLM+ze1yDbmysWP6zd1KlxuvmeF/rs9nU3yxcbahf8e0Shz831TSq0vkLa4fQ95Tq3AuXSpPoO0T6Kyt5+Tltsl6BwWg/XIYDA3gJ244sEIG+NrwKzZ8+WyZMnW8/QQy5OP/1022fq0tQffvjBuK5KlSrGzDgnM/XsKp43b57onnlmybXMNrOeu+++Wz799FPrj3UfPR082Youz9VlumZxssxWlyXrcmmdQailYcOG8sgjj+RsjiYuFy1aZPxcB7keBNKgQYMKm5+5xLh79+6iB634WcKaANQ269+Yf7CmnqPlwLrcpnPD9Ymd+Zf5juRr16XxBunQ+qe9F4uVAAxjn+fr6Nc7GHQcQT/P7t9xYYsnNd4gY/PzWX7WbXrF5Rl272uQ7c0Vix/Wbup0c6+bb0Oxnut1v+TTDqfvJ9dVJFDR7NFyaVxth7SuvU2a19guVStFe6ZfLgW7BAXvDwIIFC5gN75IABZuy50OBHQvv6uvvto6zENfuDFjxux1eEZqVZmHh3Tp0kV0BqAXRQ8V0YM2Nm7caFRXs2ZN43CRWrVq5axeExa6z545405n5ensvFxl8eLFMmTIEOvHHTp0ED1BuKKSeXiIzi7UWYa5yl/+8hdjZqFZnBzmkXl4yI033iidO3f2gjVnHWFOAGrQOoly9Y6q8vWmWvLD1upSnrIXUBz2WPGzc/Oxq1Sp1Jr5W8wEYBj7PB9HP0/2CzqOoJ9nNxbCFk9qvHaxtW1YIoc0Fam01f3+mnbPcrPvlJ91m15xeYbd+xpke3PF4oe1mzrd3Gv3bSgtEWlRc5scUHvvPdf8fG4hv4cUGo/dfZqMalR9p6zZXlWWbdOtc3Ituy5kaXTmPYXVUb1SmWzfo9u7pMZWLrn+/Kd328nycbv4zCRd+nMz4zATe42q7ZA9Uiq7y9KfXbm0PBbLe538+8suQeGkDq5BAIHsAnbjiwQgb47vApnLbvXUWU1YZSs6E+6mm24S3XPP+CyXlBh7AWZbCqs/X7lyZdosNienBc+YMUOmTZtmPb5Hjx6iS2OzFV2qqLP/PvvsM+vHTpYjZy591pl2OuMuW9FkpC4b1qW9WnS5sM4wrF+/fs6+2b59u/Tv39+6R2dJ3nfffcbBJtnKggULjCSkmcTUff80iam+fpawJwBT276rrES27alk/EKWpF/CvOh/Ozu7D5EXMRRSh13chdTp5p6wxBN0HEE/z66PwhZPRf+eqlWlXPZt8dNsdK+T6346+Fm36RWXZ9i9r0G2N1csfli7qdPNvdpG8/4yqSTNGjeU2lVF1q62377Cq+d69XtIofHY3Zf689R3Qn93qlxSJrvL905u5Xp3Mu8ptA5zX7xcsWf7c43J/L2v0PjM3xdT68rWpjjs2+f030VOrgvr74VOYucaBMIuYDe+SACGvQdjEN/atWtl0KBBsm3bNqM1mnTq06ePccCFvqBm0USYzg6cP3++9Wd6Qq0uoc1VCkkAapJRZ/SZe+Fp3TrjTpcnV65c2XqUxqv7/r377rvWn7Vr1y5tCXGuuL799lu5+eabRRNgWjRBp0nGbt26pd2i8WsiztwzUH+oS6Q1Frvy6quvyuOPP25dpsuGdeZh5qEeuuxZD/8wD2PRG/REZT1Z2e8SpQSg3xZJrt/uQ5RkG9qOgBsBxpYbPe5FoGIBxhdvCAL+CDC2/HGlVgRUwG58kQDkPQlEQA8C0USXOQNNH9q8eXM5+OCDRQ+j0JkLH374oWhyziytWrUykm26TDdXKSQBqHV98803MmzYMNmx4/8PdNAZd0cccYTUq1dPVq9ebcRj7smn9+if33vvvcb+fE7KrFmz5LHHHku7VJNzemhHtWrVZNmyZfLxxx9bSUK9sFOnTjJ06NAKl0inVjh27Ni0BKUmVw899FBp3bq1sez666+/tvYKNO8788wz5bzzznPSBNfXkAB0TRiLCuw+RLFoJI1AoAgCjK0ioPPIxAgwvhLT1TQ0YAHGVsDgPC5RAnbjiwRgol6H4jb2rbfeMk6dTU265YpIE2W61LZJkyYVBl1oAlAr/eSTT4yltps2bbKF0Th0mW6upci5KnjppZfkueeeS0vy5bpWk6GDBw82EqJOiyZMdW+/uXPn2t6iycGTTjpJLr74Yt+X/prBkAC07ZZEXGD3IUoEAo1EwAcBxpYPqFSJwH8EGF+8Cgj4I8DY8seVWhFQAbvxRQKQ9yRQAT3Z9+mnnxadEWguj00NQGfh9ezZU3SWWupy3FxBukkAap3r1q0z4vnnP/+ZNTGph4Po3n3nnntuhTMRK0L897//LdOnTxc93CR1BqR5jw5CTczpP4XuyafJVd3b8Lvvvssays9+9jM5++yzA1n2mxoACcBAh1doH2b3IQpt4ASGQMgFGFsh7yDCi7QA4yvS3UfwIRZgbIW4cwgt8gJ244sEYOS7OJoN0Fl3ixYtMvbh0732dHmtzrLT5bGp+wIG1To9VEMPytB4Nm/eLHXr1pVGjRqJnuCr+/d5UXQvRE0G6v/VmXua7NRDOw488EAvqjfq0ASg/qPPUMcGDRrIAQccYJ3A6tmDHFZEAtAhVMwvs/sQxbz5NA8B3wQYW77RUjECtrMoIEIAgcIE+HYV5sZdCDgRsBtfJACdKHINAggUJEACsCC22N1k9yGKXYNpEAIBCTC2AoLmMYkUYHwlsttpdAACjK0AkHlEYgXsxhcJwMS+GjQcAf8FSAD6bxyFJ9h9iKLQBmJEIIwCjK0w9goxxUWA8RWXnqQdYRNgbIWtR4gnTgJ244sEYJx6m7YgEDIBEoAh65AihWP3ISpSWDwWgcgLMLYi34U0IMQCjK8Qdw6hRVqAsRXp7iP4kAvYjS8SgCHvQMJDIMoCJACj3HvexW73IfLuSdSEQLIEGFvJ6m9aG6wA4ytYb56WHAHGVnL6mpYGL2A3vkgABt8nPBGBxAiQAExMV1fYULsPEUoIIFCYAGOrMDfuQsCJAOPLiRLXIJC/AGMrfzPuQMCpgN34IgHoVJLrEEAgbwESgHmTxfIGuw9RLBtNoxAIQICxFQAyj0isAOMrsV1Pw30WYGz5DEz1iRawG18kABP9etB4BPwVIAHor29Uarf7EEWlHcSJQNgEGFth6xHiiZMA4ytOvUlbwiTA2ApTbxBL3ATsxhcJwLj1OO1BIEQCJABD1BlFDMXuQ1TE0Hg0ApEWYGxFuvsIPuQCjK+QdxDhRVaAsRXZriPwCAjYjS8SgBHoREJEIKoCJACj2nPexm33IfL2adSGQHIEGFvJ6WtaGrwA4yt4c56YDAHGVjL6mVYWR8BufJEALE6/8FQEEiFAAjAR3WzbSLsPkW0FXIAAAlkFGFu8en1jLgAAIABJREFUGAj4J8D48s+WmpMtwNhKdv/Ten8F7MYXCUB