{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow360 API Quickstart\n",
    "\n",
    "Flow360 can be automatically driven via our powerful Python API. All the case setup parameters available in the WebUI are available in the Python API.\n",
    "\n",
    "By starting from the same  [evtol_quickstart_grouped.csm](https://simcloud-public-1.s3.amazonaws.com/quickstart/evtol_quickstart_grouped.csm), the EVTOL quickstart case shown previously can be easily launched."
   ],
   "id": "b289ba4d"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import necessary modules\n",
    "\n",
    "First, we import the required modules from the Flow360 library:"
   ],
   "id": "c07706a6"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import show\n",
    "\n",
    "import flow360 as fl\n",
    "from flow360.examples import EVTOL"
   ],
   "id": "10f72deb"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Create a new project\n",
    "\n",
    "Create a new project from a predefined geometry file in the EVTOL example. This initializes a project with the specified geometry and assigns it a name."
   ],
   "id": "453cd4fb"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Output()"
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     "output_type": "display_data"
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    {
     "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"
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       "<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\">[14:09:43] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Geometry successfully submitted:                                                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Geometry                                                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Python Project <span style=\"font-weight: bold\">(</span>Geometry, from file<span style=\"font-weight: bold\">)</span>                                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = geo-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">4a2412d4-786e-4aa6-a7d1-1242a3e8ae63</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = uploaded                                                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">8d95954b-152d-4574-9f87-63e755a2bcb1</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:09:43]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Geometry successfully submitted:                                                                  \n",
       "\u001b[2;36m           \u001b[0m        type        = Geometry                                                                          \n",
       "\u001b[2;36m           \u001b[0m        name        = Python Project \u001b[1m(\u001b[0mGeometry, from file\u001b[1m)\u001b[0m                                              \n",
       "\u001b[2;36m           \u001b[0m        id          = geo-\u001b[93m4a2412d4-786e-4aa6-a7d1-1242a3e8ae63\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m8d95954b-152d-4574-9f87-63e755a2bcb1\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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     "metadata": {},
     "output_type": "display_data"
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       "<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><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Waiting for geometry to be processed.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Waiting for geometry to be processed.                                                             \n"
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      "text/plain": [
       "Output()"
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     "output_type": "display_data"
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     "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"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EVTOL.get_files()\n",
    "\n",
    "project = fl.Project.from_geometry(\n",
    "    EVTOL.geometry,\n",
    "    name=\"Python Project (Geometry, from file)\",\n",
    ")\n",
    "geo = project.geometry"
   ],
   "id": "655e3174"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Group faces by tag\n",
    "\n",
    "Group faces by a specific tag for easier reference in defining `Surface` objects:"
   ],
   "id": "1b28ea58"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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\">[14:11:32] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Regrouping face entities under `groupName` tag <span style=\"font-weight: bold\">(</span>previous `_color`<span style=\"font-weight: bold\">)</span>.                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:11:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Regrouping face entities under `groupName` tag \u001b[1m(\u001b[0mprevious `_color`\u001b[1m)\u001b[0m.                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geo.group_faces_by_tag(\"groupName\")"
   ],
   "id": "cf170c76"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Define simulation parameters\n",
    "\n",
    "Define simulation parameters within a specific unit system. This includes:\n",
    "- Meshing parameters (boundary layer and maximum edge length)\n",
    "- Reference geometry parameters\n",
    "- Operating conditions (velocity and angle of attack)\n",
    "- Time-stepping configuration\n",
    "- Boundary condition models\n",
    "- Output parameters"
   ],
   "id": "ad34e7c6"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with fl.SI_unit_system:\n",
    "    far_field_zone = fl.AutomatedFarfield()\n",
    "\n",
    "    params = fl.SimulationParams(\n",
    "        meshing=fl.MeshingParams(\n",
    "            defaults=fl.MeshingDefaults(\n",
    "                boundary_layer_first_layer_thickness=0.001,\n",
    "                surface_max_edge_length=1,\n",
    "            ),\n",
