FieldProjections.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing near field to far field projections\n",
    "\n",
    "This tutorial will show you how to use field projections to obtain electromagnetic field data far away from a structure with knowledge of only near-field data.\n",
    "\n",
    "When projecting fields, geometric approximations can be invoked to allow computing fields far away from the structure quickly and with good accuracy, but in `Tidy3D` we can also turn these approximations off when projecting fields at intermediate distances away, which gives a lot of flexibility.\n",
    "\n",
    "These field projections are particularly useful for eliminating the need to simulate large regions of empty space around a structure. \n",
    "\n",
    "In this notebook, we will\n",
    "\n",
    "* show how to compute projected fields on your local machine after a simulation is run, or on our servers during the simulation run.\n",
    "\n",
    "* show how to extract various quantities related to projected fields such as fields in different coordinate systems, power, and radar cross section.\n",
    "\n",
    "* demonstrate how, when far field approximations are used, the fields can dynamically be re-projected to new distances without having to run a new simulation.\n",
    "\n",
    "* study when geometric far field approximations should and should not be invoked, depending on the projection distance and the geometry of the structure.\n",
    "\n",
    "* show how to set up projections for finite-sized objects (e.g., scattering at a sphere) vs. thin but large-area structures (e.g., metasurfaces).\n",
    "\n",
    "## Table of contents\n",
    "1. [Simulation setup](#setup)\n",
    "2. [Far-field projector setup](#farfield1)\n",
    "3. [Server-side far field projection](#farfieldserver1)\n",
    "4. [Downsampling the near fields to speed up projections](#farfieldserverdownsample)\n",
    "5. [Spatial filtering of far fields via windowing of near fields](#spatialfiltering)\n",
    "6. [Coordinate system conversion, power computation](#powercoords)\n",
    "7. [Re-projection to a new far field distance](#reproj)\n",
    "8. [Exact field projections without making the far-field approximation](#exact)\n",
    "9. [Projection to a grid defined in reciprocal space](#kspace)\n",
    "10. [Some final notes](#notes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3d imports\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Far Field for a Uniformly Illuminated Aperture <a name=\"setup\"></a>\n",
    "\n",
    "First, we will consider the simple case of an aperture in a perfect electric conductor sheet illuminated by a plane wave. The far fields in this case are known analytically, which allows for a straightforward comparison to `Tidy3D`'s field projection functionality. We will show how to compute the far fields both on your local machine, and on the server. The geometry is shown below.\n",
    "\n",
    "<img src=\"img/far_field_aperture.png\" width=350 alt=\"Schematic of the aperture\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometry setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# size of the aperture (um)\n",
    "width = 1.5\n",
    "height = 2.5\n",
    "\n",
    "# free space central wavelength (um)\n",
    "wavelength = 0.75\n",
    "# center frequency\n",
    "f0 = td.C_0 / wavelength\n",
    "\n",
    "# Define materials\n",
    "air = td.Medium(permittivity=1)\n",
    "pec = td.PECMedium()\n",
    "\n",
    "# PEC plate thickness\n",
    "thick = 0.2\n",
    "\n",
    "# FDTD grid resolution\n",
    "min_cells_per_wvl = 30\n",
    "\n",
    "# create the PEC plate\n",
    "plate = td.Structure(\n",
    "    geometry=td.Box(size=[td.inf, thick, td.inf], center=[0, 0, 0]), medium=pec\n",
    ")\n",
    "\n",
    "# create the aperture in the plate\n",
    "aperture = td.Structure(\n",
    "    geometry=td.Box(size=[width, 1.5 * thick, height], center=[0, 0, 0]), medium=air\n",
    ")\n",
    "\n",
    "# make sure to append the aperture to the plate so that it overrides that region of the plate\n",
    "geometry = [plate, aperture]\n",
    "\n",
    "# define the boundaries as PML on all sides\n",
    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "\n",
    "# set the total domain size in x, y, and z\n",
    "sim_size = [width * 2, 2, height * 2]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Source setup\n",
    "For our incident field, we create a plane wave incident from the left, with the electric field polarized in the -z direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bandwidth in Hz\n",
    "fwidth = f0 / 10.0\n",
    "\n",
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=f0, fwidth=fwidth)\n",
    "\n",
    "# place the source to the left, propagating in the +y direction\n",
    "offset_src = -0.3\n",
    "source = td.PlaneWave(\n",
    "    center=(0, offset_src, -0),\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    source_time=gaussian,\n",
    "    direction=\"+\",\n",
    "    pol_angle=np.pi / 2,\n",
    ")\n",
    "\n",
    "# Simulation run time\n",
    "run_time = 50 / fwidth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create monitor\n",
    "\n",
    "First, we'll see how to do field projections using your machine after you've downloaded near fields from a `Tidy3D` simulation.\n",
    "\n",
    "We create a surface [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) just to the right of the aperture to capture the near field data in the frequency domain.\n",
    "\n",
    "We will turn off field colocation to obtain the raw data on the Yee grid. In the local field projection computation, fields are colocated internally as needed. In theory, turning off the server-side colocation avoids double interpolation in some cases. This can produce slightly more accurate results, although typically there should not be a significant difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_mon = 0.3\n",
    "monitor_near = td.FieldMonitor(\n",
    "    center=[0, offset_mon, 0], size=[td.inf, 0, td.inf], freqs=[f0], name=\"near_field\", colocate=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Simulation\n",
    "\n",
    "Now we can put everything together and define the simulation with a simple uniform mesh, and then we'll visualize the geometry to make sure everything looks right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[monitor_near],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim.plot(x=0, ax=ax1)\n",
    "sim.plot(y=0, ax=ax2)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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\">12:27:48 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_1'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-9f87a07b-bb81-4408-9482-08e65924f548'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:48 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_1'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-9f87a07b-bb81-4408-9482-08e65924f548'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548'</span></a>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=243927;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=243927;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=493947;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=243927;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=493947;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=243927;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[32m-9f87a07b-bb81-4408-9482-08e65924f548'\u001b[0m\u001b]8;;\u001b\\.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49fc1b6bb36d48e9b0934069bf2f9edd",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:27:51 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:51 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:19 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:19 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"></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\">12:28:27 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:27 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "93ba782fb7124bd3ad13265271b7d3f7",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:36 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:36 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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 = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:40 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:40 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:41 EST </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:41 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=481389;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=481389;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=597279;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=481389;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=597279;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=481389;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9f87a07b-bb81-4408-9482-08e65924f548\u001b\\\u001b[4;34m-9f87a07b-bb81-4408-9482-08e65924f548'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "654a627313aa41a7ade40cfe6819221e",
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      },
      "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:28:43 EST </span>loading simulation from data/aperture_1.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:43 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_1.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(\n",
    "    sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Far field points <a name=\"farfield1\"></a>\n",
    "Now, we'll define the set of observation angles far away from the source at which we'd like to measure the far fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# radial distance away from the origin at which to project fields\n",
    "r_proj = 50 * wavelength\n",
    "\n",
    "# theta and phi angles at which to observe fields - part of the half-space to the right\n",
    "theta_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n",
    "phi_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we define a far-field monitor, [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html), which stores the information regarding the far field projection grid, and then we define the object that does the actual projections, [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5492d8ea31ec4139a8821e2a66adff0e",
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       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# far field projection monitor\n",
    "monitor_far = td.FieldProjectionAngleMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    # the size and center of any far field monitor should indicate where the *near* fields are recorded\n",
    "    freqs=[f0],\n",
    "    name=\"far_field\",\n",
    "    phi=list(phi_proj),\n",
    "    theta=list(theta_proj),\n",
    "    proj_distance=r_proj,\n",
    "    far_field_approx=True,  # we leave this to its default value of 'True' because we are interested in fields sufficiently\n",
    "    # far away that geometric far field approximations can be invoked to speed up the calculation\n",
    ")\n",
    "\n",
    "# helper functin to call the projector\n",
    "def get_proj_fields(sim_data, monitor_near, monitor_far, pts_per_wavelength=10):\n",
    "    # object that does projections is constructed using the near-field monitor, because those are the fields to be projected\n",
    "    projector = td.FieldProjector.from_near_field_monitors(\n",
    "        sim_data=sim_data,\n",
    "        near_monitors=[monitor_near],\n",
    "        normal_dirs=[\"+\"],  # we are projecting along the + direction\n",
    "        pts_per_wavelength=pts_per_wavelength,  # to speed up calculations, the fields on the near-field monitor can be downsampled to these\n",
    "        # many points per wavelength (default is already 10)\n",
    "    )\n",
    "    return projector.project_fields(monitor_far)\n",
    "\n",
    "\n",
    "# execute the projector, with the far field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data = get_proj_fields(sim_data, monitor_near, monitor_far)\n",
    "t1 = time.perf_counter()\n",
    "proj_time = t1 - t0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytical solution\n",
    "Before we plot and analyze the results, we need reference data with which to perform comparisons. In our simple aperture example, an analytical expression for the far fields is already available, so we'll simply implement the analytic formula here at the observation points of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analytic_fields_aperture(\n",
    "    proj_monitor, sim_size, aperture_height, aperture_width, r_proj\n",
    "):\n",
    "    \"\"\"Compute the far fields analytically.\"\"\"\n",
    "    # in Tidy3D, the plane wave source is normalized so that a total flux of 1 is injected into the simulation domain,\n",
    "    # which corresponds to an electric field strength that is inversely proportional to the square root of the in-plane domain area\n",
    "    thetas_ext = np.array(proj_monitor.theta)[None, :, None, None]\n",
    "    phis_ext = np.array(proj_monitor.phi)[None, None, :, None]\n",
    "    f = np.array(proj_monitor.freqs)[None, None, None, :]\n",
    "    E0 = np.sqrt(2.0 * td.ETA_0 / sim_size[0] / sim_size[2])\n",
    "    k = 2.0 * np.pi * f / td.C_0\n",
    "    ux = k * np.sin(thetas_ext) * np.cos(phis_ext) * aperture_width / 2.0\n",
    "    uz = k * np.cos(thetas_ext) * aperture_height / 2.0\n",
    "    Etheta = -(\n",
    "        -k\n",
    "        / 2.0\n",
    "        / np.pi\n",
    "        / r_proj\n",
    "        * E0\n",
    "        * np.sin(thetas_ext)\n",
    "        * np.exp(1j * k * r_proj)\n",
    "        * aperture_height\n",
    "        * aperture_width\n",
    "        * np.sinc(ux / np.pi)\n",
    "        * np.sinc(uz / np.pi)\n",
    "    )\n",
    "    Hphi = Etheta / td.ETA_0\n",
    "\n",
    "    # for convenience, let's encapsulate the data into one of Tidy3D's native data structures designed for\n",
    "    # storing far fields - this is the same format in which data will be returned when using Tidy3D's\n",
    "    # 'FieldProjector', so comparisons will be easier to make\n",
    "    coords = dict(\n",
    "        r=np.array([r_proj]),\n",
    "        theta=np.array(proj_monitor.theta),\n",
    "        phi=np.array(proj_monitor.phi),\n",
    "        f=np.array(proj_monitor.freqs),\n",
    "    )\n",
    "    Etheta_data = td.FieldProjectionAngleDataArray(Etheta, coords=coords)\n",
    "    Hphi_data = td.FieldProjectionAngleDataArray(Hphi, coords=coords)\n",
    "    Er_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Ephi_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Hr_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Htheta_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    return td.FieldProjectionAngleData(\n",
    "        monitor=proj_monitor,\n",
    "        Er=Er_data,\n",
    "        Etheta=Etheta_data,\n",
    "        Ephi=Ephi_data,\n",
    "        Hr=Hr_data,\n",
    "        Htheta=Htheta_data,\n",
    "        Hphi=Hphi_data,\n",
    "        projection_surfaces=proj_monitor.projection_surfaces,\n",
    "    )\n",
    "\n",
    "\n",
    "analytic_field_data = analytic_fields_aperture(\n",
    "    monitor_far, sim_size, height, width, r_proj\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot and compare\n",
    "Now we can compare the analytic fields to those computed via `Tidy3D`'s [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html), and also compute the root mean squared error between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.54 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x273.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_field_plot(phi, theta, vals1, vals2):\n",
    "    n_plots = 2\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(8, 3.8))\n",
    "    im1 = ax[0].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals1),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im2 = ax[1].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals2),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    ax[0].set_title(\"Analytic\")\n",
    "    ax[1].set_title(\"Field projection\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "\n",
    "# RMSE\n",
    "def rmse(array_ref, array_test):\n",
    "    error = array_test - array_ref\n",
    "    rmse = np.sqrt(np.mean(np.abs(error.flatten()) ** 2))\n",
    "    nrmse = rmse / np.abs(np.max(array_ref.flatten()) - np.min(array_ref.flatten()))\n",
    "    return nrmse\n",
    "\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_analytic = analytic_field_data.Etheta.isel(f=0, r=0)\n",
    "Etheta_proj = projected_field_data.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain good agreement to analytical results. Now let's see if we can repeat this simulation but compute the far fields on the server, during the simulation run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Server-side field projection <a name=\"farfieldserver1\"></a>\n",
    "All we have to do is provide the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) monitor as an input to the `Tidy3D` `Simulation` object as one of its `monitors`. Now, we no longer need to provide a separate near-field [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) - the near fields will automatically be recorded based on the size and location of the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim2 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[\n",
    "        monitor_far\n",
    "    ],  # just provide the far field FieldProjectionAngleMonitor as the input monitor\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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\">12:28:46 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_2'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:46 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_2'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \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\">             </span>View task using web UI at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1'</span></a>.               \n",
       "</pre>\n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=999288;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=999288;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=323237;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=999288;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=323237;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=999288;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[32m-ab699c92-9bb1-4630-b1df-6705e6a92bd1'\u001b[0m\u001b]8;;\u001b\\.               \n"
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       "Output()"
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      "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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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:28:48 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:48 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:09 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:09 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"></pre>\n"
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     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:16 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:16 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">12:29:17 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:17 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
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     "metadata": {},
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       "Output()"
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:25 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:25 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \n"
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     "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": []
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     "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