//akdgUQLkABMdPdbjbf7EKGEAAKFCTC2CnPjLgScCDC+nChxDQL5CzC28jfjDgScCtiNLxKATiW5DgEE8hYgAZg3WSxvsPsQxbLRNAqBAAQYWwEg84jECjC+Etv1NNxnAcaWz8BUn2gBu/FFAjDRrweNR8BfARKA/vpGpXa7D1FU2kGcCIRNgLEVth4hnjgJML7i1Ju0JUwCjK0w9QaxxE3AbnyRAIxbj9MeBEIkQAIwRJ1RxFDsPkRFDI1HIxBpAcZWpLuP4EMuwPgKeQcRXmQFGFuR7ToCj4CA3fgiARiBTiREBKIqQAIwqj3nbdx2HyJvn0ZtCCRHgLGVnL6mpcELML6CN+eJyRBgbCWjn2llcQTsxhcJwOL0C09FIBECJAAT0c22jbT7ENlWwAUIIJBVgLHFi4GAfwKML/9sqTnZAoytZPc/rfdXwG58kQD015/aEUi0AAnARHe/1Xi7DxFKCCBQmABjqzA37kLAiQDjy4kS1yCQvwBjK38z7kDAqYDd+CIB6FSS6xBAIG8BEoB5k8XyBrsPUSwbTaMQCECAsRUAMo9IrADjK7FdT8N9FmBs+QxM9YkWsBtfJAAT/XrQeAT8FSAB6K9vVGq3+xBFpR3EiUDYBBhbYesR4omTAOMrTr1JW8IkwNgKU28QS9wE7MYXCcC49TjtQSBEAuXl5VJWVhaiiAilWAKVKlUyHq1JYQoCCHgnwNjyzpKaEMgUYHzxTiDgjwBjyx9XakVABSoaX5ogLCkpiTVUSblmISgIIIAAAggggAACCCCAAAIIIIAAAgggEEsBEoCx7FYahQACURHQWX/r1683wq1Xr571t1JRiZ84EQirAGMrrD1DXHEQYHzFoRdpQxgFGFth7BViiosA40uEBGBc3mbagQACkRRYs2aNXHXVVUbsjzzyiDRs2DCS7SBoBMImwNgKW48QT5wEGF9x6k3aEiYBxlaYeoNY4ibA+CIBGLd3mvYggEDEBPgQRazDCDcyAoytyHQVgUZQgPEVwU4j5EgIMLYi0U0EGVEBxhcJwIi+uoSNAAJxEeBDFJeepB1hE2Bsha1HiCdOAoyvOPUmbQmTAGMrTL1BLHETYHyRAIzbO017EEAgYgJ8iCLWYYQbGQHGVmS6ikAjKMD4imCnEXIkBBhbkegmgoyoAOOLBGBEX13CRgCBuAjwIYpLT9KOsAkwtsLWI8QTJwHGV5x6k7aESYCxFabeIJa4CTC+SADG7Z2mPQggEDEBPkQR6zDCjYwAYysyXUWgERRgfEWw0wg5EgKMrUh0E0FGVIDxRQIwoq8uYSOAQFwE+BDFpSdpR9gEGFth6xHiiZMA4ytOvUlbwiTA2ApTbxBL3AQYXyQA4/ZO0x4EEIiYAB+iiHUY4UZGgLEVma4i0AgKML4i2GmEHAkBxlYkuokgIyrA+CIBGNFXl7ARQAABBBBAAAEEEEAAAQQQQAABBBBwJlBSXl5e7uxSrkIAAQQQQAABBBBAAAEEEEAAAQQQQACBqAmQAIxajxEvAggggAACCCCAAAIIIIAAAggggAACeQiQAMwDi0sRQAABBBBAAAEEEEAAAQQQQAABBBCImgAJwKj1GPEigAACCCCAAAIIIIAAAggggAACCCCQhwAJwDywuBQBBBBAAAEEEEAAAQQQQAABBBBAAIGoCZAAjFqPES8CCCDgo8B3330nr7/+unz22WeyZs0a2b17t9StW1datGghHTp0kB49ekj9+vV9jICqEYi3QFlZmQwbNkwWLVpkNLRx48YyYcKEeDea1iHgsYCOowULFsi8efOMsfTDDz/I5s2bpWrVqtKwYUPje3X88cfL/vvv7/GTqQ6BaAvouJk5c6Z8+eWXsmHDBqldu7YccMAB0qtXL+ncuXO0G0f0CAQsEMVvEQnAgF8SHocAAgiEUUA/YNOmTZO//vWvov9/rnL99ddLly5dwtgEYkIgEgI6xp544gkrVhKAkeg2ggyZwJAhQ2Tx4sUVRlVSUiK/+c1v5IILLhD9/ykIJF3gmWeekZdfflnKy8uzUnTv3l2uvvpqKS0tTToV7UfAkUAUv0UkAB11LRchgAAC8RYYP368vP3220YjDz/8cDnhhBOMmRPVq1eXdevWyRdffCFz5swx/mOKvyGO97tA6/wTWLlypVx33XXGzNp69eoZs2xJAPrnTc3xFejfv7/oeDr44IPl6KOPloMOOsiYnb5jxw75/PPP5bnnnjPGl5bevXvLOeecE18MWoaAAwGd9Td16lTjyrZt28r5558vrVu3lrVr18qMGTOM3/G0nHbaaXLhhRc6qJFLEEAgit8iEoC8twgggEDCBf7+97/LlClTDAX9jyT9jyUKAgh4L3DXXXcZy+vPPPNMI6muS7FIAHrvTI3xF9AZ67olRcuWLbM2dv369aIzM/QvsCpXriwTJ040ku4UBJIooMvjNVGxZcsWad68uYwaNcr4C97U8tBDD8k777wjlSpVkgcffNC4joIAAhULRPFbRAKQtxoBBBDwSUB/4dL/yNe/Xd26dasxO6Fp06bSrl270Cyv2L59u1x55ZVGfDqT4vbbb/dJg2oRSLaA7q05adIk4z+q7r//frn77rtJACb7lQht66Pw7XKC97//+7/y1FNPGZdec801ossbKQhEScCrsZi69cSAAQPkmGOO2YtBZ8z+4Q9/MLaBOfXUU+Wiiy6KEhWxIpCXgFdjy8lDw/YtIgHopNe4BgEEYiOgCa9vvvlGvvrqK+Ofr7/+WlatWmW1z4vZOLoZue6z8tFHHxlL/TKLJgJ79uxpzALSmQnFLLNnz5bJkycbIdx6661y6KGHFjMcno2AI4Egf3FzFJDNRToLafDgwUaiXZPsnTp1kjvuuIMEoBe4CamDb1f+Hf3JJ5/IPffcY9yoyx1PP/30/CvhDgQyBKI4FocPH24sja9SpYr88Y9/NA7LyVb0gKqFCxcaf1mtW8NQEAhSIIpjy4lP2L5FJACd9BrXIIBA5AVeeeUVeeONN2Tp0qU5Nz/WRrpNAL711lvGclrdh8iutGnTxtgPrEmTJnaX+vZzXQby4YcfSo0aNYxfCs2Nn3WDaP1bYF0KQkHAiUBcf3Fz0na7a0aPHi0ffPCBHHfcccaMWy0kAO3U+LkK8O0q/D148803jaW/Wn7/+9/LiSeeWHhl3Jl4gSiPxUsuucT4CyhdgTJixIicfal/ef3SSy8ZP9ffCWvVqpX4fgfAf4Eojy0nOmH7FpEAdNJrXIMAApEXMP8D3K4hbhKAOuNPE2qpp6vpcj+d7VO7dm1ZsWKFkWzbuXOnFUarVq2MX8Zq1qxpF5ovP7/iiitkw4YNRoy33XabvPbaa6JLFb/77jvZtWuX7LPPPtK+fXv5r//6LznkkEN8iYFKoy0Q91/c3PbOu+++K2PHjpW6devKmDFjjH8XkAB0q5qc+/l2Fd7X9957r3z88cdGBeqoB1tREChUIKpjUWeg9+vXz2j2r371K2MvwFwldVWI/m6qCUMKAn4LRHVsOXUJ27eIBKDTnuM6BBCItEC2j4tugKyz8HRJsDljr9AEoP6CNXDgQNm2bZvhVFJSIn369JGTTz45bb+/jRs3GkmA+fPnW55du3Y17g26aILvggsuMB6r+8Fs2rRJ5s2blzMMnT1x2WWXGW2jIGAKxP0XNzc9rWNq0KBBouNex7iOdbMwA9CNbHLu5dtVWF+nLrnq0KGD3HnnnYVVxF0I/EcgqmNxyZIlcsMNNxitsNvbT2eqazu13HjjjdK5c2f6HwHfBaI6tpzAhPFbRALQSc9xDQIIRF5AZ+CsXLlS2rZta/2jpwfqklfd9NjcB7DQBOBjjz0ms2bNspwqOk1XZwDq6YTLli0zrteE2siRI41kZJBFT0ns27ev8Ujdi1D3K9QYNCl40EEHyZ49e4w9ynQT9R9//NG47rzzzjP2LqQgYArE+Rc3t708btw4mTNnjhx55JFy0003pVVHAtCtbjLu59uVfz/rYQY63nR2u+55pgfuMPsvf0fuSBeI6lhctGiRscJDi/7+pr/H5Sr6l8DmEuFrr71WunXrxmuAgO8CUR1bdjBh/RaRALTrOX6OAAKxF3CbANRE2tVXX20d+KGbJ+ssv4oO+NDNmHVTZrN06dJFrr/++qzWmrjUEwzdFF1+qHsTphY9ndjcj0z/vEWLFkYiUmdGphb9jyj922NtZ7Vq1WTChAlSp04dN+Fwb4wEovyLm19jS7tXtwQwx9ODDz4ojRo1Sut1EoAxGgRFakpSv10VcessfB1bixcvNi7TbS6OP/74IvUQj02KQJjHIgnApLyF8WxnmMdWVL9FJADjOVZoFQII5CHg9uOSumeKPlZn0P32t7+1jUCXBpqzAHWWwtSpU/dKvmklfiUp9D+ULr74YitOTTJ27949a9wzZsyQadOmGT/TZOevf/1r2/ZxAQJux1ZUk+u62boe8KN/+6ubr+tWAJmFBCDjw62A2/EV1W9XLjedXa+n/urMdS29e/cWnY1PQcBvgTCPRZYA+9371O+nQJjHVlS/RSQA/XxjqRsBBCIh4PbjorN8dLaPWcaPHy86C9CuTJ8+XV588UXrMp0BqDMBM4suzdUDRNwUPc23WbNmaVXoYSUXXnihcdiHFj0tMXOWknnDV199JbfccovxP+32kHETJ/fGS8Dt2PI7QeHX2PrTn/4kf/nLX4ztBnT5oXm6dmrvkgCM17tejNa4HV9R/XZls9axrNsR6H5LWk477TTj+0ZBIAiBMI/F1NUe+pe8Fa0o0YPgHn30UYPsrrvuMraDoSBQTIEwj62ofotIABbzjebZCCAQCgG3Hxed4aMzfrRkW2qbq5F6OqGeDGUWnSWkdQVZdJPnb7/91njkk08+mXUGov5s+fLlovvBaOnZs6d1olyQsfKs6Am4HVt+Jyj8EnV6MErm8zt27GgsX6Qg4ETA7fiK8rcr1Uf3q33ggQdEDzDQUoxvqZP+4pr4CoR9LOpqD131oQk9TezlKs8884y89NJLxo8ff/xx69T6+PYcLQu7QNjHVhS/RSQAw/7WEx8CCPgu4ObjkrmP3uGHH27NlLMLPPUQDr32sMMOk6FDh9rd5unPJ0+eLDrLSovu7aeHoGQrX375pdx6663Gj5hZ4WkXxLoyN2NLYaKaoCABGOvXOjSNczO+ov7tMjuhrKxMHnroIXnvvfeMPzrhhBPk8ssvD00fEUgyBMI+FvUU7Pnz5xuH4jzxxBPG/81WzJnpuopFV7NQECi2QNjHVhS/RSQAi/1W83wEECi6gJuPS+ZhHr169bJO1rVrmC7B1f0CdemSlkJPILZ7TkU/Tz3x7aqrrpIePXpkvVyXKuuSZS0DBgyQY445xs1juTchAm7GVpQTFDpjdsuWLRX2sibf9aCC+vXri87E1VKjRg3jMB4KAk4E3IyvqH+71EeTf7p1xVtvvWVwHXfcccbs9JKSEid8XIOAZwJhH4t//etfjcSfloEDB0rXrl33art+c3WPZx1XbPXi2atBRS4Fwj62ovgtIgHo8qXkdgQQiL6Am4+L/ofHww8/bCGce+65ctZZZzlG0b1Y9JAPLbpP37PPPuv4Xi8u1F/0hgwZIrpJtP6Nry65rFWrVlrV+kuhJig2btxo/ExnCtasWdOLx1NHzAXcjK04JCgq6l72AIz5yx9A89yMr6h/u/Qv0DSJrnuWaTn22GON5AXJvwBePB6xl0DYx+LmzZulf//+xl9MNW/e3Ngvs1q1amntGDdunMyZM8f4XVRPrtfrKAgUWyDsYyuK3yISgMV+q3k+AggUXcDNx2XWrFny2GOPWW3QfVZOOeUUx2264YYbjOSbWfSk3apVqzq+34sLv/jiCxk+fLgxE7FVq1Zy3nnnGfvEaHJQl4xoUnLVqlXGo/r27Ss6y5GCgBMBN2Mr6gkKOx8SgHZC/NxOwM34ivq3S2cz6awmLUcddZSoRbbDdkxDTWrkWvZo58zPEbATiMJYnDlzpkydOtVoih5QpStQ9ttvP1m3bp28/PLLRvJPC9u82PU2Pw9SIOxjK4rfIhKAQb7BPAsBBEIp4ObjMmPGDNGknVl07yHdg8hp0ZN19YRds2gysU6dOk5v9+y6uXPnGkuptm/fnrVOnVWhvyzqL4YUBJwKuBlbUU9Q2BmRALQT4ud2Am7GV9S/Xeecc44dT9rPdYagelEQ8EMgKmNRD/nQZJ/OWspW9JRgnUlbUTLdDz/qRCCXQNjHVhS/RSQAGW8IIJB4ATcflxdeeEGef/55y/DKK6809iFyWoYNGyYLFy60LtckXKNGjZze7ul1Osvvb3/7m+jpxKtXrzZ+QWzYsKF06tRJTjrpJNl33309fR6VxV/AzdiKeoLCrndJANoJ8XM7ATfjK+rfrij+R5ddf/Lz6ApEaSwuWLBAXn31VdHD3cytXXRGYM+ePY3ZtBQEwiQQ9rEVxW8RCcAwveHEggACRRFw83GJe5KiKB3CQ2Mj4GZsRT1BEZtOpCGhFXAzvvh2hbZbCSyCAozFCHYaIUdCgLHlfTeRAPTelBoRQCBiAm4+LnFfphixriTckAm4GVskKELWmYQTOgE344tvV+i6k4AiLMBYjHDnEXqoBRhb3ncPCUDvTakRAQQiJuDm45J5UIFOBe/du7djgWKfAuw4UC5EoAABN2OLBEUB4NySKAE344tvV6JeFRrrswBj0Wdgqk+sAGPL+64nAei9KTUigEDEBNx8XD7//HPjBF2z6Am5elKuk6J77OnBGnr6rpbGjRvLhAkTnNzKNQhEQsDN2CJBEYkuJsgiCrgZX3y7