    "            volume_zones=[far_field_zone],\n",
    "        ),\n",
    "        operating_condition=fl.AerospaceCondition(\n",
    "            velocity_magnitude=100,\n",
    "            alpha=5 * fl.u.deg,\n",
    "        ),\n",
    "        time_stepping=fl.Steady(max_steps=1000),\n",
    "        models=[\n",
    "            fl.Wall(surfaces=[geo[\"*\"]]),\n",
    "            fl.Freestream(surfaces=[far_field_zone.farfield]),\n",
    "        ],\n",
    "        outputs=[\n",
    "            fl.SurfaceOutput(\n",
    "                surfaces=geo[\"*\"],\n",
    "                output_fields=[\"Cp\", \"Cf\", \"yPlus\", \"CfVec\"],\n",
    "            )\n",
    "        ],\n",
    "    )"
   ],
   "id": "64fb6624"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Run the simulation\n",
    "\n",
    "Run the simulation case with the specified parameters:"
   ],
   "id": "3c1570d3"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \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\">[14:11:35] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Successfully submitted:                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Case                                                                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Case of EVTOL from Python                                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = case-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">9a16e08c-935c-41bd-8ffc-a9095f7ba0e6</span>                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = pending                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">8d95954b-152d-4574-9f87-63e755a2bcb1</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:11:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Successfully submitted:                                                                           \n",
       "\u001b[2;36m           \u001b[0m        type        = Case                                                                              \n",
       "\u001b[2;36m           \u001b[0m        name        = Case of EVTOL from Python                                                         \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93m9a16e08c-935c-41bd-8ffc-a9095f7ba0e6\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m8d95954b-152d-4574-9f87-63e755a2bcb1\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case = project.run_case(params=params, name=\"Case of EVTOL from Python\")"
   ],
   "id": "d23f4889"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Wait for results and plot convergence\n",
    "\n",
    "Wait for the simulation to complete and plot the lift (CL) and drag (CD) coefficient convergence:"
   ],
   "id": "b4354d77"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      },
      "text/plain": [
       "Output()"
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     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "Output()"
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     "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"
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       "<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\">[14:15:41] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Saved to <span style=\"color: #800080; text-decoration-color: #800080\">/tmp/tmpjrj0fzrd/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">5734bde9-ec89-4ef8-9609-75c75887efdf.csv</span>                                \n",
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       "\u001b[2;36m[14:15:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpjrj0fzrd/\u001b[0m\u001b[95m5734bde9-ec89-4ef8-9609-75c75887efdf.csv\u001b[0m                                \n"
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     "data": {
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w9NNPIxQKoaGhAQ0NDfD7/eprzJgxI6YZ6Mc//jHefPNN7NixA++++y4uvPBCmM1mzJ07N/PfMIuiw0bGrhyYa8pU6SNBm3wHe16IiLIiGyvW5pNsfR4ZDxutWbMmZjrUggULAACXX345br/9drz00ksAgClTpsQ873//+x/OOOMMAMC2bdtw6NAh9bE9e/Zg7ty5OHz4MMrLy3HKKafgvffeQ3l5eaanmVVSng4bKZUXzjYiIsoOq1X+d9Xj8cDpdKY4euBQChhmc8++RzMOL2eccUbSBKUnXXWdarVkyZJMTyc38rRh12YxwWE1oV1p2GXPCxFRj5jNZpSUlODAgQMAAJfLZfh+P31dOBzGwYMH4XK5YLH0rOWWexulIV8rL4A8Xbqjg5UXIqJsURZcVQIMASaTCXV1dT0OcgwvaVDXecmznhcAKHJY0NHRf9Z5+WBHE1o7A5gxvrK3T4WIKC5JklBdXY2KigoEAoHePp0+wWazwZSF0QuGlzSYIpWXfJttBMibM6qL1PXxyosQAt/9yxq0dAbw/A/rMW1YaW+fEhFRQmazucc9HhQrv5o3jKTp4THl67AR+sfeRh5/CC2d8v/FPPj6ll4+GyIiyjWGF73CIfWqZM6/j63IYUE7+kflpbkzWn59a8shrN3Z1ItnQ0REuZZ/38JGicw0AvK0YddpRYeIVF4Cnpiw1tc0e/wxt1l9ISIaWBhe9NKEF1MeNuzKw0aO6B19uPrS4pErL6UFNlhMEqsvREQDDMOLXtrKSx427BY5LPDBihAiv1sf7ntRho1GlRfgomOHAshe9SUUFlj5+UG1p4aIiPoehhe9NOEFeTpsBEjwmlzyHX248tIcqbwUO22Yd+borFZflm1owGWPrcbtL33a49ciIiJjMLzopQkv+TjlTdnfyCP1/RlHzZ1yz0uJy4q6wa6sVl92NXkAAP/bfADhMPckISLqixhe9Ipp2M2/j80d2d/Io+5v1HcXqlOGdEqc8jnPPytafVmzo2fVF+W1mz0BfLa/tWcnSkREhsi/b2GjaMNLHk6Vdjvlyou6UF0frrwoDbslLjm81Ja6cOHUIQCA59ft7dFrt3qjvS7vbjuU5EgiIuot+fctbBTtbKM87HlRdpZuDdvlO/pDz4vLpt53dI0bQGz4yESrplH3na2He/RaRERkDIYXvWKmSuffx6YMG7WE+/7+RkrPS3Fk2AgAnDY5UHr9PVufRjvLaPX2JviD4SRHExFRb8i/b2GjRMJLUJjyc52XrsNG/aDyUqIJLw6rHF46Az0LL63eoHq9MxDC+t3NPXo9IiLKPoYXvSLhJQwJ5vzLLnBazTCbpP7R89IZ2/MCyOcPyPse9URb5LUr3fLw2Ttb2fdCRNTXMLzoFQkvAvlZeZEkCW6HJbrKbr+ovER7XtRhox5WXpRgNPvoKgBs2iUi6osYXvSK7PUThgSTlH/hBZCbdtX9jfpo5cUbCKlDQ8WayovL1vNhIyGE2vA7Z1I1AODDXc3o8AWTPY2IiHKM4UUv7bBRHlZeALnvJbqzdN9s2FVmA5kkoMhuUe9Xe156MGzkDYQRCMkL000cUowhJU4EwwKre7h2DBERZRfDi15qeDEhT7OLvDljH6+8KMM6xU5rzPCdMwsNu8prm00SCmxmnDx6MADgXfa9EBH1KQwvegn5/8jze9hIW3npm+GlWRNetJSel55UXpQhI7fDAkmScPLoMgBc74WIqK9heNFLU3nJ22EjhxUd6NuVl3gL1AHRykswLBAIZbY2S2uXYHTSKDm8fLa/FU0d/oxek4iIso/hRS9Nz0v+Vl6s8Ahlhd2+2fPS7Ilsypig8gJkPnSkDBu5I69dXmTH2MoiAMCqbay+EBH1FQwvemnDS75WXpwWtPfxyku8NV4AwGaO9iJluspudNgo+tonRfpe3uGUaSKiPoPhRS8hfyEKmGDO08qL3LDbx3te4qyuC8jr1PS0abe1U54Sre2nOTkydMSmXSKivoPhRa+YYaNePheDyA27kcpLyA8E+16fh7qvUZeeFyA6dJTpKrvRYaPoFOzpI0thNknYcdiD/S2dGb0uERFlF8OLXpHwEsrTFXYBuddDXWEX6JPVl0SVF0Az4yjjykv3YaMihxVDB8mBbl+z8eHlXx/tw/VLPuzxSsFERPmM4UUvdXsAKa+HjYKwwI/Il3cf3Fk6Uc8LEJ1x1OOely7BSBlG0u44bZQHXv8cS9fv47YERERJMLzopazzIiSY8vRTK3LIwyXqdOk+WHlpSbDOC9Dzheq6zjZSKJUYpSfGKEII7DkiV3caWnyGvhcRUX+Wp1/DBohZYTc/Ky9KIOjLO0urw0ZxKi+OLDXsuh2WmPuVHhijKy+H2v3wB+U/Z42tXkPfi4ioP2N40UuzMWO+LlKnVF7U8NIH13pR1nkpdma/YVcZNupa1VFutxocXvZqemoOtDG8EBElwvCil6bnJV8rL4WRjQ7VLQL6WOUlFBZo9crVkXiVF2Vn6UybXVMNGxldedl7JBpeGls5bERElAjDi17a2UZ5Gl4sZhMK7Zbo5ox9rOdFW/mI1/PS052l4802AqJhRqnMGEU7m4nDRkREiTG86DUA9jYCum7O2NG7J9OFsiljod0Cq7n7H92eNOyGwwJtvu6L1AHR8GJ45aWZlRciIj0YXvSKGTbq5XMxkLzKrrJFQN/qeYn2u3SvugA9Cy9tvqAyoUzt/VFEe16MnW20RzNsdLjDh2CGG0wSEeW7jMPLypUrcd5556GmpgaSJGHp0qUxjwshcOutt6K6uhpOpxMzZ87Eli1bUr7u4sWLMXz4cDgcDkyfPh2rV6/O9BSzawDsbQTIX9zqQnV9bNgo2RovQLTnJZNhI2XIyG4xqcNPCmX2kdGVF+2wkRDy7CMiIuou4/DS0dGByZMnY/HixXEf/9WvfoXf/e53+OMf/4j3338fBQUFmDVrFrzexGP5zz77LBYsWIDbbrsN69atw+TJkzFr1iwcOHAg09PMnuJaLDGfh6Whk/N2kTpAHiLpq8NGydZ4AQBHT8JLgplG2vuM7nnZ22UFX/a9EBHFl3F4mTNnDu6++25ceOGF3R4TQuDBBx/Ez3/+c5x//vk45phj8OSTT2Lfvn3dKjRa999/P66++mpceeWVmDBhAv74xz/C5XLhsccey/Q0s6f8KPzWciUeDX05bxt2AbnK4Omj67wkW+MF6NmwUaKZRtr7jKy8tPuC6uuPrigEwPBCRJSIIT0v27dvR0NDA2bOnKneV1xcjOnTp2PVqlVxn+P3+7F27dqY55hMJsycOTPhcwDA5/OhtbU15mKUUFhEzsuwt+h1RQ5rdHPGPjZspISXeGu8AJrtATIIL4kWqJPfL7J4ny+IcOTPQLYp06SLnVaMLCsAADS2sWmXiCgeQ76GGxoaAACVlZUx91dWVqqPdXXo0CGEQqG0ngMAixYtQnFxsXqpra3t4dknpnxv5fNsI7fTAo+wyzf6WniJ7CidsPLSg0Xqkg0bKVOnhQDavMY07Sr9LkNKnKh0y5WvA6y8EBHF1e9rCAsXLkRLS4t62b17t2HvFY5MR8nvYSNrdG+jPjZs1JJkR2mgZ8NGrUmGjWwWk/raRvW97ImEl5oSJyrdcnjksBERUXyGhJeqqioAQGNjY8z9jY2N6mNdlZWVwWw2p/UcALDb7XC73TEXo6jDRnkcXoocVs1so77VsNucYraRMwuzjRI1Axu9v5EybDR0kBMVkcoL13ohIorPkPAyYsQIVFVVYfny5ep9ra2teP/991FfXx/3OTabDdOmTYt5TjgcxvLlyxM+J9eUyku+Dxt19NG9jZLtawT0sOfFq/S8xA8vRu9vtFczbFRRxMoLEVEyGYeX9vZ2rF+/HuvXrwcgN+muX78eu3btgiRJuP7663H33XfjpZdewieffILLLrsMNTU1uOCCC9TXmDFjBh566CH19oIFC/Doo4/iL3/5CzZu3Igf/vCH6OjowJVXXpnxL5hNYbXy0ssnYqC+XHlJOVU6K7ONujfsAunvb9TQ4sWFf3gHz6/do+t4tedlkKbnhQ27RERxxf+XWoc1a9bgzDPPVG8vWLAAAHD55ZfjiSeewE9+8hN0dHTge9/7Hpqbm3HKKadg2bJlcDgc6nO2bduGQ4cOqbe//vWv4+DBg7j11lvR0NCAKVOmYNmyZd2aeHtLaED0vGgqL32t50XnInUZNeymHDZKb62XNzYdwIe7mgEAF00bmvJ4ZdioRtOw29Thhy8Ygt1iTvZUIqIBJ+PwcsYZZ0CIxNNGJUnCnXfeiTvvvDPhMTt27Oh23/z58zF//vxMT8tQA2O2kabyEvIBoQBgjv+FnktCiNTrvPRgV2kllKQaNtJbeVGGfBpbUg/9+INhNLbJxw0pcWKQywqrWUIgJHCwzYehg1y63pOIaKDo97ONcik8IBp2LdHZRkCfmS7d4Q8hGPn8S1L0vARCAoE09wVKtkgdEF3/Re/+Rmp4afOpjd6JNLR4IYS8NUFZoQ2SJKGiiENHRESJMLykQR02yuNPze2wIgALfCJSlOsjfS9Ks67NYoLDGv8/gHZPonSrL0ooSTRslGnlJRQWONSePIBom3WlSDBWpktzrRciou7y+Gs4u4QQ6q7D+by3kcNqhs1iggd9q++lWbPGi5Tg87dbTFAeSrdpN9WwUbo9L9ppzvtTDB3t1azxoqjs4XTpQCic0fAZEVF/kHHPy0Cjrfzn87AREGnaDTgwCO19pvKSqlkXkPusXFYzOvyhtNZ6CYTCapNvwtlGaU6V1k5zbmjpBGpLEh6rNOsOiRteUldevIEQ/vC/rfhwdzMOtvlwsM2HJo8fNrMJj195PE4aVZbwueGwyOtd0okoP7HyopO2byHf/7F3O6x9bq2XaOUlfr+LQl2oLo2qgzaQFCWqvKQxVdofDONwh1+9nbry4gEgT5NWVKir7KauvPz+jS343Rtb8daWQ9jU0IbDHX4IAfiCYVz7zIfY39LZ7TmhsMBP/vERjr7tFfzfsk1o9xmz7QERkREYXnQKa2ZW5fNsI0Bp2u1bw0apGmoV6lovaVRelAXqiuyWhP9t1UXqdOxtdKAtNqw0pAgv+5rlx2OGjdSG3eTP3XqgHY+s/AIAsODso/Dkd07Af687FasWnoUJ1W4c7vDjmqfXwR+MNjALIfDzpZ/g72v2oDMQwsMrtuHM+1bg72t2G7bxJBFRNjG86KQNL3meXeTp0qJvLVSXalNG+DuAD/6MIeYWAOlVXvQEo3S2B+haLdHb85LusJEQAr9YugGBkMBZ4ypw7VmjcdpR5Rhf7UZ1sRN//NY0uB0WfLirGff85zP1Off8ZyP+tno3TBJw7VmjMXywCwfbfPjJPz7G+YvfwaufNrBfhoj6NPa86BQzbJT3PS+azRn7yLBRqk0ZsfYvwCsL8W3Hl/E+vpnWl2+yTRkV6WwP0DVwJKu8hMNCDS9D0xw2+uf6fVj1xWE4rCbc8ZWjuzUy1w124YGvT8FVf1mDv6zaial1g7DzsAf/7+3tAIB7LzoGlxxXi/lnjcZf3t2B3y/fik/2tuB7f10Lh9WEU0aX4+wJFThzXIU6dZuIqC9geNFJW03P92Ejt9OCDshfnn2m8pJigToc+BQAUIZmAOmtshudaZT4r4MSbHxBeRaPdlp2V0p4qSiy40CbD/tbu/ecKA53+OEPhiFJQFVxNCAow0YtnYG479fSGcDdkWrKtWeNQW1p/IXsZoyvxLVnjcbv39iKm/7xEQIh+Q/ybedNwCXH1QIA7BYzvnfaKFw4dSgeXrENyzbsx74WL17f2IjXN8obpY4qL8Dxw0tx3PBSHD98EOpKXQlnfRERGY3hRafwAKq8FDtt6BCRKkAf6XlRho2KXQkadg/LfR8FkINCOj0veoaNCm0WmCQ5xLZ6AynCi1wtmVJbglc/a0Rjiy/hrB6l6lJZ5IDVHB3FdTstsFtM8AXDONDqQ93g2HDym1c341C7H6PKC3D1qSOT/n7XzzwK63c3460t8lYcN559FK48eUS348qL7Lj1vAn4xZfHY+P+Nry+sRHLNzbioz0t2HawA9sOdmDJB7sBAOOqinDD2UfhnAmVDDFElHMMLzqFBlDPyyCXNbrOS1+rvCQKGE3bAACuSHhJb9go+QJ1gDzDrMhhRUtnAK2dgaTDKErlZdKQYry2sRH+UBhNHj/KCu3djlWnSWuGjAB52nel24FdTR40tnljwsvHe5rx1/d2AgDuumAibJbkrWtmk4TffmMqbnvpU0weWoyrTukeXLq+94QaNybUuPGjGWNwpMOPtTuP4IOdTViz4wg+3tOMTQ1t+P5f12LSkGIsOPsonDG2nCGGiHKG4UWnsIjuKJ3v/0gPctmwq49NlU66zouvDWiXhzdcIlJ5SSe8pFigTlHslMNLS4otApTwMrTUibJCOw62+dDQ4o0fXpRp0iXObo9Vuu1yeOnSQ3PvfzdBCOCCKTVJ13DRKi2w4fdzp+o6tqtBBTbMnFCJmRPkDVKbPX48+tYXePydHfhkbwuufOIDTK0rwTeOr8Xso6tRnGQtHiKibOBsI53CkZmm+T5kBMgBoaPPVl7iDBs1faFetQv5iz6dnpfosFHyLK88nqpptyESNirdDlRH+lgSzThSpkl3rbwAQEWcVXYPtvmw6ovDAIAbzxmb9DyMUuKy4aZZ4/DWT87E904bCYfVhA93NePm5z/Bcfe8hque+ABLP9yLDq4dQ0QGYeVFp+i+RvkfXkoLbH1unRe15yXe0M7hbepVR1iuZGSySF2yYSMgWplJtUXAgUjYqHQ7UOV24GO0yKvsxrHnSPetARTx1np5fWMjhACOGVqcsEk3VwYX2nHLl8bju6eMwHNr9+BfH+3DpoY2LN90AMs3HUCh3YKvTRuKb9cPw6jywl49VyLKLwwvOikNu/m8r5GixKVp2O0DlRdvIARvQC59xR2SaIqGF1tIDi/eDBap0zNsBCRf66XdF1RXq9VTeVGnSScYNgKiYQgAXvm0AQAw6+iqpOeaSxVuB+adORrzzhyNLY1t+NdH+/DPj/Zh52EPnnh3B554dwdOHVOGb584DGeNq4DFzIIvEfUMw4tO2p6XfDfIZVWnSgtfG3r7V1YqIyZJXgW3m8PRYSOLCMCKYNYXqQM0lZck4UXpTym0W1Bot6CqWA4lidZ62Xuk+9YAiq4L1bV6A3hnqzxjaPbEvhNetMZUFmHBOWNx/cyj8PbWQ3hy1Q4s33QAb205hLe2HEKl246Ljh2KS46rxfCygt4+XSLqpxhedFIWqRsIw0bFTqtaeQn72pB4UnBuNGuGdeJ+/prKCyBPl+4MhLsfl0CbzmEjpeqTrPLSqPa7yOEvWeWlzRtQqz7xho2iC9XJz/3fpgMIhARGVxT2+WEYk0nCaUeV47SjyrG7yYOn3t+J59bsQWOrD39YsQ1/WLEN00eU4qJpQzFzfCVKC5LvWUVEpMXwopOyzEu+L1AHABazCZJd/r9i4ev9YaPoAnWJ1niJDS+Fkhedfv3Noupso1QNuw6lYTfxazdqmnWB6MJzDXGW+VeadYudVhTGqSgpr6EMGy3bIA8Zze5DQ0Z61Ja6sHDOeNx49li8vrERz36wGyu3HMT725vw/vYmmCTghBGlOGdCFWZNrIo784qISIvhRafosFH+hxcAsDrdQCcg+Xu/YbfZk6RZ19sCeOShFFhdQMATqbzoGzYSQqiVlJSVFx09L8rMoKpI8IhWXjohhIiZZr/nSOJp0oC8Qi8AtPmCONzuw4rNBwH03SGjVGwWE740qRpfmlSNfc2deGHdHrz8SQM+29+K975owntfNOHOf3+GSUOKMevoSsyeWIXRFUW9fdpE1AcxvOikDhsNlPDiKgI6AVPQI88TN/Vek2VzsjVelKpLQQVgdQLNO1EAr+4Vdr2BsLpkfqqGXbcz9WwjpbdFmeasVE+8gTBaO4MxDccb9rYCAEZXxB8CKrRb4LKZ4fGH8I+18g7QQ0qcOLrGredX69NqSpyYf9YYzD9rDHY3efDKpw149dNGfLCzCZ/sbcEne1tw36ufY2R5Ac4eX4mTRpfh+OGD4LLxnywiYnjRTam8DJSJEnaXGzgMSBBAwAPYe6