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     "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\">12:29:30 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:30 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></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\">             </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=537165;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=537165;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=745079;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=537165;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=745079;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=537165;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab699c92-9bb1-4630-b1df-6705e6a92bd1\u001b\\\u001b[4;34m-ab699c92-9bb1-4630-b1df-6705e6a92bd1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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      "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:29:32 EST </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:32 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(\n",
    "    sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the projected fields are already contained in the returned `sim_data2` object - all we have to do is access it as follows, and then plot and compare to analytical results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.54 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x273.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the computed projected fields\n",
    "projected_field_data_server = sim_data2[monitor_far.name]\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_proj_server = projected_field_data_server.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj_server)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj_server.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain nearly identical results, except that they are computed much faster on our servers. Note also that in some cases, the server-side computations may be slightly more accurate than client-side ones, because on the server, the near fields are not downsampled at all.\n",
    "\n",
    "To see the performance gains of using server-side computations, let's compare the time taken in each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection took 2.73 s\n",
      "Server-side field projection took 0.62 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(\n",
    "    sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
    "print(f\"Server-side field projection took {server_time:.2f} s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the server computes far fields faster than the local CPU-based computation, though it's a relatively small gain in this case. The gains in computation time are expected to be greater for larger and more complex setups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling the near fields to speed up projections <a name=\"farfieldserverdownsample\"></a>\n",
    "We can further speed up the server-side field projections by downsampling the near fields before projecting them to the far field. \n",
    "Typically, we should make sure that we are still sampling at least approximately 10 points per wavelength. Therefore, the downsampling feature is useful when the simulation resolution needs to be fairly high for the time stepping itself, but the projection step does not require such a high resolution.\n",
    "\n",
    "All we have to do is provide the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) monitor (or any of the other field projection monitors) with an additional argument, `interval_space`, which defines the spatial interval at which to downsample the near fields. For example, a spatial interval of 2 implies that every 2nd near field point is used in the projection, which in turn means that roughly half the near field points are dropped. Note that the first and last points in the original grid are always kept in the near fields. We can downsample by a different amount along each direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make an updated copy of the projection monitor, this time requesting downsampling\n",
    "monitor_far = monitor_far.copy(update={\"interval_space\": (4, 1, 3)}) # downsample by a factor of 4 along x, and 3 along y\n",
    "\n",
    "# update the simulation object with this new monitor\n",
    "sim2 = sim2.copy(update={\"monitors\": [monitor_far]})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation with downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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     "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\">12:29:55 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:55 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
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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"
      ],
      "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\">12:30:04 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:04 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "model_id": "8dd5ebd8f7da4b70b35c9f2a919dff5f",
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:30:12 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:12 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:30:13 EST </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:13 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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      "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:30:16 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:16 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></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\">12:30:17 EST </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:17 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=542799;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=542799;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=252539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=542799;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=252539;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=542799;https://tidy3d.simulation.cloud/workbench?taskId=fdve-087b5a62-71fb-41db-ac84-d509e78b648c\u001b\\\u001b[4;34m-087b5a62-71fb-41db-ac84-d509e78b648c'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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      "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:30:18 EST </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:18 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(\n",
    "    sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare the downsampled projected fields to the analytical results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.54 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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qzy721M9IvlvQByfz/RqeI4XBxKrNrGrgdM3zE2ZHq/dCw3WFfcniXHW6bGvk7hSEjkBEoKD6r+a6LiSirxDRrUT0Ss/8hxLRx4no74joi0T09NYPSBCEpaRNW7MIdo1i4+LzqKszD0yNiGZ1E5L1N+g9UrQ/ZdsuazPfRgzOpOS7C5f3kAGUClSklqj+NMUVWYv64NjY3YyLqKvWzLNCMdH0xoSIQgBXAHgqVLfdG4noIDPfYi32OwCuY+a3EdF5UAW2zp5640IjplFdJ8mubKpkd6H+VlPVdZLf8zQVleeVldmUNmzNoti1AxtB6BwEhL1W/pFcAOBWZr4NAIjoWgAXAbAHNgzgBP1+P4B/bmPDgiB0gPZszUJYuYFN0ci5jvcysyyqBrE09nZ93lCRKuNfV71ps4SjGKGzzYyHouc1y4pKl/VVZM0rRNRqDZ15e3QG1ZiulfvyDADfsj4fAvA4Z5nXAvgIEb0MwCaAp7SxYaGYpr/NKptW3HG6ZlfsVir0zt7elCkevt+qz9b47IPvN1u0raqjLPv9+87RolWcFm3NQujukEwQdh2EgKr/AJxCRDdZf5dOsLHnAbiamc8E8HQAf0ZEYi8EYVdQ29YsJSun2BRRNmL2eStmpO/zRurE2pRhez114mhslcb1mCizLv/++FSeWRdf4ijKbdvE09j7ac6lUXVsT8X1Wezvs6cnjlt9tWltHHef/POK76M2VJlSCAjqycN3M/P5JfNvB3CW9flMPc3mJQAuBABm/hQRrQM4BcBd9XdYmAVedaWGHbG/53rjZTYpY2P07zmoY3c832sDX1xecTyeZU/0+yCOvfPVZ0u1D5x5MZd0Kfftk60mZ8/pzG3FtNS3NUtJd/dcEHYZRCoFs+qvBjcCOJeIziGiAYCLARx0lvkmgCer7dL3A1gH8J0WD0cQhCWlRVuzEOam2BDRVQCeAeAuZn6kM+/XAfxXAA9i5rtJhWO/BUoCPw7gxcz8+cqNcPWzSZ934Y6eA48KoL7rZNt4lJs4UQ0mu+hNvaMyr8inynifr7fkTcWWJ0QeBYaSTsJRrfW5e2XWxHHsXX+jfa35/HxahabtZ+VtPAxi5jERXQbgBqhas1cx881E9DoANzHzQQC/DuCPiejXoAKJX8zMS+5mKuZiaxZAHaXG/n27y9sqjbEp5vtldofCoFBFtm2MWca2J3XUnLrUUmW0DbKnGXvjU2rMq1FzOLTsVZTtn2XXzsrbprwKTSHNNWOybdp68ExEF0L9xkIAb2fmyz3LPAcqro8BfIGZnz/NNuf5KOpqAG8F8A57IhGdBeAnoDxEw9MAnKv/HgfgbcgHNwrCrqOtFExmvh4qhdue9mrr/S0AntDKxubP1RBbIwhTMa/SEkR0LoBXAXgCM99HRKdOu925DWyY+ZNEdLZn1psBvALAB61pFwF4h/YQP01EB4jodGa+o3QbUP1TyiQy36jf9R7igmejRdWLOYon8kCKKHoe7YunoYx3FBYu76oypfVwah6Ley7t7KdkXhBklJy62OfU3Q4FQWH9m0koe87u0kbdm2RdDTO1iKjTKZjzYllsTVPatiMGo9T44mhcOxJY6ow9z9gWV5Wx1RwKst9z37v7kO5f8TH7fudu7FxWnYkz3+MoTlUZe1qgVByj0JCl4ORj80wcYJhTb0w2VRBSa3Ezbaq8k3aab9HW1Ckt8W8BXMHM9wEAM08dx7dQK0lEFwG4nZm/4MzypaOeUbCOS032x/3D4Yz2VBCWgy5XA10kYmsEoRk1bU1VBmad39fDATyciP6WiD6tH11NxcKyoohoD4DfgpKGJ4aZrwRwJQB8//4TGCgfpfo8rDIVp0i9Ud9LM6fceBs71saM6s0/nSQ6v8I7q1ubRk1v9qy7SY2cIszyVepJYaxMFBfG3fj6NNXNdsp5dNb94HpVWW9vuloW3n1psX4OCEudYrmsLMrWAM377eTUyQnj90oroIfkVWoAIBj0cvEzFAaJLQkGvcz3bMUmaKDcTJIlZeyMsXRunIy9TBzlFRsTYxMPx4gdNTij4AzHajl3+y0oKdPak8y62rQtLvVtTVUGZh16UI+CnwSVoflJIvoBZr5/mhUuiu8GcA6AL+hneWcC+DwRXYB66aiCsKvoetGsBSK2RhAa0KKtqfP7OgTgM8w8AvBPRPRVqIHOjZNudGEDG2b+BwBJkBARfR3A+TpT4SCAy/TzuMcBOFz1zLsuvlFumYrj85iS7yUxNpyJt7G/F0ecLucoN+a7anlfB+p6yo6hVj2JmrUlyp71+2JeknlOrZrSWJmG3bPLlBqf2jKpUjNpHM1MPSgAkBibiViUrQGaq8cuVZmXRfbDtjs+fJlPyTwnnibs9wrjaMKMwpOPzQlK4v6axBP54mjQVy9xFGViatS20yynSCswhmDQS2JqImTn2RmXZu9shbkIvxJTbH+q1pd8b9Y2pYj2bE1SWgJqQHMxADfj6QNQBUH/lIhOgXo0dds0G52blSSidwP4FIDvJaJDRPSSksWvhzqwWwH8MYB/P4ddFISlh4gq/3Y7YmsEYXrasDXMPAZgSkt8Gaqx7s1E9DoieqZe7AYA9xDRLQA+DuA3mPmeafZ9nllRz6uYf7b1ngG8dIKNTNTvyR0VBxlFJV+bJfleiedke1pFz8bjKM6pMZnteRSVsqrHRfNmlW1Rlzr1JxrNi2Pv/CJFx/aWyjKgmnpVk3pTk9a2UPLwRF/dVczF1rRAEzXHZ4cyanByT9Wrip7ExTW0C64q45uWqkFho7pbTUliZ8LQU/dXLxPnbV9ZTCAFeRW5bgZmGvNTTyl2WZg646FNW1OjtAQDeLn+a4Vd01JBEDpPxzvuCoLQETpua2Rg46GoPkWRIuLznHw1bhLlRn+PAkrjaBAk33NH9eZTYG3bn8kVZuYl3/N4IT4ysTINYl+4tPdKvaqg7jNy+32S4eBdhjPLqOXMtMmVGt930uNYlGclj5p2C2WxgNl+akYdySs3yTxrivocgHToS2p/FCruLUrem+25dsfX7y13DM563e83rUVVuB1PjI2/iniZnUrtkGt30mXYskVZG6Oyr/KxestnQ+rSbVsjAxtB6Aimf4sgCMIs6bqtWa2BDVHtuJppqNMjqlaNG3hq2yDIxekk20B5tHeSkeQ8u47sGBu9jK3iTNt3Kdm+R53xrddVaexlfN14p1Fqir6XbqO9uhJ1meYelXTv5WOW/YDse8UXC5iqs06FXz3VnpdRbnR9wWAAa0kAw3FSqyYejtS0QT+p7ZLOU5/ZrmMTa9U5SmNukt+4XpUvrsbNnLKJPX3lXKXHrlVTVseG7To2yftRZp5deThdPkrW6VNq1CuX2h2fQjOPPlK71das1sBGEFYYImDQ4efegiB0g67bGhnYWMxCequsceMsHyMGtJOSZkxZ3ofxioznY9W6yT3HtrttR05mgO0lGW9qwswp//Ps1NPyeVju+3y14HzmU5lKo6Y3qyo8Ke59Mq/n5QRC2GEvaqVwMjBntpmyvmXW+8DJC4qTqL10SVu5STpVa+WGQqNS5LOdOI7BfVU0JrYUX7NMYs8m7BXVlDZ6RcWjUXZ5yw7FI61GOfaGY/YqNWqZfDyNbRcW1eV78gzMbtsaGdgIQlcgdNrYCILQETpua1ZqYEOYbcDT5IqGv8YNUPRs3MzTy1kKTpqx4CocQcVz7OJ6Ej6aVB6uWqaovkx5NpWtxLjH6ldpiuvlNOyiXSPjw2ZeQXaEbhsbobrPWRn5mlfW78BdFjGMaJr2ctJqg9UrKqmGHlGyjKssUxAg0DE1RTVr1LRsxWIg26+uiGltjS+ez6cY2+pMUfxe3cwnV7lR++EqxiVqW4v3wSzouq1ZqYGNIKwyAQFrHX7uLQhCN+i6rVmtgQ1NrqpMS52Ou76+LvnMJ2v/3ZgR2yNI1Bmzrnz2gD2yd+fOOntsUm+FPfEqk/ZuqqPUBCGVrn+RFZtzULefe+9GpvHM66wrSOLqnGw/exn9yVZwjHpjljP2QM3L3mMUkEfxTZd3mbai8DT4auP4KwJnVZl0OnuXd+cl0yriaNq8/kXrnImC03Fbs1oDG0FYYZQ8vEQDLUEQVpKu25qVG9jUUU4WTZmSwJ66DcY78szaVcwqs6AL9wzQ/RRMoX2KvHf7txK5SnEcgSP/Pe9TYHy/j3nUC2sbv6JSv45V3SynWag086brtmblBjaCsKp0PQVTEIRu0HVbs1oDmzlVHm5KE6XBG+/hicDPfa+klkrV9tvyMKqe9ZZdm7LMorI4l7pqyzLeF5MQdrh/y27E/Cbm5cWXbsdSfLnoNzWqzmZ0KbIvbdqdSW1L3d99nTiVZVNiZp0d1ZatIaILAbwFQAjg7cx8uTP/xQDeCOB2PemtzPz2aba5WgMbQVhhqOO1JQRB6AZt2RoiCgFcAeCpAA4BuJGIDjLzLc6i72Hmy6beoGalBjZE/qj+tinLojHY6orrNZR1lq5bFyH2RO7no/jzkf+z7I1U9XzeZHDZnoZ7bsznzDKeHjkGO4ssl81Rch/UyXZahvvIhjqegrlSWOpwvey7xSkCvv2LTL+2Gpk8dsXddJ2+vm1uFqet/kzXK8n3W87aFrfCscnaspYJ87bC7Yfku07ldmQ2NqItNWbS/WvR1lwA4FZmvk2tl64FcBEAd2DTKmIlBaEjmOfeVX+CIAjT0KKtOQPAt6zPh/Q0l58hoi8S0XuJ6Kxp93+lFBsQEPZnP1ZzL6i39opnzFhecyWv1OR6lUScn2ZVx/T1L1HL5D2nrNLj1Gao6UnlFZK8EuPzqmxvKq2KmvWgVO8rctaV9iw2qo2v07pL7UyPOQ8KJhmEyMBl+WjqFRcpFdN46T61x91OnWreRXYk7ZqdtR+qiq9jr1i9RmxlZjmHbM/zLeM7pW7Mh72MmReQazNSG5OZ5rFF6jXIqzm53n3IqXV+Ran9/0XzjBWsaWtOIaKbrM9XMvOVDTf1PwG8m5l3iOgXAVwD4McbriPD3BQbIrqKiO4ioi9Z095IRP+oR2rvJ6ID1rxXEdGtRPQVIvrJee2nICwr5rm3KDbliK0RhOloYGvuZubzrT93UHM7AFuBORNpkDAAgJnvYeYd/fHtAH5o2v2fp2JzNYC3AniHNe2vALyKmcdE9AYArwLwm0R0HoCLATwCwEMAfJSIHs7MpZVcCPkKmXWoOwoufCYc5j0g8w/Gfj6dKgf+ypaAUmt8Sg0ARMMo178kHkbJ52iUfW6edqxlRGNdfTTxptJt+qa585Lj8kTKm9Nnz7OnhT3XO0o9qLAfZuYFA/WZY07614R6mumdFVi9jH3ZVL6qqEXP1NW8MrWn/kBh1h1826wtUZWpoJd5DoDXAmAAX2Dm57ey8dlzNWZuaybv8t6m123uOfcejqO4MAYojmJvHySzrLEjthITDbO2ZRQbe5LaiGGctSMRs0epSec1xdgX9/SFlJ83CCj5jpnWDyy7MwiT90BqH8J+CHbsh5kXIy6swKyWaxafM0um7V/Xoq25EcC5RHQO1IDmYgAZO0JEpzPzHfrjMwF8edqNzm1gw8yfJKKznWkfsT5+GsCz9fuLAFyrR3H/RES3QgUhfWoe+yoIy0hbtSXqZCoQ0blQ//yfwMz3EdGpU294ToitEYTpaMvWaEfiMgA3QDlRVzHzzUT0OgA3MfNBAL9MRM8EMAZwL4AXT7vdZYqx+XkA79Hvz4AyPoaigKMsVH+EPEkPIAqzn+3YlFB3ts33bMlH26RTYq9M4lNqAKVimPfRMNudNhrG6TxHnRnGqeeUelVseU/ILG9P85H3mFJvyVVvBgEh1Mdje1EAEPaC5BjDgan3kX42XpU5Llu5MefbxlVqqjKy7O9kp03mqdj3R1uZZi4tPWqqk6nwbwFcwcz3AQAz39XGhpeE6W2Nh6ZectP7zHtP6W26alGAoFaWlRtPE42i1LZEqW0xCo2rygxj9io1Zp47zacU16FIDTafc7aF1LoHASXzhrGyI4OY0Hfsjl3XJ4T5Ievz57E1LnZMYDJtwtpcPmZlT8po67E2M18P4Hpn2qut96+CcqJaYykGNkT021CjtXdO8N1LAVwKAKdvbrS8Z4KwPATtycO+TIXHOcs8HACI6G+hPK3XMvOH29j4ImnL1jx4fa3lPROE5aFFW7MQFj6w0VUHnwHgyczJEL4y4Migg5WuBIBHnnIg4wJUjYqn7UJrvp/tKOv0bkGQxIb4sqeMquDrA+XvLpvNdDLeVTSMEqXGeFD2qzvN92zcYM/zUfSsexBQqcdkDkc7SRiM815IODDHnnbdTuKVSjMQSrwjqztxvm6OlenQQtM3c13K7r2JvS+aa6ZCD8C5AJ4E9fv7JBH9ADPf33A9S0Obtua8AyfwpMrexPWRHOXAzl4MHLsTQ9ke9d7X3TpvW8yrrdQAKp7GZ1PUa1YFtudFDIycDKlp1GHX7pjPfaJkm65yY9uyQea3o8/XUK9rYM4DgQJtd0xsjWOHgHymJoWUU2h8MX4To69909pXE1Pf1iwlCx3Y6ADGVwD4UWY+bs06COBdRPQmqIC+cwF8dgG7KAhLA4FQs8z53cx8fsn8Ov/MDwH4DDOPoGJPvgr1O7yxwS4vDWJrBKE+DWzNUjK3gQ0RvRvK+zuFiA4BeA3Uc7U1AH9F6iR+mpl/SQcXXQf1zH8M4KVVWQqZbZVludTwyutmVpln2OXrjHOeU2DNMd28k32O84fJSV2bOONZAcjE1bje1FZkFBy/V2WmGV+vLDvK2SMAec9pGHPiFdkqTd5LS8+Xq9okcTIBg8zz/2RD+T3J1s6pUxPHibHxXLum179sfVk1L7ufkyg3QTvGpjJTAcAHADwPwJ8S0SlQj6Zua2Pjs2aetsZHlXdeJw7QZ8Pc+yUMKVVhHI8+1YmR1GFJtltxdImNseyBG483NIKHVw3WmVPsj+2z12NP85GNreHMNKP8RsToWwqNPU+RVXrs4wn1tik5b+UUVkoP8jW5fMtnv9vcDlTtX5uKTku2ZiHMMyvqeZ7Jf1Ky/OsBvH52eyQI3YII6LeQPlozU+EGAD9BRLdA/Sv8DWa+Z+qNzwGxNYIwHW3ZmkWx8BibWVCnGq2PpjVw7OVd9SYba5HWXwFS5QYRW16U9bzcjORDPW1Ub38i53m2Pd33/LtIqfHVn7BJ73fzRi9MlPPQBoF9aMarsrdX/8fj6yPlzgPyGVB2heN0Gfv596SxEpN1bp40xobQXsfdGpkKDODl+k8ooTS+q2G/slK1xxtn4cb0malptSeT8ZMoxmGQqMC+2L4y6mQypXYlb5Pq2hh3e+q0ZO2NbVdKRF3PvjeL0Qssu+PaIBNXk6lYXNCfqnA7NeJn0v9p+e9z5n9Hdv6kCk6btmYRrOTARhBWlS7Lw4IgdIcu25qVHthwFOdGzHbMgxsPYTzvWfT48BGElHhMZqTNESFyRtmU8QpMvYns96KxL1spfSadxsOo14g58e58TtskKqR91tJ9yQeh2fOS77o1IOweL75qwYkqU7yOWePtz+OJqUnmTVmLQnlRU61CaJFJlJqmKo333rfe+6pkmWXMPDJZj1asjanqncaFmGxLytgiQMW25GvHpEpK3rbYn/02SU2vjunLVDDP7UO67iC3fPrqq3tjprn/vIOQkKuQntjf4tgZZa/ycXxF19a+rkY98y2br1qftyNkx1o5TGoTu25rVnpgIwirBBGhN6dBtyAIu5eu25rVGtiwr1+P30umMCj0rtus8VjlpaeR8Wl2lPGw3Oe5HHOSIWR6LBnCiD11YdT3sh5X+hqxzi7wPP8u2+vUO8p6Y7ZXZWdHuZlS9mvSR0pXFTbHRZbn5Nag8cbMVPwIy+KumsbIJOtsqM600Uuqy17UbqFuNetJO8ybZTjmtI9Ryb1l1E071sa2KWqe/m1FQfJbTBiqb9lkFRFXxdGvMTsxMqnNUTE22WO143d8nbxde9O3lJtiG+O3O6ZvlKk8bI6ZAjuOJm9/fLE1Rfgqn/tw59n1znzX15dNVdQXbBq6bGtWa2AjCCsModvPvQVB6AZdtzUrNbBhcM77Dgoy/zmKCp+DZ0fC0/f0cLt1V+F6WBjkl3FrJgQhJZVCQ13bZmD1jCqrPFzWM6qMvGLj77Lr6xEFqN5PxmNKunwP0m7f6TRzrFlvqYokZgqB5dFk++C03bOlzGOaVBkyqBTM7srDuxnf/ZbUnLHskFud3Ofp+yqYu1BIaQydU8cmQJBkXIaePKIkbi9RSGOQtimmx9LA6h3lZkLaNqaofo2/83czxSbTj86jFJtlzXtbpTHXwbU/dudve5o6L55+UJ5MKB/mmjW9nnOrNOzQdVuzUgMbQVh1uiwPC4LQHbpsa1ZrYMNANIozHlDkqeQL6JFzjRoOJlugLmVeuTsyt/uypN5Ums1gfCmfx5CoQH21bC/upV3AnerEHFsVi4fpQSfznUqjNq5646ttYCRLOy4mmTcIrUrATmVgyzvy1Z5J5lnVPdXnfM0IjtI+6vk8kbjwWrd5fW3qeNVNvTECdVoe3i2wXZ8qNy+fqem7D5LMJM995FveVRLZE5Nh6trEQPLbih37QwGlWTp6md56nHb81kqNUW42Is7bHat2TVF37zZ7Rdlqjm2LoI/TjZmhIEiUGl+18pzd8fSDymXbWtfcXIt8xlp55eA69qBIMW4ztgbovq3prtYkCLsNUo3pqv4EQRCmokVbQ0QXEtFXiOhWInplyXI/Q0RMRGV97mqxUooNsxrRNq2mWRZnEY3ay5Eqi8/wVY9MlBsj3fSBsO94e5ZHl+8GHhfOK5o2Lf6MEPe5dFA8L9NB139dMjEJVsfvpCuxc/2X4fq2AQHoy8BlafDFyBjKf1vV90ldG1Ye01VQ2ySgZA9CbVzMesJBmFMl7W2U2hjTey6zfPG6qvYTKMgcc6b56sbYsSxuXbLyejT1bJNv310r44vjbPq/yaWpzZ6m8nAbtoaIQgBXAHgqVGPdG4noIDPf4iy3D8CvAPjM1BuFKDaC0BlMpkLVnyAIwjS0aGsuAHArM9/GzEMA1wK4yLPcfwTwBgDbbez/Sik2YE6eBTej+jtNvfG62TZlHklYwzNJ5gVU4Zl4+isVdCWfpru1r7ZLmddm1uGLPyr6vrsO87nYO6nepyqaZk/NBAKWYTeELI294imV0qkrWIdBqgIb+uqlqjN1nd5HdWrxZNdRfFM37sNWIyapyp7U6dnkW0cdNWYp7Egd2rM1ZwD4lvX5EIDHZTZF9BgAZzHzh4joN9rY6GoNbARhhel6bQlBELpBA1tzChHdZH2+kpmvrL0dogDAmwC8uNEOVrBaAxvOjqTjksyUSeqNNHm+6fN23Aq6ap3ZKHvbA3IzhlSEv6m7oC5dMOjpZQIEg352nn6lMEBoLWemmflBGCbT0v0vHq7nFRidBRBFiEfjzDSOY0TD7DSzTDQaIx6OkuUAIB6m85KYGZN1YWpieDO74tz1LqsfVPdaltWnKPM0S7834bNrQloxVVgw3Ew5KbNFySpr3pP2/VznHvTdb0n9K6tuFOBmBaW2JrFPiU0JS+ZZNibptxTk5hUpxj44jnNqSWIzovw82w5FyfsomZfYFsdeRcMoFytkMkl9So65rkXXLldXrYYMUlUTJ1nXDOPtGtiau5m5LNj3dgBnWZ/P1NMM+wA8EsAnSA2kHgzgIBE9k5ntAVMjVmtgIwirjDyKEgRhHrRna24EcC4RnQM1oLkYwPPNTGY+DOCUZLNEnwDwH6YZ1AArNrBh5kyWS5WnXifCP5032XNxX7+QpILuIEiqCyePvMNUlTHL9zZSJSZcV2WIe+trapr5vDFAb8+6fp+dFwzWQX21DhqoZdAbJO+N94WeXiYIgbLRemw8Jq2cjLXqEkXgoY79Gg/VtOE2eKTmx3petK3mjbd2MD6+rd8Ps/O2d5L3FI71MiP9OUi8r9TzipPrFw9TT07tZ73n52WU9X0py6TweV88ocWQR1HLA6NYhamrBtfJVKxdrdwTX5fce0aVSewPWb3Z0irgANBbHyQqcKhtTG99YNmdrP0JBr2MnQGQtTU9NY+MbenrMupBAOo5JdVtm+PE6nEcpXZmpG2L/ozxMLE7Plsz3tpR07Q6M94eWnbG2B29zHCcThs6QTPDKImjiR31mCNu9dq52NPM8pHn/qmr9lTRlq1h5jERXQbgBqh/c1cx881E9DoANzHzwak34mGlBjaCsOr4CiQKgiC0TVu2hpmvB3C9M+3VBcs+qY1trtzARj1vzasyudoMMWfiM+xl1fJp1V53WhOCkHIVdxNvKQoST8lAYdrdKu1Zoi5Tb3MDg317AAD9zXX9uqFeT9iDcHOfWsf6ptrO5gn68x4EG5uZedxbB2svCqHynDjQtwNR+t4DxcrzIV091HxGNARpL4rG2oPaPoZ465h+fxwAEB97AAAw2D6G6NgRAMDoATVvdGxLv25jeERNg56WVWc4ea82HSXek5lmPxtPn8c3u4b57LMgPy2pghx462io9aRVo6Okk3szw0FQPVyEJYC5UQyXLxMwmTdh/ReguKaL3WstwdgTpPe1sTHGnoTra5ZtUa+DfZvomfkbyv4Elq0hbVsCbVvMZxpsgENlY7ivvp/YlbAPJm3pjFJDHiUzMrYmBiJlWwJjf0bKxlA0Ag+1jdC2Jt4+lnyOjx/JzIu2jmN8TH13eERNG+nPo2PbVnyhtmHm+iCv1NjqsLE7XKHKGZrW41HTA1VJ3fl+8t4RmepmuOb2Dd22NY2Pmog2ddGdpt+7iojuIqIvWdNOIqK/IqKv6dcT9XQioj/QlQq/qNPBBGHXE1D136ogtkYQFkeXbU2lYqPTsS4G8AIAjwWwA2CNiO4G8CEAf8TMt9bY1tUA3grgHda0VwL4GDNfrkstvxLAbwJ4GoBz9d/jALwNTu67D1V5OOtJud6Qrc64qozdOynN9MlHvbs9lcq60gaUPutOFJiRfi673ku7vlp9RgJLAQDSWJn+5joGJyiPae2A8ph6JyhVJth3IoJ9B9Ty+pX27Ff7u7aJeKCVmoH6ftzfwFiPa8d6H0x40jhmmPABdo6ViGCcrl7SNTf93NPeRDBSHhQNj4OGyisKd7QXdfywOm9H7gcduV8tv36fWuaBB5JjN9fFxNOYDIbAqjJsvN/x9jjxosbbOrNqaGU6FPTDqrp2yXFb/WbSzI50GpBVc8i5hhwzKMrGWDWGuu1FVdElWwPk1WGDsi3FqkyRGlOmLNZVbIxK46svk4kFM/E2/Ww8zWDfnryN2bs3sS3J6970lQdKNY4He/Wr+sz9DUShWu9QH/MwSm1N0j/KZBbZx2X2Wd/wvZBgwnMG+tgGxj5GO6DE3qjXcHhUfd45lijE8dH71TqP3I9Q250kPqh/RJ+XAEO9bZPNSVasTXKtjG3Rtlwpxtlp9vI+CjuFe+xI+h1rXpSPu8lXOs5XP65Fx21NHQv7cQDfDeBVAB7MzGcx86kAfhjApwG8gYheWLUSZv4kgHudyRcBuEa/vwbAs6zp72DFpwEcIKLTa+yrIKwsBEJI1X8dRmyNICwBXbc1dWJsnsLMI3ciM98L4H8A+B9E1J9w+6cx8x36/bcBnKbf+6oVngHgDjgQ0aUALgWA09bWMt5SNIpz8TO2SpN0rHXUmWgce7vR+jrT2ssA+VbvIREGemQ9GKvthMMg2Z7JCzCeVjxIV5bE5uj6NEqxUcqLUWrCE09Vr/tPRnDgQWodG0qpidaVxxWv78d2rPZha6zWv3U8xpb2LLb1fh3Xn3eiGCNzDh2HIySgr72DNf26R+/7ei/ARl9tZ6OnvLaNjT1Y3zxZHce2UmoCvV+9jX2IdSZF1MveQhzHmSwG+3wA6bPu2FJlhsfUbTre0oqNPq5hzBiaZ+MNr13SSVhfQ7uTcNhzu5VbXYMjM0/fb5bSYwgm6GiyzPJvC3TO1lRV0a6jFNt2yFWI40wX7Oy9a/9jMcpGPFC/4WCQf4IXDPL3m6lv1bNUYaPU9E88UX3vhJMR7te/Yf1qbMx4bR9Y/56HgVrHMS39bm3H2NLZkVt62lGtghwfRYndGcX5YzXHY/oVrfeCxM6Y1019jBu9ABt9tQ+b+9R+DWJtM7aPIFhXdqenYw7j9c3U3jjZnxyldsdkSkXb6S2Z/m/R6rC2NaPtcRLTZ65n02vnU4UTexKk9oQdNTgICZHOUJ1aFbbosq2pPHqfoZlkmRrrYGTVyLrfu5KZz2fm8w/0JrV5gtANqMZfV+mUremLrRFWmy7bmtpZUUT0cs/kwwA+x8x/P+H27ySi05n5Di3/3qWnV1UrLIBz0enG8zEjbLuapK3QAMh49el7WNPS9zZlXv8gSL3+DT2KHuh1byCfndCLelbX4GyV4XB9gP5enYGwT3lTiSd14mmItDIS71HztgP1fPvIdoSj+vjv1/En922NcJ+uC3NEexoPaM9kaxhhaM7JOBsPMOgFGGilYkPv8wnrysjvG4Q4UdfcMa8H1nvYq4Nw9q2p/Vrvq2f4HA4QOpkQpjZFfzxKMqSSSsphqnS513W8PU68py19POYabkVxco2Gcf1rFxLBOLmJSkPqmgJAqNeVfO6l8RPGY2atlIUIYXpWTdrgl7A76th0wtZwGkvjq3lVpBRHozTeK612m3r4xra49ke9z4/F7PsSAAZaBRpYXzRKQG+9l+xLGpORrRrc39xAb6+KlTFxNOH+kxGcqASuSNsW1orNsL+JI8OsGvPAjrrD7z4+xH3aphzWdsfYmMPHRziuf6dGOY6s8xgG2Tiafes97N+jbIqxN/v18Zy43scpe5RadMKa+t3t1UrUvj0nY6Azskz2Z+DL+NTnrb89TDKkXIU1Y3f0vo+2TY2tMYajrN2J2H/NgKxi46rCg4AQkLl/3IrNlP7P0MqVrQabPU4UnIAmUoa7bmuaHPH5AH4JSqY9A8AvArgQwB8T0Ssm3P5BAJfo95cA+KA1/UU6Y+HxAA5bMrIg7FrUo7DyvxVAbI0gLJgu25omdWzOBPAYZj4KAET0GqhMhR8B8DkA/6Xsy0T0bgBPgmqadQjAawBcDuA6InoJgG8AeI5e/HoATwdwK4DjAH6u1h6yer5te0s+pQZQXtIodpUaJJ/NNHueG6dRVhIlGYUTJR59xGp7G2bEPQLCYaj3x+xnqpC43lRvfS2tGWGyE7RiE+05MVFqjunIncNawTi8E+GOI+p58V3H1LPnu47u4I77lWdyr552/3H1enR7jLHJ3HK8UQoIPe0p7NUe0wHtLZ20OcDpB5R3dOpepRadujnA6fvUe/Msff+aWn5zz4mAPiehrk0RmAqi28fQWz+SOX7bg0qedVvX03hMW3reVsTJ67TXLqRUnTHfNWqOWeeAGf3I8RWSWIdIqzaAUW64oWUgUllpu4DltzVI1ZqmcTRuDEaVUmw+++5Zc1+6qrCtCxrPPq2Sa9mYJI4vVYWTOjQ6JiXYf3ISU5OowaGKoTu8HeHIjlrfnceyNubOozv4trYxd2n7c89R9Xrk+AjDHa12GIXdsjVJ1XWt9g7WetinFZuTjW3RduXBB9ZxmmVvAOC0zdTm7F9Tx7O+adWJ0fbGVCgmXWMrXD+a6b9no65dNo7GqMTDUWTZmzQ+scjOmOsGIGdjIk4Dcwc7Wr3XCk5oVatPzlUUZGL5AGQUHFP3pgldtzVNBjanQqVfGkZQAXlbRLRT8J0EZn5ewawne5ZlAC9tsG+CsCtoK6CPiC4E8Baoem1vZ+bLC5b7GQDvBfDYafu3NEBsjSAsmC4HDzcZ2LwTwGeIyEi4PwXgXUS0CeCW1vdsAhjZ+g+x5Tm5HVpHcZxTZdLX/DTbmzLj3zLv3/b6h7FRbMydomNniNHXI37z/NtX9yDjTeksIlPlk7U3wuv7MNQ1I45sqWO9V7/efmQbtz+gPJNv3qM8k0P3HU8Um2MPqP8VQx1zs7NVT7FZ21D7daeOp9k8YQ3fOaL278wTVRzN8ZP3JErNGfv0vpuMh401rOmMCh4dzxxXPFi3etDkb9P0WXfqOdkKDQAcHafKzYjT66hec6vMXDMACMDJNTPTbC8sUWoyFkBXBTU1JkwNjEEICrR3rTfUtAoyoR35Vxe9uwLAU6GygG4kooPMfIuz3D4AvwLgM9NvtRFLb2uMOlwUT+NTasxnoxS7cV/Z7L3sPDsr0yaJrXFUYSBASNkMnqSWjq2MWF26AfVbS2zMHv3b7G8g1nYm0vFxR7fVOo/sxLhDqzBGFb79sIqN+8Y9x3HoXvW7fkDPO35UqTnbx0YYJYqNVkGseD6TcWhsTX+thwc2lZ25Rys3d+1X+3n/1ghHT9TZSUk2mVnTWvLb7a+rfae1bdDweOYYY1NPa9BLzkXSQ08Tx5ycw8jZ562IE3tjX89ixSa1KWb59Bp61GBjY4ZJqBAiXXAnHAAxsjE2xg7FE0XYtGdrFkXtgQ0z/0ci+ksAT9CTfsny4F7Q+p4JgpCjJXn4AgC3MvNtep3XQtVzcQcN/xHAGwD8RhsbrYvYGkFYPLviURSpo/w+APuZ+XVE9FAiuoCZPzu73ZsMdp5v25gRdtbzVq8m68kXYzPi4jiN8mwFTvq/h3r9RlEYBJw8Z0+r7NrelKlTkMbaJB25N9KeT4Dyqo4ltSLU693H03gao9Tc9h1VkfPb9xzHUUex2U76pRxGpCt4RmNTh1MfV2+AUFcW7W+q5+7ruo/McCv1wuxsKlOLwtS9Wdfe2FqPMOhvZI4j7TOznsTWuBkc9nlKKrVa18ecX/vabTl1eSqvmf5sjqLv/Y1nM1Iy95Q+/sDy+tJrbJ6D+9ZZArUmD/tqt2Sq7erWAmcx84eIaK4Dmy7YGqMO25mXdlyNG3eTzXxS6/ApxkUqclGGja0k2mxFceLl95O4kLQTtUtia3r91Mas6d/mYA9YKzVbSc0r9Xrv1iixM98+ouzHN7St+cbdx3BY2xhja45r5Wbn2FGMjh0GAIxNn6c4SqqMB7rMcM+yNdubKltrj46tSTLNLAXKZGz2w9TmmErF6z31ure/J6nAbo4xUal6/Vw2lE3aIyqfeemzP2VZUXbcXhG2igMAIXPyj8ooN3FECCyFBsgqN/Ekmk17tmYhNDni/wbg/wFgnl8fgZKzBUGYE2U1JcwfVNDsTdbfpY22oVobvAnAr7e3540QWyMIC6amrVlKmsTYPI6ZH0NEfwcAzHwfEQ2qvrRoXG/KEFk1I3yvtrcP+KvXlnv/6asZmZtR5CDIr3OtrKdIYKpPhiDtyZAuRhjr7rlR0McwSqt6AsBhrZ7cfWyYZCXcZak0Rqk5du99AICdo6oK/ejYYYx1d9w4zlZdCYIQPR0H099W6k+0c5Kee2KSzXCX9nr2rvdz9SdO0vu3LwoQ6arKgT6OQB8X9QegIMwcv484Kr4+ZbELVdfMflU7ZjaYVWjc1/QyZuNoKOKpH1oT0voeFdzNzOeXzK+q3bIPwCMBfELL0Q8GcJCInjmnAOJO2hqDXe+kzO7UUWzM10fs9/5j5002tk8tv+FUM1b7ZTpEO2poEIJ08UHbxnBP93zSGVDbUVq7xtSouVfHz9xrMp+O7ODYA1mlZut+ZWN2jt6bKDaRzkyKRqk6HGo7F2olZTzcQjwydka9Js3BQ8JdOvvwwIZT62ZtjGGk3ps+Vby2lnQZT+2NqUQc5s5JbMVQude1LC6z6JoB6nI1CbFzbQ6gbYp+NfdBCwWH1TpR29ZUr6siUYGIfgkqgD8CcBTApW68X1OanIaRDjpkvTMPAibIIxMEYSIIrXXcvRHAuUR0jh4wXAxVzwUAwMyHmfkUZj6bmc+G6tM0r0ENILZGEBZKW7bGSlR4GoDzADyPiM5zFnsXM/8AMz8aqpTDm6bd/yaKzR8AeD+A04jo9QCeDeB3pt2BeWOPoF2P251uL59VeNx5vi2Ziann5D4vLcLt/psQhGlvE+1xGM8jYra6dKvv75iI/WGEo7ri50hXBR3tRNjRWVBjrbwYD2p4/AGMt9Q0dhQbCsKcihP2lHe1s7UXa9pjMts5uj1KKgHvJL1hdCaT9Qy6Z6qBJq+BOt4m50djzq9Zys5OqHvNzGf3HukTFd4vvvumaRhNFW34UMw8JqLLANwAtYtXMfPNRPQ6ADcx88HyNcyclbA1NrF13xX9/n1xf1n74/tWVg02WXy2vXJjatzO4xns3535LVKAWN95Jp4lSmwNY3usft+mkvCR7bQ+zVjHg4x2lBpjbM146yhGWhX22ZpI1/fqJRV0w8TOjHZUrM14mNoaY2PMPhgbM4o5zUKL1VliovwxJp/r+fpJVqb3/0nVNQMABpz4KL/ym583r2ylljZTmajAzA9Yy29ignYnLk2yot5JRJ9DWgviWcz85Wl3QBCEulBrZc6Z+Xqo4nT2tFcXLPukVjZaE7E1grBoatuaU4jIVnKvZOYrrc+ViQoAQEQvBfByAAMAP958f7NUDmwK+rYAwNOI6GnMPLVsJAhCNQRGML0zs7SIrZkPbXR+roNbA2u6deU7rJWtP2px27uRBramKp6vFsx8BYAriOj5UOrsJdOsr45is0+/fi+AxyJ9Fv9TAJYm/VIQdgW80qEmYmsEYVlox9Y0bTJ7LYC3TbvRyoENM/8uABDRJ6H6txzRn18L1b+lU6RdVdmf/eJ8trvnptPzsRhF27G7QQfJvPJ9LKyjEEdJF1roXiekX0Mi9EzdisDUiUm7cO/VWQL9tTB5NfEw4y31zLpv1awJ9DPn0qwoXcemt66+v7bRz6wfUFlRpgu42R+zf70g7YlijgPJa6yOt8n50Zjzm2ahkfUsvN41M5/TaWQt59+e775pG1rhgc2q2RqbwKqRVPT7t21MuozOrgNQfs8W37vUNCjD/O7MvcZx4r2bTJkwsTWE9V72921+7/21ED1dQrensx+NrYjGw5wKY2rYqH3WMTa6jk1vY2/yXbOu/nq6HdfGmH3qB5TYG7PPxJweY2Jv/LamCHNOQ891NdOKrplZxv1/kM2k9W93ntWAW7I1SaIC1IDmYgDPz2yH6Fxm/pr++K8AfA1T0iR4+DQAdrW2oZ4mCMJc4MYGuKOIrRGEhdKOramZqHAZET0FqifcfZjyMRTQbGDzDgCfJaL368/PAnD1tDswa8zIOtCvutSL9mjM+/yr6dWRpNZYIoEvit3Fp9ikXVzTz4maUzIUTyrWxhFY13rgscpooki9hvEIA61k7NH9Vfavqct7yuYAh3W1TpMdZToTK05U69BVOEfreyeqPLx5who2T9Bddk2X731rOEV33DX7Y/ZvEBLCOHsc5rh4lHp0vgrSBnPe7HNpn1/A1IzwZyBkjs3j9SbVW6115r3k9NVVasz+NfaafTCv+qMoQydtjSEI04rVid3RN5xtdwY1Ql2S+zT21+Zw7/X0FTnbYt+DJs4m7XOl93g8Ao/Mb1DVnqFoBBqr9wNd7nbd1Kka9LBf97k7aa+ad2RH/fa3hpHVuVutnkjVoAn6g7SqsLY1sVXHxq083Fvfi7V9qtv45glq/Ru6o/eezQFO1dPMPph92jvoJZWHzSuNdxKFOLU3o/T4nayxtPI55f6fpDbHk/VacM3U8vZ9kL+Gvmnme0b9s69r2f+PiWjR1lQlKjDzr7SyIYsmWVGv1/1bnqgn/Rwz/13bOyQIQjGr/CjKILZGEBZPl21NnawoYlYBCsz8eQCfL1tmGTCjaooCIHJrrujnraMop9SkHlR+9BuyXXw2rRWhXj3LW15/3/GqNqzRvhl9J9WFrZG38RxM3Yl4NAbrKp28pWpA0Kb6TKMtbA5U7OXOQO3XKXt0fZkoTjpsGwa9AHdoBWWgY22GW8oD2tnabNTde2B19z79QLa790NP3oNTtWdl9mevPtGb/QA01Mcxzh4XD7cRj8aZ47c9qeQa6/MWWOqKOb8ZlUZ7XXWvGaCud5nKZu4X37Nxc59lz13+Gjemw8amii7ZGoJRZgIYWTe1D0HSu4dNnzhTN3kY1+oR5HZ+Dqn8nnU9+40wSBUbHX8S6lff/ZfYmvEIgbExO0pJofE2aKT6P21saLWkp5Y/aaOPnShbN8vOSDJxLff3dXydjr3b2RxguKXslbE1dtfxQH/P7u69rrt7b2hV5oDu7n3mSXtw5olK2TldKzfG1py00cceve0N/ZukreNJd29zjMauxpZi46uXZc6hOacDXa9rIwyszup62YJrZua5CrFPsXH/RwVECAfZyshBSKkddNQ5CoPJ1ZwO25o6eX8fJ6KXEdFD7YlENCCiHyeia9DCMzFBECpgVsGOVX/dRWyNICwDHbc1dR5FXQjg5wG8W0c23w9gA2pQ9BEAv78sMrHxooxIE4QEjs1wWFeW1JUp+1HqaRn8Ee7qNeK0G2uZ15+uK12nqyTYrz2teqSj8Pw646G6gaLtIXrGs9BVO3s7WvHoH0mef+8bqNeYtacWrCcdtk18y0l7B3jQPrWue4+pZ9v36y69R7fHtRSbvfo59gHjHW0OEsXGqDSnbg5wuo7v2a+9tX36WAfRDmj7iFqvPo5oO1Vsou1h5vgz+2GyEkzWxUYPG6bCaHJdtLdMxX2+fPiune1V5Z97p/P6judkPLvA8xy8uSfFaVbcatIZWwNSvwVlRbIBefY0o9REOnwkHCAJiw4d4clW/Mz9aVTBiPMVr8137NcN7cVvhJSq033Tc03fk5ZSZDKRIqOODlNVOD6ufpu9zRMQ9NTvOtSv+3Q8XgwghjpIs1qTmbR3vZf0brpLKyn3mD5Sx0cY7qQVigEgHqf3dmD2PUyVnv1aqTnZ2BZtVx58YB2nWfYGAE7bXNP7GSQKcahVp2DnGGikYwj1MSbHPBwn54IjNyOUknNozqk5xxvMMPbG7hlVZGfsn77PjrixU0lW3SBIlJrkf0YQeJUawKg5kyg23bY1ddK9t6G67f43IuoDOAXAFjPfP+N9EwTBocvPvasQWyMIy0OXbU2TrCgw8wjAHTPal+nRXlRa5yRGmHTr0aNvo9wAoEiPgrWnYD/Pdp97qo7f2dgagz0qd51wNfpW7403lXj8/TAZdSfPbq0aLWzF1gDAeHsHAx2DEh+5X73q7rdh0Eva3W7uOVFN21Bei6o1oeadqD2oUzcHOEN7UUe0ivWAzpjaGkYYmnMyzt7cg16AgVOvwnTS3TcIk/Wb1wPrPezVz7j3acVmPVZeW3D8PoTH71PHcfiezHHx1jGMt3cyx28/8w7C7HkLBwEGur/Mhr4eRmELKU5UnGFc/9opxSZ9b5Ypyr4Ke4FXqQGUh+d6UxN5Uh02Nk1YelsDQhAGiC29Brr2ShwxKInTyMaCRaMouf7mfg50P6WYGYMg9faBrCrs70hfEKfRD3NqcBpjY9kY7ZVnVGET53ZMtfCJB+sITG86bWM2NtT3wvXNRA02mVL717QNWO8nSorpAG5szOHjo6Sv05ZWh32xOSbTc996L1GIT9yTzXw6cb2fxNScYOpn6WPeNwgwGKnjCbStCbYOJ/bGHKOJ7Yu2h8m5cLMxKQwy9gZAco4V6jjsa1HU3dtXF8uX+eTG09gZUIkSF1JGocnsc0CTV5rusK2ZT23tCojo14joZiL6EhG9m4jWdefhzxDRrUT0Ht2FWBB2L6awWNWfUIjYGkGoQcdtTSPFZhYQ0RkAfhnAecy8RUTXQVUnfDqANzPztUT0hwBegspSy9nRqY61B4BEuSHtEQUhIdKeUjICNh1bx3HiOdkdW93urcl2Kr3+vGcPqNF+T3sd9ujb7I/JBjLPfKPtIUZHlWfR7ynvI+r1023pETbpB/ob6yrrYG19P/YO1HZMnMupm31sjdSOb2tV5rj2nHaiGCOzbcfhCAno63O85tTNWe8F2Ojr+CGj6vQI6+acb2uPScfVBFuHEd//HbWdRLFRy4yOHktibCInOyqw6kmY89Zb7yHWO7uh93Wgj2sQUC7GxlB17dxMqUw2Q8/NRAgyz72z81KvKuybec39ii7Lw4umVVtD+reKAEYUTu5PIKPeAEgUnCCkZJpRBChMs3CMDerr1zjTPdqNyUlvWNfDDwYh+sa2ONlQah+ymT9GFR0d20K4flSvVN+fQapyhzpgNNZZjIP1/ThJ25m9OrZv35r63kkbIbbGuqaNjqM5qtWQ46MosTsmY9M+VnM8iRrUCxI7Y2zZRj+1Meb9pn4dxMp20PEjCLYPq3VquxMfvgfRfXepc6rtzvjo0eT4fQqxOW9pbJ/O1rIMiPmt9y0FzlDn2tnXxxdH4+6DT6VJ4qgs2zJpFmaXbU2lZSWis4nojUT0PiJ6OxFdRkQPa3k/egA2iKgHYA+UBP3jAN6r518DVaRLEHYxumhW1V9HEVsjCMtCt21NHcXmgwD+AMCHAVwF1fziN4joLwC8nJl3ptkBZr6diP4rgG8C2ILKfvgcgPuZ2aTDHIJqf56DiC4FcCkAnLa2lnpRZv2J+pH1nDhKc/+T58ymOmjEVg0Z7V1ZI/M45/Vbz4bdyrOUH+Unz2fXe1ZdBD36tjMWkuff6rn06Ng2gr66ZGbfk2Wt+hPhPp3NsEdVBo7XDmNzoPo77Rmo+jLxng2M9Xkaa49pZIqPxgzzuNstGUJESfZD2psq/dwzmSE660DVjNDPuHXmEx9XHtT4yP1prJBWasYPqGfewweOYXTMZCqMMufDPk9BkvEQwjw/SKq9as8pHEZY93jAQPW1S47b4025VUjrZicEzvdrY1IwV5fO2RqbUKs0HHGiiJhYEdsOmV+usTFhn5N5ri1K9r0sjQ/IKZiBnXHZz8fvGaKhid9TCkdwbDvfiy2Ok6yhQGctBnsPAAB6m0fAa8q2BAPdM05XC+b1DUShirEZ6mMdRkphHsVWhmJJJ+7QijsxdiatJKx/+9EOSMfR0BFld4KhUmBo51hiY8bHdcyQZXeMUrNzv7KZo2Pbybkw5yb2xfb1UxUrOU3mPA90RmnNa5auu9iOpN+x5ln/K8psykSKTcdtTR0tPGTmP2HmjwG4l5n/LYDvBvB1AFdOuwNEdCKAiwCcA+AhADah0j5rwcxXMvP5zHz+gX6/+guC0FEISh6u+uswnbE1Jw7E1girS9dtTR3F5qNEdBkzvxW6Van2bt5IRF9tYR+eAuCfmPk7AEBE7wPwBAAHiKint1XV6hzqu9nRaRiGyaiZAn0R+kalYUuNMSqOpco4z8HtaU3wVYVMKoH2g1w2FIVBbr9MrMkoDFOVSc/r63n97eMIjUeinxsHm6q3Cq3vQbCxqd+rV+6to2fic3SxDdaZDyBK33tIOnFrjyv5HA1BpoeVqSS8fQyxzjgYb6s6ErHJRNg+huiY9pQeUPNGx7b063ai2Jjjt1W0VAlJ1S/jwaSxLDrKar1X6AlXkc8yyFfy9FX5dDOegjDweF8TeFIdri1Rg87YGkDfe2HWowd0PFWUvbbGtoR9a3nLFgHZe9ON7yjy/n0evdo3SpUaT42sZJtJ/J4Sw4awlGI9b7A9RG9L/T5DrXSQtifBnn3pe21bElsz2EAvVDZm0NM2xhT2CcLUxhj1mSw/2/zTNHFI8TgJVjUxhKT72FE0Aut+U2l207Hks6nHk8zbOo7hA9omHVc2xtia0bHt5FwksTaWopSotJ5GX4nib80rU22Krh2AUjvi+36RLZk4IwrotK2pc9QvB7CfiG4C8BAiupSIXkhEVwC4p4V9+CaAxxPRHiIiAE8GcAuAjwN4tl7mEiiZWhB2Mayb01X8dRexNYKwFHTb1tQp0BcDeD0RvRnK43k0VDvoLwH47Wl3gJk/Q0TvheoLMwbwd1Cy84cAXEtE/0lP+5M66zNeFKA8o2SEDaN02F5R9sLYnpdbcXcStUbtT3HEejAIrGe2+Qj3SNd5oFArIse20kqhRsXRnkbvyDH09ujYGl2/JlzXFUEH6yD9mI503Rv0Bsl70rEB0AoOBWEuhieD1W0cQNohN4qSZ/HQ3hQPt5POufEwq8CMt3YSj2m8NczO297JZUWZ82GfJ3Pe1HGY86s9TscjBtq9jum84gwEnyc1eV0JSx1bQTpla+zYuTAbb8ERJzbIJY5iry3KfNdZvg7uPWV3ok5UYeu+Nb+D9DelFdYoRqx/r2PLxhhb0jM2Rcf69TbWMnYGQNbWaKWGjG3pG8UmSLKtKCg4WbBsTBwldsd0ATd2B+Nh2kPPY2vGW1kFZrw9tOyMsTt6meHYirGJMucqcw4H2X2mkFq9di6+zuw+pupDZ9OirSGiCwG8BepX8XZmvtyZ/3IAvwD1m/wOgJ9n5m9Ms80m3b2PAzio/1qFmV8D4DXO5NsAXND2tgShu/BSZyK0hdgaQVg07dgaIgoBXAHgqVCB+TcS0UFmvsVa7O8AnM/Mx4no3wH4LwCeO812F17Hpk2IyOvB+yh79lk00q6Kcs/sS4mnnhl9O3EhajumM7CaN94aJdPtmjYAEAzSLKlABzSG2ptKMqjCAOEgm01FYZDMD8J8RdJcZoRF/vm/iV+JcjUgOI6T7AK3ZkY0GucynpIKqKNxGmM0zPatynb51ooXkPNQzfXyXc+617LsHprUcwomqThs2AUDmy5AKLmOnummc7Wr7tjUvSd9WTrefTQKprM/ccRJz7xk23Gq4CR1b7QaHPZ7aexOYlPCknmWjUmqbLsVdC1bU6IO2zGObtftNG4uzs2z7ZDb+ym2bYtjr6JhlIt5imsoN0XXzrU9ddXaOsrLVHakDu3YmgsA3MrMtwEAEV0LFcCfDGyY+ePW8p8G8MJpN7pSAxtBWGmYU/ldEARhVrRna84A8C3r8yEAjytZ/iUA/nLaja7WwIaynkCJcwQ0zNZ0VYrKXak5Mi/r8Bw5XlU0sj21YXZ7np4g2aj5/P4UeUp1vQqvEuKJpC/L8EgqoJbENJWd++T8hSHI9ZpKrvGsruds4aUuY76roGb3RKktMtS0SU3vXR+JGrGVjaMIQsLomM5s9MZ6BLnlc8vUVBKaxJrVjVdx7Yj6rlsTqFzBbRKHl6gmBcdsP0EAlsWO1KG2rTlFB/sbrmTmiUozENELAZwP4Ecn+b7Nag1sBGGVYcjARhCE2VPf1tzNzOeXzL8dwFnWZ285BSJ6ClSCwI9OW4gTWLWBDVGmhkDtr81pFF3mafmytfKeQ5xbRxznv5fMs7O8amRgtEGdypdlWUT2c+Oi6+L1EkNCWKB+LcP1bWX94CROQFgeylRXH9NnrtSrk1KkVHAUZ+yGux5XHWkjk7RuXZ4y6tR9SeZ5qvL61tOWbSq6B1rLUtI0PW+TZoG2aGtuBHAuEZ0DNaC5GMDz7QWI6AcB/BGAC5n5rjY2uloDG0FYZZgBibERBGHWtGRrmHlMRJcBuAEqx+MqZr6ZiF4H4CZmPgjgjQD2AvjvqrwUvsnMz5xmuys1sCHKVn8tXXYGz4GB8mfB7OxXbPWkMiN71Wcmm/0TJ9lAnGYIORk/HPvmpdWT3Qj/zPyCPkpAeVdag92d1j33wSDM9EABsh2vkwwMTx+lXFdivUwMfy8VX7dbe90+2ry+sJ6n+57159fV0JtiTup6TMsiakusGmV2psxTr6Mglq3bvm/Iid2xq6ibdbj3WRxz3n5YdW3crCCO4nT+KHLmpTYscrpaR8wwm076Qlm74toWe557+LbdScLqTEdzSt+7Xc7Jqvid1r4KCztkU+CxO3qZGPlMpKBE/fFd57rXtZhmGbtN1cR0he3ZGma+HsD1zrRXW++f0sqGLLoSySQIAqCKlFX9VWDVlngagPMAPI+IznMWM7Ul/gVU5+v/0vKRCIKwzLRgaxbFSik2oHwU+rRVGpvGZ7g9YgBLEXFG2wGU+gBkayWkNRbSOg1qmTjnMZnvRaMo8ZiMchON9WdmDLUXZl4jzntRrndVhe0xmVd32iAgDKwOvQAQ9nTtmUGYeFZpX5u0O7E5b6YvC1s1a8x5M1cnqwzln5sXXes2rq9N4jl5smBctSdsXIeiNS9qIbUldgtN1ZpS5cdzj/juWNfbtyvh1lF+jV2JhpFnWpxTZUb6n9owTlWZ1LYYW2O/d21Nur9lQoV9any2xUw3oZVmmcEoXdYs1w+M3QnS+lce+2MUKzONQ/15ECYWPDDvwqy6o977lWOgQj0unJNeX1+9M3v77cVNtqfYLILVGtgIwirTXozNQmpLCILQEToez7dSAxtCeS2X7PQSJaesT1LVPpju26afkqdfVVVNhjh5xp0qNYBRZVIvykwDgPHWOFFoXHVmGOcVG9vTyis3XPAkF/o4FHnFhnLelK3YJK96HwbjGOFQd+fe0F6Rp0Nugm4zozKgSnbQ7Ken0nNS+XTCa2x/z1ezx8Q8+DKkgmmf/DLqZiosZW2J3UxdpaZO7J9ZpiyOy47Vs6cBplJvXqlRr5YqbE2zFRr3VZsijyqc2h3za3BVHfW+8pAzarCxI+aMDgLKzUtf7ffa7sRxot5wZGxyan9c2xNq+dXu82Xq1pjvBahQ6lq6rnOjvq1ZSlZqYCMIq03tollLWVtCEISu0O1ioKs1sKF6kenJvCkr79oUqTAUBhX1a/J9l9Jn445yE3E+O8GKp3E9pq3IeFl+T8t4TSOPYlMHV7HpU+o52XE7+fWm53egVabAqbIchJQ+Vw505liUPkfmIPuMm6O4tLxr2X0wabdt31U1Ko5/e1MG27VX5nwhtSV2Cxxxzg6l2Y/pfZF4+9ayrrfu8/Sbdqv31ayxbYqZptYdZ5QaQMXTpDalnhoMKLtSFH9jx/gZqrKiQjLrz6oyI2b0E7uTf3VjCPVZ0TuW3Y6K7cuqMbb9MXGSaWd3FBJbmWnmmlVdT986cstP0fuuNh1v37JaAxtBWGna8aIWVVtCEISuIIrN0tKkPxIwuec+KfZoPMly8oze7a7WuZoU1ueIs16RPd03r0w/qJupkNkXpMlAWc/MLEG5eWaa/azafCany3lmW8YDMs/GLYWnebbRZCT1LezsBOf+suNwzP04cYViRmvGZhG1JVYNn+JiMB50kXKj5mXjO3zrqup9VFTR186u9NWssW2Kvc7Yis2JPb9hXyalq4iYPcpmXuZV4TIbk5/HMLZCvc/aFXPa8hmevn1PVZyQs3YkjhgUmGlZ+2MrcYnCpdWcOBNBF1vvnCzd4kMuVd/qVq1vss5SWrQ1i2ClBzaCsEow2BuwLAiC0CZdtzUrObCZtDeQ8Wja7G5tx8z4Oln7RttuNdC6uDEvxrPJZiupVzsrCpT1hGwPqHx72e0G1jS7xoRbrTjdz2bKin0eQ+fhtvKm9PskE0NfT10xCLBUExMLEwS56zjN9a/DxMoNMzAeVi8nzBXXK87EzHh+w6nXn7/+biJK1b2S791kqbtOdqX9+yny5NuI1UizJlO1JPTYmPR9NbYNy9uT9irNcsS5Lut2rI2J83NjbQLEudpa+tuZT9MmGlVdn4kVmtyGum1rVnJgIwgrCXc7BVMQhI7QcVuzFAMbIjoA4O0AHgk1jP95AF8B8B4AZwP4OoDnMPN9ddbny0BI5tkxDwXxNk09cXudvmferlLj9oLyfc/GRNKrPlgme8H0REmVC5NhlKKrahIn3o6pOaGqdWazGNIYmPLMqLwypLdv1ZPwVR72vdpViIHU2w1CsmrOFCs7yXkLwzRewPGc4swT8LLzXK/OUBl15NvJu4DzUpcx7wJt2xofVV5zE3Wh7J9LWayN3Q8qXabam7f7vZnsINt+DAKf8pIl2UysYt/UtDS+xXw27+vgq2puK8VudfNsHa3svqt1qWmmt5Sv95wPN3YqrSqfViM2l4zCIDkZE/dscmhNkamk27ZmWXpFvQXAh5n5+wA8CsCXAbwSwMeY+VwAH9OfBWF3E0fVf0IZYmsEoQ4dtjULV2yIaD+AHwHwYgBg5iGAIRFdBOBJerFrAHwCwG/Ofw8FYUlgRtzh2hKLRmyNINSk47Zm4QMbAOcA+A6APyWiRwH4HIBfAXAaM9+hl/k2gNOarrj08U4YTB31XadQUhzFjQswGRJZVA+Mg5CS1EOKzCOc/PfMIym7OaWRYe1CWaksjGSaoUm6ty0RuzKxejyF5H1m+V6QPIIyZczN4yBbEjeUycRxxIns7cIxO4HEmW+my5U8wmzK5I+bylbKs1nv7qE9W8OTX+PMo4qaVG3LbnCZTGuQhJAWkqO02ayxLUOg6BGu/Zt3bcwgKG/bkuxn2X5ltuV/FOVrt2Dvk6/Ni90QE8g2rnTtTlk6v21XIucxVWAdme+JYp3rv7Dfe4u2hoguhFJKQwBvZ+bLnfk/AuD3AfwLABcz83un3eYyPIrqAXgMgLcx8w8COAZHCmZmRkEIPRFdSkQ3EdFN9253N4pbEKpgNll25X9CIa3ZmvuGYmuE1aUtW0NEIYArADwNwHkAnkdE5zmLfRNKRX1XW/u/DIrNIQCHmPkz+vN7oYzNnUR0OjPfQUSnA/CWddfN/a4EgEeefIDrpivaQXlVAWPpd8qUl4J0zNjXjK7ck0oKv+kRv1E1omGUvE+Ke+ny5xQEaZlvJzh5EHHSINP1nIqmufMMvhRtXxpmpjFmL/WG7H2nkBD2naBhK4jYBA2nx2wC/fzjcbajFpEtjmUHEmcPML++Se6N7PZnBDPi0Xi221htWrM15+0/IXexfcU1S2mjinTBPVcVAG9+b7ETVB8iTUaIo/TeN2nOfb29QWzsia9dgh0gjGQ5m0nUYV+at3ktUnNsxabvUYMDJ2kh7IfJb76JohZHcWJHkrY3ntYazpdqr39SgkmLlbZnay4AcCsz3wYARHQtgIsA3JJuir+u57XmlS1csWHmbwP4FhF9r570ZKiDPgjgEj3tEgAfXMDuCcJSIYrN5IitEYT61LQ1pxgVU/9d6qzmDADfsj4f0tNmyjIoNgDwMgDvJKIBgNsA/BzUoOs6InoJgG8AeE7VShhc6al4mbZoUkXZ82S5Gs+8g4ASTcHEg9jKTZJuGJhigpRszzwvtsuqq/2zSnw7Te/s/Srbd+++ljQcTdUVy+sLXOWGMs+21TKpKkPWcmaa+VzmibjKDYVBekye9MsoNqqXZ51LFPjPzIg7XFtiSWjJ1kyg0GB2qp63WGiJIpzYFH3Ps2VHcqnM/TCxIeZ31LNU4Q3HpsRWG4UypSadVnxcPsHDp9wkqd/k2gzKKMTJNI8tUq9BzhYZfEqxnf7tK/Q5i+vdREWe5B4FGtmau5n5/Ik2MkOWYmDDzH8PwHdynjznXRGEpabLZc6XAbE1glCPlmzN7QDOsj6fqafNlKUY2LQGA9Eobq0YUhF1FI2mjwTs0ulGjTBrMO0DqjIefBlZQFZRKlOSpsX3TDqr2ORjZFzvwxdH4y5jqzWTZha4zekA5FzHZbiPMjAjHkqMzbLRllc+TWFIH67yYv+OcjZFh9aE/fx+1I0TzC3jUYqz66h/3nwqhU8N9s7zxMw0UWXKFJLyTM3Jr2dZW5dpz1st2rM1NwI4l4jOgRrQXAzg+W2suIyFx9gIglAPZtWYrupPEARhGtqyNcw8BnAZgBugimFex8w3E9HriOiZAEBEjyWiQwD+DYA/IqKbp93/lVJsTIrasoUhlMbUOCNqW0lw+jyCg/RGCvtOq7WSZ6nVjdPa+WdY1TyyzHsoi5kpU2XKVBX7uN1t11Gplu0+Atr36oUJ4XzLgjIWed38ake1T1t3n8vOw6TnqG4jWmByRcWmzfPRBnW2VWefp1ET2zpeZr4ewPXOtFdb72+EekTVGis1sBGElWaKonCCIAi16bitWa2BTUMvahnxKRCp17G7nxxOc227fl8AOlNB6th0inkrNWVevK1eFCmkvjiUqnV1hfI6ZMVZogb7mCepadY2ZjtN1K26dN3WrNbARhBWGWmpIAjCPOi4rVm5gc382rpnaZJBU/5MuFk8ibf2SrJ8Pe9tFkzq0ZTVBKIwN6v0x1dXpVnUPTMJXTY2QvsU/cbL1Bm7Vou7fFEdl8CpBWOvK3kflNmu9lWFcjuS7wGXfs7XOzN2J47Ym/EFAGTNg8lcjW2lx9TISo+1yzFxXbY1KzewEYRVhZkRdVgeFgShG3Td1qzWwGbGAU9lakqcjNabPZd21+mr+5LOK6v/Uu45uR5TVf2Xsvl1zrG9jOu1GOGlyqtK9iXwe1dANovMXYdbx8NHlVqzVF4LZ/tYCcuP+7ubVW0TX62nIttSv+K3VmmCwFJsgtw8ctI3k3V77FCT/ksuRb/FuNSORImdSZWY2Krx5c5juPW/EsU4isERZabZHbzdveOIC6/ZrO6D1ui4rVmtgY0grDQsdWoEQZgD3bY1KzWwmbR/i6GqE2peEag3ci5TV3LPuiuea9ueko3ytIpVGXd518tyl2+Cz5OyR/tB7HpReQ8rdlWZMG3o6FZJtTt023vsVhM236eQGmVFNVVpprnnGtHxFEyhHW/bW5emJI7GVWBUr6R8/zUACPo9rzoT9PvZdQTpPNcmuaqOvZ3MPpbE5Lj4/snGHjvCHiUmsTGZaco+mcyfZJkoBiXTsn35OKSMeqOWSRWcwNFs7E+u/ZmL6jINHbc1KzWwEYRVhpkRSUsFQRBmTNdtjQxsLGzPu0q9qYtPbUm2YXlO6bSgUOGxn3Xn5nnUGVuVyW3b8pamee4NAFDOXDauxlpn7rl3oLwljuN0/x2PC2GQqDipt5YqOGXqTZpFlfbfqhNvU5e5KTQ5ui0PrxRErd5ThZupWa3ba1s8Sg2gVGFjR4JBqKepz+Ggl1NggkE/sRfBoJeZZys2gUe5KVKY1fKeNMcC7E7T5jeQxOpZqkzahTyv2BjlOB6OE/tkbGQ8GqnPtgoU6vdDvQ+WJmy/A5TtcRXjsvgbtW+LuW/q0W1bIwMbQegKHZeHBUHoCB23Nas1sHEqD8+qVktVF2t324XZCQ3jacriaOpmJ2SUHXddDZ5525iRPYWBFReTqiXQ640dD4DCsDTyPusTWduzFJhALxVHcRo3YL5nKTd2vI1aR5rF5mZG2UrPLJjYU+P2vDwiuhDAW6Ac37cz8+XO/DUA7wDwQwDuAfBcZv56KxtfMeZVhdenIrtqjL1PZZlPrlJjx9XYSo3abpBTasznwFrelzFlVJmy+L9ax46eJ+PJxOdFibqS/M6TuJg0A8p0qw4GPUC/N2ukKB8PZOYFA/1mmCrEPuXGPaIYqd1xr10c83JXb27R1iyC1RrYCMIKo2pLTJ+CSUQhgCsAPBXAIQA3EtFBZr7FWuwlAO5j5u8hoosBvAHAc6feuCAIS09btmZRyMDGQ50+KpnlMypLdQbUJEqNu658JkI+nsan1NStFNqkjk2V0pN6LVlPSCkv/ufsap4+Dj0tduar/UyVmzjx1vLKjavC2MqNuS4+5can2gSeqqPzoqUqyRcAuJWZbwMAIroWwEUA7IHNRQBeq9+/F8BbiYiYubtuXIeo2+2+SQVhn63x2xa/8uu+d6fl7VZ55mUdhdiO8yhSUoMw9MawFH7fiu1DojZ77Kf+HMWpzUgUYq9yk61YHIRUmKnpu76Li93z01ZF9kWow0uecyYIgoE5DYYs+6vBGQC+ZX0+pKd5l2HmMYDDAE5u4TAEQVhy2rI1ljr8NADnAXgeEZ3nLJaowwDeDKUOT8VqKTZWpkIT6npHue81VGry3/d7P5MqKUXzps56mpIiT6tIEamcZ50fe5nk2XjOoyNvppT6TBkPS33fjtPye1yA/7l5HSZ+ts6MaFhr4HIKEd1kfb6Sma+cbKPCrKmTgVkV1+dmQLWJrZ7mfpMlv1N7qm+v0t9gfXXYnubLlHLr2VRNy63bkwlk7E3VP/L0OFLlxrUp9rHmlKdlUnHq25oqFqIOL83ARo/sbgJwOzM/g4jOAXAtlJf4OQA/y8zDRe6jICyU+pkKdzPz+SXzbwdwlvX5TD3Nt8whIuoB2A8lE3cesTWCUEF7WVE+dfhxRcsw85iIjDp896QbXZqBDYBfAfBlACfoz28A8GZmvpaI/hBKrnrbtBupW5+mTuZTumx1BhSQja3xfTe/vWbeV5EH5PO4KAxy3kmRElK5XY+XU8s7avjDKVJkgKzykkzT55vjYs/JV+PGvmZ1426A2Xtc01bWtrgRwLn6H/rtAC4G8HxnmYMALgHwKQDPBvDXKxRfMxdbA0xfD6uuUlO2TBnmt2S2ou7vbNBoDCRZRGn8ifrXEQ3H6bRYr2Wk1xWkWZyJjRil65228rCtxLjzfXVs7CrDcU7hsdQfqwpx0bZd/NmV9dTgMjtYdv/MUs1pYGuWUh1eihgbIjoTwL8C8Hb9mQD8OJQsBQDXAHjWQnZOEJYFXc6g6q96NTwGcBmAG6D+wV/HzDcT0euI6Jl6sT8BcDIR3Qrg5QBeOaOjmitiawShBvVtzd3MfL715w5qmqjDaEsdXhbF5vcBvALAPv35ZAD3awMM+IMbAQBEdCmASwHgwetrAJp7SXXjaPLfm0ypqavEJN6U57lsXpWJksj+xOOwt1lje3WeefuWr5rn1q/Jqjn+lELfuut2xHWVHQoo6dTrU26KatzYy6f7wIXnpyyDykdTj4sZiIbtpGAy8/UArnemvdp6vw3g37SyseXi99GirQHaq1IOTG6LXGIrvsNF3d+OwmHeWAoMmWygKACbejRxXvk1y8e5fnT5ulvpsTT3qYt+/75A1rq9opp2/i7et3q/5UmVG++6ZqjmtGhrFqIOL1yxIaJnALiLmT83yfeZ+UozWjwwGFR/QRC6ii6aNa1is1sRWyMINWnJ1ixKHV4GxeYJAJ5JRE8HsA713PstAA4QUU+fGJ98lYNQ7T3VUSPqxNI0XWcTkkq+gavKlGQnVBC5aoylogSe7UyCq8wUrYs92Qy+5ZtkLkxDUY0bNc3tylsef+My6fNzP1xbtRK8zNXWLAtGrcz2VfNVh1I1W0wV3kyNm1E2xiYuqZ+VbKOiH10Tu1mWHQXUU4VdBcZ+74uncZWaTDfxGcS3tFnxfPp7sz1bswh1eOGKDTO/ipnPZOazoWSqv2bmFwD4OJQsBSiZ6oML2kVBWA5YGdSqP8GP2BpBqEnHbc0yKDZF/CaAa4noPwH4Oyi5qhyaTD2pG0eTn1c/c6ouGVXGdKO1199kXR7lx3gvtqcVzUAF8MXOVGVPuZ9dj8HnTU1K3Wfd7vWvir9xaVPNY+bWYmyEDM1tzQyoulfM/RV4FMU0Psz6/eS6TVvrMlZF305BaP8OTBZRfr9cFdmOlWnSD2qSvnRlSm1bdsT+fee/Z8/L/tb9ilLeHtSJxSnLvJwXXbc1SzWwYeZPAPiEfn8bVHEfQRA0EkPTDmJrBKGcLtuapRrYzJKmSkpTpaYJdrZTrvZKSLnsJNvDyI3jM+ty6tJ4FYj8OmdJ1bNxg+95btE+Zp+R+7yi6Y6t7Fm3r16Ooewea6PvCnN7/VuE5cNWa8soU26cJTOfjIhKYQA4y0cjY0coU2sG8NffMkqN7dPP0mb6KM/KrGcXfI9Tyn5jxTW5yn+XXfvddt3W7JqBjSB0no533BUEoSN03Nas3MBmmhiXiXv4VGC8AtfziaM4V88hO/KvrzxEcZx/bu3po7SIZ7dlCkqT5+bpdL8nUVjnouUgt7K4myKmjb1SG+q2PCxUU6S6+qjyqIu+WVQ7CgC4sbK98PyTUiaxd5P8xqZRNxYdT+Ol47Zm5QY2grCqMJbUCAqCsFJ03dbs2oHNJOrMpM+/Da5yQAF5VYZUXcmPmNOsJs/6S7y9aE4xNXWo84OpfmZdpgJVexptZif4r8UMvJ2OP/cWmjPNP5cSYUYQyum4rdm1AxtB6BrMjGi8PANUQRBWk67bml0zsGkjfqZJLyXfaNdVcYqUhbhObI3jjfl6r5Q9S/cx6TlqQ51omsnUJG5mUs9jGm95kjicOkQr02BbmJZWO8c3vD+nzTyc5PcwrQ1v2p+qzZjLrlSptumyrdk1AxtB6DqMXJauIAhC63Td1qz0wGZmWU7e+iaTqTg+JskziOJidcZXh8K/kgk23IA2s5PafP47jyC5sv5TTeiyF7WbaFNNqUOTe6pKbSlbV3mH6/q/o0lsQW07Br86Y5Tw8no71jyPPWyi+tjrmvX9MAtFqMu2ZqUHNoKwSsQMDJe4P4sgCKtB123NSg9sOOKZqTb5bbVT7RaYQTR6lzXFmnQhNbGNOJtdcCk7AWO+qkzde6eOauLPtmxWwbutir3TUKaSlylCFBVXUrbPg+9/R534x7Ks1sx+tPi/aRb3YpdtzXJXVxIEIYHBiLj6TxAEYRrmYWuI6CQi+isi+pp+PbFguQ8T0f1E9Bd1173Sig0wm3ois1CBuqA47DaWrfImAxjKbbJyTHqflSoqDeNkqvooFakxTfs1Va2vjFxWqZX16dpkOyE0p+xEnFuXT4kJGvr9Zh/KapPZlJ2feT1pKGJOtuaVAD7GzJcT0Sv159/0LPdGAHsA/GLdFYtiIwgdgRmi2AiCMHPmZGsuAnCNfn8NgGf594U/BuBIkxWvvGIzC5bNkxd2D3LrdZu21ZlJMpim6XLtKjR+9af6GMt6xBURl/nhJRXffT333HX51txExaGQcucikxVVU8UxLIOaU/NWPYWIbrI+X8nMV9bcxGnMfId+/20ApzXYvVJkYCMIHUHVlpCRjSAIs6WBrbmbmc8vmklEHwXwYM+s385sj5mJqDXjJgObluhyX43dTiudt+eAeu4t91kXmEXcTJ11N60z4yo1VSpNU4VmEmWm6booKM4oDQqUFLeKvP09Yw/MubHr55hz6Kotvmwq37mqUnF863apWm8btGVrmPkpRfOI6E4iOp2Z7yCi0wHcNfUGNRJjIwgdQT33rv4TBEGYhjnZmoMALtHvLwHwwanXqFm4YkNEZwF4B9TzNYZ6RvcWIjoJwHsAnA3g6wCew8z31VnnqqknkjGlqFPdeRIWdb9MohTJo6jJmYWtAaaPuatbsbeNTCdg8ngaH9OoNU1+d2W/FY7jjGrjbiOfTcWWqpLv/2f2y1VugFS9cc+trbKYa+FTUcrmpdtP11230nHRvTGNkjMHW3M5gOuI6CUAvgHgOQBAROcD+CVm/gX9+X8D+D4Ae4noEICXMPMNZSteBsVmDODXmfk8AI8H8FIiOg9pKti5AD6mPwvCroWhwiCr/oRCxNYIQg3mYWuY+R5mfjIzn8vMT2Hme/X0m8ygRn9+IjM/iJk3mPnMqkENsASKjY6KvkO/P0JEXwZwBlQq2JP0YtcA+AT8Oe4ZlkWtEZWlfdo8p7NSf5rQ9F5lsMTYTEGrtoZ5pnE0yWYqtjGPzKc6+zEJQVL3pXjd84p/q6PcAP64G7Vs7I27KVJMqioc2+u1adqhfNLr1nVbs3jrbkFEZwP4QQCfwQxTwQShi5hMhVnXsalTEZSIHk1EnyKim4noi0T03Kk3PEfE1ghCMfOyNbNi4YqNgYj2AvgfAH6VmR8gskbJJalgRHQpgEsB4MEb663tz7IqLvPuGrxIZtGx1jDN9V2Y2jO/4OA6FUGPA3gRM3+NiB4C4HNEdAMz3z+XPZyCVmzN+lqjbTZRaYD5dNhugyCkQsWFgqBWVtS0qkxRfE0b626CL2OqTkxNnWXsbTRVbSai44kIS6HYEFEfytC8k5nfpyffqVPAUJYKxsxXMvP5zHz+iYP+fHZYEBaAScGs+muByoqgzPxVZv6afv/PUL/PB7Wx8VnSlq050BdbI6wuc7Q1M2Hhig0pd+lPAHyZmd9kzTKpYJejbioYL6/SUsVuUmLq0uSczFLdcVnUPTbHAn2NHs0Q0QUABgD+z6x3bBpatTU1aVOpWSRltVnKYmWK1JRJ6tuUKTNF++RdzwLqVrWp3Ewbd1OHrhcDXfjABsATAPwsgH8gor/X034LBalggrBbUcam1qKVZc7bqgiqFY4/A3AJMy+7VyG2RhBq0MDWLCXEHR6VuRDRd6AM0zJyCoC7F70Tc2Q3He+0x/owZq58jENEH9bbquJuZr5w0p0hoq8AeJJVEfQTzPy9nuVOgMog+s/M/N5Jt9dFxNYsDbvpWIHpjreWnQHmZ2tmxUoNbJYZIrqprKfGqrGbjnfVjpWI3gjgHit4+CRmfoWzzADAXwL4n8z8+wvYTaGAVbsfy9hNxwrsvuOdlKUIHhYEYam4HMBTiehrAJ6iP4OIzieit+tlngPgRwC8mIj+Xv89eiF7KwiCYLEMMTaCICwRzHwPgCd7pt8E4Bf0+z8H8Odz3jVBEIRKRLGZH1dWL7JS7Kbj3U3HKiw/u+l+3E3HCuy+450IibERBEEQBGFlEMVGEARBEISVQQY2M4KIvk5E/6CDKm/S0yp78HQBIrqKiO4ioi9Z07zHRoo/IKJbdU+hxyxuzyej4HhfS0S3W4GzT7fmvUof71eI6CcXs9fCbmCV7QwgtkZPE1vTEBnYzJYfY+ZHW+l5pgfPuQA+pj93kasBuLULio7taQDO1X+XAnjbnPaxTa5G/ngB4M36+j6ama8HACI6D8DFAB6hv/PfiCic254Ku5FVtTOA2BqD2JoGyMBmvlT24OkCzPxJAPc6k4uO7SIA72DFpwEcMH15ukLB8RZxEYBrmXmHmf8JwK0ALpjZzglCnpWwM4DYmgrE1hQgA5vZwQA+QkSf012BgYY9eDpG0bGdAeBb1nKH9LRV4DIteV9lyf2rfLzC8rHb7AwgtkZsTQUysJkdP8zMj4GSR19KRD9iz2SVjraSKWmrfGwWbwPw3QAeDeAOAL+30L0Rdiu71s4Aq398GrE1DZGBzYxg5tv1610A3g8lEd5ppFH9etfi9rB1io7tdgBnWcudqad1Gma+k5kj3fjxj5FKwCt5vMJysgvtDCC2RmxNBTKwmQFEtElE+8x7AD8B4EsADgK4RC92CYAPLmYPZ0LRsR0E8CKdsfB4AIctGbmzOM/u/zXU9QXU8V5MRGtEdA5UIONn571/wuqzS+0MILZGbE0F0lJhNpwG4P1EBKhz/C5m/jAR3QjgOiJ6CVRn4OcscB8nhojeDeBJAE4hokMAXgPVT8h3bNcDeDpUYNtxAD839x2ekoLjfRKp3kgM4OsAfhEAmPlmIroOwC0AxgBeyszRAnZbWH1W2s4AYmsgtmYipPKwIAiCIAgrgzyKEgRBEARhZZCBjSAIgiAIK4MMbARBEARBWBlkYCMIgiAIwsogAxtBEARBEFYGGdgIgiAIgrAyyMBGEARBEISVQQY2uwgi+tdE9P8XzNsgov/la3tPRK8lov8w4TYHRPRJIpJikIKwSxBbIywSGdjsLh4D4PMF834ewPvarlzJzEMAHwPw3DbXKwjCUiO2RlgYMrDZBRDRw4norwD8KoDXENGvehZ7AayeMkT020T0VSL6GwDf66zvhUT0WSL6eyL6I+N5EdH/S0RfIaK/IaJ3W57XB/T6BUFYYcTWCMuADGxWHCJaA3AdgJcD+A6AxwN4NRGtW8sMAHwXM39df/4hABcDeDRU75XHWst+P5RH9ARmfjSACMALiOixAH4GwKMAPA3A+dZufMlehyAIq4fYGmFZkGeRq89TAXwBwD8DeICZv01E2wDs59unALjf+vxEAO9n5uMAQEQHrXlPBvBDAG7Uzfc2ANwF4CQAH2TmbQDbRPQ/zReYOSKiIRHtY+YjbR+gIAhLgdgaYSmQgc3q8ygA/wDgXwD4IhGdCuAIMx+zltkCsO77sgcCcA0zvyoz0S8526wB2K65DUEQuofYGmEpkEdRq88RAN8HZXS+COB3AVxhL8DM9wEILcn4kwCepbMX9gH4KWvxjwF4tjZaIKKTiOhhAP4WwE8R0ToR7QXwDPMFIjoZwN3MPJrJEQqCsAyIrRGWAlFsVp8/B/B+AD8N4D4A1wLwpWF+BMAPA/goM3+eiN4DJSvfBeBGsxAz30JEvwPgI0QUABgBeCkzf1rLyF8EcCeU53ZYf+3HAHxoFgcnCMLSILZGWAqImRe9D8IcIKK/B/AUZr67YP5jAPwaM//sFNvYy8xHiWgPlCd2qTZc7wPwSmb+6qTrFgShG4itERaNKDa7AJ2tsL/I0ACANgofJ6JwivoSVxLReVDP0K/R6xwA+IAYGkFYfcTWCMuAKDaCIAiCIKwMEjwsCIIgCMLKIAMbQRAEQRBWBhnYCIIgCIKwMsjARhAEQRCElUEGNoIgCIIgrAwysBEEQRAEYWWQgY0gCIIgCCuDDGwEQRAEQVgZ/i8RbNq7wj0/qwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x273.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the computed projected fields\n",
