{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8df7288",
   "metadata": {},
   "source": [
    "# Quickstart\n",
    "\n",
    "This is a minimal Tidy3D script showing the FDTD simulation of a dielectric cube in the presence of a point dipole.\n",
    "\n",
    "Before running this notebook, make sure to have:\n",
    "\n",
    "1. [Installed tidy3d](../quickstart.html#installation-of-tidy3d-python-api)\n",
    "2. [Generate your free API key](https://tidy3d.simulation.cloud/account)\n",
    "3. [Optional - Configured your API key](../quickstart.html#installation-of-tidy3d-python-api#linking-registration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19787f48-98be-4f65-8fd1-7f46c398ca86",
   "metadata": {
    "is_executing": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import packages and authenticate (if needed)\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "\n",
    "# web.configure(\"YOUR API KEY GOES HERE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0ca3f0",
   "metadata": {},
   "source": [
    "First, we use the convenience class [FreqRange](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FreqRange.html) to define the basic frequency-related parameters of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a6c56eb-149c-4691-bd4a-7b48e8127933",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lda0 = 0.75  # wavelength of interest (length scales are micrometers in Tidy3D)\n",
    "freq0 = td.C_0 / lda0  # frequency of interest\n",
    "fwidth = freq0 / 10.0  # desired freq. bandwidth\n",
    "\n",
    "freq_range = td.FreqRange(freq0=freq0, fwidth=fwidth)  # set frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ce5b0433850c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Create a [Structure](../api/_autosummary/tidy3d.Structure.html#tidy3d.Structure) from a [Geometry](../api/geometry.html) like this rectangular prism [Box](../api/_autosummary/tidy3d.Box.html#tidy3d.Box) and assign a [Medium](../api/_autosummary/tidy3d.Medium.html#tidy3d.Medium) to represent its optical properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "269efda8-682f-4358-a052-58191856efc0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "square = td.Structure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(1.5, 1.5, 1.5)), medium=td.Medium(permittivity=2.0)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad3434a0984f8d7",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Create a [Source](../api/sources.html). In this case, it is a [PointDipole](../api/_autosummary/tidy3d.PointDipole.html#tidy3d.PointDipole), which is a uniform current source with a zero size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1b33707-0a3a-4d21-89e3-730883b80753",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create source\n",
    "source = td.PointDipole(\n",
    "    center=(-1.5, 0, 0),  # position of the dipole\n",
    "    source_time=freq_range.to_gaussian_pulse(),  # time profile of the source\n",
    "    polarization=\"Ey\",  # polarization of the dipole\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bc193890a2f273b",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Create a [Monitor](../api/monitors.html) like a [FieldMonitor](../api/_autosummary/tidy3d.FieldMonitor.html#tidy3d.FieldMonitor) to record electromagnetic fields in the frequency domain at `freq0`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dfbc36f5-2073-4b17-945e-500fd2c0d6c4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create monitor\n",
    "monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),  # center of the monitor\n",
    "    size=(td.inf, td.inf, 0),  # size of the monitor\n",
    "    freqs=freq_range.freqs(num_points=1),  # frequency points to record the fields at\n",
    "    name=\"fields\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dafe7bec31a86ae",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "All these components are used to create a Tidy3D [Simulation](../api/_autosummary/tidy3d.Simulation.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c735e33-7021-4193-9ab6-9860ae0d8a97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sim = td.Simulation(\n",
    "    size=(4, 3, 3),  # simulation domain size\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        min_steps_per_wvl=25\n",
    "    ),  # automatic nonuniform FDTD grid with 25 grids per wavelength in the material\n",
    "    structures=[square],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=3e-13,  # physical simulation time in second\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9fd94b52-2751-4e74-a107-8d94c7274c9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize in 3D\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21f75a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation grid is shaped [179, 147, 147] for 3 million cells.\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"simulation grid is shaped {sim.grid.num_cells} for {int(np.prod(sim.grid.num_cells) / 1e6)} million cells.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac6039e92546ca7",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "The [python web API](../api/submit_simulations.html) is used to [run](../api/_autosummary/tidy3d.web.run.html#tidy3d.web.run) your simulation quickly in the cloud. The output data is stored in a [SimulationData](../api/_autosummary/tidy3d.SimulationData.html#tidy3d.SimulationData) container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "53e62b20-bddd-4dc7-be88-c8dab6de5377",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:27:54 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'quickstart'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-08497039-7352-4e15-9249-73b62fad435c'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:27:54 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'quickstart'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-08497039-7352-4e15-9249-73b62fad435c'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-735</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">2-4e15-9249-73b62fad435c'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=836164;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=305933;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=836164;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=132254;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=836164;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32m-08497039-735\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=836164;https://tidy3d.simulation.cloud/workbench?taskId=fdve-08497039-7352-4e15-9249-73b62fad435c\u001b\\\u001b[32m2-4e15-9249-73b62fad435c'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=801163;https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d34726e802034e628d58e23248674fae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:28:00 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:28:00 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "864166dcb4eb4913a7232b51013878a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:28:03 EDT </span>loading simulation from data/data.hdf5                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:28:03 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/data.hdf5                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run simulation\n",
    "data = td.web.run(sim, task_name=\"quickstart\", path=\"data/data.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bfe889fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[02:49:23] INFO: Auto meshing using wavelength 0.7575 defined from sources.     \n",
      "           INFO: Auto meshing using wavelength 0.7575 defined from sources.     \n",
      "           USER: Simulation domain Nx, Ny, Nz: [179, 147, 147]                  \n",
      "           USER: Applied symmetries: (0, 0, 0)                                  \n",
      "           USER: Number of computational grid points: 4.0184e+06.               \n",
      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
      "           timestep_reduction=0.3), lossy_metal=SurfaceImpedance(attrs={},      \n",
      "           type='SurfaceImpedance', timestep_reduction=0.0),                    \n",
      "           type='SubpixelSpec')                                                 \n",
      "           USER: Number of time steps: 7.5090e+03                               \n",
      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
      "           USER: Time step (s): 3.9959e-17                                      \n",
      "           USER:                                                                \n",
      "                                                                                \n",
      "           USER: Compute source modes time (s):     0.0545                      \n",
      "[02:49:24] USER: Rest of setup time (s):            0.5844                      \n",
      "[02:49:26] USER: Compute monitor modes time (s):    0.0001                      \n",
      "[02:49:29] USER: Solver time (s):                   2.2550                      \n",
      "           USER: Time-stepping speed (cells/s):     3.56e+09                    \n",
      "           USER: Post-processing time (s):          0.2103                      \n",
      "\n",
      " ====== SOLVER LOG ====== \n",
      "\n",
      "Processing grid and structures...\n",
      "Building FDTD update coefficients...\n",
      "Solver setup time (s):             0.5530\n",
      "\n",
      "Running solver for 7509 time steps...\n",
      "- Time step    300 / time 1.20e-14s (  4 % done), field decay: 1.00e+00\n",
      "- Time step    501 / time 2.00e-14s (  6 % done), field decay: 1.00e+00\n",
      "- Time step    600 / time 2.40e-14s (  8 % done), field decay: 4.89e-01\n",
      "- Time step    901 / time 3.60e-14s ( 12 % done), field decay: 3.16e-03\n",
      "- Time step   1201 / time 4.80e-14s ( 16 % done), field decay: 7.81e-05\n",
      "- Time step   1501 / time 6.00e-14s ( 20 % done), field decay: 2.71e-06\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "Time-stepping time (s):            1.6936\n",
      "Data write time (s):               0.0081\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# see the log\n",
    "print(data.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c738a140262348",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "This monitor data can be easily plotted through available [plot methods](../api/_autosummary/tidy3d.SimulationData.html#tidy3d.SimulationData.plot_field). You can also save, modify, and custom plot the raw [FieldData](../api/_autosummary/tidy3d.FieldData.html) and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a94d3289-58f4-4310-bfad-62046ce48eb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XH3jggZn7evLJJ7HXXnvBTZRG2nfffXHdddfh3//+N3bbbTc8+eSTAIC9997bWG/SpEmYPHmyXp6FBQsWoFwuY6+99qrrHPvDsccei5122gnf+c53cPfdd+Piiy/GmDFj8OMf/xjve9/78N3vfhe33HILvvjFL2KfffbBwQcfbGw/depU3HnnnVi9ejVGjhSmRSBY7wgFgmGAE044IXRdN/znP/+ZWhYEQRiGYXjjjTeGAMIDDzwwrFarevnSpUvDQqEQfuADHwh939fzf/SjH4UAwhtuuCEMwzB88sknQwDh7bffntmOK664IgQQLlu2rKH217Pdz3/+89B13fCvf/2rMX/u3LkhgPDRRx8NwzAMX3rppdB13fC///u/jfMJw7gvwjAMd9lll/CQQw5JHefBBx8MAYQPPvhgGIZhWC6Xw3HjxoW77rpr2Nvbq9e76667QgDhBRdcoOfNmjUrBBDOmTPH2Od73vOecOrUqf13QhiG69atS8377Gc/G3Z0dIR9fX163syZM8Otttqq5v5seOmll8Lu7u7wv/7rv4z7AEBdfzfeeKPRjne/+92pY/T09IQAwq985Sv9tqWzszM8+eSTU/PvvvvuEEB47733hmEYhpdeemkIIHz99ddT6+6zzz7h/vvv3+9xrr/++hBA+PTTTxvzFy5cmDonAoDwwgsv1N8vvPDCEEB42mmn6XnVajWcPHly6DhO+J3vfEfPX7FiRdje3h7OmjUrtd9bb701BBA+9thj/bZZsPFg+bKl4R6zrgrHHH5xS/++es1d4erVqzb06Q07CM8pGPIIggB33HEHPvKRj6QYDSAtsD311FMN3dOf//xnlMtlnH322QZzcuqpp2LkyJG4++67AQDd3d0AgD/96U9Yt26dtS3EXN15550IgqDuc6hnu9tvvx077bQTdtxxRyxfvlz/ve997wMAPPjggwCAO+64A0EQ4IILLkgxQc2U4Hn88cexdOlSnHHGGWhra9PzZ86ciR133FH3D8fnPvc54/tBBx2E//znPzWP1d7erqfXrFmD5cuX46CDDsK6devwwgsvNNz2JHp6evDf//3fGD16NH7xi18Y98H9999f19+MGTP0Nr29vdZsOOqn3t7efttT7/b0mbVureO8/fbbAIDRo0f3u149+MxnPqOnPc/D3nvvjTAMccopp+j5o0aNwg477GC95tSGehlSwcYBx/XgeK39k1Bsc5BQrGDIY9myZVi9ejV23XXXutafMmWK8f21114DEDmScxQKBbz73e/Wy6dMmYLZs2fj8ssvxy233IKDDjoIH/3oR7XeCIjCVNdffz0+85nP4Ctf+Qre//7346ijjsLHPvax1CCLo57tXnrpJTz//PMYO3asdR/krfTKK6/Add2W+Spl9Q8Q1VJ85JFHjHltbW2pNo4ePRorVqyoeaxnn30W559/Ph544IFUmaCkCWkzOPXUU/HKK6/gb3/7GzbbbDNjWTJcWw/a29ut+sa+vj69vBXb02fWurWOQwhbkAu35ZZbGt+7u7vR1taGzTffPDWfBpS2NgykzqdAIGgeMrATbHSo95+gDZdddhlOPPFE3HnnnbjvvvvwhS98AZdccgn+/ve/Y/LkyWhvb8fDDz+MBx98EHfffTfuvfde3HbbbXjf+96H++67LzNDsp7tgiDAbrvthssvv9y6j3e9611Nn1cr0V8WaH9YuXIlDjnkEIwcORJz5szBNttsg7a2NjzxxBP48pe/3BADasNVV12FX/ziF/jf//1f7Lnnnqnl9dZ67O7u1vfQxIkT8eCDDyIMQ2OgsmjRIgCRBq4/TJw4Ua/Lkdx+4sSJen7yOi9atEhr+rJAg9gVK1YYCUNZ6G8AaLu+Wdfcth8a4CcHgoKNG4NVeULQOITnFAx5jB07FiNHjmza0X6rrbYCELmMc5TLZSxcuFAvJ+y22244//zz8fDDD+Ovf/0r3nzzTcydO1cvd10X73//+3H55Zfjueeew7e+9S088MADOlSahVrbbbPNNnjnnXfw/ve/H9OnT0/9EaO2zTbbIAiCmvUP630oZvUPzUv2T7N46KGH8Pbbb+Omm27C//zP/+DDH/4wpk+fbg0fNvpA/+tf/4ovfvGLOPvss/GpT33Kus7EiRPr+rvtttv0NnvuuSfWrVuH559/3tjXY489ppf3hz333BNPPPFEatD62GOPoaOjA9tvv72xn8cff9xY76233sIbb7xR8zhkhbJw4ULr8jVr1hjfqUzSYGDhwoVwXVefm2DTwGDUikU/URBBNqTXBEMeruviyCOPxB/+8IfUPz6gdvhp+vTpKBQK+MEPfmCs+9Of/hSrVq3CzJkzAQCrV69OufzvtttucF1Xh8jeeeed1P7pn64tjEaoZ7uPf/zjePPNN/GTn/wktW5vb6/O8D3yyCPhui7mzJmTGjDw8+vs7EzZudiw9957Y9y4cZg7d65xDvfccw+ef/553T8DBbE+vI3lctkwCCV0dnbWHZpdtGgRPv7xj+PAAw/UmbU2NKOxO+KII5DP5402hmGIuXPnYosttsABBxxgtOOFF15ApVLR8z72sY9hyZIl+O1vf6vnLV++HLfffjs+8pGPaE3dLrvsgh133BHXXXedYf1y7bXXwnEc7YGXhalTp6JQKFh/HwBSLx2/+93v9Lm0GgsWLMAuu+yi5QsCgWD9QkKxgmGBb3/727jvvvtwyCGHaCuQRYsW4fbbb8cjjzxi2HEkMXbsWJx33nm46KKL8MEPfhAf/ehH8eKLL+Kaa67BPvvsg+OPPx4A8MADD+Css87CMcccg+233x7VahU///nP4Xkejj76aADAnDlz8PDDD2PmzJnYaqutsHTpUlxzzTWYPHlyv7YX9Wz36U9/Gr/61a/wuc99Dg8++CDe+973wvd9vPDCC/jVr36FP/3pT9h7772x7bbb4mtf+xq++c1v4qCDDsJRRx2FYrGIf/7zn5g0aRIuueQSANE/+2uvvRYXX3wxtt12W4wbN04nYnDk83l897vfxUknnYRDDjkEn/jEJ7TdydZbb41zzjmn2ctm4IADDsDo0aMxa9YsfOELX4DjOPj5z39uHVxMnToVt912G2bPno199tkHXV1d+MhHPmLd7xe+8AUsW7YM5557Ln75y18ay3bffXfsvvvuAJrT2E2ePBlnn302Lr30UlQqFeyzzz6444478Ne//hW33HKLEaI877zz8LOf/QwLFy7Uvnsf+9jHsP/+++Okk07Cc889pytP+L6Piy66yDjWpZdeio9+9KP4wAc+gOOOOw7PPPMMfvSjH+Ezn/lMym4liba2NnzgAx/An//8Z6sx8L333otPfepTOPjgg/Hvf/8b1113HTo6OnDfffdhn332wYc//OGG+8aGSqWifSQFmxZcx4MrodihgQ2SiysQNIHXXnstPOGEE8KxY8eGxWIxfPe73x2eeeaZYalUCsMwtjuxWaKEYWRvsuOOO4b5fD4cP358ePrpp4crVqzQy//zn/+EJ598crjNNtuEbW1t4ZgxY8LDDjss/POf/6zXmTdvXnjEEUeEkyZNCguFQjhp0qTwE5/4RPjvf/+737bXu125XA6/+93vhrvssktYLBbD0aNHh1OnTg0vuuiicNUqM+3/hhtuCN/znvfo9Q455JDw/vvv18sXL14czpw5MxwxYkQIQFufJO1OCLfddpve35gxY8JPfepT4RtvvGGsM2vWrLCzszN1fmSVUQuPPvpouP/++4ft7e3hpEmTwnPPPTf805/+lGrP2rVrw09+8pPhqFGjQgD9Wp8ccsghmdYl3M6jWfi+H377298Ot9pqq7BQKIS77LJL+L//+7+p9cgKZuHChcb8d955JzzllFPCzTbbLOzo6AgPOeSQzHv0d7/7XbjnnnuGxWIxnDx5cnj++eeH5XK5rnb+9re/DR3HMSxTyO7k29/+djh9+vSwWCyGU6ZMCX/961+HX/3qV8OOjo7woosuCsMwvoZJS56sa37IIYeEu+yyizHvnnvuCQGEL730Ul1tFmwcWL5sabj3adeH44+6sqV/5//kPrE7aQJSUkwgEAg2Avi+j5133hkf//jH8c1vfhNAVHliypQpuPHGG1M1gwcDRx55JBzH0aFewaaBt5cvw+Ffvwv/t7ynpfs99YM74Usf30dKijUI0dgJBALBRgDP8zBnzhxcffXVWLt27Xo//vPPP4+77rpLDyoFmxikpNiQgfSaQCAQbCQ49thj8c477/Rbgm2wsNNOO6FardbtNykQCAYHkjwhEAgEAoFgQBAfu6EDGdgJBALBRoqtt956UCxNBIIkHMdt+cBOQrHNQXpNIBAIBAKBYCOBMHYCgUAgEAgGBAnFDh0IYycQCAQCgUCwkUAYuzoRBAHCMIADB5C3CIFAIBAMZYQhQoRwHBfueqi56nguHE80dkMBMrCrE2EYYOWKFRu6GQKBQCAQ1I1Ro0djfQTnHEdCsUMFMrCrG9EN1tbR2bKbzRnC2WrhMPhBDeX+qxfDoZ+HOobrfTCUr/1Q7dOh1mfrs58aPfcwDNG3rgf0v0uw6UAGdnWCBnOO47RuYNeSvQwShtgD1Iah38I6MAz6eahj2PbgEL72Q7ZlQ6zP1mtrmjz39cV6DUbyhIRim4MM7AQCgUAgEAwMbut97BwZ2DUFGdhtQNRDrW+okEit4w6FkEitNgzVcBLHcOjnoYLhcD05hvK1G6p9OdT6bCj001DrE8HQhwzsBAKBQCAQDAiu68B1WzsIFcKuOcjAbojD9rY2FN4ihwPTlNWGodB/9SKrrUOhfwcbw+U6DeVrMdT6cKj11VDqn0b6ZlN+LghqQwZ2AoFAIBAIBgTHceC0mLEbaskywwUysBMIBAKBQDAgtNIxQu+zpXvbdCADuwbhhKG+2YT2zgYPFQy1fqL2DKUwTKNwwnDI9etAMdyux1Dr/6b7LwwaOEh9oqdG+qbVl73WoRvqp0HoG2P3LU6go3WH2r0pWL+QgZ1AIBAIBIIBYVCSJ2SA2hRkYCcQCAQCgWBAcFy0XGMn47rmIAO7AcAWbmyENl/vPnaNhBVqYT3moWd1wXr90bey7+pBC/u30VsoaOKec+u8GE1dsxp977Tg2oRZ/W2Zn/W7zeq2VvcnX5T5fLD0SUv6ydqg/u/VRvqlkZ7K6iHXsoT3Gb9+1v5rtp9qbWfpp+EmPxAMD8jATiAQCAQCwcAgWbFDBjKwaxFqvnlZ36AHqTHGMQaJzeD7bYDV0MvrOPeaTAdbXIsxqslyZPSTtf+y+rSRt++aCm+zT1PXwHFTQunk4ZP9l2xdkJgRDog9iLZNamKSz3m65/n1SnVFon/1NUj2e7K9zdzrql8d+GZjqL/VPnX/s+uQ1d+1+rk/UH/5al+OsazGPcPO39pnDbB7WSyc4wSp5cZe1Xx+KH4f8nWpX7Luu1r9ljWGoG4ylrN92frUeD4gg4FshgVt8vkbuuxfs7j0ChqEDOwEAoFAIBAMCK7j1C3JqBdC2DUHGdgJBAKBQCAYEBy39aFYyYptDjKwG0zw0EhQ3YAN6QfN0vwZ29nCgo2IpJsJWwEtDF0l5usTqWfdgYBCWLpffWN+MgQWsukkkiHBZMhLf89YDgD1nlWyBckup2tEsz01I0TsB0li9+TV0v0cqL5Q31NhxlSIto7WW0Lb/FNfB9cz2haCherUyfqBvb91cxL9ngXXAfyQdu3oeQS6rl5mqTzLfUl9l7W8HvCwq+6n0LY4OwlFgfdBst+Sy20h2qxWJ4QLUbtYP7lGqJVt56TX9Ry+nEkG6F7ICC0bz/hmnhtZz1QKy0pIVlAnZGAnEAgEAoFgQBgMxk5KTzQHGdgJBAKBQCAYEFxHDIqHCmRg12pkhV+zQiK0WQM+WubyBm78Wn5TNdrAM10zs95s4RNLeCUrLFUrEsvP1mfTOuRn9Ee8N8PbSnsO8lXt2YP6GvLlxnUdmF9YHPKj7EzaSSI0qMMxFDoK9DwKDYaJ19tkiJXCfPXOBxoPySYzEvV3tZz27CAOy+rQmWu5LgypECxdhyAjRFsPUqFXdR2ovykrlvqa9TucKDRH4To/mYWcEfrOgh+ChafVtkiHE/lhMn/9JE3Ium9tfcTvZTcjK9YSFjTCkXrz+F9LrathhGeNZ0l6nUYyt7MejXzs4Tnpdc3l8fHofvUs4dnUsfVzg83MuhbxzqzTTkqmIRD0DxnYCQQCgUAgGBjc1ssAhbBrDjKwG0xw5qeGcDbr/tV7MN7msgycbH5yzTnq2xg5g8nJEDnb2IlmBdE28FZzdk4zQXz/GUZWWqyf8YaMkHOBNI+/bdcQRDfCKhDvGJj+aSkGifatmJDQiTlIYpBczfLFCQpAzMglkex3zTCxU6jlhUdtoB7LYuqI7QgClgBANwZtRMtIpO5n+QXW8LkLw8x10n6AMQMarcCSUwDmc0eLcyypQ83UonrzdLL6nWBjrRNdoZm76HCUZBKm1s9EFnuZdQ8TMvo+pO28+F+H7VfG53lenh2XMXLEQPLtOXtnzI/vTdu6HPE9y/vJ7pvoW+bZGD0A8EKal2bxACBnYe9q+psaLGn/qwoE9UIGdgKBQCAQCAYEx3FEEzdEIAM7gUAgEAgEA4LrovXJE63Ost1EIAO7ViHpscXmZU5nesFZQoRGzRuLr1TG/rLCq4RaiQ/ROunlPMxkTY7gbchYt9ZxCfwt0EyYYCERW/IEP5glLGs8NLhYGZZQLAcXlmeFC/WhGkw/UGHgVGgwcW0dx02HvdQ6WriejIcmOl+HWxN954ahnldL8Z9crO8nWpCILerr44Zxy9Uy1zM9EOPkEGqv+o0lyn31h2SYMfk9TE7Qz82PxO0hhRyV2N1hq+pLoq5RMolCn2vi/u7Pzy4jOg3ADAvqZvMQY/Zu1co1Qtt1SAji+zLeV+ilw7ohCnqecefysCwh43casPmepUP4r7SWV6aR2GJJPjEeBZZQLcBDsel5iaYhr8KyGbkV7FqxJApJjhC0CDKwEwgEAoFAMCA4zmBUnmjp7jYZDLlXhIcffhgf+chHMGnSJDiOgzvuuKPf9R966CEd2+d/ixcvNta7+uqrsfXWW6OtrQ377bcf/vGPfwziWQgEAoFAsOmADIpb+Scju+Yw5Bi7np4e7LHHHjj55JNx1FFH1b3diy++iJEjR+rv48aN09O33XYbZs+ejblz52K//fbDlVdeiRkzZuDFF1801ltvSHiUAcynDDBuZls5mTDjZrdmMmaUv6kVEg0skYvMTFhrG2qva2miFU5mPDDdD47VVQvWcI/LQzI8DMLiJ7YsP+5BFRohXJrJAjj1hg2T4XndKYGx71CFCOHltFdWnMEZGutmhWQpgq0zWnUYkrKf0xmr9WbJJvsrGe51w/gYlLVL97Mfp4Ia7aZsX/IMSx2DvlPf2LKadQPNfk5lyyb6XWctumx7Hbp04nmsfUkfvlpZsvz3QeHbZEiWr5clrzDuRcci9zBCfc1KCHRcP96tz34PNI/vKoyfbc2GZX1bhwTpfuPr2nPiTdA1CfkallAtEJ8xv7v4P1DbMy/HZBu5XDF1fBm2CAYDQ25gd/jhh+Pwww9veLtx48Zh1KhR1mWXX345Tj31VJx00kkAgLlz5+Luu+/GDTfcgK985SsDaa5AIBAIBJs8HMepXZ+70X22dG+bDobcwK5Z7LnnniiVSth1113xjW98A+9973sBAOVyGQsWLMB5552n13VdF9OnT8f8+fPXXwNtiQ2cpeMsEZtPb+dGAgL3gmL7ayQxwbpuxvJGEiJs65rtoW1qC7UJbsbP27ewc7yX+Vu4yXJE8w0GhRcMZ/1v8z9zXPvPhtg041Jr5qM1qgfdHr+qvcS0yz154CXWpfMhRinJfhCL5oYxc+Z4NK1OI1FYvZnryBGErMszMgZiYttUoGtvsGR1Ds3sQd+Ayb6oKVDX6xH7RskAMfOq54X2fdE/t6SPYFKoX8/vlVeeoCnOCJqedml2zsooNwhbQpiZtMWYM1qNb8+m+XyXmE6PJVp48X4rQXq/xrnzJAf+wEmSr0gkiSGN/hJa9HYWJpAflidV2CIXfIWcja3k/SyJFIIBYNgP7CZOnIi5c+di7733RqlUwvXXX49DDz0Ujz32GPbaay8sX74cvu9j/Pjxxnbjx4/HCy+8kLnfUqmEUqmkvzdSykYgEAgEgk0JWhfXyn2Kxq4pDPuB3Q477IAddthBfz/ggAPwyiuv4IorrsDPf/7zpvd7ySWX4KKLLtLfu7q68OrC/wyorQKBQCAQbIwYjKxYicU2h2E/sLNh3333xSOPPAIA2HzzzeF5HpYsWWKss2TJEkyYMCFzH+eddx5mz56tv4dhiGql3FhDMgpl6zAJo+NDFm7yeag1UUicz0vOtx4rA7VCD7aEh1p+dNn74tv17zdlA98mS8MRh4DtpYPM8JVtHgv78IdTwkstOlb/4a2Qh1SSBer1So0UUmPrM9G/TqRwzZCkDofpdUOjDRRKJm8wCmPxpAqdT6HDiY6xTlZoNrP5liSKUO1T++hlJXUkS43p8Ki5vj6WX42TB2APT9dEmGiMLqWVDpHFJc3oWK37b8R/h7EsI0NiYPG6zCybx70Y6X7uL+kkAd6PxqUPLMtdlmjh8bBu+t8Pl0EUvPiZWKX7hj14SDoAmKXXuKSAtVhP+bZnUMb9G3A5B/0GeMjVUiKN7y/zblP3EQ/JOn6FNZdf1/rDslmJdYJNCxtlIP+pp57CxIkTAQCFQgFTp07FvHnz9PIgCDBv3jxMmzYtcx/FYhEjR47UfyNGjBj0dgsEAoFAMBzhuk7r/2Sg2hSG3MBu7dq1eOqpp/DUU08BABYuXIinnnoKr7/+OoCISTvhhBP0+ldeeSXuvPNOvPzyy3jmmWdw9tln44EHHsCZZ56p15k9ezZ+8pOf4Gc/+xmef/55nH766ejp6dFZsgKBQCAQCJqHzU92oH+N+thdcskl2GeffTBixAiMGzcORx55JF588UVjnb6+Ppx55pnYbLPN0NXVhaOPPjoV0Xv99dcxc+ZMdHR0YNy4cfjSl76EarWK4YIhF4p9/PHHcdhhh+nvFA6dNWsWbrrpJixatEgP8oAo6/X//b//hzfffBMdHR3Yfffd8ec//9nYx7HHHotly5bhggsuwOLFi7Hnnnvi3nvvTSVUDCosWa+BYw+/8mldzovtqrEwaJNZiw2sO9DEksF6JzOrCKXDV0aoJsMfi8J/RsmxIO3bxdcxwn1OwmuOlgVmD2eGCPsJHeqQZDLxlspfJbNGE2XvHJ1FGt2P9HbsOelMTh2C1RnF9tBsI/dN0rbOTwZXM7NlE6XGzK3Mebq/zfl1I+UvyH3s6vOzyyotloSt7wznOcvmXE7A7+dcjVKETWddWu5Hx7I8Kxs3tKxrbs8kEexYeZ3ZzaQWGX5zVhPAjLAsLPctD9XyS6bvz6zMXLaPnDr9Wr8HNxf/D+DJ33VLBgQG/vKXv+DMM8/EPvvsg2q1iq9+9av4wAc+gOeeew6dnZ0AgHPOOQd33303br/9dnR3d+Oss87CUUcdhUcffRQA4Ps+Zs6ciQkTJuBvf/sbFi1ahBNOOAH5fB7f/va3N+Tp1Q0nlHTPuhAEAVa88zY6OjrtmTq2WrEBN6pNmw4bAzteh3WIDuxqWZxkbV9LY9cIODXPL0Psact0N2y5Ud9RLeBX0WMrW9fl/3DYwM64xtZ7IPGWN9CBnbUesJqX0Npl2YEgMVjV9yPIHDfUAzu6F7WRa8IqJllHuJHKuLo2LWns1KfHBphAfA3oe47Wp75V1yD+XgXIekb3t7pO9f7DTAyMtAWR62rdZTzPM76HCVuZZF8mpIP99h2/2sn+AoBcxn2r+8iPdcGGhovdl05VrWO5lwG73YmB/u5JINPKSd+vzO6klgVUlQ+0Mga11US/J5cb8y0a5qyBnQ3mM4jZmdCpsetTYBeooOa35eJ+cpnG0dAoWgzts5C0dertWYvRYzaD6zY5kK8Tby9fhs/9+t9YvKZBHXoNfGz3sTj5gC0xYsTI2itbsGzZMowbNw5/+ctfcPDBB2PVqlUYO3Ysbr31VnzsYx8DALzwwgvYaaedMH/+fOy///6455578OEPfxhvvfWWJn/mzp2LL3/5y1i2bBkKhUJ/hxwSGHKhWIFAIBAIBAIACPwAq1evNv64FVl/WLVqFQBgzJgxAIAFCxagUqlg+vTpep0dd9wRW265pfa1nT9/PnbbbTcjojdjxgysXr0azz77bKtOa1Ax5EKxQx0hi/s7jbzOGeXDojfRCnt1zHqjbJQJqRcDHdEbUQ6Yb4nJ/QcZb7XNoBHJRda61NWmoSjrf0spMs4Uukb5NxZy0qHRdEmxFCNHmYLa7DaxnBi9rO0Z9DLfYh4bNThaT4cTc8Z6tL2nvrtuTp8vXWdPs050TLWrrNBsymgXqe+uvrfVsdQybeybEZLVbasnJJs0jdZhU2pobUY0Oq+EUfF6guG7a8n85s8Nl93Qmrnh92pGmDMko2ujL3jWbOPlx5yMxcYzMzRLxAEJNtxg76LfFM8i9SxZs0B8z1b4cXmDTGdyakzcLJbeyk3QY4bVvt+QxYbp52uxITbgsevHs4Bb/sBfD3DcKIGipft0gGeeeQYHHnSQMf/CCy/EN77xjX63DYIAZ599Nt773vdi1113BQAsXrwYhUIhVaVq/Pjxur784sWLrb63tGw4QAZ2AoFAIBAIBoTB8bFzsOuuu2rmjVAspuvuJnHmmWfimWee0dZnmxJkYDeYYG//AXvTJA1INajN2DWip7P9pprNFrfyEgZLl7Fuoog5APCCUKFFvl5TYJwx36Z1bOS5kqVb5J5YxBrxdhvnznSSWjTPS5IFGckTCcYo9kIjSizB6CWJpX50T6nyWUkmL5E8gcDU2jmBr5NGXK0HNRk8V+uYTKaO9Enao47Itn4YPJsHIQB4Gcydvsb9MHfJ20Azd6kWNMHAJa4Z+Rom/eySpcWICPL7+U33FwTgHoB6ffYb4M8QmpszEibSOl/jPCz3bXQMSyJEAyyeLbkCSCfzRF9YZIN53qV+H4h/bwCQZzo9zyOGmuvuMtrppzV2xmPFcj24tx2f9I1niLr2GZ6jdJZVN55paiTFu47gem7DGruzzjoLd911Fx5++GFMnjxZz58wYQLK5TJWrlxpsHbc13bChAn4xz/+YeyPsmb7874dShCNnUAgEAgEggFhMOxOGh2yhmGIs846C7/73e/wwAMPYMqUKcbyqVOnIp/PG762L774Il5//XXtaztt2jQ8/fTTWLp0qV7n/vvvx8iRI7Hzzjs33T/rE8LYCQQCgUAgGBDIVLiVaLRW7Jlnnolbb70Vd955J0aMGKE1cd3d3Whvb0d3dzdOOeUUzJ49G2PGjMHIkSPx+c9/HtOmTcP+++8PAPjABz6AnXfeGZ/+9Kfxve99D4sXL8b555+PM888s64Q8FCADOxaBbKOsMwDzDBJWXH2FZ6i79tDfbVgJCnADJf1t24zYcys8Cp/raLQAw8rJAKFar2MfTUA22++1jkCjZ1nXFLLvt/QEpbhu3fdvNFWJyMEGyZCtDoU1k9oNtsihY5hlohyyGlL1/AybVAcl0qU5XR4lkTrydAshbso9EqJQBR+9BMagiARmnKRDobWHZKli0E3jiUka0ukAPoJySZjoJmZN4G1rNhAQP1Qq2SftinkOVn8vmTrUhdlJf0YSQw03wjv8xAuC/1ajtVMcoWxD5+FZy3JSNEXdW96PKGCTbOEJU/JXhwmf2lIosFD2jXCsllWThR2DzK6pkzWPdx7kFvXGH596QNv7OHXZnHttdcCAA499FBj/o033ogTTzwRAHDFFVfAdV0cffTRKJVKmDFjBq655hq9rud5uOuuu3D66adj2rRp6OzsxKxZszBnzpz1dRoDhgzsBAKBQCAQDAiOOwjJEw2+Q9Xj29rW1oarr74aV199deY6W221Ff74xz82dvAhBNHYCQQCgUAgEGwkEMZuAOB0eC26nIemKARbzvCxqwWjMHKNF6Ss0KR2+zf22/++vDpexoIwvZLtzBrJ9s1Crfa24t3R2k7D8y4N3q5kxCTud/KMUyFOXa4oMDfMCs2iGq+qG2uvrpAVstUhWL0ihV+r8TIKfVHYS2Uq6rCnawbS/YTnXLIDkyHZaJ4Jm19b1M5EILCfkKzOkE1mAKvzyc6SrQOpkmKNlRZLPivikmr9I1mCDTClANx/kfrQkBCwa2X42PUzD0j8jiyl2WqGZRsol+fwbFBL9QWHe0S6zPOOh2jVdi4vScayZms9yHirjAo7TrpPefiVRZRRoTbwBwC3v1SzuQTHZxmyYX8PkSEK13GMEHKr9iloHDKwEwgEAoFAMCB4busHdjKuaw4ysGs1MhImqpZECT6vFoNVD1umqwQYL3tMkGtxZ8/6IQ7095R1Oq1g6lqFLD2G8ZZeY7ltf45FcZ3sZvpKOvz42imWRxUH1wwF7ZPqogIAFGPhJ1k+9TWLWUq0IZ7BfO0SgnryFNP7VEkVJFCnigAxe9g/c2fzcqPLQZwMMXQ0X/erZjaymTvNKOqKEWoRMZ+qGkeK9Wyi+HrSz24gSN5b9jrM9l+nx9m7hL8gkPC544kU+ljpeUAGO8eYeX7eaWYZxjPRTNCon93TTB7fV8CnGZOn2FjkGEvH+jHPkipsiTBhRp/5dPvxyh9GZQre/6HxCQCosrqwah8Vl2/DTodt58noRtAgZGAnEAgEAoFgQHDdbKKg6X3KoLYpyMBOIBAIBALBgOANgsZOxnXNQQZ2LYLNV8goLG9JnuBi2yAjeEk0vBEOyQi10iT/cfHwq+GNZJtnFOhusgp1jVI4YavT4fs7VkbIlMIc/JpllhQL08uzhP96dj8p9zqamNh/HIpVPoBqBoVmXV0mKS5RpkNotG8KQSVl+IlQbKb/Ha3uuCxkqR4RFOpyzTAXHZvOR4f4EiFYKklG4VXHcXQ/JbuLrg+Fq5KhWVZbSy+JVmQh2X7KjfHvFM6uNyTrhAFCOttk6Ta2TrS8/9Ji1BZqtsMN+PqBef+lf/9RG9S+jRvb/tvLJfooaiMLr/IyX7pMXZy4YE264KW/aoRfM+/HLBM4gsv63bX42/HwrNGGNj2ZzynDWRaS5V2WZ31G18l3al8kuod94xTiL1QtrRrECS08bBu2JPVLsKlCBnYCgUAgEAgGhMFInpBQbHMQHzuBQCAQCASCjQTC2LUYWRlVFUbJa0qfh1SyIiY2vzk2zZO68jp8F8/LLFND2ZVV5gWVFUJsJCxbIxQbe6athzcxWwgJgKumjTBWhh+YzZ+Q9wYPtcQlsfi8RJMSoVj9PZHR7FFUMRGapexTF+mwYhgkPNuy/OwoxJUobxa30WUNM0OyofIDy8y4Jbs71U5qY5AIu4ZxJFaHWvU66jv9Puj84tBs/yFZNwh1B8dechQ2pbhnMssycY9n9GG0KmUGw1wnw88OTuNF8+zZsOZhk+uFLFPVtwSdsx8yEXI8nMl/x0aMlyQB7LcVVNPL2TwzQZaXAbNk0AbGD4pN265DHGp1HBZ2dcrRp5EVm3EMhXy+PV7s8fBrPJ23PLddXt+NP0Pop8bWrbAm0u/az9l98EzpR/Q51Mkrz3WM/zetwHpU7mxUkIGdQCAQCASCAUFCsUMHMrBrMYIMFo6/5fkWYa2ZEJHer5fBvHGPI3pb4oJflxWCN9g5Yuz423Ytj6l6UIOxq7mcY6BMocE6cKG1WdA+WhyzKrwIOL2w23zXgESCTA0mliOuQEE+dybbGjN20WcusZ+cl48JIzoHUmTza2pvdLS+jTEhOOTzphgYN8FKcSd/1gbajtrmuRFzR5Zjuh9tlVrUZ6ZPICVeJDM2EsydGzqp5AEn6WuXZJ6SvnbJPslIlODLsvzs6CeaVYFCJ0CFYZxzYqnQQeAt40cyE4DULjOY6FrsnccrihiJFCrZxPg9sd9RQMkobBufs3fseZS4FgDgwP4MSjHEyWkOzbbaGT9+bW17KBQ69LTPGDmfipcE/Pkbb1drEFJhTCH9VH2WPGGyr1b3QIGgLsjATiAQCAQCwYAwGCXFhLBrDjKwEwgEAoFAMCBEodjW5mNKKLY5yMBuEMHDd6FlmnvXeVllgrSoPh1yBYACiwXo5ImgwhoRTxthVzXNwyRmmKMOQy29Y0vbGwm5ZqGZcHBGwoRj87zy4nPk4ZkcLzmk4FtCrklQGN4Wgs/qzmQyBSXDJEOx1DoerixQIoUOU6kEB10OLBFyJWQlPgTpMvQ6RB2a+7RI82mDaDmFZFVbkskhfj/P66y+MvzeAFa2jL6r5AnmAuaqA5JnYVyaqoH722hcwJJV1ARdtCy/wISfnd/Ib6sGjLBshr8dwasRlmV5AvYyYoiTYhxL+DXahyW5gv8Oq2V2jEJq/2aSg8WHjj3PwiBjXUqYMZ59xXi7fLydvnXY88FlYegCkxxQCcgqe/5WWUkwnshG05Wq/Vrr5ApekowtH0qlFwXDDzKwEwgEAoFAMCC4g5A84Qhj1xTEx04gEAgEAoFgI4Ewdi2C9ucy5qUzYQEeQWLZq7z0F3tL8XSma7ycT/NQrFstATBDEPDj0AcPg+is2EHKhA2bDMXWKndVs41ZmbBGFl86GzRkIRfTJzAKPXkOD2NlNI2WG6Xk1DJL+J1Dh2J1uDGaoPC6DsVaNi9S2yn06lFJJXWeWdeCshKr6n6hcFbg63AWEC1zcioE55n9n8yGpVJTDvWxyvqjfqRyTC7MbFAgHX7KClk6yalESJZnxbpqmS7NR6FCuopZfnaprFjelsBoR5h1TyZD3VQejk47UVosDLPvrXpgC8vaQrKA6dtIp8bZloDpDfizKVkCL9ouli7oTHOW7YnA7o/nVPrU8S3edjDDubqtPPzKw7q+pec83oaMEme2Y7HpXDFuGz1ruSdplk9onOUez6sE/H8AOSOkr8NwxeDYnbR0d5sMZGAnEAgEAoFgQPAGIStWkieagwzsWgzOOtTiv5wMli5vSY7gzFwxx4S+iqUDAIcYO/UmDKB28kSTLF2YxYbRrGaTJ5otGG5DJmNHiQD2Y/EXZ6pSwbO9nCz/QQsbp6sraFYq7WEY7V/ND803/TDBZtne6unhl6ekDy00p6oRxMSpa57ehVoerRf6PlBR9w2dN7F5OZOt0kgwXyFtR/5xJLrXSQwA9XSQOKkAdgZPQ/vYJVI4gpgN1EknoZmw4Op21vkPw9bhel6ikodSzCf97Oh7XAWDmmAylo7jxL6G6ghZiTr1IsxIqDD876ivWIdn/XqTlVIAk82mc8rbWDy+A75Po8G8RE//fowGS1flSRXqPubHyLEoBvfSozaydQOWPOEwJr+Qj/ztyuy45axIS437y8ZG899BaJlfa58CAUEGdgKBQCAQCAYE13FaPviUsWxzkIGdQCAQCASCAUFKig0dyMBuA8B273MPJMOnzqVQbEb4tdLLpqMQbGbyhJ8uKVYXLOErHtkMbes2EjLNWFeHXW3C6MR21hBtVijW66eUVsZ2uXybnpVn3lXVGqcZJMKNFZ9CruZ8s5g4kFehzLyKKfYXiqUtHY+2UWEwKoKeTKJIhsl5CBYAqhUdzqLwoRatJ66FbrWum0UhWBXOUn3tqP25/YTow9D+mUw4oaQjHc6mUmJOnGhCIUwPJrSfXergSe+50JhvRzL0muEPCArB1koGsSxjv7k4IaI5ZIVlbfk8WU8H1+L/Z0pK1PYsrJtnMpJcLvaT03dClvwix8rW6edV/Ozj4KXKQhaWjbfP8LxT4NIRw6OPtYE8/AosKaPMDBnLbvqc3Sq/uum+45KMYZ47IRhCkIGdQCAQCASCAWFwfOxaurtNBuJjJxAIBAKBQLCRQBi7DQB6C+FC0wJ702nLpbNiPT8j/Fpm01UVirX41QGJrNgGQpe1c+UsqJUVy4/PwyBG5m7aUyzTd8+WQZvRHvLNcoI6glq6NFYcfvFYqCbHI7zqGrrGbs0sWD8Rko0z4cxATIV87FSItk0diDItjSYmMhU95TnnUkieMlOp3U7ZODcN1R9h4LNpOhnTz07no7pmCalQe+glMnEp1En9aXkTT2bDBrrvzL4h/ze9C11KjOaH+rel99VknKu/LGy9S2oQ9ZVjhnWTIVpX+dmFdH5qN67Dws/aJC993Fa8jYe2rMzMdfvfF2dVXNVgztzwzF4uYyiqsCwvhWb81rmkhDKKXdtzCWaoVYVijaxZFj4N2fNR741nwhqh2Pi5G+YiOUYux37/LMzssbAsnT+X2LjNxtCHCXKOY8iIWgHR2DUHGdgJBAKBQCAYEFwXkjwxRDDkBnYPP/wwLr30UixYsACLFi3C7373Oxx55JH9bvPQQw9h9uzZePbZZ/Gud70L559/Pk488URjnauvvhqXXnopFi9ejD322AM//OEPse+++w7eiSTAb0/yqctZ/OoAoMim80H0dmmwdKWeeLoae9Y5FeVjx1mvrKLZtI3LXymzGp9OnghtvnBs3VqVJ7KYt5C/LYOSHOxv9LaC4bZzTB07RwxKHbe/9mVjb+mF2KOLe3h52qONc5z05q6YukTz/IRPXQyaT8xT9L2oiQXGcjrmHDpWIZFEEdK9oFg1gxHhCAKD1TBaRUkQ9L2sWGJdeF1dM5U0EdJ32i5HfZdNA/mhydTpgunai04dgvI0XNOrzoOjGalkIkZmqkKSHc5MhIC+J+LKExlJFNAr2I+pQOdj86zj/yezqkjUCxtLF+2XlrN5mZ5q6XU5dDSCJ09w9s7j5xAducgSk1z+LODJXioJjPwQo2nGwtkaYyRUsPuZsXPkWccZ+fgeBZxqMTXNE0D4ufHpsvbzi3+nfU2nvQgEjWHIaex6enqwxx574Oqrr65r/YULF2LmzJk47LDD8NRTT+Hss8/GZz7zGfzpT3/S69x2222YPXs2LrzwQjzxxBPYY489MGPGDCxdunSwTkMgEAgEgk0GZHfSyj8h7JrDkGPsDj/8cBx++OF1rz937lxMmTIFl112GQBgp512wiOPPIIrrrgCM2bMAABcfvnlOPXUU3HSSSfpbe6++27ccMMN+MpXvtL6kxAIBAKBYBOClBQbOhhyA7tGMX/+fEyfPt2YN2PGDJx99tkAgHK5jAULFuC8887Ty13XxfTp0zF//vzM/ZZKJZRKTDg7wArNths+q0xYPmSFsFUIlodf3co6tpz5Oil/O8PHKcuvjvzJ2CyHhSPMElxqXY/dLqzUTmgrGcSTFWxhWUsYFTDDyFqgz8KrfF+GQxStw88349y1RxsPuVjXRBx2Y+fOwzYF1g9VFX6qsmtdTZQK001LhGAp7Jgsr+UHFGZMeLdFLYnalhGKpSQKz1WJD9TuZLjcNd3ejOSJpG+dF83XFnK6KSRuz6l9mEkToU6eUPvhx6PPRJiPzrWi2hKHYik8rZImVOjb0WHruH01g18Z/nX6PrSFYjP2kUqiyFDL6/9VNk/CVJmxeFkz4RUe4ueeeNbkCTaPh4X5qjbpQGgcQ7WVtZt7PRas28UrtLEwZ8ieBVQuMXTZ885NuhSq7dQ9m/kc5Nelt0ftl4V1y0zekmtj00rqko/3m3Pj9vIos6v7of4yYwJBqzDkQrGNYvHixRg/frwxb/z48Vi9ejV6e3uxfPly+L5vXWfx4sWZ+73kkkvQ3d2t/yZPnjwo7RcIBAKBYLjDHYRQbIsJwE0Gw35gN1g477zzsGrVKv33xhtvbOgmCQQCgUAgEPSLYR+KnTBhApYsWWLMW7JkCUaOHIn29nZ4ngfP86zrTJgwIXO/xWIRxWJMswdBgBXvvN1Q2/iomdPwlM3XxkOxDvNv4lmvatoIvzLvupCFYsNSLzU2o0HpMCYPRxrhU5Z9FlJWJQs7gi/nIVrtlWYPxVozB/k0z17zLZ5tfDnLanVcyoplmXBZpchovhG2Zf3PThPkDehazhFArhivTKF1nvlK150uNX13E/E4XXIslR5L88nPLn06SW84T8XRclRqS4W3QsospJJ0FGZW9wU/clxezMyO1SoBtVyHZNV95BRUCFZtR95fjg7N2kKAyU8652jvSc8/Os+Azs+nbGTVtBAIwxqv+smSYRSSTYRg9Xf+m6L+In9AfU/X955cj6qD9sR/yc1E8nh40Mh6RcYCyyyffaH7ky+vWNJ5uX+b6WkXT1MonZfz8tgzqsDDsmrayfEwKXt2uTX6PkOioWUALPwaluJpt8hLMpaNTwDIs1Ct4WlXtfjYOfzc65f2DBed2WDUinWGybkPNQx7xm7atGmYN2+eMe/+++/HtGnTAACFQgFTp0411gmCAPPmzdPrCAQCgUAgaB4Sih06GHKM3dq1a/Hyyy/r7wsXLsRTTz2FMWPGYMstt8R5552HN998EzfffDMA4HOf+xx+9KMf4dxzz8XJJ5+MBx54AL/61a9w9913633Mnj0bs2bNwt577419990XV155JXp6enSWbCNwwjD2r2JvEzbRMH/b4I7r5F/HGTu3tDbejidKqPncxy7r7dLmPWYkRBiedVSknTNvLCEix6ejt9IwX0zNM/bF9hHWeNPizA1/A+Zt0B59FXtFDHN/lDzBmDfuc1VJe7ZxzzujtW76eI7bx5bbXeoL+Q4AQJkxhZQTQ6QGJcnoChQZ5GrFzJJgCQP2agzROtG+yVuPGLt8XiU20LVR11z3T1KEHvixLyAJ0InloHUTrHBIFSny6vqpYxHjla6+kL4/klU56LPkJztJnZdOnoiWewExaY7up9rMnZk0oStlEFNnY33VuZPPYi3PxizYqmLUk6SVaTdZ4zcXZngH6mSVjO14++i+4yxdhd0LqUsFIM+ec228VAuIaeZekOw5WWAecsrrLuR+ntzTjj2v6L422Ht+v/JkLVpeYhGGQvxs4xERp6CeUyypwwVvezoqwxkszt7FpUUEgtZjyA3sHn/8cRx22GH6++zZswEAs2bNwk033YRFixbh9ddf18unTJmCu+++G+eccw6uuuoqTJ48Gddff722OgGAY489FsuWLcMFF1yAxYsXY88998S9996bSqgQCAQCgUDQOAbD7kRCsc1hyA3sDj300H7fWm+66SbrNk8++WS/+z3rrLNw1llnDbR5AoFAIBAIEvAGpaRYS3e3yWDIDew2JhjVc5x0OMIt9x9+BeIQbNgXLw9642keEtP+bEb4lYUNWWhSF3Pn4ddCezzNhcuFKMQY5uPlvhuHQbjg36+Sb5t9cE4/VKPEGmtDLsfC09R2nrhQzvilk+8Xn2V4V7HpqiUsm1FdS+/PsYeDub+dp0JDRVYknEKtIciLLfpOpYYoRF/RISiz3+K+VYkC5GsXhCkBNoV6dFknh44Vfbar9pHoX4v/LaFECsGmQrFQ35PedxT+UiExV4WzKGEDvulrB3iZZb8otEchWApLJzX6fuKpT6GwvAuEdoszLQHQyRy+GXpNhmCN8nx0rsn+cs0wc/qgprdjHCZWn2o1HvK07YmOylkMQ/phPzprN69PFh8s0Ak9xtrxlBGKjT77mDkdD8VaEyl8p9/lvFn8mZln3dxOCUD8ecVlG3lLIgW/RwMmpTBKjSG1riFpMZK51D3B5vH7w3XY85XmgV8rlkTST0m9gYDu71pSGMHGDRnYCQQCgUAgGBCi5InW7lNCsc1h2GfFCgQCgUAgEAgiCGPXYvD3CyMjiocbfFWahoVczbBsPE1h17Av9rEzQgUWfy1khGLBM11VWNXIdGWh1qDYlZpfYocqs3TOMguvVC2lsYyyZeqT902ehWpybH6bam+eecW5GSHRuDFsccEeGqtVfswWljXLrbFsaIu/XbE4Qs+qqtAyZWcGajfk50X+dPnA/E5dGgSh8V373nlOphcW9S2Feen6FApRuCpH90El7TmoDhr3B/nRVbKyY1X2rLr3AvKzKyr/OnXvpLJjEfdnMhuWwnz0SaFYCkvTbylI0AOUfemHrvZIS9sCJsKmdD7JECyFkJPruZ4OXeuQbGYI1jU+KTxGIdhkVmwYhv2WQHMsIVP+XMkK0RIsidTRfCTvsWzPvLgUXrxyyQjLprO2+W89me0dtZU/J+PtCqxB5CnqsOcVyvZnW1apMQL3t9RHNn7/Feu0qz0N7c8Nj4Vikz6V9cDJmK653QDLXbYK3iAwdqKxaw4ysBMIBAKBQDAguHBMXXkr9imh2KYgA7sBgL8phfpt2pDv66kcexd3yj3GJ2AmTwQ9a+JpSppggv+sigqOZuyYuL/Iiliz5IhATYeFzngeY+l85ckGAL2aQWFv6b6dsbM50/O3e+of7vmUYy/YBfaKVlW+ZEX2tGhnbeTsXa0XRcfyls2ZOc6CGleQ1mE+djzZwGV9HSimgLN4xRz1uVlLgKoUhErzTWyVR55sqhEkzSaBOjF4lcDRCRhJPToxV9TH5GdXUNfMIyE6JVE45n0T+n6cPKF8EgP1PSirJAP1au4FiQoUqg/IC8xtU9eL+p+SF1xHb0PkTknd18QAlarmd7q36B7KJyiovHL7L3oh/JydxYj9DhNJE1WzqoD2L9OMHmPsyOeMqlNQ5YnEscIEs+cn2Cy6bj5LIqFT0klGlv9rBvtvRAXSvppGe2oQO5xM495yxjqWCil8mljWqs3QDrGPIwdneXIsKcsWCfAykidcVoXC0b9D5k1nbQ17lrLnK0+OMKMjyQQgGGyt46b730wMYcylalGrGS6BAJCBnUAgEAgEggHCdVpfKUL4uuYgAzuBQCAQCAQDgueg5aFYicQ2BxnYtQg6LOtkhEbKrDRNOfKmc3n4dc3KeHrdaj2txbuWcmEArKJhp8DDr6w8jiVRgodfK7k4VLuuHIcue1UItpeJpLmP1bqKn5pv86sCuLA/7hsenunIx3HZsgq/tLPlfLedLFxMAnUjspEVMqFZXETNfawyxNE28LAsTfMQcZ7m8RJsAALytVOtofOmkCv1YzKJQovT/UCHbSuBGc9Jhrupr8tetG1bwSwtZi0ppu43vxTdt9UeFZJNyAA8VWYpn0iq0MkT6l532qKEEu375cb3YlbyxFp1D9L3ZLJIMqxH51nJB3Gom0K/iUQH8q+DLuxOoed16rMv7gsOHvIrqmMgb65DIUAKxao+8alUmroVq4nQrBm8pGvIdqvDe/ZnjBuy+zlIh0K5/17BSycY8LKH1YCHFdNls3hyBE+IiMPm9lCs7bGQZ/HIohdv18Y8LXUCUEYJRO516eQS1yOJjLbp/RrlxyylyPizJCNxxhZhtXl7ZkSsa2KoJEwIhiZkYCcQCAQCgWBAcFzHKLPdkn0KZdcUZGAnEAgEAoFgQPCcQciKbe3uNhnIwK7F4BQ5LzfjVOOsSreiwj29LPuVh19L8bqhpfwVz/biZcKojJNTZKXBuDddIQ67Bm0jASTCr8ybbi2b7lHTa0rx+awp+Wy7eJqyZQNeZoxFDeiHn/Ps4deOfLyvEYXo9vRZxCUrAtGZV6FOFvGsnSnLyrHx+ayKFIVlw6xQOIPOe7Vl6xajY3Wo8mzJ5x+FZEp+tMU6yjp1ouNTH/aqvi6zUHhBhSQplEkhSwp7U8iyqEJb7WpfRQrjJ1+zA1/fgxSCLa+JZAN+2Syx5dE1Kkf3KXU/SQDcjug+c9optBlt7zrxtaSwHt1HFIJdo461tq9qrEdoL0T3Dd1rFIrtyHvZIa5ElqtD2b8qZBwq38igbA/FOkz6oEN+QRHmSqafJLWbQq8UVtShWOZrpxUKtIsw/Z/SCM/y8KvPMjstpdDMUnhxuDKvMlGrRoYtz2RP74qD/74pBMulGDyEXrFk2+a9+Bw6WB2xsh9P62z7gt2vzijzpksRZvh52pDlaVlDipG5u6a2EghaAxnYCQQCgUAgGBBcp/W+cxKKbQ4ysBtMcJGtz9g75ZHl98bJEwZLV2YFq23g4mDLtOFXl2fJE4U0k1di3nS9fLoSTxNTt6qPMXY8ucKSPMHf0rlomLzX8kwkzT2vKn76zdrNcNR3LMLyzhxPEGH0XcCF5eRhVrQuD22Cac7ucfaOs12uShSwVMfQBdzVPUF+fJrF8ciDLTr/vGsyBdSfxNT1sv7n7B0QMzpFxfp1KWaLklC0ED3pY0fnFwTat67aF92rFcXcVfuUzxt54uWjfQT0XVW3cNsjBjroGxXNtwjPg0TSBLEy5F9HTB190nkSE0lMmMeYOiC6BzULpo6lEw8oeYLuAZU8EZSia0dMXWbyBACnmtf9FE0kWKiEf101mfgS2r+HYXxPe5Z/aJrM40lZ/LnCWTqa5s8gdp15i111H2RVsTASNGr8o42TfOLjZlWhoABAXzVuF0/K4qwrTQfgbcmoQmOrwNMCxNe7fj4uy8/TVjVG0iEErYIM7AQCgUAgEAwILgbB7qS1u9tkIAO7ViO0sD0AwPQvqEYsCGfmDC2dTdeRUQvRMewXVDUJbgmQs9eCpbqvvUxLx+1M1jAtFTF1q5jGbi2b7mOv1sQk8bd02xs76aMAoMJKT2S4pLDt43PznLQmyKg1y84XjM0IE3VBAcDJ8fqQNfR0nL2zsKu8GgWS9UTVJ7ENHWQ7Q9uq5ue9oloe9c1I1d+j26PzLzFWitCmGLp2tU13W/TzblNMHcka9eVQ9wlVH3FHbg4AyFUrWsfptkWawLbNovrFfoKxowoUuc6IHfVGjFL72sz4rplldd4OgILatlPdC6PbonXofimq+2Jdh6pukWF3Qn3UXYzOd0x7XusJSXenLW60BYliK9Xvw+1UWkCPKmfYWXOn2Kb1g9RvxISHyYoeup6uuuZqH/QZ/xNUNWQR1221ge4Nl69jPGNY9RQ/zdhZaysD+nmSc+NnjMesT2pFw0x2Xn2y3zxnlG2VMkzG3s700zSfl/PYv69mQ3akoWV6vlYMJqiZ/HoaDKSl5u5wh+s6cFt8Oo1e1ocffhiXXnopFixYgEWLFuF3v/sdjjzySL08DENceOGF+MlPfoKVK1five99L6699lpst912ep133nkHn//85/GHP/wBruvi6KOPxlVXXYWuri7LEYcmZGAnEKxnPPGvpxGoQUFVDQbWVU0PNwqF0wB6rQp3U4JBxQ9Sg+CiTpaI/lF3FdWgUA14OgtqIKWWdyrRutu7CgDg9Ebh03D1cgSr3wEABGujZX5vlPATqCSJMIhLgwGA164GSB1RQpDbGe3LW6KSgrqWR9t3jIraX+jC2rI6R/UyQC8QK9XnuqrpYxcm/gmS/xkNXCnkvKwtr8+1Sw36OvKqvJp6qXJL0UBVl/ejly0Vks0a3Dv5Apy88gFUg3Lyh9TflWchySCSoWb6XmVJE9H5RQOBXfd4j/XYAoGgf/T09GCPPfbAySefjKOOOiq1/Hvf+x5+8IMf4Gc/+xmmTJmCr3/965gxYwaee+45tLVFv9dPfepTWLRoEe6//35UKhWcdNJJOO2003Drrbeu79NpGjKwazG4YaWRFcvepsOkjgcwDIi5xouYEyMTNs9YOsOMWLE/hdi4N2TTvhezd8Ss9bHRQU+ZZcUyDRfp6ThL18N0dWuZ9o7ezpO6LwK9sfPlfluGmTHpjdhbfp71g/H2r2Z7TGZE+i8AyDMz45DYjMCuT3LynF2lgQxj6WoYGPMz163ljF2lVzN21MIuNRjIUX1XZT5cVKxalx+t2acGO2U/TPWx61Jt2OiTBjy5JGulWITAifbp0D2iztEZCXhq8OK0KzYvOeBJmLzSYIdYY6etU31G+w4881HjOk5cw1axayMUw0iXuKPqqXO1GxTH5+kZ59uec1F0KatY1XBO8jBU05Yz4QAchwyWE9dV7Sd0cgi9nLktGeUSO66WB4mBW7wrNRimpqjPwAnhwkHeday1RrXuLfMZw25+FSHgz6MwS5OmBvheLs2mAaauVf/OaqTKZjH2NvY+q+6swd4Flr606epgOgUMCrhWkU/bdHOZGrvo02ZaPFwxKCXFGqTsDj/8cBx++OHWZWEY4sorr8T555+PI444AgBw8803Y/z48bjjjjtw3HHH4fnnn8e9996Lf/7zn9h7770BAD/84Q/xoQ99CN///vcxadKkgZ3QeoLYxAgEAoFAIBgQXCcuK9aqv1YOUBYuXIjFixdj+vTpel53dzf2228/zJ8/HwAwf/58jBo1Sg/qAGD69OlwXRePPfZYC1szuBDGbjCR8WZNTB3X1dn86gDE2iSuq2MsnasYFSAOCQV5u48dzzgrWcqErWW6Ou5ZR/OzWDo+TSWFkp5jhGRGY38gxs41GDs27aUZBu6/xUskeSxDVl8LngnLGFOjDJtmqBiDw7NibUwemxcQC1aMrq/rt8Etr4s1dfqglC2sPMWUfxmF6xzKCmTZseWAPANhIKf6JclwecQSqj6qqhJjLrFU1JQwQFCN+igsK00d3bOKuQNnhxD7ixFr5CbnK0aS+oj7PSb97Chrl8ra0XlSdin9EjxFH9GtRMcs5lz4ipUMtcaJWEsSP/lGe4lR17/D5O+RmPN8EaGFOeMIVX9Su+iTfmp0nn5/errEJ8Aye23Zr+ycjHUysmLh82lVVo1liXvNZsWSFq4OjZ2vQ9NcY8emDZ9JxbDyezqDORswaZThXUdRk6yrxh9pNg2doRkM0svreCRukgj8AKtXrzbmFYtFFIvFjC3sWLx4MQBg/Pjxxvzx48frZYsXL8a4ceOM5blcDmPGjNHrDAcIYycQCAQCgWBAiEKxrf1zHOCZZ55Bd3e38XfJJZds6NMd0hDGrtXIKDzPMzBjVoAzP+wt0ZIBS0XVgQSjxCtLKB0Z19WVnfgSc3aOpnssWjrA1NjpigCMmeM+aiW237IuXp/B2AXp92n+Fs/3SxUV8mz/65i/Gy8eTto7j0uHGOnC0/ApW9bluscg7jOD+aAqHpxd5Y73FXYQdY35PEddY/1Z6kPQsybWVdF6pK3zouzMvPpeSTJNersw9kdLaO3KgZ2xI01aTjF1tFlBF61nnUT3rtJqhQmft6BkMlouMXWKcdE6tJLS3BXJO5D6Ns3YkZZOfyrGhpjmaoLSKCbOj7JsK9UQxaTvghYYktbRrEAR6goUJXXeicoNpBF0Pa3DS2qqQspuVf3o+2YmZ5Kps9WiTxYBsfrJ8fvTt+t49TQ/SFYhT6VldPLxvjyWIWvq/NSuatBiWbo6Dv2sYI+7LK+3JPvZMJqsIGFFhlaRN01r6IL0vGg6TC0X2LHrrrti1apVxrxG2ToAmDBhAgBgyZIlmDhxop6/ZMkS7LnnnnqdpUuXGttVq1W88847evvhAGHsBAKBQCAQDAie67T8z3UcuJ6LkSNHGn/NDOymTJmCCRMmYN68eXre6tWr8dhjj2HatGkAgGnTpmHlypVYsGCBXueBBx5AEATYb7/9Bt5J6wnC2A0islzhSc8V2l7ZkciA1dUk7Lq6IM986shPi+vqWAWJEns9JBbExswBsfs/ELNoZQszByTezpvI8sp6u6dj9LnxsbjGjreX5uc97nPHNHasqoanrDAKBaZP5AxNPmY+XHXdQl7hI6syiO260jzF4jn5PMK+HuZbp9g11W5X2W7klSbN80k7aK4PxAwW6fCqvmlBQpYktE1BZT8Sm0UauyBH2aiKieIsBPWL+iSmrlpSfaSO7ar7RT9QytF96Simzw3M7OIoW1Np5IjB0vpB0/qFMoErCX+XMFS6N3V+xPRVwwC+0iimfmHE2JHGjtpFjKyqCkPn7SSrF1TdWPea1Ng5pPkzMziTTJ1mHpM/FceSvcsXk8YuyyuT6+2qlqzYwK5DCymrl+3LZf8ZDJ1fnQq2LJbO9lu3+eAl0dLkUUstWCeoP5PWzC6O+4P/7Km9XCdo6gfT5z7cE2QdDEZWbGPrr127Fi+//LL+vnDhQjz11FMYM2YMttxyS5x99tm4+OKLsd1222m7k0mTJmmvu5122gkf/OAHceqpp2Lu3LmoVCo466yzcNxxxw2bjFhABnYCgUAgEAg2Ajz++OM47LDD9PfZs2cDAGbNmoWbbroJ5557Lnp6enDaaadh5cqVOPDAA3HvvfdqDzsAuOWWW3DWWWfh/e9/vzYo/sEPfrDez2UgkIFdq8Ffu4w3a569ZhPXeNZp0tYZ2a+caeL+bGo+96YzdHVG/VfT8DZazmq++ml2rr5M1v5fsUhPxytEZHli2d7ouc8Vz/ItesRAMp2Qw9k7lplH/niFmO10CnaNHTE5WmsHwOEVQ3iNX7qu3JOQJsi7MFdA0NcTZ40qtsyhihqKKskpBi+v7gXdb5bqAdWE4S3RHqHS2nmqb0iD1qEOVU30L2kHbd5RgWKwiKmjChR6ubp3iC1088r3TvnYhYoJQxtla4ZwiP0h/RR1X2gyWyVVHaWSIUgi779yTjF2fpidukh9pLV26twpCzjBUGqGkTb3cinOSmfJktedmq+1dInz0yVHE/vhnBFdA6M2si5nkOG/aIsQZNWK5ZUWdO1k/tux/z77q2UL2Bn7Ws+NLJa/Ef2ZwUz6pn4yOR02q7ez1J4NGbXpW6pmcJbZ9OhLa5F5lYrhSN6RRUkr0ejuDj30UKufoN6f42DOnDmYM2dO5jpjxowZVmbENsjAbjBhuFOyB4tvebBk2JnQNBm+AkBQYHYmxXg+GRD3lthgjSceVNJhVz6vYhnMZYE/7Km0Uz3r0rRtHhAnTPD5/OHH22gLy5pmxkxk7sT9S+WSPHaOnWyw7PKwuRKh80QLhyW3cDNUamW/5eHKfUYolgTtLoX31L4dHZKNBka5KiU+pA136QjxP4voO9nB5KiCg/pORdfblCEt3QI6jO16SJafohJidE/TQE4PfOg8VKUHT9nEOOqTbFN0fwQ+9OOHcgLUsQI9sDMHdDTAc/U9pAZy6p7RA1UwoXryGR9SKFgN6PRATl0zGtDp+0x9kt1JEMS/6+Qgm8LqiQGcnwg10571bvr572UsSpSli3ZmtzvRSS/8XuTPGD7I0/Y/bGBoSZgAaidN6MPXkTxhm99Qia0Mu5n4AtR45oLdv/UM9qjPspInWHvo9qkViuUDv+Eeih0cg+LW7m9TgSRPCAQCgUAgEGwkEMZuPcEQ1JNgl9sPWMKvQByCDTNMhwPGNBE712sxIgaANSxMSQwXtyqpZLxZEwtWqMHM8XWz5tkYu1rr5jNsGrjQmtrOGUjXYBr4ftU8flz25t3O+1onP8QhV3590MeSKgicKSBjXz6vrzeepqL0KgnGVWWpAvVJpdCIfaOSaTnPSZnFUn9QUfWAQrEOJZaY9iGVhB2HUbw+o8+JyaL7OajSvaz2pZIrKMnCJYNnKnFF9z7CzLd7SvoIEqyGZryo9qravpILjO2qQVg7lJVIotAhWWJViMGjfshIdIpORl1DSp7Q5KZpa0LXJ8lUEftaK/JI4UajdFiWCTrZt7Cat0apLZYMpEOxRiiX/Y5qtGt9oKbNiuX5aj5zeRIJD9uq6Ub+E7LfXcimbUlknJHjz1qazZ+5w92g2HUdtLqYW6sZwE0FQ+E3KxAIBAKBQCBoAYSxG0xkaUCIncsqE8b0dE77CACAX+zS8wI2XQrjsTmV/OJJEmsNlo4nUpCFBH+LZJYgFt2bTf+Wmtaibz5PT8b6qAbE11ngehxiqbgRidkGbr6q7D3AzyHeV64Qa+gKKqkiVJo3ILYkAQAnF5e6sZUcIqYuqMTaLb9chRuuUxupe0Dp2zQbqHSUWmvnRgweJZ0UvLSeRTNcVVOL1qf0eW050wBYswaUbJEjE948HErq8OgzobkjVk0xdq7SuwWV6HwoySKXYI4crX+qakuNei00krpPur6aCVPzA8bY1WRBAmL7iLmrR2vlWD/DDLuT1PfEY8GiydfX1rjGFo2dwd75sZ4uLoXH2Cl+AK69y1u0exsINmadw0jsydAX6muZYQAfsGdiahuYjJ5hd2PR2JksXbwq/aZKFl1dtJxYc6bR5ZLsYcjeuU7rGTYh7JqDDOwEAoFAIBAMCFFWbGuHYq4zDEe4QwAysNuAcLjOpb3TOh2orFcj+5VZnKyzZMByndkattzIgCXTVPa7MSwOGM3m5kijxTNOWTkvYz6tGy/PYu/0+Rhvqpa3Wq5DyXhD1tPsDXld+lCZ4M8j3sYcaRi5xi4X79kwi+5Zk9qv1qIpxi4oVxGUK5pJcZy10XpUgmudKndG9ivKNqTYHjF5BdW4gush50b7zGW8JhNrQNmjseGvafzbkVeGxXT+Xg4OGV+TcbLX/6OCrotTpRJd6l6jLNMqWWoopijwkaxkRneMkzifIIO+0EyYWt6fzUGjoAxHpx4SixgcclJREym7E50Va7aTlntOZADjgJOBTnpFw1KJZVVaNGUhY60M9inP6nhZ+s0wQWaKnVr/t7UZcx1ZsbVge24YtwZn1mz6QrY84CUBLXrJLLN4G0L2WzCfQUxPp7JwuSUTn6Z7wXi2DUuTE8FQhAzsBAKBQCAQDAhR5YnWMnYSim0OQ3Jgd/XVV+PSSy/F4sWLsccee+CHP/wh9t13X+u6N910E0466SRjXrFYRF8fM44NQ1x44YX4yU9+gpUrV+K9730vrr32Wmy33XaDeh5ZcPKKqWNZalxXh/aRepJMh7kpcQ/TyvHptaVoelUpfnvlGjsjA9bi/sl94TjjRj51RTavIx+3vS2XXpfP4z92muQkgWniyTN60wwkf+s19Ct++g3YD9NvyMlpWxsNxk71SSfTNYaVOKvV0EOSPo753OltSNdWqSCoVhFUTZ2Pk+uJ2tajfOyUsS+K0SeV5Mqpe6aQc1BQlJdmM7TvnzrPhOaOskcrCeaO+oxItkKugNClUnbqXEhrl1MmxglWLdRaNTNDlYyNXfJVo1Jdga8f2lpPRtnKiX8ObpLBS1w+XVCdzhth7EOsJ9AcktnBrgvNYCU0V0GCodN+wmrTpCF0jOx/X7U0dsgsL0Z+gXaNnd0fr3Yn2VbJYlSbAf/t8QgC3RN8HmfpuL6Q9INhKf6dcl2dTWPn5mv/KwwtGjvOEvNHKj2PqkYZR5+tm15uErHDj73z3NZrA8XHrjkMuazY2267DbNnz8aFF16IJ554AnvssQdmzJiBpUuXZm4zcuRILFq0SP+99tprxvLvfe97+MEPfoC5c+fiscceQ2dnJ2bMmGEM/gQCgUAgEAiGO4YcY3f55Zfj1FNP1Szc3Llzcffdd+OGG27AV77yFes2juNgwoQJ1mVhGOLKK6/E+eefjyOOOAIAcPPNN2P8+PG44447cNxxxw3OiSTbyN78HQuj43aO0NMB09MFbRF71xvGDBnPbuXTq5R32Kq+inV5ib8xqjerLN1cRz6e7ipEt0l3W3y7dBYYY8eYPNKAmSXDkAJ/u+XalLLh+5T2puNsJNh0oBgKW+ZZNN9h89M6IA7Pia9PXunTCm2s/Bi7Pk6pR0+7KrM5yKW97YixC8pVBBU/relRfZ8jP7u+SMfntPepY0afxVyXapejWVFiV3WpNPVZ1V5a0fdSSmOnKjdol3ylVfMKCNU5aK2d+u7lqQRaxjthgrnTFSooe5O0TkGgPa+IhclR9YoEA5nE+vC2cpJVJdj5xkXgKQta+dBpLZ1azyKJi5ZHM1LMpBNlCLuOE+sNeZt01YwgNQ9IZnbWyO5ttqyWQnYZsMYpGxszl5zWrDTjHR2eBcw1dMTUsXmkcQUi1lzvgzLZfTvzae1HnhXLIw+Wcoe8NGNvOc3Ymc+r4cfScUT3bYtDsZI80RSGFGNXLpexYMECTJ8+Xc9zXRfTp0/H/PnzM7dbu3YtttpqK7zrXe/CEUccgWeffVYvW7hwIRYvXmzss7u7G/vtt1+/+xQIBAKBQCAYbhhSjN3y5cvh+z7Gjx9vzB8/fjxeeOEF6zY77LADbrjhBuy+++5YtWoVvv/97+OAAw7As88+i8mTJ2Px4sV6H8l90jIbSqUSSqXYr2zAmgfuWZdLeJbB1NDxahKUAdvTF7/trS3Hb4EreuO3z1V9VbXcrknj2h6dvepyZi5uI2fnxrTnU8s7GaPXnovf0trUq7Xj2z2k4gbE+6IqC4CpA+xRfnwFz/4Wz6HffDN8o3w233NIW2bvG9NLK59qQ74Ys6thMWbsKJvZyHZOMFthECAMglStVX08xY4GPZE/ntcRMXSO0vgVCl26PW1K70Zau7yNGmXnRtq6UmD62ZW0Bk9pxQp5uKruMN2jjsqiJH87qgmrdX0JAlKfFzF4iexYBBU4yjPOVQwIsbzxJ52XWa2kFisUhPE5az87NeUldVJUA1b9PkNdU1i13zFN5hzXtfjYKdYnoesLkjVi63iGOA79We5zm8aOL7bUQ+WMU1alm1gryCsqxOvyoyVZSSCRGWrJiq2yaVsGt/l74zrf9HzzucIyYUtMU12Opn32/K72xdN+X7wP0tbZKgOlUNPHjmfFmr8twM7YZT2fN7yjYONwHQdeixm7VjOAmwqGFGPXDKZNm4YTTjgBe+65Jw455BD89re/xdixY/HjH/94QPu95JJL0N3drf8mT57cohYLBAKBQLBxwXWclv9J8kRzGFKM3eabbw7P87BkyRJj/pIlSzI1dEnk83m85z3vwcsvvwwAerslS5Zg4sSJxj733HPPzP2cd955mD17tv4ehiGqrO5iXeA6HTatWZC2tF8dAIRtMSNEWa9rDZ1ZzNK9wxi71UpzxjVpZc5Usbfhonp7L7LsVc7Sje2MdWYjFVPXXWQsHeLjur2r4nMr96jPOCPNyF6jc/Ti/YeFuDarx869rX00AJMtM2290pqW3oxz52/LBF5Jg2f2cQaBWAPehmIu7qd2dt1ybcSuxedDespk5Qaq2IBE13gFxea2RZ547rrI585pV5o7VYmi4BVQUExpQfsMKoZLnRexBVrPQ274FbvWTmfHBiE8xTqSbtBR14gYRZeyY6lCRYJFC6maRYK5M2rGqnnEzKUZO5OpixlJVVmjmae+rhKhGCBirui7R+yba3zX61kZu0SGcGpCtTpROzZVccKJNHUueOWJGudYo1oE965zbCUugBTzmJwO2TGIleJMVWBkhvavX+XQdaPZKfLMfKu3JGcgmUdfULYwdoyZM6YtWbE8U9bGfHIYbKaFrQRifS9/Hq3pSx+XRw34tFmFgthmGeUI6sOQYuwKhQKmTp2KefPm6XlBEGDevHmYNm1aXfvwfR9PP/20HsRNmTIFEyZMMPa5evVqPPbYY/3us1gsYuTIkfpvxIgRmesKBAKBQLApw3WiRLlW/slYtjkMKcYOAGbPno1Zs2Zh7733xr777osrr7wSPT09Okv2hBNOwBZbbIFLLrkEADBnzhzsv//+2HbbbbFy5UpceumleO211/CZz3wGQKRVOfvss3HxxRdju+22w5QpU/D1r38dkyZNwpFHHjm4J8M1K27c1cTUhUb2azxw7GNZmeRDt5pp7Javi9myFSwDdo1i7GzsFAB0MUaOtHUjmG6OtHQA0F1k89uUlqtvhZ7nrVkWn9vKmGH1V0Tzw3VxDVWesUY0hVEbd8QoPZ3bLGZmgxER+zdyxLh4X4X4HKpB3MY15eh8VmekTPI+SdYcBYDeNnufEavAffvac/H2bYY2UtV3bYsrg5BnYbrWqsoarZpaOzcXsQ2eYuhIa5dTfeRWqBJFUbOIpLWjShTE8tBnWTMBVJ8y6WdHGcVR35b9EIVcdH2IWXXbonML1KdXjNrh9paN9idBfnbaW4z72ClvPlfpGImVKXgJBk99p3q0XqjOzzXPk4Oak7KxS+ikSMsWauZOzSedZFJ/5npprZVa1kDxgkGBw3wxrXwZZ+yMacVSsooKAdM2cSaK+jMr+7xW5Qmrxo7NMzNhLRo7XkmDZ8Vyxk7p7biurtrL2L0KZ+fUPdXGnApqXUiusWOPjYqR0a8iLYyl4+ydnmfo7vo/LEfI+sYZIp53ruPoDPFWwRGNXVMYcgO7Y489FsuWLcMFF1yAxYsXY88998S9996rkx9ef/11uEwEvGLFCpx66qlYvHgxRo8ejalTp+Jvf/sbdt55Z73Oueeei56eHpx22mlYuXIlDjzwQNx7771oa2tLHV8gEAgEAoFguGLIDewA4KyzzsJZZ51lXfbQQw8Z36+44gpcccUV/e7PcRzMmTMHc+bMaVUT+zsYm7ZnoYWO8jtjNV8DlmlJFSQAYI3KgF2+Ln7j5NNv98TT9HbI35DbC3ZdDWnruK5uJGPpRrfF08WeiIXzVvyfnld545V4etGrenrdorcBAKWVa/U8n70h06A81xkPqjvGjY6PxbSU+S22iY47Id6+e1ScxFJmmbkjFJO33Ivf4nk/cJZupdIl8uXrLBlrUXttjB3PJI7f9Im9czvjyiHETKZ83xI1ZInxqvREbEOuXfnZdSjmTmnt0N6rlnej4BJjZ1b7KKp+WZvw305p7QK71q7suwiL0fFD8lyk81AZzK7SAlJ2bJJ51KdJlAbptDRjV9F6KZIsEiuTc0jXaGbFxlmSpm9ff6DMVE1q2Bg4Pp+uE52GLoehmGbH0WxYqPV6CTY2qa2zWxbG34lhhQPHUX/U3FrnmPTb07NJE5hm5gAzczugaAKLKvAMT571qjOos7LPmU6zFmKNnT0TluvtchbGLmQ63qA3zk6vrovm028JSGjs2POIztioRmGr4AFYNZXcLYFr5Mi/jjNyJjuXXm5m6WPYwXHQcn5NQrHNYUhp7AQCgUAgEAgEzWNIMnYbC/ibfGirH8o0dn2sMsJa9kZJ7NxSxswtXRNrR95ZG8+nt2me/Wr4QhmVJSjT1a6rKzI9nfv2a9H+X/7/9Lw1L/xbT6965U09vfqNKEN23fJ1el6V6UxIa1YcGffHiIlxHdZR28bavdFKX1Zgb80uy6btHjlJT69VmZ6cWePnzv2k1ipdImfpuPbHlknMq2t0F+OfTVchnl9UXnNOW3w+xNiRhsdxXTiuq7NwQ2K61Cf1VHlNxEAQsxn0RFmyuRERGxFWelHwIsaXmLpYm2YyXSTD1HI38tjqJzuW2uEprZ2rPPoCldFN2bGeun/Ily+VAK0Lpio/O9Jb+lWtkSKGTjN16nzoO2Vwl9T5UZURrbGz6LY0U5doBmmAdGUJx8zuRdUztnNSjJ5n6JuMU7XOZaSf0gZmVZ6oG/y5khEVoEohnC4kL8Jo3VxqmuuAzfrNaVaKz+O/l3IiEzs5bUO2ri5eR7N3XFfHasGGfTFjV+mJ5lcZY1dZx9a1iNly7JkbcE2wTW+XUXnCqGNNda7LPCuW7VeBRxJ4nw/HWrEuBkNjJ2gGMrATCAQCgUAwIAxGKFbQHGRg12oYb9B8Ou7q0FMsB9fV9cVvbjwDlpi6pWtjlm7p6nh6VW9aU8Z1dSN4JizTr1A2LGecupx4X7kVb+hpYureWRAzdkufXBhPP7dcT7+hmLrF7Bz62FtvXr2Rj2Ft3Jp55m22KNbmkUZmPGMiiky/Vmzv1tMd+Tb1GZ8PZ+z4mzH5Sa1Zl36DBsy3bALv09Ese3gU699RIyOmzmW+fHE1CpVdWshpXRqQnR1bVRq28uqIiXC7IsYu7CNfu14UOiPGLs6OVWyoZ2ey6P6gvigplq0vkR1b9nMgyVEhn8iObY/OUWfHFpRnWN7sSychgAuUts7llSgUvefAZJqJeSR/vlxCYxeEpoIkWSPXVuFBk0aagaNKEwltncoM1a3XWrx0dYYk6tXFJZk6/tV1zFq4NYkb7o/JaxQTy80yZflyegYZ02xetWqvHEH3CmfAy5bpch0pnlYfO8/O3rmqSolTZdmvfXFUIDQ0dtE6xNxF0wnBaQKmjx3zk6vhacfvNc64lS0+dja3gnWGxq65yhNZDLJg04UM7AYTPGTCH6SF6B+yGX6Nf+BLWNh1iRrQLVoZP5gWr4ofWNz0Mh7IpMO+gBmKpaSJEWxg562OQ6qV1+ISbiueegYA8Ob8l/S8hU/E5dj+uSJu21qLlYgNb7F2P8MGqnuxgWq1N1qHCs8DwDhujTJyrJ7u6p4CIE6iAMzz5WJwSjLpYwM7blBcLqWNRNvZYGyzjrh/x3XG5dDWVaJ1RjILFFdZ25AptZfPw83l9KAr8CmJQg3s1HcSgFfbo2OFKiwdrFPGxV29yKt/dvlEEoU2KlafdF/QPx6dPEH/hKvJAV6g/zEFKonCyavzTBgWu4VooEmGxZm2J36itFi5HJsVq3k59fKTtD3J64FedIxkrXRrKJZKiiVLYNG6yQFbMuSaSK4wkl8yEhZSSJgM06cOxSJ7gGffn7Il4dZJhosvK2VnewSw0n1cGkLTvhtvXwnsA46SHtjZByxJU+wkbEkVboZchMsjKGzvsDCpb0mYAOJBHB/MVdlzJbS0gVug8GkbQiMUa08ioT7pZQPGkmVgxwd+WSXnhktQ1nFaL9rPcK8S1IAM7AQCgUAgEAwIEoodOpCB3WCCh1/5G3I+Yjt6yvG7GC8NtrQnZrAWK6buzRVx2GE5Y7h4cfucYpU6MixOeJiyXU13svCr804cfu19OWbsljweWZu8+PgiPe/Rt+M35FbiCcZMllWIt9AV913HxDF6epSyQwGAztFbRssZs8bDOkZRcvWWXOLsIHtz9phqm17I32R9ulkXZ+zi6c07FAtajJMnyPqETKndQi5it4iZoDJTCabTUYaqJACn8JKrGDuv0svKi0X3ExkVFxLJE17itZeYgXIirGYwdmRbQdYilERRJKue6Lw9ZffiFRWzSEkhCeZOz1fsHPxKVFYM0EbFnmKMkmXcCrrkWGAsJ5a1v1JLcdKEOr5mvci+hEqHqd+qSqJIsSRJjxIGJ6QSZ2qftIlO1EgwdInm2lpPx+csTpgwVQZMU2Gj3JaeyIga5NpS01kJEX2VeJrKFfZllOuj7Xh4Nnn/JWEr4RdtF6/j+NHvwQzFphMmomli7Epsnl124ai2Vft4ebIM6xO6Fhnl1iqWPuGhVlskwEyesJcUEwgahQzsBAKBQCAQDAjqtaOl+5TKE81BBnYthmE/wKfZG3LFibp9nWFrEr9RLloVv5W+oZi6pYzJ4towrhcptpN2Kn4z52/LnM3qVHostye2F+Gmw8uejO1M/u+xtwAMHkuXBdLejf1XXLKse6u4jV3bxtPexO0BAB35zfS8Ns/OsvjEVPXG/V/utb/REyO6OBf345iuWKu05ejYZHqtMpP2O2PGzlOWNmQX4uZzcHJeyrDYV6xIUv9TbY/6gFiIvGIpgt4eOB3R9Sh0RG0gQpaSDkiTlhTrk96QtHZ9KdsTHxU/p9ahfSpGTt3HTlEliCjGjjR2jhvzVYDFWoIlT7iMvQNihw5te6JYtUJOGRlXVTKF6ruqkpgTg8WZLGK6yPaEWhEmyoGFzHg4anYimcKGMHFO6lixf63J1OkeoV0GjvmdIQj7Mae1aewM2xKWAJDUCgII84ylM9i76H4uMWauzBqxjrHZ6yrpUlnrLCa8ZabBo/sQsGvvePJElvUJKqp0XdnO2BGjDQDVHjIojn/T5SzGjtjfCtPusWmjHKIFAeN2DcazmjYgrlYS9w1MDZ6RPCGUnWAAkIGdQCAQCASCAWFQKk+0eH+bCmRgN5jghqGMvetVVgKrS/HbHC8TxjNg33onevtcx3R1JWZ0yVkKYkv4W3GBlb/ijB3ZnHgrYsZu9ctx1uvip2I93cPMbHhDgB9/4tNL9fT4qa/p6ZE7vQMAaB8TZ8q2Z5gV05tzhWleetessR7b9yOmjevu3hgRt2fx2DgDdtWYiMXqrbDMXJU96rRFrJpXLMIrFBizFYGYOsqOpZZVVF2wSpeyFVkbZaF669bA6VYsntpVXtueROdNWYX5pPUIlRaDaXvSy+xPSuoeLStWr0NlxYbqk7JidYkxZVTslIhZUVYm1G+kuWMGxSALC6VjIoaGrlU+ZzJ0lCUbZ1aqexj9IMz41Fmw0TFCJ2fOR/+ZkUBcfJ3MZJPOKY46Zc3YaVawtkExnSLPAM5T25iujrN34CboxCoaOt+YaQZj70hHWfa5Vi6LsaNsT8bSGZYeUb9llxRLRxOyWLq8obGL7htuaxKWmAExy4AtrY6emZylKzMjdw5PHaTaW4fGjo7L2sgJ6Sr7orOHDcauf7sTnpk/HPm6wchglYFdc5CSYgKBQCAQCAQbCYSxazUyNHYB07T0KqZoRV//ujoA6FFMXQ9j7LIyOHMqczOLsetiHm/tjmKGFr+q5614IWbAXvn3O3q6Xm+6wQJnLV56KW7X5H+/rqdH7BsxjJ3jd9LzjPJiXH+lNXbsjX5NvF+fFRqv9kZ6Oc+bqOetYBq7N96JdYfLx0brrhsZLx9RMDV2TrENKBRik+IEaxMopkyXzcpH90pF64aiz1xfD/KVktom+sy7EWND2aS5jJJbdHsECY0dNywms+JKoMp4KUaLSro5yqCY/PncdcqDTnkOhlWTndBZsmFcWixU7SejYjcgPzsqKRYtLqj269Jj6juxnMR+cuaODqfPlT5BmbREq6nfRFKn128WLDF15jmZ6kLAA/U/XVNabuchwjBi/6K/eJ5eTubJnKXjWjmjjaqfOWPHWLogH5tol9R9wHV1PbwUFpvuUWwW98/kOjGbuTf3o7Nq7IwSiPb5lBUbMI2dzbsumo5+15yl43o7bp5dUefh93GNXVZWbPo5yK8Pf04Ri8mf1VVL3xhZsTVKrw0HCMM2NCADO4FAIBAIBAOCC6flVTCc4T/W3SCQgV2DCB0nfruvkbnE35a5ZoX0S9y7juvqVjD2jpi6XlZSrFqOp/Nt8Vt4oT19OTlj18mqTLjrVgAAKm+9quctey7OPv3nivWbAVsvuM/dHi/G7Z20JGLvCtvHfdORobGjjLNqb1y+rMQZO84K9FJlhVi/1D4inuZVQEgn2cPewsNipK0jHzunrRNOoR1ujhgwyh6N2uQndDh+jjRA0Xn5ym8r7OtBWI6YXWIz8l7EFOqSXBkVKPS+dUH3aH6JZcdShqyuVkGecVQaTaWw6ooUmk0iX7gMxotlxYZVMysWVHbMKRrtpZJi9OmpzUL1PVmJgkNL6yhLNlRsHxIMXaLShGbskpU0wjBmbkhbB1MzR93sJvzrXMrQ7ed/X4gog5fOiZM49E8zS1fHd6vPm+vxcmldHRB7z/WxMmJrMzR0lA27lulTOXtXtrD7/L7zLc9MrrEzCmk4acaOa+y4d12VTZO2rrSaMe+sjZyx8yjKwStA8PJiGZVU9LpGVmy6tJrPS7NZsmK5Bo/72PHrvhEQeYL1DBnYCQQCgUAgGBAcBy2PxUpotznIwG4QwWnpKnubI9f2FZyxY8wP19OtWx2xR32r3tbzgirP8IorMQR+G5LgXm5F9qbq9q6KjvVGnP267Pn4GP2xIBsSvF3LX4xZttKiqM5tR99qPa8jP1JPG4wdvU2X2Vv+uni7Ss8qPe1qhipmRnpGxX3O/QXfVjV+e9mbedAVaZmo2ohTaIOTL8AtmAwXQZNBPrVRaXWUBoi0RGFfr2YvHKVVyxdGRJ8JTVpSY0dMHWXhVbXWLvYgI8aOCAdi7IpKYxeS1k5VoEBBZcvmlD6U7n3So+mKFJQJG2iGLlmBIqdYQGo2MY90PqQrJY9Bz5JDmORGYuZOTSR93uqt/8p3orJ6SSNIujYvodcjjZ2b0M0lWx0ghB9GOjTaB7/fSZ/m8mx7I+uV7cziecezYkuMSaJr3WupMAEAaxiDpRk7pkkzKk9YdGQ8apBVQ5bAWTo3ZPtSGbuhobHjurr4mUnaugqruV1hDgRGpQtVM7vam8HS8WoeFo0dh1FvumrR2FmyYnnlIH6txcZOMBBIVqxAIBAIBIIBwR2Ev02RsfvOd76DlStXDmgfDTN2CxcuxF//+le89tprWLduHcaOHYv3vOc9mDZtGtra0ozRxoxaQlH+1sUzntYofcpyppt7h7F0nLEjpq5vVew3x8GZJL86MrU8zxi7dm4MtTTa7+pXY8buOXbc4YCXmb/dbm9E/dNZiv3o2nKj9HSRsQaaqSrzrLqYpeMgdrR3Razn6x09QU+vWx1XmSCd5Cqm56kgYklyearY0AYnX9TZo07CY04zdYpFcRTTQIwCae2q6/qQU+yF40fzklmjVIGCfOySPlOx7lNlwPoRE1SqBigHZh3Ziq/LWkTtpGxM9Um1Vklb53rRvoKq6QcXKCbD8auasYtTWH2jnUlfO621UxUoiP/w+6sVS59JzZo2m1PMlqV6hdlw1kZNvZntJubRVSwZHcJToroqVaLIYmPCqL0hYi0aZ3FIvubl4kxYl7NIbvpxzitMlNiqhuZXTa/lzFwpzdIBwEoVZeC6uhJbl+5bzhLbdHccZuWJeNoJmC7OT1ee4N5z5Z50Bixn6Xhmf4EdJE9ZseUMjV3F7n+nl2c847XGjuvmLP3gG1mxG9Z9YKBwmP68ZQg3raHdmjVrcP755+OQQw7BtGnTmt5P3QO7W265BVdddRUef/xxjB8/HpMmTUJ7ezveeecdvPLKK2hra8OnPvUpfPnLX8ZWW23VdIMEAoFAIBAINmaUy2X88Y9/xJtvvgnf91EqlXD33Xdjiy22wKmnnopPfvKT6Orqgud5mDhxIj70oQ/VTZ7VNbB7z3veg0KhgBNPPBG/+c1v8K53vctYXiqVMH/+fPzyl7/E3nvvjWuuuQbHHHNM42e6kYFngFV9zthFb4dvM58lXlmid03MJBFTV167Qs9zmMbGK8Z+VFpzxN5IOVPVxl6N/RVRBYfVr8U6tVfX9V8Xcajh36z/1r4Z9dPEdSv1vOLoLfV0MiMUAPxS/Zm/1b44g7Z3xWI93bN6vJ5etiZiE3i2M+mW2nJUsSHS2Dk58k9LaOxIk0YaN1UBoVoiLVBJfwaq/TlVzcENo3WJpCU/uCTzlQSxNyXFpvVVAp2hV9ZaO5XRqdkoxdSpWrHE2Lk6Ozbhnac0UvrODQJdh9NV2jpdgQLUfsomVd+1Zg3mMRJ+dkEQZz5rpk5xd0myTDN1sIP0VqSFDMMw9ohLMHX03VN9o2tYOOSdR3q+NBtHCIKomgH9VHllAyJNuU7N8SzVJqIGR8dmx+AsXR9jimi+qauLp1ex5wJp69Yxxq5aZjoxegaxSAFvr23ayIoFA/eQo8oTvNoE09iVLXVhOUvXa2jZ4qO0Kya8ws7H9LFjfWrR2PGarsbznirJsDbYNHaBb++bYBjWnhgMwq7V+xtKOOGEE/DrX/8aEyZMQD6fRz6fx/bbb48HH3wQc+bMwc0334xSqQTf97FkyRLstttuePzxx+vad10Du+985zuYMWNG5vJisYhDDz0Uhx56KL71rW/h1VdfrevgAoFAIBAIhj8kK7Yx3HfffbjnnnvwX//1X6llN910k/H9lVdewfbbb4933nkHY8aMSa2fRF0Du/4GdUlsttlm2Gyzzepef2NGkKG/oDfjd5gupI+9FZeYnq5v9fJoe0umJgDkO2JdXWjJOCvyrFienaZqxK58za4tGw7gTvlr3ojOI1izUs8rbB4/FnhmHrFiPCu2EdA1AUx/QWJguQcYsSHk+u+1d8IptsNTWjU3Q2tHb/Kuyu/0lY6J9D9+uaLZC8omJc1azokYo1wiOzaJIJF6RyxdqeKnqhEQ4UGsRE5r6tQjhLRc5GOnabWEhpCuWbWS0qgRO+KEpLVz+S7jT+0XRxUnstkNzdypfxE6I1Xt23FMrV1WPplm7gI/VeNW14ql+drXzmyvU8NtNQgBOKHOjo32GS+na1DlXcqYUdeJmXw6b87SGdPst0PaOluFCcDU09F0menqOBPFNWV6HmOtamXFGuwdY8hIW8crT/h9zNuTZbXStMnYxcdtZ0nF1LaAsY5BhUUualSe4PBraOz8hN6UHz+5vWTFbvwYPXo09txzz7rW3WabbbDDDjtg3bp1rRvY2bB06VIsXboUQULwufvuuze7S4FAIBAIBMMQg2GxsTEzdq+88kpD6z/33HN1r9vwwG7BggWYNWsWnn/++fhN1XEi7YnjwPfTOoJNCUZtR0N/Ec8nJ/e3GdvTx9g7XgXBlq3Jfew460RsCNdStTGmyi3HWaTldxRjtyjWjg1nrFHn4TO/v4JnZ+wIQbU5TSG/JiXGEPb0REz1qlK83z5i7JToNdbYRQxXqo4rrU+sItWOVVo9qjdZ7S0Dyr8uVFo7Yu48VTM2l9CipStPmLQAZQtX/FD7cFFVAtIMUQnYAuk8ibkjzSB9ZtVaZTVjSWMXUnZsaGbJOkqlRpUbSDPoOmb2bD1PnDQDUue/jMSLa1itxixlys+OWmL+Dt0gcURTamd47pHGTtebZc2kZ4ihzeN6O74uXS/OIjH2iLN36yrkY5euMAGYGbKkreNVFDhjR8+ggDXGeA7WyPw0CN6AZ6dG9wrPWOXTvLIE+YRylq7XYBLd1PzsyhP9312GBtKiH+SVJ0z/UbWeX5vNjPtvYx7mCFqJhgd2J598Mrbffnv89Kc/xfjx47PtAQQCgUAgEGwSGBS7k01keHHIIYfglFNOwTHHHIP29vbaG9RAwwO7//znP/jNb36DbbfddsAH39jBX8CqFo3dWqar6+uJ2bQSy4CteQzGOhHjksvIinWqsT6l7+2IdXqzN637GI7oWRL1X7B2pZ7H/aqsWbGWN+hGUVrLql/0RjY/PItQa+y475uXSzFbySxSqh0b6jd/pQVSTElQ9REojy2quepQrVV1KGK4dHZpltYuMPVYFT/QNWKJ5SFeIfaBi9i00EkwdEznZYN29Q+CuG4saY+IHVFaJq1RI22drrlqnkey73g7U6VeKTs2WXmiFrQeMGYYHc/MhqV1dCUNyiCm89Dtp12ZDA333KNlZuUJ9ZmhsUOYZow4M8dt1PosVSbWZTB2a1iVCWLnsioqUDfxa1JLY8d/mwbPyzV26rcaVDhLx2rBsucYsXBcR1g2jsuYy0BVMeE+doxFy6pCYYNNY8d1zwHzxCNng9DC8gHDsz6sOwjJE5sK3vOe9+CLX/wiPv/5z+PjH/84TjnlFOy///5N76/hgd373/9+/Otf/5KBXR3gv01O2feoByE30OThvSyzXBv44CS0KG7zLCTmsLBtaWUUulxS2jgGdkvUP5+gJy4Nls8IxcZluwYuGzDCssrmhP9T1LYHasATegWEbl4nG7g5+0BIl+DyafCjQqLqngnKZQQU8qV7gOw2EkbFdIRU2JfaFpqffhjqRApqvw4t6YGRRzsFwJIoMgaqyVhoGPgIQzbIA+AkkhD0WFG33zyPeIBX+79gbHuSgawBXjLBwwUbLZoDOWTYntCeayVPhGFsTuwE5iAQiAd0WaFYDlqHJ23xaW53EpcUi38P69hAh5cMqyYkAdF0uldd9tvjOQe1kiccM54cT6s+98vxIJMPxngolQZxPPzal3FcnRxUtg/mQksyCEdgGUxH+1D7NQaJfmo6a2An2LRw5ZVX4vvf/z5+//vf42c/+xkOPvhgbLvttjj55JPx6U9/GuPHj6+9E4aGB3bXX389Zs2ahWeeeQa77ror8vm8sfyjH/1oo7sUCAQCgUAwzNFqwm5TGurmcjkcddRROOqoo7B06VJcd911+PrXv46vfvWr+NCHPoQvfOELeN/73lffvho9+Pz58/Hoo4/innvuSS2T5Ils8LALvQFXGFtW6WsuiYHT+7bkCc5aUVkeACivjgrILyttHNfrHfXGXVkTh7SLNUKxNjFzo6j09cTTqi8520HhLWJLip4HuJ622XA042UvLUZshw7FquyFoOLraUpCiG1CoD6VzUbi3L1EYoMO8ekEiVCzeXRPaeaRCAhiGimkqUJLTsJwOVlazLDkYYkUtk8ib3QIU9ucoG40bfSaJfIP/Nj6hJI+PGq3YhzpmJQk4RhfU+RgXO4sZk5DZaLL209dZ+YBOKnlQFZJsvhLie2EysZxZo6zdyWDnVP3HzfWtbBans8Y8gzvDhtDZZQUqxGK9Vk4mTNulDRRYcfloVj+UyvrW87O0oVZYdkaSP5+oun0s7ae5InhAhetl9gFm2Bo9x//+AduvPFG/PKXv8S4ceNw4okn4s0338SHP/xhnHHGGfj+979fcx8NZyh//vOfx/HHH49FixYhCALjTwZ1AoFAIBAIBPVj6dKluOyyy7DrrrvioIMOwrJly/CLX/wCr776Ki666CJcf/31uO+++zB37ty69tcwY/f222/jnHPOaTjmuynCeIO2CGsNY88Gyltx2N4CDcaOa+wYQ1VmzNbGgNWKvar0xP3Y7sd6nLzFfqOWlUE9qPbGTCsxsJztoCQEuv6hm0Poejp5gpi6pP4tbiNp7czPwA90mS6dPKGL0EfbJpk7r8bbb6D1dHHyBJUSq1KiA7Egap+hm2DuFByv/3fGMAxjVkwnH4TGp5NkvBLt1+dJ7BZjaIIE+5Fi7uolR5Jti3Yen4PR7kQShWbwqP21NYG6pJirrjcrf0UkmVuPxs5id1Lx+XTMFFHx+TI39GWWHwarRPYgbF1eNisu62a/FtVGWCkuzqM+Z20JuMaOT6tDZCVP8KQqYjY5Axn6DbSRwca41WLssjAcDYodx2m5S4aziWRjTJ48Gdtssw1OPvlknHjiiRg7dmxqnd133x377LNPXftreGB31FFH4cEHH8Q222zT6KYCgUAgEAgEAoZ58+bhoIMO6nedkSNH4sEHH6xrfw0P7Lbffnucd955eOSRR7Dbbrulkie+8IUvNLrLTQJchmItN8NK5TQC/haYZCmAuCA8ADisVE61tzlz3qGKVZStxywQuD2Bja1qBWPH90HXk7MdxIZoEsBxAcdJaezS+01+D435YdVHSG7BSaao0XPQ2q7oexCELEOW5ql1aRsng5HLMiauryHG16zzyLJtqe8Yie9Z55HcTFuYeOlMWc3YJbSCurRYwp4lo/lBEDGLhsaOtTepxYv25aSWx0dOljXkLF2ayeOMXYlblFim+bOLP3eoN02mqn/6ydDV8b4JOYtGWaTsuBksm850tWgO09Pp7TPtThqATVdoe940kjE81OEOgo1duJETdkuXLsW4ceMyB3XVahVPPPEE9t1334b22/BT+Prrr0dXVxf+8pe/4Ec/+hGuuOIK/XfllVc2ujsrrr76amy99dZoa2vDfvvth3/84x/9rn/77bdjxx13RFtbG3bbbTf88Y9/NJaHYYgLLrgAEydORHt7O6ZPn46XXnqpJW0VCAQCgWBThzMIf82i0THEhsLEiROxdOlS/X233XbD//3f/+nvb7/9NqZNm9bwfhse2C1cuDDz7z//+U/DDUjitttuw+zZs3HhhRfiiSeewB577IEZM2YYJ8/xt7/9DZ/4xCdwyimn4Mknn8SRRx6JI488Es8884xe53vf+x5+8IMfYO7cuXjsscfQ2dmJGTNmoK+vOZasXoRhqP8C9ucH6q8a/4WBr/8aOoZlO8919Z/rOPoPYaD//EpgZJUNd5SDEOUgRFCu6j9+vusDfjWI/oL4GgdhxJrQfRAxdm7EbNXBbtE9Qwj9IPoLQv1HZr9OGGpdFxA/HF2nsUzS/hCGRKSovZPbfJ3nY+4riLzs4p2qBeY1G0q+p7bfpxMGRgYnnU9yfiPXga47dU2SjdN/bHlgPGfovrMv5/doJQhQCQKUqvEfX85/RvqZxu4/4zkXhNbIQasQaUujv5D98XMj+GHI/pDxFy3n+2ppe4OQ/fmWv8Hvs00NjY4hNiSSzO6rr76KSqXS7zr1oGV1exctWoTvfe97A97P5ZdfjlNPPRUnnXQSdt55Z8ydOxcdHR244YYbrOtfddVV+OAHP4gvfelL2GmnnfDNb34Te+21F370ox8BiDrlyiuvxPnnn48jjjgCu+++O26++Wa89dZbuOOOOwbcXoFAIBAINnU4jERo1V8zL3SNjiGGOppJSGmqVqwNr732Gv7xj3/g3HPPbbgRhHK5jAULFuC8887T81zXxfTp0zF//nzrNvPnz8fs2bONeTNmzNCDtoULF2Lx4sWYPn26Xt7d3Y399tsP8+fPx3HHHdd0eweKZjOmGoGpWbFnlG1M4JoYgy1pFWXV77GjPrUVA9eqK8dFM+9TjTAJyZJben4DfaCzeBPtbxXqOZ9U6bAWt6Fp6EoTSSGk/c263ucy6ev8METecraU2euxZUHGMeNKG/xeZPtqgAWw68Wyjhv2u7whHVmTTLtf49xqLa+lCRTYMSilYhvcXzNjiI0RDQ/sVqxYYXz3fR//+c9/8Pzzz+Oaa64ZUGOWL18O3/dTVirjx4/HCy+8YN1m8eLF1vUXL16sl9O8rHVsKJVKKJVK+nszdKhAIBAIBILmEfgBVq9ebcwrFosoFoupdZsZQ2xIOI6DNWvWoK2tDWEYwnEcrF27Vp9v8rzrRcMDu9/97nfW+d/61rdwxx134LOf/WxTDRlquOSSS3DRRRfp711dXXh14cA1hAKBQCAQbGxIanxbghB45plncGAia/TCCy/EN77xjdYeawMgDENsv/32xvf3vOc9xvf1EorNwic+8QlcfPHFA9rH5ptvDs/zsGTJEmP+kiVLMGHCBOs2EyZM6Hd9+lyyZAkmTpxorLPnnntmtuW8884zQrxhGKJaGXgJKoFAIBAIBPVh1113xapVq4x5NrYOaG4MsSFRry9do2jZwO5f//qXMdJsBoVCAVOnTsW8efNw5JFHAgCCIMC8efNw1llnWbeZNm0a5s2bh7PPPlvPu//++3WK8JQpUzBhwgTMmzdPD+RWr16Nxx57DKeffnpmW5JUbxAEWPHO2wM6vyR4DU+HHPxbDOMFinl2uYXBOd6GBveF415r6yPrjK6hZ6lRSy2JdH+Na4dqVXLgyKqP2kgfULudRPtbhXrOJ3keQ0YMQb/VZBZwxpt1vSSGCwdwAC9jPzbtJPfIM/3t0tvwLm/EC9DGGBj1h/30usn6xHEbGmAf6vQYTB2jxrnVWp7VdkENhMEg/EhDuJ6LESNG1rV2M2OIDYlDDjlkUPbb8MAumagARKPhO++8EzNnzjSWX3755Q03aPbs2Zg1axb23ntv7LvvvrjyyivR09ODk046CQBwwgknYIsttsAll1wCAPif//kfHHLIIbjsssswc+ZM/PKXv8Tjjz+O6667DkD0oDn77LNx8cUXY7vttsOUKVPw9a9/HZMmTdIXXiAQCAQCQfOI7H1avc/Gd1hrDLEpoOGB3ZNPPmmdv88++2Dp0qXaK6bZmnHHHnssli1bhgsuuACLFy/GnnvuiXvvvVeLIV9//XW47E35gAMOwK233orzzz8fX/3qV7HddtvhjjvuwK677qrXOffcc9HT04PTTjsNK1euxIEHHoh7770XbW1tTbWxXvA+4G/I9Nbq5QbO2Nm287k7O/9hsDdgL99qDmbDguo/ugV2Szf5xt8svFx0PM5K0KS+F8gQrE5H+1TVAo8qVjgxs6B+D2FiXVv1gYEiPgRZ9idKUzS0Lze5U/XdvG5DhqmD/feWqsJBdXRpvq4YUf9x6LrbHqN8Fl/O2blQHdRYbnkGAXEd5WLOtS7np2dj5Izn3CCzXS6jHTnra2MgOTPnsREHr0JD6zTCiDcC3h+u5d5ZH9n6mxpqjSGGCk455RRceeWVGDFiRF3rn3HGGZgzZw4233zzmus2PLAbrJgwx1lnnZVJmz700EOpeccccwyOOeaYzP05joM5c+Zgzpw5rWqiQCAQCAQCQhgOghl8c691/Y0hhgpuu+02/M///A923333muuuWbMG1113HS644IK69t0yjZ2gf/AXwgIxO2ymV2iOPeQMgu3tj3tXhV5c1zfXnk+tO5zRrRjIXFshnsn6xrc8H3jfNesjyPdB17OdsYbEhmiWQFVaCNUDMKsWpSZ7AvruGPOdnAcnp47teMZGjT4KiXEh1sN1HT3tmaSg5oScrAd4k7U1VUOMr1nn0YgHW/oYie91/iMymDq6CDSP2q0vjlZUAkgzdVnNd10AgTJmpX5n7Y3nMRbIaGQ8Scfk2+dZpCPPGTt1kQuMsTPYO8s0f3YFLvOOdC2MXg1WylYPN9qQ7cPz1L4YS+fx5fE0sfc2Zi49nd7eYAWbrH1s1yWmGTvOhjakPxySCFtPr2/ENmN77bUXPvShD2GvvfZCLpdDPp/HTjvthC9/+cu44YYb8Oijj6Kvrw9BEOC5557DhAkT6k4Aqeuu/eAHP4i///3vNddbs2YNvvvd7+Lqq6+u6+ACgUAgEAgEmxpuueUWnHTSSRg7diy6u7vhui6uv/56HHbYYfja176GcrmM7u5ubL755vj4xz+OBx54oO5918XYHXPMMTj66KPR3d2Nj3zkI9h7770xadIktLW1YcWKFXjuuefwyCOP4I9//CNmzpyJSy+9tOmT3ZjAX8D42xi9GefyjO0ptjd1DLvGLn7LqTAGJczFbFZhREdTxxuqGKnYq3xn3I8BYygrFiapFYxdrr1LT+eL0c+pnV1XYkN0lmlQhRP4QLUaHVdRiVkZq5oB8cxP13PhEouRUz9jdT50qoHWdkUTNtaSgxhfz3V1u3OKecgp5sLQCgLRuQCagSTUqizhUH1Z1u6Y+VIatYSML/nyHp9n+sSS7HUqo7RecoTaxu+PRF9obWOSOaXlisaox+TcdSN2n9rrGKyT+QmYzxV+CxHplGPz8mzDvMfZu2iaM3acdTYiC2qas3i+n+YHTG1ZPJ1rNiuW+pzr6lhmv8en1SEK7Fj83rcxeVnsXyOwMW7NOh+0uoLDekEYoPX1YTZexu5d73oXvvnNbxrzXn/9dWy99da466678KEPfajpfdc1sDvllFNw/PHH4/bbb8dtt92G6667TvvKOI6DnXfeGTNmzMA///lP7LTTTk03RiAQCAQCgWBTxJZbbokZM2b067FbD+rW2BWLRRx//PE4/vjjAQCrVq1Cb28vNttsM+TzG5deazDAGYN29XZJDA8A5Nu6UtvUtd98zMLR26HB2LFX1bDIGLuRnQCAscX4LXJZafjWjx1TiN7k84yJrFpqtnK4jMEMqs2ZT+fbOuNp1ZcdjD3oUOyd1jT5PhD4KY1dmKDTYmYiWk4MiauYSTfv6Wknl6eNoi0STF2y9qWfYC/pUMQ45DxHsyx0TxHToiVHxGARU5fB3AW+eU8Zeist3LN/Juud0vk0lF3aLIOQpa1yvVh35anfbyK7N6RjJhlHmN8JSc85Q2PH2q91j9wn0cLoAYAbpp8FnC0rMuaL9HTt7L7lrHORze9T026pGh/XklFqMGAZ9JON4TL0doyxc9Rv1c0zJpFl9nsFxjaqY+fZcTmpyOcX9C1nz7Z1mtTbJX8/0XSasTPY0GGusXPCAE6rGbuNWGOXhXvuuWfA+2g6eaK7uxvd3d0DboBAIBAIBIJhjjDExhw6HU6QrNhBBH934S+1ncTiMLYs39ltna70rOr3GB5jnWxvxobGrhDrz4qjIoZwPGMNhzNjN74tYq3cztihvMRYsHKVZ+6pT2/g1Tf4tSqqTOOutrhPNdMVRn3r+GU4QQXwKwCAoGrv8/hNX2mLSJep7hm3UIBbVEwd3QOKEfDVWy4xlnSEpI5Pa59U18SZsI7WYOVckyXySPulzgeacaya35O0WtKPz/ViH7uED1+YYB61Hx+Rg2pBVoUNG2IGLANZzIBuI/uumRvVbtXvoa5Ioa4Daeu0xq52Gx1E/R+zqPHyWho7A6qPePZrle2rjWnkaJqzdJx15kxeD2Wfs3lcO0iELdfgNZL5aegQOUNG2eWFODpk6OpY20lb124wiUFqOZ/mjF8We2dDljcg/X49g/HzUtOc0RvujJ1gYPjrX/+KH//4x3jllVfw61//GltssQV+/vOfY8qUKTjwwAMb2pcM7AYR/HfKwyAUnuvqiB9SbZ1xCLHYNVpP1xrYubl4H/SPmocgS1WePBFbqrRtFg1ItmiPb4FnVpf6PdZQRuf4qP/crlF6XrlGKJYPiitNHrfYNSaeVn3Zza5rgZIe1EAOfgXwqzp5ImsglEyW0KHYfBySpTA8JU8EKjRIu6qqfdP9kGUTQvcNtTXvxckTBfWPSQ9l6DZWIVYnNM8DYf8vB/qfphsPkKj9emCkRgI6pEwDOtgHdNR3/J9kwrs5Pr4emGZkZGQ3XO/Q8RLtTbRbD/QSxsRZ7Y/bpv6cOATrWQYOWaFY/mJnGyJU2D3WxsKY9DzqYIMj/nIyoi2+n1fmI8kCT/zi9y5NGyHGDONjAv9tGoH8GqFYbm2UY88xGtAVmA2LHzqp5dE6NLBjtlFZ4dcayQ+2BDnjnmSymXj/9oHdsBzjtdzDDkA4HDuicfzmN7/Bpz/9aXzqU5/Ck08+iVIp+l+8atUqfPvb38Yf//jHhvY3OHbbAoFAIBAINh1QVZ1W/m0iod2LL74Yc+fOxU9+8hMjZ+G9730vnnjiiYb3J4xdi+FkvEHzN2sKeWzWVdTz2jrjt7niiJgFKq1dAcBk7rjo32PhVVvyRB9j7IJCzArmxowFAIyayJI2lvRkndaQxwh1Hl73ZnpeOSMUS+BsZyMwwq8jRunpTnUNu4uMiSXmrdoHAAjLfQgrZYRVFYpNhkdp/YQNg5sIgeXaC0A+un8cZZUTutFxfZ9CsdE+iUVJspZJ9oSYu7znoKASMwqJkKz2RC4rZk6FYENiIBNMZAosYYKSPjQD5uSMdbQVDOh8on0GCSasHqRVCnVunKD8nFzOYs+SYO5Av0OznfqIie8O+yS7EyKNatmd5AyWx/68IRQ4U8U6ryOfDsVyxq6LyTU61PwyS54IA2YErk7a5aFYg3Xsn0swCFQvPq6j/tnxkoF8Osfa20ZG4UYnpJMromkV4mXnzvdby6LECJVb2DdeNpI/t/V6dSRPNFueUzB88OKLL+Lggw9Oze/u7sbKlSsb3l/DjN2sWbPw8MMPN3wggUAgEAgEGymoBnYr/zaRrNgJEybg5ZdfTs1/5JFH8O53v7vh/TXM2K1atQrTp0/HVltthZNOOgmzZs3CFlts0fCBNwWY5XzSGrsxjKVrY7qsYvfYeL5i6lyL8BYwjY1tpXtKzCiW6+06RivGbiuW2fzUkn7OZuiBi6BHTI7Ow2UMWhZjR2/AnO1sBG0j4yLM7Yx13awrup5dTK+jNXaliLELensQlnrhlyPGI6iYRsUEzdxRCSfFmhCb4BXycIqRZpKYOmI4qlUzeaKapa1LMAGUMFHMeyiSxk7rutQhaBti6ihpgjSEWjNIdKE6drJ4fC7PdGvEeFESBSVPkHkzzE9te2Imh9geZnSOmhUjko30QMlPU+Wl4XAzZSeRLJFI+gh1KTGznbX+R7lKW+fC0f1s09hxli7HLyHTN7qOvmB6Hr/FCn48v0vdUyMKMQvXWeAau/T0ur54HpdW2ZIGamnsOLgO1LA7USUXXVZ60WuLf3tcY0fTXZW4PzzHnjyhtassecLl9l22UnIZsGnszH5I36FZfSMk3aaFU089Ff/zP/+DG264AY7j4K233sL8+fPx//7f/6u7PixHwwO7O+64A8uWLcPPf/5z/OxnP8OFF16I6dOn45RTTsERRxwhnnYCgUAgEGxicMIQTos1cQ7cTUJl95WvfAVBEOD9738/1q1bh4MPPhjFYhFf+tKX8JnPfKbh/TWlsRs7dixmz56N2bNn44knnsCNN96IT3/60+jq6sLxxx+PM844A9ttt10zu96owN+2c+zNeUSRNHYxY9cxMn77bB8RM0kVxt7ZwI2N6e3Qz8iK7WOv7CNGjwMAjNwq1vNtzVjDV9c1mye6/rA967+uLaJ+CjpG6Xmlao2s2AbKuOVYP7ePjgsxd7LrNnZExCaMaY/7sV3pl5yeKMsp0Bo7xY4kMsmITYztTXLqk7R1Rf3pkrZOadUCpVHzg2jfpEkj+xNbHwAxq1hUArq2vKvZO13+jjR/AWnoovsjLEdZksTcBcTkpbSDCa2SG2vsiHEMtU0IVPtJY6e+Jxi85DHou5tjTJ22OTGZO4JTg0ZLmtI6jhMzSTVsTvyEzYlfh8Eyaeyo3Va7E87sVFkmu+Vcch7P0E5bnACxLRDPih3BWGee5d3VE02vaYvZPZ9FBZwqnYc929M2bbB0vPGcLVPnQSw1AOQ74ulCZz413ZXxDOOMHbF7ecZKeizz1tBYWhg7U9fI9ks2R6yfeSax3t6z903TxtobEoOS7LApDOui58rXvvY1fOlLX8LLL7+MtWvXYuedd8aPf/xjTJkyBYsXL25ofwPKil20aBHuv/9+3H///fA8Dx/60Ifw9NNPY+edd8YVV1wxkF0LBAKBQCAQbLQolUo477zzsPfee+O9730v/vjHP2LnnXfGs88+ix122AFXXXUVzjnnnIb32zBjV6lU8Pvf/x433ngj7rvvPuy+++44++yz8clPfhIjR0bmsL/73e9w8sknN9Wg4YRab/tcKME1diMUC7M502eNYczPGjZd7o2zPAm8/BUvQs/1GoQKe5vurTB2aES035FbT9SzdmbHHQ6M3babx1m+XZMjxi4sjtDz+vrs+kJiE3JMY5dlCk2ZbO2jx+t57aNilpMzrRNHRQxCN3v7zyu+yamorNhSH8JKCX6Gto4MUendPmbqFHNHjF1Hm9YdwYvmVRPGxGXFWFJZuSRTpH3rFFOX18ydi4JiKqjclC4gr7R05MsX6k86H5W56tv97HQZNC8XZz26JgMWJBguYhqrlO2b0NaFCR0fR1Jbp3+Ggamtq+lrZ2jsSKhnZ+6SmkA/kQZLR0hmxcKJvexsGjvNAvnxb9Nh0whiFk23jTHCxXx8v1cZU0R6yi6mq+OZsDxDdpRio9f2xcfl+tWyk77uBctziYP/BIySYi7T8XnKs5Fp7HLtrEQi0yvnFWOXX82yfNnxOMtGTJ3hY8ezYi3ecxz8lsvX0Ni5ln7gz+x8AyXLhiQGo/KEs3EzdhdccAF+/OMfY/r06fjb3/6GY445BieddBL+/ve/47LLLsMxxxwDrwkj/YYHdhMnTkQQBPjEJz6Bf/zjH9ZitYcddhhGjRrVcGMEAoFAIBAMQ0gotmHcfvvtuPnmm/HRj34UzzzzDHbffXdUq1X861//GpDNTcMDuyuuuALHHHMM2traMtcZNWoUFi5c2HSjNhZwRo9nspHP0mimxZrYHb9NLx/Zq6dLvfQWHjN31XKsq8mz6+BazKv6eFYsezUO2iKGqnNyzNiN3Sk+hrdkrZ72h9Bvi5/i5juwqg8To8zsoC0uKbZuTcwecH0ZvTnzrNhCR7ydZ/gERv1r6Oq6Y5Zu3Ki4/zdTrEE7c/V3K9G1dNQn+dgFZdKiJTR22uaN2qgYO+X+n++Mjue0tcNp74zOWfnZURJgJZENm/TKi73EomOT/rNNaYAKOU9rsCjrktgIpxKxxY4ffQbqO9Q9GVKJtATzRdmSDs+ATVRwIK0dEUDU7LK6hzVTp74TU0f3J88QTXIfSeYOyazdRlzzU/515MOnNK46CzaR1Zv0s0uAsmE919HMorV8GMv25Cydw5j8eCP2iGcasaLHPDR1STHubcf1dmn2rotVo1hX7r/aSJbGzgaf3TeBwzV2iunljB3T2OU74/MpKO1tnrF4jsf6iXVqzIRz7zp7tYmwRlasazB26nfL+tGmsTOyZlnXSFbspoE33ngDU6dOBQDsuuuuKBaLOOeccwbsXdjwwO7Tn/70gA4oEAgEAoFgI0MYwsmwC2oaG3ko1vd9FAqsNF4uh66urn62qA9SeaJBRCndda7L3qYLLDutqKgFnj05kTE/b3TH08TY8QzAaoVlcLE3Pl6Ym8D1Lz1lVoVizOhom0lb63ljd451ZPv8a6me/vs7MYO4obEX66cxO8TtzY3fEgDQy5iIdZU1etpg7NTbENcn8mofPmM+KOu4c0zMZnYxdnUCm968I/qBdnLX/fI6AEDY16M/w3IvgiplkSqNF9WEpbd68gNrM7NhPVUf02nrhKMqiZD+iJg6Yrjok3SW6coTSj9HejrFNLblXM3ikLZOM3aqb6hyRlhR7LFvMpChPq8Ey0H1Yb1cXCPWM334Aj9DW0fz1a7ou82/kRAzdUqfRmSbFrwlPgO196zKGY7DaFXlX5fhW5euEWseiu+St9dFzN7wU6MIgJPF0nHtHTGQAWPTGONkZssqxpZRntyL0VaRgmvwepkGz5Z5XeRebRYmwsiKZZtz9k5r7BRLDQD5zvi3l2PTlBVbHBn/w+TRDM7YkR4vu/JEDZaO/TfgGjmtsWN9msun91Vk/ZznejzOPgt7t9EiDEOceOKJKBaj53tfXx8+97nPobOz01jvt7/9bUP7lYGdQCAQCASCgSEMkGXw3fw+N27GbtasWcb3448/viX7lYFdq8G1OmyaZ7K1K/3F6DausYuZqMmj42zPNSo7lTN2JZaRxmPxpOHgOhbO2K0tx2/6vWH0NjtywtZ63ugdt9LT22z/pp5+5vFF0faWeqvrA1x7st12MbM2evst9bQzJtIK9rDM33VMi8Tf/kljV2SMqc8YO45CR/Tm1MWYwtGj2bUawxm7aH8d7M3c6Y2YukB9hqU+oFxGULFr0Vz1hu+qa0lZe8RO0Kfb1qm1dWFOaez6on2Wda1Yu8aObg/SBLXl44oTADF26viKhXBDqjChGLu+3vh8EPvX6WxfdUySSTmsRiwAOLk8HGq/0qgF6rNaIU1dtEk5oRmk86Kr69F37gNGCasJ5kszLLriBPkJmrq4tPYxvqZhMitWfVIv05aUDZuU8xFDlaz84TjR75n+onmMadIegiz71ciQZeydzvZl51Hh/RM/+ov56B7n/m6dBa6x89h8VaWCsXRrS/E0PW+q7J6rpbHjLB+TBBvzibFzDY0dz2rnervod1jo6j+jFWBZsexZbPrY9V95wlbLF4hZTkNjZ4moFIys2GFOzQ3GwG4jT5648cYbB2W/wzy/WiAQCAQCgUBAEMZuMMH1LYin25W2aGQxfoMjfRZg6u1W9UZv4W9YsjoBk8krFPtn7DiDtVbp7Tq74soWndvG1UIm7BlnNR/8auTr9sfFcabs+sTBzK9u3G7j9HT7VjHDGHRGjBv36uut2LNiSeuSZzqh9hGx/x1H+whV/3UUZ+ni9kwYEc/vLkZv/Twr1ilHzFbQF2nt/FIJfrmcqppAOjFi6si/Lt8V7Z8YCU8Ja52OEUAumldRuyK/uj6VmUqsSSVZh9Yl3ZzJ1LUzxo50oLrWrapw4FSogoZi7NT8oKSY5aqZIUlaO02h5ZieLlFzNelbV9E1b03NoK6BG5j7thYzdDI+A5MxdUJiGvvP8DTOTTN3Dt9VOuGWDpnIkoVrZ+6iedGnkehO3oA+09hl6e20tx9j93jbGWOXU/52BXYwPs0zZGm6PUODV1aMXpndB5QhmgRdZ66x48w6t90kHabLNHZGFQrG2BVHRudT6antxRlnxTI9XiGDsaPjhpyNZPtiX0hXyDV0tqzYDu6fxyuK1Gz5UETYWHZ5XbvcuBm7wYIM7AQCgUAgEAwIThiYof9W7BPhRh6MHRzIwK7F4De2cZMz/UveS2uxNu+w6+3W9kVv3Jx5W87e7HidRnoj5FlonKnijF2P2h8xXQCQn7yNnh77nu3jNixaDQB47yP/p+c9+vbgZ8ruqqo6bL1HnP26+e5xG3NbxNN+Z5S1um51fL7cw4/D0xo7ll3Mstd4pnG7qg4yYbOYpZs8Oq2rA4CugsqEKzFms2Rq7IJKFWHVT2m4vETGXE7p/3Jt0fFj/7qIsXDbO+ErHz7S1BHDQRUniDUJEm+9lJmY1/51cTZs9OnF2bCkkVM1YZ0qVdBQ11/ND9SxiImk83PzCR6NZcWG5LGmfg9E8pCWrqzYs7Jm7kymjrSDoWPq44CYBSNNHfWu/k2SBi3B3KXKRtiQ1FolGDut00sydrRrmhNo4R9rdz9ZkLrNjIUzpuPft14nw3vN8ZjHm2JdizmWWcroMpOxi/bHq1Fw1qls+c3V1NgZlSfs7B1y6coT9FsATE+7nNaj1n5G5dR58PvUY9NUzzgLZlZsuvIEZzbXWLJi2xk7yLe3sbgCQb2QgZ1AIBAIBIKBIQgkFDtEIAO7wUSW/sWN3iQ787GPGve0G8dc1NeMivbBa53yt941fdXU/CxNyzpDfxZN94Txm/vIMZP1dPu2O+rp8XuvAABU2LEKTyzW0/9c0aenB5o5y33qdtoxYuEm7r21nte17bbxypttoSd7guic11XiqhxcW8b7TDObrM+53o77TY1S7OkWLFN58qiY2eDXihg7pzdm7MKeiO0kH7ugXI3YLaJviFlSb/iktSPNT04xdcRIuB2RFjDMt+ts2HLZ1NaVA/KvMzVrBGIDiFUo5pKMnaszJHPELimmLlBZsKGqOOErxs4vEXNn13TpyhOMpUtWnCCGhtpNTCSdT6ZmsJ+Hv5bBKWbFCaM+ipk68t1TGjtqP2WbksaqHwaPKhJobZ2aT8ycJgMpSzaRFWtrPd2tnLmJ2cZ4CydDb8ezZZP7BGLmNZpWrHA+vscLjLVuY78HYu/aOKvNWCmqQuHn6v+HzFk6fm05+Rcqxi7M2Rk77mlH7DavRsHB9a0xM16Hxo6uBY/K8Fqxlj7hbGahmP53a2TFsu2FsBMMBDKwEwgEAoFAMDCEYesZNmHsmoIM7AYT/M2Ov0GragRthfiNs4u9FY9nNQ7Je66cwYS1F+L9EjPTbvFLAoAKYx1WKfZtDdPKdI6M66Hmt4oZu9HrogoOIXuFzrPtRj23XE+/sTw6t8V9sd6njx03r15Fx7A2bs3Od7MdY83fhL0in7rN94z1fvmtd9LT/ohYe7dWMZBrmFcfP1/ueE/6oGRmKqGDnRtlwHK/unGsvTyzmfRHzqoePS/oY/51APxKBUG1qvVhLr2lKybEpRqTyqOLmDqnM6plyxm7isoqrSj2qa9qVprIqjhB+jli6ojhNRg71S5XMTuUDRuWFWNH2b7l6P7TGjuqBpGoW0wVKIixcwoFnelIWjsitYm0SVbOIM1gNaHj8iw6Mlf7wNF3tSBVaSJRoSHJigWkHVT6Nr5OLSR865JaR621g6nRy96fRWNnlGqInwV0nTgMxo5VnqBoghfE2/MqClz7pbM9WVSAP29ouU1rB5g1swncZ5H/ZquGj526V5jmjWfI2jzteKZsUtOqt1NMncu86/i0DVw7zW9z3k/UJ1xDV7Q8l3lGcZaubtiQd+EghGIldaIpyMCu1eA3Nn+YBOmQiVuKS151FeIi9CPb4h87DSJ4KNaw7mAPE1onq9A2D3OsUSGTtazM2NpC/NAcMToOyxa2jf6pcwtf/tDsmhibGW/+RmSNsk4N8ACgykK49A+el/sZMTEOSY/adpKeJsPk/Lt30fOCMe/S06V8/GBfp0ybebiZ9xMPeZC5qhGeZdNjuuIQDiVKTGDzuDUNhV+BOGnCLceibZ+MidU/z6Bcjc2JERvfemo/rrJMoVBsYSQlS0QDOkeVOAsK7aAxbFnbnETnXvLtxsRxqD5tSBx9Rt8LngP6f+T0qoGdSv7xVZiZDIp9Gtipc6IBngvzn5hLIViePKEsN0KVPeBTsoQeyKlkCUoOoU91nmQPQbc1ffZnHxIP4FRIlkKx9Fv1TdsTJxmK7Wf0RUtqDdBSoVgjeSAuRQbUEZJjzxsq8wYAIVmj8GcQCys6OW5srKbZwDDnsmQE9ttoS4TvAfO3RdOFqj2Bi4Pm88i6EYplHRM46l4xQrFx6NhpTydS8PAsH9jxF1RPJScZZcR4SNSzvyQT+L2WN+xiVPJE3j4AJvBQrZE80e9RTTiWGy6UWO4mDRnYCQQCgUAgGBCcMGy53YmEYpuDDOwGEYb1icU81CnFIbu2YmyQ28Xe8ogdMhg7wyAzfjMja5QspoqHOcj6ZFWJlTrLszfOjtFx2zaLmLMiC89QSBAAOiZupqe7F70dtXdlnEBAZaYAwFX7yDHGr2NcfKziBBYOJjuTCe+O9zUiNihexcK9a0q+cV6A2Q+cYehS5YN4mIS/WW/GShGR9cw4xtiNYqFaztg5a4nNYskTKiTm96lSXEGAMAg02xQoloxYArI3KYxQTJ3qZ7cz+gyUxUmQb0e5FF1PYupiA18zeYJAtwoZqRZTNifK6Nd14gcDJU30JkLKii70S4lQrL7HyCdF1y+LvjKDYgqv6VJhZHNCZanU95I+HxVajh1/ow8LI0Q2FNqXWCdRUCiWGDjV7mrCoFgdUwdLaf3A1wxJ8qhZHImO9iZDsc3+08qwVDLMlavp5AlKeAEAp5C2TOEh3nzBbuNBIVrD2sNLM3ZZUQMbsixOeDS3oq5xnoeQizEjZ0uk4M8YztLx5xGFYnkZMZdbnLgW7swIxcbPjTb2jKHnDWfkRrCyZb66KczkCZYoMywZNwnFDhVISTGBQCAQCASCjQTC2LUa/C3c0NsxXZXSK7mV+G0uNPR23Xq67Edjb67rqmRoVujtr7dcuywSMYCrmP6NG5EamrPOqOxYwYtvl1wx1sV5m3GWbVl0PutW63lc+0M6H2406o4YFe+X7StQ7JzB0jHtzlrGzlHSBBfWexbDUAAY1Z42HeWGq+NHpPV0PGGCs3SFgLEgZWVp0sPOnZINkuJtxQSQUJuSJki7SGyDq3REbofS1qnyT1U42gaEGDutsavY35qpP3TyhGtn7AqeE5cQU0yzPg8qIZbQ1mWJ013SKFGCg7qHQjev7wUio4lh1cydbzKP9KmvsVf7vVQzdzp5wp4coedrLZ12EzbW4ybIxNzFrJlnHovaQM1MHELL9yi5Qu0/DEOEDSZUpGenNXYO09jx36Rm6hhjx5ONeIIAMVHcmoOzd3SP8edHbY2d3e6EP+cokYJsTwDAKcSMnS2RguuAg3Ja5wuw31+WxQmfTibewGTWeMIJ2cEYpdcMjV26NBvfvgHCc+hgMJInJBTbFIYUYxeGIS644AJMnDgR7e3tmD59Ol566aV+t/nGN74Bx3GMvx133NFYp6+vD2eeeSY222wzdHV14eijj8aSJUsG81QEAoFAIBAI1juGFGP3ve99Dz/4wQ/ws5/9DFOmTMHXv/51zJgxA8899xza2toyt9tll13w5z//WX/P5czTOuecc3D33Xfj9ttvR3d3N8466ywcddRRePTRRwftXACYb3bsbZjMavlLmcuyvdry8Ztol8pULRuWAFyrwfR2ZDzL3kizbAdIb7eGsXvF3vgtnrNddBrdxVF6Xvtm8Ruyy/R43uZR1qrDMkMNewbaJytpFLI372pbrN0L2qP9rqnE57i6nNbVAXE2bBabaXtz5izeSGYeuhljR4mp4ywdLwXHdZKUDVvtizOCw0rUp2HiOiSzYT11fGIZ3HbF1Cmbk7AQfSfGrlQNWTassgFJFFWnz5ipM9mWfCIblpiXgufAKSWyYVUWrM6GLZmlxHQ2bIJqcLSwL7pntUGx62ljYmpn0uZElxDzzRJidM+7jt1GJGqHOr7W1ikkSoqFGcwdMVpaE8jXS+yDlm1olkVnwmaBa/CMaWWjwsyOXaZt4s8C6k9Dd8eeN7RurTJiRrPYbzZbb6cYO/7c4Ho7XmqsqFjvtph599tjZt3n7VVMHS8jZtXVcXCNHVuVa+SI2eSRgPZ1FrsT9lyqg4DWsGXCbnAEgXlftQJD8TyHAYbMwC4MQ1x55ZU4//zzccQRRwAAbr75ZowfPx533HEHjjvuuMxtc7kcJjDRPceqVavw05/+FLfeeive9773AQBuvPFG7LTTTvj73/+O/fffv/UnIxAIBALBJgRKDGvtTmVg1wyGzMBu4cKFWLx4MaZPn67ndXd3Y7/99sP8+fP7Hdi99NJLmDRpEtra2jBt2jRccskl2HLLyNx2wYIFqFQqxn533HFHbLnllpg/f37mwK5UKqFUistThc3cYFxvwLPXShbzUO7PlI/1dp2dmwMAyn78ZldllXKSmY9JcJ8l/jZMb8k825br7Th8xRBWgrgNnSyLrL19cz3d1hXp8YjpiQ5meYtj2pUgF58Qb09PXzTNvelWM5ZuVSluL2XD8rd8zlx6bVwzZBr0AkA3Y+y4npEyYLmBdBsYs8EYO8qGDUsxW0n+dUnGztXZsFRCLOoDXTqMfOuUti5U5Z50GbFSoH3e6FMbEyfMrFPaun6yYYFIH+UoxippSEzZsDoLVmeTmsIxOi9iJjULQkazrqfnkdxLM3SJT2JrkobLntvMb5J0cUmmTjFCdJ0oK1br+agUWZDWWtFnkh1Mau10hnBGu8MoBzBA7GVXM3PWYs5s7NJg5hjTbKyUyBROTHPNIP12OAvHnzE2xi4LVh87bkrM5ut1mK+cqbdrS017rEwYn+awaexqedfxTGTe+56FxeTZ9iMYe0fnbhpB20uKZRkXCwRZGDIau8WLo9qj48ePN+aPHz9eL7Nhv/32w0033YR7770X1157LRYuXIiDDjoIa9as0fstFAoYNWpUQ/u95JJL0N3drf8mT56cua5AIBAIBJs0Qj96UWrlX8vtUzYNbDDG7pZbbsFnP/tZ/f3uu+9uaj+HH364nt59992x3377YauttsKvfvUrnHLKKU2377zzzsPs2bP19zAMUWU+UA2DvTmTfodnpnk8S7TczuZHTE1nPl5eZW/efsj0dpbSRfxtr1RlHm9qFe5tt5aTbJZMtT72xtnLtCG8IHiBGCg3Xtdz07cZr7Dms4y1Mnt9LykminvTcZZuDZtOlpkCEqWQLG/GXewcxrBMWT5NTJ2hq+tbFU9zxk5XmWAVABKhCcd14bguXLJzo0oTKpPPaY8YOtLWQWUfh+r+oEIhZT/U2rpywuctCboHSFNHfUGMZpzpGK3n+hXAVyXEFLtMHmjE0OlKExmaxiRTp0uJ5eKs2FB5gAVhQlOnP82s2KzsyvT5spJiNI+mkvo4YuYSFSk0Et/DIIjZvgTTReQZZUrSMek2pDb1x8JRuc04QsDYGn0A+/s4Z5r01m4G+8SZvGSmMJKsVDpb0zVYPDZtYexsZcSMpli0dECiCoVFYwf2XCFdHcAYu2IcCci12Z/ddJ86XFeX1WfJeweAx06eZxIXE78twNTT0flw7zv+rB4yjEsjGBSNnQzsmsEGG9h99KMfxX777ae/U9hzyZIlmDhxop6/ZMkS7LnnnnXvd9SoUdh+++3x8ssvAwAmTJiAcrmMlStXGqzdkiVLMnV5AFAsFlFkD4YgCLDinbfrbodAIBAIBALB+sYGG9iNGDECI0bEGZBhGGLChAmYN2+eHsitXr0ajz32GE4//fS697t27Vq88sor+PSnPw0AmDp1KvL5PObNm4ejjz4aAPDiiy/i9ddfx7Rp01p3QjVg1CqkKhRsXtAT6+oclhXrKk1Ve2fMIlWYboNrUrqLlGXIj5x2oAdi9o6/FQfsbctg7II0c8Y972yO61lvojTJSQvTuyruE9Lb8eP2Mf1YibvJq13wt+Z8hqs7tZ3r6kYzlq4jz9dVbJYf6y25rs6pxnpJX3u9pfucvLPcQg5u3gP5WFHmHjF25FtHdTCJqaMamSXSmwWh7otkRYZkzVStrUtcG/qkbiKG0/HLWmMXVNR5q+/k2p8pkk4wIG6eNHeqrynL1HVBV5WYZmLotL4sg6Grk7gbEEJi4Yg74X5wlDkLWsfMiqVPJ/FJyNJMBSEQIEQQhgjIx463STF1IWPs+LRRC1ZNZ1+n/nVkteBlnEMjFScIRk3YjGm6pwPGYPKsWF4tQlekYPNcpgn2LMw297az9aMBo/JEPJtHCPRvzOufsTOzi4e3li70/dqZ2Y3uc3382DdCDBnG13EcnH322bj44ovx+9//Hk8//TROOOEETJo0CUceeaRe7/3vfz9+9KMf6e9f/OIX8Ze//AWvvvoq/va3v+G///u/4XkePvGJTwCIEjBOOeUUzJ49Gw8++CAWLFiAk046CdOmTZOMWIFAIBAIBBsVhkxWLACce+656OnpwWmnnYaVK1fiwAMPxL333mt42L3yyitYvny5/v7GG2/gE5/4BN5++22MHTsWBx54IP7+979j7Nixep0rrrgCruvi6KOPRqlUwowZM3DNNdes13PjIG8zsPqxAXu79HrjygVOvqg+4z7obBulp6sBf6sljV06+ypaHmvStHcYY8A4+1cJ0iwZf6Ncy/zk+Px8wjMNSGTNWV5KeVacqe1Le9MFQfotnoO/AedYGzjDOEK9OXczzSDX0HWy6Y6cYrF64vqvTpn51PUxjR1lO1fTeh5iBLx8Hm4up7NiKVuPal2Sts5pU9o6xdQFSpRXVexluRpqbZ2fYLh0H9O1SFQLyGdlw1KRiHIZTkBZsepclMdZqGvCmp2f1CqRf51LTJ1n+tgFrqfZqJiho+/mvpPMXZIUovuLztuFw9iyZKpqg0gyXkEAXUIiobmKNXUq81a3JwJpzaoNyIaMU7dp7DIYOz3tsoo3WSxdLA6s2R7bKknvwoGA/6ZtTB6fx3W8YOyd9kxktWTdAvvNWlhMpw4TOceisXOMjOB4XXom8mcQ+UUC8fnw5bxvh2Wt2CBI/14GCrE7aQpDamDnOA7mzJmDOXPmZK7z6quvGt9/+ctf1txvW1sbrr76alx99dUDbaJAIBAIBIIkQkmeGCoYUgO7TQ2GFquXMT88Q7ZI/mXxPI9lhnUwPR4xW1X28so1a0btRWLDHFiX+xnTug0ZzvKkvcli6ejtPkuj4zfwhmbo6dz0G7KNpQNipq6TLTdYOjbtqvqvboVV0qjGejufXTcrU5eoCeu4LtxCPtaeqSxYYuzIty5Q1Th0pQlFZVDWcDnwU1mkSSRZg6S2jhiEnOpH/TDwqwhJW6fSlwPf7nFIoOuq/fmInbBkw0YrenFZVq2fUt+TDF0GI0T3GC1vJcvhEItVqwoBEP/z0eSguscTDJ5ub2Jz/lsJ6U8n3rK+sIn2uH6VtTUk5jTHs0gz2D1Lv4UZmbe1fp7NVJ7IglVjx49vnC/LkCXGjtdeZRGRwE3fy04915nW9XlNXbBpnhUb9S/XGvNpikbwCIPbNK0sEJiQgZ1AIBAIBIKBIfBNQ+xWQEKxTUEGdoOJLFd4uvm5t105zq4MmG7L6VWVCFh1Bq4nKXbG84l1MnV38SXmGaUVVcnC1NXF02XG9JXVduU6xEG2t3Pb23vWW7xtXe7IbmShsTfgJCsFmMxbF3OWb1fz23Pxzvh0PoiZN9LTGbq6Epvux7MOiHVNRFQhH9WndNrMLFi3U1WaKJgVJuizUjHZubIfprJD3YSmjpigtoTGjvzrqC+J2dM1fYNKXFGCtHWpWrcJhi6RBZsrKqaI2JN8gRqpPnOxtg71Pbx5bV/ebjoP4qBc14n962qRIFoTqLJIHdpLP/d6RuUJ0mBpZi7hX6eZuzrIoaA/hopnxXqMqeK1U1V/h8x7k/vcOYzBCi373VAwPe3Syw0Gk2vsbJmsBlsZL+dVJui+doztWf9a/f54rVh7ZEJnorMIgsHOqfssn6Gx29B1h5tCEIrGbohgw/+SBQKBQCAQCAQtgTB26wk2Z3OD4eE+d6yWbKA0XJ7SYAGAU+FVKmJ2r70YZVSWfTsLN4K9qZJ3HPeC45ULYkVZrLHjjF0tDR5HLcaOT3NWRk/z5Dcvg71T+zB1dWmWDgDac25qXhvbmdPH9HRKW8d1dQGv9WvxrDPYA+WIz1/Anbb2WFPXbn6Sti5UWdAVqhBC1RfU96ofprJH9SlQ9qtnMnap2rCa8TIZOyfwM/3PKHtQM3Sqi+k7VdJwiwmmjteIBRDCyfSj02xgosZtECTmJ5hI2i7HGLtMaA0dpQI7xvmFyJvr90ezkeedZuwUi6k1gCpbmQg+1U5dYjbhf5d5GPKx4ywdZ+8sOjPzHHjWLGOt1DTfb8hrOaPF4bUmENoYTA7L89V85jJmrAE9Xb+NAeIqJLBrjbljAK9CQc9lk8UbWLM2NMKw9aHYUJInmoIwdgKBQCAQCAQbCYSxG0wYognG4jCti0aG3o6muV+awzJkHZYh61GVCva2XmF1ZXm2LOntOGNXYowcZ856y+m3MM7SlTKYPBvorbZoY+Yy1jWyXxljZ8uA7cin5wGmho7YuXZeHaPMfep6U9M6SxSscghgdVo32ZIEc5IrwGnrhEu+dR2q+orS1iGprVO7r1J1BkXZcV0anaX2qaNDOdTXlKFnfsaeg2qDkmIfLcW3tc8XsVB6I5Md8QqKsVNMHTF2TkFpvrS/msfSYNUs9ZU0aTnXZOSSoOuvs3qJ6QNjP5IsiK7eYPrs6SoGnmItUTXW1xos141/10k2ITQ1dPTpKaYxIGZOrRfWwdAYvyabj12Gzkz7BhrZr3Z2T++Ds3jswHy63kIA9WTF2uZnVeWwIksTqC+A/ZlrWgOm182ERWNnetrFs+mW5fpg8z5Oe4MOR+s6A4E/CHYnorFrBjKwazUMK4KsB7DlgcR/EHyQp8J+AbPV8JgdCh9EOOVoMNDWPlrPq7DBC0+qGBFEDzI+sDOTK1hYVu2jnuSJmnYlQbotXsZ/C3rwuxZbE8BuJcCTJIo5+7oUgvVYmTCeHOFU2LQfDeKCUjzYs5UMA+J/EiETbet/HDka6OThtnXGoViVPKFDsGqAV3XM5BYaMFctoYkc/XPw6ZhqQOcmQ7GUjKK2S/SvQ/ed5RrSAEgnRRBoMKPsTLx2lcyjzGFjg201YHUodusgpH+xNOChdiUGakXV4KTtiQ4p58ykkJznpAd0BG0ZYg5UyY4Fvhro0er0D59sWyy/XQrHkbBfR8TVlJ9ImtB7SHYzazPtK2Cjv1DHbNNhVN7GaENftY1JPPjziA90KBTL9uUbSQzxNM3O+p3bbIxq2Z1klyfrdzMD/Nz0IM5m2AxzsNtQ6NCyrk44AuA5zGTeEorNG/3gGutFc9ggr/5WDR0MSvKEhGKbgYRiBQKBQCAQbFL41re+hQMOOAAdHR0YNWqUdZ3XX38dM2fOREdHB8b9/+2dfZQVxZn/v9137p0ZYCCIhOElklFUoqAoKg6SqCs/hg1RMQkakoAoanQxkeBP1FWBVSMHXxI1JhLPakxcjEqWoKseV4IvIQfUReQkxMhRF+EXcQBFQIeXmbndvz+6q/qp6qrb93VmYJ7POX1uT3d13+rquj3Vz1PP9/niF3HdddehvV3VQXzllVdw8skno7q6GsOGDcOjjz5a+conwBa7CqJORqYTm0PrDQ2rp8fRt552kdopHlABAA4RKBZuQ+qqramK9rdmqYUr+MZeRLiXWuyoe7U2E5c7oW/xqvUttFIUIjScEEhBrW3VipxJVHdhqas2uFwBTc5EJG8ngSc0OMKhblexbrHSKYj7SjY56dAlFlpZHeGKDeVNhACxcL16mgtWtHMk0EpTKoWT8MM2rxLpz2KWOk2QOKVaxFwpcxJZeaTFKqVOrBdBEfqDQ2x3w+sUnyKtUyRMLFyhxA0uhXyDz0x4HTWe+t5Z5ap9qjq8jowm41LluJFQtlZPPWhC1ktYvr24/AWAyBpWlY7OqrsCY3InYdBE2M7S4OMqX5U3Ul2FWqcsARGo8sIqkS+hz5uUQQqEBkxQ9yvoen6/60LkjJJSDgJldlNS653clr+Nw1FcsVF70EsW9U1yxdJrP9hdsb6XNU5NKe2klXPFtra2YsqUKWhsbMTDDz8c25/NZjFp0iTU19dj9erV+OijjzB9+nSk02nccccdAIBNmzZh0qRJuPLKK7FkyRKsXLkSl112GQYOHIimpqaK1T0JHtgxDMMwDFMaXgVSipXbtUv4t3/7NwCwWthefPFFvP322/jjH/+IAQMGYNSoUbjttttw/fXXY8GCBchkMli8eDEaGhpwzz33AAC+8pWv4M9//jN+9rOf8cDukMKWoNskDkoFNKlFyDDfjkqg+Jlonaa6Emlz/NZItDhDREtrSRLq9tAa0kYsdgcsc+xEWrJsjfntyRQwYQuiSAqeqCX1qQnfcO1WOhIcEc7DqiFvxVTOhJ7DPRAESlDLnDLHLhsFR4i5db5lDqQSCGNI4yQtdUL2JFMDt2cdPBEskQnm2vnpcG5daENoE0nPQwuJ3pounMjiVqVal6o0mRPRZsLClZZz2MT1aLm9gKjvikn4oeVNBjiktEdHRgRJ1CrlxXULi1cko+HIqxKWCt3ylhEWuvCrhGVYhm1ootQZKYPixK0f0aS58FO1UsXkQdo1C464t6mq6Bq0L3HE9fjCUhdsF/PkpAFS/LTcZGsEFeTNyvOSOWLKvDpt/iMAuOQfo2O22Ik5ococOyKZZAqkSAqisKYcNFjklZSDdM6Zsq5+Fkw+wRH5kkfwhKgnNdK5hussZB5hd8XLetizZ4+yrbq6GtXV1ZYjysOaNWswcuRIDBgwQG5ramrCVVddhb/97W846aSTsGbNGowfP145rqmpCbNnz65o3ZLgbsUwDMMwTGmIqNhyLr6PDRs2oE+fPsqycOHCil9Oc3OzMqgDIP9ubm7OWWbPnj3Yt28fOgu22FUSm3iosN5QKwFN8aMIFyekHyPz7UTaMbcq6lAesR7VhALGANAavnLXknlMNKLUlGrMy0PrIJOQfky8sVMrXa+a6Ht7kjpEEibxuXRBWWLdqxKWQCIISkwBblvUZjJNWDvZliWpwVoNAsTKXCVLtF2VOp8OANxaYZELLFheujqw1oWWOjHHzgstKEKIWMqaaMa0aB6aC6HIIV7PRNRolWWOnYjQE58i+tTxVfeJ77gySlTIlECUEXOGNBeJlDURljoR9Ru2hScsSzJ1V9xiJ/tGWL8e4f2tyoYWL22+TXSdqahNwuYQEam6VLEvtot6iHr5Yn6ksFRqj8awkr5TRUSNze/Foj2FULGw1ERdXrR7+NUktZq4Qk/7BCIrmfIsUaLto0ndfphK0BYV6xNLvrDeUVHiLJ1PS9pdvwc6kSUqbpnTkSkDadotJQUXmUsoPqlZjFrLyhk9abHu2US75WFU7kTOs3Ri24DIemuySjIqI0aMwO7du5VtNmvdDTfcgEWLFuU839///ncMHz68bPXrivDAjmEYhmGYkvA9L3HwW/A5fR9uykVdXe/kwgCuvfZazJgxI2eZI488Mq9z1dfX44033lC2bdu2Te4Tn2IbLdO7d2/U1tais+CBXZnxLdp1dE6Lq827AlRtNIcI4Mr99AfTZrYu+SIqliYDJ+tCwBgAakNLXjuZS9Oeiere5kVvrWK+ne1tnb6dCzFjW8oxUVaZS0fmiCmiwuE6nUtXVx2fgwdEybap6HDajywYijZdGAFL59ihnUbCEgFik3aVG59XB0TWKWGlAwBfzqELddzStUC6Vlrq2sN7sje0kIjI5H1hLrHPDwTX8HkYJrs3/GzLeshqt6Nazl8M6ueFbSWjZzUrlhu2j7RghnMP/T0fw9uzMzjH58GbcnZfUMZrDaO0w3sqInCFfp0QXHZDSQBp+QsjtaVFyvchjEKt7cHWz/YHx+wKP/e2Z5U28bX+l5Zz7IJysp/UpOVtSYv5beEx8m6JCODwtyb6hC/mVLbFf4NAYJmUadI8kf4tPLu0voTtmw4jiR01sllcR1Zq1YntgdWuzfOldUed3ybMthahYZpqTB5jnmOnWCTD/qz8ZhUrXVRUTL0tJo2gvi7mUlJ9N2U9FT9OsWrRa6MpGcsdmaljsRQ6hrBWdd4dtc6F8yUP9lBYil+B4IkCLbH9+/dH//79y/LVjY2N+MlPfoLt27fji1/8IgBgxYoV6N27N4477jhZ5vnnn1eOW7FiBRobG8tSh2LhgR3DdDAnnzgSXk3wBtoWytF83hY8wPaGn5+3Bp+7w0HObjHAaw0+D7R7MUkZMdAVA+I+oYv7sNpgcN87HOjVhQOg3qnQZdjySfgZDOb8nR8h++l2AEB2d7Av2xIM+rL7gwGPH/6HFxkpqnoGA7dU3ReCc/XuF2w/PHizRZ/gwZjtFTx0W6v7YPeB8BoPBPX4ZG8waPx4b2t4repgVn+xqNaCQ/pUB9d7eI8MeteE15gR1xwOItqDgZu7LxiwuvuDSdkiJ7DI8EKDlShOdY0cwMtBexgw4sm/heh08CmCkkQOZ/HZ5ukDvvzlRBiGKY0tW7Zg586d2LJlC7LZLNavXw8AGDZsGHr16oUJEybguOOOw7Rp03DnnXeiubkZN998M2bNmiVdwVdeeSUeeOABzJ07F5deeileeuklPPXUU3juuec68cp4YFd+FItdPMUPAJk2is7FcmjUq+mtx5aZglj6hLXBzRBtO0WfLZp7V10TWuxI5Ci1AGXJXDaTpU7JBtEW1UfMi/EM0Xx0P30bVyNZozYTA5M+1VHbqZp25Bxijh0NSibZIpQI2HBuHY1+VbJJJLkTDFY6gGi2EW1B8U9fbNP/6e8NBzXRQC74WwxuPgk/xcBu976gnjTNm7BmiLmKvavVn7UY+PRMq3O+EM4rFHp+/p6PAQDtnzSjfceHwfds/xQAcGBXMLBr1wZ2qdAqle4dXE9tv6Btq8M+mg2t0qmefcMGCa1vAFrDc7SE1/Lp/jblmj9uCQd44eBWzNvUrb59egT9Q8wBdR1HDvraqtSoYmFhERkDxO/DawkGeN7ez4Ly4veo/Rad6hqZDs7pGZ5L7BPWM6FnKI9VrYbiU/zexO/D93NrmQkjmQdqyaLPGBLpKp5DFoudEhWbilvvSVB8opSYSYfONscuZbDOVSlz7Mxz0sS6YuHKEqHYYvXORFS0Kc1jCcgkIcq9ivaLSOmCUqh1dQ6ylGLz5s3Db37zG/n3SSedBAB4+eWXcdZZZyGVSuHZZ5/FVVddhcbGRvTs2RMXX3wxbr31VnlMQ0MDnnvuOfz4xz/GfffdhyFDhuDf//3fO1XqBOCBHcMwDMMwJVKJOXaVHNg9+uijiVkihg4dGnO16px11ll46623yliz0uGBXSVR3pDJfLtwvpUyF6vV7Pox5iWlb0V0v8xSQSxVyny7TGy9mmSmyBILWJbkqPQM3cSWv7U6VLz3lPk60XHihZy+pdN5dT2IBbEutBpSPTo6h860rkS60kjY9nikq99mnldnfOukVjqqU5emmnXCJUcsdiLqtbpXuC+wbO0LXa77NffcgXB+UJs2h0mJhtUQ20REca/QYlcXfkoduyo16tQJ3boOsaIBgOO6UhOxqibMiNEzKCvmjQlS4XdW9Qgsc+m64PqcHoGrWVi3TJGk4ppktK42R5BGSwPxeV3CYtdTXl8UBaxnnpDz2MLvl/lRU2o0rxv2DfnvSbfYVWVkBLvMDaxbXYRVMPysCr8rLSx04clFtxZydp4fWVNNhhw6P1Hg0jl2ZLvJDqTmio2eC8ICSC3rJv08/btNiMjstE+sdA6pr0OfFcF9M+V8BlQLl1h3iavasejJSYt7ma1HjiFrSuIx1jl2hrJF14xhVHhgxzAMwzBMaVQi80Q5ZWy6ETywKzP07Z1qRbkkf6sXZhlI1dZF+0mkq9KVhaXDECkLqBFgXmj1c6vo95J1mg9RWEuiaqFH2hyeLawf1DJHrWx7yRw7EcHYpodsinO58XPZMksIC5PNSteTWPJSoUVOTIYHoOaCpVbMcK6hR+bgUcunMRKWWulINLNT00uuC0udmFQPQAZJZMP7Lyx1LeHnPhkFq0bFikhk0YzC0inmIGaIVUq0n9D4E0EE4lO0WY3MPCGuOexTuvvETclrrOp5QNnlaRGHqUzQv4Slzq0L5tKJXLiO1PEL2yy0MHlkPpnoEz1kFLQahW2at0mvW9c7rK5y7Yr+Wl5Uv0rkuK0VFxj8LcrpfaEqHVlpxe9bzzPqqxajVJgvOJzuBxG8K34CwoJH74K4tybLHf1puWSOnSK/ZtBiU3TsyH7xW81arOyJWSZIHSPtOXo1jmF/ZJ1XfvNkrlsmRY8L2yNrtqwren0G7c/EAYdNm7LIjBWs/M90JjywYxiGYRimNPxKBE+wxa4YeGDHMAzDMExJ+Fmv7BqCvu/z3MMi4IFdmRAuWF9xYZCJvkQcWOpetUcuO7fO/GbihbpaIC4K648nfFtSRIvp5H5DKiKXuGdoGvReIkk9gJQrJoFTcVEqNUKFjSPpBlktPz55msoW0Bz2GTd+XpoajAZSuK2R6LAbiutSSRdF4oRsl+3jGdw30AWIQzkE6n6tjtpGBMIAkQtWBEoAQFsq2H9Ac7keCEV5xadwZ4tyos3EpctJ6eF5q11XurWES1Lo0wmZGJFyLRY0EbqzZBo1/S3bTUnJjlSo1yT06jwRcBH+LfeHLlcpUCxTioV9XQQruPFHTkoLohD33fOD+gs3XBuRM6Hle2jBE2nXlS5MR/4uwz4ogifCYCYRROSnwusS91Z3xYpPNxXp2BmuJSgr8sAJaZXQJeuI+omCqrvV9yO3p2lyfSRkbHaZwsntQqS/STpVot0TunrRfjWQIr5dkS2hQuFi6oDlEWWadkEDpujvO2N43jhkyorqliXp1EwBZxTdda7h0P3KejwAyBZIYXqa02deG0Q75q6qDTrlx6lg5ChzcMIDO4ZhGIZhSsPzkjVAC4UHrUXBAzuGYRiGYUqjEnPsyj1Q7CbwwK4EYvpV0DMukB3U5SlSD5FIV5rT0aU/DqGdtD9yOyoRsqaOT12MNDUSjfYyuBBsXoGeYbSs4orNRuutBrcObQelGcJPkwK9/h0im0SaNLNwuQJRjlMgcrUq0a9Eu07J0NEWd0Ga3K8A4IQRjTTDhE/c6iLCGYiyStDME8LVquvW7c+KFGJCt06NgtWDimU0ociBmnJku0XRpKoLVrSfiIYVuTmdA2GuXE/TsfNEmrAUHBFNHbqgRfSlWxW5JIHIRS1csMIlK6NhZeSocGNFrlHR38QtFxGRQgsxK92SYYYCT3VPCxdgtcwZG2U1seXgFK4zR3cNi6jnsN/Ko7UIV7ip6Lcs3LoWd5yM1Aw/U+H1xfXowjaBnzPzhEDpGyRk1VM00+LH0ejWdvJHlNrMnB+WYoqQp+sI70HWkh+WlhX9tRfJckOneNB1+Vwg7ldb5hgxrSIfsVyRccKpopmByPQVsi4iupX7Td2yBve1yY1NyfK4hakAPLBjGIZhGKYkgswTB09KsUMZHtiVGdoNFV0ost0VuWLJJHslwIC8aQpLiU+CJ7x9kT6bLQAg2k+zVJC3XVe1OujrrqILFVh2aoglKp2O3nAzKZpjUk1sriOV9cm2tOnNHIAb5vFU8t1SK52yPbTY0bd4GkSSmFkibqUDIkudT3QIfSL+5xuyTBwgb+H7Q4tdq6dmmGgNt4tJ7OJTD5rwXDWDgkssJjWafl1ddahxJyx0rho0kfJCq4bUrwsnnOuSAq4btYfIe6pPxhcWu7B9ZOYGGTQRbPdSmmVM5jCNTmUPnhD7g8+01qeE9aeGBE2I84luFLNcada2SNcurF8YROFD5HttV/eDWGxkhIbF1q1Z7BzNGijqJi14cOCGZUyGHF/uM5v16CR6079X67NJVJMUsP0/Ff1PzSBBnhtSeDE6hlrsqkm0gOivSs5ni8VOPgtIQBSy5mdbkjtQyQur6RoC2u+frMs+bLHuU+tbUoYOE75lPfG4rhJIUYGUYj4P7IqCdRQZhmEYhmEOEdhixzAMwzBMSfieV/5Jg2yxKwoe2FWQrOLaiP4QaaN6kNRT1PXp0UCK8JM6X2gaISU4wpJ2TJZVXBeBW85JkWPo4UqCbZGlPNKKShHXZIrWJ3TdmQJLKNRloOhRtZOAhjD4QdGjo5On2/bHtvvUVaOkFCLXY9D4Mrlfgci9qujVEZc0ddFm3eAcB1qj720NH3TCBRtphnnKp811I9yNUv8v/Ls65cZSadXI1GGqvp0ImkDYXjJowuaKBZlMLnXcdNel5oqtCQJHRDCFnxJpt8SnNvHcjzTmnDBJvOgyUp9Pu01paH+LtiBBE0CgDWbrfVJvUrpeq9TrDLeL4413RVRUn85gdcmKmfRBO7uh3pz05PpCn48EjISH0jsj1l1LgJaT8Juz6d9JF6+lD9LTiq5EtStTpJJpQxOkLZqXen8FVLcuXXf2h65YS/CER/Tt5G9dCUIzpwYT/ZwGTygBE3RdBL+QbVTL0CONGrnXaUBK5QcpSc9dpnvAAzuGYRiGYUrCz5bfYucnJSpmjHSpOXbLli3DhAkT0K9fPziOg/Xr1+d13NKlSzF8+HDU1NRg5MiReP7555X9vu9j3rx5GDhwIGprazF+/Hi8++67FbgChmEYhul++J4fRsaWb2FXbHF0KYtdS0sLxo0bhwsvvBCXX355XsesXr0aU6dOxcKFC/GNb3wDjz/+OCZPnox169ZhxIgRAIA777wT999/P37zm9+goaEBt9xyC5qamvD222+jpqYm4RtUfMeBSWxKeist7learkeY513iKaixuWXFNpLaRtGgoy6GA+H2fCKThJsiS1L00P0mkz6pl5MlKXxSUTdy3ANiJdpP1nVtr9h5qTs5rJvifqGRvaTuMKTHUlzPhjRCSnSc4n6pJuuBi8YaFUvcssLtSu916HWXL7IiZVgUBRurFgA1TRMQuWKjNGuRK1akYZLRsNqn6wftEEslJtrHlFLM1fW9NEeoiJYNXa/SJSvTbYVuKz0a1hERr/EL16Njs76MH1WOidzSYc1ENKwbpehynWg9J3qEq9AqE7tFOcWV75qvrUBkVKyQaCPu6VzRgNZfd8I/QTXNXx7n0+oJRG1Mo2LdLC0gysWPAVQdO5EqUImKJesujXoX0wiy8ecDoGmCZlWtRQBwXMuzQPbj6HdMp2I4ZAqGJ6YVUPcreWpmyXMsK3/38ehjoGPcskz3pUsN7KZNmwYA+OCDD/I+5r777sPEiRNx3XXXAQBuu+02rFixAg888AAWL14M3/dx77334uabb8b5558PAPjtb3+LAQMGYPny5fjOd75T9utgGIZhmO5EJYInfMP8XyaZLjWwK4Y1a9Zgzpw5yrampiYsX74cALBp0yY0Nzdj/Pjxcn+fPn0wZswYrFmzpqIDO9olqep41iCzRK1w1HpnOhe1D9DjhDadkgTbpukkLCrEEuG41CJnSKRtsbIp3+G0ipORbQZrmeV4hwRoiGANx6ZRRRN/G6yUikWOErNGQbHSyWwJiCx1SvAEKdtOXrz1AAkguu/6p/7GHunTwbw9JfYHBXqkU9J6Z9OtE9YREXziWPTrcrWd0HPTtwtLpissdWnVmiEnmGuWu2hyN9FtdPRP9VpF7xfNKi12WtukyPZkS52mQeeoVjiZjUNa5Ugb2TJP5Dlx3dAEMcw6dnmdXj2P5ZgkfTAlYILa8oVTwKBDSI9TrHxkvymzBNWuS4Na7w/E12mGiXZDwIQNW2aZqnhmGWGFBkgQEFmn29pIQ7RnDb/7MljpIm3Lrh0YUYk5dtYOzOTkoB/YNTc3Y8CAAcq2AQMGoLm5We4X22xlTBw4cAAHDkQPFhZKZBiGYRimq9NpwRNLlixBr1695LJq1arOqoqRhQsXok+fPnIZMmRIZ1eJYRiGYbokvufBy2bLurBBpTg6zWJ33nnnYcyYMfLvwYMHF3We+vp6bNu2Tdm2bds21NfXy/1i28CBA5Uyo0aNsp73xhtvVFy8vu+jvS23Tlwu6CRaU1d1iQsilY5uS5pMzhdQY7fiihUJzokrUZlgbHK5GYIKwsLkC8PJ93Q3PYcfd1MpAROG0zs2ty4NpJAuQ6J5Z5lzId3QdGOCdpWSJoq6X2lwROh2pcETHnHFtpJICbHaZpgwbXO9uo70LwLQ3F6I3KnC3VidijTrbMESegox2Y7CjS1TXAlXrOqud9wU/HCyeax/hG0qXbBa8IRsRz1huugXQtKNfp/4dNTPaAJ+7uCJlGxLyE9XOTIHWn91HNUFq0wN0I6xfprS9RFy/a/S/5HZAh6KIemfpOuYf+E+2ez4IrjDfA5578gxNGCiimrayeAJ8qygKQOJTiVE4I8y/cI8zUSfLgAg0mEEVFdsrZoCT1/3FP3K8FlAnhWq+5V8nQymi7tnGabSdJrFrq6uDsOGDZNLbW18AJMPjY2NWLlypbJtxYoVaGxsBAA0NDSgvr5eKbNnzx68/vrrsoyJ6upq9O7dWy51dXVF1Y9hGIZhDnXKLXUSLDwYLoYuNcdu586d2LJlC7Zu3QoA2LhxI4DA6iYsb9OnT8fgwYOxcOFCAMA111yDM888E/fccw8mTZqEJ554AmvXrsVDDz0EIJAPmD17Nm6//XYcffTRUu5k0KBBmDx5csdfJMMwDMMcYlQkeIKtnEXRpQZ2zzzzDC655BL5t4hYnT9/PhYsWAAA2LJli6LpNnbsWDz++OO4+eab8a//+q84+uijsXz5cqlhBwBz585FS0sLrrjiCuzatQvjxo3DCy+8ULCGXSnQ7ilcdfRlhJpOUy4NOQsisNJ6PiWBKfqURpESd6PiVjK5NBMiWW0uUZ+eV7q0EozBNpeqEhVr0Lyz1Vd6vyyRsFRXT7QJTYVmcL8q26n7xTO7X4SrRXGfIR4dR4kiOdXtejSsiIAVkaKZlGN1wVYJ11dr6IZvV91YUoPQ1q6uq6ZVIkg3th5NKFyvInWYnrIrbOt8IgNl9J+8ZWFUIFQNtZjrVmxHpAenf1oRzxRfdKSwnjnShenRsLrbWZLw3bmMEjZdzHIiY48tkaz0a/V+CqiXJ85FteuqFB27qKxwwbo0NWA7XaepBMM+S6eWmOQFKNTlmib9mfz/kFMKaiMlAkWz0rDeTp7WbR6dihF3u7bloynKMGWmSw3sZsyYgRkzZuQs88orr8S2TZkyBVOmTLEe4zgObr31Vtx6660l1pBhGIZhGB3f88tusXPYFVsUXWpg110Qb8Dt5FW4lU5QJuJoUnufWJEsNqmYej4AOek4WCdH2vTtjOctTKcLgN0alFBWCboQq6Sd6H4l0MLwfb7NAik02qhGlclKR8p4xGKXbY++S7HeCUusITxGTPT3xEz0lPg7+HS1thWWubRmhUsTrTpdv06UcbNq0EQUPKEFo+htFn6n46aizAvC6iE/wzJCqT9sFz1oIiqvBlHI5OiG57ULYfkSlk/dcqeW04Msok/HoI0X/7588HNZnhOCJmIWPC3rht4Gnh8FRuUyzuXzy0qaPG2yYNqfK+S8CRY7kwU6nTJb70RmCdVKRzUrqfU+eF6ZAsCCk9FsEqE1mQRPOAbtOoAGT0RzvE0BE8FFBce1J2jXAdGzQNWxI9Xt2pJ0ReF7Xn5Zjwo6KQ/siqFL5YplGIZhGIZhioctdgzDMAzDlIRXgeAJly12RcEDu07A5Iaiv4d2h7how+0uKVBN3IaKyVW4fKj+kxJgYEjXVWQuPqubM9c2Gxb3qmwFqnOl1DcVL5tPHeVEf5o6LJ46iJah7pc2i3vFROReDP5OCw+lp/pihBtRT5cltudyxUp3rXDSZdWgCV2/Lu6CDVOsyTRigONprlepDRYmTZeaXmGapSo13ZIeROGL4ImsvcFkMIT2t67tJ9B17OS0BUSuQP1IxyrAZtazy+nUsAVNWIIoZDo1XavO/g1KwESp/zKp+5W6Ak0zLGxXLc6hpgyL76fadS65QlOaMOp+pakMle3h88rWVkrQlOG/Gg0GoinDnOoewXkV92vklqXBE1k3eBa0tkbTWEyalQDQljWnDtRJ0UdUzpIMkz88sGMYhmEYpiR8j3PFdhV4YMcwDMMwTGlk/UDLroxwSrHi4IFdmaEuiiRnJO2zNKKKuvqcrOlcCW5Z6gKiUbFKxFl4622pvQohl8ZXoadKqIOfVEeqV2d1xYYuR4v7lWrWeU5QNkseWPRho6ybqqO5E+PpssIqQXW9inJpLeKVumLlNuF2bA+jYbX0S06+bnfhkgWAtKpjJyMJZRSscMFqf4u2E+0p2loE4kqtv6i1hPtZ/Ab06FgdXbcuJdvUkZ+6y1DqsvnCLZ3nPwyT+1T6jLVo2BCbvp2eTi2ePsyXZcplqMjH/WrSnqNuQfU4kys27nZ1vMilSiPwHfI8kinDiPtVeV4l3CM5PQB6irp0bD+oK5a6XcNoWC/dI9pm0bRsDZ8B9Pls0rHU102kDP5vGhnvWLYzTD7wwI5hGIZhmJIQacDKfNLynq+bwAO7MiFf4pUX++hNS3mrDQu1KZkcov2qHlLw2Ubf5ugbNHllTKeqw+/KHTQAINKIylqyURRiAje9URZpsVO+tZgftc1i58bbQbHSkcnXimad0KNSsklYvjr8VN7GXTUbhR4IECVZUC12ujVKJE+nFjwRPCEnoev31KZXJ788DBQQ1jiZKJ2kOdH16MRnldkyh1jQRPCZ1bTbPMQtVsIKJNraZNUAqMUu/BSWOtJm+j75+9T/8dgCSkTfEduVH11unbrYObTtnm/5RGnQb8k3yIFuV8parHeyPemzy2CdUzPIROuqTl28rJPgQXDI75ha3B2X6HIKy7OibWfWphPBET7RsaPBEzTLhLDYKTp2Bh1LIAqayCqWaVJFGRxlfq4fjPieV35XLM+xKwrWsWMYhmEYhjlEYIsdwzAMwzAl4WfLb7HjqNji4IFdBVFdH/F1Nw/lIuGWoib99qTfjhu50lzqBvJoSjGR+snmii1fIEVB+ymF1CFBS883BU8Q17RPXLHGNGF5PF+Ee8XXpnLTj6isutnVXK+RezH4WwZKkBRjInWYnJyuB0tQdxjF5jpMxV34st1Em4WuVmjBErqOnSjvhVcYuaaC3bk8/TaXlN5mMRcsSSkmXYbinLpbuoT+7VuCJvR2lUETYf28CvyTMrlfARo0Yt5Pt+t9jW4DNLer3ucApS1j6esAONl4arBge2usrDUllWjbFE0TRsumY2XVlIFkP9nuhW5Xqlfn0YAJEikhVm3adaZ1Op2APsOlbiV5MNB7eSimHGM6Dh7YMQzDMAxTEp7vlz14guVOioMHdgzDMAzDlEQlXLE8sCsOHtiVGdcQ/QqoJnmx7rs0iio6h2/w5NGIK9X9Eq0LFy39MVQRd1GKuBgc6b4j7lnbj6hUl6ixXAf4GhI07TziBvE8871I8p4p7i2h4UW26U0aueHV43XXme4eS5FoWACBGzZ0i1mjYe2VDoq5Bi1DIGgfWVFXKau7WmVkotgeuspEZLGMKvbi+nWxaoWfuhtKd1fHdOwMkcSu1m7ShRh+v6O7ZENLg1XrL1e/jkXJqhdg068z6dl5fnybjq0mJp26JPcr3V5Fq01dpl7clWqLepXtaDgGMEe95tPmvmGbrazs37Y0gTRCNiNSikWRsK0kBL7VEAFr06tLSh+WJs9isa7MflCUD9gXyxQPD+wYhmEYhimNbBZ+NptcrhA4eKIoeGBXZhzLW3GaWOdaw82+8oaWuwPTvfT9VrV8h6Xoqzv5YaiGwPCNkVrx6BtnhYIn/A58E7W9QHvSYmKzmObOJuFa/vBDLcIU7BRusQv3C2ugsDxl22IT1WNWKA2Z4D52i1LxcjJAQNWj0y11vmapk7p1mqVOGEFEzTw/rtsmLHLi/uiK+/lY6oLtjtwmLdN62yQFUeTq/8LyYtOxswWnFIDoF0qGjhzlgvVou94mgNlKB0SWOquVzWSdy1q05/T2ju23BGsJaEAKna/lGH5VVNOOanTqfRZm7bpgPdjeRn7o1GLXTtbbZJ8mVbQ8ZKQlziP/A1LxdcWTQ+O7LIF3XRmf59h1GVjHjmEYhmEY5hCBLXYMwzAMw5RERXTs2GJXFDywKxOOoQOaUvEAUVoo6gqkKcWM5yfriseUnEO4ApXUNsSOT90Gojq2n41TrDFXmyiuUwktr2KxmflNVVS82+RuUAeRbwieMB0PxLXY9GT1UQosbbI6/Vu4YGXQRH4T//2kCeiOG3e9Cv063c1lCZZo11ywMmDAEBwgrlVs0oMkdLe1zQVL3VtRMnpDuwGRS1APmkiYgmAKwrGV0fXrbKnE5HF5/jRMenWAWQctyf0K0OCbtvg2wKxJZ0k/KI8rYCqHtT8adBVhaX/VFasF9kBzvxItvLaw1RT3K7kximad1odzIfowkcc0Bk9UkcuhbtmDxPvKdFF4YMcwDMMwTElURO6kCxkCDiZ4YMcwDMMwTEl4ngePB3ZdAh7YlRvigkgRO3wV6Z9VoU9EjW4tvQObTpEiTtEsMfCLrR5xQZQjjY2pDqYrK8fvNam+5XBnmM6Rsp1Yi3ANNqmFbanEXOnDDl2GmiZdzMXltcfTL2k+IhkFKyuT4JIl6cPkPt0Fq0W/ilRs7ZYo2FhUbB6pxGwRw7GoWIMLNjjet7tgbbp1xUaBw65fVwziWkU7Jbn1k1KGKWVpmjDidk3SpjOmBLNFvZrasRBtQJur1eSKJc9XOVUAiKW7C9Yjt6wpArbN6n4llyEju82dWI/i1rdliP+7Kqx62uIqN52LYfKFB3YMwzAMw5SE75Vf7oSDJ4qDB3YlYAqYsO2nb2Npg6mJpmsv50+DnsulKukGW1TSTygfK5spIMF0PeX4vZpeam1J0ZUyhs2292NjWcvkddO59EAAPRm942kWI5uFzpApIWZtkvVKssyJymgZE8Tkbrcq2qcHUYR/i/4jLHJtWTWbgs1SZ+ofut3Gpu3n5GGpA0RgSWiF0tsoa9H8s6UIMZGnfl1SxglBvr933XIZrJstPlFbESsd0aYz6tQlWekAc7aIJGtnPtkixKksFjnZpqm4Xh2gBU9oAT2AGhDRatCma7cET6iZJeKXoFjWiFZpWljLLc99sV5FbppiUaX30vCb6Ug90LzxKpFSrMwDxW4C69gxDMMwDMMcIrDFjmEYhmGYkvCzPvxseV2nbLArDrbYlQvfA/zAPSYXr10uKdeRSyYVLGk3WsQ2fXvKyb04ZHGd3AEFHll8348tWS/3opQlS7sXLVk/cL+1edGSJUt71kd7Vj1vW8JirxPii0+XqI607p6f260s2tF1INuW3r+UA7JE96LKDRcHckl5bUh5bXCzweJkW8OlLZjAHi6OXIL9EDp1YsmGi+epblgTjhMsbkpZ/FQ6WKoyyoJw8atqgiWVgV9VrSxZN42sm0abH0w+b8164eKjNUvve3jPwr9FW4u2p33RRdx1TvuxaGN5D8K/0ykH6VTU7i58GTAh3LDRutp+4rcJufjmeQG27W75H5miHRSvnhNf5G0lx5jK0bYTbUPbx/HayfV7pJ950eKbl3j75fGfV6So0xZfLKJfptKBGzVclH6argmWKrqQPpqulYvoq6Jv6gt9tojnEd3mKf3W3hWA6J44DrRnRLDQZ3kVWTLhoj7rSZ/3fbkYvzdhP5ObDz74ADNnzkRDQwNqa2tx1FFHYf78+WhtbVXK/eUvf8FXv/pV1NTU4Etf+hLuvPPO2LmWLl2K4cOHo6amBiNHjsTzzz/fUZdhhS12DMMwDMOUhJetgNxJhQau77zzDjzPw69+9SsMGzYMGzZswOWXX46WlhbcfffdAIA9e/ZgwoQJGD9+PBYvXoy//vWvuPTSS/GFL3wBV1xxBQBg9erVmDp1KhYuXIhvfOMbePzxxzF58mSsW7cOI0aMqEjd84EHdgzDMAzDlITvV0B3rkIDu4kTJ2LixIny7yOPPBIbN27Egw8+KAd2S5YsQWtrKx555BFkMhkcf/zxWL9+PX7605/Kgd19992HiRMn4rrrrgMA3HbbbVixYgUeeOABLF68uCJ1zwce2FUS4qZwiW5UlYjaIn2WelAdRTspTEdUkQraKSSS1TPU13auYn6nJCZPDVakKdLkd5mjBE2o0av0O0x6YCQSlsY1+jS6UESsJkQRku2xT4EmG5Db9ZoQBetqUZtCg47o1tH9vlsVRb2K/uebderE/U6Kfs3Vf/ONghVRg8YoWMCYbi0xClZvV1sEp6GMrl+XbyqxnKfO0Wd1fT8A1rSFcl3pi4bUXwCJ0G43ly0kAlYeZNCg07bH0tXF9sejXhVtOhI1q0Syhr+dNlJVGt1KI2BlPyZVT3pGuaTVdfe5vp1GwlbR9bBTV5nuGaDeN3lSc9+k7tguGS17kLB7924cdthh8u81a9bga1/7GjKZSAOxqakJixYtwqeffoq+fftizZo1mDNnjnKepqYmLF++vKOqbYQHdgzDMAzDlISX9RXB+3Lge4GLd8+ePcr26upqVFdXl+173nvvPfz85z+X1joAaG5uRkNDg1JuwIABcl/fvn3R3Nwst9Eyzc3NZatbMfDArkAc388/o4Hy6keU2sO3sTR541SSeZO3T88PrQH0TAX8dio1R6FYK51XYn1sb8smlK+yvFlH5zJb+sRbtGOzdiRZ52hZUiZvi1weFqWYDp1NVy2HhQ6IrJ1eGHACFG6hy1uTDfbMEnZLnTg2h6UOiIIlQNrVkDHBiNQETLCCdhAmXUZrNglaVtdD1NaVzBFalhN9fyIF6NEpenOiv5KsEOr++HHtpIpZankjD6R2rc/q+5Xt0iIdbbNnlghXDJY5QH02VYlLI/cnQx4smXA7tehRnUH6POisvlcoFckV6/vYsGEDxn31q8r2+fPnY8GCBbHyN9xwAxYtWpTznH//+98xfPhw+feHH36IiRMnYsqUKbj88svLUu/Ohgd2DMMwDMN0SUaMGIHdu3cr22zWumuvvRYzZszIeb4jjzxSrm/duhVnn302xo4di4ceekgpV19fj23btinbxN/19fU5y4j9nQUP7BiGYRiGKQ2v/Dp28Hy4KRd1db3zKt6/f3/0798/r7Iffvghzj77bIwePRq//vWv4WpSRo2NjbjpppvQ1taGdDqY27lixQoce+yx6Nu3ryyzcuVKzJ49Wx63YsUKNDY25lWHStGlbLzLli3DhAkT0K9fPziOg/Xr1yce8+ijjypabo7joKamRinj+z7mzZuHgQMHora2FuPHj8e7775boauw4GXlIjTLXD8rlypN50gsQrNL2eZCLqqmWqhdRfWsXMe46G2mL1QrKwl6XDH4CUuxUI0p5foQeFOs2nS0nbxssEj9uVb1XhJtMLlfWUL9tPbWYMm2B4vXbtcVy1MjTOiAwQ0XoRHmVgFuFXxtkfpgqQz8VAZeKg0vlUa7H7i4hI5Xa1bVJqRLpE0YaglKrcDAxeUnLBRdny3SBNTug/Y31auzadbpZYw6k1SXzdqJXNXVaNFkU/TZxPUiXDQNPxuuYTG1Vz6/9ZhOXxCyWHAfs7aHSY/OcaO+RvUTlT5IlwyQyig6dnTxyCL0E2390qSlqWheWvQtTXp1Lhy5OIb+qT6Lc2uR0m1Cuy5DttP/AQ7Rsyz6/jB58eGHH+Kss87CEUccgbvvvhs7duxAc3OzMjfuu9/9LjKZDGbOnIm//e1vePLJJ3HfffcpwRLXXHMNXnjhBdxzzz145513sGDBAqxduxZXX311Z1yWpEsN7FpaWjBu3LhEH7lO79698dFHH8ll8+bNyv4777wT999/PxYvXozXX38dPXv2RFNTE/bv31/O6jMMwzBMt0QET5RzqdS4dsWKFXjvvfewcuVKDBkyBAMHDpSLoE+fPnjxxRexadMmjB49Gtdeey3mzZsnpU4AYOzYsXj88cfx0EMP4cQTT8Tvf/97LF++vFM17IAu5oqdNm0agEAVuhAcx7H6tH3fx7333oubb74Z559/PgDgt7/9LQYMGIDly5fjO9/5Tkl1ZhiGYZjuju/5FQmeqAQzZsxInIsHACeccAJWrVqVs8yUKVMwZcqUMtWsPHSpgV2xfP755xg6dCg8z8PJJ5+MO+64A8cffzwAYNOmTWhubsb48eNl+T59+mDMmDFYs2aNdWB34MABHDhwQP5dVAejkWcJWlB0v0P1m4RGlhJZSk4F83a535aSxlRdei5SwhWRY0oEWO7vVb7LEDlGo2OTHLhuHi5eU8QgraOy3TFtI9FppghYS8SaEvWqR2ICcd20QpGhooYoTaH/pUe/iv4j+o7QAAujDGWka/ipR7pmPV9Gt2Zl9dWoV3E5SVHOol3FZVAtNn2bfl/0qFgZNWiKggXU9td06nK6W03ofS5HKjFdv66k+QOxasSjXm06dkq/NekjKv22CG06QkHadAY9OrVsFBVLnztthqhXU0RrsD++vZBofNsjJkWfg+KnSPdTHTpyknTYJFSnjkbFVoW/JIfom6r3IWpTcS/ziY4VfYD17Lo3XcoVWwzHHnssHnnkETz99NP4j//4D3ieh7Fjx+If//gHAEifeaFaMwsXLkSfPn3kMmTIkMpdBMMwDMMcxHieX/alUha7Q51OG9gtWbIEvXr1kkuSudNGY2Mjpk+fjlGjRuHMM8/EsmXL0L9/f/zqV78qqX433ngjdu/eLRcxUGQYhmEYRsXP+mVfOjzl0iFCp7lizzvvPIwZM0b+PXjw4LKcN51O46STTsJ7770HINKb2bZtmzIxctu2bRg1apT1PLqyted5+HTnJ8VXzOR+JW48RUuXlg1dFw4xwzsmhV1Y3GPEJK9+h+l4s3tUmPWVVGfkbNTq7/uGsuS8nnSzkXoV4MqlmFyttKzVZSUFcImbhV6PIbVSkvtVKWNKB1REKiYT0h2TqjK4XlUBYl14WLizPOJyBYgLlriwZFnxvRbXq+3WJbvXo/unCxCLv6UbV3e9WtKxKe2flMrN1s5JwsQiEhmwdkzRRvoUBV/uN391cEr1nIVMIaDp7Yp2QRswul2VHxpJHyZcsDb3K0kJ5oXrqrhwVF+TwLBNdJhepZxiYL4cY/tbHqlGtyvtEVXEvZrkiq2iIvWhC9bJthq/V2kzpIxlGMZGpw3s6urqUFdXV/bzZrNZ/PWvf8XXv/51AEBDQwPq6+uxcuVKOZDbs2cPXn/9dVx11VVl/36GYRiG6W74ngfvIAmeONTpUsETO3fuxJYtW7B161YAwMaNGwEEVjdheZs+fToGDx6MhQsXAgBuvfVWnH766Rg2bBh27dqFu+66C5s3b8Zll10GIHgDnj17Nm6//XYcffTRaGhowC233IJBgwZh8uTJHX+RQLL1jlrnRMem1idLgm2xnb59+hbLmHjRpBYYXwkgIOcQVit6CbayIt2TwYqnl9WPyQdq0TAl3aZvzabUYHS7Yhyhk8lpoIQ+QR8wW+nocQnBMUAeE6G1oAnFUhdul2/1CSnCbOnBdKuceCZnfT9moSv18aonsBfagkDUryLLnbCMhH0pV+owgKTEiix4VktdvuRK02ZL2SbiNbRTJQcXxc1Fpon6ensF28i6EhChBo/o64mWPKtFMx7MYwqUoAERNDWYRyx2ov+1kwaiFrk2ul0L4tHLKs8xc82j6tA2M+ynzwrXsN32XKHWOZEqzCVWVBooIS119LlykKQOY7o+XWpg98wzz+CSSy6Rf4uIVZoXbsuWLYpC9KefforLL79cJuUdPXo0Vq9ejeOOO06WmTt3LlpaWnDFFVdg165dGDduHF544YWYkDHDMAzDMIUj58WV85yFJEZnJI7Pts68EHPsevToac6wYJrTQt/QTAm4LZIBBc1psckOyP2557LZ3nQ9g8UuH5kVUzJ4m1SLfkw+lNNipyRQ9yxv1h1tsdPv90FqsRMtK+aARVbS8G83uldiXzplsdhJ60aeFjsvm2yxs82l0+cp6pIyIoMCLWNpb30uYz5z7AS5pDVs/dql0hmiregcLtKHnazheZTP7/AQsNhRuqrFTmm/sM3UeXe58R0Hvu9jX8vn6HtYv1i6rHLzycc78P6VV6GteVty4QLo9+1vYfDMS/NOKcYEdCmL3UGN+FHSfyBJpnWLO0Rxeco1ci76sHASvo+6ag3b83Hbiudr0mAu2O7kVVa98tyDz3yCJxwn4Z+eYcK5Y3K5Amb3lc11laeLL9n1mv+ADkCUtknfhvwHdFk5eINS3vftAzp9UKJPOtcHdPJyyIAOgEwdFmxTXYwxF6wewCIHcHoQRa6oBHP7Wwd0se1V0XahD6jp13lacEox78x6e5om9ds0FxXySEcnzyHXLMeYXiTpQIEGSogBScJgDogGdG0GDbqgLKmvprUIJGvTKZdg2W4KiKAvjymyQ7xwpOk2y8BODrKVF0YyyG4P1ukLCB0gK9stdWcYGzywYxiGYRimJII0YGUOnmBXbFHwwI5hGIZhmJLwPb/sAzGeKVYcPLArM2qUaoKbNA83nkwnYy1hdtEa66DsyN9t6+opk2CfN2eaR5TkqqUkPRdsEW2u5vLTy5o068qh8aW2ozhfHvNZdP+y7nrVozGpKxAA3FRMvy7JBSvaVr/qXA9PUUtRwjT3K9iuul71KFjqgg0+7fp1sbmKts8clN31bWh3kaLNFg1bKEnRmkattYRUhQVhm7tLSdKpC9skyf0KRP2S9j/f9twwVIW6pKlbNmWYu6GmNYyQ0wAszwrT1A66rYreMzrHUejU0SkenmVuLsOUGR7YMQzDMAxTEr7nwyt7VGxZT9dt4IFdubFFutK32gRV+HySPeeNdXJ1aBVRJmJbLIyGKF6qpWcKwCjEoqdWt7QIWdWKR67NHJGSx5eoFrMAmjGEtJNnv6/We6pb5rTvjWWVEO3rRpknhKXOlvEg9pWiunrwgrQ8+ebI7xznEuX1DCAxyx35TrEurX2xIAnLhch7IupbpR2Xo+62aFhXu88GSx2gtrs1G0dCH84ny4HJAi3WCurLtqh5EhXgm55DtuP0fgkogRLSmknqlbU8CwrR93Olnqe5bEr5rat9MdhGqkuP0wJ36LbgvIayNEOPYpEjgRIJ1mXj8yAfiynD5AEP7BiGYRiGKQk/62k2p6UAABajSURBVMEve+YJNtkVAw/sGIZhGIYpCd8rv0CxTYGHyQ0P7CqIEkjhKjuCj66c3NkksmvZr2piubGyDnFtCC8GdXEorqw8XYA23AKON2t5QY1H8eP6hI7VrZoU6ELII9l8+GXK31RfTQazWLTlhIs2JYJF5HeJ7WG52CR2u7KZzUFkcn/RM0m9MOIy1wMulLRuyg7V/Rdzczlauye5FnP8nY+una3do2qL6QhBAXk/LOVVl6Fj3ZfYtxWXaVyH0DFMB4kdZ9hmFEGn7lelrKrpp6NM2wg/PdLfUqTGyjPC4IMtpC+a3NyA2RVL29mlLShEn/MIWBFtYm1zU+VpO1val2HygQd2DMMwDMOURKBjx3InXQEe2DEMwzAMUxKBjl2559jxwK4YeGBXJnwtsi/4w+LOqPCE0I7QSLK6B0wRtAmuVhrRVs7fcaJXlt4fy/aoLYlrpBLtm5TyKke5KAJVjY4Vrizd9SqIe8tUF2I+2KJnk1KNBceGn3pUqXBjRTUNCwrduIS+l7PCenua9QT1ukSuWnuniq5RPbdnmH5gIknHzvyl5j4sN/v0t1WkS8+QpzopRzW9zz6pGXWpCg04k85lPuRyYcf2W+pm7IMGPdCgTEL/Utop7KuFTLcpIAc4w+SCB3YMwzAMw5QE69h1HXhgVwKmt3e6zZqgO4+3/9ghBVhREq1p5aaMb5Qlxk7kRZJ1VS0bD56o6Bt0CecWbacr79u6jt2CRGxl2sGFBKeY6pa7kGYts2yPHWawqiaRd2aKXEU0S6m1XAHCibnatygLtNH6XDz5TuRXMjmQ9jG2hWLdSzhvHt8dC8rRz6FY58RnkdlMiv29shWOqSA8sGMYhmEYpiT8bCXkTniOXTHwwI5hGIZhmJKojEAxD+yKgQd2BeI7Tt7+wkJcrcWeqxAXLZv/S+AgbrtSuqEpoXqHkWebd6TOl+P7sd9iUhNVsg2tUz8MbVL0FI0C2lfUwTcEKADmtijH/+6Sm7gc01eK6If5/I8o6BnPMOCBHcMwDMMwJeJx8ESXgQd2DMMwDMOUhJ9F+efYsbWyKHhgx1SEcrqhy82h4Nroyu1bKMYo5S6Myd3YFcgrIt9EBVzZtu83tVlHN2NB7dQFpmAk9TN6DV2tTzKdAw/sGIZhGIYpCd/3Y/JIpZ+zrKfrNvDA7iChK1szuupbYldus0Lpqm1cTmJBCV38/hVilepoCrHydCSm7+3M9jJaEDuwbcrRh7pCf2O6FjywYxiGYRimJLLwkS3zoNgrQNybieCBHcMwDMMwJZH1g6WcdHGjfZeFB3Z5IoQSKyWYmGT+78z+nWjq70JuHcrB/EyItXk3fMLZrriru2ht96orucxMNey0ds3jezuy7QpphYq1WRlc1pX+n8V0XXhglzfBj2P/3pZOrgfDMAzD5EvHDOw8vxKuWKYYeGCXJ47j4gt9+8JB/pknys1nn32GIUOG4B//+Afq6uo6pQ7dBW7rjoHbuePgtu44ukRb+z58+HA6SLKlEq5YThVbHDywyxPXdQF0rqaR4zj4/PPP4ThOWB+mUnBbdwzczh0Ht3XHwW3NdCY8sGMYhmEYpiQq4Yr1D+qZ0p0Hv0owDMMwDMMcIrDF7iCiuroa8+fPR3V1dWdX5ZCH27pj4HbuOLitO47u2NY8x67r4PgcC80wDMMwTJF88vEOvHjORdj7/7aW9bzHXDkNo278Ierqepf1vIc67IplGIZhGIY5RGBXLMMwDMMwJeGBXbFdBR7YMQzDMAxTEhVJKVbe03Ub2BXLMAzDMAxziMADu06kra0N119/PUaOHImePXti0KBBmD59OrZuTZ6A+otf/AJf/vKXUVNTgzFjxuCNN95Q9u/fvx+zZs1Cv3790KtXL3zrW9/Ctm3bKnUpXZ5ly5ZhwoQJ6NevHxzHwfr16/M6bunSpRg+fDhqamowcuRIPP/888p+3/cxb948DBw4ELW1tRg/fjzefffdClzBwUNS39ThNi6cP/3pTzj33HMxaNAgOI6D5cuXJx7zyiuv4OSTT0Z1dTWGDRuGRx99NFam0HvXHVi4cCFOPfVU1NXV4Ytf/CImT56MjRs3Jh7X3fq10LEr5+JxbGdR8MCuE9m7dy/WrVuHW265BevWrcOyZcuwceNGnHfeeTmPe/LJJzFnzhzMnz8f69atw4knnoimpiZs375dlvnxj3+M//qv/8LSpUvx6quvYuvWrfjmN79Z6UvqsrS0tGDcuHFYtGhR3sesXr0aU6dOxcyZM/HWW29h8uTJmDx5MjZs2CDL3Hnnnbj//vuxePFivP766+jZsyeampqwf//+SlxGlyefvknhNi6OlpYWnHjiifjFL36RV/lNmzZh0qRJOPvss7F+/XrMnj0bl112Gf77v/9blin03nUXXn31VcyaNQuvvfYaVqxYgba2NkyYMAEtLfa84dyvmU7FZ7oUb7zxhg/A37x5s7XMaaed5s+aNUv+nc1m/UGDBvkLFy70fd/3d+3a5afTaX/p0qWyzN///ncfgL9mzZrKVf4gYNOmTT4A/6233kose+GFF/qTJk1Sto0ZM8b/wQ9+4Pu+73ue59fX1/t33XWX3L9r1y6/urra/93vflfWeh8sJPVNHW7j0gHg/+EPf8hZZu7cuf7xxx+vbLvooov8pqYm+Xeh9667sn37dh+A/+qrr1rLdLd+/fGO7f6/H3+Wf2/dMWVdVlx/m79nz+7OvryDDrbYdTF2794Nx3HwhS98wbi/tbUVb775JsaPHy+3ua6L8ePHY82aNQCAN998E21tbUqZ4cOH44gjjpBlmGTWrFmjtCEANDU1yTbctGkTmpublTJ9+vTBmDFjumU759M3dbiNO4akdi7m3nVXdu/eDQA47LDDrGW6Y7/2fJTdFcue2OLggV0XYv/+/bj++usxdepU9O5tFmT8+OOPkc1mMWDAAGX7gAED0NzcDABobm5GJpOJDQ5pGSaZ5ubmxHYW22xluhP59E0dbuOOwdbOe/bswb59+4q6d90Rz/Mwe/ZsnHHGGRgxYoS1HPdrpjPhgV0HsmTJEvTq1Usuq1atkvva2tpw4YUXwvd9PPjgg51Yy4OfXO3MMAxTLLNmzcKGDRvwxBNPdHZVuhxC7qScC+vYFQfr2HUg5513HsaMGSP/Hjx4MIBoULd582a89NJLVmsdABx++OFIpVKxCNdt27ahvr4eAFBfX4/W1lbs2rVLsdrRMocytnYulPr6+sR2FtsGDhyolBk1alRR33kwk0/f1OE27hhs7dy7d2/U1tYilUoVfO+6G1dffTWeffZZ/OlPf8KQIUNylu2O/Vq4T8uJz0p2RcEWuw6krq4Ow4YNk0ttba0c1L377rv44x//iH79+uU8RyaTwejRo7Fy5Uq5zfM8rFy5Eo2NjQCA0aNHI51OK2U2btyILVu2yDKHMqZ2LobGxkalDQFgxYoVsg0bGhpQX1+vlNmzZw9ef/31btHOOvn0TR1u444hqZ2LuXfdBd/3cfXVV+MPf/gDXnrpJTQ0NCQew/2a6UzYYteJtLW14dvf/jbWrVuHZ599FtlsVs6vOOyww5DJZAAA55xzDi644AJcffXVAIA5c+bg4osvximnnILTTjsN9957L1paWnDJJZcACCbhzpw5E3PmzMFhhx2G3r1744c//CEaGxtx+umnd87FdjI7d+7Eli1bpEag0KGqr6+Xb8/Tp0/H4MGDsXDhQgDANddcgzPPPBP33HMPJk2ahCeeeAJr167FQw89BABwHAezZ8/G7bffjqOPPhoNDQ245ZZbMGjQIEyePLnjL7ILkNQ3uY3Lw+eff4733ntP/r1p0yasX78ehx12GI444gjceOON+PDDD/Hb3/4WAHDllVfigQcewNy5c3HppZfipZdewlNPPYXnnntOniPp3nVXZs2ahccffxxPP/006urq5DO6T58+8qWR+zWnFOtSdHZYbndGSG+YlpdfflmWGzp0qD9//nzl2J///Of+EUcc4WcyGf+0007zX3vtNWX/vn37/H/5l3/x+/bt6/fo0cO/4IIL/I8++qgDrqpr8utf/9rYzrRdzzzzTP/iiy9Wjnvqqaf8Y445xs9kMv7xxx/vP/fcc8p+z/P8W265xR8wYIBfXV3tn3POOf7GjRs74Iq6Lrn6JrdxeXj55ZeN/Vm07cUXX+yfeeaZsWNGjRrlZzIZ/8gjj/R//etfx86b9Fzpjtie0bT9unu//njHdv/+4Wf6t9cOK+vy3P+9leVOisDxfQ4oZhiGYRimOD75eAceG/dt7Nn8YVnPO/rqi/G1edegrs4+77xYzjvvPKxfvx7bt29H3759MX78eCxatAiDBg2SZf7yl79g1qxZ+J//+R/0798fP/zhDzF37lzlPEuXLsUtt9yCDz74AEcffTQWLVqEr3/962WvbyHwHDuGYRiGYUrCq8BSSavT2WefjaeeegobN27Ef/7nf+L999/Ht7/9bbl/z549mDBhAoYOHYo333wTd911FxYsWCDd6UB+GUY6A7bYMQzDMAxTNJ98vAO/qYDF7pSrL8aZFbLY6TzzzDOYPHkyDhw4gHQ6jQcffBA33XST1IUFgBtuuAHLly/HO++8AwC46KKL0NLSgmeffVae5/TTT8eoUaOwePHiitfZBlvsGIZhGIYpCa/MWSeyvg/P9+FlPezZs0dZDhw4UNa679y5E0uWLMHYsWORTqcBBNlDvva1r8lBHRBkD9m4cSM+/fRTWSZXhpHOggd2DMMwDMOUREUEigFs2LABffr0URYRfVwq119/PXr27Il+/fphy5YtePrpp+U+W/YQsS9Xmc7OHsIDO4ZhGIZhuiQjRozA7t27leXGG280lr3hhhvgOE7ORbhRAeC6667DW2+9hRdffBGpVArTp0/HoTA7jXXsGIZhGIYpCa8SmSd8H27KzXuO3bXXXosZM2bkLHPkkUfK9cMPPxyHH344jjnmGHzlK1/Bl770Jbz22mtobGy0Zg8BoGQQ6YrZWnhgxzAMwzDMQU///v3Rv3//oo71PA8A5Py9xsZG3HTTTWhra5Pz7lasWIFjjz0Wffv2lWVWrlyJ2bNny/PQDCOdBbtiGYZhGIYpiUrNsasEr7/+Oh544AGsX79e5mifOnUqjjrqKDko++53v4tMJoOZM2fib3/7G5588kncd999mDNnjjzPNddcgxdeeAH33HMP3nnnHSxYsABr166VWaI6Cx7YMQxTUR5++GFMmDCh4t/zwgsvYNSoUfLNm2GYjqMSUbGVmu7Wo0cPLFu2DOeccw6OPfZYzJw5EyeccAJeffVVVFdXAwhSxr344ovYtGkTRo8ejWuvvRbz5s3DFVdcIc8zduxYPP7443jooYdw4okn4ve//z2WL1+OESNGVKbiecI6dgzDVIz9+/fjyCOPxNKlS3HGGWdU/PtOPfVU/OhHP8K0adMq/l0MwwR88vEO/GLMBfi0zDp2Z/xoBpr+7ccdomN3KMEWO4ZhKsbvf/979O7du0MGdQAwY8YM3H///R3yXQzDRBxMrthDHR7YMQyTyI4dO1BfX4877rhDblu9ejUymQxWrlxpPe6JJ57Aueeeq2w766yzlMnGADB58mQlmu3LX/4ybr/9dkyfPh29evXC0KFD8cwzz2DHjh04//zz0atXL5xwwglYu3atcp5zzz0Xa9euxfvvv1/8xTIMUzBZVMIVyw7FYuCBHcMwifTv3x+PPPKInBz82WefYdq0abj66qtxzjnnWI/785//jFNOOaWo7/zZz36GM844A2+99RYmTZqEadOmYfr06fj+97+PdevW4aijjorpTh1xxBEYMGAAVq1aVdR3MgzDHOyw3AnDMHnx9a9/HZdffjm+973v4ZRTTkHPnj1zKsDv2rULu3fvxqBBg4r+vh/84AcAgHnz5uHBBx/EqaeeiilTpgAIVOMbGxtjulGDBg3C5s2bi/pOhmGKwwvdp2U9Z3lP121gix3DMHlz9913o729HUuXLsWSJUtkBJmJffv2AQBqamqK+q4TTjhBrou0PSNHjoxt2759u3JcbW0t9u7dW9R3MgxTOAda9qKuvjj9uFz0ru+P1Q8/WfbzHurwwI5hmLx5//33sXXrVniehw8++CBn2X79+sFxHJkwOxfZbDa2TYiCAoDjONZturzJzp07ixYpZRimcF57eCnOmXtFcsEC6DN4AE6eeh7OuXpGWc/bHeCBHcMwedHa2orvf//7uOiii3Dbbbfhsssui1nLKJlMBscddxzefvvt2D49Dc///u//lqWO+/fvx/vvv4+TTjqpLOdjGCaZyQvmoP64o3HM+LFlO+f/ufEqvP3cy6iqSicXZhR4YMcwTF7cdNNN2L17N+6//35cf/31OOaYY3DppZfmPKapqQl//vOfY9uffvppLFu2DO+//z5+8pOf4O2338bmzZvx4Yel6WC99tprqK6u7vSUPgzTnXBdFy//9GFMnPcjuKlUyecbOPIYHH/uOTjriu+VoXbdDx7YMQyTyCuvvIJ7770Xjz32GHr37g3XdfHYY49h1apVePDBB63HzZw5E88//zx2796tbJ80aRLuvPNOHHfccfjTn/6EX/7yl3jjjTfw2GOPlVTP3/3ud/je976HHj16lHQehmEKY/rihaiqyeDkqd8o+VwT51+D1x5+CqkyDBK7I5x5gmGYijJlyhScfPLJuPHGGwEEOnajRo3CvffeW9bv+fjjj3Hsscdi7dq1aGhoKOu5GYZJ5sCBA9j67vv46Zhvom3v/qLOcfQ/NeLCB2/F4GFHwXXZ9lQM3GoMw1SUu+66C7169ar493zwwQf45S9/yYM6hukkMpkMdv2jGeOuLM6F6rguJs7/EV7+6SM8qCsBttgxDNOhVMpixzBM59PW1oYdH36Ie077Jlp27Czo2NHfPRdn/Xgmho0+UUa9M4XDAzuGYRiGYcrGqt88hc+2f4Jn5i7K+5h0jxrMee0/MeiYo1BdXZz2JRPAtk6GYRiGYcrG2O99Eyd/5xs4fNjQvI854wdTsXvrdmQydtFzJj94YMcwDMMwTNlIVVXhzd/9FybO+2Fe5Xse3hdf/eF0HP9PZ7ALtgzwwI5hGIZhmLLyz//3SjSMG40vNyaLhf/TdZfjf1etRTqd6YCaHfrwwI5hGIZhmLLiui5WPfAY/nnBNTnLHX7UUIyeei7GfveCDqrZoQ8P7BiGYRiGKTsX3X0Leg/sj5GT/4+1zISbZ2HdE88iVVXVgTU7tOGoWIZhGIZhKsL+/fvw/zZsxL1nTEG2tU3ZN3TMibj4iftQf8QRrFtXRrglGYZhGIapCNXVNWht2Ysxl3w7tm/i/B9h1c8f40FdmWGLHcMwDMMwFaO1tRXbNm/BPadegP27PwMAjDjvHEz6ybX48vFf4UjYMsPDZIZhGIZhKkY6ncbWv2zEmdfMAACk0lWYcPMsDBzWwIO6CsADO4ZhGIZhKobjOBh9/gScPvNCfGFIPU6b8S2072/lDBMVgl2xDMMwDMNUnD/+4lFU1/XE0DGjUN9wBGeZqBA8sGMYhmEYpuJks1ns+OgjbHnjLzj1gn9mN2yF4IEdwzAMwzAdQnt7GxzHRSqV6uyqHLLwwI5hGIZhGOYQgYMnGIZhGIZhDhF4YMcwDMMwDHOIwAM7hmEYhmGYQwQe2DEMwzAMwxwi8MCOYRiGYRjmEIEHdgzDMAzDMIcIPLBjGIZhGIY5ROCBHcMwDMMwzCHC/wfjtpfyRWCu+QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the field data stored in the monitor\n",
    "ax = data.plot_field(\"fields\", \"Ey\", z=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89907018a22f5576",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "See all our [examples](https://www.flexcompute.com/tidy3d/learning-center/example-library/) to help you with your own designs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46eea396-6ceb-43b4-987d-919592e3e26f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook serves as a basic simulation template in Tidy3D FDTD. Beginners can adapt this template to run their own simulations.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "test_examp_env",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "key": 0,
         "length": 1,
         "op": "removerange"
        }
       ],
       "key": "version",
       "op": "patch"
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