{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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. [Coordinate system conversion, power computation](#powercoords)\n",
    "5. [Re-projection to a new far field distance](#reproj)\n",
    "6. [Exact field projections without making the far-field approximation](#exact)\n",
    "7. [Projection to a grid defined in reciprocal space](#kspace)\n",
    "8. [Some final notes](#notes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:21:52] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> This version of Tidy3D was pip installed from the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d-beta'</span> repository on   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">103</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         PyPI. Future releases will be uploaded to the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d'</span> repository. From now on, <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         please use <span style=\"color: #008000; text-decoration-color: #008000\">'pip install tidy3d'</span> instead.                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:21:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on   \u001b]8;id=99272;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=334253;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m               \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m         please use \u001b[32m'pip install tidy3d'\u001b[0m instead.                                        \u001b[2m               \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: #000080; text-decoration-color: #000080\">INFO    </span> Using client version: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.9</span>.0rc1                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1                                                  \u001b]8;id=638085;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=575764;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3d imports\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Far Field for a Uniformly Illuminated Aperture <a name=\"setup\"></a>\n",
    "\n",
    "First, we will consider the simple case of an aperture in a perfect electric conductor sheet illuminated by a plane wave. The far fields in this case are known analytically, which allows for a straightforward comparison to `Tidy3D`'s field projection functionality. We will show how to compute the far fields both on your local machine, and on the server. The geometry is shown below.\n",
    "\n",
    "<img src=\"img/far_field_aperture.png\" width=350>"
   ]
  },
  {
   "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 = 20\n",
    "\n",
    "# create the PEC plate\n",
    "plate = td.Structure(\n",
    "    geometry=td.Box(size=[td.inf, thick, td.inf], center=[0, 0, 0]), medium=pec\n",
    ")\n",
    "\n",
    "# create the aperture in the plate\n",
    "aperture = td.Structure(\n",
    "    geometry=td.Box(size=[width, 1.5 * thick, height], center=[0, 0, 0]), medium=air\n",
    ")\n",
    "\n",
    "# make sure to append the aperture to the plate so that it overrides that region of the plate\n",
    "geometry = [plate, aperture]\n",
    "\n",
    "# define the boundaries as PML on all sides\n",
    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "\n",
    "# set the total domain size in x, y, and z\n",
    "sim_size = [width * 2, 2, height * 2]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Source setup\n",
    "For our incident field, we create a plane wave incident from the left, with the electric field polarized in the -z direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bandwidth in Hz\n",
    "fwidth = f0 / 10.0\n",
    "\n",
    "# Gaussian source offset; the source peak is at time t = offset/fwidth\n",
    "offset = 4.0\n",
    "\n",
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=f0, fwidth=fwidth, offset=offset)\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 past the source decay (around t=2*offset/fwidth)\n",
    "run_time = 40 / fwidth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create monitor\n",
    "\n",
    "First, we'll see how to do field projections using your machine after you've downloaded near fields from a `Tidy3D` simulation.\n",
    "\n",
    "We create a surface [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldMonitor.html) just to the right of the aperture to capture the near field data in the frequency domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_mon = 0.3\n",
    "monitor_near = td.FieldMonitor(\n",
    "    center=[0, offset_mon, 0], size=[td.inf, 0, td.inf], freqs=[f0], name=\"near_field\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Simulation\n",
    "\n",
    "Now we can put everything together and define the simulation with a simple uniform mesh, and then we'll visualize the geometry to make sure everything looks right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at y=0.00'}, xlabel='x', ylabel='z'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:21:53] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_1'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'e064203d-d990-4848-ba4e-7f3ccec7deef'</span>.    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:21:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'aperture_1'\u001b[0m with task_id \u001b[32m'e064203d-d990-4848-ba4e-7f3ccec7deef'\u001b[0m.    \u001b]8;id=972214;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=826528;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bda76992766431a95a7078a85fa7b40",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:21:55] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:21:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=964244;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=169178;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:03] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                      \u001b]8;id=877839;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=815897;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\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\">[12:22:05] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:05]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=4293;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=157535;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:08] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=626848;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=495608;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\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\">[12:22:14] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=219256;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=181270;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4473853b12d9448f82413b4b82215236",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:17] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:17]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=665139;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=499840;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=939318;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=488641;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:19] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:19]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=97987;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=171233;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=447341;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=839166;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"data/aperture_1.hdf5\"</span>             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"data/aperture_1.hdf5\"\u001b[0m             \u001b]8;id=826695;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=737037;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25021c012b984d7fad40a1b6e9b553b6",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:21] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/aperture_1.hdf5                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:21]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/aperture_1.hdf5                                  \u001b]8;id=103151;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=833513;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Far field points <a name=\"farfield1\"></a>\n",
    "Now, we'll define the set of observation angles far away from the source at which we'd like to measure the far fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# radial distance away from the origin at which to project fields\n",
    "r_proj = 50 * wavelength\n",
    "\n",
    "# theta and phi angles at which to observe fields - part of the half-space to the right\n",
    "theta_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n",
    "phi_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we define a far-field monitor, [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor), 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/v1.9.0rc2/_autosummary/tidy3d.FieldProjector)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbdc2534cf0a422c855704cb0de4e0d0",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# far field projection monitor\n",
    "monitor_far = td.FieldProjectionAngleMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    # the size and center of any far field monitor should indicate where the *near* fields are recorded\n",
    "    freqs=[f0],\n",
    "    name=\"far_field\",\n",
    "    phi=list(phi_proj),\n",
    "    theta=list(theta_proj),\n",
    "    proj_distance=r_proj,\n",
    "    far_field_approx=True,  # we leave this to its default value of 'True' because we are interested in fields sufficiently\n",
    "    # far away that geometric far field approximations can be invoked to speed up the calculation\n",
    ")\n",
    "\n",
    "# helper functin to call the projector\n",
    "def get_proj_fields(sim_data, monitor_near, monitor_far, pts_per_wavelength=10):\n",
    "    # object that does projections is constructed using the near-field monitor, because those are the fields to be projected\n",
    "    projector = td.FieldProjector.from_near_field_monitors(\n",
    "        sim_data=sim_data,\n",
    "        near_monitors=[monitor_near],\n",
    "        normal_dirs=[\"+\"],  # we are projecting along the + direction\n",
    "        pts_per_wavelength=pts_per_wavelength,  # to speed up calculations, the fields on the near-field monitor can be downsampled to these\n",
    "        # many points per wavelength (default is already 10)\n",
    "    )\n",
    "    return projector.project_fields(monitor_far)\n",
    "\n",
    "\n",
    "# execute the projector, with the far field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data = get_proj_fields(sim_data, monitor_near, monitor_far)\n",
    "t1 = time.perf_counter()\n",
    "proj_time = t1 - t0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytical solution\n",
    "Before we plot and analyze the results, we need reference data with which to perform comparisons. In our simple aperture example, an analytical expression for the far fields is already available, so we'll simply implement the analytic formula here at the observation points of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analytic_fields_aperture(\n",
    "    proj_monitor, sim_size, aperture_height, aperture_width, r_proj\n",
    "):\n",
    "    \"\"\"Compute the far fields analytically.\"\"\"\n",
    "    # in Tidy3D, the plane wave source is normalized so that a total flux of 1 is injected into the simulation domain,\n",
    "    # which corresponds to an electric field strength that is inversely proportional to the square root of the in-plane domain area\n",
    "    thetas_ext = np.array(proj_monitor.theta)[None, :, None, None]\n",
    "    phis_ext = np.array(proj_monitor.phi)[None, None, :, None]\n",
    "    f = np.array(proj_monitor.freqs)[None, None, None, :]\n",
    "    E0 = np.sqrt(2.0 * td.ETA_0 / sim_size[0] / sim_size[2])\n",
    "    k = 2.0 * np.pi * f / td.C_0\n",
    "    ux = k * np.sin(thetas_ext) * np.cos(phis_ext) * aperture_width / 2.0\n",
    "    uz = k * np.cos(thetas_ext) * aperture_height / 2.0\n",
    "    Etheta = (\n",
    "        -k\n",
    "        / 2.0\n",
    "        / np.pi\n",
    "        / r_proj\n",
    "        * E0\n",
    "        * np.sin(thetas_ext)\n",
    "        * np.exp(1j * k * r_proj)\n",
    "        * aperture_height\n",
    "        * aperture_width\n",
    "        * np.sinc(ux / np.pi)\n",
    "        * np.sinc(uz / np.pi)\n",
    "    )\n",
    "    Hphi = Etheta / td.ETA_0\n",
    "\n",
    "    # for convenience, let's encapsulate the data into one of Tidy3D's native data structures designed for\n",
    "    # storing far fields - this is the same format in which data will be returned when using Tidy3D's\n",
    "    # 'FieldProjector', so comparisons will be easier to make\n",
    "    coords = dict(\n",
    "        r=np.array([r_proj]),\n",
    "        theta=np.array(proj_monitor.theta),\n",
    "        phi=np.array(proj_monitor.phi),\n",
    "        f=np.array(proj_monitor.freqs),\n",
    "    )\n",
    "    Etheta_data = td.FieldProjectionAngleDataArray(Etheta, coords=coords)\n",
    "    Hphi_data = td.FieldProjectionAngleDataArray(Hphi, coords=coords)\n",
    "    Er_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Ephi_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Hr_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    Htheta_data = td.FieldProjectionAngleDataArray(np.zeros_like(Etheta), coords=coords)\n",
    "    return td.FieldProjectionAngleData(\n",
    "        monitor=proj_monitor,\n",
    "        Er=Er_data,\n",
    "        Etheta=Etheta_data,\n",
    "        Ephi=Ephi_data,\n",
    "        Hr=Hr_data,\n",
    "        Htheta=Htheta_data,\n",
    "        Hphi=Hphi_data,\n",
    "        projection_surfaces=proj_monitor.projection_surfaces,\n",
    "    )\n",
    "\n",
    "\n",
    "analytic_field_data = analytic_fields_aperture(\n",
    "    monitor_far, sim_size, height, width, r_proj\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot and compare\n",
    "Now we can compare the analytic fields to those computed via `Tidy3D`'s [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjector), 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: 4.51 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "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(\"$\\phi$ (deg)\")\n",
    "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "\n",
    "# RMSE\n",
    "def rmse(array_ref, array_test):\n",
    "    error = array_test - array_ref\n",
    "    rmse = np.sqrt(np.mean(np.abs(error.flatten()) ** 2))\n",
    "    nrmse = rmse / np.abs(np.max(array_ref.flatten()) - np.min(array_ref.flatten()))\n",
    "    return nrmse\n",
    "\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_analytic = analytic_field_data.Etheta.isel(f=0, r=0)\n",
    "Etheta_proj = projected_field_data.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain good agreement to analytical results. Now let's see if we can repeat this simulation but compute the far fields on the server, during the simulation run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Server-side field projection <a name=\"farfieldserver1\"></a>\n",
    "All we have to do is provide the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor) 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/v1.9.0rc2/_autosummary/tidy3d.FieldMonitor) - the near fields will automatically be recorded based on the size and location of the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim2 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[\n",
    "        monitor_far\n",
    "    ],  # just provide the far field FieldProjectionAngleMonitor as the input monitor\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:23] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_2'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'b4dc3b94-d998-4eea-99b7-17ea190c6e02'</span>.    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'aperture_2'\u001b[0m with task_id \u001b[32m'b4dc3b94-d998-4eea-99b7-17ea190c6e02'\u001b[0m.    \u001b]8;id=92971;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=348333;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc2fc4e2a9bb4eb5ab5c0acde71ff500",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:25] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=230604;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=807851;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:32] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                      \u001b]8;id=447821;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=415418;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\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\">[12:22:34] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=780342;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=534326;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:38] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=715200;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=994876;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\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\">[12:22:44] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:44]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=451732;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=751609;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4a439c723404974b6007dbddc56657b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:47] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:47]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=934832;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=5744;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=927042;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=639518;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:49] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:49]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=876429;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=800808;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=220395;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=349595;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"data/aperture_2.hdf5\"</span>             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"data/aperture_2.hdf5\"\u001b[0m             \u001b]8;id=985761;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=211249;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8aac00f5b7274d0e9659c7aaf65dadfd",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:51] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/aperture_2.hdf5                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/aperture_2.hdf5                                  \u001b]8;id=219393;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=373170;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the projected fields are already contained in the returned `sim_data2` object - all we have to do is access it as follows, and then plot and compare to analytical results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.51 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain nearly identical results, except that they are computed much faster on our servers. Note also that in some cases, the server-side computations may be slightly more accurate than client-side ones, because on the server, the near fields are not downsampled at all.\n",
    "\n",
    "To see the performance gains of using server-side computations, let's compare the time taken in each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection took 1.84 s\n",
      "Server-side field projection took 0.63 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(\n",
    "    sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
    "print(f\"Server-side field projection took {server_time:.2f} s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the server computes far fields faster than the local CPU-based computation, though it's a relatively small gain in this case. The gains in computation time are expected to be greater for larger and more complex setups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1260x380 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "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(\"$\\phi$ (deg)\")\n",
    "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "\n",
    "# get the fields in Cartesian coordinates from the projected data we already computed above\n",
    "fields_cartesian = projected_field_data.fields_cartesian.isel(f=0, r=0)\n",
    "\n",
    "# plot Ex, Ey, Ez\n",
    "make_cart_plot(\n",
    "    phi_proj, theta_proj, fields_cartesian.Ex, fields_cartesian.Ey, fields_cartesian.Ez\n",
    ")\n",
    "\n",
    "# get the power\n",
    "power = projected_field_data.power.isel(f=0, r=0)\n",
    "\n",
    "# plot the power\n",
    "fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(4.3, 3.8))\n",
    "im = ax.pcolormesh(\n",
    "    phi_proj * 180 / np.pi,\n",
    "    theta_proj * 180 / np.pi,\n",
    "    power,\n",
    "    cmap=\"inferno\",\n",
    "    shading=\"auto\",\n",
    ")\n",
    "fig.colorbar(im, ax=ax)\n",
    "_ = ax.set_title(\"Power\")\n",
    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "_ = ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-projection to a different distance <a name=\"reproj\"></a>\n",
