Hydro.ipynb

{
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  {
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   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numba\n",
    "from numba import jit, njit, prange, int64, cuda\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:42: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpL[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffL[2-s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:43: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpR[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffR[s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:42: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpL[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffL[2-s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:43: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpR[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffR[s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:96: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpL[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffL[2-s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:97: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpR[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffR[s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:96: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpL[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffL[2-s])\n",
      "/home/osborne/MethodologicalValidation/HydroWeno/Weno.py:97: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float64, 1d, A), array(float64, 1d, C))\u001b[0m\u001b[0m\n",
      "  enoIntpR[s] = np.dot(q[row, gridIdx:gridIdx+3], EnoCoeffR[s])\n"
     ]
    }
   ],
   "source": [
    "from HydroWeno.TimeIntegrate import tvd_rk3, tvd_rk3_cons\n",
    "from HydroWeno.SimSettings import Prim, Gas, Sim, Cons\n",
    "from HydroWeno.BCs import reflecting_bc, zero_grad_bc, diode_bc, fixed_bc\n",
    "from HydroWeno.PrimCons import prim2cons, prim2flux, cons2prim\n",
    "from HydroWeno.Weno import reconstruct_weno, reconstruct_weno_nm, reconstruct_weno_z, reconstruct_weno_nm_z, reconstruct_weno_log\n",
    "from HydroWeno.Flux import lax_friedrichs_flux, gforce_flux, lax_friedrichs_flux_cons\n",
    "from HydroWeno.Simulation import Simulation, Grid, GridCC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cfl(grid, V):\n",
    "    cs = np.sqrt(Gas.Gamma * V[Prim.Pres] / V[Prim.Dens])\n",
    "    vel = cs + np.abs(V[Prim.Velo])\n",
    "    dt = 0.8 * np.min(grid.dx / vel)\n",
    "    return dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sod Shock Tube\n",
    "The simplest, most classic, test case for a 1D hydrodynamic code that may be expected to handle shocks. It has a nice analytic solution too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'sod' from 'sodshocktube' (unknown location)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-68766bcc683c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msodshocktube\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msod\u001b[0m \u001b[0;31m# analytic solution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'sod' from 'sodshocktube' (unknown location)"
     ]
    }
   ],
   "source": [
    "from sodshocktube import sod # analytic solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = reflecting_bc(position='Lower')\n",
    "upper_bc = reflecting_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def sod_setup(grid):\n",
    "    mid = (grid.griBeg + grid.griEnd) // 2\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :mid] = 1.0\n",
    "    V[Prim.Pres, :mid] = 1.0\n",
    "    V[Prim.Velo] = 0.0\n",
    "    V[Prim.Dens, mid:] = 0.125\n",
    "    V[Prim.Pres, mid:] = 0.1\n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    \n",
    "    return V, U\n",
    "\n",
    "def setup(grid):\n",
    "    mid = (grid.griBeg + grid.griEnd) // 2\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :mid] = 1.0\n",
    "    V[Prim.Pres, :mid] = 1.0\n",
    "    V[Prim.Velo] = 0.0\n",
    "    V[Prim.Dens, mid:] = 0.125\n",
    "    V[Prim.Pres, mid:] = 0.1\n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    \n",
    "    return V, U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up computational grid, and Simulation objects. One using a uniform grid, with many points, the other with a non-uniform grid, and fewer points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "interfaces = np.linspace(0, 1, 2001)\n",
    "grid = Grid(interfaces, 4)\n",
    "\n",
    "nonUniformInterfaces = np.linspace(0, 1, 301)\n",
    "dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "nonUniformInterfaces[1:-1:2] += 0.4 * dx\n",
    "nonUniformInterfaces[1:-1:3] -= 0.2 * dx\n",
    "grid_n = Grid(nonUniformInterfaces, 4)\n",
    "\n",
    "sim = Simulation(grid, sod_setup, bcs, cfl, tvd_rk3, reconstruct_weno, 5, lax_friedrichs_flux)\n",
    "# sim_nmz = Simulation(grid_n, sod_setup, bcs, cfl, tvd_rk3, reconstruct_weno_log, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:9: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  if __name__ == '__main__':\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2\n",
      "WENO 5-Z: 2.935 s\n"
     ]
    }
   ],
   "source": [
    "starts = []\n",
    "ends = []\n",
    "methods = []\n",
    "np.seterr(all='raise')\n",
    "Vinit = np.copy(sim.V)\n",
    "t=0\n",
    "tEnd = 0.2\n",
    "vs = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "methods.append('WENO 5-Z')\n",
    "starts.append(time.time())\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    if t >= tEnd:\n",
    "        break\n",
    "ends.append(time.time())\n",
    "# t = 0\n",
    "# vs_nmz = [Vinit]\n",
    "# tq = tqdm(range(10000))\n",
    "# methods.append('WENO NM-Z Non-Uniform')\n",
    "# starts.append(time.time())\n",
    "# for i in tq:\n",
    "#     tq.set_description('%f s' % t)\n",
    "#     tMax = tEnd - t\n",
    "#     t += sim_nmz.step(tMax)\n",
    "#     vs_nmz.append(np.copy(sim_nmz.V))\n",
    "#     if t >= tEnd:\n",
    "#         break\n",
    "# ends.append(time.time())\n",
    "print(t)\n",
    "for i, m in enumerate(methods):\n",
    "    print('%s: %.3f s' % (m, ends[i] - starts[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sodPos, sodRegions, sodValues = sod.solve(left_state=(1.0, 1.0, 0.0), right_state=(0.1, 0.125, 0.0), \n",
    "                                          geometry=(0.0, 1.0, 0.5), t=0.2, gamma=Gas.Gamma, npts=2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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       "\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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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9rPq06dPp16Gd88999SBBx6oG2+8MejP/u0YID2LARIRBghC0c8fDb3F0M99gOStmD8JvTtcffXV+shHPpL3MaLBDyL0xABpxzPPPKP/+q//0v7779/hf9F4w4QJE/SJT3xCvXr10qGHHtrqr/46gwGCUPTzR0NvMfRjgHRN7APk9ttv14svvqjFixfrvvvu07777tvylCgwQFwxQNpRWVmpQYMG6dFHH+3UAFmyZIne/e5361e/+pXmzp2rW2+9VXvssYfGjh3b6cdkgCAU/fzR0FsM/RggXRP7APnFL36h/fffX71791bfvn115ZVXtrz0LRggrhggndCZAXLRRRe1erUFSfr2t7+t/v37d/pxGCAIRT9/NPQWQz8GSNfEPkCwawwQTwyQTujMADnxxBN3+ga1e+65R/vss0+7H7N9+3Zt3Lix5aqurlZFRYVqamq0Y8eOHr9OuXmCDh9UqcMGPq7DB1VytXP1jfm6pFKHDnxcfS/J/yz9rn5Sj8+qLsq/u6V01dXVadSoUaqrq8v9LFye/TZt2qSqqirV1dWpqamJK/BqbGzUhg0b1NjYmPtZuOLpV1dXp6qqKm3atGmne66mpoYB0kUMkE7ozADp27evrr322lZvGz16tCoqKrR169Y2P2bIkCGqqKjY6Ro+fLhGjRrV49enLns8tx9UxVWa16EDH9efRvT8v7tcXFxvXlmWafr06Vq9erU2bNjAxcXVDdfq1as1ffp0ZVm20z03fPhwBkgXMUA6oacGSN5/A7K8ZpMWrlinex4apYUr1unVNRu5zK5Y+i1aVav/c/tzStJMF/9tdu7/VdrpiuG/oHN599u8ebOqqqq0ZcuW3P9rtOPF34B4Xz3Vb8uWLaqqqtLmzZt3uuf4G5CuY4B0Qk89Bevt+B4QhIqp36QFa5SkmT4+ZKy27WjM+zg2YmqIcDH0a2xs1Ny5c1t+GBzCNDXxPSDOeqpfTU2N5s6dq8bGnf//jO8B6ToGSCd09pvQjz766FZv+5//+R++CR09KqZ+jU0Ffe7ap5SkmUbPWZH3cWzE1BDhYum3YsWKlhGydetWbdu2jauTV11dnVavXq26urrcz8KVf7+tW7e2jI8VK9r+/zIGSNcxQNqxefNmzZo1S7NmzVJFRYV+85vfaNasWVq2bJkkaeDAgTr99NNb3v+Nl+G98MIL9corr+j222/nZXjR42Lrd3VWpSTN9MsRszp+Z0iKryHCxNKvUCi0jBCusKuqqkrTp09XVVVV7mfhiqffihUrVCgU2rzfGCBdxwBpx4QJE9r8BvEzzzxTknTmmWfqpJNO2uljjjvuOPXq1UuHHHIIP4gQPS62fi8srlGSZjruiifU2NT2/3CjtdgaIkxs/RobG3P/L9Ju16ZNm5RlmTZt2pT7Wbji6NfW067eigHSdQyQiDBAECq2fjsam/TxIWOVpJmmv7ou7+NYiK0hwtDPHw295dGPAdJ1DJCIMEAQKsZ+5w2fqSTN9Osxr+R9FAsxNkTn0c8fDb0xQDwxQCLCAEGoGPuNmvWakjRT/6HP5H0UCzE2ROfRzx8NvTFAPDFAIsIAQagY+63dvL3lBxOu2bQ97+NEL8aG6Dz6+aOhNwaIJwZIRBggCBVrv6/eMklJmmnkzNfyPkr0Ym2IzqGfPxp6Y4B4YoBEhAGCULH2u7ZyrpI0068emp33UaIXa0N0Dv380dAbA8QTAyQiDBCEirXfswvWKkkz9bvmyXZfRx3NYm2IzqGfPxp6Y4B4YoBEhAGCULH227ajUYcPqlSSZlqwalPex4larA3ROfTzR0NvDBBPDJCIMEAQKuZ+37t7ipI005+eXZL3UaIWc0N0jH7+aOiNAeKJARIRBghCxdzvDxMXKUkzff+eqXkfJWoxN0TH6OePht4YIJ4YIBFhgCBUzP3+8XqtkjTTxy4do/qGpryPE62YG6Jj9PNHQ28MEE8MkIgwQBAq5n5NTQV98spxStJMLyyuyfs40Yq5ITpGP3809MYA8cQAiQgDBKFi7/fzB2cqSTPdOHZe3keJVuwNsWv080dDbwwQTwyQiDBAECr2fg9PW64kzTTgtufyPkq0Ym+IXaOfPxp6Y4B4YoBEhAGCULH3W1G7VUma6eCBmWrr4jxj3mJviF2jnz8aemOAeGKARIQBglAO/f7jpglK0kxjXl6R91Gi5NAQ7aOfPxp6Y4B4YoBEhAGCUA79hjz2DyVppkEj5+R9lCg5NET76OePht4YIJ4YIBFhgCCUQ79xVauUpJlOumF83keJkkNDtI9+/mjojQHiiQESEQYIQjn027Rthw65eLSSNNPydXV5Hyc6Dg3RPvr5o6E3BognBkhEGCAI5dLv//z+eSVppgenLsv7KNFxaYi20c8fDb0xQDwxQCLCAEEol343j5uvJM10zgMz8j5KdFwaom3080dDbwwQTwyQiDBAEMql34tL1ylJM33iynFqairkfZyouDRE2+jnj4beGCCeGCARYYAglEu/HY1NOvLSMUrSTC+/Vpv3caLi0hBto58/GnpjgHhigESEAYJQTv1+eO+LStJMf5i4KO+jRMWpIXZGP3809MYA8cQAiQgDBKGc+v3p2SVK0kzfu3tK3keJilND7Ix+/mjojQHiiQESEQYIQjn1W7Bqk5I00+GDKrVtR2Pex4mGU0PsjH7+aOiNAeKJARIRBghCOfUrFArqd82TStJMzy1cm/dxouHUEDujnz8aemOAeGKARIQBglBu/X750CwlaaZfj3kl76NEw60hWqOfPxp6Y4B4YoBEhAGCUG79/jajWkma6Wu3Ppv3UaLh1hCt0c8fDb0xQDwxQCLCAEEot36rN25TkmY6aGCm9Vvq8z5OFNwaojX6+aOhNwaIJwZIRBggCOXY78u/magkzTR6zoq8jxIFx4Z4E/380dAbA8QTAyQiDBCEcux3+d//oSTNNPBvc/I+ShQcG+JN9PNHQ28MEE8MkIgwQBDKsd/Tr6xSkmY68frxeR8lCo4N8Sb6+aOhNwaIJwZIRBggCOXYb/P2Bh168WglaaZlNXV5Hyd3jg3xJvr5o6E3BognBkhEGCAI5drvtDsmK0kzPTBlWd5HyZ1rQzSjnz8aemOAeGKARIQBglCu/X775AIlaaaf/WV63kfJnWtDNKOfPxp6Y4B4YoBEhAGCUK79pr+6Xkma6dgrnlBjUyHv4+TKtSGa0c8fDb0xQDwxQCLCAEEo134NjU06+rKxStJMc6pr8z5Orlwbohn9/NHQGwPEEwMkIgwQhHLu9+M/T1OSZrp9wsK8j5Ir54agXymgoTcGiCcGSEQYIAjl3G/Y80uVpJm+c9cLeR8lV84NQb9SQENvDBBPDJCIMEAQyrnfojWblaSZ+g6q1LYdjXkfJzfODUG/UkBDbwwQTwyQiDBAEMq5X6FQ0GevfUpJmmnSgjV5Hyc3zg1Bv1JAQ28MEE8MkIgwQBDKvd8FD89Wkma6dvTcvI+SG/eG5Y5+/mjojQHiiQESEQYIQrn3GzXrNSVppq/eMinvo+TGvWG5o58/GnpjgHhigESEAYJQ7v3WbNquJM2UpJlqNm/P+zi5cG9Y7ujnj4beGCCeGCARYYAgVCn06z/0GSVppr/Pfj3vo+SiFBqWM/r5o6E3BognBkhEGCAIVQr9rnq8SkmaKf3rS3kfJRel0LCc0c8fDb0xQDwxQCLCAEGoUug3Yd5qJWmm4697WoVCIe/jFF0pNCxn9PNHQ28MEE8MkIgwQBCqFPrV1Teo7yWVStJMS9duyfs4RVcKDcsZ/fzR0BsDxBMDJCIMEIQqlX7f/uNkJWmm+154Ne+jFF2pNCxX9PNHQ28MEE8MkIgwQBCqVPrd+vQCJWmm/71vet5HKbpSaViu6OePht4YIJ4YIBFhgCBUqfSbtXyDkjTTx4eMVWNTeX0fSKk0LFf080dDbwwQTwyQiDBAEKpU+jU2FfTxIWOVpJlmLd+Q93GKqlQaliv6+aOhNwaIJwZIRBggCFVK/f73vulK0ky3jV+Y91GKqpQaliP6+aOhNwaIJwZIRBggCFVK/e574VUlaaZv/3Fy3kcpqlJqWI7o54+G3hggnhggEWGAIFQp9VuydouSNFPfSypVV9+Q93GKppQaliP6+aOhNwaIJwZIRBggCFVK/QqFgo6/7mklaaYJ81bnfZyiKaWG5Yh+/mjojQHiiQESEQYIQpVav4seeUlJmunqrCrvoxRNqTUsN/TzR0NvDBBPDJCIMEAQqtT6/X3260rSTP2HPpP3UYqm1BqWG/r5o6E3BognBkhEGCAIVWr91m2p10EDMyVpptUbt+V9nKIotYblhn7+aOiNAeKJARIRBghClWK/r936rJI00yPTq/M+SlGUYsNyQj9/NPTGAPHEAIkIAwShSrHfjWPnKUkznTd8Zt5HKYpSbFhO6OePht4YIJ4YIBFhgCBUKfabumSdkjTTJ64cp6amQt7H6XGl2LCc0M8fDb0xQDwxQCLCAEGoUuy3o7FJR102Vkma6aXqDXkfp8eVYsNyQj9/NPTGAPHEAIkIAwShSrXfT+6bpiTNdOvTC/I+So8r1Yblgn7+aOiNAeKJARIRBghClWq/v0x5VUma6Zt3PJ/3UXpcqTYsF/TzR0NvDBBPDJCIMEAQqlT7LV9XpyTNdMjFo7VxW2n9s71dqTYsF/TzR0NvDBBPDJCIMEAQqpT7nXzjBCVppjEvr8z7KD2qlBuWA/r5o6E3BognBkhEGCAIVcr9hjz2DyVpposfnZP3UXpUKTcsB/TzR0NvDBBPDJCIMEAQqpT7jX9ltZI00+d//bQKhdJ9Od5SblgO6OePht4YIJ4YIBFhgCBUKferq29Q30sqlaSZFq3ZnPdxekwpNywH9PNHQ28MEE8MkIgwQBCq1Pt9564XlKSZ7nluSd5H6TGl3rDU0c8fDb0xQDwxQCLCAEGoUu/3h4mLlKSZvn/P1LyP0mNKvWGpo58/GnpjgHhigESEAYJQpd5v7oqNStJMRwweo+0NjXkfp0eUesNSRz9/NPTGAPHEAIkIAwShSr1foVDQp69+Ukma6bmFa/M+To8o9Yaljn7+aOiNAeKJARIRBghClUO///vwbCVppmtGz837KD2iHBqWMvr5o6E3BognBkhEGCAIVQ79Hpv9upI0U/+hz+R9lB5RDg1LGf380dAbA8QTAyQiDBCEKod+67fU66CBmZI006qN2/I+Trcrh4aljH7+aOiNAeKJARIRBghClUu/Abc+qyTN9PC05XkfpduVS8NSRT9/NPTGAPHEAIkIAwShyqXfTU/MU5JmOnf4zLyP0u3KpWGpop8/GnpjgHhigESEAYJQ5dJv2tJ1StJMx17xhBqbCnkfp1uVS8NSRT9/NPTGAPHEAIkIAwShyqVfQ2OTjh4yVkmaadbyDXkfp1uVS8NSRT9/NPTGAPHEAIkIAwShyqnf/943XUma6ZanFuR9lG5VTg1LEf380dAbA8QTAyQiDBCEKqd+D0xZpiTN9N+/fz7vo3SrcmpYiujnj4beGCCeGCARYYAgVDn1q15fpyTNdPDATLV1pfPPW04NSxH9/NHQGwPEEwMkIgwQhCq3fqfcPFFJmunxl17P+yjdptwalhr6+aOhNwaIJwZIRBggCFVu/a4ZPVdJmulXD83O+yjdptwalhr6+aOhNwaIJwZIRBggCFVu/Z5fuFZJmulTVz2pphJ5Od5ya1hq6OePht4YIJ4YIBFhgCBUufWrb2jSkZeOUZJmmlNdm/dxukW5NSw19PNHQ28MEE8MkIgwQBCqHPv95L5pJfVyvOXYsJTQzx8NvTFAPDFAIsIAQahy7Dd8avPL8f6/tz+X91G6RTk2LCX080dDbwwQTwyQiDBAEKoc+62o3aokzXTQwEzrttTnfZwuK8eGpYR+/mjojQHiiQESEQYIQpVrv/5Dn1GSZho167W8j9Jl5dqwVNDPHw29MUA8MUAiwgBBqHLtd13lK0rSTL8YMSvvo3RZuTYsFfTzR48qUpwAACAASURBVENvDBBPDJCIMEAQqlz7TVlcoyTN9Ikrx6nR/OV4y7VhqaCfPxp6Y4B4YoBEhAGCUOXab0djk46+bKySNNPMZevzPk6XlGvDUkE/fzT0xgDxxACJCAMEocq538/+Ml1Jmuk34+bnfZQuKeeGpYB+/mjojQHiiQESEQYIQpVzv4emLVeSZhpw67N5H6VLyrlhKaCfPxp6Y4B4YoBEhAGCUOXcb/XGbS0vx7t28/a8j7PbyrlhKaCfPxp6Y4B4YoBEhAGCUOXe76u3TFKSZvrbjOq8j7Lbyr2hO/r5o6E3BognBkhEGCAIVe79bhw7T0ma6dzhM/M+ym4r94bu6OePht4YIJ4YIBFhgCBUufebtnSdkjTTMZc/YftyvOXe0B39/NHQGwPEEwMkIgwQhCr3fg2NTTrm8ieUpJmmv7ou7+PslnJv6I5+/mjojQHiiQESEQYIQtFPOueBGUrSTDeOnZf3UXYLDb3Rzx8NvTFAPDFAIsIAQSj6SX+dXq0kzfSfv5uU91F2Cw290c8fDb0xQDwxQCLCAEEo+klrN29XkmZK0kyrN27L+zjBaOiNfv5o6I0B4okBEhEGCELRr9nXbn1WSZrpoWnL8z5KMBp6o58/GnpjgHhigESEAYJQ9Gt287j5StJMP/vL9LyPEoyG3ujnj4beGCCeGCARYYAgFP2azVq+QUma6ejLxqq+oSnv4wShoTf6+aOhNwaIJwZIRBggCEW/Zk1NBX3qqnFK0kzPLVyb93GC0NAb/fzR0BsDxBMDJCIMEISi35sueHi2kjTTFX+vyvsoQWjojX7+aOiNAeKJARIRBghC0e9NY15eqSTN9IUbxqtQ8Pmp6DT0Rj9/NPTGAPHEAIkIAwSh6Pemzdsb1PeSSiVppkVrNud9nE6joTf6+aOhNwaIJwZIRBggCEW/1r539xQlaaY7n1mc91E6jYbe6OePht4YIJ4YIBFhgCAU/Vq797klStJM3/rD5LyP0mk09EY/fzT0xgDxxACJCAMEoejX2vJ1dUrSTIdcPFq1dR5/JjT0Rj9/NPTGAPHEAIkIAwSh6LezL/9mopI002OzX8/7KJ1CQ2/080dDbwwQTwyQiDBAEIp+O7uu8hUlaabzH5yZ91E6hYbe6OePht4YIJ4YILtw2223KUkS9e7dW/369dPUqVN3+f5Dhw7V4Ycfrr322ksf+chH9Itf/ELbtm3r9OMxQBCKfjt7cek6JWmmY694Qg2N8f9UdBp6o58/GnpjgHhigLRjxIgR6tWrl+655x5VVVXprLPO0n777afVq1e3+f4PPPCAevfurQceeEBLly7VE088of3331+//OUvO/2YDBCEot/OGhqbdOwVTyhJM724dF3ex+kQDb3Rzx8NvTFAPDFA2tGvXz+dc845Lb9uamrShz70IV133XVtvv8555yj//iP/2j1tl/96lf6/Oc/3+nHZIAgFP3adv6DM5Wkma6tnJv3UTpEQ2/080dDbwwQTwyQNtTX12uPPfbQyJEjW739jDPO0IABA9r8mAceeED77rtvy9O0Fi9erCOOOELXXHNNu4+zfft2bdy4seWqrq5WRUWFampqtGPHjqJcdXV1GjVqlOrq6or2mFz06+lr5IzlStJMX7ppQu5noWFpX/Tzv2jofeXRr6amhgHSRQyQNrz++uuqqKjQ5Mmtf5bAhRdeqH79+rX7cbfccov23HNPvetd71JFRYV++tOf7vJxhgwZooqKip2u4cOHa9SoUVxcXLt5Df/rKB2cPq4kzXT3iPzPw8XFxcVVOtfw4cMZIF3EAGnD7gyQCRMm6AMf+IDuuusuzZkzR48++qgOOOAAXXnlle0+Dn8DwkW/nru+9YfnmwfIpEW5n4WGpXvRz/+ioffF34B4YoC0YXeegnXCCSfoggsuaPW2+++/X3369FFTU+deiYfvAUEo+rXvzmcWK0kzfe/uKXkfZZdo6I1+/mjoLY9+fA9I1zFA2tGvXz+de+65Lb9uamrShz/84Xa/Cf2Tn/ykLrroolZvGz58uPr06aPGxsZOPSYDBKHo175FazYrSTP1vaRSm7c35H2cdtHQG/380dAbA8QTA6QdI0aMUO/evTVs2DDNnTtXP/nJT7Tffvtp1apVkqTTTz9dAwcObHn/IUOG6L3vfa8efPBBLVmyROPGjdOhhx6qb33rW51+TAYIQtFv17544wQlaaYxL6/I+yjtoqE3+vmjoTcGiCcGyC7ceuutOvDAA9WrVy/169dPU6a8+VSOk046SWeeeWbLrxsaGnT55Zfr0EMP1V577aUDDjhAZ599tjZs2NDpx2OAIBT9du3Kx6uUpJkueHh23kdpFw290c8fDb0xQDwxQCLCAEEo+u3a8wvXKkkzfeqqcWpqKuR9nDbR0Bv9/NHQGwPEEwMkIgwQhKLfrtU3NOnoy8YqSTPNWt75v40sJhp6o58/GnpjgHhigESEAYJQ9OvY2X+ZoSTNdOPYeXkfpU009EY/fzT0xgDxxACJCAMEoejXsUdnVitJM/0/v3km76O0iYbe6OePht4YIJ4YIBFhgCAU/Tq2oa5eh1w8WkmaaVlNXd7H2QkNvdHPHw29MUA8MUAiwgBBKPp1zv/3xxeUpJnumrQ476PshIbe6OePht4YIJ4YIBFhgCAU/TrnT88uUZJm+tYfJud9lJ3Q0Bv9/NHQGwPEEwMkIgwQhKJf5yxfV6ckzXTwwEzrt9TnfZxWaOiNfv5o6I0B4okBEhEGCELRr/P6D31GSZrpr9Or8z5KKzT0Rj9/NPTGAPHEAIkIAwSh6Nd5Nz8xT0ma6af3T8/7KK3Q0Bv9/NHQGwPEEwMkIgwQhKJf582prlWSZvrYpWO0bUdj3sdpQUNv9PNHQ28MEE8MkIgwQBCKfp1XKBT02WufUpJmGv/K6ryP04KG3ujnj4beGCCeGCARYYAgFP3CDB75spI008C/zcn7KC1o6I1+/mjojQHiiQESEQYIQtEvzDPz1yhJM/371U+qqamQ93Ek0dAd/fzR0BsDxBMDJCIMEISiX5jtDY066rKxStJMM5etz/s4kmjojn7+aOiNAeKJARIRBghC0S/c2Q/MUJJmun7MK3kfRRIN3dHPHw29MUA8MUAiwgBBKPqFGzXrNSVpplNunpj3USTR0B39/NHQGwPEEwMkIgwQhKJfuNqtO3ToxaOVpJmWrt2S93FoaI5+/mjojQHiiQESEQYIQtFv93znrheUpJnufGZx3kehoTn6+aOhNwaIJwZIRBggCEW/3XPvc0uUpJlOu2Ny3kehoTn6+aOhNwaIJwZIRBggCEW/3VO9vk5JmunggZnWbanP9Sw09EY/fzT0xgDxxACJCAMEoei3+77y20lK0kwPT1ue6zlo6I1+/mjojQHiiQESEQYIQtFv9/1m3Hwlaaaz/jwt13PQ0Bv9/NHQGwPEEwMkIgwQhKLf7nv5tVolaaYjBo/Rth2NuZ2Dht7o54+G3hggnhggEWGAIBT9dl+hUNDx1z2tJM00rmpVbuegoTf6+aOhNwaIJwZIRBggCEW/rhny2D+UpJl+9dDs3M5AQ2/080dDbwwQTwyQiDBAEIp+XfPC4holaaZjr3hCOxqbcjkDDb3Rzx8NvTFAPDFAIsIAQSj6dU1jU0GfvHKckjTTcwvX5nIGGnqjnz8aemOAeGKARIQBglD067r0ry8pSTMNHvlyLo9PQ2/080dDbwwQTwyQiDBAEIp+XTf+ldVK0kyfvvpJNTUViv74NPRGP3809MYA8cQAiQgDBKHo13XbGxp11GVjlaSZZixbX/THp6E3+vmjoTcGiCcGSEQYIAhFv+5x3vCZStJM146eW/THpqE3+vmjoTcGiCcGSEQYIAhFv+4xes4KJWmmL9wwXoVCcZ+GRUNv9PNHQ28MEE8MkIgwQBCKft1jy/YG9R1UqSTNNHdFcf8PhYbe6OePht4YIJ4YIBFhgCAU/brPj4ZNU5Jm+s24+UV9XBp6o58/GnpjgHhigESEAYJQ9Os+j0yvVpJm6j/0maI+Lg290c8fDb0xQDwxQCLCAEEo+nWfDXX1OuTi0UrSTEvXbina49LQG/380dAbA8QTAyQiDBCEol/3+s5dLyhJM/1h4qKiPSYNvdHPHw29MUA8MUAiwgBBKPp1r/smL1WSZvrG7c8V7TFp6I1+/mjojQHiiQESEQYIQtGve63auE1JmilJM63auK0oj0lDb/TzR0NvDBBPDJCIMEAQin7d7xu3P6ckzXTf5KVFeTwaeqOfPxp6Y4B4YoBEhAGCUPTrfn+YuEhJmuk7d71QlMejoTf6+aOhNwaIJwZIRBggCEW/7rd07RYlaaZDLh6t9Vvqe/zxaOiNfv5o6I0B4okBEhEGCELRr2f0H/qMkjTTw9OW9/hj0dAb/fzR0BsDxBMDJCIMEISiX8/4zbj5StJMPxr2Yo8/Fg290c8fDb0xQDwxQCLCAEEo+vWMuSs2Kkkz9R1UqU3bevbPlobe6OePht4YIJ4YIBFhgCAU/XpGoVDQF2+coCTNNGrWaz36WDT0Rj9/NPTGAPHEAIkIAwSh6Ndzbhj7ipI000/um9ajj0NDb/TzR0NvDBBPDJCIMEAQin495x+v1ypJMx0+qFJbtjf02OPQ0Bv9/NHQGwPEEwMkIgwQhKJfzykUCjrphvFK0kyPzX69xx6Hht7o54+G3hggnhggEWGAIBT9etavxzQ/Deun90/vscegoTf6+aOhNwaIJwZIRBggCEW/nvXya81Pw/ro4ErV1ffM07Bo6I1+/mjojQHiiQESEQYIQtGvZxUKBZ14ffPTsB5/qWeehkVDb/TzR0NvDBBPDJCIMEAQin4977rK5qdh/ewvPfM0LBp6o58/GnpjgHhigESEAYJQ9Ot5c6qbn4Z1xOAxPfI0LBp6o58/GnpjgHhigESEAYJQ9Ot5hUJBn//100rSTKPnrOj2z09Db/TzR0NvDBBPDJCIMEAQin7Fce3ouUrSTGc/MKPbPzcNvdHPHw29MUA8MUAiwgBBKPoVx+zlG5SkmT526RhtrW/s1s9NQ2/080dDbwwQTwyQiDBAEIp+xVEoFHT8dc1Pwxrzcvc+DYuG3ujnj4beGCCeGCARYYAgFP2K5+qsSkma6dzhM7v189LQG/380dAbA8QTAyQiDBCEol/xzFy2vuVpWNt2dN/TsGjojX7+aOiNAeKJARIRBghC0a943vo0rLH/WNltn5eG3ujnj4beGCCeGCARYYAgFP2K66rHm5+G9fMHu+9pWDT0Rj9/NPTGAPHEAIkIAwSh6FdcM/75NKyjLhvbbU/DoqE3+vmjoTcGiCcGSEQYIAhFv+Jqairos9c+pSTNNK5qVbd8Thp6o58/GnpjgHhigESEAYJQ9Cu+K/7e/DSs87vpaVg09EY/fzT0xgDxxACJCAMEoehXfG88DevIbno1LBp6o58/GnpjgHhigESEAYJQ9Cu+t74aVuWcrv9QQhp6o58/GnpjgHhigESEAYJQ9MvHtZVzlaSZfvaX6V3+XDT0Rj9/NPTGAPHEAIkIAwSh6JePl1+rVZJmOnxQpTZvb+jS56KhN/r5o6E3BognBkhEGCAIRb98FAoFnXzjBCVpppEzX+vS56KhN/r5o6E3BognBkhEGCAIRb/83DxuvpI00w/vfbFLn4eG3ujnj4beGCCeGCARYYAgFP3ys2DVJiVppsMuGa3aut3/86ehN/r5o6E3BognBkhEGCAIRb989R/6jJI000MvLt/tz0FDb/TzR0NvDBBPDJCIMEAQin75um38QiVppu/dPWW3PwcNvdHPHw29MUA8MUAiwgBBKPrl69WaLUrSTAcPzLR28/bd+hw09EY/fzT0xgDxxACJCAMEoeiXvwG3PqskzXTf5KW79fE09EY/fzT0xgDxxACJCAMEoeiXv7smLVaSZjrtjsm79fE09EY/fzT0xgDxxACJCAMEoeiXvxW1W5WkmZI004rarcEfT0Nv9PNHQ28MEE8MkIgwQBCKfnH45h3PK0kz3TVpcfDH0tAb/fzR0BsDxBMDJCIMEISiXxz+PHmpkjTTgFufDf5YGnqjnz8aemOAeGKARIQBglD0i8OaTdt18MDmp2Etq6kL+lgaeqOfPxp6Y4B4YoBEhAGCUPSLx3fvmqIkzXTb+IVBH0dDb/TzR0NvDBBPDJCIMEAQin7xGPHiMiVppv5Dnwn6OBp6o58/GnpjgHhigESEAYJQ9IvHhrp6HXbJaCVppgWrNnX642jojX7+aOiNAeKJARIRBghC0S8uP7z3RSVpppuemNfpj6GhN/r5o6E3BognBkhEGCAIRb+4jJr1mpI004nXj1ehUOjUx9DQG/380dAbA8QTAyQiDBCEol9c6uob9LFLxyhJM01/dX2nPoaG3ujnj4beGCCeGCARYYAgFP3i84sRs5SkmS4d9XKn3p+G3ujnj4beGCCeGCARYYAgFP3iM2HeaiVppk9cOU47Gps6fH8aeqOfPxp6Y4B4YoBEhAGCUPSLT0Njkz511Tglaabxr6zu8P1p6I1+/mjojQHiiQESEQYIQtEvTkMe+4eSNNN5w2d2+L409EY/fzT0xgDxxACJCAMEoegXp5nL1itJMx0xeIy2bG/Y5fvS0Bv9/NHQGwPEEwMkIgwQhKJfnAqFgr5ww3glaaZHZ1bv8n1p6I1+/mjojQHiiQESEQYIQtEvXr8ZN19JmumMP03d5fvR0Bv9/NHQGwPEEwMkIgwQhKJfvJas3aIkzXTIxaO1dvP2dt+Pht7o54+G3hggnhggEWGAIBT94jbg1meVpJnufW5Ju+9DQ2/080dDbwwQTwyQiDBAEIp+cfvTs0uUpJm+fttz7b4PDb3Rzx8NvTFAPDFAIsIAQSj6xW31pm06eGCmJM20dO2WNt+Hht7o54+G3hggnhggEWGAIBT94ve9u6coSTP99skFbf4+Db3Rzx8NvTFAPDFAIsIAQSj6xe+v06uVpJlOvnGCCoXCTr9PQ2/080dDbwwQTwyQiDBAEIp+8du8vUEfHVypJM30UvWGnX6fht7o54+G3hggnhggEWGAIBT9PJw7fKaSNNMVf6/a6fdo6I1+/mjojQHiiQESEQYIQtHPw1NzVylJM/371U+qobGp1e/R0Bv9/NHQGwPEEwMkIgwQhKKfhx2NTTruiieUpJkmzl/T+vdoaI1+/mjojQHiiQESEQYIQtHPx2WjXlaSZjpv+MxWb6ehN/r5o6E3BognBkhEGCAIRT8fL1VvUJJmOnxQpTZte7MXDb3Rzx8NvTFAPDFAIsIAQSj6+SgUCvrSzROVpJlGvLis5e009EY/fzT0xgDxxACJCAMEoejn5fcTFilJM532h8ktb6OhN/r5o6E3BognBkhEGCAIRT8vK2q36qCBmZI00/J1dZJo6I5+/mjojQHiiQESEQYIQtHPz/funqIkzfTbJxdIoqE7+vmjoTcGiCcGSEQYIAhFPz+PzqxWkmb6wg3jVSgUaGiOfv5o6I0B4okBEhEGCELRz09dfYOOvHSMkjTTtKXraGiOfv5o6I0B4okBEhEGCELRz9P/fXi2kjTTwL+9RENz9PNHQ28MEE8MkIgwQBCKfp6eX7RWSZrp6CFjtaluGw2NcQ/6o6E3BognBkhEGCAIRT9PTU0FHX/d00rSTCNnLKehMe5BfzT0xgDxxACJCAMEoejn68ax85Skmc740xQaGuMe9EdDbwwQTwyQiDBAEIp+vhav2awkzXTIxaN138M0dMU96I+G3hggnhggu3DbbbcpSRL17t1b/fr109SpU3f5/hs2bNDZZ5+tD37wg+rVq5f69u2r0aNHd/rxGCAIRT9v37j9OSVppvN//xgNTXEP+qOhNwaIJwZIO0aMGKFevXrpnnvuUVVVlc466yztt99+Wr16dZvvX19fr3//93/XV7/6VT333HNaunSpJk6cqNmzZ3f6MRkgCEU/b/e/8KqSNNPnrnichqa4B/3R0BsDxBMDpB39+vXTOeec0/LrpqYmfehDH9J1113X5vvfcccdOuSQQ7p0AzBAEIp+3jbU1euwS0YrSTO9tGxd3sfBbuAe9EdDbwwQTwyQNtTX12uPPfbQyJEjW739jDPO0IABA9r8mK985Sv67ne/q7POOkvvf//7ddRRR+maa65RY2Nju4+zfft2bdy4seWqrq5WRUWFampqtGPHjqJcdXV1GjVqlOrq6or2mFz043rzOmvYVCVppssenZ37WbjCL+5B/4uG3lce/WpqahggXcQAacPrr7+uiooKTZ48udXbL7zwQvXr16/Nj/noRz+q3r1764c//KGmT5+uESNG6H3ve58uv/zydh9nyJAhqqio2OkaPny4Ro0axcXFVQbX1fc8piTNdNTgx/W3R/M/DxcXFxfXrq/hw4czQLqIAdKG3Rkgffv21QEHHNDqbzxuvvlmffCDH2z3cfgbEC76cdVu2qyjBz+uJM1U+dJruZ+HK+ziHvS/aOh98Tcgnhggbdidp2B94Qtf0Je+9KVWb6usrFRFRYXq6+s79bh8DwhC0c/fjh079INb/q4kzfSjYdPyPg4CcQ/6o6G3PPrxPSBdxwBpR79+/XTuuee2/LqpqUkf/vCH2/0m9IsvvlhJkqipqanlbb/97W+1//77d/oxGSAIRT9/O3bs0B+Gj2r5mSCrN23L+0gIwD3oj4beGCCeGCDtGDFihHr37q1hw4Zp7ty5+slPfqL99ttPq1atkiSdfvrpGjhwYMv7L1++XO9973t17rnnav78+cqyTO9///t19dVXd/oxGSAIRT9/bzT8xm3PKkkz3TFxUd5HQgDuQX809MYA8cQA2YVbb71VBx54oHr16qV+/fppypQpLb930kkn6cwzz2z1/pMnT9ZnPvMZ9e7dW4ccckiHr4L1dgwQhKKfvzcaPvDCEiVpppNvmqBCoZD3sdBJ3IP+aOiNAeKJARIRBghC0c/fGw3Xb96qIwaPUZJmmv4qPxPEBfegPxp6Y4B4YoBEhAGCUPTz99aG//fh2UrSTBc98lLex0IncQ/6o6E3BognBkhEGCAIRT9/b204dck6JWmmIy8doy3bG/I+GjqBe9AfDb0xQDwxQCLCAEEo+vl7a8NCoaCTbhivJM308LTleR8NncA96I+G3hggnhggEWGAIBT9/L294W3jFypJM512x+QOPhIx4B70R0NvDBBPDJCIMEAQin7+3t5wZe02HTwwU5JmWrxmc86nQ0e4B/3R0BsDxBMDJCIMEISin7+2Gn7/nqlK0kzXj3klx5OhM7gH/dHQGwPEEwMkIgwQhKKfv7YaVs5ZoSTN1O