    "projected_field_data_server = sim_data2[monitor_far.name]\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_proj_server = projected_field_data_server.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj_server)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj_server.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are virtually unaffected even though we used just a fraction of the data in the projection! Let's see how this changed the computation time, again compared to the client-side projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection took 2.73 s\n",
      "Server-side field projection took 0.57 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(\n",
    "    sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
    "print(f\"Server-side field projection took {server_time:.2f} s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The server-side projections are now faster than when we used all the data. Note that this is a fairly small problem, so the speed-up is marginal, but will be much more significant for larger problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial filtering of far fields via windowing of near fields <a name=\"spatialfiltering\"></a>\n",
    "For surface field projection monitors, it is assumed that near fields go to zero near the edges of the near field monitor. In case the near fields do not go to zero, they are truncated, which can result in high-frequency artifacts in the far field.\n",
    "To alleviate these issues, we can apply a spatial filter via a Gaussian windowing function by specifying the `window_size` field in the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) monitor (or any of the other field projection monitors). This defines the size of the transition region over which the window is applied relative to half the size of the monitor, along each tangential direction. For more details, see the documentation [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html).\n",
    "\n",
    "In the following, the windowing feature is demonstrated by repeating the aperture example from above for a larger aperture size, but using the `window_size` field instead of a physical PEC plate with a hole, to achieve an equivalent \"aperture\". The `window_size` field here was chosen based on trial and error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# size of the new larger aperture\n",
    "new_aperture_width = width * 1.6\n",
    "new_aperture_height = height * 1.6\n",
    "\n",
    "# new projection monitor with windowing sizes chosen to approximate the physical aperture defined above\n",
    "window_size = (0.37, 0.37)\n",
    "new_monitor_far = monitor_far.copy(update={\"window_size\": window_size})\n",
    "\n",
    "# new simulation object without a physical aperture, but with windowing enabled in the projection monitor\n",
    "sim_window = sim.copy(update={\n",
    "    \"monitors\": [new_monitor_far],\n",
    "    \"structures\": [],\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
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      "text/plain": [
       "\u001b[2;36m12:30:19 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_2'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=919602;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
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       "Output()"
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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\">12:30:21 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:21 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
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       "Output()"
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     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:30:42 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:42 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
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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"
      ],
      "text/plain": []
     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:30:51 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:30:51 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:00 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:00 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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 = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
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     },
     "metadata": {},
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:03 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:03 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
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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": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=948473;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=948473;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=86870;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=948473;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=86870;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=948473;https://tidy3d.simulation.cloud/workbench?taskId=fdve-623c9da0-5e81-43b1-b9f5-06bcb8e1c127\u001b\\\u001b[4;34m-623c9da0-5e81-43b1-b9f5-06bcb8e1c127'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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       "Output()"
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      "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\">\n",
       "</pre>\n"
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       "\n"
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     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:05 EST </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:05 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \n"
      ]
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     "metadata": {},
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   ],
   "source": [
    "sim_data_window = web.run(\n",
    "    sim_window, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the results, we notice the excellent agreement between the analytical results for the aperture, and the projected fields resulting from windowing the near fields by an amount equivalent to an aperture of the given size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 3.03 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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M0B34+biXWgE7qjzPtS/TgKNULeXoQjrXz96Q3Ldv9Tfauzf4+XGO/S7Jy5cv14CjPCIwMLDydgjKM5w2yNgYEwYsBG4XkYNnuY0pxpjVxpjVmnqV8m3Lly8nNDSUyJwcaN8emjSp/kbDwqBrVxpnZtK4cWMdaKxULeaUgGOMCcQKN++JyCcnWSUPqDo9aaR92TFE5HURSRKRpCbO+LBTSnmtVatW0btXL/xWrXJO95RD376YVavo37cvq1atct52lVI1ijOuojLAHCBVRKafYrVFwFX2q6n6AAd0/I1StVdFRQWbNm1iaGws5Oc7N+D06wdFRQxt3pyMjAyKi4udt22lVI3hjDE45wITgRRjTLJ92f1ANICIvAp8CYwEtgBHgElO2K9SqobKysri6NGj9HeMv3F2wAH6iGCz2di8eTM9eujMFErVNs64imolYP5kHQH+Xt19KaV8Q0pKCgCdCgshIgLij59ZohpiYqB5c9rt2VO5Lw04StU+OpOxUsrtNmzYgJ+fH40yMqyrp/yc+FFkDPTrR8TGjYSEhLBhwwbnbVspVWNowFFKuV1KSgrd27TBb9Mm53ZPOfTrh9m6lYFxcZWtRUqp2kUDjlLK7TZs2MDFzZuDiMsCDsCoRo20BUepWkoDjlLKrYqKiti6dSsDAwKsrqnevZ2/kx49ICiIviLs3r2bPfbxOEqp2kMDjlLKrTZt2oSI0HH/fkhMtAYZO1twMCQl0c4+Yah2UylV+2jAUUq5lSNsNNq5E7p1c92OunYlYvv2Y/aplKo9NOAopdxqw4YNRIaGErBnj9WC4yoJCfgdOkRXHYejVK2kAUcp5VYpKSmMbN3aepKQ4Lod2cPThdHR2oKjVC2kAUcp5TYiwoYNGxjQsKG1wJUBx77tPuHhbNy4kYqKCtftSynldTTgKKXcZteuXezbt48ufn7Wnb+jo123s8aNoWlT4m02iouLycrKct2+lFJeRwOOUsptHGNhog8dsm7PYP7wLi/Vl5BAy8LCY/atlKodNOAopdzGMRYmIjfXtQOMHRITqZuTg58xOg5HqVpGA45Sym02bNhA5xYt8MvPd+34G4eEBExREYNiY7UFR6laRgOOUsptUlJSGBEVZT1xU8ABGNaypbbgKFXLaMBRSrlFWVkZmzdvpl+9etYCNwacXnXqkJWVRVFRkev3qZTyChpwlFJukZGRQVlZGfE2m3V7hlatXL/TBg2gZUviyssB6zYRSqnaQQOOUsotHGNgWjruQeXqK6gcEhJoZr8nlY7DUar20ICjlHKLlJQUAvz9Cc3Odk/3lENCAkFZWYSHhuo4HKVqEQ04Sim32LBhA/3bt8cUFLg94JijR7mgfXttwVGqFtGAo5Ryi5SUFC5wjLtxZ8Cxz7czpGlTUlJSEBH37Vsp5TEacJRSLrd//362b99Or7p1rQXumOTPIT4egB7Bwezbt4+dO3e6b99KKY/RgKOUcrmNGzcCWFczNWwIzZq5b+cRERAVRZviYgAdh6NULaEBRynlco6xL80cMxi76woqh4QEGu3adUwtSinfpgFHKeVymzZtol5EBEGZme4df+OQmEhAZiZRLVtWtiYppXybBhyllMulpaUxoG1bzIEDngk4CQlQUsKQ6GjS09Pdv3+llNtpwFFKuVx6ejoDGzWynrhzgLGDPVSdW78+6enpeiWVUrWABhyllEsdOnSIvLw8ugcFWQs80YLTqRMAnf38OHDgAHv27HF/DUopt3JKwDHGvGmM2WOMOWnntjFmsDHmgDEm2f74pzP2q5TyfhkZGQC0Ky6GJk2sh7uFhUFsLDGHDwNWl5lSyrc5qwXnbWDEn6yzQkS62R+POWm/Sikv5xjz0nTvXs+03jgkJNDot9+OqUkp5bucEnBEZDmwzxnbUkr5lvT0dPyMoc62bR4POIFbtxIeEqIBR6lawJ1jcPoaY9YbY74yxnjwU04p5U5paWn0i4rCHDrkmQHGDgkJmLIyztMrqZSqFdwVcNYCrUWkKzAT+OxkKxljphhjVhtjVufn57upNKWUK6Wnp3Ne06bWE0+24NjD1aBGjXQMjlK1gFsCjogcFJEi+9dfAoHGmMYnWe91EUkSkaQmnhiIqJRyKpvNRkZGBkl16lgLPBlwOnYEPz+6BQaybds2SkpKPFeLUsrl3BJwjDHNjbHmZjfG9Lbvt8Ad+1ZKec6OHTs4evQo7W02aNrUug+Vp9SpA7GxxJaWYrPZyMrK8lwtSimXC3DGRowx84HBQGNjTC7wMBAIICKvApcBNxljyoGjwDjRmbaU8nmOsS4tDh6EDh08XA0QF0fTrVsBa2xQvP1O40op3+OUgCMiV/7J6y8BLzljX0qpmsMRcMJ27YLevT1cDRAXR53lywG9VFwpX6czGSulXCYtLY3oiAj89+6FuDhPlwNxcZjDh+nerJkGHKV8nAYcpZTLpKenc15kpPXES7qoAAa3bKlXUinl4zTgKKVcJj09nXMaNLCeeEMLjj1kJYWH6003lfJxGnCUUi5RVFREbm4uiUFB4OcHbdp4uiRo1Qrq1KGjnx/79+9H59tSyndpwFFKuYTjJputS0ogJgaCgz1bEFhBq317Io8eBXSgsVK+TAOOUsolHOGhcWGhd3RPOcTF0WDPHkDvKq6UL9OAo5RyibS0NPyMIWT7dq8LOAE7dhAWHKwtOEr5MA04SimXSE9Pp3dUFObwYa8LOKa8nMExMRpwlPJhGnCUUi6Rnp7OwObNrSdeFnAA+jVpol1USvkwDThKKaez2Wykp6fTMzzcWuANc+A42ANO1zp12LZtG6WlpR4uSCnlChpwlFJOl5uby9GjR+lgDISEgGOyP2/QqBE0akS7igoqKir0pptK+SgNOEopp3OMbWl1+DC0b29dnu1N4uJofvAgoJeKK+WrvOxTRynlCxxjW+rv3u1d428c4uII27kT0EvFlfJVGnCUUk6Xnp5Ow/Bw/L3tEnGHuDj8du6krd50UymfpQFHKeV06enpDI6JwZSXe23AARgSFaUBRykfpQFHKeV0aWlp9G3UyHrixQGnd/36pKWl6U03lfJBGnCUUk51+PBhcnNz6VKnjrXAGwNOu3YAJAQGUlhYyN69ez1ckFLK2TTgKKWcynGTzbbl5dCwITRu7OGKTqJuXYiOprV9DhztplLK92jAUUo5lSMsNDtwwDtbbxzi4mhUUADolVRK+SINOEopp8rIyMAYQ2hurtcHnODsbIKDgsjMzPR0NUopJ9OAo5RyqoyMDDpGRmJ27vT6gGP276dXbGxlt5pSyndowFFKOVVGRgYDW7Wynnh5wAEY0KyZBhylfJAGHKWU04gIGRkZ9IqIsBbUgIDTPTSULVu2UFFR4eGClFLOpAFHKeU0+fn5HDhwgPiAAGuB/XJsr9S6NQQGEmcMpaWlbN++3dMVKaWcSAOOUsppHF090SUl1h3EQ0M9XNEfCAiAtm2tG4KCdlMp5WM04CilnMYREhoVFECHDh6u5jR06GDdEBQNOEr5Gg04SimnycjIIDAggODsbO8ef+MQF4f/tm3Uj4jQgKOUj9GAo5RymoyMDJJiYzH799eYgGNKShgQE6MBRykf45SAY4x50xizxxiz8RSvG2PMi8aYLcaYDcaYHs7Yr1LKu2RkZDCwWTPrSQ0JOAD9GjfWgKOUj3FWC87bwIg/eP1CoL39MQWY5aT9KqW8REVFBVu2bKFHWJi1oH17zxZ0OuwBp2tICDk5ORQXF3u4IKWUszgl4IjIcmDfH6xyMTBXLKuA+saYFs7Yt1LKO+zYsYOSkhLijLGuUIqN9XRJf65ZMwgPp43NhoiQlZXl6YqUUk7irjE4rYAdVZ7n2pcppXyEo4un1ZEj0LatFXK8nTEQF0fzgwcBvau4Ur7EqwYZG2OmGGNWG2NW5+fne7ocpdQZcASc+nv21IzxNw5xcYTv3AnopeJK+RJ3BZw8IKrK80j7smOIyOsikiQiSU2aNHFTaUopZ8jIyCAiLIyAbdtqXMDx276d1s2ba8BRyoe4K+AsAq6yX03VBzggIrvctG+llBtkZGTQPzYWU1xc4wIONhsDIyM14CjlQ5zSSW6MmQ8MBhobY3KBh4FAABF5FfgSGAlsAY4Ak5yxX6WU98jIyOC6mBjrSU0LOMA5DRrwdXKyZ2tRSjmNUwKOiFz5J68L8Hdn7Esp5X1KSkrIzs6ma6dO1oKaFHDsl7MnBgWRn59PYWEhDRo08HBRSqnq8qpBxkqpmikrKwsRoa3NZt1gs0UNmgWiXj1o1oyY0lIAMjMzPVyQUsoZNOAoparNMXalxcGDVuuNMR6u6AzFxdGksBDQK6mU8hUacJRS1eYIBWG7dtWs7imHuDjq5Obi5+enAUcpH6EBRylVbRkZGUQ2bYp/Tk7NuEXD8dq3x/z2G51bt9aAo5SP0ICjlKq2jIwMBkVFgc1WY1twAAa2aKEBRykfoQFHKVVtGRkZnOO48qgGB5ykiAgyMjKwLvxUStVkGnCUUtVy4MABdu/eTWJQkLWgJnZRtW0LxtDRz4/Dhw+za5fOQ6pUTacBRylVLY7LqmPKy6FxY2jY0MMVnYWQEGjdmqjiYkCvpFLKF2jAUUpViyMMNC0srJndUw5xcTTcuxfQgKOUL9CAo5SqloyMDIwx1M3Lq/EBJyg7m5DgYA04SvkADThKqWrJyMggPjoas3NnjQ845uBBzomN1YCjlA/QgKOUqpaMjAwGtWxpPanhAQdgQLNmGnCU8gEacJRSZ01EyMjIICkiwlrgAwGnW2goWVlZlJeXe7ggpVR1aMBRSp213bt3c+jQIToFBFgL2rXzbEHVER0NQUHEiVBeXs62bds8XZFSqho04CilzlpaWhoA0cXFVkCoU8fDFVWDvz+0a0fLI0cASE9P93BBSqnq0ICjlDprqampADQuKKjZ3VMOcXHU370b+P17U0rVTBpwlFJnLS0tjbDQUAKzs30m4Phv20bLZs0qW6eUUjWTBhyl1FlLTU2lT7t2mP37fSbgUFLCgJgYbcFRqobTgKOUOmupqakMbN7ceuIrAQc4t0kT0tLS9KabStVgGnCUUmfl0KFD5Obm0j001FrgQwGnc3AwhYWF7Nmzx8MFKaXOlgYcpdRZcVxl1B4gIABat/ZoPU7RtCmEh9PGPgeOdlMpVXNpwFFKnRXHINwWRUXQtq0Vcmo6YyAujib79wPoQGOlajANOEqps5KamkpAQADhNf0eVMeLiyNk+3ZCQ0O1BUepGkwDjlLqrKSmptKuTRvMli0+F3BMdjad4+K0BUepGkwDjlLqrKSlpTEgJgaKi6F9e0+X4zxxcSDCoFattAVHqRpMA45S6oyVlZWRmZlJ3/r1rQXx8R6tx6k6dQKgd3g4O3bsoKioyMMFKaXOhgYcpdQZ27p1K+Xl5ST6+1sLfCngdOgAxtDRZgP0nlRK1VQacJRSZ8zRdRNz+LB1aXWjRh6uyInq1oXYWFodPAjopeJK1VROCTjGmBHGmHRjzBZjzLSTvH6NMSbfGJNsf1znjP0qpTzD8Uu/wW+/+VbrjUN8POE7duDv768DjZWqoaodcIwx/sDLwIVAPHClMeZkn3gfikg3++ON6u5XKeU5aWlptGrZkoD0dJ8NOH4ZGXRo21ZbcJSqoZzRgtMb2CIiW0WkFPgAuNgJ21VKeanU1FTOjY2FAwcgIcHT5ThffDyUljIoKkpbcJSqoZwRcFoBO6o8z7UvO96lxpgNxpgFxpgoJ+xXKeUBImJdIu4Yd+OLLTj20NY3IoLMzEzK7bduUErVHO4aZPwfIEZEugDfAu+cbCVjzBRjzGpjzOr8/Hw3laaUOhM7d+7k0KFDdAsKshb4YsDp2BGARD8/ysrK2Lp1q4cLUkqdKWcEnDygaotMpH1ZJREpEJES+9M3gJ4n25CIvC4iSSKS1KRJEyeUppRyNseYlNijR62rp3zxZzUsDFq3Jso+B46Ow1Gq5nFGwPkVaG+MiTXGBAHjgEVVVzDGtKjydAygnxZK1VCOMSlN8vOt1htjPFyRi8TH02DnTkADjlI1UbUDjoiUA7cA32AFl49EZJMx5jFjzBj7arcZYzYZY9YDtwHXVHe/SinPSE1NJSI8nMDMTN/snnKIj8c/M5PIFi10oLFSNVCAMzYiIl8CXx637J9Vvr4PuM8Z+1JKeVZaWhrntmuHWbfON6+gcoiPh+JiBsfEaAuOUjWQzmSslDojqampDHKMu/HxFhyA/g0akJaWhoh4uCCl1JnQgKOUOm0HDhxg165d9AgJsRb4csCx33SzS0AABw8eZNeuXR4uSCl1JjTgKKVOm2MsSruyMqhfH5o392xBrlSvHkRGEnP0KICOw1GqhtGAo5Q6bY6xKM0KCnz7CiqH+Hga7d4N6JVUStU0GnCUUqctLS2NwMBA6mzb5tvdUw7x8QRu2UK98HANOErVMBpwlFKnLTU1ld6xsZj8fN++gsohPh5z5AiD27TRLiqlahgNOEqp05aamsp5jnE3taQFB2Bg48bagqNUDaMBRyl1Wo4ePUpWVha9QkOtBbUh4NivpOoRHMzOnTspLCz0cEFKqdOlAUcpdVo2btyIzWYjHiA8HFq18nRJrtewITRvTvuyMgDWr1/v4YKUUqdLA45S6rQkJycD0HL//tpxBZVDfDxN9+4Ffj8GSinv55RbNShV24gIBQUFZGdnVz4iIiKYOHEiderU8XR5LpGcnExERAQh27bByJGeLsd9EhIIfOstWjRv7tMBp6SkhPfee4+CggJiYmJo3bo1MTExNGnSBFNbwqzyKRpwlDoDR48e5bHHHuPll1/m0KFDJ7z+yCOPMG3aNKZMmUKIY7ZfH5GcnEz/+HjMqlW1Y/yNQ3w8FBUxNCnJJwNOaWkpb775Jk8++SQ7duw44fXQ0FCmTJnC448/Tqhj/JVSNYB2USl1mr777ju6dOnCU089xciRI5kxYwaffvop69ato7CwkO+//54OHTowdepU2rZty0svvURxcbGny3YKm83G+vXrGeYYd1PbAg4wuGlTNm/eTGlpqYcLco7S0lJef/112rdvz0033URkZCT/+9//2L9/P+vXr+fzzz/nhRdeYOzYsTz//PMkJibyzTffeLpspU6fiHjlo2fPnqKUNygsLJTrrrtOAGnTpo0sXrz4D9dftmSJXNynj3QBuaBnTzly5IibKnWdjIwMAWTlVVeJgEh2tqdLcp/8fBGQtRMnCiDr1q3zdEXVVlJSIqP695euIBd27y5f//e/YrPZTrn+ihUrpEOHDgLIxIkTZe/evW6sVqk/BqyWk+QIbcFR6g+sW7eO+Ph43nzzTe6++25SUlI4//zzj13pyBF4/HHo3RsiIxk8fDifrVrFeuCTNWv4tls3bL/95pH6ncXRNdO+rAxCQyEqyrMFuVPjxtCkCbH2e1LV9G4qyc/nm549eX/lSpKBL9etY/iYMZjISEhKgocegqKiY97Tv39/kpOTefDBB5k/fz6dOnVi1apVHqlfqdOlAUepU1i3bh1Dhw4lMDCQX375haeffpq6dev+voIIzJ8PHTvCP/8JderAsGFwzz0wcyZ8+CE5PXtyUUYG5dHRcNddYL+vUU2TnJxMQEAAjfbsseaG8atlHx3x8dTLzaVu3bo1N+Ds3Qv33UdpZCQXbdxITmIifPghvPQSTJsGI0ZARAT8618QFwfvvAM2W+XbQ0JCePzxx1m7di316tXjggsu0JCjvNvJmnW84aFdVMqT1q5dKw0bNpTo6GjJyso6cYVffhHp18/qruneXeT770+6HZvNJg9feaW8A1JhjEidOiJvv+3i6p1v5MiR0rlzZ5GWLUWuvtrT5bjfTTeJ1Ksnfc45RwYNGuTpas7chx+KhIaKzRh5H+Te0aNP3SX1008ivXtb53avXiI//njCKjt27JB27dpJeHi4/PTTTy4uXqk/hnZRKXV6HC03YWFhLFu2jDZt2vz+ogg89pjVHbVlC7zxBvz6KwwceNJtGWN4cO5c5o8YQYIx7IuLg2uvha++ctN34xzr169nQIcOsHMndOni6XLcr2tXOHCAoW3bkpycjPWZWkMsWwYTJnAwNpZuAQG8OnAgj3788akv/e7TB376Cd59F/LyoF8/q4WnSmtOZGQky5Yto1mzZtqSo7yWBhylqvjDcFNWBtdfDw8/DBMnQmamFVb8/f9wmwEBAXz44YcEJiTQeetWjsbFweWXw5o1Lv5unCM/P5+8vDyG1qtnLejVy7MFeYL9ex5Uty4HDhwgJyfHwwWdpo0bYexYSlu3pnteHsWxsXz66acEBwf/8fv8/GDCBEhPhylT4N//ts75kpLKVTTkKG+nAUcpu8zMTIYNG3bycFNUBBdfDHPmWIMw33nHGq9wmiIiIvjvf/9LWUgI48LCkEaN4KKLIDvb+d+IkzluT9CtrMz6xde9u4cr8oDERAgKIuHIEaCGDDTeuRNGjkTq1OHaFi0osNn473//S8OGDU9/G2Fh8Oqr8H//B++/DxdeCPv3V75cNeQMHz6cjRs3Ov/7UOosacBRCti3bx+jRo0CYMmSJceGm927YfBg+OYbeO01q4vqLGZ2jYqK4sknn2TRr7/yze23W38NjxgB+/Y555twEccv81Y7d1oDjMPCPFuQJwQFQbduNN2xAz8/P+8POAcPWrNNFxbyw/33M2/FCh555BHatWt35tsyxuqimjsXVqyAAQOgyoSAkZGRLF26lNDQUEaNGsXuGjqQXvkeDTiq1istLeWyyy4jOzubzz777NhfAtu2Qd++kJoKn39uNddXw6RJk+jatSs3vvACJR9/bG3/4ovBiycETE5OJrJVK4I2bKid3VMOvXrhv24dHdu39+6AU1ZmdYFu3Ej5Bx9w3csvExcXx80331y97U6caI0dy8mxfibS0ipfioqK4j//+Q979uzhkksu4aj9knqlPEkDjqrVRISbb76ZZcuWMWfOHPr37//7izk5MGSI1SS/bBnYW3iqw9/fn+eff56cnBym//qr1dW1cqXVBeClkpOTGdaxI+zZU+sDDkVFXNimjXcHnBdegP/9D157jVeyskhPT+fZZ58lKCio+tseOtRqxSkrg/POs8ah2fXs2ZN58+axatUqJk+eXLMGYiufpPeiqmEOHTpEXl4ee/bs4ejRo8c8jDFERERQr149IiIiiIiIIDIykrDa2KVwmp599lnmzJnDgw8+yIQJE35/YccOK9wcOABLlkCPHk7b55AhQ7jkkkt48sknmZSZSfNx4+Dpp2HSJIiJcdp+nOHo0aOkpaXxQMeO1oLaHnCAIWFhPJeTQ2FhIQ0aNPBwUcfZtQsefRRGjWLf2LE80q4dQ4cOrex+dYquXWHpUqvbdsgQ+P57aNsWgL/85S889dRTTJs2jbi4OB599FHn7dfHHD58mNzcXA4ePMiBAwc4ePAgBw8epKKigjp16lQ+6tatS+PGjYmMjCTiDMb9KQ04XkdEyMvLIy0tjbS0NFJTU8nIyCA3N5e8vLyT3uDxz0RHRxMfH0+nTp2Ij49nwIABxMXF1fo7BH/22Wfce++9XHHFFcd+EOflWR/c+/bB4sVODTcOzzzzDPHx8Tz44IO88fTTsGiRNRHgggVO31d1bNq0iYqKCnpUVEBgYO28RNyhQwcIDSXRfiXR+vXrGTx4sGdrOt5990FpKTz/PI8++igHDhxg+vTpzv9ZT0iwgv955/0ecmJjAbjnnntIT0/nscceIy4ujvHjxzt33zVQVlYW33//PZs3byY1NZXNmzeTfRYXGISFhdGqVSsiIyNp3749nTp1omPHjnTs2JGoqKha/5l+POOtzYhJSUmyevVqT5fhUiUlJWzatInk5GSSk5NZt24d69evPybERERE0KFDB6Kjo2nVqlXlyd2sWTPq1q1LSEhIZdK32WwcOnSIQ/n5lOTmUpKXR352Nrlbt7IzO5vdO3YgpaUUA2HNm9OlXz96nXcefYYPJ7xNm1o1O+3atWsZMGAAiYmJfPfdd9SpU8d6YdcuGDQIfvsNvv0WzjnHZTXcddddTJ8+nTVr1tD9v/+1rs5avBiOvxWEB73xxhtcf/31HO7Xj7olJeDjP5N/atAgSouKCF67lueff57bb7/d0xX9btUqa2zMtGmkXX01iYmJXH/99cyaNct1+0xOtkJORIQVclq3BqxxbRdccAE//fQTS5cu5dxzz3VdDd5GhMPZ2fz8v//xy5IlrP/hBw7u3EkdwAQG0iwqihatWxPZpg1NYmIIbtWKkKgowpo0ISIiAn9//2Na5o8cOcKePXvIy8ur/EN3x44dpKens7/KFW1hYWF06dKFbt260b17d7p160ZiYiIhISEeOxTuYoxZIyJJJyzXgOMepaWlbNy4kdWrV7N69WrWrFlDSkoKZWVlgHVydu3ala5du5KQkFCZzJs3b35sKi8ttQamZmVZY0Qcj+3brctC8/Ph8OEzr8/Pj9LISEI7d8a0bWtN1d6nj/UXe2Cgsw6DV9i5cye9evXC39+fX375hebNm1sv7NplfVjn5lpXTPXr59I69u/fT/v27UlISGDZV19hEhKgbl1Yt85rjvktt9zCu++8w35/f8yVV4Irf1nWBHfdBS+9RHT9+pw3YgRvv/22pyuy2GxWGN+5E9LTGTVuHCtWrGDLli00adLEtfteu9YK5Q0aWGPV7CGnoKCAPn36cODAAX755RdivKz7tdrKyyElxQqW6elIVhaHU1IIzM0luKLizLdXty40aQItWljH0PGIjra6ANu0gSrzF4kIe/bsITU1lbS0NDZt2sT69etJTk6u/CM5ICCAhIQEkpKSSEpKomfPnnTp0uXP50GqYVwacIwxI4AXAH/gDRF56rjXg4G5QE+gAPiriGT/0TZrcsApLS1l06ZNlUFmzZo1bNiwgdLSUgAaNGhQebL16NGD7t2706ZNG/wcLSgi1gdVWpo10VZaGmRkWAP6cnKg6g9PYKD1AxAdDa1aWT8gTZpYNwhs3BjCw61LXB2PwEDrip1Dhyjbt48tycls/uEHflu1ihbFxXQMDKStMQTba6VuXWvW3n79rJaNwYOt7dRQR44cYeDAgaSnp/PDDz/QxdHlkpdnhZudO60rRaoONnahWbNmcfPNN/PFF19wUVkZjB1rDRK97Ta37P/P9O/fn+jiYt5fs8aaA2jyZE+X5FkffgjjxnFL376sPHLEewYbv/mmNenkvHl816oVQ4YM4ZlnnuGuu+5yz/5//RUuuADq17dCjj3MpKen06dPH1q1asWPP/5Ys8eQlJXB8uXw3Xfw44/w88+Vf0yWBgWRBaSVlpIXHEzT3r1J6N+fuB49CGzY0JpaoU4dKxSVllpTRJSWWvNr7d1rPfLzrcfOnb//0er4HAarhb11a2jf3np07Pj7o1WryqkrbDYb2dnZJCcnV/7+Wb16NQUFBQAEBgaSmJhIz549K38Pde7cuUaHHpcFHGOMP5ABDANygV+BK0Vkc5V1bga6iMiNxphxwFgR+esfbbemBJyCggI2btxY2c2UnJzMpk2bKltm6tWrd8yJlJSURGxsrNUqU1pqTfeflmY9UlN//7rq3XzDwqwWFceJHRdnJfqYGGje3CldSyUlJXz66afMnj2bpUuXEuPvz7SBAxnXujX1Nm60WhUqKqBePetqorFjrTlcQkOrvW93sdls/PWvf2XhwoV8/vnnjB492nohN9caR7B7N3z9tctbbqoqLy+nXbt2REdHs/z772H4cOuXRUaGFVQ9yGazUa9ePV7q25erv/0W1q+v3WNwALZuhbZt+WT4cMYtXUpRUZFzrk6qjv37f/98WLmSC0eOZN26dWRnZ7u3e2LNGutms+HhVsixzyW1ZMkShg8fzgUXXMCiRYsICKhBQz+PHLFacz/9FL74AgoLrZnLu3blUJcufJyXx1PffUdmWRkDBw7k+uuv59JLL/29y7s6bDbrysXsbKvF3vFHbmam9fXBg7+vGxZmjRGrGno6drTOieBgRITt27dX/tHt6EkoLCwErJaeTp06VXZtObq3XN765ySnCjjVvikm0Bf4psrz+4D7jlvnG6Cv/esAYC/2cHWqhzfdbLOiokJ27NghS5culddee01uu+02Of/886VZs2YCVD6aNm0qw4cPl3vvvVc++OADyczMFFtJiUhmpsg334i8/LLI1KkiF14o0ratiJ+fdUM7xyMyUuT880VuuUXkpZdEFi8Wyc0VOdVN8Vxky5Ytcsstt0hQUJAEBATI5MmTZWtKisiiRSKTJok0amTVGxIicvnlIv/7n0hFhVtrPBsPPPCAAPLcc8/9vjAnR6RNG5GICOsmgx4wY8YMAeTHH38U2bxZJCBAZMoUj9RSVWZmpgCSMnSodZPQsjJPl+R5NptIw4ayZcgQAWTdunWerkjkjjtEjBFZs0bWr18vgDzxxBOeqWXtWpGGDa3PsszMysWvvfaaADJ16lTP1HUmbDaRpUtFrrzSOu9BpEEDkauuEvn0U9m+ebPceOONlZ+P119/vaSmprq/xp07rTpfeUXktttEhg0TiY4+9neKn59IbKzI8OEit95q/V75+muRjAyxFRfL1q1b5aOPPpL7779fRo4cKS1atDjmd1qTJk1kyJAhcsstt8isWbNk8eLFkpOTIxVe9nnPKW626YwWnMuAESJynf35ROAcEbmlyjob7evk2p9n2dfZe6rturMFp6ysrHIQ1/bt2ysfOTk5ZGVlsWXLlmMmrgoNDSU+Pp6EhAS6xcXRvVkzEiMiaFhUZDUrbt9upe6tW62vq3YphYZaf205/uJyJO24OOsvHy+Sl5fHv//9b15//XXKy8u55pprePzxx2nRpIk1d8vChTB/PhQUWFdQXHutdalzy5aeLv0Ec+fO5eqrr+a6667j9ddft1rQsrOtlpvCQmvekN69PVJbUVER0dHRDB48mE8++QT+8Q+YMcO6j1B8vEdqAliwYAGXX345h7p2taYaWLnSY7V4leHDKd6+nTppabz11ltcc801nqtl2zbrs2PSJHj9dSZOnMinn37Kjh07PHcJ+/r11pic4GCrJScuDoA77riDGTNm8PLLL1d/0kFX2L0b3n7buoHuli1Wd9uVV8Kll8LAgeTv388jjzzC7NmzAbj22muZNm0are1jjrzG4cNWC4+jNyAj4/dH1Z4BPz+Iivq9N8A+1KEwPJxNhw6xbvdu1m/ZwsaNG9m8efMxF7+EhITQtm3bytZnx6N169a0bNmSZs2aubVl05UtOJdhjbtxPJ8IvHTcOhuByCrPs4DGJ9nWFGA1sDo6Otplae+tt96SwYMHS6dOnaRhw4aVadWARIBEg/SpU0eujomRJ3v2lI+GDpWfR4+W7WPHStGYMWIbMkSkQwfrr/6qadnxaN5cpHdv6y+ABx8UefNNke+/90hrjDPk5eXJbbfdJoGBgRIWFiZPPPGEHDlyxHqxuFhk/nyR886zvnd/f5HLLvNYa8jJfPLJJ+Lv7y/nn3++lJSUWAtXrxZp1cr6y2z1as8WKCIPPvigGGMkPT1dJD9fpG5dkQkTPFrTAw88IEF+fmKrU0fk9ts9WotXeeABsfn7S8OQEM+3SNxwg0hQkEhenuTk5Ii/v7/ccccdnq1JRGTDBpEmTUSaNhX54QcRESkvL5fRo0eLMUbmzZvn4QKrWL1a5G9/s1pOQWTAAJF33xWxf8aVlJTIs88+K/Xq1RN/f3+58cYbJScnx8NFnwWbTSQvT2T5cpG33xZ56CGR8eNF+vQRadnSagU8/ndZRIRIhw5iGzxYii6+WLb/5S/yy5gxsmDoUHmqVy+5JiZG+oeFSWuQevbfoY7fpw0aNJCOHTvKoEGD5JVXXnHpt8YpWnBqZRfVsgkTZG1EhGypX19+q19fDoSHS0lIiNhO9h9c9REebnVn9Otn/RK/9VaRJ5+0TpalS0W2bLF+4fuozMxMGTt2rADSunVr+fDDD8VWNbBlZorcfbdI/frW8erXT2TBApHyco/V/NVXX0lgYKD07dtXDh06ZC386COr6Tk62vog9gK7d++W4OBgmeLomvrHP6ywmJXlsZpGjhwpf2nXzvq/9KZfSJ722WciINfFx8vAgQM9V0denhVubrhBRETuuOMO8ff3955fvmlpIu3aWTW+846IiBw5ckSGDBki/v7+8sknn3iutooKkc8/Fxk48PfP9ttvF6nS1WSz2eTTTz+Vdu3aCSAjR46UzZs3e65mVysutj5vli2z/r/+7/+srq/LLxc591xrWMWp/qi3P2zGSElIiBwID5fd9epJVv36khwRIUsuucSlpbsy4AQAW4FYIAhYDyQct87fgVftX48DPvqz7bp0DM7rr4sMHiwyapTIuHEi111nndwPPijy3HMic+aILFxojYFJTrZaXnw4uJyppUuXSteuXQWQAQMGyPr1649d4dAhkRdftPp+wQqFM2eKFBW5tc7vv/9eQkJCpFu3blJYWGj9BfPII1ZN554rsnu3W+v5MzfeeKMEBwfLrl27Tvjl5W7l5eVSv359eat/f+t4pad7pA6vlJcnArJw4EAJCQmRYk99NlQJwfv27ZPQ0FCZ4OFWvxMUFPzeunvvvSLl5XLo0CHp06ePBAUFyTfffOPeeo4cEZk1S6R9e6um6GjrM3///mNW27x5s5x//vkCSHx8vHz99dfurdOblZRYPwPr14ssWSLyySdWL8X06Var0O23W79Tx42zfscOHiwyY4ZLS3JZwLG2zUisK6mygAfsyx4Dxti/DgE+BrYAvwBt/myb3jTIWJ2ovLxcZs+eLY0bNxZ/f3+ZOnWq7D/uQ0LKy62g2Levdao1bCjyz3+K7Nnj8vp+/vlnCQ8Pl06dOsmePXtEDh8WueIKq46rr/bKwJqZmSnGGLn//vutBY7uh9xct9eydu1aASTjvPNE6tWrEYPI3aplS9k+aJAAsnz5cvfvf+/eY7oxn3jiCQFO/GPDG5SWitx4o/WzN3q0yMGDsm/fPunWrZvUqVPHPcevoEDk8cetbjMQSUoS+eCDEwbOHzp0SO655x4JCAiQ+vXry8yZM6VMB9d7PZcGHFc8NODUDAUFBXLTTTeJMUaaNWsmc+fOPbbbymHlSpExY6Ty6qsbbxTZuNElNf3666/SoEEDadOmjeTm5op8+aXVVG6MyNNPe/U4qEsvvVTq168vBw8etJqL/f2tq2TcbPr06QJISefO1pV96lhjxkh5u3ZijJHHHnvM/ft/6CHrZ2nTJjl69Kg0a9ZMRowY4f46zsRLL1nnc0yMyGefyZ7du6VTp04SHh4uK1eudM0+09OtoQR161rHa+RIqwvmuM8Am80mH374obRq1UoAmTx5suz2shZedWoacJRL/frrr9K7d28BpG/fvvLLL7+cfMXUVKv5MjjYOv0GDxb5+GPrrzwneO+99yQkJESio6Nl+/LlIhdfbO2nQwery9HLrVq1SgB5/vnnrQUTJlgfzvn5bq1jzJgxEt+2rUhgoNW1oI71+OMiIOcmJsp5553n3n0fOGCNcxs7VkREXn/9dQFkyZIl7q3jbKxYIZKQYP1MXnih7Fq+XNq3by9BQUEyZ84c5+yjvNwaJzVsmLWfwECRiRNPOd5u3bp1MsjeGtetWzdrugZVo2jAUS5XUVEhc+bMqZwf6KqrrrJaUE4mP1/kqadEWre2TsOWLa0xUOvWnVULS3l5udxzzz0CyIi+feXQPfdYLUWhodZ+HFdP1QCDBg2SqKgoKS0tFdm0yTo+Dz7otv07xt884QiHCxa4bd81xtdfi4C8dOml7h+H89RT1v/Lr79KRUWFxMXFSc+ePU/ecuqNSkut8Rrh4SJBQXLkjjvkL4MHCyC33nqrdd6fKZtNJCVF5NFHf58LplUrK4ju2nXSt/z2229y3XXXiTFGGjVqJK+88oqUe/CCCHX2NOAotzlw4IBMmzZNgoKCpG7duvL444//fgXT8crLrQkEhw///TLFmBirW2bFitOaXK6wsFAuO/98uQpkQ+vWYgsMtLbz17+K7Njh5O/O9b744gsBZP78+daCv/zFGgdz/BgnF1mzZo0A8svVV1vH0VuuyvEme/eKgGy86ioB5Pvvv3fPfo8csS69vuACERFZtGjRsedKTbJrl9WyAmILCJDU1q1lMsjF/ftL/um0WJaXW9NR3H231QUN1mfIkCHW2L9TfHYcPnxYnn76aQkPD5eAgAC54447rIsQVI11qoCjN9tULrN161buueceFi5cSIMGDZg6dSq33norDRs2PPkb9uyBRYusadEXL7ZuZREYCO3aWZOFdehgTY5os8G+fUhBAXkpKeQsX06vo0cJAuteLZddBuPGQdKJ8z7VBDabjY4dO9KoUSN++uknawr8pCR48km47z6X73/69OnceeedFF1xBaHffWfdWb3qDV+VpW1bShMTCfnPf3jkkUf45z//6fp9zpxp3afs++9h4ECGDRtGamoq27ZtI9BLbtB6xpKT4YMP4OOPYetWyoFfQkKI7N+fqK5dMY0aQcOGEBBgTcCXnm5NWrdli3VPp8BA6z5yY8fCmDHWzSpPYv/+/bzyyivMmDGD/Px8Ro0axXPPPUecfSJCVXO5bKI/Vz20Bcd3/Pzzz3LJJZcIIKGhoXLXXXdJXl7eH7/pwAGRDz+0xn+MHSsSH29dUVRlzoViYyQXZFNgoGy/8kqRX37x6gHEZ+LFF18UQH7++WdrwfDh1hUgjgkWXWj06NHSvl07qyVtzBiX76/GGj9epEkT6dG1qwwZMsT1+ystFYmKsqY4EJGNGzcKIE8++aTr9+0ONpvImjWSd/XVsjE4WHaAHD3+djaBgSIdO1pj6+6+W+T990X+pPVl9+7dcv/990tERETlfDYrVqxwy7ek3APtolKelpKSIuPHjxc/Pz/x9/eXwYMHy/PPPy9bt249rfeXHDkiqz74QMYNGCAhIC1atJCXX37599mJfcjBgwclPDxc/va3v1kLli2zflxdPCNoeXm51KtXTx659FJrf6+/7tL91Wjvvy8CMuOvf5WQkBA5evSoa/c3d671f/LFFyIiMmXKFAkJCTm97pwaprS0VGbPni1RUVESAvKXPn3kh3nzpPg059LKycmRmTNnytChQyUgIECMMXLFFVfI2rVrXVy58oRTBRztolJut3XrVubMmcPnn3/Opk2bAOjcuTODBg2iadOmNGrUiMaNG9OoUSPy8vL49ddf+fXXX0lOTqakpITGjRszbdo0br75ZufctddL3X777bz88svk5OTQskUL6NMH9u61muhddEfmtWvX0rNnT5KvuIKuH30EO3eessm/1isshKZNybz4YuIWLuT7779n4MCBrtmXyO93ct+wgX2FhURGRvK3v/2NN954wzX79AIlJSXMnj2bJ554gt9++42goCC6dOlC79696dWrF1FRUezbt4+9e/dSUFBAfn4+K1asYN26dQB06NCBiy++mMmTJ9OhQwcPfzfKVbSLSnmlzMxMefbZZ2XAgAESHh5eeR+Tqo/Q0FAZOHCg3HnnnfLBBx9Yc8TUAlu2bBFjjDzouIJq4ULrL/gPPnDZPp999llr/pvu3a37qak/dt55Ut6xoxhj5JFHHnHdfr74wvq/nztXRESeeuopAWSDl9xqxNUOHz4sH3/8sdxzzz0yePDgU35WhIWFSd++feWpp55y/x2+lcegLTiqJigtLT3mL7JGjRrRqVMn/P39PV2aR4wZM4ZVq1axfft2QgIDrbuLh4XB6tUuGfg7evRo9m3ezA9bt8Ljj8ODDzp9Hz5lxgy44w7GxMdzqGlTli1b5pr9DBpk3Tk8K4tyY2jTpg3t2rVj6dKlrtmfl7PZbKSlpbFnz57K1t6GDRsSHBzs6dKUB5yqBcfPE8UodSpBQUE0b96cxMREBg0aRGJiYq0NNwBTp04lPz+f+fPng78/3HUXrF0LS5Y4fV8VFRUsX76cGyIjrQVjxjh9Hz5n9GgAJjdtyk8//URxcbHz97FqFSxfDv/4BwQG8umnn7Jjxw6mTp3q/H3VEH5+fsTHxzN48GASExNp0aKFhht1Am3BUcqLiQhdunTB39+fdevWYUpKIDYWEhPh22+duq81a9aQlJTEjh49iCwosFoM9PLwP5eYyF4/P5qkpPDdd98xaNAg525/7FjrsvDt2yEsjP79+7Nz504yMzNrdfhXykFbcJSqgYwx3Hbbbaxfv57ly5dDSAjcfrs1T9DatU7d13fffUcdoNXmzVbLhIab0zN6NI02b6YB1jF0qrQ0+PxzuOUWCAtjzZo1/PDDD9x6660abpT6ExpwlPJy48ePp2HDhrzwwgvWghtvhPBwePppp+5n2bJlXN2qFaa4WLunzsSYMZiKCm6MiXH+GJxnn7VC7a23AvDCCy8QFhbG5MmTnbsfpXyQBhylvFzdunW54YYb+Oyzz0hPT4d69ayQ8/HHkJXllH2Ul5ezYsUKxoeHW+HJ2d0svqx3b2jalMuDg1m1apXzxuHs3AnvvguTJ0OTJmzfvp358+czadIk6tWr55x9KOXDNOAoVQNMnTqV4OBgnnrqKWvB7bdbc+E884xTtp+cnMyhgwfpuWsXjBgBQUFO2W6t4O8Po0aRmJtLRUkJq1atcs52n38eysutwcXAM/b/67vuuss521fKx2nAUaoGaNasGVOmTGHevHnk5ORAy5bWX/Zz5kBmZrW3v2zZMpKAOgcOaPfU2Rg9msDDhxlkjHMu3d6xA156CcaPhzZt2L17N2+88QZXXXUV0dHR1d++UrWABhylaoi7774bYwxPO8bePPwwBAdX+wacIsK7777LlObNrdaIkSOdUG0tM2wYBAdzY8uWvP/++9hstupt76GHrNmLH38csG6AWlpayrRp05xQrFK1gwYcpWqIyMhIrr76aubMmcOuXbugeXO4+25YuBB+/PGst/vTTz+RkpLCWH9/6N/funOzOjOhoTB0KCNKS8nKymLx4sVnv63162HuXJg6FVq3Zt++fbzyyitcccUVtG/f3nk1K+XjNOAoVYPce++9lJWVMX36dGvBnXf+HnTOck6rWbNmkRAaSqO8vMqJ69RZGD2asPx8+jdowKxZs85+O/fcAw0aVLbMzZw5k6KiIu6//34nFapU7aABR6kapF27dowbN45Zs2ZRUFBg3bbhscesFpxPPz3j7e3du5ePP/6YR7p3txbo+JuzN2oUAA8lJvKf//yH3NzcM9/G//5nPR56COrX59ChQ7zwwguMGTOGzp07O7lgpXybBhylapj777+fw4cP8+KLL1oLJk2CTp1g2jQoKzujbb399tsEl5RwcVoanHMOaBfI2WvVCs4/n/NTUmhQUXHmd/muqLBa4mJj4aabAHj11VcpLCzkgQcecEHBSvk2DThK1TAJCQmMHTuWF198kYMHD1qXiz/9tHU11euvn/Z2bDYbr776KrNbtiSwoMC6akdVz4wZ+B86xNzISGbPnk3ZmQTOefNgwwb4v/+D4GCOHj3Kc889x7Bhw+jdu7fralbKR2nAUaoGeuCBB9i/fz8vv/yyteCii6zJ+R59FA4ePK1tLFmyhLpZWVz2229www2QdMKtXNSZSkyE22/nwtxconbu5Isvvji99x09at25vVcvuOIKAObMmcPu3bu19Uaps6QBR6kaqGfPnowaNYonnniCrKws675Rzz4L+flw221wGpcpz3rlFWYHBGAaNoQnnnBD1bXEww8jLVsyOzCQ11555c/XF7HuEp+ba03caAy5ubk89NBDDBo0iIEDB7q+ZqV8kAYcpWqoV155hYCAAK666ioqKiqsFph//hPeecea6fgPrqrKy8uj3uefc055Oebf/9ZLw50pPBzz/PN0Liuj7eLFZP7RRIwicP/98MorcMcdMGgQNpuNSZMmUVZWxhtvvIHRm54qdVY04ChVQ0VFRfHyyy/z448//j753yOPWFP7z5xp/eI8Rch5d+ZM/i1CcffucM01bqu51rj8ckoGDOAJ4P3nnz/1ek88AU89ZXURPvccAC+//DKLFy/mueeeo127du6pVylfJCJe+ejZs6copf6YzWaTyy+/XAIDA2XdunWOhSI33igCIo8/fsJ7ysrK5K3QUCkHEcd7lPOlpUmpMTI/OFiOHj164uvTp1v/RxMnilRUiIhIamqqhISEyMiRI8Vms7m5YKVqJmC1nCRHGDnLycFcLSkpSVavXu3pMpTyegUFBXTu3JmGDRuyevVqQkJCrDE4kyZZM+I+8wxceCGkp0NGBikLFhC/Zg05F11Em9MdBKvOSvbf/kbM/PkkJybS9a9/xXTsCHFxsHIl/P3vcNllMH8+BARQVlZGv3792LZtGykpKbRo0cLT5StVIxhj1ojICVdJVCvgGGMaAh8CMUA2cIWIFJ5kvQogxf50u4j86WxiGnCUOn1fffUVI0eO5B//+AfP2bs6KC+Hv/0NPv74mHV3ArktWtAzJQX/Ro3cX2wtIkVF/JqYSMucHCKPf/Gii+CTTyrv3P7www/z2GOP8fHHH3PZZZe5vValaqpTBZzqjsGZBiwRkfbAEvvzkzkqIt3sD50qVSknu/DCC7nxxht5/vnnef311ykpKbHmx3nvPXjzTWzz5vHE2LGEA49cfz09d+zQcOMGJiyMpK1beXbqVMKAB0eOpHzePJg9GxYsgKAgysrKmDt3Lk888QQTJkzQcKOUs5ys3+p0H0A60ML+dQsg/RTrFZ3ptnUMjlJnpqioSJKSkgSQpk2byoMPPig7duyQ4uJiufTSSwWQ++67T8d2eIDNZpN//etfAshFF10khw8fll27dsmjjz4qLVq0EEASExOlsLDQ06UqVePgijE4xpj9IlLf/rUBCh3Pj1uvHEgGyoGnROSzP9u2dlEpdeZsNhtLlizhpZde4j//+Q9+fn7ExMSQlZXF9OnTueOOOzxdYq322muvcdNNNxETE0Nubi5lZWVceOGF3HLLLYwYMQI/P72wVakzddZjcIwxi4HmJ3npAeCdqoHGGFMoIg1Oso1WIpJnjGkDLAXOF5Gsk6w3BZgCEB0d3TMnJ+ePvyul1Clt27aNWbNmsXDhQh5++GGuuuoqT5ekgAULFnDvvfcyZswYbr75Ztrr/b+UqhZXDTJOBwaLyC5jTAvgOxHp8CfveRv4QkQW/NF62oKjlFJKqT/jqkHGi4Cr7V9fDXx+kh03MMYE279uDJwLbK7mfpVSSimlTqm6AecpYJgxJhMYan+OMSbJGPOGfZ1OwGpjzHpgGdYYHA04SimllHKZgOq8WUQKgPNPsnw1cJ396x+BztXZj1JKKaXUmdAh+0oppZTyORpwlFJKKeVzNOAopZRSyudowFFKKaWUz9GAo5RSSimfowFHKaWUUj6nWjMZu5IxJh/w1L0aGgN7PbRvb6LHwaLHwaLHwaLHwaLHwaLHweLJ49BaRJocv9BrA44nGWNWn2za59pGj4NFj4NFj4NFj4NFj4NFj4PFG4+DdlEppZRSyudowFFKKaWUz9GAc3Kve7oAL6HHwaLHwaLHwaLHwaLHwaLHweJ1x0HH4CillFLK52gLjlJKKaV8Tq0OOMaYKGPMMmPMZmPMJmPMVPvyhsaYb40xmfZ/G3i6VncwxvgbY9YZY76wP481xvxsjNlijPnQGBPk6RpdzRhT3xizwBiTZoxJNcb0rY3ngzHmDvvPxEZjzHxjTEhtOR+MMW8aY/YYYzZWWXbSc8BYXrQfkw3GmB6eq9y5TnEcnrH/bGwwxnxqjKlf5bX77Mch3Rgz3CNFu8DJjkOV1+40xogxprH9ea06H+zLb7WfE5uMMU9XWe7x86FWBxygHLhTROKBPsDfjTHxwDRgiYi0B5bYn9cGU4HUKs//DTwvIu2AQuBaj1TlXi8AX4tIR6Ar1vGoVeeDMaYVcBuQJCKJgD8wjtpzPrwNjDhu2anOgQuB9vbHFGCWm2p0h7c58Th8CySKSBcgA7gPwP65OQ5IsL/nFWOMv/tKdam3OfE4YIyJAi4AtldZXKvOB2PMEOBioKuIJADP2pd7xflQqwOOiOwSkbX2rw9h/TJrhfUf9o59tXeASzxSoBsZYyKBi4A37M8NcB6wwL6Kzx8HY0w9YCAwB0BESkVkP7XwfAACgDrGmACgLrCLWnI+iMhyYN9xi091DlwMzBXLKqC+MaaFWwp1sZMdBxH5n4iU25+uAiLtX18MfCAiJSKyDdgC9HZbsS50ivMB4HngHqDqQNZadT4ANwFPiUiJfZ099uVecT7U6oBTlTEmBugO/Aw0E5Fd9pd+A5p5qi43moH1w2qzP28E7K/yYZaLFf58WSyQD7xl76p7wxgTSi07H0QkD+svse1YweYAsIbadz5UdapzoBWwo8p6tem4TAa+sn9dq46DMeZiIE9E1h/3Uq06DkAcMMDedf29MaaXfblXHAcNOIAxJgxYCNwuIgervibWZWY+famZMWYUsEdE1ni6Fg8LAHoAs0SkO3CY47qjasn50ADrL7BYoCUQykma6Gur2nAO/BljzANYXfzveboWdzPG1AXuB/7p6Vq8QADQEGuIx93AR/bWf69Q6wOOMSYQK9y8JyKf2BfvdjQr2v/dc6r3+4hzgTHGmGzgA6yuiBewmlcD7OtEAnmeKc9tcoFcEfnZ/nwBVuCpbefDUGCbiOSLSBnwCdY5UtvOh6pOdQ7kAVFV1vP542KMuQYYBYyX3+cZqU3HoS1W+F9v/8yMBNYaY5pTu44DWJ+Zn9i75H7B6gFojJcch1odcOxJcw6QKiLTq7y0CLja/vXVwOfurs2dROQ+EYkUkRisgWFLRWQ8sAy4zL5abTgOvwE7jDEd7IvOBzZTy84HrK6pPsaYuvafEcdxqFXnw3FOdQ4sAq6yXz3TBzhQpSvL5xhjRmB1ZY8RkSNVXloEjDPGBBtjYrEG2f7iiRpdTURSRKSpiMTYPzNzgR72z49adT4AnwFDAIwxcUAQ1g03veN8EJFa+wD6YzU1bwCS7Y+RWONPlgCZwGKgoadrdeMxGQx8Yf+6DdZJuQX4GAj2dH1u+P67Aavt58RnQIPaeD4AjwJpwEbgXSC4tpwPwHyssUdlWL+8rj3VOQAY4GUgC0jBuvLM49+DC4/DFqyxFY7Py1errP+A/TikAxd6un5XHofjXs8GGtfS8yEImGf/nFgLnOdN54POZKyUUkopn1Oru6iUUkop5Zs04CillFLK52jAUUoppZTP0YCjlFJKKZ+jAUcppZRSPkcDjlJKKaV8jgYcpZRSSvkcDThKKaWU8jn/D20S1MQqiZJZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x273.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "projected_data_window = sim_data_window[new_monitor_far.name]\n",
    "\n",
    "# compute the analytic fields associated with the new window\n",
    "analytic_field_data = analytic_fields_aperture(\n",
    "    new_monitor_far, sim_size, new_aperture_height, new_aperture_width, r_proj\n",
    ")\n",
    "\n",
    "# plot the field distributions as before, in 2D and for a 1D slice at phi = pi / 2\n",
    "Etheta_analytic = analytic_field_data.Etheta.isel(f=0, r=0)\n",
    "Etheta_proj = projected_data_window.Etheta.isel(f=0, r=0)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(8, 3.8))\n",
    "im1 = ax.plot(\n",
    "    Etheta_analytic.theta * 180 / np.pi,\n",
    "    np.real(Etheta_analytic.sel(phi=np.pi / 2, method=\"nearest\")),\n",
    "    '-k',\n",
    "    label=\"Analytic\",\n",
    ")\n",
    "im2 = ax.plot(\n",
    "    Etheta_proj.theta * 180 / np.pi,\n",
    "    np.real(Etheta_proj.sel(phi=np.pi / 2, method=\"nearest\")),\n",
    "    '-r',\n",
    "    label=\"Projection\",\n",
    ")\n",
    "ax.legend()\n",
    "\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other far field quantities and coordinate systems <a name=\"powercoords\"></a>\n",
    "So far, we've been looking at the electric field in spherical coordinates. However, we can also look at the fields in other coordinate systems, e.g., `E_x`, `E_y`, `E_z`, and the radiated power, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 907.2x273.6 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 309.6x273.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_cart_plot(phi, theta, vals1, vals2, vals3):\n",
    "    n_plots = 3\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(12.6, 3.8))\n",
    "    im1 = ax[0].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals1),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im2 = ax[1].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals2),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im3 = ax[2].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals3),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    fig.colorbar(im3, ax=ax[2])\n",
    "    ax[0].set_title(\"Ex\")\n",
    "    ax[1].set_title(\"Ey\")\n",
    "    ax[2].set_title(\"Ez\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "\n",
    "# get the fields in Cartesian coordinates from the projected data we already computed above\n",
    "fields_cartesian = projected_field_data.fields_cartesian.isel(f=0, r=0)\n",
    "\n",
    "# plot Ex, Ey, Ez\n",
    "make_cart_plot(\n",
    "    phi_proj, theta_proj, fields_cartesian.Ex, fields_cartesian.Ey, fields_cartesian.Ez\n",
    ")\n",
    "\n",
    "# get the power\n",
    "power = projected_field_data.power.isel(f=0, r=0)\n",
    "\n",
    "# plot the power\n",
    "fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(4.3, 3.8))\n",
    "im = ax.pcolormesh(\n",
    "    phi_proj * 180 / np.pi,\n",
    "    theta_proj * 180 / np.pi,\n",
    "    power,\n",
    "    cmap=\"inferno\",\n",
    "    shading=\"auto\",\n",
    ")\n",
    "fig.colorbar(im, ax=ax)\n",
    "_ = ax.set_title(\"Power\")\n",
    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "_ = ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-projection to a different distance <a name=\"reproj\"></a>\n",
    "We can re-project the already-computed far fields to a different distance away from the structure - _we neither need to run another simulation nor re-run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html)_. Instead, the fields can simply be renormalized as shown below.\n",
    "\n",
    "Note that by default, if no `proj_distance` was provided in the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html), the fields are projected to a distance of 1m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.54 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x273.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# new projection distance\n",
    "r_proj_new = 20 * wavelength\n",
    "\n",
    "# re-project our far field data above to this new distance\n",
    "reprojected_field_data = projected_field_data.renormalize_fields(r_proj_new)\n",
    "\n",
    "# now all the fields stored in 'projected_field_data' correspond to this new distance\n",
    "# compare to the analytical fields at this new distance\n",
    "analytic_field_data_new = analytic_fields_aperture(\n",
    "    monitor_far, sim_size, height, width, r_proj_new\n",
    ")\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_analytic = analytic_field_data_new.Etheta.isel(f=0, r=0)\n",
    "Etheta_proj = reprojected_field_data.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More accurate field projections <a name=\"exact\"></a>\n",
    "In the field projections used above, the far field approximation is used: it is assumed that the fields are measured at a distance much greater than the size of our simulation in the transverse direction. Accordingly, geometric approximations are invoked, and any quantity whose magnitude drops off as 1/r^2 or faster is ignored. The advantages of these approximations are:\n",
    "* the projections are computed relatively fast\n",
    "* the projections are cast in a simple mathematical form which allows re-projecting the fields to different distance without the need to re-run a simulation or to re-run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "However, in some cases we may want to project to intermediate distances where the far field approximation is no longer valid. `Tidy3D`'s field projection functionality allows doing this very easily: simply flip a switch when defining the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html)! The resulting computations will be a bit slower, but the results will be significantly more accurate.\n",
    "\n",
    "**Note**: when the far field approximations are turned off, we can no longer simply use `renormalize_fields` to re-project the fields at a new distance. Instead, we would need to re-run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "Below, we will demonstrate this feature by looking at fields only a few wavelengths away from the aperture. Note that our analytical results also made far field approximations, so here we'll make our simulation domain a bit larger and measure the actual fields on a monitor, so that we can compare these actual fields to those computed by the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "Also, this time we'll use the [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html), which is the counterpart to the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) where the observation grid is defined in Cartesian coordinates, not angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# project fields only a few wavelengths away from the aperture\n",
    "r_proj_intermediate = 4 * wavelength\n",
    "\n",
    "# create a field monitor to measure these fields at the intermediate projection distance,\n",
    "# so that we have something to which we can compare the 'FieldProjector' results\n",
    "monitor_intermediate = td.FieldMonitor(\n",
    "    center=[0, offset_mon + r_proj_intermediate, 0],\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field\",\n",
    ")\n",
    "\n",
    "# make a larger simulation along y to accommodate the plane at which the intermediate fields need to be measured\n",
    "shift = 1.2 * r_proj_intermediate\n",
    "sim_size3 = [sim_size[0], sim_size[1] + shift, sim_size[2]]\n",
    "# move the sim center\n",
    "sim_center = [0, (sim_size[1] + shift) / 2 - sim_size[1] / 2, 0]\n",
    "sim3 = td.Simulation(\n",
    "    size=sim_size3,\n",
    "    center=sim_center,\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[\n",
    "        monitor_near,\n",
    "        monitor_intermediate,\n",
    "    ],  # provide both near field and intermediate field monitors\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim3.plot(x=0, ax=ax1)\n",
    "sim3.plot(y=0, ax=ax2)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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\">12:31:07 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_3'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-40454c45-9ab7-4941-9dc6-61fccf40573e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:07 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_3'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-40454c45-9ab7-4941-9dc6-61fccf40573e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e'</span></a>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=668041;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=668041;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=766804;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668041;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=766804;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668041;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[32m-40454c45-9ab7-4941-9dc6-61fccf40573e'\u001b[0m\u001b]8;;\u001b\\.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "version_minor": 0