/HQtmUMW5lRFnjZfAoebE6AAWSF4d09rwoQcRskuCyJe/uceuovCjTmpWeF4fVjNICG5o6/Njf2hkTXj7cfQQAcGxdSdzXUlbZ3X6oA0+9L6+oO3tiVd4tklhb6sJ3Tx2J7546EofafXj9s0Ys+7QB7249jC8OduBPB7/An1Z+AatZwtS6QTh5VBlOGFGKqXUlSbdpIKL8xfCi00BapA4ACgrcCAsJJknI06V7Mby0JVsBVwkvpaOAw1sBAAXwYo/OYSPtkFGqUKBnnRel8qIMGynXmzr82N/ixbgquWoihMCHu5oBAMcOG5Tw9SqK7Nh+qAO7m+Qp1f11yEivskI7vnFCHb5xQh3avAH8b/NBvL3lIN7Zehh7mzuxensTVm9vAgBYzRImDinGCcNLMW3YIEypK+Hu10QDBMOLTqEB1vNSUmBHBxwoQifgbwdQ2Wvn0hEZAopbGVGGjQaPBNrlhtZCqRMenQ27yrTnZDtKK9SeF0/qnpcKTXipLnbgs/2tMdOltx/qQEtnAHaLSQ008VRqXqes0I5j6xIHnXxT5LDiK5Nr8JXJNRBCYOdhD97ddhirvjiMD7Y3oaHViw93NashEABqih2YUleCyUNLMHFIMSZUuzGIM5mI8g7Di05qw+4AGTYqLbB2CS+9x6OGlzh/XJVp0qWjgP0fA0BaPS/RmUap9+NRZiO1+YJxd4kWQqjDRsosI+117XRp5Qt30pDipBsrKsNPAHD2hMoBMdstHkmSMLysAMPLCvDN6XUQQmDPEbkS88GOJny4qxmfH2jDvhYv9n3SgJc/aVCfW1PswIQaN8ZXu3FUZRHGVRVhRFkBF8sj6scYXnQaSCvsAsoquw5AQq9vEaBMe05eeRmlDm0VoFNdkTcVvTONgOiwkRBygOn6nKYOv9r8W67ZhFGZcaTdIkDpd5maoN9Foa285PuQUTokSUJtqQu1pS5cNG0oAHl140/2tGD97mZ8vKcZn+1vxc7DHjnQtHjx+sYD6vNtZhNGlhdgTGURRpUXqGvnjCgrYB8NUT/A8KLTQFqkDpB3lo5uztg3Ki/OruGl8wjQKfc/oHQkYJOn1RZKXviDYQRD4ZT/d62s2ZJqphEgN9/aLSb4gmG0dga6hRdlyKis0BZTTVFW2dVWXtbtbAYATE0xDKQMPxU5LKgfOTjlOQ5khXYL6kcNRv2o6OfU5g1g4/42fLqvBZsb2rCpoQ2fN7bB4w9hU+S2liQBNcVOjCwvwIiyAgwfXIDhZS7UlRagttQJu4XBhqgvYHjRSVmkbqD0vAxyWeHpI+FFWbOlW+VF2RagqBqwFcRUXgDAGwyjMEV40bujtKLYacWBNl/cpl1lgbquTaPRyouy43UQmxrkadKpKi+njynHsXUlOG9yTdLhJYqvyGHFCSNKccKIUvW+cFhgb3MnNje0YdvBdmw90I6tkZ9t3iD2Nndib3Mn3tpyKOa1JAmodjtQN9iF2kGuSOXHidpBLgwZ5ERFkWPADusR5RrDi07qVOkBEl5KXDbsE31jZ+kOnzJs1OWPq7bfBZADDIACSa6AePzBuCvXarXq3NdI4Y6El3jTpbtuDaCo6hJePt7TgrCQZyFVFydfTbbYZcUL15ys69xIH5MpOuQ0U9OILoTA4Q4/th/qiF4OdmBnkwe7Dnegwx9Sh6DeQ1O317WaJVQVOzC0xIWaEieGlDhQXeJETYkTNcXy9VR/HolIH/5N0mmgDRuVFtiwNVJ5Ef72Xt2c0ZNotpHS71I6Qv5pkysvbpMcErz+1H0vrcmmYceRbIsAZQsAbbMuEJ023eYLos0b0EyRLtH1npQbkiShrNCOskI7jh9eGvOYEmx2HvZgd1PkcsSD3U2d2H3Eg/0tXgRCQr7d1JngHeSNRauKHagqlnccr3I7UFnsQGWRfF+l24HBBbYB8+8MUaYYXnQaSLtKA/Jqtu2RyovP04reXD0j4bBRk6ZZFwDscs+LW/LGPC8ZvTtKK5Q+l3g7S6vTpLsMGxXYLXA7LGj1BtHY6sWHuyLNurUDZ9pzf6cNNtPirMsTDIXR2ObD3iOd2Nvswb5mL/Y1d2Jfcyf2t3ixt7kTbd6gHGAPtGPLgcTVTLNJQnmhHZVuO8qLHKhw21FRZEd5kR0VRQ71elmhnUOJNGAxvOh04sjBeOenZ8EyQNKLw2qG3+QCAPg72no1vCRs2D3cddhIrrwUphFelAqKntlGQPItAhoTVF4AoLrYiVZvG/Y1e/Hh7mYAqftdqP+wmE0YUuKM7FNVGveYdl8QDS1eNLR4sa+lE40tXjS0etHYKv9saPHhcIcPobCQb7d6AbQkfd8SlxXlkVClBJqyIpt8u9COwYXy9cGFNjYbU15heNHJYTUPuN1uQ9YCIAT4Pcn/ATWaJ9LzUqDteREiTuUl0rCrhBcda71Eh430N+wC8bcISNTzAsiBZnNjG9buPIKDbT5YTPLqsDRwFNotGF1RmHAvK0Cu4Bxq96Ox1YsDbT7158E2Hw62aa/7EAwLNHsCaPYEklZyFEV2CwYX2jC40I7BBdqfNpQW2DC4wC7/LLRhkMvGqg71aQwvlJCwFQKdQNDbew27Qgh44g0beZrkHaUBYJDS8yIPGymzjToDqVfZTXfYSN0iIMmwkXZtFoUy4+i/G/YDACbUuLmeCHVjMZvUnphkwmF5N/SD7XKQORT5ebDdh0Ntfhxql6s4h9r8ONzhQyAk5CErXxA7Dnt0nUuRw4LSAjnYlLpsGBS5XuKyotRlQ4lLvj3IZUWxy4oSJwMP5Q7DCyVkssvhJextS32wQXzBMCLtRrHDRkrVxT0EsMnDW0rlxSUi4UVHw26bV1nnpWeVl0AojMMdicOL8mX0eaMcBKfWluh6P6J4TCYJgwrkQHFUZVHSY4UQaO0M4lCHD4fb/Tjc7ouEGz+aOvzyfZHHmjr8OOLxIyzkvxtt3iB26gw7gFxdKnZaUeKKXJw2uJ1WFEcubqdFvV7ksMLtsMg/nRYOa1FaGF4oIZNDDgOiF9d58WiGfmKmSqv9LiOj90V6XhyiE4DQ1fMSbQbW91dBWQ+m1Rtb1TnY5oMQ8nTZUlf3vXSqu/yfdLLNGImySZIkFEeqI6PKUx+vVHUOR4JMU4cfRzr8aPL40dTuxxFPAEc88mNHOvxo7gygpTMAIeS+nnafvFZOumwWE4rsFhQ6LChyWFBoly8FkUuh3YICmwUFdjNckZ9Oq3zdaTPDFbk4rWY4Ij+t3AIibzG8UEJWp7xhoMnf0WvnoKzxYreYYhcA69rvAqiVFwtCsCGYMryEwgL+oFydceocwklUeWnQLFAXb5prVZf1XDjTiPoqbVVHr1BYoM0bUINNS2cALR451Bzx+NHaGURLZwCtXvm+1s4A2rxBtHoDavXTHwzjcNCPwx3+rP0uFpMEh9UMh9UEu0X+Kd+O3me3mNTVs2OuW82wmU2wW02Rx8ywWaLXtffLx8ceYzFJKXeqp8wZGl6GDx+OnTt3drv/mmuuweLFi7vd/8QTT+DKK6+Muc9ut8Pr9XY7loxnc8nlaFOw9yoviVfX7TLTCACsBerVAnSqeyKlem0gzkymBBL1vBxQwkucZl0gtvIyuMCG2tKB1fxN+c1sklAS6YMZgYLUT9AIhYVasWnzBtAemVLe7g2iI3J/u0++3uEPwaP89AfR4Quh0x+CJxCUf/pD6AyE1KHmoPraBvzSKZgkqCEnGoKiIalrANKGKXu8YGWNPa7rT7s1eqzNbMr74GRoePnggw8QCkW/IDZs2ICzzz4bF198ccLnuN1ubN68Wb2d7/8B+jK7S668WIPpl4CzJeGO0vEqL2YLYHECwU4USN6UPS+eSLiRJLmyo4c7UeUlsnpuVZx+FyB2+vTUuhL+uSaKMJsktQ8G6HmoF0LAHwrD6w+jMyCHGa96CcMbDMEXCMOn+elVbgfD8AXD8AZC8EeuR48LR+4LxTlGvl/ZmBWQt5RR3j/XJAlwKIEmTsXJYTXHuT8y5GY1wWmLPG4zw2GRbzu1x9nMan9TbzE0vJSXxw6w3nvvvRg1ahROP/30hM+RJAlVVdw9ty9wFZUAAGyh3hs2UgJGt8pI0w75p7byAshDR8FOFMKb8h8NZQVep9WsO0wUJ1jnpbEtcbMuIE9TLbCZ0eEPpdyMkYgyJ0lSpGphRjFy++UaDsvBydclDKnXI+FJDT2BELzaUBQTrrTPi31+159KMFOImODUfWZkNpw8ejCe/u6Jhry2HjnrefH7/XjqqaewYMGCpF8U7e3tGDZsGMLhMI499lj88pe/xNFHH53weJ/PB58vWhNsbW3N6nkPZAWF8jokdtF7w3bKWi0F2vAiBOCLTJN2ddlp2VYIdBxEATrhTRFePAFlzyT9sxyUyovyf2rKDInoGi/xw4skSRheVoBP97V2W3qeiPKDySTBYTJHlkHIbXBSK06aqpK22qReV6pQkVDU6Q9FHg93q1Ipj3X6NfdFQpGjl2eH5Sy8LF26FM3NzbjiiisSHjN27Fg89thjOOaYY9DS0oL77rsPJ510Ej799FMMHTo07nMWLVqEO+64w6CzHtgK3SUAACuCQNAHWOL3cxipI97qukFNmLJ2CQv26Cq7qRapUx5PZ72VIrsFkiTnp9bOIMqLuoaXxJ/Rby6ZjM/2teL44ay8EFF2aStOuQhOQojUBxkoZ/PI/vznP2POnDmoqalJeEx9fT0uu+wyTJkyBaeffjpeeOEFlJeX409/+lPC5yxcuBAtLS3qZffu3Uac/oBUXKz5ku2lGUdK021Mz0tA04Nj6TJGrlmozpOi8pKwGTgJk0lCUWRnYKXvJRwW2HFIXgsjUeUFAMZVufHVY4ey34WI+r3e/ncsJ5WXnTt34vXXX8cLL7yQ1vOsViumTp2KrVu3JjzGbrfDbs99RWAgKClywiuscEgB+DpaYHflfrgj7r5GSuXFZJGbdLU0WwQc0Vl50TtNWuF2WtEameYJAEvX78Xe5k4U2S1c8p+IKAdyUnl5/PHHUVFRgXPPPTet54VCIXzyySeorq426MwomSK7BR2R7v/W1iO9cg7qbCNtwFAqL12rLoC6UF0BvCl7XpTKi95p0gplunRLZwDeQAj3vSLPjrvmzNG92n1PRDRQGB5ewuEwHn/8cVx++eWwWGL/L/myyy7DwoUL1dt33nknXn31VXzxxRdYt24dvvWtb2Hnzp347ne/a/RpUhySJKFTkodB2lt7Z3NGZbZRgV3zZ0epvHTtdwEAm7zGRIGO2UaeDCsv6oyjzgAee2c79rV4MaTEiStPHp7W6xARUWYMHzZ6/fXXsWvXLnznO9/p9tiuXbtgMkXz05EjR3D11VejoaEBgwYNwrRp0/Duu+9iwoQJRp8mJeAzuYAw4GnvrfASpzoSiISXeJUXu9zzUih1pmzY9QbS2xpAoWwRsOOQB4++9QUA4MezjuJGi0REOWJ4eDnnnHMSdiWvWLEi5vYDDzyABx54wOhTojQEzHJ46Wxv7pX374w3bKQsmhe38hIdNtJbeUk3dCiVl0dWbkOHP4SJQ9w4f/KQtF6DiIgyx12rKKmgRR6G8Xl6Z/2c5JWXOOFFbdhNXXlRg1GGPS/KNO5bvjQ+7n5GRERkDIYXSkpYXQCAgKetV94/fs+LUnlJ3LCrZ4XdTBt2tU25Z42rwEmjytJ6PhER9QzDCyUlImEg5O2t8BKnOpK08hJd5yVleMlw2EhZZdckAQvnjEvruURE1HM5W2GX+idTZBgm7O2dnaXjzgjSUXkpkHzwB8IIhQXMCYZ04gYjHaaPLIXTasZVp4zAmMqitJ5LREQ9x/BCSZkdkS9nf+9UXjrj7Sqtp+cFcsDpDIRQaI//x1yZbZTuVOlxVW5suGNWwlBERETG4rARJWVxRsJLwNMr769unmjXW3mJrPMiyQEnWdNupj0vABhciIh6EcMLJWUvcAMALIFeGjbypdnzEtnbqBDyMclW2VWagdOtvBARUe9ieKGk7AXyXj2WUGeKI40R3R5A52wjzVRpQCRt2u0MhOXXzqDyQkREvYfhhZJyRcKLPexBMBTO6XuHwyL+0E7SyoscXiwIw46AGn7i6WTlhYioX2J4oaRcRXJ4KYAXLZ2BnL63NxgNHgW6e14K1auFSL5QXU96XoiIqPcwvFBSymyjAnhxxOPP6Xt3+KLBw2HRWXkxmQBrtGk3Wc9LZ7zVe4mIqM9jeKHkbNEekiOe3FZeOjVrvMQsv5+s8gJopksnX2W3M14/DRER9XkML5ScMvUYPhzpyG3lRZ0m3bUykqzyAmg2Z0w8bCSEgCcSbBw2/jUgIupP+K82JRdZbt8l+dDS4c3pW6szjexdwovOykuh5FUDSle+YBjKZuds2CUi6l8YXii5SOUFANraWnL61uoaL12HdXRXXrzwJqi8aCsyDC9ERP0LwwslZ3EgDPnLvaO9OadvrS4i13XYSK28pAgvUuLNGZX7bWYTLGb+NSAi6k/4rzYlJ0kImOXhGW97bvc3UgJG4p6XFMNGSRp2PZxpRETUbzG8UErByNRjvyfHw0aJdn0ORsJLqspLkobdTDdlJCKi3sfwQimFrXIYCHhyW3np8Cmzjbr2vESGjRJWXiJr00i+hOElYVWHiIj6PIYXSi3StBvy5njYKBuVlxTDRg5WXoiI+h2GF0rJFKlkhH253Vnak2j5/pSVF6VhN3HPS8JgREREfR7DC6VkdkT2C/K3IxwWOXvfuAEjFABEJJCkqLwUojPh9gCdgQQzmYiIqM9jeKGUrC43AMApOtGcw80ZlanSMT0vStUFSFx50azzkrDnxS/vkM2GXSKi/ofhhVIya/YKamzN3Sq7HfEqL0q/CyTAYo//RHt0nRdPgvCScA0ZIiLq8xheKDVND8mBNl/O3jbusJHa7+IAJCnOs6AZNkq8qzSnShMR9V8ML5SaZhjmQA4rL9HqiGbYKNVMI0BXwy4XqSMi6r8YXig1zXL7vVF5KYhbeUnQ7wIAtsg6L0lW2O1k5YWIqN9ieKHUIuu8FMCX08pLR7zqSDqVF3SiM1K96YpTpYmI+i+GF0pNWbEWnWhs7Y2elzizjRLtKA2olSKzJCAFvQjFmd6tVF50LVL35q+BJy8A2hp1nTcRERmL4YVSi4QBl+TFgbbc97zEnW2UNLwUQEBu5k3UtOuJF4wS+eBR4Iv/AU9fBHhb9Z08EREZhuGFUosMGxUit7ON4m7MqFRerEl6XiQppk8nXt+LOtvIpuOvgPKeDZ8AS74JBHP3GRARUXcML5SaXVN5afVBCONX2Q2FBXxBeSE5V7zZRskqLwAkTeCKt1Cdcp/TqqPyooQXkxXY8RbwwveAcPxGYCIiMh7DC6WmWTfFHwqjJQer7Ho0jbZpV16AmKbdeOere6p0OASEI8+/6FE5wHy2FPjvzUAOQhwREXXH8EKpaYZgAJGTpl2lMiJJgN2i+WOqs/Ki7dOJF16UYaOUs4202xGMmQV89U8AJLkPZs1jyZ9LRESGYHih1CJDMGYI2BHISdOuR13jxQJJu5KuWnlJEV4iM6QK4UWzJ0nlJdVso6Dmd7U4gIkXAafdJN/e/HLy5xIRkSEMDS+33347JEmKuYwbNy7pc5577jmMGzcODocDkyZNwssv8wui10XCCyDv1HwgB5WXhMM6auUlxbCRLbrKbrzKi+6p0kpYMtsAU+SvS+WE2MeIiCinDK+8HH300di/f796efvttxMe++6772Lu3Lm46qqr8OGHH+KCCy7ABRdcgA0bNhh9mpSMyQxYXQDkYZjGnFRe4kyTBtKovCh9Op1o7vR3e1j3InXxwpJyneGFiKhXGB5eLBYLqqqq1EtZWVnCY3/7299i9uzZuOmmmzB+/HjcddddOPbYY/HQQw8ZfZqUiqZpN6eVl66VkXQrL/CipcuwUTAUhj8Ujv/6XcULS8r1YO7WvCEioijDw8uWLVtQU1ODkSNH4tJLL8WuXbsSHrtq1SrMnDkz5r5Zs2Zh1apVCZ/j8/nQ2toacyEDRIaOXMjNQnVqz4u9y1TmgI7tAQDN5oyd3XpetOu+pJxtFK9BmJUXIqJeZWh4mT59Op544gksW7YMDz/8MLZv345TTz0VbW1tcY9vaGhAZWVlzH2VlZVoaGhI+B6LFi1CcXGxeqmtrc3q70ARyjCMlJvKS2cgwbBRUMfGjEBMpahrz4sSXrrNZIon3tRsK8MLEVFvMjS8zJkzBxdffDGOOeYYzJo1Cy+//DKam5vx97//PWvvsXDhQrS0tKiX3bt3Z+21SUOZeozc9Lx0+BIMG+mtvGimd3fteVH7Xazm2JlM8cSrvCjhJcjwQkTUG3QsL5o9JSUlOOqoo7B169a4j1dVVaGxMXbzu8bGRlRVVSV8TbvdDrvdntXzpDiUSobUqa6ym/KLvwcSNtTqrbyoi9T5Eg4bpRwyAuJXXpQgE2DPCxFRb8jpOi/t7e3Ytm0bqqur4z5eX1+P5cuXx9z32muvob6+PhenR8moPS8++IJhtHqDKZ7QM9Gp0hn2vNgSr7DbqXd1XSB55SXkA8Lh1K9BRERZZWh4+fGPf4w333wTO3bswLvvvosLL7wQZrMZc+fOBQBcdtllWLhwoXr8ddddh2XLluE3v/kNNm3ahNtvvx1r1qzB/PnzjTxN0iNSyRhslftdDrQaW3XwRHpeCjJd5yWySF28dV469S5QByTveQE4dERE1AsMDS979uzB3LlzMXbsWFxyySUYPHgw3nvvPZSXlwMAdu3ahf3796vHn3TSSXjmmWfwyCOPYPLkyfjHP/6BpUuXYuLEiUaeJukRqWSU2+RQYfTu0h5fomGj9CovheiExx+CLxidYRQdNtIxappsthHAoSMiol5gaM/LkiVLkj6+YsWKbvddfPHFuPjiiw06I8pYJAyUWuXm10ajKy8Jh43S7HmR5PNs6Qygosgc+9pWHdk9XuXFZJJX3A35WXkhIuoF3NuI9In0vAyyyOHF6MpL4qnSaVZelPCiadpVKy96ho0SbQSprvXCygsRUa4xvJA+kR4St0npeTF42CjRbCO9lRfN1G5AxPS9RGcy6Sg8xqu8aG8HPKlfg4iIsorhhfSJVF4KI+HF6LVePIkCht7KS2TYyAQBV5fp0ro3ZdS+X9fKC7cIICLqNQwvpI9SyRByJeKg4ZWXOMNGQuifbWR1AZL8x7sAXjRrKi8JqzrxJNoIklsEEBH1GoYX0idSeXFEwkuuKi8xa7FoqxypKi+SFLvKrie6yq43nUXqEoUlVl6IiHoNwwvpE+l5sYbkHg9llV2jKH0pBdphI22VI1XlBZCrL5AX1muNqbzIVZ20Gna7hqXIa7PyQkSUewwvpE+k8mIJyuGlMxBCu8+4VXaTVl5MFsCso9k20lTrgD9m2KjTH+7+2okEElRe1C0CGF6IiHKN4YX0iQzBSP52FNnl4NBoYN9L3J4XvTONFMqWBlLXht0E07DjSVh54eaMRES9heGF9ImEFwS9qCqSw8sBg/peAqEwAiF5SMoVr/KSqt9FoQ4beeNOldY12yhRYOLmjEREvYbhhfSJTD0GgLpCOVgYtdaLMmQEdJkqnWgIJxGbHF6c8MUOGwXSmG2UsPKiNOyy8kJElGsML6SP2Sb3mgAYWiB/+RtVeVEqIxaTBJtF80c0mGDaciLW6LBRi2a2UUYbM3abbcSGXSKi3sLwQvpoph7XuOQvf6N6XjqU2UDdVtdNsGBcIrbobKN4lZf0tgewx97Phl0iol7D8EL6RcJLpUOpvBgTXjoTLSIXTLBUfyKa2UatnQGEw/JwV9yZTImk2h6A67wQEeUcwwvpF+l7qbDLVYwDBu0s7Ym3xguQfuVFHTbyIiyAtsjU7swWqeu6wi4bdomIegvDC+kXmXo82BoJLwZVXjyJho3SrbxEho2KTPL5KjtLq9sDWFOsFSNEksqL0vPCjRmJiHKN4YX0iwwbDbLIza9GV1667yidbuVFDhglkfNt7vRDCBHdmNGW4o9/yA9AxH9Pbg9ARNRrGF5Iv0h4KTbLFZcOvzGr7EZ7UrruKJ1u5UWuFLnNkcpLZwC+YBjKrgbddqzuStuM2/U9uTEjEVGvYXgh/SI9L/aQBwWRqogR1ZfOyLBRQZYqL4UmOWw1ewJqMzCgY7aRWlWR5KniMa+dQeVl/8fAur8CBu4JRUQ0EDC8kH6RSgb8Hah0y1/eRvS9JJwNlPZsIzm8FER6Xpo7A/BEhoxsFhPMJin587X9LlKXYzPpeXnpWuCl+cC+dfqfQ0RE3TC8kH7KFgH+dpQXyeueNBpQeclaz4tmnRcAaPH401ugLtFMI+196cw26jgo/2zZo/85RETUDcML6acJL0rl5aAhlRdl48Qe9rxYle0B5IDR0hlIvIZMPIlmGmnvS2d7AH+7/NNzWP9ziIioG4YX0k/Z38jXjqpiObzsazau8tKtOpJ25UUe5rIL+XnNnkCGq+tmofIiBOBTwkuTvucQEVFcDC+kn6bnpXaQXHnYfST765wo1ZECe3Z6XmzhSHjpjIaXtHaUTlp50Rlegj5ARJqFGV6IiHqE4YX00wwb1ZbKwWB3U/bDS8Kp0mnPNpIDhjUsh5AWT0CdyZTWjtLx3k8JL3obdv0d0escNiIi6hGGF9JPE17qIuFlV5MHIstTf5WNGV1dqyMZrvNiDnkBCLnnJZ2tAZJVXpRAEw4CIR1r3Sj9LgDDCxFRDzG8kH6anpchg5yQJLlKcrjDn9W3SdhUm+E6L5IIw44Amjv9iftp4tFTeQH0Ne2y8kJElDUML6SfpufFbjGjOjLjaFeWh47UqdL2BLON0mzYBQAnfDGL1KVXeUnSsAvoa9pl5YWIKGsYXkg/W5H8M/JFPNSgvhdlaCdh5SVemIjHZAbM8no0LvjgC4bRHNmcMb2elzjDRpKk2SJAx+8fE17YsEtE1BMML6SfWnlpB4RQ+16yHV46IvsldRvaSRYmErEpWwTIQ1v7W+TX0DfbKEVYSmeLAO2wkb9Nnn1EREQZYXgh/ZSeFxEGAp0xTbvZlLjnJckwTiKRvpdyhxyI9rd0xn/teFKFpXQ2Z9SGF4DVFyKiHmB4If2s0R4S+DsMCS9CCHX/oYJuPS9pNuwC0fBil1+zIVJ5SathNxuVF19b7G32vRARZYzhhfQzmaIBxt+mWesljSXyU2jzBREKy1Ov3Q5r7IPJpi4nEhk2GmxTKi+R8NJ1DZl4lPdLVHlJZ3PGbpUXhhciokwxvFB6NDOOlMrLvpZO+IPhrLy8sldSkd0SOyMoFIiuUJtW5UU+31KrHF4y2h4gUeUlnS0CGF6IiLKG4YXSo1nrpazQBqfVDCGAfc3Zqb4caJXDS7nbHvuAtq8kg8pLiTUQc7fTpuOPfiDF1Ox0NmfsGl462fNCRJQphhdKj6byIkkSakvlL/Bs9b0cbI+El8Iu4UXbV5JBz0uJpUt4seoYNkrVY5NW5aU99jYbdomIMmZoeFm0aBGOP/54FBUVoaKiAhdccAE2b96c9DlPPPEEJEmKuTgcaXxZkbHUtV7kBtRsN+0eaJWDQIW7y39zbRVEkvS/YCS8FJm6Vl56uD2A9v501nlR+mQ4bERElDFDw8ubb76JefPm4b333sNrr72GQCCAc845Bx0dHUmf53a7sX//fvWyc+dOI0+T0qGpvADI+gaNKSsv6VRdAHXYqMgcu65KjzdmBNLbWVoZNiqpk38yvBARZUxH7Txzy5Yti7n9xBNPoKKiAmvXrsVpp52W8HmSJKGqqsrIU6NMaXpegOxXXg5Gel4qEvW8pNPvAqiVjgIpdv8lXQ27qd4zk4bdkjrg4CaGFyKiHshpz0tLSwsAoLS0NOlx7e3tGDZsGGpra3H++efj008/TXisz+dDa2trzIUMpF1lF0DtoEjl5UhfrbzI5+uSYisvuoaNdFde9DTsRoaNWHkhIuqxnIWXcDiM66+/HieffDImTpyY8LixY8fisccewz//+U889dRTCIfDOOmkk7Bnz564xy9atAjFxcXqpba21qhfgYBu+xvVDY5UXg5nq+fFmMqLA13CSzYqL2rPSybDRmzYJSLKVM7Cy7x587BhwwYsWbIk6XH19fW47LLLMGXKFJx++ul44YUXUF5ejj/96U9xj1+4cCFaWlrUy+7du404fVJ07XmJVF5avUG0eAKJnqWbWnkpym7lxSFiA0ZWel7S2ZjRp1Rehsk/WXkhIsqYoT0vivnz5+Pf//43Vq5ciaFDh6b1XKvViqlTp2Lr1q1xH7fb7bDb7XEfIwN06Xlx2swoL7LjYJsPu5o8mOQqzvil/cEwmjrk3pSKogSzjdKuvMjH28Kx4SW9jRkTVV4y2JhRqbwEPIDfozYUExGRfoZWXoQQmD9/Pl588UW88cYbGDFiRNqvEQqF8Mknn6C6utqAM6S02SLhRbNuSbaadg93yFUXi0lCibPL1gCZVl4iw0aWULQvxSQBdouOP/rBFIvU6d2YUYjo51VUBZgivxsXqiMiyoih4WXevHl46qmn8Mwzz6CoqAgNDQ1oaGhAZ2f0H/vLLrsMCxcuVG/feeedePXVV/HFF19g3bp1+Na3voWdO3fiu9/9rpGnSnolCS89bdpVV9ctssNk6rKWS6aVl8iwkSnYicLIRo9OqxlSqrViwiEg5E/+nnorL0FfdGsDWyHgGixf59AREVFGDB02evjhhwEAZ5xxRsz9jz/+OK644goAwK5du2AyRTPUkSNHcPXVV6OhoQGDBg3CtGnT8O6772LChAlGnirp1aXnBQBqB2VnlV1lX6Nu/S5Ajysv8HtQ7LSi3RdMb6ZRsvfUuzGjdmsAWwHgKgXaGxheiIgyZGh4EUKkPGbFihUxtx944AE88MADBp0R9ViXnhcgewvVHYiEl4p44UWtvGS2SB0CcnjZ29ypc3VdTXjp6TovkdWIYXUBJrOm8sJhIyKiTHBvI0qPgT0v+iovmU2Vhr8DJS6510TfjtKRsGSyyoEj7ms7Yo9NRKm8KFUrV2SdIz3hRQjg81eB1n2pjyUiGiAYXig98cJLZK2XvUc6EQyFM37pA21yQCnvOtMIyLzyohnaUcOLTUfBMdVMI0DTsJuq8tI1vKTR87LzHeCZi4GlP0x9LBHRAMHwQumJ0/NSWeSAzWxCMCywv0XHtOEEDKm8KOcb9KLEIf9xd1qzMNMI0CxSl6ryEgl6SvBLJ7zsXSf/bNiQ+lgiogGC4YXSY4+ssBv0AqEgAMBkkjC0VP4i78mMo+Q9L0olJMPKC4DBdrkHy5VW5UVHeEk1bKT0B2VSeTkY2YXdcwjwtqQ+nohoAGB4ofQoX8BAzNCRusdRD/pekldelEpIJovUydOiy+xy2Eqr5yXZ++lu2FWGjTKovBzaHL3e9EXq44mIBgCGF0qPxR5dZC2LTbtCCDW8xK28BCN7E6VbeZEktfpy+vACTB5ajAumDkn9vHQrL8lm1vm7Vl50NuwKEa28AAwvREQROdkegPKMrQDwNsf0vUTDi44dluNo6QzAH2n2Leu6ozQQ7StJt/ICyCEj0IHhbuCf80/R9xw9lRclvIiwvKCdJcE2FZlWXtoaAJ9ml3SGFyIiAKy8UCaUvpc4a71kWnlRqi7FTmv8fYeCGfa8ANG1XvxpnJueSo822CRr2k022yhZxUY7ZAQATdsTH0tENIAwvFD61BlH3YeN9mQYXg4k63cBelh5iZxvoCP5cem+n9kKSJG/Qsm2COg2bBQJLyFf7Oq7XSlDRsow3eFtyc+ZiGiAYHih9MVZ62XYYBcsJgmHO/xY+fnBtF8yab8LkJ3KS6opzfHeL9FQECD30+jZnFH5nJTVia2uaLNvsqEjJbyMOFX+yWEjIiIADC+UiThrvRTYLbisfjgA4LaXPoU3EErrJaML1BlReYmusqub3o0g1abdZJWXLj0vkgQ4labdJOHl0Ofyz6PmyD87DgC+tu7HBf3AyzcBm/6T/FyJiPIEwwulT+15if0iveHsMagosmP7oQ48sjK9KoGxlRdl2CidnhedG0GqC9Ulee2uPS9AdOioM8mMo4Ob5J9DjwNcZfL1eH0vm/8DrH4EeOlade0dIqJ8xvBC6YtTeQGAIocVv/iyvPv3Q//bip2H9Vc6ogvUJQgLPZ1tBKTXsKu38qJnrZe44SXFdGlPE9ARGX4rOwooHSlfb4rT97Lvw8hzDgO73k1+vkREeYDhhdIXp+dF8eVjqnHK6DL4g2Hc/tKnunYWB1IsUAf0rPKSScOu7sqLjs0ZlQqVrSh6X6rp0sqQUXGt3Cujhpc4FS0lvADAZ/9Mfr5ERHmA67xQ+hJUXgBAkiTccf7RmP3gSvxv80G88mkjZk+sSvmSSbcGECLzvY2AzKZK66686NicMdmwUaLwogwZlR0l/xw8Sv7ZNbyEw8C+j6K3N/4bmPNrwMT/LyEacISQ152KuUTug4i93e3+OM9Rrnc7Rsj/npWO6LVfleGF0peg50UxqrwQ3z9tFB7631bc+a9PceqYMhTYk/9R07UpI5Bh5aUns4309ryksc4LoCO8RCov5WPln2rlpUvPy5HtgK9FPk+zDWhvAPasBupOjD2udR/wl/OAmmOB8x9KPouKBg7lSyoUAMLByCWkuR4ERKjLfZHbIhR7vAjJYVr7HOW+mNuh6Jegej3U5XaXS6L7E15E9y/yeF/Aeo/p9sUfJwxAxL4mul5PcmzCY8KAQJxzi/O60FflzprRM4FvPZ/b99RgeKH0Jam8KOadORpL1+/FniOdeHjFNvx41tiEx3oDIbR0BgAk6HnRBoOMKi89WOdF92wjPeGlMHqf3sqLGl4i/4fTda0XZcioapIccD5+Vh466hpeVt4HHN4qX7wtwCVPZhYESf6iCIeAcED+0g8FItf9cW4HE1zXHpfoOZFAoT6mvb/r7WCcY4Ndrie4EElmeRakZAIgRa6b5duSJN9nMkVuRy6Okl49ZYYXSl+SnheF02bGz8+dgB88tRZ/fns7LjtpWMJm3EPtctXFZjHB7YzzR1KpgpgsgDmDP7KZNOzqrbykatgVovsidUDqhl2l56WsS+WlvUEOQ8prKeGlZiow4vRIeHkJmPXLyD86AFr2Ah/+Vb5usgJbXgGWzAW+8UzycNa6D1j/DHBoCzDuS8DYc7t//kIAu1cDDR8DlROBIcd2r+oIIb9W8y7AWQIUlAPOQYApspJyOCR/Dp5DQGez/A+jKfIPp8kif8EGOiMXj7z6cdf/uw8FI1/8PvkLXDkm3pd6yB8NEF1DQsgfuZ4sYASQ8//LzTXJJH95mSyRizn2p2SOXNfeNsU+pr6GSXOf9jFT9D7tf3PtRfu4ZOryBar5wlUu8b5k0eUYSYr/PO3j6m3NsTH3xXlOt+fFO0Z7H3Qco9yGJlAkO59k768NItpg0j8xvFD6dFReAGDW0ZWYUluC9bub8dAbW3Hn+RPjHqeurltohxTvL1NPZhoBmmEjA3peUlVegl75yxWILlIHJK+8+NqBlt3ydaXy4hwkXzqPyENHVZHPUhteRs+Qm5Nb9wB71wFDp8mPvf2A/GU87GTgjIXAM5cA296Qf85dEhuqgn7g82XAh08BW1+LlKYBfLwEcA8BjrsSOPYKOZB9/Czw0RJ56EphtgNDpgHD6uUQsP9jOdh0/T0lk/wZhEPy79Tfw4BkkoftTFZ55WWzNXLbIl83ae5THlfu63ZMgusxx1k09yu3LbGPqcebo48p/wOgvW3ShBSzNRo4iPowhhdKX4qeF4UkSfjJ7LH45qPv42+rd+G7p4xE3WBXt+MMnWkE6A5bcd+zpz0v2ve0an73ZOFFqboUlEcrNABQOgrYu0Zu2q2aKH/x748069ZMlc/lqHOAT18ENv5TDi+t+4B1f5GPOf1mebXebz0PPH0xsH0l8NhsoKROnpbd3gi0NcYGsbp6oHoK8MlzQOte4I27gRX3xg43WAuA2uOBxk/l19n1bvcp25IZKB4i/5npPCKHImUquHxAJKCVyDe1vRQmq/y7WZ2R1Ynt0S9dtQJgkYOBxSYHKLMt/pe0xR4bKuIGDOV65DW6hpK4wSLOflxEZBiGF0pfGmHgpFFlOHVMGd7acggPvP45Hvj6lG7HJJ1pBESHZHJaedEZmFJtD6AMGVldsV9wXTdn1Facug4ZKUpHRsJLpO/l8Fb59a2u6KykCefL4eWzl4CZdwBvPyhXXerqgRGnyccMOwn49ovAUxfJVZGGj2Pfp7ASmDwXmPptoGy0fN/ZdwCfLpUXw9u7BoAEjDxDPm78l+U/E0LI57TzXblp2GwHqo+R+3EqJkSDXigg/94dB+Xg4CqTg0smQ4JENCDxXwtKn46eF62fzBqHt7a8jaXr9+L7p4/EuCp3zOOpKy/KEE6mlZdMdpXWOVSlrvOSoOcl3kwjIFpRCQcBXyvgKI4+puxpVB4nvADR6dLKkFH15GgwGn22XC06sh3Y+jqw9gn5/tNvjg1ItScAV78BbHwJsLuBwgo5tBRWAMV13YOExQ5M/rp8ObxN/n2KukyBlySgbIx8mXZ5/M8DkCsVRVXdn09EpBMHNil9anjRNwwzaWgxzp1UDSGA+17Z3O3xg5F9jRKvrqtzCCcRdZG6Xqi8+OI06wLRIRCg+9BRyvAS6THR9rso7IXyFEYAeP4quYG19kS5StJV2Rjg1BuBE66WKzZ1J8rvkaoCMngUgwcR9SqGF0qfXVN50bmC7oJzjoLZJOH1jQewdmfsDBv9lZdMh4107D+U6D1TVl50DhtpV9dVqENHR2LvP5Rm5UUbXgA5iADylGgAOOPmfj2rgIioK4YXSp9SRRBh3Qu/jSovxMXThgIA/u+/m2O2DdDf89LThl0DKi+ptgdINGwEaKZLayovQV+0stK150VZZbd1r9z4uj/Sq9I1vBw1S24kBYChJwAjz0z+OxAR9TMML5Q+q+aLWGffCwBcN3MM7BYTVu9owj/W7lHv1115yXjYSGnY1TnbSAj9lZdU2wMkDS9xZhwd3iZPrba7uw/NOAdFe2M+f0U+R1uRPAtJy1EMTLxIboad8QtWXYgo7zC8UPpMpmiASSO8VBc7cf1MeVbMnf/+DA0tXoTDQg0vFe4UlZeeNuyGg/I6JqmEAtH1TXRXXhKFlwQ9L0D88KIdMuoaOiQpOnS0IbIsd82U+GtyfOX3wIKN0RlGRER5hOGFMqP0vfj0hxcAuPrUEZg8tBht3iBuefETHPH4EQzLQ0iDC1JVXjLtedEEBz3VF+0QUMqelxT7JqnhpbD7Y/HCi9Ks23XISKGEl62vyz9rpsQ/zmKTZw4REeUhhhfKTCYLvwGwmE349cWTYTOb8MamA/jTSrn5tLTABpslwR/HnlZeLJGFyAB9PTrqEJCUegNDdXuAFD0v9hThRQi512VnZHG3rs26CmWIKBSpIHXtdyEiGgC4zgtlJs21XrSOqizCdTPH4NevbMYjkfBSXpgkJPS08gLIFRJfq76mXW2PTap+kVTbA+hp2N30b2Dzy7ErzlZMiP96SuVFwfBCRAMQKy+UmR6EFwD4/mkjMWlIdGG2hP0uQM8rL0B6Tbvq7KYUVRcg9caMyXpe3EPkn+pqs1ZgyHHAaTfFX5cFiA0vjmJg0IjU50hElGdYeaHMZNjzorCYTbjv4sn48u/fQiAkjK+8pLPKrrqXko730115iTNsNPpsYObt8t45Q48Hqo5JHdC04aVmKmcSEdGAxPBCmcmw50VrbFURbp49Dnf/ZyOOG16a+MCsVF6UVXb1NOymsa5MqkXqEq2wC8gr2Z5yQ+r30Cook6dH+9s4ZEREAxbDC2VGHTZKvrN0Kt89dSS+Nm0oSly2xAfluvKiBBE9lRflnILe7hssAskrL5mQJKBivLzx4dDjs/OaRET9DMMLZcYeWe4+w2EjraTBBZBXnQWy1POiY7ZRWpUXzTFBb/fAk2yqdKbO/Q2w6z1g7Jey95pERP1IThp2Fy9ejOHDh8PhcGD69OlYvXp10uOfe+45jBs3Dg6HA5MmTcLLL7+ci