+aJ9XQ2JTj6dAR7kF/NPTGAPHEAIkIAwSh6OevrYb1DU36xJXjlKSZxr+yOsfToSPcg/5o6I0B4okBEhEGCELRz197Da/4e5WSNNP/3jc9p5OhM7gH/dHQGwPEEwMkIgwQhKKfv/Yazlu5SUma6dCLR6tm8/acToeOcA/6o6E3BognBkhEGCAIRT9/u2o44NZnlaSZ7pq0OIeToTO4B/3R0BsDxBMDJCIMEISin79dNbz/hVeVpJm+dPNEFQqFHE6HjnAP+qOhNwaIJwZIRBggCEU/f7tquGnbDh0xeIySNNO0petyOB06wj3oj4beGCCeGCARYYAgFP38ddTwwkdmK0kz/fKhWUU+GTqDe9AfDb0xQDwxQCLCAEEo+vnrqOHMZeuVpJkOH1Sp2jo6x4Z70B8NvTFAPDFAIsIAQSj6+euoYaFQUP+hzyhJMw17fmlxD4cOcQ/6o6E3BognBkhEGCAIRT9/nWk47PmlStJM/Yc+wzejR4Z70B8NvTFAPDFAIsIAQSj6+etMw9qtO3T4oEolaaYZy9YX8XToCPegPxp6Y4B4YoBEhAGCUPTz19mGv3qo+ZvRL3h4dpFOhs7gHvRHQ28MEE8MkIgwQBCKfv4623Da0nVK0kxHDB6jjdvoHQvuQX809MYA8cQAiQgDBKHo56+zDQuFgk65eaKSNNN9L7xapNOhI9yD/mjojQHiiQESEQYIQtHPX0jDPz27REma6Su/ncQ3o0eCe9AfDb0xQDwxQCLCAEEo+vkLabihrl59//nN6C9VbyjC6dAR7kF/NPTGAPHEAIkIAwSh6OcvtOH5D85UkmYa+LeXevhk6AzuQX809MYA8cQAiQgDBKHo5y+04ZTFNUrSTB+7dIw2b2/o4dOhI9yD/mjojQHiiQESEQYIQtHPX2jDQqGgk2+aoCTNNHzqsh4+HTrCPeiPht4YIJ4YIBFhgCAU/fztTsM7n1msJM30X797tgdPhs7gHvRHQ28MEE8MkIgwQBCKfv52p+G6LfXqe0nzN6PPqa7twdOhI9yD/mjojQHiiQESEQYIQtHP3+42/Pk/vxn9okf4ZvQ8cQ/6o6E3BognBkhEGCAIRT9/u9vwjZ+M/tHBlaqto39euAf90dAbA8QTAyQiDBCEop+/3W1YKBTUf+gzStJMf3p2SQ+dDh3hHvRHQ28MEE8MkIgwQBCKfv660vD+F15VkmY6+aYJ/GT0nHAP+qOhNwaIJwZIRBggCEU/f11puHl7g468dIySNNPzC9f2wOnQEe5BfzT0xgDxxACJCAMEoejnr6sNB498WUma6af3T+/mk6EzuAf90dAbA8QTAyQiDBCEop+/rjact3KTkjTTIReP1qqN27r5dOgI96A/GnpjgHhigESEAYJQ9PPXHQ1Pu2OykjTT0Cfnd+PJ0Bncg/5o6I0B4okBEhEGCELRz193NBw16zUlaabPXPOUGhqbuvF06Aj3oD8aemOAeGKARIQBglD089cdDbc3NOpTV41TkmYa8/LKbjwdOsI96I+G3hggnhggEWGAIBT9/HVXw+vHvKIkzfTdu6Z008nQGdyD/mjojQHiiQESEQYIQtHPX3c1rF5fp4MGZkrSTIvXbO6m20LqZAAAIABJREFU06Ej3IP+aOiNAeKJARIRBghC0c9fdzb84b0vKkkzXfl4VTecDJ3BPeiPht4YIJ4YIBFhgCAU/fx1Z8Px81YrSTMdc/kT2lrf2A2nQ0e4B/3R0BsDxBMDJCIMEISin7/ubNjUVNAJ1z+tJM300IvLu+F06Aj3oD8aemOAeGKARIQBglD089fdDe+YuEhJmumrt0xSoVDols+J9nEP+qOhNwaIJwZIRBggCEU/f93dcENdvT46uFJJmmna0nXd8jnRPu5BfzT0xgDxxACJCAMEoejnrycapn99SUma6ewHZnTb50TbuAf90dAbA8QTAyQiDBCEop+/nmg4d8VGJWmmQy4erRW1W7vt82Jn3IP+aOiNAeKJARIRBghC0c9fTzX81h8mK0kz3Th2Xrd+XrTGPeiPht4YIJ4YIBFhgCAU/fz1VMMxL69Qkmb6xJXjtG0HL8nbU7gH/dHQGwPEEwMkIgwQhKKfv55q2NDYpOOva35J3kemV3fr58abuAf90dAbA8QTAyQiDBCEop+/nmz4+wnNL8n7n7/jJXl7CvegPxp6Y4B4YoBEhAGCUPTz15MN12+p1+GDml+Sd/qrvCRvT+Ae9EdDbwwQTwyQiDBAEIp+/nq64UWPNL8k7zm8JG+P4B70R0NvDBBPDJCIMEAQin7+erph1evNL8l76MWjtbJ2W488RjnjHvRHQ28MEE8MkIgwQBCKfv6K0fC0f74k701P8JK83Y170B8NvTFAPDFAIsIAQSj6+StGw8o5zS/J+8krx2l7Ay/J2524B/3R0BsDxBMDJCIMEISin79iNGxobNLnrn1KSZrpr7wkb7fiHvRHQ28MEE8MkIgwQBCKfv6K1fD2CQuVpJn+63fP8pK83Yh70B8NvTFAPDFAIsIAQSj6+StWw3VveUneqUt4Sd7uwj3oj4beGCCeGCARYYAgFP38FbPhxY/OUZJmOuvP03r8scoF96A/GnpjgHhigESEAYJQ9PNXzIYLV29WkmY6aGCmpWu39PjjlQPuQX809MYA8cQAiQgDBKHo56/YDX9w74tK0kyXjXq5KI9X6rgH/dHQGwPEEwMkIgwQhKKfv2I3fH7hWiVppiMGj1FtHf/edBX3oD8aemOAeGKARIQBglD081fshoVCQaf+dpKSNNPvJywqymOWMu5BfzT0xgDxxACJCAMEoejnL4+Gf51erSTN1O+aJ1Xf0FS0xy1F3IP+aOiNAeKJARIRBghC0c9fHg3rG5r06aufVJJmGjnztaI9biniHvRHQ28MEE8MkIgwQBCKfv7yanjb+OYfTPifv5vEDybsAu5BfzT0xgDxxACJCAMEoejnL6+G67fU66ODm38w4QuLa4r62KWEe9AfDb0xQDwxQCLCAEEo+vnLs+Ggkc0/mPBHw14s+mOXCu5BfzT0xgDxxACJCAMEoejnL8+Gi9c0/2DCJM20eM3moj9+KeAe9EdDbwwQTwyQiDBAEIp+/vJu+KNhzT+YcNDIObk8vru8+6HraOiNAeKJARIRBghC0c9f3g0nL6pRkmb66OBKrdtSn8sZnOXdD11HQ28MEE8MkIgwQBCKfv7yblgoFPSfv2v+wYRDn5yfyxmc5d0PXUdDbwwQTwyQiDBAEIp+/mJo+PhLrytJMx13xROqq2/I7RyOYuiHrqGhNwaIJwZIRBggCEU/fzE0bGwq6As3jFeSZrrnuSW5ncNRDP3QNTT0xgDxxACJCAMEoejnL5aG97/wqpI00/HXPa0djU25nsVJLP2w+2jojQHiiQESEQYIQtHPXywNt+1o1KeuGqckzfTozOpcz+Ikln7YfTT0xgDxxACJCAMEoejnL6aGt41fqCTN1H/oMyoUCnkfx0JM/bB7aOiNAeKJARIRBghC0c9fTA1rt+7QUZeNVZJmGv/K6ryPYyGmftg9NPTGAPHEAIkIAwSh6OcvtobXjJ6rJM102h2T8z6Khdj6IRwNvTFAPDFAIsIAQSj6+Yut4crabTrsktFK0kzTX12f93GiF1s/hKOhNwaIJwZIRBggCEU/fzE2vPCR2UrSTD/+87S8jxK9GPshDA29MUA8MUAiwgBBKPr5i7HhwtWbddDATEmaaeHqTXkfJ2ox9kMYGnpjgHhigESEAYJQ9PMXa8Of3DdNSZrplyNm5X2UqMXaD51HQ28MEE8MkIgwQBCKfv5ibfhS9QYlaaZDLh6tpWu35H2caMXaD51HQ28MEE8MkIgwQBCKfv5ibvj9e6YqSTNd+MjsvI8SrZj7oXNo6I0B4okBEhEGCELRz1/MDWcsW68kzXToxaO1fF1d3seJUsz90Dk09MYA8cQAiQgDBKHo5y/2ht+7e4qSNNPFj87J+yhRir0fOkZDbwwQTwyQiDBAEIp+/mJv+OLSdUrSTIddMlqvb9ia93GiE3s/dIyG3hggnhggEWGAIBT9/Dk0/PYfJytJM1066uW8jxIdh37YNRp6Y4B4YoBEhAGCUPTz59Dw+UVrlaSZ+g6q1KqN2/I+TlQc+mHXaOiNAeKJARIRBghC0c+fQ8NCoaBv3vG8kjTTFX+vyvs4UXHoh12joTcGiCcGSEQYIAhFP38uDZ+Zv0ZJmunwQZVaWcvfgrzBpR/aR0NvDBBPDJCIMEAQin7+XBq+9W9BBv6NV8R6g0s/tI+G3hggnhggEWGAIBT9/Dk1fOMVsQ65eLQWr9mc93Gi4NQPbaOhNwaIJwZIRBggCEU/f24Nf3jvi0rSTGc/MCPvo0TBrR92RkNvDBBPDJCIMEAQin7+3Bq+snKjDhqYKUkzzamuzfs4uXPrh53R0BsDxBMDJCIMEISinz/Hhr8cMUtJmum7d03J+yi5c+yH1mjojQHiiQESEQYIQtHPn2PD5evqdNglo5WkmZ6sWpX3cXLl2A+t0dAbA8QTAyQiDBCEop8/14bXVb6iJM30+V8/rW07GvM+Tm5c++FNNPTGAPHEAIkIAwSh6OfPteGW7Q367LVPKUkz3Txuft7HyY1rP7yJht4YIJ4YIBFhgCAU/fw5Nxw9Z4WSNFPfSyr1j9fL8xvSnfuhGQ29MUA8MUAiwgBBKPr5c25YKBT0o2HTlKSZTr5pgmrr/P4Zusq5H5rR0BsDxBMDJCIMEISinz/3huu21Osz1zQ/FetLN0/UL0bM0qm/naTPXvuUTrj+aX3xxgk6+aYJOvnG5uuL/7xOumG8vvDP68Tre/464fqne+T6/K+f1icve1yf//Wbv87rOvH68TrphvEtf7Zv/ef/wj/ffvKNE3TKzRP1vbunaP6qTXn/6xMF93uw3DFAPDFAIsIAQSj6+SuFhq+s3KhPXjlOSZpxGV0n3zhBjU2FvP/1yV0p3IPljAHiiQESEQYIQtHPX6k0XLt5u/4wcZF+99QCjatapdnLN2jGsvWaumSdpiyu0ZTFNZq6ZJ2mLlmnF5c2X9OWrtP0V9+41vf4NWNZ919TF6/Rb+8fpamL17S8bWYRrlnLN7S6Zv7zsaf988/1zT/b9S1/xtOWNv/5T5i3WkddNlZJmmnGsvV5/6uTu1K5B8sVA8QTAyQiDBCEop8/Gnpz7ffjPzd/785dkxbnfZTcuTZEMwaIJwZIRBggCEU/fzT05trvhrHNP8dl0Mg5eR8ld64N0YwB4okBEhEGCELRzx8Nvbn2G/HiMiVppjPvmZr3UXLn2hDNGCCeGCARYYAgFP380dCba78nq1YpSTMNuPXZvI+SO9eGaMYA8cQAiQgDBKHo54+G3lz7TX91vZI00wnXP533UXLn2hDNGCCeGCARYYAgFP380dCba7+la7coSTMdeemYvI+SO9eGaMYA8cQAiQgDBKHo54+G3lz7rd28veXngTSV+c8CcW2IZgwQTwyQiDBAEIp+/mjozbVfXX1DywCpq2/I+zi5cm2IZgwQTwyQiDBAEIp+/mjozbVfU1OhZYCs2bQ97+PkyrUhmjFAPDFAIsIAQSj6+aOhN+d+RwweoyTNtKymLu+j5Mq5IRggrhggEWGAIBT9/NHQm3O/T101Tkmaae6K8v4iyrkhGCCuGCC7cNtttylJEvXu3Vv9+vXT1Kmd+4FNDz74oCoqKvT1r3896PEYIAhFP3809Obc74Trn1aSZpr+6vq8j5Ir54ZggLhigLRjxIgR6tWrl+655x5VVVXprLPO0n777afVq1fv8uOWLl2qD3/4wzrxxBMZIOhx9PNHQ2/O/U65eaKSNNNzC9fmfZRcOTcEA8QVA6Qd/fr10znnnNPy66amJn3oQx/Sdddd1+7HNDY26vjjj9fdd9+tM888kwGCHkc/fzT05tzvq7dMUpJmmjBv1/9hrdQ5NwQDxBUDpA319fXaY489NHLkyFZvP+OMMzRgwIB2P+6yyy7TN77xDUnq1ADZ/v+3d7fBWVb3vsdvJppgZ5DYF1aleFncWDt6PG13dzy1WvaWPdvd1mp3Z6pTexCnHW1VzrQ6xQurNdYnVHws0FatFGuJVK3ScoEgKiISQawBU8AHBCFQnoKEhJAEkvzPi2gqFZEFuddav/v6fmbWC26D1wpfo/kNCba32/bt23tPQ0ODFQoFa2xstF27dnk5ra2tNm3aNGttbfX2TA79ODQslaPc75zx8y1JM5u5dF3wu9CQo9SvsbGRAXKQGCB7sX79eisUClZbW7vH66NHj7aqqqq9/pz58+fboEGDbMuWnt/KHrkfA6S6utoKhcKHTk1NjU2bNo3D4XA4nKKdM2+abkmaWfWDfw5+Fw5H6dTU1DBADhIDZC9cB0hzc7Mdd9xxNnPmzN7X9meA8DsgHPpxaKh9lPt99/5aS9LMHlu8JvhdaMhR6sfvgBw8BsheuH4JVl1dnRUKBSsrK+s9/fr1s379+llZWZmtXLlyv57L94DAFf300VCbcr+LJi2yJM3sj4vXhr5KUMoNwfeAqGKAfISqqiobNWpU74+7urps0KBBe/0m9La2Nquvr9/jnHvuuXbmmWdafX29dXR07NczGSBwRT99NNSm3O/ihxZbkmb28EvvhL5KUMoNwQBRxQD5CFOnTrWKigqbPHmyLV++3C655BKrrKy0jRs3mpnZiBEjbMyYMR/580fyp2DBA/rpo6E25X6XTfmrJWlmk15cFfoqQSk3BANEFQNkH8aPH2/HHnuslZeXW1VVlS1cuLD3rw0bNsxGjhz5kT+XAQIf6KePhtqU+10xtc6SNLP75u3flwmXKuWGYICoYoBEhAECV/TTR0Ntyv2uemypJWlmE557K/RVglJuCAaIKgZIRBggcEU/fTTUptzvmidfsyTN7K6n3wh9laCUG4IBoooBEhEGCFzRTx8NtSn3+8VfllmSZnbrUytCXyUo5YZggKhigESEAQJX9NNHQ23K/W6ZudySNLMbpy8LfZWglBuCAaKKARIRBghc0U8fDbUp97tj9uuWpJldN60+9FWCUm4IBogqBkhEGCBwRT99NNSm3O/eZ960JM1szJ9eC32VoJQbggGiigESEQYIXNFPHw21Kff79fMrLUkzu/KPS0JfJSjlhmCAqGKARIQBAlf000dDbcr9fjt/lSVpZv+v5tXQVwlKuSEYIKoYIBFhgMAV/fTRUJtyv9/XrrYkzexHD78S+ipBKTcEA0QVAyQiDBC4op8+GmpT7vfIojWWpJn9YPLLoa8SlHJDMEBUMUAiwgCBK/rpo6E25X5/+muDJWlmIx5cFPoqQSk3BANEFQMkIgwQuKKfPhpqU+73lyXrLUkzO/++2tBXCUq5IRggqhggEWGAwBX99NFQm3K/p+o3WJJm9u1fLQh9laCUG4IBoooBEhEGCFzRTx8NtSn3e27FJkvSzL45fn7oqwSl3BAMEFUMkIgwQOCKfvpoqE253wtvbrYkzeysu+eFvkpQyg3BAFHFAIkIAwSu6KePhtqU+9WubLQkzWz4nc+HvkpQyg3BAFHFAIkIAwSu6KePhtqU+y1evdWSNLNhtz8X+ipBKTcEA0QVAyQiDBC4op8+GmpT7le3dpslaWanjX029FWCUm4IBogqBkhEGCBwRT99NNSm3K9+XZMlaWb/dtOc0FcJSrkhGCCqGCARYYDAFf300VCbcr/XNzRbkmb2hRueDn2VoJQbggGiigESEQYIXNFPHw21Kfd7e3OLJWlmJ1fPCn2VoJQbggGiigESEQYIXNFPHw21Kfdbu7XVkjSzE699KvRVglJuCAaIKgZIRBggcEU/fTTUptzv7007LUkz+5efzQh9laCUG4IBoooBEhEGCFzRTx8NtSn329zcbkmaWZJm1t3dHfo6wSg3BANEFQMkIgwQuKKfPhpqU+63rbWjd4Ds7uwKfZ1glBuCAaKKARIRBghc0U8fDbUp92tp3907QNp2dYa+TjDKDcEAUcUAiQgDBK7op4+G2pT7te3q7B0gzW169+8ryg3BAFHFAIkIAwSu6KePhtqU+3V2dfcOkHd3dIS+TjDKDcEAUcUAiQgDBK7op4+G2tT7HTemZ4Bsam4LfZVg1BvmHQNEEwMkIgwQuKKfPhpqU+839GczLUkzW7dtZ+irBKPeMO8YIJoYIBFhgMAV/fTRUJt6v8/9/ClL0szeadwR+irBqDfMOwaIJgZIRBggcEU/fTTUpt7vlOtnW5Jm9tamltBXCUa9Yd4xQDQxQCLCAIEr+umjoTb1fv9649OWpJmt2JDfT6TUG+YdA0QTAyQiDBC4op8+GmpT73fqzc9YkmZWv64p9FWCUW+YdwwQTQyQiDBA4Ip++mioTb3fV2591pI0s1fXvBv6KsGoN8w7BogmBkhEGCBwRT99NNSm3u/fx821JM3s5dVbQ18lGPWGeccA0cQAiQgDBK7op4+G2tT7/eedz1uSZrZg5ZbQVwlGvWHeMUA0MUAiwgCBK/rpo6E29X7/fc8LlqSZzXtjc+irBKPeMO8YIJoYIBFhgMAV/fTRUJt6v2+On29JmtmzKzaGvkow6g3zjgGiiQESEQYIXNFPHw21qff7n4kvWpJmNutvG0JfJRj1hnnHANHEAIkIAwSu6KePhtrU+33nN7WWpJllS/8e+irBqDfMOwaIJgZIRBggcEU/fTTUpt7vggdesiTN7MlX14W+SjDqDfOOAaKJARIRBghc0U8fDbWp97vwwUWWpJk9unht6KsEo94w7xggmhggEWGAwBX99NFQm3q/H0xebEmaWc2iNaGvEox6w7xjgGhigESEAQJX9NNHQ23q/X708CuWpJn9vnZ16KsEo94w7xggmhggEWGAwBX99NFQm3q/UTWvWpJm9uD8VaGvEox6w7xjgGhigESEAQJX9NNHQ23q/a6YWmdJmtl981aGvkow6g3zjgGiiQESEQYIXNFPHw21qfcb/dgSS9LMJjz3VuirBKPeMO8YIJoYIBFhgMAV/fTRUJt6v6ufeM2SNLN75rwZ+irBqDfMOwaIJgZIRBggcEU/fTTUpt7vumn1lqSZ3TH79dBXCUa9Yd4xQDQxQCLCAIEr+umjoTb1fjdMX2ZJmtnYmStCXyUY9YZ5xwDRxACJCAMEruinj4ba1PvdMnO5JWlmN05fFvoqwag3zDsGiCYGSEQYIHBFP3001Kbeb9ys1y1JM7tuWn3oqwSj3jDvGCCaGCARYYDAFf300VCber+757xhSZrZ1U+8Fvoqwag3zDsGiCYGSEQYIHBFP3001Kbeb8Jzb1mSZjb6sSWhrxKMesO8Y4BoYoBEhAECV/TTR0Nt6v3um7fSkjSzK6bWhb5KMOoN844BookBEhEGCFzRTx8Ntan3e3D+KkvSzC6f8tfQVwlGvWHeMUA0MUAiwgCBK/rpo6E29X5/WPiOJWlmFz+0OPRVglFvmHcMEE0MkIgwQOCKfvpoqE293+OvNFiSZjbiwUWhrxKMesO8Y4BoYoBEhAECV/TTR0Nt6v2mL11vSZrZd35TG/oqwag3zDsGiCYGSEQYIHBFP3001Kbeb86yjZakmZ0z4cXQVwlGvWHeMUA0MUAiwgCBK/rpo6E29X7z39xiSZrZWXfPC32VYNQb5h0DRBMDJCIMELiinz4aalPv9/LqrZakmQ27/bnQVwlGvWHeMUA0MUAiwgCBK/rpo6E29X6vNTRZkmb2f255JvRVglFvmHcMEE0MkIgwQOCKfvpoqE2935sbmy1JM/v8L2aHvkow6g3zjgGiiQESEQYIXNFPHw21qfdb09hqSZrZidc+Ffoqwag3zDsGiCYGSEQYIHBFP3001Kbeb9P2NkvSzD4zJrPu7u7Q1wlCvWHeMUA0MUAiwgCBK/rpo6E29X5NO3dZkmaWpJl17O4KfZ0g1BvmHQNEEwMkIgwQuKKfPhpqU+/Xtquzd4Bsb9N8Hw6WesO8Y4BoYoBEhAECV/TTR0Nt6v26u7vtuDE9A2Rzc3vo6wSh3jDvGCCaGCARYYDAFf300VBbKfT77LUzLUkzW7u1NfRVgiiFhnnGANHEAIkIAwSu6KePhtpKod8p18+2JM3srU3Noa8SRCk0zDMGiCYGSEQYIHBFP3001FYK/apunmNJmln9uqbQVwmiFBrmGQNEEwMkIgwQuKKfPhpqK4V+/zFuriVpZi+93Rj6KkGUQsM8Y4BoYoBEhAECV/TTR0NtpdDv279aYEma2VP1fw99lSBKoWGeMUA0MUAiwgCBK/rpo6G2Uuj3g8mLLUkzm7JwTeirBFEKDfOMAaKJARIRBghc0U8fDbWVQr/Rjy2xJM3sl8+8GfoqQZRCwzxjgGhigESEAQJX9NNHQ22l0O+eOW9akmb200eXhL5KEKXQMM8YIJoYIBFhgMAV/fTRUFsp9PvLkvWWpJl9c/x86+7u7n29s6vbOnZ32e7OLuvs6rauD5zu7n8cdX3VsLu759dmV2fPr9kHf53e/7Xs2N3VR7fG+xggmhggEWGAwBX99NFQWyn027i9zZK05/+G/pkxmZ147VN2cvWs3tf253xmTGZDrp5hxwudIVfPsM+M6bn7cen0D71PQ/7pbfd1PjNm/3+tzr+v1nZ3MkT6CgNEEwMkIgwQuKKfPhpqK5V+V/5xidPg4BzcWbRqa+jkJYMBookBEhEGCFzRTx8NtZVKv86ubluydpu9vbnF3mncYSs3t9iWlnZr2rnLtrV22NYd/ziNLe29Z0tLu21ubrdN29tso+DZtL3N1m9tsd8/Os3+/m7P+9z43vu0oamt93zw7d8/e/x9mttsS0u7bd3RYdtae8677/1abW5ut22tHda0c5d97Z4XLEkz+8uS9aGTlwwGiCYGSEQYIHBFP3001EY/fT4bjqp51ZI0swdeeLvoz8oLBogmBkhEGCBwRT99NNRGP30+G179xGuWpJndMyeff+RxMTBANDFAIsIAgSv66aOhNvrp89nw59PqLUkzu3P260V/Vl4wQDQxQCLCAIEr+umjoTb66fPZ8Pq//M2SNLPbZ60o+rPyggGiiQESEQYIXNFPHw210U+fz4Y3ZcssSTO7Zebyoj8rLxggmhggEWGAwBX99NFQG/30+Wx4y8zllqSZ3Th9WdGflRcMEE0MkIgwQOCKfvpoqI1++nw2vH3WCkvSzKr//LeiPysvGCCaGCARYYDAFf300VAb/fT5bHjn029Ykmb282n1RX9WXjBANDFAIsIAgSv66aOhNvrp89nw3mfetCTN7OonXiv6s/KCAaKJARIRBghc0U8fDbXRT5/PhhOee8uSNLOrHlta9GflBQNEEwMkIgwQuKKfPhpqo58+nw1//fxKS9LMrvzjkqI/Ky8YIJoYIBFhgMAV/fTRUBv99Pls+MALb1uSZvaTqXVFf1ZeMEA0MUAiwgCBK/rpo6E2+unz2XDSi6ssSTMbVfNq0Z+VFwwQTQyQiDBA4Ip++miojX76fDZ8qHa1JWlml/7hlaI/Ky8YIJoYIBFhgMAV/fTRUBv99Pls+IeF71iSZnbJ7xcX/Vl5wQDRxACJCAMEruinj4ba6KfPZ8OpL6+xJM3sB5NfLvqz8oIBookBEhEGCFzRTx8NtdFPn8+Gj73SYEma2chJi4r+rLxggGhigESEAQJX9NNHQ2300+ez4ROv9gyQ//vbhUV/Vl4wQDQxQCLCAIEr+umjoTb66fPZ8M9L1luSZvbd+18q+rPyggGiiQESEQYIXNFPHw210U+fz4YzXvu7JWlm3/lNbdGflRcMEE0MkIgwQOCKfvpoqI1++nw2fKp+gyVpZt/+1YKiPysvGCCaGCARYYDAFf300VAb/fT5bDjrbz0D5H8mvlj0Z+UFA0QTAyQiDBC4op8+Gmqjnz6fDZ9ettGSNLNzJzBA+goDRBMDJCIMELiinz4aaqOfPp8Nn1neM0DOYYD0GQaIJgZIRBggcEU/fTTURj99Phs+u6JngHxz/PyiPysvGCCaGCARYYDAFf300VAb/fT5bPjc65ssSTM7+5cMkL7CANHEAIkIAwSu6KePhtrop89nw+ff2GxJmtnX732h6M/KCwaIJgZIRBggcEU/fTTURj99PhvOe2+A/Pc9DJC+wgDRxACJCAMEruinj4ba6KfPZ8P5b26xJM3srLvnFf1ZecEA0cQAiQgDBK7op4+G2uinz2fDBW/1DJD/uosB0lcYIJoYIBFhgMAV/fTRUBv99HkdICt7Bsh/3vl80Z+VFwwQTQyQiDBA4Ip++miojX76fDZ86e1GS9LMhjNA+gwDRBMDJCIMELiinz4aaqOfPp8NF743QP7jjrlFf1ZeMEA0MUAiwgCBK/rpo6E2+unz2fDl1VstSTP793Fzi/6svGCAaGKARIQBAlf000dDbfTT57PhK+/0DJBhtz9X9GflBQNEEwMkIgwQuKKfPhpqo58+vwPkXUvSzM64jQHSVxggmhgg+zBhwgRLksQqKiqsqqrKFi1a9JFve//999vpp59ulZWVVllZacOHD9/n2+8NAwSu6KePhtrop89nw1fX9AyQ0297tujPygvZHj0wAAAUDUlEQVQGiCYGyEeYOnWqlZeX26RJk2zZsmV28cUXW2VlpW3atGmvb3/BBRfYxIkTra6uzlasWGEXXXSRDRw40NatW7ffz2SAwBX99NFQG/30+Wy4ZO02S9LMThvLAOkrDBBNDJCPUFVVZZdffnnvj7u6uuyYY46xsWPH7tfP7+zstAEDBthDDz20389kgMAV/fTRUBv99PlsuLShZ4B8+ZZniv6svGCAaGKA7EVHR4eVlZXZk08+ucfrF154oZ1zzjn79fdobm62/v372/Tp0z/ybdrb22379u29p6GhwQqFgjU2NtquXbu8nNbWVps2bZq1trZ6eyaHfhwalsqhn/7x2bDunZ4/hvfUm+cEf79L5YT4GGxsbGSAHCQGyF6sX7/eCoWC1dbW7vH66NGjraqqar/+HpdeeqkNGTLE2traPvJtqqurrVAofOjU1NTYtGnTOBwOh8PhlNCZMGWaJWlmp1w7PfhdOAd+ampqGCAHiQGyFwc7QMaOHWtHHHGELV26dJ9vx++AcOjHoaH2oZ/+8dlw6ZqeP4b3Szc+Hfz9LpXD74BoYoDsxcF8Cda4ceNs4MCBtnjxYufn8j0gcEU/fTTURj99Phuu2LDdkjSzf71xTtGflRchPgb5HpCDxwD5CFVVVTZq1KjeH3d1ddmgQYP2+U3ot912mx1++OH20ksvHdAzGSBwRT99NNRGP30+G76+odmSNLMv3vB00Z+VFwwQTQyQjzB16lSrqKiwyZMn2/Lly+2SSy6xyspK27hxo5mZjRgxwsaMGdP79rfeequVl5fb448/bhs2bOg9LS0t+/1MBghc0U8fDbXRT5/Phm9u7Bkgn//F7KI/Ky8YIJoYIPswfvx4O/bYY628vNyqqqps4cKFvX9t2LBhNnLkyN4fJ0my128or66u3u/nMUDgin76aKiNfvp8NnxrU4slaWb/mwHSZxggmhggEWGAwBX99NFQG/30+Wy4cnPPAPlf1bOK/qy8YIBoYoBEhAECV/TTR0Nt9NPns+GqLTssSTM7mQHSZxggmhggEWGAwBX99NFQG/30+Wz4TmPPADnpOgZIX2GAaGKARIQBAlf000dDbfTT57PhmsZWS9LMPvfzp4r+rLxggGhigESEAQJX9NNHQ2300+ez4dqtPQPkxGsZIH2FAaKJARIRBghc0U8fDbXRT5/Phuu27bQkzeyEa2YW/Vl5wQDRxACJCAMEruinj4ba6KfPZ8P17w2QoT9jgPQVBogmBkhEGCBwRT99NNRGP30+G25oarMkzexffjaj6M/KCwaIJgZIRBggcEU/fTTURj99Phtu3N4zQIZczQDpKwwQTQyQiDBA4Ip++miojX76fDbc1NwzQI4bkxX9WXnBANHEAIkIAwSu6KePhtrop89nwy0t7ZakmSUpA6SvMEA0MUAiwgCBK/rpo6E2+unz2bDxAwOku7u76M/LAwaIJgZIRBggcEU/fTTURj99Phu+u6Ojd4B0dTFA+gIDRBMDJCIMELiinz4aaqOfPp8Nm1p39Q6Q3Z1dRX9eHjBANDFAIsIAgSv66aOhNvrp8zpAdv5jgHTsZoD0BQaIJgZIRBggcEU/fTTURj99Phs2t/1jgLTv7iz68/KAAaKJARIRBghc0U8fDbXRT5/Phjvad/cOkLZdDJC+wADRxACJCAMEruinj4ba6KfPZ8PWjn8MkNaO3UV/Xh4wQDQxQCLCAIEr+umjoTb66fPZsG1XZ+8AaWlngPQFBogmBkhEGCBwRT99NNRGP30+G+7q7OodIE07+WemLzBANDFAIsIAgSv66aOhNvrp89mwq6u7d4Bs3dFR9OflAQNEEwMkIgwQuKKfPhpqo58+3w3fHyCbm9u9PK/UMUA0MUAiwgCBK/rpo6E2+unz3fD4q2dYkma2oanNy/NKHQNEEwMkIgwQuKKfPhpqo58+3w2HXjPTkjSzddt2enleqWOAaGKARIQBAlf000dDbfTT57vh537+lCVpZmu3tnp5XqljgGhigESEAQJX9NNHQ2300+e74cnXzbIkzWzVlh1enlfqGCCaGCARYYDAFf300VAb/fT5bnjK9bMtSTN7a1OLl+eVOgaIJgZIRBggcEU/fTTURj99vht+8YanLUkze2Njs5fnlToGiCYGSEQYIHBFP3001EY/fb4bfummOZakmS1bzyevfYEBookBEhEGCFzRTx8NtdFPn++Gp978jCVpZvXrmrw8r9QxQDQxQCLCAIEr+umjoTb66fPd8LSxz1qSZla3dpuX55U6BogmBkhEGCBwRT99NNRGP32+G55x23OWpJm98s67Xp5X6hggmhggEWGAwBX99NFQG/30+W74H+PmWpJm9vLqrV6eV+oYIJoYIBFhgMAV/fTRUBv99PluOPzO5y1JM6td2ejleaWOAaKJARIRBghc0U8fDbXRT5/vhv911zxL0sxefGuLl+eVOgaIJgZIRBggcEU/fTTURj99vht+7Z4XLEkze/6NzV6eV+oYIJoYIBFhgMAV/fTRUBv99PluePYv51uSZvbcik1enlfqGCCaGCARYYDAFf300VAb/fT5bnjuhBctSTObs2yjl+eVOgaIJgZIRBggcEU/fTTURj99vht++1cLLEkze6p+g5fnlToGiCYGSEQYIHBFP3001EY/fb4bnn9frSVpZtPq1nl5XqljgGhigESEAQJX9NNHQ2300+e74Y8fedWSNLM7Zr9uLe27bcd7p7WDcyCnacdO++Ofptn2HW1e+pkxQPoCAyQiDBC4op8+Gmqjnz7fDcfNet2SNOP08Tnjtme99DNjgPQFBkhEGCBwRT99NNRGP32+G769ucX+7aY5wT9hL7XDANHCAIkIAwSu6KePhtropy9Ew66ubmvb1dl7dnZwDvRs39Fmj/5pmjW38iVYShggEWGAwBX99NFQG/300VAb34SuiQESEQYIXNFPHw210U8fDbUxQDQxQCLCAIEr+umjoTb66aOhNgaIJgZIRBggcEU/fTTURj99NNTGANHEAIkIAwSu6KePhtrop4+G2hggmhggEWGAwBX99NFQG/300VAbA0QTAyQiDBC4op8+Gmqjnz4aamOAaGKARIQBAlf000dDbfTTR0NtDBBNDJCIMEDgin76aKiNfvpoqI0BookBEhEGCFzRTx8NtdFPHw21MUA0MUAiwgCBK/rpo6E2+umjoTYGiCYGSEQYIHBFP3001EY/fTTUxgDRxACJCAMEruinj4ba6KePhtoYIJoYIBFhgMAV/fTRUBv99NFQGwNEEwMkIgwQuKKfPhpqo58+GmpjgGhigESEAQJX9NNHQ23000dDbQwQTQyQiDBA4Ip++miojX76aKiNAaKJARIRBghc0U8fDbXRTx8NtTFANDFAIsIAgSv66aOhNvrpo6E2BogmBkhEGCBwRT99NNRGP3001MYA0cQAiQgDBK7op4+G2uinj4baGCCaGCARYYDAFf300VAb/fTRUBsDRBMDJCIMELiinz4aaqOfPhpqY4BoYoBEhAECV/TTR0Nt9NNHQ20MEE0MkIgwQOCKfvpoqI1++miojQGiiQESkaamJisUCtbQ0GDbt2/3chobG62mpsYaGxu9PZNDPw4NS+XQT//QUPuE6NfQ0GCFQsGamppCf+ooiwESkff/geZwOBwOh8PhxH0aGhpCf+ooiwESka6uLmtoaLCmpibvK97n77pw6MehYakc+ukfGmqfEP2ampqsoaHBurq6Qn/qKIsBknPbt/N1jMrop4+G2uinj4ba6KeJAZJzfOBqo58+Gmqjnz4aaqOfJgZIzvGBq41++miojX76aKiNfpoYIDnX3t5u1dXV1t7eHvoqOAD000dDbfTTR0Nt9NPEAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAyQHJkyYYEmSWEVFhVVVVdmiRYv2+faPPvqoffazn7WKigo7+eSTbcaMGZ5uir1x6Xf//ffb6aefbpWVlVZZWWnDhw//2N4oPtePwfc98sgjVigU7Nxzzy3yDbEvrv22bdtml112mR111FFWXl5uQ4cO5d+jAbn2u/vuu+2EE06w/v3726c//Wn7yU9+Ym1tbZ5uiw+aN2+enX322Xb00UdboVCwJ5988mN/zty5c+0LX/iClZeX2/HHH2+/+93vin9ROGOAlLipU6daeXm5TZo0yZYtW2YXX3yxVVZW2qZNm/b69gsWLLCysjK7/fbbbfny5XbttdfaoYceavX19Z5vDjP3fhdccIFNnDjR6urqbMWKFXbRRRfZwIEDbd26dZ5vjve5Nnzf6tWrbdCgQXbGGWcwQAJy7dfR0WFf+tKX7Otf/7q9+OKLtnr1anv++edtyZIlnm8OM/d+U6ZMsYqKCpsyZYqtXr3aZs+ebUcffbRdccUVnm8OM7OZM2faNddcY0888cR+DZBVq1bZJz7xCbvyyitt+fLlNn78eCsrK7NZs2Z5ujH2FwOkxFVVVdnll1/e++Ouri475phjbOzYsXt9+/POO8++8Y1v7PHaqaeeaj/84Q+Lek/snWu/f9bZ2WkDBgywhx56qFhXxMc4kIadnZ122mmn2W9/+1sbOXIkAyQg136//vWvbciQIbZr1y5fV8Q+uPa7/PLL7cwzz9zjtSuvvNK+8pWvFPWe+Hj7M0CuuuoqO+mkk/Z47fzzz7ezzjqrmFfDAWCAlLCOjg4rKyv70AfshRdeaOecc85ef87gwYPt7rvv3uO16667zk455ZSi3RN7dyD9/llzc7P179/fpk+fXowr4mMcaMPrrrvOvvWtb5mZMUACOpB+X/va1+x73/ueXXzxxXbkkUfaSSedZDfffLN1dnb6uDI+4ED6TZkyxQYOHNj7ZVpvv/22nXjiiXbzzTcX/b7Yt/0ZIGeccYb9+Mc/3uO1SZMm2eGHH17Mq+EAMEBK2Pr1661QKFhtbe0er48ePdqqqqr2+nMOPfRQq6mp2eO1iRMn2pFHHlm0e2LvDqTfP7v00kttyJAhfP1yIAfScP78+TZo0CDbsmWLmTFAQjqQfu9//9z3v/99e+WVV2zq1Kn2yU9+0q6//nofV8YHHOi/Q++991479NBD7ZBDDrFCoWA/+tGPin1V7If9GSBDhw61W265ZY/XZsyYYYVCwXbu3FnM68ERA6SEMUC0HewAGTt2rB1xxBG2dOnSYl0RH8O1YXNzsx133HE2c+bM3tcYIOEcyMfg0KFDbfDgwXv8jsedd95pRx11VFHvig87kH5z5861T33qU/bAAw/Ya6+9Zk888YQNHjzYbrjhBh9Xxj4wQEoLA6SE8SVY2g7mS7DGjRtnAwcOtMWLFxfzivgYrg3r6uqsUChYWVlZ7+nXr5/169fPysrKbOXKlb6uDjuwj8GvfvWrNnz48D1emzlzphUKBevo6CjaXfFhB9Lv9NNPt5/+9Kd7vPbwww/bYYcdZl1dXUW7Kz4eX4JVWhggJa6qqspGjRrV++Ouri4bNGjQPr8J/eyzz97jtS9/+ct8E3ogrv3MzG677TY7/PDD7aWXXvJxRXwMl4ZtbW1WX1+/xzn33HPtzDPPtPr6ej6BDcD1Y/Dqq6+2JEn2+GT1nnvusaOPPrrod8WHufb74he/aFddddUer9XU1Nhhhx3G9/EEtr/fhH7yySfv8dp3v/tdvgk9QgyQEjd16lSrqKiwyZMn2/Lly+2SSy6xyspK27hxo5mZjRgxwsaMGdP79gsWLLBDDjnE7rjjDluxYoVVV1fzx/AG5Nrv1ltvtfLycnv88cdtw4YNvaelpSXUu5B7rg3/GV+CFZZrv7Vr19qAAQNs1KhR9sYbb1iWZXbkkUfaTTfdFOpdyDXXftXV1TZgwAB75JFHbNWqVfb000/b8ccfb+edd16odyHXWlparK6urvd3h++66y6rq6uzNWvWmJnZmDFjbMSIEb1v//4fwzt69GhbsWKFTZw4kT+GN1IMkBwYP368HXvssVZeXm5VVVW2cOHC3r82bNgwGzly5B5v/+ijj9oJJ5xg5eXldtJJJ/E/0ArMpV+SJFYoFD50qqur/V8cvVw/Bj+IARKea7/a2lo79dRTraKiwoYMGcKfghWYS7/du3fb9ddfb8cff7z179/fBg8ebJdddplt27YtwM0xd+7cvf437f1mI0eOtGHDhn3o53z+85+38vJyGzJkCP8jwkgxQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAwQAAACANwwQAAAAAN4wQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAwQAAACANwwQAAAAAN4wQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAwQAAACANwwQAAAAAN4wQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAwQAAACANwwQAAAAAN4wQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDcMEAAAAADeMEAAAAAAeMMAAQAAAOANAwQAAACANwwQAAAAAN4wQAAAAAB4wwABAAAA4A0DBAAAAIA3DBAAAAAA3jBAAAAAAHjDAAEAAADgDQMEAAAAgDf/Hx0boCrMsjCkAAAAAElFTkSuQmCC\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd28320ca90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(grid.cc, vs[-1][Prim.Dens], label='Uniform high resolution')\n",
    "# plt.plot(grid_n.cc, vs_nmz[-1][Prim.Dens], '--', label='Non-uniform low resolution')\n",
    "# plt.plot(sodValues['x'], sodValues['rho'], 'C4--', label='Analytic')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The high resolution solution is clearly in very good agreement with the analytic solution. The non-uniform grid, is extremely non-uniform over short lengthscales, and when this method is applied on a uniform grid, it matches the normal WENO5-Z solution. These slight ringing effects are very minor, and shouldn't cause any trouble in the actual simulation, as we should never have a grid where the spacing varies in such a horrible way (print `grid_n.dx` to see quite how bad this is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(grid_n.dx / grid_n.dx[0])[4:20] # First 4 cells are ghost cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overpressure shock wave\n",
    "This is a very similar problem to the sod shock, just harder to capture, due to the magnitude of the pressure.\n",
    "The same Riemann solver can be used to find the analytic solution, and some nice animations are shown on [here](http://cococubed.asu.edu/code_pages/ppm_1d.shtml) -- hosted by F. Timmes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = reflecting_bc(position='Lower')\n",
    "upper_bc = reflecting_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def overpressure_setup(grid):\n",
    "    mid = ((grid.griBeg + grid.griEnd) * 2) // 5\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :mid] = 10.0\n",
    "    V[Prim.Pres, :mid] = 100.0\n",
    "    V[Prim.Velo] = 0.0\n",
    "    V[Prim.Dens, mid:] = 1.0\n",
    "    V[Prim.Pres, mid:] = 1.0\n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    return V, U\n",
    "    \n",
    "interfaces = np.linspace(0.0, 5.0, 401)\n",
    "grid = Grid(interfaces, 4)\n",
    "nonUniformInterfaces = np.copy(interfaces)\n",
    "dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "nonUniformInterfaces[1:-1:2] -= 0.2 * dx\n",
    "grid_n = Grid(nonUniformInterfaces, 4)\n",
    "sim = Simulation(grid, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_z, lax_friedrichs_flux)\n",
    "sim_n = Simulation(grid_n, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vinit = np.copy(sim.V)\n",
    "tEnd = 0.4\n",
    "t=0\n",
    "vs = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    if t >= tEnd:\n",
    "        break\n",
    "        \n",
    "t=0\n",
    "vs_n = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim_n.step(tMax)\n",
    "    vs_n.append(np.copy(sim_n.V))\n",
    "    if t >= tEnd:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "overpressurePos, overpressureRegions, overpressureValues = sod.solve(left_state=(100.0, 10.0, 0.0), \n",