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      "text/plain": [
       "Output()"
      ]
     },
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     "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:31:09 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:09 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:30 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:30 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"></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\">12:31:38 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.065</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:38 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.065\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "version_minor": 0
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      "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:48 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:48 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m8\u001b[0m%, exiting.                             \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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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 = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
      "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:53 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:53 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></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\">             </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=794689;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=794689;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=62010;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=794689;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=62010;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=794689;https://tidy3d.simulation.cloud/workbench?taskId=fdve-40454c45-9ab7-4941-9dc6-61fccf40573e\u001b\\\u001b[4;34m-40454c45-9ab7-4941-9dc6-61fccf40573e'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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       "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"
      ],
      "text/plain": []
     },
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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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:31:57 EST </span>loading simulation from data/aperture_3.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:57 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_3.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data3 = web.run(\n",
    "    sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html), this time turning off the far field approximations, and then run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html) again.\n",
    "\n",
    "The [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html)'s xy observation grid is defined in a local coordinate system whose z axis points in the direction along which we want to project fields, in this case the +y axis. The mapping between local and global coordinates is as follows:\n",
    "* `proj_axis=0`: local x = global y, local y = global z\n",
    "* `proj_axis=1`: local x = global x, local y = global z\n",
    "* `proj_axis=2`: local x = global x, local y = global y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "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"
      ],
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     },
     "metadata": {},
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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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the projection monitor which projects fields without approximations\n",
    "xs = np.linspace(-sim_size[0] / 2, sim_size[0] / 2, 100)\n",
    "ys = np.linspace(-sim_size[1] / 2, sim_size[1] / 2, 100)\n",
    "monitor_intermediate_proj = td.FieldProjectionCartesianMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field_proj\",\n",
    "    proj_axis=1,  # because we're projecting along the +y axis\n",
    "    x=list(xs),  # local x coordinates - corresponds to global x in this case\n",
    "    y=list(ys),  # local y coordinates - corresponds to global z in this case\n",
    "    proj_distance=r_proj_intermediate,\n",
    "    far_field_approx=False,  # turn off the far-field approximation (is 'True' by default)\n",
    ")\n",
    "\n",
    "# execute the projector, with the projection field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data_noapprox = get_proj_fields(\n",
    "    sim_data3, monitor_near, monitor_intermediate_proj\n",
    ")\n",
    "t1 = time.perf_counter()\n",
    "proj_time_new = t1 - t0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare the following three results:\n",
    "* Directly-measured fields at the projection distance\n",
    "* Projected fields with approximations turned off\n",
    "* Projected fields with approximations turned on (just to compare the accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "76803ef931bf441e8dc512909c21ae1a",
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       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute projected fields *with* far field approximations, to facilitate an accuracy comparison\n",
    "monitor_intermediate_proj_approx = td.FieldProjectionCartesianMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field_proj_approx\",\n",
    "    proj_axis=1,  # because we're projecting along the +y axis\n",
    "    x=list(xs),  # local x coordinates - corresponds to global x in this case\n",
    "    y=list(ys),  # local y coordinates - corresponds to global z in this case\n",
    "    proj_distance=r_proj_intermediate,\n",
    "    far_field_approx=True,  # turn on the far-field approximation\n",
    ")\n",
    "\n",
    "# execute the projector, with the projection field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data_approx = get_proj_fields(\n",
    "    sim_data3, monitor_near, monitor_intermediate_proj_approx\n",
    ")\n",
    "t1 = time.perf_counter()\n",
    "proj_time_new_approx = t1 - t0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection *with approximations on* took 5.95 s\n",
      "Client-side field projection *with approximations off* took 13.23 s\n"
     ]
    }
   ],
   "source": [
    "# let's see how long this took compared to the previous case when the approximations were turned on\n",
    "print(\n",
    "    f\"Client-side field projection *with approximations on* took {proj_time_new_approx:.2f} s\"\n",
    ")\n",
    "print(\n",
    "    f\"Client-side field projection *with approximations off* took {proj_time_new:.2f} s\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, when the approximations are turned off, the projections take longer. Now let's see if it was worth it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to plot fields\n",
    "def make_cart_plot(phi, theta, vals1, vals2, vals3):\n",
    "    n_plots = 3\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(9, 3))\n",
    "    im1 = ax[0].pcolormesh(ys, xs, np.real(vals1), cmap=\"RdBu\", shading=\"auto\")\n",
    "    im2 = ax[1].pcolormesh(ys, xs, np.real(vals2), cmap=\"RdBu\", shading=\"auto\")\n",
    "    im3 = ax[2].pcolormesh(ys, xs, np.real(vals3), cmap=\"RdBu\", shading=\"auto\")\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    fig.colorbar(im3, ax=ax[2])\n",
    "    ax[0].set_title(\"Ex\")\n",
    "    ax[1].set_title(\"Ey\")\n",
    "    ax[2].set_title(\"Ez\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$y$ (micron)\")\n",
    "        _ax.set_ylabel(\"$x$ (micron)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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\">12:32:16 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Colocating data that has already been colocated during the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">solver run. For most accurate results when colocating to custom    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">coordinates set </span><span style=\"color: #008000; text-decoration-color: #008000\">'Monitor.colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to use the raw data  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">on the Yee grid and avoid double interpolation. Note: the default  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value was changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D version </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">.               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:16 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Colocating data that has already been colocated during the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msolver run. For most accurate results when colocating to custom    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcoordinates set \u001b[0m\u001b[32m'Monitor.colocate'\u001b[0m\u001b[31m to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to use the raw data  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mon the Yee grid and avoid double interpolation. Note: the default  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mvalue was changed to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D version \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m.               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation: 0.43 %\n",
      "Normalized RMSE for |E|, with far field approximation: 22.85 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the actual measured fields\n",
    "fields_meas = sim_data3[monitor_intermediate.name].colocate(x=xs, z=ys)\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_meas.Ex.isel(f=0, y=0),\n",
    "    fields_meas.Ey.isel(f=0, y=0),\n",
    "    fields_meas.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Measured fields\")\n",
    "\n",
    "# projected field without approximations - get them in Cartesian coords\n",
    "fields_proj_noapprox = projected_field_data_noapprox.fields_cartesian\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_proj_noapprox.Ex.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ey.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Projected, no approximations\")\n",
    "\n",
    "# projected field with approximations - get them in Cartesian coords\n",
    "fields_proj_approx = projected_field_data_approx.fields_cartesian\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_proj_approx.Ex.isel(f=0, y=0),\n",
    "    fields_proj_approx.Ey.isel(f=0, y=0),\n",
    "    fields_proj_approx.Ez.isel(f=0, y=0),\n",
    ")\n",
    "_ = plt.suptitle(\"Projected, with far field approximations\")\n",
    "\n",
    "# RMSE\n",
    "Emag_meas = np.sqrt(\n",
    "    np.abs(fields_meas.Ex) ** 2\n",
    "    + np.abs(fields_meas.Ey) ** 2\n",
    "    + np.abs(fields_meas.Ez) ** 2\n",
    ")\n",
    "Emag_proj_noapprox = np.sqrt(\n",
    "    np.abs(fields_proj_noapprox.Ex) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ey) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ez) ** 2\n",
    ")\n",
    "Emag_proj_approx = np.sqrt(\n",
    "    np.abs(fields_proj_approx.Ex) ** 2\n",
    "    + np.abs(fields_proj_approx.Ey) ** 2\n",
    "    + np.abs(fields_proj_approx.Ez) ** 2\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, no far field approximation: {rmse(Emag_meas.values, Emag_proj_noapprox.values) * 100:.2f} %\"\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, with far field approximation: {rmse(Emag_meas.values, Emag_proj_approx.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without approximations, the projected fields match the measured ones extremely well! Instead, when approximations are used, the match is very poor. Thus, the accurate field projections can be extremely useful when the projection distance is not large compared to the structure size, but one still wants to avoid simulating all the empty space around the structure.\n",
    "\n",
    "We should also note that this more accurate version of field projections can be run on the server in exactly the same way as before: just supply the projection monitor with its `far_field_approx` field set to `False` into the simulation's list of `monitors` as before. Everything else remains exactly the same, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim4 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[monitor_intermediate_proj],  # only need to supply the projection monitor\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim4.plot(x=0, ax=ax1)\n",
    "sim4.plot(y=0, ax=ax2)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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\">12:32:17 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_4'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-c98cd029-1ff9-470b-a61b-75004ddc810c'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:17 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_4'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-c98cd029-1ff9-470b-a61b-75004ddc810c'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c'</span></a>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=853949;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=853949;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=758302;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=853949;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=758302;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=853949;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[32m-c98cd029-1ff9-470b-a61b-75004ddc810c'\u001b[0m\u001b]8;;\u001b\\.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "117d657123e94c87a658bfcdee5d0fa9",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:32:19 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:19 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:40 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:40 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"></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\">12:32:46 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:46 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">12:32:47 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:47 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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      "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:55 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:55 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \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\"></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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:32:56 EST </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:56 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:59 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:59 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=361686;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=361686;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=94688;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=361686;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=94688;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=361686;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c98cd029-1ff9-470b-a61b-75004ddc810c\u001b\\\u001b[4;34m-c98cd029-1ff9-470b-a61b-75004ddc810c'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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       "version_minor": 0
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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"
      ],
      "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\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\">12:33:01 EST </span>loading simulation from data/aperture_4.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:01 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_4.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run the simulation\n",
    "sim_data4 = web.run(\n",
    "    sim4, task_name=\"aperture_4\", path=\"data/aperture_4.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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: #800000; text-decoration-color: #800000\">WARNING: Colocating data that has already been colocated during the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">solver run. For most accurate results when colocating to custom    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">coordinates set </span><span style=\"color: #008000; text-decoration-color: #008000\">'Monitor.colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to use the raw data  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">on the Yee grid and avoid double interpolation. Note: the default  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value was changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D version </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">.               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Colocating data that has already been colocated during the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msolver run. For most accurate results when colocating to custom    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcoordinates set \u001b[0m\u001b[32m'Monitor.colocate'\u001b[0m\u001b[31m to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to use the raw data  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mon the Yee grid and avoid double interpolation. Note: the default  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mvalue was changed to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D version \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m.               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation, computed on the server: 0.28 %\n",
      "\n",
      "Client-side field projection *without approximations* took 13.23 s\n",
      "Server-side field projection *without approximations* took 0.92 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the projected fields as before and plot them\n",
    "projected_field_data_server = sim_data4[monitor_intermediate_proj.name]\n",
    "\n",
    "# plot the actual measured fields from the previous simulation\n",
    "fields_meas = sim_data3[monitor_intermediate.name].colocate(x=xs, z=ys)\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_meas.Ex.isel(f=0, y=0),\n",
    "    fields_meas.Ey.isel(f=0, y=0),\n",
    "    fields_meas.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Measured fields\")\n",
    "\n",
    "# projected field without approximations computed on the server\n",
    "fields_proj_noapprox = projected_field_data_server.fields_cartesian\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_proj_noapprox.Ex.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ey.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Projected, no approximations, computed on the server\")\n",
    "\n",
    "# RMSE\n",
    "Emag_proj_server = np.sqrt(\n",
    "    np.abs(fields_proj_noapprox.Ex) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ey) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ez) ** 2\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, no far field approximation, computed on the server: {rmse(Emag_meas.values, Emag_proj_server.values) * 100:.2f} %\\n\"\n",
    ")\n",
    "\n",
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(\n",
    "    sim_data4.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "print(\n",
    "    f\"Client-side field projection *without approximations* took {proj_time_new:.2f} s\"\n",
    ")\n",