9OkdPSwYTctgSzNNgJ0NuxmUHnRPk8r2WyjTFUfA0z/HvtdiGjAMjy8PPvss1iwYAFuu+02rFu3DpMnT8asWbNw4MCBuMe/++67mDt3Lq666ip8+OGHuOCCC3DBBRdgw4YNRp8qpaOHDbtpCWah5yWThl09lRezJbqPUNzwkqTnhYiIMmJ4eLn//vtx9dVX48orr8SECRPwxz/+ES6XC4899ljc43/7299i9uzZuOmmmzB+/HjcddddOPbYY/HQQw8ZfaqUDnXYqGc9L7pktfKS5Z4X7XHxtghQF6mLs6s0ERFlxNDw4vf7sXbtWsycOTP6hiYTZs6ciVWrVsV9zqpVq2KOB4BZs2YlPN7n86G1tTXmQjlgi3wZ97BhV5esVF7SmB2VTuVFe1zXyosQrLwQERnA0PBy6NAhhEIhVFZWxtxfWVmJhoaGuM9paGhI6/hFixahuLhYvdTW1mbn5Cm5XA4bqYvG5bryojO8JNqcMeiNbvDI8EJElDX9foXdhQsXoqWlRb3s3r27t09pYMjlsJE6VVrHireJqOuxpNPzonfYKEEw0lZ5lGOIiKjHDJ0qXVZWBrPZjMbGxpj7GxsbUVVVFfc5VVVVaR1vt9tht/fgS40yk9PZRmmseJuIOmxkQOUl0RYBSrCzugCTWd9rERFRSoZWXmw2G6ZNm4bly5er94XDYSxfvhz19fVxn1NfXx9zPAC89tprCY+nXtIrlZdsrPNiROUlwRYB2V6gjoiIAORgkboFCxbg8ssvx3HHHYcTTjgBDz74IDo6OnDllVcCAC677DIMGTIEixYtAgBcd911OP300/Gb3/wG5557LpYsWYI1a9bgkUceMfpUKR1KePG3A+EwYDIoB4eCQDgoX89K5SWNjRl7WnkxYo0XIiIyPrx8/etfx8GDB3HrrbeioaEBU6ZMwbJly9Sm3F27dsGk+eI76aST8Mwzz+DnP/85brnlFowZMwZLly7FxIkTjT5VSoe2mhDoMG4qsLaakbPKS5pTs5XX7lZ5MWB1XSIiys32APPnz8f8+fPjPrZixYpu91188cW4+OKLDT4r6hGrE5DMgAjJQ0eGhRdf9HpPwktaexulWXmxJpgqzcoLEZEh+v1sI+olkpSb6dJKIDDbezY0ZWTlxZJgZ2mu8UJEZAiGF8pcLhaqy8YCdUCa67xkWHnpus6Lurouh42IiLKJ4YUyl4sZR9nYGgBIc2+jdCsviYaN2PNCRGQEhhfKXC6GjbJWeYkM3QQ88rL9yaRdeVGqOux5ISLKBYYXypx2urRRsl15gYi/+7NW2rONlGEjhhciolxgeKHMKcMhRg4bqQvG9XAVZe3y/Kn6XtJe50Vp2E2wwi7DCxFRVjG8UOZy2fPSkwXqAHl5fnMkAKVaqC7dFX1TNezaDJpGTkQ0QDG8UOZyEV7UyksPe16A6NBRqmGjQJrvmWpjRlZeiIiyiuGFMpeLzRmzVXkBNE27SSovQgAhX3rvye0BiIhyiuGFMpfL2UbZrLwkmy6tHfrRXXlJtDGj0vPCqdJERNnE8EKZU4eNWo17D3WqdDYqLzoWqtMOKbHyQkTUJzG8UOZsuZgqncXKixJekjXsKmFJMgNma3qvm6jnhSvsEhFlFcMLZS4nw0ZZ7HmxpVF5Sef9Us42YnghIsomhhfKXE6mShtQeUk22yiTHpt467wIwY0ZiYgMwvBCmcvFbCO18pKNht1IiEg2bBTIoMcm3gq7gU5AhGPfl4iIsoLhhTJnd8s/jRw2UisvOWrYTXeBOu3rhvxAOCRf1wYkK8MLEVE2MbxQ5pSeF39b6s0OM5XVyouOht10twYAYoOOMiSlVKOsBYCJf82IiLKJ/6pS5pRhIxFOvV9QpgzpedFTeUmj0qM9N6VnhtOkiYgMw/BCmbMVAJDk60Y17WZzkTqrjkXqMqm8mEzRfZPUygvDCxGRURheKHOSpJlxZFDfSza3B3CWyD+9zYmPyaTyAnSfLs3VdYmIDGPp7ROgfs5WKK+w6+8HlZeCcvln+4HExygVpHQrJlYX4G0B/jYXMFnk6wAXqCMiMgDDC/WMvQhog/HDRtmovBRUyD87DiY+Rgk2hRXpvfagEUDbfuDwltj7K8an9zpERJQSwwv1jNGr7GazYbcwUnnpOCjPjpKk7scowUap0uj1jaeBPR9EthWwACarPJRUPaVHp0xERN0xvFDP2A3e3yib2wMogSTolStFDnf3Y9TKS2V6r+0qBY6a1bPzIyIiXdiwSz2jNKQatbN0NisvtoLognGJho7aG+Wf6Q4bERFRzjC8UM8YOdtIiOxWXoDYoaN4Mh02IiKinGF4oZ4xctgoFIjuD5SNyguQfMZROBwNL6y8EBH1WQwv1DPqsJEBs420Gx1mq/KizjiKE168zUA4GDmOlRcior6K4YV6xsjZRkq/CyTAbMvOa6rDRoe6P6b0uzhKAIs9O+9HRERZx/BCPaPsLG3EInXaHZ7jTWvORLJho0zXeCEiopxieKGeMXLYKJN9hlJJtlCd2qzL8EJE1JcxvFDPGDnbSN0aIEv9LkDy2UZq5YX9LkREfRnDC/WM3ciGXSMqL8mGjZQ1XtJcoI6IiHKK4YV6xmbgVOlAhjs8J6MOG8Vp2OUaL0RE/QLDC/WMOmzUTyovypCQr0UzmymCDbtERP2CIeFlx44duOqqqzBixAg4nU6MGjUKt912G/x+f9LnnXHGGZAkKebygx/8wIhTpGzRDhsJkd3XNqLy4iiRN00Euve9KGu/sGGXiKhPM2Rjxk2bNiEcDuNPf/oTRo8ejQ0bNuDqq69GR0cH7rvvvqTPvfrqq3HnnXeqt10ulxGnSNmizDYSIblSkq3F5ABjKi+SJA8Lte2Tw0tJbfQxVl6IiPoFQ8LL7NmzMXv2bPX2yJEjsXnzZjz88MMpw4vL5UJVVZURp0VGUMILIM84ymZ4CWjWecmmQk14UXBrACKifiNnPS8tLS0oLS1NedzTTz+NsrIyTJw4EQsXLoTH40l6vM/nQ2tra8yFcshkMm5nabXyksVABMSfccStAYiI+g1DKi9dbd26Fb///e9TVl2++c1vYtiwYaipqcHHH3+Mm2++GZs3b8YLL7yQ8DmLFi3CHXfcke1TpnTYi+TZRtmecWRU5SXeQnVKkHEUc2sAIqI+Lq3Ky09/+tNuDbVdL5s2bYp5zt69ezF79mxcfPHFuPrqq5O+/ve+9z3MmjULkyZNwqWXXoonn3wSL774IrZt25bwOQsXLkRLS4t62b17dzq/EmWDzaD9jdRF6gwYNgJiw4vSrMs1XoiI+ry0Ki833ngjrrjiiqTHjBw5Ur2+b98+nHnmmTjppJPwyCOPpH1y06dPByBXbkaNGhX3GLvdDrud/6fcq4xaqE6pvGSzYReIP2zUzplGRET9RVrhpby8HOXl+voB9u7dizPPPBPTpk3D448/DpMp/faa9evXAwCqq6vTfi7lkN2gheqCPvlnNqdKA8mHjbg1ABFRn2dIw+7evXtxxhlnoK6uDvfddx8OHjyIhoYGNDQ0xBwzbtw4rF69GgCwbds23HXXXVi7di127NiBl156CZdddhlOO+00HHPMMUacJmWLsspu1ht2Daq8JBs2YuWFiKjPM6Rh97XXXsPWrVuxdetWDB06NOYxEVnILBAIYPPmzepsIpvNhtdffx0PPvggOjo6UFtbi4suugg///nPjThFyia7QT0vAQM2ZgQSDBtxmjQRUX9hSHi54oorUvbGDB8+XA0yAFBbW4s333zTiNMhoxk2bGTAInVAtLriOQyEQ4DJrNmUkeGFiKiv495G1HM2gxt2s115cQ0GIAEQcoABOGxERNSPMLxQzxk128ioyovZArgiCyYqfS/qsBEbdomI+jqGF+o5u1v+adgidVmuvADRCkv7gditAVh5ISLq8xheqOeMGjYyqvICxM448jYD4UDkfoYXIqK+juGFek5p2M36bCMjKy+a8MKtAYiI+hWGF+o5peelv8w2AmKHjdisS0TUrzC8UM8ZtUidURszArHDRurqugwvRET9AcML9ZxRw0bq9gBGVF4YXoiI+iuGF+o5I6ZKC6HZHsDg2UYcNiIi6lcYXqjnlNlG4UC0WtJT2tcxfNiIa7wQEfUnDC/Uc8qwEZC9oSOl6gIYVHnRhpfI1gCsvBAR9QsML9RzJjNgdcnXs9W0q2zKKJkBszU7r6mlBJWQHzi8Vb5eWJn99yEioqxjeKHsyPbmjEb2uwDy9GtlZeAj2+WfHDYiIuoXGF4oO9RVdrMUXpTKixH9LoqCsi63OWxERNQfMLxQdmR7xpHRlRege1gpYOWFiKg/YHih7FA3Z8xSeMlF5UU7TOQoNmYlXyIiyjqGF8qObA8bqZUXI4eNNOGFQ0ZERP0GwwtlR7aHjXLS86IJLFxdl4io32B4oezI+myjHA8bsd+FiKjfYHih7LBlu2FX2VHayIZdTWBh5YWIqN9geKHsUBp2szZsZOCO0goOGxER9UsML5QdSs9LtoeNjKy8aAMLG3aJiPoNhhfKjmwPG+V6kTpWXoiI+g2GF8oOpWE361OlDay82N2A2S5fZ+WFiKjfYHih7FCHjfpR5UWSgEkXAxVHA5UTjHsfIiLKKktvnwDlCZtSeelH2wMAwAWLjX19IiLKOlZeKDuyPWyUi8oLERH1SwwvlB1Zn22Uo8oLERH1OwwvlB1K5SXoBYL+nr8eKy9ERJQAwwtlh7JIHQB4W3r+ernYHoCIiPolhhfKDpMZcBTL1zuP9Pz11EXqGF6IiCgWwwtlj3OQ/NPb3PPXUrcHYM8LERHFYnih7HGUyD9ZeSEiIgMxvFD2KJWXbIQXVl6IiCgBhhfKHmeJ/LOzueevxcoLERElwPBC2ZPVyosy24iVFyIiimVYeBk+fDgkSYq53HvvvUmf4/V6MW/ePAwePBiFhYW46KKL0NjYaNQpUrZlM7yoi9Sx8kJERLEMrbzceeed2L9/v3q59tprkx5/ww034F//+heee+45vPnmm9i3bx+++tWvGnmKlE1Kw25PZxuFQ0AostAdKy9ERNSFoRszFhUVoaqqStexLS0t+POf/4xnnnkGZ511FgDg8ccfx/jx4/Hee+/hxBNPNPJUKRuyVXlR+l0AVl6IiKgbQ8PLvffei7vuugt1dXX45je/iRtuuAEWS/y3XLt2LQKBAGbOnKneN27cONTV1WHVqlUJw4vP54PP51Nvt7TIq7u2trZm8TchXUJ2wCeApoNATz5/T5P8OgDg8QPeUHbOj4iI+izle1sIkfJYw8LLj370Ixx77LEoLS3Fu+++i4ULF2L//v24//774x7f0NAAm82GkpKSmPsrKyvR0NCQ8H0WLVqEO+64o9v9tbW1PTp/6ok3gR8VZ+el7i3NzusQEVG/0NbWhuLi5N8haYWXn/70p/i///u/pMds3LgR48aNw4IFC9T7jjnmGNhsNnz/+9/HokWLYLfb03nbpBYuXBjzXuFwGE1NTRg8eDAkScra+wByKqytrcXu3bvhdrtTP4Eyws85N/g55w4/69zg55wbRn3OQgi0tbWhpqYm5bFphZcbb7wRV1xxRdJjRo4cGff+6dOnIxgMYseOHRg7dmy3x6uqquD3+9Hc3BxTfWlsbEzaN2O327uFoa7Vm2xzu938i5ED/Jxzg59z7vCzzg1+zrlhxOecquKiSCu8lJeXo7y8PKMTWr9+PUwmEyoqKuI+Pm3aNFitVixfvhwXXXQRAGDz5s3YtWsX6uvrM3pPIiIiyj+G9LysWrUK77//Ps4880wUFRVh1apVuOGGG/Ctb30LgwbJM1L27t2LGTNm4Mknn8QJJ5yA4uJiXHXVVViwYAFKS0vhdrtx7bXXor6+njONiIiISGVIeLHb7ViyZAluv/12+Hw+jBgxAjfccENMb0ogEMDmzZvh8XjU+x544AGYTCZcdNFF8Pl8mDVrFv7whz8YcYoZsdvtuO2227Las0Pd8XPODX7OucPPOjf4OedGX/icJaFnThIRERFRH8G9jYiIiKhfYXghIiKifoXhhYiIiPoVhhciIiLqVxhedFq8eDGGDx8Oh8OB6dOnY/Xq1b19Sv3KokWLcPzxx6OoqAgVFRW44IILsHnz5phjvF4v5s2bh8GDB6OwsBAXXXQRGhsbY47ZtWsXzj33XLhcLlRUVOCmm25CMBjM5a/Sr9x7772QJAnXX3+9eh8/5+zYu3cvvvWtb2Hw4MFwOp2YNGkS1qxZoz4uhMCtt96K6upqOJ1OzJw5E1u2bIl5jaamJlx66aVwu90oKSnBVVddhfb29lz/Kn1aKBTCL37xC4wYMQJOpxOjRo3CXXfdFbP/DT/r9K1cuRLnnXceampqIEkSli5dGvN4tj7Tjz/+GKeeeiocDgdqa2vxq1/9Kju/gKCUlixZImw2m3jsscfEp59+Kq6++mpRUlIiGhsbe/vU+o1Zs2aJxx9/XGzYsEGsX79efOlLXxJ1dXWivb1dPeYHP/iBqK2tFcuXLxdr1qwRJ554ojjppJPUx4PBoJg4caKYOXOm+PDDD8XLL78sysrKxMKFC3vjV+rzVq9eLYYPHy6OOeYYcd1116n383PuuaamJjFs2DBxxRVXiPfff1988cUX4pVXXhFbt25Vj7n33ntFcXGxWLp0qfjoo4/EV77yFTFixAjR2dmpHjN79mwxefJk8d5774m33npLjB49WsydO7c3fqU+65577hGDBw8W//73v8X27dvFc889JwoLC8Vvf/tb9Rh+1ul7+eWXxc9+9jPxwgsvCADixRdfjHk8G59pS0uLqKysFJdeeqnYsGGD+Nvf/iacTqf405/+1OPzZ3jR4YQTThDz5s1Tb4dCIVFTUyMWLVrUi2fVvx04cEAAEG+++aYQQojm5mZhtVrFc889px6zceNGAUCsWrVKCCH/ZTOZTKKhoUE95uGHHxZut1v4fL7c/gJ9XFtbmxgzZox47bXXxOmnn66GF37O2XHzzTeLU045JeHj4XBYVFVViV//+tfqfc3NzcJut4u//e1vQgghPvvsMwFAfPDBB+ox//3vf4UkSWLv3r3GnXw/c+6554rvfOc7Mfd99atfFZdeeqkQgp91NnQNL9n6TP/whz+IQYMGxfy7cfPNN4uxY8f2+Jw5bJSC3+/H2rVrMXPmTPU+k8mEmTNnYtWqVb14Zv1bS0sLAKC0VN41eu3atQgEAjGf87hx41BXV6d+zqtWrcKkSZNQWVmpHjNr1iy0trbi008/zeHZ933z5s3DueeeG/N5Avycs+Wll17Ccccdh4svvhgVFRWYOnUqHn30UfXx7du3o6GhIeZzLi4uxvTp02M+55KSEhx33HHqMTNnzoTJZML777+fu1+mjzvppJOwfPlyfP755wCAjz76CG+//TbmzJkDgJ+1EbL1ma5atQqnnXYabDabesysWbOwefNmHDlypEfnaMgKu/nk0KFDCIVCMf+QA0BlZSU2bdrUS2fVv4XDYVx//fU4+eSTMXHiRABAQ0MDbDZbt001Kysr0dDQoB4T77+D8hjJlixZgnXr1uGDDz7o9hg/5+z44osv8PDDD2PBggW45ZZb8MEHH+BHP/oRbDYbLr/8cvVzivc5aj/nrnu9WSwWlJaW8nPW+OlPf4rW1laMGzcOZrMZoVAI99xzDy699FIA4GdtgGx9pg0NDRgxYkS311AeU7YLygTDC+XcvHnzsGHDBrz99tu9fSp5Z/fu3bjuuuvw2muvweFw9Pbp5K1wOIzjjjsOv/zlLwEAU6dOxYYNG/DHP/4Rl19+eS+fXX75+9//jqeffhrPPPMMjj76aKxfvx7XX389ampq+FkPYBw2SqGsrAxms7nbbIzGxkZUVVX10ln1X/Pnz8e///1v/O9//8PQoUPV+6uqquD3+9Hc3BxzvPZzrqqqivvfQXmM5GGhAwcO4Nhjj4XFYoHFYsGbb76J3/3ud7BYLKisrOTnnAXV1dWYMGFCzH3jx4/Hrl27AEQ/p2T/blRVVeHAgQMxjweDQTQ1NfFz1rjpppvw05/+FN/4xjcwadIkfPvb38YNN9yARYsWAeBnbYRsfaZG/lvC8JKCzWbDtGnTsHz5cvW+cDiM5cuXo76+vhfPrH8RQmD+/Pl48cUX8cYbb3QrJU6bNg1WqzXmc968eTN27dqlfs719fX45JNPYv7CvPbaa3C73d2+SAaqGTNm4JNPPsH69evVy3HHHYdLL71Uvc7PuedOPvnkblP9P//8cwwbNgwAMGLECFRVVcV8zq2trXj//fdjPufm5masXbtWPeaNN95AOBzG9OnTc/Bb9A8ejwcmU+xXldlsRjgcBsDP2gjZ+kzr6+uxcuVKBAIB9ZjXXnsNY8eO7dGQEQBOldZjyZIlwm63iyeeeEJ89tln4nvf+54oKSmJmY1Byf3whz8UxcXFYsWKFWL//v3qxePxqMf84Ac/EHV1deKNN94Qa9asEfX19aK+vl59XJnCe84554j169eLZcuWifLyck7hTUE720gIfs7ZsHr1amGxWMQ999wjtmzZIp5++mnhcrnEU089pR5z7733ipKSEvHPf/5TfPzxx+L888+PO9V06tSp4v333xdvv/22GDNmzICevhvP5ZdfLoYMGaJOlX7hhRdEWVmZ+MlPfqIew886fW1tbeLDDz8UH374oQAg7r//fvHhhx+KnTt3CiGy85k2NzeLyspK8e1vf1ts2LBBLFmyRLhcLk6VzqXf//73oq6uTthsNnHCCSeI9957r7dPqV8BEPfy+OOPq8d0dnaKa665RgwaNEi4XC5x4YUXiv3798e8zo4dO8ScOXOE0+kUZWVl4sYbbxSBQCDHv03/0jW88HPOjn/9619i4sSJwm63i3HjxolHHnkk5vFwOCx+8YtfiMrKSmG328WMGTPE5s2bY445fPiwmDt3rigsLBRut1tceeWVoq2tLZe/Rp/X2toqrrvuOlFXVyccDocYOXKk+NnPfhYz/Zafdfr+97//xf03+fLLLxdCZO8z/eijj8Qpp5wi7Ha7GDJkiLj33nuzcv6SEJplComIiIj6OPa8EBERUb/C8EJERET9CsMLERER9SsML0RERNSvMLwQERFRv8LwQkRERP0KwwsRERH1KwwvRERE1K8wvBBRn3X77bdjypQpvX0aRNTHMLwQEQGQJAlLly7t7dMgIh0YXoiIiKhfYXghIgDAGWecgfnz52P+/PkoLi5GWVkZfvGLX0DZ/uwPf/gDxowZA4fDgcrKSnzta19TnxsOh7Fo0SKMGDECTqcTkydPxj/+8Q/18SeeeAIlJSUx77d06VJIkhRz37333ovKykoUFRXhqquugtfrjXk8HA7jzjvvxNChQ2G32zFlyhQsW7ZM1+/n9/sxf/58VFdXw+FwYNiwYVi0aBEAYPjw4QCACy+8EJIkqbcB4J///CeOPfZYOBwOjBw5EnfccQeCwaD6uCRJePjhhzFnzhw4nU6MHDky5ncnIgNkZXtHIur3Tj/9dFFYWCiuu+46sWnTJvHUU08Jl8slHnnkEfHBBx8Is9ksnnnmGbFjxw6xbt068dvf/lZ97t133y3GjRsnli1bJrZt2yYef/xxYbfbxYoVK4QQQjz++OOiuLg45v1efPFFof0n6NlnnxV2u138v//3/8SmTZvEz372M1FUVCQmT56sHnP//fcLt9st/va3v4lNmzaJn/zkJ8JqtYrPP/885e/361//WtTW1oqVK1eKHTt2iLfeeks888wzQgghDhw4oO5yvn//fnHgwAEhhBArV64UbrdbPPHEE2Lbtm3i1VdfFcOHDxe33367+roAxODBg8Wjjz4qNm/eLH7+858Ls9ksPvvss7T/GxCRPgwvRCSEkMPL+PHjRTgcVu+7+eabxfjx48Xzzz8v3G63aG1t7fY8r9crXC6XePfdd2Puv+qqq8TcuXOFEPrCS319vbjmmmtijpk+fXpMeKmpqRH33HNPzDHHH398t+fFc+2114qzzjor5vfTAiBefPHFmPtmzJghfvnLX8bc99e//lVUV1fHPO8HP/hBt/P+4Q9/mPKciCgzHDYiItWJJ54YM5RTX1+PLVu2YMaMGRg2bBhGjhyJb3/723j66afh8XgAAFu3boXH48HZZ5+NwsJC9fLkk09i27Ztut9748aNmD59esx99fX16vXW1lbs27cPJ598cswxJ598MjZu3Jjy9a+44gqsX78eY8eOxY9+9CO8+uqrKZ/z0Ucf4c4774z5va6++mrs379f/f27nqdyW885EVFmLL19AkTU9xUWFmLdunVYsWIFXn31Vdx66624/fbb8cEHH6C9vR0A8J///AdDhgyJeZ7dbgcAmEwmtXdGEQgEcnPyEcceeyy2b9+O//73v3j99ddxySWXYObMmUn7U9rb23HHHXfgq1/9arfHHA6HkadLREmw8kJEqvfffz/m9nvvvYcxY8bAbDbDYrFg5syZ+NWvfoWPP/4YO3bswBtvvIEJEybAbrdj165dGD16dMyltrYWAFBeXo62tjZ0dHSor71+/fqY9xo/fnzc91e43W7U1NTgnXfeiTnmnXfewYQJE3T9fm63G1//+tfx6KOP4tlnn8Xzzz+PpqYmAIDVakUoFIo5/thjj8XmzZu7/V6jR4+GyRT951N7nsrt8ePH6zonIkofKy9EpNq1axcWLFiA73//+1i3bh1+//vf4ze/+Q3+/e9/44svvsBpp52GQYMG4eWXX0Y4HMbYsWNRVFSEH//4x7jhhhsQDodxyimnoKWlBe+88w7cbjcuv/xyTJ8+HS6XC7fccgt+9KMf4f3338cTTzwR897XXXcdrrjiChx33HE4+eST8fTTT+PTTz/FyJEj1WNuuukm3HbbbRg1ahSmTJmCxx9/HOvXr8fTTz+d8ne7//77UV1djalTp8JkMuG5555DVVWVOgtq+PDhWL58OU4++WTY7XYMGjQIt956K7785S+jrq4OX/va12AymfDRRx9hw4YNuPvuu9XXfu6553DcccfhlFNOwdNPP43Vq1fjz3/+c1b+mxBRHL3ddENEfcPpp58urrnmGvGDH/xAuN1uMWjQIHHLLbeIcDgs3nrrLXH66aeLQYMGCafTKY455hjx7LPPqs8Nh8PiwQcfFGPHjhVWq1WUl5eLWbNmiTfffFM95sUXXxSjR48WTqdTfPnLXxaPPPKI6PpP0D333CPKyspEYWGhuPzyy8VPfvKTmIbdUCgkbr/9djFkyBBhtVrF5MmTxX//+19dv98jjzwipkyZIgoKCoTb7RYzZswQ69atUx9/6aWXxOjRo4XFYhHDhg1T71+2bJk46aSThNPpFG63W5xwwgnikUceUR8HIBYvXizOPvtsYbfbxfDhw2M+GyLKPkmILgPRRDQgnXHGGZgyZQoefPDB3j6VfkWSJLz44ou44IILevtUiAYM9rwQERFRv8LwQkR54Ze//GXMlGbtZc6cOb19ekSURRw2IqK80NTUpM4c6srpdHabxk1E/RfDCxEREfUrHDYiIiKifoXhhYiIiPoVhhciIiLqVxheiIiIqF9heCEiIqJ+heGFiIiI+hWGFyIiIupX/j+seUgXZHooKwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case = project.case\n",
    "case.wait()\n",
    "\n",
    "total_forces = case.results.total_forces.as_dataframe()\n",
    "total_forces.plot(\"pseudo_step\", [\"CL\", \"CD\"], ylim=[-5, 15])\n",
    "show()"
   ],
   "id": "6d271bf3"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flow360",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