    "We can re-project the already-computed far fields to a different distance away from the structure - _we neither need to run another simulation nor re-run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjector)_. 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/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor), the fields are projected to a distance of 1m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.51 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "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(\n",
    "    monitor_far, sim_size, height, width, r_proj_new\n",
    ")\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_analytic = analytic_field_data_new.Etheta.isel(f=0, r=0)\n",
    "Etheta_proj = reprojected_field_data.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More accurate field projections <a name=\"exact\"></a>\n",
    "In the field projections used above, the far field approximation is used: it is assumed that the fields are measured at a distance much greater than the size of our simulation in the transverse direction. Accordingly, geometric approximations are invoked, and any quantity whose magnitude drops off as 1/r^2 or faster is ignored. The advantages of these approximations are:\n",
    "* the projections are computed relatively fast\n",
    "* the projections are cast in a simple mathematical form which allows re-projecting the fields to different distance without the need to re-run a simulation or to re-run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjector).\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/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor)! 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/v1.9.0rc2/_autosummary/tidy3d.FieldProjector)._\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/v1.9.0rc2/_autosummary/tidy3d.FieldProjector).\n",
    "\n",
    "Also, this time we'll use the [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionCartesianMonitor), which is the counterpart to the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor) where the observation grid is defined in Cartesian coordinates, not angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at y=0.00'}, xlabel='x', ylabel='z'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:52] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_3'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'f0a7c9ea-5deb-4d76-bbaa-4470aecdffe3'</span>.    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'aperture_3'\u001b[0m with task_id \u001b[32m'f0a7c9ea-5deb-4d76-bbaa-4470aecdffe3'\u001b[0m.    \u001b]8;id=651796;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=602492;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8758f4acc8144046961eab1037a233da",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:54] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                      \u001b]8;id=622070;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=317598;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=353860;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=746223;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:22:57] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:22:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=546793;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=623047;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:00] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:00]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=152650;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=277670;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\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\">[12:23:06] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:06]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=212297;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=217645;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "538b3a5d9b6a486ca83b10c01c000b8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:08] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=218203;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=816836;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=964655;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=604482;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:10] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=191687;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=376194;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:11] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=810989;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=786934;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"data/aperture_3.hdf5\"</span>             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"data/aperture_3.hdf5\"\u001b[0m             \u001b]8;id=503130;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=676324;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c26d8ef41ad4be7b9cb49675191994b",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:13] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/aperture_3.hdf5                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/aperture_3.hdf5                                  \u001b]8;id=802707;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=385581;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data3 = web.run(sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionCartesianMonitor), this time turning off the far field approximations, and then run the [FieldProjector](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjector) again.\n",
    "\n",
    "The [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionCartesianMonitor)'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": 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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "692922fe131a40968708e0323852e641",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the projection monitor which projects fields without approximations\n",
    "xs = np.linspace(-sim_size[0] / 2, sim_size[0] / 2, 100)\n",
    "ys = np.linspace(-sim_size[1] / 2, sim_size[1] / 2, 100)\n",
    "monitor_intermediate_proj = td.FieldProjectionCartesianMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field_proj\",\n",
    "    proj_axis=1,  # because we're projecting along the +y axis\n",
    "    x=list(xs),  # local x coordinates - corresponds to global x in this case\n",
    "    y=list(ys),  # local y coordinates - corresponds to global z in this case\n",
    "    proj_distance=r_proj_intermediate,\n",
    "    far_field_approx=False,  # turn off the far-field approximation (is 'True' by default)\n",
    ")\n",
    "\n",
    "# execute the projector, with the projection field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data_noapprox = get_proj_fields(\n",
    "    sim_data3, monitor_near, monitor_intermediate_proj\n",
    ")\n",
    "t1 = time.perf_counter()\n",
    "proj_time_new = t1 - t0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare the following three results:\n",
    "* Directly-measured fields at the projection distance\n",
    "* Projected fields with approximations turned off\n",
    "* Projected fields with approximations turned on (just to compare the accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdf462e4744544b18ba47003be75e1c8",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute projected fields *with* far field approximations, to facilitate an accuracy comparison\n",
    "monitor_intermediate_proj_approx = td.FieldProjectionCartesianMonitor(\n",
    "    center=[\n",
    "        0,\n",
    "        offset_mon,\n",
    "        0,\n",
    "    ],  # the monitor's center defined the local origin - the projection distance\n",
    "    # and angles will all be measured with respect to this local origin\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field_proj_approx\",\n",
    "    proj_axis=1,  # because we're projecting along the +y axis\n",
    "    x=list(xs),  # local x coordinates - corresponds to global x in this case\n",
    "    y=list(ys),  # local y coordinates - corresponds to global z in this case\n",
    "    proj_distance=r_proj_intermediate,\n",
    "    far_field_approx=True,  # turn on the far-field approximation\n",
    ")\n",
    "\n",
    "# execute the projector, with the projection field monitor as input, to do the projection\n",
    "# let's also time this, for later use\n",
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "projected_field_data_approx = get_proj_fields(\n",
    "    sim_data3, monitor_near, monitor_intermediate_proj_approx\n",
    ")\n",
    "t1 = time.perf_counter()\n",
    "proj_time_new_approx = t1 - t0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection *with approximations on* took 3.45 s\n",
      "Client-side field projection *with approximations off* took 11.61 s\n"
     ]
    }
   ],
   "source": [
    "# let's see how long this took compared to the previous case when the approximations were turned on\n",
    "print(\n",
    "    f\"Client-side field projection *with approximations on* took {proj_time_new_approx:.2f} s\"\n",
    ")\n",
    "print(\n",
    "    f\"Client-side field projection *with approximations off* took {proj_time_new:.2f} s\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, when the approximations are turned off, the projections take longer. Now let's see if it was worth it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to plot fields\n",
    "def make_cart_plot(phi, theta, vals1, vals2, vals3):\n",
    "    n_plots = 3\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(9, 3))\n",
    "    im1 = ax[0].pcolormesh(ys, xs, np.real(vals1), cmap=\"RdBu\", shading=\"auto\")\n",
    "    im2 = ax[1].pcolormesh(ys, xs, np.real(vals2), cmap=\"RdBu\", shading=\"auto\")\n",
    "    im3 = ax[2].pcolormesh(ys, xs, np.real(vals3), cmap=\"RdBu\", shading=\"auto\")\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    fig.colorbar(im3, ax=ax[2])\n",
    "    ax[0].set_title(\"Ex\")\n",
    "    ax[1].set_title(\"Ey\")\n",
    "    ax[2].set_title(\"Ez\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$y$ (micron)\")\n",
    "        _ax.set_ylabel(\"$x$ (micron)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation: 0.59 %\n",
      "Normalized RMSE for |E|, with far field approximation: 23.17 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAErCAYAAACirgCsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAADrvklEQVR4nOy9eZgsRZU+/EZEZlb13WAuqygCCqKIgoKXn4wKjugFcUFFgUdlkWV0REUUBUdZVUAQUWFkUBEdRdx1HBUUhE8dcQHGhVFc2Ua9rMLdujozI+L7IzMiTmRl1tJdfburO97nqXujIqMyI7MrT+WJ95z3MK21RkBAQEBAQEBAQEBAQMC8A5/rCQQEBAQEBAQEBAQEBATUIzhsAQEBAQEBAQEBAQEB8xTBYQsICAgICAgICAgICJinCA5bQEBAQEBAQEBAQEDAPEVw2AICAgICAgICAgICAuYpgsMWEBAQEBAQEBAQEBAwTxEctoCAgICAgICAgICAgHmK4LAFBAQEBAQEBAQEBATMUwSHLSAgICAgICAgICAgYJ4iOGwBAQEBM8SOO+6Io48+eq6nMTRuvPFGMMZw4403zvVUFj0YYzjzzDPnehoezjzzTDDG5noaAQEBAYsewWELCAhYkLjyyivBGLOvdruNJzzhCTjxxBNx7733zvX0hsL73/9+fP3rX5/raQQsQGzcuBFnnnlmcNoDAgIC5jGiuZ5AQEBAwGzi7LPPxk477YROp4Mf/ehH+NjHPoZvf/vbuO2227BkyZKRHON3v/sdOJ+99a/3v//9OPTQQ3HIIYfM2jEC5haTk5OIok3/k7xx40acddZZAID999/f2/bud78bp5566iafU0BAQECAj+CwBQQELGgcdNBB2HvvvQEAxx13HLbYYgtcdNFF+MY3voEjjjii9jMbNmzA0qVLBz5Gq9UayVwD5j/yPIdSCkmSjHS/7XZ7pPsbBaIomhMnMiAgICDARwiJDAgIWFT4p3/6JwDAHXfcAQA4+uijsWzZMvzpT3/CC1/4QixfvhyvfvWrARSO29ve9jZsv/32aLVa2HXXXXHhhRdCa+3tsy6H7eGHH8ZJJ51kP7vzzjvj/PPPh1LKG6eUwoc//GE85SlPQbvdxlZbbYUDDzwQN998M4Ait2nDhg349Kc/bcM76bH+8pe/4HWvex222WYbtFotPPnJT8YVV1zRdd7/93//h0MOOQRLly7F1ltvjbe+9a2Ympqa9nW88847wRjDhRdeiMsvvxyPf/zj0Wq18IxnPAM///nPu8Z///vfx7Of/WwsXboUm2++OV760pfit7/9bd/jpGmK008/HXvttRc222wzLF26FM9+9rNxww03NM7nQx/6EHbYYQdMTExgv/32w2233eaNNX/zP//5z1i9ejWWLl2K7bbbDmeffbb3t6X7vPjii+05/uY3vxnonD71qU+BMdb193j/+98Pxhi+/e1v275qDpvJH/v973+P17zmNdhss82w1VZb4T3veQ+01rjnnnvw0pe+FCtWrMC2226LD37wg0NftzvvvBNbbbUVAOCss86y3y8zj7octjzPcc4559hrseOOO+Jd73pX13dpxx13xIte9CL86Ec/wqpVq9But/G4xz0On/nMZ7xxWZbhrLPOwi677IJ2u40tttgCz3rWs/C9730PAQEBAQEFwtJZQEDAosKf/vQnAMAWW2xh+/I8x+rVq/GsZz0LF154IZYsWQKtNV7ykpfghhtuwLHHHos999wT1157LU455RT85S9/wYc+9KHGY2zcuBH77bcf/vKXv+Cf//mf8djHPhY//vGPcdppp+Fvf/sbLr74Yjv22GOPxZVXXomDDjoIxx13HPI8xw9/+EP85Cc/wd57743/+I//wHHHHYdVq1bhhBNOAAA8/vGPBwDce++9+H//7/+BMYYTTzwRW221Fb7zne/g2GOPxdq1a3HSSScBKMLtnve85+Huu+/Gm9/8Zmy33Xb4j//4D3z/+9+f8fW86qqrsG7dOvzzP/8zGGP4wAc+gJe//OX485//jDiOAQDXXXcdDjroIDzucY/DmWeeicnJSXz0ox/FP/7jP+LWW2/Fjjvu2Lj/tWvX4hOf+ASOOOIIHH/88Vi3bh0++clPYvXq1fjZz36GPffc0xv/mc98BuvWrcMb3/hGdDodfPjDH8Y//dM/4de//jW22WYbO05KiQMPPBD/7//9P3zgAx/ANddcgzPOOAN5nuPss8/29vmpT30KnU4HJ5xwAlqtFlauXDnQOR1zzDH46le/ipNPPhnPf/7zsf322+PXv/41zjrrLBx77LF44Qtf2Pf6HnbYYXjSk56E8847D9/61rfw3ve+FytXrsS///u/45/+6Z9w/vnn43Of+xze/va34xnPeAae85znDHzdttpqK3zsYx/DG97wBrzsZS/Dy1/+cgDAU5/61Mb5HHfccfj0pz+NQw89FG9729vw05/+FOeeey5++9vf4mtf+5o39o9//CMOPfRQHHvssTjqqKNwxRVX4Oijj8Zee+2FJz/5yQAKp/Dcc8+13/G1a9fi5ptvxq233ornP//5fa9PQEBAwKKADggICFiA+NSnPqUB6Ouuu07ff//9+p577tFXX3213mKLLfTExIT+v//7P6211kcddZQGoE899VTv81//+tc1AP3e977X6z/00EM1Y0z/8Y9/tH077LCDPuqoo+z7c845Ry9dulT//ve/9z576qmnaiGEvvvuu7XWWn//+9/XAPSb3/zmrvkrpWx76dKl3v4Njj32WP2oRz1KP/DAA17/4YcfrjfbbDO9ceNGrbXWF198sQagv/jFL9oxGzZs0DvvvLMGoG+44YaufffDHXfcoQHoLbbYQj/00EO2/xvf+IYGoL/5zW/avj333FNvvfXW+sEHH7R9v/zlLzXnXB955JE9j5PnuZ6amvL6/v73v+ttttlGv+51r+uaD/3baq31T3/6Uw1Av/Wtb7V95m/+pje9yfYppfTBBx+skyTR999/v7fPFStW6Pvuu8+bw6Dn9Le//U2vXLlSP//5z9dTU1P6aU97mn7sYx+rH3nkEW9/APQZZ5xh359xxhkagD7hhBO8a/GYxzxGM8b0eeed512PiYkJ7zsy6HW7//77u45dnYPBL37xCw1AH3fccd64t7/97RqA/v73v2/7dthhBw1A/+AHP7B99913n261Wvptb3ub7dtjjz30wQcf3HXsgICAgACHEBIZEBCwoHHAAQdgq622wvbbb4/DDz8cy5Ytw9e+9jU8+tGP9sa94Q1v8N5/+9vfhhACb37zm73+t73tbdBa4zvf+U7jMb/0pS/h2c9+Nv7hH/4BDzzwgH0dcMABkFLiBz/4AQDgK1/5ChhjOOOMM7r20U9OXWuNr3zlK3jxi18MrbV3nNWrV+ORRx7Brbfeas/lUY96FA499FD7+SVLlljGbiY47LDD8A//8A/2/bOf/WwAwJ///GcAwN/+9jf84he/wNFHH42VK1facU996lPx/Oc/3wsLrIMQwuaLKaXw0EMPIc9z7L333vb8KA455BDvb7tq1Srss88+tcc58cQTbduwlGma4rrrrvPGveIVr7Chg8Oe07bbbotLL70U3/ve9/DsZz8bv/jFL3DFFVdgxYoVPc/b4LjjjvOuxd577w2tNY499ljbv/nmm2PXXXe119yMHea6DQJzXieffLLX/7a3vQ0A8K1vfcvr32233ez3AQC22mqrrnluvvnm+N///V/84Q9/mNacAgICAhYDQkhkQEDAgsall16KJzzhCYiiCNtssw123XXXLkXHKIrwmMc8xuu76667sN1222H58uVe/5Oe9CS7vQl/+MMf8Ktf/cp7yKe47777ABThmdttt5330D8o7r//fjz88MO4/PLLcfnll/c8zl133YWdd965ywncddddhz5uFY997GO998Z5+/vf/26P3XSsJz3pSbj22mv7irx8+tOfxgc/+EHcfvvtyLLM9u+0005dY3fZZZeuvic84Qn44he/6PVxzvG4xz2uaxxQ5HZRVI8z7Dkdfvjh+OxnP4tvfetbOOGEE/C85z2v6VS7UL2+m222GdrtNrbccsuu/gcffNDrG+a6DYK77roLnHPsvPPOXv+2226LzTffvOueqM4dKL4f5rsBFCquL33pS/GEJzwBu+++Ow488EC89rWv7RmWGRAQELDYEBy2gICABY1Vq1ZZlcgmtFqtkcryK6Xw/Oc/H+94xztqtxvHYKbHAIDXvOY1OOqoo2rHbIqHXiFEbb+uCLNMF5/97Gdx9NFH45BDDsEpp5yCrbfeGkIInHvuuTYfcbYxMTExo88/+OCDVkTmN7/5DZRSA3/f6q7vINd8Nq/boMW0B5nnc57zHPzpT3/CN77xDXz3u9/FJz7xCXzoQx/CZZdd5rGLAQEBAYsZwWELCAgIqMEOO+yA6667DuvWrfNYtttvv91ub8LjH/94rF+/HgcccEDPYzz+8Y/Htddei4ceeqgny1b3gLzVVlth+fLlkFL2Pc4OO+yA2267DVprb1+/+93ven5uFDDXqe5Yt99+O7bccsue7NqXv/xlPO5xj8NXv/pVb+51YaQAakPrfv/733cJmyil8Oc//9lznn//+98DQE8RFGD4c3rjG9+IdevW4dxzz8Vpp52Giy++uCuscNQY9LoN6nwBxXkrpfCHP/zBMs1AIX7z8MMP97wnemHlypU45phjcMwxx2D9+vV4znOegzPPPDM4bAEBAQElQg5bQEBAQA1e+MIXQkqJSy65xOv/0Ic+BMYYDjrooMbPvupVr8JNN92Ea6+9tmvbww8/jDzPARS5UVprW7iYgrIQS5cuxcMPP+xtF0LgFa94Bb7yla90ydYDRcgkPZe//vWv+PKXv2z7Nm7c2BhKOUo86lGPwp577olPf/rT3jncdttt+O53v9tXKdGwNPR6/PSnP8VNN91UO/7rX/86/vKXv9j3P/vZz/DTn/609u9F/7Zaa1xyySWI47hvyOIw5/TlL38ZX/jCF3Deeefh1FNPxeGHH453v/vd1jmcLQx63Uzx+Or3qw7mvKjKKQBcdNFFAICDDz546HlWwziXLVuGnXfeeUYlJwICAgIWGgLDFhAQEFCDF7/4xXjuc5+Lf/3Xf8Wdd96JPfbYA9/97nfxjW98AyeddJKV1q/DKaecgv/8z//Ei170IitjvmHDBvz617/Gl7/8Zdx5553Ycsst8dznPhevfe1r8ZGPfAR/+MMfcOCBB0IphR/+8Id47nOfa0Ux9tprL1x33XW46KKLsN1222GnnXbCPvvsg/POOw833HAD9tlnHxx//PHYbbfd8NBDD+HWW2/Fddddh4ceeggAcPzxx+OSSy7BkUceiVtuuQWPetSj8B//8R/2YZ3ixhtvxHOf+1ycccYZXl2wmeCCCy7AQQcdhGc+85k49thjrQT+Zptt1vcYL3rRi/DVr34VL3vZy3DwwQfjjjvuwGWXXYbddtsN69ev7xq/884741nPehbe8IY3YGpqChdffDG22GKLrvDUdruNa665BkcddRT22WcffOc738G3vvUtvOtd72rMPRz2nO677z684Q1v8P6Wl1xyCW644QYcffTR+NGPfjTSUFyKQa/bxMQEdtttN3zhC1/AE57wBKxcuRK77747dt9996597rHHHjjqqKNw+eWX4+GHH8Z+++2Hn/3sZ/j0pz+NQw45BM997nOHnuduu+2G/fffH3vttRdWrlyJm2++GV/+8pc9QZiAgICARY850aYMCAgImGUYWf+f//znPccdddRReunSpbXb1q1bp9/61rfq7bbbTsdxrHfZZRd9wQUXeJL7WnfL+pvPnnbaaXrnnXfWSZLoLbfcUu+77776wgsv1Gma2nF5nusLLrhAP/GJT9RJkuitttpKH3TQQfqWW26xY26//Xb9nOc8R09MTGgA3rHuvfde/cY3vlFvv/32Oo5jve222+rnPe95+vLLL/fmc9ddd+mXvOQlesmSJXrLLbfUb3nLW/Q111zTJev/zW9+UwPQl112Wc/rZiTvL7jggq5tqJGJv+666/Q//uM/6omJCb1ixQr94he/WP/mN7/peQytC7n997///XqHHXbQrVZLP+1pT9P/9V//pY866ii9ww471M7ngx/8oN5+++11q9XSz372s/Uvf/lLb5/mb/6nP/1Jv+AFL9BLlizR22yzjT7jjDO0lHKgcxzknF7+8pfr5cuX6zvvvNP7nCl9cP755zdeMyOpb0oMVOdexX777aef/OQnD33dtNb6xz/+sd5rr710kiTePKqy/lprnWWZPuuss/ROO+2k4zjW22+/vT7ttNN0p9Pxxu2www61cv377bef3m+//ez79773vXrVqlV688031xMTE/qJT3yift/73ufdIwEBAQGLHUzrEWWGBwQEBCxSbL/99li9ejU+8YlPzPVUZox3vOMd+PznP48//vGPaLVacz2dgXHnnXdip512wgUXXIC3v/3tPcceffTR+PKXv1zL0AUEBAQEBMw3hBy2gICAgBkgyzI8+OCDXTLr44obbrgB73nPe8bKWQsICAgICFjICDlsAQEBAdPEtddei6uvvhqTk5ND1daaz/j5z38+11MICAgICAgIIAgOW0BAQMA0cd555+GPf/wj3ve+9+H5z3/+XE8nICAgICAgYAEi5LAFBAQEBAQEBAQEBATMU4QctoCAgICAgICAgICAgHmK4LAFBAQEBAQEBAQEBATMUwSHLSAgICAgICAgICAgYJ4iOGwBAQEBAQEBAQEBAQHzFMFhCwgICAgICAgICAgImKcIDltAQEBAQEBAQEBAQMA8RXDYAgICAgICAgICAgIC5imCwxYQEBAQEBAQEBAQEDBPERy2gICAgICAgICAgICAeYrgsAUEBAQEBAQEBAQEBMxTBIctICAgICAgICAgICBgniI4bAEBAQEBAQEBAQEBAfMUwWELCAgICAgICAgICAiYpwgOW0BAQEBAQEBAQEBAwDxFcNgCAgICAgICAgICAgLmKYLDFhAQEBAQEBAQEBAQME8RHLaAgICAgICAgICAgIB5iuCwBQQEBAQEBAQEBAQEzFMEhy0gICAgICAgICAgIGCeIjhsAQEBAQEBAQEBAQEB8xTBYQsICAgICAgICAgICJinCA5bQEBAQEBAQEBAQEDAPEVw2AICAgICAgICAgICAuYpgsMWEBAQEBAQEBAQEBAwTxEctgBceeWVYIw1vn7yk5/M9RQDAgICPAS7FRAQMI4ItitgOojmegIB8wdnn302dtppp67+nXfeeQ5mExAQENAfwW4FBASMI4LtChgGwWELsDjooIOw9957z/U0AgICAgZGsFsBAQHjiGC7AoZBCIkMGAhnnHEGOOe4/vrrvf4TTjgBSZLgl7/85RzNLCAgIKAbWmvsuOOOeOlLX9q1rdPpYLPNNsM///M/z8HMAgICAnpj//33bwyZvPLKK+d6egFzgMCwBVg88sgjeOCBB7w+xhi22GILvPvd78Y3v/lNHHvssfj1r3+N5cuX49prr8XHP/5xnHPOOdhjjz3maNYBAQGLGb3s1mte8xp84AMfwEMPPYSVK1fa7d/85jexdu1avOY1r9nU0w0ICAgA0Nt2/eu//iuOO+44b9tnP/tZXHvttdh666035TQD5gmY1lrP9SQC5hZXXnkljjnmmNptrVYLnU4HAHDbbbdhr732wpFHHokLLrgAu+++Ox71qEfhpptuQhQF3z8gIGDTYRC79fvf/x677rorPvaxj+H1r3+93f7Sl74Uv/rVr/DnP/8ZjLFNNeWAgICAgZ+5KH784x9j//33x2tf+1p88pOfnO0pBsxDhKfsAItLL70UT3jCE7w+IYRt77777jjrrLNw2mmn4Ve/+hUeeOABfPe73w3OWkBAwJyhl916whOegH322Qef+9znrMP20EMP4Tvf+Q7e8Y53BGctICBgztDvmctgzZo1OPTQQ7Hnnnvi3/7t3zbV9ALmGcKTdoDFqlWr+ibAnnLKKbj66qvxs5/9DO9///ux2267baLZBQQEBHSjn9068sgjceKJJ+Kuu+7CDjvsgC996UvIsgyvfe1rN+EsAwICAnwM8syV5zle9apXQUqJr371q2i1WptodgHzDUF0JGAo/PnPf8Yf/vAHAMCvf/3rOZ5NQEBAQG8cfvjhiOMYn/vc5wAUeSB77703dt111zmeWUBAQEBvnHLKKbjpppvwxS9+EY95zGPmejoBc4jgsAUMDKUUjj76aKxYsQLvete78PnPfx5f/epX53paAQEBAY1YuXIlDj74YHzuc5/DXXfdhf/+7/8O7FpAQMC8x9VXX42LL74YF154Ifbbb7+5nk7AHCM4bAED46KLLsKPf/xjXH755TjnnHOw77774g1veEOXylFAQEDAfMJrX/ta/OY3v8Epp5wCIQQOP/zwuZ5SQEBAQCNuu+02HHfccXjNa16Dt7zlLXM9nYB5gJDDFmDxne98B7fffntX/7777oupqSm85z3vwdFHH40Xv/jFAAqloz333BP/8i//gi9+8YuberoBAQEBPe3W4x73OADAwQcfjC222AJf+tKXcNBBBwVZ7ICAgDlHL9tlVCSf85zn4LOf/WzXdmPbAhYPgsMWYHH66afX9n/iE5/Av//7v2PLLbfExRdfbPt32WUXnHvuuXjLW96CL37xi3jVq161iWYaEBAQUKDJbn3qU5+yDzVJkuCwww7Dv/3bv4VwyICAgHmBXrbr/vvvx4YNG3DCCSfUbg8O2+JDqMMWEBAQELDg8da3vhWf/OQnsWbNGixZsmSupxMQEBAQEDAwQg5bQEBAQMCCRqfTwWc/+1m84hWvCM5aQEBAQMDYIYREBgQEBAQsSNx333247rrr8OUvfxkPPvhgSN4PCAgICBhLBIctICAgIGBB4je/+Q1e/epXY+utt8ZHPvIR7LnnnnM9pYCAgICAgKERctgCAgICAgICAgICAgLmKUIOW0BAQEBAQEBAQEBAwDxFcNgCAgICAgICAgICAgLmKRZlDptSCn/961+xfPlyMMbmejoBCwhaa6xbtw7bbbcdOO+9HtLpdJCmaeP2JEnQbrdHPcWAMUWwWwGziUFtVz+7BQTbFeAQ7FbAbGIx2a1F6bD99a9/xfbbbz/X0whYwLjnnnvwmMc8pnF7p9PBxPKVQD7ZOGbbbbfFHXfcMa8NSMCmQ7BbAZsCvWzXIHYLCLYrwCHYrYBNgcVgtxalw7Z8+XIAwEn/cQNaS5YBAOKo8MwFZ0giZtuxKPpj5toRZ4hMP/f77efKlSTOGMrN4Kx4X+037w3oIhRH94qUt535/d74sinIcRlcHKzgDIKZz5p5Ff3mc6bNtAZTeTFYSTAly3YOaFWMUTlg+rUCM3o2WhXjALBybPFZBVDNG7LNG1ez3f+cJEOUnaOZi5ayOFZ5TC2zcqwE8rKdZdBlGzKFzvOyP7VjVJ5Bq+K4KsuhZbFPlWb2+GsnO9jjjI/b71gT0jQF8knEux8BiLh7gMyw5rbPI03TeWs8AjYtzHfq1v/9HZYuWw5ZfhclAFXeD1IDKG8NBW1vE6X9W0ahW2uqSX6quihObYzZVrVDdlzddtTbI8aYHVfYsu7+7mM5u8XsdmbnwLR29kkrZ0NIvx0HkO3KazOvX5N+3T2e7Idp5WxPxdaxmvEeBv2DMH9VWTPu+hgDylVnzTjAI/sZzUXR5gKacaxbtx6P22NVT9vV124BwXYFeDDfpzuu/SxWLF3i7gcK0qeV+z0394XXp9zvuf/bXnkOkKlrkzFammcX5Z4RlLTPDipzbS2V/Z3XUkLrmn6loSSZv1T+/7p7W9P7fmCim0GifYxx+572c8HBzLOcIGMYBxOiu7+0GTwWts24AKzNcJ8DF9bGMM4BkXj76BojXNvuz+yf2DJGtqGOOSv71m7YiJ1Wv2ZR2K1F6bCZH/mVKzdDe+ny0kkzDhhHXHoxfpuhFQnbrh9T7kMwCPNAwlB5COnuB1DjlvngA4QS+I6ef779HoTMRwX3+2odNvLQwojD5j20qIaHHPPezKHuQaWKfmMGeVDy+ktnLM+AvDTqWQZt2nnmHLmpSevINY2nzpvYUKzgDBr6wVtLwUoD552S7E3dByw+mO/UsuXLsWz5CuuwKcC2NQrnDCi+9sYx09r1m/eDH9d/T22J7QOrHVfnRA1qm+r3070N8J1AOjfW5Zj1tkmszpaR7YPYNd8xVPXHpw+tlTl2je0DXXHYwDjxmrnbThw5zSP3cFQ6bPbjA9iuJrsFBNsV4MN8n5ZHDMsjBqB4jvKdMG81yUJL06+dc1VYvHLnEpop26YOmLsHJXS5qKvynDhazumSaVbrpEny266lqh2jVMWpI2Pcebi259zVOa89QJ0gTh21mjbjvjPGuXPk/DGuX8SRv4+c22OKJAYT0n1OG6cuAtOlc6WFtW2MEQePaTBrYzTMd4BBEydQgxEfDZx8J4iJ8xw5ADoyz9QL324tSoctIGC+gDHRZYAAAKqmLyAgIGAeoNFuAcF2BQQEzEuMu91a1A7b8naMiXaMWDCPHWuZcEfB0S6Zt5ZwTFo74pZ5irlj08wihyCryWLAFeRhoIZYHa87BqvrawgtojCrsEVvuYrEo/oVZE6CrnqsFDeeSnXVeBBUV8TNSo/H/Emyyp1b1pBJCW5WWGQGnXaK3WSknXaKEEnTLpk3nqfQWdGOhlwxE3EMFtUwbCyURwyoR5cN0dreq7JCnRlGXzHtSwIPYXN6MWlVhr7oI1EE1bnCsGT+uZgx9ftrtpd1tqprodXYEq1qbRidiSZja0MWmfJCnHwbU+xVQ1TCJh3bZv86vD/zpvU0RZxJGGT3Nu6Ps/PhQ9ncJrsFBNsVUA+ddqBj7kISAS+M0bFjyu8vt/VLd9B5Ws+A0fQFKaGy3I6RpF07Ximb8qA9Jk1Zpszv16S/jICQusK2dd8fqqavCi58w8bIe8OIccFs6CMX3I5hwjFlXPismu1PYo+FA0r2rIGB42WbC9E43oZTRgm0FxJZsqzchVyCC2If/X5yEewYwx6a57NBMO52a1E7bMtigSWJQCviXrijddIi57DFnKMcgpgzm68Wkzyw2twv+GGF7kda9w158UJdSFuTH1ovL6XysNbv61f3zDbIg48m7eIc3A2l+4Xx1OVb1Gwb9ByraAqL8sI5S2Nf5NwZhy0Hytw2JlOwVuGYcZkB+VTx0bQDPVUYB5V2/LDJ0mjwyu9MPzDesOLTtAoUEIDyu13eChyAMjYIrAgzQeE/mMUdYXuH2H+JpvwzOo6jOyRlEMfM7LPrOH0cNFTmUZ1zLRi39pfmeTGt3I6tYyZ8h6kprNL4d9UQSuLIeTaxzpEDsZu8Pq9t0PBIe272gzSfrRIeWTd+QDTaLSDYroBa6KkOFA1zqzhrdQ6Z/ewIHbPm/qxrnzQkUqay1gnTUkOmZcil0l0OmZLK9tFtNDRyWPjhkMw6ZtV+bp5JE+E