    "print(f\"Server-side field projection *without approximations* took {server_time:.2f} s\")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we get an excellent match, an even smaller error than the client-side computations, and over an order of magnitude speed-up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reciprocal space monitor <a name=\"kspace\"></a>\n",
    "\n",
    "In addition to [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) and [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html), one can also define the far field observation grid in reciprocal space using [FieldProjectionKSpaceMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionKSpaceMonitor.html).\n",
    "\n",
    "To demonstrate, we'll compute the far field associated with a Gaussian beam propagating at an angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the Gaussian beam source positioned the same as the plane wave source above\n",
    "gaussian_beam = td.GaussianBeam(\n",
    "    center=(0, 0, -0.1 * wavelength),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    source_time=gaussian,\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_theta=np.pi / 6,  # angles are with respect to the source plane's normal axis\n",
    "    angle_phi=np.pi / 4,  # angles are with respect to the source plane's normal axis\n",
    "    waist_radius=2 * wavelength,\n",
    "    waist_distance=-wavelength * 4,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the k-space far field projection monitor\n",
    "monitor_far = td.FieldProjectionKSpaceMonitor(\n",
    "    center=[0, 0, 0],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=[f0],\n",
    "    name=\"far_field\",\n",
    "    ux=list(np.linspace(-0.7, 0.7, 100)),\n",
    "    uy=list(np.linspace(-0.7, 0.7, 100)),\n",
    "    proj_distance=50 * wavelength,\n",
    "    proj_axis=2,  # projecting in the +y direction\n",
    "    far_field_approx=True,  # use far field approximations\n",
    ")\n",
    "\n",
    "# create a simulation with the new source and monitor, and no PEC sheet\n",
    "sim5 = td.Simulation(\n",
    "    size=[10 * wavelength, 10 * wavelength, 7 * wavelength],\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=[],  # no PEC plate\n",
    "    sources=[gaussian_beam],\n",
    "    monitors=[monitor_far],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax) = plt.subplots(1, 1, figsize=(7, 3))\n",
    "sim5.plot(y=0, ax=ax)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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\">12:33:02 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'kspace_monitor'</span> with task_id                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ef35642a-3ec2-404b-8026-6efe8853271d'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:02 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'kspace_monitor'\u001b[0m with task_id                         \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ef35642a-3ec2-404b-8026-6efe8853271d'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d'</span></a>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=622265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=622265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=447712;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=447712;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[32m-ef35642a-3ec2-404b-8026-6efe8853271d'\u001b[0m\u001b]8;;\u001b\\.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14c63f824878458bafe1ed9d3c0581aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:04 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:04 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
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       "Output()"
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:29 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:29 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
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     "metadata": {},
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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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     "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\">12:33:33 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.198</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:33 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.198\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:34 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:34 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
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       "Output()"
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    {
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      "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\">12:33:48 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:48 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \n"
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      "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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     },
     "metadata": {},
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
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       "Output()"
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:51 EST </span>status = success                                                   \n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m12:33:51 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></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\">             </span>View simulation result at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">h?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>               \n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=494394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=494394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=559156;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=494394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=559156;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=494394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef35642a-3ec2-404b-8026-6efe8853271d\u001b\\\u001b[4;34m-ef35642a-3ec2-404b-8026-6efe8853271d'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m               \n"
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       "Output()"
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       "</pre>\n"
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       "\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\">12:33:53 EST </span>loading simulation from data/kspace_monitor.hdf5                   \n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m12:33:53 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/kspace_monitor.hdf5                   \n"
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   ],
   "source": [
    "sim_data5 = web.run(\n",
    "    sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot and compare\n",
    "Extract and plot the fields. We use a polar plot, and observe that the far field spot is located along the `phi=45 deg` line, as expected. The angle `theta` is expected to be near `30 deg`, which is nearly what is observed in the plot. The small deviation is due to the way the fields are plotted - a better way would be to project the fields orthographically on the surface of a sphere prior to plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_297577/2534941820.py:16: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
      "  im = ax.pcolormesh(\n"
     ]
    },
    {
     "data": {
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E4Bbt8AkAbiUiFwA7gMncwpVyEhISbu7bt++TX375ZVLXrjLXluh4pk+fbjn55JP7XHHFFT+YTKYJLpdrQysus0DrXUQAfsKx716Hoi4JGTpt2bIiWCsRkSElJeXVM88888rFixfHR0ZG6p0kIfwqOzsbF1xwQVFOTs4/CwsLX9U7PcEoIrUP95rme1v2ry9dIiuChQoiMqakpCyeOnXq5E8//VQyf9EpZGRkYMuWLYmnnXbaU0lJSffpnZ6gJG0AHRsRmVJSUlbceOONF7z44osxMnpedCYRERH48ssvEwYNGvRwcnLyo82f0flIAOigiMhitVq/+Mc//nHuE088ES2Zv+iMLBYLPv/88/izzjprZkpKyjMyh9AxRNINtEMionCr1fr1Aw88MPyRRx6ROh/RqZlMJixbtix+9OjRt1it1v9IEDiGDL5vepEA0AJa5r921qxZZ95zzz0ReqdHiGBgNBqxcOHCuAsvvPBaq9X6qgQBlYwE7kC0ap/V//d//3f6zzNmh99CPfROkhBB4zZjL4S/923cRRddNMlqtc7p7EGAyPfqH6kCClJEZLJaraseeuihgbfddlu4Z/8t1AMSCERn5v0dIBDefvvtuNGjR19jtVqf1jdl+pNG4A5A6+r58V133TV05syZEQDwKh+sc4wEAdEZ1f/cv8oHYTAY8MEHH8QNHTr0FqvVOkuflAlfddiRwG2hDfL66Kabbhr18MMPN9ng6/ky1A8OQnQ0zT3wGI1GLF26NO7iiy++KykpqaqwsHB2YFIWXEJpJLCUABqQkpLyztVXX/3Xxx577LgJsBrL6KVaSHRUTX22638fzGYzPv300/jTTjvtoYSEhJv9n7ogQ4CByOdNLxIA6klKSpo5atSoS2fPnt1oP/+mnvYlCIiOpKnPc2PfA4vFgpUrVyZkZGQ8ZTKZRvgnZcHJMxeQtAGEIIvFcm5mZuYj7733XlxbOjNIaUCEurZ+hiMiIvDFF18kpqamLiGibu2XsmDnnxXBiCiciH7UFq3aTUT/buCYMCL6kIj2E9EPRM3/B0oA0BBRr9TU1IVffPFFYnh4eLPHt6TOX4KACDUtzfhb8vnPzMzEkiVLUqxW69c+rCcQ2vw3ErgGwLnaolWnAxhLREPrHfN3AMXMfAKAlwA829xFJQAAIKKYlJSU1cuXL09JT09v12tLaUCEAn99TocNG0bPPPNMd6vVutyXlcVCmT8GgmmL6VRoP5q1rf5UzpcCmK+9XgJgTHPjMjrFf0hTiMhgtVo/ffHFF7sOGuTbbKy+9PyRQCCCla+fS197vF1//fWWiRMnDklOTu7wYwTUNoBWTQWRTERbvbabjrs2kZGIfgJgA7CamX+od0gGgCOAutQmgFIASU2lt9N3A7VarS9effXVA6dNm2Zuzfmv8kGfvkDex0rXUaGX1j6MtPYzO2fOnNidO3feGBMTs628vHxxqy4SCrQqoFYoaG49AGZ2AzidiOIBLCeivzBzm1ZW69QBIC4u7qpBgwZdO3v27Gg97i/BQASSniVQo9GITz/9NGHgwIGvENFeZt6pW2L8zN+9epi5hIjWAhiLuktrZgPoCiCLiEwA4tDM0radtgqIiAampqb+Z/ny5fEGQ9v+DO2ReUsVkfCX9vpstfVzHh8fj1WrViWnpqZ+ri0P2QH5Xv/fkjYAIrJqT/4goggAf4W2prmXTwBcq72eAHU95iaXfOyUJQAiSktLS/t01apVSbGxsXonpw4pFYj2EKwPE3379sW7776bfs0113xFREOY2aF3mkJEOoD5RGSE+uC+mJk/I6LHAGxl5k8AvAXg/xHRfgBFACY3d9FOtyYwEYVZrdatCxcuPGXMmDHtWgLy15dOAoFoqVD5DD733HNVL7zwwic2m21qc0+poSS220l85r1v+HzeN3eercuawJ2uBJCSkvL6Aw88cEJ7Z/7+JPMNiaYE69N+U+67777IH3/88cLVq1f/HcCbeqenPYXSXECdqgRgNBpHDx06dOmGDRsS/DVteaC+jBIMRKh/1srLy3HKKafkZ2VlncHM2X65SYDFdT+Jhz7wls/nfXX7CCkB+BMRRaempv6/RYsW+S3zB3zvFtpaDU3JKzo2PZ70/fm5iomJwTvvvJM0bdq0j4jorI5SFWQMoRJApwkAqamprz722GPWrl276p0Uv5CA0PGEYtWOr8477zzD2LFjT1m+fPmNAF7XOz1tRSAJAMHGaDSeO3z48ItuvPFGi95pCRQJCKGnM2T4DZk3b17c2rVrnyCilcycpXd62oSkBBBUtKqf9xYuXOjXqh9vgaoG8oV0Lw0+wfYZqS9QnxNPVdDUqVMXh3pVEEECQFBJTU199fHHH7dmZmbqnZSg0dLFPUT7CfbMXm9jxowxXHTRRacsX778ZgCv6p2e1iICTBIAgoPFYhkzdOjQi2644YZOU/XTFhIY2kYy+baZO3du3Jo1ax4jos+Z+Yje6WkNKQEECSKKSU1Nfe+DDz4IWNWPt2CsBmqt9pofPtR1lP/PltDj/zM6Ohrvvvtu8pQpUxYT0fCQrAoiaQQOCqmpqa898cQTyVL1ExihHiQ6U+YezM4991waN27cyUuXLr0FwP/0Tk9H1yEHglkslvOGDRu2+Ntvv9Xl6d+bZCwi1OgdqCsqKnDqqafmHz58eGCoVQUl9zqFL37ifZ/Pmz9toC4DwUJmOoSWIqLoxMTE+XpV/Qgh2sZTFZSamvpRcytaBSOjgXze9NLhAkBycvK/HnrooaSMjAy9kyI6ICM1von2M3r0aDr77LP7mkymi/ROiy+IJADohoiskZGR1996661heqfFQ+/itGid1mbyoR4Ygunz+vzzzyckJSW9HEprCXtGAksA0EFqauqzTz31VILFIr0+ReA1ldmHclDQS7du3XDFFVckR0VFXaN3WnxhJPJ500uHCQBE1CM+Pv5vU6ZMMeqdlvqC6alK1NVe1TlNfakbu16wBYNg/Jw+9thjsdHR0U8QUdCU6psiVUA6SUtLm/uf//wnqa3LOwrh4cnILYamt+ae4HwJCqKupKQk3HLLLQkJCQl36J2WlpIAEGBEdFpmZuawCy64QL5WolmNPem3tlh+fEBQr+35uaXpEQ277777IiMiIu4hohi909Icz1QQvm566RABIC0t7X+vvPJKUjD3GAvG4rVoXP0n/IZKAy0JFJ5zjgWdps/TKxgE8+czKioKDz30UHxycvKjeqelOdIIHGBENLJfv34nDR48WO+kiCDWsqd+3zLgCCMdt9UvDTSVlvpVSFIKaNzNN98cFhkZeR0Rpeidlo4kpAMAEVFqauqrL7/8cqLeaRGhyzsj9q7G8bxuLINvToTRcFyJoSXnAYENBsH89O9hNpvx9NNPJ6Smpj6nd1qaIyWAALFYLJeNGTOmy0knnaR3UkQQa+6p3xdRRkODW7jBADMRwg1Uu68xEUYDIoyGOk/+wVgtFGwmT55sjI+PH0dEvfROS2OkF1CAEJExPj7+peeeey5e77S0VCg8aXVUjVX1eP/s/bQeYTQg3EDaZqjdmhJnNiLObES0Sc3g6wcJTymiId6ljvrpFCqDwYA5c+YkpaWlzdU7LY3xTAcdKgEgZGcDjY6Ovn7y5MmJMuWDaEx7ZZ7RJkPttRp7Snczw93MvIqeUoFDYTiZEWEkOBTP+ccf77mXW5uw0UgNH9eZnH/++dS1a9chRNSfmXfqnZ7jhNiSkCFZAiAiQ2Rk5KOzZs0K+m5h9UkpIDC8M+y6VS3HeubU37yf9qOMBkSb1K0lrGHG2i3RYkSc2VC7RRip0et4qoJa+uTf3qWCUPs8EhHmzJmTlJ6ePlvvtDQk1HoBhWQJwGAwXHTRRRdFJyZK269oP96NtgBgMaD2tXd7QUOlAHcj06pbw4ywa4/tdjfD7lZgMVDtPmcT07F7nviNRI1ev7N46aWX8Oabb4KI0K9fP8TFxZ1BRMMA/AdAEoBtAK5mZoeuCUVolQBCMgCkpqY+9uCDD8brnQ4RfLyfjuuPvPUuCXhn7GYfx4/EJYaDvNoDWFHrcdwOBW6HGwDgcGr/Kg1n3HFm9Xy7m2GAUiftDuX487yrgzpbNVB2djZefvll/Prrr4iIiMDEiRMxcuTIxP379893uVyPMvMiInoVwN+h8yIyJFVA/kVEp/bo0aPriSeeqHdSRAipX+1j1jL+cINaPZNoURtv48wGJFo8mxGJEWZ1iw1DfHw44uPDm7xPbGYMYjNjEJ8ejUjtnOQwE5LDTEi0HKsWakpDo4o7O5fLBbvdDpfLhaqqKlxyySUGt9t9AoDPtEPmA7hMvxSqpBHYz9LS0h6dNWtWst7paIuOtF5wMGkoo2xr5hmVGgUAMIebYLCoGbfRYoRB+9IqCoO1R3LFrcBldzV5PWuY+pWrcCkodSqINh2rWqpwKY1WCXlXBwHqMW0tCYRK/X9GRgbuvfdedOvWDRERETj//PMxdOhQxMXFwel0XgXgVQBZAPTvERJiJYCQCgBElNizZ8/R559/vt5JEUGsfv964Fg3SwAwE2l98dUdavdMNXOPNhlgDlMnlDVHtWxa8aQTrQAAV7UDLrsTAOCsdMJRqb6uKauBvbga8TCjpvz4KmrvBmJ7ba6u1O5rrBrJ8/t09Cqh4uJifPzxxzhw4ADi4+Nx5ZVX4osvvkB8fDzZ7fb7ieg1vdPoQfBtHim9hVQAiI+Pv23mzJlxwTznjwg90SYDorUnc0u0GWGx6szDYbFhsESZAQDmKDNMEepro9kEg+XYV4fdCpoS1z2u9ppVBVXq9YqrYaxSA0Sps/HzLQbyyuDb58k/1Hz99dfo2bMnrFY10F5++eXYuHEjysvLMXr06PglS5acA6AaQLae6fQnIuoK4D0AqVA/CK8z85x6x5wD4GMAB7Rdy5j5saauGzIBQJv24eZrr71WVnsRddRv+K3bBfTYMZ7GXs/Tf0u7eHpLPq23ej2zuXaf4lYbfBWnC87KagCAo7wKNcXlAIDqEnuD1/K0JxhLa1DqdGttA1qDMlMT1UHHegWFSjDYu3cvJk2aVPvzn3/+icceewzXXHMNJk2ahIMHD6JHjx5YvHgxEhIS6pzbrVs3bN68GVVVVYiIiMCaNWswaNAgjB49Gv3790/4/vvvH8zOzj4ANfPTncE/D6guAPcw83ZtVtRtRLSamX+td9x6Zr64pRcNmQAAYNSoUaMiY2Nj9U5Hu5B2AP/zrgIye83D4xmR6wkAURGm2if08IRwRCSE1762xEYCAMLiY2COaroBGAAS/tIXrrJSAEB1oeffMlTZigEApnATqgqqEJ0ejarC4wODd1CqcGnBQNt1bNDYsVy/tdVAga7/P/HEE/HTTz8BANxuNzIyMjB+/Hg888wzGDNmDB588EE888wzeOaZZ/Dss8/WOXfIkCGYMGECBgwYAJPJhDPOOAM33XQTxo0bh0mTJiEvL+88qI3Bdwb0l2oAwT+N9sycAyBHe11ORL9BbfOoHwB8EjIBoEuXLg/OnDlTOv4Lv4vJjAcAhCfFITwpFuFJcQAAY1QMKFxtFCZLOGDQFp9T3GCH+uTP1ZVNXjvhBCtMEWowMBgNqLBVIiYuDCitAQAUad1IG2IktT1AzWDqjhIOJWvWrEHv3r3RvXt3fPzxx/j2228BANdeey3OOeec4wIAAPz73//Gv//97zr7evXqhS1btmDOnDmuWbNmfV9SUlITiPQ3iVDbQcBHyUS01evn15n59QZvQdQDwBkAfmjg7WFEtBPAUQD3MvPupm4aEgGAiBJ79eo1cMiQIXonRQSZhqp/jjUCU53+/hFGA6JN6s/RJgOiItSPf3hCOMK1p/7I5MgW39vU+7RjP7jU+nzUVMJUWggAMBfb4CrMRURKQm1JojKnsMFrxcSpJRB3STWKHAoijIbap3pFqxayuxt/8g+lBuFFixZhypQpAIC8vDykp6cDANLS0pCXl+fz9a655hrL008/fQsRPcusb0RUSwCtCgAFzDyo2esTRQNYCuAuZi6r9/Z2AN2ZuYKILgKwAkCfpq4XEgEgLi7uhhkzZsRI469oCe96f0+FivckbwAQZTLWqfaJ1rp7RqbEIjItCVFpSQAAszUVxgS18dEYlwSKUGcfYVPzS9Ra+g6AMT9LPTfmMKoOH0Fc7wwYLGr7gcErelXYji85eHopORRCtaLUaRBu6Mk/FDJ/h8OBTz75BE8//fRx7xERWvMdT0hIwLBhw2JWrFgxHMDGdkhmm/ipDQBEZIaa+S9g5mX13/cOCMy8koj+S0TJzFzQ2DWDPgBojb+3XXvttSGxKLToWMxd+4DiUwEASmQClLBoAACbI6Dg2BfdwGrVjcFpB8Wrdf+oKm7wmnE90mtfK15jCCpyKhAbaYab1dJEQ9VBnhKNpzTQmmki9Oz/v2rVKgwYMACpqerfNDU1FTk5OUhPT0dOTg5SUlq33svdd9+d+OOPPz4I4G/tmFyf+asNgNTI+BaA35j5xUaOSQOQx8xMRIOhPv80XOTUBH0AAHBKv379IjvivD/SENx6DU354F3949nvPQ7Aezpms1d3z4iEcESmqJ0LojOsiMqwwpzatcVpOVDigElrDgg3hiMmSq3uiYpOhjFcLTFYomLUdgMA9gN/NngdTzVUdI2a8dvd6mCxCCPBzc3X+QeiGqikpAQ33HADfvnlFxAR3n77bZx44onN9uTxWLhwYW31DwBccsklmD9/Ph588EHMnz8fl156aavSNWLECBgMhiFEZNF1PiCi1rYBNOcsAFcD+JmIftL2PQygGwAw86sAJgC4lYhcAOwAJjdXJRb0ASAxMXHSVVdd1fFyfxFQtdMrmNWc2hJlRlis2qM4IikK4YlxiO2pPpmbU7vClHkCAIDj0uCKVquAKmFBhdZn317thKvx9tpa1fHdAABh5giYTcd6MFfu34eYrilQHOrTvsvuQllW/SrduukH1Mnj7G6GkajRAWL+dOedd2Ls2LFYsmQJHA4Hqqqq8NRTTzXbkwcAKisrsXr1arz22rFxWw8++CAmTpyIt956C927d8fixYtblS4iwgUXXGB+6623RgFY3drfr60I/qkCYuYNAJq8MDPPAzDPl+sGfQAICwubNG7cOKPe6RChw/PUb8CxjLMtitxqnX2R3YW8SrWjSbHdiSqnAkV7wDIbCLHh6nGp2gji5EgTrM20KUelJ6Fkfzai02PgqlankXBUOlFSUo1ok6G266dDOT7aHCsFBaZHUGlpKdatW4d3330XAGCxWGCxWFrckycqKgqFhXVrJJKSkrBmzZp2Sd/kyZPjV65ceQ10DABAaM3dFNQBgIhS+/Xrl5CcHNJT/4gAqj95mue1Z2pnk9bzxxJlQZjW8yYsIQaRKQkwJ6v10qa0buC4NACAK64L0IIKhfV/FCJJy/gzEiLQIz4CBVUO1CREAADSopIRpbYrw+xyIFLrNlq2Z1+D14vS6pSOTSVNqHApMBPBQY0PAvNn9c+BAwdgtVoxffp07Ny5EwMHDsScOXPapSdPezj77LOhKMoYIiK9egP5qwTgL0EdAMLCwi6ZMmVKyC36IoJLokWr9jEQzOHqR94cba6d68cSE4mwxDgYErQGyLgUuLVqn2IHkKfN6fNHURWyyqqRXaQO4CqxO+HWqmEszYwqzq10ITNGvWZ4oh2mSnWUcFR5CRxl6vQQnhHDEWU1cFY4j7uGd/dW9V91vy9P/m1pAHa5XNi+fTvmzp2LIUOG4M4778QzzzxT55jW9uRpDxaLBaeffrrpyy+/7Adgly6JCDFBHQCSk5Ovvfzyy5sffik6rfqNvvXfa4+JuQ4U27E3vwIAsCenHEcK1C6b1VVOuLSeOgaTAeGRahVQZnIkitLVRuVql4KTrVFNXj88ST02PL4YJQeKERYbBnO0WkKI0BoavBeR8eZmrh0gBvi3ITgzMxOZmZnwjMeZMGECnnnmmXbrydMerrrqqqQtW7ZMhF4BIMRmAw3a9QCIKMJkMvXt6PP+h8qUvMHs+GqfhhZ/B4wmAwwWIwwWI8zhJpijwpF8Wm9YYiNBUbEwxiXBGJcEJTwOdrLAThYU2VvQ0quxHS6F7XAp9h0swfZDxdh+qBi/5pXjQLEdB4rtyK9ywaw44I5Ng9GaCaM1E6aktCavaTEQSp1Kg0tYBrquOS0tDV27dsXevXsBqCN6TznllNqePADa1JOnPVx00UWGsLCwiXrd31MF5Ouml2AuAZw7btw46fsv2sS7FGAwEozaYiwGixHG8DCYo9Q6ekNkDBChPokr4bGo0FpfC6ocyC6vxr48tQRwIKcMJflqlU1VeQ1c2spfBgMhPLLpeQoLqlyIC1M/0jGRCcChn2FMSEF40lEAaluExabexzMLaVXZ8bMbGABU1puBtP4C8k3p0aMHYmJiYDQaYTKZsHXrVhQVFbWoK+fcuXMxbdo0OBwO9OrVC++88w4URWmXnjztITExEampqfFElK7NnxNw0gjcDjIyMq6dNGlSx5j5TQSMP+Zizy2pRlaRmumXFdlRWqDN8lmcC6ddzbDJaIQ9Up0zyFFz7Kk+0mJEYm2voKYDhCUmEmYt4zdpbRUWsxEWZ90lI9vD2rVr4d25oiWTsgHA6aefjq1btx63v7168rSHKVOmxO7Zs+cSAAFfJ4Cg7xO9r4KyCoiIyO12jxw+fLjeSREhrO4AMYCMBhhqN1Ln9dc2CgsHm8PUzRQGu4ux5Wg5iuzHN8Y2pqowG1WF2agsyEV5kR3lRXZkFVQhu9iO7GI78iodKHcoKHco4Ig4GOKSYIhLgik2DqbYuEave6zKh+o0BHtvbfXxxx/j2muvBaB25VyxYkWbr9ka7dF5Z/z48WFJSUnXtT01rUChtSRkUAYAAAMGDx5sMpmCtoAiQhwZCGQ0HNtMFsBgAgwmuBhwKowat4Iat4Lyaheq7U51q3LCUV4ER3kR7MV5tVtVwdEW3bfaraDarUAxRwBhUUBYFCg8ChUHDsMcFQ5ThFnbTKi0VcFoaXgITFNrBTfXAExEOP/88zFw4EC8/ro64WQwdOWsqqrCkSNH2nydPn36wGw29yails/s107UNgDfN70EZQBITk6eMm3atCS90yE6N0VhKArDrTDcLnVTXApcDvuxrbqidnNUlsEUEQ1HVSmqqxyITY5EdZUThZUOFFY6UFbdeGnCUxIxhVtgCrfAYDTAaNE2r4bsCpfS6BO/p/6/seDg6XCwYcMGbN++HatWrcIrr7yCdevW1TlOr66cK1euxMyZM/HLL7+0+VqXXHJJGIAxbU+V7+qXzlqy6SUoA4DJZLp87NixoVOR1kbSE0gf3ks5cgMjbVt3TTfY7VaDh0uB4lLgcCkY2jMRToXhcgMuN6CQETCZAZO5do6gxhiJUORQ6vRsaouMDHXt9JSUFIwfPx4//vhjbVdOAAHryqkoSp0qnwkTJmDy5Ml48cUXsWuX2ouztVVCV155ZWxGRsZ17ZFOX4RaL6CgCwBEZE1JSYnqKCt/Cf14L5voZjXDV7SNFQa7ldoNihtQXHAmdtfqZYEwkwFhJgMiLUaYLAaYLAYYTAYYTRZUF+fBaLKADL7PUtIzvl5jsMEIg1HdyGhA0f4CkJFABgPI0PBXVGHGG44sLHfnws2MCjixhHPwPmfjS85vtDdQZWUlysvLa19/9dVX+Mtf/hLQrpyeTN1gMICIUFNTg5KSEgDAlVdeiV69euFf//pXm+4xbNgwKIoytK1p9RkBRoPvm16CsZJ94Nlnny3dP4UuDC6122W40YhobeH3uEgzYrSePKWRZlhiEuGsrqg9x+1S54owWSJgjlCnizaZjTBp9fdGg29PeS2ZTfJHdymSyQw7q6WYDVyM/ohFH4rCt1yI31CBk3H8IPq8vDyMHz8egDqyd+rUqRg7dizOPPNMv3blZObaaiXPv5s2bcI333yDAwcO4MILL8SECRMAAHfffTf69u2Lw4cPo1u3bq26n9FoREpKiomIkpi5ySmRO7OgCwCJiYlnjRgxovEuEUI0oLl6cUCde58VRmxmDNwON9xOF9zVaubNNdUgZ+tWFIyyqlNHmywRsMSoE9dGxoTVjgyOjzAjUht/YDICBniW+lLg+mOnz/crVVzYr1RhiCEeW7gUzIwjXI0xULt1noRobEFJgwGgV69e2Lnz+Hu256Rs3txud+2TvofL5cLs2bOxbNkyTJ48GevWrUO3bt1w4YUXIioqCpGRkRgxYgRee+01PPnkk1AUBYZGSkJNGTFiRNjOnTsHAviqHX+lJoXaXEBBVwUUGRl5zsCBA/VOhggRbuY6vV7Un7l2v5vVaRIcCoPdrGb8DkXdqmvgrLTDWWmHUlUO1FSCqstB1eWINBuQHGlBcqQF6XHhyEyMRFl5DWITIxCbHIcoazdta9m6AQkRZiREmBFhMoCcdnVz1YAd1WBHNdxOJ9xOJxSHq7bxmRUFrCjHXeszZz7GmJJq5wauhoIwGGoznigYUYn2adNojT///BO//fYbAPVJnIiwd+9eLFmyBMwMg8GAb775Bq+//jruuece/Otf/0JxcTF27NhRe40ZM2bUNk63JvMHgBEjRsTFx8ef1fbfyBe+NwDr2QgcdCUAl8vVu1evXnonQ3RgnmmXnZXVcFZqi7lXlkEpLYQhRn2KjolKQkKEWoWTEROOktRo2L1W6DJpT/TVcdFQXGom7T0fUGxiBLomq3MAZSRGtChdBbv+AAC4tfsobkZJSXWdY35zVyKKjEg3hGG/UtXotfRaHbKgoAAvvvgiunXrhpNPPhklJSW488478fvvv+OMM87Anj17MHz4cAwcOBD79u3DGWecgTFjxuCrr77Czz//jBEjRgBQG6iHDBmCwsJCJCW1rkPggAEDEB0dfU47/nrNCrUSQFAFACJKOu2004ytjfii8zrWBZKO2+95z6moT/7tIbN7PACgvNIBl3ZNo4kQHqFNCJcYiT6pantAZmw4UqPUZq1oswEGrf2A7eXganViOU9VVGM8v8NBtx2/uiqxx1UFJxgOKPgWRaiBujaBgQiVcCMK+iyhkZycjH79+uHQoUM4cOAASkpK0Lt3b8yfPx9z5szBG2+8AavViujo6No5hTIyMpCXl4effvoJBw4cQM+ePbFv3z44nc5WZ/4AcMIJJ8Dlcp3QXr9bi5C+jbq+CqoAAGDgiBEjmh4vL0QTPNU+6muGm48tpO5QGK5qFxwVambrqHSipkTNjGuKSmGIs8GiLQBvCItBYrhan981LhxOr9W3IizG2qkhWqJ3YiQStdJEFBxwFOXinEnXo6aiHM5qOy4bfApu79cHB/NLcf/GHSiqrMGJUdG4K7bLcYO6zjcn42yjmq7fnVXYyqW40GDF524b/kAV+iAKe1CBHqg7BsofXY3vvPNOTJs2DYMHDwYA7NixA0899RTKy8tRU1ODIUOGICIiAm+++SY+/vhj9O7dG6+88gpGjRqFHTt24IknnsBtt92G6upqGAwGlJWVwe1WSz+jRo3C0aNH6zQe+8pgMAS8IVhKAG0QHx9/1ogRI+L1ToceZH1g33gyxqb6xCtQg4BnqmSHwnDZXbVVQDVlNXCUqU/g9sJSWOJtcOXHq9e1RCHSrPbPt0ZGAoiAWeudEx9hRqa20EtplRNVWpWN0UCI0ebwSYsPR0ZMw/37w8Is+PqDNxBRfASV+3fj/MffwkCTCa9v/Q1Te3TDUEThxUN/4oviAgw1xtb5fRszwpCAz5V8/MAlsMKCkxDd9Alt4GmUTUxMRFWVGgjtdjtmz56N0aNHY9q0abj88suxdetW9OjRA6NGjcK1116L8847DwBw5MgRpKWlYc6cOVi8eDG6deuGcePGYdy4cYiIUP+u0dHRmD59epsHpA0fPtyya9euAQjgKmEhlP8HVyNwdHS0NACLoBPHVegSbUKfpAj0SYpA/9RYDMqMx6DMeAzpkYDhvRIxvFcihvVKxMCu8fhrXyv6pcSgb1IUhmbGIjXKjAQLYLUfhbEiHyi1IcpZDndxPsrzi1FT4wQRYUtBEcZ0UQdgXZBkxff2Y2sEu5lR6nTXadzuaojAeGMa3AxEw4wJlI6rKAMXkBXGppeP9cnhw4drX3saccvLy7Fnz57abppOpxMHDx7EyJEjERcXh/vuuw+KokBRFPTv3x/PP/88du7ciRkzZmD8+PHIzc1FZmYmrrvuOpSVlWHUqFHo379/nRlI22MqmBEjRsTFxcUFdFIxA8jnTS9BFQBcLtcJJ5wQ2Co7Efq8M8W6vYC4Ti8gh8KoditwVDrhqHTCWeFEdYld3QpLYc8vhjs/G+78bCj5h2EsyW5VenonhKN3QjgyYkxINDqRaHTCVHoUVJoLKs2FOz8b5VvXYchtj+Pkh/+HEd3SkG4wINpohGJ3w2V3wVLoQIHLWZvulvwN2otnoNaSJUtw2mmnYcqUKViwYAEAtQ+/2+1GTEwMKioqsGrVKgCAzWbDWWedhT/+UBuyhw8fjnXr1qGsrAw33HAD/va3v+HRRx9FUlISPvvsM5xxxhkAgH379mHPnj1444038NJLLyEysn2n7xk0aBDFxMSMbteLNoGglgB83fQSNFVAWgOwSRqARVsdawM41g4AAA4FsLsVhGvLLdaE18AUYUJMejSqC8thCg+D0aw24oYbjDABMGqZYWR0MsK1RX1jLEYkR6pfnWq3Au+p+c0tnNnLaDBgw79vwv5Nu3Dbqk0YZLCAFUZNWQ2clQ6126qWZs/v4rmNvxd/91S7rFmzBq+88gqSkpLw/PPPIz4+HuPGjYPb7YbRaMQ111yDl19+Gbfffju6d++Orl274sMPP8Q555yD6OhoMDN+/fVXFBcX4/bbb8ftt99eew/PNYYMGVK7whgz15Yw2kufPn