ithxPDp2AozF2HUpDQqJAGPL+44gAei9KTUigEDEBNx8XNatWyf9+vWzWnzYYYfJ0KFDHQmsX78+LVl4+OGHi54KTEEgLgJuxhYJiri8BbTDLwE344tvl1+9Qr1JFGAsJrHXaXMQAowt75VJAHpvSo0IIBAxATcfF23qJZdcIlu3bjVaXbduXZkyZYojgY8++khGjhxpXXvyyScbdVEQiIuAm7FFgiIubwHt8EvAzfji2+VXr1BvEgUYi0nsddochABjy3tlEoDem1IjAghETMDtx0WTeJrMM8u4ceOkWbNmtgrTp0+XF1980bru+uuvly5dutjexwUIREXA7dgiuR6VnibOYgi4HV98u4rRazwzjgKMxTj2Km0KgwBjy/teIAHovSk1IoBAxATcflxmz54tkydPtlp9/vnny+mnn26rMHDgQPnhhx+M66pUqSJTp06V6tWr297HBQhERcDt2CJBEZWeJs5iCLgdX3y7itFrPDOOAozFOPYqbQqDAGPL+14gAei9KTUigEDEBNx+XHQvv6uvvto6zKNp06YyZswYqVy5ck6JzP3NdOafzgCkIBAnAbdjiwRFnN4G2uK1gNvxxbfL6x6hvqQKMBaT2vO0228Bxpb3wiQAvTelRgQQiJiA24+LNldn782cOdNq+TnnnCO9e/fOKrFz50656aabZOnSpcbPS0pKjL0A27RpEzE5wkWgYgG3Y4sEBW8YArkF3I4vvl28XQh4I8BY9MaRWhDIFGBsef9OkAD03pQaEUAgYgJefFzWrl0rgwYNkm3btllJvT59+oge7FFaWmqJbNy40ZgdOH/+fOvPunbtKrocmIJA3AS8GFsk1+P2VtAerwS8GF98u7zqDepJsgBjMcm9T9v9FGBsea9LAtB7U2pEAIEQCqxatUr69++fNbKysrK0P09N2KX+4Pbbb5eOHTvmbJ0eBDJq1CgpLy+3rmnevLkcfPDBUrt2bVm+fLl8+OGHojMAzdKqVSsZMWKE1KxZM4RqhISAOwF+cXPnx90I8O3iHUAgHAKMxXD0A1HET4CxFWyfkgAM1punIYBAkQRWrlwp11xzjaunDxs2TDp16lRhHW+99ZZMmTJFduzYYfus/fff39j3r0mTJrbXcgECYRXgF7ew9gxxxUGAb1ccepE2xEGAsRiHXqQNYRRgbAXbKyQAg/XmaQggUCSBoD4u2jw92ffpp58WnRG4Z8+evVpcv3596dmzp5x55pkVHhRSJCoei0BeAkGNLZLreXULF8dEIKjxxbcrJi8MzfBNgLHoGy0VJ1yAsRXsC0ACMFhvnoYAAgkS2LRpkyxatEjWrFlj7A1Yr149Y7Zf+/bt0/YFTBAJTY2hAL+4xbBTaVKiBfh2Jbr7aXyIBBiLIeoMQomVQJLHFgnAWL3KNAYBBBBAAIH4CyT5F7f49y4tRAABBBBAAAEEEPBDgASgH6rUiQACCCCAAAIIIIAAAggggAACCCCAQEgESACGpCMIAwEEEEAAAQQQQAABBBBAAAEEEEAAAT8ESAD6oUqdCCCAAAIIIIAAAggggAACCCCAAAIIhESABGBIOoIwEEAAAQQQQAABBBBAAAEEEEAAAQQQ8EOABKAfqtSJAAIIIIAAAggggAACCCCAAAIIIIBASARIAIakIwgDAQQQQAABBBBAAAEEEEAAAQQQQAABPwRIAPqhSp0IIIAAAggggAACCCCAAAIIIIAAAgiERIAEYEg6gjAQQAABBBBAAAEEEEAAAQQQQAABBBDwQ4AEoB+q1IkAAggggAACCCCAAAIIIIAAAggggEBIBEgAhqQjCAMBBBBAAAEEEEAAAQQQQAABBBBAAAE/BEgA+qFKnQgggAACCCCAAAIIIIAAAggggAACCIREgARgSDqCMBBAAAEEEEAAAQQQQAABBBBAAAEEEPBDgASgH6rUiQACCCCAAAIIIIAAAggggAACCCCAQEgESACGpCMIAwEEEEAAAQQQQAABBBBAAAEEEEAAAT8ESAD6oUqdCCCAAAIIIIAAAggggAACCCCAAAIIhESABGBIOoIwEEAAAQQQQAABBBBAAAEEEEAAAQQQ8EOABKAfqtSJAAIIIIAAAggggAACCCCAAAIIIIBASARIAIakIwgDAQQQQAABBBBAAAEEEEAAAQQQQAABPwRIAPqhSp0IIIAAAggggECMBcaNGydz5syxWti0aVMZP358jFsc36aVl5fLrbfeKv/+97+NRpaWlsr9998vrVq1KnqjFy9eLDfddJNojFo6deokw4YNK3pcBIAAAggggEAUBUgARrHXiBkBBBBAAAEEECiiAAnAIuJ7/OjXX39dJk2aZNXaq1cv6du3r8dPKby6zHdtwIABcswxxxReIXcigAACCCCQUAESgAnteJqNAAIIIIAAAtESWLlypVxzzTVFCbpjx45yxx13WM8mAViUbvD8oZs3bxZNqG3atMmou1q1asZMznr16nn+rEIr1Pd+4MCBsnv3bqOKBg0ayNixY6V69eqFVsl9CCCAAAIIJFKABGAiu51GI4AAAggggEDUBEgARq3Hwh/vtGnTZMaMGVagp556qlx00UWhC1xnKOpMRbOce+65ctZZZ4UuTgJCAAEEEEAgzAIkAMPcO8SGAAIIIIAAAgj8R4AEIK+ClwLr1q2T/v37y86dO41qq1SpYsz+0xl2YSs//PCDDBo0yNoLsGbNmvLwww9L7dq1wxYq8SCAAAIIIBBaARKAoe0aAkMAAQQQQAABBP5fYO3atXLvvffmRbJ9+3ZZsWJF2j26zLNZs2Z51dO2bVu58sorrXtYApwXXygvfuyxx2TWrFlWbD179pR+/fqFMlYN6oEHHpB//vOfVnynn366nH/++aGNl8AQQAABBBAImwAJwLD1CPEggAACCCCAAAIeCcyfP1/uvPPOtNoy9/Pz6FFUEyGBjRs3ylVXXSW7du2yoh49erTsv//+oW3FZ599JnfddZcVn84C1KXB7AUY2i4jMAQQQACBkAmQAAxZhxAOAggggAACCCDglQAJQK8k41XPf//3f4v+Y5YDDzxQ7rnnnlA3sry8XK699tq0Ga2XXHKJnHzyyaGOm+AQQAABBBAIiwAJwLD0BHEggAACCCCAAAIeC5AA9Bg0BtXpabo6+2/Dhg1Wa/r27Su9evUKfetefPFFmT59uhVn06ZNRZejl5SUhD52AkQAAQQQQKDYAiQAi90DPB8BBBBAAAEEEPBJgASgT7ARrvb999+X+++/32pBaWmpTJ48WerUqRP6VulhIAMHDkyL84477hBd1k5BAAEEEEAAgYoFSADyhiCAAAIIIIAAAjEViGoCUBM9ixcvFj34RPep0/3eWrZsKT/72c8c7/lWVlYmS5YsMf7RPe/0f9erV09atWoleqiJH7PG9Bka96pVq4xnbt682Yhdk2t68IrusacJt2KW++67T/71r39ZIRx88MFy++23uw5JTxP+7rvvZNmyZbJlyxbRA2i0rVWrVjVO623cuLHojD23pwxff/31xnPM0qNHD2NGIwUBBBBAAAEESADyDiCAAAIIIIAAAokU8CsBWOgpwHqSsCb1zHLcccdZpwtr8mz27Nny17/+VTQBmK3UqFFDNOHTu3dvI6mUrWzdulVeeeUVee2112TdunVZr9FE4GmnnSYnnXSSVKpUyfW7sWDBApk5c6boQRWa9MtV9tlnHzniiCNET7DVRGTQZdu2bfL73/9edBmwWS666CI59dRTCw5FT+Z944035NNPP5U9e/bY1lO/fn056KCDpEuXLvLzn/9ctE/zKc8++6z8+c9/tm7R+6dOnSqVK1fOpxquRQABBBBAIHECzABMXJfTYAQQQAABBBBIikBUEoBr1qwxlqV+/fXXjrqmYcOGcvPNN8t+++2Xdv0XX3whDz30kGh9Tkq7du3klltuMWbpFVJ0ttsTTzxhJL/yKTr7sGfPnnLppZdKlSpV8rnV1bWarHvggQfS6hg1apS0adMm73pXrlwpDz/8sKh5oeXMM8+U8847L6/b582bJyNGjEi7R2cw6kxGCgIIIIAAAgjkFiAByNuBAAIIIIAAAgjEVCAKCcDIAzpyAAATvUlEQVSzzz5bbr31VsdJO7OrdCbZvffeay0p/fjjj40koi4ZzqfobLQ777wz76W5moh68MEHRWccFlp0SfONN94odevWLbSKvO6bNGmSvP7669Y9tWrVMmbP5bssefny5aJ776XO5swrkP9cXEgCUJca6+m/qbMYf/Ob30ifPn0KCYF7EEAAAQQQSIwACcDEdDUNRQABBBBAAIGkCYQ9Adi9e3f5/vvvjX3ztOjMuA4dOsihhx5qJPZ0dpzO5vvkk0/k888/36v7OnfubCTQtI6hQ4ca+85pqVatmlGH1qXLfXV5sSat5s6dK0uXLt2rnnyXwepBGmPGjNlryasuQz3kkEPkwAMPFJ2lqDMLddmt7gmoy4MXLVq017M1AanJNC+WItu93wMGDJAff/zRuqyQ/f/Ky8uNeBcuXLjX43QmoR7I0bx5c6Pt6qHt12XROltS9+775ptvjP7QUkgCUO8bMmSI9c7o/9Y9HTUZTEEAAQQQQACB3AIkAHk7EEAAAQQQQACBmAqEPQGoCT5zxt4BBxwg/fr1y7kc9aOPPjKSbjt27Ejrrbvuuksee+wx47APLd26dRNN6GniL7No4unFF1+U559/Pu1HOhNOT8J1shxXE4k33XRT2sw/Td7pPno6E62i03Q10amz8MyEpxmE7kd44YUX+voW6sEcl112mWgCzywnn3yyMZsun5LtndLDPfr37y+6pNquaDJQ+1L3e9RkYb5LgLX+iRMnyptvvmk9ShONTz75JPsA2uHzcwQQQACBRAuQAEx099N4BBBAAAEEEIizQNgTgKZ9p06djFld1atXr7A73nrrLWPfudSiy2c3bNhg/JEernH++efbdmlmAklvGDhwoHTt2tX2Xt0z8KuvvrKu05g1IajJLCdFE546Wy11RqMmsLRdbk/Irej5OgNRk6WpRQ9l0YNY8im656Ee1GIWTZrqUmhNAuZbNJmrszXzLXrIiyb8Uss999xjzLykIIAAAggggEB2ARKAvBkIIIAAAggggEBMBaKQANQZc7p3X7YZe5ndorPXBg0alPWUYF3OettttxnLiO3K6tWr5Q9/+EPabLhf//rXcvXVV1d4q+4zmLnUdPDgwXL00UfbPTLt5zoL7tprr007MdjvfexmzZplzJRMLbr3oi6VzqeMHj1aPvjgA+sWPdVYD2QJsuhSbk06phadgfirX/0qyDB4FgIIIIAAApESIAEYqe4iWAQQQAABBBBAwLlAFBKAOmNPZ+45Lc8884y89NJLe12e7www3TPw3//+t1VP69at5b777qswDN37bsGCBdY1OnNx2LBhTkNPu06XIb/wwgvWn+kMuvHjxxdUl5Obpk2bJjNmzEi7VJNorVq1cnK7dY0emKLvlVk06abJtyCL9pv2X2rRpcS6pyAFAQQQQAABBLILkADkzUAAAQQQQAABBGIqEPYEoM7WmzJlSoX75mV2zXvvvWfsBZha9t13X3nggQfy6kWdDaez4syiS1mffvrpnHVs3LhRrrjiirRZgzqLT/ccLKToMmJdTpxadH9Av5YBjxs3TubMmZP2vMcff1xq166dV/iaNNQZeGZp1qyZjB07Nu+ThPN6aMbFOoMzc7Zmr169pG/fvm6q5V4EEEAAAQRiLUACMNbdS+MQQAABBBBAIMkCYU8AtmzZcq9knl1/ZUucnXDCCXL55Zfb3Zr285dffnmvhJ/OkqtatWrWerItO9V9+5o0aZLXc82LNaGYGbPug/jzn/+8oPrsbho5cqRx+EZq0dmUuv9gPkVnEapTatF9BC+99NKC9vPL59kV2WkiVhOyFAQQQAABBBDILkACkDcDAQQQQAABBBCIqUDYE4Ca7NKkVz5l6dKlovvupRY99VdP4c2nZNsTT08CzrUX4Z/+9Cf5y1/+kvYIXTbsppgnF5t16Kw23YvQjzJ8+PC0g0d09uVzzz2X96PWrl1rLPk1T282K9DDWDT2X/ziF6InOpeWluZdt9Mbtm/fbpz0nFo6d+4sN954o9MquA4BBBBAAIHECZAATFyX02AEEEAAAQQQSIpA2BOA3bt3l2uuuSav7li+fPleM72uuuoq6dGjR171vPbaa/Loo4+m3fPII49Iw4YNs9aTufQ1r4c5vLhPnz6ih4H4UTL3L6xUqZI8++yzBT0q2yzA1Ipq1aolBx10kHEqb7t27Yx/7E54zicQTT5ecMEFabcceeSRxmnMFAQQQAABBBDILkACkDcDAQQQQAABBBCIqUDYE4C6dPTKK6/MSz9bAlCTiJpMzKfkmwDMnEGXz7OcXnvuuefKWWed5fTyvK7TQ1I++eSTtHumT59e8Ew9PYhF7y8rK7ONQ5ONmgTs2rWr8c8+++xje09FF2zZssVYcpxa9CTmzJmhrh7CzQgggAACCMRMgARgzDqU5iCAAAIIIIAAAqYACcDc70K+CUBdqrx48WJfXy4/E4B6SMo///nPtPiffPJJVzPzdDn2//zP/8j777+/15LgXFDVqlWTE088Uc4+++yC9wxct26d9OvXL+0Ruvw482AQXzuLyhFAAAEEEIiYAAnAiHUY4SKAAAIIIIAAAk4FSAB6lwC87bbbZNGiRVaFOott6tSpTrui6NdlnnqsAU2cOFEaNWrkOjadkacHjOj79sUXX8iPP/6YdlpytgfoATBDhw4t6Pnff/+9XHfddWnVnnbaaXLhhRe6