5b8b5Y5xBJC5ssiskUnDwOK7dzkm7vyOXDubIuRO0ObaM8y6nDSh+/hjn9plsEIy73VrUDltAwFyDRzFY1J0EqzF9Yx4QEBAwm2iyW0CwXQEBAfMT4263FrXD9g8TMZZMxB6T1hLCtiNRMGgAkHCGiAiQUIaNa8PYlGqBSoLlhNGRNQqJvVZM61Y/ub9SSseaMYImmzetoDaspuq+y9PN82v6bBMzpm0fCRvQgCYcgBmvtbuN/P31nmJVtU6U2aycCQhe3LAicuGsXEswwrAZcREmM7C8WMFhrRR8STkmnYSeKgRGdNohDNtwq9WBYQsYFowxcNgIPCgw8PLmUIyEQWoNGkWjG27xQZj2KkNWjKmGS3Zvp8doCsFumked0NKMyzjV2D/PPjaKfzREEVTVJc34kinTdBsVQqr29xJAqR6zYZ5uAjUiJEC3emT1t2UIpm3cV6oDNj3U1EZoobuYNcBn0Lx+Eiap0ty2aWiiJMyYYc+U9MebNg1xpIxclUmTGdl/SsIgjTJvKj3WjIZBKttPwyS1nYs7xfqHGMrCMVFv8Gh4JOOEMRNsgPBIx6px7saLxIVKitj0CdIXISehjzyJy31wyCRyc0kie0yfhXNMnRM9Se14xrmzKcSG6KoipbsIhcjWVG+pfopxt1uL2mELCJhrcNGQwzZECFRAQEDApkST3QKC7QoICJifGHe7tagdts3bEZZOxGhHHEnp8SeCOVZNMCTCtQ2rxvMpMFKzi5VsTGNuVNMqLEEtC1atoUNzEUg9HUZYNbsf4WSbWUPuAl1ltSkc08kpM2wYqtLhlD2DbUvLnlEJcg2zsKS09lm4hrpSdfOhp2ZAmQDB/JxDw7DFHIh4cSPHSQtxq9yPTMHKHDaWdWwbyQRYq2jzjOSwpUPK9MYJeNxtQNQY0PMBcwMjwmHSrxkDtDZ5so6pFowN9C2qY8/Mccz+Gxm2ytjqdtuHPvmxgKtpZlFzYw+ZFz5U5AAAX1faHNLv654nfMarqyRAvf3XHsPWnf/WuJ/q8Xqhwd4XNdnIbwKPYGu0DbLbBrsFBNsVUA891YEWaGTSamuf1UrtZ17emmG3VJq7WmppDmXbWW3emkolZFbmnkkNVeahyVTWMmk0h432K6ntfjTJbVN1eWtae8JQA2iNeKCEm40OonZVMMK2OVZNxMJj4WzempfD5jNvQMGwcdMnUoi4bCeiPm8ticE7RsQkIoImEaQodQGIMImIY/u38XLbqjXhapg3+2w8TA7bmNutRe2wLW9FWNaKuhyzpPyytiMOUYY5smwKyMrQOBoml6c2lK6pvleV3i/Gki8HUfRqKk7IosSp5ojYc9hg1HJ4ZGtdVOvsoMaRq0tCZ0y5H3XSDxDHqcuh6u+YNdWMMnm3UmvrhEmtXVFgBWTG8Gptx1cNnzmmvaQVw2aMWlUR1Bi9pvDXhMdIkuIGj5Kl4FlJv2cdsKh05OIWWFy0RTqc6kgTRd9I2wcsenBW/HBLmHBebT0n6rwBQL9vUTWksbnGmRnfJBjSXYetqwZaXTjgdNAvrLEp7HsGAk19p0RcWk6LwbKKg9dY561bjKRLyKRGSXJoMRJec5141DO0vQ69QouC7QqoRZ5Bp35YYN8aZ6WiJA1f1EpBpt0iIjSsUWWZC4OkY0i4o0qldapkJp1wSOoUIGWmrCNXdczMeC21tSeSLDxL2+e3DYZ13OqcNdNvlcpz0ma+U2edNyJAQh25wglzSpJFH7dOXDE2J2Mzuz/rsHVciCPPYueYZZELj0xziHKMSnNPYZI3FPGuKwZuQygNeTIAxt1uLWqHLSBgriGiGLyGomd6SLnJgICAgE2EJrsFBNsVEBAwPzHudmtRO2xLYo6lMUcrqrBqsmROpqbA0o1FO58CMwxbPgU9VfTrqU4h8Q5AG1ZtytXo8mqGVEMB6EosrTlh5Eyj2LVJ/QgWxUCpdMPixKreMMK8MS6gRfnnpeEuKncMmojAKFOHkpkrqWEGf+XaCoHAsVlSObZNKhrWSFaVlL/qRFk1w6RlUlsmrWibfmXbUrl+pbULW6hZOeeVFShD8XPmQl7bEUcsjNiME55px9wxrpzZcNl2xJDESwEAcTxh2TadtcBF2W5PYRgEhi1gWFRZLsqMMA0MUmOtX72zpnqMHmNEpelVRTij3O7mNSS71rP8BzmOqflGx2iFusgAoN6GFWOa2Xozvu+UyejmEgUuTN3YpOo1Ncfy6kcStrJRjGTY61oVIxmR6EiwXQF1kGkKJZjHlPl1zWRtP1Blz+rFQiirJrOcMGPKY9UMMyYz5dqVcEcjNCIzPyRS5mU/YdJSpXuyadW+UYdEegwbc2O89A/GkMhyTCbd+MgxaKyT25BHkRhmTCCfzMs+4di2VEKVbZkpiLi4jlxw8JLNFHFu2TYtFZjtjyzj6YVHSgVVVx+OlAeohk0COWSaDnz9xt1uLWqHLSBgrsGjpH7FZwxWewICAhYnGu0WEGxXQEDAvMS4261F7bAtizmWJRwtwRDrMvFxar1j1bJJsHTSttWGdQAA2dlgEx11Z4MVnLB9eeoludLY7DpUVw1srG+U2JwD1ppwTFoUA4Zta7Vdf9IGKxMqWdIGk+XqBo8sIwceORZOK2hTqLVk45gZb97UQJGcNG/1SNWzarnyWbWpcpUqUwqd3LFqU9K0FWHYNKZyaY9lWLg0VyTPzf+/CsGZK2rOGRJSDJ0WSV9Sri61hGsvbwkkotjvlGRoEbatZdi2qAVV/j3UxsETYAGAsQaGrUb8ICAAKFdROfOIJsMQNahAA+iVn+aYHjuWMjqqB9PTlJMFTCvXymPSaG4vzbWlzJDNHVP1wkl0OgPm3gI+40ZnXqc3Ugc/N1CTfspgate2/dwxm1y4Ki20LAplNqslAUxf3wnWiJEMscLcZLfMtoCAKlSWQ1Lxsh6sGi1obcfWCYcQARKZ5o25apRVcyIlyuakVVk1J0CiLCufKo3UPvfQNhrZNjO2Lnet6S6lDJxoyCvllW2NrJp57mGuxEtCxNYSpZGUz2CcMajEXCfDpCnLtmlSFFxJbq8vZdt4IiDI34yXbSGVZcm0VFawREtln3c1GSMI86aVApPdbBuy4nnZ/M0HwbjbrUXtsC2JGJZGDCybBJ9aD8B30tBZB71hLYDCSTMOm+5sgJ7cAADIN05CdgpK1tQDkZ20VrmoCU21K3gcQZhaF3EE0SbOGHXMknbRbpH2VAes1bZj7I84z62zB+3+/N5Pvg0tYvZz2mrSwVN0pCGOOTFWmfL7jZPWyZV1uqZy57BNSeUcOanQKT88mUqkuXPSTHuKOGypMdg9irMZhy0R3LaXJMI6bxOJwLJWcT2WxAITpcO2PhXWeVuWRFiamGMy6+xNRAKt9goAgGoP57DxOK5XLdKDG6GAxQWjbmp+gJXWqJVdJKiKgnhhjarm4V+rQu3W9rvQx0ZnoSZkr7qtCw01w8B4UVSubNfVo2TUeZtBKLfnvJnpat95o/0UVfESg6Yaco3OW+l9c6/fPWTRMFgGboVNukRNAD9UtQeqQi16CJXIRrsFBNsVUAsjJtLkpFmFx0oNNbPdqTtmnhrkIKqPxrmQVefNtonDRkIfezlp9e1u0RFF2sOLjlDnDaTN7DZPXASahEdqpMqldpjPS11sK9rMG5NMlfskCpdacds2Ii1CulDKxpkr7fajnJNW7KsMoUxcEWumuHXMAICXx2WSNzpvxb4GF14ad7u1qB22gIC5RshhCwgIGDeMey5IQEDA4sO4261F7bDxzlrwRIOlk+BpybBNbYBa9zAAQG1c61i1jWshy3a+oYNsQ8HCZRs6lmHLy/+rkrJNDJtLquRetXez6hC1E8u2Re3EMmzRRMe2edJ2TFpKGLaJpeCmjICSYCXVjahlj6+j7qhHzTmRlfZXv82KM12Rlqpg0IBixciEMubShS92cuUxbBvLVTDKqnVyhU4Z+jiZSkymrr2xbKdSYbK8rmmu7HEN6yaV7hkWaf5PSBjkRHndJ2KB5e2ivawdYVnZXt+KsKwcszGTWJYV7c3aEXIT5qkYygU8ZMJd30Egogg86r4NmVrUt2ZADzAlwbW07BJvIteqkvqDhDXKvKa/fxgk08ovVWLn0LB07MUM1tWg9OtOOtEk5pgg7vqZyocK5daolhox03Ur4XXMnCdgQla/u09Td50mAJiAR8YKBs1Ml4alcvtZwqoxt3LeHE5pTp93lRboBVfncnBZ/ya7BQTbFVAPlWZQgjfWW6uLSqLiIirN3NgacRGZOZn+XuIihm3LJ3PHyFGJ/1xZxowybKmqFxqh/VJrZDUhkQaDiI70Col0LJv2hEYc28YsexYzyqppGx4pNUhbI6mJIDdhkoD7GyjpGLOBXIdEAKjPC+NW1t9J8guyT5XmgIk6a9i9AgApvX30w7jbrfk/w4CABQzGGVjNE3ddX0BAQMB8QJPdMtsCAgIC5hvG3W4taoeNT60H7xSsGjqloMi6vzuGbf3DUGUOW/rwesuqpes2IttQ5CrJzpRr18RGK6l7io0AhYSqS7bkiCbisu3y1qJ2izBsCaIl7bKdIlpSMHssSz1WTZVtpiSYiRme8BegdWzy1cwyqwJ4/XztZ0BzPVzRyExplCQZMuWYNMqwbcyk1za5aus7uWXV1nVyTGaUbStW0zbSfLZUuhU5s1IundR/FU5Cm0FE5Yq04BAl27asHWFJGZO9rB1j8/JvsKwdYeWy4rpPZpETSVEKm7WKMVJzJ7CS9b52XfMSHELUrCHV9QUEoGCTbH5ZHZryyuqYMZmT/DTp56cpwrbZPLcebJuBql9V9UDDT+pYNS4s86YZYf15RPLvXF4teETm0px7a9peqRHlctSomJJjnjRh4Byz1qsQd+0pk7yTgkmrYdu8fpfDVuQgulV082whmGPQfCEZx9K54/eenxzgHOy+muwWEGxXQCN65a01yf1XxxZ5cN3tYfPWiuO4Z4e6vLWqiEhTPptj1UZbONtn29w4wZjdRnPYCvvi2iTGws434UCZroeEM8IUdh+/YNpMLpmGFk7IZSCYkgFcgZVlBRRXYOSZ2P6NK8+dltlDM8vG+HC2Ztzt1qJ22FhnHVisgQ1/hzRO2iMPWoctW7sW6dpCXCRduxHpuo1lewOyDUW9rWxDhqzjlIkAX32oyWFjgtsK8yIW4KbuRSIQtwuKlycCcVnXK16aWodNZROQJuSS1CGJlroHK6ZU7ZdcCxLDy7h7KCMPSrruIQzwVdWsUSLUv3IGpkMFRUgY5MaMOnIS68trt76TY13Znswk1neKa7AxldhY9ueZhMy1a5sb2oRESkW0EXwLaB02DnvD8sjdvJ1YIG4V12WinWH9ksIZ22xJYh3JzZbE6Cwp/gZTUiGTxlGNoErBkjyvsXo9wFgDw9Yvjilg0YLJ3HOKugQm6pQbFRURIU6Xyv22ccyo86bc8bRSpC1t8rgXDtnksFEnzariurqT4ML9ANOakowDgpxT2a8B4uw5oQ2N+lwEpUlYN5qVbos+7QRKSL1IqZxzJsliFTXxgwiRCO7eFw4Yq+03HxFcW0dOcG0fyQRnXgil3Ue5vXAGy3bXfPxrkzU9NdagyW7ReQQEUOiqnapur3lOUjVh1tR5mwnos5mWunHBoim00bV1ZXxzSGSv/fRDIRbi9kHDJd0+qRiJv928r+5HahpaWT2mBrcCJArK1HKLu8cOCuqQM+IkKaU8Z6puTO2+BlQgBkZrt84991x89atfxe23346JiQnsu+++OP/887HrrrsOtZ9hMP9dyoCABQxRrvjUvQICAgLmI3rZrWC7AgIC5iNGabf+v//v/8Mb3/hG/OQnP8H3vvc9ZFmGF7zgBdiwYcMszX6RM2zY+DDAM8hHHoR65EEAgFr3MNJHivDIqYfXYerv6117rREayZBuKBigdH2K3DJsJkFWeiGRdeCCeSGRpsJ8NBEhI221NLb7jJc6qdu4IczS/EE559DcsWZmFVtnGRAVc2eMA7LsNwn7dfWUKtBk9VlpGjKgHeskXZuGRE7l0oqLrO/klmFb18mxfsq0M9s/NZUjL69rNpUjN3VTpLL9lGkzzJpuWC0rQiLL6y44eNmOYo6kZMmyKYlJMq/JZa1y7q6UgNIu/JKuqLNsiCUzhBy2gGlA5WAyde+9cMR62X3KpEHlboyUvny/LBP78wzaMGlZ5rFqhk0r2jUMWxNo+IqRpSehjyxKrM1icWLrS0LE0NrUpiRC14zbuWu678q9T8l2KpZkwyBJaZKMhDyZez2T2gooSeXElKiIiRpy1VwwwqRxGuLo2LZYuHpJsXC1JLlkNnpHKBcSacQGuHJiAx7DxvwV2uqKco+qKF0YdS7IpZdeigsuuABr1qzBHnvsgY9+9KNYtWpV389dffXVOOKII/DSl74UX//614c+bsCmA2uoj2i3E+n2vvsy4dJDSLpXwQWDFobp1xDaCXHUgYYhVvvp/e+HJwKA/znHdGlQqf1+6Jb1737vh0c2j+/XT7ez8sA0KmwmGDSMsRezRvfV73vljx+d3brmmmu891deeSW23npr3HLLLXjOc54z1L4GRVgKCwiYQ4iIN74CAgIC5iN62a1hbdcXvvAFnHzyyTjjjDNw6623Yo899sDq1atx33339fzcnXfeibe//e149rOfPZNTCQgIWCQYxG6tXbvWe01NTQ2070ceeQQAsHLlylmb/1gybD/4wQ9wwQUX4JZbbsHf/vY3fO1rX8Mhhxwy9H7k2ocgZQfqkQchS4YtfXg9Jh8sLvzUw+sx9XDJtq2dwtTa4g839cgUspJhyzo58slSar5kf6oyrxR1KyEJd6xP1IkQl5LyhUxtsYKUkKKFvVaVzKoE4xxakNyQsligzlOwvFy5jmK3Gk0lvAeAWanW5CNau9XnTCm7Wj1FCmF3pLI5YTRvbf1UjnVl3tojGzNkUyZvTSEt2xlh2/JMOrYtNwxb6lgBKW3buz5cFHkzAHiUIDIlFGKBbKoYH7dytErRESUVKdwt/RICNdcqyYcrvkh1Fqr9AQsLo7JbUDlY3sCwae0zZlQ4RBoRIsK2yQw6L1m1LAVMW0nbjzwjuWoSdflsfYVGvPw1l6umqW2KMpdfS/cdKbDSZOm8YNmKEyEFtachlkTz0iizBgBp7li1TGpMSXffZ5Z5UySfzWfZmsSP7CXgJG+ti20rWTXOEJs8Z+baMeeunzOS2wa7P8q60eP4IiQkMoANJzrSZLfMtmFw0UUX4fjjj8cxxxwDALjsssvwrW99C1dccQVOPfXU2s9IKfHqV78aZ511Fn74wx/i4YcfHu6gAQNjZHYLzewKh4uUoWN4WSRZAbZgMhPcJo0WrE/5LKI0NGGDWHkPMsHALXtHxkgOVn6WCwZt8rOI2I9gTgI/Va4/4YyIdDihD27fw/ZRCX6aL1swdu44g6COZav2mfeFrH/D82bN+RVj/ONwxiyrxgSzf5vimjrxNkbHiKYx7u9Kn1NNm5O29x0QnBy3fsygGMRubb/99l7/GWecgTPPPLPnfpVSOOmkk/CP//iP2H333Yee16AYS4dtw4YN2GOPPfC6170OL3/5y6e9H7XuESiVFmGQDxehj5MPPoKph10YZOfvRRjk5N87mFpbPCRlG1Kk64uHmclUYlI6daHi/3pVIIDeEMzWv0g4w0T52bbUnnJRHYpwykKZkn7hRRIhnyycSh5HYGXoI6KseBgDgCi2D19MyW6VN6IIR1XgFLiXsG9slZfITxUjpUYmFWmbByHnsE3liqhBuvDIwhkrr+lUbp23rOOctDyTyNPiXOVU8TdSeQpVPsgq1eywmRpFPEqQR8XDomhNIG4XyptSRq4mi1R9H74EY1bUJMn7PLhWPytYg0pkCIlcaBiV3WKyDFFsUGu0DhtxepjK/XBHYw9y4rDlqQt3zFLisKUuTIk6bNKFRw4rNKJNSGQU2+MwzoHWRHka0i4s2RqSAFgMaGmERnIXDsOYDZvsJZbkREV8UQDqqAHAlJROEVY6J20ql04RVirP0VOq2+b3EiCxD0XcPVjR/khwxNyERHL3IOb1M8TldaVOnH1QIwIkgoPUb3OCJmZ7PoQCQqPdAqztWrt2rdfdarXQavl1KtM0xS233ILTTjvN9nHOccABB+Cmm25qPP7ZZ5+NrbfeGsceeyx++MMfDjzvgOExKrvFkxhcCDQtqwhTm0u6OmumXhfS3C5A02+dTHOwUpCISecscKGtA6YFhy4ds+oY1AkUpQDILOltYe7BVPltZ0vQFVqpKiGRsk/oZd3xqqCOmXkPFNemzknzHTP37Fm0u8cYmyISDl4qPYpYEOeN2xBC6hBzzhrHMO6eU019YSYEuKlHnESuNnES2X3SZ1xGnDcuROGwDxEWO4jduueee7BixQrbXbVZdXjjG9+I2267DT/60Y8Gnst0MJbr+AcddBDe+9734mUve9lcTyUgYEbgZR5d12uaifuXXnopdtxxR7Tbbeyzzz742c9+1nP8l770JTzxiU9Eu93GU57yFHz729+227Iswzvf+U485SlPwdKlS7HddtvhyCOPxF//+tdpzW2xI9itgIWCRrtFbNf222+PzTbbzL7OPffcrv088MADkFJim2228fq32WYbrFmzpvbYP/rRj/DJT34SH//4x0d/YgFdCHYrYKFgELu1YsUK79XPYTvxxBPxX//1X7jhhhvwmMc8ZlbnP5YM26igNq6DQoZs/Qak60r5/nUbbRhk5++TmPx7p2x3rNDI1LoU68vV10mpMGnYI692R3GMarKpz7AZihqQ5aqw1BoTG91n7GoFJ6sYglmREtlJwcuwPpnm4LGT++dxWZ9Ntf3QJRv2RKWwpw8r8a+cNDRl25R24UVpTkMMFdKSkdqYSiuJn6cKecnC5an0wiBNeKRMJy2zJtPy/zyFKpkDTRg2yrQx7soa8DgBLxm2iHxW5sugjZhAA7smOENShrEmkVvxbsnhQiI5ERmg0NOQxja5IJdddhn22WcfXHzxxVi9ejV+97vfYeutt+4a/+Mf/xhHHHEEzj33XLzoRS/CVVddhUMOOQS33nordt99d2zcuBG33nor3vOe92CPPfbA3//+d7zlLW/BS17yEtx8881Dzy9gNCjqKqY+q0ZERDyZ/gZWTaUd2zb9Os+APO0er1TBvqG5jpJ5782ThsBUVkl5KShShESW7SgBM+x+FAOtdv35G1ZNMiKWpAcSS9LEJlEpf8qsAb5QUicn4d258kIljY1LpSIhli7c0rw3ECSx3bQFZ17brm4zibi8ZjFniGxbef1xyTC0I9NHQia1+80p5LzL3xOmK3XbGIaJDWiyW4CzXdNZqe6HdevW4bWvfS0+/vGPY8stt5zx/gI2HZjgBYsinWCI4uVvtFDEfuRAyaxRm2Lsh8zqf2OL8MgyTUJQsQzlhezx8n6RgtlSTAB51hIKrOwXufLqnTkJfPeMV4RHGnadkegqd3/0q8M2CEdEl3CpWEhVaIQ+Y9IQzrpnzyrzZgXZDKuWcMKMcddfKUVlnke7xxhxKW4ZVB5H9plVxJFjPGlbcMLCdbNqpp/+PwgGsVuDQmuNN73pTfja176GG2+8ETvttNNQn58OFoXDNjU15SUOVkM1AgLmCixySpUUahqiI8Pmgnz4wx/GgQceiFNOOQUAcM455+B73/seLrnkElx22WXYbLPN8L3vfc/7zCWXXIJVq1bh7rvvxmMf+9ih5xgwOILdCpivaLJbgLNdZoW6F7bccksIIXDvvfd6/ffeey+23XbbrvF/+tOfcOedd+LFL36xO17pQEdRhN/97nd4/OMfP9S5BIwWwW4FzFcMYrcGxRvf+EZcddVV+MY3voHly5fbiIDNNtsMExMTM55rHRaFw3buuefirLPO6uqXGzdA6hzp2g1I1xZFsbO1G5y4yNrU5q2lGzJMrSvaj2QS68sVWcqw9ap2D3QnhLpYZga6fmLlmSdzuzLEE2FXg2TMbWFuIRVUWqyi0yLa3uq3krbNqGRuXfFdrQZi26jQiN1dRdrayxGpWX1OiaCHVNomHUupnFS/1F7emslRk1OTllnLy/9VRnLYiCy5IufJCcPG0o5d6Vd5iih37BywGfmMyy8x7YcjjiQq9tOKuGXblvQTX6iAc7fPaj8wWB4IML1ckJtuugknn3yy17d69eqe8tiPPPIIGGPYfPPNG8cEjAZNdqsoZJ0PxarptOOLiJTstKY5bFOTln1XeWbzSLRyOSVKOluipeorq92Ue8DpamtuJPtd+QAkDeyaEIVYElBkiZt8PR4NlXsrvXw2FwHQseVHlGvLKttWHy1QZ+Nkj/zXJobN2BKPxSd5a61IWFYt5gyt0g6Z6IZYqHq2jTMoKyZA2TZAo7lwcB2a7JbZNiiSJMFee+2F66+/3gpZKKVw/fXX48QTT+wa/8QnPhG//vWvvb53v/vdWLduHT784Q93CQYEbHo02S3RisHjCLoUB9JC1bJtNC9JlWwaE9zaICY4VGlLJGFcVJp7ohguf0o58QvOIE3OG41USqR9vmJcQZTskUydjYtTJzKUKu2xZk3tah/tL9rDxTbVMWm03xcOYY3thDzTmHNlgrt2A6tmnkcLVs0xbC7PzY3nsctD40kEHjtWzdh/xrn7LRiQVasKkIghiqiPym4BwMc+9jEAwP777+/1f+pTn8LRRx891L4GxaJw2E477TTvwXTt2rXYfvvtkW/oIFca2YaODYmcWjuJbH3ppK1PkW0ohUbWZzYMcn2urdDIpNTomB9wEhJpQKvKA9reKLLy3TBOmmDu8wknAiSphC7VI5UiipGVkCT6MOWBOBLGkWHFm8brNiioi+fR/DXGSCqNvOZhRiptwxA1actceeGJJmxR5Smkcc5oXxnqpZS0/cU+S+eNiCDwOIFocLBs2CRfbg0/F8yuzqwXmX2YmogFJhKjyDmkw0aUlKr9wOCKRb1yQW6//fbaY69Zs2ao3JFOp4N3vvOdOOKII/qunAfMHE12ixml12FDH6mTVvbrLLVtleX2AUmlOWSWkXbpvBEnrVc4JFD/4wqUP9hZVLZj+4Mt2gq8j3iJV0eSR15Y6LALTaZNRZG82pHSOW8bjQIwUbmtOmxTNQqy1fBIg2popBMJYWiRUOukri0V2iYkUnAbrkkdOnMe7UgjVk5IoMl5YwzWLg+CJrtltg2Dk08+GUcddRT23ntvrFq1ChdffDE2bNhgIwWOPPJIPPrRj8a5556LdrvdpcJmFo9mU50tYHA02S0exRCtFklV8MOrec1CkDQP/FJBGcdMKs+u6PIhX8Y5lLFZgoPHxT5ElkPGxnEQEKZGbjuyC988dbVwVVtBlqJnUVvZ+rrRhLLPXXEqiWq3Jmqx3Yv1TQ5aVeOnV/237r767VWhESpmZBf/BbMOFvfEQKiNJqGJNaGPdB+DOmk09LHJSRPl+KoaZK3DZsXjBn/mGqXdaqr1O5tYFA5bEysREDDX6MewzUYeyHSQZRle9apXQWttV5YCZhfBbgXMV4xypfqwww7D/fffj9NPPx1r1qzBnnvuiWuuucYuJt19991WSS5g/iPYrYD5ilHarbnAWDps69evxx//+Ef7/o477sAvfvELrFy5cqi8mnzjJDKlCqZtsliVzju5FRfJNmS2xtqG3JfvN2GQHeVCIiWhyw0owyYYI+wZkJXDhKayrzRpVXsr16pmFbua+F8HLSXm8qvYSxa/jm2rfkZZ5s2X6q+Kiqg8s+GPKku97ZZV5L6Mr60vRcC4AC/DLDMhwMWyss0houL7EMXc1ZObyrGkI8r+YRm2ekXIqmJRPwybCwIA22677UDjjbN211134fvf/35g16aJUdktKFmwamU4oCcWkmVOICTtQE917BjLqk1Neqya7JRhxmnm2lnuwiBJqLVSLgQbQE/bU6yMluHHgtuHbklknaN2Ytk2LRVEu1wVpzviAoyUHvDqSCpToI0IKFVl/c3/2rH+ioQAFjUjSyERU2NNacuYbcwkKT8iPYZtY+qYNxfe7YdHNjFXEXlAcGGQ3LJvScSRlHagFXHL4icRR0ramTRjyvAu7dg2pTVaurzu3AkhFGxbcWxRSvwPoY7daLfMtmFx4okn1oZAAsCNN97Y87NXXnnl0McLGByjslus1QZrtUk9SD89w/6OE3vDifiITJ1YmirF1SQdm+VQaRmF1PZTRYwYW6QUlEktySSULMcTxkxm0kY2yZS0M+nVwqXll4wdVKQsk//s5tg4iqayH02oE8xgFdaorg4a49yvp1YjYMcEs0yZDX0kUv48ce1BBEU4DXckDFs1JFIkhFUjzKll3rhoLA8DAGyIR65R261Njfk/wxrcfPPNeNrTnoanPe1pAIqQiqc97Wk4/fTT53hmAQHDwaz41L2GAc0FMTC5IM985jNrP/PMZz7TGw8A3/ve97zxxln7wx/+gOuuuw5bbLHFUPMKcAh2K2ChoJfdGoeV6oDBEexWwELBuNutsWTY9t9//5HEj8pMQooceWcKslOsxuSTuRP3yCSyqXIFVWlb2T5V2gqM0ORTJ+1aOY59r2vjkXuBSpbWrQBUVyXq9+GzSh7L1FT2fQhwwt/VnV/TjSA4s6vMgjNkDZ+Z7o1UJ+tP2ypPray/zFN7XVSWQkUuP86InkSJgDSlBzKFqdQV/Z4qV/0nh8wJjARHVKdONI3VnmFyQQDgLW95C/bbbz988IMfxMEHH4yrr74aN998My6//HIAhbN26KGH4tZbb8V//dd/QUpp89tWrlyJJEmGnuNixqjslmHXvMLWfZg0nXaK/C8AKu1YJi2fnPJYNdOmeWsqy1xOSZo358lW5+kljpPV1iyGiN3KOWXYKISxU1xAm5C4KLbnwRJXxBu0eHgDlHZsm1RudZvmsE2VZUao6Mhk5li19Z3cY9Uc2yYtI9ckQNIEKjqSRI5haxFhoyTidv+tiFs2byIRkIlj1oCCMTQ5bork1LQjQBmp8Qiw67WiyGceRgCh0W4VJzTwfgLmP0Zmt1pLwJLEPi1opbxyQ6afig/RfDfDuFCGTUhp8261VM5mpTl0O7FtoySq0qx2fMHgmWNp15baY9VMtA9l3ihrpiTJuSf91LZVWbbq2CZUc6xYDatGx9FSUHQ8ZdI499k2Kipi9uFYN27tNmXAeBI3Fr+uHU9Ep6joCOPcPZuSci/mve2n4dFcWOGaQTDudmssHbZRQU6lkCjDgjLjpLmEU5kqL3HUOWS6Z/LooKhLDvX7mXeT2aRR7lPdlhrmlSR/88WmlHLly16FHtCBq/M7eSX5lZPzoCpo1Emj/VbVqbEtPGfTqj1W/h8GTSGWRtBE0FCN3CUdq9wpWU7lyoZMTQzpsHFyDSjUNJzUYXNB9t13X1x11VV497vfjXe9613YZZdd8PWvf90m7//lL3/Bf/7nfwIA9txzT+9YN9xwQ5c6UsAmQumsGRGRRnGRtONCHyc3OMeskyLvFEq4eSf1nDTTr7IckoRE0ocZVRPyU4eiLhIJoSkfjkScQyVGQEkhUon3mWpbxDF0uSDCchLymWdgUZEro7XyJWtroLW2ipFaO4GRIiTShUKavk7eHQa5MaXhkcR5IwIkObENWumeIeFA+dBU3u+TpBZSITRS7L8VcaTlNaPOmyd8kjgnrgmS2BUlzLwEBO+taFk35zq7BUzPdgUsfLD2ErC2y21j3m+vc95YQlWtS7uTpeCJU71WJkRaKt8Bo2GTROXW2LKu8bbtjqnS3Kaf+MIomvTrxvBIe042jJz2VUIih4lDRtOivb+4Xeu8VcIgnZAaJ8+Y/gIb4DtgTIhaB6xQl6x3wGi4Ix1v1LmLZ9Ny/3Fin0kZ5z2dNNpmVQW/Hhh3u7WoHbaAgLlGEnHENSs+bBp12IDhc0Fe+cpX4pWvfGXt+B133HFOlJACAgLmN5rsFjB92xUQEBAwmxh3u7WoHTYjH6slYU6kLzVbV0+tyqiZBQ7TL5jfNkiIfHPCmU0gpHUxEk6q0EdkhTqp1sAw8qq+5Kld3RAVermObYNj1AZl1qpgzGfbOJGRNedetB2TZhPsU59hE2ZFXXDk9how5Jm7HiouVuN57iT5jXw/4xyiDHHUUtpwR1ObDSjYszqWjqLKurl6bm61XGlSeqBSW24YiBEybAGLA1opv2Za2rFtPTUJ1dlo24Zhk50U2YZJ2849ts20p+xKtOxkLuog9ZPwrQhQQ2gBjQSwLFkiIbIy5DiWEMTGUpiVXcZJCE3UAcx9naVgpkabIvXnqKx/heWmxJEx41R0RGkgK+fhQiN9yX7DpBXhkcU1WtfJ7ZipVLo6kpSJr5Qrqb1ehGETgru6kxHHVHk90kRYVm1JIgau+aaU9n+vyhBL5AouhV2iBTGU6EiT3QKC7QqoB59YCr50iff7alk1pTwBMMus5Sb8uU3CJBWEYdmltHW4dJ4iIuHaTcybZb4ywqRVxlORJfc82FyDkjJyBv3DIKdfUqnKtDWFR1YZM7Pdl8zvFobynikpM0ak+ek+mpg0y85FiS8WUtrzLiaNMm92vo6FQ82zG1eD25txt1uL2mELCJhrtCJWu+LDo/lvPAICAhYnmuwWEGxXQEDA/MS4261F7bA5dk11rfRWUVSK12W7vrJ8WV+wUiybbnfsWdzFqnWPEYlA1DYFZgXitknydJKqNLFT0ITPOCpkr+GvYjAaM8y5W5U2DBvjdrtm3DJvnDHw8vw53NosXZQQ3OVtCs5IDpuTmaZJ9YmXVC+RljdSHjFEpohlrry2LFdmeJTYlTpesm505S4CbB4a3VZl11yBbNdmlXYTVN0qNxtSprdhxUeOwWpPwBwhTwGRENGRSt4aaecbC4aNiotkG4joSGeKtFPknZJhS6UtaSIzRVaiNcnXqLeZliWjSeyZgChVm0QiEZEV6boVYbpqq/LMrajTHDYprZR/ubOel41upRL/mVRe7hrgy/RPNuStTaYSadnOieS3rOSwWRGCBoaN5rDlxG7yiCMqC9hKqZCWdrDKqtWJhVi2re1+4j3NgkhAlGM4Y8iYQt6nPEx1zk0r1cF2BdSBtSaKPDbT0SDrT7dpIkpixYaU9Ng2UDauZjzddzW3jUZTKVlf0NtJ9vv9BnR8dZs7nfqSTNNFVWCurk5hNR+Y5oF5DFpDgWrTxwkD5/X3Ewsh0Vwsipvz08h4tx/6m1Av62+356PJvR0Hu7WoHbaAgLlGIoR1Wj2IZkcxICAgYC7RaLeAYLsCAgLmJcbdbgWHrQeY4KToNbz8MxuSTJx9l+/m+qpsXFy2JwS3TNqEYJgoVy4mBEPcKoswtyMrqRpNuHbcJqxau+VyPeII0UTLtq2cf5QUCjwAQGKWwcVoZP1JrprLYQPi8pwypcHL1aSEFIBNc2Xz2ZYkwqmsxQKyXDWJYmFXq6NYkCLaE/b4dGWujhHjXNiC2l6cvBDghmGLEqtcxOPE9vss3OhXYJpiqptWgQICtJTQeeqYpiy1bJsm+WyykyKfnLLtbEPBtmUbO5BGJXIyRbahHJMpy6rlHVLeJJUuX5PkswG+KhpVJCv+dzm4KlWQSbG/WPo/O3Tl19g1meU276QoxkpX141aHJUFH2y12mS6Ka1tzpb02LYyF5XksNF8to20cHYqXb5ZpmxbeQwbiM3SXh4bI/e4zWGLGJj5vZAcKi+uTZQI9zcgZQNypSF7nLvgDKJV7K8jlbVhXCqSY1wU7B5CJLJnLkiwXQF14EtWgC9d6joqKpGM9ld/rymTRmyAJmOhFHSZz16waqp7f0rZ3Hco2bj/uly15vw0mstWUb6sYCasWhOqbBu1p5w4IXUKvHR8lUEr/vcVxr1nx36Kjlx4z531UV799189p6q6OVeDO1rjbreCw1bCSZ5SWpghLr8ogmkk3Mj6E4dNOYbWmByp/Xpr5qtGwx0TzjAhnPNm2u12hHhp8SVPlsWIlxZORNSOEE0YJy2GKGuMRO0E0YRpO+eNRYlL7IxiFx5J+sEjpxhiQyIH+9KahwoGbT8imLteMdeIy+sVc7/fOGkTJJFeKm0fiqTS7oFHC/sw1XdOXCDnRTgYE8KKkag8BafGloRHUsPESbglt0mxAiIy1L8rrsjZaAotJoIjqav/MQY1QQLmCHkOzZUN+fFqr2UZ5JRz0urCIGVnyjpv+WRmwyDzyRwZaZvyJiqVVoBEkZBIwD2A+A8DRjKaWXEkJTVEQ70cTmyvEUDhceREACphT+YhS+epeyjS2oZHalKTTTNYpVOtneiIVC78UWlXh83YGmqPinprxVzS3NVfzDOJPFOunbprZOo1SlKXiTprSmnPftiQyMyJLympIYWboyofTKJYoyzo4AmNiJqwKPqAwhnDFFO2bUMipYZg7hoMgka7VRx04P0ELB7ouA0dT1Q6qSKQExAysG2tUBcG2cvpanTqpPust0+DXv01bboIbOdRhZLdfaNEXepGNXSQMki8vu2J09Vto46ZcI7WdJ0xb5+MewSCJmk6LnWn+5lLx4Nf23G3W8FhCwiYQ8QRtw4shR4DidmAgIDFiSa7BQTbFRAQMD8x7nZrUTtshgLmJHSHCQZRJnqLWECVYTwTSkFqw6C5fRQS/o5ZK/dMtrNa0ZEqw2bCIOOlCZJlcdmOkXhsW8mkTSSIlxYrVaKdWLaNJxFEqwiJZK22lb9mcWLlUlkc21UUzTi0KL8CVHSkT5gkowIk5Pwk03aRIhYMcSm3GiuOVuRWsJV2IZFLEpJI3y7mmJOk+kFgBUKEC19UeQoZOYbNrspXVrroZw2rJgjDJloTlgEQ9HtCpbh70Oz9EJFC4hTjkAAbMDcoVox1IT4CAHlqQ4FU6guKUPn+bGPBy+STKfLJgp3LNmQeq+aJjti2CwuSKQn/8ZhvacOhKcNmQiaF1NA1DBsT3IZK8iwHL1lDleWWYVNpbgvlCrpaPiJIpe25mLIcaa684tS0bdh/JbULiUwd20ZDImWuHMPXUESbMm1ccChu2DluBZe0puUBhKevMlkXUs26bZPgzlbHnCEjbanrxUua0GS3gGC7AuqhkyXQyZKGjSQCpkZIiBWxxWaAt51TFs60qyGRBg2MXPHx7vDquhIE7n1dyGMf2zQbTFsPYTRWl5dVZeJ7lTmigiJ0bJUxI9s9lo6yZPQZs4RmzBO58549m9oV6KSG0WzAuNutRe2wBQTMNZLAsAUEBIwZmuwWEGxXQEDA/MS4261F7bBZhi2JbK6FiIUn9GFyNxKpMWGLYWukplAyKUxat0JZFMWmbSc0YvK6ookIiclVm4hs3lrisW0J4qUFYxYvmfBz2NoFqybaCclVi10RwigucteAYtVDlP1kRUPXrWZUVjWYFRQhifOMio64xM3ufDYnW23aE00FYEumrR8Mw8XtvpdYZkxOTYIbUQYlbfFsVVnhouIiXg5bmSwbJTFE5BL/TVtE3Oaa0FIFYsAcQAPBGkRHhtxPwCKCVoBy+RU6z0gRbSfWQVkqmeXIJw3b5vLWsk7uCY2YdtbJoazoiLL5XlL7dk5am+iml5TMusgZhKRFtk1BVQVe5mbJREKmJqJBQsVuvqaIt5LSJetX8lFsm66uN6Bg9+vbkuSumf9d4WzpBJFy5fLTcgWVO7bNtPPML6JtVukVLTMipV2hliBMP8mZ5cpn1erAODBVsR+tiNsSKVO5QkLYQZO/kSmNiIhkKe2XPeiHJrtltgUEVKGSJVAtKjpS/42rK9WhK6wazW3TdGxNHhyUcqycduImjSVBvM/6zwt1QiJ14+YdGlg41oNtKwfUtrueF809T0pFeYxZZXwd2wbGmo/hzdHvV1ODW65xt1uL2mHjUVQokMWxrWtGnbSi5pD7Mph02SRXFYetOxTSoBoSSZ00UXpyUTuy9daSZQlpuzDIeGkb8ZJiBtHStnPelk4gKtu8vQSsVYxhSRusVYZERrGtdcGiGJqXf3Yhum6oxpukAnt/krMWjMEUnY8Fs6GisWJo6zKRXmsMkdveCM4YGMvLqbu6RSov/o55FEHm5QNfj5BIez5cWCeNR4lz0mKBuBWVbReiFMUcUTmmFblE1mRI1c2mmGo1Bqs9AXMDrSR0lnuJ9yZk0HPS0pyERE7Zfs9JI2GQ6frM2r58MreOSarcApWs3L/G9lE7J8nCVlJuTyrnYEKLecohTX2xRELQ+kcm9DDLSf0jouA2g/BIqckCkfZrKgJl6KOuWUySytVbky70MSeKkVIq5MZprtgeVw9KdtWENP9rRWpNkgdVXRNOWQghSdsGCiVLW+tScOJ4KqRR0W5F3KtDF3M2VBh6r1yQYLsC6qDjCei4ISQScM4Z6ap1qqpOmoFyjpz2xncLm3Ttu7qvXn095j5v0fBc0nXH142r6atd4AeanS7eNL5BaKTfnOhcykW+QTDudmtRO2wBAXONIOsfEBAwbhh3eeyAgIDFh3G3WzNy2LIsw5o1a7Bx40ZstdVWWLly5ajmtUkgkqh4xRGiMsRQpdKGAmmpoQjDZlaFZaogyjFtqXvKznPGLJPGBPdYNRt6SST7k6WxZdjipS3LpEXtlmXS4qVtT3SEG3GR1oQTGmlN2DBIlrSdlL+IfTatKufPBxAdgStVUAiQmLa2bUHYxHbkwo9aGpUV+vL/pPkamiTRdZxh0oRlCiL6Ecnyf2brt1G2TanE/h3rkv6L0yb7ExycMGxRTNvCtk09uSQSdtWG6+Fu+iRiDQzb/Dce44pxt1sFq6Rd7bUsI6IgmRPryHKokukp2LYa+X7CtslM2naaSS+KICVsVD1DrknNypJVoz+AmbQsGxOMhEEKKzoiU07mnvm1kGprr5GainTVvRK2RN9RU23sNmX960K0aR02SUIiVa6svfHZtsyFYGepbVOGrQqfYaOMXJWbBFjqlwMwIeGsZNrSiHslCYxgSkLqt2VSI7NlV4rfMD2E6EiT3QKC7ZpNjLPtUnEbKm6j6WtWF5HGBmHHPMGSmp03fE5XtjVikDEjqGc7qxjVOQzDtgH+H7VXSKXtr5Ht7/N9UXFaP6AG4263hv6WrVu3Dh/72Mew3377YcWKFdhxxx3xpCc9CVtttRV22GEHHH/88fj5z38+G3MNCFhwMCs+da+A0SHYrYCA0aGX3Qq2a7QItisgYDQYd7s1FMN20UUX4X3vex8e//jH48UvfjHe9a53YbvttsPExAQeeugh3HbbbfjhD3+IF7zgBdhnn33w0Y9+FLvssstszX3G4LEAj6OCpeoUxWZFO0aUda+C0oLaIpZQJQumlctp8MebVU8nBc8T4UoGJMKyajFh2Ip8tlLwYukEabdt28tbS9pgE0UiL0va4KRdJ+uveVTkrgEAj1w+W1MSKD0nk6MCX4DErKwr5lZIFANKMgpKc7QGKEbfFBroyVKX7cmU2zyzPC3z1jKXRxLF3OWa5K54rSLy2BSMMydLzhnJYXN5a3ErQlQypK1EoEUKgNv2sDlsDYUc5RgUcRwXLDS7ZeEVjzV5XcqJdaS5x7ZJIyKSSajUMHLSk/VPy/tnUtYLK6Xk3qHiIzSHzSVv+/eZKJkeIbUtDyATCV6ybbod2aLcNIdNK1UvOgInoz3sTy2NilBE1t8wULS0iPTEP+AERaQTHdFKE+YthcpcSREqeETZMwOayyaixGfYGmTCXTSAssW1ze9MtQxBagReyDkVoivTTyZuslvA9GzXpZdeigsuuABr1qzBHnvsgY9+9KNYtWpV7diPf/zj+MxnPoPbbrsNALDXXnvh/e9/f+P4ccZCsl2TmUKcTS/Xq/i6E2aZuUfXmnrxjfaAz5KwxKD30gjS97swyBnN1nkDzede7VUeWaq7RhZdw1+hySG+U6O0Wz/4wQ9wwQUX4JZbbsHf/vY3fO1rX8Mhhxwy1D6GxVAO289//nP84Ac/wJOf/OTa7atWrcLrXvc6XHbZZfjUpz6FH/7wh/PWeABEdCSJbIihVi78hhEHggkGXj6sq1TZEDutdB+HjdnQRyZcO25HVugkbkcQbaMSmUCUqo/xkjaiibIeWNuvvcbbRfIuDYPkE0udk0ZFR5I2tHAqkZ6TVt7IXlX5Pk4HZ+62Yow6b4Aow2zKK1gMGjCZkyrN1TlpVI0xiTgmywe9KeFCIo1zRZXa6MNUUc+o+9jFpXCqkyYkUgjqsAkk5d9sIhFYVv7NEuESWYcVHeGM1RrT2TSwiw0LzW5BKWho67hoJa1jRh0dqrQoU+mcpFRacRGZKqcGWRFTag6JrFPDBXHYTC+DsRSCMSfikUobGq6ktk6aIiHonuiI57zRukgDiAbQzZX3deIptaq1pH6aXw/NPaxI6dQgtSSqtHkKSUMi+9SD1EpatVpvuxCQJmySMQjjhEUMXBpHuLimkoRwprmEJLUubWhnQpQxda9Q13o02S2zbRh84QtfwMknn4zLLrsM++yzDy6++GKsXr0av/vd77D11lt3jb/xxhtxxBFHYN9990W73cb555+PF7zgBfjf//1fPPrRjx7q2PMdC8l2pVJjSmrUrJf2Rfdarq9U3fOzwx+ur2Jq1QT2OqdBQo0HcTkGOQ/WeE9Wx838WHXodR79LsOw3wtzTukQhmuUdmvDhg3YY4898LrXvQ4vf/nLh/rsdDGUw/b5z39+oHGtVguvf/3rpzWhgIDFhJgzxKLbUORjQM+PC4LdCggYLZrsFjC87broootw/PHH45hjjgEAXHbZZfjWt76FK664AqeeemrX+M997nPe+0984hP4yle+guuvvx5HHnnkUMee7wi2KyBgdBil3TrooINw0EEHjWJaA2NRq0SKiRaiiZYXchMTkZEinLEUk0gEqUsk3Yprg3fPhGNrbChlwi3DJmJhWTWRRJZVi9qJV2ONsmqmvyv0kUr5W9GRtqvDxiNbX0OLqHiPMjyyTnZ1CHAA2ltZdyGR4GZVmt4k9cehK/SAz7AZ9irqMBtulUQSrahgDzaW29NcYKr8G0UJdyFKkqyQq/qQSMAxqowzW2ONsnatxAmNLG/HmIhNO7L9sRruOhahZKEOW8CQoGGBypfAd2ybk8CXmbKsmkqVFx5pmLcmVm36DBtg7IEgodMxCYmM2soPgzQhe1JBNdQ8qq2FNKCstqoJuWlaoJXm2iknyKGI/VBKeyGRlFUzbUnCI6uy/hRWdKSx3pqwYxRv2egBnnPHtgnHSNbVk5vKFSYq5QuAwcO5KJrsltkGAGvXrvX6W60WWq2W15emKW655Racdtppto9zjgMOOAA33XTTQHPZuHEjsiwbKwGOxYiOVIjyiiDQSNi2maNpHl1MGrEfdbdN3X567WMm4DXBkNVbsu5a0THVffR67NiU133QOTAU36tBMSq7NVeYkcN2/fXX4/rrr8d9993X9eN6xRVXzGhiAQGLAREpJE6RBYZt1hDsVkDAzNBktwBnu7bffnuv/4wzzsCZZ57p9T3wwAOQUmKbbbbx+rfZZhvcfvvtA83lne98J7bbbjsccMABA85+fBFsV0DA9DEquzVXmLbDdtZZZ+Hss8/G3nvvjUc96lGNsbPzGaIVQ7QTL1+CopFhy6SXt1b9LCPJiyL29yHiqGwXBbuBgknjZX+Rw2YYtpZti1bLy0ljdVL+lWLZiIpVAc2jglkDCqERIuXfVXG+Wp2+bDMGlGrdYIxZuV3GGBi625Rt01zDMWsKg0RIG6lqWpmesm1J6vLGJpLi+k+mOaZKpisleRx5rizDRss0UIl/Tm5iyrBFpNDikkRgIimu47JWhOWl8MwEYd6ibMgcNiLLXXf+AaPFQrBbWkpAOMENLRVU2ab5XoWsv2HVpGWyKKumUolMDc6wUUsntbarklQav0xP8/KiirYb6wpha6/kRp1dpefXhQYWrh+aFmXrctgAkLw1n61XhG2j+WnKk+Yv/wZ56rGDpr+Q8jd5082y/4Zhk1zY3xQhFGQpRx2ZuecaeQOrVpeXBwwvQtJkt8w2ALjnnnuwYsUK2z8bq9TnnXcerr76atx4441ot9sj3/98wrjbrlRqdPLeDBUwOgaqDo3HJBuqtsHbpnV9v6ofX/1MsX3Q2fZG3e1H2SOak0X1NLz+hvH9PkcxW1/DOgaxejzOhsxhmyd2a7qYtsN22WWX4corr8RrX/vaUc5nk4InbfCkDdHgsPEkQp6UYYukvlHxAOEUzOrAyhBDLjh4+ZDPOAc3+yvVKQETElm249i120m9YxbF9c5b7MZTBUgdxX4YpAmP5JELhfRqs/W+AzlzYZAaxAhylLGQpq3dB+AGceZuMGMQOAmp4owhKjfEXNkVkYQ4T0nEsaSss7axfCidiAUmy7CvNJfOYaPJ9pUHMYo6oZNWRB3DyCoM0TDIiUSgHZWhrkOGRMYNKz5Nq0ABM8NCsFsAvJBIwNkhTUIJqZ1SUtswSCW1JzpCFSBLPw5Sw3PYjJWTxDEz74Hy3i3thu+k0ba2n4ntaRARD0kcuQHCIZsUFAeF/8DV/KMvK06aCY/UnkPnhztSARLXr2pDIrWSnlKkCacEXKikylPb5rGrKymlQlSGUZowyUi7+Rb2rrfta6pN2QtNdstsA4AVK1Z4Dz512HLLLSGEwL333uv133vvvdh22217fvbCCy/Eeeedh+uuuw5PfepTh5j9eGLcbVcnV2BE0a/JEarDsGG7vZyuumPSW6AaLkztXZ3N8M+jfmzdPTaMyE8V1TQs6oT4Dhm6+kWPsVWnzB9Lj++P6+fsDYJeoh+9HNFOPvii3ajs1lxh2trhaZpi3333HeVcAgIWHYxqUd0rYPQIdisgYOboZbeGsV1JkmCvvfbC9ddfb/uUUrj++uvxzGc+s/FzH/jAB3DOOefgmmuuwd577z2jcxkXBNsVEDAzjMpuzRWmzbAdd9xxuOqqq/Ce97xnlPPZpGATS8AmJkDTvBmptyY7qQ1VLMKLyppGqp6R8/ZtwiC5258pIQAUDJtrO1aNJxF4jXAIFRdBFNsxiGInOkKZNBH7YZBGyp8LX2ikVx22XudnapZp7VZhlLbMG8BIFKS2yzOMaUA69orLbnqeM4ZMuZV7K1YgOGLuGDbDmiVWdERZ1m0qV0ilq0XkQoFUz9V0wDBs7jiGVUsijiWEVTMM29IkwhJbdG74OmxxzVJUXV/AzLEQ7JYFYdIoPDl8y15VxD2kWx12giJO6p3WXlOAN6YejkU331yfYWMe22ZCH5uYtGKeLuTTG6dmxqxVUbdqLnUzE08/59i2Boatoa2q51C+51xYWf+iXEPBtjEuwM1nsxTKjvHZv2Lfbl5NrJpUekYr/E12y2wbBieffDKOOuoo7L333li1ahUuvvhibNiwwapGHnnkkXj0ox+Nc889FwBw/vnn4/TTT8dVV12FHXfcEWvWrAEALFu2DMuWLZv+Sc1zjLvtWj+lIOPcvm9iuIDejHcT21b9Pjcx6P0YMcqGSU1Kd1QYs7rPyjpbournUfe+H5pq1dK29548U7k+n5GjY+qYu36MXXUOtWxbgy/U5CRVz7OJ3eOMYcPUcHXYRmW31q9fjz/+8Y/2/R133IFf/OIXWLlyJR772McOta9BMW2HrdPp4PLLL7fhCHEce9svuuiiGU8uIGChoykJNgohkbOCYLcCAmaOXsn7w9quww47DPfffz9OP/10rFmzBnvuuSeuueYaK0Ry9913gxMV44997GNI0xSHHnqot5/5JA4wGwi2KyBgZhil3br55pvx3Oc+174/+eSTAQBHHXUUrrzyymnPsRem7bD96le/wp577gkAuO2227xt45IMy+Iy/0tJy7JRhk3EMWSWAQBUmkOSHDaDWrESsg8uuMtnSyLbFkls2TuPVYtjl4cWkRy2OAaLnKw/ix3zBlG0dS/5fpMjwbhj3oioSF2x7GqfWQ2hq0icudUxxhiElzBck88GBoiizRSz6XKCC3SYsseZMsVgmVv56OQuny0WDFlU7IcybK5grGPS0lwhr0u8r6zUiZoVo1Ylb87ls7m8tXbE0TLFwWW9LHcTqiti1eMHjBYLwW7VgQp0OPELZZmsQtDDFM4mxbV1dwHl7n5/TMMM4CT8Ycf6+3NtWg7FzrGPXZ0rVFfnaQUBJ0bis2dUgMTmF1Ixkob8OwWAWUaOD8DUCVtaQMXczkk32LthV/Sb0GS3zLZhceKJJ+LEE0+s3XbjjTd67++8886h978QMO62a0OWQ2fSMfc9cki7hTvQczvQzYzVjW1iyapj6sSHqkzZIGMM8prtde/7oelZIeLdzy60Te/XLhauYQzQzWZZnYEahs6Mcf1ujlWhj34sXO9cOv93ZmOWY1CM0m7tv//+AxVFHyWm7bDdcMMNo5zHnIAlLSLSUSZ0cw5ehiHKqSnwrHR62kR9jdQ3aty3FR0RFdER17YOWNVJM2GQRESkUH3s7vfCHYVwbcZrwyC1ILXXGK9RiWR9wyIZHEWtwMBLJ02X7wF0O24kPNLccBy2VBs4GHhc9E8xZRNWM+7CIzljVno1Ehy5qZ1X3rlZpK3iHa0/VG0PFhLZbcSSiNvaazFnaJUOWyviaJcOm5ZDhkSyBtGRMfgBHkcsBLtVha886Ds9VI3RORfOkWsWBgERGvEdr3q4kEhp72nT3wxVeRJrqms516gr89ZUz9Ef0+zINX6GmzGif5glUXekYiiqzwPkTB23JrtltgWMHuNuuzZm0gofAaUy6QBOVrGt4tD1+Vw1NLHuuz+sM5ZX+2vCjqnIT9Px85p7b5D7sepQ1DlpviPG652xivJ21Mthawi37OX02T7WI5yyzvEc0PGrbtuYDR4iP+52a0Z12B5++GF88pOfxG9/+1sAwJOf/GS87nWvw2abbTaSyQUELHQ0JbuOQwLsuCLYrYCAmaFXkn6wXbOHYLsCAqaPcbdb03bYbr75ZqxevRoTExNYtWoVgCKG+n3vex+++93v4ulPf/rIJjlbYC1St6xkxDTn0HkRBimiGKJse6EtDWUA7H5JSCTj3Ek2c2FDGcGFZdKqrJpl0iISBkkESCBinz2zcydhkIz3DYMcVGBkUFSZt6bwSFcGQNsyAExoMNOOOeKynXFtZVs5c8UNM6U9tg0AcqmQlaIfUmsbMql082pYHbpWksobOebMhmcWDFvZFhwt4b4/wyAWjiGs9geMHgvBbllQaXhZz7K5oapS46wu3FGjKSSSop4EczXZzPaYUcbOfcdpIv+wmK1QyekyTlbwg4Q4UgbMvK+2qwybJ+tv6rMpCa0Mc+/CKbnHtpG/aXlpqtc2b2DWpvs3AJrtltkWMHqMu+2azBRUJrvCEoHBQhibWLKm7YOwZ02pEvUsnPJKBTXVbKwelzLeAGrD6AZh61mFGaJhsIaxYjUsmWnXsWpJxK3AWlNI5KBsXL/+ujFd2/qwbNVQTM4YJrPBfxPG3W5N22F761vfipe85CX4+Mc/jigqdpPnOY477jicdNJJ+MEPfjCySQYELFQUceGBYdtUCHYrIGDmaLJbZlvA6BFsV0DAzDDudmtGDBs1HAAQRRHe8Y53jE1dFN5aAt5eCs0FdOpYMJSsGotiu1rNlLQr2l7COF3NJuwKE2R/5eop4xwwjBltE0ERVHLb7Jhq8ety/wWTVpO3xhhh4Sqsmlckm+SumX30Yd44Y3YVjDOXt8ahvVrZNJ/N3guKQVvmjYFxtwJvLh/Xrso9Zy7nLVMMWTk+UwqqZLiyctU9U9y2pYbNcatK8zbFv9fFWNM4bMFACno7tq0dOYYtz4e76WPBGhi2+W88xhELwW71gi+IpGtzwpTU5H6oz2FTaM5to6ve9MfPjDdf3eq6Z1N6WhNr1i9PeLZRl2sCNBearstL61Xou1ceG1DksumyKHZXDps0eXG6dnXesG26gYGYKZrsltkWMHqMu+3amEvICsPWj6Wi7/vlYzblig3KntH+qZqcd9rWSttC9X5pjW4Gjd6j9H6sMtxNLFuVWQN854JxZsdQBooxRio4uTFCcNduyEsz0UP+9uFy4sz76njaXx3TtL1XjtxkPkQO25jbrWk7bCtWrMDdd9+NJz7xiV7/Pffcg+XLl894YpsCNiSSc+dsRTGYCYOUbafYVfYBTsWrJ6iT1hQSacZQQREh6p00EuLoCYfwyDlYVFyEkXOqhEF6QiPEUeu+QH6fGUrtTDUM0giQKF3pNx/igC7Doxh5WOTkQZBpVtRrQ6EkaUMSNXXYnNNIwyRl6TgprV14pPIfUAcJBaIGMSbGwvQXDlvRbkeuPlwW9XZ2644Tctg2HRaC3ZoOCufNCZDUgYZEmveu3d3nv++u4TOdOc53DBK61Pvz9SGR9L35XVBKWq2maphl3Zyq9dhmE+OeCzKOGHfbNZXLIiRyCNGPpr46ZwwgC0sNQiBVEZE6MTKpNGTZr7X27i+qukudMCv8o3T3/UiUZb3QyMo5DuOw0b7CGSv7GfPCI60jx1ybcTeGi8p4szhd67Axq5IddTl69ePrlLd7OXW0b1C1y6khHLZxt1vTTmI67LDDcOyxx+ILX/gC7rnnHtxzzz24+uqrcdxxx+GII44Y5RwDAhYsREnR170CRo9gtwICZo5edivYrtlBsF0BATPDuNutaTNsF154IRhjOPLII5HnRR2EOI7xhje8Aeedd97IJjibsHXYuLAhjDrLXOhjntlVzSIkcogQHe5YLyc6MoAACRf1wiGc10v2E5asEB3pw6qZbUCthH+/cEh7euUxldZWuLsaHmkPB0Azx6qZkEjGYEVHNLRd3ZHKl/5XZnyFbXMMmwt3yMwKGBFNUNqt4KkKi9APgpG6H9xJwnLmGLaYc9tWQ970jDnmstofMHosBLtVRTWkkL6vyuYb1AmN1G2vtnuhu1YbqzBz3V/qcWDVmtC0Il5lw1QDq9a83/I3Rwjymdjb7jN13T/juibsjGKm4ZFNdstsCxg9xt12TaYKUyzvG85YJ5JDZfSbPlcNa6wNiRyASZO5tvePUtrVOfTGKFJGw2e3NXnW6OprEB8ZVnSkKjjisWfMtWmoJGXhDINGx/OIO7bNMm1k3+RzTaGUzcIk9eGUgAurr/YPInCSRByddPDn8nG3W9N22JIkwYc//GGce+65+NOf/gQAePzjH48lS5aMbHIBAQsdgsPWnKv2B4wewW4FBMwcTXbLbAsYPYLtCgiYGcbdbk3LYcuyDAceeCAuu+wy7LLLLnjKU54y6nltEmgRQ4sEjHGbQ4bIMWyMJHdDKV9GuybnwANhxuw4QQRIotiT3be5Zzxyrj6vMmk1Yyh7Noi4CFCbtzYos9Z1mkSApJrPZsc0iJFwrW1QrtYMzIzhbpWecQ1tWTvHtgnNIJVZdTGx4oAqpfwzSRk2ujJXn8PWJKTAGbM3MlUYogwbY+4z0ZDSsBzMCqxU+wNGi4VitwaFGkCAhKIqKOJv685nA9Azb01qPRbFSGcTPQtk1/yGaCVrf0+o0Ejz/mqEDWYpn63JbpltAaPFQrBdnVQiEk4aH+gvDGIwUybNz0NTtm1yy6RU3v1DBUVc291PKlceg9aU81Zsry/zUS0F0g/ULjAh/PdWM0E05qTRfDZeMmWUefPESIyIWuaLleRlkWrOGbig+2jOMTPtQZg3A5r/RsfT3Lck4oVATDpEDtuY261pOWxxHONXv/rVqOey6VGKfWglnJPGIyqx5URHaDikkvV/2pofWk90pJdCI6/rrw99HNhJc5Pw+d5+jlof540xX3ikKTzSgIqRAC4wSjPnpGmiJEmdN62d2lyhL2mO1a1GpzWgtAlTdM6dJkIjVUG6JgESrwYIcdisLw3XFtzd7Bkf7qbnDSs+Q5ZzCxgAC8VuMSEAlXt9wygq0pDJJh+OqkdW+zcV2BzfBNGQ9/JMYMMgyW+IlhKad4dE0vHAcE5Zk/Jlr2T82vE9VqqD7Ro9FoLtmswkhJC1YY5NIiEG1ZDJxhBHKgRCRD+sQ0VUHGXunDTqsBVt9BzT7fiVqq15ah0xT9W1Ty1GNYDDxisOWpPDZttCgJcCdnkmiNAIB8+cU2edM65syGNe2h3qxCnq0EUcLC8X6rkTKZEV0ZNeYiEAEHHV4LD1V6TMlUbEGaayIRy2Mbdb057ia17zGnzyk58c5VwCAhYdWI9XwOgR7FZAwMzRy24F2zU7CLYrIGBmGHe7Ne0ctjzPccUVV+C6667DXnvthaVLl3rbL7roohlPbrahRQItEoArj1Wz4iJau34ATA+xim2YLqCWSaNjBpXd78eqde3TTnx07Fp1l1WmjYZHatvfHSIJ+CzZIGwb51U2rVxBI6wbnU8d8wbhjxn0PM151DFsnLmbPRoyBIyuGFX7A0aPhWC3hkFTjbMm+f5+Y2cTbIC6AGyWEg3q7sHa428C1q2ObaNQSkJUxnaPmd0/WpPdMtsCRo9xt11TuUKUq6Gl9+vH1oc7ahKmaMMXpfZEQbxwRxLKKPNBxph+CZWnRVuSdpcgUHG/1tVQpGMGRTUs0vSZfk4ZNtofJXY8jxLbLyJuz0lE3J63Yd0k1xBRcT/L3DFwUio7hnFmxa2qZQKkSSEZOmxSNwuWlM9yQjFIzmzNvEEw7nZr2g7bbbfdhqc//ekAgN///vfeNjYGJx4QMB/AUU9zjwE7P5YIdisgYOZoslvo0R8wMwTbFRAwM4y73Zq2w3bDDTeMch5zAiM6Aq0se6a1Alm+9Bg2s2bZxLT1Zawq7UY2rK74NR03A1ZtoHkOgUHy2Yp+1zZsm2AunrwX2+Zy1Zg9VpVNK7aDbNcwvBcdVl14biIX6n7/KJNWvGd2rLl68ZAr8FUpW9ofMHosBLvVC02M2mzBsW96LFYo5xpNhbM3BWYq5U/RZLfMtoDRY9xtVyYVlFQDsWrVgtrzgVVTeepYs4y0aX+FeSv215TDprr6esHPWeNozGEjTJrJe1N5avPZVJaCx2U7d3luWrvSSjoy+WkMWjkmzVxrxpg3xrBtStUX5daagZkSTrxgxoBuhk1Og23LhvjNG3e7NW2HbSGgcNjKZG4aEkmf4mucs6F+9gYNTaxzzGi7KZRxJk5azbjpoCk80mBY5w0gjhFjxFF2G7TWXphl9fg2BBL+fmfyOEuvFGPMnge97ANEdXkQrP4zw+4nIGAcMUgY5HzFpgiPnK9osltmW0BAFabu2SBOWp3D1s9JaxIUUZrUUlPahu9Va6yZtidAkueNoY+mXXXSukIiST1fr5/29VGABVwIJOCHPgLOYeNRDKaEmy9x3sxxm9paJRBRWQO4vI6ChKBzxWCegorHVVW2mTdG2/fcPj4zrmfFeTNKkYNi3O3WtJ/Wzz33XFxxxRVd/VdccQXOP//8GU0qIGCxgDHW+AoYPYLdCgiYOXrZrWC7ZgfBdgUEzAzjbremzbD9+7//O6666qqu/ic/+ck4/PDD8c53vnNGE9skEAlg6WDCvdSFPA4hmw2gWSO0iR2r6Wtk0oCebFrXZ3vNYYSgU2pi2wBfSt+GQZId0PDIYl/ajrWLKYR58/fd/bmq/s+wLFvd1aI3N11oH5ZW5w0rPot48X5WsSDsVh80hUWqvnXY9IwY6IDZg1aqb9iUblhpHmUopEGT3TLbAkaPcbddaa4gouFrqAHdMv0e2zZALbW6UEmVq9p6awU758IdVdbNqmklISnblnWLjqg8c9v71F4bKCRSkZIfFfERE/qolQQvawozLpo/Q9uqW9yothYkvbGjylORCSHlDKJ8YpK5ckIj4LbMErf/FI/VZk/DB4krpDm8un79MO52a9pP7mvWrMGjHvWorv6tttoKf/vb32Y0qUFw6aWXYscdd0S73cY+++yDn/3sZ7N+zICAUYOz5td0MOx98aUvfQlPfOIT0W638ZSnPAXf/va3ve1aa5x++ul41KMehYmJCRxwwAH4wx/+ML3JzQMEuxUQMHP0slvTsV2jtlsLEcF2BQTMDKO2W5sa03bYtt9+e/z3f/93V/9///d/Y7vttpvRpPrhC1/4Ak4++WScccYZuPXWW7HHHntg9erVuO+++4bajxEd0SKBjtrFi7bJC/Fwr7p92P2bY5i294pdbh0Xza9SeETzCLoUMKEvD0akpCJWMptgzH9RmCKt9CUYs7UwTJyxfZWxzYIzxMK9It79SkTxijlsu+vFh3zV7IPunx4/HjIQmp5b9TUshr0vfvzjH+OII47Asccei//5n//BIYccgkMOOQS33XabHfOBD3wAH/nIR3DZZZfhpz/9KZYuXYrVq1ej0+kMPb/5gIVgtwIC5hq97Nawtms27NZCxEKwXYY9M+ya7PPS5cvkrRl2TZm+Mm/N9Nvx2v+sktqyaypXxasyxn2+YNJMEWzDmhlxEcOu2f6MtPPMvmxfKeVv9mX33dCuvuqOT9taFsdQveaQNcy9up/KMf1rXVxXk++n6LWtGUP/HsXfB2XVLL+ffrbf96H6nRkWo7Rbc4FpP70ff/zxOOmkk/CpT30Kd911F+666y5cccUVeOtb34rjjz9+lHPswkUXXYTjjz8exxxzDHbbbTdcdtllWLJkSW18dy9Q58i+otamedUdW8SFAzbEq8sZq3vNA1QduLpX443E6l8xb34lgvd4NThyja/6/dQdd1ilPNbjNSyGvS8+/OEP48ADD8Qpp5yCJz3pSTjnnHPw9Kc/HZdccgmA4ofw4osvxrvf/W689KUvxVOf+lR85jOfwV//+ld8/etfn8YM5x4LwW7NFP1CIzc1mOCzVl9tNsA58xLt5xrmgXVTopfdGvbKjNpuLVSMu+2SQ9Sx7QXrqJXOmnngp6DhwfTe8PorY+r34xy24th+eCN16Lxaa4o4UZUxTa8mDPJZMxfVMBc6pus8esyBOsrmmtGwU9tuGFO3H+O0jaJWpFRqqO/VKO0WsOlZ52nnsJ1yyil48MEH8S//8i9I0yJ+t91u453vfCdOO+20kU2wijRNccstt3jH4JzjgAMOwE033VT7mampKUxNTdn3a9eunbX5BQQMg36y/tXvaqvVQqvV6ho/nfvipptuwsknn+z1rV692jpjd9xxB9asWYMDDjjAbt9ss82wzz774KabbsLhhx8+2EnOIwS7FRAwcwwijz2I7ZoNu7VQMS62K9itgPmKUcr6G9b5sssuwz777IOLL74Yq1evxu9+9ztsvfXWo5huF6a9rMkYw/nnn4/7778fP/nJT/DLX/4SDz30EE4//fRRzq8LDzzwAKSU2Gabbbz+bbbZBmvWrKn9zLnnnovNNtvMvrbffnsAgGJi4FfGoqFew+x7kJdmbE5fTOs5e3EtR/qKdT7Ua9j9D4Ne5w0UYTD0u3vuueeO7L5Ys2ZNz/Hm/2H2Od+xEOxWQMBco5/NBgazXbNhtxYqxsV2BbsVMF8xiN0aFHMRMTPjOmzLli3DM57xjFHMZdZw2mmneStya9euDUYkYF6AqRxM5bX9AHDPPfdgxYoVtr+OXQsYHsFuBQRMH012y2wDgu2aLcx32xXsVsB8xSB2axBMJzJgFBjKYTv55JNxzjnnYOnSpV0hCVVcdNFFM5pYE7bccksIIXDvvfd6/ffeey+23Xbb2s80hZFlSiOrxiw3eNnDhtty1vsDTTUfmljZpt1VJfPrj9V3yOxjgDhj1jSmsb/HNe5xvMbjNH5g8KLjw9z0AGCD8ev6AaxYscJ76GnCdO6Lbbfdtud48/+9997rqZPde++92HPPPfvOab5godmtmYLPswqhTWUI5itGkXsxSsxJ/aAmu2W2YTDbNRt2ayFhHG1Xk90SI8qn55xZiXgoZisoUREKxpnNo2KMQZfjvf7KGJOXSs0RIwWqtZJWPl8qaQtZe9L5Srki1mXfoNatKY+trkA2bTMu7FyaCmrTMWZc3ZgqOPfrkzHe0G4YU7cfxjGyHGDB+XDfqwHs1iCh3L1Y59tvv33w+QyJoRy2//mf/0GWZbbdhNn8AUmSBHvttReuv/56HHLIIQAApRSuv/56nHjiiUPti9YBqa/f5TCK2l0AvTa61jmjXXR79ZqabVLr2mRJ6shVT2eT/L4P6zB5dfB04zbWr15ev+3A0DX1WF1NvYYaeUwO6bApWbzq+ofAdO6LZz7zmbj++utx0kkn2b7vfe97eOYznwkA2GmnnbDtttvi+uuvtw7a2rVr8dOf/hRveMMbhprfXGKh2a2AgDlHk90y2wbEbNithYSFZruGzRMy3yQO8rPNqTOkAWUcAVIbVbm6YV2/3qaGWK68MVqb/Qjwsj6vQmodMh4ltg6biBJbh43Hia3DxiNAq2L/Zu2Ww39+pA5gXbsJjY4bqcNWzJ3UYTPOY5zYtohcm5M2E+68nRPnHFnGmX2u5BG3/VzUj2Gc2b8JY8w+JvFKP/2s+X6IhnZU6R8aA9itKht8xhln4Mwzzxz+WLOAoRy2G264oba9qXHyySfjqKOOwt57741Vq1bh4osvxoYNG3DMMcfM2ZwCAqaDIna624EcNp4a6H9fHHnkkXj0ox9tc0ne8pa3YL/99sMHP/hBHHzwwbj66qtx88034/LLLy/mwBhOOukkvPe978Uuu+yCnXbaCe95z3uw3Xbb2R/ucUCwWwEBo0WT3TLbhsGo7dZCQrBdAQGjwyB2a5BQ7ulEBowCM8ph63Q6+NWvfoX77rsPirAWjDG8+MUvnvHkmnDYYYfh/vvvx+mnn441a9Zgzz33xDXXXNNFT/YDDYk0vzFK+wzbTIN16OpOsQpWVnv32DN/vFktk9qNoz+CnBXbAH9lje5TVX40mxi3kS7MNd4IqnkcnUw/Jq2JPSu/e43sW8MxhobHqjH73qt7J9Ph9qlytxRX7R8S/e6Lu+++G5ywhfvuuy+uuuoqvPvd78a73vUu7LLLLvj617+O3Xff3Y55xzvegQ0bNuCEE07Aww8/jGc961m45ppr0G63h57ffMG4261+aJLI7xcKKRgDx/ALBQGzD8b5ACvwo1E/GwhNdstsGwKzYbcWKsbZdiURRxG/OPxvsARh1ZR7U+ytsFkCHJoXbZkriHKQ4hrIa44Zue+U4lU5/+57xkjiF9tdW5G2kfAHHEul8gzMjIncZ6uf64dqSGMd48ajuD5UMkosC8erDBsZI6Ko/GzJYgkOXl4nzphrc2Z/Tzhn3hj72YjXhkEyEh5J2bYmVk2UNW67+3nxnYrqf+9qMYDdGiSUe64iZqbtsF1zzTV47WtfiwcffLBrG2MMcoAv4Exw4oknhlCigPFHnxy2YdHrvrjxxhu7+l75ylfila98ZeP+GGM4++yzcfbZZ09rPvMNwW4FBIwAA+SCDINR262FiGC7AgJmiBHarblgnaftsL3pTW/Cq171Kpx++ukjXyHeVMilRi41FCjDRoopol5spCnio46t8pg0UJaM2fFVFs4IljBWZcOYnZfdrXa5cAr1bBuDf151bNuMmLbakL7hWLUudqyOVVPKjSuqZnYdg/Vj4yrzaqLHdV0ia7UQuWmT/mFFR5jKa/PehhYvCRgIC8FuLSToeVbEexhUi8MuJjTZLbMtYPQYd9slOIOIOKQy4h4asmQJhdJWNCQnbfo/bZt7TzENxl3BZkc6cisOxKgYidIwDF/xk827x3AGmZsxMaRlylLLRqksdUxWnlr2Silp89wMa8a48Bg50+aEIe1VONvNizJqvJZha2LVaF4e4wI8Trr6KSMmStaKcQYhSDtyuWdNY5py20bFqlXHDxNBMEq7takiZiim7bDde++9OPnkk8fScBhkugiJpI6Z1o5i17oqRjL4vr0wR+b6uHWqtHPYmHPAOIPnyNkvOXMOn9Z+v53jkM7bSBy3Xg7QTJw0GuZo+7X/2apzRh02wGUpk891HbcB9q9UddDsH4fbbYw6cjLru28PI2bYAnpjIditXmgKh5wtmChLMS+kaOc/6EPWIA9po8RIQyNHzLAF9Me4265YcESCQ3LqhPV33tz2wZ03zrV12JTU1kHQqtJmxTG11mB505iyHbWtI0cFSLy2ktClA0RDH01bK9kYWjkIqmIjps+GOJK2118JfXQCJL6TRkMhi33MTycNKH5zBGfQw/zmbcLIgNnAtH/dDz300NpQhYCAgCFgVIvqXgEjR7BbAQEjQC+7FWzXrCDYroCAGWLM7da0GbZLLrkEr3zlK/HDH/4QT3nKUxDHsbf9zW9+84wnN9uQqshFVdC29kY3w6btWIOqoIdBXU00wV1/wbDBtWuYN8YcS8bKz5u52JWeAdg2Kh6gwOw+aTil0rprzsX+ak/PR78wyCq7VsdwDcuqqbyeTath0roYOLvCpXxZhaabtBJ+YPtqwiDBBUwxGDYkw8aUbCicPf+NxzhiIditYdDEuFH9EceO1ds1QUSOZhODhEfOVs02WsOp5/E3QRhkP3GRuhpK3WNml/FssltmW8DoMe62qxXxMiSyfKZiui/blpSflao5ZLKuXYRHlu1IW2ZMKxfuJ3NlWSIpFYTQrq2axpRCJkpAlQIdSmnHpuWpZdA8Vq0mJLJJuKQJ1fu+MSSyQabfsWDcl9L32DTTLvYrhM+6NQmK2M+NSKa/H6tm+iPOhhIdGXe7NW2H7fOf/zy++93vot1u48Ybb/QL5zE2741HQMC8QAiJ3KQIdisgYAQIIZGbHMF2BQTMEGNut6btsP3rv/4rzjrrLJx66qme5O44wcj6S61tilUmtWPVtPYYNsqsSdKuy98wzBVd4OaM2bGxYLXMm6BpUmDQ5ao3g2PkBOvPtlEmjUPX5rRNO59tkLy1Ss7asKyaXQUh/UzlHpvmjbH7K1ev8sy1qXqWUrWsGl3t8latuYA27JkQjnnjAqwsUAnFoXkph5sPJ+vPVN7AsIXE/dnAQrBbWkqgcm/WFndvAGXdmpT+BXPbKPG1qdg2oGDD5xL5JmDTDOqYMiZEI4NG+4dh06KGsYr81g2CJrtltgWMHuNuuyZigSgRSInEfm2OmtaQyj8/n0lTHqs2SJ6bY8Y0VMnSc8HsowOXyrLnkdKQ5Zgo5q6thMuLy5W9X7TSkHlZADuJXU6dZfsaGLYhc9mq+WtNDFtdDhnNT6vK8zexaXYfHsMG+zku6D5Gk59mILh7Vq4ycgZJVHyWZ70jErxrOOZ2a9oOW5qmOOyww8bScBjk5c2eSY2sfDigDltGbnql9VAPKuZhhxMKlzPn3PGcIRaO5jVtqd0XlTFtK5ZwMAhu5lIfKgkO8HLuNAxS6e4QSTMfGh5p5jsMuoVDtGvTMXUOm1LE6aoPffScNCVJGGTuO2fldp2RtjGCSpG2BPo8CHp/Zs6tk8aok8YFYEJSogQsKuc7rOiIUvXzmeOH1YWKhWC3BgUnjhkTDKxvHTZnn7LKw7vpl1o3Onnd+wtCJL3CG2udtB4OGq3DVD+Gef8Dsxga2WS3zLaAkWPcbVc7EeAR9x7M6xwsulDibbc2STQ4ZsM7ciZUMtLcOloy14i0c7pUOUZpTcYoW6+3WHd2zpsmz4xdfeTcaL3fQUKt6X3t1d8lDhPjhAgg/dQxq4qHmPE8IqGS1tEj+yafG5VwCIBaxwxAw358py6JONgQDtu4261p3/lHHXUUvvCFL4xyLgEBiw9jnAA7jgh2KyBgBBjz5P1xRLBdAQEzxJjbrWkzbFJKfOADH8C1116Lpz71qV0JsBdddNGMJzfbmMoV4lwhUwqd3DFsGWXVlGPbDAYJHbEhkYyGRzLEhJYWsgyP5Ayxcv1xuYIWCwatu9k2wZhdkRGMufpuikHbcEdNQhtdBTgGx7ZRFq6XEEkdGsMgm0Q/KKtWE8LItAJIiKOtlVFh1SybRpJ7UfbpPPOYNMq2UdGRgW5MGm5QsmqarnJHMVhe9LOE3OzDyvRWSxGQ/oDRYyHYrSqqwiL0PW+gw+yqJmMQrNueFdudjZED2Dy6T9eHrj5/ruPLwtEVb7+fhitlVixAV+oxNe+3t6iIJyzQxMgxfyW6iplK/DfZLbMtYPQYd9s1kXDE7aiR7TKg/U19dewZ4FJVulk11dWfK23DM5uYN619dkxJ1zb9WumebFqxD3h9ZixFE8tWZ2eqLDplz7yQSPK86bFwfWT4m5i0pOyvioUI3sC81dghysJV+2lf9X3t/jkDy4ZIBxhzuzVth+3Xv/41nva0pwEAbrvtNm8bC6EwAQGDQUnnqFb7A0aOYLcCAkaAJrtltgWMHMF2BQTMEGNut6btsN1www2jnMecYCrXiHKFKUkZNmXZtFwqm7dW5LB1xyFXE/INzKqFYMxj26Jy9VuwopAkYBi2MoGTAXGZqxarerZNc21XLhRzOSWaCAUwomTCtfby1qj0P+0HfC2DLon/JjVDkrfWV1xE5Xa8J9OvcvdZSaRXq6xaZti01O5Tl0IfOssA01bKb1PmrSpCYsBpzo8TF9F2NZvbopgsioE4cfswc8mGvOl1AxWv57/xGEcsBLs1HRQ5bNy260DZtkw35a2xWsGlURBl48C2NbFqg3++vnB2HWPWS8a7bk50pXzW0WS3zLaAkWPcbVcrEmjFwrMfdWwb7afvm7Y37aNZ0KR//ttUE/NGWDUjRuKVEFB+bhrQzcbZ/sq4YRg2GgVVzVWz/cwxb54wCBUXachFa9UybENI7ZdozFsbgGGj7dox5bO1jobIYRtzuzVth20hYGMuwTKJKamQlTffVO4cNuq8Nd3odWiihTljiEVxHMEYYl4mvApu27HgFYetaLc1t8IkCgzKhBpxWEeuECUpf7ShYQZpBoiaMEgqRuI7dL0FSBrrpDWEQTJJHTM/xNE5ZiQ8UuWAJGGOU52ynRLnLYPOUjum6Ev7CpBopWwtp6aaTkxw93DLuX1Q0lEMFpf7jGJPhdLU8NBDCg3pPHNzrvQHBAwLNoDQCBf+IpILWySiSNBev1vK0UBVohJ0HDlOn+11c/b651hcoUlRsUnIoz580V8EalKj9eooCVpTiZN29xj6IOYf1/zfOzRyumiyW2ZbQEAVS2ocNmWfr9y4JjXuJietaXu/cMtGYZIeTp0JoWyqBVc3N+rQAd0OnRnTD9X7vCo8Ysb0CzGkTlWhtFgfzgg0O2O9hECGccC6ttFzqgmt55WxnDGwIRy2cbdbQ/0i3n333UPt/C9/+ctQ4wMCFh3GOAF2XBDsVkDAiDHmyfvjgmC7AgJGiDG3W0MxbM94xjNwyCGH4LjjjsMznvGM2jGPPPIIvvjFL+LDH/4wTjjhhHldzDGVClEZDmko8KlcWlZtMpPNSal9VkTqEjWrbRfuqNAuV5kzpa0wSaw42uVfSGlthUlizi1lrbWT+4diQNlmGsQdJ6sSNWGQI0OFYbPCIbrCnlHJfkn6S7ZNpx3LnoGsiNB+nZPwx7Rk4LLMhUcqBZXmXW0lZd/6ToxzcLPKTdg2HkcQrVaxzygGM2GeidufVsNdVJ1ljgms9AeMBgvNbll4dXkIk1PDTHHBPQESw7wJybywRhMSKRgjIkfVupPdU6kTGqH9ft/w5UPcvGeHdZsu+2RXtkVz+KJtK9lT5t/tkzJsxg6J+n4aAmXCnyrXti5EqW7cMGiyW2ZbwGiwkGzXRMzRit33X2ndmF5SDResih71+1yVmesXQtmLhaMiJXVlBpoETpqOX1ffsd/zJNBto/oLdwwXwljLsPVgz/qGNZJ0oOK9m3tdlEI/ds18rmtbPPhvwrjbraEctt/85jd43/veh+c///lot9vYa6+9sN1226HdbuPvf/87fvOb3+B///d/8fSnPx0f+MAH8MIXvnC25h0QsDDQpFo5BjVBxgXBbgUEjBi91HaD7RoZgu0KCBghxtxuDeWwbbHFFrjooovwvve9D9/61rfwox/9CHfddRcmJyex5ZZb4tWvfjVWr16N3XfffbbmO1J0cgWeK0xm0oqOdHKJybT4g6a5i1lOCQtXJyNL0bSCkZCikUnELduWRBxZGYdbZdvMihEVJmkJd9x2xC1jphg86X+iLoIqywYUeWumt07qvxEDCI105a0RERFPst/0y8xnylKSt5Y5ho2yaqZflWNVmkNlxf5kmlkmTaW5a8vmHDY//6dkQuMIPInKdmz3L9oJOCno7S7NkAybbMhhG7YAd0AjFprdqsLLk+L+d5gKjThZZ8e2NeWwCabtyjUd0yt/rSrrP0jh7GrZgfkqPMJqFnEHEfco2LDCTnAu0O+RgAnKnnE/b62uTVaxTU4Lle3ul+g/XTTZLbMtYDRYSLZrSSywJBGekJtBlWHqZtjQczvQn3Ur+v38udq5NOaw9WfhmnLq8prtde/7oalERxOL3ksOv98YoJv1asolo2Ncv5tjlVGrY/e98ZXtdZEBdnw8RA7bmNutaYmOTExM4NBDD8Whhx466vlsUqS5gsiLkMgNZcjcZOocto2pC4lMpSLhkarvjVanrJNEHEn5ANWqOGxpGVZHnbd2pJGXTkUr4miVh5RKo62Lzyqt0RLG2XMCJBBEHKBQFynhO27aqE2ShzMDpbV3MzbWCxskDLJ0zJjMwcyNQRQg9dSkraem0o6rrTY1SZw3J0Ci0o4Nc5SdwomTWeb6styFQWa5dc4UER1pAhMcvHzwlUkEHpcOWxJBtAtlSC0VRLkfAdjwNC2HdNiIIEq1P2C0WCh2qw7OMeNEtMI5ZpwzcLMIkXDkHaNWyzwnjdZNM20Fahfq7R4dz20fKvtzDwHUMbNzrFkwmQ+oPixQ5805wc6J4lxAmb+BEmBc2jHmo00WiDc5Zg1tzhl4+TtSV3+pKRRqpmiyW2ZbwGixEGzX0jjCBHm49pynilnp9XzVVAe3l1NH9+c7Z93jq45fXZgldfboZ5sUMOvmUfe+H/rVVOxyxojgneurd7ioQ2ZQG4JotjXUeuznjHn7b1jUq54nfVt1EFk8uBsz7nZr/vwqBgQsRuRZ8ysgICBgPqKX3ZpF2/XQQw/h1a9+NVasWIHNN98cxx57LNavX99z/Jve9CbsuuuumJiYwGMf+1i8+c1vxiOPPDJrcwwICJinmCO7NSosell/nUlsSHOs7ziGbV3ZTqXCZMnSVEMi61ZPKJqqwztWTdjVpiTilr2jbFuWCBseKbVbDYo5sys6Le18bqWdGAkkgzZiJLw+PJIx2H4zX91VfK0BXo01Is3fFAZZ3gxN4iI67TgmbarjC41MTdq2ZdM6KWQZEmn70pywbTlUWu7DEx1RjaIjJpyMV4RGeBIDAKJ2AmmETKTP1Jl1Q82Gu6W0rNSFI/0BAT3BHatGYb+7pN5Owbx1tzljSMoxqXIrrAl3q8VSw46hdoiiKSTSFzRxYw2r1ku6n1HhHzpuAOGOYVC3UjwIG8UZI2GIgzBjzl5Uz9of76T8eVnrkTJvPE48Nq1af41zN6/qijs9z5lEnzbZLbNttvDqV78af/vb3/C9730PWZbhmGOOwQknnICrrrqqdvxf//pX/PWvf8WFF16I3XbbDXfddRde//rX469//Su+/OUvz9o8A7qxrMUxkbjfx6ZQxTo0sWpNqAbR1H2+ieGrsnGN4ZXluH6MHeCzdnVjh0UdE2bbfRixJjasTgzKH0uPP0CI45B0UC8RpMawSw6wdAjRkTmyW6PConbYAgLmHETtsqs/ICAgYD6iyW6ZbbOA3/72t7jmmmvw85//HHvvvTcA4KMf/She+MIX4sILL8R2223X9Zndd98dX/nKV+z7xz/+8Xjf+96H17zmNcjzHFEUHoECAhYN5sBujRLTtlbr1q3D8uXLRzmXTY6pXIKVIiMmb21dJ8f6KcO25dhIBEhyw7BJ5Vesr6yeeKsdgtv3kZe3JjFZtieSCJNZcZyJWHhCJ2lSrKpOKI2sFB1pC05ipt3qUIssPBdFts3Kg3bLIzSfTTHbLtW8wUi7cQVWa19oxMjbq7w+by3PHNuWT5G8NcKqVZg0yrbJqaniXDsp8g5pTxY3nhECyTsplBUiySHLfpVKqHLJTWZEgp8sw1GWggsGUbKfPBEQZYy0SjNE7VbX5aDiDprFDRetHkUhx+7bcByKOI4jFoLdAtDFMjHCtnHatmwbgyhtCe/k9vutEgnRKdl9zmpZNQBIjZEheQ6ykuNKc9fM//WCJn7+mmMBmVewvg7VQtQzQVMORhWiwmJRcQ83rwZWTQgwZfr9otkmZ8IvAcDBo5JVixPHqkWJ7WdcuHxEwW1OnTDXjvmiI3WFcb3rMI28tia7ZbbNBm666SZsvvnm1lkDgAMOOACcc/z0pz/Fy172soH288gjj2DFihVj56yNu+1qRxwTRIK9iTRTDXmyo0DjMT3mrMc2rev7Vf346meK7YPOtjfqbtteTFRtf8P4fp+jmEF1kJ7gDQJX9HicAXk0BMM2B3ZrlJi2xXr2s5+Na665Bttuu+0o57NJMZkq6Fh6YZDrp3Ks62Tldomp0mHLMwmZlw8zxGFrqlDPakJWuGCYitwPbat8gNqYSiwp22kSeQ6bX/ejfMga6I7ncKntHMx4YWCmVBs0tBUp0aWR1IwVYZG2XX6qIi7iCY3YNnHkiBokUzmQF44WFQ7xHTMnLqImN9i27KQ2zDHbMImchES6dunEpTlk+beTmYQs/3YyVUR0REM3xCLYMDFOHm4Tgbhd7KcpnNJz2PiQtLpuKNio5z89P45YCHarcFRyL2TQ1A3sDud132NW2jgRC4ikvDcygTgtFzO0gtQu9LEORUhksbH6w08FRlwf69rOGXHMhBNDKYRRiBMkqOPZ4Jz1CKnshaZwnSYltTp77tl2zojYi6uZpmkoY5RYJ00rWVH3JJ81YZBRAhG5tukXUQRhhEYi7hw1M/eIIYqcuFWTClw1jGqoumxNdstsA7B27Vqvu9VqodXqXvAaFGvWrMHWW2/t9UVRhJUrV2LNmjUD7eOBBx7AOeecgxNOOGHa85grjLvtSgRDO/K/Y/WPMv1CkUc3p97z6HbwqDNZ5/zV7afXPmaCOoemegvXXSvP4anso5cJ2JTXfdA5MADpMLHdA9it+Yxpi4487WlPwz777IPbb7/d6//FL34RaoEEBAwIneflqk/1lc/11BYkgt0KCJg5mu2Ws13bb789NttsM/s699xza/d16qmngpX5gE2v6v06HaxduxYHH3wwdtttN5x55pkz3t+mRrBdAQEzwyB2azbwvve9D/vuuy+WLFmCzTfffNr7mTbD9qlPfQpnnHEGnvWsZ+HrX/86tt56a7z73e/GV77ylbExHp1UQscFu2ZCEtd1MitAkqYS2RRh2AxLkyvLcmmtUaN2b8NUGGN2NZRxZldDo1ggL485RcIgp0gYpNSxrd/hsW0JlcbtlbxarsIyDWbqg3FtGTaAuVBIszretKsmEKGRqny/Xcnw5Pv9MEg1ucG2TUik6mz0WDXTzidTZBsN8zZlQyINq5Z1cqiSVcs6uWXYtNSurTRUzQWjoVlccPDESKALu8+4wrAZBiAjogiqNVxIJFTDis8YSMyOIxaC3QLghUQyXmHVyhDeoqaXqSfIbRikTCREVrRFqqBKti2Z0gMybN39dQxbwhnK26hsu9BHkdAyBEQMhd6DA4RFks76CVdQtyrdtEBLQwlNGCQnkvlUUp9JZUMWtWwu1WES26vbaQil2Y8gYZA8Jm3hWDURuTlY1k3wWtGrKttmr8l0Ypqa7JbZBuCee+7BihUrbHcTu/a2t70NRx99dM/DPe5xj8O2226L++67z+vP8xwPPfRQX9Zp3bp1OPDAA7F8+XJ87WtfQxwPaafnAcbddrUFx0TEpxUS2Ivd6ff1nQ4r0a9eYhdr1uOc9ACCKYOUbB7kPFjDxehipmbhmgG9z6PfZZgu25YNo24ygN2aDaRpile+8pV45jOfiU9+8pPT3s+MgrjPOusstFotPP/5z4eUEs973vNw0003YdWqVTPZbUDAooHOMuis2002RcEDRo9gtwICZoYmu2W2AcCKFSs8h60JW221Fbbaaqu+4575zGfi4Ycfxi233IK99toLAPD9738fSinss88+jZ9bu3YtVq9ejVarhf/8z/9Eu93ue6z5imC7AgKmj0Hs1mzgrLPOAgBceeWVM9rPtB22e++9F+9///vx8Y9/HLvtthtuv/12HH300WNlOFKpwHKFVCqsp3lrpehInipkpp1J5KVghcxdDpvSujaPza7CMlLENHKFbPNMIjIr3rm27F2a0wLdGmkpg9tUSmBgaVibmMns6gnj2q56mFw2pV0EeRPbRvPZirbZiSJS/hJMEsn+mrw1mqumJzcUBbMB5Bs6yDYUbFveSZFv6JA26S+Z0Hyy+D/r5LYtU2kFRgrREZNzqHowbE6gweT+xO3Iy3kzf2vGHavG48gKn6hhc2oCw7ZJsRDsFjgv7l0v78mwao5tE3EEUdoPleWW1RKZgCrz1kQiIQ3bJjUSIjpCYVizQnzEsHDa297NsDnhEk90JBHevWZzR0k+Gz0PmpdHBTq68tf6sGzVRWXKrNm59cn3YsSeM06ksgWHrGHJKJSSNseVMmz0nERULzRS5LM5Jo2TXGgh3LU022n5GHpupr+u5MJQMv9zsFL9pCc9CQceeCCOP/54XHbZZciyDCeeeCIOP/xwqxD5l7/8Bc973vPwmc98BqtWrcLatWvxghe8ABs3bsRnP/tZrF271ubWbbXVVhAzFK7ZlBh325UIhtY0a0l0M0SscRvQnAU3LTZ5AAxadmA25FQGOaPZOm+g+dyrvfQxuY51nK4Yy9Qw36k5YthGhWk7bDvttBN23XVXfOlLX8LBBx+Ma665BocddhjuvvtunHLKKaOcY0DAgoXKM6i8+6FBjYFi0Tgi2K2AgJmjyW6ZbbOFz33uczjxxBPxvOc9D5xzvOIVr8BHPvIRuz3LMvzud7/Dxo0bAQC33norfvrTnwIAdt55Z29fd9xxB3bcccdZm+uoEWxXQMDMMIjdGrVY0igxbYftiiuuwOGHH27fH3jggbjhhhvwohe9CHfeeScuvfTSkUxwNjGVKyBXBatmcshSibxcfc6mcqSWYVM250zlyjJiWumeqo2F8hlRQTNFtEkOWxQLROUqd9xSkA0FuuuKdMuk/7KE4ACXZoUYNoeNaUbaxf+0cLbSlVVXuipCVSJJgWxIJ+tv89aytDlvrWzLST9vLdvg2DYj5Z9t6CDbULTzTo50Q7F/w6rlJG+taJd/I6lsu2k1qFCucwyAyKT9rKrq/AIQcWbZC9lJoSaKlfBhi2Fqpb3yArQ/YPRYCHbLwjAzXHhy+I5Vc/lsPI4gklIlMlWQZd6ayATi2i+t9CT5jay/YIx8x/2VTToe8PPWEk5yeUkOW6FYKWy/X9zbnZMtu8GFn783TYaErjhz7hQSDasWVRg2yqoZRksJDhmVjLtU9vy06mbXAICp+ty2Yr+ELa3JW4uS2EZkRLGAKNX2eMQ95g0AkoijRdqWVSPnNLQqZAVNdstsmy2sXLmysUg2AOy4447e6v3+++8/UA7ROGDcbddEXMj6N/056r6OrO6ZAwCjin5Uublu597nVOO2RgwyZsAc2jnDqM6hZoymfdXt9I9Ktmle/xld8yXo933J4sGv/SB2a/vtt/f6zzjjjFqRolNPPRXnn39+z+P99re/xROf+MSB59cP03bYqOEwePrTn44f//jHOOigg2Y0qU2FLC