3gcrn6tNsFWyCEFgQLngAAYMDw4cOlAVj4XbWWYxvLatSFYWoXiSkGeXXhCFfcMGqLt5uslVBq1CkU4sJiEBuuDrJSTGG16wIDgIkA0kYTG+xlMFSXqm+U2uDKVatSnHlHUJmrtknGhlkwJCMFu4pKUeF2w6Vl7kWKE4mGY19Ph8LHZfz1x0Co+1r5R6ln7dq1sFqtGDlyJI4cOQKHw4EtW7Zg3LhxsFjUr+mFF16IJ554Aps2bcLw4cNx9dVXIysrC1dccQWysrJwww03YPr06XXWHfAMDDMa6/ZS8rQrtPckdAaDAVar1URECcxc3K4Xb4jOT/S+CpoAEBcXN1xGAAtfubmpaZEZTgaMWgZtdyuwGAyIaOcekuElx+rIobiOjSi2l8Fdqmb0SrENzgJ1iuXD+7PgsBUhNsyCwpwSfLvnCK7qmonTIqLxdXYuhipR+MZeiv7m6BZV//hDYmIilixZgqqqKnzzzTdQFAVWqxWlpaWIi4sDMyM6OhpXXnklXnjhBQwfPhwJCQl46qmnsHfvXpx00knHZfIAGtwHtH6wV0sMHz7c8vPPPw8AEDyr1gSJoAkAMTEx5wwcODCEYqcIJseejgnHhkGpXUCdnjECCmB3M4yklQCqXTCU1dSWALyf/tmtwO10IsKu9hJSyktgTFAbaI1xSThSWI7r7vs/2AoKQWTA36dNxB03XIOi4lJMvfVuHMo6iu5dUrBg1j+QEFu3R87BP7Jw78pNcDPgcrlxdlQ8BhqikBiVimfzD+NttwtdDGEYH2ZFjdszoR3Xqdryt8jISCQkJMBisWD79u1YsWIFFi9ejLi4OCiKUvukfu+99+L222/H888/j+uuuw7Jyck49dRT1XQ2MA2EHkaMGBG3aNGi4QhAACCdG3V9FTQBwOVyde/du7feyRAdjHfG6agNAJ5BYwqM5XUHYHmmiFYcbriqa+AoU7s5RiRVIEx7mqeoWMDuxrM3XI4zTuyFcrsDQ6+/H2P6puG9T1fjnJO74b7HbsNz85fi2VfexhPTxqKmqBRVNrUG4qTkeCweq454rcirhL3ADgBIM1nwUuoJKK12oVRbBrJccaNamw7i+Cog/7UHVFdXY/jw4Zg0aZKatrQ0ZGVlATj2tM7MCA8PxzPPPIN3330X33zzDSZOPLYee2NP+4HWp08fiomJ6Reo+0kVUCsYjcYws9YAJ0QwKM8urp0qoqakAmGFan2+OSoc8VERSAq3oOaPXxFmNKCvNRaHftqKT77ZhM/unYbqA79j/AlpuHTOd7h38ClwlFehplhtQ7AXVsJerF63urgaNdrC7xU16vgEu1tp16qf1gwCO+mkk+B2u/Hll1+if//+2LlzJ6ZMmQLg+Pp6q9WKO+64Azabrd3S3J60lc4yA3U/aQT2EREZevbsKbm/aBXvdgD1iVhdOMXz2nvQmENh2D2NwGQA4EacVynArT15u50KwhOaXqjF41B+MXYdysWg3hnIL6tAWrzaQJwaG4X88kqUHcyBs9KBmlI1o68pc9Rm+jVlNai0ezJ+RoU2r5Anjc4Gqn4aavxtb2azGePHj8eCBQvQr18/WCwWzJs3D0DD9fVhYWHo2rVlE+MFWlpaGlwuV2qg7hdC+X9wBAAA1uTkZL3mrxId0LEMsm4VEAxqWwAALbNVgwAARJfVQPFMxOZQ++N7MuqwWDssXiUAY3gYjGYTKh1OTP7gCzx67iAoWflgN6P4d3VdW3d1DaiV2YGnBOA9l1HdBW6Orw5qbyNHjsSJJ56I77//3q8LxPibxWKBwWBo2Yx8bSRTQbROerdu3ULnr+YnMh1E63k/5buZ60wK5x0M6s8FV+FS4GaDdpwCN6tP426HAme1C5ZKtWBaU1oDS7Ta/dEUbofRYoBiIvxj3TaMTbdimDkMJfuPIsFixv5fD8MaEY68sirEm0woyyqDs8IJR+WxOYic2liESpfb66mfYQ9EC68PUlJSajN/T9/9UGSxWExEZGDm9pkNsAn+yP+JqCuA9wCkQu3l8Dozz6l3DAGYA+AiAFUArmPm7U1dN2gCQPfu3WUVMBFQS2rysFepQgwZ8a+YngDURteXSrNQoDiRVhKG/+vdBzENTEnAzHj4mx3oYjLjsshklB5Sp20YHhOPj3b+iasyMrHkyBEMjYxDxdEKOKtdcGlVPQ6nuzajt7sVr9dcp+oH8PT/9+/foaVCNfMHgOTkZOXgwYNJAPL9fS8/dWh1AbiHmbcTUQyAbUS0mpl/9TrmQgB9tG0IgP9p/zYqKAIAEaV37949Su90iNDnaQ9orFuodymgnzEGQ0xxWOqwaSUBwmc1+TjJHIm/RSThi+oivHfgMK6LTYMpwoSaMgeMFvXrvbumEl8W5KNXZCSu+0ldyOTmbt1xVWYm/rVnDz7PtyHVbMG/Mpvv2eYpATgUrs34649mrrvvWEQIluAQ7Lp27WrYunVrOvwcANSpHdq/CMDMOQBytNflRPQbgAwA3gHgUgDvsTqXx2YiiieidO3cBgVFAEhMTDwhIyMjdB8vRNDyNASrPK/VXDOdwlHFLjDUJ283E7Y7KnB3dDdUuBgDTDF4vvwILg1PhsXlhqXSAbPWANrTaMDK7qcB8Bo/UAOgpgZPJHQHoC7o4i52oAKOOnP6qA3Rx14L/+vevbsFQBcAu/x9L3/3AiKiHgDOAPBDvbcyABzx+jlL2xfcASAyMrJXly5d9E6G6CDqjw4+vjRwLCA4oM6542SGogDl7IZRMaBCUWAAoVRR++RbDGrPIs8gMovXt7z+SOSGeuuo01Krr+sHg9oFa+qMWTh+AJhovR49ekQBSA/EvVpZAEgmoq1eP7/OzK8ff22KBrAUwF3MXFb/fV8FRQAAkKn11RUiaLRXUb5I61nkmZlUfa3+q6DhzL7heX6k6qe1MjIyTImJicG81mwBMw9q6gAiMkPN/Bcw87IGDskG4N0XN1Pb16igCABOpzMtLS1N72SIDqShBWOOVQcdq1N3gsFaxmwkIJKMsLkciCETquBGFBlR4VK0xbuPLTnp/brxNLT/07tk/K2Tnp6OyMhIvwcAgn8agbUePm8B+I2ZX2zksE8AzCCiRVAbf0ubqv8HgiQAGAyGiPDwlg26EcIXDVcHeXUR1aqAPI2tvSkSW13lGGaMx4/uMvQ1RKFaYRjg6VrqmUbi2DUbWofY+/7eP9ef0rl+I2/9Bt/61xOtk56eDiLqFoh7+Wnuo7MAXA3gZyL6Sdv3MIBuAMDMrwJYCbUL6H6o3UCnN3dR3QMAEVH37t1lFLAIuC+UfGSjGtVw4zXXYQyleJxpiMNnig0/ucsQRyaMN6XWVtF45hBSX3t/yRvPoJsKBg3ta6inT/33hO/S09PhdDr9X81A/mkEZuYNaGaQsdb75/amjqlP9wAAICEhIUE+2sJv6lcHeTLXsQZrnf3qa8KVxvTa/c1V8wSCZPxtFxERASJq3+XGGqH/J6blgiEAhEdFyRAA4X+NBYK6X9m6T+N1g0PDrxu+V9NP8I016Ep1j/8Yjc39r7WdOhWEv+/SfoIhAJjaY/FnIVqq8UAAHP/8Vj8gUAPnNHe/lgeDxo4R7SIgWbPe6x/4otkGayJ6m4hsRPSL177TiWgzEf1ERFuJaLC2n4joZSLaT0S7iGiA1zkziWg7EU2qdwuzTAMtQtHxC9Afv3mmcjh+O3ZM3Wse20T7Ih9yZiIaS0R7tbzsQW3fqUT0PRHNJ6IG805PCcDXTS8t6bH0LoCx9fY9B+DfzHw6gEe1n4G6c1HcBHUuCs/ghTMBDAYwtd61pAQgdNFQRnt8Jl43U/YM4pKqmtDT0vyfiIwAXoGan50CYAoRnQLgbgCXANgK4PxGz2/Fppdmc15mXqcNPa6zG0Cs9joOwFHtdYNzUQCo8DrvuDRYLJbQKTOJDqd+EGh4FHF9Lf/INhcs5Gk/MHwoAQwGsJ+Z/9TOWwQ1bzNCzcMUNPoBoE4xHfRdAL4kouehliKGa/sbnIuCmbcS0c9QI+fs+mmQKiARTBrKkI+f7qFtubZk+oHnQwBoKB8bAnWq5c8B7APwasM36RxLQt4KYCYzLyWiiVBHqJ3X1AnM/DSApxtKg8lkCqE/mX+1Zvm+zkjWTRC+OnjwoJWISKuh8Bkz70Az0yuHmtYGgGsB3Km9/gjAm9prn+eiAOB2u92tTEbHIxlbcJIn9tDXvXv3goMHD7bkf7I1+RgAgJhBIdQ+1NppK44CGKW9PhdqkQhQ56K4RusNNBQtmIsCgMvhcITOX0wIEapauhrYFgB9iKgnEVkATIaat7UMK75vOmm2BEBECwGcA3W60iwAswDcCGAOEZkAVEPt8QO0Yi4KAC6n0ykBQAjhVy2t+WFmFxHNAPAl1Ibft5l5d0vvQzpm6L5qSS+gKY28NbCBY32eiwKA0+Vy+XiKEEL4xpe6f2ZeCfWB1te76PpE76tg6IDvcjqdeqdBCNHBtbbxtxU3Csht2kNQBAApAQghAsD/OTNLCcBXDrvdrncahBAdnKIEZgHmDtUGEABFRUVFMg5ACOE3DocDiqLUBORmIRQA/LF6mU+Y2e1wOKQOSAjhN3l5eTCZTDb/34lDqhuo7gEAANxud420Awgh/CUnJwdEdKT5IzuXoAgAJpPJlpeXp3cyhBAdVE5ODqqrqw/4/UYMKQH4ioiyc3KaGzAshBCtc/ToUaWwsPAP/9+JAUXxfdNJMDQCo6am5sDRo0ebP1AIIVrh4MGDlYqiBCSTkV5APioqKtp39OhRRmitpyyECBGHDh2qwbF1S/xLAoBvXC7X0UOHDlUCiNY7LUKIjufw4cNuAP6vZ2aWkcCtkHPo0KFqSAAQQvhBXl6eAUAAuoFCSgCtkHP48OHQ+av5iawFIIR/1NTUuJk5IH3NpQ3Ad3m5ublS/y+EaHdutxtutzswo4BDbDbQoOgGyszOmpoaGQkmhGh3NpsNJpMpP2A3lHEAvnM6nU5ZGlKI9tfZqxa1UcAtWtKxzVimgmgVs9mck5WVpXcyhBAdzMGDB2G323/XOx3BKGgCQE1NzcYdO3bonQwhRAfz/ffflxcUFGwIxL0IaiOwr5tegiYA5Ofnr9u0aVO53ukQQnQs69evtwPYFrAb+mEqCCJ6m4hsRPRLI++fQ0SlRPSTtj3akqQGSy8gANim/UfF6J0QIUTHwMw4fPgwI1CjgOG3gWDvApgH4L0mjlnPzBf7ctFgCgDZBw8eZGYGkfQIFUK0XVZWFgwGw+HArQcMvzTqMvM6ovZvzQ+aKiBmZqPReCQ7OzCN9UKIjm/btm1wOBzrA3nPVrYBJBPRVq/tplbcehgR7SSiVUR0aktOCKYSABwOx4Zt27YNyszM1DspQogOYNOmTeX5+fnrAnfHVg8EK2DmQW248XYA3Zm5goguArACQJ/mTgqaEgAA5Ofnf7dp06YKvdOhh87eV1sIfwh4AzCgyzgAZi5j5grt9UoAZiJKbu68oAoAALZt2LChSu9ECNHRdMYHDGbGoUOHGEDg6pWZAcXt+9ZGRJRGWuMpEQ2GmrcXNndeUFUBAcg6cOBA6MylKoQIWkePHoXBYMgKWAOwhv2wwhcRLQRwDtS2giwAswCYAYCZXwUwAcCtROQCYAcwuSW/d1AFAGbmzMzM7KNHj6Z26dJF7+QIIULYtm3b4HQ6A9oArC4J2f5T2jDzlGbenwe1m6hPgq0KCE6nc8O2bYGtshNCdDybNm2qsNls3+mdjmAWdAHAZrN99/3331fqnQ4hRGhbv359FQLeAAxd2gBaK6iqgDTbvvvuu0oAUXonRAgRmpgZWntiQGeYZDA4hGY1DsYAcPiPP/5wORwOWCwWvdMihAhBu3fvhsFg+C3QDcBqCUAWhGk1ZmaDwfDVd991nqq7zthFTwh/WrZsWZXNZns78HfWpxtoawVdAACAnJyc9xcuXFisdzqEEKFp0aJFlU6nc2XAb8wMVtw+b3oJygAAYN1XX33lCnTpTYiOrLOUNPPy8lBaWmpj5mYHQvmFH6aD9pegDADM7ASw6+eff9Y7KUKIEPPpp5+6KysrF+pzdykBtAubzfbO0qVLZVoIIYRP3n///aLS0tKPdLl5iHUDDdoA4HQ6Vy5evFjGAwghWsxut2PPnj01zKzTGsAsVUDtgZmLy8rKcnNycvROihAiRHzzzTcAEPjG3xAVtAEAACoqKhZ88sknLr3T4U+dpWFOiED44IMPivLy8t7XLQEMsNvt86aXoA4AZWVlS99//33pDipEO+nIDxyKomDt2rUuAN/rl4rQGgcQjCOBazHz/vT09OqqqipERkbqnRwhRBDbvn07jEbjNmbWr9aA/TMbqL8EdQkAABRF+ezrr7/WOxlCiCD30UcfVWRnZ7+jdzpYUXze9BL0AcBmsy344IMP9BnQIYQIGcuWLbMz85f6pkKqgNrb5m+//dZZXV2N8PBwvdPSrjpyfawQgfTbb7+hqqrqT2Yu0zUhnnEAISLoSwDM7FYUZdHSpUtD568qhAiol19+uSw3N/dpvdPBYKkCam/5+flzXnjhBekNJEQ76Gglz5qaGqxYscKuKMrneqdFRgL7ATMfzM3NPbRv3z69kyKECDLLli1T3G73h7r2/qkVWm0AIREAACA/P//ZefPmleudDiFEcHnhhReK8vPz5+idDgAyEMxfXC7Xxx999JHd4XDonZR20dGK4SK0dJTP3/79+3H06NEjzPyn3mkJRSETAJjZ4Xa7l65YsSJ01lsTQvjV3LlzywsKCp7VOx3HyGRwfmOz2WY/9dRTMiZACIGqqip8+OGHlU6nc5neaalD2gD8g5kP2Gy2PTt27NA7KUIInc2fP9/hcrne1BaQCg6yJKR/5eTkPPrkk08W6Z2Otugo9a8itIXy55CZ8fzzz5cVFhYGR+OvFxkH4F/fbdiwoSQvL0/vdAghdLJmzRrY7fZ1zFygd1rqYAa7FZ83vYRcAGBmLi8vf/bll1+W1cKE6KQef/zxgpycnP/TOx31MUMCgL9VVVW9984771TW1NTonRQhRIDt378f+/bty2bmn/VOy/FkKgi/Y+Zqh8Px5iuvvBJyESCU611FxxOKn8f777+/OC8v70G909EgP5UAiOhtIrIR0S+NvE9E9DIR7SeiXUQ0oCXJDckAAACFhYVPzZ49u6S8XAYHC9FZ7Nq1C5s2bTqkKIrO0z43zk9VQO8CGNvE+xcC6KNtNwH4X0suGrIBgJkr7Xb7s08//XTItAWE4tOWEMHkH//4R2FeXt4tzMx6p6UhzAzF7fZ5a8F11wFoqvfjpQDeY9VmAPFElN7cdUM2AABAaWnpvLfffrvYZrPpnRQhhJ+tX78ev//++8/M/IPeaQlCGQCOeP2cpe1rUkgHAGZ2lpWVPfjII4/ouwiEECEsFEqmzIwZM2YU5ubm3qp3WprTykbgZCLa6rXdFIi0hnQAAAC73b7wk08+sR04cEDvpAgh/OSTTz5RbDbbWmbeo3damtT6cQAFzDzIa3vdxztnA+jq9XOmtq9JIR8AmFnJz8+//e6775YFY4TogNxuN+65556i3Nzcu/ROS0voNA7gEwDXaL2BhgIoZeac5k4K+QAAAIqirN68efPBXbt26Z2URoVCMVt0XsH8+Zw/f76zvLz8I2Zu9olWb8z+GQdARAsBfA/gRCLKIqK/E9EtRHSLdshKAH8C2A/gDQC3tSS9obAofLOYmYno5hkzZnyxbt26RL3TI4RoH9XV1Zg1a1aJzWb7p95paSnFDyN7mXlKM+8zgNt9vW6HKAEAADNv2bdv30/r1q3TOynHCeanKyE8gvFz+vLLL1fb7fb/MnNoVPHKVBD6yc3NvW3GjBmFQdpFWAjhg9LSUrz00kslhYWFz+mdlhaTyeD0w8x7bTbbmo8//jhoVg0LxqcqIRoTTJ/XJ598sqKqqupJZq7SOy0txZDpoHWVl5c388477yysqKjQOylCiFb6/fffMX/+fFtZWdmreqfFJ1IC0BczHy0rK/u/O++8UwaHCRGC3G43Jk6cWGSz2SYxs0vv9HRkHaIXUH0lJSX/++yzz6755ptvBp977rmkVzqCqTgdbF7lg3onQf5/GnEL9dD1/+f555+3Hz169H1m3qpbItpAzyd6X3XIAKB1C71y+vTp23bv3m2Njo7WO0mdQjBk6r7wJb0SLAJj7969ePHFF3Py8/Pv1zstrcKAomOdvq86ZAAAAGY+EhcXN+uOO+54+u23344L9P07YoYRahl8e2rud+9o/996lAK8qn4mMnPIrfUBAAyWEkCwKCsre/Xzzz+/Zs2aNUPGjBmjW1VQKOnMmXxbNPZ362iBwZ+ee+45e05Ozv9j5m16p6XVGOAWTO8cLDp0APBUBV1//fXbpSroeJLZ+58EhpbZs2cP/vOf/+Tk5+c/oHda2oZ17dbpqw4dAACAmbPi4+Mf/cc//vHMO++8E5CqoGD8cktmH1wa+v8Its9NoKqBOkLVTy2WRuCgU1pa+trKlSuvWbNmzdDOUhUkGX7oqf9/FmwBwV+effbZqpycnPkhXfVTS9oAgo5WFTRx+vTpO3bv3p0cExOjd5LanWT4HU9nCAi//fYb5syZk1tQUBCci7z7iNk/k8H5S4cbCNYYZs4qKyt75B//+EepP+8T6C/pq3xQMv9OQo//a39+nt1uNyZNmlRos9muZGaH324UUP6ZDtpfOkUJwKO0tPT1VatWXbNy5cqhF110UcgFP8noBdBxSgaPP/54VW5u7nxm3q53WtpNiLUBhFwm2BbMzDabbfz111+f+/vvv7f79f31RZSnfNEUz+fDX58Rf3yuP/vsM/d///vf3/Lz8ztE1U+o6lQlAABgZhsRjbvwwgu/3r59e1JcXMDHiLWYZPrCV57PTDCXCn777Tf8/e9/z8nPz7+AmZ16p6ddMcDu0JmOvtMFAABg5p+io6Nv/9vf/va/tWvXJhiNxjZfs72+cJLpi/bg/Tlqj89me3UJLSoqwoUXXlhgs9kuZObCNl8wyDBYGoFDQUVFxYd79ux5a+bMmeVtvVZ7fMGkmkf4S3t9ttr6OXe5XBg3blxxfn7+Tcz8S5sTFIwYYIV93vTSKUsAHvn5+Q8sWrRowMCBA0dce+21lkDfXzJ8EUjtXSrw1e233162f//+/1ZWVi4P+M0DSJEqoNDAzAoRXXr//ffvOOmkk04YMmSIz9fw9Yskmb4IBq0NBq2tCnr99ddrli9fvrGgoOBfPp8cQlh6AYUWZq6w2WznXXHFFXnZ2dk+nevLF0eqeESw8vVz6etDz4YNG/iRRx45mJ+fP4E7+oLdzGC375teOnUJwIOZD5lMpivHjh274scff0yMiIhot2tLpi9Cgb96Dx0+fBiTJk3Ky8/PPy+U1vZti1CqAur0JQAPl8u1/ujRo7OmTp1a0pKHlJZ8USTzF6GmpSXVlnz+KysrccEFFxTm5eVdzsxZbU9dCNCqgGRN4BBUWFg4b9OmTZ889dRTbXpSkeoeEera+hlmZkycOLEkJyfnIZfL9X37pSy4MQBFYZ83vUgAqMdms93w8ssv7/7ggw8aHaDS1NOPZPyiI2nq89zY94CZcdddd5Vv3bp1cUlJyRv+SVmQCrE2AAkA9TCz02aznXvXXXftWbp0qav++4196OWpX3RUTX22G/o+PPzwwxWLFi36wmaz3erflIm2kkbgBjBzBRGNvPXWWzeHh4f3GTduXKNDhSXTF51FSxqKH3/88cq33nrr2/z8/CnMHDr9IduRjATuAJi5ND8//6zp06f/uXr1agU4/oMvmb/ojBqbjXT27NlV8+bN25Sfnz+emUNnYdx2xNpcQKFSBSQlgCYwcxERnXXVVVf9sHjx4h4ACJCMX4j6pYG5c+dWz549e0t+fv44Zj6u6rTTCLHJ4KQE0AxmzrfZbGdNnDjx8OS177Jk/kIc8yofRL9591U//vjjP3XI2T19pk4G5+vWHCIaS0R7iWg/ER03hTYRXUdE+UT0k7bd0JLUSgBoAWbOsdlswyZNmnTQUx0khABeeOEF+2OPPfZjfn7+6JBf0L09+GEyOCIyAngFwIUATgEwhYhOaeDQD5n5dG17syXJlQDQQloQGDJt2rQ/Pvvss05ZvymEtyeeeKLq2Wef3WSz2f7KzNV6pycYMNSRwL5uzRgMYD8z/6ktnbkIwKXtkV4JAD5g5vz8/Pyh119//b6GuogK0RkwM/75z39WzJ07d21+fv7YjrOebztgbu1I4GQi2uq13eR11QwAR7x+ztL21XcFEe0ioiVE1LUlyZVGYB9pDcNDb7311vU1NTUnTZ061ax3moQIFGbGvffeW/H+++9/abPZJnXW3j5NaWUjcAEzD2rDbT8FsJCZa4joZgDzAZzb3ElSAmgFrYvo8JkzZ/70xBNPVHX0CQ6FAIDq6mpceeWVpQsWLFhqs9kmSuZ/PGa/VAFlA/B+os/U9nndlwu92mDeBDCwJentkAGAiLoS0Voi+pWIdhPRndr+2US0RysmLSeieG1/DyKye7Wgv+p1rXO0Itlz3vfQppE+a+7cuR+NHz++1G63B/R3FCKQcnJyMHjw4KK1a9f+Ozc39zrvQV5EFE5EPxLRTu379m9t/wyt1woTUbLX8ecQUanX9+1Rr/cmE9F2IroroL9gcNsCoA8R9SQiC4DJAD7xPoCI0r1+vATAby25cEetAnIBuIeZtxNRDIBtRLQawGoADzGzi4ieBfAQgAe0c/5g5tMbuNatAEYCeIKITmLmPZ43tC5v1yUmJs4488wz//3ll18mZmQ0VDUnROjasmULxo8fb7PZbJMdDsfaBg6pAXCuNoLeDGADEa0CsBHAZwC+beCc9cx8cQP7JwM4E8ACIopm5op2+jUChpX27Sio5VczAHwJwAjgbWbeTUSPAdjKzJ8AuIOILoGa9xUBuK4l1+6QAYCZcwDkaK/Lieg3ABnM/JXXYZsBTGjB5QzQGvehDQSrr6ioaJ7JZNo5ZMiQj5YuXZrampXFhAhG77//vvOee+45rPX0OdDQMdoiL56M2qxtzMw7AICowa9NYzwHMxr5vgU1blGVTisuyysBrKy371Gv1w9BfaD1SYesAvJGRD0AnAHgh3pvXQ9gldfPPYloBxF9R0Qjvfa/CWATAAMzN1qscrlc67Ozs4dccskl++fPny+9IkRIc7vduPvuu8vvvvvujTab7YzGMn8PIjIS0U8AbABWM3P971t9w7Qqo1VEdKrX/mUAtkJ9si1v0y+hB5kKIngQUTSApQDuYuYyr/3/hFpUWqDtygHQjZkLiWgggBVEdCozlzHzl1CLXs1i5kNEdMb999+/Ytu2bYNfeumlGKOx0XnkhAhKZWVluOyyy4p37979bn5+/r0tmdRNaxA+XWtXW05Ef2HmXxo5fDuA7lqV0UUAVgDoo11nPtQeLCGJIWsCBwWtLnIpgAXMvMxr/3UALgYwzbM+KTPXMHOh9nobgD8A9G3NfbXG4fMXLVr06rnnnltcWlraxt9EiMDZv38/BgwYULh169YZeXl5d/s6oyczlwBYC2BsE8eUeer2taoNs3cjcUjzTy8gv+mQAYDUSse3APzGzC967R8L4H4Al3ivT0pEVm24NYioF9SnkT9be39mVmw22/3btm27dcCAAYW///57ay8lRMB8/fXXysiRI4/+8ccffy0rK/ugpedp35947XUEgL8C2NPE8WnadxRENBhqPlTYpsQHDVkQJhicBeBqAOd6dTW7CMA8ADEAVtfr7nk2gF1aHeYSALcwc1FbE1FRUfHhn3/+OWbUqFHZq1atCp1yoehUmBkvvPCCfdq0abtzc3MHeBpvfZAOYC0R7YLaZXE1M39GRHcQURbUfuu7iMgzP80EAL8Q0U4ALwOY7CmNhzpmQGH2edMLdZC/e1AjopSUlJTlY8eOPXXevHlxMTExeidJCADA4cOHMXny5OI///zzk7y8vJtlQre2OcEcwbOTevl83uV5v25r40jgVumoJYCgwsw2m802YsWKFfedeuqp+WvWrJHSgNAVM+O1115zDB48OOuHH364XBvcJZl/GzEAN/u+6aVD9wIKJloR9w0iWjV16tTFF1544Slz586V0oAIuCNHjmDy5MnFf/zxx2d5eXm3heJgq2DmDqFaFSkBBBgzZ9lstrNWrFjxgFYaCJ1PiwhpzIzXX3/dceaZZ2Zt3rz5itzc3Gsk8+/cpASgA6008BoRfT516tTF48aNO+Xll1+Oi46O1jtpooPKysrClClTivft2ydP/X7kqQIKFVIC0JGnNLBs2bIHTz311Py1a9eG0EdHhAJmxhtvvOEYNGhQ9qZNm+Sp38+Y1SogXze9SAlAZ1pp4FUi+nzy5MmLL7744pPnzJkjpQHRZllZWZg6dWrx77//vjIvL+8WyfgDQ0oAwmfMfMRmsw1fsmTJQ6eeemr+8uXLFemiK1rD4XBgzpw5NYMGDcrevHnzlbm5uVdJ5h8YDN+f/vUsAUgACCLMzKWlpf87fPjw6bfddtuS/v37F6xbt07vZIkQoSgK3n//fVefPn3yn3zyyf/k5eWd5HA41uidrs5EuoGKNmPmowAmEdGJkyZN+u8JJ5zQf968eUn9+/fXO2kiCDEzVq1apcycObOotLT0k7y8vAeZOV/vdHVWoVQFJAEgiDHzXgBjiOjMCy644LWhQ4f2eOmllxJ69uypd9JEkPjhhx9w++23F2ZnZ2/Kzc29g5kP6p2mzszTCBwqpAooBDDzlry8vIGffvrppGHDhu2/8cYbS202m97JEjras2cP/vrXvxZddtllG7dt2zY6JyfnEsn8g0MoVQFJAAgRzMxut3t1Xl7eiQsWLLjttNNOy3r44Ycry8tDb80M0Xpaf/6Sc845Z/fXX399aU5Ozghm/lnvdAmV2gYgjcDCT5hZqaqq+iAvL6/X//73v0f69u2b98ILL9TU1Mg0Lh1ZUVER7rjjjvKBAwceXLZs2XV5eXn9mHmD3ukSdYVaI7DMBhriiCgqKSnpYYvFcsP06dOj7rjjjqjU1FS9kyXayZ49e/DMM8+UrFq1qqKqqurfFRUV72irb4kglGkM5zsjuvl83v2V+3SZDVQCQAdBROGRkZFXx8TEPHjWWWfF//Of/0wcMGCA3skSraAoCr744gt+/PHHCw8dOnQoLy/vUUVRvvB1dS4ReJnGcJ4R3tXn8x6q2i8BQLSdttLS2V26dHksOTn5lH/+85+Jl112mcFiseidNNGMsrIyzJ8/v+bFF18sq66uXpObm/s4M/+qd7pEy2UYwvm2VgSAR+wSAEQ7I6IeKSkp9xsMhsuvvPLKyBkzZsT07duqpY6FnzAzNm/ejJdeeqlo/fr1ldXV1a+XlJS8wszFeqdN+C7DEM43h2X6fN6s6j8kAAj/ICKLyWS6JCUl5cGUlJTu99xzT8KECROM4eHheiet0yoqKsK7775b88orr5RVV1dvO3r06NMA1neUpRE7qy6GcL6pFQHg3xIARCAQUQ+r1XqnwWCYfPbZZ5unTZuWdN555yEqKkrvpHV4BQUF+Oyzz9zvv/9+0S+//FJpt9tfKSsre7s91p8WwaGLIYz/bvE9ADxR86cEABE4RGQEMCQ1NXUaEV3cu3fviKuuuirhkksuMXXp0kXv5HUIzIy9e/di2bJl1QsXLiwvLCwsqq6u/rC4uPhDAL/J037Hk24I4+lm3wPA0w4JAEJHRNQ7Njb2iujo6KtiYmLSJk6cGHXFFVdEnnbaaVDblUVLuFwubNy4ER9++GHp559/7nC73XsLCgrm19TUfMLMMny7g0szhPG1pgyfz3vOeUACgAgORJRgNBovTEtLmw6g/1//+lfTlClTEkaNGoWwsDC9kxd0SktL8cUXX/CCBQsKf/zxR5fJZFqXnZ39LoC1zFytd/pE4KRRGF/VigDwgksCgAhCRGQGMDI9Pf1qRVHO7927t2nkyJGRw4cPjx44cCC6dOnSqUoIiqJg//792L59OzZs2FC6fv36GpvNVuFyuZYVFBQsBLBDqnY6r1QK46lG36tQ/+M+qEsAkNlARZOY2QngGwDfEBHl5eV13bRp08CUlJRRZrN5hKIomT169KCRI0dGDB8+PGbAgAHIzMzsEEFBURTs27cPW7du5Q0bNpRu2rTJYbPZXCaTaX9FRcW3JSUlmwBsY+YCvdMqgodMBy06JO3J9rC2LQfUgWc5OTkZ33///UCr1Xq2xWIZqShKt27duh0XFIxGo67pb4rD4cCff/5ZJ7PPz893mUymfeXl5WtLS0s3AdjOzIV6p1WI9iIBQLSJFhSytO1joDYodPnhhx8GJicnnx0eHj7C5XKlmUwmS1hYmCk1NZW7detm6N69e3j37t0ju3TpYujSpQvS09ORmpoKs9ncbumrrq5GTk4Ojh49ipycHGRnZ7sOHTpUdejQoZrDhw9zQUEBOZ1Op9vtrjabzYfKysq+9crspXum8IlnNtBQIW0AIqCIyAQgFUA6gHSTydQlMTGxT1hYWE8AGU6nM8VoNIabzWZzUlKSEh4eDpPJBLPZ7PmXLBYLDAYDud1udjqdcDgc7HK54HQ64XK5UFVVheLiYnI6nU5mrjKZTLnMfKSqqurP4uLiP5g5B4BnK5A5dkR7sVIYX4F0n897DYekEVgID22cQgIAC9SSqgmA2eu1EYCr3ubU/q0GUCKNsSLQiOgLAMmtOLWAmce2d3qaIwFACCE6KVkQRgghOikJAEII0UlJABBCiE5KAoAQQnRSEgCEEKKTkgAghBCdlAQAIYTopCQACCFEJyUBQAQNIupKRGuJ6Fci2k1Ed2r7PySin7TtIBH95HXOQ0S0n4j2EtEFXvsnE9F2Iror8L+JEKFBJoMTwcQF4B5m3k5EMQC2EdFqZp7kOYCIXgBQqr0+BcBkAKcC6ALgayLqy8xubf+ZABYQUTQzVwT6lxEi2EkJQAQNZs5h5u3a63IAvwGoXV6J1EUGJgJYqO26FMAiZq5h5gMA9gMY7Dncc1mv10IILxIARFAioh4AzgDwg9fukQDymHmf9nMGgCNe72fhWMBYBmArgK1aMBFC1CNVQCLoEFE0gKUA7mLmMq+3puDY03+TmHk+gPl+SJ4QHYYEABFUtDWIlwJYwMzLvPabAFwOYKDX4dkAunr9nKntE0K0gFQBiaCh1fG/BeA3Zn6x3tvnAdjDzFle+z4BMJmIwoioJ4A+AH4MTGqFCH1SAhDB5CwAVwP42aur58PMvBJqr5461T/MvJuIFgP4FWoPotu1HkBCiBaQBWGEEKKTkiogIYTopCQACCFEJyUBQAghOikJAEII0UlJABBCiE5KAoAQQnRSEgCEEKKTkgAgdEVE44lobiPvRRDRd0RkbOC9/yOie1t5TwsRrdOmlxCi05IAIPQ2AMD2Rt67HsCy9h7dy8wOAGsATGruWCE6MgkAQhdE1JeIVgO4C8CsRlbumgbgY69z/klEvxPRBgAn1rveVUT0o7Zq2GueUgMR/UtbLWwDES30KjWs0K4vRKclAUAEHBGFAVgM4G4A+QCGAniUiMK9jrEA6MXMB7WfB0KdD+h0ABdBXe3Lc+zJUJ/mz2Lm0wG4AUwjojMBXAGgP4ALAQzySsYv3tcQojOSOlChh78C2AngKIAyZs4lomoA3nX9yQBKvH4eCWA5M1cBABF94vXeGKjTRG9RJxRFBAAbgEQAHzNzNYBqIvrUcwIzu4nIQUQxsmCM6KwkAAg99AfwM4DTAOwiohQA5cxc6XWMHUB4Qyc3gADMZ+aH6uxsfkH4MADVLbyHEB2OVAEJPZQDOAlqINgF4N8AXvE+gJmLARi9qoXWAbhM6xkUA+BvXoevATBBCyQgokQi6g5gI4C/EVG4tsrYxZ4TiCgJQAEzO/3yGwoRAqQEIPTwPoDlUFf4KgawCEBDXUG/AjACwNfMvJ2IPoRadWQDsMVzEDP/SkSPAPiKiAwAnFDXBtisVRXtApAHtdRRqp02GsDn/vjlhAgVsh6A0I226Mt5zFzQyPsDAMxk5qvbcI9oZq4gokiopYibtGCyDMCDzPx7a68tRKiTEoDQhdYTKK6xzB8AtIx6LREZ2zAW4HUiOgVqe8J87ZoWACsk8xednZQAhBCik5JGYCGE6KQkAAghRCclAUAIITopCQBCCNFJSQAQQohOSgKAEEJ0UhIAhBCik/r/DyqlbDRhAnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the computed projected fields\n",
    "far_data = sim_data5[monitor_far.name]\n",
    "\n",
    "# We can compute the theta and phi angles associated with the given reciprocal coordinates\n",
    "coords = far_data.coords_spherical\n",
    "theta = coords[\"theta\"]\n",
    "phi = coords[\"phi\"]\n",
    "\n",
    "# plot\n",
    "Etheta = far_data.Etheta.isel(f=0, r=0)\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, tight_layout=True, figsize=(7, 5), subplot_kw={\"projection\": \"polar\"}\n",
    ")\n",
    "ax.grid(False)\n",
    "# im = ax.pcolormesh(np.squeeze(phi), np.squeeze(theta) * 180 / np.pi, np.abs(Etheta), cmap='RdBu', shading='auto')\n",
    "im = ax.pcolormesh(\n",
    "    np.squeeze(phi),\n",
    "    np.squeeze(theta) * 180 / np.pi,\n",
    "    np.abs(Etheta),\n",
    "    cmap=\"RdBu\",\n",
    "    shading=\"auto\",\n",
    ")\n",
    "fig.colorbar(im, ax=ax)\n",
    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "\n",
    "label_position = ax.get_rlabel_position()\n",
    "_ = ax.text(\n",
    "    np.radians(label_position - 8),\n",
    "    ax.get_rmax() / 1.3,\n",
    "    \"$\\\\theta$ (deg)\",\n",
    "    rotation=label_position,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    ")\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Far Field for a Finite-Sized Structure\n",
    "The above examples are very useful when simulating thin structure with a large extent in the lateral direction, such as a metasurface or metalens. If the structure is small enough, we may instead want to enclose it in a closed surface, which now serves as an equivalent surface in the spirit of the equivalence principle, without having to worry about whether the fields decay at the monitor's edges or not. To learn more, see the [sphere radar cross section](https://www.flexcompute.com/tidy3d/examples/notebooks/Near2FarSphereRCS/) and [plasmonic nanoparticle](https://www.flexcompute.com/tidy3d/examples/notebooks/PlasmonicNanoparticle/) case studies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes <a name=\"notes\"></a>\n",
    "* Since field projections rely on the surface equivalence principle, we have assumed that the tangential near fields recorded on the near field monitor serve as equivalent sources which generate the correct far fields. However, this requires that the field strength decays nearly to zero near the edges of the near-field monitor, which may not always be the case. For example, if we had used a larger aperture compared to the full simulation size in the transverse direction, we may expect a degradation in accuracy of the field projections.\n",
    "Despite this limitation, the field projections are still remarkably accurate in realistic scenarios. For realistic case studies further demonstrating the accuracy of the field projections, see our [metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/) and [zone plate](https://www.flexcompute.com/tidy3d/examples/notebooks/ZonePlateFieldProjection/) case studies.\n",
    "* The field projections make use of the analytical homogeneous medium Green's function, which assumes that the fields are propagating in a homogeneous medium. Therefore, one should use PMLs / absorbers as boundary conditions in the part of the domain where fields are projected. For far field projections in the context of perdiodic boundary conditions, see the [diffraction efficiency example](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingEfficiency/) which demonstrates the use of a [DiffractionMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DiffractionMonitor.html).\n",
    "* Server-side field projections will add to the monetary cost of the simulation. However, typically the far field projections have a very small computation cost compared to the FDTD simulation itself, so the increase in monetary cost should be negligibly small in most cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to perform far-field projections in Tidy3D FDTD.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "far-field projection, near field to far field transformation, Tidy3D, FDTD",
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