{
 "cells": [
  {
   "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 matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# tidy3d imports\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "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(geometry=td.Box(size=[td.inf, thick, td.inf], center=[0, 0, 0]), medium=pec)\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]"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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],\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"near_field\",\n",
    "    colocate=False,\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": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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\">14:17:38 UTC </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-67cbda09-48fb-4c95-86e8-c1cc4ab32b38'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:17:38 UTC\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-67cbda09-48fb-4c95-86e8-c1cc4ab32b38'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4c95-86e8-c1cc4ab32b38'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=234977;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=368781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=234977;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=376048;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=234977;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32m-67cbda09-48f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=234977;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[32mb-4c95-86e8-c1cc4ab32b38'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=126982;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e862d448f4d4beaaf4f10060d52294a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:17:40 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.041</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:17:40 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.041\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:17:41 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:17:41 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "670fb3edd0274aaea84448dc898dc557",
       "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\">14:19:41 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:41 UTC\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\">14:19:45 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:45 UTC\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef49e4b1fbc490b84a3b570e07afc6d",
       "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\">14:19:47 UTC </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;36m14:19:47 UTC\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:19:48 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:48 UTC\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                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4c95-86e8-c1cc4ab32b38'</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                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=182104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=53088;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=182104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=699921;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=182104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34m-67cbda09-48f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=182104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-67cbda09-48fb-4c95-86e8-c1cc4ab32b38\u001b\\\u001b[4;34mb-4c95-86e8-c1cc4ab32b38'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "338dcf69f5a04234a9555785980b3044",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:19:50 UTC </span>loading simulation from data/aperture_1.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:50 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_1.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\", verbose=True)"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "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": [],
   "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",
    "\n",
    "# helper function 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"
   ]
  },
  {
   "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(proj_monitor, sim_size, aperture_height, aperture_width, r_proj):\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(monitor_far, sim_size, height, width, r_proj)"
   ]
  },
  {
   "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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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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(r\"$\\phi$ (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();"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:19:55 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:55 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">9-4af1-90d7-c8d766f2d5d2'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=749952;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=38364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=749952;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=765445;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=749952;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32m-3d5f8ac4-8aa\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=749952;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[32m9-4af1-90d7-c8d766f2d5d2'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=862207;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79bba02885a44786907cf8badea9cbe2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:19:57 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.042</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:57 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.042\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:19:58 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:58 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1eacb299e24e4dc29d29d4b9e8628791",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:22:37 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:37 UTC\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\">14:22:38 UTC </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:38 UTC\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be9ed984cfef4dffa0db71ac2e9748e5",
       "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\">             </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;36m            \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\"><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": "129bafcbd0df4790af226fd05d7a8cf3",
       "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\">14:22:41 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:41 UTC\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\">14:22:43 UTC </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">9-4af1-90d7-c8d766f2d5d2'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:43 UTC\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=495740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=315589;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=495740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=750656;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=495740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34m-3d5f8ac4-8aa\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=495740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3d5f8ac4-8aa9-4af1-90d7-c8d766f2d5d2\u001b\\\u001b[4;34m9-4af1-90d7-c8d766f2d5d2'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4603f483006544aca1d586b5801e24b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:22:46 UTC </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:46 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</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: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True)"
   ]
  },
  {
   "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: 4.55 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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 3.83 s\n",
      "Server-side field projection took 0.01 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
    "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
    "print(f\"Server-side field projection took {server_time:.2f} s\")"
   ]
  },
  {
   "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": [
    {
     "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: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make an updated copy of the projection monitor, this time requesting downsampling\n",
    "monitor_far = monitor_far.copy(\n",
    "    update={\"interval_space\": (4, 1, 3)}\n",
    ")  # 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]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation with downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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-c517e169-54a1-44e3-afdb-f373392f3751'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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-c517e169-54a1-44e3-afdb-f373392f3751'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">1-44e3-afdb-f373392f3751'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=186718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=948441;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=186718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=335684;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=186718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32m-c517e169-54a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=186718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[32m1-44e3-afdb-f373392f3751'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=630107;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e6cfba8653046c1b9563de97e478161",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:22:49 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.041</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:49 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.041\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:22:50 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:22:50 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd61ebb97fb146e9bd1dcb09d566d9f0",
       "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\">14:33:19 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:19 UTC\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\">14:33:24 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:24 UTC\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be42089bdaba4f219547a9660667f97b",
       "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\">14:33:25 UTC </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;36m14:33:25 UTC\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\"><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": "febc568720fa4b4490d669bc08c0e3ca",
       "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\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">14:33:27 UTC </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">1-44e3-afdb-f373392f3751'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:27 UTC\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=775331;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=823855;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=775331;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=83273;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=775331;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34m-c517e169-54a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=775331;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c517e169-54a1-44e3-afdb-f373392f3751\u001b\\\u001b[4;34m1-44e3-afdb-f373392f3751'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2163b2f0f7247f09c256fff21fce572",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:33:30 UTC </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:30 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</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: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True)"
   ]
  },
  {
   "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: 4.55 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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 3.83 s\n",
      "Server-side field projection took 0.01 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
    "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
    "print(f\"Server-side field projection took {server_time:.2f} s\")"
   ]
  },
  {
   "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": [
    {
     "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: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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(\n",
    "    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": {
      "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>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-da3cea9c-09c0-4d24-be95-c028aa7c7c26'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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-da3cea9c-09c0-4d24-be95-c028aa7c7c26'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0-4d24-be95-c028aa7c7c26'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=41925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=976132;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=41925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=548651;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=41925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32m-da3cea9c-09c\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=41925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[32m0-4d24-be95-c028aa7c7c26'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=302815;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7efa1f00ef1a493c81b84c2a1e4c0048",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:33:32 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.028</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:32 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.028\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:33:34 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:34 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22b834451b654422b8d1a7e1694df900",
       "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\">14:33:59 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:33:59 UTC\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\">14:34:05 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:05 UTC\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\">14:34:06 UTC </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:06 UTC\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d72e390cb1f64441a12c541230d71e69",
       "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\">14:34:08 UTC </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;36m14:34:08 UTC\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">0-4d24-be95-c028aa7c7c26'</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                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=886053;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=702605;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=886053;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=582569;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=886053;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34m-da3cea9c-09c\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=886053;https://tidy3d.simulation.cloud/workbench?taskId=fdve-da3cea9c-09c0-4d24-be95-c028aa7c7c26\u001b\\\u001b[4;34m0-4d24-be95-c028aa7c7c26'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8b46018ee874bd598bd30e877d1910f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:34:11 UTC </span>loading simulation from data/aperture_2.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:11 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_2.hdf5                       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</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: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_window = web.run(\n",
    "    sim_window, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\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: 3.02 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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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",
      "text/plain": [
       "<Figure size 1260x380 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 430x380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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(r\"$\\phi$ (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(phi_proj, theta_proj, fields_cartesian.Ex, fields_cartesian.Ey, fields_cartesian.Ez)\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(r\"$\\phi$ (deg)\")\n",
    "_ = ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "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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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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(monitor_far, sim_size, height, width, r_proj_new)\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();"
   ]
  },
  {
   "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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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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\">14:34:13 UTC </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-a3991acf-ea96-46e2-89d0-fa423b2e517d'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:13 UTC\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-a3991acf-ea96-46e2-89d0-fa423b2e517d'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea9</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">6-46e2-89d0-fa423b2e517d'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=6031;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=157937;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=6031;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=992344;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=6031;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32m-a3991acf-ea9\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=6031;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[32m6-46e2-89d0-fa423b2e517d'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=136541;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "996cef53582f4e0abf9d20d731a3c26e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:34:15 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.098</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:15 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.098\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:34:16 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:16 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05fe0fa3301c4b08a88eda9a33ca8737",
       "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\">14:34:25 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:25 UTC\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\">14:34:30 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:30 UTC\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e950c5b45ba04add8edfc6d8573d7fa1",
       "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\">14:34:37 UTC </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;36m14:34:37 UTC\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea9</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">6-46e2-89d0-fa423b2e517d'</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                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=572475;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=149519;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=572475;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=100713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=572475;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34m-a3991acf-ea9\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=572475;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3991acf-ea96-46e2-89d0-fa423b2e517d\u001b\\\u001b[4;34m6-46e2-89d0-fa423b2e517d'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "109f56d5e0e94f6a8b0639f9fd751905",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:34:41 UTC </span>loading simulation from data/aperture_3.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:34:41 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/aperture_3.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data3 = web.run(sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\", verbose=True)"