9DInNXh60IfaRt57yZfpkrqNzUAEsbf4SBsiaPcCEuoqz9onJlnTeZa+ukUVEMrZ2Tlvf4ERQk7Mj2kRCXDiOhO4rbUBitnQNjbgoF1p+2J7XXoHUlPLLsl65AoU470KZN5Pv11KQNg5Sd1IZBZhs6Ngwy2+ict2xDinR96aR1cqTrU9s2/xvHLJ/MkZVzTJW2D5nVmijuejEkpVOb5AoiLR/KyMMsJwIJPBHgSXF80W5BpsUc6gxOL6gsh4pqGLYsqETOBhaC3SqcFg0WlcIJcQyWGpGc2H53Cln/YozIckTt0vZI96NFw32rocKitEmCaQhmQiW1tyhh7qd6WX8XEpkQx4w6aTxxYigiEcTBjL3aclZohHPPUTXwHhoqoZL0nV/Dxzkvdu41IZEt4vTkEbfXSUoOYYWouHNUkhj07jXOmCYOWyHrL7rGdMv6uwU+K1iVcM95i2I3BvCdtET4zps5p1iwslanc96aBAvq0GS3zLaA0WPcbRfPOuBZ/WIGgFqnwnOwaFmhus8o1TC+yekbwHkbxNEZZtxcYVCHsm5cHyeNeQ4bqzhgxG6T/THSrh3fb04EPOv03E4xiN0atVjSKDHyypE77rgjfvzjH496twEBCxJaqnqGrWEVKGB2EOxWQMDgaLJbZlvApkOwXQEBg2EQuzVqsaRRYuQOGwD8wz/8w2zsduRIpQKkwlSukJeryTT0Mc980ZGsU4plZKlj2HLHsKkapo2TEBfGBWTZlq0Jj23TqmhrrQdOYO0FwfwV5IyblXANUba5BkS51mG+woMemtHVLsuqSRseqfPMqu50Fcsm8v2WSauEQWYbHduWbSiudbo+Q7rBhE1mNhSShkYaVm1SahvGJbVGaqZYOUHHBmgvfCspPztBz5kwbCIWiA1jkWVOdGTIpGqVZpA1srQqDTlsmxrjYrcYF2AxtyHH4AK8ZNt4nFuWSiQRoraRmleWBY7b2iskT5FPklDBMuqApwqCmeLazLuHTBkA+rV3QiMuDFIkwjJ80UTksWo0PLJWdCSOSBFtJ1jCKuGRw0Aw5kUmcMKsASUbxbrZNiE4pFC2rcp56SpxUDKbkgv3+1D+ZgCF9D8N56QMm/ld4IJ7DFuUkLZl2LjHrAHAkkRgogyL9di2iCMxgjQkJDIuywCIOvWGBjTZLbMtYNNiHGwXyybBMvLIqeofnAdhyVgT20ajfehxiDBaz+NU25VnOt0w53kvWNFgJ7tKpFTHNYQ7doVBGnae+2Ms+1YZr2v7mcfW1bJtlWMAxfdqUMyV3br77rvx0EMP4e6774aUEr/4xS8AFHm1y5YtG3g/s+KwBQQEDAatVe2PgJ7vIRYBAQGLFk12y2wLCAgImG+YK7t1+umn49Of/rR9/7SnPQ0AcMMNN2D//fcfeD+L2mEzQh4pERGRuRP9yFNJ2hlkyQzJdBKSMmwNxVCBuhy2xH5OtQr+RqkJV4hbDfYnaZKfNiuomdKIyn1mSlvmKdbM5sUJxqyUKl3Acn1OwrucnP8/KnlrWhW5awCgpM1bQ1lJHijYNpRt2UmtoEjeSZ3oSEPeWrohRVayaen6zDJvprj5pFSYLPNLUuUYtl45bLX5NpxhwjBpmZPLzidziDJfRGYS0ojQpDnJCRrupldpDlWz+qXSkAcS0ADGS2n/cqUwiqENw5ZkEFnRVhlh2+II0YSjgaKaHDbvEIIhtwIgyuZqmRxRoFvAxzA2nky/l7fminjzGtGRIi/UzdeIp3Ah6kVHaD5bJReiDkWuliZt118n60+l8ZOouNfTiENKl99q2lxpJygFV9aFMQatSxYuimoFqjhhtoqC4U5QxLBnImKVvDXXbpXXbyIxTJuwvwOtCsMWmxw2zjyRGM6GK5bbZLfMtoCAKni6EXyq4VvWh+1i1bx5sr022kdJ92BOn8u84vXK22af38gDva581kPdQqvsw7TNBhPXI8qgVpSpB6vGqvvivFJKxdleu+/Kdi8CgjBplFVzOWzMzkdXbDhrYPe6Tifd2LitirmyW1deeeWMa7ABwWGzL20dJk2Syp1CoJyatCEtMk+t80ZDIo2B6E4oL3/UowSKJJTbz0kJrWjwXW8IzhDxzLZpGM9k6URQFbCYM2TlmIxrGFEdzYlogBEd0YyoajWEyFScNCaLLzojqxc6yzyVSCtAMtWBnCqcNJXm1kmjoiP5ZNoYBmmct2xDik4pNmKctF4OGzWtVCiBo079zoV4TUqNuHxI5Ymr7aal+56oHnHR/aCVrmfYgkpkQAOKUEAOmFpfSoHFxX3BlXOuaLx+9ftJv3PWweJEpZEzUs/NLU7wRPt1Ccv9MhJmQh02E/rIE2EXO6KJyIYTCyo6EkfgsQntJAqXSWRDPqsqkaOAICqRxtGpKi2adhpx5Hm5aCOUdZwoFGdgubuO9LfFjlHac9ScCiWz4iK0bmeUNIRBJsI6asvbxTWaSASWECfObG9HArERLhHcto3DKoYQHWmyW9XzDAgwYOlGK45kUSMI0ig0omocLSWhJHG0iKgPyPMYiDNmnSrS732Xe/XXtLuctLr7YrZDJutsYjV0UNQ4XpW2E3fqdsZYRfDJOWyu3/422XaDg2fHk882CZBURU0qYEM4bONutxa1wxYQMNeQaQ5ZU2xbhlXqgICAeYomu2W2BQQEBMw3jLvdCg5bCROqorQm4YkaMi9FNJT0hEZcO3NtwpgZMCHAyxUElWfg5ViRTHgSz/1AZZenhCSsWu7kp1Ma/qKQqZJVUxyZDY90/bFmEKVogFlb6AqDbAJN4rU1AXLHpOWpDX3Ueeak/PPUCnTknSnkk0aaPyVsW+bVVqsLg+x0csumrc+7hUYmpUKmjeiIY9V8Fk3bVeWEwyqvFCURTL8LJ42IHPpMWDWKoBIZMDSiCCwS3iozkrZtC1XIEBc1BLtti6LsGqfMGBH6ENzWNJQxR1SGastUNpYC4DaE0tVS40Skx4RBxu0I0UQpQNKOINomnNOJpPDEMWzeKmwU25VaFiV2/poxf0W2bDPA2k3GtF2gFRyITeQDU57EfbGdedL4RsRjKleQJWPVVH9T5couCgvFvd8TuorLahg2ETk7LyJOpPydfD8XHBMlQ1ll0wBgeSuy7YlEYKJk42LBLFMYc+bCI0uJ/3gIwaSgEhkwLFjWAcsIs+OFI1bCEKvPUoOyZ1lq9+2FRDawc037t5FCNEqBzlHWt6m9rY2cmYV7g1VENKhN51TYSPD6Ni2fQmpfFv8Px5LRkEhwAVZGlM2EhaueU5VNZEPI+o+73QoOW0DAHELlOVRWoxKZz//VnoCAgMWJJrtltgUEBATMN4y73QoOWw8opT0WzDBohax/wfqoPIUsV3VqGbMcNsnRFEQ1oOPqiqhyzmwie55Jr52W7Y2pRFIWApzKlS0AnkQc7XK1JJfK5rCpiFuxAKU1lB5Ohr4WNNFX1qx2EWEWleXIJ6ds27BtsjMFWbazTm5X9/NJ0u7kRGBEY1I6Zq34X1mGLdP1oiMUtLg4AJt1L1TBrAFGxrw8zVlYgZFZQ0hkKD4b0AAmBFiUuFwJumqctO29JtqyMV6fc7eSaqT0807qGLGEW5ZbJsLm8kbtyDJsquF+cNL8LidOJIJI+XMi8R9bMRTRbnkiKSJxzBuLaQ6bWcHl3ursIDDp7pwxmEVmWgLFsExUDr8VcaTSSeYbyArDZnLSpFDgdlXesWpNjBznzMths+VYIm5ZNUEESHxWLfKYNbOdMmyGSWsLIjpCcthizj0RlkHQZLfMtoCAKtTGtVCc5oH5+WG+wAdh0Mz2zAma1TFmOs989oyMpzoDRlxCKz/HVxExEtpvbCiNqvEYNqUaGTd3OqNl26qsGq+5F6uMWjWawvzPCatG+02fYemqDJwRiGJUmISLiq12kRGNjBwZz2rsOeMC2su5889VbdzQde5NGHe7tagdNiPYITjzEsDroL2b3rUVbdM6O2UfI3V4AEfx0wvPuICKUtc24iYRtzXhRMRcW3BbNy7NFdK8VDDLhX2ISHOFLHZhgMZJK0RWULYBlPeBeZZQsJGBUORzgqoxqYoypD1nhbpQBa2UDY9UWW4NoExze5OoNIdKjeqiRFYKishMIjftVHpOmBGso30mDHJSahsGmZIHJamdkyZ1GQoJEzZp2qxRVZKCho9VDd2gCCGRAUMjSorwkVKIQyvpBTEz8nBg7Ey13g79vjLyg8xjs4CSWfVGGgYpU+lC/HooTAJGxKR0OBJu9ydiYcMgRRJBtIsQzqidEOctcaIjUWwFVlgUF84qyiT6AZXEAF8FkXlOGkfMSxVd7sIgjcM2kQjkytlPA6m0DUef4swqDXPBIIySZEXQqvZ6EYdNCO7CKUlIZCsRtXXWJuLukMhlbefEtQVHq1zQa0WuTUMiTXhoNKDTC4x/aFHApoeemoSOWL3yIhUDIf221mTX80S5SC5JeGSeeqGMZjG4qEGZ2c/atIYst4tO1fGuTiV10upDJYvp9Qub7L73mxa8BgEXVXtOwqtrwh151XnznDRi/3nFYRPchaYT586rjcm5XVyrjrehleVvVtEvnD3n3DljXNjfNC8cUtBFum5iw9T1HQTjbrcWtcMWEDDXUFkOVbNYoMZgtScgIGBxoslumW0BAQEB8w3jbrcWtcNGGTazwsmZY9uKUBWSKFmiWquiKhriMWqkrfIUvFxZkHnqVgiUtHXdGBdQmWHYEkSGJcu1a0tSN64sSwAUbFCak34S+mgWeIowSCLLXVOHbRgwwrxpwA9JIEm/roQBCUmgK1lSWenwrhV9085VhWFz5w0UzCDtc21/zua9YN3bTL9ru3pFVVEGTsQVbH803C1VDaeg/QEBdWCcg7UmrFAQ0csBg2OSNOfQNXV4aF0z+t0VcWTrIooksqpZKnNhydFEZO/HfiuS3aIjRL7f1Ftrt6zQiGgnEKadxC7kptW2K68sTvzVVmZER5qZNvr7bERHOJyMPWdw4YGiOCcaBplE3LJY1TDIjeV1EZxZ25sLZ5+10o2hkHZ+NCSSMQhSToCGZRpWLYm4FRJZUgl/NP8viSnDZvZBwiMjbsM/W0JAcEAMTrA12i2zLSCgCjW5AQrkobgqOtLw7ACgSK8gfSYlpReTRkMfJXnm8MbbtjumSnOPMXMMmyb92rODLky8u+QJvf+rTNuwLFuVWQN8do1GNRTjnRCUe3Yhz7uCeyJRVUaOJxERJREQlG0jbUHCI2mopGPeOj5TZ6JDqABJnFi2jYZNaipkUkyu6C/banIYWf/xtluL2mELCJhrqDSHrFHlDMVnAwIC5iua7JbZFhAQEDDfMO52a1E7bO1YoBULtEiuACfFSnnEPYZNlOyYytJaxm0QeX6Kupy47v6obPu5EJrkVEjSzuv6CatGF3hUDa02KNPmFbe0O6wkDdesmNEkXk3i1rVSrhC1csV5tXRtqbWXU1aV6u+Vb9aEKptm/k9MfgeR+C+K/Bo2whX85XFkmQFVrigNCq1U7crObK72PPTQQ3jTm96Eb37zm+Cc4xWveAU+/OEPY9myZY2f6XQ6eNvb3oarr74aU1NTWL16Nf7t3/4N22yzDQDgl7/8Jc477zz86Ec/wgMPPIAdd9wRr3/96/GWt7xl1s5j0YJHLtYfAIQgrFp9UWkO2NwvnnTsKiiPI8hy5VNmue1XNL80y+yqpExz717uheqKrTtmbFdqRTux/VE7QVTmsxX9ZamCKHHnS3PYorgi5d87D5kxBs502XYCIzHniEuRISvKwTnaZb6XTPy8NYMiOqM4fppLTJG8Ylljh5tgojyAgj2zZVoibgWlKKtWsG31hbGB4ryM4BTNW2tHTnSkFXGbrxcLBsZgjzsImuyW2RYQUIXubIRm7qG4yqrZ/syJhzSJhZi2krKWMVPETqk0t6IfKs0aGDZlowi00q4tfSbNsGU0Csgvc6KcyND/397Zx8pRnff/e86Zmd37ZhvHBkPAJOAq5IUgYmIbkjbmRZi2QvRFSFHzQ0AotEgmNY5CTNUEUhU1dUFEpTRJESJUBDVt09BKtGkpEDUCJ6ZQV3IwKEShIBJeGoPf753dOef3x8w55zmzM/tyfe3de+/zkS7MnnnZs+vdZ+c53+elQm0rH+/P633vIkttN+ry1pyqJkUpd82rbTafmCpyslQkyl7DbStZqbDJJHa5b1SRk0lUfbxSgQrnttNpfy9Ni5EAYZ5bSW0z0wPksM1zu7WoHbb8xywPNWkk+Rc3jaULSVHKV+bK4gSyXdzwxInvp0YqI9pwx3Lxkaqwyn6hcnpVaE3ZSet1DSB0bLQLrDqKapFB4ZFOp9VU9ILKx3XtzZ8ODNzsvkjK3cCZIAzSkkhB+q0JxGTbFiNJpEDcIMUSCuMVjfn+UVGz4QyTboaVQHuh0wwanWEOtgDLseBTn/oUfv7zn+Oxxx5Dq9XCtddeixtuuAEPP/xw7Tk333wzHn30Ufz93/89li5dis2bN+O3fuu38NRTTwEAnn32WZx44ol46KGHcNppp+Hpp5/GDTfcAKUUNm/efMxey2LEyKhw2orHIN/euiqKUQIUvRClJI5UHCErFhmytBU4b4qERNIkfF2EHQHdf+TqEtppj7UocNgaPiSy0YBo5A6bSJoQjbF8O4orq5DR3mt1SOHfJyUBaftRKoFYW0emsPek4JI2od22jk3a1s5+pJFE4sLRdeUiWpmIOEg29FFJGThvCXG8rGOWlLa9c+YdOhr66HuvydBJtTdnonDYeji8lDq7ZfcxTBkzcxhGkeIiNVUiqb2h9wdV4Y4ZtU2ttnfkiJ3KQ7qps1ekXrR8uoWmqRetMCXDkPG6MEhfSZIuNtOiJNWFmqoWzbshK76jQonAmfPh7oI4b7I2PNIuPFNHTtBQditgJApSpX7cOXU+JFLGEaSt7jstkZFxGnovWyRssuULltCecLJtjwl7shlavASAmem/D9t8t1vzzmG744478Oijj2LXrl1IkgTvvPPOsKfEMLNGa10Zx15VBngu2LNnD7773e/imWeewXnnnQcAuOeee/Brv/ZruPPOO3HKKad0nLNv3z7cf//9ePjhh3HRRRcBAB544AG8//3vxw9+8ANs2LABn/70p4NzzjjjDOzYsQP/+I//yA4b2G4xC4s6u2X3MQsHtl3MQmG+261557ClaYorr7wS559/Pu6///6jutZEI0KzGeFwmuFAsVpwJFaIi342Wdsnj+ts3PcSa6cwxYovJSPKmoUqbIDvxSalcomXgvafAAJFjrYbqGo9oKQIVmqr6NWy4FgjKgofuH2lMrKWchlawBYA8aqZD2EswpmEQFbsTqQvQKKEqFXYbOhjU0qMKa+wjRXPOREpRGP55yGeiJFMWFUtciXIo4km4on886CTwRS2rGWQodNQZK1ZVoHpwY4dO7Bs2TLnrAHAJZdcAiklfvjDH+I3f/M3O8559tln0Wq1cMkll7ixs846C6tXr8aOHTuwYcOGyufat28fli9fPvcvYh4yl3YLUuUqW6EoCSHDQhyux5pyPSBFu+VWJk0UwxR2SLRSyDhfoVStNvRYHpKYh0S2yHZnoaC6EtaW8oppsMJqi47EMSlAknibODYBUYREiigG7HiUuJLQkJF/3UKG4ZF0Hvb/pJS/hLcFYUikD43UUedKLA1fTNvaKWNpW7uQSFrwqS4kkoYfKqL0KymcIkaLjpS3raoWK6qgeZWwqrhIoKrJsOhKHhLZMc1a6uyW3ccsHObKdpmZaRgVhjlW9TujNsbaIFosRJPCZVSNy1ptaGKzNFHeMqeqeYVNZyYodKZTWgCNKnve3lUpciajKSemI0WDimphSkf4/tSldFQp31WpHHbc3xeBfMd9uKNUwqljeQES33rF22gaym73+16aUrVJESnp/m1kHEFO5/fBMokgC/VMEYVNE4VNKn9uHnLp1TY3XuoPJ+hvHQZT2Oa73RqsadQI8KUvfQk333wzzj777GFPhWGOGvvjVPUHAPv37w/+ZmZmjur5Xn/9dZx44onBWBRFWL58OV5//fXac5IkwbJly4Lxk046qfacp59+Gt/61rdwww03HNV8Fwpst5iFRDe7dSz7Ge3duxef+tSnsGTJEixbtgzXXXcdDh482N+cjcGv/uqvQgiBRx555JjNcaHBtotZKAzLbs0V805hmw0zMzPBje7+/fsBAJNNhWYzwpE0w5FWvmqbtr1kqtukhLU20Hqy2K6OdXUJk1ESNNa2SKKkySh2OW8yTlxBExklRIUjDVUj6Us/0/HSSm1UMQ6Esc90RUYeTe6ahaxou/cACOKO3f7Sqrs/jxR7kWQ1KFEQReNsSYqBJFIEja7LtIx/nXTlipbpV8LnrY0pr6pNRgKTdtV6MkY8kf97JJMJ4sl8u7FkDMnURH7MRBPRRK4GpMl4lzeqkyzVyCoKuNgVvtNOOy0Yv+2223D77bd3HL9t2zb82Z/9Wdfn2rNnz0Bzmy27d+/GFVdcgdtuuw2XXnrpcXnOhUid3TJRAhPFvp1GJnO1CYBQsW+o3W758snptFep2i0YcoyNFhAzR6Bsg/t2y+WwlZP865rHVlG1MkqTzmXs89lElIR5a1ZhC8r6k9dHVDXTI3/NzUf4/9ttmsNm/9801deTIlTYUlJoZKymEFQdithquu3z2fx2omSooDnVTATNsO3raVbkrZVVNbudK4/oGalBqbNbgLddx4LZ5N9avvKVr0AMkKfHzI46u6XbLWQzIlDoqcKms07lrUpVK+ewDaKqZWnmPp/l/DR3fOrVviwlal+qnZKWUhXdoHa7PEbH8+3BVJ2ymlYez1U1PxZst/12MlPUXRACqlDQ2tPSbxPVTbVy+6JTjXZxEZUoKKtUJsq9pyrOfEGTTEPa340kgizUM0XUVC0lZPH+SiXde13+vaj6HbFRG7bFQz8My27NFYvCYfvTP/1TfOlLX+oYn0oijDUipOOaVPjK3I+w1l7qDgt3LCWOV4J2qdiIbqVBpUcLDX0UUjnHTEUJVJLfNKnGmHPkoli5oidSCVfJUilf1bL8o155ExB8cf2roE4cvZHpBxp+FBQ8cNv+i0X7aAQ9y4IE1dTJ7SpRUNYApBniZlEpMzNIDudfzjH6QmBDJo0Lj0wqQhPK7wUNiQwdNonGVOGkTcRoLLFOWoLGkjxkLF4ygWRJ7pwlSyagJnJnXkaDOWwm0zCyokpkYbheffVVLFmyxI03Go3K63z2s5/FNddc0/W5zjjjDKxatQpvvvlmMN5ut7F3716sWrWq8rxVq1YhTVO88847gcr2xhtvdJzz/PPP4+KLL8YNN9yAP/qjP+o6H6Y7dXYLRdERY394hOztvMVJ3ssIANotaOq8FeMmaQK2H2S7BWmP1xqmGK8LY7KPKeUFGfqjax1J0GpgURKEQVY5b4gS9/ry8M/IvQc2PJI6cpJUhpTwISWKhAAaI1zFxGZUfQMlSWhRyzp3UmKs6I2ZZvWVIcuVJcvbZYfNzkUJ0h9OCkRkOw62ffijnVe/ThqQh4zKINy8N3V2y+47Fswm/9aya9cu3HXXXfiv//ovnHzyycdkfkxOnd3KZlrQUvblpGlyjB0LQh/7KChCHbCq0MduTpobb2t3/5A7aSDb3jGjPV+7hULWOWv9fGPoElJdGKR9bI+h9zp0nFbBTorXrYSASn34IwAYLV1IqEq8M5ZXzyxCHLWBcuGh0lXJVJmBKY7XmYZy/6YZpH3OOPILgHHk/t0lqSpptIbIKpw3LYFW/rnql2HYrblkJEIit23bBiFE178XXnhh1te/9dZbsW/fPvf36quvzuHsGWb26HYeL1/+08WiwZIlS4K/Oodt5cqVOOuss7r+JUmC888/H++88w6effZZd+4TTzwBrTXWr19fee21a9cijmM8/vjjbuzFF1/EK6+8gvPPP9+N/ehHP8KFF16Iq6++GnfcccdcvD0jDdstZrFSZ7eo7ZpreuXf1nH48GH8zu/8Du69997aRanFxrG0XWy3mFFlGHZrLhkJha1fdWC2NBqNyhvdiUaE8UaEljZBaEtdGIst3iGkQCvKVY/WdNJRzl+3UxcKWQ6fdMqcVD4kMkqgirCkKGk4VS2KFaJCoo4ShciWlE8komI1dTxRQa8emphuV2rzVWa/QuxXX/tX1LphV7OFlC7UKii5LZXvARW33cqJIn06dDNxhQ2iMR+KWtfXRB5pQwm7MlT822kg1T4Msqq1CV2NSiSIwkZKZZMwyMaSBGMn5Kv7zRPG0Fg2lR8zNe5DIicnICfyz4OIOovRdENnGlpUVIk8Rqs973//+3HZZZfh+uuvx9e+9jW0Wi1s3rwZn/zkJ90K9WuvvYaLL74Yf/M3f4N169Zh6dKluO6667B161YsX74cS5YswU033YTzzz/fFRzZvXs3LrroImzatAlbt251uW1KKaxcufKYvJZhMyy7ZaSCUYlrpyGM9mqb1BDarjirarWNhEqi3YIpQkpMO3Wltk0rdeNop15J05nvkZRlvjR3XQ/Kkuqez8XbhrygSFEARUpXvj/vt1aogLSsf9zIC64AMMoXXsljHLuvP4qifD1g1aZ820iDJLKGsDOMO1fVbAEjICvCJduZduONSPoeTT16Xdpr2ueXgQrmxyMSBhkr6YukBOMi6KcGlIuY+LB3JX0BFknfi9pXXk+d3bL7AB8KZ6n7PPfLbPJvgbwlyQUXXIArrrhi1s+90DiWtqvWbhVhjVWqGlXry+GPQLnwke+9lqVtsu3DHXWaBSGRWUVIZLmUvyZqW0vbe4pqVS3VqFTeqoqOaKAyJLIuHJLaD1Vzf0bVM/sYsMWUrHrmC7Ol2gSqmn3usCCbQVKMx0Qxs8VF8sf+XkwVqhftlWsyWTrGqnPV1sVk2t0DAnBKGpIIQNuNWVWtSm0bRBnrx26NMiPhsK1cuXLB3tQxTDd0qivLyR7L1Z5vfvOb2Lx5My6++GLXOPsv/uIv3P5Wq4UXX3wRhw8fdmN33323O5Y2zrb8wz/8A9566y089NBDeOihh9z46aefjpdffvmYvZZhwnaLWazU2S3A265RyL/953/+ZzzxxBP47//+71mdv1Bh28UsRvqxW6PMSDhsg/DKK69g7969eOWVV5BlGXbt2gUAWLNmDSYnJwe61tJCYdNk5aQOKX0OGc0ni2KFdjNXYNo2rrqduhYAdQqbUCrIg7PNumneWpRI12IgaUSIYn/MZJHXNZZEGIt9w1TaUNUlpiuS86BCtU2SFWcgzPOQwuduGEhfRlxKsrJNtqXyjXKjGLClw6MYKPJUZDtCZEuHt9quUW65rK+ukseK9z5//wTEkXwFJmn7VaSq5F9KRw6bfa/HIpcrF0/EaCzN59hY0nB5a41lU2gsyz9jY+9aiqTYllPLIKeW5ddXzcp513G8FTYAWL58edck/fe85z0wpfeu2Wzi3nvvxb333lt5zu233155M8bkzKXdMiouFKoin4AmURsDFAobjCbbMZDZ72MbolGckxGFrZUCdltnRGFrkWa3RGHT2itrdQqbJVDafE5rnsNWlOmPYm8fk2aY2xbn30EjI8C2CZGRV9tkFNokgnD2zpC8NUBbhU0It524X0TlbF9LGswU38dYerWtJQUaxN5QNU13KTYC5L8nUvgVb7dNxml+miKREeUcNaqg2WOtehZcW/j3In8/6HsERHXL+RX0s1I9Cvm3TzzxBH7yk590VLj97d/+bfzyL/8yvve973V9XmbubJdOW9BKVhYtMlr7XDSqtpFG2K6tUilvzak72vict8y476DO6o+xuW25IpePt7R2OWllhY3mqvntUFVrdRQdCdW1bqX9e+G/orStUf443xZBmyNau8A+VyLD7c5rAy6rrqNTVWGfMw1TnJDns9liIQKiuHj5GKNsjmDbXU0C0NZwpW1XjEqnbRhbpyGpdlM0AGSZy2fsB1bYjjNf/OIX8eCDD7rH5557LgDgySefxMaNGwe61rJmhImxOBhTpApYHmKYf0B/EUkcJE5aayb/0KUzbbStxN7Or5VlTWSFt16+8bUIIVyIpYx8EZEokYEzaB22KJFoFNuTzQhTzfy5JhsRppp+3DpsTeV79TRoCI30oZK0Upr9rvZdRcudKMNtG94UtWCi/Iskkqa7oRNZBlUY47jLF0SovLeGVMJXj1TSFSaJxiK0m7ZSVH7thFR06ubw+f4hylVFipoRkklfaCQu+q01lvgwyMYJkz4kcumUc9Lk0ndBLX1X8foGCSyySc0VVSLngfFg+mcu7RZkBGN7kQEuNBJAHqJonSOjAVMcp9uAsjdHbVekBFkCERWhJw0NZDY8suUdsxbZ1j4M0hDnDTWrluG8yd0BKb7kQiWjxG/HiQuJhIqdY4YoLjlpwl+7V0gk/GKUEMI7bzBwFrB4GUkEKE2KGRXHZhouXEpH0oXP6wFvwjqdNDtOHDblQxtj5X+XJEStcwaEYZWCLMoJESatl239IEVH6uwW4G2XzbvtRb9qD82/Xbt2LYDe+bfbtm3D7/7u7wZjZ599Nu6++25cfvnlPZ+TmVvb1S0MsuoYrTuPpSFw+eKu/833oXnESSMORXAMcfZ00EstrPAYFhTJx8uLw95Jq+7DNmhPtiqo05WHMvowbdqf1t/NGVLMRMA6dan2qSD09YWVt4vnMYY4YN7pMplw91hCaoiMOGay+zGCFp5R5N8auZPnZmz7tmW6ttiGkN1tfpl+7NYoMxJFRwbhG9/4BowxHX8D3/QwzAig07yyVeff6BsPpn/YbjELiXq7dexsF82/3blzJ5566qnK/NuzzjoLO3fuBJBXuP3Qhz4U/AHA6tWr8d73vveYzHOhwbaLWSgMw269/PLLuO666/De974XY2NjOPPMM3HbbbchTTvky57MO4VtLplqRJgsVCsXJkhDVaRAowhVHEsU9jXzN/idwy0cmS5638xEaNuSsa5/m3bbRleHx+Q91vJtpSSk7b2jpAuJVJFw2+PNCOOFMjTZjF0Y5FQzcuGRk80IzciGRyo392ZHeKQPs6HJ7vn/+1DZaBikkHnyPwDoDCIqPvRxDFEojmgQhU1rV9K17sNHe22oOEIrznu6yERBT+TXaU23kU3YcAZfpleTFbbyNYEinNW2SkikS4aNm5FT1eKJBpIleUGRZGo8KN8fFyvGcmoZZKGqqallwMQJxVwGi3HQmYFG5zl1CiHDQEZ50ZECQVcMFQmRNNqpb0bHrhgJTOKOEartt3Ub0EXYWpy56+bj1WGQPlSSqnyzK0AShEpSVU1IFwZpipYGdjw4xr0hof2yClMGb9uUNDDFNJUQgLTft8IOGpK8L43rz5ZpQBdFRzJDVuXpy+9SaMSiJA1VpL854bhTz2qKh+QhkfZl00IjfoyW7696X/I5h497UWe37L5jxWzyb5nRwNQoG27/AAqH6UfR70Gutlk1qHdaTH2REFP5uK64yGxDIjPjVfTMmEAR99f0Slp5f5WCXjdO90tyT6ULlUzFtaf0xGjt47e7HUd6snW7Vq/PFWUYduuFF16A1hpf//rXsWbNGuzevRvXX389Dh06hDvvvHOgay1qh41hhk3WylD1O5VlPXKCGIZhhkSd3QKOre2aTf5tmV77GYZZmAzDbl122WW47LLL3OMzzjgDL774Ir761a+ywzYI47HERCwhRRSURm4or0y54h4HFaZcDlkLB6fzXI/DaYaD00UulW24nWlk7WJVwpjaBHSXw6ZIPpuSSEjhEKuqjSU+P20sVi5vbSzxBUiaSmKcFCCh202ittGSz+WG2kKQhqrllReqqrlCI5Ff4ZdtwBRlxJMmRMUXQAIwruAAyacjjRLbh6fddjadusIk8XTqSvvGR1quPK9rqEly2MpQhc3nsBGFbaIJ1Wy47WRqvNge88VFJpZATi7Lt6eWQU7lqhqaUzCNXJGrS2itw2hT5NB0jjNMFUZFgVplygXZ6Q1pVel/orwhaxMVzqtqMBqmUOREHibgr02uSY939CpAAoRqW6Vd8bltRkinqgXFRcgxwbl1TyngvmnGkCbW2jhbJ4ojhBHubVQAMlt8ScKt0NK3uU5VC5/f29M8n0z4bVE9TvPSfK4azXmjRVWKa6DahndT0BR8W4B+qLNbdh/DlBG9ckxJ6XaLLL7fWSm3SbiS8rNX2qQSrrCFUAbK2Lyu8PNLG07TvDGaT0a//2E+GQCI4Jp2f65uodjuPd+qMv7lx+Um2lXHl6/T7WuvhIBwhd6kqydwNAgpK1UzWcpH66asBdfq8bmi9GO35rodSRX79u3D8uXLBz5vcTtskcR4LIMPOe1r04ykCz0cjxUOFIVGlk7Hzkk7MN3GkSIk8khR/WamrV1ft0yb2r5u9oZBSeH6p0VkeyyJkCjfb805bGS7GSnnjDUi6ZzN8Tgct9uxlCiiJhGT8E/3o4/O0JkOhPQ3Slq4GyhaCEEAkEVLJaMUTHGDpkl/NikVTFEhLo4SqGZeaCQaa6B9JA+DzKZTtKeL/nattt9OW52VpEhicueUrcMmXSUiGceICmdQNRPEE3mFx3hiDFGxrSamIMaLMMiJKUi7PbXMO2nJJEySv1iDwVZpdJohq7A3up+bXmZRYmx1xF4/VEH1SOKkEacLio5rmIw4adaRKzlpflyH41XfvTpHht5UkB9qU1V9Ngh9FH5bekeufLyraCuAomha3ofN3nBJkccBIq9sZu/73FoSqfpo4G9q8qH8AQ2tMabaapbvnSqdNIDY4ZpwRvIbJQVIP7U6hy10DrthyI1nv9TZLYBtF1NPXZEICVfvx/fioudpGYxL59xJSOUrQKKm31cVOjMQxbm5I5JfMymVd7epTUr4XmZ54Q673xf6KGZlZ1383xAnra4Hmn++KufOP0bHvjoHLCYpL0qA9GETQU8235fWj7v9kQwrc9v7qERBxbaKo3IL33QRXCYqKBjnF839NnXeRM24JCky5WMGpR+71W87ktny0ksv4Z577hlYXQMWucPGMMPGZKYyRIdXqRmGGVXq7BbAtothmNGkH7vVbzuSfvtHnnXWWe7xa6+9hssuuwxXXnklrr/++kGnv7gdtolEYjKRiGWeWA4AkQSSIqNyPFYYj/MV56lEYbJQZo40MxwuVLWDM20cSfNtq6odTjOnqqWZrlXYLEoKp6TRQid5WwG/TUv2275qVFVrRtKdG0sZKGyRU/PgSvwrIYJxIFzV7QihsWFBwsvQbrUbRbor/US5vm0lVa0o123SaZiZXFUzyTREWpTyb6aIxvPtbDqFLsIgs7TltnWr7Uv1kp4sdfiVocitzqlmAlX0elLNBNF4rpKJsQmIZq6eyYkpyGJbTCwBmnlZf52MwcT58box6banp0M5vRctrdGq0DRbc5BUzSxMjFTQwq8k14fjkRVpqQKlxSpNphzWGJFx2s/NhU2WjncqFEK1rWPSNZ/nkkoYKmzCbftQyWolLSjxX4MksYKZNi7CQRhAFvZf28IeBtCFamaML48dvtWirwIj7miByrL6uUpWnFcaryzPT66fK4ilORgN0Yf9sO+pAAAhIWtChaqos1t2H8OUkUkMqRTqPh3uu5FE7jfdositqk5bLkqmk0Ld7Utpi5wClCnh+rABnSobkH8XbQl8JQRR3kRQ+r8cWqlJSGRGFPr64iPV36u6kEiqkkmEyhtVz2j/2XC7U2GzfYFz9cwWgFOV6SSqpLbZY8ICbwqqSHORSUTux2I3TovN0fu0DhXORUupPExzgLDYfuxWv+1I+u0fafnZz36GCy+8EBdccAH++q//uu85Uxa1w8Yww4b2dimPMwzDjCJ1dsvuYxiGGTXm0m712z8SyJW1Cy+8EGvXrsUDDzzQka/XL4vaYVPTB6ASYCweQ1Ss2CRKYLooGJIogbFi5SDNNCZn8tWbw60MM4VXf7iVYaZYjZku/t/S1Tls3XLZqvLZYindSkgjUr40vxReYVO+ZH8z8kU8GqXiIhGNWSYKG23Aavf7BHy6aE1Xto3PHQHCdVm6+m1X6IWEkL4YiUmtqtaESYrtdsupbWineeNeALLdgpk5ko9rDdP2+WyuJG9FQ81gSqUYaFvQREQJRKPp5mVVNdFoOlVNTkw59cwQVc0k4267JSLMFJ+ZQwP28ki1QVSxsp1yWBFTQ1Y0baUfkd6V70xlAQoBCWmbWJcUOFORt4bacaLC2YsQpaVSdauaJVXc6I9alaoW5K2JcJxg1ShtjI8akMK9ZwKAsTavmLyBLwigyYps59vcO3etVlXzU6kZ94VGAhWNKmhE5aT7eyIkhFUjivfOKap9UGe37D6GKWPzj5TNP8s0tCzahZAGykL6z6/7jSdjUkpXfMxeFwAy2XYFMrK0Po9SuLwqjSzIz9JuWxbny1QjKdT3VBsoYZWysGCIz22jDbW9kpaRHFeqvFXRLYfNvQfoVNns8XQ7zFXLx+tUNSmEU9O8kkYUtkQFLZFcpFIpby1Q24p7LZVEPictDlU1SRU2d82oq6pmoef0wzDs1muvvYaNGzfi9NNPx5133om33nrL7Vu1atVA11rUDpuc3g8Za+h2iiTOb9yjeMyFJ6aZQEMZt+2dtwjTLe+kWeetlfmxVvGPr0mVSOrB5zcPnV842gcuJo4cddLybTvuHTZFvoh5BchqJ62qwpj7kkvRtZoYgCDx34bTAHkCsCiKFhipAG2LA0S+4lzWBooeUrLRBrLcMTPtFkyraCTYbjmHzbRTmFareNMyN650FvaGcm9sYahJFTohJYJeT0VIpmg0IYpCKSJpeuetMQ4T5XHLOm66bZP48Uw1nIOeau0ctiMVoRTd0Ka6/0s/VeeYxUlmcqdNO+fC0/03x+8Me3MZNxY6DsWPJP0ukRC8jnDKbo5D3XYddaGS5X1VYZM1SFLNTQoS/ii8w+uctFIRkX6+jfQMakOrHeWasEby3ghdXShGzNX7Gji+pr/qngV1dsvuY5gyMo6gmol3wpQOqj1S500Qpw4AslbbjWmynSkZ9OvS7ia/DamKPq2x9A5ImjlnLlPaV0CUAiop7tNSicw5JhlMceOm0syFSuZOWP66yk5aZx+2sDfbbPuwAfVFR8Jtv7/eefOhj9bZEiSEkTppwp7XpbiIddLyom72mCgo8FbljEmy3Y+TVl58BwDZHm279dhjj+Gll17CSy+9hFNPPTXYN2iLkdnpcgzDzAmpMUh1xR/f9DAMM6LU2i22XQzDjCjDsFvXXHMNjDGVf4OyqBU2MX0AIjaQWQrTzsvIy9Y0GoXaFsdjrqBHmvnE0nZmkBYrCi1tYB1867m3MuMSGFuZcZ479ezpSrgsrZpIopJ5tU368EWipEm6WiL9Kq+SwnnjSlb38BHkuetCcihBkjpsqELkV35p+W2j8x5PAGBir4ZFvphBUNgg8z2ghCbKm9ZAEQZpssyHWekMprwiTJU2Ek5Fi56IOAasqhbFLrTTqNi1JdBRE6YoPAOisGUy9qpamiHNrPpq3Pah1mAlrTODykYAnAfC1GGKcEj7GaGGX6O+kn4V5dC94LGzEyawE95e+eJDktgYd22iChl0UYYGpUJJq1Xg4F+TMWF4JF2tNqXJ918cvHgOsl0uqV8OZwTQ2QaBKGyiIsyUnjubUFOgeI8qwkyFjIKiMf1QZ7fsPoYpo5IEMo5gVEXIY6YhSWqDHddFL1dBlDSTRMiKoiQyjnxRMiUhiyJistWCjn27H2WPKaltuuhhm7W88qZiBd20yp6GLsZ1M0JW/L6bzLjjm6V7PK+sdapq9LsxqNLWT9GRum1J+qmpRPntmJTeLyll+RgpLlJzLA19LLdNouoZDX1UxTFB+f44glTKHVNX+t8ipISQCmoAgzPf7daidtgYZtjQuPhgnFepGYYZUersFsC2i2GY0WS+263F7bAdfgeQLcik6YtJxA2glRe5ENFhSKu2RQ2MJ7kC0zK5cgYAbW1QpLP5VRRtkBnlxuznICwSEE6lcmVb9G60SvPQaHnoXkqaO770lnRtuGpzHsh2vsKritdUymNRFSvFRvuiBVnbbQc5GrodHO+VNx2uRNtpVawMd6y425YEKnKqmpaRU95MlLjcOqNi6EJVa2uvnqUtr6q16HimnfL29pH+E/eB/DNRteLDeftMHVZds8oaVdU0Gbf7ehEWxSDjgR2ymBo13j9n2MjZq/LlXLgyHeXp5xj6nIM0ie64TtU863LPgMBm+WtUF2wB4BQ00aGwdSp1vSdLVqSFBgzJW7O7dBtCRgO9/3V2y+5jmA6iOM8Vr8g5NzoLiodZNc2pbklM8tlaTq3RrTa0VXTStruGTiOnkmnSDihqalewRKeZU8x0FjklLUszX3cg9TlsWZq5Xl10XGcmUN68OmjcmHu5pRyquchhC+7ZlCCNrmWlkiaU8HlrUoQ5bDYvjeSt9SrTTwuHyCR2FRDD8v2+TH+YtxbX5qcFChupQeCw0QLRIDls89tuLWqHLdu/F1k2nRuRxBacGINICueNhMkZEibXiBIk9uY+jl1PpNBhy59DEzmc/h52+3CECevFGMo3QsV+oDJcqVuIThX+x5reFJDNoIScIoeo+h/6HuFQppxIT6vM0ZsZegNTukYt5EbFCAmoyI27sE2p3LYWyhWKaWuDVlERtJ0ZFwpLw1/zYjOZ3y4ctv0zgzlsqTbBza4bnwerPcxwsE4ZtTHVzlv4Gar7RFWV1yjbjCqnji4QAf4Yup8+R71TVzWPzpBseu1+GdgJrLArHQtCgzhjdF8pDLK2YEuv4iI18/QXlpWPXXERlJw3FeUVfQesEllltwC2XUw1otGEaIyFxW3sdpbVOnL5/7XrzSaTKAilzIqiZKapfZ/Wpj/eNBO3rbWGSvPjaaXpjG6nGbIWuX7qt6scOZMZ1xPWaEMcNU32G/c6/Eus/p5QB0+oaoMnybiQMnDGXIihEq5giCTOW9kxc85TQqs0kqIjNcVCZFKEn0ofBinItixVgLQ9b8vOGD23zjETqmq8cDQHyEKZ73ZrUTtsDDNs8pjqTkMxH+KpGYZZnNTZLbuPYRhm1JjvdmtRO2x6/17o9pGipHuuqiGKIZ3a1nTbiBpOYTNRAlMobFAxZKHeRLaABQm1g/SKjhGipLJ1/4TUKWNVSez5eBYuow+gQrlTuiTvu2O6vIbwUUWCu/tPccTAr7H7a6qavxHC9cAz8P3wMgNkLbudhQqbKzADV0CGKmm0dcNM2/fiO8AKG3OMMcZAw6v09nG+7b+TBugaht0NQT6TdYp+flxxDGi4tXHn0f3+HBGqbVXPI3x57GCcvIayAteX+tZLQesjBDFQxuj+QcIaaXEReq2auQxcXMSdSEIiQe2zhAuY1Tp/OMBzzPeVaub4IxvjEEnDDxClTZCQSJCWPc6iZBlkUihW7TQIn1RZUSyMqGQ6y5zalqtwfrtqPMo0tFXqSEhmWXmzIZGahD7SAiRam0Ahy4/VgRpHx2eLpMU3lC8oUh6noY2ChEo6RS4IiZRBmKM7lihjdf3TKsMjSTl+WVOyX0SJC210Klp+slfQaFsmu8++Rikhs/7DLua73VrUDhvDDJu2MWhVrPi054HxYBhmcVJnt+w+hmGYUWO+261F7bDpQwehTRsinQamDwMARBTDWIUtTnxuW9LMS8IDuepm1TaVAMXKgM+Nivwqp1RhDoFNlOxDyaplNiWx6xqmlsaE0H5/6TyaG2M/2h1FDuwCMnnqfpv5UsLm3fRBfcHtUE0w0Lb1gPH7aBGYzBjYRa7MeFUt08YVEWllxjVGn25r1xx9JtOu8ExL++0DrcEUNi7rz8yGQOUmjzNDGmoHx5iBFDb6tQzz1ESl+ibISbQhtM9ro8WOTBflrdgmkzWifH7+/8yYoAWJPaVWaeuaZ2Y6jq1VwGarpHVR5NwxVaX+B0QI7X9njPZ2XkgIG+0BuFxkYTTMgC1Z53t5bOb4IxpNyLHxvD2PpaSyAUWeV6llD1XghG76HDfS6qesvDm1rU/lrfJ4raFT32KI5s75vDVdylfz+Wz5c3o1zh5Tpi6fjSJL+Ww0v6133hptRC0r1S5aMKROGZszJY3kqtljyipa3bh/zYX9avdvcOa73VrUDlv70CG0dTuUd6PEOWaIkrxXF3JHjoZN+nF/vKjq7yWkd3qEcB/aYBzo7bT14dTVhjMKUek0imBuxX4ZwYbKiNI1q0KtMl3tvIXFD0pOHZ1zH1+SXqGjVc6hMflNqtsm43X98sJtH+5ot+uOT9vahVkemh6sD1uqTXAD7MbnwWoPMxx8+CNdKKn+vFR9B+zjfqEOUBb0ZPM/crkjVxxDQhlpuCPth+avTQslGeJ0+Sc18A6cFIBGp/OmjXGOojElp60unLFboY8uPdD6ukbNdWods36KndRgOn4bpL++LO2z1xTSz10U8xkwJLLKbgFsu5hqbHE3F+ZYVXwECIqPOOdOZ5UOXT+OHO3ZSp0x6mhlaavS2aMhkSbT1Q5h4LCF1S7966DFRsi47v87B8A7MSiHP3ZuC1KsQygZOGN1Pc6qQiLtc6okrjxPxlFYLKQfx6wq3FGqUlgksV1BGGS4aC9ai8duLWqHjWGGTWZMTdGR0TceDMMsTurslt3HMAwzasx3u7WoHbb2oSNOJVGJTaycDpIpZWTVthgiJgoaUdPsOKzqJpVT4OpWHARdcbDHWXoob7VJ5UTBC46TUaikkXBNQ8rdA4WqZkM7a0KLtAkLd9iV/oyMhwqbL0HeLUyrlwKgyRetrvCJXbyi/U40CX3UpdBHr55pUmhEV46nmVfSqKqWtv0Kz+EBi460alZ8WvPAeDCjQfk75L53MCWVmxxX8aNV95ErmwFjOgsFGWGc1O2VNK+6GZD9IGGTJVXNELXNtQmDIEoaIMncrdomhbcDVe0AgLLyFapaHWGL9Ng6Va0c7lihyAlayr+kxvVsU1KneJV+Ezr+fQC4YFOt3eGGnCtoqOQsqLNbANsuphrRGINojvswYrqTKGymQm0z5dBJ7RUtq7yF19BAO3Xb9hipM6LaeaWO9oHTpJ9boKRl1b3ijNahakYUOTeX0r66x72gSlrVWFkxs8guPc4kUeHoOBD2UhOSKmalUvtUMbP3xuX7W3uM8vfEHcqZlOFjen4ZO68BQiLnu91alA6bdTAOTKduTBZfHKEkZNFsS0gBaZvyRRlEXBwTtQGVuW0RFzfp1GFT1mGTlQ4byg6boA4bLYs2QCXHPhw2Q8IjaR8ykGMNmSOtcBlUV7SOWTeHrZgHddhQumk8lg6bNn6OgcNG+uW1axy2YNz4be+kGbfdIg7bzOGDxfz6+/Ifga6MnU77annMLCbsZ+rggQN5aK/9PsJ/BzID9x2bM4et5BFIdDps1ZUjfUxkZ6VJ4bfdeSIIt6wa73wu77DRfDY7B0Ecs+4Om3fC3P/Jdr3DZjqPJ9c5Ooetz3+Q0m9E1zB8Yu8DOy8kDhzo33bV2S2AbRcTYj9P+w/ldQJQFQZInRoaKml7s5bDJ4nDRkMf/WkayLzDFoRH9nLYWjWOWZbBmIpxbbo7bKZzX93jXvR02EQ3h43ks9ljRBg22eGwtdt9OmzkPHtv3OGw0fvgGoeNChBVTbIpxZj9XC0Gu7UoHbYDBw4AAD78xa8PeSbMQuUXv/gFli5dWrs/SRKsWrUK33z9tdpjVq1ahSRJjsX0mHmItVsf+eD7hjwTZiFz4MCBWtvVj90C2HYxHmu33rvp/w15JsxCZjHYLWH6lQIWEFprvPjii/jABz6AV199FUuWLBn2lEaa/fv347TTTuP3qg/27duH