bgsVIIAAAgggEFcBEoBx7VnahQACCCCAAAKJFyAB6F0CcPTo0fLBBx+kVagHg9SoUSMS75ku2dVTf1PLiBEjjKW5XpfNmzcbydKFCxfKxx9/LJqwy1b2228/GTVqlOgS4XyKLmXWJc2p5fe//70xs5CCAAIIIIAAAtkFSADyZiCAAAIIIIAAAjEVIAHoXQJQDwzRZcOp5a677jIOu4hCee+992TMmDFpoQ4aNEh++ctf+h7+smXLjBOU1a+8vDzteX379pVevXrlFcPrr78ukyZNSrvn5ptvliOOOCKvergYAQQQQACBJAmQAExSb9NWBBBAAAEEEEiUAAlA7xKA2fYMPPPMM+W8886LxDv1ww8/yMCBA9Ni9XPPwWwo//jHP2TChAlpPzrssMOMpcD5lGnTpomeRJxaNCHYoEGDfKrhWgQQQAABBBIlQAIwUd1NYxFAAAEEEEAgSQIkAL1LAK5atUr+8Ic/pFXYtGlTeeihhwo+STfId1Fn3unJuVu3brUeq7P/dBZgkEX37ktdEly/fn3R2ZX5lLvvvls+/fRT65ZC6sjneVyLAAIIIIBAHARIAMahF2kDAggggAACCCCQRYAEoHcJQK3phhtukCVLlqRVqifP6gm0USi659+8efOsUFu0aCFjx44NNPQHH3xQ5s6daz1TTwV+6qmn8opBlw2vX7/euqdLly5y/fXX51UHFyOAAAIIIJA0ARKASetx2osAAggggAACiREgAehtAjDbPnq1atWSe++9V5o1axb690qXzery2dQyZcoUqVu3bmCxZ56m3Lhx472WBVcUjJ4wPGDAgLRLrrjiCjn++OMDawMPQgABBBBAIIoCJACj2GvEjAACCCCAAAIIOBAgAehtAlCX0Q4ZMkS+/fbbtIo1+XfjjTdKq1atHPRK+iW6JHfWrFnSqFEj6datW97353PD0qVLZfDgwWm36L6AXbt2dVyNnnysh3a0bNnS8T3mhV999ZWx31/qQSCdO3c27JyW2bNny+TJk9MunzhxouFHQQABBBBAAIHcAiQAeTsQQAABBBBAAIGYCpAA9DYBqLXpYRqaxNqyZUta5dWrV5czzjhDTjzxRKlZs2aFb1RZWZlo3+iMwnfffdfYl+/iiy+WU045xfc3UWfP6Sw6s/Ts2VP69evn+Ll9+vSRnTt3yiGHHGKcIHzUUUdJnTp1bO//17/+Zez1t3HjxrRr8z2JOHMJcZs2bWTUqFG2z+cCBBBAAAEEki5AAjDpbwDtRwABBBBAAIHYCpAA9D4BqDXqARQjR46UPXv27PUA3dOuQ4cOctBBB0m9evVElwjv2LHDSBiuWbNGvvnmG1m8eHHaYRxaSVAJwBdeeEGef/55K25d/quJudLSUkfjQBOA2h6z6H377ruvtG7d2vi/tWvXNtqsSc5NmzbJsmXLDK/UpKN5b6dOneT222+XkpISR8/WxOPll18u27dvt64Pys1RgFyEAAIIIIBAiAVIAIa4cwgNAQQQQAABBBBwI0AC0J8EoNb65Zdfis5GW7t2rZsuCjyRtXLlSunfv3/aMtxhw4aJJuOclMwEoJN7sl2jCcNbbrlF9ARfp+X999+X+++/37q8UqVKMmnSpED3MHQaK9chgAACCCAQNgESgGHrEeJBAAEEEEAAAQQ8EiAB6F8CUGvesGGDcajGnDlzss4GdNKNOvutY8eOcuGFF0rbtm2d3OL6mnvuuUc++eQTq57jjjtOrrzySkf1avJwxYoVjq7NdpG299hjj5VLLrnEdql05v2Zy3+PPvrovfY0LDgwbkQAAQQQQCDmAiQAY97BNA8BBBBAAAEEkitAAtDfBKBZ+6pVq+Rvf/ubkVTTgzbsSo0aNYxlwoceeqh06dIl8AMs5s2bJyNGjLDC1P0LdRmwxmVX9AAPXcL80UcfycKFC0UP9ti2bZvdbcYsPd0zUPcc1Nl/+RbdO1CTlLt377Zu1Ta0a9cu36q4HgEEEEAAgUQKkABMZLfTaAQQQAABBBBAAAE/BHRWoCbINGGle+DpfnW6L6Am1xo2bCgtWrSQxo0bO973zo8Ytc4bbrhBlixZYlWve+udcMIJeT9O9/rTZcU6K3D16tXG3oa6R2DVqlWNNusS3/3220+aNGmSd92pN8yYMcOYbWkWTfylJjFdVc7NCCCAAAIIJECABGACOpkmIoAAAggggAACCCCQKjB37lxjD0OztGzZUh544AHHh4EEqamHrejSY00wmkX3Dzz88MODDINnIYAAAgggEGkBEoCR7j6CRwABBBBAAAEEEECgMIGbb75Zvv76a+vm6667Tn7xi18UVpmPd7355psyceJE6wm6fPrOO+/08YlUjQACCCCAQPwESADGr09pEQIIIIAAAggggAACtgKZewEecMABMnLkSNv7grxAlxhrYnLZsmXWY4cPHy7t27cPMgyehQACCCCAQOQFSABGvgtpAAIIIIAAAggggAAChQmMHj1aPvjgA+vmwYMHi56uG5aSOfuvW7ducu2114YlPOJAAAEEEEAgMgIkACPTVQSKAAIIIIAAAggggIC3AnqC8aBBg2Tnzp1Gxc2bNzf2AqxcubK3Dyqgtl27dsmAAQOsvf/0UJExY8ZIgwYNCqiNWxBAAAEEEEi2AAnAZPc/rUcAAQQQQAABBBBIuMD7778v3377raXQvXt3adasWdFVdNnvO++8Y8WhS5Q7d+5c9LgIAAEEEEAAgSgKkACMYq8RMwIIIIAAAggggAACCCCAAAIIIIAAAg4FSAA6hOIyBBBAAAEEEEAAAQQQQAABBBBAAAEEoihAAjCKvUbMCCCAAAIIIIAAAggggAACCCCAAAIIOBQgAegQissQQAABBBBAAAEEEEAAAQQQQAABBBCIogAJwCj2GjEjgAACCCCAAAIIIIAAAggggAACCCDgUIAEoEMoLkMAAQQQQAABBBBAAAEEEEAAAQQQQCCKAiQAo9hrxIwAAggggAACCCCAAAIIIIAAAggggIBDARKADqG4DAEEEEAAAQQQQAABBBBAAAEEEEAAgSgKkACMYq8RMwIIIIAAAggggAACCCCAAAIIIIAAAg4FSAA6hOIyBBBAAAEEEEAAAQQQQAABBBBAAAEEoihAAjCKvUbMCCCAAAIIIIAAAggggAACCCCAAAIIOBQgAegQissQQAABBBBAAAEEEEAAAQQQQAABBBCIogAJwCj2GjEjgAACCCCAAAIIIIAAAggggAACCCDgUIAEoEMoLkMAAQQQQAABBBBAAAEEEEAAAQQQQCCKAiQAo9hrxIwAAggggAACCCCAAAIIIIAAAggggIBDARKADqG4DAEEEEAAAQQQQAABBBBAAAEEEEAAgSgKkACMYq8RMwIIIIAAAggggAACCCCAAAIIIIAAAg4FSAA6hOIyBBBAAAEEEEAAAQQQQAABBBBAAAEEoihAAjCKvUbMCCCAAAIIIIAAAggggAACCCCAAAIIOBQgAegQissQQAABBBBAAAEEEEAAAQQQQAABBBCIogAJwCj2GjEjgAACCCCAAAIIIIAAAggggAACCCDgUIAEoEMoLkMAAQQQQAABBBBAAAEEEEAAAQQQQCCKAiQAo9hrxIwAAggggAACCCCAAAIIIIAAAggggIBDARKADqG4DAEEEEAAAQQQQAABBBBAAAEEEEAAgSgKkACMYq8RMwIIIIAAAggggAACCCCAAAIIIIAAAg4FSAA6hOIyBBBAAAEEEEAAAQQQQAABBBBAAAEEoihAAjCKvUbMCCCAAAIIIIAAAggggAACCCCAAAIIOBQgAegQissQQAABBBBAAAEEEEAAAQQQQAABBBCIogAJwCj2GjEjgAACCCCAAAIIIIAAAggggAACCCDgUIAEoEMoLkMAAQQQQAABBBBAAAEEEEAAAQQQQCCKAiQAo9hrxIwAAggggAACCCCAAAIIIIAAAggggIBDARKADqG4DAEEEEAAAQQQQAABBBBAAAEEEEAAgSgKkACMYq8RMwIIIIAAAggggAACCCCAAAIIIIAAAg4F/g/MALJKtwyLjwAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.semilogx(timeHistory.index, timeHistory['CO'],'-o')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel(r'Mole Fraction : $X_{CO}$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustration : Modeling experimental data\n",
    "### Let us see if the reactor can reproduce actual experimental measurements\n",
    "\n",
    "We first load the data. This is also supplied in the paper by [Zhang et al.](http://dx.doi.org/10.1016/j.combustflame.2015.08.001) as an excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>NC7H16</th>\n",
       "      <th>O2</th>\n",
       "      <th>CO</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500</td>\n",
       "      <td>0.00507</td>\n",
       "      <td>0.0293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>525</td>\n",
       "      <td>0.00492</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>550</td>\n",
       "      <td>0.00466</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>575</td>\n",
       "      <td>0.00416</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600</td>\n",
       "      <td>0.00355</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T   NC7H16      O2        CO       CO2\n",
       "0  500  0.00507  0.0293  0.000000  0.000000\n",
       "1  525  0.00492  0.0286  0.000000  0.000000\n",
       "2  550  0.00466  0.0285  0.000000  0.000000\n",
       "3  575  0.00416  0.0263  0.000243  0.000101\n",
       "4  600  0.00355  0.0233  0.000968  0.000251"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expData = pd.read_csv('data/zhangExpData.csv')\n",
    "expData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define all the temperatures at which we will run simulations. These should overlap\n",
    "# with the values reported in the paper as much as possible\n",
    "T = [650, 700, 750, 775, 825, 850, 875, 925, 950, 1075, 1100]\n",
    "\n",
    "# Create a DataFrame to store values for the above points\n",
    "tempDependence = pd.DataFrame(columns=timeHistory.columns)\n",
    "tempDependence.index.name = 'Temperature'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we simply run the reactor code we used above for each temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation at T=650K took 9.85s to compute\n",
      "Simulation at T=700K took 11.86s to compute\n",
      "Simulation at T=750K took 3.99s to compute\n",
      "Simulation at T=775K took 5.76s to compute\n",
      "Simulation at T=825K took 6.73s to compute\n",
      "Simulation at T=850K took 6.59s to compute\n",
      "Simulation at T=875K took 5.78s to compute\n",
      "Simulation at T=925K took 6.62s to compute\n",
      "Simulation at T=950K took 4.53s to compute\n",