   ]
  },
  {
   "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": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa3be96ca03946c2a0b1992e08092375",
       "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"
    }
   ],
   "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(sim_data3, monitor_near, monitor_intermediate_proj)\n",
    "t1 = time.perf_counter()\n",
    "proj_time_new = t1 - t0"
   ]
  },
  {
   "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": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1401775cc45243f5af4e56e912b11e8d",
       "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"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection *with approximations on* took 8.05 s\n",
      "Client-side field projection *with approximations off* took 16.00 s\n"
     ]
    }
   ],
   "source": [
    "# let's see how long this took compared to the previous case when the approximations were turned on\n",
    "print(f\"Client-side field projection *with approximations on* took {proj_time_new_approx:.2f} s\")\n",
    "print(f\"Client-side field projection *with approximations off* took {proj_time_new:.2f} s\")"
   ]
  },
  {
   "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)\")"
   ]
  },
  {
   "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\">14:35:05 UTC </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;36m14:35:05 UTC\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: 0.29 %\n",
      "Normalized RMSE for |E|, with far field approximation: 22.73 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAErCAYAAACirgCsAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsfXm4HEW5/ltV3T1zcpKAgYQQQEBAFtkD5JKLAtd4w+LCVdaHXQhuUSGKgrIjIIIYBCSiAl4VWVxwuSxChJ8oUQEXQAFF2QQTdpKcnDnT3VW/P7qr6que7jkz55wk5yT1Ps8kdap7uqt7pr+pr97vez+mlFLw8PDw8PDw8PDw8PDwGHXgq3sAHh4eHh4eHh4eHh4eHuXwDpuHh4eHh4eHh4eHh8cohXfYPDw8PDw8PDw8PDw8Rim8w+bh4eHh4eHh4eHh4TFK4R02Dw8PDw8PDw8PDw+PUQrvsHl4eHh4eHh4eHh4eIxSeIfNw8PDw8PDw8PDw8NjlMI7bB4eHh4eHh4eHh4eHqMU3mHz8PDw8PDw8PDw8PAYpfAOm4eHx6jFZptthuOOO251D6Nr3HvvvWCM4d57711l57z++uvBGMPTTz/d8b4PPvjgkM93xx13YOedd0a9XgdjDK+//vqQj9UJHnjgAcycORO9vb1gjOFPf/oTzjnnHDDGhnS84447Dpttttmg+z399NNgjOH6668f0nnWJIzWe9HpZ+nh4eExVuEdNg8Pj66gJ/v6Va/X8da3vhVz587FkiVLVvfwusKFF16IW2+9dXUPY6Xha1/72kqZXL/yyis49NBD0dPTg6uuugrf+c530NvbO+Ln0YjjGIcccgheffVVfOUrX8F3vvMdbLrppivtfB6jDy+88ALOOecc/OlPf1rdQ/Hw8PBY5QhW9wA8PDzGJs477zxsvvnmaDQa+PWvf42rr74at912Gx599FGMGzduRM7xxBNPgPOVt6504YUX4uCDD8ZBBx200s6xqnD00Ufj8MMPR61WM31f+9rXsP766484S/nAAw9g2bJlOP/88zFr1qwRPXYZ/vGPf+CZZ57BN77xDZx44omm/4wzzsBpp5220s/vkWHTTTdFf38/wjBc5ed+4YUXcO6552KzzTbDzjvv7Gz7xje+ASnlKh+Th4eHx6qCd9g8PDyGhP333x+77bYbAODEE0/Eeuuth8suuww/+clPcMQRR5S+p6+vrysmhjofHu0hhIAQYpWc68UXXwQArLvuuiN2zHbfjarzBUGAIPA/Y2VoNBqIomhEFzw0oz7asDocSA8PD49VCR8S6eHhMSL4r//6LwDAU089BSDLKxk/fjz+8Y9/4IADDsCECRNw5JFHAsgm55/61KewySaboFarYeutt8all14KpZRzzLIcttdffx0nn3yyee+WW26Jiy++uGWFXUqJyy+/HDvssAPq9TomT56M/fbbz+RtMcbQ19eHb3/72ya8k57r+eefxwc/+EFssMEGqNVqeNvb3oZrr7225br/9a9/4aCDDkJvby+mTJmCU045BQMDA0O+j7vuuive//73O3077LADGGN4+OGHTd9NN90Exhgee+wxAK05bJttthn+8pe/4P/9v/9nrm+fffZxjjswMIB58+Zh8uTJ6O3txf/8z//gpZdeaju+ffbZB8ceeywAYPfdd3fu23333YdDDjkEb37zm1Gr1bDJJpvglFNOQX9/v3OMdt+NIo477jjsvffeAIBDDjnEuY6qHLbvfve7mD59Onp6ejBp0iQcfvjheO6559peF5B9t4477jiss846WHfddXHsscd2nJv36quv4tOf/jR22GEHjB8/HhMnTsT++++PP//5z85+Or/xpptuwuc+9zlMnToVvb29eO9739syxn322Qfbb789HnroIcycORM9PT3YfPPNsWDBgtJj3njjjTjjjDOw0UYbYdy4cVi6dCkA4JZbbjH3Y/3118dRRx2F559/3rz/7LPPBuccCxcudI570kknIYoicw1lOWz6s3z22Wfx7ne/G+PHj8dGG22Eq666CgDwyCOP4L/+67/Q29uLTTfdFDfccEPX9+3ee+/F7rvvDgA4/vjjzfdZj6Msh61TG8MYw9y5c3Hrrbdi++23N8/6HXfc4ey3bNkynHzyydhss81Qq9UwZcoUvOtd78If/vAHeHh4eKxs+KVJDw+PEcE//vEPAMB6661n+pIkwezZs7HXXnvh0ksvxbhx46CUwnvf+17cc889OOGEE7DzzjvjzjvvxKmnnornn38eX/nKVyrPsWLFCuy99954/vnn8aEPfQhvfvObcf/99+P000/Hv//9b8yfP9/se8IJJ+D666/H/vvvjxNPPBFJkuC+++7Db3/7W+y22274zne+gxNPPBF77LEHTjrpJADAFltsAQBYsmQJ/uM//sNM5iZPnozbb78dJ5xwApYuXYqTTz4ZANDf3493vvOdePbZZ/GJT3wC06ZNw3e+8x388pe/HPJ9fPvb347vf//75u9XX30Vf/nLX8A5x3333Ycdd9wRQOYcTZ48Gdtuu23pcebPn4+Pf/zjGD9+PD7/+c8DADbYYANnn49//ON405vehLPPPhtPP/005s+fj7lz5+Kmm26qHN/nP/95bL311rjmmmtMWKy+b7fccgtWrFiBj3zkI1hvvfXw+9//HldccQX+9a9/4ZZbbnGOU/bdKMOHPvQhbLTRRrjwwgvxiU98ArvvvnvLdVBccMEFOPPMM3HooYfixBNPxEsvvYQrrrgC73jHO/DHP/6xkhVUSuF973sffv3rX+PDH/4wtt12W/z4xz82zulg+Oc//4lbb70VhxxyCDbffHMsWbIEX//617H33nvjr3/9K6ZNm9YyTsYYPvvZz+LFF1/E/PnzMWvWLPzpT39CT0+P2e+1117DAQccgEMPPRRHHHEEbr75ZnzkIx9BFEX44Ac/6Bzz/PPPRxRF+PSnP42BgQFEUYTrr78exx9/PHbffXdcdNFFWLJkCS6//HL85je/MffjjDPOwM9+9jOccMIJeOSRRzBhwgTceeed+MY3voHzzz8fO+20U9trT9MU+++/P97xjnfgS1/6Er73ve9h7ty56O3txec//3kceeSReP/7348FCxbgmGOOwZ577onNN9+84/u27bbb4rzzzsNZZ52Fk046CW9/+9sBADNnzqz8LLuxMb/+9a/xox/9CB/96EcxYcIEfPWrX8UHPvABPPvss8aeffjDH8YPfvADzJ07F9tttx1eeeUV/PrXv8Zjjz2GXXfdtYNviIeHh8cwoDw8PDy6wHXXXacAqLvvvlu99NJL6rnnnlM33nijWm+99VRPT4/617/+pZRS6thjj1UA1Gmnnea8/9Zbb1UA1Be+8AWn/+CDD1aMMfXkk0+avk033VQde+yx5u/zzz9f9fb2qr/97W/Oe0877TQlhFDPPvusUkqpX/7ylwqA+sQnPtEyfimlaff29jrH1zjhhBPUhhtuqF5++WWn//DDD1frrLOOWrFihVJKqfnz5ysA6uabbzb79PX1qS233FIBUPfcc0/LsQfDLbfcogCov/71r0oppX7605+qWq2m3vve96rDDjvM7Lfjjjuq//mf/zF/68/lqaeeMn1ve9vb1N57791yDr3vrFmznPtxyimnKCGEev3119uOUb//gQcecPr1faG46KKLFGNMPfPMM6av6rtRhXvuuUcBULfccovTf/bZZyv6M/b0008rIYS64IILnP0eeeQRFQSB03/ssceqTTfd1Pytv5df+tKXTF+SJOrtb3+7AqCuu+66tmNsNBoqTVOn76mnnlK1Wk2dd955Ldey0UYbqaVLl5r+m2++WQFQl19+uenbe++9FQD15S9/2fQNDAyonXfeWU2ZMkU1m03nmG95y1ucz6DZbKopU6ao7bffXvX395v+n//85wqAOuuss5x7FEWROvHEE9Vrr72mNtpoI7XbbrupOI6d6yneC/1ZXnjhhabvtddeUz09PYoxpm688UbT//jjjysA6uyzz+76vj3wwAOVn0PVZ9mJjQGgoihy+v785z8rAOqKK64wfeuss4762Mc+1nJuDw8Pj1UBHxLp4eExJMyaNQuTJ0/GJptsgsMPPxzjx4/Hj3/8Y2y00UbOfh/5yEecv2+77TYIIfCJT3zC6f/Upz4FpRRuv/32ynPecsstePvb3443velNePnll81r1qxZSNMUv/rVrwAAP/zhD8EYw9lnn91yjMFk4JVS+OEPf4j3vOc9UEo555k9ezbeeOMNEwZ12223YcMNN8TBBx9s3j9u3DjD2A0Fmj3Q13Lfffdh9913x7ve9S7cd999ALLQvUcffdTsO1ScdNJJzv14+9vfjjRN8cwzzwzpeJQZ6uvrw8svv4yZM2dCKYU//vGPLfsXvxvDxY9+9CNIKXHooYc6n9vUqVOx1VZb4Z577ql872233YYgCJwxCSHw8Y9/vKNz12o1ky+WpileeeUVjB8/HltvvXVp2NwxxxyDCRMmmL8PPvhgbLjhhrjtttuc/YIgwIc+9CHzdxRF+NCHPoQXX3wRDz30kLPvscce63wGDz74IF588UV89KMfdXLPDjzwQGyzzTb4v//7P9O3/fbb49xzz8U3v/lNzJ49Gy+//DK+/e1vd5wjSMVg1l13XWy99dbo7e3FoYceavq33nprrLvuuvjnP/9p+rq9b52gWxsza9YswxIDwI477oiJEyc641x33XXxu9/9Di+88MKQxuTh4eExHPiQSA8PjyHhqquuwlvf+lYEQYANNtgAW2+9dYvAQRAE2HjjjZ2+Z555BtOmTXMmqwBMaF87Z+Hvf/87Hn74YUyePLl0uxan+Mc//oFp06Zh0qRJXV/XSy+9hNdffx3XXHMNrrnmmrbneeaZZ7Dlllu2OIFbb7111+fV2GCDDbDVVlvhvvvuw4c+9CHcd9992HffffGOd7wDH//4x/HPf/4Tjz32GKSUw3bY3vzmNzt/v+lNbwKQheENBc8++yzOOuss/PSnP205xhtvvOH8XfbdGC7+/ve/QymFrbbaqnR7O3GKZ555BhtuuCHGjx/v9Hf6Weqcya997Wt46qmnkKap2UbDhDWKY2SMYcstt2ypozdt2rQWMZa3vvWtALKcsv/4j/8w/TrMkF5T1TVss802+PWvf+30nXrqqbjxxhvx+9//HhdeeCG22267qst1oHNEKdZZZx1svPHGLc/GOuus43w3ur1vnaBbG1N8DoDsWaDj/NKXvoRjjz0Wm2yyCaZPn44DDjgAxxxzDN7ylrcMaYweHh4e3cA7bB4eHkPCHnvsYVQiq0BXz0cCUkq8613vwmc+85nS7XoiO9xzAMBRRx1Vmb+k88hWFvbaay8sXLgQ/f39eOihh3DWWWdh++23x7rrrov77rsPjz32GMaPH49ddtllWOepUpVUBWGGTpCmKd71rnfh1VdfxWc/+1lss8026O3txfPPP4/jjjuuRRRmpL8bQPbZMcZw++23l15b0RkbSVx44YU488wz8cEPfhDnn38+Jk2aBM45Tj755FUmOU/ZtaHgn//8J/7+978DyMRCOkXV96iT79douG+djPPQQw/F29/+dvz4xz/GL37xC1xyySW4+OKL8aMf/Qj777//Khmnh4fH2gvvsHl4eKxSbLrpprj77ruxbNkyZwX88ccfN9ursMUWW2D58uWD1v7aYostcOedd+LVV19ty7KVhUdOnjwZEyZMQJqmg55n0003xaOPPgqllHOsJ554ou37BsPb3/52XHfddbjxxhuRpilmzpwJzjn22msv47DNnDlzUBn/wcI/RxKPPPII/va3v+Hb3/42jjnmGNN/1113rbIxbLHFFlBKYfPNN+/aed90002xcOFCLF++3HHsOv0sf/CDH2DffffFt771Laf/9ddfx/rrr9+yv3aMNJRSePLJJ1sWA1544YWWkgd/+9vfAKBFGbHsmvQ1aBVXjSeeeMJ51qSUOO644zBx4kScfPLJpkZhUbF0pNHpfevmuzwcG9MOG264IT760Y/iox/9KF588UXsuuuuuOCCC7zD5uHhsdLhc9g8PDxWKQ444ACkaYorr7zS6f/KV74Cxljbyc+hhx6KRYsW4c4772zZ9vrrryNJEgDABz7wASilcO6557bsR1fNe3t7W2TbhRD4wAc+gB/+8Id49NFHW95PZe8POOAAvPDCC/jBD35g+lasWFEZStkpdKjjxRdfjB133BHrrLOO6V+4cCEefPDBjsIhy65vZUE7j/T+KqVw+eWXr5LzA8D73/9+CCFw7rnntrCESim88sorle894IADkCQJrr76atOXpimuuOKKjs4thGg55y233OLI51P87//+L5YtW2b+/sEPfoB///vfLd//JEnw9a9/3fzdbDbx9a9/HZMnT8b06dPbjmm33XbDlClTsGDBAqfUxO23347HHnsMBx54oOm77LLLcP/99+Oaa67B+eefj5kzZ+IjH/kIXn755cEvfhjo9L5ph7WT7/NwbEwZ0jRtCemdMmUKpk2bNqwSHh4eHh6dwjNsHh4eqxTvec97sO++++Lzn/88nn76aey00074xS9+gZ/85Cc4+eSTneT/Ik499VT89Kc/xbvf/W4cd9xxmD59Ovr6+vDII4/gBz/4AZ5++mmsv/762HfffXH00Ufjq1/9Kv7+979jv/32g5TS5IPNnTsXADB9+nTcfffduOyyyzBt2jRsvvnmmDFjBr74xS/innvuwYwZMzBnzhxst912ePXVV/GHP/wBd999N1599VUAwJw5c3DllVfimGOOwUMPPYQNN9wQ3/nOd0ol6u+9917su+++OPvss3HOOee0vUdbbrklpk6diieeeMIRvXjHO96Bz372swDQkcM2ffp0XH311fjCF76ALbfcElOmTGlhWkYK22yzDbbYYgt8+tOfxvPPP4+JEyfihz/84ZDz4YaCLbbYAl/4whdw+umn4+mnn8ZBBx2ECRMm4KmnnsKPf/xjnHTSSfj0pz9d+t73vOc9+M///E+cdtppePrpp7HddtvhRz/6UctEvQrvfve7cd555+H444/HzJkz8cgjj+B73/teZY7TpEmTsNdee+H444/HkiVLMH/+fGy55ZaYM2eOs9+0adNw8cUX4+mnn8Zb3/pW3HTTTfjTn/6Ea665ZtCC0WEY4uKLL8bxxx+PvffeG0cccYSR9d9ss81wyimnAAAee+wxnHnmmTjuuOPwnve8B0BW12/nnXfGRz/6Udx8880d3YOhoNP7tsUWW2DdddfFggULMGHCBPT29mLGjBkteXvA8GxMGZYtW4aNN94YBx98MHbaaSeMHz8ed999Nx544AF8+ctfHtb1e3h4eHSEVStK6eHhMdZRJelexLHHHqt6e3tLty1btkydcsopatq0aSoMQ7XVVlupSy65xJGYV6pV1l+/9/TTT1dbbrmliqJIrb/++mrmzJnq0ksvNTLnSmWS7JdcconaZpttVBRFavLkyWr//fdXDz30kNnn8ccfV+94xztUT0+PAuCca8mSJepjH/uY2mSTTVQYhmrq1Knqne98p7rmmmuc8TzzzDPqve99rxo3bpxaf/311Sc/+Ul1xx13tMj6/+xnP1MA1IIFC9reN41DDjlEAVA33XST6Ws2m2rcuHEqiiJHpl2pcln/xYsXqwMPPFBNmDBBATAS/1WfoZaHH6wcQdX7//rXv6pZs2ap8ePHq/XXX1/NmTPHSKQXpeCrvhtl6FTWX+OHP/yh2muvvVRvb6/q7e1V22yzjfrYxz6mnnjiCWcMVApeKaVeeeUVdfTRR6uJEyeqddZZRx199NHqj3/8Y8ey/p/61KfUhhtuqHp6etR//ud/qkWLFqm9997bKa2gr+X73/++Ov3009WUKVNUT0+POvDAA53SB0plsv5ve9vb1IMPPqj23HNPVa/X1aabbqquvPLKju6Pxk033aR22WUXVavV1KRJk9SRRx5pym8kSaJ23313tfHGG7eUc7j88sud72CVrH/ZZ6nHXsSmm26qDjzwwK7vm1JK/eQnP1HbbbedCoLAGUfZZ9mpjQFQKtdPbc/AwIA69dRT1U477aQmTJigent71U477aS+9rWvtbzPw8PDY2WAKTWE7HIPDw+PVYBNNtkEs2fPxje/+c3VPZRh4zOf+Qy+//3v48knn0StVlvdw/FYTdBM6y233OKUgyjDPvvsg5dffrk0NNfDw8PDY+2Bz2Hz8PAYlYjjGK+88kqpYMNYxD333IMzzzzTO2seHh4eHh4eXcHnsHl4eIw63HnnnbjxxhvR39+Pd77znat7OCOCBx54YHUPwcPDw8PDw2MMwjtsHh4eow5f/OIX8eSTT+KCCy7Au971rtU9HA8PDw8PDw+P1Qafw+bh4eHh4eHh4eHh4TFK4XPYPDw8PDw8PDw8PDw8Rim8w+bh4eHh4eHh4eHh4TFK4R02Dw8PDw8PDw8PDw+PUQrvsHl4eHh4eHh4eHh4eIxSeIfNw8PDw8PDw8PDw8NjlMI7bB4eHh4eHh4eHh4eHqMU3mHz8PDw8PDw8PDw8PAYpfAOm4eHh4eHh4eHh4eHxyiFd9g8PDw8PDw8PDw8PDxGKbzD5uHh4eHh4eHh4eHhMUrhHTYPDw8PDw8PDw8PD49RCu+weXh4eHh4eHh4eHh4jFJ4h83Dw8PDw8PDw8PDw2OUwjtsHh4eHh4eHh4eHh4eoxTeYfPw8PDw8PDw8PDw8Bil8A6bh4eHh4eHh4eHh4fHKIV32Dw8PDw8PDw8PDw8PEYpvMPm4eHh4eHh4eHh4eExSuEdNg8PDw8PDw8PDw8Pj1EK77B5eHh4eHh4eHh4eHiMUniHzcPDw8PDw8PDw8PDY5TCO2weHh4eHh4eHh4eHh6jFN5h8/Dw8PDw8PDw8PDwGKXwDpuHh4eHh4eHh4eHh8cohXfYPHD99deDMVb5+u1vf7u6h+jh4eHhwNstDw+PsQhvuzyGgmB1D8Bj9OC8887D5ptv3tK/5ZZbrobReHh4eAwOb7c8PDzGIrzt8ugG3mHzMNh///2x2267re5heHh4eHQMb7c8PDzGIrzt8ugGPiTSoyOcffbZ4Jxj4cKFTv9JJ52EKIrw5z//eTWNzMPDw6MVSilsttlmeN/73teyrdFoYJ111sGHPvSh1TAyDw8Pj/bYZ599KkMmr7/++tU9PI/VAM+weRi88cYbePnll50+xhjWW289nHHGGfjZz36GE044AY888ggmTJiAO++8E9/4xjdw/vnnY6eddlpNo/bw8Fib0c5uHXXUUfjSl76EV199FZMmTTLbf/azn2Hp0qU46qijVvVwPTw8PAC0t12f//znceKJJzrbvvvd7+LOO+/ElClTVuUwPUYJmFJKre5BeKxeXH/99Tj++ONLt9VqNTQaDQDAo48+iunTp+OYY47BJZdcgu233x4bbrghFi1ahCDwvr+Hh8eqQyd2629/+xu23nprXH311fjwhz9str/vfe/Dww8/jH/+859gjK2qIXt4eHh0POeiuP/++7HPPvvg6KOPxre+9a2VPUSPUQg/y/YwuOqqq/DWt77V6RNCmPb222+Pc889F6effjoefvhhvPzyy/jFL37hnTUPD4/VhnZ2661vfStmzJiB733ve8Zhe/XVV3H77bfjM5/5jHfWPDw8VhsGm3NpLF68GAcffDB23nlnfO1rX1tVw/MYZfAzbQ+DPfbYY9AE2FNPPRU33ngjfv/73+PCCy/Edtttt4pG5+Hh4dGKwezWMcccg7lz5+KZZ57BpptuiltuuQVxHOPoo49ehaP08PDwcNHJnCtJEhx66KFI0xQ/+tGPUKvVVtHoPEYbvOiIR1f45z//ib///e8AgEceeWQ1j8bDw8OjPQ4//HCEYYjvfe97ALI8kN122w1bb731ah6Zh4eHR3uceuqpWLRoEW6++WZsvPHGq3s4HqsR3mHz6BhSShx33HGYOHEiPve5z+H73/8+fvSjH63uYXl4eHhUYtKkSTjwwAPxve99D8888wx+85vfeHbNw8Nj1OPGG2/E/Pnzcemll2Lvvfde3cPxWM3wDptHx7jssstw//3345prrsH555+PmTNn4iMf+UiLypGHh4fHaMLRRx+Nv/71rzj11FMhhMDhhx++uofk4eHhUYlHH30UJ554Io466ih88pOfXN3D8RgF8DlsHga33347Hn/88Zb+mTNnYmBgAGeeeSaOO+44vOc97wGQKR3tvPPO+OhHP4qbb755VQ/Xw8PDo63destb3gIAOPDAA7Heeuvhlltuwf777+9lsT08PFY72tkurSL5jne8A9/97ndbtmvb5rH2wDtsHgZnnXVWaf83v/lNfP3rX8f666+P+fPnm/6tttoKF110ET75yU/i5ptvxqGHHrqKRurh4eGRocpuXXfddWZSE0URDjvsMHzta1/z4ZAeHh6jAu1s10svvYS+vj6cdNJJpdu9w7b2wddh8/Dw8PBY43HKKafgW9/6FhYvXoxx48at7uF4eHh4eHh0DJ/D5uHh4eGxRqPRaOC73/0uPvCBD3hnzcPDw8NjzMGHRHp4eHh4rJF48cUXcffdd+MHP/gBXnnlFZ+87+Hh4eExJuEdNg8PDw+PNRJ//etfceSRR2LKlCn46le/ip133nl1D8nDw8PDw6Nr+Bw2Dw8PDw8PDw8PDw+PUQqfw+bh4eHh4eHh4eHh4TFK4R02Dw8PDw8PDw8PDw+PUYq1ModNSokXXngBEyZMAGNsdQ/HYw2CUgrLli3DtGnTwHn79ZBGo4Fms1m5PYoi1Ov1kR6ixxiFt1seKxOd2q7B7BbgbZeHhbdbHisTa5PdWisdthdeeAGbbLLJ6h6GxxqM5557DhtvvHHl9kajgZ4Jk4Ckv3KfqVOn4qmnnhrVBsRj1cHbLY9VgXa2qxO7BXjb5WHh7ZbHqsDaYLfWSodtwoQJAIA//OUJjJ8wAakE0lx7JVUKWoVFSkDCarIMJs9CF4848j8YwG0TPN9JAGa1iTNA6H0YM/tzZvcRzL6XMYDpwSgJpqQ9sdOuGDAdKHNXJBTjTp/K91UKkPnx6FGlylY4ipCFLl6ysEavFYC+Y+CMmSEymdrrUxKQadavlL1WmZjtZl+Zmu3OvoV75dy7Cih9P+i9YRyKi3zAAopnj9Kyvn68ZftdzHesCs1mE0j6EW5/BCDC1h3SGIsf/T6azeaoNR4eqxb6O/X43/6OCRMmWDtFnkH63Dn9sOYglQr6W69gn+tUwTzcEsrsnx2HHLcLm0hRZh8ZtY+s3G4KxoxtEMRmCAYIYkB0m/YzpcC0fZApWG4/MluS9TOZEFtC7EupXZH2oml/J3a33Q1xbC61N/rHgNt+Hpj9FWP2vbk9UgU75dgsY1jJsfPty5YtwxZbb9vWdg1qtwBvuzwc6O/TO77wI7DIrYFI5w4yN160T+V9Uir7E66U3VcqY/SkUua9SipIsr99fJU5ppLK2D6ZStuv7D7OuGQKldsHJSVUmpLrSM0+duzlbef65eDzjzKwApPE9Fykos0Y6RPCvJ9xYfbhxJYybT8ZM20uuJ2DctLPmTUxjIFzeww7x7UGnXFmzpUdH+Q4ZAzMHr/YpyE4Q9Low6/OeP9aYbfWSodNf+i94ydg/ISJiKVCKu2kRVVMWqpAJxwagjhjZrLBmUkaFNw6JYIx47AJztyJyiCOC4CKSQNpF5wy+2PPW/fhwpkwUIctJZNCOkGkE0dz+g4mLfTho/epylHlsI5XNslStg1kEyy9PXUnYc49Im1nIlYFTu6X47Dljw9x2PT2TkM/eK0XTEQt/SptT917rH3Q36nxEyZgwsSJ5nlTStk2YGxZlZOWSrsolZJFKaWyv7P3UtunTL/+G8jsQacBTsJ51hkEt23jk4DYRG6dN8FdG6qPJbg9LnXSHIdNpq7DRhd5UuKwaUeNOmnmfXShyLUfIBM4apNRMUFzkd9gLoyzBZDJGBfl9oYxa5Oo80ZtEHXezPEC1/bbWZa9HnRmu6rsFuBtl4cL/X1i0TgEPb2mP3Oq7DyBOc6WmYQByBa49ZwiTSQ4ddKkngcQR4srqMQeT7G8zRVk/lsvoaAtoWIKUu+jUihYB0zlxk9JaRws6rzpvwFAduCwUUdvOGDC2gzkx2RcgJGpFzft1DpvgNmHKQam8jkeBNnHOnp6/iXBoC0+BzPhh9miEWEZAjshNg4YdfAYAw+y93Li1NHpKHXenHliwakDss+9uF8VRtJuXXTRRfjRj36Exx9/HD09PZg5cyYuvvhibL311l0dpxt40REPj9UIxoRZ5XJeZEXMw8PDYzSh0m552+Xh4TFKMZJ26//9v/+Hj33sY/jtb3+Lu+66C3Ec47//+7/R19e3kka/ljJsGppNV4QlUspdcW7HrAGaDcrbJWE+RfasilVz9tGruWniruyS1V/NCDkrvu1AV1Y1HV4MnUF2H3SXAoD8SyxpmIJSWfgUXLYtJffLCXWoHJQyKwY0PDILjbKrJiJfDsrYx+wdQtTMPpD5gyYDsyquGLcr6IzbFS66cg77uTOOapZN32vuhoPq1XqleDnL2QFEGIIFJQwbG+SL57FWo8hyp+S5o8+mZsUpQ05ZtZRE+KXKhgilEoj1SnQFw6bf0w5FZg3I2TPJzHbbrwwzplIGoZeHpWXkMlutr4mRFXJmw6EYM9fUsuZaxq4r1fL8MpnYVWsaMpnGNoxJppZhS1NrPzoIhQLc0CXKsCltn4UActvAOAfjud3iAZTUIU3SMmh6jDyA0tfBBaCsbTcRADwgtoyER3aIKrsFeNvl0R40zNH0EaasGP5o+yxjlhrWSzmhlCll1XTUFAl3lFJB5u/NjkmYNM2Sxc3SfiVTw6AV2TPNmnUSEjnUMMgiaFjkoCGRQpT2cxISyQptAOBhBFmynXNmWE7KegnBoaQNfRSBjnSwYZAIOJDknx9l3pRl2ySZG2bmKe+XZM5YlmczCEbSbt1xxx3O39dffz2mTJmChx56CO94xzu6HlsnWKsdNpXHPEsSUtQJnLwrknehJxU0lFGQ8J+w4JgFZWE7SeqE+LHUhvPYnKxCWE6Vk1AS8sgKoTWmLbL/GYhTIlwCVpKJoCSTPD2ZS5UqTP7yIVbkvwAkZwXKYdX1RI8xRcJLVamTG+TXIMLQOrsyAURuJNOmdd6UNCGfdCKmlAQYub+dGFXnM+jeeACuEXRQ1ufhkaMYBlm2aJI9m7bfcd7IMxunNrxIO2mpdEMfzTFJ3kcn4MQR0M4YlzRknCHMH2RJnyEOQGqDoJy2CaFR1s9gyuYBt4WTi0acM5rPBgCpa4eNY5bEth2TtrQOm+OkFR02bVc4dy0hnSjRkMg4zo7JOVgYmX4W5HkYKoBSue3W5xcSLLeJCjBxNCxNoHI7DyWJ5c136uJzrbRb5Fo8PIpQhYmWm3Om266jprdTJ811xmD2lYldZDJtaXPb0kRaxyxpOo6W009CHB3nrWR//Xf2vyzpc22ALNoEvX9FqKQT+kjAS56z4nNJc9XK9pHCddJ4wWFTMnUdunwsKogg84VyEXCw3D4rqWy4o2TQxodzdxFfmRxjBpHvI2FNHyQzc0Vu/nHDHpVUXTttK9NuvfHGGwCASZMmDes47bBWO2weHqsbPAjt5ItAteElPTw8PFYnquwW4G2Xh4fH6EQndmvp0qVOf61WQ61Wa3tcKSVOPvlk/Od//ie23377kRlsCdZqh02iXbhehnJ1w+qEeCBbPQ657QtMP8nHlAnQzFZPGWWAZAKWxvkAk8ET34Fyho2EOzoJ5kJkyedA9n+J8qNh45SEAgnVUa2r9UlqQ7BiqUpX8YsCBlWgKpjO/a0IIw3Nfc2OGXAGkS/RhKIGLkhSv2bbZAIkeXIpY+b627Jt3aDL/T3D5tEtKLOm/5akbUMf7bNZZMI18xanLqtmwyCJeIkTZmnf2wkMq8aYefY5Y5BGdERBL59KbtvZxuw/phhY/owzZcMdFRSUZuWYvS9VTw59ljMbSq6jKD5EbaxMM2YNgIqblklLSJuER4IqyLUwbPnfxedb/y0sw6aobeDcYefMb0EQggkdXtQaJeFETNCwb9ZGlKoDeIbNY6iQFfajUgUSblijy7ZlrFlrf6Gd5KkSMoWMm6Y9VFYta5cLkBQZNEdNsritE3Eim8HhPHOypA+wjByvYNUYJ6yZJP1CGOXrMoaNBxFYzqqpNAXPGX8lBUSQM/okSkJxty1y5i2FNKya/hsABLi5Ju5EWLi3Q383+BBCIjuxW8USFGeffTbOOeectsf92Mc+hkcffRS//vWvux5TN1irHTYPj9UNLipy2Lp1FD08PDxWEarsFuBtl4eHx+hEJ3brueeew8SJE03/YOza3Llz8fOf/xy/+tWv2tbeHQl4h60NinWDaI5Vmcx0GasWcgautIhInLFpAJA0DXvG0jjLswLAktjJndAru3DirWXrym0RJBeChZGlgUVoGbYgBAoSpwq5GAnyFV7CwFFBkSRfco+l0vmjWT6MtKv7sdlHmtX9mMR4V9Vqc4UIgNCwZqzAYmbtgNx322dZzoBHJvmVp7G9fpK7glS40t6UbTM3R1avRndTkIqAhZFZqaKQPqzIo0MokjuqKvLWnFqThWczJs+yYd4kFSAhbZqPWvGdp3lrLqum2TbbHwpObALPGbcs/0HbgyxHL9+FvJcxZtg0pRQ6Es4oi0woqaFGoxgceytlxqyVtSmrVpLT4trs2Da5sH9zK8mfRQbk7SC04kdB6IofFf5XVW3GiVBSG1vWAarsFuBtl0dnKAqNaBQFRvT/ZXlrWU5aKwuXppIwbylk/pyqtJxVUzJFmhDmrQtWzZHyT9O2LFp1PbbBmTbGRel+xfw0M16ScwaZOvlpjGtRomq2TR9D5A6OTJoO26aZSppjJ4LAYcycdv55iMDtN/eAiH5IAELnNstyAZKhoBO7NXHiRMdhq4JSCh//+Mfx4x//GPfeey8233zzIY+rU6zVDpueCBSnHZxoiw3mpGUOhXUSgIKTlsRgSSNvN4E83JElDbDEhkSqeCAb00C/Db9JYjMhoAnuIIaDghV+7FmYOWkssA4bq9XBorwoYGkoJTNJ7EUnRP+VTQSzdiJhnLRmKtHIH8o4VRhIdVuafeJUOuFVZeAk9DETJcgdNm4FCkLOUc89tVr+f8BdJy4y71P2sxEBgii7FzyNzecBPnioZCcTnG5DKKso+kra3sMjBy1VRNv6qSqKi5SFQWZt+2zSBRfqpJkCsyTMsgqC/vBWOGnamnKpzEoNFX+ibcYAke+vqAfSKTqpCVl8bmmtSzKBQ1FcRLfT1NgP18HrRB0uNuIAiotMlQXZZEppBdw0BUKbe+EKhrh9DLDCSkraRScqjiTdsG/VpfPWLrTI2y6PbkGFRrK/bTi2/puKklSJkbjCJOVhjZI4Zo6TRhRfHWXIxC6ulCpGFkIeBwt77CgMsgTF99GwxU5gl8WKx7X2hpewT/oesYLzRvdVPM33hQmPpKGPtJ05YLnNZ9YZk1xp0wfGreov69rgV2Mk7dbHPvYx3HDDDfjJT36CCRMmYPHixQCAddZZBz09PcMeaxl8HTYPj9UIEYQQQVTyKk+MHQxXXXUVNttsM9TrdcyYMQO///3v2+5/yy23YJtttkG9XscOO+yA2267zdl+3HHHgTHmvPbbbz9nn1dffRVHHnkkJk6ciHXXXRcnnHACli9fPqTxe3h4jH5U262h2y4PDw+PlYmRtFtXX3013njjDeyzzz7YcMMNzeumm25aSaNfyxk2Ch1+Iwt9lFXT3m0g3DBIzd4E+buLrBpLBvJ2Ayxn0pAMQPZnBfZkswHVzPZXA6SdxHbVljJscFdVGA2hKQuDDCLDqvGkFyyXimY9smXtQvHACKBAuCvCVNpbh1fFUqGZr2qtiFPDsK2IJQaSbIwDiSxfxa8QMBAkmTTk3LBtoeCGQasFHLUgu27DtAnLutVDjig/dsgZIv0ZSdtuYdt0qCQRgaFsG2jtphHCSDJsN910E+bNm4cFCxZgxowZmD9/PmbPno0nnngCU6ZMadn//vvvxxFHHIGLLroI7373u3HDDTfgoIMOwh/+8AdH6Wi//fbDddddZ/4uxnQfeeSR+Pe//22KRx5//PE46aSTcMMNN3R9DR6DoyzX2shjK8q20VBJVIZBxpoJJ+0qVq0zSX8yQD0YbqX8U6nMUqFUlj1bbRjqM11g3krrs1WGRBaHoMVIJBi3IU0O21byPkdURPdxQepRMpdJU61rtJkoFesspJSewzNsHsNEmQAJlf532iU12WRL/TbargprHLyWWuU+JWGQxf1GE/S4HLESmTpMDd02WN3ITurMabn/TDdE2TbXUUuKCKYA+vdCSWVqsygSeaEkjMS/KPvx6wIjabdUR7+FIwvPsHl4rEbwIKp8dYvLLrsMc+bMwfHHH4/tttsOCxYswLhx43DttdeW7n/55Zdjv/32w6mnnoptt90W559/PnbddVdceeWVzn61Wg1Tp041rze96U1m22OPPYY77rgD3/zmNzFjxgzstddeuOKKK3DjjTfihRde6PoaPDw8Rj/a2a2h2C4PDw+PlY2xbrfWaoZNqtYisJRVo7lUbqFmm0sVcAZuGLT8/7hRyqqpxnLLqjX6oAZyJq3R5zBs6UC2v4wTyGa2UprGMVRavhLMhC6OyCE0YxQGEPXsC8iiOlgtj6lNmmA9vdk+zkH0MZqZMAkAkPwHqbhd4YLNW0ukcli1FXGat1PTHkgk+vN2fzNFM9Ey4kQgobDSZsRFuBUaiQKOKGfQeiKButA5bNnKyLiQO+1xoW4LRPnnFUmGhNt2ZD7HAIFWL2EcjOcr1GnTFi9XzK5QV63Kd5vDxioYNtbdak+z2cRDDz2E008/3fRxzjFr1iwsWrSo9D2LFi3CvHnznL7Zs2fj1ltvdfruvfdeTJkyBW9605vwX//1X/jCF76A9dZbzxxj3XXXxW677Wb2nzVrFjjn+N3vfof/+Z//6eo6PIYHas+UsiuTmTR/qwAJzSlNC0yaNEw4QItoV0EzaFRohEIfe5iLpCOPYYhvdIR2hbRLQPRVnD5m8uKEFTvh3EhtO3lz+prKSheMAKrslt7m4dEtHOGRgp3JGDP7d1GUBEChHEB1Tpo9Zmu+WafgXDgsW5kMfhm7NZLohCkq26es4HYnUDI1zJgosm1lpQt4QFgoZj8zZgteK2lZOMFt6RcBZj5LLT4yEhjrdmutdtgo6FfCKBQy6zi4qoPMhj82B2z4Y7M/+z9pgOdt1eyH7MsK8ckVy4zDphp9UHk7WdGPtJH9AMd9/UjjzEGQzQQy1g6bLcZBHTcmOHgeBsmjADzMPtKgHhmHLehpIBiXjRGEDpdc2Pfq8EkZ2geu8AOv/8ocraw9kLhO2vKBbJzLmin6cmdzeSNBfzPfp5maEMr+ZopUtjpvQNFhy8YYBdwIiYyLBHqi7AHT/4+vB46TptvjI9vujQTi3ADEkiHNHbBIAKk+T1jPQiSBbOLD8slRSurigQOlinPd0eQ8DMtVi1R2nk6LOL788stI0xQbbLCB07/BBhvg8ccfLz334sWLS/fXybNAFg75/ve/H5tvvjn+8Y9/4HOf+xz2339/LFq0CEIILF68uCXcMggCTJo0yTmOx8jBLihl/xdFQGzNQ9uXShLiSAQ99N+2vzwMsp2j1ino7662sZyxUgeOE6XYMhGorB+kv82POt1GnTPSNiIdSO02E2pe4ehwYWugydQKhwwnPIpOglDQV8mL16nUnovW51Sp7TNzDxLG3fYTpLU6O0Cl3QKM7fLwoBhKOJsaJGQSKA+r7AScC6SDPKvtnC2rXGidNyaEcV7K3ksdupFCpQNC1BuLNdloW9uSTsbVjbMnpTKfOW0D5DMs9LEuvyOCM6fe22AY63bLO2weHqsRg+WwDaWI40ji8MMPN+0ddtgBO+64I7bYYgvce++9eOc737nKxuHh4TF64HPYPDw8xhrGut1aqx02hdZVzGIYpJXqtzLxQsY25LG5wmkDAIsLrNqy17PzNfpMf7xsBeK+jIWL+xoOw6ZZtaTRNGxa2ixfCeKCg+cMkwgD8Cj7SMNxPYZhC3vr5jhUB4dzW9le6ZDJICVskbuyrBfZaX2nWEoMmJDIFMvycS5txHhjRcZSLWskhnnrbyZY0bThkUn+XilVaRInY8xUtA9ISGTGsGXXOr4WmL51xmVXOL4eoDdn1VbUAozP9x1IpGn3hByp0gyfDY9UiiHKw0IF3BAl05YJzBrbMEKNRBCAB62PIZNZX6dFHNdff30IIbBkyRKnf8mSJZg6dWrpe6ZOndrV/gDwlre8Beuvvz6efPJJvPOd78TUqVPx4osvOvskSYJXX3217XE8hg6eq3V2wuZW7eLUautghdoIhlQcUBAWi0YocGM/aW3FYqi5FgFipuai4JYYo3oYnLmhlkMtyaMYd9k0zS7lB1SMm3qUjHMgX5V1fi8SK2cNumod2MVaRxREpna/4bJwkoRdSc0OhnZ7JzbJXDOHyl+dospuAdZ2eXgUwbL6HqvhvOUS+KWLpYQlK4q7KUlYeUmeeXrcwtd/VQqSFK+nfDGYl26nzgxl50zfKHN0umXjgLFvt7zoiIfHagTjrPIF2CKO+lXlsEVRhOnTp2PhwoWmT0qJhQsXYs899yx9z5577unsDwB33XVX5f4A8K9//QuvvPIKNtxwQ3OM119/HQ899JDZ55e//CWklJgxY0ZnN8HDw2NMoZ3dGspEysPDw2NlY6zbrdHvUq4iDJa3FgkOIXXR6wGHTdO5a2gsAwDIvqVINau2Yqlh2OLlfWguzfLWmktXIF6R5ZXFS/uQ5Axb0kiQ9Od5a80UaZwzUKmEypNKGEkGEZGwDFRPgKCefaRJbxNhbyblL+PEkZeO9GpTEELpleOB7Bp41GMloWn+g2JmYUyRVflGIYdN5629sSLG6znD9kZ/E8saNp8tztm2NFFIYr2SZYteFiGMqApDkLNmSwPbHpdf84R6gNf7s1XmdXtCw7Y1UssCDiTClhiQAr05O6kUR1nGXiRCmHQQwClYW5rP1iXbxgU31+dedPdrKfPmzcOxxx6L3XbbDXvssQfmz5+Pvr4+HH/88QCAY445BhtttBEuuugiAMAnP/lJ7L333vjyl7+MAw88EDfeeCMefPBBXHPNNQCA5cuX49xzz8UHPvABTJ06Ff/4xz/wmc98BltuuSVmz54NANh2222x3377Yc6cOViwYAHiOMbcuXNx+OGHY9q0aV1fg8fgYMwVR+oENIm/23w0zkgyeIHSomyXKXZvimVbJo0zlhfMzvYLTVF7VshnI21dXJvZ8zJQ5o2yeoOLmVDWLD9AS78SmS1hSlkGisrrC2ELzRZ/PTXrFTfNNiUFEQvh1g5zUc2yreoVa8a6z2GrslvAkGyXx9oDbR/SVNn2MFi38uNVsUtunyDMmyTCJGWMXKmwRgEjVRS7DENhsjoSJilh0+jfvIRhc9g4Lswx6D6cGGQ+TGdopI411u2Wd9gKYDQkktkaa1ylbhhknAuMDPQBA7ny47LX8v9fh1z+OgAgXfY6Bl7LHLnmshUYeD0rKBwv7UPclx1vYOkA4tyhiZfHSHMnJulPIFOteuQ6AprWFhGHyJ2OoB4g6Mk+0qhhBUvoexnnVpgkCE19NiS5yIZMrNMhiyGRVilO13EaSNyQyOX5dSxrJHijP3NCX18RY3nuvDUbCZI8JLI5YK8vTaRx2GhSMSMTOsYZRB4SGYTcOGwDeUjk8prA+HHloZfN8ZljGqeBo4pnJrK1AGWEM2MKLA+P5EoCyszEAKMYScImupwMM1a+stNWRKEChx12GF566SWcddZZWLx4MXbeeWfccccdRljk2WefNSIzADBz5kzccMMNOOOMM/C5z30OW221FW699VZTg00IgYcffhjf/va38frrr2PatGn47//+b5x//vkO0/e9730Pc+fOxTvf+U5wzvGBD3wAX/3qV7sev0dnYEq5Ydyw35fsq0SeH+IwaQjGdBRdJjRCilCaryK3T4OUqsVRK4LTkEdyTr34RR2zUJSHQYaCHoOZ309BhEkyR063q0NEzHCrHkfO7fPLiHOm/xfCCYU2zhm3YiQqbtrPgfOsbiYyG6t0rUsSsug6b6mpmdkWTo3NknqbZTkZVRM7XW9NX4cjusKymm0dospu6W0eHkVwztoL33QIxq3ioNNmDAq6v/wZ4IhKa7JltQvbO2TF2mvt9h1taCueMohjW9Yuhk+W9QOuLaD2ohM2qxPHTHBmf786wFi3W95h8/BYjRAjyLABwNy5czF37tzSbffee29L3yGHHIJDDjmkdP+enh7ceeedg55z0qRJvki2h8dahEq7BYyJlWoPD4+1D2Pdbq3VDptU2UsUktq1p01rgLE4BtKMMcpCIvMwyIE+h1kDALn0FSRL38g2v74MA68tt+2cYRtYOoCBpdnxmsubiPuyFdmYhkQm0tZOKixNRSTsSLNqQT1AND5jg9JmakIoAcvI0fpsoh6ZVWGZ14FjSUxC/Qr3i9y3OGffYqlMiGF/0zJsywcSExK5fEWMRt6OBxIM5NeXxKlh25I4rVz50uBBRBg2YRi2sJYdL6oFJsSyv5kahq2ZSiuyML58jY8zBh7lnzVZuWdMmdX9UERGxYEpaUOjMPQVtqrY6bEQT+2xmqBkHj5oV5bLWTXXbnDCwun3OrL65PdKKlI3rU0dnDI2zREdqWDVTD/npqYlI6GPgts2DYmkIeuC2GoGt21vBhUUcZk0E/5I7p5DzOl9U2bajCXWJnEB6HCitBBGRcVFdKRCmjq10opRE2UwAgGEYWOCsm3c7afXra+zLNSR9CseQKnuKpK0y/nwtsujCoIzJCWMGAAnPFLbByPZLpUlhCX5jqVWCp4jS93QbYlWZkhxtz6bRpElK2PNRjpQebhiHSuT2RssnJSyanRbFkJpI6Jo2+5L23D6i7U7W8ZAI0WGYGfGut1aqx02D4/VDRFw44Q6SEf/ao+Hh8faiUq7BXjb5eHhMSox1u3WmHTYfvWrX+GSSy7BQw89hH//+9/48Y9/jIMOOmhYx7SMCpwcNl1AmaVNsDjPYUsapli27O+D7MvFRnTeWt8yDLye9TVeWWraA6/1of+17H0Zw5Ydr9kXI16enacvSdHMV6CaUqFZsggryBgjztCT5371NO2qrUwVuNAFtQV4mJ03qNdMCQEZJ+BNW1Db/K+ZIyXN+hfNCc5ER7J2nEo08hy2ZiINq7W8EZti2U0iNDLQn6DZn+ezDSRIc6YyHeiHTLJx0QRgwMZY8yACDzJ2kIcRwvo4AEAS50Irsc2Dk6lCmpQX5S7eS8CVF2dM2H6pkJN22cNuVs65zUGRzC7Rdyk6UpXr30X+v8cYwUjZLaZkLnWvGSjLAmtBEtOGZqwUZL76LJ22Pa5UlhGTSoFXPTM0AbyETbNMGxyZft3OBEjKWTU7Xst20WeTE9tH8407ER0BY7ZANg+yfF1kQiMtKW+MW/El2pZWLIQxDgS5lH4SG+l/xE2w3E6ppGkZNkkZNpkxbhqDCJA47FkQulLblIVDzsrRMgWaSXPYRrdNi6Z3gnYaJd52rVkYKbsl8hw25rBmmoFRJhWc5qVpcM6Q5pQ/58xE+3DJHBsm8pmcZAqM6/x/axgkkeLvhFUrohNWbLB9hivAUUS7wuGDXdNQr7lKXIRxZn+XCJtV1CIYTESkyMJVMXRd57CNcbs1Jh22vr4+7LTTTvjgBz+I97///SNyTPMlg/3hF5zBzNaTJlgeEokkhmrkQiMrlpq26s/+j5euQHNppiLZXNqHgdey/v7XGmiUOGx9/QmW585Ff6rQr2uvKRjnjUIwGxIZcWb2SZVCL4mBErnEpQg5Qq0e2RhAmouRpI0mAh0eEGvRkeoHWP+eS9gf91RZBbr+ZopmPvYVzdQ4aUkzRTyQHTceSNDU/f3LkTSW5/v0Q8a5I5k7bkXwIDJV6kUQIc2VLZOe8dlYkh5j6KnBX0ZCW2mYaxRwq1YnOMK8vxZwJLmRj6UywjOpUuBkksNG4AkXglXksI1+et6jO4yY3ZIpmCJOGkgYJKyjw8HA8tBHwZlZZMkcJns4O39ixkmTCuZ5oOCFkBRjKxmts5b1hZw7fcaRgxUUoQ6bowbJ7CKa4My5PhOmTsbOGLPXXQirMU4akIVFIld55VZAiDpq2W7ShhIqCeh9qShTKmyINA9MPwtCGy4t61C5XWNSGvuqZJr9jcKkqY3jxkgYJA3LNM6c7hOhHTt3HTPlOGx52BljUEp1JQhRabcAb7vWMIzkfCuzQ9bxSmnaBlEKMhN9RSbq2vHiyizsIOCAruVKJvZMKjsHENapob6HUsXvr61SWyVAMbhz0d5mVr1vKCg6tQBaFl3K9qF9Vc5eWU3cIhyV3pJ7QK+PE7GPzJGzx6D7O/ey5L7R8wwlJHKs260x6bDtv//+2H///Vf3MDw8hg0uOHgJRa+SMbDc49EVvN3yWFNQZbcAb7vWNHi75bGmYKzbrTHpsI006AoII8nzTKYmFIbJxDBsLG1CDmRMmRpoGIYtWZExPs1lfWguyxm2ZSvQ7MveR1m1gaUD6MvFN96IUyxPcpYqlWgQxsxI0CvlJN43pWXYUrNSJO0+/QmSes6kxdKUCpBxApnXSlOpbGHWVJpaafo24X1aDEUqZVi1VCo0SXhkml9TUVwk6c9ZtcZyxDnDloVE5gIoSbM0JJLxBngegpQGkWUHK1alzcoNY3iDsGc9Ob3QEwnUg6y9Ik5Ry1deBhJpWLVA2mtNJRAEJBRJktXqIQqP0NpTFN1IbHusZVASUBKCZd/FlFnbkDohkcowWSqlrBonK7ESXNowSMqq6e89lfR3GTYaEmnDHDlhySh7pt9KZfoFc9kzK/hEw9SZwyZSVs3t7+CZ0UvsitkVdiUtW8513UnpljfRQkyESYOw/UqFYGm+j4gs2yYTWzpFplb6X6ZG9IAybxTUrjlhSVTWXwggD79kOjyTkdIDTugjM++j/bquZje1sKrsVnZsb7s8WqFDIvX8KpHKCZ9zmDfd1iHa3LIrUgLIwx05WMayIWPVNBRTRiVEKlVZ/oOi7PtcxZ5RlgioZpvaHWOwcxdRFbJMGbMq9owyZnRaJykTiXJGrptQaWcuXbhuK0CC0rBJXnEPOQmzBFxmrduQyLFut9YKh21gYAADAwPm76VLl67G0Xh4WLCgfMVHViXGeqw18HbLY7Siym4B3nat7fB2y2O0YqzbrbXCYbvoootw7rnntt1HO+mc2RwQpsjKqkxNkrhqNowMvmo2oHK2Le7Lc6r6m0gbA3k7RjOX7E8aiSmQ3WjYvLXlic1b60ulIzriyvlnf0ScGUEbCUAwvb/NZ6s3JdJcsSRtpkibmkGzcvSyLOm9KDVtlmNIsq6yIiSSrMxmq7SWbdOrOlIqIwaSJtLkqKVJk+StxUhyARIZN53VZd1iXJgctiBqZf9o0UbObZFtIRIEUdZe0UzRH2thlAQ9edHxesAxkI+xkUjU8vemyub+pBxk5Y+sUA8jn40XEnBpv8fajSq7xZTKmBvN9hKJf8FJrimzMteCq6wDyHI6tGQ/E5DcMvplKDJsmrXjDjvm5qW19MEVKKGFvqmISBmTpreh2IeKVfFil35OaZf5Bxnbpu1bSXQBUzJj3HS/yWFLABXa94mkdR+ZWJuqFFhg92FmHyLxT4RJKi0Alfgnsv46z07xgAiRBNnfpj8w90T3Z4IjrrDUYKiyW3pbt7jqqqtwySWXYPHixdhpp51wxRVXYI899ijd9/rrr8fxxx/v9NVqNTQaja7P6zHyqLJbgjMowp4xzsyXLstfsoaryERxCcOkZTlrOdvGrNw/FRcBSN5aYRzleWikzVhlTlYZg1QU0TDlRcgxy45H9+0UZbllRVaNMmh0DlbGpEll+8vYuSpmju5TBfeay1nIohgJjYqy6bbl924oOWwjbbdWNdYKh+3000/HvHnzzN9Lly7FJptsYpTFGAnXcSYBaZr9KCMPbcmdNxk3M1UwACqJbShfnJj/ddhh0kiMs5T02xprTanQn1rHrEGctCqHTU9sqHKkYCRkT6nSSRetxyZTCZVSTnxka3mkgz705fXWJHHelExLhUdoWFACmBpFWnxEBJF1BtPQqkSm0rT7myn6889moBaYEM44lYhTXVtOIs7vWRooEv7JrBgkcdgY50ONiAQXrLTOVbvaVx5rB6rsViZ8oUzosmD261cMKzRSEpKZMCKmmOPIKdX+u0bVJqtVKNEi+kEds6GEOGq4Iettxmmcraod7AyqtDYZUB4GTh0wpdyQSOLIKRJCaVQlc+e6uA8rOHImhFK5xzSgtd2K10KcsGxcwu0zHjQJj+TChACl+e9MVw5bhd3S27rBTTfdhHnz5mHBggWYMWMG5s+fj9mzZ+OJJ57AlClTSt8zceJEPPHEE+bvbie+HisPlXYLruhXKpWtvZbatoQC1yq25Lhc/xFw41wIThQlhetE0O9hpcNUEo5HHQcajse462iUhfXR6yv+X2wHBQdhMAeEzq0S0k4LbXcB3TpjdD5mHDbi4ClF9ilz4lR1+GSV81Yl0kLve5njS+918ThFAbmuQiJH0G6tDox+DnAEUKvVMHHiROfl4TEaoFd8yl4eaze83fIYrWhnt7q1XZdddhnmzJmD448/Httttx0WLFiAcePG4dprr618D2MMU6dONa8NNthguJfkMULwdstjtGIk7RaQRQZsttlmqNfrmDFjBn7/+9+vhFFbjEmGbfny5XjyySfN30899RT+9Kc/YdKkSXjzm9/c8XFY/uJwJaS5XqKlYSuFJHSdMA6ZOiIeAJDGiZHOV6mCbNraYIqwamXMWKpAhEbc8eq/u10IYHSlSXAwKmtarLHBh+fDD4WmLqJdbRC6TRVksRVJ5FfSpfpl2rrq5LTJfY9TUrZAguyjTHhkEAgbBklXsbsMjeSCm3p5xX6PNQsjZbe0LdLMDeehFRdBtkINACol4YxcGYnsVCkT+SfAHGbFDUPM+0BCH5nLpDk10TR5k28XnErtF+X4LWPHaFRAIRTRXFTVrRikiCFNJC8GH5Qn05c8i8RGUiKHVTJjyobSKwllwuqrmbfSEEo66CLz58ZwZbsIwqqRMEjDttHwSMbtoZUyr05RZbf0tk7RbDbx0EMP4fTTT7fv5xyzZs3CokWLKt+3fPlybLrpppBSYtddd8WFF16It73tbR2f16NzjJTdCniewkEYEpPuIJl99BwxkryLHEeAZaIiendu2/Sb104AIzv24OxZVnu1lT2LAm4YMpdV46UMm6012RnbVoa2rJqqmt+01qJNiDhcy/6JFl0qYeZkeYhlkYUbDJRVA8o/D9pPpf/pvQay+9ZSnaHduUfIbgFDiwwYLsbkrPDBBx/ELrvsgl122QUAMG/ePOyyyy4466yzVvPIPDy6g2fY1h54u+WxpqCTleqlS5c6LypEofHyyy8jTdMWhmyDDTbA4sWLS8+99dZb49prr8VPfvITfPe734WUEjNnzsS//vWvkb9QD2+3PNYYrO7IgOFiTDJs++yzT1ergVXQEp+MkXhkhtJVU0YLppLEcJowroqCHYOAJvNbyX5lVq2z/DS6v/3ftsnqDWkzEqvLBDOsGhPc5H5xUZCKLgMjS/e6i+SxcF4sSs1tW2jRD07ilAW4lqEmIiFUUMRl0VInd42VjLOsrwqqgmHLEu9tW69YKWUvnYqtKAUn2d8m9nc+FgAIBEdQpk7kGbY1DiNltzTzb/KhaF4ZrC1RHDapn7VKZQOteUtlOWRZ1AHMcSiTRlkzUxib2CZDPMsUTKVm/FXiHoNeusMscTAmnb+B/HmlLJtO4ysUh7bPsmrpK5zV+ctlIXNbyjhhIQtRGoZJGwbzpv8uA2X68/8tqyYAUZLPxjjJzc0YjG5+vSrtFmBsl85b0jj77LNxzjnndHGWcuy5557Yc889zd8zZ87Etttui69//es4//zzh318DxcjZrfgFs4uFtHW0yfOmYkS0EJJQjAobtkdR2K/4lyGMXNYG9vPBXdYtSiwcxfdDpx+7u4jbFszZFFQwbBV5LN1G5FUZNb0/4O1BxJp2s2UtBNpWLhmIg2LRxk43aZsm0ylw7xp0Dy4TtDKqrX2VxXLHko0Vyd2q6hqWqvVUKvVnL6hRgYMF2PSYRspMGS/X4ITpTKQiUOe3G9QUvdLpYOrTeiQRC6YaUecGXXHiFt1x6jNl5DWYevJv1x1zsx7egRDT358EQkEPYFpizCfWISB67yFucqZdpyEGDS8j8M6hpy5hksbtCjgaAT5PgE3zlsQCqS5YyaCCFK3C2GQWoCEgnGRjS9/r67Jph09HkRmO01sBahyU7mRTAuGxjhvUJA6lEwqpNyGlXEyQWJmUtSdEeEV4+kmkdZj7UImWJGYsDcmU1OTTRInTTFre1JZHl5EQZ92KgDiiIcw1zEzdgAkPJA4KKXOh5JO2N9gjprKZlnZvkC5Y8K4uTIG17HTz7KC66TpxTCpWtXXJFpDKIvILj2fcAJk0U8Zh1dwDs5zG1d0YKkCMfnNMY54ShYJqdhJ5YCITKd2XkVg22RhSYLZsG+VH74b0ZE2k01tu5577jknf6k46QGA9ddfH0IILFmyxOlfsmQJpk6d2tFYwjDELrvs4oTteYw+CM6ckMhUKtuGtT9K2e8WFcdgRvio/HtXVGWkIXXUMaNhjbpdC3ipYxYJXuq8RQEvdfAEZwj1ojVd1CbzJdpvxttlHTYpqf0iwmikP5ZFx8y2qUM2QGrn6pq6drvddyApP54iKuDtFCurQJ0021f+mQHDrMPWgd3qZKGpXWTA448/3vF4usVa7bB5eKxuRAFHWLLiw8ZATRAPD4+1E1V2C7C2qxPBiSiKMH36dCxcuBAHHXQQAEBKiYULF2Lu3LkdjSVNUzzyyCM44IADOr8ADw+PtQ6d2K1OFppWF9Zqh02HEApWoKxjGrpjhUbKwIQwIYYm1JCTsMNIQOS1vmg7ilPDhqWKW0ERqQzzJpi75KlXZkJGWTWO8YFl2KIwP35viKCefbxhPUDQk7NRUYignq/41iNAhydSpq1EeIQzlzzSK0IhZwj1WCJh2LZxkcCKfCxBKBDWsnaaSsh0HIBWdtKERzYFVNAaHunUWQtCiFpPdh2BZdhElPcJbuqw8YBbxeuq8ARycamCERehK8/ZqrsVIwkCG84JlYdEiqjl3rVDVViEZ9g8KqHZFhK6rZ8LwTJmDchWhLVd4VxBkkBH/Z12nmnACQ13BEU028YZmK4TRiIQDOsH2FIojv1MXSatql0IeTT/V7BH5phUXKPi0ZGKCgiRZ1mhJTySMmx0ZZtaZHqazB7q8FPCPDJl7rFw7iOHyJk3LmwIZXYfc5snKMPm/haVsZLKLlVXh0fm/ZKIjCilnPCpTtAunKtb2zVv3jwce+yx2G233bDHHntg/vz56OvrM7XWjjnmGGy00Ua46KKLAADnnXce/uM//gNbbrklXn/9dVxyySV45plncOKJJ3Z1Xo9Vj6AQEkmhw/GE4Dbahfxuq0KfRlkkDWXYimGKNYclE7adz10o2xZVtEPOEQpm2vq5DgQ38yEbicCcPnrZxRqX7UCZtOzv7P9U2nSOmDzHMe1PFWIpW9qUbSu2gYxVo6xbM0lNe6AkbDKVhZIBTkmncvsyWF26qjlbJ0It7Y5VhLZbnSw0jURkwFCwVjtsHh6rG7WAla748MA7bB4eHqMTVXYL6N52HXbYYXjppZdw1llnYfHixdh5551xxx13mHCjZ599FpwsIr722muYM2cOFi9ejDe96U2YPn067r//fmy33XZDvyAPD481HiNlt0YiMmAoWKsdtpBnL8FIsr2TT0Bl/VW5qAgXTk4YAPAoQNCTs1h9AyZ/LKwHSHLWq55KpHkR7VRJ6GUjWji7zt0VCb0aExXy1sbnX8CeSKA2MaNvw94QUW9o2mFvPWuPq2fMGvJ8tjwPjGmmLQgh9Qp2IYfNynXDri4JjlDYFahxOYPYEwWoRXnx8JqATPLciZQWX5xgjs24QNLsz8cVEXl+l4Wj+WqaWdOsWtAzHkHO6oW1AEFo8+Z0f7baRoRRBlmlkUqZBOhUwuSw0dV6JiK7HM+7e6SqYqrTIaweeawlyHPAjFAF40T8QhhREEaSthRjKJPDcVmichERJ98qoblXSZZnBWQMGs1X09tphEKVRH0ZiEAG426umm4zJW2/fk92seYcCoKIBilSMsWW6EgVKTCb75tKUh6BMHD6b3NKcwNVaUFxRphKXmAqy9sBhM7NhSrNc6OCM5WgNjxnXxXjhmVVIDkw5NUp2uWCDMV2zZ07t3Kic++99zp/f+UrX8FXvvKVrs/hsXrBS1gSyuqWCYnQKRctT0SPSd9XlZ9m+0Vpftq4SJQyaT2hMOxYKLhh50LOzLyntW2ZtayPW6EmksdKy6Vk21ouL7sHJbZHQjkRANqWSVVk0jTDJivaCgM5axZLZdi0/qZl0jR7tqKZoplktqSZUjYurWTbaJ6b/gFqJ0pC89bKUJyzCc66sjcjabcGiwxYGVirHTYPj9WNSAgTluFAdKc26eHh4bGqUGm3AG+7PDw8RiVG0m4NFhmwMrBWO2zaW6eyrCy1rJqTgwGQXDVBJN0FeJjdRvo/z3PCgp4QYZyvOMQSURya4/XqcTQSRDmbRhm2YuHsSMdzM6sG2SO4yQ8LeyPUJmasU22dGmrrZGxbbWIPwnGahYoQ9GT9LKoTho3ksA1S/JnmaIQkJrwnEujJGbbxtQD9OZvYTKwELM0HyZQcs1jhpNkDMZAxbDJpQiaZSmR1Dltk2DbDsIUCUS3P26tZVi0IOaJ8XOPIGGtOTLpVJSou5BliACQHhjMTc58VY8zunxIhukEVyzcSBcg91kwwpVyWRVmbxSkzBWVKe1TJ9wP2WWYMbn5aQto508NSm2PF0mZ5v1MEWjPl0uZmAW6blsIwZT44oJ8lxgGu87oEYc8iwxAqzh3GkcLketC8NQmTD5JIl23T2y2z7q5ga9B8kmIuCl1Rt0Vz3Xw2k9fCrFqeYMotyMtypd8wtAXGZUE9sgxE6VcZlUhh7HCW92IPQcuXdIJ20QnednlUociuVbIdJftXHYfmN5UrOrqsGp2vRCXtOokaqgUcNc2eCY560Mqk1YQwbUGefWpXKctuGTY3Z6szhq08xzYlz3JaYuNo3tpAYhm2RiIRp5nNHUhtXlpvPndqpNKwbT2RcJg3oy6ZCodt0+2qAt3673ZoV/6A2sduMdJ2q11kwMrAWu+wBbloBiN11VBVB4fAysdzE06oHaG00USahx3K3jqUljxNqfNn66TxSCDS4ZGJNA4bYB84mrAecg6Re29BT2DERaKCwxZNyMMgJ/YinJgJfUQTehGMy/p5Ty9YLtwB47BZGeii+IhJ6AUjIZHMGLRxocB44qQ102wsiVR4gxxHhwpwYWu1JaFAEoXmHqiSEgrZfdOy/oGpTK/FRWgYZFQLEOVlDaJaYJy0nigwycXUSHPOSpN+U2kNpq5XpK9J7x9LhVCHHQXdKQpFwiY7O/B12DyqoO0SDZHTzhIArkMGOTM/6nQRgn7NmVJgMs7+kG64oxEPkQlYGpt+li+mOPunMVSS7WNKciSxfX6lbHmWzRjM4hc3bcWFFUIKIrOgpFQExq0dVchDudPEOmoVNlsqOCGResElJg6Z6Ssk6VsJfOUk+5eBCgsIxhxxAjOx4wxN48iBOG8MOsVCcGX7yeSWsQBC19gcTO6fcaj8PKksL2XQhdaIQaXd0hfk4VGAKCykdDtBrnLSdHsoTlpPaBdvtRx/LbCOWY04aXSfKGAVzyxdoMn6OBkvDTXnrEy4qBVlokf0+aWiSVWh3onMxO0AV5ikQRyvRiIxkNo2ANQSibqw7+uPMhve37SOWX8zxYDIHbaAO86bdq6Sivpw+u8iqhy24ndGcN7yvWqHsW631mqHzcNjdSMkLB+F8rL+Hh4eoxRVdgvwtsvDw2N0YqzbrbXaYdOS9FzZlWJGVpYrw02o9H0QmZVgQeTyg1zkQ6WSMGwkHFAwI/EfNBIk9Zxhi1PU8v2K+2tGToQCPH9vJtmvGbYQYS40UpvYg3BiFnQZTRiHaELGsAW9dfCerJ9FdbB61q+ZNuXIY9twGgYGrssNcJiVpnrAMaATd0OBuJ6zZIVVFL3S8jpnhhELIoGmFiZppkjy0FGZSFvslsjE0mLYnDFwXawyV/fJygcEeZub8MgJ40JMyJm/8bXAtKmUL5XmLa50mZBIIjTCAcSM7pOvZHW5Wh2QcFwKLzri0RZK2Yx8TotSM8t6MV6qcM9khdx+kT1Lm6Q/Y89Y2gRytk01G5Bx3k6ahmFD/r+Km25IZAV0qDlIqDkLQyuEFNWB3MayKAUoi03FNSqYpqJ8PZA9xzosKElh2DS9shynyqw20yR9KqFdDFmnMNEQgrslUErYtnrAkRghJBsSGXKGJLe5gWDIF7Gz4uVa44gBDFpwxoZg0Z8uHQap6L2AjRZQKrsmWfV7V4IquwV42+VRDcqQJG1CItuxb+1YNSMuIqwAWhRwEmHjhj7WAsuwjcvZtjoJg6wHlp3T0VhZ27LigWAkRYSwfyYiybLgWXi5FV9iJKzdYLAyJ8jmaTa20goxSXASEqkQE7YtSW1EQW7mEAmOJMz6BxJp7B9l2lbE3GzX1xwF3IRH0nYzkVjBsnb2ObUW4C6ybYMxrWVCI/T/bjDW7dbodyk9PNZgaEWqstdQcNVVV2GzzTZDvV7HjBkz8Pvf/77t/rfccgu22WYb1Ot17LDDDrjtttvMtjiO8dnPfhY77LADent7MW3aNBxzzDF44YUXnGNsttlmYIw5ry9+8YtDGr+Hh8foRzu7NVTb5eHh4bEyMdbt1lrNsAkZQ8i4JXfDWfUoWwEheWssCE2RZx7lOWO9iVlRdvLWBDfMmIiEYdXiRoK0N1uVkE0JmZavFGtGjgmGMGeJRCQMqxb0WPn+aEKvYfmiCeMQ5WxbMHEdsHGZ0Afr6c1WrwGwMFu1VoKIjhRig50CsLlISk0I1IJsvOOkQJyv4sgeK75BV0iigOP1Fdlx+8PU5JwlsUSqE1RTCZm3ixKwVPZV578Zpo0IsESRzacbFwmsk5dZmFAPnNU2I9MrrDRvceXGiA8ogOVVibmCs8Qu85vTbLfsXgJatL3Y3y1uuukmzJs3DwsWLMCMGTMwf/58zJ49G0888QSmTJnSsv/999+PI444AhdddBHe/e5344YbbsBBBx2EP/zhD9h+++2xYsUK/OEPf8CZZ56JnXbaCa+99ho++clP4r3vfS8efPBB51jnnXce5syZY/6eMGFC8XQeI4XcLmk7pWRixTeAXM8fYKxgR4isfqU0fwWrpuKBrHugH6rZyA6TxFADWRuEYTNMm0wtw5amrka3BucmHxhBZNg2FUTWNiVN0+YgeR+M23xjHpjjU7EoCW5zUGFZpVTB5HrE0q4sr4iz8RYT8xPNtklbvFaSnDAKwYiEObO5saFgrkR4bm8GAioXzhHlEQMJWQ0OVSZIAuQr+iQ3UZsPs+BeMB20KDjNx1Xkvih0KTpSYbf0Ng+PwRAQ4a5OWBb6vsFk+muEVaMMWz0QJietJxROrhpl2Oq6FBN5BiNBGTaGUAdZcQZdwisTa4ptG3BtrLLiS0VRu0HLnTBeWq4DjEOJXPCOBxA6QooL1HNhtkQxJEIzbHDyd3U7EsxcdyOn5mrCsmoDgUQjye9LnJooq0aSms+jv2nbK0i7mTBTTqmZpCS3TnUlQDKUYtnOsca43VqrHTakMZDG1Spo+oHT0OE6QkBpxcjQTiz0+0TSRChdR03/ryckIgyQ1LPJUdhMkcbWwdOhkJLMBrhgpg6JiAREblxEPTQ138JxPSYsM+ytWyettw7Rm02g2biJ4Hmbj5towiOViOz/xBCY8zOqeqRsTbiAYZzM9qe1ybL3WOqaGtWeKPvaLW/EWNHM2v3NFM2cVpepVZVU0obxZOfWDpsVG9F9EVGAzNQgs2P3hMKGRNYD48jVA2GMTkhERzhz808VmeToyVrmr+YPeKqQ5s14EONTRFVMtRzCas9ll12GOXPmmDogCxYswP/93//h2muvxWmnnday/+WXX4799tsPp556KgDg/PPPx1133YUrr7wSCxYswDrrrIO77rrLec+VV16JPfbYA88++yze/OY3m/4JEyZg6tSpXY/ZYwgoLiJJCTASckN+1FnZhEAm1rmhgiJp00wsWBpD5XURHSet2TBOmmo2rJM20G8mKlL30XDwipBIxjl4FJi2WQir1QEdnqntKzLngpv3UrXeBFBamMQ9l34iaUgzFR3Jwn6sowZkjptJxk+lWYiKSWhPMeyboky5TjDmOGa6vSJmZuI4LhSoSRuOpe1TKqwASapARA5I6JV21OE6bXSIaYnzNhS0ywUZiu3yWPNRNlGmohTtnDbqpGXPlLBtLXpWofrYEwnrjBEBknGh7achkZFwnbTIhDHbdsAZeGpDwPVCF0tsGLiz+EXSbFiZqB0RZKK2klHhN+4uphuRJS6Mwwaa0iIsmRCKCGGuuluLQmP7mqRWW8CVWXDWtqYW2NDtFXFKhFbgtOlno8MjBWdYoduMmRpvgjM0cwViIZXjvFWhylHrpJYuxVi3W6N/hB4eazBoaYniqxs0m0089NBDmDVrlunjnGPWrFlYtGhR6XsWLVrk7A8As2fPrtwfAN544w0wxrDuuus6/V/84hex3nrrYZdddsEll1yCJEnKD+Dh4THm0c5ueVl/Dw+P0YixbreGxbDFcYzFixdjxYoVmDx5MiZNmjRS41olYMkAWNIAJEn+pOGRNCmUgguzEqyCuIVh4zJ1bqxeJeGCm1ptaaOJIM7OI+MYspm3U1m6Gs04NzL2PCJ13uqRYdWCeoSwNxMPEfUIQV57jfX0go+bYNv1XtPWCfxKy2bzwKzWFOsZ6e9zJmObr8QoZlamsqT1PFST2zIEgoEkBtsE1Z5QoD+2yar9+T0YSGSlBKyGs3JNVoM0qxYF3Ej2jiPhkT2RQD1fnRsX0pBI7sj0lkHXKwIyJk1qgo1ZkYFuQyKjgFUwbNkBly5d6vTXajXUaq2lA15++WWkadpStHGDDTbA448/XnruxYsXl+6/ePHi0v0bjQY++9nP4ogjjsDEiRNN/yc+8QnsuuuumDRpEu6//36cfvrp+Pe//43LLrus9DirG2PdbgG5lH8u0+zUZCvs47BxhFWjUQRmJZisFKtGn2HSZLMB1d+X9Tcblm0baEA29T4JZG7P0mbOsElp7FoVeBTYqIMoBA+z8wsSBsmkdFYWFbchlMgjA9qVYNGQyrJtqYRhzQaItLVm2lbEEgP5KjCtRdRMJJGtloOG80QBd8LB6ap/GBPhptRKZ9fysKM4FKgHeei54sh3Qcit1LdQNjSLhqxrM1Rk2mgdJw2Vy4J3w7hV2S3A2i6PkcdYt11ZWJz99qWG3WkvPpJNqO3vfTeS/fR3flwonDJENAxSs2eUVaPtgDPwJAsNZ3EMxJntYylh2NJma0gkKXmiYhI6Lm2YuOqgRiUjYneMC1OKiQk7H2VhzTJsQehETukasSKIwPN5XxDWDLMVcoWQW+YNyMKwraAKx4q8HiZnDDwXF+GcgceWSaPhkRoBZxCJThtxP/80vweiw/BI+53onm8a63ar6ytetmwZrr76auy9996YOHEiNttsM2y77baYPHkyNt10U8yZMwcPPPDAyhirh8cah8FWezbZZBOss8465nXRRRetlnHGcYxDDz0USilcffXVzrZ58+Zhn332wY477ogPf/jD+PKXv4wrrrgCAwMDq2WsZfB2y8Nj5DDWV6rHErzt8vAYGYx1u9UVw3bZZZfhggsuwBZbbIH3vOc9+NznPodp06ahp6cHr776Kh599FHcd999+O///m/MmDEDV1xxBbbaaquVNfZhg6XNLOaYFB+tWpXONlL56XylIwjtioi0uRbaEw65MKyaiEKIep6830yQNPLV7FSa1WlVITjCBDe5cDwMIHImSdQjBPWaaYucfWG1Ophm0uq9WU4IAF7vzZg1ACzqgcrFRsxKdSFOWreZssn+gmcruwAQkAWR8VFgV12YzWELiHxuPRBo5CvX4+uBWYXpb6YYSLIVoGbaGcNWlHelq2000dhJOi5I+RoRAHI8ToQCJMn1YAzQtBpjgDLiDoBeS2p2m8NWUcgxzfuee+45h80qY9cAYP3114cQAkuWLHH6lyxZUplbNnXq1I72187aM888g1/+8pfOeMowY8YMJEmCp59+GltvvXXbfVcF1jS7BaCVTTIS/4VtVIiD5ubqnN2kaXPYkgFIyqQ1VmSH6O+zrFp/n2HV0kYTSX9uz2LXnum+dvYMcHPYRBiYaAElJUQ+dg6YnGFwYfIyWNI0YkmQic11LdwbKrpBi8qawthSWTlr83+KvnzVeHkjcRg2vU/RTpWB5thEQbl96okEevOV/jhVGBdati3O89lSZQUSlOKGowhhWX/N8itu2baiUr+V8ndzg7tFld0CrO3yGD7WSNtF0KnYiODciajR370qcZHeKHAKXo8jbJt+vsaFwj6bgqFeyrDxTJgOABsYsKxaMmBZtdi2VTyQ5fPCii/JZqO81EkSd1T2RINxKzTCgpCUQIks2xaENjKh1gNuxOQiG0UV1IEgj2QIY8O2iSCEyHOhaXSUtiWMcZPbLzj5bGLLpHUr3JFFK2h7Mfg9GC7Gut3qymF74IEH8Ktf/Qpve9vbSrfvscce+OAHP4gFCxbguuuuw3333TeqjQdLmmBJgQUgDltpOCTyBydX3xkUQQQRZA8wDxtmQiLjBGFT116LSa0295ylgiVRaJxAHgVGnZLV6vZhjequk1az/SrMa65Ryjyn0VUQZYprgH1Sc9CQSB2GU/zNZ0yQfbJrCTnDQK4kWQs4BpJsn4EwRaNuw4toqNFgif3U0DuGnEyIdDvk3BjvkDMEpMYKVW0zIZEVz61SQAqb2K+UHQPLnbe4y5BI6hwW+wFg4sSJgzpIABBFEaZPn46FCxfioIMOAgBIKbFw4ULMnTu39D177rknFi5ciJNPPtn03XXXXdhzzz3N39pZ+/vf/4577rkH66233qBj+dOf/gTOeaky5erAGme3iqIjilnlxKKDVBbqXaEAqYhjJvv7oBq589boM45c2mgi7us3be2klTts8aAOGw9DCB0mHgUQJkw8QZi/l/5IcS6gcturojpYpBUpa+5Cm27TkEAQxVepzLPaSKSpO6RDIvviFMsb2ViWNxKTPN8fp8ROWWGSdg5bjdgnE7JN6kSNrwdoRtlxxteskzZOKkhlBZ10GGRNAHUzybGh2cZAS2WEmBRzzTgVUNKfTDf11zSq7Jbe5jEyWNNsF4XgbNDfdh325oQWdxAGSeun0dDHcSHHePMMWsesJmxIZD1gCLWTkjTA4mzhisV5Cg0KTtrACkgdPt7os86ZdtyoOFPctGGQSbNUTbyTOaATEhlGmQOHbH4HItxk5oY9vaatghgq1LY6BnQYelBHPZ8b6nvNmDJzG84kOBt5p0Z/B7LpcGu4LMVwWbCxbre6cti+//3vd7RfrVbDhz/84SENyMNjbQItpkuRDMEwzZs3D8ceeyx222037LHHHpg/fz76+vqMauQxxxyDjTbayIRVfvKTn8Tee++NL3/5yzjwwANx44034sEHH8Q111wDIHPWDj74YPzhD3/Az3/+c6RpavLbJk2ahCiKsGjRIvzud7/DvvvuiwkTJmDRokU45ZRTcNRRR+FNb3rTUG/LiMLbLQ+PkUWV3QKGZrs8yuFtl4fHyGGs2621WtafpU2bcF8GKV0KiYREsqCwHwWRm2ZJ04TzqDACi3VSfWwSTZWUpQwbI1QPExw8X0UBF2BhyYoKZdVIm0V1IE84pdQ4SCKqSVTlgRUbaanDpkU5FDS5FHDm1P8xbQjzYISCmZXompCIQ53sz01/LBXiklpHejUbaF1xKYZECs6MDLZgMHXVKKuWPbC2rd/ryvozh9o3Hwkn5ydsG0WzglGogiici/Z3i8MOOwwvvfQSzjrrLCxevBg777wz7rjjDiMs8uyzz4KTRN2ZM2fihhtuwBlnnIHPfe5z2GqrrXDrrbdi++23BwA8//zz+OlPfwoA2HnnnZ1z3XPPPdhnn31Qq9Vw44034pxzzsHAwAA233xznHLKKZg3b17X4/cYIpRCaTgJDfV2wiBjN4QnZ89ko8+KixBWTTZWIOnLVpDjvn7DpCV9DcKwxUj682Pm9lA209KaklxwU6KEhnfzMDCsWtEOMmJDlV4pbjagchuHMLV16egtgA39U7T2WqoMqxan0pHwBzJWTTNsyxoJlg9k7f5mYvbtb9p6QkqqlpqRQJaQHxDWXzN14yKBZppd90AiTdmRZiKNQFLZ8QCXRI1gox00JLNRAkpZu01RDIdUXRZiq7JbepuHRxU0a5ZKWV6DlPxGVYVBRmWhxURcpCoMcnwUEFbNHrMeMNS0uAgkeF7SBHEDPM7aGduWRyM0+4l97KsUZQIyJk2SyAE5SGRVlaw/TYthnENE2dyNh4EJKy/O+7StZM2Gy7bVxmXnihIoqdNxEiNeFeZMGws4ES6igjGDM20psTHtxESasHL/7nHL51JDERsx7x3jdmtYDtvChQuxcOFCvPjii5AFp+Xaa68d1sA8PNYGZAU4Ww1FPMTVnrlz51aGQN57770tfYcccggOOeSQ0v0322yzQfNcdt11V/z2t7/tepyrE95ueXgMD1V2Cxi67fIYHN52eXgMHWPdbg3ZYTv33HNx3nnnYbfddsOGG25Yuoo36pHE2avbuFzG7UIAl2ZFg0quaulplYSWAUuspCtkaldSZOpKuVKYwrDCxikH5JhhaCVdo7rLvJWxasXCiqTgIpAzbXrsTuFsBpUvwSpGY4kVeE63cRBhEgYISXPIsmsdl9oE/zisZtj06jJl26ogHGYsHy95MDkbnFUTnBT6Jt9lXsa0oTXvQ49xIClfFaoC5wy8xFCU9XkMH2uE3crhMkolNkzJjvLWpM5VI3lrsr8Pad9yABmTpvPW4r4G4hVadGTAsGppI0aa53+lOYukUgVZktPJBTMMm4gSiHwlXNRDYxPDVJbnbkRNu4Jdq1shFVraoDCRpbla+rFNlbU3A6k0bSqCtEznsA0kWNaITf9Avk+aSqSJluUuF/FgjKGZfzQDAYfIr6k/EuiJsuNMqIeGmZ+QdPaTbIMvBHhg803ys1qlFc7AK+znMDRHKu2W3uYx8liTbFcZKHPSEj1D5OKjAsMWkfz0umbhBDfy/TXhFsWmgiL1wLJtocojEOJ+MC0uEveDNS3DJvuWAgBUY4UVaGq4okxpro6c5tEHaTO2bcKwFSOrig64Bo2K0UxaUaxJt4O61UkQtZpl25oNqFyEjiexifTiPTGQ2yEq1sQp0yacLOL8fwmp7LiMmBPRHJBkHqdZ0OI+gjMIaT9Xql1AmdiRwli3W0N22BYsWIDrr78eRx999EiOZ5WCycROaHLnpFh7jDpzehv9WDOVSKIeCQCChEQGIVSg627UzaRJpSmYUZds46zp4wiqTGmTTBGEmUoQcidNO14FZ8yGPArisAV2f6qA6VyzlhujKpF2QsA4g54nMGbDb1KljPOUciBQ2qjChC3EqcJAkJp2TBTXdFsqYgAGmWFw5io9GueN2bhlwbpz0ijo+VOl3L9zmzKQdKkSWbHiU7UK5DE8rAl2C0D240ptFfmxdURJdBhkmrh1gWhCvA7daTYqwyCby1bk7QaS3HlLGk0kuVOT9CfGUUvj/Ee3WW7XmGCmpqQIOYKefLJBwyA5UcUVHGm+ECXqDahaFq4DKaHi2F7zIHXYssBRKw6kfck4VSYUUjtsK5qpqRG5rBGb8MhmM0WS9yexhMwnGFIpKBIeyXTSPg21Jg5bmhaFlVp/ilOpgCxyqWUyQf+0zpuWcFMw1loqY8Or5vdD8duq7Jbe5jHyWGNsV46q0DZHAZrZyXytQlxMp0HUAhsSWQ+s0BhVgww4Q2TES6zzFqoETIc+NleQdr9py75lkCtyh62/DzJX0VX9fUj7szYVZTLh4s3EddhiPQe0DltV/V2KLAwyd9gEN7V4qcOWqYbrurw1BL2Z8yji2Mw9keTq6EBWg5gqCRcFrQAEUeboKcGJ4i6DFLrNMS6fjkoyL5LK2thUAWlQrvxt2tIVFbFO3cgJnYx1uzVkh63ZbGLmzJkjORYPj7UOg6lEeowsvN3y8Bg+xrra2liEt10eHsPDWLdbQ3bYTjzxRNxwww0488wzR3I8qxQqyUKDGOcmfJB+ZIpxIg9NWCfyNyMr3SyXsQfnJmQRSdPKr0pJ6m6kTuiOFiBhpLI9OLd/09pvRMYVIrSS/KRGUQurptk2xl1WzQiMMLO9THSEMYDnd0cqZWVfFUzIDVeW1UoVoPJ9UqmQ5mGTMbOrLgFniAK7j5bZjqU0jFW2SqNXbNonrwrO3JVnGiqpCUSSdEof0Cop/6JmQkpXj0rGGHcZZxQKbsI1i/0eI481wW4VwZS07H9hlZSlOiQyNWybkxhP2w27akzDIJvLViDO2bbm0j7EfdmqbdJIkPRnx4wbCWQzZ4xyBkqmqlTWnwkOEeoSJcKEzahC+CQTme3jpD6bjBPwRAum2JpGxXIsjvAIkbK3bRqC7ZYUATL5/v6mFhpJ0cyZt3iAMmypCYmUabXoiDajQnDwQDNsgoRT2hXndsJK1G6FhIGIuZbdzqMeJNM/FVDMRgZwsMr6bN2iym7pbR4jjzXRdlE43/VCSGTAK0IiBXfEzfR3r+bUPLTPixsSyRDmAdMsGbBhkMmACYPEQJ8Jg5QrltkwyBVLTTtZ0XBKnWhbqRm2tDFAhEYSSBOJkJqQ8YxtK38odfg4ExxcRwqFwvQH9YgwbDWkua1M6k2EOZsXxgkCzaolTcOqQUpwPfcEmf+SOaCW8g+jXsj8nEoxp6ajzN9Zk9xER4VSITRtZkIom1w6n6/+nAYSaer7UrSb83WL1WG3nn76aZx//vn45S9/icWLF2PatGk46qij8PnPfx5R1GF5sBxDdtgajQauueYa3H333dhxxx0R5vSsxmWXXTbUQ3t4rDWoSoINxgA9Pxbh7ZaHx/DRLnnf266VA2+7PDyGh9Vhtx5//HFIKfH1r38dW265JR599FHMmTMHfX19uPTSS7s61pAdtocffthIfT/66KPOtjGTDJuLfWQR/3l8rwgHFyEp5rUVC23zwLaD0OSqKcKwMZK3ptLUrGzoVeXsOCSHjbCAigdW1ITmoRHBEDDuCoroHD0elLNppdfmfo5Wst/2M5AcCWUT74VihkkTDIV2zqpxhVTats5LTRUnK852hbiYN1YGhzUrYdiK+1RBn0fwcrGR4rg0O5h0K+tPVg6L/R4jjzXCbmkQ9t/JW6NS/kqZti3eGtv2QL8jQ61zLeK+frNSHPc10Fzal7cHEPdl7232xQWGza4cA5phs8+rkfIXDDIXAUhju7JM9+WCI40yJk/WIzMuGScmb00lsSPiVCbr79wuUnw6JnmycaoMs7aCiI7o9kAzRTygGbbEyWFL8n2KOWzmmjkz+WxCcIh8NVkmEirPWyum3rmFg9szbDTER//PGKlAIq3tk0o5tnsoBbPpuKpslLddKwdrlO0qoPid0d/1KtaNCofpHLaQMyM0kgmdkbI++TMQcpdhY83MxiBuVOStLYVcscy0tShT2rfM5Pg2l/Y5pU7iFXkOW4UgU0oiEbTNkx0wbE45FM4g8glT0EOFm5pIe3LRkXrNMHuyacVOQinB01b9BA4icEIYNhO9wQNEYSZikkqGUOencYUwn9PVA24jpVJp7FTIOeI8AiAKuBNR4Hyuej5IrHhVgfWhYHXYrf322w/77bef+fstb3kLnnjiCVx99dWrzmG75557hvrWUQMVN6HiXLTDxJCQX8+ykEENvb+UAMudJ5gsTFcEgBccusJ5Wr4mJQ+LYjwTM4F2uogDxkk/FRopERJxwiDR6pR1AvoWwZgJrxFWiwQKee47MudGu6Epcd6kYki5dYB0vyL9ThgTlBPKUzbhoM5Y2aXx1rvdAgnlhH9WgYZqWudt0MM7CFmF6MgY/wEerVgj7FbZglKZbZHSERqBDiVMmqRGkBUgSQcGStUgk75+EwYZ98VoaodtOREdaSR2IkJVIsnzYxwKwSCivPZOnBZqruUTkkiAR3nNykbThkQ2EzvJkKlTy9K5/ornlqqZ0eT4YkhkM0mNAEkSu2GQzQHb1qIjaSqNo0ZDIzlx2KTgSNPcYZMkFLQyDJIbsYSiGp4NA+OIpQ7t1AIKwthhxhSY0uGTpbck3w8lP0TVqLJbepvHyGNNsF3tUOaQBRUOm34uOHkWaoGt/VonYiSUVYkEM8fkyYCpR+mGRDYg+3InjdSjVI0+pHl/vHRFach42hggIZFWkCnO22kzdULHhxMSyaM8rLyRIMxrN/JGgiiv+RY0E6tCKV1REyrwZJXIuWlzIqDHTEx3YPojEZi5TsgZtDi2cD6PYnhknhYjy51v/TcAJzRyREMiR4ndeuONNzBp0qSu3zesOmyvv/46vvWtb+Gxxx4DALztbW/DBz/4QayzzjrDOayHx1oDLzqy6uHtlofH8DDWk/fHKrzt8vAYOjqxW0uXLnX6a7UaarXaiI3hySefxBVXXNE1uwYMw2F78MEHMXv2bPT09GCPPfYAkMVQX3DBBfjFL36BXXfddaiHXi3Qqw+sIGvvgJdI/5ckKmZMWr5CIaXLvA0G+mXiJGSxilVj3GXVtNAIY87+lElzhjHIkOhwWMX46def8dbzkCoATnhkKhVE/u6UMcNYKUVZOMqwocCwtZ8YVDHcVYn3pnSRYpDMJurr+CKZ2oc6rbhx3YYZhQJmRarY7zHyWNPsFoAWds2GR1qmXxEJfJUQto2IjqSNppWfbjSRNrS4iGXSmn0xmst12GRsVo6T/gRNHfZjpJyVwzjrr7lIGaJE11uzX3RGQn5EKMyqsYxjyFgn7ceQup5cEjtsW7H+GqDZ77wNl1XToTtNIrGv66ENJBJJ3pfE0g2D1G0i8S+TpmX7UhtmxIQwwlFpEEHkDJuSCkqSazdlAIDllEnIWbVa0zJsPaFwwo50wVctPhJzZcPXFTOWigqQDBdVdktv8xh5rIm2qzQdoGRCXZTy17/DIedEgAc2PFLYUGDBgEDYfQKdEZLGllVLm2Bpbh8b5bUp075liJdmokzNZX2lIeNpI0azLw+PJOHiup3GskJ0pFygCQCpR8kKDFseBhlyc/ygJzDHSZsppKnzVlVihZs0HMU5pE7B0fV8eQCm55SxMOk3QRQg141CKhgCzaQpZkJR41QhzKPL3NBVhWbeX2RTjZQ/Yy1hkcDw2bZO7NYmm2zi9J999tk455xzWvY/7bTTcPHFF7c932OPPYZtttnG/P38889jv/32wyGHHII5c+Z0N3gMw2E75ZRT8N73vhff+MY3EATZYZIkwYknnoiTTz4Zv/rVr4Z6aA+PtQZUtbLY7zHy8HbLw2P4qLJbepvHyMPbLg+P4aETu/Xcc89h4sSJpr+KXfvUpz6F4447ru353vKWt5j2Cy+8gH333RczZ87ENddc0+XIMwyLYaOGAwCCIMBnPvMZ7LbbbkM97GoDKyvOxzhR2iiXuy9j4xRd8aab2xV2LTsmFQWheWjCsm1FVs22uWHVlHLzKtqtUdCvcuUPb+E6WDHvTzdNsipzpLV1oW3KtnEQtk3a/DfKqkllx04XWgZbNO60YCxjluHTeR8pyWfjzOatCW4ZvrI8nU6RSRGXMWx+0rMysKbZrRYoaZkmKvGfNKGMrDMRHYmbhr1KGgNIm7rdNEnzTZK3lvQnpaIjjVSimT+U+n/KsNGvc8SZER7CgO1n3Mr9p3FqxUtIwjxtC8qqyXRQQ5Ax9zbXVD+3zaRV1r+ZWCYtTaSR4E+aqREaSeIUaS7/LeMmpL6/cMu0aIaNBxFkGOXb7SSAcQbOE9MWgRY+4aa0QCQ4evIV9f44tcn8RMqcsofK2CZ772l7uKiyW3qbx8hjTbZdlblMVaIjmq1nVo49JBL/VKQnJO2AM5O3BsKqsXgASj/L/X0mx1c2VkBp+X5S6iTua5SWOqFRB/Hy7NhUkMkRZ2pKR7isKv/dXqsN5xMRJwybQNCTCxgRoaeqnDjAzneZ4HbuG0TgeVkoGWTXzIPIMmw8gNI5f2mIgOfiJqTgNW1T28QT6Xxmmi3V9hZwP2MqRqL/Hgl0YrcmTpzoOGxVmDx5MiZPntzReZ9//nnsu+++mD59Oq677jor7tIlhuywTZw4Ec8++6xD9wGZdzphwoShHnb1o6icSMMgqcNW5qhRZ6Wdc1YCVeqwsWonrcyRI6GP1ElTcB2cMuhnQ6E897wYDlkpoELr1lEVO+K8aQWzlDhvDNaB4lCmrociIU1ZvQ/b1hhqmA+NqJQqCx8CAMkU8bMtNU+dNMDeM87UkFeVfQ7bqsUaY7eK9qfE3jBF6j6m1rlRcdOqKyaxVWBsJiQkcgBpwzppNLyHhkE28pCb/lSiP3WFd6pCIlMS8gxI8Ga+IBKlJvQy6AmMeEkaJ5B67DR5XqYm1Icq7RYdN0X+pk6Ncd6kQqKdzSTN/5dIk3x7KpHEOjzShkGmzX6kuWCLTJpIdZgpUV5jXJhEfUVEUrJtzPzPtRBCkELkk5wBYZUqeyJpRFB6IoE4tAInVPky+18a1basXlJuv2DDI50wdnQPn8O26rHG2K5BUOa4OduZdQQCQcMjbdidIMrQgjMTvhcwWCctaQLGAWlC5s+yajZMGKQa6EeiVR8dQZEmEuO8ucq5OmScCjKZkMimNCJBTakqbaV7vfa6dTuSEmEuXiKj1NhElUoTBlmFLEzbOmw8zNwAFvVD5fV9VR4SqQb6wcNafuwQLM3bMoUIdJipMgtICVdG6TFzMLNzhoJjIB8XJ2qQZTX3ViZWh916/vnnsc8++2DTTTfFpZdeipdeeslsmzp1alfHGrLDdthhh+GEE07ApZdeipkzZwIAfvOb3+DUU0/FEUccMdTDenisVRAVFH0Vbe8xPHi75eExfFTZLb3NY+ThbZeHx/CwOuzWXXfdhSeffBJPPvkkNt54Y2eb6pJsGLLDdumll4IxhmOOOQZJktd3CEN85CMfwRe/+MWhHnaVggmRJVySemcOeyaEGwZZFqpYIVBSKr3tnLzw5SjWdgMydq+MVWNWflUxbmuyVbBqnXwpJAn7q0Sx3hzpMwMw7dRcI1PSuXeMMIh6zZcLm3DKOHNCH60YCTPlATIxEpug2no9g4MON6snl7dJoj5gw4uc8NYUkKb0AIPUK3hdrhIxVh6y6ec8Kwdrgt1qVyeSUUl7RdkoaUMiZWpER2QSI21mq8NpnCDV4h5NIkUdp4btomxbI5XoNwybIgybZdpS8pDp59Rl2ADBcrvSb+sJyaY0ZQJkM4XUY6RS1Wnq1BFqG3ION8oglcrYylRaWX9tg5JEGvsiE+nI9+vQx3Sg34REpkkTMi5n2GSe1M9lmoVxkm0AwGgtNcEMwxZE3Iyrv5mgJ783zUSa+xpLhViLDOTvS6UtXyC4tV8KqKTTOBvE9hdQZbf0No+RxxphuzpE8XeUMjGUJRHMMlC03qrgpJYbs+wNkwmgS53IFCy1ZU9oPUrTbtLalLZNS50kjaS01AkNI9ch1f2F0HHLsLm20rl2cx3KMmycIcrnH5GUiEw9N1UZCsm1fYgGkOasWhIFCPJr4tGKrMRVfg8AQEWNrCQMAJYmUKbdBE/D/F4HEEQshOdhU6GgjKck4jDk8yuEwurPPeUMabc1kjrA6rBbxx133KC5bp1iyA5bFEW4/PLLcdFFF+Ef//gHAGCLLbbAuHHjRmRgHh5rAwQvFRot7fMYPrzd8vAYPqrslt7mMfLwtsvDY3gY63ZrSA5bHMfYb7/9sGDBAmy11VbYYYcdRnpcqwZBBAQRWBAaudKsKHW+Ism46W9h1Ry9++pPelCmDbBFuM2xK5g8fU7GjbiIYlTQQznMlFkhbrNQUbaq2tG6RhWz5uxD+/NVdHJ8zrjJ41AKhsFiZOwMNraYXlPWRd5bgMDgeXsKyjBxtAA4g4JeBxdE1h+SuSyb+cPei6qijFXgYKXFvDsp8O3RHdYYu9UNSmTvVRKb3C/ZTAwLlwl6ZCuoMrbJ8Ul/4hbITmwOhmbV+lNVKjpCUZbrLZjdP0qkERpxREdS6eRoGPlrKuNfIulfBpreQcuL6LHqXLY0tUIjaSqRahYrkYZJo3lrVHSEMmyAZdJUWsxhE+Z/kSfhy1Q559J5a+MiYUoOUJGUNLSr9DQ/z+b1rhw7UmW39DaPkcWaYLuyMj7doVR0pMCq6Xy2bDKes3AgwiREdARpCuSsGpOJlfJvNqwQUxJDxba8SZKXN5HNxLSTRtPJ5dVRB0kjcQpmA0AzTo2dbEpVyrA1yWSlNSpBs1cZs5btD8Ow9QgOwLU5APIyAKQkQC5SwqOmyVvjUWCuSdQjcB0lQO9FzjYi7MkYSgAqTcwcUPAsSgDIhNv0vaaMJ2VFaa5Ytzlsgkj/A7nt7kKQZKzbrSE5bGEY4uGHHx7psaxyMBEaZ82oK9IwyGLtM+IwaWRO3WDhj11sb+ekOY4cFeVwk/2zfjWow1IGqawTJ5XqPq63QjHSelXSPBaZwIkNj9QqkVl4og6ndJ032m8OXeFiVqpDKruDPo5SCtJEPlojqRigNzChkNqdDDgT4EzXG+lumYZXrPgMUUTIow3WFLs1KEwdNiI6QgQ6HOdNSqvAGCe23lnThkFmbVvbpz8tD+/R/doCFFUijWUgJoI6bKmy4TxpM7Xt2DppSkpI5zrK6wsNBurU0B997QgpSR0nBSV1eKR1zGgYZNrst04lERfJVCJbncmUC/CEOn55rTYSfilTe96EjJGON05Vi/2nEz4q2jSS4o1Vdktv8xhZrA22S1SEyZntFX1G2IJzM+nOQt/0fIJMo2RinA6Q8EgVx4ARYmo6tSmNTSTtpD92lB/14hIVGNF1KbNwcb3IhYLD5i5yAW46B6e1yBgj9lRZpV1I2AmJtYdcMKeGmw43FyGHCHPbU68hyUMig0YTot4w9yD7Pzb1O7lMoKR1drWNEwGDYDYkUqttMwYn9NFeR7UtcvfLwyM7oxA6wli3W0Me4lFHHYVvfetbIzkWD4+1DqzNy2Pk4e2Wh8fw0c5uedu1cuBtl4fH8DDW7daQc9iSJMG1116Lu+++G9OnT0dvb6+z/bLLLhv24FY2lAihRJSLjlAmrbXtSOxTVm2QmmwOOhEoqQi3pGUFqsIggc7CINuh44g+PeZ2if5U4r+kn4ZHMtjr5mBm1Zgzy8kJ5rJt5nAVVBqruAf0jum3KkXYNiizOpeFaubnVDY8kitLrQuuIHLVkZ6wuzWQTH64ZOXQZ+6vFKwJdqsU+llzwgQJ+yQp20bl/t1wQ12HLY0l0piwTaQmmV0hdleL4xKmhzJsZlWVwywjZzXZ7DGiph6XMgwfDYOk4zXXkl/rYKVUqqIR0gJ7ld0uy25Rtk3J1DBslG1T0oqRSMJmMmFl/SmYEHb/uAkZZXLZxfDLtCQMcoC0pbLiKbS8SZmAgYQy9SWHy7ZV2S29zWPkscbarjYoC5fjtAwG+a5xxszvuWC07A7AtC0kokwsTawdTJomDFIlsWXV4pjYxMSwUWksTUikbEpXtn8gLw1CbGMeoOBEJaQKxGYOLusvASPEEbU8e9r2cSPilDYlRJjb7ZCTcHNpxaXiBKJpr1VfNyd1Oo1YVRobkRallGEqmVLOvebmM2AkPJKEq5LPryokshj6OFIY63ZryA7bo48+il133RUA8Le//c3ZxsbAhXt4jAZwlNPcY4CdH5PwdsvDY/ioslto0+8xPHjb5eExPIx1uzVkh+2ee+4ZyXGsHgQhEIRDY9XayPpXCo1U9rcyauY4hFUz/WSlWGHk2TSzEE7PyZjJN1OAu5ptaarBWcYqKOnks2n2KsujgzlvGdtWBqnKV5EVGa9UMEnQGaumL6OabTP6IwomVlspu5IUBd39cHYTr+8xfKwRdmuQZ4w+myZvjYLkWNFCq2mcoKwAK80nKxZ71avFsWpNoE8dxsd9HrUpzVafmdlHlchTq1SVFoMtinuQDe7llu/ljq9gRJWEw7CZdkrvnW3LpIm0RNZfydSUXaFgXCANcjntpIk0l2mXiSApiPa8GQsoSbt1Zd6wg+T3Ift/5G1Jld3S2zxGHmuE7SLo9nsiCmxaeb/dbtg2zsCUZdhMDptSRlwjK3tCWCXC6GvWScax6Zc0x5cIJKXNtEV8qSg0UmYzOyucTWyoLO6j50X2+KKZIo0yQ8tjYcablUlJ7TWVCDpRtpGKVSHSEyPp5ErrUk2MKcfa2PIL1QWrVyXGut0assO2JkAF9ezlOGDMCpDwCseMCoAUt5m+QT78wnuqjkfFRTSKYZBloN89OhcpfieHvTLHOAl9ZOWSjXpbWXf+Xnr9TCknPFKj6LwVj0mvs+islTl3xbBKE0JJnEEJG36aStd5E8aps+pzUZexRlUJuCMpEODhoQoCHUYZMk2duma27TpMZnKi3ET5MuetrA4bVTtLlV14ocdLiRCIKqg+lqpEjhCKqmNALkJEQg1NfUuZQlIhF30fqRNcEB3RkDI1oVmqYn9J68Ol0qkVVyY6kkrlhEKWoZParN3mcLQVDvC2y6NLdDNZFo6zRoUt3P2cP6kQU4Uok1XUpU5aYkSZspBxrVzrCiRJaisLQiLUSctsH0y7WbLw0nq99v+ysPJMjMQe31HdzWMxg7qEJMJR0jieSanoFEiouVmAkqlNZ1ESirR5Pn/jAFGDVOWCaiRUspMwxJEMjxzrdmvILOBFF12Ea6+9tqX/2muvxcUXXzysQXl4rC1gjFW+PEYe3m55eAwf7eyWt10rB952eXgMD2Pdbg3ZYfv617+ObbbZpqX/bW97GxYsWDCsQa0qqCCCCmrZK9SvOiCC7MXtS/EASkTZiwe5UEmbV87aKf3e4quwze4vMlER/VJwBEYGW00t++LpRFC9iFX2BaXbnXukyldoVUv5Af1i5S9nkIS5dA4qyQqOyl7kELqWh16h0avC+qVXT/SLXremwgW31077BANCnr0E3Z8xc7xIsMKLZy/OUAvyV5fVFzlrHbeo+Cw6wVVXXYXNNtsM9XodM2bMwO9///u2+99yyy3YZpttUK/XscMOO+C2225ztiulcNZZZ2HDDTdET08PZs2ahb///e/OPq+++iqOPPJITJw4Eeuuuy5OOOEELF++fGgXsJKxJtitriBla40ymRqmSklZEPVQeQiiKm1nrBl9KbM67LbRpl22L1pWmGUelikrlp6NeIp065u1gz6vVKrAVMn81VrXJwtNTNu/0kG2d7qPUm4IZm4S9bjarTRLcl0rG1V2azi2y6M91ibbVQxd65SBc6cd+W893UFK81AxJfOogtSWCNH2JLeJMpWQMntl9jJ/PlNpbKJKlQkzjKUssZHW7sR5JIKW9C+3n2XbWm2oRC5EQo7ZlO5x9HWkTWntqbQ2hl6fTMlvgSy7J5mwE9NGSf+2KFnK0NNpHyfzqG4+95HGWLdbQ3bYFi9ejA033LClf/Lkyfj3v/89rEF1gm4nph4eoxHUqSy+usVNN92EefPm4eyzz8Yf/vAH7LTTTpg9ezZefPHF0v3vv/9+HHHEETjhhBPwxz/+EQcddBAOOuggPProo2afL33pS/jqV7+KBQsW4He/+x16e3sxe/ZsNBoNs8+RRx6Jv/zlL7jrrrvw85//HL/61a9w0kkndX8BqwDebnl4DB/t7NZQbNdILzStifC2y8NjeBhpu7WqMWSHbZNNNsFvfvOblv7f/OY3mDZt2rAGNRi6nZhWQQX1jFELIkBkLyUim9smolz6P2xh0CqZs8LLsGZlL8KkSTCTL0VfetWUMmvF9dOyL1s72rfsi0qZqkFBGDVVeKGTF/0MivmAgCMaYJi2AtvGCqxZVkDTfYmSVxkTJ0qOFeSvkMO0s7+zV8CLbFv+6jaRunBe+uoWl112GebMmYPjjz8e2223HRYsWIBx48aVhtEAwOWXX4799tsPp556Krbddlucf/752HXXXXHllVdmH4NSmD9/Ps444wy8733vw4477oj//d//xQsvvIBbb70VAPDYY4/hjjvuwDe/+U3MmDEDe+21F6644grceOONeOGFF7q+hpWNNcFuAehI3KcsD6zYV7W/WV1OW99D2TC6ymu325Vh+p6qY7j9OQNW2GhWvNuMf6ioYqSUUi25r0Umj7JjZfsUGTSZvzJmk7Bupq3cVz4GJQv3t0V4xB1nKm1e7cpAO7vVre1aGQtNayLWGNvVBSpFuUrYmipRC65z64uhQlScyCkVUs7Wa7ZfplmeaVUEQDtmzN2vyMCVMXNVUQwuA1d1XD3e7LqsHXH6UzfKQt8Dm7tWEqVB7x25rywXe+kmunBls2pl5xqpOdeqxpAdtjlz5uDkk0/Gddddh2eeeQbPPPMMrr32WpxyyimYM2fOSI6xBd1OTKvghETmL+QOmn1FTiik4gGkCEsdL+10FV9FJ0wpN8RFJ46XvcxYyasKg60elDlnRSeNOjwt94s4mAYFZ6zowA32qv5wbOiCORV13kpeHOWvTpw86tSF3NL3AWcIhX2VOmmCoZ6/uhUdKfss6GeydOlS5zUwMFB6nGaziYceegizZs0yfZxzzJo1C4sWLSp9z6JFi5z9AWD27Nlm/6eeegqLFy929llnnXUwY8YMs8+iRYuw7rrrYrfddjP7zJo1C5xz/O53v+viTqwarAl2a0hoEzI4mCNHUXTM3G3lThjdv8y5qDoenWA4/StBeKQI2SassMxJo/2rEoOFP66s29TObnU77RnphaY1FWut7WoDzhgEt4IjHFZhunQhu1irkTgj1A7KNDWCTDR8vAraAaoK79YoOlsjgaJzWAZ3MU6V9gP2uqu2F+dkg6FsLrk6xT1G0m6tDgxZJfLUU0/FK6+8go9+9KNoNjMJ0Hq9js9+9rM4/fTTR2yAReiJKT3HYBPTgYEBZ6K7dOnSlTY+D49uMJis/yabbOL0n3322TjnnHNa9n/55ZeRpik22GADp3+DDTbA448/XnruxYsXl+6/ePFis133tdtnypQpzvYgCDBp0iSzz2iCt1seHsNHJ/LYxe9rrVZDrVZz+obyXCxatAjz5s1z+mbPnm1Y/zUVY8V2ebvlMVqx1sr6M8Zw8cUX48wzz8Rjjz2Gnp4ebLXVVi0GeaQxlInpRRddhHPPPbelXwY1yCAbbyeLHYbxGkxGefBDjSi6/ZqNRD0MVXEM1i1p28lqTRcrOmVgbT4QVsHylQqwVN43K/1fD7q7fs0OlvUDwHPPPYeJEyea/pX9fK3pWBPslofH6kaV3dLbgM4Wm1bGQtOairFiu7zdGlloQmwsyM6PdnRit0Yzhl2Hbfz48dh9991HYiwrDaeffrqzIrd06dKWHxMPj9UBJhNbyLPQDwATJ050HLYqrL/++hBCYMmSJU7/kiVLMHXq1NL3TJ06te3++v8lS5Y4ye5LlizBzjvvbPYp5jEkSYJXX3218ryjAd5ueXgMHVV2S28D/GLTysJot13ebnmMVnRit0YzunLY5s2bh/PPPx+9vb0tIQlFXHbZZcMaWBWGMjEtC8UAgGaq0GyXdDFCKCvaXMRgNSCq2Npi92DsWbfkWoekot2fjKjqXM5Khma4Kli0lpjz0pN2sE9Vvtxg76U5dIOfBSItzzFre/yyMXTJKkZRhOnTp2PhwoU46KCDAABSSixcuBBz584tfc+ee+6JhQsX4uSTTzZ9d911F/bcc08AwOabb46pU6di4cKFxkFbunQpfve73+EjH/mIOcbrr7+Ohx56CNOnTwcA/PKXv4SUEjNmzOjqGlYW1jS7NSSQAs5FMN45K0wLYNN29nf2f3UBWOb8X7XdjquCxe+ydMZQwNuEx+hi2KzNPV1VGCyMZ6Xdqna5LHl/J4tNK2OhaU3CWLRdI2q3BkFWxoL8bewRQ1l6p2Lc/R2vsH1c5M+44MbetLOTXGjb1p4JK9rPTvLYBpXCdwqJV9hMMnZGDli8Jn3dVds7EbuiKCtDtQqm3NXowG6NZnTlsP3xj39EHMemXYWVWYBuKBPTKjQTiYFEltYtK2Kls9Hki03Pr8dGjU/VbzR11lpKnxnPq+L0Q/zM2tWF4yV3jTH3XGZcjBcERqgKUZlD0+VTrzoQA+jGaSz26fpxcaN133bQdU7K+rvEvHnzcOyxx2K33XbDHnvsgfnz56Ovrw/HH388AOCYY47BRhtthIsuuggA8MlPfhJ77703vvzlL+PAAw/EjTfeiAcffBDXXHNNdi2M4eSTT8YXvvAFbLXVVth8881x5plnYtq0aebZ23bbbbHffvthzpw5WLBgAeI4xty5c3H44YevdOWyTrGm2S0AQ/px0T++VU+O+6POwTuZqOT/tzpy2ZmqwnmqJiH6vbywg5408S6cy05R5fSUfR+KThp13ug2LT5C+xkX4KbNwQR5r2kz46gybmtlUudVsEKNqpJxrnSftspu6W0dYmUsNK1JWCNtVxfopqZgNhdpvQ9SKZQurVDng3Nj59otxGi7xDkz9rFoqzR0dyZmll1HrOzzmipF7KC7+OUehzqDdtHLddTKzuuOjdoQp1/beWI0nHtQZnPpvSt897qdmq2KupEGI2S3Vhe6ctjuueee0vaqxmATUw+PsYIsprp18j2UeOrDDjsML730Es466ywsXrwYO++8M+644w6Te/Dss886E96ZM2fihhtuwBlnnIHPfe5z2GqrrXDrrbdi++23N/t85jOfQV9fH0466SS8/vrr2GuvvXDHHXegXq+bfb73ve9h7ty5eOc73wnOOT7wgQ/gq1/9atfjX1nwdsvDY2RRZbf0tm4w0gtNaxK87fLwGDmMpN0aCgYGBjBjxgz8+c9/xh//+EcTudQphpXD1mg08PDDD+PFF1906uMwxvCe97xnOIdui8Empp1Ch0QyVmS1bLtsoXKkV7OKC7wOm0ZWXarOW8asVX75il/WfKWE7j8Y21Zk1aq+5mXsG+8oZLKCXaPHG4Rh6CiUsogqJq2iXos5B2UG4/7uzimT7FXWPwTMnTu3ctXz3nvvbek75JBDcMghh1QejzGG8847D+edd17lPpMmTcINN9zQ9VhXF8a63eoUjHOostVRLpwwH2dlVa8gC1baFom7gmtXfJXDrOVHA2Xdyt/nHs8dZvsVbMa5CfXsNDzRMHhFliq/T2VsW8Z6tTJpTlsIMNk6hiLzNuhxqsJFO6gTNBJiUh2jym7pbV1gZSw0ralYW2xXkXVp+XuQuXVWPonYIf1scFsLVjHLcmfbhPnfMPqCm++eiALLfgtubCITDDzK3isGUlOLtSm17bN2J2RAmrNtEWdomo+wnB3U78/+d22ofiIibtm2iLssnL4OEdmICU5YfHp9nIR/0nsBYuNUgZXMNthZKk3/odM1WVIvrgwrnW0bQbs1FHzmM5/BtGnT8Oc//3lI7x+yw3bHHXfg6KOPxiuvvNKyjTGGNF259GK7iamHx5jBCOWweXQGb7c8PEYAI5wLMtILTWsivO3y8BgmVmMO2+23345f/OIX+OEPf4jbb799SMcYssP28Y9/HIceeijOOuuslbbKsrLRVApNqcCgzOokIysXjDFoE+gwcAVOabjpAqlyV1bpufSCA2d29UKClefZFfuGItjBuGHbikxbsZD3oIemhyXH6Ggl2LBX5azaoDlunaAiDw2AYdJazmNyAWU5w5Z0JzrCZAKWVqtEeows1gS7NRQwLqAcpkevqhK2zWkzZwVZ6BXkpl1BzlaILYMW5YaQrhqXJcQLxpzcN6ff5Gy5VrWYXzeScJk2/TvAjPAIJ203D03Y+9hyf1vbIowGZdg4ZxCBZj8ZRMDMuPTYiu2WwrSFH4eVQbxV2S29zWPksSbbrlQqiA5166lQR6rsvCSVKM2fUoDNuWLcbetnMIgIq8TBw2xqzMMAPMraNK/XiTqIBHgj+86LgENIy6DpMWp2KVUKIYlEKNuniDKGLWTMvFcwkLbt54xBRDY/jUeabRP2OqLAibbQ123YMxohwIVlJ3ng5LHpeaqE/TykgiMIY/e1RcM7EV0ZSdatE7vVSf3IbrFkyRLMmTMHt956K8aNGzfk4wzZYVuyZAnmzZs3pg1HIhViqcDBwHKamjPrXHDCpDvChgXnarjrWlm+fn5+2AeXMeuYSVUtNuIcq7Sm1+AOjaJqjYMoAdEzdPIsceYGSZUftOAAEcdIgxVXR9pdV6dOXFmII32/UlZQhJ6ftJmS1sGLV49KpEdnWBPs1mDfDf0sM8CE/DiPqRPKR8RFBEm8L/YL66TRCYQz4WDVExUaFuSG8FhHT7DykEwmmDMWjcowyIL9qrJmjjNZdHK4TdJ3hEBEIQxSO1pBZN4rZQqVsx1MuA4eD8Js/zCCyN/DgwgiyCeIATfDF4Kb82ZOGidtKj7gXgNnNjyy0zBJhS7rh45xtbWxiDXCdg0DdHIvHaeN9tvtMp9tSEVsIuOkzcDy5xGcm7a7cGVtIg9DE/rII2EWsZL+BCLMF2UiiSiR+bjKHDa6Gu8q63bisNEwSLpwpm0o7RcRt+MKuRkvj4S5DuqkOc6bFkEKQuO8sSC0i/jE8VU8gMoHr1Rxfmgds3YCdasMHditTupHdnVKpXDcccfhwx/+MHbbbTc8/fTTQz7WkJcqDz744NJQBQ8Pjy6gVYvKXh4jDm+3PDxGAO3slrddKwXednl4DBMd2K3nnnsOb7zxhnmdfvrppYc67bTTMqe/zevxxx/HFVdcgWXLllUepxsMmWG78sorccghh+C+++7DDjvsgDAMne2f+MQnhj24lY0kzV6MKcNeCcYsqwYFpoke0g+ljKc7nJoSzJ7IQNH8WLJKxBkJj0SxvyinPcZRxWoBLsOlIbtb0a2U6S8Jv3TOLxP7Xmn3ZzKx+3cdEplWFM72k56VgTXBbrUF53bCXJRmJpLyijBsegVZhAFEHv4jQg4RctLOV2oDjiiPc4m4Kqwi2xAd2wfTZwQ/ACecx2He9MqvoG03HNIJjyShO2qQyABGmCdHBrsQYpjdrip5fWHYNB5EhklTMjX9TKZQvEzWnzvvNe0wsiIAjEFooYCAm/MKzhDloZK1gBM2zQ3dbAdeEUo/FFTZLb3NY+SxptmuVKpBhXR0OBwNi5NSQUoddueybrR6kQ7HS6WCyu0XDYNUIrDPZhCBhTlLHkSGdRJhaEIiRRhAmP7Y2MSgJ0AaZycTcYowzfrThmaXKAvIjcS/YMqElHNkgiRAFlZIQcPHy8IgewRDT24zeoQNqQ56AiuGEgmXbcuvI6jXyPWFNiRS27IwysJFgczGinw7Y1lYJLK5MQ2D1B+VItdNI7Ho55dKVfoZryzxkU7sVif1IwHgU5/6FI477ri2+7zlLW/BL3/5SyxatKglrHK33XbDkUceiW9/+9udDR7DcNi+//3v4xe/+AXq9TruvfdeJ0yQMTbmjIeHx2qBD4lcpfB2y8NjBOBDIlc5vO3y8BgmRtBuTZ48GZMnTx50v69+9av4whe+YP5+4YUXMHv2bNx0002YMWNGV+ccssP2+c9/Hueeey5OO+20lVLMdFUglhKxlNmqpi5QzZQpOqpUOdvGiRgJMIykblosO/+fCpAoZgVOVFGYBFaARK8WZ3Uj85XglRwvXLYAQiVdi/LU9C+n9ICTB5bf1ZZ8NtKuyjnT27tBO0GTsly1NCll1ZDGUPm4VJeFs5lMKhg2n7i/MrAm2K1SlOWg0qR6UpxZcWHzEoisPw8Du7JMcjSytk1Y78lzNJpSkdwMQFsxumqsbVPWpjkXre1Mhtom8pt2yG3iP+fgWoq7Qyn/KnDCXumV/ogKfhimy65aU2ZMBJEpkK1kStocSpIC2STPjYf2vZRtC/RqfSTA9bnIajll1SjbJkpYQyrkUixZM1Koslt6m8fIY421XQVQ1qXYDwC0CnYqlZmLpEpBQrM1DIrncycwy7zxAODalpB2GAI6hy2qg0VZnVFRbyKoZ89p2mhC5O0gTgyrlsYpwnrONqUSMjeKkQm/SqFtY1b+JL8MRqMR7ENaFOIoK5xN89Z6BDc2NOQcQY9mzwIzrqAnsP09obkOHgXm+kQ9Mtet/0cQZvcGyO6VZtWI6EhKfgcUYTlp3prDpLXJ13NYtpUwh10dduvNb36z8/f48eMBAFtssQU23njjro41ZIet2WzisMMOG9OGI04V4jQPh9SMOQBI7TAp42i0OG/0QCPwvaKKjGVOmmKA0MaInJ1DmfdKpWydM8agU1QVOhMeGS6KTlrZRMEJ26GOmUwLzlti9nEUG6vqshXR4fWWC42UC4pApmS8CVQSm34VN7O3DnTnsEHK8pDOLsM8PTrDmmC3uoJTT8cmj2vnjUfWScuUxLQohg2nCXoCJLkKWlAPkDYzx6RHKrQG8FgUf5QHddgCmiRvQ3h4QQzF1grioMpuncC8tSDWoccW6LEQwQ9WCFOUudPFkwiChP9JbQMKIYHaSWNcIIh6suNEPaY/CIU5PhfuubRjFgUckbBt7byFglunmAiNrPSabFV2S2/zGHGMdds1aPijTu0g85u0LHSOTPilUojz2Mc45WYfKUi4trSqhIILQFinQ4ncGQkiKzoShCY8MnNoslC2pN5E0MzsoIwTyNwOqlQa0Q1ZJouYjSI7P4PjsLkCTe2l2agSb4sNJeGZxkmrWyctpM5bPTLXJEibR4G5bude5M6bEqEJg4QIbGgp7GI9vQ6plAlLlcR5cwRjKkIiq1Dcp5P6lA7GuN0a8pN/7LHH4qabbhrJsXh4rH3wifurFN5ueXiMALzoyCqHt10eHsPEKLBbm222GZRS2Hnnnbt+75AZtjRN8aUvfQl33nkndtxxx5YE2Msuu2yoh15lyGT9JULOEeefVSgYUiJCrz1ayRR4vmJD/fAq577bnEm9KsEZXKETwrbpk2V9OfNGTia4Tf7ksOolDNwhASmr1JKoP0jivgYtN9BuO/KR0jp3OrmzJQySsmp5rQw3PFGhJXRxOKgqEUCZv5QwfyQMUiWxGbuKm0DOthnWrUMweq5Cv8fIY02wW+3YY8U4GMu3M8tMKc5N8rjiDZNUzoMQImfVgnqEtJGxRCKyK7JJf4Igb6fN1Kza1h0KzY6pLMyH9tPV4R5h20FPYEIvecTBtcR/ZMM5RVF6moZFDmK7qE0S3Ip1OCGGeiwBd5guHaYoBEeqQyJrPeTUAqlecS788Gv5fsYFhGbYaj0Iopo5fhDZkEgdHikER0/e3xMFqJWwbSGpw2ZDJgmTCBsZ0o50o2IBnaDKbultHiOPNcJ2dYhE1zLL/3ZER2ioXYFts0IjQMr1Psy8PxMa0bXGrIgGD2s2HLBWB2tmbR7VIeo6uiCCzBk2EScI85NJwrCVgQsG3p8z4k2JSLaGlGdh5Jpts8dy61iW11sLa1amP6wHCHtze94TGLsd9oYQ9axf1Gs2tLMekfDIunMPgDw0MmchFWXVRASZ96eJdOrM6Y8qTrP5ddYPxIYhLQ+VBOznvtJER8a43Rqyw/bII49gl112AQA8+uijzrZiaJyHh0cFZGrDP4v9HiMOb7c8PEYAVXZLb/MYcXjb5eExTIxxuzVkh+2ee+4ZyXGsFqQye3FGV29g8sBohgZHxrIBMEwbMLisvywkuJWlXTlpXeTYxXIDWgpVMAbohFrCvEEqm/MGZWPBST6bvq7WQXQeHcvIMaoYxhZWTV+4pEyaclm1vJ2Je7Syai1iIOaCKlZGOrmmMsaOSPa3CKDoh5rkrUFKu6retfBJBRWvRr/xGItYE+wWAFdcBHBFR/QuzApeMCGgdB5YaPM1VBA6hVON6Ei9BpGvJgc9CdJY52soWyQ1Vaj32x8/LVfdJCup1D6WM2wcYc3mrekVYUGKu1I57UzEw0r5mwKvgjJtBcEjKtZE8rwEYaZ07loUCNMngpxtCwWS/PqDUBg7rKQtlk3l+ynDRvup6EgQ1RDW8tXvWoAgL6EQhAJBzjLWIoFx+T2ICNsWBRwhKT8QErYt+587tpeTnJgycz2ktewqu6W3eYw41hjbNQio3H+Z1HtK8tZCziBz5jmWltGJFEMqaQ5b1k4UENK8tTSPjBExWM6Ys2YDrN6btZMYIsl+58PeHqj8vEpKU9IjrMhb42XFt6MUIs99C5vSKSzdUeFsbbMiTuyjcHLVqOhIND7Mx15D2FvP23XS7jEMG+vpbREdYbUeKJHbORFBEYl//XnEkuYaWobQZdukLcVAcgpXhZS/gzFut4bssK0J0NS6VJZ6TqWNspFwwyBpeGQ7UKeMfgc7Eb1h5NjcRjVCErUvxeyxBLeOnGTMTJoUswNmynWewOzkpqgmqTpcqavaiya7m1AcKtZBnB5XaTGpduTIex2nqlN0mqQtB3ESZWrUILOVmtZ2MRxqMKgkLg2j7Da00sMDQK4MaWRurSMXRGBBPjkJYptUHkau8llPNjmRcQIZZ/tHcWInKqlyEuu1kiPrTyByp0Y7Y83CDzCdeNBwHqteFlhlylCYkEweBUYlkrapkAq4aB/zh2wzVVzTtioqiHsAQE8kMNDMxpLE0oQpKkmT5iMkRIVTO2N6Iqfhio7k4UqhDX0MQm6ctyDkpr8nEujJr3VcJJwx1nLHMuTMOm/EGTW1PMktGUm1yCq7pbd5eHQDOlkXgrUIURTFKdL8sU8VjPOWCo44pfvYkEgdjhdwhiB3QJiIoUS+6BqE4Hm4Mu9pmu8wT8YBucMWSOk4bFWg6rZAFt6t22ksjVhJGqdGUTJbCLPH1P06LDw7Ljch44zbdlC3dpOKjoS9IcLeWt6uI+ztaWkHvXXwnsw55fVxYLqd/8+iHki9uCdCIL93SkRIUuuY0XBH48il1rGWylUUrvxcyxz0EXTkxrrd6kp05Nlnn+3q4M8//3xX+3t4rHXwifsrHd5ueXiMMEZB8v7aAG+7PDxGEGPcbnXFsO2+++446KCDcOKJJ2L33Xcv3eeNN97AzTffjMsvvxwnnXTSmCzmqFdQRWHFVjv6RS+3vCZZvo0EnCjlSppWoSycJSX14SQDUr1arezKteI5s4YsDMgIkDCbhE7JQc6YKVDghmW2H1cVnNBHJcFkCatGJfvTxJH1N6waYd5o7bNuBEccMRWZlodHVl10VR04faziuYbxoKs4hopLGLaSPo+hYY23W4zb7yjj5rvPmA2bZJwDuUgBS0IT2kJlm3lo6wyJZgIR5yGRzcRI+Vcl19PEelOHKCl/RkXASY03bhPjaThPjy0rwEMbtinC0Er8B2Fp2QJnXCQygYM5LFSoV8KJ6IgW86gFHEGgwxQ50kQzbApSttZ/U0EdaZvrBTKbbMRFQmHCIKNaYFi1qBZgfH4/JtRD9DhsW9auC27GXguEadP/aR02E+ng3BfW0W9RFarslt7mMTJY022XZk+oNHuqlCPtr/ejjIv+7ma1dFnetiGRccqRP16IpYLIf6ITrsyzEYoQCPN6YzKF0kJn9V7w/DssZWrmMTxN4cq8WJgyKYJDRFq4ydaxjPN22kwhm7Z+m2XYqoVLTBSDsEJMIrR1KoOewJRAcRm2GgKHVcuuNZrQiyBv8/o4G/7Z0wuu2/n/SoRQQS7rH9ZNGYREZaGQAJCklj2LpUIjt4PZZ6NMv/5spFJo5vtU1twr2KaRYtnGut3qymH761//igsuuADvete7UK/XMX36dEybNg31eh2vvfYa/vrXv+Ivf/kLdt11V3zpS1/CAQccsLLG7eGxZkDK8pWdMVATZKzA2y0PjxFGld3S2zxGBN52eXiMIMa43erKYVtvvfVw2WWX4YILLsD//d//4de//jWeeeYZ9Pf3Y/3118eRRx6J2bNnY/vtt19Z4x1RVBUYLevLilhn7aKzX8WmZfu6xQPpykHZCic9t5NkygGhaP4FTFuzZ5RtE23YNipMYs6m2cMOctiKuziCIg4zlfUzmYAWwmapZtJcVs20qZR+UWiEPGw6jpyVrKyzotw3YdgM+1axaNNSoLtwvrJztYtpbweVVuSwpaN/tWesYE2zW6XsMhUd0W3OoZQuii2BIH9egtjINiNpguXfv2Bcj/keZyu+tl1VEFYn1seNxKzyaoESSQRKsjHk7JZg5pl1Cr3S1eF6gECzfWEAQXLYGEl8N6IqnJQuYbwyn83YSiKHH/LWHLYosIxWkkikiRYaITnG5BhSKQhRUp6DM1OAWwhu2DaaqxbWAiO8Mq4eGKGRHiI60hMJw7bVAoEaERoJuW5rJq/IKtrxVNl3pdCV+kiV3dLbPEYGa5rtagfKtum2lnkX0rIytFhySHL441QizvsbibQMuuSI8699IJWdIwU1cD2fCBIokkPPeydk7WKueg7KtFEhJBEG4Cv6AWQRC1lfbCIHkn4brSClcgpuU1up28zJYWOGteOctCOb7yvqIYKe3G7WawjHUaERm7cmesdn7+2dCN47MWvXe00Om8OqBVkenBIRZN5OEmk+m1gqR45f5xHSHLasnYtSpbI0by1pk882UhjrdmtIoiM9PT04+OCDcfDBB4/0eFYpdL0aXnCMqlAWQVLlpMUkIdPWnICjCmSOUTguVYak46ITDGOMWOaomXYHzhsVJmEmRAhmXMXQGQ0TWtMufLAs9LFDJ80JidQ1zmi9s7R8ZYTePqMWRx02LlxludLwSNJXVKCsWpHRx5SpaTuOYgdQMi0NqRxOmKVHOdYUu2VQsghBn11ak03xACx32BCGYEk+5aj1GIcNMoXQzlszKVVEc04vmHXCIo4kV4wMpK3ZZsaSKie0Ryfhi8hVONM1hEQ9hKjndcporaAwANOhnUFoxFPARWtNyeJ4GbWhVqwjFDYkUjtI/bFAMw+DbCZW4cw5HmeOnyxD69Qx4iVptUkhbD23YkjkOBMGGWB8Xi9pAgmPHF+3ddhqATfKkPWAt4RECkcB034nqBiJIkJUxfCjTlBlt/Q2j5HFGme7CmhRhuS2X/9f1s5C7fLF6FRaR04wNBIb8qyfh2aqTEqL4Aph7oAwJaFK0iBYr3RTYIjqrlV+tEJIcV/DtEU9c9jSxoCp35bUm8YZG05IJKMOWxg4TppZ6KpHJgwyqNcQThiX7d/Ta0Ieee9E8HET8mudCNRyhy3MnbSwx4SNqqCGpnG67H1PpK2x1kikCX0cSGlIpFXEbCblDlsqVaktGlHRkTFut9ZqlUgPj9WOJDZFt1v6PTw8PEYjquyW3ubh4eEx2jDG7dZa7bAFeTgJZ8wwU3QVsgpVQiI6aoiuJqQSJNmSSNCSRYOq5O+sVhBpmzAAS/c7bBtnCEnYZCdsm65BJ5y+vE3G0iIo0m3oY6prliXlbBuhqlXctDL5iW1nhy1Z6SeslqJMlxEk4ABl3kgYldmnWNeqJOzMCbsi90cBYFKvvFWlJZdDpWkpe1HFJnp4FKEKTJt5bnlgvq8MwP9v7+2jLKmq8//nnFNV93b3TM8wMMOADiDiD/E1MGSGURIJQxziy/KF5QoJywVIIBgHw0vEMb6AfoMvgMFIMGQhKgZYica3lWBQBExURmRB0IUOxBAJLMIM6jjMTL/dqjrn90fVOWefulXd9/Z0T3dP789aMKfr1q17bt1b+9Y+e+9nw6ZHJm0I+/3SmoxzyHIc5fSaozL+yqVBqqQDUY51J3eF9W4FuTHCJvwxYhn0DYoGStn7gcStFEcDCVQZVZNxBJCUSHf9krTnaqSNStyLWntaI+sfK3TKlfIJsiJMkbmGKt9HHpwvkgYqhdtHRtKNoySU71/qImkxlpfnYEnbR9jaSmKw3L8dSbTLecZKuHEQVSM2PGjpQs7JPmiONNot+xjD9Eut+ITw6XJK+wiNvXZparMUAqksozuZj6rFUrqsIpF70TghDIS9jqJWWHph9wGJUBNhIyOls0NxNOJEkaJ2C+lIkRKpxssWKZ028nIckXYpJtfIy8gbtbEmaJvibZmQ0tnNomdmmY0QRySqR+xmu+XERVSr5SX7B5eS8bAbozUEU7Y2MLH916dEppBBhC0YlynjE5nGhBUdyXWQHtmx9746FB2ZqBEgmb2UyIVttxa1w8Ywc06Wuj4vXdsZhmHmI012yz7GMAwz31jgdmvaDtuePXuwdOnSmZzLfidWhbRzNaomG9pC91KvBpQFlk5elkrQhrnXdntdfYSbC2mKGpNVJV9/IX0BuvIF6EkkoLRdYWqOtrma1vJ4oihuoJs8VAjERrqMDmvSpoiqiTx1UTiTpdATxWoUSENDo3N38Zic9Mdoyj0m0t62hs1IScQJSCQtisk+CpBUuCR3xwlwndRzl7ceRNqk8nOL+rukikaO3c9ZCE0cFyIHgt0C0N2moq6eTWeALL5bBvCRcKUhWsUKKrSGINdXUxWYk9KX0q36Fqu8tk7DF9bTFgC0RoOuFtumrypRTqyEFszHgwO+xQCpYRNJmzT9jl09W1VYqPY9wNv2wlb6cVv5htlAsYrvVoHbPmo+JoWzyVmaQ5d1aJqkTGht3D5C+Do3RSJsLSLTP5io2rq1ZYMxhspzMxgrF2ErxuVxlHI2v+ylXdawlWMpaj9Tmj8wtcxUN012yz7GzDwHjO0qyStCXar8HgfNk6WPqvn9RK0ASaoFJsrbD6pLEKucZFBJdGz0OTf+HkdFiMuoUoCQruZdRrGPsEWJi7CZyNsh1Rp3tspG1TJSw5anmRsbrd1YV86FjbJRmymp7aVRNTKO2i1vN1stJy4l2qRubWAIou3r2UxS1q0lA15gJCkeN/EAUlEcO4iqaT9OtcZoKTQ1nmkn619E23K3j/3MqG2l2Qu06XbYQLv+ezIdFrrdmrbD9ju/8zu48847sXr16pmcz37FXuwSU6dBBjobJA2SOmc2kj2Ra6+Oo01lXDppxgTpkZOlRQKlMiRJ4fEpkQKt8pc6lj49Js4FWqVjkjc4bxEAbVXOYFMjBWk4JyDodncCQicN1hnTWeGQldtFuZIROGmdcZiJ8WKcdWA65Tj1Kx+B0MhkMqzuJBGlODuOYpiomItQCsbe5Ol8EuetfC45Zt3rFK+lAE1ukm1KZKvG6E+GaWjYaOZ/eH4hciDYLYtL/WtQRTQy8osscBmR7m+L/Rbrmm1AcdNgbxToOI99Wo4eSJGNlSlAZf+2wmHrTi2WSgaF9BERFIlKoZGI9A2iNyEiTlzfOBEl7po01GGr/KDbv4qFOW/PrHBHS0lM1DhsdSk5EblB7ETS7VP0Z6tJm5TC9XNTUrjjtyKJgfIma0nLK0PSNMgh4qQtaUVu3FLS2fYk8un8ivxW2DlKeGGpYF7GQNOc7n5pslv2MWbGOZBsVx32xlxJ2X3jLv246ry59EjyPZd0e0q2UxE1aASWTpVOTzIEGdhWe4Ept93EiVs40q02TGmTTGccorynUeV9Tpx1nPOmUy/mlKdpoMTbrELdbXuFlFCJTxOXTkW37Z20pO1tZavte6wNDEG0SocsannnjIx16bxmkJjIraCIT2vskBTH0TTHRO6FRux4nIiOUCetS3TEkPEkjtqMsMDt1rRd1eOPPx7r16/Ho48+Gmx/+OGHuRcIw/SIybJy1af6XzZrr7lz506cddZZGB4exvLly3Heeedh7969kz5nfHwc73rXu3DwwQdjyZIlOOOMM7Bjxw73+I9//GP80R/9EdasWYOBgQEcd9xx+Ju/+ZvgGN/97neLiEPlv+3bt8/K+6yD7RbD7DvNdmt2bddihm0Xw+wbC91uTTvC9vnPfx5XXHEFTj75ZHz961/HqlWr8IEPfABf+cpXFozxsNLOTdG1IpJW/5hdOM5JnzW7spDmmnR790WVqSZFmCREXC22pCiSl5iQldrErg5HEnHZcr4VSUzkftU4jYpjtbQvUs+NFybRpoi4FdhqfFPbq0cY44VGdO7FRXIfVRN5x6dBZqkbB1G1zjiJqpEIG8ktNimNsOVBb6hqEW7xr4+S2dQEI5VbATORX5WHzl0qg4Av9hVUK6Qacm9KtaJzsdoHUZ9hdd2w4jOLErNnnXUWnnnmGdx1111I0xTnnnsuLrjgAtx+++2Nz7nkkktwxx134Mtf/jKWLVuGzZs3461vfSt+8IMfAAAefPBBrFq1CrfeeivWrFmD++67DxdccAGUUti8eXNwrMceewzDw8Pu71WrVs3OG63hQLBbXQhR/x0VcApDBkQkZ5JIm1vTlKooskcoYS2UdAX2+XjHFdbn4x0nw687NrVZBwX0wdRcak/sxhFJfYzaSdA3yK0UJ22IuLx+iay/qYuIV96fIEJMYUqkdJL5aWk/83b402jtcBJJJFFh+zqkF1F1dZiKIkTkuTaqligZ9FizUbWBpJr6WJ8GmZStAhJFxRV8hI22a6FS/haNUHVEUHWFXmiyW/YxZsY5UGwXjZzUC0v4x8uMQSSRRAfdtoTeHwXbG3rsAhmWJP7aNu6Y0t3rtSOJuEwTFEJClJE3ISMIaftaJpDWJnXGXYRNk3sddz/TGYccIoJqpPSjru9llSC7gd7zuNRwH+0DudcRrbaf48CQ226iNrSV7Y/aXsI/ajuxkbQ8F0VUrRQUyXUgNGLTIEdTTca5u9+dyDTGyu1jnRxjZap8hwiT0J5sReRt8u9G0aPPR2L7ZoHbrX0SHfnwhz+MVquF3//930ee59i4cSO2bt2KdevWzdT8GOaAxqQpTNp9s2nS2cmn3rZtG+6880488MADOPHEEwEA119/PV73utfh2muvxeGHH971nOeeew4333wzbr/9dpx66qkAipuH4447Dj/84Q9x0kkn4R3veEfwnKOPPhpbt27FV7/61S6HbdWqVVi+fPmsvL9eYLvFMPtGk92yjzGzA9suhpk+C91uTdth27FjBz760Y/ipptuwkte8hI8+uijOOeccxaU4YiVQKJ8tMniBEWECes7iNBIXQ2brUkbz3we7wQpwhzP8trCS5rTmzWs1NLaCSV9o9eBRLlxO1IuktaKvAx0K5OYsI1hYwUTTbYy4QvWjTFhbYyth6F1a3kKkRUrSiLruGib6Yw5QREzPuojaRNjfkxWo3SW+gJckuet87w2t7uQuLURgO5CXBnFvm4t9hE7ZDHQsg2Bc78yBR9lM1r5xWYV1wo6dIs+lJGMGY6w7d69O9jcarXQarX6ew3C1q1bsXz5cuesAcBpp50GKSXuv/9+vOUtb+l6zoMPPog0TXHaaae5bS9+8YtxxBFHYOvWrTjppJNqX+u5557DihUrurb/1m/9FiYmJvCyl70MV155JV796ldP+/30y4Fgt7q+g0L6bdVVRxtBJvsIQdpTCBE0kpdOtlrBlNeXlgqSSFirdnHN5uMdqLEJN87L2jVaVE9rNNxLVmoxmornrSS1bA9CltLT1boM2DYaUhY1e/a8kPfkok0wTnRESeGjatq4yJoubWZuALT9abS2txVJl92QGxPU0zRF2Oz+SgoXVSuibT7C1o5qImmRH7eJPY+ldAIjMWkKTIVGXPsCEARIPMH/zhkh+m+evcBXqhciB4LtojTJtofb7T2SzzCqi7RVqY+uWQr7NBgraOPv8IzNRjACeXkxJfEglLUxKoFJy3sKlUDkZdPtqA1Z1oSJdAKGCKkBRdTNRdXSjs8eqtbqU+w1FNTNE/E0kkEk4sRH26KY2McBCBs9Uwm0yzjyUv0gsv25jIMaNfvveCDl7+vWRlM6LuY7kflo21gaRtWo0Ih7naCerb5umEIbrE+LObRbd9xxBz7ykY/gJz/5CdrtNl7zmtfg61//el/HmLbD9oIXvADHHnssvvzlL+P1r3897rzzTvzhH/4hnnzySbznPe+Z7mEZZlGhsxQ6617x0aWBX7NmTbD9iiuuwJVXXjnt19u+fXtXCmIURVixYkVjLdn27duRJElXVOzQQw9tfM59992Hf/qnf8Idd9zhth122GG48cYbceKJJ2JiYgKf/exnccopp+D+++/HCSecMO331A9stxhm32myW/YxZuZh28Uw+8Zc2a2vfOUrOP/88/HRj34Up556KrIswyOPPNL3cabtsH3uc5/DmWee6f4+/fTTce+99+INb3gDnnjiCdxwww3TPfR+IxK+voDK9LvG0kYgt2vRxPHPtZfw18bXqDk501wHKw51ebyjHRJty2k9W7hiG9awlfLXZNV2kETYaC1EO1KuXq4VSaTarhwb5MZ+Ybu/uEL4pt7KVKJrNVL+Iu94NchsAmZitNhlbAR6fKQYT4zD2HFnHHqsGOfjXj0pT9NA+rZJPUmQCIKLptEIm21gOdByY9Vq+bknftlcwH+sQiqY3K76592RCpSqe27pWoYNep16VLvreZNhtKnNXbfNd5966qmg3qspurZlyxZ84hOfmPS1tm3b1tfcpssjjzyCN73pTbjiiivw2te+1m0/9thjceyxx7q/X/WqV+Hxxx/Hddddh3/4h3/YL3M7EOwWgO4IryRRt+B7WV5HtDE8rXmTWfBcaVdhibIqohimvGZNFDsVNJmMu2ib7mROHTIva9hopBwg9aLk2qI1cSqJ/bidhCvFJKpG6zVsVC24NivnxppQIQRUKROutG+BEkvtMhNs5kRuwoiVW93PdKOSpK1ni4jNps8t6t/82EbV2pGvoRuMlWvTQqNqrUi648ZSwCZJKBphsx8pwho2izaFOiQAaFLnNp0O2k12yz7GzDwHjO0i9N4cWbt6tl4jLNooMqbqgzaiAwzarBojkZe758bfA+VGI7Hqka0Y0kbb8jZMWmYWRW1Xry+SDkSrzAYo75FkTloWVWrYbAaEodGdaqTHqWD7xt1BDZtSvi4/bvlMgyiGVmXrAZXAKGI3Ix95s/VqnSysUbP/dpxKZFir5u5xe7jfrdathfoN3VoOTexTdA1zY7eyLMOf//mf45prrsF5553ntr/kJS/p+1jTdtio4bCccMIJuO+++/AHf/AH0z3sfiUpUyKpuEhu6O+XcWHyvKJ97MVDjJMutf/Sbu97JzLsHS8u3LFOjtEODRdnbv+pBEiofG1CfuBpIftArNwFQp23oVj5nm9GVYRUSmNQXge5Box1Yivny4mOVIVGsiItykyMuhs7PbrHOWZmfMRtz0bHnJOWjowhI3K3Qa8Skk9cTacCUPaDKh3Y2EraRk60QKeZkwg3uYYqjaDIw15TTohBKideUsWQG12jSNqVvVkkN8km6k9pSKcZdFQTYStvfoeHhwOHrYnLLrsM55xzzqT7HH300Vi9ejWeffbZYHuWZdi5c2ejXPTq1avR6XSwa9euIMq2Y8eOruf87Gc/w8aNG3HBBRfgAx/4wJTzXrduHb7//e9Pud9McSDYLcB+5wQZdztsRkh/zQKu56DR0t0oIK9/rhiQ7iaAphcHAkLJOER58yEnxnwKkOuhSFIiJ5GspinNwt14xKHQCE2JtLLVUezFRnrowyaFv96VhGuN0opkbUqgcmsz3rlrEovqei5JiXR2W0knDNKKlH99JWtT2duRDHqseVERv9BY9NIMnTOBbkfNvp+8PAPSGNt1EkIU+/dzL9Rkt+xjzMxzINgumvZWt9jR/DzjF1vIF7VbCr7GMSOL1FobpGRxxvfI9dddbgRS7Xu7pa4lkkBSOkBR1IK0Tk/uBdZE5lMea/vQGuNbIhntykyok9a0QA2pGtLglbsvMTLyDpuKi35xKBwzmz6uVezOdyfz96/UOcvItjpBkep4vLTzVSdtlKZE5t5udsr+bPTzm+w7YO1dNV1WNdyzNTEXduuhhx7C008/DSkljj/+eGzfvh2/9Vu/hWuuuQYve9nL+jrWPomO1HHUUUfhvvvum+nDMswBSZM6VNMqUBMrV67EypUrp9xvw4YN2LVrFx588EGsXbsWAHDPPfdAa43169fXPmft2rWI4xh33303zjjjDACF0uOTTz6JDRs2uP1++tOf4tRTT8XZZ5+Nq666qqd5P/zwwzjssMN62nc2YbvFML0zmapdv7aL2TfYdjFMb/Rit2ZaN+B//ud/AABXXnkl/vqv/xpHHXUUPvnJT+KUU07Bf/3Xf9XW+Tcx4w4bABx00EGzcdgZx8r6a1MKbACQRjgvXhPNDUlSBUPREd8km6ZG7p0ovPW94xn2lBG2ItpWrD6P0pWITu4azGpt3KKLCWSXBekLK1waYJIoDCRlEW2iMFYWxS5pRT7lsh35uZMVCilos9UyWiX8KoaWIoyyUdERu0qUpTBpGWEbG4Ee3VM8d2Q39Ejxxdfjo8hGilX5dGQMaTnORsaRjRfP1Wnmo23kfFTD1MI2hFUSMqmLsJUFtO0EeqiYo0pTxOWqVQQ4MYXyQMW/UVw01QbK1a7YvqCPWKjIR9VkRNLKlFvtMlF/KZG6kyJX3ZEBK40+0xx33HE4/fTTcf755+PGG29EmqbYvHkzzjzzTKcQ+fTTT2Pjxo344he/iHXr1mHZsmU477zzcOmll2LFihUYHh7GRRddhA0bNjjBkUceeQSnnnoqNm3ahEsvvdTVtimlnCP5qU99Ci94wQvw0pe+FOPj4/jsZz+Le+65B9/+9rdn5b32y0KxW0FEDCjSZOz3j34vhYCxac80giQjt8prpITLBZIRIEspah1DKNvQuuWv8YkxmMS34nAS1u1Bl/Zjo27QeZECZKmLspFIOS2kD+SpSbRNUnlqlYQryw2CQHY9XpCG0srApRW2G0SYbGPtKNPIbMsWLZHHpX3UxtlViiKRqlhJJ4DQ1UqApD76aJuX7I9JS4BCXKQ8NSoUGqEpn0B3pEzYrH6aFgYBKXz6Z7802S37GLN/WQi2K9emqwgja4iOVbcpKYKUY7s9odGSDoI2GzZ1mUZx0ki5iFJufFZUK5Mua6odSbRLsZ9MC2TaC791cj+Oy3uHKIoQlXL4hSBb6sdAEGGjpSWCRtjsYxWCM1LJnkBNhA00wiZVEVkDkBl/btKOb0eSahNE0+z5GE/9vSxNfbRlP2NpHojp2XvZMVLqQ9Mjafp4J8sbRfbqqH72lF7SKCm92K1edQN6LUPR5e/e+9//frfg/fnPfx7Pf/7z8eUvfxl/+qd/2vP8Z8VhYximN4zRtelipsZ4zxS33XYbNm/ejI0bN0JKiTPOOAOf/vSn3eNpmuKxxx7D6Oio23bddde5fScmJrBp0yZ85jOfcY//8z//M375y1/i1ltvxa233uq2H3nkkXjiiScAAJ1OB5dddhmefvppDA4O4hWveAW+853v4Pd+7/dm7b0yDDPzNNkt+xjDMMx8oxe71atuQK9lKM888wyAsGat1Wrh6KOPxpNPPtnP9Be3w2Zl/XNtkFuBDW3cEqVGGOGxC5RFTnT3Ks1EmZdLVxz2jGcu2vbcaMdF2yY6OTrl9izV0LYuItfuNYNomBQuuiSkgCpXCdJYYSIpxntj5Wvk2hE6WbG60rQCIYWAErk7FwAQGenORbEiW75mJd+aio7YWjU9PuKjaiO7kY/sBQB0do8gGx0vx6NIRwrZ23RkHHkZcUzHM+hy7nknd+/dVJZ/RTlPFSuo8n2rciUtakeIbGuATupC3FElBB4TaVyQqIBrA1A9UUTMIFi9crniJPIW9XezojsZdE0etq3nmw1WrFgxaZPso446KliNB4B2u40bbrihsbD9yiuvnFK98vLLL8fll1/e93yZCuXqqq9hCFdVg1XYuqcb7Wu/dAQoe11ngFXQ0nljtA2udcc4TLscZx0XYauVsKY/kjrvlqu2c7cF9nHs69mSNkQcuzGsJLVU7rorakr9+7bvXQofSZLwMveKNtE2suuXUAnh7HosBVJdSvlrEwiTUDEDKiXu6t+EcJG6WApnZ2Ppo2q0Vk1J9BRVs5FCWpfXJGVu8ySMEK6gzQCB9H/ffbMb7JZ9jGGmohpdmyxSEtSmkbrQ4nnltRkpIhcvSRTOj/PEuGs5zTUmIi/q4+7jconxzEe/rV5AJH2EPJK+ppRem7FUUFEpnETaiQTZSa6fhvY1xk2RthphM6BbZMnafw3foiPXBmnq6/gyortgu5HQCBttQWXbUgURtkz7e9xcB7VqtXVrFTE9O6ZRtV6jY/bzzvZR1r8Xu9WrbkCvZShr165Fq9XCY489hpNPPhlAsSj+xBNP4Mgjj+xj9ovcYYvKlBMBnzYCeEfJ/pABQPW30H7PipTIUHRkrJM7oZG9ExmeGy1ufPaMZxgdKx2UiRxZ+cXO0hxZGYLOM408UFYrjimkcD/IQgooa0Riicj252lFyGt6u1UL6v1NACmCL5+XKAldFvfq6k+4NTpGA3l5I9YZ933VxkacGmQ+shed3cW4s2cU6e7RcjziUiI7IymysdJpHc/cOE9z6Nw6bKHgiFTkHFhHbaD4GsftCPFQMa8ozZr7t1klOpKCZVKfdmXy3IuRkBvBImWseE0TJd5JUwnZ3l8vD6NNfYSNldaYyRCiXhmS/HgbIWoFAKm4jtA5WYjxCw/QGUxeOkl5CqgyhTKPfXpPPABZFtabLAVKtViT256HqVc/0zpUQqubj5RkrJyThijxqZIycmpnhqhEBgX5XaeqtBnESdNwSc8o3BcrfOIdKt9fUxJxgslTuIDi5sLeUyjhnTQpvHhJEoXqjlMJisTS37QIkLHxN4NdKlEAIKRb6Mu18efC+B6jQohSdKT3G6Emu2UfY5g6mkRCJhMPsVAnzY1Julwn006FNY+Uu+9JMhn0vHWKr4nCgHNWhCtpaUVEBKgiCESvX3fNZlOrtvprN65dbCn+rr/+6KKQcdt8inOu4e6XcqNhb5lyQwU9/H1gSs71OBHIo/2Dx0lao3XSUm0CBUh7Tsc6ea0CZIf0Ha46ab1+1nXbQzG+2t0amQu7NTw8jAsvvBBXXHEF1qxZgyOPPBLXXHMNAOBtb3tbX8da1A4bw8w1eSdDXtNCIOdVaoZh5ilNdss+xjAMM9+YK7t1zTXXIIoivP3tb8fY2BjWr1+Pe+65p+/a00XtsMk8hcxTCBkFqxvW0TYyXFH1j/sVAm2MW4ntEGlTl5rY8aIjo2Mp0okyvEzGWSdHVq5c5Ll2UbLqirRdfVaRdCmRMpJIWsXHmKU58nJVXOdhjyC/6uP7AsVk9Sh1KzQGpi45xmgvGqAzJ1mr046LsOnxUZcemY2Mo7OniKpN7NqLtIy2TeweQzpSRBk7Iyk6e4vjZOMZ8k4ZZUxzlwpZjbDZlEipBKKybUFOUynt88h7p9LheRwhT4pzJOPICyXo3J3v6rs3JMXMr+hHrqDXKC8vnvW5SDNTKpHMIsJG1Gjqo43wyshFxrU2wYosxUvAS0ibhqi88I7QGaBsdCyDcSnQLRhSVG90q9yeufRHabydCFIiA1EfOpluESAh/XsKUj6VT0UOItsqCqKMdRQiS+UUhADt72GHrgelEIjL1HAdGbdqrUk6fBUvry/86rrwK+40wqeET3eMSYoXTXeMSEqkkqKIpqH8bGxOU1MPNdqTTth0S+VTIgUVKTFFa776I9XCKpFMv+RGB6Ij1RZGU0Vdqm2OqmPVEG1LIoVO2WN1ItMuxbGTaYyVGTFFe6Tie9vOJMYUjaqVWUiRcvdLNL25pXyLDkWufXsJ0mtdChGmIpP7zqZMP3oqbFRNA0HvYGu3jAFJiSRRtdzfp05k2ouLZEQ0L/fRNrttstTHXqJqU7Wrmg5UeCTXGnkfNbNzZbfiOMa1116La6+9dp+Os6gdNoaZa3SWQac1KpEZr1IzDDM/abJb9jGGYZj5xkK3W4vbYctTIE8hjIYsoyWKrMLmJK+/XMt2Tw0Kz8mqDlDm95be+mgnx4StVetoJzSSTuRknCHr2KazHejUFvvnQZTNRthklEDGZQPHpOUES+JW5MU6tK/Q2FVpum1XntqR6ioyLVZCiufRhuIAwgLZclw00i1ERMzEmBMXSUfGnJR/NjKGid3FPhO7O5jYXUiEd0ZSpCNlhG3MR9hSrd2KEV3NDlelpY/Iucic7hIpKc6bhCRRNdtcW6UZJIkACFJvQ57s63qkKgRGUMqI20aUQnnJ2j41svO0ISWSm88yDRghuppl2ygwLTrXJlzNrC1xIo8UtVeyPGQCVcrLCyIyZIKxl6uubi8H3mZoHRTbu9cnYyqSQhuDQ5LIdpPwD91eqQXx9WRlZA0omvBqv9RthTlUeUZyUaxiA4AxorLKTc4feSkvrw9IK9ZEfk+kCCNmzWMvVuCkwbNJ5MDrIG0ehPD7Stu+RXrb2kfpmqPJbtnHGKaOnIhc2L/tv4GtqhFeC45T/iulgP22CSncPVgSSVKTpsn9j3L3P6Od3I0HE+WiR/QeaSBWpBVH7qJzRYTNjrNaYSHXzkPKIOLuI2yi1n5UqbM9GiaMsDkREU1aTnmZ/jTXDWNTW6NGa/7seZnISCStElWbIJG0uqgazXhq+kyB4vME/OdbhUZR7d/9sNDt1qJ22ETZod4ggRBlzy4VQ5XfB6lNcEHV/bAVKZGhw5Zpg7EyH3aMpDumExnS0knrkHE6Po68dHryzhjybHKHTUUJZKmglk8kyAeWFHPRppIKaNMHM+yxBijJMFiKdYxnObKyB5MNgae5gY79DV/teTPapRKaLPXjzjiyscIZS0fGnRpkh6RBTuyeQIeM073FeDzX6NjzqEMVTosSQGINrzRIRsoUBtezTXtREiXcOGpnyG2/t3biOtqbXMOkPiUywKZjCRGmWtHeJ9L3eXEppX2G+zklkumbUggndFyK76LWNF0bJFXGgH6j7I99cMNARCmkAJQVPBISSpaLHJEI0iYNcSKco1Y6ccW28rpqUEQzlb/rVNFC59Rfj0EapIxqr1MhvKCUEALKqUsJyFJcSRrvJLmUI1k4asV5bHZ660QDaLalkqI2NaoqTCJB0h1zcs6I8wvi/Ioah82rhpL3b7T7ngghnUOuQRYm3dz6EB3hlEimT7QuhG6aUuPqnDSqVlwnCqFzf5+DHMjL73CWabd9jJSBKOmdrsIxK+zmWJojIWmQSbBP95iqvMZS1gq5eXERstBMVDJoH1y7X+15o/0Tydiejlz7e1BarpNqU3HedNe4Q52wyhgonbScPt6dEll10ujnONXnRxFS+LVybZzzRj1Z+p2x/fgmcwCrLHS71afGCsMwM4lOM+g0rflv/q/2MAyzOGm2W7Nru3bu3ImzzjoLw8PDWL58Oc477zzs3bt30ueccsopEEIE/1144YWzNkeGYeYnc2W3ZopFHWGDzoA8gyApRYUASfGwoCufTeFqGg0ikbZgXCpR5LlGVqbvZZ3cpUHmE2PIxveW28emTInMpSIpkQNkNkvcSEbSRfCiWJJO9BlGO5Gbm12ZoSmItXXsVD5aayfhjSz1oiNphny82J6Pd5x8fzqeuahaNp65NMh0b4qRcsVmLDeVCJufj0UJgbL1GhJpMGBXq0pRlzaK/mzFuRLIS1GSbDxD1Lb92TJ3YTZeoDRkLv13wygvfmBk5Feycp/m0WdGJIxuiLA1SM8yjJOwp2I47nrxK6+Z9lG1XIcF67WHLY4GoIxGOVlqH3lTwkCV14eUCZRN/6n2GkKZJkmiaoZG2Mj3uy5aRAlaawC1LQxAUyib3p+AW62VxvfeFIau5JdRNdDUx+bj1gkIhL3RiCgCSYeiaaYwJPVRZ/485n4c2N/GudieBWEU0s6FRiqlUH4uohAe6Se7qMlu2cdmi7POOgvPPPMM7rrrLqRpinPPPRcXXHDBpH0lAeD888/HRz7yEff34ODgrM2Raaap91peiapVozFB1K3r60Wziop/JfkyZ1Igy+z9ncAYKQ9RsrjuWpGsTZtMlI+qRSRSlwT7SxLB8607XCo0TXOmAiRkjr201KCRNk3ukagQEt2eat14T0ojaVSohUbTin9z97yJ6n2t/ewy7exnNfWxFz0Q95kBsDJZNNpWjcTuS0+2ubJbM8XidtgYZo7RnQx5zQ0hN59lGGa+0mS37GOzwbZt23DnnXfigQcewIknnggAuP766/G6170O1157LQ4//PDG5w4ODmL16tWzMi+GYRYGc2G3ZpJF7bAJnZV1GJKsTioIYTvVh9LHcgrhY9qomhZbugaHmfb5vbmGLqNUtG5Npx23XdPGs/ARNiFV43Y7ztLciQZkqUbWkKfsa9eKf7Uxvq1Bk2S0Ma45rslzVwyv08wVbuapj2RlY74pdjaWoUPq1sbK5aCx3GDMCp8YuGgbRQm/Kl78q8vtZV55RyMv6wUjHSEvm5Hr3EDb91ddRWlo5ltfP0Pl0xVy1woBZFWrzxo2rRsaZ8//1R5mbjBWCMfWXUkVrFT76LS3SRp+TL+hzXL/BlLTmiwaeStrv1Bt5mxXmYvov1SViFIgWmTc2MW2eliOpcIkXVE1es0G78nXorkFWSFcbVth5+w+U04hiETVR9V8ZkZRu1famDwPa9JcVE3XR9uq4iJN56fazoBEHgX8+RA6c+dLKhmIseSiP/GRJrtlHwOA3bt3B9tbrRZarVbvL1Jh69atWL58uXPWAOC0006DlBL3338/3vKWtzQ+97bbbsOtt96K1atX441vfCM++MEPcpRtP5PromFQnXy/IbVI4dg/35AoUvOL+KGNYAkhkJX3CkWwvtjeUdKN9wZ1bpmza60gehZG0mzULIkkaXYvgn3cvpW6tsnGk1EXkWxqj0Bry2jj6k5ejZTVR9uqx6Cfi8518BmZSvSvV6QUML5kN4i22RZORhv3sdLG2Xbcj25AL3ZrPrOoHTbowtkQeeZTa4z2aT7akBsYTy/haxr6p1/goIDdOj0k9VFnHehSxMM6cXZfoUqVSNK3SEiFTBbiHjJKiLMX+QzGTCO3vTYyHaRw0rB6sW2SN1Wj8gadu/ehO5kLN+s09f3R0tw5Unmauz5zHR2mQdpx4QD5G01/+yXcPkqI4MbUvgfbh406adUQuKZ/1/WAUqoiYGBzGLywQWAYqYNef+Ya0Z0cuqaUVHeadJKYRU+Zwu3Sc0kapAFqr++cLMTQNGO6JqNN1RmxjhlRNhMGovxqKikg83pHzj4u3NgLXhQpeN4ZtP3FDNCoINlE1Tkrnhhuo6pswXZ7tU6ZSukfD/xDkqYYKDdWna5+HDOjnfKm3c/t0/QereOufHqoS4MEnAgMhAyOp0rFyFwUYhD9FLQ32S37GACsWbMm2H7FFVfgyiuv7ONVQrZv345Vq1YF26IowooVK7B9+/bG5/3xH/8xjjzySBx++OH4yU9+gve+97147LHH8NWvfnXac2GmR1OvNSpQQVPpwm00LbL+nqqO4PqVwouxSe0WXIQEUuvgSeEXYqRARJ004ni1arfLLueMOnfV7ZSowWnLahySLoeN2vzAeetWbGzqj5ZlxAkjKY51jhn9PPr5LIDw8zDB5wGn3KthIKR9LX+ucpIGmWvTeM6a6MVuzWcWnMN21VVX4Y477sDDDz+MJEmwa9euuZ4Sw0wbrXXoQJLtzIED2y3mQKLJbtnHAOCpp57C8PCw294UXduyZQs+8YlPTPp627Ztm+ZMgQsuuMCNX/7yl+Owww7Dxo0b8fjjj+OFL3zhtI+7WGDbxRwo9GK35jMLzmHrdDp429vehg0bNuDmm2/ep2MJYwrZ42AVNHIrvlQGWpIVZCWE789GVk9ocXm/nr+NUk223Y1JSiRNjQwidTR8TYp4qyswvayI1GK/3DoPBAQMiWq5FZg8jHx5yf56cZG8IkHuxsb0JT0tS9UEQaV0pQy3WzEDkk4KqfzKPY1kVKIadsbG+DnmfZ7PPDXIa2Qg8nSanwszL5lJu+UER6xEu6muqha7adBoG4laGx9oof18Gl+OpIMX0bFyu/bblfSRN2sblSBiJdK4dU0hBGRpV4kOSLld+u00+u2eO8k8a94I3VZ9aq2JronqCR1Gz+i+jbL7U0TShM683WyM1E1yA0F71tn0Rzs3pXyaKeAjcDT1XwiIUtLcCsz0U8DfZLfsYwAwPDwcOGxNXHbZZTjnnHMm3efoo4/G6tWr8eyzzwbbsyzDzp07+6pPW79+PQDgv//7v9lh64GZsl2ZRpesvyERHdMQySm2Ebl4Ux/doftXoQIfombcHIUTQRRONkThBI2mTZL+SLdV7xGbrr+6lL+6XnZ2XJceGUTKTEV6n0QzdeW8VqNnjVHOHs67ex49vzRZC0RbSgt/fOlfKyfv1/Zky/rws3qxW/OZBeewffjDHwYAfOELX5jbiTDMDGByDSO7DchC6AnC9A7bLeZAoslu2cf6YeXKlVi5cuWU+23YsAG7du3Cgw8+iLVr1wIA7rnnHmitnRPWCw8//DAA4LDDDutrnosVtl3MgcJM2q25YME5bNNhYmICExMT7m9XDG1XM41xq50u4gZACulXV4Rvoh0rQZomCtcosa7BYhRJqDKio8hYSOFr0qLEyfQXUbLy9aVqlOCmQiN12+czQT53w6KuW2ghY9rcVQn/XCeTK3yzbKmEK1qVSkAl5Sq0kpBxOZbSfQYiit3yjohi14jYyAiwtSFSQcPmT/vC3SBi0eciTd7RyGtW0q1gCrN4abRbMiqjwD6X337tCjn67mhbUWfpx7RmVZOIdx3V5q5O7l8IX+eW++209s1mJYi8PvImyXUcRO8AF4UrHvP7u22o1EOQKKDbpxIR89sbxDxqasa6ol5TyO4LKs1fiaTVR+S8zTfaizg1IpWvt5XSvxcb/QdCKX/b3Dyo1Tbu3CgRaDX0RJPdAmbPdh133HE4/fTTcf755+PGG29EmqbYvHkzzjzzTKcQ+fTTT2Pjxo344he/iHXr1uHxxx/H7bffjte97nU4+OCD8ZOf/ASXXHIJfvd3fxeveMUrZmWei50mu2UjatXatcnGNNJj6/BptAig9VbhPPqJ+oQloaI2Iiek6IrEue10fxKRq3tNUff6PWQOVUXgmiJc1fo/+/hUUbO644WNr9H1+GRIKVxmVSEFIOyLuvNhpIAiNWX2JZQS7jWUDMdV4ZV+REfmwm7NJIvCYfvYxz7mVolqMdr96BmS5qJUy6VEBs4C6bURK+m611snrVAWUm7bRNk8TE1IKOvIxQp52UNNpx2oiDps5Q+slBClAAlFSAUZxW5sn0tVIiUxIlKEIXtKLwIqk9LgIAolvUFToSNlXzORXkQkaUoHMNQxE26/RIZjAIgGIkRl77WoHSEmY+ukqXYSjEXSLuYY+zFU7Av4ZShAYm9uNUmDNGb6qaUcYWOaaLJbRoig95qhgiIagRiP1WCkKZFp7r+vRa+eYv9+C8aVpE6YX8SSZJFLae9oCWJLJRl7URCaQomeUihd3x7yusZUUidpuiHdRpyzQMHSPV6f4hgcjzhgTiykwXmDzmGsPSdiTTSlHJUUd4uoiiPZ3CGlAGv/reYI4Jx5iMylQQap/zJy70nKuP8+bHO0Un3bbbdh8+bN2LhxI6SUOOOMM/DpT3/aPZ6mKR577DGMjo4CAJIkwXe+8x186lOfwsjICNasWYMzzjgDH/jAB2Ztjoudye63gn5rlTF1NKpOBHUsqEBJ0dKx2+HoVwgDqL8XqkufBKoOWOhwVR3CqoNW57A1vT4w9fx7SVOsOl56ks+h39enVAVFLIVTa8fe5EkA2opVUfMsjRMgMdoEipHWUFEBkl5Z6BG2foShZo0tW7ZACDHpf48++ui0j/++970Pzz33nPvvqaeemsHZM8z00ZlGnnb/p/tJzGbmBLZbzGKlyW7Ntu1asWIFbr/9duzZswfPPfccPve5z2HJkiXu8aOOOgrGGJxyyikACqXKf//3f8evf/1rjI+P4+c//zmuvvrqnmrrDmRm03ax3WLmK3Nlt2aKeRFh67XoeLr01f+FrLzSFB0lw0hP20bTlHTyrgNxsQo6kCg3XtqOMFbKhcatyIX1wxC1/8GhUTKddiBJ5I2ustoUShklbh/VGoCKonK79FGtSJKVa4HEpmhKEaQT9owgYh2ATyVUEjLx6YYqKearYgUVk3EZcUy0xoATBNG+r4n0YiRANcJWjAeUxED5QJtE0myELRmKEQ3Y7QmidsuPB4qxSNo+wpa0IWxaqox8epGMvNCIjFw0woCkQfZ+5rrQuYYWNSqRC2C1Z7EzZ3arFByhIiJ2TKO9xoAIkBikuY+qaRJtC44zxcp0GGETpJeXgMx8dAwAYil9BI6IWkiyjxBeSIimR+aGtgeg0vyk95sxMCTahjLaNqkts1GralSNSO8Xx9b1+9J0xzzzvxU0kpanQYqjSTv+2FQsiqZBVudXwQSiSP63wGgdSvgDEDFJg5TSpfg3pf4LEYol9EKT3bKPMfOb2bRdTXYr1yYQHQF85KcxNbJOXt74aFzQ2zaIIpmeok1NNKUo1qdTTh1Ba7JJTdG2qaiLhAHdNnvK6FnDcfo9R/a8aJBUUUMe18JF3FQkASLZL+znJwFl+2Fq49prae0l/oF968O20O3WvHDYei06ZpgDDd3RtXKyC2G1Z7HDdotZrDTZLYBt10KAbRezGFnodmteOGz98OSTT2Lnzp148sknkee5U3w65phjgtSIvqkrKtc5VBk9UhpOXCSTcFG1diQxWEaPJsoPfEk7co0JO7l22w3Jva7ias9iHzHTWYfI9PuVV0lWW1Uy4KJtcXsQcauMMLUiRGWEK4olknI8mKhAHMWujHjZ2Qa5ayAoDHFiHXHiIlMyjqBiG9VqIY3Hi/FAhDgtau7yNHeFqACgJor3pQRI4+xwAnU1bG0lXQQtGSqOHQ/FaC0rVvbiJQmSpUX0LB4eQjw8WO4zgGiw2C4HhiBaRR2hSNoQrWIfE8UwKilfNHJ1ehrCrUoXLRH8HKdXwcYRtsXCjNqtUnDErTgjjPba7yKV7KctPLSh0TZDxHNM0IS+aeXSRWICIREydrWz2tX6Kgk3lqTeTZCoGo2wKemFMwLhISmcsk+wwgvj6rZ0pf2HqLPtTfVnNKpGH7fjPK+tTzNZ6qNnacdHzzIypoIiWoetXJqERqy4iNIwKOvfotgVfIgIMLkVsiqPkae+bk1rXxxSqdV20UFTyLX0s9a/0Feqmd6ZSdvVJDgCVOusTNe/TXVrNApXX+fWEGmrXHPVvydjMnG3XoXf6qJ1/TCV8Ecv76eXfereT5DtJYUzX0IKCFu3TOT7tfRmSGvfDgay0irA1ipL4T4/QSyT1mHj7H5r2Ba63VpwDtuHPvQh3HLLLe7v448/HgBw7733urz1fhFGw6D8Auow/UWp0gGQBnl5NxFp32F9MFZItU0v0uW/UaOCzS77mlQIJBKISqcvSyNk7cJxyLMMOivSaeiPu1BEXCRK3HOjRLlx3FJISuctihUGEpuuGTlnM4mkF08p5xJL6fyyIC2gOnbpObJQWAQgk8ilRKp2gniocIx0mpH+bMb34FACcry4EYo7OfJOmS5acWqdiIESLs0yakdIlsRuDACt4Zbblixto3XQ0nI8iGRp6bAND0IOFfULoj0EUZ5rMTDknDQTtWFUcRyjEred9o3T8D8w021lBxQpHbUqkQvAeDC9M5N2ywqOGJLKaM2MqY5rREfCsXH2izpp1HmrkhKbRlOqnaOmvROXlj/AsRLhWHt7U5osCOHqzKHh1SmNJg4YKTpXMMjJj7kVlWxSnkXZd7M4TiW10aYQutTISR6nThq1z6WgiKmIiziHLacOm1eGnAzrhBlNlCEBGPvLnZGbJ+33FTQNkjippnZhMiuFR3q/+WmyWwDbrgONmbJdNiWyLk0v1APqFvGqpkw29WSrU5Js6lUb9LjdB+dtX5iuqvdsz28qJ43eg2rymJDKK0CaigKkTYPXvs9uVQHSfu6C2PnCVJXbFe3P1n9K5EK3W/NCdKQfvvCFLxQ5zJX/puusMcxcojsaupPX/Df/jQfTO2y3mAOJZrvFtutAg20Xc6AwV3brv/7rv/CmN70JhxxyCIaHh3HyySfj3nvv7fs4Cy7CNmu49BANY1dZhYQsVziVlIhtepGSzks3sQpWqwE0rkwDvrv9rkhCRcW4EytEcfGaWZojK784eR7D6CJKRVeMaKhZSB+di2Lp0iCTVoSkjDwtGYyxtBwPxApLyNj1k1M+XcmGq7uizVbenkTVRJQ4WWmZtBEPFSvO+XgHOrVtCzIYclKsRKtKFKIywpZ3crePrpxAJ56SKKi4bIsw4GX74yXF6ydDsYvqJUt9GmR7+VI3VkuXQy5ZXhxv6XIXbUNrCCYuoqlGxe49QcVB7zUavQhOjZ1rn1kOOjcuClLdzjC1lO0l/HexmlpU/KtN+D2lKZHagIzro23ueZOsYJaWsmgjQiJrQBh108a388iNb5GilYE2tkWKcCIixohC2hllpI0UnYOssKry2jHCp9AYGilq6KFWTYMMImsoI2k5iappv6+Nnpm00xhVMymNsNnIlw4ibL1gXCaD9tE2JBBW9Ekqf3ybGhkhfJ80DZKcl6CVjai0QpiCJrtlH2OYOuoERyx1diaMxjVtJ2mQwdgL/NCoWt12+3fd2M+vQRBoP0Xj9pWmaJ6cKppWMxbaR9hseYxFl9lqEoB2PTm9GInRhowBUT6dbgf890GRlIniO7MvKZFzY7fe8IY34EUvehHuueceDAwM4FOf+hTe8IY34PHHH8fq1at7Ps6Ci7AxzIFEnuaN/80WO3fuxFlnnYXh4WEsX74c5513Hvbu3Tvpc8bHx/Gud70LBx98MJYsWYIzzjgDO3bsCPapk4b+x3/8x2Cf7373uzjhhBPQarVwzDHH4Atf+MJMvz2GYWaZyezWbNouhmGY6TIXdutXv/oVfv7zn2PLli14xStegRe96EX4+Mc/jtHRUTzyyCN9HWtxR9hKeeyAhsL0KGq5hd1EFSvAQLGCPZQ05yFLIpecRJJI/0fYU0aInhtNkaXFOOtoZOUXJ899b4imVW4VSSgbHYskojIClbQiDJbHXz4YY9lAETFa2o5cPdtgrNBSlRo2JYKVVreAYYraGQAwMoIoa7wCafyBIahOITSSLB1yRZwm1xB2jolyNWfZeIZsrIywpbqx6FPaNgSxbxVAm2TbqFo0NIC4FBRJhgeRDA8Vjy8ZgigjaXLJcsihorZNDg0DA8V2kwzAxEVE0MRtmKiItmkVO4GG3CCQQK+bbb9lxEYb1/y3un22OOuss/DMM8/grrvuQpqmOPfcc3HBBRfg9ttvb3zOJZdcgjvuuANf/vKXsWzZMmzevBlvfetb8YMf/CDY7/Of/zxOP/109/fy5cvd+Be/+AVe//rX48ILL8Rtt92Gu+++G3/yJ3+Cww47DJs2bZrx93mgYoQIvjG0nrKpySndnpNatdwgqFtzUTgd1rA1HddFzXIDVa6maukj9D56J1wUXxvjxsVjZUaBEa6mFgq+oE0aJxEtSHNtYXxUqGqB7XQbr0fdHHkr31AQdXPRs6wTRM9ctI2Ki6Sdxqiaq1ubbFWermgH26V/rray/rkrDhGIybHj+mO78CsRI0EpPNJHMW6T3bKPMUwVrU3X9TiVcEYdTVL1oWhJJZJWRsupkFs1oqYbtrvxDNe8Tec5/da8Ne1Pt2tVH02zf0tSn2bnTNtPSST1Ly4j8pmQ2rNKHVov0Do3Sk4yz3phLuzWwQcfjGOPPRZf/OIX3WL13//932PVqlVYu3ZtX8da3A4bxcXbpUuVMUKSVDeJRPkfQUOUDK0il91bkVSgWPqbkAEi/rG0HWHXaFSOY4x1itcc7eSub1ueeSemCPH76frsROEdmkg6BcjCMSuOv2zAp0QuG4wxVG5vRdL1k7OpkUr43kpdKTKkVb3tTSbiGKJVOmydNlTpDJlco22fRvqzqXYL+fgEACAb60Cn5bnOjXfwyIUjyPsrUiK9qInrrWYdtnbinLRoyM9FDA5DDhTb5eBS57yhNQSTWCdtACZuu7EuHbaMFLXm2qvuFXn86KKvfnYAdCdHXhPnbkq/2Fe2bduGO++8Ew888ABOPPFEAMD111+P173udbj22mtx+OGHdz3nueeew80334zbb78dp556KoDCMTvuuOPwwx/+ECeddJLbd/ny5Y0h/htvvBEveMEL8MlPfhIAcNxxx+H73/8+rrvuOnbY+sAKi+yv2+Kqs0exfyshfKai+102znmLpRdlKrZ5extbP8TQtE1vf4QR7nWkEP4ahE+P0cY7N023NII6ZkCQBu9SIXOvDOn2pWmNRDjEZGnhqAGFE0e2T+mk9XJ9T1OQoG+sMElDMX4dTXYLmD3bxRwYTMdJ6/V4dSqQNA0y2F5x0uqcs8nTJnXt9ir7M2WyzuHq3keGj2d+XE1zNDp3adnUeaPHrr4/J0ZCUhbpeF9pct56em4Pdmv37t3B9r76ONcghMB3vvMdvPnNb8bSpUshpcSqVatw55134qCDDurrWJwSyTBziMlN439AYTzofxMTE/v0elu3bsXy5cudswYAp512GqSUuP/++2uf8+CDDyJNU5x22mlu24tf/GIcccQR2Lp1a7Dvu971LhxyyCFYt24dPve5zwWrnlu3bg2OAQCbNm3qOgbDMPObyeyW4Ro2hmHmIb3YrTVr1mDZsmXuv4997GO1x9qyZUttGQj979FHH4UxBu9617uwatUqfO9738OPfvQjvPnNb8Yb3/hGPPPMM33Nf1FH2IwQLs0vfMCmxWR+Wy6dd1tE2so0FGEgbGFl2d9BCC/m0VIS7ag4zmDse7aNDuRYNlhE7PaOZy6qtmci8z3cstyNaaSHoqRwQiZJJJFEZcplrDBYRvOWtCMnNDKURFhKUiLtfFrlioqSXk47ODMkfdTIyK/EJG3IMiUSA0NulSICXBqkUBKqXUrjj3eQjRWr0jrNkLsIW9iXyPV5UxLS9m9KIkjX5y1xx4wGykjbYNtF0grJ/jKqNrTUbx9Y6tIdTTLoxzQNMmohc60ajBsH6WOVz8G1HkB/pFojrXlWWq7erVmzJth+xRVX4Morr+zzVTzbt2/HqlWrgm1RFGHFihXYvn1743OSJAnSGwHg0EMPDZ7zkY98BKeeeioGBwfx7W9/G3/2Z3+GvXv34t3vfrc7zqGHHtp1jN27d2NsbAwDAwPTfl+LCW2a+zlWsVEqGvlVQjiJZW18FGyyoutqZK3ucSckQtJgbNgtBVwkDdrPR3bZNF3OV7lIv0EopGITQhWJtvV74YmKAIcfk6ibFeXQukhzBIAs9WmQRLKfRuEahUbsY/uKVC7bga6KB4/X/a5VqQiTiD4ibE12yz7GMHXMVbpsXRpkdftUUTWjdWO0TddE5yabx0zSmPZIUh3rImPFWAcRN5fyqFXXvsWBVNe+k81hNjHaIEd366zJ6MVuPfXUUxgeHnbbm6Jrl112Gc4555xJX+/oo4/GPffcg3/913/Fb37zG3fcz3zmM7jrrrtwyy23YMuWLT3Pf1E7bAwz1xR1cfXbgd6Nx5YtW/CJT3xi0tfatm3btOfZCx/84Afd+Pjjj8fIyAiuueYa57AxDHNg0GS37GMMwzDzjV7s1vDwcHDP1cTKlSuxcuXKKfcbHR0FABd4sEgpoftc3FrcDpuNGlGJY/e/ctG2ZiVWGo1W2UxZkpovWa5qKmGCRtS2Tmw0zTEY5268pKzrmhjIMVIKjYx1chJh0248kfl50BUFmsfbIjVsSSRJs2wfSaNRtcFYkSbapP6urnG2PV/lv0aVNWxRC8JGr3TuonJaKihblJqMIyrFSPLxDnTHCo2kpKF2/RdXKOmiaiqJ3bho0u3FToAy2ucibINBtM1EZRPvuOXGho6jFnSpLxtE1bRB6nrQNgsx9Fu7Zulog6imGqlTvn6vxqPX1Z7Vq1fj2WefDbZnWYadO3c21p6tXr0anU4Hu3btCqJsO3bsmFSSdv369fh//+//YWJiAq1WC6tXr+5SltyxYweGh4c5utYH9tvStLDoo2r+R0hJ39BaCkCK7u8utWWFJnN3+GqqSNtk2LlQMRJtAFWz4klbDwjh9yGqzkVz7XJ/Y0jRWxU654bIWld0yfQgwU9W5YunTPHj22vdmqsZkX5Fm2wP9/f7uKwEemMgZH0WSZU+a9ia7JZ9jGGq5No01pc24WTeK3XtNFJnGzXnufHjyle5rt6ql1pLQ2pQq5E3V//WIEZSF02byQjbVDVkRudunOe5sw+TWYOpImX2PauGiB1FkntTWak3EzX1Z3XbZpq5sFsbNmzAQQcdhLPPPhsf+tCHMDAwgJtuusmJsPXD4nbYaqA/3EajkCIDILIOjCLqkeVNQKxiqDINMSqr7SNpyFgjKZ2bwVhhvHS8RtMcE7kf2+1ZrjFebk9z48K0nUxPGfpVUjiHLZbSpWW2lXSOWStSGCxzk6jzZh3MSHpHTYpKppFTOlFAKTpiVAzRKnqcSYD0DZIwZa82k4xDlGlEaqiiskZTh4KX8ik/sD3f4sT1f0OUeHXKUvREJu3AeQuctNLBNlELsOIiKg7ERdLMqtXBOWyaKEMag0a1PHeTXPtoM9qY2pvgptdpotfVng0bNmDXrl148MEHnULRPffcA6011q9fX/uctWvXIo5j3H333TjjjDMAAI899hiefPJJbNiwofG1Hn74YRx00EEuKrhhwwZ885vfDPa56667Jj0G0x9SeIEOIbwQkhE+1VlLA21I/0UiBhKkM7rVF+Il9TiHBUUfjsq06CddSKqwx1qN8ybiuOh/idI+0v3tv25xjXwWDY6bMBr93ko32S37GMP0i3e2jFefLW+upfR9FqH9zb0w3nkTQjgFwGqqMFU3tGMplStt6NeRqjp7kzlqM6Eo2StVJUc6tmid1/Ze6+XYjamVbrEoPO900Z/2DvbbKo6daHbyZoK5sFuHHHII7rzzTrz//e/HqaeeijRN8dKXvhTf+MY38MpXvrKvY7HDxjBzSMcYqBpHvDNLxuO4447D6aefjvPPPx833ngj0jTF5s2bceaZZzqFyKeffhobN27EF7/4Raxbtw7Lli3Deeedh0svvRQrVqzA8PAwLrroImzYsMEpRP7Lv/wLduzYgZNOOgntdht33XUXPvrRj+Iv/uIv3GtfeOGF+Nu//VtcfvnleMc73oF77rkHX/rSl3DHHXfMyntlGGZ2aLJb9jGGYZj5xlzZrRNPPBHf+ta39vk4i9thE6L4j35O1TQZK7RhNIRdGZGZl4HWietJJssojhLCFdgnSqFT5gIlyqBVph4OxNJFcSYy7SJsE7l28tepNsHYypk25eAq4VclpBCktxqR75fCRdsGY+UES+y/SgrYFkl0fcPQnnUqAnTiz5dd0W/BrfLKKIZJijRIk6WALdRv6lFEqawyi7h8rSh2ETYRJb6dQGIjZomLxmmV+KiaioOomt2eGSDL7Pn1kbRc10fVmi5nRVeR+owu5AaoW2+bzTqQ2267DZs3b8bGjRshpcQZZ5yBT3/60+7xNE3x2GOPudxrALjuuuvcvhMTE9i0aRM+85nPuMfjOMYNN9yASy65BMYYHHPMMfjrv/5rnH/++W6fF7zgBbjjjjtwySWX4G/+5m/w/Oc/H5/97GdZ0r9PtOlOh3S9yWC8WIcRThBJwLfuKPMdybOJGkg5ltqnJOZCQLr+bFN/NxW5BKg9stuVFEEa5pzTS8pgHVJBKJ9KL2Tuxu7QZDzZaZsqqlbYQWL7XKZB7Oyj20bTICURixJy8rTRPm5YmuyWfYxheoFG1Sw05dFGY4z2/RelJE1BtICxNgZw7ZaCbB9UhTZIdKkmAtXTvBFG2ZrSE+eauvclqXw/udcSle1AETmrO3fVfelYkkhaMBY122sicN3jmYu2LXS7tbgdNoaZYzraNxwOts/ias+KFSsmbZJ91FFHdakQtttt3HDDDbjhhhtqn3P66acHDbObOOWUU/Cf//mf/U2YYZh5RZPdAjjCxjDM/GSh2y122OqoKUwXVNaeipRoDeGarhZRpFjFiMoar0QpJKr4ImTaIC1XjrLcjwci6ca5MehkpRS21i66k+Y+91abZilT5Va0feQnVr55d6zCpt4RGQNl4+zyWF2LGkR0xK782mhVeZJcbZuMYphWKSSRdXxhLomqNRXLFgfw9RpuHMVAGc00MvLRNFdPl/jHIx9hg4pdo+/MFOceKFZU3HmvRNXouW6Cnp/GczYFumHFh+v2mcmQJDHAmKJGzY7tV1AIuGg5NKlJK45gHwi2WTGSVBhne6rRNvuj0ZTzH7YQ8NtUY7TN7xMKoNjVchrBrn3JyenhSTYiJezVKGh0S5NGshooI2kCgKFjlJEuncP2TTCarE43rb4HK9iVqJqV5o5iv4odxfV1vdQ2WvtMMyPomETepkOT3bKPMUw/CInCRgEAjIuwSCv4Jf19iTHG1+ZKAVU+kkOH46iwVHkGyDIKrdMOZFn/qbMOVDk2Okeedfqac50FnQoqBFLdPhWN8v2TRBLrpP3DiJisj5SRmjRF6mXt4zJK/Dj2YxVF7rNTStaOpRTEDAm/XYhwn1nIvljodmtxO2z2B0yY+sLzJkUxId0PMmQOlI6a+6GNEojSQRAygix/SJNY+f5e0isO0h5rqTbIy08lN9JlqeQa0LDOhf9mNYgrFqpwNgRNxAdonzU69imR3ukT5KYJIOk0MoIp5yjgb3YgpEsVRRQDUe7OnSQ9fxqL/KkKJT1maTy0jLzYCb0pKQ0KZFSkP5aPW9XH3BikmXfS6lIfc20C9T1f3xxexfY8NtmSfm1MR5tAsc9tXwCrPcz8QAjhepMJIYiTRsRCpPE3RF2Om+19JpCWvSSVFL7nILFV2tB+kFN/2euctNB5g1tMqtomey3ZzHU345o+kT3/uFMnhdoYfyAAhU0T1omTkogp5U7ww2QdP9a5T2vUyi1QhU5a3DwveqNE08GJiJMTGplsEat8nlXxhYz8dtp3lLznnlQkKzTZLYBtF9MbQvhFJAl/XyOl6HKChPHXt4JEXu6hIom8LGsItgf7RK6lroyThh5rofqjXYhp6s9mtHSLMYKqR0rVLUjSoCJJ/+41JbNuvzrxDwBdAiG9OGnVlEgplXNwA4eNOGmFU2fvdwWU7b9bHUd2jGA7FSOpU4qkTt2+OnEL3W4tboeNYeaYIqe621AshHxqhmEWJ012yz7GMAwz31jodmtRO2ymjOSIapDUpkE2RYKM9ik0gTBJucqgcx9tUxFE7qNCSbkaGkcRdLlCnRuDNK+PANlaorwifmEXuY3pjgIB3WlEduFCwK9QK0GjaeV0g5XwykHt+5SRO7qJALd8JQRgyhVnoyFU5k9ZQ7SyacXbF817iWqjGiJsbptyq3S5KVokAMXKnb0Yc23c6h2NqtEIZjFfdDHZ4s50V344wsZMFxf7Fv77WixEk0gajYaVrUakEc4+SGEg82KspUGpoVFcD9KnaWty/VgF+F5kkIP0yJp0bSmEE0NRdCzpHBGMfeRNVCJvk8+lsPVkLGrskI1uGeO3qTiMJdJVcterTQKa9D+yaYq9ihDQlW3a0oRG2Kz4klT++JUIGtBsJ8vqfT/eh5TIhb5SzcwdVkBCV3LQ3NdRC/c1tVcojZjZvwGb+ii7jmdtV3E4AyGL6yXPQql7nRZpkDRVUeocukyPnCzaZgkjb9qnbtp9ou4I3mTQiByNmFVplNivpljXPN4UVbPHrIuqCalcaindriJJBGEEZPl5SEHGJA2SpkdWo3BUjKTJPO2LAMlCt1uL2mFjmLkmMwZpjcOdLQDjwTDM4qTJbtnHGIZh5hsL3W4tbofNrjhqURsxCyJB1cbOdrVHyvC59ri2xk3ngMz8duVr22ydm5IKcZkDrCFqxUVy46NtGqTGimyf9K3ShoSkNsSvdNv31Rwtcu9TaLeqK+z7QhlhtOdAa8BMUrNROWYxie7VXyMjsvqt3P7VaBoA5Kn2Y2NI/V+lBqcmOklPYbX41Muk2zcM/95Rqa+Z8h2HzIWsP7OwKaJMAhLeTthrVlVF5e2lY1yADbkGRCmEJLRwNiA3BnFZK5LrQvSoOH5YMxv8PYXtUYHd8fVpPsrv62RjJYJIvyRjW5dXFUXqW+yHRtJMTcaA3Y2OdebqxAS8kIiQykfQ8tz9RoiaGpn6qXSLLDkbiHJ13a10UzGSKMxAkMRW1j1OMxSa6oSrhYJTsNDlsZm5gUr2A+ReQ1IRD19vSyNtNqpmhOmKoBXHLg8EQNDm27kOm2uX132e+SgRjZLptBNE22idm9YNEbaGyFvdWLlt/ciVhAgpK39PHm1rkufvlviv1LBVatWaomp14iJF3RrRQ7AZFpEMJP7rhEaolL9oECCpq3ebioVut9hho8IjdptVgBSyPi0yuLvXgfNmnwfqxDmBksiJchgZ+YuOiGkoIaGIwpeJlXtJ6sgZOiZexFRKN/Q7Lmq2C3KjRB83QsDeKhkZeZVMQfOxwh52U33/AwWz4sXLf2XgmDnHi6ZmpZqkM3pH1oqwUIdtOk5aHdX0Kyp+QM9fP3S0cb2ygu0LYLWHmTukgEupljSVVwj3ZRaiSH8EimuE9mqzNkMJb1eUEdB2f2kQm1LsxxgvbqT6c9gsVcfNOmCSpDVKEIdNhkJJdHHEvQ86RnVsT0hlIjUOixAkxb0u7VsI36+J/iYY0key2lPS7j/1qQHtvVZMrcbZogtXQnhRkerilt1GVSLp74zo/o2ajvRmk90C2HYxk+Nu0OHTGKsCJPY3WjiRLwFjU7Qz7dQjEUmI8hiGfCeNMcgz68h5J1EbA51pf0y7aKMjNxcZJYGTVi86ktc6ZLoHh42mPO4LNF2yyWGTFQfMjnsVHan2VXMLgxFRfRShcEidkxY4ZtVUSeKk+XnXC43Q7dNhodutxe2wMcwckxvTIDoy/40HwzCLkya7ZR9jGIaZbyx0u7W4HbZylZEWoFM5Z4CkxRTLQeUfNKpk/AqlTYmRCKJ0QbTNpVv6sD9E5lIlg2JwGfm5CAFFVlJdBCqIEpngq9hL1Ki6WBFG3RpWMgRZ0w+ikH4Vp+mlgxRIIYvVa/tYXUQs015gBWE0jaaFAt1RNTuHqkx/P5E1KnJAIwB0FZ+KH/S7+JM2rPikC8B4MHODKP+z3zUNEUTZ7APCAPYqEEq4770SPu3aGEAF11e5D+j1JdylXRU56udrGqYO+ygZTdGm22kbgLpUSRrZbn5RGkkyPguARp6Mz8dy9p5mCAjtol5G6yCjQMgagaomsaq6uVXGpmJbvWGRYQSNRsqoqIjbRtIdrQBJ9fVIRkPXfKagyW4BbLuYelTlYhWktxpAqk6k39dFxkiapIqkj8xpA1N+D7U0jdE2X6lhYJR0+zghN21ce4Ai6hb7fdwx89rIm/2b/jvZmNJL/zXKdHqxdW1T9RG2uuiVEFSOXzSnMkq/f1NUjUbh6sRFqq9PUy79+wnnp6SA6eOma6HbrUXpsNl0wj179hQb8o5rfg2d+xoE0gFSbfgAAA0fSURBVDOsSG2kP8rkw606NiS/uKlxqZEqTAGs+zEmKTFF/g912ETXNGbTYRMCEPTFKumPvdK3w0bSIIEeHDb4Y1CHzf6xrw4b3I0j6RlFUrzGR/a6effCGHRt7nSnr3aczGKgarfCBQlS02G6t1evF+ewoaI+a29sYLquL2D/OWyoOmzl88LatvAm0I4D9Vtjmm17uV3ozNszm8pojO8pabS3cVWHjaZH9ntS6AmhzhbdNqXDJuodNlHnsFVer+Kwue9VD/NvslsA2y4mxH6fsvER51BVHwN8eiTd5hw27Z0uY6gTZZyB0sa45xaOlt8/cNjIc6nDpnPt9qevX++w6dBhM9N12Pq7Vqq1a377FA6bqDps0j3uez3WO2zIvcNmahw2U3HYjDM9Pt3REEO/Tw5b5V7bSIFsfKQYLwK7tSgdNvvDdPTLjp/jmTAHKr/+9a+xbNmyxseTJMHq1atx2/anG/dZvXo1kiSZjekxCxBrt178/71ojmfCHMjs2bOn0Xb1YrcAtl2Mx9qt//jAW+d4JsyBzGKwW8L0Ggo4gNBa47HHHsNLXvISPPXUUxgeHp7rKc1rdu/ejTVr1vC56oHnnnsORxxxBH7zm99g+fLlk+47Pj6OTqfT+HiSJGi32zM8Q2ahwnarP9hu9Y49Vz/72c9w7LHHQjas5ANT2y2AbRfjYbvVH2y3emex2a1FGWGTUuJ5z3seAGB4eJgvih7hc9U7kxkOS7vdntfGgZlfsN2aHnyueud5z3velLaL7RbTD2y3pgefq95ZLHar9ypjhmEYhmEYhmEYZr/CDhvDMAzDMAzDMMw8ZdE6bK1WC1dccQVardZcT2Xew+eqd/hcMbMJf796h89V7/C5YmYT/n71Dp+r3lls52pRio4wDMMwDMMwDMMsBBZthI1hGIZhGIZhGGa+ww4bwzAMwzAMwzDMPIUdNoZhGIZhGIZhmHkKO2wArrrqKrzqVa/C4ODglM2OFxs33HADjjrqKLTbbaxfvx4/+tGP5npK85L/+I//wBvf+EYcfvjhEELg61//+lxPiTnAYbvVDNut3mC7xexv2G5NDtuuqVmsdosdNgCdTgdve9vb8M53vnOupzKv+Kd/+idceumluOKKK/DQQw/hla98JTZt2oRnn312rqc27xgZGcErX/lK3HDDDXM9FWaRwHarHrZbvcN2i9nfsN1qhm1XbyxWu8UqkYQvfOELuPjii7Fr1665nsq8YP369fjt3/5t/O3f/i0AQGuNNWvW4KKLLsKWLVvmeHbzFyEEvva1r+HNb37zXE+FWQSw3QphuzU92G4x+xO2W92w7eqfxWS3OMLG1NLpdPDggw/itNNOc9uklDjttNOwdevWOZwZwzBMPWy3GIZZiLDtYqaCHTamll/96lfI8xyHHnposP3QQw/F9u3b52hWDMMwzbDdYhhmIcK2i5mKA9Zh27JlC4QQk/736KOPzvU0GYZhHGy3GIZZaLDdYpjZJ5rrCcwWl112Gc4555xJ9zn66KP3z2QWIIcccgiUUtixY0ewfceOHVi9evUczYphDmzYbu0bbLcYZv/DdmvfYdvFTMUB67CtXLkSK1eunOtpLFiSJMHatWtx9913u2JOrTXuvvtubN68eW4nxzAHKGy39g22Wwyz/2G7te+w7WKm4oB12PrhySefxM6dO/Hkk08iz3M8/PDDAIBjjjkGS5YsmdvJzSGXXnopzj77bJx44olYt24dPvWpT2FkZATnnnvuXE9t3rF3717893//t/v7F7/4BR5++GGsWLECRxxxxBzOjDlQYbtVD9ut3mG7xexv2G41w7arNxat3TKMOfvssw2Arv/uvffeuZ7anHP99debI444wiRJYtatW2d++MMfzvWU5iX33ntv7Xfo7LPPnuupMQcobLeaYbvVG2y3mP0N263JYds1NYvVbnEfNoZhGIZhGIZhmHnKAasSyTAMwzAMwzAMs9Bhh41hGIZhGIZhGGaewg4bwzAMwzAMwzDMPIUdNoZhGIZhGIZhmHkKO2wMwzAMwzAMwzDzFHbYGIZhGIZhGIZh5inssDEMwzAMwzAMw8xT2GFjGIZhGIZhGIaZp7DDxjAMwzAMwzAMM09hh41hGIZhGIZhGGaewg7bAcavf/1rrFq1Ck888cQ+HeeUU07BxRdfPCNz2lfOPPNMfPKTn5zraTAMM4uw7WIYZqHBdovZXwhjjJnrSTAzx6WXXoo9e/bgpptu2qfj7Ny5E3EcY+nSpTM0s+nzyCOP4Hd/93fxi1/8AsuWLZvr6TAMMwuw7WIYZqHBdovZX3CE7QBidHQUN998M84777x9PtaKFSv2yXB0Op19noPlZS97GV74whfi1ltvnbFjMgwzf2DbxTDMQoPtFrM/YYdtHvP85z8fn/nMZ4Jt9913HwYHB/G///u/Xft/85vfRKvVwkknnRRsP+WUU3DRRRfh4osvxkEHHYRDDz0UN910E0ZGRnDuuedi6dKlOOaYY/Bv//ZvwXNoeF5rjauvvhrHHHMMWq0WjjjiCFx11VXB/ps3b8bFF1+MQw45BJs2bQIATExM4N3vfjdWrVqFdruNk08+GQ888EDwvHe/+924/PLLsWLFCqxevRpXXnll13t74xvfiH/8x3/s6/wxDDM3sO3ysO1imIUB2y0P2635Bzts85j169cHF5oxBhdffDEuueQSHHnkkV37f+9738PatWtrj3XLLbfgkEMOwY9+9CNcdNFFeOc734m3ve1teNWrXoWHHnoIr33ta/H2t78do6Ojtc9/3/veh49//OP44Ac/iJ/97Ge4/fbbceihh3a9RpIk+MEPfoAbb7wRAHD55ZfjK1/5Cm655RY89NBDOOaYY7Bp0ybs3LkzeN7Q0BDuv/9+XH311fjIRz6Cu+66Kzj2unXr8KMf/QgTExO9nTyGYeYMtl0etl0MszBgu+VhuzUPMcy85eqrrzYvfelL3d+33HKLWb16tdmzZ0/t/m9605vMO97xjq7tr3nNa8zJJ5/s/s6yzAwNDZm3v/3tbtszzzxjAJitW7e65/z5n/+5McaY3bt3m1arZW666abGub7mNa8xxx9/fLBt7969Jo5jc9ttt7ltnU7HHH744ebqq6+unZsxxvz2b/+2ee973xts+/GPf2wAmCeeeKJxDgzDzA/YdnnYdjHMwoDtloft1vyDI2zzmJNOOgnbtm3D3r17MTIygr/8y7/EX/3VX2HJkiW1+4+NjaHdbtc+9opXvMKNlVI4+OCD8fKXv9xtsys3zz77bNdzt23bhomJCWzcuHHS+VZXmh5//HGkaYpXv/rVblscx1i3bh22bdtWOzcAOOyww7rmMTAwAACNq1EMw8wf2HZ52HYxzMKA7ZaH7db8I5rrCTDNrF27FlJKPPTQQ/jOd76DlStX4txzz23c/5BDDsFvfvOb2sfiOA7+FkIE24QQAIq86Sr2wp2KoaGhnvbrZW7Vedhw/sqVK6f1GgzD7D/YdnnYdjHMwoDtloft1vyDI2zzmMHBQbz85S/HV77yFVx77bW47rrrIGXzR3b88cfjZz/72YzP40UvehEGBgZw99139/W8F77whS6/2pKmKR544AG85CUv6etYjzzyCJ7//OfjkEMO6et5DMPsf9h2edh2MczCgO2Wh+3W/IMjbPOck046Cddffz3e9KY34ZRTTpl0302bNuF973sffvOb3+Cggw6asTm02228973vxeWXX44kSfDqV78av/zlL/HTn/50UjnboaEhvPOd78R73vMerFixAkcccQSuvvpqjI6O9i2D+73vfQ+vfe1r9/WtMAyzn2DbVcC2i2EWDmy3CthuzT/YYZvnvPKVr0Qcx7jmmmum3PflL385TjjhBHzpS1/Cn/7pn87oPD74wQ8iiiJ86EMfwv/93//hsMMOw4UXXjjl8z7+8Y9Da423v/3t2LNnD0488UR861vf6su4jY+P4+tf/zruvPPOfXkLDMPsR9h2se1imIUG2y22W/MVYYwxcz0Jppnf+73fwwknnIBPfvKTPe1/xx134D3veQ8eeeSRSUP5C4m/+7u/w9e+9jV8+9vfnuupMAzTI2y72HYxzEKD7RbbrfkKR9jmIVpr/PKXv8TNN9+Mn//85/jGN77R83Nf//rX4+c//zmefvpprFmzZhZnuf+I4xjXX3/9XE+DYZgpYNsVwraLYeY/bLdC2G7NTzjCNg/57ne/i1NPPRUvfvGL8fnPfx7r16+f6ykxDMNMCdsuhmEWGmy3mIUAO2wMwzAMwzAMwzDzlAMj4ZZhGIZhGIZhGOYAhB02hmEYhmEYhmGYeQo7bAzDMAzDMAzDMPMUdtgYhmEYhmEYhmHmKeywMQzDMAzDMAzDzFPYYWMYhmEYhmEYhpmnsMPGMAzDMAzDMAwzT2GHjWEYhmEYhmEYZp7CDhvDMAzDMAzDMMw8hR02hmEYhmEYhmGYeQo7bAzDMAzDMAzDMPOU/x9LA+JpnPlnWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "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 + np.abs(fields_meas.Ey) ** 2 + 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();"
   ]
  },
  {
   "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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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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\">14:35:06 UTC </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-436ff86b-18eb-4768-98a5-7e7af6bfdd83'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:06 UTC\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-436ff86b-18eb-4768-98a5-7e7af6bfdd83'\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\">14:35:07 UTC </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4768-98a5-7e7af6bfdd83'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:07 UTC\u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=650222;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=669540;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=650222;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=969931;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=650222;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32m-436ff86b-18e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=650222;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[32mb-4768-98a5-7e7af6bfdd83'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=414753;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bf697f9fa7e4214bf81f0446bbbf7e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:09 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.042</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:09 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.042\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:10 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:10 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "202cc570d88b409da63815e4d072f42c",
       "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\">14:35:24 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:24 UTC\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\">14:35:28 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:28 UTC\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a0f4fefe50e4f56a91e90917167d4e9",
       "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\">14:35:30 UTC </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;36m14:35:30 UTC\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:31 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:31 UTC\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                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4768-98a5-7e7af6bfdd83'</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                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=26468;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=439056;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=26468;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=655457;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=26468;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34m-436ff86b-18e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=26468;https://tidy3d.simulation.cloud/workbench?taskId=fdve-436ff86b-18eb-4768-98a5-7e7af6bfdd83\u001b\\\u001b[4;34mb-4768-98a5-7e7af6bfdd83'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0c83897bb2f40a89c18a5a0fed1f654",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:33 UTC </span>loading simulation from data/aperture_4.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:33 UTC\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(sim4, task_name=\"aperture_4\", path=\"data/aperture_4.hdf5\", verbose=True)"
   ]
  },
  {
   "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 16.00 s\n",
      "Server-side field projection *without approximations* took 0.04 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "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(sim_data4.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
    "print(f\"Client-side field projection *without approximations* took {proj_time_new:.2f} s\")\n",