    "                                                                     right_state=(1.0, 1.0, 0.0), \n",
    "                                                                     geometry=(0.0, 5.0, 2.0), \n",
    "                                                                     t=0.4, gamma=Gas.Gamma, npts=2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(grid.cc, vs[-1][Prim.Dens])\n",
    "plt.plot(grid.cc, vs[-1][Prim.Pres] / 10.0)\n",
    "plt.plot(grid_n.cc, vs_n[-1][Prim.Dens], '--')\n",
    "plt.plot(grid_n.cc, vs_n[-1][Prim.Pres] / 10.0, '--')\n",
    "plt.plot(overpressureValues['x'], overpressureValues['rho'], '--')\n",
    "plt.plot(overpressureValues['x'], overpressureValues['p'] / 10.0, '--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shu-Osher Problem\n",
    "One of the coolest looking hydrodynamic test cases. This implementation is based on the Athena [test suite](https://www.astro.princeton.edu/~jstone/Athena/tests/shu-osher/Shu-Osher.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = zero_grad_bc(position='Lower')\n",
    "upper_bc = zero_grad_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def shu_osher_setup(grid):\n",
    "    transition = np.searchsorted(grid.cc, -0.8)\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :transition] = 27.0/7.0\n",
    "    V[Prim.Pres, :transition] = 31.0 / 3.0\n",
    "    V[Prim.Velo, :transition] = 4.0 * np.sqrt(35.0) / 9.0\n",
    "    V[Prim.Dens, transition:] = 1.0 + 0.2 * np.sin(5 * np.pi * grid.cc[transition:])\n",
    "    V[Prim.Pres, transition:] = 1.0\n",
    "    V[Prim.Velo, transition:] = 0.0\n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "#     V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0))\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    return V, U\n",
    "    \n",
    "interfaces = np.linspace(-1.0, 1.0, 5001)\n",
    "grid = Grid(interfaces, 4)\n",
    "lowRes = np.linspace(-1.0, 1.0, 401)\n",
    "dx = lowRes[1] - lowRes[0]\n",
    "lowRes[1:-1:2] -= 0.2 * dx\n",
    "grid_l = Grid(lowRes, 4)\n",
    "# nonUniformInterfaces = np.copy(interfaces)\n",
    "# dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "# nonUniformInterfaces[1:-1:2] -= 0.2 * dx\n",
    "# grid_n = Grid(nonUniformInterfaces, 4)\n",
    "sim = Simulation(grid, shu_osher_setup, bcs, cfl, tvd_rk3, reconstruct_weno_z, lax_friedrichs_flux)\n",
    "sim_l = Simulation(grid_l, shu_osher_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)\n",
    "# sim_n = Simulation(grid_n, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vinit = np.copy(sim.V)\n",
    "tEnd = 0.47\n",
    "t=0\n",
    "vs = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    if t >= tEnd:\n",
    "        break\n",
    "t=0\n",
    "\n",
    "Vinit = np.copy(sim_l.V)\n",
    "vs_l = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim_l.step(tMax)\n",
    "    vs_l.append(np.copy(sim_l.V))\n",
    "    if t >= tEnd:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(grid.cc, vs[-1][Prim.Dens])\n",
    "plt.plot(grid_l.cc, vs_l[-1][Prim.Dens], '--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collela & Woodward Interacting Blast Waves\n",
    "\n",
    "Also based on the Athena [test suite](https://www.astro.princeton.edu/~jstone/Athena/tests/twoibw/TwoIBW.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "class Validator:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def validate(self, recon, grid):\n",
    "        fails = []\n",
    "        okay = True\n",
    "        for var in range(Prim.NumVars):\n",
    "            if var == Prim.Velo:\n",
    "                continue\n",
    "            for direction in range(2):\n",
    "                fails.append(np.argwhere(recon[var, direction, grid.griBeg:grid.griEnd] < 1e-20).squeeze() + grid.griBeg)\n",
    "        fails = reduce(np.union1d, fails)\n",
    "        if len(fails) > 0:\n",
    "            okay = False\n",
    "            print('YELL:')\n",
    "            print('  %d fails!!' % len(fails))\n",
    "            print('  ', fails)\n",
    "            print('~~~SCREAM~~~')\n",
    "            \n",
    "        return okay, fails\n",
    "        \n",
    "    def transform(self, prim):\n",
    "        trans = np.zeros_like(prim)\n",
    "        for var in range(Prim.NumVars):\n",
    "            if var == Prim.Velo:\n",
    "                trans[var] = prim[var]\n",
    "                continue\n",
    "            trans[var] = np.log(prim[var])\n",
    "        return trans\n",
    "            \n",
    "    def inverse_transform(self, trans):\n",
    "        prim = np.zeros_like(trans)\n",
    "        for var in range(Prim.NumVars):\n",
    "            if var == Prim.Velo:\n",
    "                prim[var] = trans[var]\n",
    "                continue\n",
    "            prim[var] = np.exp(trans[var])\n",
    "        return prim\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = reflecting_bc(position='Lower')\n",
    "upper_bc = reflecting_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def collela_woodward_setup(grid):\n",
    "    transition1 = np.searchsorted(grid.cc, 0.1)\n",
    "    transition2 = np.searchsorted(grid.cc, 0.9)\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens] = 1.0\n",
    "    V[Prim.Velo] = 0.0\n",
    "    V[Prim.Pres, :transition1] = 1000.0\n",
    "    V[Prim.Pres, transition1:transition2] = 0.01\n",
    "    V[Prim.Pres, transition2:] = 100.0\n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    return V, U\n",
    "    \n",
    "interfaces = np.linspace(0.0, 1.0, 2501)\n",
    "grid = Grid(interfaces, 4)\n",
    "# lowRes = np.linspace(0.0, 1.0, 401)\n",
    "# dx = lowRes[1] - lowRes[0]\n",
    "# lowRes[1:-1:2] -= 0.2 * dx\n",
    "# grid_l = Grid(lowRes, 4)\n",
    "# nonUniformInterfaces = np.copy(interfaces)\n",
    "# dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "# nonUniformInterfaces[1:-1:2] -= 0.2 * dx\n",
    "# grid_n = Grid(nonUniformInterfaces, 4)\n",
    "sim = Simulation(grid, collela_woodward_setup, bcs, cfl, tvd_rk3, reconstruct_weno, 5, \n",
    "                 lax_friedrichs_flux, reconstructionValidator=Validator())\n",
    "# sim_l = Simulation(grid_l, collela_woodward_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)\n",
    "# sim_n = Simulation(grid_n, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.zeros(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "np.seterr(all='raise')\n",
    "Vinit = np.copy(sim.V)\n",
    "tEnd = 0.038\n",
    "t=0\n",
    "vs = [Vinit]\n",
    "ts = [0.0]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    ts.append(t)\n",
    "    if t >= tEnd:\n",
    "        break\n",
    "# t=0\n",
    "\n",
    "# Vinit = np.copy(sim_l.V)\n",
    "# vs_l = [Vinit]\n",
    "# tq = tqdm(range(10000))\n",
    "# for i in tq:\n",
    "#     tq.set_description('%f s' % t)\n",
    "#     tMax = tEnd - t\n",
    "#     t += sim_l.step(tMax)\n",
    "#     vs_l.append(np.copy(sim_l.V))\n",
    "#     if t >= tEnd:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "idx = -1\n",
    "print(ts[idx])\n",
    "plt.plot(grid.cc, vs[idx][Prim.Dens])\n",
    "# plt.plot(grid.cc, vs[idx][Prim.Velo])\n",
    "# plt.plot(grid.cc, vs[idx][Prim.Pres])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lax shock tube\n",
    "As discussed [here](http://doi.org/10.1137/S1064827502412504)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = zero_grad_bc(position='Lower')\n",
    "upper_bc = zero_grad_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def lax_setup(grid):\n",
    "    transition = np.searchsorted(grid.cc, 0.0)\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :transition] = 0.445\n",
    "    V[Prim.Velo, :transition] = 0.689\n",
    "    V[Prim.Pres, :transition] = 3.528\n",
    "    V[Prim.Dens, transition:] = 0.5\n",
    "    V[Prim.Velo, transition:] = 0.0\n",
    "    V[Prim.Pres, transition:] = 0.5710\n",
    "    \n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "#     V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0))\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    return V, U\n",
    "    \n",
    "interfaces = np.linspace(-0.5, 0.5, 401)\n",
    "grid = Grid(interfaces, 4)\n",
    "# lowRes = np.linspace(-1.0, 1.0, 401)\n",
    "# dx = lowRes[1] - lowRes[0]\n",
    "# lowRes[1:-1:2] -= 0.2 * dx\n",
    "# grid_l = Grid(lowRes, 4)\n",
    "# nonUniformInterfaces = np.copy(interfaces)\n",
    "# dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "# nonUniformInterfaces[1:-1:2] -= 0.2 * dx\n",
    "# grid_n = Grid(nonUniformInterfaces, 4)\n",
    "sim = Simulation(grid, lax_setup, bcs, cfl, tvd_rk3, reconstruct_weno, 5, lax_friedrichs_flux)\n",
    "# sim_l = Simulation(grid_l, shu_osher_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)\n",
    "# sim_n = Simulation(grid_n, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vinit = np.copy(sim.V)\n",
    "tEnd = 0.16\n",
    "t=0\n",
    "vs = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    if t >= tEnd:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(grid.cc, vs[-1][Prim.Dens])\n",
    "plt.plot(grid.cc, vs[-1][Prim.Velo])\n",
    "plt.plot(grid.cc, vs[-1][Prim.Pres])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Einfeldt Strong Rarefaction\n",
    "Once again, refer to the Athena [test cases](https://www.astro.princeton.edu/~jstone/Athena/tests/einfeldt/Einfeldt.html). See [also](https://arxiv.org/pdf/1410.4194.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bc = zero_grad_bc(position='Lower')\n",
    "upper_bc = zero_grad_bc(position='Upper')\n",
    "def bcs(grid, V):\n",
    "    lower_bc(grid, V)\n",
    "    upper_bc(grid, V)\n",
    "    \n",
    "def einfeldt_setup(grid):\n",
    "    transition = np.searchsorted(grid.cc, 0.0)\n",
    "    V = np.zeros((Prim.NumVars, grid.griMax))\n",
    "    \n",
    "    V[Prim.Dens, :transition] = 1.0\n",
    "    V[Prim.Velo, :transition] = -2.0\n",
    "    V[Prim.Pres, :transition] = 0.4\n",
    "    V[Prim.Dens, transition:] = 1.0\n",
    "    V[Prim.Velo, transition:] = 2.0\n",
    "    V[Prim.Pres, transition:] = 0.4\n",
    "    \n",
    "    V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "#     V[Prim.Eint] = 3.0\n",
    "#     V[Prim.Pres] = V[Prim.Eint] * ((Gas.Gamma-1.0) * V[Prim.Dens])\n",
    "#     V[Prim.Eint] = V[Prim.Pres] / ((Gas.Gamma-1.0))\n",
    "    \n",
    "    bcs(grid, V)\n",
    "    U = prim2cons(V)\n",
    "    return V, U\n",
    "    \n",
    "interfaces = np.linspace(-0.5, 0.5, 1001)\n",
    "grid = Grid(interfaces, 4)\n",
    "# lowRes = np.linspace(-1.0, 1.0, 401)\n",
    "# dx = lowRes[1] - lowRes[0]\n",
    "# lowRes[1:-1:2] -= 0.2 * dx\n",
    "# grid_l = Grid(lowRes, 4)\n",
    "# nonUniformInterfaces = np.copy(interfaces)\n",
    "# dx = nonUniformInterfaces[1] - nonUniformInterfaces[0]\n",
    "# nonUniformInterfaces[1:-1:2] -= 0.2 * dx\n",
    "# grid_n = Grid(nonUniformInterfaces, 4)\n",
    "sim = Simulation(grid, einfeldt_setup, bcs, cfl, tvd_rk3, reconstruct_weno, 5, lax_friedrichs_flux, reconstructionValidator=Validator())\n",
    "# sim_l = Simulation(grid_l, shu_osher_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)\n",
    "# sim_n = Simulation(grid_n, overpressure_setup, bcs, cfl, tvd_rk3, reconstruct_weno_nm_z, lax_friedrichs_flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b558cd3ea05246bb87d2f0e1ef15078d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YELL:\n",
      "  2 fails!!\n",
      "   [503 504]\n",
      "~~~SCREAM~~~\n"
     ]
    }
   ],
   "source": [
    "np.seterr(all='raise')\n",
    "Vinit = np.copy(sim.V)\n",
    "tEnd = 0.15\n",
    "t=0\n",
    "vs = [Vinit]\n",
    "tq = tqdm(range(10000))\n",
    "for i in tq:\n",
    "    tq.set_description('%f s' % t)\n",
    "    tMax = tEnd - t\n",
    "    t += sim.step(tMax)\n",
    "    vs.append(np.copy(sim.V))\n",
    "    if t >= tEnd:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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"
    },
    {
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ECBAgAABAssnIAC4fPauTIAAAQIECBAgQIAAAQIECBAgQGDRBQQAF53YBQgQIECAAAECBAgQIECAAAECBAgsn4AA4PLZuzIBAgQIECBAgAABAgQIECBAgACBRRcQAFx0YhcgQIAAAQIECBAgQIAAAQIECBAgsHwCAoDLZ+/KBAgQIECAAAECBAgQIECAAAECBBZdQABw0YldgAABAgQIECBAgAABAgQIECBAgMDyCQgALp+9KxMgQIAAAQIECBAgQIAAAQIECBBYdAEBwEUnnvsFTpw4Ef7n//yf4Zd/+ZfDj/zIj4T169eHSqVS+/Pud7977ieax55/8Ad/EH74h384bNy4MXR2doZbb701vOtd7wrf+MY35nEWuxIgQIAAAQIECBAgQIAAAQIECBRVoK2oBWvGct18881LVu2hoaHw4z/+4+FP/uRPomsePnw4fPrTnw5ZYDALRP7Kr/xKlC9BgAABAgQIECBAgAABAgQIECDQWAK+ACzo/dq2bVvty7zFKt573vOeyeDf2972tvC5z30uPP744+ETn/hE2LVrVxgfHw8f/vCHw+/+7u8uVhGclwABAgQIECBAgAABAgQIECBAYAkEfAG4BMhzvUT2xd0jjzxS+5N9DXjw4MGwY8eOuR4+5/3+6q/+KnzmM5+p7f/2t789fPaznw2tra21dHb9d7zjHeGhhx4K2deAH/zgB8Pf//t/P6xZs2bO57cjAQIECBAgQIAAAQIECBAgQIBAcQR8AVicexH+7b/9t+Hv/J2/Exa7K/B//I//sVbrtra28Du/8zuTwb86RTb24Ec/+tFa8ty5c+HjH/94PcsvAQIECBAgQIAAAQIECBAgQIBAgwkIADbYDbvR4g4ODoa//Mu/rJ3mh37oh8KWLVumPeXf+3t/L/T09NTysi8ELQQIECBAgAABAgQIECBAgAABAo0pIADYmPftukv9xBNPhMuXL9eOf8tb3jLjeTo6OsIb3/jGWn52zMjIyIz7yiCQCoyMj4TnzzwfHnv58XD8wvE0W5oAAQIECBAgQIAAgQIJZG32x459s9aGz9ryFgIEyidgDMDy3dNZa/Sd73xnMv/OO++cXJ9uJcv/0pe+FEZHR8O+ffvC3XffPd1uTbPtA3/622HitdpWolrnU1fW81tCuJqq5Nbrq9G2+sbq+ePtVy4YbatMd96r23KnSs51ZZ/cnrWr1as03TXmtG/1BGcvHQ/7B74bXrn8bBivDNVPGTZ3PBx+5//4SNi59pbJbVYIECBAgAABAgQIEFhegZfOvBLe++cfCscuPzlZkJaJzrCh/Z5w15oHws0rbw6dbSvC5bGRMDY+GrLg4Ej1d6z6OzoxFsYnqv9Cqv8jafIM1ZXcv1WizflESdY/9iPvK0lNVKPsAgKAZb/DSf2OHj06uWWm7r/1HbZu3VpfDUeOHJlXADB/ncmT5FZeeeWVXKoxVr/06n+r/v/YdP/v1hjlX9JSxhHDWoPiRz/7j8MfveMPw+033bSkRXExAgQIECBAgAABAgSmCuw7eTL8+Of/cRhvOxlljleGw/HRp8Lxk09F2yWmCkxMZP/wEQCcKmNLEQUEAIt4VxaxTNkYgPVl9erV9dVpf1etWjW5/fz585Prc1nJBw/nsn8j7CP4d2N3KWtYvOuzHwpf/en/FtpbjT5wY5qOJkCAAAECBAgQIHD9ApdHx2tt8/EVcfDv+s/oSAIEii7gX+FFv0MLXL7h4eHJM2bj/M22rFixYjJ7aOhqd87JjVYIzFPg4orHwm/81VfmeZTdCRAgQIAAAQIECBBYSIHf+Kv/HbK2uYUAgeYR8AVg89zrWk07Ozsna1yfDGRyQ7Jy6dKlyS1dXV2T63NZyboMz7ZkXYAfffTR2XYpXN7K8TteK9PVbsBX17Kseqr+m1Zh6vaJyWPyx1/7uKvXytamnjefH5+tvm/9N82dbnt+W3796rFXyjARWsPq0Ne6JdzRd29427a3hJaW0fDhp34mTLRcDTz/wXP/b/i5739z6Olsv3oCawQIECBAgAABAgQILInAwPBI+Mzznw6VnquXq4x3hl995OPVf0asCH996GvhW2efDOdGjobRcLH6r43R6pjirdU/bbU/LZPrLclY48m/FZJhgWb+N8rVcjTe2pRKNl4VlLhpBAQAm+ZWX6lod3f3ZI2v1a33woULk/teq7vw5I6vrVxrfMF0/0ZIf/On/r9GKGbhyvjkmR8LXzj06avlWv10+PRj+8P//da7rm6zRoAAAQIECBAgQIDAkgh86rF9IVTb5Pnl7Tt+PLzz3jfUNr3z9dlkkT+dz7ZOgEAJBHQBLsFNnE8V8oG5a03Ukf+Kr4xj+s3Hzb7XL/Dzj76nevDVN2OVlsvhk7u/FCayGcMsBAgQIECAAAECBAgsmUDWBv9UtS2etcmvLpXw/kd/6mrSGgECpRQQACzlbZ25Unffffdk5nPPPTe5Pt1KPb+trS3cfvvt0+1iG4FrCmxYuSHc2XflbWJ953OVp8LTh8/Vk34JECBAgAABAgQIEFgCgacPnw3nKvHXf3f13R+yNruFAIFyCwgAlvv+TqndI488EuqTf/zN3/zNlPz6hmx8wMceuzIobHZMe7vx2uo2fucv8M7b/8/ooNZVz4fPPj37OJHRARIECBAgQIAAAQIECNywwB9X2+BZWzy/vPOOuK2ez7NOgEB5BAQAy3Mv51STbAzAH/zBH6zt+xd/8Rdhpm7Af/zHfxwGBgZq+/3oj/7onM5tJwIzCbx121ujrJa2i+FL+3frBhypSBAgQIAAAQIECBBYPIHx8YnwZ/ueDVlbPL+8detb80nrBAiUVEAAsGQ39vd///dDpVKp/fnwhz88be1+8Rd/sbZ9dHQ0vO997wtjY2PRfqdOnQof/OAHa9v6+vrCz/zMz0T5EgTmK7B59eZwc9em6LCz498Jz786GG2TIECAAAECBAgQIEBgcQT2HOsPA+G70ck3rtwcNq2O2+nRDhIECJRGwCzABbqVX/3qV8P+/fsnS5QF4upLtj0L7uWXd7/73fnknNd/4Ad+IPzET/xE+MxnPhM+//nPh7/1t/5W+Pmf//mwadOmsHfv3vBrv/Zr4fDhw7XzffSjHw1r1qyZ87ntSGAmge/d/Mbwx/v/eDK7bdWL4cvPnQx3buyZ3GaFAAECBAgQIECAAIHFEfiL77wa2la+FJ08a6NbCBBoDgEBwALd549//OPhk5/85LQl+trXvhayP/nlegOA2Tl+7/d+r9bF90/+5E/Cl7/85dqf/LlbWlrCv/k3/yb8s3/2z/KbrRO4boFHbnkkCgC2dB0JX37+1fB/v3XXdZ/TgQQIECBAgAABAgQIzE3gf+87EVq6rnzoUT/ikY2P1Ff9EiBQcgFdgEt+g2eqXldXV/jiF78YPv3pT9e+ANywYUNtcpCtW7eGf/SP/lHIvkacqQvxTOe0ncBsAvetvy/Kbmk7H54+diAMDI9E2yUIECBAgAABAgQIEFhYgf6hkfDtVw9Vx/+7EJ04baNHmRIECJRKoDJRXUpVI5VpCIFs8pEs2JgtR44cCVu2bGmIcivk9Qtkf9V832e+LwxcvjK5THamoaPvCv/PO34y/F+vv+X6T+xIAgQIECBAgAABAgRmFfjSt4+H937u90PXlk9P7tfb0Ru+8hNfqY0fP7nRSikF/Pu7lLd13pXyBeC8yRxAgMD1CGST09yz7p7o0JbOo+Er+66OdRllShAgQIAAAQIECBAgsCACX3/xdMja3vnlnvX3CP7lQawTKLmAAGDJb7DqESiSQNbIyC+tXUfDEwfP5DdZJ0CAAAECBAgQIEBggQW+/uKpkLW980v6cj6fZ50AgfIJCACW756qEYHCCty77t6obK3Vt5D7TwyEU+cvRdslCBAgQIAAAQIECBBYGIGTg5fCC68OhKztnV/Sl/P5POsECJRPQACwfPdUjQgUViBtZFRah0Ol43R40leAhb1nCkaAAAECBAgQINDYAt946XStzV1pjV+6py/nG7uWSk+AwLUEBACvJSSfAIEFE7h55c1hfdf66HzZm8hvHtANOEKRIECAAAECBAgQILBAAtnL9vTrv5u6bgo3r7p5ga7gNAQINIKAAGAj3CVlJFASgWwikPRNY2vnsfC4AGBJ7rBqECBAgAABAgQIFE3gqUNnpwQA0545RSuz8hAgsPACAoALb+qMBAjMInDnujuj3JYVx8N3XxkIA8Mj0XYJAgQIECBAgAABAgRuTODCpdFaW7ul89XoRHetvStKSxAgUH4BAcDy32M1JFAogdv7bo/KkzVGxidCyN5MWggQIECAAAECBAgQWDiB3UfP1dra2Uv3/HL7mrhNns+zToBAOQUEAMt5X9WKQGEF0sZGS9tgqLReCM8cPlfYMisYAQIECBAgQIAAgUYUeLr6kr3Sej60tJ2Pip++lI8yJQgQKKWAAGApb6tKESiuwNburaGjpSMqYPZG8pnDvgCMUCQIECBAgAABAgQI3KDA09WX7OnXfytaV4SsTW4hQKC5BAQAm+t+qy2BZRdoa2kLu/p2ReXIGiXPHql2T8j6AlsIECBAgAABAgQIELhhgaxt/XT1JXvLinj8v529O0NrS+sNn98JCBBoLAEBwMa6X0pLoBQCU7oBVxslg8Oj4aVTcdeEUlRWJQgQIECAAAECBAgsg8BLpy6EcxdHpnwBmLbFl6FoLkmAwDIICAAuA7pLEmh2gdv6bosIWl8blNg4gBGLBAECBAgQIECAAIHrFsi+/suW1uQLQOP/XTepAwk0tIAAYEPfPoUn0JgC6VvHK+OSTIRnqt2ALQQIECBAgAABAgQI3LhANgFICONTvgC8bU38Mv7Gr+QMBAg0gkBbIxRSGQkQKJdA+tax0no5VNrPVicC6S1XRdWGAAECBAgQIECAwDIJZF8AVtrPVWcBvhyVIG2LR5kSBAiUVsAXgKW9tSpGoLgCG1ZuCN0d3VEBs68Anz8+EC5eHo22SxAgQIAAAQIECBAgMD+B/qGR8MKr56d8/dfT0ROytriFAIHmExAAbL57rsYEll2gUqmE9M1jS8fJkE0CvOdo/7KXTwEIECBAgAABAgQINLLA3tfa1FkbO7/s6tsVsra4hQCB5hMQAGy+e67GBAohsKN3R1SO1hUnamkTgUQsEgQIECBAgAABAgTmLbD76JWxtVtWxAHAtA0+7xM7gACBhhUQAGzYW6fgBBpbIG181N9OPvPabGWNXTulJ0CAAAECBAgQILB8AjN9AbijJ34Jv3wldGUCBJZaQABwqcVdjwCBmsDO3p2RREvtC8ArMwFPTFT7AlsIECBAgAABAgQIELgugT31LwA7TkXHb+/dHqUlCBBoHgEBwOa512pKoFAC6ReAldbh6gxl58PJwUvh+MBwocqqMAQIECBAgAABAgQaRSBrT7/cX21Pt1wMLW0XomJv79kepSUIEGgeAQHA5rnXakqgUAKbVm8KK1pXRGW68hVgCLuPmAgkgpEgQIAAAQIECBAgMEeBya//kvH/2iptYXP35jmexW4ECJRNQACwbHdUfQg0iEBLpSWkbyDr4wDWGy0NUhXFJECAAAECBAgQIFAYgT0zzAC8pXtLaG9pL0w5FYQAgaUVEABcWm9XI0AgJzD9OIAh1BstuV2tEiBAgAABAgQIECAwB4H6y/T6y/X6IekQPPXtfgkQaA4BAcDmuM9qSaCQAmkjpN5IyRotJgIp5C1TKAIECBAgQIAAgQILZG3o+sv0lqQLsAlACnzjFI3AEggIAC4BsksQIDC9wI6+HVFGfQzAgeHRcOj0xShPggABAgQIECBAgACB2QWOnRsKpy9cru3UkswAvKMnbnvPfia5BAiUTUAAsGx3VH0INJBA2ghpaa9O/lG5VKvB7upXgBYCBAgQIECAAAECBOYusPe18f9CGAstHaejA30BGHFIEGg6AQHAprvlKkygOAJZI6RS/S+/1LsqXG285HOtEyBAgAABAgQIECAwk8Du1wKAlfazoVIZi3ZLJ+CLMiUIECi9gABg6W+xChIorsCK1hVh8+rNUQGvjgNY/RrQQoAAAQIECBAgQIDAnAUmJwBJxv/rW9EX1nSumfN57EiAQPkEBADLd0/ViEBDCezs2xmVtz4O4Lde7g9j4xNRngQBAgQIECBAgAABAtMLjFfbznuPXXmJXn+pXt/T1391Cb8EmldAALB5772aEyiEwJRxADtO1sp18fJYePHk+UKUUSEIECBAgAABAgQIFF3g4OkLYbA6mV62pBOAGP+v6HdP+QgsvoAA4OIbuwIBArMIzPQFYHbI7iMmApmFThYBAgQIECBAgACBSYE9kxOAZAHAKy/V65m+AKxL+CXQvAICgM1779WcQCEEdvTuiMpxZbayKwMW5xsx0U4SBAgQIECAAAECBAhEAruPXn15Xp9Yr76DLwDrEn4JNK+AAGDz3ns1J1AIgbQLcDZbWTZrWbbseW0Mk0IUVCEIECBAgAABAgQIFFhgb/0LwJah0NIWD6WTvnQvcDUUjQCBRRIQAFwkWKclQGBuAn2d1RnJVsQzktXfWH735YFweXR8bieyFwECBAgQIECAAIEmFRgdGw/ZJHrZknb/ba20hq2rtzapjGoTIFAXEACsS/glQGDZBNIuCfVGy+VqQ+b544PLVi4XJkCAAAECBAgQINAIAvtOnA/DI1denNdfptfLvaV7S2hvba8n/RIg0KQCAoBNeuNVm0CRBNJBiesBwKyMe45dHcukSGVWFgIECBAgQIAAAQJFEZjs/lst0JQZgHu2F6WYykGAwDIKCAAuI75LEyBwRWDKF4Arrs5atufIla4MrAgQIECAAAECBAgQmF4gmgDEDMDTI9lKoMkFBACb/AFQfQJFEEgnAsl/AZhvzBShrMpAgAABAgQIECBAoGgCe+oTgFQLlm9LZ+U0AUjR7pbyEFgeAQHA5XF3VQIEcgJTvgBsu1BtuVys7ZGNZzJ0eSy3t1UCBAgQIECAAAECBOoCl0bHwnPHB15LjlcDgKfrWbXftK0dZUoQINA0AgKATXOrVZRAcQWygYnbKm1RAetjl4yNT4TvvKIbcIQjQYAAAQIECBAgQOA1gedeGQwjYxO1VKX9bKi0jEY26XjbUaYEAQJNIyAA2DS3WkUJFFegvaU9ZEHA/JKfvWy3cQDzNNYJECBAgAABAgQITArsOXp10rz6S/R6ZndHd1jbubae9EuAQBMLCAA28c1XdQJFEki7JuTHLtl7zBeARbpXykKAAAECBAgQIFAcgd2zjf/XsyNUKpXiFFZJCBBYNgEBwGWjd2ECBPICJgLJa1gnQIAAAQIECBAgMDeB6AvAFSejg9KX7FGmBAECTSUgANhUt1tlCRRXIJ2dLN8F+KWTF8LA8EhxC69kBAgQIECAAAECBJZB4MKl0bC/Omlefcn3osm2pW3s+n5+CRBoPgEBwOa752pMoJAC6dvJK7OXXZ3991u6ARfyvikUAQIECBAgQIDA8gl8++WBUJ0zb3LJv0TPNpoAZJLGCoGmFxAAbPpHAACBYgikjZNKZSxks5jVlz25sU3q2/wSIECAAAECBAgQaGaBfPff0DIcWtoGI460jR1lShAg0FQCAoBNdbtVlkBxBdZ0rgm9K3qjArasODWZjho3k1utECBAgAABAgQIEGhegfxL8nQG4JZKS9jWs615cdScAIFIQAAw4pAgQGA5BaZOBHJisji7j5gJeBLDCgECBAgQIECAAIGqQP4leTr+36ZVm0JHawcnAgQI1AQEAD0IBAgURmDqOIBXvwA8dm4onD5/qTBlVRACBAgQIECAAAECyynQf3EkHDx9cbII6fh/JgCZpLFCgEBVQADQY0CAQGEE0jFK2juvBgCzQu4xEUhh7pWCECBAgAABAgQILK/AnmPnogK05YbPyTLSl+vRzhIECDSdgABg091yFSZQXIH0LWVbGgDUDbi4N0/JCBAgQIAAAQIEllQgP/5fduGulaej66cv16NMCQIEmk5AALDpbrkKEyiuQPqWcqwyUP1OeWiywPkxTiY3WiFAgAABAgQIECDQhAJx23g8jLWejBTSl+tRpgQBAk0nIADYdLdchQkUV2Dr6q2htdIaFTA/mPHuo/1hYmIiypcgQIAAAQIECBAg0IwC+S8AK239YSxcjhh8ARhxSBBoegEBwKZ/BAAQKI5Ae2t72Nq9NSpQfjDjU9VJQF7pH47yJQgQIECAAAECBAg0m8CJweGoXZxvM2cWq9tXh/Vd65uNRX0JEJhFQABwFhxZBAgsvUD6pjIdyyT/pnPpS+eKBAgQIECAAAECBJZfYG+1Z0x+6Zxm/L9KpZLfxToBAk0uIADY5A+A6hMomkA6DmBvTzy7WTzWSdFKrzwECBAgQIAAAQIEFl8gGxonv6zri9Npmzq/r3UCBJpTQACwOe+7WhMorED6BWBojwcz9gVgYW+dghEgQIAAAQIECCyRQPpSvKPrVHTlKW3qKFeCAIFmFBAAbMa7rs4ECiyQzlZ2fvx4tbTjkyXOGjsmApnksEKAAAECBAgQINBkAllbOO0CPByyNvPVxReAVy2sESBwRUAA0JNAgEChBNLGytjESKi0n50s48DwaDh4+uJk2goBAgQIECBAgACBZhI4dm4onL6Qm/G3cin0j8RfAKYv1ZvJR10JEJheQABwehdbCRBYJoE1K9aEno6e6Op9vcYBjEAkCBAgQIAAAQIEmlYgHRKnN2krV0IlbOve1rQ+Kk6AwPQCAoDTu9hKgMAyCWSzlaVvLDetH4xKkzZ6okwJAgQIECBAgAABAiUW2F0dEie/bL35Qj4ZNq3eFDrbOqNtEgQIEBAA9AwQIFA4gXTQ4q5Vp6MypoMeR5kSBAgQIECAAAECBEossPtIHADs67k6XE5W7bQtXWIKVSNAYB4CAoDzwLIrAQJLIzBlHMDWE9GFv3VsIIyOXZ0YJMqUIECAAAECBAgQIFBSgfHxiZC1hfNLS0c8/l/als7va50AgeYVEABs3nuv5gQKK5B2AT4zcjQq69DIWHjxZNzVIdpBggABAgQIECBAgEAJBV46dT6cvzQa1Wxw7OUovaNnR5SWIECAQCYgAOg5IECgcAJpo+XM8OmwaU1czHTskzhXigABAgQIECBAgED5BHYf6Y8qdXNPRzh24XC0zReAEYcEAQKvCQgAehQIECicwNburaG10hqVa9fmi1HaOIARhwQBAgQIECBAgEATCKRt4Ds3j4eh0aGo5sYAjDgkCBB4TUAA0KNAgEDhBNpb28Pm1Zujcq3ri992mgk44pEgQIAAAQIECBBoAoHdR+M28aYN56Nar2xbGTas3BBtkyBAgEAmIADoOSBAoJAC6TiAHV3xTMDffWUgXBodK2TZFYoAAQIECBAgQIDAQgtcHh0P36m2gfPLqlVn8slwa8+toVKpRNskCBAgkAkIAHoOCBAopEDadWEovBKVc2RsIjx/fDDaJkGAAAECBAgQIECgrAIvvDoYsiBgfhlrPZFPhvQlepQpQYBAUwsIADb17Vd5AsUVSAcvPnr+UNh506qowGkXiChTggABAgQIECBAgECJBNJJ8LavW1mdAORQVMO0DR1lShAg0NQCAoBNfftVnkBxBdK3l4cHDofXb+qOCrznyLkoLUGAAAECBAgQIECgrAJ7khmA37C1LxwcOBhVd0fPjigtQYAAgbqAAGBdwi8BAoUSSLsAXx6/HG7deDkqo4lAIg4JAgQIECBAgACBEgukXwDetakrvHIhHibHF4AlfgBUjcANCggA3iCgwwkQWByBtZ1rQ3dH/MXfmt6z0cX2nRgMFy+PRtskCBAgQIAAAQIECJRNIGvzZmMA5pcNa+MJQbK8bd3b8rtYJ0CAwKSAAOAkhRUCBIokkM1elnYDnmg7GVpbrs5qNj4RwrdfntrwKVI9lIUAAQIECBAgQIDAjQpkbd6s7VtfsjZxW+eperL2e8uqW8LK9pXRNgkCBAjUBQQA6xJ+CRAonEDaDfhodZDjO26OvwrcbRzAwt03BSJAgAABAgQIEFhYgbTNe/uG1VMnAOnZvrAXdTYCBEolIABYqtupMgTKJZB+AZgNcnzf5t6oksYBjDgkCBAgQIAAAQIESiiQtnnfsKU6AUj/waimxv+LOCQIEEgEBAATEEkCBIojkM5iljVy7tuaBgDNBFycO6YkBAgQIECAAAECiyGw52jc5s3axOkMwGnvmcUoh3MSINC4AgKAjXvvlJxA6QXSt5gnh06G2ze2R/U+ePpi6L84Em2TIECAAAECBAgQIFAWgaytm7V580vWK8YXgHkR6wQIXEtAAPBaQvIJEFg2ga3dW0NLJf5rqr062HFHW7xt77H+ZSujCxMgQIAAAQIECBBYTIE9x+Kv/7K28Lre4XBxNA4K7uzduZjFcG4CBBpcIP5XdINXRvEJECiXQEdrR9i8enNUqaPnD4W7bumJtu1OukREmRIECBAgQIAAAQIEGlggHf/v7mpb+Oj5w1GNutq6woaVG6JtEgQIEMgLCADmNawTIFA4gekmAnnDFuMAFu5GKRABAgQIECBAgMCiCKQzAGdt4QP9B6Jr3dpz65SeM9EOEgQINL2AAGDTPwIACBRbIB3MOGvs3Fed9Sy/pG9F83nWCRAgQIAAAQIECDSyQNrbJWsLmwCkke+oshNYHgEBwOVxd1UCBOYokE4EkjV20i8AX+kfDicGh+d4RrsRIECAAAECBAgQaAyBVweGw6sDl6LCvqE6A/CBgfgLwLTNHB0gQYAAgaqAAKDHgACBQgvs6NkRle/wwOFw67qusKqjNdq+96iJQCIQCQIECBAgQIAAgYYXSLv/rl7RFnauXz11BuCe7Q1fVxUgQGBxBQQAF9fX2QkQuEGB9G3mpbFL4cTQ8XDP5ngcwN0CgDco7XACBAgQIECAAIGiCaRD3by+2gYembgcXj7/clTUdNzsKFOCAAECVQEBQI8BAQKFFljXuS50t3dHZZyuG/AeMwFHRhIECBAgQIAAAQKNLzBl/L9q999DA4fCRPW//JKOm53Ps06AAIFMQADQc0CAQKEFKpVKSN9oHuw/OO1EIBMTcUOo0BVTOAIECBAgQIAAAQKzCGRt273H4mFu3lCdACSdAfjmlTeHle0rZzmTLAIECAgAegYIEGgAgbQb8JUvAOOZgM9cuByOnh1qgNooIgECBAgQIECAAIFrCxw+czGcuzgS7Xjflt4p4/+lL8ujAyQIECDwmoAvAD0KBAgUXiDt0pC99dy6tiv0rWyPyp6OkRJlShAgQIAAAQIECBBoIIF0jOt1qzrC5r6uKTMACwA20E1VVALLKCAAuIz4Lk2AwNwEpnwBWO0CnHUNvq/aBSK/pGOk5POsEyBAgAABAgQIEGgkgT1HzkXFzb7+y9rAaRdgAcCISYIAgRkEBABngLGZAIHiCOzo2REV5sTQiXBh5EK4v9oIyi/PJo2kfJ51AgQIECBAgAABAo0kkPZuyV5+Z+MCCgA20l1UVgLFERAALM69UBICBGYQ2NazLbRU4r+usolA7t8WfwG492h/GB0bn+EsNhMgQIAAAQIECBBoDIGsTTtlApDqDMCvXnw1DI3G416nw+U0Rg2VkgCBpRaI/0W91Fd3PQIECMxBoKO1I2xatSna88DAgZDNgpZfhkbGwguvns9vsk6AAAECBAgQIECg4QT2nzwfsrZtfsm+AEy//utq6wrZLMAWAgQIXEtAAPBaQvIJECiEQDq2SfYF4LrVK2qTgeQLaBzAvIZ1AgQIECBAgACBRhTYnQxtk03+sb7a9k0DgFkbORsX0EKAAIFrCQgAXktIPgEChRCYMhHIwMFaudKvAJ89HA+WXIjCKwQBAgQIECBAgACBeQikY1vfv/VKz5fpAoDzOK1dCRBoYgEBwCa++apOoJEE0rFN6o2femOoXhdfANYl/BIgQIAAAQIECDSqwDPJS+16m/fgay/B6/VKJ8urb/dLgACBVEAAMBWRJkCgkAJpF+DDA4fD+MR4eCCZCOSFVwfDhUujhayDQhEgQIAAAQIECBC4lkDWls3atPmlPvld/SV4PS9tI9e3+yVAgEAqIACYikgTIFBIgbRxMzw2HI5fOB7u2dQb2lqujnsyPhHCnupswBYCBAgQIECAAAECjSiQzf6btWnrS2u1rXtvtc17YeRCbRbg+vbsN20j5/OsEyBAIC8gAJjXsE6AQGEF1nWuC6vbV0fly96Adra3hjtv6Y626wYccUgQIECAAAECBAg0kEA6/t+dG7tDV0drSLv/tlRawraebQ1UM0UlQGA5BQQAl1PftQkQmLNANrtZOg5gvRFkIpA5M9qRAAECBAgQIECg4ALpDMBvmGECkM2rN4cVrSsKXhvFI0CgKAICgEW5E8pBgMA1BdIuDvUxUOqDItdP4AvAuoRfAgQIECBAgACBRhNIvwCst3Xrbd96fdKX4/XtfgkQIDCdgADgdCq2ESBQSIHtvdujch3sP1hLpxOBvNI/HF4dGI72lSBAgAABAgQIECBQdIGsDZu1ZfPLAzN8AZi+HM8fY50AAQKpgABgKiJNgEBhBdK3nAcGDtTKunP96tC9oi0q9zOHz0VpCQIECBAgQIAAAQJFF0jbsFkbd9dNV8bBrg9/U6+DAGBdwi8BAnMREACci5J9CBAohEDayDlx8US4OHIxtFRnRrtva29URt2AIw4JAgQIECBAgACBBhBIu/9mbdysrTs2PhYO9R+KapC2jaNMCQIECCQCAoAJiCQBAsUVyGY5q1T/yy/1N6EmAsmrWCdAgAABAgQIEGhEgWePnI2KXR//7+ULL4fL45ejPAHAiEOCAIFrCAgAXgNINgECxRHIZjnbtHpTVKD6YMj1xlE9c8/Rc9U3pRP1pF8CBAgQIECAAAEChRbI2q57j/ZHZbx/65paut7mrWf2rugNa1Zcyatv80uAAIHZBAQAZ9ORR4BA4QTSN531LwDTAOCFy2PhxZPnC1d+BSJAgAABAgQIECAwncC+E4Mha8Pml3obNw0A7ujZESqVuGdM/jjrBAgQSAUEAFMRaQIECi2QTgRSnwl4Q09n2NTbGZX9WROBRB4SBAgQIECAAAECxRXYfSSexG5zX1e4qXtFrcBTAoC9O4pbESUjQKCQAgKAhbwtCkWAwEwC6ReA+cbQ/dv6osOerXYDthAgQIAAAQIECBBoBIF0ApD6139Z2fNt3iy9vXd79mMhQIDAnAUEAOdMZUcCBIogkH4BeGjgUBifGK8VzUQgRbhDykCAAAECBAgQIHA9As8kvVfyAcD6sDf182ZdgC0ECBCYj4AA4Hy07EuAwLILpF8ADo8Nh1cvvForV76RlG14/tXBMJSMo7LsFVAAAgQIECBAgAABAonAhUuj4YVq2zW/1Hu39F/qD2eGz+SzQtomjjIlCBAgMI2AAOA0KEXYdOjQofALv/AL4c477wyrVq0Ka9euDY888kj4D//hP4SLFy8uSBEPHjwYPvjBD4aHHnoo9PX1hfb29tp13vzmN4df/dVfDSdOnFiQ6zgJgYUUWN+1PqxqXxWdst4l4t7NvaElNxZyNpPat16OZ1KLDpQgQIAAAQIECBAgUACBvcf6q71arhaktdqovXdTb21Dva1bz21raQubuzfXk34JECAwJwEBwDkxLe1OX/jCF8J9990XPvaxj4Xnn3++FvA7e/ZsePLJJ8Mv/dIvhQceeCDs37//hgr1qU99Ktx9993hN37jN8LTTz8d+vv7w+joaMiu841vfCP8yq/8SrjrrrvC//pf/+uGruNgAgstkM12lnYDPjBwoHaZVSvawh03d0eXNBFIxCFBgAABAgQIECBQQIF0/L87N3aHro7WWknTAOC27m2hvaW9gLVQJAIEiiwgAFiwu/PMM8+Ef/AP/kEYGBgIq1evDr/2a78Wvv71r4e//Mu/DP/0n/7TWmlfeOGF8Lf/9t8Og4PxJ+JzrcrXvva18O53vzsMDQ2FlpaW8FM/9VPhc5/7XHj88cfDH/3RH4W3v/3ttVOdOXMm/N2/+3fDSy+9NNdT24/AkgikXR7qMwFnF3/ARCBLcg9chAABAgQIECBAYOEE0pfW+aFt6i+761dL28L17X4JECAwm4AA4Gw6y5D3/ve/vxaYa2trC1/60pfChz70ofCmN70p/MAP/ED43d/93doXe1mxsiDgb/7mb15XCX/91389jI9fmTThP//n/xx+7/d+rxboy7oY/9iP/Vj4/Oc/Hz7wgQ/Uzp0FCbMvES0EiiSQfgGYHxTZRCBFulPKQoAAAQIECBAgMBeB9AvAKADYf6W3S/08AoB1Cb8ECMxHQABwPlqLvG/2Bd5XvvKV2lV++qd/uhb4Sy+ZjQuYdc3Nlt/6rd8KIyMj6S7XTGdfFGbLunXrwnvf+95p9//lX/7lye1Zl2ALgSIJbO/dHhUn3y2iPlhyfYdj54bCycFL9aRfAgQIECBAgAABAoUSON4/HI4PDEdlyvdqyfd2yXYSAIyoJAgQmKOAAOAcoZZit6wbbn3JuuVOt2Rddn/yJ3+ylnXu3Lnw5S9/ebrdZt12+fLlWv6OHTNPHd/b2xvWr19f26++/6wnlUlgCQXSRs+rF18NF0euTI5z+4busPK18VLqRdp95Fx91S8BAgQIECBAgACBQgmkX/91V8e13rl+da2MI2Mj4cjgkai8aW+YKFOCAAECMwgIAM4Asxybv/rVr9Yum836m83MO9Pylre8ZTIrG89vvsvrXve62iEHDsSfkufPk41BeOrUqdqm+v75fOsEllMgG/i4Uv0vvxwaOFRLZjOmvb46G3B+SRtV+TzrBAgQIECAAAECBJZTIG2r3re1tzpW+5W2bhb8G5sYi4qX9oaJMiUIECAwg4AA4Awwy7H5u9/9bu2yt912W8jGAJxpufPOOyez6sdMbpjDys/+7M/W9jp9+nT4r//1v057xL/7d/9ucnt9/8kNVggss0BnW2fYtHpTVIrZugE/c+RstK8EAQIECBAgQIAAgaIIPJu0VaPx/wbijzbWd60PPR09RSm6chAg0EACM0eZGqgSZSjq8PDw5Bd3W7ZsmbVKa9asCdlXghcuXAhHjsSfg8964GuZ73nPe0L2teH/+B//I7zvfe8LTz31VHjHO94RbrnllnD48OHwqU99qjYrcLb7v/pX/yr80A/90FxOG+1z9OjRKJ0mXnnllXSTNIF5CWRvPo+dPzZ5TH4ikAe2rpncnq3sPtIfxsYnQvZ1oIUAAQIECBAgQIBAUQSyNureo/1Rce7PtWXzL7mzndKhcKIDJQgQIDCLgADgLDhLmTU4ODh5udWrr4z3MLlhmpV6APD8+fPT5M6+qbW1NXzyk58Mb3/728NHPvKR8PGPf7z2J3/U2972ttoMxNcT/MvOs3Xr1vzprBNYcIEdPTvC145d7QKfHxz5wW190fXOXxoN+04Mhjs3elsawUgQIECAAAECBAgsq0DWRr1wOe7im/8C8KVzL0Xly9rAFgIECFyPgC7A16O2CMdkXwDWl46OjvrqjL8rVqyo5Q0NDc24z2wZWdfh7AvAvXv3TrtbNvPvJz7xiXDs2NUvrKbd0UYCyySQDn6c/wJwQ09n2NzXFZXs6UMmAolAJAgQIECAAAECBJZd4NnDcRs1a8Pe1H3l33pZ4V7sfzEq486+nVFaggABAnMVEACcq9Qi79fZ2Tl5hbnMunvp0qXa/l1dcZBj8iSzrHzlK18Jb3rTm8IXvvCFsHnz5lqX3+PHj4fsulmX4t/+7d8OK1euDJ/5zGfCo48+Gr797W/Pcrbps7LzzPbn8ccfn/5AWwnMUSAd/DgLAI5PjE8e/eCtcTfgZw4bB3ASxwoBAgQIECBAgEAhBJ5JAoD353qyZG3btAvwrr5dhSi3QhAg0HgCugAX5J51d3dPlmQu3Xqz8f+yZS7dhSdPXF3JAof/8B/+w9Df3x82btwYHnvssdpvfZ9s/MH3vve9IZtp+OGHHw4vv/xy+Cf/5J+EJ598sr7LnH6vNY7hnE5iJwKzCKTjnwyNDoUTF0+Ejas21o56YGtf+MLulyfP8LQA4KSFFQIECBAgQIAAgWIIpG3UrA1bX45fOB6yNm5+2dnrC8C8h3UCBOYu4AvAuVst6p7ZF4Dr1q2rXeNaE2icPXu2NgFItvN8x9r7sz/7s8luvT/3cz8XBf/yFbznnnvCu971rtqmbJKQ3bt357OtE1h2gZu6bgor21ZG5Xip/+oYKekXgC+evBD6L45E+0sQIECAAAECBAgQWC6B/qGR6jjV8Zju+Tbsi+fi7r/d7d0hawNbCBAgcD0CAoDXo7ZIx9x99921M+/fvz+Mjo7OeJXnnntuMu+uu+6aXJ/LSjb2X3158MEH66vT/j700EOT2/PXnNxohcAyClQqlSmzoOW7SNx9S0/oaIv/invmiG7Ay3jLXJoAAQIECBAgQCAn8OyRePy/jtaWcM+mq5PW5V9uZ4dl4/9lbWALAQIErkcg/tfx9ZzBMQsm8H3f9321c2Xde7Ov7mZa/uZv/mYy63u/93sn1+ey0tZ2tdf3bEHG7FwjI1e/lsofN5fr2IfAUgikXSDys6Rlwb/Xb+6NivF0MsZKlClBgAABAgQIECBAYAkFnj4Uv5y+d3NPWNHWOlmCKQFA3X8nbawQIDB/AQHA+Zst2hHvfOc7J8/93//7f59cz6+Mj4/XZu/NtvX19YW3ve1t+exrru/YcXXa+GwykNmWfKAxf9xsx8gjsJQC6Sxo6SxpD+YGUc7KZSKQpbw7rkWAAAECBAgQIDCbwDPJF4APbosnscu/3M7OYwKQ2TTlESBwLQEBwGsJLWF+NuPu93//99eu+IlPfCJ84xvfmHL13/zN3wz1brzvf//7Q3t7e7TPX//1X9c+C88+DX/3u98d5WWJH/zBH6zN8Jut/5f/8l/C3r17s9Upy5/+6Z+Gz3ZbNfcAAEAASURBVH72s7Xt2UzB999//5R9bCCw3AK7euNZ0PJdgLOyPZA0orJuFuPjE8tdbNcnQIAAAQIECBBocoGsTZq+nM6P/zcxMRHSl9vpJHhNTqj6BAjMU0AAcJ5gi737b/3Wb4Wurq7aGIA//MM/HH7913+9NlPvl7/85fDP//k/D7/0S79UK8Idd9wRfuEXfmHexcm+GvyX//Jf1o4bHBwMb37zm8OHPvShkJ3/2WefDX/+539emwX4He94RzVQMl7b79//+38fWlo8KvPGdsCiC6RvQc8MnwnZn/qSvkUdHB4N+0/GAy3X9/VLgAABAgQIECBAYKkEXqy2SbO2aX7Jt11PDZ0Kg5cH89m+AIw0JAgQmK/A1QHh5nuk/RdF4IEHHgh/+Id/WJuBd2BgoBacSy+UBf+++MUvhu7u7jRrTul//a//dThz5kzIgo3nz5+vBRmzQGO6ZF8XfuQjH5mcDTjNlyaw3AKbV28OHS0d4fL45cmiZF0l1m5cW0tv7O0Mm6p/Xu4fnszP3rTecfP1/W9n8iRWCBAgQIAAAQIECNyAwNPVNml+ydqsWdu1vqTj/3W1dYVbVt1Sz/ZLgACBeQv4rGveZIt/wNvf/vawZ8+e8C/+xb8IWbBv5cqVtfH+Hn744fDRj340PPPMM+G222677oJk3YP/03/6T+GJJ54IP/uzPxvuvffeWjCxtbU19Pb2hmz23w984APhW9/6VvjFX/zF676OAwkstkBrS+uUmYDTxlLaDfjpQ/Fsa4tdRucnQIAAAQIECBAgkAqkbdIHbo3H/3vx3IvRIdt7toeWin++RygSBAjMS8AXgPPiWrqdb7311vCxj32s9mc+V33rW98asvEi5rJkgb7sj4VAIwtkE4E8f/b5ySqkjaUHqhOBfHHvK5P56dvWyQwrBAgQIECAAAECBJZIIG2T5rv/ZkVIX2qnQ98sUTFdhgCBEgl4hVCim6kqBJpRIJ0IJB0sOT+Ycuaz78T50D800oxU6kyAAAECBAgQIFAAgawtmrVJ88uD1ZfW+SUNAO7s3ZnPtk6AAIF5CwgAzpvMAQQIFEkgfRuajQGYX+7Z1BM6WuO/6nZXZwO2ECBAgAABAgQIEFgOgWeTtmhHW0u4Z1NvVJS0V0vW68VCgACBGxGI/1V8I2dyLAECBJZBIH0benLoZOi/1D9ZkhVtreGezT2T6Wwl7XIRZUoQIECAAAECBAgQWESBpw/FE4Dcm72wrgYB68u54XPhzPCZerL2m/Z6iTIlCBAgMAeBq3/LzGFnuxAgQKBoAlt7toa2Sjyc6YH+A1Ex0zFVnjnsC8AISIIAAQIECBAgQGDJBNKX0WlbNe3+297SHrZ0b1my8rkQAQLlFBAALOd9VSsCTSOQNYhu7bk1qm/aZSKbCCS/PHP4bBgfn9tkOfnjrBMgQIAAAQIECBC4EYGsDZp2AU7HrE7HtM7aum0t8QvvGymDYwkQaE4BAcDmvO9qTaBUAumYKGmjKX2rOjA8Gl46daFUBipDgAABAgQIECBQfIH9J8+HwWpbNL+kbdV0TOt0zOv8sdYJECAwVwEBwLlK2Y8AgcIKpI2itNF0S29nuLlnRVT+tOtFlClBgAABAgQIECBAYBEE0vH/NlXbqRurf/JL2gU4HfM6v691AgQIzFVAAHCuUvYjQKCwAumgyOkXgJVKJaRvVrNuwBYCBAgQIECAAAECSymQvoR+4NY1Uy4/JQBoBuApRjYQIDB/AQHA+Zs5ggCBggmkXYCPXzgezl8+H5VyagDQRCARkAQBAgQIECBAgMCiCzydTEaXtlGzNmzWls0vvgDMa1gnQOB6BQQAr1fOcQQIFEZge8/20FKJ/zpLZwJOJwJ5/tXB6vgrI4Wpg4IQIECAAAECBAiUW6D/4kjYfyJ9SR1PVpe2YbM2btbWtRAgQOBGBeJ/Md/o2RxPgACBZRDoaO0I27q3RVdOuwHfu7k3tLdWJveZqE4CvPtI/2TaCgECBAgQIECAAIHFFHjmSDwETUdbS7hnU290ybT7b9bGzdq6FgIECNyogADgjQo6ngCBQgikXSPSiUA621vD3UkDyziAhbh1CkGAAAECBAgQaAqBtPvv66svqLMgYH5JX2Lv6N2Rz7ZOgACB6xaI/7a57tM4kAABAssrMGUm4P6XphToga1xF4t0EOYpB9hAgAABAgQIECBAYIEE0pfPD26L26bZZQ6cOxBdLW3jRpkSBAgQmIeAAOA8sOxKgEBxBdKJQF489+KUwj6YzLKWvYUdH6/2BbYQIECAAAECBAgQWESBrM357JF4Erp0ApDs8ukXgGkvl0UsolMTIFByAQHAkt9g1SPQLAK7endFVT12/lgYGh2KtqVvWfuHRsJLp+KBmKMDJAgQIECAAAECBAgsgMD+k+erE9CNRmdKX04Pjw6Ho4NHo33Sl9xRpgQBAgTmISAAOA8suxIgUFyB7b3bQ6X6X32ZCBPhYP/BerL2u7mvK2zs6Yy2PXkwHow5ypQgQIAAAQIECBAgsAACTx+K25xZu/TmpF16aOBQtQUb907Z0WMMwAXgdwoCBKoCAoAeAwIESiHQ1dYVNq3eFNUl7UJRqVTCQ9vXRPs8mTTGokwJAgQIECBAgAABAgsgkI49/cA04/+lQ9hsWrUprGxfuQBXdwoCBAgIAHoGCBAokUA6SHI6E3BW1YeTcQCfEgAs0ROgKgQIECBAgACBYgqkbc45jf/Xt7OYlVEqAgQaUsAXgA152xSaAIHpBNJxANO3qNkxD9+6Njr0wKkL4dT5S9E2CQIECBAgQIAAAQILJXD2wuXw4skL0ekeSl5KZ5kH+uMZgE0AEpFJECBwgwICgDcI6HACBIojkA6S/FL/S1MKd9ct3WFlR2u0PX0jG2VKECBAgAABAgQIELgBgbSt2dXeGu7e1DPljOnL67R3y5QDbCBAgMA8BAQA54FlVwIEii2QfgF4ePBwuDx2OSp0W2tLuH9rX7QtHZQ5ypQgQIAAAQIECBAgcAMCTxw6Ex39hq29ob3aJs0vWZv18MDh/KbgC8CIQ4IAgRsUiP/WucGTOZwAAQLLKZB+ATg+MR4ODhycUqR0HEATgUwhsoEAAQIECBAgQGCBBJ46GM8AnA5Jk10ma7OOToxGV7yt77YoLUGAAIEbERAAvBE9xxIgUCiBVe2rwsZVG6MyTTcRyEPb43EA9x7tD8MjY9FxEgQIECBAgAABAgRuVODS6FjYc6w/Os3D29dE6SyRdv/N2rSrO1ZP2c8GAgQIXK+AAOD1yjmOAIFCCqTdgF/sf3FKOR/Y1hcqlaubL4+Nh28lDbOrudYIECBAgAABAgQIXJ9A1sa8PDo+eXDWBn1wmglA9p3dN7lPtuLrv4hDggCBBRAQAFwARKcgQKA4Amk34PRtalbSns728Lqbu6NC6wYccUgQIECAAAECBAgsgMATSfffrA2atUXTZf+5/dEmAcCIQ4IAgQUQEABcAESnIECgOAJTvgA8N/ULwKy0DyVvXp9MGmfFqZGSECBAgAABAgQINKpA2sZM26D1eqUvrQUA6zJ+CRBYKAEBwIWSdB4CBAohcNuaeLDkQwOHpswEnBU0HXvl6cNnw8TERCHqoBAECBAgQIAAAQKNL5C1LbM2Zn5J26BZ3tDoUDgyeCS/my7AkYYEAQILISAAuBCKzkGAQGEE0i8AxybGwoH+A1PKl86+dubC5fDSqQtT9rOBAAECBAgQIECAwPUIZG3LrI2ZX9I2aJaXtVUnqv/Vl0qohB29O+pJvwQIEFgQAQHABWF0EgIEiiKQzZa2adWmqDjpmCpZ5pY1XWFD94pov6cOxW9oo0wJAgQIECBAgAABAvMQePLgmWjvm3tW1Nqg0cZqIm2rbl69OaxsX5nuJk2AAIEbEhAAvCE+BxMgUESBtBtw2qjKylypTsGWdsF4yjiARbydykSAAAECBAgQaEiBdPy/7Ou/rA2aLmlbNW3LpvtLEyBA4HoEBACvR80xBAgUWiAdNHn/2XhWtXrhH6o2wvLLk4fit7T5POsECBAgQIAAAQIE5iOQ9i6ZaQKQtK2atmXnc037EiBAYCYBAcCZZGwnQKBhBdJG075z+6aty8PJTMAvnrwQzibjtEx7oI0ECBAgQIAAAQIEZhE4ff7SlPGlH9kev3yuH24G4LqEXwIEFlNAAHAxdZ2bAIFlEbh9ze3RdY+dPxYujlyMtmWJuzf1hK721mh7+qY2ypQgQIAAAQIECBAgMAeBJ5OxpVd2tIa7bumecuSFkQvh5QsvR9vTl9lRpgQBAgSuU0AA8DrhHEaAQHEFslnTWirxX2/p2CpZ6dtbW8IbtvZGFUkba1GmBAECBAgQIECAAIE5CKQvle/f2hfaqm3PdEm//muttIbtvdvT3aQJECBwwwJT/wa64VM6AQECBJZXYEXrirCte1tUiOkCgNkO6VgsTxkHMHKTIECAAAECBAgQmL9AOgNwOvRM/YxpG3Vr99aQtWUtBAgQWGgBAcCFFnU+AgQKIZB2A953dqZxAOOxWHYf7Q+XRscKUQeFIECAAAECBAgQaDyB4ZGxsPdYf1Twh2YY/y8NAKZt2OgkEgQIELgBAQHAG8BzKAECxRVIx05JG1f1kj+4bU19tfZ7eXQ8fPvlgWibBAECBAgQIECAAIG5CuypvlAeGZuY3L2lEsKD2/om0/mVdAbgXX278tnWCRAgsGACAoALRulEBAgUSSB9ezpTALB3ZXu44+bVUdGfOng2SksQIECAAAECBAgQmKvAk8mQMq/b2BO6O9unPTwdAzB9iT3tQTYSIEDgOgQEAK8DzSEECBRfIG08nRo6Fc4OTx/Ye+jWuBtw2mgrfm2VkAABAgQIECBAoCgC6cvkmcb/67/UH04MnYiKnbZho0wJAgQI3ICAAOAN4DmUAIHiCmQDKHe0dEQFnOkrwLRRls3aNjFxtdtGdBIJAgQIECBAgAABAjMIjI9PhCerbcn88vD2eMiZel769V9bS1vY1hNPZFff1y8BAgRuVEAA8EYFHU+AQCEFsgbUzr6dUdlmDAAmjbJT5y+Hg6cvRsdKECBAgAABAgQIELiWwIsnz4f+oZFot4dunT4AmLZNt/dsD+0t03cVjk4oQYAAgesQEAC8DjSHECDQGAJpF4p0kOV6LbatXRlu6l5RT9Z+nzh4JkpLECBAgAABAgQIELiWQPr13y29nWFzX9e0h6UBwNv7bp92PxsJECCwEAICgAuh6BwECBRSYEoA8Nz+actZqVTCo9vjcQAfPyAAOC2WjQQIECBAgAABAjMKPJlMJpd9/Ze1Nadb0gCgGYCnU7KNAIGFEhAAXChJ5yFAoHAC6UzA+87tm3Fsv0eSbsC+ACzc7VQgAgQIECBAgEDhBdLJ5NKxpvMVSMcAvG3Nbfls6wQIEFhQAQHABeV0MgIEiiSQfgE4eHkwnLgYz7RWL+8jO+IvAA9VxwB8dWC4nu2XAAECBAgQIECAwKwCJwaHQ9aGzC8PJ71M6nmnh06HM8Nxj5O07Vrf1y8BAgQWQkAAcCEUnYMAgUIK3LLqlrCybWVUtrSrRT3zzo09obuzrZ6s/eoGHHFIECBAgAABAgQIzCLwxIF49t9VHa3hzo3d0x6Rfv23onVF2LJ6y7T72kiAAIGFEBAAXAhF5yBAoJAC2XgraVeKmQKArS2VkHbR0A24kLdVoQgQIECAAAEChRRI244PVb/+a2ud/p/c2dA0+WVn787Q2tKa32SdAAECCyow/d9GC3oJJyNAgMDyCaSzqe07Gze28iV7dMe6fDL4AjDikCBAgAABAgQIEJhF4JvJJHLfkwwxkz80bZOmY1fn97VOgACBhRAQAFwIRecgQKCwAulYKjN9AZhV4NEda6J6PP/qYDh38XK0TYIAAQIECBAgQIBAKtA/NBKeOz4QbX5khvH/sp1eOPtCtO8da+6I0hIECBBYaAEBwIUWdT4CBAolkHYBzsZbGZ8Yn7aMr9/cF1a0Xf1rcWIihCcPxmO5THugjQQIECBAgAABAk0t8NShMyFrO9aXjmqb8r4tvfVk9Ju1RdOX0mmvlegACQIECCyAwNV/6S7AyZyCAAECRRNIvwAcHhsOxwaPTVvMrKH2wLa+KC8dyyXKlCBAgAABAgQIECBQFUi7/96/pS90tk8/pt/RwaNhaHQocrtjrS8AIxAJAgQWXEAAcMFJnZAAgSIJrOtcF9asiLv2poMu58v7aNJV4/GDZ/LZ1gkQIECAAAECBAhMEXgiGf/v0XmM/5e1VbM2q4UAAQKLKSAAuJi6zk2AwLILzGcm4Kyw6UQge4/2h4uXR5e9HgpAgAABAgQIECBQTIGhy2NhT7XNmF9mCwC+cC4e/y+bACRrs1oIECCwmAICgIup69wECBRCIO0GvP/s/hnLlXUBbm252gAbHZ8Izx4+N+P+MggQIECAAAECBJpb4JkjZ0PWZqwvWVPywVvjHij1vOw3nQHYBCB5HesECCyWgADgYsk6LwEChRHI3qrml9m6AK9a0Rbu3dST333KmC5RpgQBAgQIECBAgEBTCzyedP+9d3NvWF1tU860pAHAtK0603G2EyBA4EYEBABvRM+xBAg0hEA6q9rB/oNhZGxkxrKnXTZMBDIjlQwCBAgQIECAQNMLpG3FR5IxpfNA2eQfhwcP5zeFtK0aZUoQIEBggQQEABcI0mkIECiuwK6+XVHhRidGw4GBA9G2fCJttD19+Gy4PDqe38U6AQIECBAgQIAAgepL5fHw9KF4uJj0ZXKe6aVzL4XxiavtykqohLStmt/fOgECBBZKQABwoSSdhwCBwgp0d3SHW1bdEpXvhbPx4Mv5zDQAODwyHr71cjywc35/6wQIECBAgAABAs0p8K1j/WFoZCyqfNqWzGembdCt3VvDyvaV+V2sEyBAYFEEBAAXhdVJCRAomkA6uHLa+MqXd82qjnDHzavzm8ITydguUaYEAQIECBAgQIBAUwqk4//dvmF1WFttS860pGNRp23UmY6znQABAjcqIAB4o4KOJ0CgIQTSxtVsAcCsQumb27Rx1xCVVkgCBAgQIECAAIFFFUjH/5ut+29WkLQNagKQRb09Tk6AQE5AADCHYZUAgfIK3LH2jqhy+87si9JpIm28PXnobBgfn0h3kyZAgAABAgQIEGhSgaxtmL4kTtuQKY0ZgFMRaQIElkpAAHCppF2HAIFlFUi/ADwxdCKcHT47Y5nSxlv/0Eh44cTgjPvLIECAAAECBAgQaC6B518dDAPDo1Gl014k+cxTQ6fCmeEz+U0hbaNGmRIECBBYQAEBwAXEdCoCBIorsK17W1jRuiIqYNoFI595S29X2LKmK79pyhveKFOCAAECBAgQIECgqQTS7r9Z23FTX9x+zIOkX/91tnaGLau35HexToAAgUUTEABcNFonJkCgSAJtLW1hV9+uqEizBQCzHdOvANMuHtHJJAgQIECAAAECBJpK4JvJJHFp2zHFSAOAWdu0taU13U2aAAECiyIgALgorE5KgEARBV635nVRsa4ZANy+Nto/CwBOTBgHMEKRIECAAAECBAg0oUDWJnwiCQB+z4647ZiymAE4FZEmQGApBQQAl1LbtQgQWFaBdIyVawYAk0bcicFL4fCZi8taBxcnQIAAAQIECBBYfoFDpy+GrG2YX2Yb/y/bL217mgE4r2edAIHFFhAAXGxh5ydAoDACaQBw/9n9YXQ8Hrg5X9gd61eF9as78ptC2tUjypQgQIAAAQIECBBoCoF0aJj1q1eErO040zI2PhZePPdilJ22TaNMCQIECCywgADgAoM6HQECxRVI37JeHr8cDg8cnrHAlUplyjiA33wpnrltxoNlECBAgAABAgQIlFbgsQOno7o9umNNyNqOMy1HBo+ES2PxF4Np23SmY20nQIDAQggIAC6EonMQINAQAms614QNXRuisqZdMaLMauKNO9dFmx57KW7sRZkSBAgQIECAAAECpRfIxv9LXwqnbcYUIW1zrutcF9Z2zj5mYHoOaQIECNyIgADgjeg5lgCBhhO4fe3tUZnTxliUWU2kjblj54bCEeMApkzSBAgQIECAAIGmETh6dihkbcL8krYZ83nZuglAUhFpAgSWWkAAcKnFXY8AgWUVmO9MwLdvWB3WrorHAfQV4LLeQhcnQIAAAQIECCyrQNoWzNqKWZtxtmXf2X1Rtu6/EYcEAQJLICAAuATILkGAQHEE0sGWnz/7/KyFy8ZyeePOuHvGY8YBnNVMJgECBAgQIECgzAJpWzBrK842/l9m8fyZuM0pAFjmJ0TdCBRTQACwmPdFqQgQWCSBNAB4/MLx0H+pf9arpV060re+sx4skwABAgQIECBAoFQCaVswbSumlT1/+Xw4ev5otDntlRJlShAgQGARBAQAFwHVKQkQKK7A9t7toa2lLSpg2iUjyqwm0kadcQBTIWkCBAgQIECAQHMIZGNBz3f8v3TM6bZKW9jVt6s5wNSSAIHCCAgAFuZWKAgBAksh0N7SHnb1xg2utFGWlmO6cQC/YTbglEmaAAECBAgQIFB6gfTrv7mM/5cOObOjb0foaI3HmC49nAoSILDsAgKAy34LFIAAgaUWSLsBXysAOP04gKeXutiuR4AAAQIECBAgsMwC6fh/37Nj/uP/3bnmzmWuhcsTINCMAgKAzXjX1ZlAkwu8bu3rIoFrBQCzndNuwN+sTgQyMTERnUeCAAECBAgQIECg3ALfPBC/BE7biNPVPp0AJG2LTneMbQQIEFhoAQHAhRZ1PgIECi+Qzrq2/9z+MDY+Nmu508ZdNvbL0bNDsx4jkwABAgQIECBAoDwC2fh/afsvbSOmtR0dHw37zu2LNqe9UaJMCQIECCySgADgIsE6LQECxRVIG11Do9VgXjIzW1p64wCmItIECBAgQIAAgeYS+OaBM1GF5zL+3+GBw+HS2KXoOF8ARhwSBAgskYAA4BJBuwwBAsURWN+1PqztXBsV6FrdgI0DGHFJECBAgAABAgSaTiCdACQb/6+lpTKrQzoByIauDVPaobOeQCYBAgQWSEAAcIEgnYYAgcYSSL8CTMdmma42aRcP4wBOp2QbAQIECBAgQKCcAmkAMG0bTlfr5848F2329V/EIUGAwBIKCAAuIbZLESBQHIHXrYknAknfzk5X0rSRZxzA6ZRsI0CAAAECBAiUT+Do2fmP/5cppG1MAcDyPRtqRKBRBAQAG+VOKScBAgsqkDa+5vIFoHEAF/QWOBkBAgQIECBAoGEEsp4f+WUu4/9l+6dtzLQNmj+ndQIECCymgADgYuo6NwEChRW4c+2dUdleufBK6L/UH21LE8YBTEWkCRAgQIAAAQLNIZB2/310+7XH/zs9dDqcGjoVAaW9UKJMCQIECCyigADgIuI6NQECxRXY3rs9dLR0RAVMx2iJMl9LpN2AjQM4nZJtBAgQIECAAIFyCTx24HRUoTfujCeUizJfS6TdfztbO8O27m3T7WobAQIEFl1AAHDRiV2AAIEiCrS3tIfb1twWFe16AoDGAYwIJQgQIECAAAECpRPIxv87cmYoqtcbd62L0tMl0u6/2SR0rS2t0+1qGwECBBZdQABw0YldgACBogrctfauqGhpIy3KfC1hHMDpVGwjQIAAAQIECJRXIB3/b83K9nDHhu5rVjj9AvCOtXdc8xg7ECBAYLEEBAAXS9Z5CRAovEA6CPNzZ5+7ZpmnHQfwxbhLyDVPYgcCBAgQIECAAIGGEUjH//ueHetCS0vlmuVPXy7fuSYeg/qaJ7ADAQIEFlBAAHABMZ2KAIHGEkgnAnnp3Evh0tila1biTTvjLh9Zo3BiYuKax9mBAAECBAgQIECg8QS+UW3r5Ze5jP+XtSkP9B/IHxbSl89RpgQBAgQWWUAAcJGBnZ4AgeIKZOOwVKr/1ZexibGw/9z+enLG33QikJf7h6eMCzPjwTIIECBAgAABAgQaRuDImYvh6Nn5j/+XtSmztmV+uX3N7fmkdQIECCypgADgknK7GAECRRJY1b4qbOuJZ2JLu2pMV97bNqwO61bFMwinXUOmO842AgQIECBAgACBxhL4+ounogJnbcDX3Xzt8f9eOPNCdFw2+2/W9rQQIEBguQQEAJdL3nUJECiEwOvWvC4qx1xmAr4yDmDcDTjtGhKdVIIAAQIECBAgQKAhBb62P+7++6bq7L9ZW/BaS9qm1P33WmLyCRBYbAEBwMUWdn4CBAotkI4DmDbWZip8OvZL9nbYOIAzadlOgAABAgQIEGg8gaxt9/Vksrc371o/p4qkMwCnL53ndBI7ESBAYAEFBAAXENOpCBBoPIE0AJh1AR6fGL9mRd6UNP5eHbgUXjp14ZrH2YEAAQIECBAgQKAxBPafOB9OnY8niPve2+JeINPVJAscpl2AfQE4nZRtBAgspYAA4FJquxYBAoUTSAOAF0erAz0PHr1mOXfdtCrc3LMi2u/r++MxYqJMCQIECBAgQIAAgYYSSL/+29zXFbatXXnNOrx84eUwODIY7ecLwIhDggCBZRAQAFwGdJckQKA4Auu71oe1nWujAs2lG3A29kvaBSQdIyY6qQQBAgQIECBAgEBDCXwtebl7veP/9XT0hI2rNjZU3RWWAIHyCQgAlu+eqhEBAvMQyAJ56VeAcwkAZpd4c3UQ6PySTQQyPj6R32SdAAECBAgQIECgAQXGqm26x6ptu/yStv3yefn1757+bj4Z7lp715wmDokOkiBAgMACCwgALjCo0xEg0HgC1x0AvC0eBLp/aCR855WBxgNQYgIECBAgQIAAgUjgOy8PhIHh0Whb2vsjyswl0pfJaVszt6tVAgQILJmAAOCSUbsQAQJFFUgbZdlEIHNZsnFgtq+Lx4FJu4rM5Tz2IUCAAAECBAgQKJbA116Mx3beWR3/eWNv55wKOeULwHV3zek4OxEgQGAxBQQAF1PXuQkQaAiBdFa2E0MnwumhuMvHTBV5c/IVYDpY9EzH2U6AAAECBAgQIFBcgbRNN9fuv6eGToWsLZlfsi7AFgIECCy3gADgct8B1ydAYNkFbu2+NXS1dUXlmOtXgN+76/9n7z6A5Djuw9//Luc7XEQ4ZCIdDjhkgABBAsxJgYpUjpbl57KeJOtJsvWqXP7bVsmySpJVf/9Lsp6CRQWLkilRIqlAEiRAkACInMMh58s5xzd95O7NrwHc7d7u3s3uflsF3/ymZ3p6PkPjGj3T3XoY8O7zDdLTN6DKIkAAAQQQQAABBBCIHgHTltvjtOncaazDf00bc1buLHdRbCOAAAITIkAH4ISwc1EEEPCSQFJikszPn6+qdLLxpIpvFdw+V68g3NnbLwcvN93qcPYjgAACCCCAAAIIeFzAtOVMm86dbp+rF39z57m37fn/FuYvFNPWJCGAAAITLUAH4EQ/Aa6PAAKeEFiUv0jVw268qUxXUJidJmVTc117RHZYc8aoTAIEEEAAAQQQQAABTwvYbbnFTluvICs1oDofrz+ujrPnmlaZBAgggMA4CtABOI7YXAoBBLwrYM8DGOgQYHNHd9ym3wjvOBPY/IHe1aBmCCCAAAIIIIBA/AqMdf4/I2YvALK4cHH8QnLnCCDgKQE6AD31OKgMAghMlID9dvZCywXp6O0IqDp3WAuBHLjcKB09fQGdy0EIIIAAAggggAAC3hHo7OmXA5caVYU2zNMve1WmK2jpaZErbVdce0TsNqbKJEAAAQTGUYAOwHHE5lIIIOBdATMHYGLC8F+JA4MDUtlYGVCF18wpkOTEBP+xvf2DsueCbjj6M9lAAAEEEEAAAQQQ8KzAngsNYtpyvpTktPHWzNZzPvvy7J/2CJLkxGSZN2mefRgxAgggMCECw//anZDLc1EEEEDAGwJmhba5eXNVZew5XFSmK8hOS5ZlMya59jjzAJ6pUzEBAggggAACCCCAgPcF7OG/y6bnSU56SkAVt4f/zp80X1KSAjs3oAtwEAIIIBCCAB2AIeBxKgIIxJaAPUfLiYYTAd+gPQ/gaywEErAdByKAAAIIIIAAAl4R2Gm14TbcVhRw1ey2Y1lhWcDnciACCCAQaQE6ACMtTPkIIBA1AmUFupEW6BeA5gbXW43DY9dapKmjJ2runYoigAACCCCAAALxLtDc2StHrjYrhg3WYm8q0wpONpxUe+y2pcokQAABBMZZgA7AcQbncggg4F0B+wvAs01npbu/O6AKr5w1SdJThv9KHXSmjtl1jtWAA8LjIAQQQAABBBBAwAMCO8/Wy8Dw9H+SmpwoK2flB1Szzr5OOdd8Th3LAiCKgwABBCZYYPhfqxNcES6PAAIITLSAaaQlOP/zpf7BfqlsCGwhkLTkpBsmiH7tDB2APkt+IoAAAggggAACXhd49UytquKa2fnOC94kte9WgVk8ziwi50tmcbkF+Qt8IT8RQACBCRegA3DCHwEVQAABrwhkpmTK7LzZqjrBDQMuVOcyD6DiIEAAAQQQQAABBDwt8OppvYjbxnnFAdf3ZL0e/js7d7aYtiUJAQQQ8IoAHYBeeRLUAwEEPCFgz9ViT+Y8UiXvsOYBPFfbLtebO0c6hTwEEEAAAQQQQAABDwhcbuiQC/UdqiZ3zmcBEAVCgAACUS1AB6BHH9/FixflC1/4gixatEiysrKkoKBA1qxZI9/4xjeko0P/Ygr1Fl588UX52Mc+JvPmzRu6Vl5enixYsEDe/e53y3e/+11pa2sL9RKcj0DUCNjzAAbzBeCS0jzJTU9W92q/SVaZBAgggAACCCCAAAKeEHjtjP76Lz8zRRZPzQ24bvZLY/ulcsAFcSACCCAQIQH9L9UIXYRigxN45pln5EMf+pC0tLT4TzSdfnv37h3684Mf/ECee+65oQ47/wFj2GhsbJSPf/zj8rvf/e6Gs821T58+LU899ZSsX79eli9ffsMx7EAgFgXsDsDTTaelp79HUpNSR73dpMQEuWNekfzxaJX/2O3OUJL3rJ7hj9lAAAEEEEAAAQQQ8J7AdqsDcIPTpkt02naBpN6BXjndeFodSgeg4iBAAAEPCPAFoAcegrsKBw4ckMcff3yo8y87O1u++tWvyo4dO2TLli3yqU99aujQyspKefTRR6W1tdV9alDbzc3Ncv/99/s7/97xjnfIz3/+c9m1a5fs2bNHfvOb38hnP/tZmT59elDlcjAC0S5gr9bWN9AnphMw0LTRGipi3iYPuJeTC7QgjkMAAQQQQAABBBAYFwHTVtthdQDe6XQABprONZ0T0wnoTosKF7lDthFAAIEJF+ALwAl/BLoCptOts7NTkpOT5fnnnx/6+s53xD333CPz58+XL33pS2I6Ab/5zW/KP/7jP/qyg/r5mc98Rvbt2ydpaWnyq1/9St72trep81evXi2mU/Db3/629Pf3qzwCBGJZICc1R2bmzJRLrZf8t3mi/oSUF5b745E27rQmi65v75ETVS1SPi1vpNPIQwABBBBAAAEEEJgggWPXWqSxQ3fg2S91R6qaPWVMaXap5KYGPnx4pLLJQwABBMIlwBeA4ZIMQzm7d++W7du3D5X0yU9+UnX++Yo38wKWlZUNhd/5znekt1f/ovIdN9LPV199VX76058OHfIv//IvN3T+uc9NSEgY6ox072MbgVgXsIcB2426ke5/ZmGmzHL+uBPzALo12EYAAQQQQAABBLwlsP1MrarQnKIsmZ6v23PqACuw5/+z25LW4YQIIIDAhAjQATgh7De/6NNPP+3PMHPz3SwlJibKRz7ykaGspqYmefnll2922Ij7/uM//mMo3yz28Td/8zcjHksmAvEoUFb4Rie7797NF4DBpI3WkBEzDyAJAQQQQAABBBBAwJsC9gIgdltutFrbbUXm/xtNjHwEEJgIAToAJ0L9Ftc0X+aZZFb9XbVq1S2OEtm0aZM/77XXXvNvB7LR09Pjn/fPzAGYnp4+dJoZ5nv58mW5cOGCdHV1BVIUxyAQswL2W9vKxsob5nUZ6ebvtOYB3H2hQbp6GUo/khl5CCCAAAIIIIDARAiYNtqeC43q0mZRt0CTmS/6VOMpdbj9MlllEiCAAAITJEAH4ATB3+yyJ0688ZXRvHnzRhx2u2jR8ISyvnNuVt7N9h06dMjfwbd06dKhxUY+97nPSVFRkcycOVPmzJkj5stA0zm4devWmxXBPgRiXsB+a9sz0CNmcudA0/rbnFXjXIvG9fQNOA3LhkBP5zgEEEAAAQQQQACBcRLYfb5BTFvNl0wbbv1thb5w1J/nm89LZ1+nOs5+mawyCRBAAIEJEmARkAmCty9rvrqrq3tjmOBoK+/m5+cPfSXY3t4+9NWeXdZI8fHjx/3ZAwMDYhb7OH1ar3BqvhJ88cUXh1Ye/trXviZf/vKX/ecEunHlypURD71+/fqI+WQiMJECeWl5YiZvvtp21V8NMw/gwoKF/nikjbyMFFk2Y5IcuNTkP8zMA3jn/GJ/zAYCCCCAAAIIIIDAxAu8aq3+a9pwpi0XaDpWf0wdOi1rmhSkF6h9BAgggIAXBPgC0AtPwalDa2urvybZ2dn+7VttmGHCJrW1td3qkJvub2gY/grp61//+lDn30MPPSRmARLTCVlTUyPf/e53h74CHBwclL/7u7/zDxm+aYG32DljxgwZ6c/atWtvcSa7EfCGgP3mNpiFQMwd3GkNHXmFeQC98WCpBQIIIIAAAggg4BKw52q223CuQ2+6eaxOdwCWF5Xf9Dh2IoAAAhMtQAfgRD+BN6/vnncvNTV11FqlpaUNHdPZqT83H+1E89WgL5lrmqG+zz77rKxZs0ZMmcXFxfJXf/VXQ/vMgiMm/f3f/72YzkASAvEkcEMHYMPw17OBOGy0vvY7cb1Falu7AzmVYxBAAAEEEEAAAQTGQcC0zUwbzZ3sNpw772bb9ktiuw15s3PYhwACCEyEAB2AE6F+k2v6FuMwWWYI7mipu/uNjoSMjIzRDlX57uuYDPMVYFJSkjrGBBs3bpR3vvOdQ/vNPINHjhy54ZiRdpgFRUb6Y744JCHgZYHFBYtV9SobKsVM8hxoWjFzkmSl6v/f2nGW1YAD9eM4BBBAAAEEEEAg0gJ228y03UwbLtDUO9ArJxtOqsPLC/kCUIEQIICAZwToAPTIo8jJyfHXJJBhvb4v+QIZLuwv2NlwX8d87bdixQp3ttp+8MEH/fGePXv824FsmHkMR/ozderUQIrhGAQmTMBeva2rv0vMJM+BppSkxBsmkLaHmARaFschgAACCCCAAAIIhF/AzNHsTuvmFoppwwWazjadFbNYnDvxBaBbg20EEPCSQOB/u3mp1jFYF/NlXmHhG6tNjbaARmNjo/g6AM08e8Ek9/GjLTbiPra2tjaYy3AsAlEvkJ+eL1OzdEe1PcRjtJvcaM0DuP10LcPpR0MjHwEEEEAAAQQQGAcBM8WRvQCI3XYbrRr2/H8zcmaIWUyOhAACCHhRgA5ADz2VxYvfGHJ45swZ6eu79VDDkyeHPzMvKysL6g7Ky4c/Se/v7x/xXHd+cjILRo+IRWZMCpQV6P//CroD0JoHsLqlW87UBLdwT0zCclMIIIAAAggggMAEC5ytbZfrzV2qFnfOL1LxaIG9AjDDf0cTIx8BBCZSgA7AidS3rm3m3TPJfN23b98+K3c43LZtmz+44447/NuBbMyaNUtmzpw5dOiFCxdG/Brp7Nmz/iJLS0v922wgEC8C9hCOo/VHg7r124qzZFpeujqHYcCKgwABBBBAAAEEEJgQgVedkRnuNDk3TeaVZLt3jbpNB+CoRByAAAIeEqAD0EMP47HHHvPX5sc//rF/270xMDAgTzzxxNCuSZMmyd133+3ODmj7Xe9619BxLS0tsmXLllue85vf/Maf5+uc9O9gA4E4EFhStETd5amGU2Imew40JSQkyEbrTbI91CTQsjgOAQQQQAABBBBAIHwC9kvZjfOKxbTdAk09/T1S2VipDrdfHqtMAgQQQGCCBegAnOAH4L782rVr5c477xza9cMf/lB27tzpzh7a/uY3vylmVV6TPvvZz0pKSsrQtu//bN26degXl/nl9bGPfcy3W/383Oc+J77VgP/2b/9WTEegnX72s5+JKcukRx99VNzzAQ7t5P8gEAcC9jCO7n5nCG/jmaDufKM1DHjXuXrp6RsIqgwORgABBBBAAAEEEAifQHdfv+w4W68KvGtBcMN/Tzeelr4BPW2TvYicugABAgggMMECdABO8AOwL/+d73xHMjIyhuYAfOCBB+RrX/ua7Nq1S15++WX59Kc/LV/60peGTlmwYIF84QtfsE8PKDZDgP/pn/5p6NgjR46I6Xg0XxyaYcfmOp/5zGf8nYe5ubny7W9/O6ByOQiBWBOYlD5JpmdPV7cV7DBgM5m0+2VyR0+/7L/UqMokQAABBBBAAAEEEBg/gX0XGqWzd3g+dNNWu9N6aTtabezhv7NzZ0tOas5op5GPAAIITJgAKztMGP3NL7xixQp58skn5UMf+tDQl3lf+cpXbjjQdP4999xzkpMz9l8wX/ziF6WhoUG+/vWvy6lTp+QTn/jEDdcpKSmRp59+WubPn39DHjsQiBcBMwz4StsV/+2a1d7es+A9/ni0jYKsVCmflitHrw5/aftKZa3cPveNVb9HO598BBBAAAEEEEAAgfAKbHPaYu5UUZonps0WTLI7ABn+G4wexyKAwEQI8AXgRKiPcs23vvWtcvjwYfn85z8vprMvMzNTzHx/q1evHuqwO3DggMybN2+UUkbPNl8Xvvbaa/LhD39YZs+eLWlpaZKXlydr1qyRf/7nf5bKykpZv3796AVxBAIxLGDPA2g39gK5dfuN8ivWpNOBlMExCCCAAAIIIIAAAuERsDsA71pQHHTB5qWwO9lTx7jz2EYAAQS8IMAXgF54Cjepg1mt91vf+tbQn5tk33LX5s2bR1zZ1z7RdPDRyWerECMwLGA35sx8L119XZKerFf3HT7jxq1NTqPyu1uHV9U2XwPWtnZLcU7ajQezBwEEEEAAAQQQQCBiAtUtXXKyqlWVb9pqwSTTFjzTpOeFLi8qD6YIjkUAAQTGXYAvAMednAsigEA0CZjhHAnO/3ypf7BfTjac9IUB/Vw5M1+y0/T7lu18BRiQHQchgAACCCCAAALhFDBTsbhTTnqyLJ8xyb1r1O1TjafEtAl9ybQVywrKfCE/EUAAAU8K0AHoycdCpRBAwCsCmSmZMjdvrqpOsMOAU5MTZcNtes4/e+iJugABAggggAACCCCAQEQE7DaYWbAtOSm4fxbbw39NW9G0GUkIIICAlwWC+5vOy3dC3RBAAIEICdhDOo7WHQ36SpsW6qEl5u1z/8Bg0OVwAgIIIIAAAggggMDYBEzb69UzderkMc3/V2/N/8fwX2VKgAAC3hSgA9Cbz4VaIYCAhwTshUDG0gF413zdAdjY0eusDNzsobukKggggAACCCCAQGwLHL7SJE1OG8ydxtIBeLz+uLsIYQVgxUGAAAIeFaAD0KMPhmohgIB3BJYULlGVudByQVp79OTR6oCbBDMKMuW24iyVYw9BUZkECCCAAAIIIIAAAmEVeKVSf/03ryRbSidlBHWNjt4OOdd8Tp1jLxqnMgkQQAABjwjQAeiRB0E1EEDAuwILCxZKcqJexONE/YmgK7xpQYk6hw5AxUGAAAIIIIAAAghEVGBbZY0qP9jVf83JZjG4gcEBfzmJCYli2ookBBBAwOsCdAB6/QlRPwQQmHCB1KRUWZC/QNXjaH3o8wAeuNQozdYwFHURAgQQQAABBBBAAIGwCJg218HLTaqssQz/PVJ3RJVx26TbJCM5uK8IVQEECCCAwDgJ0AE4TtBcBgEEolvAHgY8lnkA180pkDRnRWBfMmuA2BNR+/L4iQACCCCAAAIIIBA+AdPmcq+/Ztpkpm0WbLI7ACuKKoItguMRQACBCREY/pfohFyeiyKAAALRIWAvBHKsTq/+FshdpKckye1zC9Wh9lAUlUmAAAIIIIAAAgggEBYBu821zmmTmbZZsMl+Cby0aGmwRXA8AgggMCECdABOCDsXRQCBaBOwV3e71n5NGroagr4Ne64ZMw/g4KDzKSAJAQQQQAABBBBAICICpq1lLwBit8kCuXB9Z71cbbuqDrVfEqtMAgQQQMBDAnQAeuhhUBUEEPCugJnfJT0pXVXQfgOsMm8RbFpYrHKqW7rlVHVwKwqrAggQQAABBBBAAAEERhSorG6TqpYudcxYOgDttp+Z+2/epHmqXAIEEEDAqwJ0AHr1yVAvBBDwlIBZBbissEzVaSzDgOcWZcn0fD1R9LZTtapcAgQQQAABBBBAAIHwCbzijLhwp9JJGXJbcZZ7V0Db9vx/ZoRIUmLww4gDuhgHIYAAAmEWoAMwzKAUhwACsStQXliubm4sKwEnJCSI/cbZDAMmIYAAAggggAACCERGwG5rmdV/TZss2GR3ALIASLCCHI8AAhMpQAfgROpzbQQQiCoBe44XMwxkLPP32R2Aey40SHt3X1RZUFkEEEAAAQQQQCAaBEwba/d5PW/zpgVFQVfdtPnsDkC7bRh0oZyAAAIIjKMAHYDjiM2lEEAgugXsRp5ZBOR6+/Wgb2rDvCJJThx+69zbPyg7z9YHXQ4nIIAAAggggAACCIwssMNpY/X0D/gPMm0w0xYLNl1suSitPXreZlYADlaR4xFAYCIF6ACcSH2ujQACUSUwM2em5Kbmqjofrjus4kCC7LRkWT07Xx1qD01RmQQIIIAAAggggAACYxJ4+VSNOs+0wXLTU9S+QAL767+ijCKZkjUlkFM5BgEEEPCEAB2AnngMVAIBBKJBwMwVs7R4qarq4drgOwBNAZsWlKhyTON0LMOJVSEECCCAAAIIIIAAAn4B07Z6+aTuALxnkW6D+Q8eZcNeAdiMDBnLPIKjXIZsBBBAIGICdABGjJaCEUAgFgWWFS1Tt3Wk9oiKAw02LyxWh15p7JSztW1qHwECCCCAAAIIIIDA2AVOVbfK9eYuVcDdC8fWAWh/AcgCIIqVAAEEokCADsAoeEhUEQEEvCNgfwF4vP649Pb3Bl3BRVNyZGpeujrvJesNtcokQAABBBBAAAEEEAhKwG5blU7KkHkl2UGVYQ7u6e+Rkw0n1Xn23NAqkwABBBDwoAAdgB58KFQJAQS8K2BP9twz0COVjZVBV9gMGdlsvYG2G6lBF8oJCCCAAAIIIIAAAn6BrSdr/dtmwwz/Hcuw3VMNp6R3QL/wpQNQ0RIggEAUCNABGAUPiSoigIB3BPLS8mR27mxVobEsBGIKsOeg2XuhUVq6dONSXYgAAQQQQAABBBBAICCB5o5e2XepUR179yI9BYvKHCGwh//OyZsjOak5I5xBFgIIIOA9AToAvfdMqBECCHhcoKK4QtVwrAuB3DGvUFKTh/8a7hsYlO2VdapsAgQQQAABBBBAAIHgBV45XSv9TtvKl9KcNtf6uUW+MKif9gIg9oiQoArjYAQQQGCCBIb/5TlBFeCyCCCAQLQJ2I0++61woPeTmZost88tVIczDFhxECCAAAIIIIAAAmMSePmUXv13/W2FkpGaNKay7Lae3RYcU6GchAACCIyzAB2A4wzO5RBAIPoF7C8AL7ZclKaupjHd2D3WasDbKmtkwPW2ekyFchICCCCAAAIIIBDHAqYtte2Unv9vrKv/Nnc3y4WWC0qTDkDFQYAAAlEiQAdglDwoqokAAt4RmJ8/X9KS0lSF7DfDKnOE4J5Fk1VuXVuPHLnarPYRIIAAAggggAACCAQucNhpS9W396gTxtoBeKz+mConNTFVFuQvUPsIEEAAgWgQoAMwGp4SdUQAAU8JpCSmSHlhuarTWDsAZxZmytziLFUWw4AVBwECCCCAAAIIIBCUwMsn9fDf25y2lmlzjSUdqT2iTltUuEhSklLUPgIEEEAgGgToAIyGp0QdEUDAcwL20I+xLgRibuyehSXq/uw5a1QmAQIIIIAAAggggMCIAnZbaqxf/5mL2C95K4r0YnAjVoRMBBBAwEMCdAB66GFQFQQQiB6BpcVLVWVN43BgcEDtCzS4Z5HuADx8pVlqWrsCPZ3jEEAAAQQQQAABBN4UqG3tFtOWcie7reXOG2l7cHBQ7Je8S4qWjHQKeQgggIBnBegA9OyjoWIIIOBlgWXFy1T1WnpaxCwGMpa0enaBZKclq1O3WhNXq0wCBBBAAAEEEEAAgZsKbKvUi39kOSv/mrbWWNKl1kvS2N2oTrXbgCqTAAEEEPCwAB2AHn44VA0BBLwrMDlzshRnFKsK2kNEVOYIQWpyotw5v0gdYc9dozIJEEAAAQQQQAABBG4qYLehNjptLNPWGks6VHtInVaYXiil2aVqHwECCCAQLQJj+5swWu6OeiKAAAIREkhISJCKYj0HjD1EJJhL320NA95+uk56+sY2pDiY63IsAggggAACCCAQKwK9/QPyymn9BeBYh/8ak0M1ugPQfP1n2oAkBBBAIBoF6ACMxqdGnRFAwBMC4VwIZPNC/TVhW3ef7L3Q4In7pBIIIIAAAggggEA0COy72CitXX2qqputxdZU5iiB/QXgshI9Bcwop5ONAAIIeEqADkBPPQ4qgwAC0SRgfwF4uvG0dPZ1jukWSnLSZWlpnjrXXsFOZRIggAACCCCAAAIIKIEtJ6pVvHhqrkzOTVf7Ag3ae9vldNNpdTjz/ykOAgQQiDIBOgCj7IFRXQQQ8I5AeWG5JCYM/zXaN9gnJ+pPjLmC9jDgl07WjLksTkQAAQQQQAABBOJNYMsJ3Xa6r6xkzARH647KwODwdCzJCcli2n4kBBBAIFoFhv/lGq13QL0RQACBCRLITMmU+ZPmq6uHMg+gPUfN2dp2uVjfrsonQAABBBBAAAEEELhR4Gxtm5yr0+2m+xZPvvHAAPfYw38XFiyU9OSxfU0Y4CU5DAEEEIioAB2AEeWlcAQQiHUBexiw3VgM5v4rnCHARdmp6pQXrTfZKpMAAQQQQAABBBBAYEjAHv5bkpMmS6bp6VWCobLbdAz/DUaPYxFAwIsCdAB68alQJwQQiBqB5SXLVV0P1h6UwcFBtS/QIDExQeyvAF88rueyCbQsjkMAAQQQQAABBOJJ4MXjevjvvWWTxbStxpJMW84e1UEH4FgkOQcBBLwkQAegl54GdUEAgagTWF6sOwDrOuvkatvVMd/HfU5j1Z12OysBN3f0unexjQACCCCAAAIIIOASaGzvkb0XG1x7REKZ/+9iy0Vp6m5S5bECsOIgQACBKBSgAzAKHxpVRgAB7wjMyJkhBekFqkLmK8Cxpo3ziyQ1efiv5v6BQdlaqd9oj7VszkMAAQQQQAABBGJRwLSVnCaTP6WnJMod84r8cbAb9vDfoowimZY1LdhiOB4BBBDwlMDwvzI9VS0qgwACCESHQEJCgthDQg7WjL0DMDM1WTZaDVbmAYyO/xaoJQIIIIAAAghMjIA9/HfjvGJJT0kac2XsDkDT1jNtPhICCCAQzQJ0AEbz06PuCCDgCYEb5gEMoQPQ3JA9DHjrqRrp6RvwxL1SCQQQQAABBBBAwEsCpo20rbJWVSmU4b+moJt1AKoLECCAAAJRKEAHYBQ+NKqMAALeErDnATzddFraetrGXMl7y0rUua1dfbLHmQuQhAACCCCAAAIIIKAFdp9vkLbuPrXzHqstpTJHCdp72+VM0xl1lD3aQ2USIIAAAlEiQAdglDwoqokAAt4VWFy4WJITk/0VHBgckCN1R/xxsBuTc9Nl2fQ8ddoLrAasPAgQQAABBBBAAAEj8OKJagWxbMYkKclJV/uCCUwbzrTlfCk5IVlMW4+EAAIIRLsAHYDR/gSpPwIITLhAenK6LC7QDcNQFgIxN2QPAzaN28FB1+zWE37XVAABBBBAAAEEEJhYAdM2sjsA71vH4MqHAABAAElEQVSkR1IEW8NDNYfUKYsKFolp65EQQACBaBegAzDanyD1RwABTwjY8wDajcdgK3nf4snqlCuNnXKqulXtI0AAAQQQQAABBOJZwLSNTBvJnew2lDsvkO0b5v8rWRbIaRyDAAIIeF6ADkDPPyIqiAAC0SBwQwdg7SE1fCTYe1g0JUdKJ2Wo015kGLDyIEAAAQQQQACB+BbYcqJGAZi2k2lDjTWZLwoP1x1WpzP/n+IgQACBKBagAzCKHx5VRwAB7wjYjcO23jY523R2zBVMSEhwhgHrISwvWI3cMRfOiQgggAACCCCAQAwI3DD812k7mTbUWNP5lvPS3N2sTrfbeCqTAAEEEIgiAToAo+hhUVUEEPCuQElmiZRml6oKHqg5oOJgA3sIy6HLTVLT2hVsMRyPAAIIIIAAAgjEnEBta7ccdNpG7nRvmZ5CxZ0XyPbBmoPqsJKMEpmaNVXtI0AAAQSiVYAOwGh9ctQbAQQ8J2C/IbbnkAm2wuvmFEp22vDqwub8l/gKMFhGjkcAAQQQQACBGBR4+WSNs0Da8I2ZNtO6uQXDO8awtb96vzprxeQVIX1RqAojQAABBCZYgA7ACX4AXB4BBGJHwJ4H0H6LHOydpiYnyqaFxeo0e6iLyiRAAAEEEEAAAQTiROCFE9XqTu9aUCRpyUlqX7CBPXpjRcmKYIvgeAQQQMCzAnQAevbRUDEEEIg2geXFy1WVL7VekvrOerUv2OB+ayjL9tN10tnTH2wxHI8AAggggAACCMSMQEdPn7xSWavu595FoQ3/reusE9N2c6eVJSvdIdsIIIBAVAvQARjVj4/KI4CAlwTm58+XjGS9cm+ow4A3O18AJiUOT2bd3Tcgr56p89JtUxcEEEAAAQQQQGBcBUznn2kT+ZJpK91rLZ7mywv0pz1yIyslS0zbjoQAAgjEigAdgLHyJLkPBBCYcIHkxGSpKKpQ9ThYqyeTVpkBBJMyU2XN7Hx15PPHqlRMgAACCCCAAAIIxJPA88f08N/bnbn/TJsplLS/Rs//Z9p0pm1HQgABBGJFgA7AWHmS3AcCCHhCwJ4H8FDNoZDrdZ81DNjMA9jXP/zWO+QLUAACCCCAAAIIIBAlAr1OG8ieE/mBxVNCrv2B6gOqDLMACAkBBBCIJQE6AGPpaXIvCCAw4QJ2B+DRuqPS098TUr0eLNeN2saOXtlzoTGkMjkZAQQQQAABBBCIRoHd5xukpatPVf3+xaHN/9fR2yEnGk6oMpn/T3EQIIBADAjQARgDD5FbQAAB7whUFFdIgvM/X+oZ6BHTCRhKmlGQKYun5qoi/swwYOVBgAACCCCAAALxIWC3gSqm58m0SXoO5mAljtQdkf7B4UXWkhKSZGnR0mCL4XgEEEDA0wJ0AHr68VA5BBCINoHc1NwbJoy255QZyz3ZXwG+cLxaBgcHx1IU5yCAAAIIIIAAAlEpYNo+9vx/dhtpLDd2oEYP/y0rKJPMlMyxFMU5CCCAgGcF6AD07KOhYgggEK0C9pCRfdX7Qr6VB5fooS1Xmzrl6NWWkMulAAQQQAABBBBAIFoEDl9plqqWLlXdB0Ic/msKszsAmf9PERMggECMCNABGCMPkttAAAHvCKyavEpV5mDNQekfGB5WojIDDBZOzpFZhfpNtD0EJsCiOAwBBBBAAAEEEIhKgeePV6l6zy3Kknkl2WpfsEHfQJ+Ytpo7rShhARC3B9sIIBAbAnQAxsZz5C4QQMBDAisnr1S1aettk9NNp9W+YIOEhASxh7jQARisIscjgAACCCCAQDQL/PlYtar+/eWTxbSRQkmnG09LR1+HKoIOQMVBgAACMSJAB2CMPEhuAwEEvCNQklki07OnqwqFZRiw08h1p9M1bXK2ts29i20EEEAAAQQQQCAmBUyb54zT9nEn++WoOy/QbXuu5pk5M6UooyjQ0zkOAQQQiBoBOgCj5lFRUQQQiCYB+yvA/dX7Q67+ihn5UpyTpsrhK0DFQYAAAggggAACMSpgL/5R4rSJlk+fFPLdMvw3ZEIKQACBKBGgAzBKHhTVRACB6BKw5wE0b5dDXbU3MTFB7Imu7aEw0aVEbRFAAAEEEEAAgcAE7Jee9zuLf5i2USjJtM3sl7T2S9xQyudcBBBAwEsCdAB66WlQFwQQiBkBeyXgus46udx6OeT7s4e6HLrcJFXNejW8kC9CAQgggAACCCCAgIcEqp2Vfw86bR53eqB8ijsc0/a19mtS01mjzmX+P8VBgAACMSRAB2AMPUxuBQEEvCMwK3eWFKQXqAqFYx7A2+cWSk56sirXXhFPZRIggAACCCCAAAJRLvD8cb34R05asqx32kShJvvrv/y0fJmdOzvUYjkfAQQQ8KQAHYCefCxUCgEEol3ArEh3s2HAod5XanKi3LuoRBVjD4lRmQQIIIAAAggggECUCzx/rErdwd1OW8i0iUJNB2oOqCKWlywPeVVhVSABAggg4CGB0P/W9NDNUBUEEEDASwL2MGD7LfNY62oPA951rkGaOnrGWhznIYAAAggggAACnhVo7uiVnWfrVf3stpDKDCLYW71XHW233VQmAQIIIBDlAnQARvkDpPoIIOBdAXsS6Uutl8TMBRhq2rSwWNJcb737BwZlywk9f02o1+B8BBBAAAEEEEDACwIvnqiWPqet40vmyz/TFgo11XfWy/nm86oYe/SGyiRAAAEEolyADsAof4BUHwEEvCuwMH+hZKVkqQqGYx7AzNRkuXO+bvgyDFgxEyCAAAIIIIBAjAj88eh1dSd3OW2gbGcOwFCT3SbLTM6UssKyUIvlfAQQQMCzAnQAevbRUDEEEIh2gaTEJFlevFzdRriGAT+0RK98t62yVtq7+9S1CBBAAAEEEEAAgWgWaO3qlVcq9eiJR5bqNtBY788e/mtW/01ODL1jcaz14TwEEEAg0gJ0AEZamPIRQCCuBexhwPtr9ofF476yEqeRmuAvq7tvQF4+xTBgPwgbCCCAAAIIIBD1Ai+drJGe/gH/faQkJci9ZZP9cSgbdgfg6imrQymOcxFAAAHPC9AB6PlHRAURQCCaBezJpE81nJLWntaQb2lSZqqsv61QlfPHI3qFPJVJgAACCCCAAAIIRJnAH47o4b8b5xVJXkZKyHfR1NUkpxtPq3JWT6YDUIEQIIBAzAnQARhzj5QbQgABLwksLV4qKYnDDdVBGZSDNQfDUsVHlk5V5Zi35J09/WofAQIIIIAAAgggEI0CZmqTradqVdUftto+KjOIwB6RkZ6ULuWF5UGUwKEIIIBA9AnQARh9z4waI4BAFAmkJaXJkqIlqsb2pNMqM4jgwfIpkuQaBtzZ2+80lBkGHAQhhyKAAAIIIICARwVM55+Z4sSXzNQnDyyOzPDfZcXLJCVp+IWt75r8RAABBGJJgA7AWHqa3AsCCHhSwB5Ssqd6T1jqWZCVKrfPLVBlPWcNlVGZBAgggAACCCCAQJQI/MFa/ddMfWKmQAlH2lu1VxWzasoqFRMggAACsShAB2AsPlXuCQEEPCVgTyp9rO6YdPR2hKWONxsG3OV8CUhCAAEEEEAAAQSiVcBMafKyM7WJO9ltHndeMNtmLuZTjafUKfbLWpVJgAACCMSIAB2AMfIguQ0EEPCuwPLi5ZKckOyvYP9gvxyoOeCPQ9l4YPEUcY0Clg6nwbytUs+XE0r5nIsAAggggAACCIy3gGnLmDaNL5m2TriG/5o22MDg8NDi1MRUqSiu8F2KnwgggEDMCtABGLOPlhtDAAGvCGSmZN4wD+CeqvAMAy7OSZO1c/QwYHvFPK84UA8EEEAAAQQQQCAQgT9aw3/XzSmUwuy0QE4d9Zi91Xr4r1mwzczZTEIAAQRiXYAOwFh/wtwfAgh4QmDNlDWqHuGaB9AU+qi1It6WEzXCMGDFTYAAAggggAACUSLQ3dcvpi3jTo8sneIOQ9reV7VPnb9qMvP/KRACBBCIWQE6AGP20XJjCCDgJYFIzgP44JIpkuAMjfGltu4+2X66zhfyEwEEEEAAAQQQiBqBV502jGnL+JJp4zxYHp4OQDMH87H6Y76ih34y/5/iIEAAgRgWoAMwhh8ut4YAAt4RGJoHMDEy8wCW5KTLmtl6GPAfWQ3YOw+fmiCAAAIIIIBAwAJ/OFKljl0zq0BKctPVvrEGB2sOipmL2ZfMHM3Lipf5Qn4igAACMS1AB2BMP15uDgEEvCJg5gFcWrRUVSdc8wCaQh9xvgJ0pxeOV4sZQkNCAAEEEEAAAQSiRaCnb0BeOK47AB8O4/Bfe/6/8qJyMW00EgIIIBAPAnQAxsNT5h4RQMATAvYQk3DOA/jQkqnqHludoTOvnWEYsEIhQAABBBBAAAFPC+w4WyctXcPDf01lH7JecoZyA/uq9fx/dtsslLI5FwEEEPC6AB2AXn9C1A8BBGJGwF4I5FjdMTFz0YQjTclLl9Wz8lVR9hAalUmAAAIIIIAAAgh4TODZw9dVjVbMnCRT8zLUvrEGXX1dcqTuiDqdBUAUBwECCMS4AB2AMf6AuT0EEPCOgJljJjlC8wCau3zYWg34+WNVYobSkBBAAAEEEEAAAa8LmKlL/uy0XdzpUatt484Ldvtg7UHpHej1n5aYkCgrSlb4YzYQQACBWBegAzDWnzD3hwACnhGI9DyAD1tDZMwQGoYBe+bxUxEEEEAAAQQQGEHglco6aXUN/zWr/76lYtoIZwSXtfv6bnVCeWG5ZKdmq30ECCCAQCwL0AEYy0+Xe0MAAc8J2HPNhHMewGmTMsQMlXGnZw5fc4dsI4AAAggggAACnhR41mqzmNV/zRQn4Uq7q3QH4Nopa8NVNOUggAACUSFAB2BUPCYqiQACsSIQyXkAjdFbrTflzx+rlq5eVgOOlf9+uA8EEEAAAQRiUaCzp99Z/bda3dpblukFzlRmkEF7b7scrTuqzlo7lQ5ABUKAAAIxL0AHYMw/Ym4QAQS8JBDpeQAfrZgqZsiML7U5qwFvPVXrC/mJAAIIIIAAAgh4TuDlUzXS4XQC+lKi05Z5eEn4OgDN6r/9g8PlmzmZmf/Pp81PBBCIFwE6AOPlSXOfCCDgCYFIzwM4OTdd1s0pUPfKMGDFQYAAAggggAACHhN45pCesmT9bYVSnJMWtlra8/+ZF7IZyeFZXThslaQgBBBAIMICdABGGJjiEUAAAVvghnkAq/bYh4QUv3WZnjB7y4lqaXe+BCQhgAACCCCAAAJeEzCjFV46WaOqFc7FP0zB9vx/66asU9cjQAABBOJBgA7AeHjK3CMCCHhK4IZ5AOuPSVtPW9jqaIbMJJmxM2+mrt4BedHpBCQhgAACCCCAAAJeEzAvKrv7BvzVSnbaMA+VT/HHoW40dTXJyYaTqhjm/1McBAggECcCdADGyYPmNhFAwDsCy0uWS0piir9CZk6avdV7/XGoGwVZqbJxXpEq5plD11VMgAACCCCAAAIIeEHAHv67cX6R5DttmXAl08YadP7nS+lJ6VJRVOEL+YkAAgjEjQAdgHHzqLlRBBDwioCZc8Z0ArrT69dfd4chb9vDgLdV1khzR2/I5VIAAggggAACCCAQLgHTNtlWqRcrC/fwX7uNZRb/SEkafhEbrnuhHAQQQMDrAnQAev0JUT8EEIhJAXvumV3Xd4X1Ph8onyypScN/xff2D8qfj1eF9RoUhgACCCCAAAIIhCJg2iamjeJLpu1i2jDhTPb8fwz/DacuZSGAQDQJDP/rMJpqTV0RQACBKBdYN1VPPn2m6YzUddaF7a5y01Nk88JiVZ49xEZlEiCAAAIIIIAAAuMs8OxhPUXJJqftYtow4Uq1HbVyrvmcKs5+CasyCRBAAIEYFqADMIYfLreGAALeFVhStESyUrJUBe0hKipzDIE9DHjH2Xqpa+seQ0mcggACCCCAAAIIhFeg3mmTvHZGv/x8S8XUsF7E/vovOyVbygrLwnoNCkMAAQSiRYAOwGh5UtQTAQRiSiA5MVnWTF6j7incHYD3lpVIRkqS/xr9A4Pyx6MMA/aDsIEAAggggAACEybwp2NVYtomvpSekij3lUV2+O/qyavFtMFICCCAQDwK0AEYj0+de0YAAU8I2MOAzTyAg4PDDeFQK5mZmiz3LdYNaYYBh6rK+QgggAACCCAQDgG7TXLvosmSlRbezjn75Srz/4XjyVEGAghEqwAdgNH65Kg3AghEvYDdAXi9/bpcbr0c1vt6qzWUZs+FBrne3BnWa1AYAggggAACCCAQjIBpi7x+vkGdEu7hv1dar8jVtqvqGmunrFUxAQIIIBBPAnQAxtPT5l4RQMBTAvMmzZPC9EJVp3CvBmwm085JH36bbj4wfM6acFtVgAABBBBAAAEEEIiwwO8PXnNGPQxfJMf58u/uRSXDO8KwtadqjyolPy1f5ufPV/sIEEAAgXgSoAMwnp4294oAAp4SSEhIEPsrQHuoSqgVTktOkgfLp6hifuc0ukkIIIAAAggggMBECTxttUUeXjpF0l3zFoejXvZL1TVT1khiAv/8DYctZSCAQHQK8DdgdD43ao0AAjEicPvU29WdmNXqBgYH1L5Qg7ctm6aKOHK1Wc7UtKl9BAgggAACCCCAwHgInKpqlRPXW9SlHltequJQA9OWsjsA7ZeuoV6D8xFAAIFoE6ADMNqeGPVFAIGYErAbo03dTXKq4VRY73HDbYVSlJ2myvzdQT0njsokQAABBBBAAAEEIiTwtNUGmZybJuvm6ilRQr10ZWOlNHTpOQbXT10farGcjwACCES1AB2AUf34qDwCCES7wLTsaTIzZ6a6jXAPA05OShT7K8DfHrga1hWH1Q0QIIAAAggggAACNxEYGBgUM/+fO5k2SlJigntXyNs7r+1UZZRml8qM3BlqHwECCCAQbwJ0AMbbE+d+EUDAcwL2V4D2kJVwVPgdK/TQmiuNnbLvYmM4iqYMBBBAAAEEEEAgIIG9TtvjalOnOvbtYR7+awq3OwA3TNugrkmAAAIIxKMAHYDx+NS5ZwQQ8JSA3QG4v2a/9Pb3hrWOS0pz5bbiLFWmPQRHZRIggAACCCCAAAJhFrDbHvNKsqV8Wm5Yr9LV1yX7qvepMtdPY/ivAiFAAIG4FKADMC4fOzeNAAJeElg7Za2qTmdfpxyqPaT2hRqYFYftCbafPXxdevrCu+BIqPXkfAQQQAABBBCITQHT5vjDkevq5h5bPk1MGyWcybxI7Rno8RdpVv6121r+TDYQQACBOBKgAzCOHja3igAC3hTIT8+XsoIyVbmd1/XcNSpzjIE9xKapo1e2VdaOsTROQwABBBBAAAEEAhcwbQ7T9nAnu23izhvr9q5ru9SpSwqXSF5antpHgAACCMSjAB2A8fjUuWcEEPCcwO3Tbld12nF1h4rDEcwszJTVs/JVUU87i4GQEEAAAQQQQACBSAvYw39Nm2RGQWbYL2u/RLXbWGG/IAUigAACUSJAB2CUPCiqiQACsS1gT059rP6YNHaFf5GOx6zFQF48US0tXfptfGxLc3cIIIAAAgggMN4CrU5b48Xj1eqyb7faJCpzjEFdZ52cbDipzl4/lfn/FAgBAgjErQAdgHH76LlxBBDwksDKkpWSkZzhr9KgDMrr11/3x+HaeHTpVElOHJ5rp9uZj+dPR6vCVTzlIIAAAggggAACNwj8+Vi1mDaHL5m2iGmThDvZbSfTtlpWvCzcl6E8BBBAICoF6AD06GO7ePGifOELX5BFixZJVlaWFBQUyJo1a+Qb3/iGdHR0RKTWpty5c+cOTcRrJuOdPXt2RK5DoQggcKNAalKqrJ68WmW8du01FYcjyM9Klc0LS1RRDANWHAQIIIAAAgggEGYBu62xaUGxFDhtknCnndf0HMpm8Y+UpJRwX4byEEAAgagUoAPQg4/tmWeekYqKCvnWt74lp06dGurwa2xslL1798qXvvQlWbFihZw5cybsNf+Hf/gHOX/+fNjLpUAEEAhMwB4GbOYBHBwcDOzkII56hzXkZue5eqlq7gqiBA5FAAEEEEAAAQQCE6hp6ZIdZ+vUwZEY/mvaTPb8f+unMfxXwRMggEBcC9AB6LHHf+DAAXn88celpaVFsrOz5atf/ars2LFDtmzZIp/61KeGaltZWSmPPvqotLa2hq325rr//u//Lunp6ZKTkxO2cikIAQQCF9hQukEdXNNZI2ebzqp94QjuLSuRnLRkf1Gmj/H3h1gMxA/CBgIIIIAAAgiETeC3zoJjA673mVmpSXJ/2eSwle8r6FzzOanpqPGFQz+Z/09xECCAQJwL0AHosf8APvvZz0pnZ6ckJyfL888/L1/5yldk/fr1cs8998j3v/99+bd/+7ehGptOwG9+85thqX1/f/9Q56L5aa5nhhuTEEBg/AXm5M6RqVl6PpxIDANOT0mSh5ZMUTf4m/10ACoQAgQQQAABBBAIWcB8lffU/iuqnIeduf8ynE7AcCd7+O/kzMkyJ29OuC9DeQgggEDUCtAB6KFHt3v3btm+fftQjT75yU8OdfzZ1TPzApaVlQ3t/s53viO9vaGv3mnK2bdvnyxcuFC+/OUv25ckRgCBcRIwc2/aw4Dtxmy4qmIPAz5Z1SrHr7WEq3jKQQABBBBAAAEE5OjVFqmsblMS71o5XcXhCm42/Ne0rUgIIIAAAm8I0AHoof8Snn76aX9tPv7xj/u33RuJiYnykY98ZGhXU1OTvPzyy+7soLfNYiNm7j+Tvve970lqavgn4w26UpyAQBwL2B2Ae6v3Sldf+OfnWze3UKbmpStp+w29yiRAAAEEEEAAAQSCFLDbFqWTMmTdnPCPNurt75U9VXtU7Rj+qzgIEEAAAaED0EP/Ebz66qtDtTGr/q5ateqWNdu0aZM/77XXXvNvj2Xjr//6r6W9vV0+/OEPy+bNm8dSBOcggEAYBdZNXSeJCcN/NXf3d8v+6v1hvMIbRSUlJoj9FaBZoa+3fyDs16JABBBAAAEEEIg/gZ6+AfndQT3FyLtWlkqi0wYJdzpQc0A6+zpVsaZNRUIAAQQQGBYY/lfm8D62JkjgxIkTQ1eeN2/e0ByAt6rGokWL/Fm+c/w7gtj45S9/KX/4wx8kPz8/bPMJBnF5Do1lgf4+kWanwVdzUqStNpbvNOz3lpeWJ0uKlqhyd1zboeJwBe9apYfg1Lf3yNZTPK9w+VIOAggggAAC8Szw8qkaaezQ0xW9M0LDf1+9+saHFD7vxYWLpTCj0BfyMxAB02Y3bXfThjdteRICCMScwPAykDF3a9F1Q11dXVJXVzdU6enT9T/K7TsxHXbmK0Hz5d7ly5ft7IDixsZG+dznPjd07L/+679KcXFxQOcFetCVK3qyX/u869ev27u8H/+f2506vrmEmVk2VSUrnvB8VTkncNUv0nUbcBoMHQ36mkULRG7/v0RWflQkMfyTPtt3G+3xHdPukMO1h/23YRYC+X+c/4U73VacLStnTpL9l5r8RT+174rcv3iyP2YDAQQQQAABBBAYi4BpU7jT6ln5Mrsoy70rbNvbr74xj7qvwI2lG32b/BxJYKBfZP9PRHZ9V6Su0nWk85VmVpFIWo5IqvPMktKcf06YUSLOvymG/i3h/mlOezMeOsZVTDxsmnkm/3pnPNwp9xgDAnQAeuQhtra2+muSnZ3t377Vhq8DsK1NT6p7q+Pt/V/84helurp6aKGRT33qU3Z2yPGMGTNCLsNzBdS+8YWm5+oVDRUyDYpnPy9y4hmR9z7xRmMiGuo9QXU08wB+95DTEHsznWk6I9Xt1TI5K/wdc+YrQHcH4JaT1dLgfAlYkMV8oD5/fiKAAAIIIIBAcAKmLWG+AHSnd1sjD9x5oWxXtVeJaSu5052ld7pDtm8m0O38+/NXztzyZ1+6Sa7TodfufBFo/pBGEQj/kPZRLkg2AmMWYAjwmOnCe6L5AtCXAlmIIy3NeQvjpM7OTt9pAf985ZVX5Ec/+tHQMGOz8AerYwVMx4GhCpgGxn+/3xlWoIeDhFpsrJ1vhgDnpDhvXF3JXtnOlRXS5lsqpklq8vCvgt7+Qfm9NV9PSBfgZAQQQAABBBCIOwHTljBtCl9Kc9oaj1RM9YVh/WkP/81NzZWlRUvDeo2YK8y0xU2b/KadfzF3t9wQAgi8KTD8rz5IJlQgPX14Nc6enp5R69Ld3T10TEZGxqjHug8w5/3lX/6l8+X2oHz2s5+ViooKd3bYts3Q5JH+7N69O2zXoqAoE7jgDNHY8r+irNLjW93kxGS5fZoZcj6cdlyNzDyAeRkp8mD5lOELOVtP7dcTdqtMAgQQQAABBBBAYBSB/9mvh/+atkZuesooZ40t2+4ANCMpkphyZmRM0xY3bXISAgjElQBDgD3yuHNyhr/2CWRYr5n/z6RAhgu7b/GrX/2qnDp1SswQ3f/1vyLXCTPaPIbuOkXN9rt/rKtq5ntQyYpV/kh5ppCR8kfKC+O51mVGrpN1XXWvTl6mM+lyjvOWNz1P5Pw2kd/8pTMvYL056Y208/+ILH6HyPRVvj38tARM4/WFiy/49+64vkP6nXlaItGgNUNynjl0zX+tI1eb5WRViyyakuvfxwYCCCCAAAIIIBCIwKmqVjl6tUUdai88pjJDCHqdL9l2Xd+lSrij9A4VE1gCV/aK7PgPvdO03d/5fZE5m0S6mkVanfnazfDfHuffnOZPn/PxSYLz7dBQm9/5R4P5aWLzbxi1z4ntf9foKxEhgMAECtABOIH47kubLwALCwulvr5eRltAwyzg4esADHauva9//etDl73vvvvkmWeecVfBv+0r2/w0KwWbVFJSIvfcc4//mLjcWPLOuLztkG963n0iH/y1yI8ecob+vvl1q5kg+E9/J/LJ599sNIR8lZgrwCwE4k7N3c1ypO6ILC9Z7t4dlu2N84pkcm6aVLe88WWxKdRM3P3/Pro4LOVTCAIIIIAAAgjEj8BT1td/po1h2hqRSAdrD0p77xsfRvjKZwEQn8RNfpoFPEwb3L1AoFngw7TVS998MW8W/zB/SAggEHMCdAB66JEuXrxYtm/fLmfOnJG+vr6hOfpuVr2TJ0/6d5eVlfm3A9nwDS/+8Y9/LObPSMmsSvz+979/6JBNmzbRATgSFnkjC5gGxWansbHln4aPu+IMAz/tfOG24IHhfWz5BaZmT5V5k+apSa3NCneR6ABMSkyQd6yYLt/bdtZ//d8euCZffmiRJCeZt7skBBBAAAEEEEBgdIG+/gH57QE9lchjK0qdEQzmy7DwJ3v4b1lBmRRl0Hl1S+nTzsv3K3t0tmmj+zr/dA4RAgjEmAD/svPQA9248Y3l6s2Xd/v27btlzbZt2+bPu+MO/ZWQP4MNBLwmsOH/FsmfrWv10j87LyCdN5GkmwrcOV2vYLf9SuTmann3qlJVh7q2bnnltDP0g4QAAggggAACCAQosP1MndS2Do8oMKe9e+X0AM8O/jC7A5Cv/0YwHHBG4Lz0L/oA0zbf8Bm9jwgBBGJWgA5ADz3axx57zF+bW32dN+D8xf3EE08MHTdp0iS5++67/ecEsmEW/xjtz6xZs4aKMj99x27dujWQ4jkGgVsLJDkTP28yQw5cqeqwyMlnXTvYdAvcVXqXO5QTDSekpqNG7QtXMK8kR5bNmKSK+x9nGDAJAQQQQAABBBAIVOB/9uq2Q8X0PJk/eXiu80DLCeS46vZqqWysVIfSAag4dHDqORHT9nanzX8vYtroJAQQiAsBOgA99JjXrl0rd975xhc/P/zhD2Xnzp031O6b3/ymnDhxYmi/WcU3JUX/hW066hKciVjNn4997GM3nM8OBCZUoOK9IkULdBXMgiCkmwosK1kmOSm60fza1dduemw4dprFQNzpxeM10tTx5ryN7gy2EUAAAQQQQAABS6ChvUeeP16l9tptC5UZYvDaNd0myknNkYriihBLjeHT7YU/TJt86Xti+Ia5NQQQsAXoALRFJjj+zne+IxkZGUNzAD7wwAPyta99TXbt2iUvv/yyfPrTn5YvfelLQzVcsGCBfOELX5jg2nJ5BIIUSEwSueuL+qRLTkf3tQN6H9GQQEpiiqyftl5pvHLlFRWHM3hbxTRJdc351+PM4/O7g9fCeQnKQgABBBBAAIEYFfiNs/hHb//w1C6pyYny9mV6ipFw3ro9/Hf91PWSnMgU9zc1vrpf5LJeLVnucv5dadrmJAQQiBsBOgA99qhXrFghTz75pOTm5kpbW5t85StfkfXr1w8twPH9739/qLam8++5556TnBz9ZZDHboXqIHBzgfJ3iORM1Xm7vqdjIr+APQ/gzus7ncZ1rz8/nBt5mSlyf/lkVeQv91wemgpA7SRAAAEEEEAAAQRcAmbaoF/tvezaI/Lwkili2haRSL0DvbLzmh4txfDfEaRft9raOdNEyoennxrhTLIQQCCGBOgA9ODDfOtb3yqHDx+Wz3/+82I6+zIzM8XM97d69Wr5+te/LgcOHJB58+Z5sOZUCYEABMw8I2v+Qh949CmR1mq9j2hIwG7Mtve2y4GayH0x+fjqGUr+xPUWOXq1Re0jQAABBBBAAAEE3AIHLzdJZXWbe5fYbQqVGWJwuPawtPXq691RyuKIN2VtdYZlH/2NzlrrtMWZ+0+bECEQBwJ8I+3Rh2wW4PjWt7419CeYKm7evDnkr3UuXLgQzCU5FoHgBVZ9XOSVb4j0db1xrvMWVw7+TOROhrXbmEUZRVJeWC7H6o/5s7Zf3S5rp671x+Hc2DivSEonZcjVpk5/sb/cc0mWTl/qj9lAAAEEEEAAAQTcAvbXfzMLMuX2uYXuQ8K6vf3KdlXewvyFUpJZovYRvClw8Ocipq3tS8npIqYtTkIAgbgT4AvAuHvk3DACHhDIchqES96tK7LvJ07jZEDvIxoSsIcBR3IewMTEBHmv9RXg7515ADt6+ngaCCCAAAIIIIDADQLt3X1i2gru9N7V08W0KSKVtl3Zpoq2R0yozHgOnKHZcsB5ye5Opg2eWeDewzYCCMSJAB2AcfKguU0EPCew2nrz2HRR5LxuzHmuzhNUobtK71JXPtd8Tq60XlH7whm8x2m0OwuJ+1Or07D/wxG9qp8/kw0EEEAAAQQQiGuB545cl/aefr+B6fd79yo9pYg/MwwbV9uuypmmM6qkzTM2q5jgTQGz2F7DOc2x8iM6JkIAgbgRoAMwbh41N4qAxwRKV4mUlOtK7fsvHRMNCZQXlUtBun5Ta698F06qac4Q4E0LilWRv3IWAyEhgAACCCCAAAK2gN1GMG2IKXnOMNMIpW2X9Qvj/LR8WVrEVCU35ba//iucLzIjMtPI3PT67EQAAU8J0AHoqcdBZRCIIwHzidmqj+obPvmcSHud3kckiQmJcsc0PbF1JIcBG3J74u7dFxrkbK2ebJtHgwACCCCAAALxLXCmpk32XmxUCI+vidzXf+ZC9vBfM1VKUmKSqgOBI9DdKnLst5pixYdEDfPQuUQIIBDjAnQAxvgD5vYQ8LRAxXtFzETEvjS0GMgvfBE/XQL2PIC7q3ZLl28RFddx4dq8t2yyFGalquLsN/wqkwABBBBAAAEE4k7g13v1CAHTdrhn0eSIObT3tsueqj2q/Lum66lSVGY8B6bzr7djWCDB6SRd9r7hmC0EEIg7AToA4+6Rc8MIeEggI19k8WO6Qvt/Is5S1nofkWyYtmHoS0AfRXd/t5hOwEil1OREedeq6ar4p/ZfkZ6+AbWPAAEEEEAAAQTiU6C3f0BM28CdTNvBtCEilXZe2ym9rhVtkxOSbxglEalrR1259vDf+Q+I5EyJutugwgggED6ByP3tHL46UhICCMSygD0MuN6Z1PnSrli+4zHdW15aniwvXq7OtefAUZlhCOzVgOvaeuSlk9VhKJkiEEAAAQQQQCDaBV46WSOmbeBOdtvBnReO7a2Xt6piVk1ZJdmp2WofgSNQWyly+XVNYYb/khBAIK4F6ACM68fPzSPgAYGZ60XMhMTuZL+xdOfF8ba9wp1pBA8MRu6LvHkl2bJmtvOVpiv9ksVAXBpsIoAAAgggEL8CT1ptglWz8sW0HSKVTJtn+9XtqvjN0zermOBNgYM/1xRZzuJuCx7U+4gQQCDuBOgAjLtHzg0j4DEBsxiI/UbSzFliJi4mKQG7A7Cms0ZO1J9Qx4Q7eHzNTFXktspaudbUqfYRIIAAAggggEB8CVx12gJbT9Wom7YXEFOZYQiO1B2Rhq4GVdKm6ZtUTOAI9PeJHPpvTVHxuEhSit5HhAACcSdAB2DcPXJuGAEPCpgJic3ExL7kTPB8w6plvrw4/jknb47Mzp2tBF6+/LKKwx08snSK5KQl+4s10zP+eq+e78efyQYCCCCAAAIIxIXAk7svOaMQhm8122krPFoxdXhHBLbsqU/m5s2VGbmRXXE4ArcR+SLPvCjSZk3ZYr9sj3wtuAICCHhQgA5ADz4UqoRA3AmYCYnNxMTuxDBgt4Z/2/4K0J4Lx39gmDYyU5PlbcunqdKe3HNJ+t2tfpVLgAACCCCAAAKxLGAW/7CnBHlsxTTJcr0wjMT9b7uyTRW7aQZf/ykQX3Dgp76tN36WrhYpKdP7iBBAIC4F6ACMy8fOTSPgQYGVH9aVMhMX157S+4jE7gA81XhKrrVdi6jM+6xhwNeau24Y9hPRClA4AggggAACCHhGYMuJGqlp7Vb1+cDaWSoOd2DaOpWNzsIWrsTwXxeGb7OtVqTyT77ojZ98/ac9iBCIYwE6AOP44XPrCHhKwHwBaCYodie+AnRrDG2blYAnpU1S+yP9FeDS6XlS4fxxp5/tuugO2UYAAQQQQACBOBH4hTP8151WzJwki6fluneFffuVK6+oMvPS8mRZ8TK1j8AROPykyIAzB6AvJWeILHmnL+InAgjEuQAdgHH+HwC3j4BnBMzExGYuQHc69EtnIuNe9564305KTJK7pt+lHCI9D6C52IfW6Tf7W53FQC43dKh6ECCAAAIIIIBAbAtcqu+QV5w2gDt90GojuPPCtb31ylZV1MbSjZKcODxHscqM18BM1GwP/138dpF0/RI3Xnm4bwQQEKEDkP8KEEDAOwIrrGHA7c7qcqdf8E79PFKTu2fcrWqyt2qvtPZEdtXktyybKjnpww1t08b8pTMXIAkBBBBAAAEE4kfgv63f/blO2+AtEV78o6O3Q/Zc36OQN0/frGICR+Dqfmf6nJOaguG/2oMIgTgXoAMwzv8D4PYR8JRA8UKR6Wt1lew3mTo3LqMN0zZISqLzxeSbqW+wT167+povjMhPsxjIu1ZOV2U/ueey9PQNqH0ECCCAAAIIIBCbAuZ3/q/3XlY3965V0yU9JUntC3fw6tVXpWegx19sckKybCjd4I/ZeFPAbjPnzxaZdQc