      "Simulation at T=1075K took 8.07s to compute\n",
      "Simulation at T=1100K took 4.86s to compute\n"
     ]
    }
   ],
   "source": [
    "inletConcentrations = {'NC7H16': 0.005, 'O2': 0.0275, 'HE': 0.9675}\n",
    "concentrations = inletConcentrations\n",
    "\n",
    "for temperature in T:\n",
    "    # Re-initialize the gas\n",
    "    reactorTemperature = temperature  # Kelvin\n",
    "    reactorPressure = 1.046138*ct.one_atm  # in atm. This equals 1.06 bars\n",
    "    reactorVolume = 30.5*(1e-2)**3  # m3\n",
    "\n",
    "    gas.TPX = reactorTemperature, reactorPressure, inletConcentrations\n",
    "\n",
    "    # Re-initialize the dataframe used to hold values\n",
    "    timeHistory = pd.DataFrame(columns=columnNames)\n",
    "    \n",
    "    # Re-initialize all the reactors, reservoirs, etc.\n",
    "    fuelAirMixtureTank = ct.Reservoir(gas)\n",
    "    exhaust = ct.Reservoir(gas)\n",
    "    \n",
    "    # We will use concentrations from the previous iteration to speed up convergence\n",
    "    gas.TPX = reactorTemperature, reactorPressure, concentrations\n",
    "    \n",
    "    stirredReactor = ct.IdealGasReactor(gas, energy='off', volume=reactorVolume)\n",
    "    massFlowController = ct.MassFlowController(upstream=fuelAirMixtureTank,\n",
    "                                               downstream=stirredReactor,\n",
    "                                               mdot=stirredReactor.mass/residenceTime)\n",
    "    pressureRegulator = ct.Valve(upstream=stirredReactor, \n",
    "                                 downstream=exhaust, \n",
    "                                 K=pressureValveCoefficient)\n",
    "    reactorNetwork = ct.ReactorNet([stirredReactor])\n",
    "    \n",
    "    # Re-run the isothermal simulations\n",
    "    tic = time.time()\n",
    "    t = 0\n",
    "    while t < maxSimulationTime:\n",
    "        t = reactorNetwork.step()\n",
    "        \n",
    "    state = np.hstack([stirredReactor.thermo.P, \n",
    "                       stirredReactor.mass, \n",
    "                       stirredReactor.volume, \n",
    "                       stirredReactor.T, \n",
    "                       stirredReactor.thermo.X])\n",
    "\n",
    "    toc = time.time()\n",
    "    print('Simulation at T={}K took {:3.2f}s to compute'.format(temperature, toc-tic))\n",
    "    \n",
    "    concentrations = stirredReactor.thermo.X\n",
    "    \n",
    "    # Store the result in the dataframe that indexes by temperature\n",
    "    tempDependence.loc[temperature] = state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the model results with experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(tempDependence.index, tempDependence['NC7H16'], 'r-', label=r'$nC_{7}H_{16}$')\n",
    "plt.plot(tempDependence.index, tempDependence['CO'], 'b-', label='CO')\n",
    "plt.plot(tempDependence.index, tempDependence['O2'], 'k-', label='O$_{2}$')\n",
    "\n",
    "plt.plot(expData['T'], expData['NC7H16'],'ro', label=r'$nC_{7}H_{16} (exp)$')\n",
    "plt.plot(expData['T'], expData['CO'],'b^', label='CO (exp)')\n",
    "plt.plot(expData['T'], expData['O2'],'ks', label='O$_{2}$ (exp)')\n",
    "\n",
    "plt.xlabel('Temperature (K)')\n",
    "plt.ylabel(r'Mole Fractions')\n",
    "\n",
    "plt.xlim([650, 1100])\n",
    "plt.legend(loc=1);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}