    "print(f\"Server-side field projection *without approximations* took {server_time:.2f} s\")\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:34 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:34 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAE8CAYAAACCZ7VcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAASj9JREFUeJzt3Xl4E9X+P/D3ZG/TJi0tdGGtCIKAglhqEUWh16oo4oLARYoLIggIorIoi+JSr4ogsl1X7g9BEB8veoWLQlkEqWBZvoIgIiJ7W9qmSZs0mSRzfn/0Zto0aZO0SSYtn9fz5FEmZ2ZO5nTyzsycOcMxxhgIIYQQicikrgAhhJArGwURIYQQSVEQEUIIkRQFESGEEElREBFCCJEUBREhhBBJURARQgiRFAURIYQQSVEQEUIIkRQFESFe7Ny5ExzHYefOnVJXhYTA/v37oVKpcObMmbCvu7S0FFqtFps3bw77uiMVBRG5oi1fvhyrVq2SuhqNsnbtWixevFjqarj5+OOP0b17d2g0GnTp0gXvv/++3/PabDbMnDkTqampiIqKQkZGBrZu3eq17N69ezFgwABER0cjOTkZzzzzDCorK/1e10svvYRRo0ahY8eOfs8TLAkJCRg3bhzmzp0b9nVHLEbIFaxHjx5s4MCBHtOdTierqqpiTqcz/JXy05AhQ1jHjh2lroZo5cqVDAB78MEH2QcffMDGjBnDALA333zTr/lHjhzJFAoFe/7559k///lPlpmZyRQKBdu9e7dbuUOHDjGNRsP69OnDVqxYwV566SWmVqvZnXfe6dd6Dh06xACwvXv3BvwZg+XYsWMMAMvLy5OsDpGEgoiwyspKqasgmfqCqDmIpCCyWCwsISGBDRkyxG366NGjmVarZWVlZQ3Ov2/fPgaAvf322+K0qqoq1rlzZ5aZmelW9q677mIpKSnMaDSK0z788EMGgH333Xc+6/rMM8+wDh06MEEQ/PloIdOzZ082ZswYSesQKSiIWpjz58+zxx9/nKWkpDCVSsU6derEJkyYwGw2G2OMsU8//ZQBYDt37mQTJ05krVu3ZnFxceL8y5YtY9deey1TqVQsJSWFPf3008xgMLit4/fff2cPPPAAS0pKYmq1mrVt25aNGDGClZeXi2W+//57dvPNNzO9Xs+0Wi3r2rUrmz17ts/6+zOf1Wpl8+bNY507d2YqlYq1a9eOvfDCC8xqtXosb/Xq1Sw9PZ1FRUWxuLg4dsstt4hfVh07dmQA3F6uUNqxYwcDwHbs2OG2vC+++ILdcMMNTKPRsISEBDZ69Gh2/vx5tzJjx45lWq2WnT9/nt13331Mq9WyxMRE9txzzzGHw+FzG2zcuJHdfffdYhteddVVbMGCBW7zDhw40KPuDYXSrbfeyq677jqv73Xt2pXdcccdPuvVkE2bNjEAbNOmTW7T9+7dywCw1atXNzj/Cy+8wORyuVu4MMbYG2+8wQCws2fPMsYYMxqNTKFQsBdeeMGtnM1mYzExMeyJJ57wWdcOHTqwRx991GM6ADZ//nyP6R07dmRjx44V/+3ah3bv3s2mTJnCEhMTmV6vZ+PHj2c2m40ZDAY2ZswYFhcXx+Li4tgLL7zgNfSeffZZFhcXJ3kgRgJFWM8DkpC6ePEi+vXrh/LycowfPx7dunXDhQsX8OWXX8JisUClUolln376abRu3Rrz5s2D2WwGALz88st45ZVXkJWVhYkTJ+LEiRNYsWIFfv75Z/z4449QKpXgeR7Z2dmw2WyYMmUKkpOTceHCBXz77bcoLy+HXq/Hr7/+invuuQfXXXcdFixYALVajT/++AM//vhjg/X3Zz5BEDB06FDs2bMH48ePR/fu3XHkyBEsWrQIv//+OzZu3CiWfeWVV/Dyyy+jf//+WLBgAVQqFfbt24ft27fjjjvuwOLFizFlyhTExMTgpZdeAgAkJSXVW79Vq1bhscceQ3p6OnJzc1FUVIT33nsPP/74Iw4dOoS4uDixrNPpRHZ2NjIyMvDOO+9g27ZtWLhwITp37oyJEyc2uB1WrVqFmJgYTJ8+HTExMdi+fTvmzZsHk8mEt99+G0D1NQ6j0Yjz589j0aJFAICYmJh6lzlmzBg8+eSTOHr0KHr27ClO//nnn/H7779jzpw54jSDwQCn09lgHQEgOjoa0dHRAIBDhw4BAG688Ua3Mn379oVMJsOhQ4fwyCOP1LusQ4cOoWvXrtDpdG7T+/XrBwA4fPgw2rdvjyNHjsDhcHisR6VSoXfv3mI96nPhwgWcPXsWN9xwg8/P54vr7/+VV17BTz/9hA8++ABxcXHYu3cvOnTogDfeeAObN2/G22+/jZ49eyInJ8dt/r59+2LRokX49ddf3drkiiR1EpLgycnJYTKZjP38888e77l+dbl+zQ0YMMDtF3ZxcTFTqVTsjjvucLsusnTpUgaAffLJJ4yxmvPrGzZsqLceixYtYgDY5cuXA6q/P/OtXr2ayWQyj+sGrusTP/74I2OMsZMnTzKZTMbuv/9+j+s8tX+B1ndqru4REc/zrE2bNqxnz56sqqpKLPftt98yAGzevHnitLFjxzIAbMGCBW7L7NOnD+vbt2/DG4FVn+aq66mnnmLR0dFuR32BnJorLy9nGo2GzZw50236M888w7RardvpWW9Hit5etY8eJk2axORyudd1t27dmo0cObLB+vXo0YMNGjTIY/qvv/7KALCVK1cyxhjbsGEDA8B++OEHj7LDhw9nycnJDa5n27ZtDAD7z3/+4/Fe3c/kUt8RUXZ2ttvfUmZmJuM4jk2YMEGc5nA4WLt27bz+jbmOFtevX99gna8E1GuuhRAEARs3bsS9997r8WsRADiOc/v3k08+CblcLv5727Zt4Hke06ZNg0wmcyun0+mwadMmAIBerwcAfPfdd7BYLF7r4joy+PrrryEIgt+fwZ/5NmzYgO7du6Nbt24oKSkRX4MGDQIA7NixAwCwceNGCIKAefPmuX0ewHNb+KOgoADFxcV4+umnodFoxOlDhgxBt27dxO1T24QJE9z+fcstt+DPP//0ua6oqCjx/ysqKlBSUoJbbrkFFosFv/32W8B1B6rb7b777sPnn38O9r9nYTqdTqxfvx7Dhg2DVqsVy65ZswZbt271+ar9C7+qqsrtiLs2jUaDqqqqButXVVUFtVrtdV7X+7X/W19ZX+spLS0FAMTHxzdYzh9PPPGE299SRkYGGGN44oknxGlyuRw33nij13Z31aGkpKTJdWnu6NRcC3H58mWYTCa/D/HT0tLc/u26n+Kaa65xm65SqXDVVVeJ76elpWH69Ol49913sWbNGtxyyy0YOnQoHnnkETGkRowYgY8++gjjxo3DrFmzMHjwYDzwwAN46KGHPEKhNn/mO3nyJI4fP47WrVt7XUZxcTEA4NSpU5DJZLj22mv92h6+1Ld9AKBbt27Ys2eP2zSNRuNRx/j4eBgMBp/r+vXXXzFnzhxs374dJpPJ7T2j0Rho1UU5OTlYv349du/ejVtvvRXbtm1DUVERxowZ41bu5ptvDnjZUVFR4Hne63tWq9UtXOub32azeZ3X9X7t/9ZX1td6XFgQHkzdoUMHt3+7/v7bt2/vMd1bu7vq0JgfRi0NBdEVyt8d1puFCxfi0Ucfxddff43vv/8ezzzzDHJzc/HTTz+hXbt2iIqKwg8//IAdO3Zg06ZN2LJlC9avX49Bgwbh+++/dzsSq1snX/MJgoBevXrh3Xff9bqMul8CUqnvM/pSXl6OgQMHQqfTYcGCBejcuTM0Gg0OHjyImTNnBnSEWVd2djaSkpLw2Wef4dZbb8Vnn32G5ORkZGVluZW7fPmyX9eIYmJixOtSKSkpcDqdKC4uRps2bcQyPM+jtLQUqampDS4rJSUFFy5c8Jh+6dIlABDnT0lJcZtet6yv9SQkJACAXz8IXOoLrfra2Nt0b8tw1SExMdHvurRUdGquhWjdujV0Oh2OHj3aqPldN/adOHHCbTrP8zh9+rTHjX+9evXCnDlz8MMPP2D37t24cOECVq5cKb4vk8kwePBgvPvuuzh27Bhef/11bN++XTx1Vh9f83Xu3BllZWUYPHgwsrKyPF6uI5bOnTtDEAQcO3aswfX5+2u0vu3jmhasGyN37tyJ0tJSrFq1ClOnTsU999yDrKwsr6eSAv0lLZfL8fe//x1ffvklDAYDNm7ciFGjRnl8caanpyMlJcXn65133hHn6d27N4DqU5i1FRQUQBAE8f369O7dG7///rvHEeC+ffvclt+zZ08oFAqP9fA8j8OHD/tcT7du3QAAp0+f9vp+RUWFxzTXUXawuerQvXv3kCy/OaEgaiFkMhmGDRuG//znPx47KeD7VERWVhZUKhWWLFniVvbjjz+G0WjEkCFDAAAmkwkOh8Nt3l69ekEmk4mnS8rKyjyW7/qC8HZKxcWf+R5++GFcuHABH374oUfZqqoqsQfgsGHDIJPJsGDBAo+jiNqfT6vVory8vN46udx4441o06YNVq5c6fYZ/vvf/+L48ePi9mkqVyjUriPP81i+fLlHWa1WG/CpujFjxsBgMOCpp55CZWWl155sjblGNGjQILRq1QorVqxwW9aKFSsQHR3ttn1KSkrw22+/uV1jfOihh+B0OvHBBx+I02w2Gz799FNkZGSIR7p6vR5ZWVn47LPP3EJj9erVqKysxPDhwxv8/G3btkX79u297iMAPIZ0+u9//wur1RqUU3l1HThwAHq9Hj169Aj6spsdiTpJkBA4f/48S05OZtHR0WzatGnsn//8J3v55ZdZjx49xHuBXD1+vPWsmz9/PgPA7rjjDrZ06VI2ZcoUJpfLWXp6OuN5njHG2L///W/Wtm1bNm3aNLZ8+XK2ZMkSlp6ezpRKJcvPz2eMMTZ16lTWp08fNmfOHPbhhx+y119/nbVt25a1a9fO7V6juvyZz+l0srvvvptxHMdGjhzJ3n//fbZ48WI2YcIE1qpVK7fPNXfuXAaA9e/fn73zzjvs/fffZzk5OWzWrFlimaeffppxHMdeffVV9vnnn4t3unu7j8i17TIyMtjixYvZ7NmzWXR0NOvUqZPbvVau+4jq274NKSkpYfHx8axjx45s4cKF7N1332V9+vRh119/vUd93nrrLQaAPfvss2zt2rXsm2++aXDZLj179mQAWPfu3f0q769ly5YxAOyhhx5iH374IcvJyWEA2Ouvv+5WzrUd6t6jNXz4cPEeoX/+85+sf//+TKFQsF27drmVO3DgAFOr1W4jK2g0Gr/vhZo8eTJr27atx/07AFhUVBS766672IoVK9jcuXOZTqdjer2eXX311Wzt2rWMsfr3Idfnqtvrs76/h549e7JHHnnErzq3dBRELcyZM2dYTk4Oa926NVOr1eyqq65ikyZN8rih1VsQMVbdXbtbt25MqVSypKQkNnHiRLcv2T///JM9/vjjrHPnzkyj0bBWrVqx22+/nW3btk0sk5eXx+677z6WmprKVCoVS01NZaNGjWK///57g3X3dz6e59k//vEP1qNHD6ZWq1l8fDzr27cve+WVVzxuiPzkk09Ynz59xHIDBw5kW7duFd8vLCxkQ4YMYbGxsX7d0Lp+/Xpxea1atWrwhta6/Akixhj78ccf2U033cSioqJYamoqmzFjBvvuu+886lNZWcn+/ve/s7i4OJ83tNbmCrA33njDr/KB+OCDD9g111zDVCoV69y5M1u0aJHHF359QVRVVcWef/55lpyczNRqNUtPT2dbtmzxup7du3ez/v37M41Gw1q3bs0mTZrETCaTX3U8ePCgeENqbQDY9OnT2fDhw1lUVBRLSUlhS5cuZStXrmTR0dFs3LhxjLHgBNHx48cZALf95krGMRaCY05CSMR677338Oyzz+Kvv/7y6Pl1pRg8eDBSU1OxevVqcRrHcZg/fz5efvnlkK9/2rRp+OGHH3DgwAHqNQe6RkTIFYUxho8//hgDBw68YkMIAN544w2sX79essdAfPTRR3jttdcohP6Hum8TcgUwm8345ptvsGPHDhw5cgRff/211FWSVEZGRr33PYVaQkJCQI+suBJQEBFyBbh8+TL+/ve/Iy4uDi+++CKGDh0qdZUIEdE1IkIIIZKia0SEEEIkRUFECCFEUnSNyE+CIODixYuIjY2lni6EkCsKYwwVFRVITU1tcODixqIg8tPFixfx/vvvQ6Hwf5MxxmAwGPwaHkSlUokNLAiCJD16FAqF2+ez2WwhGdqkIRzHuQ3x73A4PIYUCgdqj2rUHjVaUntwHIf4+PiAflS/8cYbOHfuHNq1axfw+nyhIPJTbGwsFAoFoqOj/f5FwBhDp06d/BqJmed5cdwwvV5f77NdQkkQBPF5LSqVShzWPtyMRqP4RZOQkBCSX2C+UHvUoPao1pLaw+l0oqSkxO8gco3XGBsbG/C6/EFB5CdXg8lkMiiVSp/lBUGAIAhQq9U+dxqe51FeXi4+msFisSA6OjqsO5trJ1MqlVCr1bBarXA6nSH7w6tPRUUFBEFATEwMbDYbqqqqwv7lR+1Rg9qjWktrD57nIZPJxJcvdrsdQOienUSdFSTG8zxKSkqgVCqRmJiIxMREKJVKlJSUhO30g2sns9vtSExMREJCAnQ6HUwmk9dh8UOloqICJpMJOp0OCQkJSExMhN1uR2lpaZOewxMIao8a1B7VqD1Cj4JIQrV3MtevGplMhoSEhLDtbHV3MtevzNjY2LDubLV3MtevTJVKFdadjdqjBrVHNWqP8KAgkoi3ncwlXDtbfTuZS7h2Nm87mUu4djZqjxrUHtWoPWqEOuQoiCTQ0E7mEuqdzddO5hLqna2hncwl1DsbtUcNao9q1B41wtFTkoIozPzZyVxCtbP5u5O5hGpn82cncwnVzkbtUYPaoxq1Rw2HwxGWa3EURGEUyE7mEuydLdCdzCXYO1sgO5lLsHc2ao8a1B7VqD1quEKI4zi/ego3BQVRAORyeaNvYGvMTuYSrJ2tsTuZS7B2tsbsZC7B2tmoPWpQe1Sj9qhRO4TUanXIR5OhIAqQ3W4PuHGbspO5NHVna+pO5tLUna0pO5lLU3c2ao8a1B7VqD1q1A2hcNwzRje0BkAQBMjlclitVrchR+oryxhDVVUVKioqoFAooNPpmnzRT6fTwWAwoLi4GPHx8X4dMruGGnI4HIiPjweAJp3CUKvViI6ORnl5ORwOB7RarV/zmc1mVFZWIiYmBmq1usmnUfR6vdu28OdXm91uh8FgoPYAtYfLldgedrsdjDGPkHI6nR6n41zfZaFEQRQAVxAJggC73Q6FQlHvzuYaWcFisYijK9hstqDUIyoqCjzPw2w2+wxExph4F3VMTAycTiecTmeT6yCXy6HVasHzPBhjPsfgczgcsNvt0Gq1YpgHQ0xMDHieh8lkgkqlavDLzzVGGbUHtYfLldoeDocDgiC4tY8gCHA4HJDJZGJ9w3VvEgVRAMrLy9GxY0fI5XLxV1x9v/yqqqpgsVhw//33SzYmFSGEeGM0GrF582ao1WoolUrxyDQqKsrrd1qwgrE+FEQBEARBHPjUdS7aZDJ5nNfmeR4VFRVQq9XQ6/Vo1aqVhLUmhBBPCoVCPP1mNBqhVqvrvUZH9xFFqPouCLouvCoUCklGCCaEEH/Z7fYmdxQJBgqiJqgbRjabTWzUQJ/1QQgh4SQIAgwGg+QhBFAQNZkrjGofCSUkJFAIEUIims1mg1wulzyEAAoiQgghEqMgaiLXkZDryMjhcKC0tDTsjxAmhJBAqNVqOJ3OiHiEBAVRE9S9I1ytVovXjAwGA4URISRiyWQyxMfHR8TzjCiIGqm+YUlqHxmF6wmShBDSGK4n30odRhREjeBrbCyVSoX4+Hg6IiKERLxIeNIrBVGAHA6HX/3ulUol3UdECGkWpA4jCqIAcBwHo9Hod797qbtEEkKIv6QMI/qmDIBarY6YfveEEBJsUoURfZsGQBAE6HQ6CiFCSIslRRjRN2oAXMPFE0JISxbuMKJvVUIIIR5qh5HJZArpuiiICCGEeOUKo2A8LLAhFESEEELqpVKpQv5wTwoiQgghDfL1qPOmirggys3NRXp6OmJjY9GmTRsMGzYMJ06c8Dnfhg0b0K1bN2g0GvTq1QubN292e58xhnnz5iElJQVRUVHIysrCyZMnQ/UxCCGE+CnigmjXrl2YNGkSfvrpJ2zduhV2ux133HEHzGZzvfPs3bsXo0aNwhNPPIFDhw5h2LBhGDZsGI4ePSqWeeutt7BkyRKsXLkS+/btg1arRXZ2dsifxU4IIaRhHIvwAdEuX76MNm3aYNeuXbj11lu9lhkxYgTMZjO+/fZbcdpNN92E3r17Y+XKlWCMITU1Fc899xyef/55ANXPaE9KSsKqVaswcuRIn/UwmUyYMWMG0tPTER0d7bM8z/OwWq0YPnw4WrVq5eenJYSQ0CsrK8OGDRug0Wj8GorMYrFg3LhxMBqN0Ol0Qa9PxB0R1WU0GgGgwS/z/Px8ZGVluU3Lzs5Gfn4+AOD06dMoLCx0K6PX65GRkSGWqctms8FkMrm9CCGEBF9EB5EgCJg2bRpuvvlm9OzZs95yhYWFSEpKcpuWlJSEwsJC8X3XtPrK1JWbmwu9Xi++2rdv35SPQgghpB4RHUSTJk3C0aNHsW7durCve/bs2TAajeLr3LlzYa8DIYRcCULbJ68JJk+ejG+//RY//PAD2rVr12DZ5ORkFBUVuU0rKipCcnKy+L5rWkpKiluZ3r17e12mWq2GWq1uwicghJCW4Yob4ocxhsmTJ+Pf//43tm/fjrS0NJ/zZGZmIi8vz23a1q1bkZmZCQBIS0tDcnKyWxmTyYR9+/aJZQghhHgSBCHk18gj7oho0qRJWLt2Lb7++mvExsaK13D0ej2ioqIAADk5OWjbti1yc3MBAFOnTsXAgQOxcOFCDBkyBOvWrUNBQQE++OADANXPEZo2bRpee+01dOnSBWlpaZg7dy5SU1MxbNgwST4nIYREOkEQUFpaGvIhfiIuiFasWAEAuO2229ymf/rpp3j00UcBAGfPnnUbBbt///5Yu3Yt5syZgxdffBFdunTBxo0b3To4zJgxA2azGePHj0d5eTkGDBiALVu2QKPRhPwzEUJIc+MKIbvdHvIhfiIuiPy5rWnnzp0e04YPH47hw4fXOw/HcViwYAEWLFjQ6LqFepgLQgiJBLVDKDExEQ6HI6Tri7hrRJFMoVDAYrFIXQ1CCAmZuiHkzw2vTUVBFACHwwGz2YyKigqpq0IIIUEnRQgBFEQBcTgc0Gq1MJlMfoVRhI+eRAghIqlCCIjAa0SRLjo6Gmq1WuzOGBsb67UcYww8z4ezaoQQ0ihShhBAQdQorvCpL4wEQYDBYHDr2UcIIZFI6hACKIgarb4wcjWqw+FATEyMZPUjhBBfGGOShxBAQdQkdcNIq9WKjRofHx/ym8AIIaSxGGMwGAwQBEHSEAIoiJqsdhiZTCZwHIfExEQAoCAihEQsq9UKh8OBNm3aSBpCAPWaCwqtViv+v1qtlrxRCSHEHyqVKiK+ryiImsh1TYjjOGg0GlitVrrPiBAS8eRyOWw2W0R8X9GpuSbw1tukoqICJpMJ0dHRkMvlUleREEK8UqlUkMvlPm9FCQcKokaqr8ujqzHLy8vdTtkRQkik0Wq1UCgUkocRBVEj+Op3HxsbC4fDQTe0EkIinq/7IsOBgihA/t78pdVqaYgfQkizIHUYURAFyNVQ/vS7p8dGEEKaCynDiL4pA6BSqeB0OpGUlBQRXR4JISSYpAojCqIAyGQy6PV6CiFCSIslRRhREAXAZrPR6TZCSItXN4xCfSsKfasGgDofEEKuFLXDSK1Wh3RdFESEEEK8coVRWVlZSNdDQUQIIaResbGxsNlsIV0HjTVHCCGkQdHR0SFdPgURIYQQSVEQEUIIkRQFESGEEElREBFCCJEUBREhhBBJURARQgiRFAURIYQQSUVcEP3www+49957kZqaCo7jsHHjRp/z7Ny5EzfccAPUajWuvvpqrFq1yqPMsmXL0KlTJ2g0GmRkZGD//v3BrzwhhLRAFoslpMuPuCAym824/vrrsWzZMr/Knz59GkOGDMHtt9+Ow4cPY9q0aRg3bhy+++47scz69esxffp0zJ8/HwcPHsT111+P7OxsFBcXh+pjEEJIi1BRUQGz2RzSdUTcED933XUX7rrrLr/Lr1y5EmlpaVi4cCEAoHv37tizZw8WLVqE7OxsAMC7776LJ598Eo899pg4z6ZNm/DJJ59g1qxZwf8QhBDSAlRUVMBkMkGr1YZ0PRF3RBSo/Px8ZGVluU3Lzs5Gfn4+AIDneRw4cMCtjEwmQ1ZWlljGG5vNBpPJ5PbiOC40H4IQQiKMK4R0Oh0N8eNLYWEhkpKS3KYlJSXBZDKhqqoKJSUl4lNV65YpLCysd7m5ubnQ6/Xiq3379lCr1XA4HCH5HIQQEilqh1A4HozX7IMoVGbPng2j0Si+zp07B0EQYDQawfO81NUjhJCQCHcIARF4jShQycnJKCoqcptWVFQEnU6HqKgoyOVyyOVyr2WSk5PrXa5arfZ4GBTP85DL5SgpKUFiYiI9MpwQ0qJIEUJACzgiyszMRF5entu0rVu3IjMzEwCgUqnQt29ftzKCICAvL08sEwidTgelUomSkhKfR0Z0Go8Q0lxIFUJABAZRZWUlDh8+jMOHDwOo7p59+PBhnD17FkD1KbOcnByx/IQJE/Dnn39ixowZ+O2337B8+XJ88cUXePbZZ8Uy06dPx4cffoh//etfOH78OCZOnAiz2Sz2oguETCZDQkKCzzAym82w2+0BL58QQsJNyhACIvDUXEFBAW6//Xbx39OnTwcAjB07FqtWrcKlS5fEUAKAtLQ0bNq0Cc8++yzee+89tGvXDh999JHYdRsARowYgcuXL2PevHkoLCxE7969sWXLFo8ODP5yhVFpaanX03QVFRWorKwMeZdHQghpKqlDCIjAILrtttvAGKv3fW+jJtx22204dOhQg8udPHkyJk+e3NTqieoLI1ejxsTEQC6XB219hBASbGazGRaLRdIQAiIwiJqTumGkVqthtVqh0+nE/yeEkEjE8zyqqqoQFxcnaQgBEXiNqLlxhRFjDFarFRqNRvJGJYQQX5xOJ9RqdUR8X1EQBUHtcZhsNhvdZ0QIaRZ4no+I7ysKoiaqfaEvJSVF7E1HPeYIIZFMo9FAoVD4dStKqFEQNUHd3ia1u3YbDAYIgiB1FQkhxCuO4xAfH+/3fZGhREHUSPV1eXSFkUKhkPxXBiGENITjOL/uiww1CqJG8NXvXiaTIT4+nkbrJoREPH9v0g9pHcK+xmbOYrH4dfMXx3E0Fh0hpFmQOowoiAKgUChgNpv9vvmLjogIIc2FlGFEQRQAhUIBrVYbEf3uCSEk2KQKIwqiADgcjpA/qZAQQqQkRRhREAWAHutAItnWYz/j7x++At5B97CRpqkbRqH+7gtorDlBELBr1y7s3r0bZ86cgcViQevWrdGnTx9kZWWhffv2oaonIaQBeccLMHntIgiCAAtvhUqhlLpKpJmrPZam0WgM7br8KVRVVYXXXnsN7du3x913343//ve/KC8vh1wuxx9//IH58+cjLS0Nd999N3766aeQVpgQ4m7XiUOYvHYRik0G1D9uPSGBc4VRqJ8k4NcRUdeuXZGZmYkPP/wQf/vb36BUev7aOnPmDNauXYuRI0fipZdewpNPPhn0yhJC3O3+/f8wcc1ClFvMaB0bB6GBR6gQ0hgymQw6nS6k6/AriL7//nt07969wTIdO3bE7Nmz8fzzz7s9uI4QEhp7/ziCCZ+9g3JLJVLjElBpqwIoiEgIyGSh7U7g19J9hVBtSqUSnTt3bnSFCCG+7T99DE+tfhtllgqk6BPonjXSrDXqwXhWqxW//PILiouLPQb2HDp0aFAqRgjx7sCZExj3r3+gxGxEij6RQog0ewEH0ZYtW5CTk4OSkhKP9ziOg9PpDErFCCGeDp39HY+vykVxRTlS4xIhoxAiLUDAJ/6mTJmC4cOH49KlSxAEwe1FIURI6Pxy/g88vupNFJkMSI1LoBAiLUbAQVRUVITp06cjKSkpFPUhhHhx9MKfePTTXFwyliJVnwAZV9+uy6i/Aml2Ag6ihx56CDt37gxBVQgh3hy7eBqPffoGLpaXVIdQiHswERJuAV8jWrp0KYYPH47du3ejV69eHvcUPfPMM0GrHCFXuhOFZ/HYp7k4ZyhGij6RQohIIqKG+AGAzz//HN9//z00Gg127tzp1mOH4zgKIkKC6O3v1uLU5YvQKFUQmAA5DQ9Jwozn+ZAP8RNwEL300kt45ZVXMGvWLPp1RkiIzbvnMVzdph2+KNiOQmMZVAolEmP0Dc7DaKAfEiQ8z6OkpCTkQ/wEnCQ8z2PEiBEUQoSEQYeEJMy66xFsm74YiTF6j/v2CAkVVwgplcqQD/ETcJqMHTsW69evD0VdIp5KpaIvAiKJPSd/QUmlEXHR7g9lNNuqcLG8BBabVaKakZaodgglJIS+g0zAp+acTifeeustfPfdd7juuus8Oiu8++67QatcpJHJZDCZTIiOjqYjQhJWa/dvAwODSuG+yxoslUjRt0KZuQKVNgvio+npwaRpwh1CQCOOiI4cOYI+ffpAJpPh6NGjOHTokNsrGJYtW4ZOnTpBo9EgIyMD+/fvb7D8hg0b0K1bN2g0GvTq1QubN292e58xhnnz5iElJQVRUVHIysrCyZMnA66XzWaD0+lEaWkpHRmRsDl+6S/sP30MOo3WbXqV3QalXI73Rk7FlxNexeM3D0GP1DRoVRqJakqaOylCCGjEEdGOHTtCUQ/R+vXrMX36dKxcuRIZGRlYvHgxsrOzceLECbRp08aj/N69ezFq1Cjk5ubinnvuwdq1azFs2DAcPHgQPXv2BAC89dZbWLJkCf71r38hLS0Nc+fORXZ2No4dOwaNxv+dljEGvS4aleYqlJYUI6FVXP0NxewQnHbAbgR4ugOeNN6X+/4Li60KbeMSAFYzekm52YRr2rTFgA7tIZPJkN7279VvMDPAmyWqLWkW7MbqvyVmB/73m5rn7SgpM0CpUCAhPhYyOMT3wELbfZtjLDj3YZ85cwZvv/02li5d2qTlZGRkID09XVyOIAho3749pkyZglmzZnmUHzFiBMxmM7799ltx2k033YTevXtj5cqVYIwhNTUVzz33HJ5//nkAgNFoRFJSElatWoWRI0f6VS+TyYQZM2YgvWc7KOQylFQ4oZRzSNDJvQ61Yq5ywmwTMGZwAlrFNmpsWUJQydtx82ffwWizITG65keTU2AoNFfh1Vt6Y9z1XSSsIWmOyioc2LDbAI1KBpWCA28XGvxOs1gdGDdlLoxGY0g6LgT8DXn77bd7He330qVLuHTpUpOCiOd5HDhwALNnzxanyWQyZGVlIT8/3+s8+fn5mD59utu07OxsbNy4EQBw+vRpFBYWIisrS3xfr9cjIyMD+fn59QaRzWaDzWYT/20ymcT/VyllSIwFSiqcKDU5PRqOtwswmJ1QK+k6EmmaA4VlqOTtqHIIqODtiFEqwHEcjDYeerUKw7q0l7qKpJnzFULhEHAQ9e7d2+3fTqcTf/75J/744w+sWrWqSZUpKSmB0+n0GMcuKSkJv/32m9d5CgsLvZYvLCwU33dNq6+MN7m5uXjllVfcpj311FMQmAAnc0KuAOJjOZSZBJQYBcTrZJBxHHgHQ5lJgFzGoFQwWJ1WVDlD2weftFx9U2Lxxm298MXxszhQaEC5lYdGIYfV4cTQLqnQqhmqnFVSV5M0M1anEwITYLMLMFkYlHIgLpYDgwCnl3NkAgvtNfGAg2jRokVep3/00UdYunQpRo8e3eRKRYLZs2e7HWmZTCa89tprsDALnM7//WLgAI2WQ2WlAryRQaNxwmxWQC5nUGqcsDo5nLIVQqugUclJ43VpB7zULg5/lmmw9Q8Ttv9pAuMYbujkwPGq4wAAviIKDqsK0a1Dewc8aRnMNjmqnPGwWWRQKBhUWgcsDWSNLcQj6Qbt4sXgwYMxZcqUJi0jMTERcrkcRUVFbtOLioqQnJzsdZ7k5OQGy7v+W1RUhJSUFLcydY/ualOr1VCr1R7TbUo9BHWtIxw1oFAJsBns4C0Ap+SgilOCdzI4eYaixKug1ik9lkNIoNRJwD3dgUGVNrBjRsS0isGFNlHgKh2QnzKBczKUt+8Kpqe/N9Iwm8kOK7sEpmCQt1LBKmv4dJydhfbHdNCCaPv27bj99tubtAyVSoW+ffsiLy8Pw4YNA1DdWSEvLw+TJ0/2Ok9mZiby8vIwbdo0cdrWrVuRmZkJAEhLS0NycjLy8vLE4DGZTNi3bx8mTpwYcB2ZTAYmq7PZZAIYV92rhOOq32eMgXFO2BXRkCk9A42QRmEM2vNVUFhkYH9WwVElh8xoBewAAwfurA18nziAnlVEGmBX2ABOBnCs+vvMRxAxWYQdET3wwAMe04qKirBv3z7cfvvtbu9/9dVXAVdo+vTpGDt2LG688Ub069cPixcvhtlsxmOPPQYAyMnJQdu2bZGbmwsAmDp1KgYOHIiFCxdiyJAhWLduHQoKCvDBBx8AqB6Iddq0aXjttdfQpUsXsft2amqqGHZNIdgF2Aw2yJQyKGOU4Mt52MptUGrpVykJPsVpI+TFFggqGcAAxcVKAAxMLQcEBlm5DfLLVXC2iZa6qiTCyZQyOCwO2MptUMepwfkIo1AKOIj0es8BF/V6Pbp27RqUCo0YMQKXL1/GvHnzUFhYiN69e2PLli1iZ4OzZ8+63bvTv39/rF27FnPmzMGLL76ILl26YOPGjeI9RAAwY8YMmM1mjB8/HuXl5RgwYAC2bNkS0D1ELvHyeGgU1fPZeTsMRgO0Ki3i/ndPkV1hh6HMAK6SgzZai5t1NyMuLq5pG4UQAGXn/8Lpc3vBKaOhUFUfZQuCAA4A9799greYEX9ejm6db4csxANVkuarnJXDoDRAHi9HRUUF5Ca5+B3mjVUe2iGkgnYfUUsn3keUno7o6OgG70DmeR7FxcVQq9V45JFH0KpVKwlrTloCs7EMh3Z+DQdvgyoq2ustFAAgOJ3gbVW4pu9tSL2qe5hrSZqLsrIybNiwQfwx7ms0BYvFgnHjxoXsPiK/bnShrHLnaxgMlUqF+Ph42m4kaBx2HmAMjAlo6FngMrkcHDic/e1A9TyE+KBSqZCYmAi73S7Z8GV+BVGPHj2wbt068HzDf9gnT57ExIkT8eabbwalcpHI4XD4NRaTUqmESqUKc+1IS6VPTMZ1twxBVKwetipzgz9ylJooVFWacOGPo2GsIWnOpA4jv64Rvf/++5g5cyaefvpp/O1vf8ONN96I1NRUaDQaGAwGHDt2DHv27MGvv/6KyZMnN6o3WnPAcRyMRiM0Go1fAwLSCN0kmKJj46BUalCFhu8VkslkkMnlOH/yFyR3ugbqKG2D5QkBasKopKQEpaWlkTfo6eDBg1FQUIA9e/Zg/fr1WLNmDc6cOYOqqiokJiaiT58+yMnJwejRoxEfHx/qOktGrVZDLpeHtYEIAQAmCDhxYBdMZUVQaeq/RuSiVEfBZqnEud//D1df3z9MtSTNnVRhFFCvuQEDBmDAgAGhqkvEEwQBOp2OQoiEXVnxeZReOgsmCHA6eACqBnvFcRwHuUKJS38eR+pV1yI6Ni5sdSXNm7cwCjX6Rg0Az/MUQkQS8W3a4pq+tyI5rRvkChXsNius5grwVgucDofXa0YKlRoOuw1njh2QoMakOQv3NSN6PgEhzYBMJkdSx65I6tgVTocd5ZcvoazoHEov/gVbVSXsNmf1UZBSBblCCY7jwHEcFEoVLp8/hbZX94QuIcn3igj5n9pHRna7PaTroiAipJmRK5RISOmAhJQOuPr6/qgwXIah6BxKLvwFs6kMNktldTmlEnKFCnyVGad//RnX3TLE57UlQmpzhVHd8TyDjYKIkGaM4zjoWrWBrlUbdOzeF1WVRpQVnUfZpTMoL7kEu9UCQXCivPgCyi6dRUJqR6mrTJoZlUrldUSdYKIgIqQFiYrRo22MHm0794DdZoWh+ALKCs+irPAcyorOURCRRlEoQhsVAV95HzRokMcD4wDAYDBg0KBBQakUIaTplGoN2rTvjG7pt+Omu0ej8/WZUleJEK8CjrmdO3fiyJEjOHToENasWQOttvpmOZ7nsWvXrqBXkBDSdDQAKolkjeqLvG3bNhQWFuKmm27CX3/9FeQqEUIIuZI0KohSUlKwa9cu9OrVC+np6di5c2eQq0UIIeRKEXAQubp/qtVqrF27FlOnTsWdd96J5cuXB71yhBBCWr6ArxHVvYN7zpw56N69O8aOHRu0ShFCCLlyBBxEp0+fRuvWrd2mPfjgg+jWrRsKCgqCVjFCCCGRIeKG+OnY0ft9CD169ECPHj2aXCFCCCGRQxAEmEymkK6DRvAkhBDilSAIKC0thdPpDOl6KIgIIYR4cIWQ3W4P+RA/FEQBCPUwF4QQEglqh1BiYmLkDfFzJVMoFLBYLFJXgxBCQqZuCKlUqpCvk4IoAA6HA2azGRUVFVJXhRBCgk6KEAIoiALicDig1WphMpn8CiNvT80khJBIJFUIAfQYiIBFR0dDrVaL3RljY2O9lmOMgef5cFaNEEIaRcoQAiiIGsUVPvWFkSAIMBgMkMnogJMQEtmkDiGAgqjR6gsjV6M6HA7ExMRIVj9CCPGFMSZ5CAEURE1SN4y0Wq3YqPHx8SG/CYwQQhqLMQaDwQBBECQNIYCCqMlqh5HJZALHcUhMTAQACiJCSMSyWq1wOBxo06aNpCEERFivua+++gp33HEHEhISwHEcDh8+7Nd8GzZsQLdu3aDRaNCrVy9s3rzZ7X3GGObNm4eUlBRERUUhKysLJ0+eDFq9XU+pBaofjyF1oxJCiD9UKlVEfF9FVBCZzWYMGDAA//jHP/yeZ+/evRg1ahSeeOIJHDp0CMOGDcOwYcNw9OhRscxbb72FJUuWYOXKldi3bx+0Wi2ys7NhtVqbXGfXNSGO46DRaGC1Wuk+I0JIxJPL5bDZbBHxfRVRp+bGjBkDAAE9fvy9997DnXfeiRdeeAEA8Oqrr2Lr1q1YunQpVq5cCcYYFi9ejDlz5uC+++4DAPy///f/kJSUhI0bN2LkyJGNrq+33iYVFRUwmUyIjo6GXC5v9LIJISSUVCoV5HK5z1tRwiGijogaIz8/H1lZWW7TsrOzkZ+fD6D6+UmFhYVuZfR6PTIyMsQy3thsNvG6j+tVW31dHmNjY6HT6VBZWQmHwxGsj0kIIUGn1Wqh0+n8vkk/VJp9EBUWFiIpKcltWlJSEgoLC8X3XdPqK+NNbm4u9Hq9+Grfvr34nq9+97GxsYiJiYHdbm/SZyOEkFBz/XiWMowkC6I1a9YgJiZGfO3evVuqqng1e/ZsGI1G8XXu3DkA/t/8pdVqoVQqw1llQghpFKnDSLJrREOHDkVGRob477Zt2zZqOcnJySgqKnKbVlRUhOTkZPF917SUlBS3Mr179653uWq1Gmq12mO66xSdP/3u6bERhJDmwteIMaEk2RFRbGwsrr76avEVFRXVqOVkZmYiLy/PbdrWrVuRmZkJAEhLS0NycrJbGZPJhH379oll/KVSqeB0OiW/+YsQQkJBqiOjiPrJXlZWhrNnz+LixYsAgBMnTgCoPqpxHdnk5OSgbdu2yM3NBQBMnToVAwcOxMKFCzFkyBCsW7cOBQUF+OCDDwAAHMdh2rRpeO2119ClSxekpaVh7ty5SE1NxbBhwwKqn0wmg16vpxAihLRYUhwZRVQQffPNN3jsscfEf7u6Vs+fPx8vv/wyAODs2bNug4n2798fa9euxZw5c/Diiy+iS5cu2LhxI3r27CmWmTFjBsxmM8aPH4/y8nIMGDAAW7ZsgUajCah+NpuNTrcRQlq8umEU6ltROEYPzfGLyWTCjBkzkJ6ejujoaJ/leZ6H1WrF8OHD0apVqzDUkBBC/FNWVoYNGzZAo9E0eIbHdV+kWq3GM888A6PRCJ1OF/T60M97QgghXrmOjMrKykK6HgoiQggh9YqNjYXNZgvpOpr9Da2EEEJCy5/LEU1BQUQIIURSFESEEEIkRUFECCFEUhREhBBCJEVBRAghRFIURIQQQiRFQUQIIURSFESEEEIaZLFYQrp8CiJCCCH1qqiogNlsDuk6KIgIIYR45Rr0VKvVhnQ9NNZcADiOk7oKhBASFq4Q0ul0IX8MBB0RBUCtVsPhcEhdDUIICanaIRSOB+NREAVAEAQYjUbwPC91VQghJCTCHUIABVFAeJ6HXC5HSUkJhREhpMWRIoQACqKA6XQ6KJVKv8KITuMRQpoLqUIIoCAKmEwmQ0JCgs8wMpvNsNvtYa4dIYQETsoQAiiIGsVXGFVUVKCyshJKpVKiGhJCiH+kDiGAgqjR6gsjV6PGxMRAoaDe8YSQyGU2myUPIYDuI2oSVxiVlpaipKQEarUaVqsVOp1O/H9CCIlEPM+jqqoKcXFxkoYQQEdETeYKI8YYrFYrNBqN5I1KCCG+OJ1OqNXqiPi+oiAKgtrjMNlsNuraTQhpFniej4jvKwqiJqp9oS8lJUW8ZkQ95gghkUyj0UChUETEfZEURE1Qt7dJ7Q4MBoMBgiBIXUVCCPGK4zjEx8f7fV9kKFEQNVJ9XR5dYaRQKCT/lUEIIQ3hOM6v+yJDjYKoEXz1u5fJZIiPj6fRugkhEc/fm/RDWoewr7EedrsdM2fORK9evaDVapGamoqcnBxcvHjR57zLli1Dp06doNFokJGRgf3797u9b7VaMWnSJCQkJCAmJgYPPvggioqKGlVPi8XiV797juOgUqkatQ5CCAknqcMoYoLIYrHg4MGDmDt3Lg4ePIivvvoKJ06cwNChQxucb/369Zg+fTrmz5+PgwcP4vrrr0d2djaKi4vFMs8++yz+85//YMOGDdi1axcuXryIBx54IOA6KhQKmM1mv2/+oiMiQkhzIWUYRcwNrXq9Hlu3bnWbtnTpUvTr1w9nz55Fhw4dvM737rvv4sknn8Rjjz0GAFi5ciU2bdqETz75BLNmzYLRaMTHH3+MtWvXYtCgQQCATz/9FN27d8dPP/2Em266ye86KhQKaLXaiOh3TwghwVb3Jv3ExMSwnNmJmCMib4xGIziOQ1xcnNf3eZ7HgQMHkJWVJU6TyWTIyspCfn4+AODAgQOw2+1uZbp164YOHTqIZbyx2WwwmUxuL4fDgejo6OB8OEIIiUBSHBlFbBBZrVbMnDkTo0aNgk6n81qmpKQETqcTSUlJbtOTkpJQWFgIACgsLIRKpfIIs9plvMnNzYVerxdf7du3p8c6EEKuCHXDKNTffZIF0Zo1axATEyO+du/eLb5nt9vx8MMPgzGGFStWSFK/2bNnw2g0iq9z585JUg9CCJFC7TAyGo0hXZdk14iGDh2KjIwM8d9t27YFUBNCZ86cwfbt2+s9GgKAxMREyOVyjx5wRUVFSE5OBgAkJyeD53mUl5e7HRXVLuONWq2GWq1uzEcjhJAWwRVGDZ09Csp6Qrr0BsTGxuLqq68WX1FRUWIInTx5Etu2bUNCQkKDy1CpVOjbty/y8vLEaYIgIC8vD5mZmQCAvn37QqlUupU5ceIEzp49K5YhhBDinUwma/CAIBgiptec3W7HQw89hIMHD+Lbb7+F0+kUU7hVq1Ziz43Bgwfj/vvvx+TJkwEA06dPx9ixY3HjjTeiX79+WLx4Mcxms9iLTq/X44knnsD06dPRqlUr6HQ6TJkyBZmZmQH1mCOEkCuVTBbaY5aICaILFy7gm2++AQD07t3b7b0dO3bgtttuAwCcOnUKJSUl4nsjRozA5cuXMW/ePBQWFqJ3797YsmWLWweGRYsWQSaT4cEHH4TNZkN2djaWL18e8s9ECCHEt4gJok6dOoEx5rPcX3/95TFt8uTJ4hGSNxqNBsuWLcOyZcuaUkVCCCEhELHdtwkhhFwZKIgIIYRIioKIEEKIpCiICCGESIqCiBBCSINa7BA/hBBCIh/P8yEf4oeCiBBCiFc8z6OkpARyuTyk64mY+4gIIYREDlcIKZVKREVFhXRddEQUAJVKBUEQpK4GIYSEVO0QSkhICPkQPxREAZDJZDCZTBRGhJAWK9whBFAQBcRms8HpdKK0tJTCiBDS4kgRQgAFUUAYY9Dr9bDb7X6FEYUVIaS5kCqEAAqigCkUCiQmJvoMI7vdHpZnvRNCSFNJGUIABVGjqFSqBsOI53kYDAZwHCdRDQkhxD9ShxBAQdRo9YWRq1EVCoX4MD9CCIlEdrtd8hACKIiapG4Y2Ww2sVHj4+PpiIgQErEEQYDBYJA8hAAKoiZzhVHtI6GEhAQKIUJIRLPZbJDL5ZKHEEAjKwREJpPB4XB4dEKw2+3iqTmn0wm73Q6HwwGHwxHyMZoIISRQRqMRTqcTjDHY7XafP5xDPegpBVEA4uLiUFpa6vbrQRAEOBwOcBwHuVwOq9UKs9kMuVwOxhg2b94MhSK0m5kxBp7nwRiDSqWC3W4X/z+cv3QcDgfsdjuUSiUAiP8f6s9fmyAI4HkeHMdBqVSK/69SqcJ2lErtUYPao0YktQdjDFVVVRAEAefOnYNCoWiwPUJ9KwoFUQBcDeX643Ud/bh2MplMBo7jwPM8BEGAQqGAWq0W//BCgTEGg8EAh8OB+Ph4KJVKcVplZaU4LdTMZjPMZjNiYmKg1WrFaZWVlW7TQslut8NkMkGhUIjX6JRKJQwGA5xOZ1iu21F71KD2qBFp7aHValFVVQWZTAa73Q673d5gMFMQRRiO48RTdHa7HTKZDGq1WmxAVxjZbDYIggC5XB6y3nOCIIg99tq0aeO2njZt2qC0tBRGoxGJiYkh7cFXUVEBi8WCuLg4xMbGitNVKhUUCoX4x1/7vWBzDVWvVqvdznmrVCoolUqUlJTAZDKF9Hw4tUcNao8akdgetc/iyGQy8DwPu93u9l1WW6h/MFBnhUZwXSfiOM5rw7m6bjPGUF5eHpJfE66dzG63e92RZDIZEhISxJ0+VDfXVlRUwGQyQafTed2RYmNjodPpYDKZUFFREZI6+LoPwtd9X8FA7VGD2qNGc2iP2t9Xrh/Q4UZBFKDa57zr+/UAAHK5HAqFAg6HI+g7m6+dzCXUO5uvncwllDubvzfjhfLLj9qjBrVHjebUHlKHEQVRADiOEw9pGwohF5lMhvj4+KDubP7uZLXrEIqdzd+dzCUUO1ugd4SH4suP2qMGtUeN5tgeUoYRBVEAXA3pTwi5KJXKoO1sge5ktesdzJ0t0J3MJZg7W2OHJQnmlx+1Rw1qjxrNuT2kCiMKogC5escFIhg7W2N3Mpdg7WyN3clcgrGzNXVsLGqPGtQe1ag9akgRRhREAXA6nY3uPdKUna2pO5lLU3e2pu5kLk3Z2YI1QCO1Rw1qj2rUHjXqhhFjrFHL8RcFURg1ZmcL1k7m0tidLVg7mUtjdrZgjxJM7VGD2qMatUeN2mFkt9ubtCxfKIjCLJCdLdg7mUugO1uwdzKXQHa2UA1VT+1Rg9qjGrVHjXA9RYBuaPWT69BUEAS/fh24yttsNq/jNMXExMBoNKKwsBA6nc7jD0cQBJhMJjidTuj1enHsumCKioqC3W5HUVER9Hq916FGLBYLzGYztFot5HI5LBZLUOsgl8uhVqtRVlYGm82G6OhojzKuMfvkcjmioqJgtVqDWgeA2sOF2qNGS24Pp9MJQRDAGIPT6fRZ3rX9Q3WKjmOhPvnXQpw/fx7t27eXuhqEECKZc+fOoV27dkFfLgWRnwRBwMWLF8EYQ4cOHXDu3DnodDqpq9WsmEwmtG/fnrZdI9C2azzado3n2nZnz54Fx3FITU0NyZBMdGrOTzKZDO3atYPJZAIA6HQ6+qNuJNp2jUfbrvFo2zWeXq8P6bajzgqEEEIkRUFECCFEUhREAVKr1Zg/fz7UarXUVWl2aNs1Hm27xqNt13jh2nbUWYEQQoik6IiIEEKIpCiICCGESIqCiBBCiKQoiAghhEiKgqiOCRMmgOM4LF682GfZZcuWoVOnTtBoNMjIyMD+/fvd3rdarZg0aRISEhIQExODBx98EEVFRSGqefjZ7XbMnDkTvXr1glarRWpqKnJycnDx4kWf817p287X569rw4YN6NatGzQaDXr16oXNmze7vc8Yw7x585CSkoKoqChkZWXh5MmTofwIYZebm4v09HTExsaiTZs2GDZsGE6cOOFzPtp2nt58801wHIdp06Y1WC5s244R0VdffcWuv/56lpqayhYtWtRg2XXr1jGVSsU++eQT9uuvv7Inn3ySxcXFsaKiIrHMhAkTWPv27VleXh4rKChgN910E+vfv3+IP0X4lJeXs6ysLLZ+/Xr222+/sfz8fNavXz/Wt2/fBue70redP5+/th9//JHJ5XL21ltvsWPHjrE5c+YwpVLJjhw5IpZ58803mV6vZxs3bmT/93//x4YOHcrS0tJYVVVVuD5WyGVnZ7NPP/2UHT16lB0+fJjdfffdrEOHDqyysrLeeWjbedq/fz/r1KkTu+6669jUqVPrLRfObUdB9D/nz59nbdu2ZUePHmUdO3b0GUT9+vVjkyZNEv/tdDpZamoqy83NZYxVf0krlUq2YcMGsczx48cZAJafnx+SzxAJ9u/fzwCwM2fO1FvmSt92vj5/XQ8//DAbMmSI27SMjAz21FNPMcYYEwSBJScns7ffflt8v7y8nKnVavb555+H4BNEhuLiYgaA7dq1q94ytO3cVVRUsC5durCtW7eygQMHNhhE4dx2dGoO1QOajhkzBi+88AJ69OjhszzP8zhw4ACysrLEaTKZDFlZWcjPzwcAHDhwAHa73a1Mt27d0KFDB7FMS2Q0GsFxHOLi4ry+f6VvO38+f135+flu5QEgOztbLH/69GkUFha6ldHr9cjIyGj226shRqMRANCqVat6y9C2czdp0iQMGTLEY5t4E85tR4OeAvjHP/4BhUKBZ555xq/yJSUlcDqdSEpKcpuelJSE3377DQBQWFgIlUrl8YWclJSEwsLCoNQ70litVsycOROjRo2qd4DEK33b+fP56yosLPRa3rUtXP9tqExLIwgCpk2bhptvvhk9e/astxxtuxrr1q3DwYMH8fPPP/tVPpzb7oo7IlqzZg1iYmLE165du/Dee+9h1apV4DhO6upFtLrbbvfu3eJ7drsdDz/8MBhjWLFihYS1JFeCSZMm4ejRo1i3bp3UVWkWzp07h6lTp2LNmjXQaDRSV8fDFRdEQ4cOxeHDh8XX3r17UVxcjA4dOkChUEChUODMmTN47rnn0KlTJ6/LSExMhFwu9+jFVVRUhOTkZABAcnIyeJ5HeXl5vWWam7rb7sYbbwRQE0JnzpzB1q1bGxwu/krddi7+fP66kpOTfW4v1zR/l9mcTZ48Gd9++y127Njh8yFttO2qHThwAMXFxbjhhhvE77ldu3ZhyZIlUCgUXp/SGs5td8UFUWxsLK6++mrxNX78ePzyyy9uX7Cpqal44YUX8N1333ldhkqlQt++fZGXlydOEwQBeXl5yMzMBAD07dsXSqXSrcyJEydw9uxZsUxzU3fbuR6l/PDDD+PkyZPYtm0bEhISGlzGlbrtXPz5/HVlZma6lQeArVu3iuXT0tKQnJzsVsZkMmHfvn3NfnvVxhjD5MmT8e9//xvbt29HWlqaz3lo21UbPHgwjhw54vFDcvTo0Th8+DDkcrnHPGHddgF1bbhCeOs1N2jQIPb++++L/163bh1Tq9Vs1apV7NixY2z8+PEsLi6OFRYWimUmTJjAOnTowLZv384KCgpYZmYmy8zMDNfHCDme59nQoUNZu3bt2OHDh9mlS5fEl81mE8vRtnPn6/OPGTOGzZo1Syz/448/MoVCwd555x12/PhxNn/+fK/daOPi4tjXX3/NfvnlF3bfffe1uC7IEydOZHq9nu3cudPtb81isYhlaNv5r26vOSm3HQWRF96CqGPHjmz+/Plu095//33WoUMHplKpWL9+/dhPP/3k9n5VVRV7+umnWXx8PIuOjmb3338/u3TpUohrHz6nT59mALy+duzYIZajbeepoc8/cOBANnbsWLfyX3zxBevatStTqVSsR48ebNOmTW7vC4LA5s6dy5KSkpharWaDBw9mJ06cCMdHCZv6/tY+/fRTsQxtO//VDSIptx09BoIQQoikrrhrRIQQQiILBREhhBBJURARQgiRFAURIYQQSVEQEUIIkRQFESGEEElREBFCCJEUBREhhBBJURAREiE+/vhj3HHHHSFfz5YtW9C7d28IghDydRHiDwoiQiKA1WrF3LlzMX/+/JCv684774RSqcSaNWtCvi5C/EFBREgE+PLLL6HT6XDzzTeHZX2PPvoolixZEpZ1EeILBREhQXT58mUkJyfjjTfeEKft3bsXKpXKY0j92tatW4d7773Xbdptt92GadOmuU0bNmwYHn30UfHfnTp1wmuvvYacnBzExMSgY8eO+Oabb3D58mXcd999iImJwXXXXYeCggK35dx7770oKCjAqVOnGv9hCQkSCiJCgqh169b45JNP8PLLL6OgoAAVFRUYM2YMJk+ejMGDB9c73549e8QHDQZq0aJFuPnmm3Ho0CEMGTIEY8aMQU5ODh555BEcPHgQnTt3Rk5ODmqPb9yhQwckJSW5PWWXEKlQEBESZHfffTeefPJJjB49GhMmTIBWq0Vubm695cvLy2E0GpGamtro9T311FPo0qUL5s2bB5PJhPT0dAwfPhxdu3bFzJkzcfz4cY8naaampuLMmTONWichwURBREgIvPPOO3A4HNiwYQPWrFkDtVpdb9mqqioAgEajadS6rrvuOvH/k5KSAAC9evXymFZcXOw2X1RUFCwWS6PWSUgwURAREgKnTp3CxYsXIQgC/vrrrwbLJiQkgOM4GAwGn8t1Op0e05RKpfj/HMfVO61ud+2ysjK0bt3a5zoJCTUKIkKCjOd5PPLIIxgxYgReffVVjBs3zuNopDaVSoVrr70Wx44d83iv7um0P//8Myh1tFqtOHXqFPr06ROU5RHSFBREhATZSy+9BKPRiCVLlmDmzJno2rUrHn/88Qbnyc7Oxp49ezymf/311/jqq69w6tQpvP766zh27BjOnDmDCxcuNKmOP/30E9RqNTIzM5u0HEKCgYKIkCDauXMnFi9ejNWrV0On00Emk2H16tXYvXs3VqxYUe98TzzxBDZv3gyj0eg2fciQIXjrrbdw7bXX4ocffsDy5cuxf/9+rF69ukn1/PzzzzF69GhER0c3aTmEBAPHavfpJIRIZvjw4bjhhhswe/ZsANX3EfXu3RuLFy8O6npKSkpwzTXXoKCgAGlpaUFdNiGNQUdEhESIt99+GzExMSFfz19//YXly5dTCJGIQUdEhESoUB0RERJpKIgIIYRIik7NEUIIkRQFESGEEElREBFCCJEUBREhhBBJURARQgiRFAURIYQQSVEQEUIIkRQFESGEEEn9f9JQvaYo9IsAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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();"