8CCCAgF+ADkA/BRsIIOAJAftNZeWfRVqrPVE1r1QiMyVT1k1dp6ozHsOAP7BuprpmXVuPPH+8Su0jQAABBBBAAIHYFDC/883vfnf6oNU2cOeFa3vLpS2qqDVT1khuamTnHFQXjIagx5mW5ehTuqbLP+SM9+Of+xqFCIH4FuBvhPh+/tw9At4TKH+HiNPB5U+D/c6Exs5cgCQlYA8D3n51u/QORHa+xAWTc2TtnAJVj5/vuqRiAgQQQAABBBCITQH7d75pE8wryYnozfY6c0Fvv7JdXePemfeqmMAROPGMSHeLiyJBZPn7XTGbCCCAAHMA8t8AAgh4TSDdeaO7+DFdK7MasJl0juQX2DR9k3/bbJg5APdXO3O/RDjZb/p3nquXMzVtEb4qxSOAAAIIIIDARAqcrW0T8zvfnew2gTsvXNt7qvZIa6+e53jzjM3hKj52yrGH/952j0ienroldm6WO0EAgbEK8AXgWOU4DwEEIiew0loMpK5S5PLuyF0vCkuenDVZygvLVc23Xt6q4kgEDy2ZIgVZqaroX7zOV4AKhAABBBBAAIEYE/hv63e9aQuYNkGkkz38t6KoQkwbiOQSaDgvckF/JSn2lDquw9lEAIH4FaADMH6fPXeOgHcFZq4XKZir62e/2dS5cRnZb8BfuvSS86FkZL+UTEtOkves1m+U/2ffZenscYZqkxBAAAEEEEAg5gS6evvlf/ZfUff1bmfxD9MmiGQaGBwQe47je2beE8lLRmfZB3+h650+SWThI3ofEQIIIOAI0AHIfwYIIOA9gQRn3hL7zeWx3zpzmzDU1P2w7HkAr7VfkxMNJ9yHRGT7g2tnqXJbuvrk2cPX1D4CBBBAAAEEEIgNgWcPX5emDj3P8PvX6oXBInGnR+qOSG1nrSqaDkDFITLgvIC1OwAr3uvMp51uHUiIAAII0AHIfwMIIOBVgWXOxMUJrncUPU7n3/HfebW2E1KvBfkLZEbODHXtFy++qOJIBDMLM+WuBcWq6J9bQ4NUJgECCCCAAAIIRKWAGVnwkx0XVN3vmFcoc4qy1L5IBPbw37l5c2VO3pxIXCp6yzy/TaRFf515w0v06L07ao4AAmEWcP3rOswlUxwCCCAQikDuNJF59+sSGAasPBKcLyXvm3mf2mc3llVmGAN74u+Dl5vkyJXmMF6BohBAAAEEEEBgogWGfr9f1b/fP7J+dsSrZToezdQm7sTXf26NN7fNQnnuNGWpyNRl7j1sI4AAAn4BOgD9FGwggIDnBOxhwJd2itSd8Vw1J7JC983SHYDnms/JuaZzEa/SvYtKZEquHl7yX9YXAhGvBBdAAAEEEEAAgYgKPLHzoiq/dFKGmDZApJNpz1xs0de+d+a9kb5sdJXf0SBy4lld5xXWQno6lwgBBOJcgA7AOP8PgNtHwNMCCx4SySzUVTxovenUuXEXLSlaIiWZuiH+4qXIDwNOTkoU+yvAZ5x5AOvbuuPuGXDDCCCAAAIIxKJAbWu3POfM/+dOH7p9lpg2QKSTPaLBtHXKC8sjfdnoKv/oUyL9rnZXUqrI0vdE1z1QWwQQGFeByP/tPa63w8UQQCCmBJKdhkzF+/QtHfxvp7HTp/fFcZTozJNovxEfj3kADfn7182UVNc/Anr6BuSXey7H8dPg1hFAAAEEEIgdgV/uviQ9/QP+G0pNTpTH1+i5h/2ZYd64YfjvjHvETH1CcgnYU+MsetR5cV7gOoBNBBBAQAvQAag9iBBAwGsC9jDgtiqRM5H/ws1rDCPVx54H0KwEfKX1ykinhCWvKDtN3lIxVZX1s10Xpc/1jwWVSYAAAggggAACUSHQ6/wutxf4etuyaVKQ5bycjXCqaq+SY/XH1FXuncXwXwVy/ZCI+eNOdpvZncc2Aggg4AjQAch/Bggg4G2ByYtFSlfpOtpvPHVu3EUrJ6+U/LR8dd/20BmVGcbgoxtmq9KuN3fJ88er1T4CBBBAAAEEEIgugRec3+VVLV2q0h8dh8U/zAXtNkxuaq6smmy1BVXN4jDY/1N907mlInPv1vuIEEAAAUuADkALhBABBDwoYL/RrPyTSFutBys6MVVKTkyWu2fqRp/deI5UzZbNmCTLnT/uxGIgbg22EUAAAQQQiD6Bn+y4oCq9cuYkWTo9T+2LVPDCxRdU0Zumb5KUxBS1L66D3k6Rw7/SBKatnJik9xEhgAAClgAdgBYIIQIIeFBgybtEkjOGKzbgzAF4+MnhmK0b5gE8WHNQajvGp5P0Y9ZXgLvPN8iJ6y08FQQQQAABBBCIQoGTVS3yuvO73J3sL/7deeHcNm2X/dX7VZH3z7pfxXEfnHhGpLvZxeDMjbj8g66YTQQQQODmAnQA3tyFvQgg4CWBdOeN8+K36xqZYcCDg3pfHEe3T71dslOy/QKDMij2BNr+zDBvPLJ0qpj5AN3J/nLAncc2AggggAACCHhX4Cc7LqrKmd/xDy/Rc/6qA8IYvHjpRacFM9y+y0rJkg2lG8J4hRgoav8T+ibmbhbJn6X3ESGAAAI3EaAD8CYo7EIAAQ8K2MOAa0+KXN3nwYpOTJVSk1Llrul3qYubRvR4JLMq4AecFYHd6emDV6Wpo8e9i20EEEAAAQQQ8LhAc0evPH3gqqrlB9bOEPO7fjzS8xeeV5cxw3/TkvRLRnVAvAX1Z0UubNd3vfIjOiZCAAEEbiEwPn+T3+Li7EYAAQQCFph1h/N2c7Y+nMVAlMd9s+5T8Z6qPdKshoio7LAGH3Q6AJMTnSEob6au3gH51d7LvpCfCCCAAAIIIBAFAr/ed1k6e/v9NTW/2z94+/h8XVbXWSf7qvXL3QdmP+CvCxuOgN32zSgQWfQoNAgggEBAAnQABsTEQQggMOECic5fV8udCY7d6chTIj0d7j1xvX3HtDvUW/L+wf5xGwY8OTddHnaGArvTEzsvSv/A8DAedx7bCCCAAAIIIOAtAfM7217I68ElU8T8jh+PtOXiFjX8NzM5U0zbhvSmQL8zB/bBX2iOZe9z5snmC0mNQoQAArcSoAPwVjLsRwAB7wks/4BTp+GvzKSnVeT477xXzwmqUWZKpmws3aiu/qcLzorJ45Q+tkF/IXClsVO2nKgep6tzGQQQQAABBBAIReD5Y1Vifne7k73Qlzsv3NvPX7SG/87YJOnJ49P5GO57iUh5px2fNqtdteLDEbkUhSKAQGwK0AEYm8+Vu0IgNgXySkXm3avv7cDPdBzn0YOzH1QCr19/XRq7GtW+SAUrZ+bLktJcVfyPXjuvYgIEEEAAAQQQ8KbAD1/Vv7MrpufJ6ln541LZ+s562Vu9V13rwVm6TaMy4zGwF/+YvkZk8uJ4lOCeEUBgjAJ0AI4RjtMQQGCCBOzFQC6+KmImRCYNCZjJstOTht+Wm2HA47UYSEJCgnxswxz1JHada5CjV5vVPgIEEEAAAQQQ8JbAoctNsveifmH4yY1zxPxuH4+05dIWGRgc8F9qaPhvKcN//SAt10VO/9kfDm2w+If2IEIAgVEF6AAclYgDEEDAUwILHxExEx67kz0fijsvzrbNMGB7NeA/n7cajBE0eeuyqVKco+eisb8oiODlKRoBBBBAAAEExiBg/66e4sz794g1t+8Yig34lJut/svwXxffIWfuP1cHqaRkiZS/w3UAmwgggMDoAnQAjm7EEQgg4CUBM9FxxeO6RqYDcGB4xTqdGX+RPQx4T/UeMSvrjUdKS06Sj67XcwE+c+iaVDV3jcfluQYCCCCAAAIIBClwvblT/nDE+cLMlT7izOubkjQ+/1Q0w39NW8WdWP3XpTHgfBm5/6euHc7mkneKpOXofUQIIIDAKALj87f6KJUgGwEEEAhKYMUH9eGt10TOvqT3xXF05/Q7JSM5wy9ghtS8ePFFfxzpjQ+smyXpKcO/XvqcVQV/svNCpC9L+QgggAACCCAwBoGf7Lgo5ne1L2WkJMkH1s70hRH/+dLll9TwX9OGuYPhv8PuZrqbRj0/o6z86HA+WwgggECAAsP/QgvwBA5DAAEEJlxgylKRqct1NQ5Yb0Z1blxFpuG8ecZmdc/juRpwQVaqvGvldHX9n++6KO3dfWofAQIIIIAAAghMrID53fyL1y+qSrxrValMykxV+yIZ2MN/zVQm7heZkbx2VJS998e6msWLRKav1vuIEEAAgQAE6AAMAIlDEEDAgwL2YiAn/yDSPj7DXD2ocUOV7GHA+6v3S01HzQ3HRWrHJ5yJw92ppatPntp/xb2LbQQQQAABBBCYYAHzu9n8jnanj9+hf4e788K9baYo2VNlDf+d9UC4LxO95bU5bbcTz+j6m6//xmlxFn1hIgQQiHYBOgCj/QlSfwTiVWDpe0SSh1e7lYFeERYD8f/XsLF0o2SnZPvjQRmUFy6+4I8jvXFbcbbcu6hEXeZHr56XftcQI5VJgAACCCCAAALjKjDg/E7+8WsX1DXvcX53m9/h45XM13/9g8PzOJsv/0wbhvSmwIGfOfNcO21cXzJt32Xv80X8RAABBIISoAMwKC4ORgABzwhkTBIpe5uuzj5niISZKJkkaUlpcveMu5XEn87/ScWRDj55p/6C4EJ9h2w5UR3py1I+AggggAACCAQg8NLJGjlf166O/AvrC36VGYHgj+f/qEo1bZfMlEy1L24D06Y1bVt3KncW/8gscO9hGwEEEAhYgA7AgKk4EAEEPCew+hO6Sg3nRM5v1fviOLKHAR+sPShV7VXjJrJ+bqEsnpqrrvcD5ytAEgIIIIAAAghMvMAPrd/Ji6bkyPrbCsetYlfbroppm7jTo3MfdYfxvW0WuGu6pA3WfFLHRAgggEAQAnQABoHFoQgg4DGBmbeLFJfpSu35oY7jONowbYPkpOYogT9f+LOKIxkkOPPT/IX1FeDu8w1y+EpTJC9L2QgggAACCCAwisCRK82y81y9OuqTztd/5nf3eCX767+8tDxZP3X9eF3e+9fZ+yNdR7MIXukqvY8IAQQQCEKADsAgsDgUAQQ8JmAaqfab0FPOUJKWax6r6MRUJyUpRe6dea+6+HgPA35LxTQpyUlTdfjBdr4CVCAECCCAAAIIjLPA9145q65YlJ0mb1s+Te2LdGB3AN4/634xbReSI9B8VaRSD48WM/JlHDtoeQ4IIBB7AnQAxt4z5Y4QiC+BisdFUrKG79lMJL3vJ8NxnG89NPshJXC0/qhcaL6g9kUySE1OlI9umK0u8dyR63K5oUPtI0AAAQQQQACB8RG4WN8uf3R+F7vTJzbOlrTkJPeuiG6faTwjlY2V6hqPzHlExXEd7H9CZNCZA9CXUp2FWcwCeCQEEEAgBAE6AEPA41QEEPCAQLozx1yF1SDa73QA9vd5oHITX4V1U9dJQbqeLPq588+Na8U+uG6mZKQM/6PCrAT8g+3OfI0kBBBAAAEEEBh3gf/P+R3s/Cr2p6zUJPnguln+eDw2/nD+D+oyJZklsmoyw1uHUEwb1rRl3anivSJpeloXdzbbCCCAQCACdAAGosQxCCDgbYHV1oTIrc5bbXvYhLfvIGK1S05MlofnPKzKf+7cczI46Gr5q9zwB5MyU+V9a2eogp/ce1nq27rVPgIEEEAAAQQQiKxAnfO799d7r6iLfMB5UZeXMX5Db00bxB7++/DshyUxgX+aDj2Yyj+JmLasO9kL37nz2EYAAQQCFOBv2QChOAwBBDwsMLVCZPoaXUEWA/F7PDpHr6h3ufWyHKk74s8fj42/uHOuJCc6cza+mbp6B+QnOy/6Qn4igAACCCCAwDgIPLHjgnT3DQ8tTUlKkE84i3+MZzJtkCttuhPy4bn6ZeV41sdz17IX/zBtXLMACAkBBBAIUYAOwBABOR0BBDwiYH8FeO5lkXo9wbVHajru1VhStERm5sxU13323LMqjnRQOinjhsnFn9h5Qdq7GaodaXvKRwABBBBAwAiY37n2y7e3Ly+VqXkZ4wpkD/+dlTtLFhcsHtc6ePZiDedFzm7R1ePrP+1BhAACYxagA3DMdJyIAAKeEih/TCR9kq6S/QZV58ZNlOCsGPeWuW9R9/vnC3+W3oFetS/SwV9tuk1doqmjV36557LaR4AAAggggAACkRF40vmd29ypf/d/+q65kbnYLUrtH+gX0wZxJ7P4h2mrkByBff+lGdLzRMrfofcRIYAAAmMUoANwjHCchgACHhNIcd5er/iQrtTBn4v0dul9cRo9OlcPA27oapBd13aNq8aCyTly76ISdc0fOhOR9/YPD0VSmQQIIIAAAgggEBYB87v2h686X5e50n1lJTLf+d08nmlP9R6p66xTl7TnKlaZ8RT0djqLfzyh73j5B0VMG5eEAAIIhEGADsAwIFIEAgh4RMAeItHZKHL8aY9UbmKrMTN3plQUOXMlutJ4rwZsLv1Xm/VXgNeau+T3B6+5asUmAggggAACCIRb4NnD1+Rqk9PB5Eqftr7Md2VFbPOZs8+osssKymRO3vjOQagq4KXg6FMinQ26Rqs+rmMiBBBAIAQBOgBDwONUBBDwmECh07k0d7Ou1O7v6ziOo0fmtmOSWQAAQABJREFUPqLu/qVLL0lHb4faF+lgzewCWT0rX13mP185KwMD47cqsbo4AQIIIIAAAjEuYFbd/c9t59RdrnJ+F5vfyeOZTJvjhYsvqEvaIxRUZjwFzjOS1/9T3/Hcu0WKF+h9RAgggEAIAnQAhoDHqQgg4EGBNX+hK3V1n8iVvXpfnEYPzX5IkhKS/Hff2dcpL11+yR+P14Y9F2BldZu8dLJmvC7PdRBAAAEEEIgrga2nauVkVau65/Ge+89cfMulLWLaHr6UmJAodAC+qXH5dZGqwz6aN36u+7SOiRBAAIEQBegADBGQ0xFAwGMCCx4WyZuhK2W/UdW5cRMVZhTK+mnr1f0+d+45FY9HcI8zD+D8kmx1qe9tY8VmBUKAAAIIIIBAGATM13//+6XTqqTbirPkvrLJat94BL8/+3t1mQ3TNkhRRpHaF7eB3VadNEtk/gNxy8GNI4BAZAToAIyMK6UigMBECSQli9hfAR77rUhr1UTVyFPXtd+077y2U+o768e1jomJ/z975wFfRbH98V96TyiB0AKhd5QWehEQVKQIDxClg/Up2Av67DxsKDwVQeVh7yCKoAhIk15DL6EmdEjv9T9zeUn23KB/affuvfs77+Mnc87u3TnzXd7O7OzMOR6wXwW46WgiNhy2i3vjUK9YGQmQAAmQAAm4H4G1h85jy7Ek0TAd+0/3xY6UU+mnsP6kWuVmkL61+xo0CxdTVCzkPXJyFNF3AZ4luzYsTIdNJwESuIoEOAF4FWHyUiRAAiYh0GIE4G3ImFaQC2z6r0mcc64b3SK7IcDAJr8wHwsPL3S4U32vr4IqYf6iXvsVCuIgFRIgARIgARIggUsm8O7vseI3VcsE4LbmVYXNEYrecVCo/lckwT7BuCFSxbijXBijFuSVkPAJBJoPK9FZIgESIIGrRIATgFcJJC9DAiRgIgKBKqh1s8HSIT0BmJctbRbUAtWgskf1HqLl82LnQW8RcqT4eHni7s61RJWrDpzDtji5SkGcQIUESIAESIAESOBvE9isVtevOShX+d/XtTZ0H+xI0WMM++y/vaJ6wd9bfgh0pE+mqUuPTTd/LN1pNgQIkAnT5AnUSIAESODyCDj26X95PvJXJEACJHDpBOwDJ6efBfRWYAr61eknKOxP3I+9CXuFzRHK7dHVER7sJ6p6Z6mMUyQOUiEBEiABEiABEvjbBN5bJlf/RYT64R8tq/3t31+tE3ef342DyTLWb5/afa7W5V37OnpsqseoRom+26ixTAIkQAJXjQAnAK8aSl6IBEjAVAQiGgNRnaRL696HWuombRbUWldqjcpBlUXLfzz4o9Adofj7eKlVgDVFVUtVNuCdx5OFjQoJkAAJkAAJkMClEdB96e+qTzXKXZ1qQfe9jhb75B9Vg6uiecXmjnbDnPXZJ//QY9eIRub0lV6RAAm4PAFOALr8LWQDSIAE/pRAm3vloZPbgPiN0mZBzdPDE/aBt3Vsntx8FSvRwXJnmxooG+gjarVfsSAOUiEBEiABEiABEvh/Cdj3peWCfHFHm+r/7++u9gl6bPHL4V/EZfXqPz0WsbzEbwJObJEY7HewyKPUSIAESOCKCPDJe0X4+GMSIAFTE6h/MxBmN9hdP8PULjvKuX615TbgpOwkrIhf4ajqi+sJ8vPGOLUiwSi/7DyF/adTjSaWSYAESIAESIAE/iYB3YfqvtQoYzvWRKCvt9HkkPIfx/9AYnaiqKtPLW7/tQGxX/0XFgnUU2NXCgmQAAlcIwKcALxGYHlZEiABExDwVNtcou+SjuxWW11TTkibBbXI0Ei0jGgpWv5jrOO3AWsHRrSrgVB/+VJin7VQOEqFBEiABEiABEjgTwlMt4v9p/tY3dc6Q+y3/+qtv9VD7T7OOsMxZ9eZqiZo7WNTtx4LeMnxkLPdZP0kQALuRYATgO51P9kaEiABewIthgMq822xFOQBG2cVq1Yu2K8CXHV8Fc5lnnM4khB/H4zqIGMB/rz9BA6dTXO4L6yQBEiABEiABFyZwJFz6fgpRn7o1H2s7msdLQlZCVgev1xUy+Qf/8Ox4QOgwBB6RWdEbjFSsKJCAiRAAlebACcArzZRXo8ESMBcBALKAs2GSJ82zwZyM6XNglrPqJ4I8A4obnl+YT50LEBnyJgOUQjyLQlMXqBytUxfftAZrrBOEiABEiABEnBZAtOXx0L3oUWi+9bR7aOKVIf+/fngz8jTH17/J76evuhZo2eRat2/OemlP0brsWpgOesyYctJgAQcQoATgA7BzEpIgAScSsA+GUjGeSDma6e6ZIbKg3yCcGONG4Ur82LnqUTJhjcHcfTaKWUCfTHC7gXlh63HEZeQce0q5ZVJgARIgARIwI0IHD2fjjlbjosWDVNbf8uqBCCOFj2WmHtgrqj2xqgbEeYXJmyWVLZ9CWQlyaa3e0Dq1EiABEjgGhDgBOA1gMpLkgAJmIxAxQZA7W7SqbXvqq0XBdJmQc1+G3BsUix2J+x2ColxKkB5gE/JKsB8tYTBPouhUxxjpSRAAiRAAiTgAgT+szQWuu8sEn8fT4zrWKtIdejf7ee242CyXMk/oM4Ah/pgysoK8oG170nX6vYCKtSTNmokQAIkcA0IcALwGkDlJUmABExIwP7L6vlYYP+vJnTUsS61qtQKVYOrikrnHZgndEcp5YP9cGcbGRj8+83xOHaeqwAddQ9YDwmQAAmQgGsSOKxi//2wNV44P6JdFCqE+AmboxT71X+RIZHQYw7Ly76FQOJhiaE9V/9JINRIgASuFQFOAF4rsrwuCZCAuQjoFYAVG0uf1rwjdQtqnh6e6Fu7r2j5gsMLkJnnnBiJd3epBb1ioUjy1EqGaUsPFKn8SwIkQAIkQAIkcBEC/1F9pWHxHwJV7L97Ote6yJnX3pSRm4FfD8uPrLfVuQ16zGF5sV/9V6kZENXJ8lgIgARIwDEE+BR2DGfWQgIk4GwCHh5A+welF8fWAPGbpc2CWr86/eCh/lckqTmpWHx0cZHq0L8VQ/wxvG0NUade0cCMwAIJFRIgARIgARIoJhB7Jg0/bpOx/0aquLp6Zb0zZNGRRcjIK1m9f7GPjc7wy+l16jHnsbXSDb1DRY9RKSRAAiTgAAKcAHQAZFZBAiRgEgJNBgIhlaUza7kKUG8BblelneDy/f7vhe5I5d4utW0rF4rq1CsauAqwiAb/kgAJkAAJkIAkYL/6T2f+vbuTc1b/ac/mHJgjHOxUtRMigiKEzZKK/ZgzpArQhHERLflvgY0mAScR4ASgk8CzWhIgAScQ8FZZ8OwzAu/+UcViOeIEZ8xV5T/q/UM4tPXMVhxMksG7xQnXUNErFkaplQtG+SnmBPafTjWaWCYBEiABEiAByxPQfeP87ScEh9Edajol8692Qo8dYs7GCH9uq3ub0C2pJB4F9JjTKG3uAbx8jBaWSYAESOCaEuAE4DXFy4uTAAmYjkDLUYBvcIlbhSoT8Lr3S3SLlrpW64py/uVE6525CvBuFbco2M+72J9CvQpwCWMBFgNhgQRIgARIgAQUAd036j6ySEJU3zmuU80i1eF/7ZN/lPcvj87VOjvcD9NVuH4GoMecRaLHonpMSiEBEiABBxLgBKADYbMqEiABExAIKAO0GCkd2fIZkJEgbRbTfNQX6P51+otWzz80H9n52cLmKKVMoC/GdJQvMAt2nMTuEymOcoH1kAAJkAAJkICpCew5mQLdNxpF9526D3WG5ObnYv7B+aLqvnX6wsfT4qvcMpOALZ8KLmg+HNBjUgoJkAAJOJAAJwAdCJtVkQAJmIRA23tVwGWvEmdy04HNs0t0i5YG1lUxEg2SnJ2MJUeXGCyOLY5VLzGh/iWrAHXtby/Z71gnWBsJkAAJkAAJmJSA/cr4ENVn2n88c6Try+KWITE7UVSps/9aXjZ/DOSklWDQ2ZD1WJRCAiRAAg4mwAlABwNndSRAAiYgUKY60NhuQLp+JpDnnNVuJiBic6F6aHW0qdRGuGMfyFscvMZKWIAP9FZgoyzefRrb49WXdAoJkAAJkAAJWJiA7gt/3XVKELhLJf7Qfaez5Nt934qqW1RsgZphcjW/OMEKSm6WCjUzXba0YV+gbJS0USMBEiABBxDgBKADILMKEiABExJo/4B0Ku00sP0babOgZp8MZOOpjTiSfMRpJEbpQOaB8mXmrcVcBei0G8KKSYAESIAETEHgjUX7hB964m90hyhhc6RyOPkw1p9aL6q0H1OIg1ZRtn0B6DGmUTqMN2oskwAJkIDDCHAC0GGoWREJkICpCFRpDkR1ki79MRUoyJc2i2ndqndDGT8Zk8Y+oLcjkehEIPd0qS2qXL7vLDYctnbMRgGECgmQAAmQgKUIrIk9h1UHzok239e1NkL85QczccI1VuxX/+mxRM+onte4VpNfPj8PWD1NOlmrK1C1pbRRIwESIAEHEeAEoINAsxoSIAETEujwkHQq4SCwe560WUzz9fJFv9r9RKvnxc5DTn6OsDlSGdGuBsKD/USVr/6yR2U9NKQ9FEepkAAJkAAJkIB7EtB932t2q/8iQv0wsl2U0xqclZeFHw/+KOrXicX8vGTfLU6wgrJrLpB0VLa04yNSp0YCJEACDiTACUAHwmZVJEACJiNQpztQ+Trp1Kq3oGaWpM1i2oB6A0SLdUBvZyYDCfT1xvjudYRPW44lQccDpJAACZAACZCAlQgs2nUaMXFJoskTutdDgK8huZk4eu2VX4/8itScVFHRoHqDhG45paAA+ONt2Wy98q9mZ2mjRgIkQAIOJMAJQAfCZlUkQAImI+DhAXR6VDp1eidw4Ddps5hWK6wWWkW0Eq3+et/XQne0cnvr6qheLlBUq+Mf5RdYe7JWAKFCAiRAAiTg1gTy8gvw5m8y9l/N8CAMalXNqe223/7bvkp76MRilpYDi4AzuyUCvfpPjz0pJEACJOAkApwAdBJ4VksCJGASAg36AOH1pDMr37T8KsAhDYYIJlvPbMXehL3C5kjF19sTj/aU9+nAmTTM2RLvSDdYFwmQAAmQAAk4jcDcrccRq/o+ozxyYz34eDnvlW73+d3YcW6H0SUMrj9Y6JZT9E6SVVNksys0AOrfIm3USIAESMDBBJzXWzi4oayOBEiABC5KwFM9Bu3jscRvAI78cdHTrWLsXr07KgRUEM39eq9zVwH2aVYFjSqHCp+mqozAWbnWTtwigFAhARIgARJwSwK6r9N9nlEaVwlF76aVjSaHl+1X/0UERqBLtS4O98NUFeoxZPxG6VLHhwE95qSQAAmQgBMJ8CnkRPismgRIwCQEmv4DCLPbqmL/5dYkrjrKDR9PH/yjnuJikAWHFiA5O9lgcWzR09MDT9xUX1R6IjkLn621C7AtzqBCAiRAAiRAAq5P4PN1R6H7PKM8cVMDNafkvC2lOu7fwsMLjS5hYL2B8Pb0FjbLKX+oeNJG0WPMJgONFpZJgARIwCkEOAHoFOyslARIwFQEvHyADuOlS4eWAcc3S5vFND0B6O1RMojPyldZ/mJllj9HI+lSrwLa1ionqn1veSxSsnKFjQoJkAAJkAAJuAuBVNXHTV9+UDRH94Wd64YLm6OV+QfnIzMvs7haLw8vDKxr8YmuE1uBg78XM7EV9BhTjzUpJEACJOBkApwAdPINYPUkQAImIdB8GBBUUTqjMwJbWCoGVkT3GipTskG+2fcNCgoLDBbHFj1U8Own1YoHoyRl5GLmCvliZDzOMgmQAAmQAAm4MoEPVh5CQnqOaIJe/af7RGdJoYpzZ7/9t1v1btBjB0uL/dgxSIVT0WNMCgmQAAmYgAAnAE1wE+gCCZCACQj4BADtH5CO7P1ZZXDbI20W026vf7to8bHUY1h7Yq2wOVppXr0sbmpcSVQ764/DOJ0it0aJE6iQAAmQAAmQgAsSOJmciQ9XHRKe92wUgRaqL3SmrD25FgeT5ce3QfUGOdMl59d9ejew5yfpR9v7AT3GpJAACZCACQhwAtAEN4EukAAJmIRAqzGAf5h0xuKxAFtGtESdMnUEE2cnA9HOPNarPoxhj7JyC/DWbzI4unCaCgmQAAmQAAm4IIE3F+lkVyUr770uEg/XGc36cs+XotpaYbXQtnJbYbOcsvJ12WQ/NaZsPVbaqJEACZCAEwlwAtCJ8Fk1CZCAyQj4hQBt7pVO7ZwDnLXuxJLeXjS0wVDBZEX8ChxPOy5sjlbqVAzG4FaRotpvN8dh94kUYaNCAiRAAiRAAq5KYOfxZMzdGi/cHxodiToV1XjFiXIs5RhWxq8UHtzR4A6nbkkWzjhD0TtGds2TNbe9r/SHZXkGNRIgARJwKAFOADoUNysjARIwPQE9AegbXOKmjne34rUS3YKlW2vdimCfEiaFKMQ3e79xOolHbqyHQF+vYj9UOCJMWrgbOi4RhQRIgARIgARcmYDuyyYt2KP6tJJWBPt546Ee9UoMTip9ufdLNRIocSzENwR9avdxkjcmqXaFXv1XwgR69Z+eAKSQAAmQgIkIcALQRDeDrpAACZiAQKDKMNvmHumIXgVo4ViAgT6B6Fu7r2Dy/YHvkZGbIWyOViqG+uPeLrVFtatjz2PZvjPCRoUESIAESIAEXI3A73vPYO2h88Lt+7rWRniwn7A5WknLScO8WLnSTWf+1WMFy8qZvWr13w+y+W3VB+WAMtJGjQRIgAScTIATgE6+AayeBEjAhATaqWQg6mt2iagvuhZfBWi/DTg1JxU/HvyxBJGTSnd1qoVKaiLQKHrFRG5+Sbwk4zGWSYAESIAESMDsBPJUH/bvhTIJWZUwf4ztWNPpruu+Pz03vdgPTw/PUqFCig9apWCL/Wdc/RfK1X9WufdsJwm4GAFOALrYDaO7JEACDiCgVwHab9vQcV10djeLSlRYFLpU6yJa/8WeL1Cgt0g7UQLUFuDHVUIQoxw8m46vNxwzmlgmARIgARIgAZch8NXGOOi+zCiP31Qf/j4lYS+MxxxV1n2+ffKPbpHdUCW4iqNcMF89Z/cBO+dKv3Q4mQDnZmmWDlEjARIggQsEOAHIfwkkQAIkcDEC7e6HLX5L8TG9CvDVYs2KheGNhotmH005WioIuDjBQcptzauiSVX1td0gby85gJSsXIOFRRIgARIgARIwP4FU1XdNXSyTjzWtGoZ+11V1uvOr4lfhWKr8wHZHwzuc7pdTHbCP/ad3kNh/RHaqg6ycBEiABEoIcAKwhAVLJEACJFBCQH+5tR/A7VZbXk/tLDnHYqXoStGoV1YGH/9s92dOp+Dp6YFnezcSfiSk5+C9ZbHCRoUESIAESIAEzE5g+vKDOK/6MKNMvKUhdF/nbNEr/41Sv2x9tIpoZTRZq3xWTdTqONFG0bH/9E4SCgmQAAmYkAAnAE14U+gSCZCASQjoCUB/lcXNKMsnGzVLlT08PGC/CnDDqQ3Ym6CCXztZ2tYqj56NIoQXs/84grgE5yYqEQ5RIQESIAESIIG/IHD0fDpmrToszujRMALtapcXNmcoB5MOYu3JtaLqOxveCT02sKws/7dquiH2n231n9pBQiEBEiABkxLgBKBJbwzdIgESMAEBnb1NJwQxyt6fgZPbjRZLlW+peQvK+8sXETOsAtQ34Wm1QsLbsEIiRwVRn/yLDKJuqZvFxpIACZAACbgUgVdUEivddxWJl+rTnr6lQZHq1L+f7PpE1F/WryxuqXWLsFlKORlTOvNvm3u4+s9S/wjYWBJwPQKcAHS9e0aPSYAEHElAB3L2VxOBRllu3ViAvl6+uL3B7UYaWHh4Ic5mnBU2Zyg1w4Mwol2UqHrhjlNYE3tO2KiQAAmQAAmQgNkIrNx/Fot3nxZujVR9Wu0KwcLmDEX38T8fUh9ADTKo/iD4efkZLBYr/j5JNljvGGlv99FYnkGNBEiABJxOgBOATr8FdIAESMDUBPxVcgn7Ad2+BcDxzaZ2+1o6N7j+YPh6+hZXkVeQh6/3fV2sO7MwvnsdlAn0ES68MH8X8gwrKsRBKiRAAiRAAiTgZAK5qo966efdwovyQb6Y0KOusDlL0bH/cgtKEmvpMcDQBkOd5Y7z6z22HjiwSPrRfjwz/0oi1EiABExIgBOAJrwpdIkESMBkBKLVlg6dFMQoS15QYV8McV+Mx9y8XM6/HPrU7iNa+e2+b5GZlylszlDKBPrisZ71RdX7T6fhs3VHhY0KCZAACZAACZiFwKdrjyL2TJpw5/Fe9REWID9oiRMcpKTnpkP38UbpW6cvwgPCjSbrlPXYb+lLsr1BFQC9Y4RCAiRAAiYnwAlAk98gukcCJGACAnoVYMeHpSOHVwKHlkmbhbRhDYeJ1iZlJ2Fe7Dxhc5YyNLo6GlVW98wgby3ej3Np2QYLiyRAAiRAAiTgfAK6b5qq+iijNK0ahkGtIo0mp5Xn7J+D1NzU4vo94IGRjUYW65Yr6LHf0T9kszs9Bvg5f6u2dIoaCZAACZQmwAnA0kxoIQESIIHSBKLvBkKrSrteBVhQEqxbHnRvrU7ZOuhQtYNopA4QrrcDO1t00PQX+zUWbqRm5eGNX/cJGxUSIAESIAEScDaBNxftQ2q27Dtf6NsIui9ztuhtv5/t+Uy4cUPkDYgKixI2yyi21X8vy+aGVgNajZY2aiRAAiRgUgKcADTpjaFbJEACJiPgEwB0fUo6pTPA7f5B2iykjW0yVrT2eNpxLDpiFxNHnOE4pXVUOfS/voqo8NvNcYiJSxI2KiRAAiRAAiTgLAI74pPxzaY4Uf1tzauiZY1ywuYsRffpp9JPiepHN7HwZNdeFQP6xBbBA12fBLwtnAxF0qBGAiRgcgKcADT5DaJ7JEACJiJw3R1AeD3p0O+vAPklgbHlQffWWkW0QrPwZqKR/935XxUaUcXHMYE8dXNDBPp6FXui3Xr+p11q0aY5/Ct2jAUSIAESIAHLEdB9pU5SZewydZ/11M0NTMFC+/fxzo+FL9dXuB7XV7xe2Cyj5KtVmnrMZ5RytQE9NqSQAAmQgIsQ4ASgi9woukkCJGACAl7eQLd/SUcSDgFbPpU2i2geHh4Y02SMaO3+xP3447hdbBxxhuOUSmH+eLCbzKC4Ta0AnLMl3nFOsCYSIAESIAESuAiB7zfHY/PRRHHkgW51EBHqL2zOUtaeXIt9iTJ0xqgmo5zljvPrjfkSOLtH+nHDRECPDSkkQAIk4CIEOAHoIjeKbpIACZiEQEOV/bZqS+nMiteAnAxps4h2Q3UVCyg0SrR21s5ZQnemMqZjFGqGBwkXXvt1L5IzrblqU4CgQgIkQAIk4BQCiek5mPzLXlF3jfKBGNuxprA5U5m9c7aoXvf1Ov6fJSUnHVj2b9n0iKZA4wHSRo0ESIAETE6AE4Amv0F0jwRIwGQE1Ko39HhBOpV2Glj/vrRZRPP08Cy1CnDz6c3YdmabKQj4eXvhuVsbCV/OpeVAB12nkAAJkAAJkIAzCLy+aC8S1CSgUV7o0xi6zzKD7Di7A+tOrhOujGg8ArrPt6Ssmw6knpRN7/kS4GlRHpIENRIgARciwKeWC90sukoCJGASAjU7A7W7S2f+mAZkJEibRbTetXqjYmBF0VodC9AsckODiujRUPr3+fqj0NuBKSRAAiRAAiTgSAJ62+9XG+JElTc1rgTdV5lFPtjxgXAlPCAcfWqpHRBWlPRzgB7jGaV2NzUOVP9RSIAESMDFCHAC0MVuGN0lARIwCYEez0tHspOBlW9Km0U0Xy9fjGg0QrR2WdwyHEw6KGzOVJ5XKyv8fUq6PB10feLcHcjLL3CmW6ybBEiABEjAQgR0n/PsvJ2ixTrxx3N95Ep1cYKDlX0J+7A8brmodVTjUfD3NkdsQuGYI5QVr6swL6mGmvROkBcNOoskQAIk4DoESt6GXMdnS3h69OhRPProo2jQoAGCgoJQrlw5tG7dGm+88QYyMjKuiIH+/dy5c3HffffZrlm2bFn4+PigfPnyaNeuHV544QWcOnXqiurgj0nA7QlUvg5oMlA2c4P6Yn7ePJNe0rlrq/2j3j8Q4hsiKpm1wzyxACPLBWJCd5nBeffJFHyy9qjwmQoJkAAJkAAJXCsCH685gj2q7zHKwz3qoUqZAKPJqeUPd3wo6g/zC8OgeoOEzTKKHtNtshvLXHc7ULmZZRCwoSRAAu5FgBOAJryf8+fPR7NmzfDWW29h3759tgm/xMREbNq0CU888QSaN2+O2NjYy/J8+/btiIiIwMCBAzFjxgzbNZOSkpCXl4eEhASsW7cOL774IurXr49vvvnmsurgj0jAMgR0RmC1+q1YClRiicXPFatWKgT5BGFog6GiyQsOL8CxlGPC5kxlXKeaqBcRLFx467d9OJmcKWxUSIAESIAESOBqE9B9zduL94vLNqgUglEdooTNmcrh5MP47chvwoXhDYcj0CdQ2CyjLH0JKMgraa6XH3DDMyU6SyRAAiTgYgQ4AWiyG7Z161YMGTIEKSkpCA4OxqRJk7BmzRosXboUd911l83b/fv3o3fv3khNNS5H/3sN0ddNS0uzndyhQwdMnjwZixcvxpYtW7Bo0SLcc889Kp6tp63+O++8E7/88svfuzDPIgErEihXE2hzj2z53p+Bw6ukzSLasIbDEOBdsoqhoLAA9isJnInCx8sTk25TWfsMkp6Tjxd/2m2wsEgCJEACJEACV5/Ayz/vhu5zjPJK/ybQfZNZ5KMdH6FQ/a9Ign2CMbSh/LhXdMzt/8ZtAHbPk83UY74ykdJGjQRIgARciIB5ehwXgnYtXZ0wYQIyMzPh7e2N3377DRMnTrRty+3WrRs++OADvP66ikOhRE8CTpky5ZJd0ZN7gwcPxq5du/DHH3/gqaeeQo8ePWyrCnv27GlbFai3B3uoTKf5+fl48MEHUaiDZVFIgAQuTqDz40BgeXls0UT1xVgO8uUJ7qmV9S+L2+urrTEGmX9wPuJSZbBzw2GHF1tHlcOQVnLw/uuuU1i6R2VyppAACZAACZDANSCwbN8ZLNwhw+vovqiV6pPMIsfTjmPBoQXCHb2yP9Q3VNgsoRSo+MC/PiWb6l8G6PSItFEjARIgARcjwAlAE92wDRs2YNWqCyuHxo4da5v4s3dPxwVs2LChzTxt2jTk5qoth5cg7du3t23tbdSo0Z/+ql+/fhgwYIDt+MGDB6FXJVJIgAT+hIB/mNoOoib8jHJqOxDztdFimfLIxiPh71USKDy/MB9migWob8RTNzdAuSDD1m1le+7HXci0W5lhmZvGhpIACZAACVwzAunZeXj2B5n4o2ygj60vumaVXsaF/7vjv9B9dpHoFf3DGg0rUq31d8e3wPHNss1dngACykobNRIgARJwMQKcADTRDZs3r2SZ+ejRoy/qmV7BN2LEhWybOnbfsmXLLnrelRpvuOGG4kvoSUAKCZDAXxBoMQqo0ECeoOPGZF/Ybi8PuLdWPqA8BtcfLBr5Y+yP0CsLzCJl1eTfxFsufEgp8ul4UibeWryvSOVfEiABEiABErgqBN5YtA+6jzHK0zc3hO6LzCKn00/jh9gfhDs6uVc5f/OsUBTOXUslJx1Y8oKsoXwdoPWFUEzyADUSIAEScC0CnAA00f3SW3K16Ky/LVu2/FPPunTpUnxs9erVxeWrWcjOzi6+nJeXV3GZBRIggYsQ8PIGek6SB9LUVp/V06TNItroJqPhpwNl/0/yCvNMtwpwYIuqaFNTvtjM+uMwYuKSitzmXxIgARIgARK4IgKbjyaqbPNHxDXa1iqHQa2qCZuzFR37L1cnMvuf+Hj6YFTjUUWqtf7+MRVIPSnbrMd43uaZsJXOUSMBEiCBv0+AE4B/n9U1P3PPnj22OurUqWOLAfhnFTZoULLSqOg3f3bu5dpXrFhR/NOiLcfFBhZIgARKE6jbA6jdXdrXvAMkx0ubBbTwgHAMqjdItFSvLDiVriZFTSI6zqlOCOJrCL5eoMKdPjlnO3LyVOwfCgmQAAmQAAlcAYHsvHw8pfoUYyhtP29PvDqgmS3W9hVc+qr+9GTaScw5MEdcc0DdAagYWFHYLKEkHQPW/Ec2tXY3oF4vaaNGAiRAAi5KgBOAJrlxWVlZOHfunM2batX++qtg2bJlbasE9clxcVc/uH5MTAwWLLgQBLhp06bFMQcvBVV8fDz+6r+TJ+2+rF3KxXkuCZiVQC/1hdjDsGI2T235WfKiWb29pn7pVYC+niVfy/MK8qBXGJhJ6lQMxoQedYVLe0+l4v3lDHsgoFAhARIgARK4ZAK6LzlwRoYCefjGeogKD7rka13LH8zcPlOs/tN997im465llea99uLngbysEv/0mK7Xv9XYzqPExhIJkAAJuDABTgCa5OalpqYWexIcHFxc/rOC3iasJS1NDiz+7Py/a9dbf8eNG2fLAKx/M2mS3bbGv3mhyMhI/NV/0dHRf/NKPI0EXIhARRVXruUo6bAOJH10rbRZQNMrBwbWGyhaOvfAXOiVBmaSuzvXQsPKMsPhu8sOYP/pkmeymfylLyRAAiRAAuYnoPuQ95bFCkebVA3FuI41hc3ZSlxqHHScXqMMqj8IlYIqGU3WKB9bB+yaK9vaagygx3YUEiABEnATApwANMmN1CsAi8TXt2TVTJHN/q+fn5/NlJkpgwrbn3ep+gMPPIBNmzbZfjZy5Ej06dPnUi/B80nA2gR0RmA/OaGEhY8B+XmW4zKmyRjoOEJFouML6ZUGZhIftQX4jX80g5dnydf93PxCPPH9duTrPcEUEiABEiABErgEArrv0OEkdF9SJLqP0Vt/vQ1hJ4qOOfPvzJiZ0HF6i0TH7x3bZGyRap2/BSr78S8qy69R/MsAekxHIQESIAE3IsAJQJPcTH9//2JPcnJyist/VihK0hEQEPBnp1yyffLkyfjoowtb9Fq3bo333nvvkq9R9AO9Nfmv/tuwYUPRqfxLAu5FICi89IDx9E5g0yz3auffaI1eQaCzCBplXuw8HE05ajQ5vdykahj0SkCjbFPJQGavPmw0sUwCJEACJEAC/y+BT9cewdZjMqHUXZ1qQfc1ZpIjyUcw/9B84dLt9W9HhcAKwmYJZfNs4GSMbGrXp4FAmSxMnkCNBEiABFyPACcATXLPQkJCij35O9t609PTbef/ne3CxRf+i8LMmTMxceKFr1w6ycjChQuL4wz+xc/+9JCOY/hX/1WuXPlPf8sDJODyBFrfpbaMNJbN+P0VtWf/jLRZQLu72d3w9yr5wJFfmI/3tl3+x4VrhWxC97qoZReX6c3f9uHY+YxrVSWvSwIkQAIk4GYEjpxLx+u/7hOtqqn6lofs4s2KE5ykvB/zPgoKS5JeBXgHYEzTMU7yxonVpqsY7Etfkg6E1wdaW3AlpKRAjQRIwA0JcALQJDdVrwAsX768zRudPOOvJDExEUUTgDrO3pXKV199hfvvv992mRo1amDx4sUID1ermCgkQAKXR8DLG+j9pvxtdgqgg0tbTHRG4Dsb3ila/cvhX7AvQb4giROcoPj7eOHVgc1EzVm5BXhiTgwKuBVYcKFCAiRAAiRQmoDe+vv49zHIzFXbSQ0yeUBT6D7GTHIw6SB0X2yUOxrcgXL+FlzxtkSNzbKSjSgujOG8SkKYyIPUSIAESMB1CXAC0ET3rlGjRjZvYmNjkZdXEo/D3sW9e/cWmxo2vLLAtD/99BNGjBihXnALoFflLV261LZyr7gCFkiABC6PQI32QLMh8rcxXwLH1kubBTSdETjYRyY3enfru6ZreXTNchjetobwa92hBHyy9oiwUSEBEiABEiABewI6bMTGI4nCPKxtdbStdeEDvzjgZEWvxC9U/yuSIJ8gjGo8qki1zt84FZJo6+eyvU1U6JKanaWNGgmQAAm4CQFOAJroRnbs2NHmjV7dt3nz5j/1bMWKFcXHOnToUFy+1IKe7Bs8eLBtslGvPtQr/2rXrn2pl+H5JEACf0bgRrWlxLdke7/ttIWPAjrYtIUkzC+s1IvF8vjliDlrF2/HBEyevLkBqpaRsVVf/WUvDp5NM4F3dIEESIAESMCMBGLPpOL1RXJle/VygXj65iv7UH8t2rr97HYsPrpYXHp4o+Eoo5NeWEn0WGzBI7LFvupjZU8VsoVCAiRAAm5KgBOAJrqx/fv3L/Zm9uzZxWVjQa/U+/TTT22mMmXK4IYbbjAe/tvlNWvWoF+/ftDJRMLCwrBo0SI0bmwXs+xvX40nkgAJXJRASCWVEORpeejUDpUQ5L/SZgFtWKNhpbYWvbPlHdO1PNjP25YV2OhYdl4BHvk2Bnn5JbGSjMdZJgESIAESsC4B3Tc8qvqIHNVXFImHSiyvM8wHqT7FTFJYWIi3N78tXNIf6fQEoOVEj8X0mMwoOvFHaGWjhWUSIAEScCsCnAA00e2Mjo5Gp06dbB7NmjULa9euLeXdlClTsGfPHpt9woQJ8PGR8SmWL18ODzXq0P+NGjWq1O+1Ydu2bejdu7ctjmBQUBAWLFiAli1bXvRcGkmABK6QQPTdQAW7FQC/v2y5hCB6e9HYJmMFzPWn1mPdyXXCZgalfZ1wjGofJVyJUVmBZ6w4KGxUSIAESIAESGDmykOIiZcx5Ea3r4k2Jtz6u+r4Kmw6vUnctLua3oVQ31Bhc3tFJ2VbqsZiRtFjtTb3GC0skwAJkIDbEeAEoMlu6bRp0xAQEGDbltuzZ09MnjwZ69atw7Jly3DPPffgiSeesHlcr149PProo5fs/cGDB9GrVy8kJSXZfvvKK6/YVgDu3LkTf/bfmTOqk6SQAAlcHgEdRNo+IYgONv2r3crAy7u6S/1qSIMhiAiMED6/tektkYVQHHSi8uRNDUplBZ629AB2nZAveU50kVWTAAmQAAk4mcDuEymYumS/8KJWhSA8cZPKImsyyVdbXqdumSq8qhxUGbc3uF3YLKHoMVi2XX+ux2pM/GGJ289GkoCVCXAC0GR3v3nz5vjmm28QGhqKtLQ0TJw4Ee3atUO3bt3wwQcf2LzVk3961V5IiF1ssb/RllWrVsE4offwww+jadOmf/nf9OnT/8aVeQoJkMCfEohS8T2bDpKHd34PHJAxeOQJ7qf5efnh3uvuFQ3bk7AHCw4tEDYzKAG+Xnhz8HXwVNu4iiQ3v9C2zSs7T8UNopAACZAACViagN7y++h3MdB9Q5HoPmPKoOtMl/VX+/fzoZ9xIPFAkau2vw80fwC6b7aUHFgC6DGYUfQYTY/VKCRAAiTg5gQ4AWjCG9ynTx9s374denJOT/YFBgZCx/tr1aoVXnvtNWzduhV16tQxoed0iQRI4E8J9Po34B8mD/+sgk9nWyu5RP86/VErrJbg8M7Wd5CVlyVsZlBaVC+L+7rWFq7sPZWqVnvIFyhxAhUSIAESIAFLEHhr8X7sOZki2npPl9porvoOs0l2fjbe3faucKte2XroXbO3sLm9kpOuEn88LJupk5/oMRqFBEiABCxAwEMFgy35bGWBBrOJ5iAQHx+PyMhImzNxcXGoVq2aORyjFyRwLQls+RT46UFZQ7sH1MBzkrS5ubYibgUe+F212yAPtXgIY5vKGIGGw04r6tV+/d5dDT3xVyR6hcfXd7dDdM1yRSb+JQESIAESsBCBNQfP4c6P1sP4FlU/IkR18R3g5+1lOhKf7PoEb256U/g1vft0dKp2Ifa4OODOym/PAmvekS3sq/QWI6SNGgm4IQG+f7vhTb2MJnEF4GVA409IgARI4LIINB+utpjYDbbXqS32x7dc1uVc9Uedq3VG60qthfsf7fgIiVmJwmYGRb/IvT3kevh4lewFLlCfzR76eiuSM3LN4CJ9IAESIAEScCCBpIwcPPJNjJj8033EFBU2woyTf8kq1t0H2y+EESrCpPvgjlUttuX1ZAyw1i6sUQ3FQI/NKCRAAiRgEQKcALTIjWYzSYAETEBAZefGrSoAtzHeTmEBMH88kJ9nAgcd44LOUv5oK5nEKC03DTNiZjjGgUuspWHlUDx8Yz3xqxPJWZg4b4d6AeQiegGGCgmQAAm4MQH9zH967g6cSpFhK57o1QBNqtqF+TAJB923puTIrcqPtHwEui+2jKgEKPhJjbUK1d8i8fIF+qgxmZU4FLWdf0mABCxLgBOAlr31bDgJkIBTCISr+J1dHpdVn9oBrHtP2txca1y+MXrXkrGHvt33LY6mHDVly+/pXBtta8ktvwu2n8R3m+NN6S+dIgESIAESuPoEvtsUj192nhIX7lgnHGM71hQ2syiHkg/h671fC3d61uiJJuFNhM3tlfXqA+PJbbKZndVYLLyutFEjARIgATcnwAlAN7/BbB4JkIAJCbSfAFRoKB1bNhlIOCxtbq6Nbz4evp7qC/z/JK8wD1M3q6/xJhQvFfhPbwUOC/AR3r3w0y4cOmutRC4CABUSIAESsAiBw+fS8cL8XaK1ZQJ9bFt/PY0p48UZzlWmbJoC3bcWie5zH25plwSj6KC7/j1/EFj6smxdeH2gw0PSRo0ESIAELECAE4AWuMlsIgmQgMkIeKtJr77/UU4Ztt/kZV5IEFKgtgRbRKoEV8Gdje4UrV1ybAk2nNwgbGZRKocF4LWBTYU7GTn5mPD1NuTkWee+CQBUSIAESMACBHLzC2yxX/Uz3yivDmiGiFB/o8k05dXHV2Nl/Erhz4jGI1AtpJqwubWix1Q6+ZoeYxmlzzRAj8UoJEACJGAxApwAtNgNZ3NJgARMQiAyGmg9TjpzZBWwaZa0ubk2ruk4lPErI1o5ecNk5BWUrFgQB52s3NSkMoZGVxde7DiejCmL9wkbFRIgARIgAfch8Nbi/YiJTxYNGhodiZuaVBI2syi6D3194+vCnfCAcOg+11Ky8SPg6GrZ5Oi7gRrtpI0aCZAACViEACcALXKj2UwSIAETEuj+HBBq9yV+sbLp7SoWkVDfUDzYXH2dN0hsUix0PECzyr9ubYjaFYKEezNXHMKqA2eFjQoJkAAJkIDrE1i+7wzeXy775VrhQfjXrY1M2zjdh+r4f0bRYTeCfGTfZTzudmUdVmXJ87JZZWoA3e1s8gxqJEACJODWBDgB6Na3l40jARIwNQH/UKDfu9LF3Azgx38CFtoKPLDuQNQvq+LxGOTdbe8iMSvRYDFPMdDXG9Nubw5fL9mFPvzNNpy2ywxpHq/pCQmQAAmQwKUSOJmciUe+jRE/8/HysPUBui8woyRnJ2N6zHThWsNyDdGvTj9hc2ulaOuvHlMZRY+5/IKNFpZJgARIwFIE5NuLpZrOxpIACZCACQjUvgFoNVY6cmwtoDPWWUS8PL3wdJunRWtTc1LxztZ3hM1MSpOqYXjiJjlpeS4tBw9+tRV5KlYUhQRIgARIwLUJ6Gf5ePVMT0jPEQ15+uaGaFotTNjMpEzfNh16EtAoT0Y/CU8PC7326XAqOqyKUfRYq2Zno4VlEiABErAcAQv1BJa7t2wwCZCAqxC48SWgTHXp7dIXgXMHpM2NtZYRLXFz1M2ihd/v/x57zu8RNjMpYzrURPcGFYVLGw4n4O0l+4WNCgmQAAmQgOsR0HH/Nh6RK9F7NorA6A5Rpm2M7jO/3ve18K9njZ7QfaxlJEFtfV5st803TI2xblTjKgoJkAAJWJwAJwAt/g+AzScBEjABAb0dpZ/croO8LGDefWorcL4JHHSMC4+0egQB3gHFlRWiEDohSGFhYbHNTAVPTw9MGXwdqpYp8Vn7996yg1imYkZRSIAESIAEXJOAjvs33S7uX7WyAXjjH9fBw8PDlI0qKCzAK+tfgf5bJH5eftB9q2UkXyUQm3sPkJsum9z3P2rrb4i0USMBEiABCxLgBKAFbzqbTAIkYEICNTsBbe6VjsVvBNaYdxusdPbKtUpBlUplKNx6Zit+PvTzlV/8Gl2hTKAv3r2jOXRMKKM8ouIBnkjKNJpYJgESIAEScAECp5KzLhr37907WiAs0Me0LZgXOw/bz24X/umsv1WDqwqbWyur3wbiN8gmthwF6HArFBIgARIgAXACkP8ISIAESMAsBHRW4HK1pDfLJgEn5YBenuBe2sjGI0u9rLy56c1S8YzM1Orm1cviKRUTyiiJGbl44MstyGU8QCMWlkmABEjA1AT0M/vBr7ZcNO7f9ZFlTOt7UlYS3t6sJr8MUj2kOkY3GW2wuHnxxFZg+auykWVrAj3VOIpCAiRAAiRgI8AJQP5DIAESIAGzEPANAvq/r7wxrCbLV8HH54wDcuwy2ZnF56vsh96u9ETrJ8RVE7ISMHXLVGEzmzJGxYTq1ThCuLXlWBJe/3WvsFEhARIgARIwL4F/L9zjcnH/NM1pW6chKTtJgJ3YZiJ0n2oJ0WOkuXersClqC3CR6KQnAz5g1t8iHvxLAiRAAooAJwD5z4AESIAEzESgelugw3jp0bl9wG/PSpsba92qd0PXyK6ihTohyLYz24TNTIqOCfW6ig1VvVygcOvDVYcxP+aEsFEhARIgARIwH4EftsZj9uojwjGzx/3Tzu44uwNz9s8Rft9Y40Z0qNpB2NxaWfKCSpy2Xzax06NAZLS0USMBEiABixPgBKDF/wGw+SRAAiYkcIOa7Kt8nXRs0yxg70Jpc2NtYvREkRBEN/WldS8htyDXtK0OC/DB9DtbwNdLdq1PfL8de06mmNZvOkYCJEACView60Qynp67Q2Dw9fbE+3e2NHXcv3yVKEwn/tBJs4pEJ9OyX0lfdMwt/8YuBTbMlE2rfD3Q5Ulpo0YCJEACJMAVgPw3QAIkQAKmI+DtCwxUE34+cjUZfnoASD1lOnevhUOVgyvj/uvuF5c+kHgAn+/+XNjMpjSpGoYX+zUWbmXm5uOezzYjKUNt56aQAAmQAAmYioB+Nt/7+WZk5ZZkz9UOTurfBE2rhZnKV3tnvtjzBXaf3y3M9153L3RSLUtI6mngB5X11yhqAhQDPgS8zJuwxeguyyRAAiTgSAJymYIja2ZdJEACJEACf04gvC5w02R5POO8GuiqTMEF8iVFnuQ+2p2N7kS9svVEg96PeR8n0sy9pXZodHXo/4xyLCED47/ehvyCklUaxuMskwAJkAAJOJ6AfibrZ3NcgszaPqxtdQxqFel4hy6hxvjUeLy77V3xi1phtTC84XBhc1tFj4X05F/6WdnEni8DFeTYQZ5AjQRIgASsS4ATgNa992w5CZCA2Qm0GAk0uFV6eWgZsG66tLmp5uPpg+faPadSopQkRcnMy8Sk9ZNQWGjuibQX+jZC8+plxJ1Zuf8spvym4jlSSIAESIAETEHg7cX7oZ/NRtHP7udulSu5jcfNUNZ94ItrX4TuE43yfLvn4WOVlW+rpwJ6TGSUuj2B1ipxGoUESIAESOCiBDgBeFEsNJIACZCACQioxBLo+w4QUkU6o4NdnzBvQgzp7JVp11W4DoPqDRIXWRm/EgsPmzseop+3F2YMa4kKITID4/TlB7Fwx0nRHiokQAIkQAKOJ/CLeha/uyxWVBwe7Gd7duv4f2aWnw7+hHUn1wkXh9QfghYRLYTNbZVj64HfX5HNC6kM9J8B6LEThQRIgARI4KIEzN27XdRlGkmABEjAQgQCywG3qQGtYRUcdCKM79TqwMwkS4CY0HICyvuXF22dvGEyzmWeEzazKRGh/iqAfAt4e8qXkce+i2FSELPdLPpDAiRgKQI7jyfj4W/lhzT9rNaJnPSz28yi+77XN74uXKwYWBEPtXhI2NxWyUgA5owFCvNLmuihXmkHfgQEybFCyQkskQAJkAAJaAKcAOS/AxIgARIwO4FaXYAOE6SXiUeAH/+pBsDm3gornb48LdQ3FM+0fUb8ODk7Gf9e/29hM6PSKqocnu8rt5Jl5ORj3CebcDY124wu0ycSIAEScGsCZ1KybM9g+6Qfz/RuiOia6qObyeW1Da8hJUdmlv9X238h2DfY5J5fBff0mOenB4HkOHkxnfE3qqO0USMBEiABEihFgBOApZDQQAIkQAImJNDtWaBatHRs78+WiQd4Y40b0bOGiu1jkMVHF+O3I78ZLOYsDmtTHYNbVRPOHU/KxN2fbVJZJw0rGMQZVEiABEiABK42Af3MvUtlZT+lJgGNMkQl/BjVPspoMmV5edxy/HrkV+HbTVE3oWtkV2FzW2WjWuWnxz5GqaEm/jo/brSwTAIkQAIk8CcEOAH4J2BoJgESIAFTEdBBvQfNBgLsVicsfg7QsXAsIBPbTEQZvzKipTohSFKWubdCe6h4RC/3b4KWNcoK37ceS8IT3283fUIT4TQVEiABEnBRAjpxhn7mxsTJPkOv+tPPaP2sNrPovk4n/jCKXiH/ZLRa/WYFid8MLJooW6rHRAM/VHvavKSdGgmQAAmQwEUJcALwolhoJAESIAETEghTq8gGqIGuiAeYB3w/Gkg/b0KHr65L5QPK4+nop8VFE7IS8OrGV4XNjIpOCjJzeEtUKxsg3Psp5gT+szRW2KiQAAmQAAlcfQLvqYQf+plrlMhyAS6R9EP7rD942ce+fbz14wgPCDc2yT3L6Srm77cjgPwc2T4dIznULlGaPIMaCZAACZCAgQAnAA0wWCQBEiAB0xOo20NtdXlMuplyHPjhbqCgQNrdULu55s2ltjotOLQAvx/73fSt1dklZ41sjWA/b+Hr20v24+ft8qVUnECFBEiABEjgigjojL9v/rZfXEM/i/UzuVyQr7CbUfn18K+ltv52qtoJ/Wr3M6O7V9enfP2hcwyQEi+v2+4BoF4vaaNGAiRAAiTwlwQ4AfiXeHiQBEiABExIoKtaBRfVSToWuwRY9aa0uaGmt2jpYOchviGidXpblP3KCHGCSZT6lULwztDmsEsMjEe/jcE2u21pJnGZbpAACZCASxPYfDQBD32zTbRBP4P1s7hehOxLxEkmUc5mnMUr618R3uitvy+0f8H025aF05erLFNtP7xC/lrH/esht0PLE6iRAAmQAAlcjAAnAC9GhTYSIAESMDMBHetm4CwgOEJ6uWwSsO8XaXNDrWJgRTzZWsY80luBX1jzgkvE07uhQUU807uRuDPZeQUY+/FGHDmXLuxUSIAESIAELp/AobNptoy/+hlrlIm3NIR+FptddNxC/YFLZ743yrNtn4XuC91e9swH/nhbNjOk8oWYyF5yNb08iRoJkAAJkMDFCHAC8GJUaCMBEiABsxMIUZN///ivCgdo9xifcxdwdp/Zvb9i//rW7otukd3EdVbEr8D3B74XNrMqYzpEYWh0deHe+fQcjJq9AefTsoWdCgmQAAmQwKUTOKeepaNmb0RiRq748dDoSIztWFPYzKrMi50H3bcZpWeNntCZf91ezh1Q4U3uk830VAnRBn+qPoBaYPJTtpwaCZAACVwVAnZvjlflmrwICZAACZCAIwhEqS0wN74ka8pJBb4aCmTKLIfyJNfX9Fbg59s/j/L+5UVj3tj4Bo6mHBU2Myra/5f6NUanujJ4+5HzGRjzySZk5KiYRxQSIAESIIHLIqCfoWPVs/RYQob4fdf6FfByP/Nn/NVOx6XG4bWNrwn/dZ+nV/+ZPWOxcPpylGw1lvlmGKDHNEa5aTIQGW20sEwCJEACJHAJBDgBeAmweCoJkAAJmI6ADoLdbIh0K+EgMGecSgqSL+1uppXzL4eXOsgJ0My8TExcNRF5BeafQPPx8sT0O1ugUeVQcWdiVCzAB7/cirx8uWVNnESFBEiABEjgogTyCwox/qtt0M9SozSpGor37mgBb/XsNbvkFuTiyZVPIj1XhoXQcf/K+pc1u/tX5p9OaDZXJTY7u1deR491WquxDYUESIAESOCyCZi/B7zspvGHJEACJGABAmolGfpMAypfJxsbuxj4/WVpc0Otc7XOGFxvsGjZ9nPb8eH2D4XNrEqIvw9mj26NqmUChItL957Bv37c5RIxDYXjVEiABEjAiQR0zLwXftqFJXtOCy/0M/a/o1ojyC4LuzjJREhxQJYAAEAASURBVMr0bdOx49wO4VH/Ov3RNbKrsLmlsvRFFc94oWxaRBPg1qkq7Ika81BIgARIgAQumwAnAC8bHX9IAiRAAiYh4KMmj4Z8AQTK7aS2wNk755jEyWvnxqOtHkWN0BqighnbZ2DTqU3CZlYlItQfn4xpjVB/GdD8qw3H8M7vsWZ1m36RAAmQgOkITF1yAJ+tk2Eg9LNVP2Mrhvibzt+LObT+5HrM2qESfRlE93FPRz9tsLhpcdtXwGo10WeUALXicchngG+g0coyCZAACZDAZRDgBOBlQONPSIAESMB0BMpEXhgge8pJJMz7JxC/2XTuXk2HAn0CMbnjZHh5qOzI/5OCwgI8uepJJGYlFplM/bdOxRB8NLI1fL1lt/zW4v2lXmZN3RA6RwIkQAJOIjB79WFMW6oSRxjEV233/XBEK+hnrCuI7rN0GItC9b8i8Vb9+mudX4Pu69xajq0H5o+XTdRjmsFq8q9cLWmnRgIkQAIkcFkE5JvGZV2CPyIBEiABEjAFgRrtgZtfk66omHj4SsXNSZQrIuRJrq81rdAU919/v2jImYwzeHb1s9CTga4g0TXLYeqQ60vtcHrux52Yt/W4KzSBPpIACZCAUwjM3RKPF+fvFnXr3aJTBl+HNrVksihxkokUvX35uTXP4UzmGeHVhOYT0Lh8Y2FzOyXpGPD1HUB+jmxa7ylAzU7SRo0ESIAESOCyCXAC8LLR8YckQAIkYEICrcYCLUdJx9LPAl+qOHlunhl4bJOxaFu5rWj7yviV+Gy3Wj3gInJL08p4/tZGwlv1TohHv4vBkt0yppU4iQoJkAAJWJSAfjY+/v32Uq3X2X77XFellN2shq/2foXlccuFe+2rtMeIxiOEze2U7DT1oXIokHFONq3NfaXHM/IMaiRAAiRAApdIgBOAlwiMp5MACZCAqQnoJQ+3vKm+mHeRbupset+NVF/Xc6XdjTQvTy9M7jQZOjuwUaZunoodZ2UwdeNxs5VHdaiJh3vUE27prJb3f7kFaw+eF3YqJEACJGBlAusOnbc9G/Uz0iiP96qPYW1rGE2mLsecjcEbm94QPuq+bFLHSfD0cOPXtfw8YI76cHl6p2g7ancHer4ibdRIgARIgASumIAb9yhXzIYXIAESIAHXJODlo2LmfApUaCD9P7Qc+PlhqNSy0u5GWnhAOF7t9Co81P+KJK8wD4+vfBzJ2clFJtP/Hd+9DsZ2rCn8zMkrwLhPNiImLknYqZAACZCAFQnoZ+G4TzZBPxuNclenmri/a22jydRlHffvsRWPIa9ATYYZ5OUOL0P3aW4reiyy4BFg/6+yieH1gUGzAS+7mMbyLGokQAIkQAKXQYATgJcBjT8hARIgAdMTCCgD3PEtEFRBurpVbYe1z7Anz3B5rV2VdhjXdJxox/G045j4x0SXiQfooVZyPtu7IQa3qibakZ6Tj5GzN2DPyRRhp0ICJEACViKw83gyhs9aj7RsOWk2qGU1TLyloYqlWvIRyMxc8gvy8fSqp3Eq/ZRwU4e06Fyts7C5nbJSrXjc8olsls74O1RlAvYPk3ZqJEACJEACV4UAJwCvCkZehARIgARMSKCs2v409BvA2186t+QFYPt30uZmmk4I0rxic9EqHQ9wZsxMYTOzol9gJw9ohluaVhJuJmXk4s6P1mPfqVRhp0ICJEACViCw+0QKhqnJv5QsOfnXq3GEemY2dZnJP32vPtj+AVafWC1uW+tKrfFA8weEze2ULepj5LJJsll6rDL0a6C866zelA2gRgIkQALmJ8AJQPPfI3pIAiRAApdPoFpLYMAH6vd2qyHm3QscWHL51zX5L709vfF659dLxQN8P+Z96IlAVxEvTw+8rTIDd6ort4ElpOfgjg/XYf9pTgK6yr2knyRAAldOQH/40JN/+kOIUfQzctrtzeHt5TqvNmuOr4Huk4xSIaCCre/SfZjbyoHFwPwJds1TY5SBHwHV29rZqZIACZAACVxNAq7TS17NVvNaJEACJGAlAo36ATe+JFusYw19OxyI2yjtbqRVCqqENzq/IQKoF6IQT616CnEpcS7TUj9vL8wc3hLRNWVyk/P/mwQ8wElAl7mXdJQESODyCcSeSVWrn9dBfwAxSvva5fHhiFbw9/Eymk1djk+Nx5OrnlQ9UklMXi8PL9vkn1vH/Tu+RY09RqpYxPny/tz8OtCwj7RRIwESIAESuOoEOAF41ZHygiRAAiRgQgLtHwTa3Ccdy80AvhwEnNkr7W6kRVeOxiMtVZBxg6TmpOKh5Q8hMy/TYDV3MdDXG7NHtUbrKBUfySDn0nIw9MP1iD2TZrCySAIkQALuReDQ2TTbs04/84yiP4x8NNK1Jv8yVN87ftl4JGXLhE4TWkxAq0qtjM1zr/K5A2rMMRjITZft6qBWA7a5W9qokQAJkAAJXBMCnAC8Jlh5URIgARIwGQEdEL3Xv4GmavBtlMxE4PMBQJLrrIgzuv93yiMajUCvqF7i1P2J+/H86udVQuSS1RfiBBMqQX5qEnB0NFrVsJ8EzFYvxutwUL0gU0iABEjA3QjoVc5DPliHs6nZomn6Wag/jOgPJK4iBYUFtoRUBxLVZJhBukV2w6jGowwWNysmHQM+VbsR0s/KhukxSfcXpI0aCZAACZDANSPACcBrhpYXJgESIAGTEfBUj/z+04E6N0rHUo4Dn92mBubnpd1NNJ1M46X2L6F2mAws/suRXzBzu+skBdG3I9g2CdgaLaqrLM8G0S/GQ2auZXZgAxMWSYAEXJ/ArhPJF538a66egbNHt4b+MOJKMiNmBpYeWypc1n3TpI6TXCp5iWjA/6ekngI+6QvosYZRanYB+r0H6LEJhQRIgARIwCEE+MR1CGZWQgIkQAImIeDlAwz+BKjWWjp0Xq1G+FxNAmbKLUnyJNfVAn0CMfWGqQj2CRaNeG/be1h0ZJGwmV0J8ffBx2OicV2knATUW+NuV6tkYuLc8x6a/b7QPxIggatLYJt6lg1VzzT7mH/NqoXhY7UaWj8LXUmWHF1SKulHqG8o3un2DoJ9Zd/kSu36S18zEi58YEw8LE+r0gIY8jng7Svt1EiABEiABK4pAU4AXlO8vDgJkAAJmJCAbxBwx7dAhQbSuZMxF7YDZ6VIu5toUWFRtgDrnh6y63v2j2ex69wul2plqHrx/fQik4DJmbkqSP56bDyiXrooJEACJOCiBPQzbJh6lqVkqYRVBtGrnz8b2wZhAa41+bcvYZ9t66+hKdBJP97s8iYiQyONZvcp67HE5wNVnOHdsk0VGwHD5gD+odJOjQRIgARI4JoTkG9B17w6VkACJEACJGAKAoHl1AB8LhBm9+JxfDPwxT+AbPeMJ9epWic81uoxcQuy8rMw/vfxOJ1+WtjNrugX4M/HRpdKDJKWnYfhs9Zj1QG7WEtmbxD9IwESIAFFYHXsOYyYtQH6WWaUtrXKueTk36n0U7h/6f2lEk/pvqhdlXbGJrpPOScD+Op24MQW2aZytYDhPwB6DEIhARIgARJwOAFOADocOSskARIgAZMQCKsKjPgRCKksHYpbrzL1DQH0AN4NZVjDYRhYV61KMMiZzDO2rIw6O6Mrid4C94laCdipbrhwOyu3AGM/3oTFu11rUlM0ggoJkIDlCPy68xRGf7wRmbn5ou36GTd7VLTLxfxLy0nDP5f+E2cyzoj29K/TH3c2vFPY3EaxTf6pMcTR1bJJoUVjjkrSTo0ESIAESMBhBDgB6DDUrIgESIAETEigvEqMMXI+EFRROnf0D+DroUBuprS7gaaTgjzT5hm0riTjIO4+vxuPr3wceQVy1YnZm6wzYH40shVubBQhXM3JL8C9n2/GtxvdN8OzaDAVEiABlybw5fpjuP+LzcjJKxDt6NEwwvaMC/D1EnazK7kFuXhsxWPQWeeN0qJiC/yr7b/cM+lHTrr6gDgYOLzS2GQ1xqigPjj+BJSpLu3USIAESIAEHEqAE4AOxc3KSIAESMCEBMLrqklANTAPLC+dO7Qc+GaYW04C+qhkKG91eQuRIXIL9Mr4lXhl3SsoLCyULEyu+Xl7YfqdLdD3uirC0/yCQjwxZzve/f2Ay7VJNIQKCZCA2xLQz9v/LD2AiT/sgHpkCendtDLeH9YC+hnnSqLbNGndJKw+IVfBRYVGYdoN0+Dr5YbJL/Tk3xdq8u/IKnmr/MMubPsNryPt1EiABEiABBxOgBOADkfOCkmABEjAhAQqNrywHTigrHQudsmFr/luGBOwjH8ZvNv9XegsjEaZc2AOZsTMMJpcouzj5Ym3h1yPIa3kpKZ2/s3f9uP5n3ZBTwhSSIAESMAsBPQzST+b3losV8lp/4ZGR2La7ddDP9tcTWbtnAXdlxilrF9ZTO8+HbrvcTvRY4QvBqltv2r3gFF0W/XKv0pNjVaWSYAESIAEnETA9XpUJ4FitSRAAiTg9gT0AH34PMBPfa03it7K8/kAICvZaHWLcq2wWrZJQD8vP9Ge6THTMfeASpLiYuLl6YFXBzbFfV1rl/L807VH8cCXW5BlF1ur1Ik0kAAJkIADCGTn5WP811uhn0328mC3Ovj3bU3h7YKTf3P2z8G0LdNEk3Qf8073d9wz42/x5J9c7Qj9QVHvLqhyvWBBhQRIgARIwHkEOAHoPPasmQRIgATMR0AP1HWGPvtJQJ0Y5JO+QEaC+Xy+Qo+aV2yO1zq9Bg/1P6O8tPYlrIhbYTS5RFnHOHzypgZ4vk8jFWNKuvyLCrA/8r8bkJyRKw9QIwESIAEHEkhMz8HwjzZgwfaTolb9zHpBPbse7VnfJWPkLTm6BC+te0m2SfUtkztNxnUVrhN2t1AyE4HPbgOOrZHNCSh3Ib5wZTdss2wpNRIgARJwKQKcAHSp20VnSYAESMABBKq1vPDVXg/gjXJyG/DxrUCazGZoPMVVy91rdMfTbZ4W7ucX5uPRFY9i46mNwu4qyugONfGf25urWFOyq19/OAG3vb8aR86peE0UEiABEnAwgcPq2TPg/TXYcER+UPLx8rA9s0apZ5cryrqT6/DEyidUHEOZxOTx1o/jxho3umKT/trn1FPA7N5A/AZ5no4nrJOLcduv5EKNBEiABExAQL4VmMAhukACJEACJGACAnol4OiFpbMDn9mlBvy3AMnxJnDy6rowtMFQjGs6Tlw0Oz8bDyx9ANvPbhd2V1H6qKQgH49ujWA/b+HyobPp6D99NdYfOi/sVEiABEjgWhLQz5zb1LNHTwIaJUhl+J09Khr6meWKsvPcTkz4fQJ05l+j3NX0LgxvNNxoco9y4hHgvzcBekxglOLJvyZGK8skQAIkQAImIcAJQJPcCLpBAiRAAqYjoBODjP4FCK0qXTt/AJjVUw3890i7G2jjm49Hv9r9REsy8jJw35L7sC9hn7C7itK+Tji+uactKoTIOIdJahvwsFnrMWez+03musq9oZ8kYCUCP2yNtz1z9LPHKJVC/fHtve3QsW640ewy5djEWFsfofsKowyqNwgPNn/QaHKP8undagzQC0g8LNsToiZvR6kPhxGNpZ0aCZAACZCAaQhwAtA0t4KOkAAJkIAJCYTXuTAJWDZKOpdyXH39Vy8Ax9ZJu4trOn7eC+1fKLVdKyUnBXcvvhtHko+4ZAsbVwnDvH92QINKIcL/3PxCPPpdDN5YtBcFzBAs2FAhARK4OgT0s2XKb/vw8Dcx0M8cozSuEmp7NulnlCvKoaRDGPvbWCRlJwn3e0X1wjNtnnHJOIaiIfZK/Ca1C+BmFQpEbf81SrlawJhfgYoNjFaWSYAESIAETEaAE4AmuyF0hwRIgARMR6BsjQuTgOH1pGs6K/CnarXc3gXS7uKat6e3LSlIh6odREsSshIw7rdxiEuJE3ZXUaqWCcD397VH9wYVS7n83rKDuO+LzUjLzit1jAYSIAESuFwCKVm5uOvTTXjn99hSl+jRsCK+vacdKoX5lzrmCobDyYdtk3+6bzBK+yrtMbnjZHh5ehnNrl/epyb4dDKwLDnZiQi13Xe0OqbHChQSIAESIAFTE+AEoKlvD50jARIgAZMQCFVbe8YsAqpFS4fysoBvhgGbP5Z2F9d8vHzwdte30TJCJUQxyOmM0xi9aDSOpRwzWF2nqGMBfjCiFcZ2LB1kf9Gu0+j/3mocPJvmOg2ipyRAAqYloJ8l+pmydG/pxFGjO0Rh5vBWCLKLT2raxtg5pvuAcYvG4VzmOXHk+grX2/oO3Ye4lWz8CPh6KJArYzciso3a9qs+AoZEuFVz2RgSIAEScFcCnAB01zvLdpEACZDA1SYQqLICj/gRqHeTvLLOeDh/AvD7JKBQbu+SJ7qWFuAdgHe7vYsm5WUw86JJwKMpR12rQf/z1svTA/+6tRFe6d9ErVDxEG2IPaNe2N9djcW7Tws7FRIgARK4FAJL1DNEP0t0wiGj6GfOS/0a4/k+jUs9f4znmbkclxqnvoeNwZlMObHZrEIzvN/jfQT6BJrZ/UvzrUD17789Cyx4VPXvMrsx6vQAhv8ABJS5tGvybBIgARIgAacR4ASg09CzYhIgARJwQQK+6sVmyBdA84tkNVz5OvD9GLVCINMFG3Zxl4N9gzHjxhloWE4lRDHImYwzasfTaOgtYK4qw9rWwCejo1EmUK5USVXbgPWWvbcW72dcQFe9ufSbBJxEQMf7+8/SA7jrs03QzxKjlAvyxedj22BEuyij2aXK+pmvn/36Q5BR9IeiGT1mQPcZbiO5aoX/96OBNe+UblKz24HbvwJ8g0ofo4UESIAESMC0BDgBaNpbQ8dIgARIwKQEvLyBvuqFoPPjpR3cNRf4uDeQKl+OSp/oOpYwvzB82PPDUpOAZzPP2laB6CDwrio66+b8BzqiYeXQUk3QL/FjP9mIxPScUsdoIAESIAF7AufTsjHq4422jwf2i8F1so+fHuiAdrXL2//MZXSdCX7Ur6NKTf7pD0T6Q1GIr0yy5DINu5ij6Wprs47xu3te6aNdngRumwF4+5Y+RgsJkAAJkICpCXAC0NS3h86RAAmQgEkJqGy56Ka2BfWeAnjYBTo/vhn4sBtwaodJnb90t4omARuVbyR+rOM/6RfCXed3CbsrKZHlAjFXJQfpf72K82gny/adxS3/WYWNR2SQe7vTqJIACVicwIbDCbZnxcr9Z0uR6KeeLd/f2x7Vyrru1tid53baPvjYJ/yoX7a+7QOR7iPcRnTf/cENQNw62SSVIAv93gNumKj6fRk+Qp5IjQRIgARIwKwEOAFo1jtDv0iABEjAFQi0Hgfc+S3gZ7eCLCUemNUL2PeLK7Tib/lYNAloHxMwMTsRYxeNxcZTG//Wdcx4UoCvF94ecr0tNqB9XMCTyVm4/YN1eG9ZLLcEm/Hm0ScScCIBveV3+vJYDP1wHU6nZAtPdIjRZ25piKnq2aKfMa4qm09vtmWAT8lJEU3QfcGsXrPgVpN/u9SKv1k9geRjoq22Pv7O71X4j2HSTo0ESIAESMClCHAC0KVuF50lARIgARMS0IHAxy4GytSQzulsgV+prIHLX4OaOZLHXFQL9Q3FzJ4z0Sy8mWhBumrrvYvvxbJjy4TdlRQPtaJDZwfWMbrCg+XWrnz1kv/Gon0YOXsDzqbKl3xXaiN9JQESuHoE9JbfMSpMwOu/7oN+RhilQogfvhjXFnd1rqUWi7nuarGV8Sttz3b9jDdKi4ot3Gvln+6jdSKv70aqOL4ZxqYCoVWBMb8CtdWqQAoJkAAJkIBLE+AEoEvfPjpPAiRAAiYhULEBcNfvQPV2dg6pl8Ll/wa+VhOBmUl2x1xT1ZOAH/T8AG0qtxENyCnIwcPLH8b8g/OF3dUUHaNr4fhOaH+RWF2rDpyzbfNbtu+MqzWL/pIACVxFAkv3nEavqSuxXIUJsJeOdcJtzxBXjven2zT3wFyM/308svJVMgyDtK/S3hbzz20SfmSnAt+olX06kZe9RKp+7i71YSuisf0R6iRAAiRAAi5IgBOALnjT6DIJkAAJmJJAUDgw4kfgOjXZZy/71eqBD9XqgdOuGyvP2KQgnyBM7z4d3at3N5qRX5iPiX9MxEc7PkKhfRR8caa5lYqh/vhMrQR8uEc96G18RtErAEfP3ohnftiBdLssn8bzWCYBEnA/Avr/80/P3aESBG3CuTSZIEgv9NPPjE/GREOvAHRV0c/uGTEz8Pya523PdGM7boi8Ae90ewcB3gFGs+uWz+y5ELN334LSbWg+HBipPmiFRJQ+RgsJkAAJkIBLEuAEoEveNjpNAiRAAiYl4K1e+vq/D/R8pXRykASVLfcjtV14h4oj5Abi6+WLN7u8if51+pdqzbQt0/Di2heRV5BX6pirGHQswAk96tq28VW8yMv8F+uP2VYDbj7KBCGuck/pJwlcCYEtxxLRWyUF+mrDsVKXCQ9WW37VRwP9zLCPI1rqZBMb8gvy8fK6l/HetvdKedm3dl9M6ToF+tnvFhLz9YXJv3P7ZXN0Yq+b3wD6vqMy/bruRK5sFDUSIAESIAFNwEN95ZJBO8iFBBxAID4+HpGRkbaa4uLiUK1aNQfUyipIgAQcSuDwShVPaDSQca50ta3vujBJ6ONf+piLWXQ3OmXTFHyy+5NSnneo2gFTukyBXjHoyqJjfT36XcxFt/vpFYL3dqmNh9TKH19vfld05ftM30ngYgSy8/LxztJYvL/iYKlYf/r8Hg0rYvKAZi696k+3Q8f5e3Llk1gRv0KrQsY1HYfxzce7dDzD4gblZgK/PAlsKd1nIaAsMEjZa3UpPp0FEiAB9yDA92/3uI9X2gpOAF4pQf7+sgjwAXRZ2PgjEnA9AskqG/A3ahvRiS2lfY9oql40ZgPhdUsfczGLngT8bPdneHPTmyhU/zNKg3INbFvGKgVVMppdrqzbqFf9TVqwB5m5+aX8rxcRjFcHNkOL6uoFkkICJOAWBDYdScCTc7bj4FmZBEM3LlBl9n3u1kYY0jrS5SfGTqSdwAO/P4ADiQfEffOAB56Kfgp3NLxD2F1WOX/wQqKPUztKN6GSSm41+FOgXM3Sx2ghARJweQJ8/3b5W3hVGsAJwKuCkRe5VAJ8AF0qMZ5PAi5MIFcFUP/liYuvNtAr43q/CVzvHi9Xi48uxtOrnkZ2vsyUGx4Qjre7vo3rK17vwjfygutHzqXjkW+3Ycux0klddAywEW1r4PGbGiDYz9vl28oGkIBVCaSpWH9v/LoXn647quKZlqbQonoZvDX4ekSFu/bqZt2ybWe2YcKyCUjIkuEMfD19MbnTZPSM6lkagCtatn8L/PwIkKOSfthLqzFAr8mAG6zKt28adRIggQsE+P7NfwmaACcA+e/AKQT4AHIKdlZKAs4lsOUzYOHjQJ7afmQvzYaoicApgF+I/RGX0/XLpM4cmZidKHz39vTGv9r+CwPqDhB2V1Ty8gswc+UhTF2yH7n5pWcHKof545X+TdC9YYQrNo8+k4ClCegs38+oRB8nkmX2Ww3FW8cG7V4X93WtDW8v19/y//Ohn/Hc6ueQW5Ar7nkZvzKYesNUtIxoKewuqWSqjzULH1Pxd78r7b7+CNdnGtBsUOljtJAACbgVAb5/u9XtvOzGcALwstHxh1dCgA+gK6HH35KACxPQGQd1XMCz6q+9lI0CbpsJVG9rf8Tl9GMpx3D/0vtxNOVoKd+HNhiKx1s/Dh9Pn1LHXM2w60Qynvh+O3adSLmo672bVsaztzZE5TA3yZh50VbSSALuQSA+MQMv/7z7/9q7E/g6ynLx40+bpUnatGnSNU23dF9poSulQEErW1n0XkRARdkURFAEtFcFuexcwA1lF72icP0rSG1ZVEpbSveFLnRf0zZp0n1J0jTL/3kmnXCanHNyTpazTH6vn3HmzMyZed/vTIfMc95F3lu712+BRuZ0kMe1mf+Q7u39bo+nlRbwe3rp0/LHdX+sk+3cDrny6wt/LT3Tq/uqrrNDPK3Y8bHI324VObyzbq47D65u8tt5UN1trEEAAc8J8P7tuUvaoAIRAGwQG19qrAAPoMYK8n0E4ligrFjkvR+JLHvVTyG0DemkO0WmTI/70QcPn9Dg2Nx75eM9+gJWK43tNtYZQTgzJbPWlvj7aLUBX/5omzz9z41yoryyTgGsn7A7LhggN57Tl0FC6uiwAoHoC9ggHy9qjd5fz94spSfr/htOSWotd39+kHxjUh9P1PorKi6SH8z5gSwvrNs37aTsSfLkeU9KenKc10av0BqNcx4Xmac166vqXlMZdZ3IJU+KJMd/E+7o/wsiBwjEhwDv3/FxnZo7lwQAm1uY4/sV4AHkl4WVCLQsgTV/E5mhwb4TfmqPdRkm8kWtDdhtRFyblFeWyy+W/0JeXftqnXJ0TevqBAG90C+gFW7H/uMy/c3VMn/z/jpltRW52lfYA5cPk3MHdva7nZUIIBB5AWvu+7O318r2/frDjJ90dr8sHeF3hPTO8kagaNneZU7wb19J3dHprxtynfxgzA+0mXOc91+av0rk77eJ+BvoI6WDyGU/Fxn+RT9Xm1UIIOBlAd6/vXx1Qy8bAcDQrdizCQV4ADUhJodCIJ4FDmwTefNbInkL65bCmshO0ZqCZ2uQMCG+X8hmbJkhD3z8gJRVlp1WzoRWCXLnmXfK14d9XVq3iv/+tGyk4L8u3y0PzfxUDhWf3qeWW/CLhnWTn0wbKj0yaBbsmjBHINICeQeqm/u+/6n/5r6ZbZPlhzqYz3+OyYn7EX7N1h2p/ellT0tF1emjmNtgH9PHT5cvDfxSpC9D056vXP/7YjX+5unAWvrjU53U+5zqH9Y65NTZxAoEEPC+AO/f3r/GoZSQAGAoSuzT5AI8gJqclAMiEL8Clfoy9vEvRT54WF9a/ASNuo0UufxXItmj4reMmvO1+9bKd2d/VwqLC+uU49ycc+XhSQ9LRkpGnW3xuOLA8TJ58r0N8vqSnX5HELUmhbef319uPjdXUpIS4rGI5BmBuBQoPVkhL2hz32e1ua+/Jvs6xodcN7633D11oGSkJcdlGWtn+lDpIbn/4/vlg7wPam+S7LbZ8vSUp2VYltY6j+eU/4nIW1rrb++auqWwGo0X/Fh/TPuuSGuet3WBWINAyxDg/btlXOf6SkkAsD4htjeLAA+gZmHloAjEt0CBvri8qZ2V+3uB0ZpyMvF2kfO1RmByWtyW05qd3Tf3PllcsLhOGbzWJNgK+EneIfmpNi+0ub9ktQDvu3iwTBvZ3RO1jPyVkXUIxIKA1YCbsSpfHn9nvew+VOI3S2f2ypAHrxguw3toM1GPpEX5i2T6vOlSWFL3hxfr7++xyY/F9w8vJ/Vazn1SZL6O5Ouv1l+XoSJX/kZ/QBvtkStKMRBAoKECvH83VM5b3yMA6K3rGTel4QEUN5eKjCIQWYHyEyIfPqYvM9pHkb+Oyzv2FZmmLzq550U2X014tgqt8fj8qufluU+ekyr9n2+yJsE3j7xZbhl5iydGCbayVVZWyf8tzZPH310vBwM0C7bAw08uGyqje3X05WAZAQSaQGClBuBtdN9lOw76PVqWNve1QPx/nJkjra0KoAeSjfL77Ipn5ZU1r9R5zlrxvnXGt+RbI78lCfFcI27Tv0Rm3S1ycHvdK2Y/mk3+vsi598T9gFp1C8caBBBoiADv3w1R8953CAB675rGRYl4AMXFZSKTCERPIG+JyNt3iBSt85+HEVeLTP1vkfRu/rfHwdqF+Qvlh3N/KPtL6w6aMTRrqDx6zqOSm5EbByUJLYuHisvkqfc3ymuLdojGBP2mK0dly73a71g2/QP69WElAuEI5B8ukSfe3SBvrtjt92sW6/vqhN7yfR3ht0Oa9rnqkbTzyE6npvWa/XWbw3Zs01EeOuchsW4X4jYdyRd5T2vDr33TfxG6Dhe54tm47zbDf+FYiwACDRXg/buhct76HgFAb13PuCkND6C4uVRkFIHoCViH5lYT0Jo3VZw+eIaTqeR0bRL8Q5Hx2mw4IT5fXq1JsAUBFxUsquPcJqGN3HXmXXLtkGs9MUCIW8C1ew47tZEWbj3grjptbv0D3jI5V249r5+0bRPfg7+cVjA+IBAhgZIyrWU8d4s8N2eLlJ6s9HvWCbmZTq3bYdneae5bqbXG/7z+z87I6yXldZs5T+w+UR4+52HpnBanI5FXlIssfVn7y31I5MSRutfV+vqbrDUCJ/9Aa/15o//GuoVkDQIINFSA9++GynnrewQAvXU946Y0PIDi5lKRUQSiL1C0QWsDaufl/kYKttx1HiJyiQYJ+06Ofl4bkANrEvzS6pecJsHlVfqCVyuN7z5eHjz7Qclul11rS/x+tP7IbPTRR2atkx37i/0WpEt6G7nnC4Pki9osMcEjzRL9FpSVCDSRQHlFpY7CvUue+ecmKThS6veovbPSZPolQ2Tq0K6e6nfTav39ZP5PZHnh8jrlTtTAmP2Y8tWhX43fH1O2zBZ5V2v9BaoV33OCyGXPiHTVPv9ICCCAgB8B3r/9oLTAVQQAW+BFj4Ui8wCKhatAHhCII4FKrcViNR/+rc1+Txz2n/GhV4p87gGRTO0nMA7T2v1r5UfzfiTbDm+rk/vUxFS5Y/Qdcu3ga+O7z6paJTtRXiF/+HiH/PKDTXK0tG7w03Yf2LWdBgIHy+eGdPFUwKIWBR8RaLCAG1C3kbc3Fx7ze5x0rU17x4X95etn95E2ido/nEeS/YDyp/V/kl8u/6WUVtQNevZp30ceP/dxsW4V4jLt3yLy/k9ENsz0n/1U7Tf18w+KjLpeR/ht7X8f1iKAAAIqwPs3t4EJEADkPoiKAA+gqLBzUgTiX+CYjuT4rwdEVr7mvyyttSnwuFu043NtApWW6X+fGF5bWl7qNF/747o/+s3l8Kzh8sDZD8igzEF+t8fryv3HTsgz/9oof1q0M2D/gDZQyH3aP+D43Kx4LSb5RqDJBRZt3e8MsLN8p/+Rtq3y7DXjemk/fwOlU7s2TX7+aB5w08FN8uCCB2Vl0Uq/2fjyoC/L98/6vqQlpfndHtMrS/WHrnlPiyzUEXz9dYFhmR91XXXwr22nmC4KmUMAgdgQ4P07Nq5DtHNBADDaV6CFnp8HUAu98BQbgaYSyFssMlP7OipY5f+IKdqvlY1+aMHAxPh76bUBQn780Y9lb/HeOuWzkYJvGHaDM4plSmJKne3xvGLj3qPy0Mx1MndjUcBinD+os9yrNQKHZrcPuA8bEPC6wPqCI84AHx+s1x9FAqRz+neSH182RAZ389a/leKTxfLbT34r//vp/0pFVUWd0vdo18PpNmFc93F1tsX8ipNai3HJixr8e0qk5KD/7HYdIXLx4yJ9JvnfzloEEEDAjwDv335QWuAqAoAt8KLHQpF5AMXCVSAPCMS5gDb9kmW/q24WXOq/9otk9BK54Kciw7+ozaPiq9nb0bKj8syyZ+QvG//i90LZS+49Y++RC3pe4LmmsR9uKNRaTRtkXb6fju5PaVx+RrZ8V5s09u+ig8GQEGghAhYk/8W/N8ms1fmiXWn6TUO7t5cfXjxYJg/o5KlngzV1/mDnB/LYksek4HiB37J/ZfBXnP7+4q7Wnw3wsep1kdmPihzZ5bdskqY1/S7U5sCjvxp3/z3zXyDWIoBAJAV4/46kduyeiwBg7F4bT+eMB5CnLy+FQyCyAlZLYu7/iCx+IXBTKRsoxEYMHnJ53PWTtGzvMvnZgp/57RvQoM/OPlvuG3uf5GbkRta9mc9WWVklM1btkafe3yg7D/gfKKSVNm+8dER3ueOCATKoG4HAZr4kHD6KAhsKjsovLfC3JnDgr1dmmtw9daBMG5mt3cHpPw4PpR1HdsgTS56Qubvm+i1VTrsceXDSgzK221i/22N2pUVx12v/fv/Wfvz26YBX/pKN7jv+WyLn3StitdtJCCCAQAMEeP9uAJoHv0IA0IMXNR6KxAMoHq4SeUQgzgQObq9+iVrz18AZ7zpcA4E6kuLgS7UX3Ph5QS7TPqBeXP2iM1pweWXdwTISWyXKtUOudZoFpyd7KxBWVl4pbyzZqbWeNss+7SswULp4eDf5zgX9ZVg2L8iBjFgffwLW1NcJ/K32X+PNStSpXbLWhh0g14ztJcmJ3hoI4rAO+vTcJ8/J6xteF7/PPg2OfXP4N+WmETeJDZYUN8kCfxve0R+vnhDZsyJwtgdfVj24VacBgfdhCwIIIBCCAO/fISC1gF0IALaAixyLReQBFItXhTwh4BGBXUt11MQfi+xcELhA3c/QQOB0HWL2C3EVCNx8cLP898L/luWFy/2WLTMl0wkC/seA/5CkBB0QxUOpuKxcXvlomzw/Z6scPVE3COoW9XNDusq3z8+Vs3rH3yAwbhmYt2wBa+q6dMdB517/17q6/YC6Ojay702Tc3XqK2112UvpZMVJeWPDG05ff0fK/HcFML77ePmv8f8lfTv0jZ+i24j262eIzHlSZO/qwPnuM7k68JczJvA+bEEAAQTCEOD9OwwsD+9KANDDFzeWi8YDKJavDnlDwAMCbrOq2Y+IFK4NXCCrETjpLpFhV4kkxMcLtAUH3tn2jjy17CkpLPY/AEDP9J7y3dHflal9pkrrVt6qEXTweJm8Mn+bvDp/e9BA4GgdNfhmDY58YVg3SfBYc8jANzRb4lmgQpu9v7+2QJ6fu1VW5gXo11QLmJ6SKN+Y1Fdu1KlDmrcC/fZ8+9fOfzmjoVuzX3+pU2onuXfsvXJRn4vip49D67N27ZvV3VUUrfNXrOp13UZUB/76XRhXP04FLhBbEEAgVgR4/46VKxHdfBAAjK5/iz07D6AWe+kpOAKRFbDaFuv+LvLhYyJF6wOf2wYLmXiHdq5+vUhyWuD9YmiLjYRpzYJ/v/b3crLypN+cDc0a6nSIPzF7ot/t8bzycPFJ+d3H25xagUdKA9cI7JmZKt/UQMl/jukp7TxWSyqerx95/0zgSOlJeXP5biewvWO///4ubW8L/N14Tl8n+Nch1XuBv3m758mvV/xa1h3wHyBLap0k1w+9Xm4ecbPETVcHZXo9P/mTyILfiBzY8tlFr72UmSsy5b/0xygbsMpbP9rULiqfEUAgOgK8f0fHPdbOSgAw1q5IC8kPD6AWcqEpJgKxIuDWvvhQR1jcvzlwrtKyqjtbH3OjSFtdjoO088hOeXLpk/Jh3ocBczum6xj59hnfdjrIbxVHfR8GLJDPBgue/O+CHfLivK1ySIOCgVLb5AS5fFQPuW58Lxneg34CAzmxPnICa3Yflj8u3CF/X7lHSk5qDbEAqb0T+MuVGyb1Ea8F/qzIi/MXy69W/EpWFq0MICDyhT5fcH7MyEnPCbhPTG04prWzbWCqJS+LlBwInLVOA0Um/0BHqv9S3NRCD1wYtiCAQCwL8P4dy1cncnkjABg5a87kI8ADyAeDRQQQiJxAhdYUW/0XkXlPaSBwU+DzJrQRGfGfGgy8RcT6C4yDtLRgqTyz/BlZVbQqYG7P7HKm3HrGrTKx+8T4aToXsDSnbzim/QK+vnin/E6bBu8+VHL6xlqfRuZ0kK+M6yWXn5Htub7TahWVjzEmYPfprNX58tqinfJJkGa+lu0eGalO/35Xa+1Vr/XxZ019FxUskhdXvSiLCxYHvEojOo1wmvuO6jIq4D4xtaFQay8ueFZk1RuBR6W3DHcZKnKuBv6GXqk1/hJiqghkBgEEvCnA+7c3r2u4pSIAGK4Y+zeJAA+gJmHkIAgg0FABaxq8YZbIR8+I7NZBQ4KlXtp8dvytOnKwjsYY4wNr2Ev1B3kfOP1nbTu8LWCpzuh8htwy8hY5p8c5nusjsLyiUmatKZCXtEbgql2HAxrYhjStFWh9BF45uodM6pcliQk0vQsKxsYGCdg9OW/zPnlrxW55T/v4Kz2pz58