1atX4+2338ayZcu6Hjs9PY00TWv3J0mCZrM5xzNk5itstwaD7Vb/2Pfq+eefx/ve9z5XoKCKXnYLYNvFeNhuDQbbrf5ZbHZrUSpsUkq8+93vBgAsWbKEvxR9wu9V/3QzHJZmsznSxoEZLdhuzQ5+r/rn3e9+d0/bxXaLGQS2W7OD36v+WSx2a/ZZxwzDMAzDMAzDMMwxhR02hmEYhmEYhmGYEWXROmyNRgO33XYbGo3GsKcy8vB71T/8XjHHEv589Q+/V/3D7xVzLOHPV//we9U/i+29WpRFRxiGYRiGYRiGYeYDi1ZhYxiGYRiGYRiGGXXYYWMYhmEYhmEYhhlR2GFjGIZhGIZhGIYZUdhhA3DHHXfgggsuwPj4eM9mx4uNe++9F+95z3vQbDaxfv167Ny5c9hTGkn+8z//E5dffjlOOeUUCCHwyCOPDHtKzAKH7VY9bLf6g+0Wc7xhu9Udtl29Wax2ix02AGma4sorr8SNN9447KmMFN/61rewdetW3HbbbXjuuedwzjnnYNOmTXjzzTeHPbWR49ChQzjnnHNw7733DnsqzCKB7VY1bLf6h+0Wc7xhu1UP267+WKx2i6tEEr7xjW9gy5YteOedd4Y9lZFg/fr1+OhHP4q//Mu/BABorXHaaafhpptuwrZt24Y8u9FFCIHvfOc7+I3f+I1hT4VZBLDdCmG7NTvYbjHHE7ZbnbDtGpzFZLdYYWMqSdMUzz77LC655BI3JqXEJZdcgh07dgxxZgzDMNWw3WIYZj7CtovpBTtsTCX/93//hyzLcNJJJwXjJ510El5//fUhzYphGKYetlsMw8xH2HYxvViwDtu2bdsghOj698ILLwx7mgzDMA62WwzDzDfYbjHMsSca9gSOFZ/97GdxzTXXdD3mjDPOOD6TmYesWLECSim88cYbwfgbb7yBVatWDWlWDLOwYbt1dLDdYpjjD9uto4dtF9OLBeuwrVy5EitXrhz2NOYtSZJg7dq1ePzxx10yp9Yajz/+ODZv3jzcyTHMAoXt1tHBdothjj9st44etl1MLxaswzYIr7zyCvbu3YtXXnkFWZZh165dAIA1a9ZgcnJyuJMbIlu3bsXVV1+N8847D+vWrcNXvvIVHDp0CNdee+2wpzZyHDx4EC+99JJ7/NOf/hS7du3C8uXLsXr16iHOjFmosN2qhu1W/7DdYo43bLfqYdvVH4vWbhnGXH311QZAx9+TTz457KkNnXvuucesXr3aJEli1q1bZ37wgx8Me0ojyZNPPln5Gbr66quHPTVmgcJ2qx62W/3Bdos53rDd6g7brt4sVrvFfdgYhmEYhmEYhmFGlAVbJZJhGIZhGIZhGGa+ww4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyIwg4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyIwg4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyIwg7bAuMXv/gFTjzxRLz88stHdZ2NGzdiy5YtczKno+WTn/wk7rrrrmFPg2GYYwjbLoZh5htst5jjhTDGmGFPgpk7tm7digMHDuC+++47quvs3bsXcRxjampqjmY2e3bv3o1f+ZVfwU9/+lMsXbp02NNhGOYYwLaLYZj5Btst5njBCtsC4vDhw7j//vtx3XXXHfW1li9fflSGI03To56D5UMf+hDOPPNMPPTQQ3N2TYZhRge2XQzDzDfYbjHHE3bYRphTTz0Vf/VXfxWMPf300xgfH8f//u//dhz/L//yL2g0GtiwYUMwvnHjRtx0003YsmULTjjhBJx00km47777cOjQIVx77bWYmprCmjVr8K//+q/BOVSe11pj+/btWLNmDRqNBlavXo077rgjOH7z5s3YsmULVqxYgU2bNgEAZmZm8JnPfAYnnngims0mPv7xj+OZZ54JzvvMZz6DW265BcuXL8eqVatw++23d7y2yy+/HH/7t3870PvHMMxwYNvlYdvFMPMDtlsetlujBztsI8z69euDL5oxBlu2bMHNN9+M008/veP473//+1i7dm3ltR588EGsWLECO3fuxE033YQbb7wRV155JS644AI899xzuPTSS3HVVVfh8OHDleffeuut+PKXv4wvfOELeP755/Hwww/jpJNO6niOJEnw1FNP4Wtf+xoA4JZbbsG3v/1tPPjgg3juueewZs0abNq0CXv37g3Om5iYwA9/+ENs374df/zHf4zHHnssuPa6deuwc+dOzMzM9PfmMQwzNNh2edh2Mcz8gO2Wh+3WCGKYkWX79u3mgx/8oHv84IMPmlWrVpkDBw5UHn/FFVeYT3/60x3jn/jEJ8zHP/5x97jdbpuJiQlz1VVXubGf//znBoDZsWOHO+cP/uAPjDHG7N+/3zQaDXPffffVzvUTn/iEOffcc4OxgwcPmjiOzTe/+U03lqapOeWUU8z27dsr52aMMR/96EfN5z//+WDsf/7nfwwA8/LLL9fOgWGY0YBtl4dtF8PMD9huedhujR6ssI0wGzZswJ49e3Dw4EEcOnQIf/iHf4g/+ZM/weTkZOXxR44cQbPZrNz34Q9/2G0rpfCud70LZ599thuzKzdvvvlmx7l79uzBzMwMLr744q7zLa80/eQnP0Gr1cLHPvYxNxbHMdatW4c9e/ZUzg0ATj755I55jI2NAUDtahTDMKMD2y4P2y6GmR+w3fKw3Ro9omFPgKln7dq1kFLiueeew3/8x39g5cqVuPbaa2uPX7FiBd5+++3KfXEcB4+FEMGYEAJAHjddxn5xezExMdHXcf3MrTwPK+evXLlyVs/BMMzxg22Xh20Xw8wP2G552G6NHqywjTDj4+M4++yz8e1vfxt33nkn7r77bkhZ/0927rnn4vnnn5/zefzSL/0SxsbG8Pjjjw903plnnuniqy2tVgvPPPMMPvCBDwx0rd27d+PUU0/FihUrBjqPYZjjD9suD9suhpkfsN3ysN0aPVhhG3E2bNiAe+65B1dccQU2btzY9dhNmzbh1ltvxdtvv40TTjhhzubQbDbx+c9/HrfccguSJMHHPvYxvPXWW/jRj37UtZztxMQEbrzxRnzuc5/D8uXLsXr1amzfvh2HDx8euAzu97//fVx66aVH+1IYhjlOsO3KYdvFMPMHtls5bLdGD3bYRpxzzjkHcRzjz//8z3see/bZZ+MjH/kI/u7v/g6/93u/N6fz+MIXvoAoivDFL34RP/vZz3DyySfj93//93ue9+Uvfxlaa1x11VU4cOAAzjvvPPzbv/3bQMZtenoajzzyCL773e8ezUtgGOY4wraLbRfDzDfYbrHdGlWEMcYMexJMPRdeeCE+8pGP4K677urr+EcffRSf+9znsHv37q5S/nziq1/9Kr7zne/g3//934c9FYZh+oRtF9suhplvsN1iuzWqsMI2gmit8dZbb+H+++/Hj3/8Y/zTP/1T3+f++q//On784x/jtddew2mnnXYMZ3n8iOMY99xzz7CnwTBMD9h2hbDtYpjRh+1WCNut0YQVthHke9/7Hi666CKcddZZeOCBB7B+/fphT4lhGKYnbLsYhplvsN1i5gPssDEMwzAMwzAMw4woCyPglmEYhmEYhmEYZgHCDhvDMAzDMAzDMMyIwg4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyIwg4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyIwg4bwzAMwzAMwzDMiMIOG8MwDMMwDMMwzIjCDhvDMAzDMAzDMMyI8v8BPKOtuzkUaaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "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\n",
    "    + np.abs(fields_meas.Ey) ** 2\n",
    "    + np.abs(fields_meas.Ez) ** 2\n",
    ")\n",
    "Emag_proj_noapprox = np.sqrt(\n",
    "    np.abs(fields_proj_noapprox.Ex) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ey) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ez) ** 2\n",
    ")\n",
    "Emag_proj_approx = np.sqrt(\n",
    "    np.abs(fields_proj_approx.Ex) ** 2\n",
    "    + np.abs(fields_proj_approx.Ey) ** 2\n",
    "    + np.abs(fields_proj_approx.Ez) ** 2\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, no far field approximation: {rmse(Emag_meas.values, Emag_proj_noapprox.values) * 100:.2f} %\"\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, with far field approximation: {rmse(Emag_meas.values, Emag_proj_approx.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without approximations, the projected fields match the measured ones extremely well! Instead, when approximations are used, the match is very poor. Thus, the accurate field projections can be extremely useful when the projection distance is not large compared to the structure size, but one still wants to avoid simulating all the empty space around the structure.\n",
    "\n",
    "We should also note that this more accurate version of field projections can be run on the server in exactly the same way as before: just supply the projection monitor with its `far_field_approx` field set to `False` into the simulation's list of `monitors` as before. Everything else remains exactly the same, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at y=0.00'}, xlabel='x', ylabel='z'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:29] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'aperture_4'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'74527ae2-b1d5-486b-8706-6666b06adf0e'</span>.    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:29]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'aperture_4'\u001b[0m with task_id \u001b[32m'74527ae2-b1d5-486b-8706-6666b06adf0e'\u001b[0m.    \u001b]8;id=93314;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=557925;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "275b662d32c54c1f8e883cac29962f06",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:31] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:31]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=843431;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=102101;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:38] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                      \u001b]8;id=235188;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=251288;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\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\">[12:23:41] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=425582;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=628766;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:44] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:44]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=651185;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=174855;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\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\">[12:23:52] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=285984;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=252405;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b1fdb6d992d41dcb264280780ab9926",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:56] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:56]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=34988;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=104813;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=546700;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=593861;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=586580;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=844014;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:57] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=63006;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=59970;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"data/aperture_4.hdf5\"</span>             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"data/aperture_4.hdf5\"\u001b[0m             \u001b]8;id=182762;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=467288;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d67c1755611447a9b79381022617555",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:58] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/aperture_4.hdf5                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:58]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/aperture_4.hdf5                                  \u001b]8;id=98151;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=57316;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\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\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation, computed on the server: 0.33 %\n",
      "\n",
      "Client-side field projection *without approximations* took 11.61 s\n",
      "Server-side field projection *without approximations* took 0.97 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "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(\n",
    "    sim_data4.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "print(\n",
    "    f\"Client-side field projection *without approximations* took {proj_time_new:.2f} s\"\n",
    ")\n",
    "print(f\"Server-side field projection *without approximations* took {server_time:.2f} s\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we get an excellent match, an even smaller error than the client-side computations, and over an order of magnitude speed-up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reciprocal space monitor <a name=\"kspace\"></a>\n",
    "\n",
    "In addition to [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionAngleMonitor) and [FieldProjectionCartesianMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionCartesianMonitor), one can also define the far field observation grid in reciprocal space using [FieldProjectionKSpaceMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.FieldProjectionKSpaceMonitor).\n",
    "\n",
    "To demonstrate, we'll compute the far field associated with a Gaussian beam propagating at an angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the Gaussian beam source positioned the same as the plane wave source above\n",
    "gaussian_beam = td.GaussianBeam(\n",
    "    center=(0, 0, -0.1 * wavelength),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    source_time=gaussian,\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_theta=np.pi / 6,  # angles are with respect to the source plane's normal axis\n",
    "    angle_phi=np.pi / 4,  # angles are with respect to the source plane's normal axis\n",
    "    waist_radius=2 * wavelength,\n",
    "    waist_distance=-wavelength * 4,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at y=0.00'}, xlabel='x', ylabel='z'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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, -0.1 * wavelength],\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:23:59] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'kspace_monitor'</span> with task_id                                        <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'b471f19a-d6ab-4354-b57d-f5dc9c419305'</span>.                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:23:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'kspace_monitor'\u001b[0m with task_id                                        \u001b]8;id=51493;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=981575;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         \u001b[32m'b471f19a-d6ab-4354-b57d-f5dc9c419305'\u001b[0m.                                           \u001b[2m             \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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae249d3f88e94be3bdb654571b239a50",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:01] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.036</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:01]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.036\u001b[0m                                                      \u001b]8;id=843717;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=981803;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=794923;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=489992;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:04] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:04]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=310867;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=325155;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:08] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=129857;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=8897;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\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\">[12:24:15] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:15]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=254031;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=868744;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f05012fc707e40e788242381bc786670",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:19] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:19]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=159383;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=555291;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=786648;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=219784;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=703653;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=625830;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:20] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:20]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=413046;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=316410;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"data/kspace_monitor.hdf5\"</span>         <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"data/kspace_monitor.hdf5\"\u001b[0m         \u001b]8;id=385299;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=279259;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\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\"></pre>\n"
      ],
      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd7f42af8dba417eb50c7696b61ae3f0",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:24:22] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/kspace_monitor.hdf5                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:24:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/kspace_monitor.hdf5                              \u001b]8;id=865846;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=405613;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data5 = web.run(sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\")\n"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/80/rm392zc51jz32327xblmmg2c0000gn/T/ipykernel_14312/2062883249.py:16: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
      "  im = ax.pcolormesh(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAHqCAYAAABY/wcsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAACX80lEQVR4nOzdd1hT5xcH8O/NYi/ZCIgLBw4UleJERdEqaGutW+tetFWqrfhztdZirbVWq+LW1oFbUatWUZy4oFirVdwLwc2GQHJ/f2RIGMq4cDPO53nug2TcnLQkOTnved+XYVmWBSGEEEIIBwR8B0AIIYQQ/UGJBSGEEEI4Q4kFIYQQQjhDiQUhhBBCOEOJBSGEEEI4Q4kFIYQQQjhDiQUhhBBCOEOJBSGEEEI4Q4kFIYQQQjhDiQUhhBBCOEOJBSFaLj09HZMmTUKNGjVgYmKC1q1b49KlS+rrWZbFrFmz4OzsDBMTEwQEBODWrVsa54iNjYW3tzc8PDywdu3aqn4KhBADQokFIVpu1KhROHr0KP744w9cvXoVXbt2RUBAAJ48eQIAWLBgAZYsWYKIiAhcuHABZmZmCAwMRE5OjvocI0eOxMyZM7FlyxaEh4fj0aNHfD0dQoieY2gTMkK0V3Z2NiwsLLBv3z706NFDfbmPjw+6d++OuXPnwsXFBV999RWmTJkCAEhNTYWjoyM2bNiA/v37AwBq1KiBmJgYODg4wN/fHxs3bkTDhg15eU6EEP1GFQtCtFh+fj5kMhmMjY01LjcxMcGZM2dw7949JCcnIyAgQH2dlZUVfH19ERsbq75s1qxZaNCgAaysrPDBBx9QUkEIqTSUWBCixSwsLODn54e5c+ciKSkJMpkMmzZtQmxsLJ4+fYrk5GQAgKOjo8b9HB0d1dcBiqGQly9f4vnz51i6dGmVPgdCiGGhxIIQLffHH3+AZVlUr14dRkZGWLJkCQYMGACBoGwvXzMzM9jY2FRSlIQQokCJBSFarnbt2jh58iQyMjLw6NEjXLx4EXl5eahVqxacnJwAACkpKRr3SUlJUV9HCCFViRILQnSEmZkZnJ2d8fr1axw5cgS9evVCzZo14eTkhOjoaPXt0tLScOHCBfj5+fEYLSHEUNGsEEK03JEjR8CyLOrVq4fbt29j6tSpMDY2xunTpyEWi/Hjjz9i/vz52LhxI2rWrImZM2fin3/+wfXr14s0fRJCSGUT8R0AIeTdUlNTERYWhsePH6NatWro06cP5s2bB7FYDAD4+uuvkZmZiTFjxuDNmzdo27YtDh8+TEkFIYQXVLEghBBCCGeox4IQQgghnKHEghBCCCGcocSCEEIIIZyhxIIQQgghnKHEghBCCCGcocSCEEIIIZyhxIIQQgghnKHEghBCCCGcocSCEEIIIZyhxIIQQgghnKHEghBCCCGcocSCEEIIIZyhxIIQQgghnKFt0wnRISzLIisrCzk5OZDL5epDJpOBYRgIBAKNw8TEBCYmJmAYhu/QCTFIOTk5kEqlnJ1PIpHA2NiYs/NVBkosCOFBXl4ekpOT8fTpU40jOTkZ6enpSE9PR0ZGRpF/Z2RkgGXZMj2WQCCAubk5zM3NYWFhAQsLC/W/zc3NYWVlBUdHRzg7O8PZ2RkuLi5wdnaGo6MjRCJ6iyCkvHJycmBiUQ3Iz+bsnE5OTrh3755WJxcMW9Z3KULIe0mlUty9exeJiYnq4+HDh+oE4vnz52AYBvb29kU+zK2srIpNAgr+29jYGEKhUKM6AaBIFSM7O7vYBKXgv1NTU4skOQXjU8Xm7u4OT09P9VGzZk2IxWKe/0sTor3S0tJgZWUFUcNPASEHrxVZHvKvb0dqaiosLS0rfr5KQl9HCKmA58+f48qVKxoJRGJiIu7duwcjIyPUrVsXnp6eqFu3Lnx8fNQf0s7OznBwcOD8g1koFEIoFKp/NzU1ha2tbZnPI5VKkZKSopFs3L9/H2fPnsX69etx69Yt5Ofno1atWhrJhqenJ7y9vVGtWjUunxYhOo0RG4MRSip8HlYgfP+NtAAlFoSUUkpKCuLj4xEXF6c+Hj16hFq1aqF+/frw9PREUFCQ+gO2evXq6kqCrpFIJHBzc4Obm1ux18vlcjx69Egjmdq3bx/+++8/PHjwAB4eHvDx8dE4ypPgEEJ0Dw2FEFKMjIwMnD17FhcuXFAnEU+ePIGnp6fGh2WzZs1gZWXFd7ha5fXr10USsDt37sDd3V393+2DDz6An58fTE1N+Q6XkEqjGgqRNBvOTcVCJoX07/VaPxRCiQUheJtIxMTEICYmBpcuXYK7uzv8/Pw0kghtfjFrszdv3mgkG+fOnUNycjJ8fX3h7+8Pf39/SjSI3lElFkY+ozhLLHLj1lBiQYg2KpxIXL58GW5ubuoPuQ4dOqBGjRp8h6m3WJbF/fv31f/9T5w4QYkG0TuUWBCi5+7du4f9+/dj//79iImJoURCi7Asi3v37mkkGs+ePUOnTp0QHByMoKAguLq68h0mIWWiSiyMW4wFI+IgsciXIufySkosCOGLXC7HxYsXsX//fkRFReHGjRvo0KEDgoOD0bNnT9SqVYvvEEkJWJbFrVu3cODAAURFReHMmTNo0qQJgoKCEBwcjObNm9OiX0TrqRILE9/xYERGFT4fm5+L7AsrKLEgpCrl5OTgr7/+QlRUFA4cOIDc3Fx8+OGHCA4ORrdu3ajRUke9fPkShw4dwv79+3Ho0CFYWlqiZ8+eCA4ORkBAACSSin8bJIRrlFgQoqNkMhlOnjyJzZs3Y+fOnbCzs0OvXr0QHByMNm3a0CJOekYqleLkyZPYv38/9u3bh/T0dHz66acYPHgwWrdurbNTfIn+USUWZn4hnCUWmbG/UWJBSGVgWRb//PMPNm3ahK1bt0IqlaJ///4YPHgwWrZsSWVyAyGXy3Hu3Dls3rwZ27Ztg6WlJQYNGoRBgwahYcOGfIdHDJwqsTBv8yVniUXG2V+1PrGg1J7olIcPH2L+/Plo0qQJWrdujadPn2L16tV48uQJlixZglatWlFSYUAEAgHatm2LFStWIDk5GUuWLMGtW7fg4+OD5s2bY9GiRUhKSuI7TEIMCiUWROvl5eVh165d6NKlC2rXro2TJ09i2rRpSElJwaZNm9C9e3ca7iCQSCQIDg7G9u3bkZycjM8//xx//vknatSogR49emD//v2QyWR8h0kMECMQcnboAhoKIVrrwYMHWL16NdauXQtjY2OMHTsWn332GZycnPgOjeiQx48fY926dVi9ejUAYPTo0Rg5ciSqV6/Oc2RE36mGQiw7TOFsKCTt5EIaCiGkLFiWxbFjx9C7d2/UrVsX//77L9avX487d+5g2rRplFSQMnN1dcWsWbNw7949LFu2DBcuXECtWrXw6aef4vTp02Xehp4Q8m6UWBCtkJGRgWXLlqFhw4YYMGAAvLy8cPv2bezduxfdunWjTn9SYSKRCMHBwTh48CD+++8/uLu7Izg4GN7e3li9ejWys7P5DpHoKRoKIaQKvXz5EkuXLsXSpUtRs2ZNfPHFF/j0009hbGzMd2jEAGRlZWHLli1YsmQJUlJSMHnyZIwfP57WOyGcUA2FWHcOAyOq+Hsam5+DN9HhNBRCSHGePHmCr776CjVq1MCZM2ewfft2XLp0CUOHDqWkglQZU1NTjBo1CleuXMH69etx8OBBuLu7Y/r06Xj27Bnf4RGikyixIFXq1q1bGD16NGrXro27d+/i+PHjOHbsGDp37kzTRAlvGIbBhx9+iNOnT+PAgQNISEiAh4cHQkJC8ODBA77DIzqOYTgaCmF0YyiEEgtSJRISEtC/f380btwYUqkU8fHx2LNnD1q1asV3aIRoaNeuHf7880+cO3cOL168QL169TBs2DBcv36d79CIrhIKwXBwQEiJBSFITExE37590bp1a9jb2+PmzZvYuHEjrYpItJ63tzciIyPx77//wsjICD4+PhgyZAju3bvHd2iEaDVKLEilSEpKwtixY9G0aVPY2tri9u3bWLp0KW1NTnROnTp1sGrVKty4cQNCoRANGzbEl19+iefPn/MdGtERhjYrhBILwqk3b94gLCwMdevWxevXr3HlyhVERETAxcWF79AIqZAaNWpgw4YNuHTpEu7du4fatWvj22+/RXp6Ot+hEaJVKLEgnMjOzsZPP/2EWrVq4dKlSzh58iS2b98OT09PvkMjhFONGjVCVFQU/vzzTxw9ehS1a9fG0qVLkZuby3doREtRxYKQMpDL5diwYQPq1q2Lbdu2Yfv27Th27BhatGjBd2iEVKq2bdvi9OnTWLduHVatWoX69etj27ZttJInKUIgEHJ26AJKLEi5/f3332jbti1mz56Nn3/+GRcvXkRAQADfYRFSZRiGQc+ePZGQkIA5c+Zg0qRJ6Ny5M80gIQaNEgtSZq9fv0ZISAjatGmDTp064b///kO/fv1o2W1isIRCIYYNG4abN2+iWbNm8PHxwdSpU6n/ggAAGIGAo6EQ3XiP1Y0oiVaQy+VYv3496tWrh9u3b+PKlSv4/vvvYWpqyndohGgFS0tL/Pzzz7h06RIuXbqE+vXrIzIykoZHDBz1WBBSjPj4eLRp0wZz5szBypUrcejQIdStW5fvsAjRSo0aNcKJEyfw008/ITQ0FJ07d8a1a9f4DouQKkGJBXmn9PR0fP7552jbti06d+6M//77Dx999BEtv03IezAMg4EDB+LGjRto1qwZWrZsia+//pp2UTVAVLEgROn48eNo3Lgx/v33Xxr2IKScVMMjFy9exOnTp+Ht7Y1z587xHRapQpRYEIOXnp6O8ePHo1evXvj6668RHR1Nwx6EVFCjRo1w5swZjB49Gl26dMGUKVOoekH0EiUWRMPx48fRpEkT3LhxA1euXMGECRNotgchHBEKhZgyZQri4uJw5swZNGvWDLGxsXyHRSoZ7W5KDFJGRgYmTpyI4OBgfPXVV4iOjkatWrX4DosQvVS/fn2cPXsWI0aMQEBAAKZOnUrVCz3Gxc6m6h1OdQAlFgQnTpzQ6KUICQmhKgUhlUwoFOLrr7/G5cuXcerUKTRr1gznz5/nOyxCKow+PQxYfn4+/ve//yEoKAihoaE4ceIEateuzXdYhBiUBg0a4OzZsxg+fDg6d+6M8PBwyOVyvsMiHDK0BbJEfAdA+PHo0SMMHDgQr169woULF+Dl5cV3SIQYLJFIhG+++QYBAQHo168fYmJi8Pvvv8PR0ZHv0AgHuJrRQbNC9NCpU6cQFBQEFxcXMAyDvXv3alw/Z84c1K9fH2ZmZrCxsUFAQAAuXLigcRsPDw8wDKNxzJ8/X+M2q1evRo0aNdCsWbMi9+fCgQMH4O3tjXr16uHSpUuUVBCiJXx8fBAfHw8bGxt4e3vj+PHjVfr48+fPB8MwmDRpkvoyf3//Iu9Z48aN07hfVFQUPD09Ua9ePRw4cKBKYybahyoWZZCZmYmmTZtixIgR+Pjjj4tc7+npid9++w21atVCdnY2fvnlF3Tt2hW3b9+Gvb29+nbfffcdRo8erf7dwsJC/e+HDx9iwYIFiIyMxJMnTzB8+HDONjSSSqUICwvD6tWrsWLFCgwaNIiT8xJCuGNpaYmtW7dizZo16mbqWbNmQVjJjXuXLl3CypUr0aRJkyLXjR49Gt99953694Lr2eTm5mLixIlYv349WJbFiBEj0LVrV0gkkkqNV5cYWsWCEosy6N69O7p3717i9QMHDtT4fdGiRVi7di3++ecfdO7cWX25hYUFnJycij1HWloarK2t0aRJEzg5OXHWKX7v3j3069cPeXl5uHz5Mjw9PTk5LyGEewzDYPTo0fjggw/w6aef4uTJk9i8eTOqV69eKY+XkZGBQYMGYfXq1fj++++LXG9qalrie1Zubi6EQiG8vb0BKIZ1cnNzKbEoQCBgIBBwsFoxF+eoAjQUUkmkUilWrVoFKysrNG3aVOO6+fPnw9bWFs2aNcNPP/2E/Px89XWNGjVCkyZNYGVlBS8vr2Jf5GW1d+9eNGvWDK1atUJsbCwlFYToiMaNG+Py5cuoWbMmvL29ceTIkUp5nIkTJ6JHjx4ICAgo9vrNmzfDzs4OjRo1QlhYGLKystTXWVpaYvjw4XB2doaLiwvGjx+vUYUlhocqFhw7cOAA+vfvj6ysLDg7O+Po0aOws7NTX//FF1+gefPmqFatGs6dO4ewsDA8ffoUixYtUt9m7dq1WLBgAUxNTWFiYlLuWORyOebOnYuff/4Z69atwyeffFKh50YIqXpmZmZYv349fv/9d/Tp0wfffvstQkNDOduvJzIyEvHx8bh06VKx1w8cOBA1atSAi4sL/vnnH3zzzTe4efMmdu/erb7N7NmzMWnSJAgEAkoqisEIGDAcVBu4OEdVoMSCYx07dkRCQgJevHiB1atX49NPP8WFCxfg4OAAAAgNDVXftkmTJpBIJBg7dizCw8NhZGSkvs7W1rZCcWRmZmLYsGGIj4