   ]
  },
  {
   "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\">             </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-ed7427fb-fc07-4c7f-9436-742848d593ce'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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-ed7427fb-fc07-4c7f-9436-742848d593ce'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc0</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">7-4c7f-9436-742848d593ce'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=32359;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=423210;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=32359;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=357681;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=32359;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32m-ed7427fb-fc0\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=32359;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[32m7-4c7f-9436-742848d593ce'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=783179;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7abef936fa54785948f4c0915af3a6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:37 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.198</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:37 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.198\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:35:38 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:38 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "195cc8f242d4466599502bedae44f0df",
       "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\">14:35:54 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:54 UTC\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\">14:35:59 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:35:59 UTC\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9e6a2f3f0fd4c2599e0e6415724e959",
       "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\">14:36:01 UTC </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;36m14:36:01 UTC\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:36:02 UTC </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:36:02 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11da77506ea343f1866bf12bcebc561a",
       "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\">14:36:04 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:36:04 UTC\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\">14:36:06 UTC </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc0</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">7-4c7f-9436-742848d593ce'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:36:06 UTC\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=792323;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=846842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=792323;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=986026;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=792323;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34m-ed7427fb-fc0\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=792323;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed7427fb-fc07-4c7f-9436-742848d593ce\u001b\\\u001b[4;34m7-4c7f-9436-742848d593ce'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f627547d3c4241dfa98724c98bb774ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:36:09 UTC </span>loading simulation from data/kspace_monitor.hdf5                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:36:09 UTC\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/kspace_monitor.hdf5                   \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor far_field projects to a distance comparable to the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">size of the monitor; we recommend setting                          </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">``</span><span style=\"color: #808000; text-decoration-color: #808000\">far_field_approx</span><span style=\"color: #800000; text-decoration-color: #800000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #800000; text-decoration-color: #800000\">`` to disable far-field approximations for </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this monitor, because the approximations are valid only when the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">observation points are very far compared to the size of the monitor</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">that records near fields.                                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor far_field projects to a distance comparable to the\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31msize of the monitor; we recommend setting                          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m``\u001b[0m\u001b[33mfar_field_approx\u001b[0m\u001b[31m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[31m`` to disable far-field approximations for \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis monitor, because the approximations are valid only when the   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points are very far compared to the size of the monitor\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthat records near fields.                                          \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</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: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data5 = web.run(sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\", verbose=True)"
   ]
  },
  {
   "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_5711/2904520726.py:14: 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": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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(1, 1, tight_layout=True, figsize=(7, 5), subplot_kw={\"projection\": \"polar\"})\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(r\"$\\phi$ (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();"
   ]
  },
  {
   "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 the 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 periodic 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",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "title": "Performing Far-field Projections in Tidy3D | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "02769fb9bca440da914d05f8a22ec96c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_f4133051a10f4967901518320cf33da2",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_1'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_1'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "0466b5422e0f415684764efc6380dbf1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_877a7cb918d14fab92978c7fa892b62d",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">2.2/2.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m2.2/2.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "078c45addbec49e89078edb0b1c22ab8": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_ebaa2442542f4b818ea199a2d11cf575",
       "msg_id": "",
       "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\">Computing projected fields <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Computing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "1464875837ef4a369686ffd73049455e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "15380b3f53fa4c1d84916880d38c1ea3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "197b048b02af48d6a7c88d050d7c59cb": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_76a2972d60424f0697ab3d36b3fea17f",
       "msg_id": "",
       "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\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "1fb23992b6e747c28365e829d3244c46": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_67567bef63b346489a0f98516df2db3a",
       "msg_id": "",
       "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\">Computing projected fields <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Computing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "256d4c3c1b11421f801a49ef3bdd15eb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "27baa6c6ef5f479691a72f9e74acf7e2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "28021fe4a6cc42238b7b930b6b07174e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "283cede014c8475f99605fc9d8982ed0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_a4bc2f49083842c69414e38c589e3ba2",
       "msg_id": "",
       "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\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "3109929252cb4b5bbefa61de300095af": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "313fa01670a14bd1882ce4b482a07a8b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "32f90e41ddbd40528b13c6eef13d5e62": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "399ba201c5ec44ae88a2fddb9d984afd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3b6f6bc9b49942e8b85b0db25084509d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_3109929252cb4b5bbefa61de300095af",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.3/1.3 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m1.3/1.3 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "3c6f57fef5bd46018bb92c0632fe719e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "497e1db88bbd4741aaa2f32f1b88ea86": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_32f90e41ddbd40528b13c6eef13d5e62",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_4'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_4'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "4a3a0f28f0ed4f7ead0ce672b3c8033e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_69295e9d161647bc8ad749c83fd95912",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.7/1.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m1.7/1.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "4dbe589b059a4eb0b753878c15f4e7fc": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_d7300a9737de48ac94b656f1199c551c",
       "msg_id": "",
       "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\">Processing surface monitor 'near_field'... <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Processing surface monitor 'near_field'... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "50dbc261e4e242ddbb09074fd0498f27": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_27baa6c6ef5f479691a72f9e74acf7e2",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">538.7/538.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">17.2 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m538.7/538.7 kB\u001b[0m • \u001b[31m17.2 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "581b76c3e6a8415399c536a5e8fd750b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "67567bef63b346489a0f98516df2db3a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "69295e9d161647bc8ad749c83fd95912": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6bc7443872784f45971f6e1ac07a9065": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6f900d1fbef34de686a0689a7919f6d0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6fc544faea904424aeb0958ec618fda1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_ea6f6294f5e4486a8a42a82fe8e9d648",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_2'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_2'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "76a2972d60424f0697ab3d36b3fea17f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "772a15abc94343cf9933218d47543c40": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_15380b3f53fa4c1d84916880d38c1ea3",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_3'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_3'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7da225bb8e204f9184a59eb6ebfd025b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_e2ef840b0d9142dd94618d910b927585",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.7/1.7 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">16.0 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m1.7/1.7 MB\u001b[0m • \u001b[31m16.0 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7ea9902dd09c451eac37184778313e42": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_3c6f57fef5bd46018bb92c0632fe719e",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">868.7/868.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">8.7 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m868.7/868.7 kB\u001b[0m • \u001b[31m8.7 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "877a7cb918d14fab92978c7fa892b62d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "93169a58dde74471884cbac79cc1896e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_1464875837ef4a369686ffd73049455e",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_3'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'aperture_3'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "99eb7c8aa04a4077bf65eb65d80eb35a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_d98f0a953efa4d27b4f31808666fdfb8",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_1'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_1'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9b22a875639042f1997fe3cb3fc5239a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_6f900d1fbef34de686a0689a7919f6d0",
       "msg_id": "",
       "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\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9f53231566594667a5da210edb35eae6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_6bc7443872784f45971f6e1ac07a9065",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_2'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'aperture_2'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "a0a64bbaa3eb459985f04ee0ce20201e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_d46fe238eb5547b9acf1def88f4c88ed",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.2/1.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m1.2/1.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "a1f42a0942e34bd1a8d5d3478954bf47": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "a3ef8424a74d4edb93abafb65801936e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "a4bc2f49083842c69414e38c589e3ba2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "af18cf601bda4c48a16856c3a475ef22": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_a1f42a0942e34bd1a8d5d3478954bf47",
       "msg_id": "",
       "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\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "bae34f38d6b74e40832b0a9cdf86708e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_a3ef8424a74d4edb93abafb65801936e",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">539.5/539.5 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">2.8 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m539.5/539.5 kB\u001b[0m • \u001b[31m2.8 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "bb79454d7bb4430fb251f08cbd1db7a6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_d60cfa012f7e45acbd61fbea7789a0c0",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'kspace_monitor'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'kspace_monitor'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "c898e31cbf4c48e9bf659256a9b68c17": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_28021fe4a6cc42238b7b930b6b07174e",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'kspace_monitor'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'kspace_monitor'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "d46fe238eb5547b9acf1def88f4c88ed": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d60cfa012f7e45acbd61fbea7789a0c0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d7300a9737de48ac94b656f1199c551c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d98f0a953efa4d27b4f31808666fdfb8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "dc0868332c0d4f5db901221a24cd5696": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_581b76c3e6a8415399c536a5e8fd750b",
       "msg_id": "",
       "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">2.5/2.5 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m2.5/2.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e2ef840b0d9142dd94618d910b927585": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "e44f7cea2aba4c8cb96418608dc46cb6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_399ba201c5ec44ae88a2fddb9d984afd",
       "msg_id": "",
       "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\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "ea6f6294f5e4486a8a42a82fe8e9d648": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ebaa2442542f4b818ea199a2d11cf575": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "f4133051a10f4967901518320cf33da2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "f7437c0256b0475481bfcc1a3f6eb27f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_256d4c3c1b11421f801a49ef3bdd15eb",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">538.2/538.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">9.8 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m538.2/538.2 kB\u001b[0m • \u001b[31m9.8 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "f7b662b3f78c4a33aa6b3daae630a96a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_313fa01670a14bd1882ce4b482a07a8b",
       "msg_id": "",
       "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_4'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_4'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}