gaXiP9nLLuf3kEh3Z2mv3ZGVVpczeOdsZ0XzN/jUBFfq07yO3j7o9PvoxLS/TgT3+IbL0FZHt8wKWydnQbaQG/u6p/u8JTX2DW7EVAQSaVID37ybljNuDEQCM20sX3xnnARTf14/cI+AZAQ2YyY751YHAzf8KXqz0bJGzbqjuJ7BDj+D7RnlreWW5/H3z350RNPcWBx5FNLdDrnx92Nfl0txLpY3VevRQsmDo4m0H5CUdOfjfOpKqjq8QNHVq10amndFdrtJg4AhtIuy1ptJBC8/GJhew+2/tniPyN+3b7+1P9si+YyeCnsMGqpk6tKt8dWJvmZib5bn7z0b1nbVtlryy5hXZenhrQIse7Xo43RXYMymxdYwPzHR4l8iyV0WW/0HkWODnrFPY/p8XOfs7In3PY3CPgFefDQgg0JwCvH83p278HJsAYPxcK0/llAeQpy4nhUHAGwIFq0U+/pXImr+JBBhUwymojarb/3MiZ35NZOBFMV0rsKyiTN7a/JZT2yb/eH7A65SZkinXDr5Wvjzoy5KRkhFwv3jdkH+4RN5YkudM+YdL6y1Gbqe2csmI7nKR1sAalt3ec8GYegHYoUECFvRbrf36vaM1UN/RZr7bgwzo4Z6ge4cUpzn6l8f2lK7tU9zVnpnvK9knf9n4F/m/Df8nthwodUnrIreOvFWu6n+VJMVyTWsNZIr9WLT8f0U2viOiNRoDpoRkkZFf1gGmbtcmv0MC7sYGBBBAIBICvH9HQjn2z0EAMPavkSdzyAPIk5eVQiHgDYGjWpPDanVYc65jBcHL1LaLyKivaK3Ar4p0GhB83yhutdo3b2952xk1ePex3QFzYrUAL+57sRMIHN5peMD94nWDNcWcs7FI/qT9r83eUFhvrUArp/XDZoHAi3U6s1dHHaCzVbwWn3w3g0ClVi1dueuQE/Cbtbqg3v4nLQuJeg+dN7CzWNDvgsFdPNfM18q4dv9a+dO6P8k7294JOEq57ZfTLke+MfwbckX/K2K7FvLeT0VWvqZ9+/2fyHEd4CNYSuskMuYbImNv1qGbuwbbk20IIIBAxAR4/44YdUyfiABgTF8e72aOB5B3ry0lQ8AzAtav07q3RRY9L7IrcCf1NeXNHq0Dh1xdPZpjjL70ndSajTO3zpTfr/29bD60uSbr/haGZg2VawZdIxf1vUhSE1P97RLX6wqPlsqMT/J1BNbd9fYV6Ba0c3obmTKos5yrwZtz+neSjDSt4UNqcQKHdbTpuZuK5MMNRRpQLtTmvfqsCCHZ4DNf1Cbm03TwmSxtcu61VHyyWN7d/q78ddNfgw5GZOUe2HGg3Dj8RqePv5ht6nt8v0YytUa4Bf72rKj/cll/sWNvEhkyTaO83ru+9QOwBwIIxLIA79+xfHUilzcCgJGz5kw+AjyAfDBYRACB2Bewl79lv9cRhP+fSNnR4Pm1JsLWz9NIDQbawCEp7YPvH4Wt1lRx/p758uraV2VR/qKgOUhPTpdL+l4iV/S7QqxWoBf7xttSdEz+rgM0vKnBwLwDJUE93I1WEXBUzwwnGDh5QCftNzBDkhMZRMT18dL8RHmFjth7WBZu3S9ztQbp8p0HQ6o9agZWg/TK0dnat2SO9O/SzkssTlnsWbJm3xon6Ge1/YrLi4OW8ayuZ8k3h39TJveYHJvPklIdOGj9TO0K4q8iW2ZrE9+KoOWRZL2m1sx37I0iXYcF35etCCCAQBQFeP+OIn4MnZoAYAxdjJaUFR5ALelqU1YEPCRQdlxrhLxZ3el7XvDAmVPqRO3Ty/oJHHq5yICpIm3SYw5j/YH1To1Aq7ljg4cES3079JXL+10u03KnSde23mvaZsGMFXmH5D3rw02nnQeCBzN8rVKSWjtNhMf1zZTxfbNkdK8MSUlK8N2F5TgRsIDfyp2HNOB3wAn6WcDvRHmQvt5qlat3Vpo2Ge+u/Uh28+yAMnuO7XGa9/5j6z/qrU2c3DrZGWjo2iHXyuDMwbW0YuDjiWPan9+71f2/bv6niPadWm/KGae/AFyrtb7/Iyaf6/Xmnx0QQKDFCfD+3eIuud8CEwD0y8LK5hbgAdTcwhwfAQSaXaBwfXUgcLX1CVVU/+lslN1+U6qbhw26RCQts/7vRHAP66D/zU1vOh32BxswxLLUWms5ju061mm+d2GvCyUrNSuCOY3MqSwYuC7/qLy7tsAJCG7YW0/Nz1rZSk5oLSO0yecZORlyRs8OTm3BXplpsVnrqVbeW9JHu847dLCOlRr4dadPdfTeMu0vMpzUr3P1wDEW+BvSPd2T1/lA6QF5f/v7zmi+KwrrbxJrA3tYNwJfGvglsYGGYioVH9Cg33siG7S23yYd1KM8hJq/6d1FzrhGJw38dR4YU8UhMwgggEB9Arx/1yfUMrYTAGwZ1znmSskDKOYuCRlCAIGGClRorbltH2rn8H/RpmP/0CbCWpukvtRKa4b1maRNhLWvqIFaM7Bjn/q+EbHtFZUV8tHuj+T1Da/L/N3zpUr/FyxZMHBM1zEytfdUubD3hdIptVOw3eN221ZtJlzd51uRUyssnBphbqEz0pJkpAUENTBocwsUWRNRLzardsscS3MbsCPvYLET2P00/4g26z0kn+gAHoe0T79wUxtt7j2xX5b2CdlFztd+IXtntQ33EHGxv/0w8GHeh/Kvnf+ShXsWSkU9TWLteXBuj3PliwO+KJNzJuuAJ4mxU84DW/UZPUuDfjrtXBB8BF8319b/6aCLtbbfddU/4LSmVq9LwxwBBOJLgPfv+LpezZVbAoDNJctxgwrwAArKw0YEEIhXgTJtMmovl6s1GLhZa5XU06S2ppidtDaJNREe8HkR60g+RjqQtxGDZ2yZ4YwgnHc0rya7gRbs5f/MLmfKeTnnybk554o1GfZicKv0ZIUs3nbAGVF4/uZ9sr4gvNqBvn7tUxJlcPf2MlSnwd3SNSjYXgbpnObDvkrhLx8pPSkb9Lqs10DfOp2v07l9Li6rp0+3AKdqpX0+DunWXsbnZjr9Pk7MzfLsNdp6eKvM3jlbPsj7QFYXra73RwAj69GuhxP0s75CY6Z7gPITItZVw+Z/VzfxLdJa26GkhGSR/vosHv7F6i4c2niv78ZQGNgHAQS8JcD7t7euZ0NLQwCwoXJ8r1ECPIAaxceXEUAgHgRsBEkbRdhqBW6do8HAEGsZJWlNotzzNRj4OXEGE8nMFY2iRbXETt942uTv7S1vO6N8Hj+pfSGGkCwoYIFAm8Z2GyttrBm0B9Oh4jInIGhBwcXbD8ia3YdDHiTCH4cNMNK3U1snMDhEg4GDNfA0mNqC/qikQmv1bdt3XIOwRzTYpwE/nVvT7d2HQmjS6feI1Svtn9ywbA34aX+OEzTYN65PpnTQGpxeTKXlpbJs7zJnYKB5u+bJ9iPbQypm++T2TjcANkiQDe5hPwBENWlzbtm/RQfv0ICfBf22fyQS4rNKrFa2ddEw/Eta40+7aEjNiGpRODkCCCDQ1AK8fze1aHwejwBgfF63uM81D6C4v4QUAAEEwhEoOaT9TL1fHRAMtb8p9/jtczQadK5Ok6vnHfRzFFOJ9pX1kTYRtr7A5uyaI/Y5lJSqTenGdxsvE7InyITuEyS3Q654sXagWRzV2mfLdRAJp4npqWam+46FMLBAPZDpbay2YLpTQ9CCgtaEeGDXdElP8WZgqjbH/mMnnNqWVuPSavbZfKP2zdiQ5ti1j92pXbLTT6ON7DyqZ0en/8YOqd50tYD+5kOb5eM9HzuTBf9OVGhtuRCS/Ts+v+f5cmnfS+Xs7LMlKSHKRod3i+yYXx3s26qj9h7aGUIpTu1izXv7X1gd8LPBmtp6ry/T0DHYEwEEvC7A+7fXr3Bo5SMAGJoTezWxAA+gJgblcAggED8C1kzYaqis05qB1ky4eF94ee/Y91RAUIOCvSaIRDEgaME/6yfQgoEf7vow5GCgFbhzamcZ3328M1lAsFvbbuE5xNHeFnDZc7i0JiBog01YH3RHS8ubpBQ9M1Orawn61Bbso33SJVhVwjhM1lffZu1zcfWuw9U1+yzgp1PR0dCCVPUVuaPW5LOm1tbsepSO1mxBP6/3xVhYXChLCpbIwvyFTtDPPoea2mqt5HN6nCMX9LzACf6lJaWF+tWm3+/gjlMBPw367dAafge3h3eOtp2rm/UOvrS6pnWSBgFJCCCAQAsQ4P27BVzkEIpIADAEJHZpegEeQE1vyhERQCAOBSp1pNF8HU1z0z+rawjuXq6FCD7oRp1Stu8h0nOcTuOr591GikShVo41I7Tgwtxdc52ageEEGKxMfdr3qQkIju4y2rODibjXzw0KrtMRZ61/Ogtw2Xzb/uNiLRkbm1KSWjsjEI/p01HG9MmUs3p3lPYxWlPw2IlyWbhlv9aaPOiMxLtKA3+2rrEpUQOg/bu0c/pWtH4WrY9FC/p1Tm/j2dqnrllRcZET8Fuyd4ksLVgacrNe9/tdUnVwE63pd0GvC5zm+8nWL16kU7nWmt27RmT3MpFdSzTg97HI4fr7Ij09mxoEzx6lzXsv0G4VviCSM0aHMdfmviQEEECghQnw/t3CLniA4hIADADD6uYV4AHUvL4cHQEE4lTguNYGtL6rrLnwtrkix0OvpVNTYmvW1uPM6mBg9miR7vrym9Erov0IWnBr48GNTjDQAoKr9q3SPvE02BlG6t2+t1gg0AYVObPrmdIrvZfngzbGU1xWrs1aj1UHBX0Cg0caWVvQagOO1WDghYO7yoVDukhu5+gObLC58Jj889O9OpBKoSzbcVBOVjQu6tlFg3oW5HP6TNSm0dZEup+WMVlH6/V6sn9vu47ukpVFK2WF9tVpNf1C7cfPtbH++0Z0GiGTsic5tf2GdRoW2T79LOp9eFd1oM8N+OV/IqI/LISd2mltYmvaa0G/3Ck07Q0bkC8ggIAXBXj/9uJVDb9MBADDN+MbTSDAA6gJEDkEAgh4W8BeiIs2aN9W8zQYOEcnnZdqX4INSamZGgg8o7omjAUErUZMRu+IBQUPab7n75nv1BC0WoIFxwvCLkVmSqYTDBzVZZQM7zRchmQOkag2RQy7BA3/ggV4Co6UOoNcrPMZ7GJL0XFnEIyGHHmA1oy7bGS2XHZGdydQ1pBjhPudAm0GPeOTPfLWyt2yVms+NiS10YCe9XtotfncWn22nNXOmwPM+DOypvdr9q2RT4o+caZVRavkQOkBf7sGXWfN7i3gZ335WXP8Dm06BN2/yTZazecDW0QKVum0WmtB21yn40UNO0VKhkjvs3WaVD2QR5ehEXu2NSzDfAsBBBCIvADv35E3j8UzEgCMxavSAvLEA6gFXGSKiAACTStgL83WHM5qBlpQcOfChgcELWf20tx1mEiXITrpC7MzDdbRLzs2bb5rHc2CWTuP7pSFexbKooJFsih/kRwpCz8YZDWWbCCRoVlDnYDgsKxhMihzkGdHGq7F6Hw8UV4hWwpPjYB7qglxQ/rKs/7wpmkg8LIR2dIrq2n7dztcfFLeWZMvb2vgb8HW/WE1b87ukCLDenSoDvadGgk5nvs29HcN61tXfLLYqU376f5PZd2BdbL+wHrZfHCzlFeF30Q6PSndGa13XPdxTuCvb4e+zV+rtlT/be/bWP3scoN9e9eGPjqvP6A0HazDCfido30HaNCviz7HWnu/pqc/CtYhgAACoQrw/h2qlLf3IwDo7esbs6XjARSzl4aMIYBAvAhYQHD/JpG8RaemxdUv2o3Nf3r2qaDgqcBgVn8Rm9Iym6VWTUVlhaw/uN4JCC4uWCwrC1dKcbkOlNKAlNAqQazp8ICOA2RAxgDp37G/DMwYKD3Se0S2OWMD8t6UX9mno+XaCMRLtWntsu3ar96uQ1JWHloT7DNyOjg1Ay8d2V2yM7Q5eQNSodZWnLtpn7yrgb85G4tCat6blNBKzuzVUc7UvgptUI7ROnVpn9KAs8fnV+zfwZ5je2TL4S2y9fBW2XBggxPw2354u/YK2rDm0e2S2jkBv7HdxsqYbmNkcMfeQgD4AAAtYElEQVTBOjBMQvMAHd+vzx+tsVy0XicN+Dlz/Xx0T+PPZ8+fnLHatcFZ1bX8OusPFQT8Gu/KERBAoEUJ8P7doi53wMISAAxIE90NO3bskF/+8pcyc+ZMycvLkzZt2ki/fv3k6quvlttvv13S0prmF/p33nlHXnjhBVmyZIkUFRVJ586dZezYsXLLLbfIxRdf3GwIPICajZYDI4BASxYo1maA1lm+BQVtQJH8lSIlB5tGJEWbB9qLeGa/6nmWzXXKzNXahE3XdLC8UvvA0/4DrS+z5XuXy/LC5bKvJMyRkmuVOFX7RbTaghYctKlnek/p1b6X9E7v7TR7bNVKBwrwcLJ+Bedv3i8frN8r/15XKIUhjqY7sKsGkHpr02sdKdcG07B+AzukJtVIWW3OQ1rDL+9gsXyqTXpX7T4syzXoaLUQQ0nWb9/UYV3lvIFdZGK/LGnXJjGUr8XtPo7XiUOy+9hu2XVsl1hwb+uhrU7Az/rsO1HRuFGOrZn8yM4jnaby47qNk8GZTRjwsy4JijXId2Cbjrxr0/bPlvfpDxHhjmYe6CpazWQbqMMJ+Onc+jO1Hx9ICCCAAAKNEuD9u1F8nvkyAcAYvJQzZsyQ66+/Xo4c0WYTftLAgQOdwGD//voi1sBUqTVHLMj38ssvBzzCTTfdJM8//7z+yNr0zSp4AAVkZwMCCCDQdAL20n5oZ3UgcI8GAy0gaPOS8PsLC5opCwB26FU92EhGT13WyeY2+Iitb0TtQQua2AAHFgi0oKD1fbb50GapqKoImqVQN6YnpzsDjGS3y5auaV2le9vuYn2jufOs1CxP1R6srKySZTra7j+0Se7M1QVitQXDSW2TEyRdRxOu1OtiI/UWl4V3HdI1yHfR8G5y5egeMiE3S2ukeSf4agPdHNIAn42AnX8s3wn0ucE+m+8+urvBtVtrXyNrAj+w40A5o/MZzjSq8yjJSc9peJNeq1FsffAd2aWDcezWuU42KIc9Pyzgd2C7SFlogd3aeQ34uV1XkW4jTk0jda6T/ajg8YB8QA82IIAAAs0owPt3M+LG0aEJAMbYxVqxYoVMmjRJSkpKpF27dvKjH/1IpkyZ4nx+/fXX5cUXX3RybEHApUuXSnp6eoNKYMd97LHHnO+OHj1a7r33XqeG4ZYtW+SJJ54Qy4cl2++RRx5xlpvy/3gANaUmx0IAAQTCELCg4OE87ZNL++Eq/FSnddWTDThSeTKMA4Wxa1JbDQT2EEnvLtJemxin6yid1tTY5s5nXd+ui0jCZ7XLgh3dBkGwJpJr96+VtfvWypr9a5zaVA1tKhnsXImtE53AoAUFO6V2Eqtl5U5ZKVliAUL73DGlo1iTy3iqTVihwcBF2/bLP1blyzur8+Wg1uZrjmQj8V4wqItcPipbLhjcRVKSEprjNM1yTGuaa31UHjxxUA6fOCw2oI0F+faX7ncCfUXFRVJYUig2LyopEqvB2tTJ7e/SBr4ZkjXEqdlnfV6GPAiOlkFshPFjBRrY0ya5FthzAnw+gT5b31z//jVY6dQUdoJ9pwJ9tpyuAUASAggggEBEBHj/jghzzJ+EAGCMXaJzzz1X5s2bJ4mJiTJ37lyZOHHiaTl88sknnWCdrbz//vvlgQceOG17KB82btwow4YNk/LychkzZoxzntTUz/r5KS4ulvPOO88JMFo+1q1bJ42pbegvTzyA/KmwDgEEEIiiQIUGf/Zv8QkKanDQOu+3Jn/NFRg4rbhaE8yCgLWDg207i9RMnaqX2+iPX7VqCR0rO+bUDNx0aJNsOnhq0mUL2kQqWaDGahTaYAs2b5/cvvqzrfOZrElySkKKpCSmiC27k3229alJui4hVSz4GKmA4smKSvl4iwYDtWbge2sL5Ehp4wJZ6SmJMqlfJ/nc0K5OM9/2WmswEskCcGUVZU5zWmtS6y7b3PqWtEE1jp88LsdOHqteLtdlvXdsm82P6+ejJ446QT4L9B3VWm/NEVgOZGEj8fbr0E9sgA434Gd9Wto9UiedLNE+9jSod2xv9XT01NwCfccKT23TudXsa6Ias3Xy4LsiIVlr8A0Q6TxQp8EinU7NrVZfYssZpdmXhGUEEEAgVgR4/46VKxHdfBAAjK7/aWdfvHixjB8/3ll36623ynPPPXfadvtgTXeHDx/uBOUyMjKksLBQkpLC+6P6tttuk9/+9rfOsRcsWCATJkyoc56FCxfWBB9t/2effbbOPo1ZwQOoMXp8FwEEEIigQIUGgqzG4AENDlqA0Jk2V3+25oHa7DHiKUGDCU5Q8FRA0F220UFTtQ8x60dM51UaTNnXuko2le6TbVpLK0/7XdtxZIfkHc1zmmM2ZCTVSJbVAoqJrRIlSWtGJrVOcgKCNve77LOPfc/5n84tgOh+rlm29fo/Z727j37D3V5V1Ur2a9Ng6ytwrw7oYSP5HjlxUkfw1dqjp6XqzxaLtf77MtslS1bbZOmcbvM2NTFa3wCa7zGsGbfVsHPmoSzX2ressjrQ5xvka6qm4acVs4k/2GA1VqPUmuxasC9Xg325bbMlt43WJq2okFbWb6f1t2dTzbI2269ZZ8s6acAy4kmD0k7T/o59tVafTu7cAn4ZvbUWr24nIYAAAgjEnADv3zF3SaKSIQKAUWH3f9Lp06fLo48+6my0AJwbDKy9tzXdtaa5lt577z2ZOnVq7V0CfrY/vHNycmTPnj0yePBgJ5AYaGfbvmHDBunRo4czEElT1kLgARRInfUIIIBAHAmUn/isnzALElpA8JDO3WVrZhiNAKE/Qg10SZv2NQHC8pR0yU9qIzt19NldGiQsaFUl+VUnpaCyVAoqimVv+TEpj5W8+ysP62JWwMKrnRLbSg+tCZrdOkV6tEqSnKrW0kNHYu5xsly6lpVIotYyFGfS/p5tHiv3mgYnnWb57bXJvjXb75CjQb4+nwX62utngnwxe++RMQQQQCCQAO/fgWRa1np+pouh6/3RRx85uWnbtq2cddZZAXNmzXPdNH/+/LACgNu2bXOCf/Z93+O4x/Od23YLAO7evVu2b98uffvqL70kBBBAAAEEXAFr1ueOBuyu851b7cGje6oDg9bH2NF87XtMJ5vXTNpcUZtnNnuyAIv23+ZMejL7A6jnqcnfua1e4/6E1pKvXWEUJCRIoc4P6Of9unxAB8c6oHNnWeclHhrIwp8F66oF0rQVRoY2le6gc6up10WnzuWn5vb51LJti80/sLWqptPv5qngngXzLMhn/XC6yzYwR2sNApIQQAABBBBAwHMCsfn3ieeYQyuQ9bVnyfrbs773AiWrmecm9zvu5/rmn36qfTqdSr7Hcdf5zn2323kIAPrqsIwAAgggUK+A1RSykYBtCpSsWak1bawdHLSAofVtZv2XOZMOYqD9t0UqaX1B6azBns4anBxZz0mLtQ3sQQ0OHtXAoE1HTs3dz0c1QOiuO6bbSnV/CxqWaK1EWy51lltJpbWlJTWLQBsN2rXRe62tTbrcVgdAceY+n9M0SNxOt7XTbRkaxMvQZQv4Vc8rJLlZctYEB7X+AW1ADQveuZPzWQfZsc/utjRtMk/tvSYA5xAIIIAAAgjEp0DgKFN8liduc11aWir79unLjSZrohssdezYUayW4PHjx52mucH2rb3Nqv66qb7z9OxpdSOqU16eNukKI/mex9/X8vO19gcJAQQQQAABC3q1tb78dLKRQYOlsuPVo5naiKY1gUELENpnHezA+ktzplM1/Up0HoHBD9I0iJSmtb+0KmOw3AfdZr3pndTJAoOlGhgsUZcTOpUrz0mdO5M2LfX9rPUrP9um+5XrdtvPjlWpn60WY5Wus7l9dta762q2234WfKzebiWo0mM4O+ty9dF0wVmunvsu6551ku86bVntpNPWnfpGgp4kQbcHnus2tbX6aJ/t89m6ZN1mQT137rucrEE8+2yT9ZTse/5Tp4/RmebU+rG0/izdKTVTl22yde781HYL8PkZFCdGC0e2EEAAAQQQQCCKAgQAo4jve+qjR4/WfGzXrl3NcqAFNwB47Fh4HUCHcx47h5vCPY9v8NA9BnMEEEAAAQQaJZCs/12yqWPv0A6jwR9noAQLBFrzX9+5DaDgTBpUtMDiiVqfbZ2OAOtss+Vmrn1oASqrYWaBqw6NCCSGBsNeTSpg/ebZfamjN1dPaZ/NUzpUB+gsSFczaV+U1h+l+znFXda57U8T3Ca9PBwMAQQQQAABBKoFCADGyJ1gNQDdlJxcfyOTNm3aOLuXlJS4XwtpHs553HPYgcM9T0iZYScEEEAAAQSaU8BqsrlBFqfHv0acTEegdYKA5dpfYbn+N9sm67vQWdbBUILNK7WunvWHaHNn0rp+djxnvS3bevvsLvvur+us/0ILZrpzq57nLjvz6rp+p6/TfWr28/l+7XVmFDAF2dZc37Pglw3Y4judts7drnnzt95Zp9uc77v76vES9G8rHSm5ejq1rCMq11lvo9w6+9o+p5ZtPye45wb2LNDns2zHJSGAAAIIIIAAAjEuQAAwRi5QSkpKTU7KyurvDP3ECX3Z0JSaqn+EhpHCOY97Djt8uOepr8lwvjYBHjduXBg5Z1cEEEAAAQSiKGCBJSeYGMU8cGoEEEAAAQQQQAABBBooQACwgXBN/bX0dG0aciqF0tzW+v+zFEpz4VOHdWbhnMc9R0POU1//gr55YhkBBBBAAAEEEEAAAQQQQAABBBBAoPkEbJA7UgwIWM28rCzt0FlTfQNoHDx40BkAxPYNt68938BcfefxrcUX7nksbyQEEEAAAQQQQAABBBBAAAEEEEAAgegLEACM/jWoycHQoUOd5c2bN0t5ufb/EyCtX7++ZsuQIUNqlkNZcM9h+/oex993fbeHex5/x2MdAggggAACCCCAAAIIIIAAAggggEDkBQgARt484BnPOeccZ5s1vV22bFnA/ebMmVOzbdKkSTXLoSz07dtXsrOznV19j+Pvu3PnznVW9+jRQ/r06eNvF9YhgAACCCCAAAIIIIAAAggggAACCMS4AAHAGLpAV155ZU1ufve739Us+y5UVlbKH/7wB2dVRkaGTJkyxXdzvcutdNS+K664wtnPavgtXLjQ73dsvVsD0Pa375EQQAABBBBAAAEEEEAAAQQQQAABBOJPgABgDF0zGxV38uTJTo5efvllWbBgQZ3cPfXUU7Ju3Tpn/Z133ilJSUmn7fPhhx86wToL2N1www2nbXM/3HXXXZKQoKMZarrjjjukpKTE3eTM7bOtt5SYmCi2PwkBBBBAAAEEEEAAAQQQQAABBBBAID4FCADG2HX7xS9+IampqU4fgFOnTpVHH33UqaU3e/ZsufXWW+Xee+91cjxw4EC5++67G5R7++4999zjfHfp0qVizYjfeOMNsWWb22dbtmT7DRgwwFnm/xBAAAEEEEAAAQQQQAABBBBAAAEE4k8gMf6y7O0cjx492gnCXX/99XLkyBGZPn16nQJbAG/mzJmSnp5eZ1uoKx5++GEpLCyUV155RVasWCHXXHNNna/eeOON8tBDD9VZzwoEEEAAAQQQQAABBBBAAAEEEEAAgfgRoAZgDF6radOmyapVq+R73/ueWLAvLS1NrL+/MWPGyOOPP+4E7Pr379+onLdu3VqsmbEFEq2PPxsYJDk52Znb51mzZslLL70kth8JAQQQQAABBBBAAAEEEEAAAQQQQCB+BVpVaYrf7JPzeBXYtWuX9OzZ08l+Xl6e5OTkxGtRyDcCCCCAAAIIIIAAAggggAACMSvA+3fMXpqIZozqXRHl5mQIIIAAAggggAACCCCAAAIIIIAAAghEVoAAYGS9ORsCCCCAAAIIIIAAAggggAACCCCAAAIRFSAAGFFuToYAAggggAACCCCAAAIIIIAAAggggEBkBQgARtabsyGAAAIIIIAAAggggAACCCCAAAIIIBBRAQKAEeXmZAgggAACCCCAAAIIIIAAAggggAACCERWgABgZL05GwIIIIAAAggggAACCCCAAAIIIIAAAhEVIAAYUW5OhgACCCCAAAIIIIAAAggggAACCCCAQGQFCABG1puzIYAAAggggAACCCCAAAIIIIAAAgggEFEBAoAR5eZkCCCAAAIIIIAAAggggAACCCCAAAIIRFaAAGBkvTkbAggggAACCCCAAAIIIIAAAggggAACERUgABhRbk6GAAIIIIAAAggggAACCCCAAAIIIIBAZAUIAEbWm7MhgAACCCCAAAIIIIAAAggggAACCCAQUQECgBHl5mQIIIAAAggggAACCCCAAAIIIIAAAghEVoAAYGS9ORsCCCCAAAIIIIAAAggggAACCCCAAAIRFSAAGFFuToYAAggggAACCCCAAAIIIIAAAggggEBkBQgARtabsyGAAAIIIIAAAggggAACCCCAAAIIIBBRAQKAEeXmZAgggAACCCCAAAIIIIAAAggggAACCERWgABgZL05GwIIIIAAAggggAACCCCAAAIIIIAAAhEVSIzo2TgZAqcEysvLayzy8/NrlllAAAEEEEAAAQQQQAABBBBAAIGmE/B95/Z9F2+6M3CkeBAgABgPV8mDeSwqKqop1bhx42qWWUAAAQQQQAABBBBAAAEEEEAAgeYRsHfxPn36NM/BOWpMC9AEOKYvD5lDAAEEEEAAAQQQQAABBBBAAAEEEECgcQKtqjQ17hB8G4HwBUpLS2X16tXOFzt37iyJiVRGDV/R+9+wqupuDdHFixdL9+7dvV9oSuhJAe5lT17WFlko7uUWedk9WWjuZU9e1hZZKO7lFnnZwy60Nft1W+GNGDFCUlJSwj4GX4h/AaIu8X8N47IE9sAZO3ZsXOadTEdHwIJ/OTk50Tk5Z0WgCQW4l5sQk0NFVYB7Oar8nLwJBbiXmxCTQ0VVgHs5qvwxf3Ka/cb8JWr2DNIEuNmJOQECCCCAAAIIIIAAAggggAACCCCAAALREyAAGD17zowAAggggAACCCCAAAIIIIAAAggggECzCxAAbHZiToAAAggggAACCCCAAAIIIIAAAggggED0BAgARs+eMyOAAAIIIIAAAggggAACCCCAAAIIINDsAgQAm52YEyCAAAIIIIAAAggggAACCCCAAAIIIBA9AQKA0bPnzAgggAACCCCAAAIIIIAAAggggAACCDS7AAHAZifmBAgggAACCCCAAAIIIIAAAggggAACCERPoFWVpuidnjMjgAACCCCAAAIIIIAAAggggAACCCCAQHMKUAOwOXU5NgIIIIAAAggggAACCCCAAAIIIIAAAlEWIAAY5QvA6RFAAAEEEEAAAQQQQAABBBBAAAEEEGhOAQKAzanLsRFAAAEEEEAAAQQQQAABBBBAAAEEEIiyAAHAKF8ATo8AAggggAACCCCAAAIIIIAAAggggEBzChAAbE5djo0AAggggAACCCCAAAIIIIAAAggggECUBQgARvkCcHoEEEAAAQQQQAABBBBAAAEEEEAAAQSaU4AAYHPqcmwEEEAAAQQQQAABBBBAAAEEEEAAAQSiLEAAMMoXgNMjgAACCCCAAAIIIIAAAggggAACCCDQnAIEAJtTl2MjgAACCCCAAAIIIIAAAggggAACCCAQZQECgFG+AJweAQQQQAABBBBAAAEEEEAAAQQQQACB5hQgANicuhwbAQTCEiguLpYnnnhCxo4dK5mZmdK2bVsZPHiw3H333bJjx46wjhXuzl/+8pelVatWNdP27dvDPQT7I1Aj0Nz3cmVlpcydO1emT58u559/vnTr1k2Sk5Olffv2Mnz4cLnttttk1apVNflhAYHaAvZMtWerPWPtWWvPXHv2Pvnkk2L3b1Old955R6666irJycmRNm3aOHP7bOtJCDSFQHPey/Zv4W9/+5t8+9vfdv59dOzYUZKSkiQrK0smTpwoDzzwgBQUFDRFMTgGAs7fupF4LvtS2z2em5tb8/dvnz59fDezjAACHhNoVaXJY2WiOAggEIcCmzdvlksuuUQ2bdrkN/cW2Hjttdfksssu87u9MSv/8Y9/yLRp0047xLZt24Q/gk4j4UOIApG4l3v16iV5eXlBc9S6dWv5wQ9+II899pjzh33QndnYogRmzJgh119/vRw5csRvuQcOHCgzZ86U/v37+90eykoLUt9yyy3y8ssvB9z9pptukueff17sXiUh0BCB5ryX7UeUSZMmybFjx4Jmzf4+eeGFF8R+SCQh0FCB5ryXg+XJ/k546qmnanbp3bu38CN4DQcLCHhOgACg5y4pBUIg/gSOHj0qY8aMkY0bNzqZv/nmm+Waa66R1NRUmT17tjz66KPOH+BpaWkyf/58GTVqVJMV0v6wHzZsmOzcuVO6dOkihYWFzrEJADYZcYs6UKTu5cTERKmoqHACNF/60pecl9Ts7GwpKSlx/s0888wzcvDgQcf+Rz/6kTzyyCMt6jpQ2MACK1ascO4Xu1fatWsndn9MmTLFuXdef/11efHFF50vWxBw6dKlkp6eHvhgQbbYcS34bGn06NFy7733Sr9+/WTLli1OTW/LhyXuT4eB/2uAQHPfyx999JFMnjzZyZkFAu0HSPtbxWr/FRUVOTUD7d+LBbsTEhLEAjgXX3xxA0rCV1q6QHPfy4F87bxW89tqtdpkf8MQAAykxXoEPCJgNQBJCCCAQDQFfvKTn1hNZGfSJsB1sqJBvyoNeDjbzzvvvDrbG7PizjvvdI574YUXVn3961+vyYcGABtzWL7bQgUidS9r07Oqd999t0pfPP1Kay3Eqs6dOzv3s/3b0aCL3/1Y2fIENKBRc198/PHHdQDsGew+j++///4620NZsWHDhppntgZMqrSJ2WlfO378eJWtt/PY/ak1v0/bzgcEQhFo7nvZ/va4+uqrq9auXRswO2+99VaVdh/i3Msa4A74TA54ADYgoALNfS/7Qy4vL68666yznHv3wQcfrNLAn7NscxICCHhXQLxbNEqGAALxIFBWVlbVoUMH54+OIUOGVGmtJr/ZvvXWW5197IVx8eLFfvcJd+WSJUuqtOlZlfZLVWUvrAQAwxVkf1+BaN7Lvvlwl3/1q1/V/JvR5j3uauYtWGDRokU194Q9U/0lewbbs9ietRkZGVV2X4ebtL+0mvMsWLDA79dtvRto1D4r/e7DSgQCCUTqXg50ft/1Wgu75l5etmyZ7yaWEahXIFr3sv1dYM/gQYMGVZ04cYIAYL1Xih0Q8IYAna7ok4+EAALRE7AmvocPH3YyoAG4gH1B3XDDDTWZfPPNN2uWG7qgv3yKNTW2pjs//OEPxZq7kRBojEC07uVAebZmnW6yZpckBLS2Ug3CN77xjZpl3wXrj+9rX/uas+rQoUNOk3Lf7fUt65/H8ve//93ZzQYYmTBhgt+v2Hp98XS22f72PRICoQpE4l4ONS88a0OVYj9/AtG4l23gnJ/+9KdOdp577jlnEDF/eWMdAgh4T4AAoPeuKSVCIK4ErI8dN2nzXnexztz63bE+AC1ZP4CNTU8//bSsXLlSBgwY4PRB1djj8X0EonUvB5LXX/RrNln/VCQE3HvURv3Vpl8BQXyfxeE+b63/1D179jjH9j2Ov5O523fv3k2n8/6AWBdQIBL3csCT19rAs7YWCB/DEojGvay1rkW7YpCvfvWrcv7554eVX3ZGAIH4FiAAGN/Xj9wjEPcCn376aU0ZrLZIoGSDHrgjUq5bty7QbiGttxfUn/3sZ86+v/nNb0SbAIf0PXZCIJhANO7lYPmZM2dOzWZt0lmzzELLFXCfnfYstWdqoOT7LHa/E2jf2utD/Xdg32vMeWqfl88tS8C9L5vzXg5VlGdtqFLs508g0veyDfY0a9Ys6dix42mj//rLG+sQQMB7AgQAvXdNKRECcSWwa9cuJ79WI0X7mwqa9549ezrbbfQ931/cg37Jz8Zvfetbop3Sy1e+8hX53Oc+52cPViEQvkA07uVAubT7++c//7mz2QLcV1xxRaBdWd9CBEpLS2Xfvn1OaXNycoKW2l4M7ZlsKS8vL+i+tTe6/w5sfX3ncZ/ptm+457HvkFqmQKTu5VB0P/nkE5k5c6az64gRI4QfW0JRYx9XINL38sGDB+Wuu+5yTm+jtOtgYW5WmCOAQAsRIADYQi40xUQgVgWOHj3qZK1du3b1ZtF9IbUdjx07Vu/+/nb44x//KO+//77owCPyzDPP+NuFdQg0SCDS93KwTN53332yc+dOZ5fbb79dsrOzg+3OthYg4N6fVtRwnrfhPmvDOU9TPNNbwKWjiLUEwrnH7KvufRbuvVzrtHU+2g+RN910k+jAOc62hx9+uM4+rEAgmECk7+V77rlH9u7dKxMnTnT6wQ6WN7YhgIA3BQgAevO6UioE4kbAfv20lJycXG+efZvqlpSU1Lt/7R32798v3//+953VjzzyiHTt2rX2LnxGoMECkbyXg2Xytddek1//+tfOLlYb5aGHHgq2O9taiIB7f1pxw3nehvusDec8jX2mt5BLRzFrCYRzj9lX3fss3Hu51mnrfPzOd74jS5cuddbbIGbTpk2rsw8rEAgmEMl7ee7cufLKK6843T/YwB+tWrUKljW2IYCARwUIAHr0wlIsBJpawP5QaOz06quv1slWSkqKs66srKzOttorfJv9pqam1t5c7+e7775brPnwuHHjxJoBk1qmQGPvY/t+tO/lQFfuww8/lBtvvNHZnJmZKX/961+lIf9WAh2f9fEr4D5rrQThPG/DvX/COU9jn+nxezXIeWMEwrnH7DzufRbuvRwsj48++qi89NJLzi5jx46VZ599NtjubEPAr0Ck7mX7N3DLLbc4o63feeedMnLkSL/5YSUCCHhfgACg968xJUQgpgXS09Od/IXSNMdGLHNTKE3Y3H1t/sEHH8jvf/97sdFQ7ZfP1q15/Pn6sNx4gUjdy4FyajVRLr/8cudl1/59WCff9EcVSKvlrXfvTyt5OM/bcJ+14ZynMc/0lncFKbErEM49Zt9x77Nw72X3fLXnzz//vEyfPt1ZbQPZ2LPWbWZce18+IxBMIFL3sjVP37Bhg1i/q+4geMHyxTYEEPCuQOAh4LxbZkqGAAINEHBHKWvAV2u+0r1795pld8E6iV+0aJHzB/qhQ4eCDgTidhJvnRa7TXrc49Q3f/zxx51dxowZ4/wRZH8I1U42OrCbZsyYUdM58jXXXOOuZu4BgXi/l/1dgrVr18pFF10k1p+Q/dt46623ZPz48f52ZV0LFbCaJllZWWJdIfgO1OGPwzqKd4MmvgN1+Nu39jrfgT/qO4/7TLdjhHue2uflc8sRiNS97E/0z3/+s9x2223Opt69e8s///lP6dSpk79dWYdAvQKRupfdv4Ft4Dv7+9Zfcp/5NreRgi116dJFLrjgAn+7sw4BBOJUgABgnF44so1ApAXsV+7mSEOHDnWaKdqx169fLxMmTPB7mvLyctmyZYuzrSG1mtwmQBZstNF/60vf/e53a3YhAFhD4YmFeL+Xa18E+3fx+c9/3gnsJCYmyhtvvCEXXnhh7d34jIDY83bevHmyefNmsWeq3S/+kj2L3RTu89bO4Sbf47jrfOe+28M9j+9xWG55ApG4l2urvv322/K1r31NKisrxX7Q/Pe//13vSNe1j8FnBGoLROJedrt9+N3vfic2BUs2Wrz7d/J5551HADAYFtsQiEMB2sDF4UUjywh4SeCcc86pKc6cOXNqlmsvWPNG99fJSZMm1d7MZwSiLhCNe9lqWNkv+vn5+U6zdmvmfsUVV0TdggzEpoB7j9qzdNmyZQEz6fssDvd527dv35pRp32P4+9k1im9pR49ekifPn2cZf4PgVAEInEv++bDgn1XX321Ezi3mrRW869fv36+u7CMQIMEIn0vNyiTfAkBBDwjQADQM5eSgiAQnwLnn3++dOjQwcm8BS+qqqr8FsR30IWrrrrK7z7BVtrgCHbsYJON4ucmaw7s7uuuY45AMIFI3ctuHgoLC53g3/bt251V1rfltdde625mjkAdgSuvvLJmXaBaIFa76Q9/+IOzX0ZGhkyZMqXmO6Es2CA5bhDaavgtXLjQ79dsvVsD0Pa375EQCFUgEveym5ePP/7YuaetJYH9vfLee+/JsGHD3M3MEWiUQCTuZffv2WBza9Juyebufva3MwkBBLwlQADQW9eT0iAQdwLJycniNre1vtn+53/+p04ZFixYIC+//LKz3poj2Ih7/pI7uis1SfzpsK65BSJ5L1t/mV/4whec/iytXM8884zcfPPNzV1Ejh/nAjYC+uTJk51S2DPVnq2101NPPSVuP5k2WmRSUtJpu9gLofusveGGG07b5n646667nAGX7PMdd9whJSUl7iZnbp9tvSVrhmz7kxAIRyBS9/LKlSvl0ksvdVog2EAfM2fOlLPOOiucrLIvAkEFInUvB80EGxFAoMUI+O/8pcUUn4IigEAsCNxzzz1Ov2UbN26Ue++91+mfyvrdS01NldmzZ8sjjzziNLuxzz//+c9jIcvkAQG/ApG4l60Wir2Q2ouppeuuu86pCbhmzRq/ebKV9uJqTTNJCPziF78Qa9ZrQbipU6c6o5laLT/7bB2/v/DCCw7SwIED5e67724QmH3X/i089thjYt032Pnuu+8+p8mk9VlpHdKvWLHCObbtN2DAgAadhy+1bIHmvpftXrUfWuwHF0sPPfSQUwMw2LPWBk2wiYRAOALNfS+Hkxf2RQABjwtoFV8SAgggEHWBTZs2VelLoLX/9Tu1b9++SkcuC5pP97vafCHofoE2ahPgmnNrE+BAu7EegaACzX0v273p3uuhzrXmbNA8s7FlCehgBlX2TA10/2gAr8ruY39Jf5Sp+Z49MwOlioqKqm9+85s1+/o714033lhl+5EQaKhAc97L2kw+6P3r756+//77G1oUvtfCBZrzXg6F1v52tnu6oX9Dh3IO9kEAgegL0ARYn3QkBBCIvkD//v2dGiFWM2TMmDFifU+lpaXJoEGD5Hvf+56sWrVKLrvssuhnlBwgUI8A93I9QGyOusC0adOcZ6o9W622nj1r7Zlrz163dp7dx41JrVu3drpusCaT1sdfdna2WDN5m9vnWbNmyUsvveQMXtOY8/Ddli0QiXu5ZQtT+kgJcC9HSprzINCyBVpZDLJlE1B6BBBAAAEEEEAAAQQQQAABBBBAAAEEvCtADUDvXltKhgACCCCAAAIIIIAAAggggAACCCCAgBAA5CZAAAEEEEAAAQQQQAABBBBAAAEEEEDAwwIEAD18cSkaAggggAACCCCAAAIIIIAAAggggAACBAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEPCwAAFAD19cioYAAggggAACCCCAAAIIIIAAAggggAABQO4BBBBAAAEEEEAAAQQQQAABBBBAAAEEPCxAANDDF5eiIYAAAggggAACCCCAAAIIIIAAAgggQACQewABBBBAAAEEEEAAAQQQQAABBBBAAAEPCxAA9PDFpWgIIIAAAggggAACCCCAAAIIIIAAAggQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAwMMCBAA9fHEpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAuQcQQAABBBBAAAEEEEAAAQQQQAABBBDwsAABQA9fXIqGAAIIIIAAAggggAACCCCAAAIIIIAAAUDuAQQQQAABBBBAAAEEEEAAAQQQQAABBDwsQADQwxeXoiGAAAIIIIAAAggggAACCCCAAAIIIEAAkHsAAQQQQAABBBBAAAEEEEAAAQQQQAABDwsQAPTwxaVoCCCAAAIIIIAAAggggAACCCCAAAIIEADkHkAAAQQQQAABBBBAAAEEEEAAAQQQQMDDAgQAPXxxKRoCCCCAAAIIIIAAAggggAACCCCAAAIEALkHEEAAAQQQQAABBBBAAAEEEEAAAQQQ8LAAAUAPX1yKhgACCCCAAAIIIIAAAggggAACCCCAAAFA7gEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8LEAA0MMXl6IhgAACCCCAAAIIIIAAAggggAACCCBAAJB7AAEEEEAAAQQQQAABBBBAAAEEEEAAAQ8LEAD08MWlaAgggAACCCCAAAIIIIAAAggggAACCBAA5B5AAAEEEEAAAQQQQAABBBBAAAEEEEDAwwIEAD18cSkaAggggAACCCCAAAIIIIAAAggggAACBAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEPCwAAFAD19cioYAAggggAACCCCAAAIIIIAAAggggAABQO4BBBBAAAEEEEAAAQQQQAABBBBAAAEEPCxAANDDF5eiIYAAAggggAACCCCAAAIIIIAAAgggQACQewABBBBAAAEEEEAAAQQQQAABBBBAAAEPCxAA9PDFpWgIIIAAAggggAACCCCAAAIIIIAAAggQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAwMMCBAA9fHEpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAuQcQQAABBBBAAAEEEEAAAQQQQAABBBDwsAABQA9fXIqGAAIIIIAAAggggAACCCCAAAIIIIAAAUDuAQQQQAABBBBAAAEEEEAAAQQQQAABBDwsQADQwxeXoiGAAAIIIIAAAggggAACCCCAAAIIIEAAkHsAAQQQQAABBBBAAAEEEEAAAQQQQAABDwsQAPTwxaVoCCCAAAIIIIAAAggggAACCCCAAAIIEADkHkAAAQQQQAABBBBAAAEEEEAAAQQQQMDDAgQAPXxxKRoCCCCAAAIIIIAAAggggAACCCCAAAIEALkHEEAAAQQQQAABBBBAAAEEEEAAAQQQ8LAAAUAPX1yKhgACCCCAAAIIIIAAAggggAACCCCAAAFA7gEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8LEAA0MMXl6IhgAACCCCAAAIIIIAAAggggAACCCBAAJB7AAEEEEAAAQQQQAABBBBAAAEEEEAAAQ8LEAD08MWlaAgggAACCCCAAAIIIIAAAggggAACCBAA5B5AAAEEEEAAAQQQQAABBBBAAAEEEEDAwwIEAD18cSkaAggggAACCCCAAAIIIIAAAggggAACBAC5BxBAAAEEEEAAAQQQQAABBBBAAAEEEPCwAAFAD19cioYAAggggAACCCCAAAIIIIAAAggggAABQO4BBBBAAAEEEEAAAQQQQAABBBBAAAEEPCxAANDDF5eiIYAAAggggAACCCCAAAIIIIAAAgggQACQewABBBBAAAEEEEAAAQQQQAABBBBAAAEPCxAA9PDFpWgIIIAAAggggAACCCCAAAIIIIAAAggQAOQeQAABBBBAAAEEEEAAAQQQQAABBBBAwMMCBAA9fHEpGgIIIIAAAggggAACCCCAAAIIIIAAAgQAuQcQQAABBBBAAAEEEEAAAQQQQAABBBDwsAABQA9fXIqGAAIIIIAAAggggAACCCCAAAIIIIAAAUDuAQQQQAABBBBAAAEEEEAAAQQQQAABBDwsQADQwxeXoiGAAAIIIIAAAggggAACCCCAAAIIIPD/AdJUZ6uVf5xeAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12b964e50>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(grid.cc, vs[-1][Prim.Dens])\n",
    "# plt.plot(grid.cc, vs[-1][Prim.Velo])\n",
    "plt.plot(grid.cc, vs[-1][Prim.Pres])\n",
    "plt.plot(grid.cc, vs[-1][Prim.Eint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.any(sim.V[Prim.Pres] < 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}