/HuXPn0KhRowqdjxDCr6FDh6JBgwbo3bs3rl69ioiICBgbG1fonI8ePcKXX36Jo0ePlniuMWPGqP/duHFjODs7o3Pnzrhz547G9HQrK6sKxaLPVE2vXJxHF9BQCMfMzMxQp04dfPDBB1i7di1EIhHWrl1b4u19fX2Rn5+P+/fvcxbDgwcP0KZNG7x69QqXLl2ipIIQPdGyZUtcvnwZN27cQMeOHfH06dMKnS8uLg7Pnj1D8+bNIRKJIBKJcPLkSSxZsgQikQgymazIfXx9fQEAt2/frtBjE/1FiUUlk8vlyM3NLfH6hIQECAQCdUWjok6fPo2WLVuibdu2OHLkSIUrH4QQ7eLs7IyYmBjUrVtXnWiUV+fOnXH16lUkJCSojxYtWmDQoEFISEgodiZKQkKCOg5SOoyyebOih64MhVBiUQYZGRnqFx+gmGmRkJCAhw8fIjMzE9OnT8f58+fx4MEDxMXFYcSIEXjy5An69u0LAIiNjcXixYtx5coV3L17F5s3b8bkyZMxePBg2NjYVDi+1atXo1u3bpg7dy5+++03iMXiCp+TEKJ9jI2NsXHjRkyePBn+/v7YunVruc5jYWGBRo0aaRxmZmawtbVFo0aNcOfOHcydOxdxcXG4f/8+oqKiMHToULRv377YaalEu7xv7aXCdu/ejS5dusDe3h6Wlpbw8/MrV8Mw9ViUweXLl9GxY0f176p+iWHDhiEiIgI3btzAxo0b8eLFC9ja2qJly5Y4ffq0egEqIyMjREZGYs6cOcjNzUXNmjUxefJkjb6L8sjPz8fkyZMRGRmJQ4cOoX379hU6HyFE+zEMg6+++goNGzbEgAED8M8//2DevHmc7vMjkUhw7NgxLF68GJmZmXBzc0OfPn0wY8YMzh7DEDAMR82bZeyxeN/aS4WdOnUKXbp0wQ8//ABra2usX78eQUFBuHDhApo1a1b6OFmWZcsUKdEq2dnZ6N+/P+7cuYMDBw7Aw8OD75AIIVXsxo0b6NmzJ3x9fbF+/XpaQ0JLpKWlwcrKCnXHb4XQyPT9d3gPWW4Wbq0YgNTUVFhaWpbpvgzDYM+ePejdu3eZ7ufl5YV+/fph1qxZpb4PDYXosNevX6Nr16549eoVTp8+TUkFIQZKtQ37jRs3EBQUhPT0dL5DInpALpcjPT0d1apVK9P9KLHQUU+ePEG7du1ga2uLv/76i5MeDUKI7nJ0dERMTAxkMhk6deqE58+f8x0SURIwDGcHoKiEFDzeNUGgIhYuXIiMjAx8+umnZbofJRY66MaNG2jdujX8/Pywc+fOCi2iRQjRHxYWFjh48CBq166NNm3a4N69e3yHRPB2gSwuDgBwc3ODlZWV+ggPD+c85i1btuDbb7/F9u3byzxrkRILHXP+/Hm0adMGQ4cOxapVqyASUf8tIeQtIyMjbNmyBd26dUPr1q1x5coVvkMiHHv06BFSU1PVR1hYGKfnj4yMxKhRo7B9+/YSl3l/F/pU0iF//vkn+vXrh/DwcISEhPAdDiFESwkEAvz6669wdnZGhw4dsG/fPnTo0IHvsAwW10t6W1palrl5s7S2bt2KESNGIDIyEj169CjXOSix0BGRkZEYOXIk1q9fX+bxLkKI4WEYBmFhYXB0dESPHj0QGRmJnj178h2WQeJqd1O2jOfIyMjQWCFVtfZStWrV4O7ujrCwMDx58gS///47AMXwx7Bhw/Drr7/C19cXycnJAAATE5MyLdlOQyE6YNOmTRg1ahR27dpFSQUhpExGjBiB33//Hf369XvvAklEv1y+fBnNmjVTr0ERGhqKZs2aqaeOPn36FA8fPlTfftWqVcjPz8fEiRPh7OysPr788ssyPS5VLLTcxo0bMXHiROzdu7dcY12EEPLxxx9DKBRi4MCB6l1SSdVhBIqDi/OUhb+/P961VNWGDRs0fo+JiSl7UMWgxEKLrVu3Dl9++SX279+vseInIYSUVa9evbBt2zb069cPMpmMqp9ViHY3JVphw4YN+PLLL9ExwwzbOg3nOxxCiB44EBSCDlnmGDFiBHbt2sV3OERPUWKhhTZt2oTPP/8c+/fvhwuMAQDjGA9+gyKE6DTVe4g7TBAZGYmhQ4di3759/AZlIAQCcLK7KYfbwFQqGgrRMlu3bsW4ceOwb98++Pv7w5+9r35DUP2MYO/zFh8hRLcU/lKiev/YsmULBg4ciG3bttFskUrG9XRTbacj+Y9hiIqKwujRo7Fr1y507ty5xNtR9YIQUhrveq/o1asX/vjjD/Tv3x/Hjx+vuqCI3qPEQkucOXMGAwcOxB9//IHAwECN64qrUFByQQh5l+LeIwq/l3z88cdYvnw5PvroI/z9999VE5gBUm2bXuFDR5o3aShEC/z7778ICgrCokWL8NFHHxV7m4gCQyIqNDRCCCmspC8dJb1PDB06FCkpKejevTvOnj2L2rVrV15wBqrgBmIVwVJiQUrjwYMHCAwMRGhoKMaMGVOuc1CCQQipSBVzypQpSE5ORmBgIM6ePQtHR0fuAiMGh4ZCePTixQsEBgaid+/emDFjxntv/77EYRzjQUMkhBiY0rzu3/fewTAMfvrpJ3zwwQfo3r070tLSuAuQAFztbErNm+RdMjMz0bNnTzRu3BhLliwp9dhZaaoSlFwQYhhK81ovbSVTIBBg3bp1cHBwwMcff4zc3NyKBUcMFiUWPMjLy0Pfvn1hamqKTZs2QSgUcv4YVL0gRH9V1utbIpFg586dSEtLw9ChQyGTyTh/DEPESeMmR1NWqwIlFlWMZVmMGzcOT58+xZ49e2BkZFTmc5Sll4ISDEL0R1lfz+XpuzI3N8fBgweRkJCAKVOmlPn+pChuFsfiZofUqkCJRRX79ddfcejQIRw8eLBM29AWVtY3DEouCNFtZX0NV6SZ297eHocOHcLvv/+OdevWlfs8xDDRrJAq9Ndff2HGjBk4fvw4XFxcqvzxC74x0QwSQrQfn18IatWqhR07diAoKAj16tVDmzZteItF19EmZKRSJCYmol+/foiIiECrVq04OWdFkgMaIiFEe1X09cnVF4dOnTphwYIF+Pjjj/Hw4UNOzmmIVNumc3HoAh0JU7elpqYiODgYY8aMweDBgzk9d0XfQCjBIER7cPF65LoaOWHCBPTu3Ru9e/dGVlYWp+cm+okSi0omk8kwYMAA1K5dGz/88APf4ZSIEgxC+KPNrz+GYbB06VJYWFhg+PDhYFmW75B0DjVvEk6FhYXh3r172LJlS6VMK+WaNr/BEaJvuH69VVbvlGoa6oULF7T6C5K2MrTpptS8WYn++OMPrFmzBhcuXKjQDJD3KW4fkYqiZcIJqTy6mLzb29sjKioKbdq0gZeXF3r37s13SERLUWJRSf7991+MHz8eu3btQt26dfkOp9wKvwFSokFI2VVFIlEVr80mTZpgw4YN+OyzzxAfH49atWpV+mPqA0ObFUKJRSXIzMxEv379MHny5CJboFeWyqhaFIemrBJSOlVZlajK12KfPn1w8uRJ9O/fH2fOnIFEIqmyx9ZVXPVHUI+FAfviiy9ga2uL2bNn8x1KpVKND+tiWZeQymAor4mffvoJ+fn5CAsL4zsUooWoYsGxTZs2Yd++fUhISIBIVLX/eauqalEc6skghkzfE4nCjIyMsG3bNvj4+MDf3x9BQUF8h6TVGIabxksaCjFAiYmJmDBhArZu3QpXV1e+w+EFJRjEkGhDQsHXa61u3bpYuXIlPvvsMyQkJMDNzY2XOHSBUMBAyEFiwerIUAglFhzJyclBv379MGbMGPTo0YO3OPisWhRUUgyUcBBdpA2vKW00YMAAHD9+HAMGDEBMTEyVV2mJdqIeC45MmTIFEolEK+Z4a/OHt6GMQRPdpwt/q9rwWv/111/x5s0bve8pqwiBsmJR0UNXmjcpveTA7t27sWnTJvz999/UIV0GNGxCtJE2JxLayNTUFNu3b0erVq3QsWNHBAQE8B2S1uFqKEROiYVhePbsGcaOHYsVK1agZs2afIejpi1DIqVBU1gJ33TltVKQNr1WGjZsiJ9//hkjRozA1atXK3VBQKL9GJYWfq+Qvn37Qi6XY+fOnVrXsauLb5Yl0aY3UaK76DVReViWRdeuXeHh4YHVq1fzHY5WSEtLg5WVFT5ccgxiE7MKny8vOxN/fhGA1NRUWFpachBh5aCKRQXs2LEDJ06cwLVr17QuqQB0q2rxPsU9D217YyXaRV/+9oujjX/7DMNgzZo1aNy4MT755JMqWxyQaB9KLMrp2bNnmDBhApYtWwZHR0e+wzFIlGwQFX1OInRJjRo1sHDhQowaNQr//vsvDYkoUY8FKZWJEyeiQ4cO+PTTT/kOhRRAe5voP0oitNvo0aOxc+dOfPXVV1izZg3f4WgFkQAQcbKOBQfBVAFKLMph+/btiImJ0dohEPIWVTV0HyUSuqXwkEi3bt34DolUMUosyujZs2eYOHEili9fDgcHB77DeS996rPgyrv+e1DSwQ/6Gy09XfgbdXd3x8KFCzF69GgaEgENhZD3mDhxIjp27Ii+ffvyHQqpBKX5gNOFN3ZtQkmDYRo1ahQNiSgJOEosZJRY6J/Dhw/j2LFjuHnzJt+hlAlVLbhVlv+W+pqE0N8TP3Tp74lhGKxatQoNGzbEiBEj0Lp1a75DIlWEEotSys3Nxeeff4558+bpxBAI0Q6V8QFc1g8XSgIIX2rUqIH//e9/mDhxIi5dumSwe4kIGQGEgop3XgoZ3ejeNMz/y+WwcOFCWFhYYOzYsXyHUi5UtdAf9P/RMOlStaKgr776Chs2bEBERARCQkL4DocXXPVYcHGOqqAb6Q/PHjx4gPDwcCxbtgxCoZDvcAghRGcYGRlhyZIlmDFjBp49e8Z3OKQKUGJRCpMnT8ann34KPz8/vkMhRG8ImZIPol+6deuGzp0745tvvuE7FF5wsbMpV1WPqkBDIe9x5MgRnDhxQucaNotDwyGkslV2UqA6v8zAdjjS1WGQgn755Rc0bNgQY8aMMbgvaTQUQtRUDZvff/89NWwSUoWokqF/3N3dMX36dEyYMAEymYzvcEglosTiHX7++WeYmZlh3LhxfIdCCCkGJR+65auvvkJmZiYiIiL4DqVKCRmGs0MX0FBICVJSUhAeHo7Dhw9TwyYhhVT1B7jqDVXGsrw8PuGGkZERFi9ejCFDhmDw4MEGsyInVwtkCWgoRLd9//336Ny5M9q0acN3KJzSh7Faol8kAgYSAXff0vStiqFvr9nu3bujSZMm+Omnn/gOhVQSqlgU4+7du1izZg3i4uL4DoUQnVJSEsD1h3vhx1FVMoj2YxgG8+fPR+fOnTFx4kQ4OzvzHVKlo+ZNgpkzZ2LAgAFo2LAh36EQwpvSTAetjPHftxWMoo+ruq68z4VoB19fXwQGBmLu3Ll8h1IlRAKGs6MsTp06haCgILi4uIBhGOzdu/e994mJiUHz5s1hZGSEOnXqYMOGDWV+vpRYFJKQkIA9e/Zgzpw5fIdSafSttEq0X2ma0sqbNJR0fn2gz6/VefPmYf369bh9+zbfoeitzMxMNG3aFMuWLSvV7e/du4cePXqgY8eOSEhIwKRJkzBq1CgcOXKkTI9LQyGFhIWFYfz48XB3d+c7FEK0kupD+10VAC4/2FXJRsG1K96uZ8EWue5dCsdsaOthaJP69etj0KBBmDFjBiIjI/kOp1LxNRTSvXt3dO/evdS3j4iIQM2aNfHzzz8DABo0aIAzZ87gl19+QWBgYKnPQ4lFATExMTh37hw2bdrEdyiE8KakhKEyqgAmxTzY2w97zZkgpVFwAS3qw9B+c+bMgaenJ+Lj49G8eXO+wzF4sbGxCAgI0LgsMDAQkyZNKtN5aChEiWVZTJs2DV9//TVsbW35DqfS6XOJlfBDNZRR3GEiLP6oyOMU7LnQx94LQ3iNurq6IiQkBGFhYXyHUqm4XtI7LS1N48jNzeUkzuTkZDg6Ompc5ujoiLS0NGRnZ5f6PJRYKEVFReH+/ftlzswI0XWlbcws2kxZ8d6IwowFApgJFYexgIGxgIGJUKA8yp6MFBcv0S7Tpk3DxYsXERMTw3colUbIcJRYKP9+3dzcYGVlpT7Cw8N5foaaaCgEimrFvHnz8PXXX8PMzIzvcAjRO8YCxXeYkvKCivQ6FExqpHLN85V2+MNQ9yDRBtWqVcMXX3yBefPmwd/fn+9wdMKjR49gaWmp/t3IyIiT8zo5OSElJUXjspSUFFhaWsLExKTU56GKBYDjx4/jzp07GDNmDN+hVClDKLWSiiupQlF0uENRWShYbVBVH8rLXCSApfJQnUt1qB6v7M9H+2eQGNpr84svvkBsbCwuX77MdyiVQsBFtULAqFfetLS01Di4Siz8/PwQHR2tcdnRo0fLvGkcJRYAwsPD8cUXX8Dc3JzvUAipMiX1FxQe+qgMVmJhoUOgcZiLBLASl38pfdWwSXEJENE+tra2GDNmjNaV9LnC17bpGRkZSEhIQEJCAgDFdNKEhAQ8fPgQgGIW5NChQ9W3HzduHO7evYuvv/4aN27cwPLly7F9+3ZMnjy5TI9r8EMhFy9exPnz57F9+3a+QyFEpxgLVNNONaefSkq4nCtWYsX3oWzluIVUrviZx7LqHozSDIEUP4OkbNNXCXdCQ0NRp04d3LhxA/Xr1+c7HL1w+fJldOzYUf17aGgoAGDYsGHYsGEDnj59qk4yAKBmzZo4ePAgJk+ejF9//RWurq5Ys2ZNmaaaAgDDsoY9B+ujjz5CrVq11PN2DdE4xoPvEEgVKvxBX3goQHV94cSg8O9ipvyJReHHrCZRJAuFeyNUSUPBD3rVZcUlFipvNytjNG6jec6i5y7ruhiVwdCGQQoaNWoUZDIZ1q9fz3conEhLS4OVlRVm74+HsVnFK+I5mRn4Nqg5UlNTNXostI1BD4Vcv34dhw4dUmdxhJCKKzysUU0iRDWJEPZGb49qEgGqSQTq37l4PFXvhZhhYCwQqBtG36e4mSP6soGZtpLJZJg5cyZq1qwJExMT1K5dG3PnzsXUqVOxdetWPHz4ECzLYtasWXB2doaJiQkCAgJw69YtvkMvF6GAq+EQvp9J6Rj0UMiPP/6IIUOGoHr16nyHQkiVK22lonAFQlWpUF1e+CcX7MwlAACZcppHnlzxM7tAKSFbVvSy0lDF+XYGiUEXbXnx448/YsWKFdi4cSO8vLxw+fJlDB8+HFZWVggODsbPP/8MFxcXLFmyBBs3bkTNmjUxc+ZMBAYG4vr16zA2Nub7KZB3MNjE4v79+9i2bRuuXr3Kdyi8i2Dv03CIAeD727e1teLDQPCOQOTKJEEulZX5/KrhlIz8osmHSsEhkXdRJVEylq3SqaiGMgxy7tw59OrVCz169AAAeHh4YOvWrbh48SLCwsLQtm1bmJubY8aMGejVqxcA4Pfff4ejoyP27t2L/v378xl+mdHupgZi8eLFCA4ORt26dfkOhRCtpJpFIWYY5fCC4jAXCTSOkoY+7IxEsDMSwcZMDBszcYVisXIwg5WDGapZGqGapRHMRQLYG4mUR/mGU96u4MloVGn4TsAMQevWrREdHY3ExEQAwJUrV3DmzBl0794dzZo1g4+PD549e6axvLSVlRV8fX0RGxvLV9jlxtesEL4YZMUiIyMD69evx6FDh/gOhZBK974PysJDIFU1JdOqhlWRy1hlWUCmrFjk5+Qrfmbnl/n8isRBkXAI8xWVCwEUP3O0vHKh76ZNm4a0tDTUr18fQqEQMpkM8+bNw6BBgwAAvXr1wunTp2FjY6NxP0dHRyQnJ/MRMikDg0ws/vjjD9SpU6fMi34QQt5STftUTfE0Nla8nYhMFD/FZoo+CaHydqrLBRx0oFlWtwAA5KblQrU0kEm6FAAgESiSkoJDIu9SXM8FJRGVa/v27di8eTO2bNkCLy8v9RbdLi4uGDZsGHx9fQEABw8exIQJE3iOtuIEHFUbBFSx0E4sy+K3337DlClTwGjhinuEVLb3TSMtPJ1UtbqlKoEo7+Zh72NTsxoAID9Hqr5MVbGQZuQpfmYqrstNk6K0hMzbJEgden7ZKheFzwdQ0lERU6dOxbRp09S9Eo0bN8aDBw8QHh6OYcOGwcXFBQCwcuVKjcQiJSUF3t7efIRcIVyt8KqNq8QWx+ASi5iYGKSkpOhc809lowZOUlFmjop9dsTKfgpjS0UtQVWpEKkqGsaKSgZTqHLBFmq0LA0LZ8XaADmvcyBWnl/4OgcAIMlVJCWpeWU7b3FrX1QFQ2ncBICsrCwICk0HFgqFkCtn/9SsWRMODg64ceMG4uLi4OPjg7S0NFy4cAHjx4/nI2RSBgaXWKxYsQIjRowo04YqhOiikhbCet+00vdVKsxFisvNjRRvHxLzijVmFmbrVRMAkJeZo75Mmp4FAMh9kw4AyHmt2MI560Xpt3IG3saull806ajqhMIQBQUFYd68eXB3d4eXlxf+/vtvLFq0CCNGjAAAMAyD0NBQzJo1C2FhYfj5558xc+ZMuLi4oHfv3vwGXw4ChoGAg2oDF+eoCgaVWCQnJ2Pfvn24du0a36EQovcsayiGNiQWikqGyEwx3VRVsRCINd9+WOW3Vbm07I2aKiITESxMFFUMoSrpSM8FUPbKRfG7phpe0uHh4YEHDx4UuXzChAlYtmwZcnJy8NVXXyEyMhK5ubkIDAzE8uXL4ejoWOI5ly5dipkzZ2LChAl49uwZXFxcMHbsWMyaNUt9m6+//hq3b9/G2rVr0aJFC7Rr1w6HDx/WyTUshOBmtlHFlpKrOga1pHd4eDhOnDiBv/76i+9QtBYNh+iP91UsCi98pdr7Q3W56tu9qlKh2hTMTDm0YaQc6jC2UbzRmyh/qi43slEkFOVNLFQVCwv3tx9QOS9TAQBZz98AALKfvQYAZKZkKi5/+baCoapmpBaTWGQoKxWqPonMAsMwqorFu5YBL3hfLmjzMMjz588hk71dV+Tff/9Fly5dcOLECfj7+2P8+PE4ePAgNmzYACsrK4SEhEAgEODs2bOcPL6vry+GDRumk02cqiW9l0RfhYm5RYXPl52Rji86N9b6Jb0NpmIhk8mwatUqg94ThBA+WXkqhjgEJopEg5Eov3kqp4RCrvjwYqWKhEKerUgW8tPSyvxYjICBmYOp5oUFEgxV0lSaKkbBWSKA4U0/tbe31/h9/vz5qF27Njp06IDU1FSsXbsWW7ZsQadOnQAA69evR4MGDXD+/Hl88MEHFX78cePG4ZdffsH48eN1tuFeUGDL84qeRxcYTGJx/PhxZGdnIygoiO9QCNFqhXsrVB/CqkqFqkKh+mlqp/gAN3NQfCMztrVS/lR8ozKyrvg3NQCQODpD4uisiO1FCgAgw1ox7CE2ew4AEJkoKhrpSRllOreqWpMjZ9UVG23YkEzbSKVSbNq0CaGhoWAYBnFxccjLy9NYyKp+/fpwd3dHbGwsJ4lFv379MGnSJFy6dAmtWrWq8PlI5TOYxGLTpk0YOHAgxGJuG80I0Talb9rUHPqo6qlsojrNFP8o9LiMTDG1lM1RJAei1JcAAPnrZ+V6nJIqF8DbpCE1r+xLiKsYSuUCAPbu3Ys3b97gs88+A6DoW5NIJLC2tta4HZcLWZmamuKTTz7B5s2bdTaxoOmmeigrKwu7d+/GyZMn+Q6FEJ1k76SoDJjYKWZTmSunlpo6KKoSpk62AAAz5U8AENsreiOEVorLBJaKZk6BmeI+XHwOq4ZXxMr+DVXfRsGprGmPSj+UopoRkyOXF6hcvL3eEJs3C1q7di26d++uXmeiqgwePBj9+/fHzz//DJFI9z62aFaIHoqKioKrqyuaNWvGdyhaj9az0H+FF8JSfQQX3qVUNSRiJqrcXnSpYz2N3wXKigUjVfRYCGwUU0xF9orKRf7zJzCycVD8O+leqR/HVJkUqZYLf5WdB3ORas0K1QZmpZs5UnCp74rS5sbNgh48eIBjx45h9+7d6sucnJwglUrx5s0bjapFSkoKnJycOHvsDh06QCwW49ixY+jWrRtn5yWVwyASi82bN2Pw4ME62/hDiLZRVSrMqysa+8yUP8WObgAAoX11AABjrahayE2s1BWKPGNlxUKkWoy74sxq1VI8XqFFl9IevoSFi6LakvY4vdTnK1hyFjJvZ4kY0rBHYevXr4eDg4N6R1IA8PHxgVgsRnR0NPr06QMAuHnzJh4+fMjplgkCgQADBw7E5s2bdTKxEHC0uZ2O9G7q/+6mz58/x5EjRzBw4EC+QyGkUhXemVM1rqu6vPA479vrFYdqt08TIQMnYyFMhAxsbYwhNhfDyNIIRpZGMLExVk8rrSwPMxXHM9Ycz1hzZFhUR4ZFdeQ5eCLPwRMC94aKw9QCkjpNIKnTpEznVz0HK7EQVmIhpHIW5iIG5qK3/w3KQ1t2Rn3y5AkGDx4MW1tbmJiYoHHjxrh8+bL6epZlMWvWLDg7O8PExAQBAQG4devWO88pl8uxfv16DBs2TGMowsrKCiNHjkRoaChOnDiBuLg4DB8+HH5+fpw0bhY0aNAg7N69GxkZZWvM1QaqWSFcHLpA7ysW27dvR6tWrVCzZk2+QyFEb6kqFSLXOgAA1kpRBs83V1QyshnF2hUZUjmQD2Tns4Byp9E8uWbjpLgCb56mHh6Kx1etiZGnWBPjzd2XMLVVDIWUpXIhY9kivRbavDLn69ev0aZNG3Ts2BGHDh2Cvb09bt26pbFL6IIFC7BkyRJs3LgRNWvWxMyZMxEYGIjr16+XuPjUsWPH8PDhQ/XKmAX98ssvEAgE6NOnj8YCWVxr0qQJatWqhX379ql3QSXaSe8Ti82bN2PIkCF8h0GI1lBVLYrrrVBt1iVRLoalXuzKUpEYmNgqmjZN7BUfVKYFmjW5du25YhlvR3PFY1czUfy0q6Zs2BQpGzaf34XEoz4AQHr/Rpkfp+Ay39ky1bAHdz0UVenHH3+Em5sb1q9fr76s4JcqlmWxePFizJgxA7169QIA/P7773B0dMTevXtL3EOpa9euKGktRWNjYyxbtgzLli3j8JkUxTAMBg0ahM2bN+tcYkHNm3rkzp07uHz5Mvbv3893KDqFGjhJeakrFVaKWQOvlZuQvspWVA5SMhVTPV9nKxo0s4pZoMpI+UFvZVT+tyfT6or1LlSrd6p+vr77Rr3+Rp5y59TsNznFnKGowk2vChVLQLhu3IyKikJgYCD69u2LkydPonr16pgwYQJGjx4NALh37x6Sk5M11p2wsrKCr68vYmNjtX5zxoEDB2LWrFl49uwZHBwc+A6n1LgaJtOGobbS0OvEYtu2bQgMDIStbeV9qyJEVxT+YCy4voW6UqGsXqh2JFXtGGpkpVqmW7HYlYlyESyxnaI5U+TkDoCbKaSFXX6iWPTKw0axHkWulSIxcLJQJDGq4j2TfAtit7oAAOntf8r8OBn5cvWiYNkyxc88tuA7ufZXL+7evYsVK1YgNDQU06dPx6VLl/DFF19AIpFg2LBh6rUlCu/jweW6E5XJ3d0dfn5+2LFjByZOnMh3OKQEep1Y7Nu3D2PHjuU7DEIMVkqmojJx55ViWONxmqI6kKysErxRVi5kyr4FSYH1J2yVQyBuNuXfiVg1ayX3jaLhLzft7eJYUmVsJumKskpZp5pqY6+FXC5HixYt8MMPPwAAmjVrhn///RcREREYNmwYz9Fxo3fv3oiKitKpxIKGQvREcnIy4uLiNKZGEWIICq/O967V+lT9BYVX4VRVKsTKLdElFsoNxpRLaBtVU1QshLaKIQdYKcrSMgvFT9UQCJf+TlJULnLUCYCiglHdUhGDJF+5x8jD6xC5KPoKZLeulvlxsmWsunKh6rXIlMk5Xbuisjg7O6Nhw4YalzVo0AC7du0CAPXaEikpKXB2dlbfJiUlBd7e3lUWZ0UEBwfjm2++QVpamlZvxFWQUMBAyMGMDi7OURX0NrE4cOAAWrVq9c6tewkhVePWC8ViVzeeKmZkPFL+npOlrFgokwWhMtExNlUkNG52ZnjwUlHt8Kpe/g8RU3trxeMpd0d9ffe1uiFV8lrxWCbKGLJlpVveu+B4d8F1Lfhc66JNmza4efOmxmWJiYmoUaMGAEUjp5OTE6Kjo9WJRFpaGi5cuIDx48dXdbjlUrt2bdStWxd//fUXPvnkE77DIcXQ28QiKioKwcHBfIehs6iBU/eVvGfI296KIluoKz/YBRLFrBBV5UKknJGhXjrbwlrxU7lct8xEMUskk1V8SKuaNe+9fruNOVf+e6ZIToyVsRopV890tFB8iRDbp6pvq9pfRLW9elkV37DJjcpYcXPy5Mlo3bo1fvjhB3z66ae4ePEiVq1ahVWrVgFQzKyYNGkSvv/+e9StW1c93dTFxQW9e/fmPJ7KEhQUhKioKJ1JLGgoRA9kZWXh2LFjCA8P5zsUQkgBd5SNmOnKhCMzVdHzkK/cBOxtxUKRyGRlKMZU3F0scPneKwBAi5rVyv34qp1WJWaKiknqg1RIzJQVi0zFY5V1gSwZW/Ylvi/jTZGVgOvVq4cbNxTTZXNycvDVV18hMjJSY22I91VgW7ZsiT179iAsLAzfffcdatasicWLF2tMz/z666+RmZmJMWPG4M2bN2jbti0OHz5c4hoW2ig4OBhBQUHIz8/Xib1DaFaIHoiOji52rJEQ8lZxOy4KlO9cQrFm5UJorJgVIjZTNFIypsphCSPFuhaskaL3IkM5fVQ1rfRJuqLvQTUEwqX7rxVDJHbKYRNL5WwRoZmiiiK//w8Eyj1FjG2fAgAyk1+V67FU/52yZXLOhjq8vLxw7Ngx9e8FPyAnT56MgwcPYseOHbCyskJISAg+/vhjnD179r3n7dmzJ3r27Fni9QzD4LvvvsN3331XsSfAI19fXwgEAsTGxqJdu3Z8h0MK0cvEYv/+/QgODqa9QQjRUm+Uwxk5rxVTHPOyFbM2GKFyYS5zRVUiL1dzrYJ6Hja49kSxW2nbOuWfRi5RNqWqptUCgFigamRVVE9S897ucJpTxhkgpUk6RCJRsRt1paamYu3atdiyZQs6deoEQLFPR4MGDXD+/HnOl8rWRUKhED179kRUVJROJBYMR0MhuvKZpnd7hcjlcuzfvx9BQUF8h0KIVhIAMBNqvvRVpVpGKAAjFECgPIRi1SFSHMYSCI0lYIyMwRgZgxUbgRUbQS42hlxsjOx8ObLz5UjNyUdqTj6S3+Qg+U0OHr/KwuNXWUh/na0eBuFKSoYUKRlSZEjlyJDKITe2gNzYAkIrWwitbJGfdA8iS0uILC0hsTCFxMK0zI8hUB4F91apqFu3bsHFxQW1atXCoEGD8PDhQwBAXFwc8vLyNBaxql+/Ptzd3REbG1vhx9UXqj4LXaCaFcLFoQv0rmIRFxeH7OxsnchiCTF02a9TALytWKjkF/pdrFyF85FybYu29ezxSJmguFmVf50LkbI5NfVBKoQS5SJhUs0m18rgACN8s2E16tWrh6dPn+Lbb79Fu3bt8O+//yI5ORkSiURjG3JAdxaxYlkWa9euhUwmw5AhQ2BqWvZErjS6du2KgQMHIjExEZ6enpXyGKR89C6xOH78ODp16gSxWMx3KIRovcLNYIzyAqbQFYyywqHalpwRKIYsIFC8heQrhwrylTM1s5XNmG+U00lzlQth5SgXpcpNfc7VU1BTbGwGsGaKvg/GWNH3ITBR/J52/YZ6VosqoSgvxRbqmg2bZVnfwh0m6Nu3LwDF5lq+vr6oUaMGtm/fDhOT8idKfGNZFgzDwMbGBkuXLsXFixexePFiWFhYqK/jirm5OVq3bo3jx49rfWIhADdbnuvKEIOuxFlqMTEx8Pf35zsMvVAZ0+EIKShfmq04cjI0DmlmGqSZacjNeIXcjFfIyZIqjzzkZOXhZYYULzOk8HK2RGpuPlJz8ysUh0AiVB+Ft5vPyJdzNvyhUvi1ZW1tDU9PT9y+fRtOTk6QSqV48+aNxm1SUlKK7cnQJqqNyvr06YPly5fj+vXrCAsLg1wur5T+AH9/f8TExHB+Xq4VHEKr6KEL9CqxyMvLw5kzZyixIKSUZGwFZzcwAsVRwnllLAsZy0IuUxyyfDlk+XJ1QlEZ5EIx5EIxWOXBSIzBSIwhkIjUh8hYApGxBEKJEELlzBculXV6YUZGBu7cuQNnZ2f4+PhALBYjOjpaff3Nmzfx8OFD+Pn5cR5rRcnlcnVCIRC8/Vto2LAhpk6diujoaPXOpyXtkFpeqsSC6/OSitGrxCI+Ph4SiQSNGjXiOxRCSBkwAuHb4ZUCWJkMrEymTkhUh1SmOHLzFUcjB3PkyVnklXP/DoGQgUDI4M2bnHcmBVytRzBlyhScPHkS9+/fx7lz5/DRRx9BKBRiwIABsLKywsiRIxEaGooTJ04gLi4Ow4cPh5+fn1bNCFElFAKBAAzDQCqVIioqCteuXVPfpmfPnujXrx/mzZuHjIwMzqsWvr6+SE1NLbLaqLZRLZDFxaEL9KrHIiYmBh06dNDImgkhmt611RarLF+of8pVvyvuJc/LV16u6KFg5Irf1at5Kl96YuWnr2pTMfX6GKq9SUSKJkxBMckE55SPoeoPeX3jwduekXJkCWfz3+CE7BWaM5ZoL6gGIQPks3KcYl/hFrIgAwt3mKA9qsEIxT+/x48fY8CAAXj58iXs7e3Rtm1bnD9/Hvb2ik3TfvnlFwgEAvTp00djgSxtonqfPXv2LC5duoTU1FSsXr0akydPhpeXFwBAIpFg3Lhx+PXXXxEZGYlRo0ZxGoORkRFat26NmJgY1K9fn9Nzk/LTq09g6q8gRDdJzKwgMbOCWHWYmGscIrEQIrEQQpFAcVTS1Lvix7UVVYocuRxJ8hzEy9JgD4nG/U6yr3Ef2QiEHXrDEZnIx2GU3KAaGRmJpKQk5Obm4vHjx4iMjETt2rXV1xsbG2PZsmV49eoVMjMzsXv3bl77K2TF7J+Sn5+PkJAQBAYG4vr160hISEBqairOnTuHZ88US6nL5XI4OTmhd+/e+P3339WXcUkX+iyEAu4OXaAjYb4f9VcQ8n6qBZ9UvQ9vD0VPBCuTg5XJIcuTQZYne9sbkZcPWV4+5MqDzc0Bm5sDJi8XTF4uhPI8COV5kAgZSIQMzCUimEtEsDWXwNZcAlMzxWFsJoaxmRgSi2qKw9wGEnObynmyLKs45DLFUUFSVo69+c/RQ2wHowJvnbmsHNfYdLSBDVwZEzgwRugEOyQjFynIfccZtY+qV0H1U5UECJULlxXsZXjw4AEOHDiA5cuXY9WqVdizZw+mTJmC+Ph4nDlzBsDbBZ26deuGW7duISMjg/OKsi70WQgYroZD+H4mpaM3iQX1VxCi+0xtXTQOYxsnGNs4wczKCGZWRjA2FcPYVAxrE8VhKhbAVCyAWMBALGBQy1IIRi4DU8FE4nlu0fsfyn+BOgIT1BJorsvwDLmQA3DF22miNowY5hDqTGLx/PlzhIWFYfv27RqXq5KAHTt2oHfv3pg1axb++usvAIo1g2QyGdq3b6++/fjx42FnZ4eTJ09CKpWqEwsrKyvUr19fo/+CK61atdKJPgtDojc9FtRfUTlol1Pd83Y9hbe7mL69vPivPOq1GKRy5U/FB2u+cpdSWY7iAzIvUzGTwyhLsaw2I83U+GkqVmzypdq/w9FSsW5EDTvFWhJZyo2+8nKsNR5fbKJYc0KWLy3183wXSdJV9b9ZqWK/EnV/iLJf5NXtl0XuV9JaFFfy05HM5mKkqHqR6zJZGYQAjArNjjGFEFkomqBo4zRuhmEQFRWF9PR09OrVS70h2YsXLzBhwgRcuHABffv2xd27d7Fr1y5MnjwZnTp1wpMnT9RVjfz8fDg4OKBx48Y4evQorly5gpYtWwIAHBwccO/ePdSqVYvz2FV9FidOnNDaPgsBR1NFdaV5U28+hc+dO4e2bdvyHQYhpJwsnNxh7VoH1q51YOXiBisXN1g7WMDawQIWNiawsDGBm50Z3OzM4GRtDCdrY9iYiGFjIoaJiIGJiAGTnwsmn9sqwRt5Hg5IX6C3yAEiRoDsYubnam8R/t3kcjlyc3NhZ2eHTz75BFevXsWlS5fU1587dw7Xrl1DXFwcFi5ciM2bN6Nhw4aYPn063rx5g0aNGmHdunUA3g6XODk54cmTJzhx4oT6PDVq1IBYLEZqaioqQ9u2bbV6uXNDmxWiN4lFXFwcfHx8+A6DEK2j6qMo+O+360woDqmchVTOQs4qDplUDplUjvycfOTn5CMvMwd5mTmQpmVBmpYFeWY65JnpkKW+hCz1JQTZqRBkp8JSIoSlRAhbEzFeZOWhuqUx0nLyUNfRHBk5eXBzVFQmqjmbQ2wsRDUXW5haWcLa1aPSnr88OxPy7Ezk50g1DrlMXuBQ9JIU/m8mY1k8keciAzKsyXuCebl3sSj/Hh4jB/FsGn6V3YcpI4Qcil6LgrIgg2kJs0L48PJl0QqNQCCAkZFi59r27dtDJpPh5MmT6uv37duHUaNGQSKRICwsDDVq1MClS5cwZcoU1KtXD6NHj8avv/6KvXv3QiaT4fHjx0hMTET9+vVx5coVPH78GIBiX5RBgwbBzs6uUp6bj48P4uLiKuXcpOz0YigkJSUFSUlJaNasGd+hEELKycHNCsDbqakAYGSiGFJxraboa6jvrBhqcVUOsTiaKT4UzZX7fAiUQzTyzDROYkrNk6OmwBSfG7khVzn1NlfO4rDsOaoxYvgwVrCACAIAj5GN2lAM+bxm85ABGRxhxEkcFTV69GgIBAKsXLkScrlcPWScnJyMr7/+GtHR0ejWrRvu37+P2NhY3L59G3Xq1IFMJsM333yD77//Hg0bNsSPP/6I4OBg9f4fn3/+OS5evIgJEyYgPDwc165dw+zZs+Hv74/FixereywaN24MCwuLIvufcMXHxwc3btxAZmYmzJRLumsTrmZ06MqsEL1ILOLi4lC3bl1YWlryHQohWqvgKpuF97eQsYoPAKnyw1OSo+hHUPVY5Cl7I6TpWYqfr98AAIQ2im/BYpvXyK/mDlH2K6SLrVHNRIga1ooP/zyZ5jd5c2NFsvDATDFlU9V3Ia/QEqDFy75yTv3v/ExFr0V+oeeW8fTthmfFhWDECODIGCFHuQJINstCDAFMIIQdo3gODWCOs3gNY1YIMRicxms4wkgrEgu5XA5zc3PUqFEDgObqmIsWLcI///yDvXv34vXr18jOzsa+fftw7tw51KlTB3379sUff/yBP/74Ax9++KH6fo8fP8bBgwfRrVs3bNy4EWfPnsXZs2excOFCtGvXDqtXr8bjx4/VH/JmZmbqtS0qg4uLC+zt7ZGQkIA2bdpU2uOUF1fDGLoyFKI3iQUNgxCi25rUsQXwdrEta2UDqJMyQQGA6haKf9e0UczAqGaiGGowYRXJyapVq7Fy8w7cf6gowdd3tsU3vdqja9O6AICc/HzMu3wNhx8+hTRfhpbW1hhr6ggb4fs3LSwu6VBd1gbVwOAVDuM5ZGDhBmO0hW2Znn9lEQgEOHv2rHqjLlXFIikpCdHR0ejVq5e6ybJLly5o0aIFTp48iY8++ggdO3ZEzZo1sXHjRtjY2KBFixZ4/PgxfvzxR7x8+RKdOnWCQCBAu3bt1DtKx8fHIyIiAv/73/8qrUJRGMMw6uEQbUwsDI3eJBa0TToxdKoPudIsJqmqIagqFKqf2crqgolyVohUWU3ITVU0REpepytu91LRhCexUmzjzZhZQihWfNib2yiHKUxV39YVZXOxso5rqaxYuCqTg5cZymqIrOILJ1V3dsS8b75ALUkeWAAbI3djwK+R2Ne/KzxtrTDnxGWcfPIMP7ZoArzKxZJH9/F9+gMssKul8d/hfcWTfiJnjduLGAbtYYv2BZKJSijAFOvy5cu4du0aevXqVeSDXCaTQSgUol69ejhz5gzGjx+vXu/BysoKN27cwLx58wAoZnWIRCIEBQVhx44duHjxIjp37ox169ZhxowZ+OSTT1C3bl2cP38eHTp0wLfffou6dRUJ27Nnz7Bz506sXLkSd+/eRZ8+fTBy5Miq+Q+gpM19FgyjOLg4jy7Qi8QiPj4ekyZN4jsMQkgFtK6lqlgofjcRK6oRVsotzlX9FMDbSoWNcgFMoXIb9p4tFNMN8x7dAgB8E9AKa6IvISH5JZzNTbDj2j3M82mEVvbVkJqbjm88amPYtSu4Ic1CfYnm+hTvIy3nviRcefbsGcaPH49jx47B2toaK1euxPTp09GzZ091QiEUCpGfnw8PDw9cvHhR3YMgk8lgZmaGJk2aYM+ePejWrZu6H6JPnz74/vvvcfr0aXTu3Bnt27fHgQMHEB8fjzt37uD333+Hu7u7Rix2dnZo2rQpxo8fj4EDB/IyLO3j44M9e/ZU+eOWhgAMBCVM9S7reXSBjrSClOz58+d49OgRNW5WIm2cd0/er/AMEM3L3j07RJonUxyZeZBm5iE3TYrcNCmk6TmQpucg52Uacl6mIfv5KzAmZpA9fwL2xWOwLx5DmPYUwrSnsGJyYcXkwsVcDBdzMerZmqKerSmaOlmgqZMFmrtao7mrNfxqVYNfrWrcP3+ZHHvibyArNx9epqaIv/MUeXI5mhqZIyctF3mZUjjLhLATiHE9NxMpOXnF/PdRHJkcVFMAbl5LqnUjDh48iDdv3uDJkyeIioqCs7MzZs6cCUBzpUyRSAR7e3tkZWXh4sWLAN72WYwcORJ//PEH7ty5o77P0aNHYWZmhsOHDyM+Ph4AYGlpCX9/f4wcORLu7u6Qy+Uay3wLBAK0adMG48aNg6WlJWQyGedLd7+Pj48Prl+/jqysrCp9XF2wbNkyeHh4wNjYGL6+vuq/g5IsXrwY9erVg4mJCdzc3DB58mTk5OSU+vF0vmKhaty0srLiOxRCSAU0d9b8litSztQ0UW5cZi5W/DRj8gAoNz9LVexJwaQqhmTynz/Bv/ceo8Pnc5GTlw8zsRirPw1AHZEYN16nQSwQwEKs+bZnLRDitXIztdIoaRGtqiIQCJCVlYWNGzciKCgI5ubmaNq0KUaOHInhw4dj//79CAoKgkwmA8MwYBgG/v7+2LJlC06ePImOHTuqqxOjRo3CihUr0LdvX/V00NjYWISGhsLc3Bw1a9bUeOzitkcv7npVklKVqlevDjs7O1y5ckXrtpfncyhk27ZtCA0NRUREBHx9fbF48WIEBgbi5s2bcHBwKHL7LVu2YNq0aVi3bh1at26NxMREfPbZZ2AYBosWLSrVY+p8YvH333+jefPmfIdBiNYruPJm4dkhb3stFNerFoGSKGdOqHotsl8rvrWIjFNhXt0eWcmvIJQoGx+Vu4iq2iBFMsV9LSwUO3aaWCiqEhZGig8lBzPFLaXKx+KoKABPVyecnjMGadm52BF9CZP3ncT6zr6KK1kWeZl5AN7OCpEDyGcBaYHHV/33UF3EdzJRGMMwuHr1KiZMmKC+rEuXLmjVqhWioqIQFBSk3s4cAJo0aYJWrVrh5MmTuHjxIlq1aoXc3FwYGRlh06ZN2LRpE9atW4e0tDRMmzYN48ePLzZ5eN+251xvi14WDMOgefPmiI+P17rEQrFXCDfnKatFixZh9OjRGD58OAAgIiICBw8exLp16zBt2rQitz937hzatGmDgQMHAgA8PDwwYMAAXLhwofRxlj1M7XLz5k00aNCA7zAIIeXkbilRHFZizUN5uaM4H47ifJjnvoJ57iuIXj9UH8xLxZH/+DbyH99GXsojMK+ewoll4WkswSRvT9SvZomN/9yGlVyAPJZFer5mdSJVng9rjrZvLzisVJnu3LkDV1dXdaX20qVL6N+/P44dO4bk5GSkpaWpP+RVQxLDhw+HjY0NZs2aBUCxpTkANGjQAPPmzcOxY8fw6NEjTJw4EQKBACzLavXGXsVp0KABEhMT+Q6j0qWlpWkcubnFrzYrlUoRFxeHgIAA9WUCgQABAQElrlTaunVrxMXFqYdL7t69iz///FNjuvH76HzF4ubNmwgMDOQ7DEK0Rmlmh6i+gcuhuX5F4dkhEuXsEGGaomIhlCg+gIViIQTi1wAApvCqPcoNwITKPTpE9or9RQSmihkl1UwUH4Y2Joo1Diq+7+i7yVkWeXI5GlhbQAQGF5JfoJ2lDXJy8vEkPxcv5fnwEJqon3tG/tvShbZVKlTq1KmDe/fu4bfffkNoaCiSk5MxcOBA9OvXD48ePdKoHKgqD82aNcPcuXPRpk0brFy5En379kW1atUgl8vBMAycnRUzXVSzQ/isPpSXp6cn9u7dy3cYRXA9FOLm5qZx+ezZszFnzpwit3/x4gVkMhkcHR01Lnd0dMSNGzeKfYyBAwfixYsXaNu2LViWRX5+PsaNG4fp06eXOk6dTywSExPV87MJIbpH8uqe4h8C5duRamlsZd8Dk6f8Npar2OhMnqpYlEv28qn6HHkvUgAA/1u/H53q1YBNbh4ypXnYFZ+IC0+eY1HjRhBlyNDN2hYrnz2BhVAEJi8fa9KTUVtkgtoiE6TmlW8spqqTD7lcDmNjY7Rv3x4xMTFYvnw52rdvjxo1auD48ePo06cPLCwsir2fl5cXVq1ahY0bN+Lvv/9GREREkSEPkUh3PxY8PT21smLB9ayQR48eacy8US3LzoWYmBj88MMPWL58OXx9fXH79m18+eWXmDt3rro5+H109y8IirXvX716pZ5LTQgpSnO3U2VzHaNZqVB9trytWEB5O2XlIkvRl8C8ZmBqa4KsF1kaS28Db3cNlUkVtzXJViQCrHJ5bYGVYjqpSPkzfE0k9h45jht37sPE2Bh+LZrhh/9NRb3aHupz5uTk4utv52Pbgb+QK5Wia5uWWDJpOBxtrYt9ri8ysvH59mNISc2EhZEYdS3Msbx9CzRRbmk+wswRbJ4c3z2+izxWjsYSc/QxdkC2jC2whkXBPUMK/+S/giEQCJCbmwtvb2/cuHEDQ4YMUV+XnJwMX19fpKenF0kuVBWITz/9FK1bt8b27dvx9OlTdaVCH3h6euL+/fvq/hF9ZWlpWaopvXZ2dhAKhUhJSdG4PCUlBU5OTsXeZ+bMmRgyZAhGjRoFQLEce2ZmJsaMGYP//e9/pdpBXKcTi8TERDg5OdFS3oTooJMxJzGme1u0bDwa+TIZZq3cig8/HYIrW5fAzMQYrFyG0J9W4dDZOGyeOQGWQhaTI7ah7+TZiP5uHHJfKRbpUi3WBQBzWzcBWjdB1jNFMpORokhusl8ohmMkjADjbapjvE11pCqX9i5vpYJPRkZGaNmyJf744w+cOHECHTt2BADs2bMHDg4OxVYsVIkFy7JwdXXFpEmTSvUhoUtcXFxgYmKCO3fuoGHDhnyH8xZHQyFlLXpIJBL4+PggOjoavXv3BqCoXEVHRyMkJKTY+2RlZRX5uyg4fbk0dD6xoGEQQt5PWOhd7W1zoeIfeaq+DLmqoqH4XTU7RN13kC4tUqlQ7fEhz1PuwZGjua+ISaqit0Jio1jESmam+NDbv+AbAABjpFipc83Mz1G9+2eIv3EH7Zp5ITUjExv2H8fGGRPQsbkX2Kw0rJo0BN7jvsPFWw/R1LbsU8wzcvM1nw+K9pfkaVQsCu+pgiLX8aVly5Zo06YNRowYgS1btuDBgwdIS0tDeHj4O++nSjD0LakAFM+pbt26SExM1K7EgkehoaEYNmwYWrRogVatWmHx4sXIzMxUzxIZOnQoqlevrv67CQoKwqJFi9CsWTP1UMjMmTMRFBRU6mnEOp9Y1KtXj+8wCCGl8OamopdCYqlc4ttMMTwhUK4r8UJZeTBPS4b0LouLV28jLz8frauJkXPnP+RlZqM6gOrW5jgZexUejWoDAHKVy4wDQPZLZYVCOS02R/kzN634rnld5uLigiVLlmDIkCEYPHgw0tLSMHv2bNSuXRssy+pk8yUXtLHPgs/ppv369cPz588xa9YsJCcnw9vbG4cPH1Y3dD58+FAjyZwxYwYYhsGMGTPw5MkT2NvbIygoSL30e2nofGLh6+vLdxiEaKWCs0M0+ywAVaWi8LoW6m/r6qkacvU51N4oPqRVlQpW1ZuQp7iTavdQaZriQz73jWL3UIlFyUtmy+UsvvnjT/h5usPLTfGGl5KaAYlICGtTY43b2pub4llG+VZXVFUqsguUHlQzYPIKVSVKu2R3Ve0JUhjLsrCzs8OuXbuQlJSEOnXq8BOIltHGxIJBmUcxSjxPeYSEhJQ49BETE6Pxu0gkwuzZszF79uxyPpoeJBYFG5cIIdoj/bGiAiFNV1QNxMpt0sVmikRBaKxorhOKRZh+6Bz+vZuE3cOD8PrGAwBA5pMXAMvideIjAIAsR5HQyHLzIH2TgfSHiqEV1QZpucopsYp/52r8zMwu/cqalYXrpfFVFQlTU1N1UqGaKmrIPD09cerUKb7DMGg6/Rd4//79IkvOEkLer3CPxdtv6cqLlZVRofL3t9/wCzQ6pis+yFnVypnKNS/yMhSzQowsJaWKZebhWETfeoSdn/WEs6WZ+nJ7cxNIZXKk5Uhhafz2XC+zc2BXqIpRWqrnUbBiUXg2SOGfin+jyGXayNCTCgCoWbMm7t27x3cYGgQMAwEHQ1NcnKMq6OxfYWZmJtLS0vRqqpQ2i2DvYxzjwXcYRItlKWdeSJVLZqu3XFdWEkQmircbkbFiiERoLMT8v//DiaQUrO3kC8uX6Xjz8m2/RA2pDCIBg6OXbiDA1QlyqQz30zORlJGNOqwYaY8Ut81TDr0U7KNQJTeZ+Ypkp2CzJtFvzs7OSE5Ohlwu15oGVQYcLZBV8VNUCZ1NLJ4+fQqxWAxbW1u+QyFEq8nYtz0Sqm/c75slUriCcU+WiTP5b/BYnoM0VoYxpi7wM1ZM8zaXK5Z+3p3zEkcyXyGTlaGhsRlCPWrB1ajkysIPf1/HoYdPsaRdc5iJRHiRrUgMzMUiGIuEsJCI8XEtN/x85QasJGKYQIAF/9xAY2tLNLa2UicPZfG2YqHZV6F4rkVnfhDd4+zsjPz8fLx8+RL29vZ8h2OQdDqxcHZ2NtjOZ0KqUoZcBntGAm+xBbZIkyGVs+oqgIxlcSjnJQ7lvMJYcxe4iSXYmvkMUxP/wwonT5ioKhbGircboUTxLXLHHUXvxIjjmls4T/f0RA8nR7AyFmPtXSFNlyL0zN/IY+VoaWmFL5xqICMpQ12pUG0mJlU2jxbXmJlN2YLBMDMzg6WlJZ4+fao1iYUA3GzMpR31l/fT+cSCEPJ+hfcPed8sEfWMCOU7WV2hGRoy5sWem2VZHMt5jd4mdmhhZAETIYMvLKtjxPNExGanoZNR8VXFk60UO1AKxYq58Uwxm5sYCQQIrVkLoTVrQaZcyEouLf/uIiXNACn479KsWUF5inZzdnbG06dP0aRJE75DAQD19vVcnEcXUGJBCCkyFFI4wRAy0Pi6JJWz6ipAiiwPqawMNQSK/TZUy4HXEhnjSlYGWokUC2KplgVXNaCpKheqTcwKJxaqplDVUuGq6a0yZaWk8MJWb5cjLzq8kc3VnuxEJ6gSC8IPnU0skpKSKLEgRAuks4pMwpLRfDuxEojwRs7/NM+CVJWK4vopClcqipsBQpUK3eDi4oKkpCS+w1Djc4EsPuhsYvH06VPafIyQMippSETVzFlS5QJg305FBZAnZ9/2L8gVPzPz5RAK5JAo3/3ylB/er1RbrytPpbpemMdoXP6+mFUKTw9VxVW4clHcbYlh0LaKBdfbpms7XekFKYKGQgjRDubKSkUGq9n7kM7KYCnQru8uUrliF1MZC+XBFjioIqEvtC2xMDTa9aovA0osCCm/slYuCk5ZBYB8lkWOshpgzghhDiGu52fCBmJI5QxyWDnuy3LQQmipnj2iOreQ0fz0Ljz19W1MhWMufqii8BLcqssLdlWUdnluoh+0LbGgWSE6IjU1FTY2NnyHQYhBkLJypONtv8QbNh8p8lwYM0LYC8TwFVnhVP5r2DJi2ArEiM57BQtGiIZCs3ecteq8Lykp7rZvf6/U0EglsLGxQWpqKt9hqNGsEB2Rnp4Oc/Pip78RQkqn5GmoKoornrK52CNPUV96Qv4KJ+RAI8YcH8IezWGJHIEcUXnPkQM53BhjDBI7Q8YyxVQsNB+hcMWi5FiLTwYKVyjelTQQw2Bubo709PT335BUCp1MLFiWRUZGBiwsLPgOhRCD4MqYYJLAA0DxCQLDMGgvqob2qKa+vLQJQ2UpOPxRUhXiXU2dlJjoLgsLC2RkZPAdhhrNCtEBOTk5kMlklFgQwpHClYu3l7/tvShp2e+3fRkF1rwo5lyq3oqiCYfi8pLGjwuvQFGeJIEYFgsLC62rWOhITsAJnUwsVH8wNBRCCFEpy/oTVKnQb+bm5sjLy0Nubi6MjIz4Dsfg6GRikZGRAZFIRH8whHCs8Idq8b0XxVccCl9f+MP7bQWjpMpFaWOk5krybqpqdkZGhlZ8TtBQiA5IT0+HhYWFznTIEkK4V9IU0tLM9CjNfYjuMjExgUAgQHp6Ou2AzQOdTCwyMjJoGISQKlBc70VJM0cKX1+0X0N1ruIrGoRwhWEYmJuba00Dp6FNNy3zehunTp1CUFAQXFxcwDAM9u7dq3F9RkYGQkJC4OrqChMTEzRs2BAREREat8nJycHEiRNha2sLc3Nz9OnTBykpKRq3iYqKgqenJ+rVq4cDBw5oXKeqWBBC9IfmKpglH0VXzyx8FL1P0cfSPIj+qUgD57Jly+Dh4QFjY2P4+vri4sWL6utu3ryJNm3awNXVFd9//32pzqcaCuHi0AVlTiwyMzPRtGlTLFu2rNjrQ0NDcfjwYWzatAn//fcfJk2ahJCQEERFRalvM3nyZOzfvx87duzAyZMnkZSUhI8//lh9fW5uLiZOnIjly5fjt99+w/jx4yGVStXXZ2VlwdTUtKyhE0LK6V0fwiUnAcV/8L9NDN794U9IRZiZmSEzM7PM99u2bRtCQ0Mxe/ZsxMfHo2nTpggMDMSzZ88AACEhIRg8eDD27duHffv24dy5c1yHrvPKPBTSvXt3dO/evcTrz507h2HDhsHf3x8AMGbMGKxcuRIXL15EcHAwUlNTsXbtWmzZsgWdOnUCAKxfvx4NGjTA+fPn8cEHHyA3NxdCoRDe3t6KIEUi5ObmQiKRAABkMhlEIp0cxSFE55X0Db+kqaolq9jXr9ImI1SRMExCoRByeeHJyu+3aNEijB49GsOHDwcARERE4ODBg1i3bh2mTZuG169fw8fHB02aNIGLiwvevHnz3nMy4Ga6qY4ULLhferx169aIiorCkydPwLIsTpw4gcTERHTt2hUAEBcXh7y8PAQEBKjvU79+fbi7uyM2NhYAYGlpieHDh8PZ2RkuLi4YP368xtCHXC6HQKArq6YTQgipagKBoMyJhVQqRVxcnMbnk0AgQEBAgPrz6bvvvkNAQABMTU0hEAgQGBj4/lgYhrNDF3D+tX/p0qUYM2YMXF1dIRKJIBAIsHr1arRv3x4AkJycDIlEAmtra437OTo6Ijk5Wf377NmzMWnSJAgEgiL9FJRYEKJ9SlvJeHv7yiklUIWCAOVLLF68eAGZTAZHR0eNyx0dHXHjxg0AwIcffojnz58jLS0N9vb2nMWrTyolsTh//jyioqJQo0YNnDp1ChMnToSLi4tGFlgaVlZWxV5OiQU/Itj7fIdASjCO8eA7BEK0ytWrV/H48eNKObeRkVGZkgqGURwVpSMFC24Ti+zsbEyfPh179uxBjx49AABNmjRBQkICFi5ciICAADg5OUEqleLNmzcaVYuUlBQ4OTmV6nFYavQiRGdQBYHw5fbt22W6vZ2dHYRCYZFZimX5fCoOw7JgOPjc4uIcVYHTxCIvLw95eXlFqgkFm2h8fHwgFosRHR2NPn36AFBM33n48CH8/PxK9TgCgYCSCx7Qt2JCiK5o0qQJOnToUKb7SCQS+Pj4IDo6Gr179wagqJBHR0cjJCSkEqLUT2VOLDIyMjSywHv37iEhIQHVqlWDu7s7OnTogKlTp8LExAQ1atTAyZMn8fvvv2PRokUAFMMbI0eORGhoKKpVqwZLS0t8/vnn8PPzwwcffFCqGMozdkYIIcRwlHfIPDQ0FMOGDUOLFi3QqlUrLF68GJmZmepZIuXCyhVHRXFxjipQ5sTi8uXL6Nixo/r30NBQAMCwYcOwYcMGREZGIiwsDIMGDcKrV69Qo0YNzJs3D+PGjVPf55dffoFAIECfPn2Qm5uLwMBALF++vNQxUGJBCCHkXcqbWPTr1w/Pnz/HrFmzkJycDG9vbxw+fLhIQ2dZMKwcDAdJARfnqAplTiz8/f3fOQzh5OSE9evXv/McxsbGWLZsWYmLbL0PJRaEEELepSJN/iEhITT0UQE6ucqUkZERcnJy+A6DEEKIlsrJyVEvqsg7GgrRftq0uQwhhBDtk5GRoT17SrGs4uDiPDpAJxeDqMjmMoQQQvQfbVbJH52tWFBiQQghpDh5eXnIzc2Fubk536Eo0FCI9rOwsEB2djZkMhmEQiHf4RBCCNEiqqFybalYKBbI4mJWCA2FVBpVFkp9FoQQQgpTVbRNTU15jsQw6WTFQpVYpKenl7ifCCGEEMOUnp4Oc3Nz7dlTysCGQrTkv3rZCIVCmJqaUsWCEEJIEVo1I8QA6WTFAqAGTkIIIcVTVSy0hoFVLHQ2sbCwsEBaWhrfYRBCCNEyWjfV1MASC50cCgEABwcHPH/+nO8wCCGEaJlnz57BwcGB7zAMls5WLJydnZGUlMR3GIQQQrRMUlISnJ2d+Q7jLVYOcLG/lY5ULHQ6sXj69CnfYRBCCNEyT58+1arEwtB2N9XZoRBKLAghhBRH2xILQ6OziYWLiwslFoQQQopISkqCi4sL32G8pWre5OLQATQUQgghRK9oXcWCdjfVDZRYEEIIKUwmkyElJUW7EgueLVu2DB4eHjA2Noavry8uXrz4ztu/efMGEydOhLOzM4yMjODp6Yk///yz1I+n0xWLN2/eIDs7GyYmJnyHQwghRAs8f/4ccrkcTk5OfIfyFo/rWGzbtg2hoaGIiIiAr68vFi9ejMDAQNy8ebPYKblSqRRdunSBg4MDdu7cierVq+PBgwewtrYu9WPqbGJhZ2cHkUiEp0+folatWnyHo/fGMR58h0AIIe+VlJQEGxsbGBsb8x2KGp+7my5atAijR4/G8OHDAQARERE4ePAg1q1bh2nTphW5/bp16/Dq1SucO3cOYrEYAODh4VGmx9TZoRCBQAAXFxc8fvyY71AIIYRoicePH6N69ep8h1Gp0tLSNI7c3NxibyeVShEXF4eAgAD1ZQKBAAEBAYiNjS32PlFRUfDz88PEiRPh6OiIRo0a4YcffoBMJit1fDqbWABA3bp1cevWLb7DIISQ96KqX9W4desWPD09+Q5DE8ezQtzc3GBlZaU+wsPDi33YFy9eQCaTwdHRUeNyR0dHJCcnF3ufu3fvYufOnZDJZPjzzz8xc+ZM/Pzzz/j+++9L/XR1digEADw9PXHz5k2+wyCEEKIlEhMTtTex4OI8AB49egRLS0v1xUZGRhU/t5JcLoeDgwNWrVoFoVAIHx8fPHnyBD/99BNmz55dqnPodMXC09MTiYmJfIdBCCFES2hlYsExS0tLjaOkxMLOzg5CoRApKSkal6ekpJTY3Ors7AxPT08IhUL1ZQ0aNEBycjKkUmmp4qPEghBCiN7QysSCpwWyJBIJfHx8EB0drb5MLpcjOjoafn5+xd6nTZs2uH37NuQF9jZJTEyEs7MzJBJJqR5XpxOLevXq4fbt22VqKiGEEKKfMjIykJSUhHr16vEdigbVXiFcHGUVGhqK1atXY+PGjfjvv/8wfvx4ZGZmqmeJDB06FGFhYerbjx8/Hq9evcKXX36JxMREHDx4ED/88AMmTpxY6sfU6R6LGjVqAAAePHhAU04JIcTA3bp1CzY2NrC1teU7FK3Rr18/PH/+HLNmzUJycjK8vb1x+PBhdUPnw4cPIRC8rTG4ubnhyJEjmDx5Mpo0aYLq1avjyy+/xDfffFPqx9TpxEIkEqF27dpITEykxIIQQgycahiEYRi+Q9Ek52jb9HKeIyQkBCEhIcVeFxMTU+QyPz8/nD9/vlyPBej4UAhAfRaEEEIUtLK/wgDpdMUCoCmnhBBCFLQ2saBNyHRLo0aNcOXKFb7DIIQQwrOEhAQ0btyY7zCKMrBt03U+sfDx8UFCQgLNDCGEEAOWlZWF69evw8fHh+9QDJ7OJxb169eHXC6nPotKREsRE8INei1Vnn/++Qe2trZwdXXlO5Qi+JxuygedTyxEIhG8vb0RFxfHdyiEEEJ4EhcXBx8fH+2bEQLQUIgu8vHxocSCEEIMmCqxIPyjxIIQQojO0+rEgmU5qljQrJAq4+Pjg7///ltjbXNCCCGGITs7G9euXdPixEIGyDk4WN2YpKAXiUWDBg2Qn59PDZyEEGKA/vnnH9jY2MDNzY3vUAj0JLEQiURo2rQpDYcQQogBiouLQ/PmzbWzcRMAK5dzdugCvUgsAKBVq1a4cOEC32EQQgipYhcuXEDLli35DqNkXAyDqA4doDeJRYcOHXDy5Em+wyCEEFLFTp48CX9/f77DIEp6k1i0b98e//77L16+fMl3KHqFFvQhhFv0muLWvXv3kJSUBD8/P75DKRlVLHSTvb09GjZsiFOnTvEdCiGEkCoSExODVq1awczMjO9QSsTKZJwdukBvEgsA8Pf3L3ZveUIIIfopJiaGhkG0DCUWhBBCdBLLsrqRWMjl3B06QMR3AFxS9Vm8ePECdnZ2fIdDCCGkEt2/fx9Pnz7V7v4KQJkUcDCMoSOJhV5VLKjPghBCDIcu9FcYIr1KLAAaDiGEEENx4sQJ7R8GAcDKZZwdukAvE4vjx4/zHYZeoGlxhFQOem1VHMuyOHHiBDp06MB3KO/HctRfQdum86Nz5864efMm7t+/z3cohBBCKsk///yD169fo127dnyHQgrRu8TC2toa7du3x/79+/kOhRBCSCWJiopC165dYWxszHco70VDIXogODiYEgtCCNFj+/fvR3BwMN9hkGLoZWIRFBSEmJgYpKam8h0KIYQQjiUlJSE+Ph49evTgO5TSoSW9dV+tWrXg6emJI0eO8B0KIYQQjh08eBC+vr6wt7fnO5TSMbAFsvQysQAUwyFRUVF8h0EIIYRjUVFRNAyixfQ2sQgKCsKff/6J/Px8vkPRSTQdjpDKRa+x8snKysKxY8d0KrGgTcj0RKtWrSAWi3H27Fm+QyGEEMKRY8eOwdXVFfXr1+c7lNJTLeld4YOGQnglFArRo0cP7Nu3j+9QCCGEcCQqKgpBQUFgGIbvUEgJ9DaxAIA+ffpgx44dkOtIlkcIIaRkUqkUu3fvRp8+ffgOpWxoVoj+6Nq1K3JycmhTMkII0QOHDh2ClZUVWrduzXcoZcLK5ZwdukCvEwuxWIz+/ftj06ZNfIeiU6ipjJCqQa+1stm0aRMGDx5MwyBaTq8TCwAYNGgQdu7ciZycHL5DIYQQUk6pqanYv38/Bg0axHcoZUdDIfrF19cXdnZ2OHjwIN+hEEIIKaddu3ahUaNGujUbRIXlKKlgKbHQCgzDYNCgQdi8eTPfoRBCCCmnzZs362a1wgCJ+A6gKgwaNAiNGzfG69evYWNjw3c4hBBCyuDJkyc4deqUzvbLcdV4Sc2bWsTT0xNNmzbFzp07+Q5F61EzGSFVi15z77d161Z07NgRzs7OfIdSPrRAln4aPHiwzma7hBBiyFSzQYhuMJjEYsCAATh//jwSExP5DoUQQkgpXb58GXfu3MFHH33EdyjlR7NC9JO9vT0+/vhjrFq1iu9QCCGElNLKlSsxaNAgWFhY8B1KudEmZHps7NixWL9+Pa1pQQghOiA1NRVbtmzB2LFj+Q6FlIFBJRYdOnSAvb09du3axXcoWomayAjhB732ird582Y0atQIzZo14zuUipHLuTt0gEElFgzDYNy4cVi+fDnfoRBCCHkHlmWxYsUKqlboIINKLABg2LBhSEhIwN9//813KFqFvjERwi96DWo6deoUkpKSMGDAAL5DqThq3tRvNjY2GDx4MH777Te+QyGEEFKCpUuXYtSoUTAxMeE7lApj5TLODl1gcIkFAISEhGDLli14+fIl36EQQggp5NGjR9i/fz/Gjx/Pdyh6YdmyZfDw8ICxsTF8fX1x8eLFUt0vMjISDMOgd+/eZXo8g0wsGjduDF9fX6xZs4bvUAghhBQSERGB7t27w8PDg+9QOKFa0puLo6y2bduG0NBQzJ49G/Hx8WjatCkCAwPx7Nmzd97v/v37mDJlCtq1a1fmxzTIxAIAJk+ejCVLliA3N5fvUAghhCilp6dj+fLlmDRpEt+hcIaVs2Bl8oofcrbMj71o0SKMHj0aw4cPR8OGDREREQFTU1OsW7euxPvIZDIMGjQI3377LWrVqlXmxzTYxCIoKAjW1tb4/fff+Q6FEEKI0sqVK1G/fn106NCB71B0nlQqRVxcHAICAtSXCQQCBAQEIDY2tsT7fffdd3BwcMDIkSPL9bgGm1gIBAJ88803WLBgAWQ6sppZZaFudEK0g6G/FnNzc7Fo0SKEhYWBYRi+w+EMJ9UK5QEAaWlpGkdJlfcXL15AJpPB0dFR43JHR0ckJycXe58zZ85g7dq1WL16dbmfr8EmFoBi/5C8vDza9ZQQQrTAxo0bUa1aNfTs2ZPvUDjFdY+Fm5sbrKys1Ed4eDgncaanp2PIkCFYvXo17Ozsyn0eESfR6CixWIypU6ciPDwcn376qV5lyIQQokvy8/OxYMECzJkzBwKBQX/nfa9Hjx7B0tJS/buRkVGxt7Ozs4NQKERKSorG5SkpKXBycipy+zt37uD+/fsICgpSXyZXJjMikQg3b95E7dq13xufwf/fGzFiBJ4+fYrDhw/zHQovDL30Soi2MdTX5M6dOyGTydC/f3++Q+Ec10MhlpaWGkdJiYVEIoGPjw+io6PVl8nlckRHR8PPz6/I7evXr4+rV68iISFBfQQHB6Njx45ISEiAm5tbqZ6vQVcsAMDExASTJk3CDz/8gO7du/MdDiGEGByWZREeHo6pU6dCJNK/j6WCSUFFz1NWoaGhGDZsGFq0aIFWrVph8eLFyMzMxPDhwwEAQ4cORfXq1REeHg5jY2M0atRI4/7W1tYAUOTyd9G//4PlMGHCBMyfPx9nzpxB27Zt+Q6HEEIMyp9//onk5GT1hx3hTr9+/fD8+XPMmjULycnJ8Pb2xuHDh9UNnQ8fPuR86IlhWbbsE2P1UFhYGBISEnDo0CG+Q6kyhlpyJUQXRLD3+Q6hSrAsizZt2iA4OBjTpk3jOxxOpaWlwcrKCvcWfA4Lk+KHK8oiPTsXNb9eitTUVI0eC21j8D0WKl999RXOnTuHU6dO8R0KIYQYjAMHDuD27duYOHEi36FUGpblaFYIS9um6xQ7OztMnToV33zzDQyhiEPVCkK0myG8RmUyGcLCwjBz5kxYWFjwHQ7hCCUWBUyaNAn37t3Dvn37+A6FEEL03qZNm5CZmYmxY8fyHUql4npWiLajxKIAc3NzzJw5E9OnTzf41TgJIaQy5ebmYtasWZg7dy4kEgnf4VQqSiwM3OjRo5Gbm0t7iBBCSCVasWIFrK2tMXDgQL5DIRyjxKIQiUSC77//HrNnz0ZOTg7f4VQKQxi7JUQf6OtrNS0tDfPmzUN4eLhBrLLJylmOlvTWjf4//f8/Wg79+vWDnZ0dli1bxncohBCidxYuXIiGDRvSooR6ihbIKoZAIEB4eDgGDhyIkSNHqlceI4QQUjEpKSlYtGgRjh07ZjD7M8llcsg56I/g4hxVgSoWJejatSuaNWuGuXPn8h0Kp/S1tEqIvtK31+z//vc/BAYG4oMPPuA7lCpjaM2bVLEoAcMwWLJkCXx8fDB8+PAyrZNOCCGkqPPnz2Pr1q24du0a36GQSkQVi3do2LAhQkJCEBISYhCLZhFCSGWRyWSYOHEiwsLC4OHhwXc4VcrQKhaUWLzHrFmzcOvWLWzdupXvUCpM30qqhBgKfXjtrlq1CqmpqZgyZQrfoVQ5WtKbaLCwsMDPP/+MKVOmIC0tje9wCCFE5zx//hzTp0/H0qVLYWxszHc4pJJRYlEK/fr1Q/369fHtt9/yHQohhOicsLAwdOjQwWCnlxraUAg1b5YCwzBYunQpWrRogREjRsDLy4vvkAghRCdQwyY4SwoosdAzXl5emDhxIkJCQnD8+HGdm3+tD2O0+iSCvc93CKVCfzfaYxzjoTN/Nyqqhs1p06YZXMOmIaPEogxmz56N+vXr4/fff8ewYcP4DqfU6MOhcunam31ZlOe50d9b5dG15GLZsmVITU3F1KlT+Q6FV3K5HHI5BwtkcXCOqkCJRRlYWFggIiICQ4cORZcuXeDi4sJ3SKQK6NIbuTZ4338vSjwMw+3btzF9+nQcOHDA4Bs2DW0ohGFpgYYyGzp0KF69eoX9+/dr/ZAIvYmXDiUP2oH+XktH2/9e5XI5/P390bRpUyxdupTvcHiTlpYGKysrXAn5FBZGFd8aPj1Xiqa/bUdqaiosLS05iLByUMWiHH799Vd4eXnp3JCIodP2N2Py7v9HlHTojqVLl+LJkyc4dOgQ36FoBUXFQsbJeXQBJRblYGNjg1WrVmHIkCEICAhA9erV+Q6JKFHyoL8o6dANt27dwv/+9z8cPHgQZmZmfIejFVQLXHFxHl1AiUU59ezZE8HBwRgzZgwOHDig9UMi+oiSCKJS3N8CJRtVTy6XY8SIERgxYgQ6dOjAdziEJ5RYVMDixYvRqFEjbNiwAcOHD+c7nCL08Y2VkglSWgX/VvTttaCts0OWLFmCp0+f4vDhw3yHolVYOUfNm1Sx0H+qIZFBgwahS5cucHV15TskvaKNb5xEN1FFo/LdunULM2bMwKFDh2gIpDCuVs3UkR4LmhXCgc8++wxPnjzBkSNHIBBoxyrpuvqmSckE4QO9XiomLy8P7du3h6+vLxYvXsx3OFpDNSskfkQwzCXiCp8vQ5qH5uuiaFaIIViyZAl8fHwwf/58TJ8+ne9wdIa2vCkSQhWNipkxYwaysrIwf/58vkPRSnKZHHIOqg1cnKMqUGLBAUtLS2zbtg3t2rVD+/bt0bZtW17j0eY3REomiK7QhR4Nbei1OHz4MJYvX45Lly4Z/EJYRIESC440b94c8+fPx4ABA5CQkABbW1te4tDGN0C+3/gIqShdSDL4kJSUhCFDhmD58uWoX78+3+FoLUObbko9FhxiWRYff/wx8vPzERUVxcsUVG1506NkghgCQ369yWQydO7cGTVr1sT69eur/PF1garH4uLAbpz1WLTacph6LAwJwzBYt24dmjVrhsWLF2Py5MlV+vh8vslRIkEMUeG/e21JNKrC3LlzkZKSgoMHD/IdCtEylFhwzMbGBpGRkejcuTPatGmDVq1a8R1SpaFkghBNhjJkcvz4cSxcuBDnzp2jqaWlwMpYsLKKDw5wcY6qQIlFJfjggw8wZ84c9O/fH/Hx8bC2tq70x6yqNzFKJggpnapMMqqyifPZs2cYNGgQFi1ahCZNmlTJY+o6uZyjWSE60mOhHYsu6KGvvvoKDRo0wODBgyHjYPMZvkWw9ympIKScquL1UxVfLqRSKfr27Qt/f3+MHj260h+P6CZKLCqJQCDA5s2b1RvyVKbKfEOhhIIQ7uj66+nLL79EWloa1qxZQ/sjlQErZzk7dAENhVQia2trREVF4YMPPkDjxo0xaNAgvkMqNV1+8yNE26leX1x/KajMIZEVK1Zg165duHz5MvVVlJFcBsgFFU8K5DpS/KbEopLVq1cPkZGR6NOnDzw9PdGyZUu+Q3onSigIqTqVlWBw7cSJE5gyZQqOHDkCd3d3vsMhWo4SiyoQGBiI7777Dr1798alS5fg4uLC2bkr+oZEiQQh/ONy2irXVYu7d+/ik08+wZIlS3hfVVhXsTI5WAEHC2TpyJLe1GNRRSZPnowuXbrgo48+Qk5ODifnpKSCEP1U0dcmVxWQ9PR0BAcHY/DgwRg5ciQn5zREqummXBy6gBKLKsIwDCIiIiAQCDBmzBjwueCprjeQEWII+H6dyuVyDB48GM7Ozvj55595i4PoHkosqpCxsTF2796N48ePV3gXwPJ8I+H7jYoQUnblfd1WtGoRFhaG69evY9u2bRCJaNS8IuQylrNDF9BfSxVzdnbGgQMH0KFDBzg5OWH48OFV8riUUBCi2yLY+1XW5Pnrr79i7dq1OHv2LKpVq1Ylj6nPDK3HghILHnh7e2Pv3r3o2bMn7O3t0bNnzzLdvyxvLpRQEKI/yjqLpDyNnJGRkZgxYwaio6NRr169sgVICCix4E3Hjh2xceNG9O/fH0ePHoWfnx+n56eEghD9VVnTVI8ePYqRI0di165der3PUVWTsyzkHCxuJdeRzcipx4JHn3zyCX766Sf07NkT169fL9V9SvNGQkkFIYahNK/10iYfly9fRp8+fbBy5Up069atYoERTVzNCKEeC1Ia48ePR3JyMgIDA3Hu3Dm4ubmVeNv3vUFQQkGI4SlN9eJ9QyK3bt3Chx9+iDlz5mDw4MHcBkgMDiUWWmDOnDlITk5Gt27dcPr06WKbpd71pkEJBSHkfQlGScmF6ovNZ599htDQ0MoL0IDJZXLIGQ52N9WR5k0aCtECDMNg+fLlqFevHnr27ImMjIxS3Y+mjxJCCivL+8Lr16/RvXt3tGvXrsJT4In2WrZsGTw8PGBsbAxfX19cvHixxNuuXr0a7dq1g42NDWxsbBAQEPDO2xeHEgstIRQKsWXLFpiYmKBHjx7IzMxUX1fcNxBKKAgh71Lce0TB95I3b96ga9eucHd3x5o1ayAQ0MdBZeFz5c1t27YhNDQUs2fPRnx8PJo2bYrAwEA8e/as2NvHxMRgwIABOHHiBGJjY+Hm5oauXbviyZMnpX5MhuVzCUhSRFZWFnr06AGBQID9+/cj1KyhxvWUUBBCyqrwl5MFqf+ga9eusLOzw65du2BkZMRPYHouLS0NVlZWOOL7Acw4WGQsMz8fgRfOIzU1FZaWlqW6j6+vL1q2bInffvsNgGJFVTc3N3z++eeYNm3ae+8vk8lgY2OD3377DUOHDi3VY1KKqmVMTU2xf/9+5OXloXfv3siHYkyNhj0IIeVV8P1DCjm6d+8OGxsb7Ny5k5IKPSaVShEXF4eAgAD1ZQKBAAEBAYiNjS3VObKyspCXl1emhdIosdBC5ubmOHjwIDIzM/GwS338kvUf3yERQvTAgtR/kNjGHWZmZti9ezeMjY35DskgyGVyzg5AUQkpeOTm5hb7uC9evIBMJoOjo6PG5Y6OjkhOTi5V7N988w1cXFw0kpP3ocRCS1lYWODw4cPIzs5Gz549kZWVxXdIhBAdlpqaisDAQJibm2Pfvn0wMTHhOySDwbIsWDkHh7Jzwc3NDVZWVuojPDy8UuKeP38+IiMjsWfPnjIloZRYaDELCwscOnQIMpkMPXr0KPVsEUIIKejNmzfo0qULqlWrhr1791JSoeMePXqE1NRU9REWFlbs7ezs7CAUCpGSkqJxeUpKCpycnN75GAsXLsT8+fPx119/oUmTJmWKjxILLacaFhEIBOjWrRtev37Nd0iEEB3y7NkzdO7cGc7OzjT8wROudze1tLTUOErqk5FIJPDx8UF0dPTbWORyREdHv3MbiQULFmDu3Lk4fPgwWrRoUebnS4mFDjAzM8OBAwdga2uL9u3bIykpie+QCCE64N69e2jTpg3q1auHHTt2UKMmTxRTReUcHGWfxBkaGorVq1dj48aN+O+//zB+/HhkZmaqd9YeOnSoRsXjxx9/xMyZM7Fu3Tp4eHggOTkZycnJZaqYU2KhI0xMTNQbA7Vu3Ro3b97kOyRCiBZLSEhA69at0aNHD2zatAkSiYTvkAgP+vXrh4ULF2LWrFnw9vZGQkICDh8+rG7ofPjwIZ4+faq+/YoVKyCVSvHJJ5/A2dlZfSxcuLDUj0nrWOgYlmUxY8YMrFy5En/++SftQEgIKSImJga9e/fGtGnT8M0334BhGL5DMkiqdSyiGjaHmVBY4fNlymQIvh5fpnUs+EB7hegYhmEwb948ODo6onPnztixYwftREgIUdu5cyeGDRuG3377TV3uJvySy1jIwcG26TqyuykNhZRSeHg4WrZsCQsLCzg4OKB3794awxGvXr3C559/jnr16sHExATu7u744osvkJqaqnEehmGKHJGRkRq3+fbbb+Hq6oq2bdsiMTGx2Hi++OILrF69Gn369MGmTZu4f8KEEJ2zYsUKfPbZZ9i2bVuxScWKFSvQpEkTddOfn58fDh06pL5+1apV8Pf3h6WlJRiGwZs3b4qcw8PDo8h7WOF9RlavXo0aNWqgWbNmuHDhAufPk2g3qliU0smTJzFx4kS0bNkS+fn5mD59Orp27Yrr16/DzMwMSUlJSEpKwsKFC9GwYUM8ePAA48aNQ1JSEnbu3KlxrvXr12tUGaytrdX/Pnv2LA4ePIh9+/bhwoULCAkJwV9//VVsTP3794ednR0+/vhjpKSkIDQ0lEqehBgglmUxZ84cLF26FH/99Rdat25d7O1cXV0xf/581K1bFyzLYuPGjejVqxf+/vtveHl5ISsrC926dUO3bt1KnMIIAN999x1Gjx6t/t3CwkL974cPH2LBggWIjIzEkydPMHz4cFy/fp27J6uDWLkcLAfvzaxcN3Y3pcSilA4fPqzx+4YNG+Dg4IC4uDi0b98ejRo1wq5du9TX165dG/PmzcPgwYORn58PUYF14q2trUucQ/z69Wu4uLigSZMmyM/Px4YNG94ZV0BAAE6cOIEePXrg5s2b+O2336hJixADkpWVhZEjR+Ls2bM4ffo0vLy8SrxtUFCQxu/z5s3DihUrcP78eXh5eWHSpEkAFD0a72JhYVHie1haWhqsra3RpEkTODk5ITs7u0zPRx/RUAgpFdUQx7vWT1c12IgKbT4zceJE2NnZoVWrVli3bh0K9s8GBgYiJycHpqam6NatW6lWVPPx8cGlS5cQFxeHzp07l7hrHSFEvzx+/Bjt27fHo0ePcOnSpXcmFYXJZDJERkYiMzPznWsaFGf+/PmwtbVFs2bN8NNPPyE/P199XaNGjdCkSRNYWVnBy8sL33//fZnOTXQfVSzKQS6XY9KkSWjTpg0aNWpU7G1evHiBuXPnYsyYMRqXf/fdd+jUqRNMTU3x119/YcKECcjIyMAXX3wBABCLxTh8+DCePXsGa2vrUlcf3NzccPr0aYwYMQItW7bEvn374O3tXaHnSQjRXufPn0fv3r3Rs2dPLFu2rNRrVFy9ehV+fn7IycmBubk59uzZg4YNG77/jkpffPEFmjdvjmrVquHcuXMICwvD06dPsWjRIvVt1q5diwULFsDU1JRW+QQUy3FzULFg5bpRsaDppuUwfvx4HDp0CGfOnIGrq2uR69PS0tTL50ZFRUEsFpd4rlmzZmH9+vV49OgRJ7GxLIvw8HCEh4djw4YN6NOnDyfnJYRoj40bN2LChAmYP38+QkJCytRbJZVK8fDhQ6SmpmLnzp1Ys2YNTp48qZFcxMTEoGPHjnj9+rVGD1hx1q1bh7FjxyIjI4MW4CpENd10p5sXTAUVn26aJZfhk0fXtH66KQ2FlFFISAgOHDiAEydOFJtUpKeno1u3brCwsMCePXvemVQAgK+vLx4/flzi7nRlxTAMpk+fjk2bNmH48OGYM2cO5DrS8EMIeTeZTIYpU6Zg8uTJ2LdvHz7//PMyN2xLJBLUqVMHPj4+CA8PR9OmTfHrr7+WOyZfX1/k5+fj/v375T4H0S80FFJKLMvi888/x549exATE4OaNWsWuU1aWhoCAwNhZGSEqKioUq3Jn5CQABsbG84z/V69euHcuXMIDg7G1atXsXHjRpibm3P6GISQqvPmzRsMGDAADx48wIULF1C3bl1OziuXyyv0xSYhIQECgQAODg6cxKOP5DIWcg4GB+Q6MhRCiUUpTZw4EVu2bMG+fftgYWGh3sveysoKJiYmSEtLQ9euXZGVlYVNmzYhLS0NaWlpAAB7e3sIhULs378fKSkp+OCDD2BsbIyjR4/ihx9+wJQpUyol5kaNGuHSpUvo27cvWrVqhW3btqFx48aV8liEkMpz6dIl9O/fHw0aNEBsbCysrKzKdZ6wsDB0794d7u7uSE9Px5YtWxATE4MjR44AgHpfiNu3bwNQ9GNYWFjA3d0d1apVQ2xsLC5cuICOHTvCwsICsbGxmDx5MgYPHgwbGxvOnq++YWUsuOg60JUeCxoKKaUVK1YgNTUV/v7+Guunb9u2DQAQHx+PCxcu4OrVq6hTp47GbVT9E2KxGMuWLYOfnx+8vb2xcuVKLFq0CLNnz660uG1tbfHXX3+hb9++8PPzw+rVqzn5AyeEVD6WZbF48WL4+/tj7NixiIqKKndSASh2Oh06dCjq1auHzp0749KlSzhy5Ai6dOkCAIiIiECzZs3Ua1S0b98ezZo1Q1RUFADAyMgIkZGR6NChA7y8vDBv3jxMnjwZq1atqviTJXqDmjcNSHR0NAYPHgx/f3+sXLlSq5t/CDF0r169wvDhw5GQkICtW7eWuOgV0V6q5s2tDvU5a94c8OwGNW8S7dG5c2ckJCTg5cuXaN68OeLj4/kOiRBSjLNnz8Lb2xsCgQB///03JRVEp1BiYWAcHR1x+PBhjBo1Cu3atcOSJUtoaIQQLSGXyxEeHo6uXbti6tSp2L179zsX4SO6QcaynB26gJo3DZBAIMC0adPQrl079O/fHydOnMCaNWtga2vLd2iEGKzk5GQMGzYMd+7cwalTp+Dj48N3SIQjMlZxcHEeXUAVCwPWpk0bJCQkAAAaNmyIPXv28BsQIQaIZVls3rwZXl5esLe3R3x8PCUVRKdRxcLA2draYvfu3diyZQtGjhyJHTt2YOnSpVS9IKQKJCcnY9y4cYiNjcXatWvRu3dvvkMilYCrYQxdGQqhigUBwzAYNGgQrl27hszMTHh5eWHv3r18h0WI3mJZFlu2bIGXlxdMTU1x/fp1Sir0mGoohItDF1BiQdScnZ2xd+9eLFy4ECNGjMCgQYPw8uVLvsMiRK8kJyfj448/xuTJk7F69Wps2bKFKoREr1BiQTQwDIPBgwfj2rVrSE9Ph5eXF/bt28d3WIToPJZlsXXrVnh5ecHIyAjXrl3Dxx9/zHdYpArIOZoRwsWy4FWBEgtSLGdnZ+zbtw8//fQTPvvsM/Tp0wcPHz7kOyxCdNLt27fRo0cPfPnll1i1ahUiIyNhZ2fHd1ikisjA0VAI30+klCixICViGAZDhgzBjRs3YGlpiYYNG+KHH37gbCdWQvRdVlYWZs6ciSZNmsDDwwM3btxAnz59+A6LkEpFiQV5L0dHR6xfvx5//fUXduzYgcaNG6s3LSKEFMWyLPbu3YuGDRvi6NGjOH36NJYvX06LXRkoQ1sgixILUmqtW7fGpUuX8MUXX6Bfv340PEJIMVTDHqNHj8bMmTNx7tw5WpfCwNGsEELeQSQSISQkBImJiTQ8QkgBhYc9bt68iZEjR0IgoLdZYljoL56Ui4ODg8bwSP369bFp0ybI5XK+QyOkSuXn52PNmjXw9PSkYQ9SLKpYEFIGrVu3xuXLl/Hdd99hxowZaNasGQ4ePEgbmxG9x7Isdu3ahUaNGmHBggX45ZdfaNiDFIt6LAgpI6FQiCFDhuDmzZsYMWIEPvvsM3To0AGxsbF8h0ZIpThx4gQ++OADfP7555g8eTKuXbuGvn370rAHIaDEgnDIyMgIX375Je7cuYOOHTuia9eu6N27N65fv853aIRw4u+//0a3bt3w0UcfoXfv3rh16xbGjh0LsVjMd2hEi8k5GgaR60bBghILwj1LS0t8++23uH37Ntzc3ODj44Phw4fj1q1bfIdGSLlcu3YNAwYMQJs2bdC4cWPcuXMHYWFhMDMz4zs0ogNoKIQQjjg6OmLp0qW4du0aWJZF48aN0a9fP/VW7YRouwsXLqB3795o0aIFLC0tcfPmTfz000+0twch70CJBal0tWrVwoYNG3Dz5k04OjqidevW6N69O06fPs13aIQUwbIsjh49ik6dOiEgIAB169bFnTt3sHLlSri5ufEdHtFBNCuEkEpSo0YNLFmyBPfv34ePjw+CgoLQtm1bmkVCtIJcLsfu3bvRqlUrDBgwAB07dsSDBw/w008/wcXFhe/wCNEZlFiQKufg4IDvv/8eDx8+RFBQEEaOHAlvb29s3rwZUqmU7/CIgcnJycG6devg5eWFL7/8EoMHD8aDBw8wc+ZMWouCcEJRbeCix4LvZ1I6DEtfFQnPsrOzsWHDBvzyyy9IS0vD2LFjMXbsWPqWSCrVgwcPEBERgdWrV8PR0RFTpkzBoEGDIJFI+A6N6Im0tDRYWVlhhnEtGDMV/x6fw8rxfc5dpKamwtLSkoMIKwdVLAjvTExMMH78eNy4cQMbNmzA5cuXUbNmTfTv3x+nTp2iYRLCGblcjmPHjuHjjz+Gp6cnbt68iR07duDff//F8OHDKakghAOUWBCtIRAI0K1bNxw8eBDXrl1D9erV8dFHH8HLywu//vorXr9+zXeIREc9f/4cP/30Ezw9PTFw4EDUq1cPiYmJ2L17Nzp27AiGYfgOkegxQ5tuSkMhRKvl5ORg586diIiIQHx8PD755BMMHjwYnTp1gkgk4js8osXy8vLw119/4Y8//sDevXvRunVrjBs3Dr1796bKBKkSqqGQaUY1YcTBUEguK8f83Hs0FEJIRRgbG2Pw4ME4c+YMLl68CAcHB3z22Wdwc3PD5MmTERcXR0MlRI1lWcTGxiIkJAQuLi4YP348PDw8cOXKFRw/fhyffvopJRWEVDKqWBCdI5PJEBMTg02bNmHXrl1wcXHB4MGDMXDgQNSqVYvv8AgPbt68ic2bN2Pz5s149eoV+vbti8GDB6Nt27a0fwfhjapiMVXiwVnF4ifpfa2vWFBiQXRadnY29u/fj02bNuHw4cNo2bIlBgwYgODgYLi7u/MdHqlEd+/exb59+7BlyxZcvXoVPXv2xKBBg/Dhhx/CyMiI7/AIUScWoWLuEotFeZRYEFJlXrx4gR07dmD79u04c+YMvLy8EBwcjODgYDRv3py+ueo4mUyGixcvYv/+/YiKikJiYiI6dOiAfv364ZNPPoG1tTXfIRKigRILQvTI69evcejQIURFReHQoUMwMzNDUFAQgoKC0LlzZ5iYmPAdIimFzMxMHD16FFFRUTh48CDy8vLQo0cPBAUFITAwEFZWVnyHSEiJVInFl6IanCUWv+Y/oMSCEL5JpVKcPn0aUVFRiIqKQkpKCrp06YLOnTvD398fjRo1omqGlpDJZLhy5QpiYmIQHR2N6OhouLm5oVevXggKCkKbNm1oNhDRGarEIkTIXWLxm4wSC0K0CsuyuHbtGg4cOICYmBicOXMGRkZG6NChAzp27Ah/f394eXlRolFFZDIZ/vnnH8TExCAmJganTp2CTCZD+/bt4e/vj549e6JevXq0zgTRSZRYEGKA8vLyEBcXp/5gO3PmDIyNjdGhQwf4+/vDz88PjRs3pmZAjmRnZ+Pq1as4d+4cTpw4USSR8Pf3h7e3N1UliF5QJRbjBe6cJRYr5A8psSBElxRONC5cuIDMzEw0atQIPj4+6qNx48YwNjbmO1ytlp2djStXriAuLk59XLt2DdbW1vD19VVXiCiRIPpKlViMZdwh4SCxkLJyrGQpsSBEp7Esi3v37iEuLg7x8fHqD8i0tDR1suHt7Y369evD09MTrq6uBjeMIpPJ8PDhQ9y6dQs3btzA33//jbi4OFy/fh3VqlVTJ2PNmzeHj48P3N3daWiDGARKLAghpcKyLB48eKBOMq5cuYLExETcu3cPYrEYdevWRd26deHp6alx2NnZ6ewHKsuyePbsGRITE4sct2/fhlwuR61ateDp6Qlvb291MuHq6qqzz5mQilIlFqPgxllisQaPKLEgxFBIpVLcu3ev2A/fpKQkmJiYwNnZWX24uLgU+d3JyQmWlpZVtux0bm4uUlNTkZycjKdPn+Lp06dISkpS/7vg77m5uXB1dS2SMHl6esLDwwNisbhKYiZEV6gSi+Fwg4SDHTSkkGM9JRaEEABIT0/H48ePi/3ALnikp6cDAMRiMSwsLGBhYQFzc/Mi/zYxMYFAINA4AMW24AWPrKwsZGRkID09Henp6UX+nZeXBwCwtLR8Z8Lj7OwMV1dXmJmZ8fbfkBBdo0osBqE6Z4nFZjyhxIIQUnpZWVklJgEFf2ZnZ4NlWXUCIZPJwDCMRqLBMAxMTU3VyUhxCYq5uTksLS1pwTBCKkFOTg5q1qyJ5ORkzs7p5OSEe/fuaXXzOCUWhBBCSCXJycmBVCrl7HwSiUSrkwqAEgtCCCGEcMiw5sURQgghpFJRYkEIIYQQzlBiQQghhBDOUGJBCCGEEM5QYkEIIYQQzlBiQQghhBDOUGJBCCGEEM5QYkEIIYQQzlBiQQghhBDOUGJBCCGEEM5QYkGIlggPD0fLli1hYWEBBwcH9O7dGzdv3lRff//+fTAMU+yxY8cO9e0ePnyIHj16wNTUFA4ODpg6dSry8/M1Huvbb7+Fq6sr2rZti8TExCp7joQQ/UeJBSFa4uTJk5g4cSLOnz+Po0ePIi8vD127dkVmZiYAwM3Nrcg2699++y3Mzc3RvXt3AIBMJkOPHj0glUpx7tw5bNy4ERs2bMCsWbPUj3P27FkcPHgQ+/btw8CBAxESEsLL8yWE6CfahIwQLfX8+XM4ODjg5MmTaN++fbG3adasGZo3b461a9cCAA4dOoSePXsiKSkJjo6OAICIiAh88803eP78OSQSCQ4cOIA1a9Zgx44diI+Px+eff46LFy9W2fMihOg3qlgQoqVSU1MBANWqVSv2+ri4OCQkJGDkyJHqy2JjY9G4cWN1UgEAgYGBSEtLw7Vr19S/5+TkwNTUFN26dUN4eHglPgtCiKER8R0AIaQouVyOSZMmoU2bNmjUqFGxt1m7di0aNGiA1q1bqy9LTk7WSCoAqH9PTk4GAIjFYhw+fBjPnj2DtbU1JBJJJT0LQoghosSCEC00ceJE/Pvvvzhz5kyx12dnZ2PLli2YOXNmuR/DwcGh3PclhJCS0FAIIVomJCQEBw4cwIkTJ+Dq6lrsbXbu3ImsrCwMHTpU43InJyekpKRoXKb63cnJqXICJoSQAiixIERLsCyLkJAQ7NmzB8ePH0fNmjVLvO3atWsRHBwMe3t7jcv9/Pxw9epVPHv2TH3Z0aNHYWlpiYYNG1Za7IQQokKzQgjREhMmTMCWLVuwb98+1KtXT325lZUVTExM1L/fvn0bnp6e+PPPP9GtWzeNc8hkMnh7e8PFxQULFixAcnIyhgwZglGjRuGHH36osudCCDFclFgQoiUYhin28vXr1+Ozzz5T/z59+nRs2rQJ9+/fh0BQtOj44MEDjB8/HjExMTAzM8OwYcMwf/58iETUUkUIqXyUWBBCCCGEM9RjQQghhBDOUGJBCCGEEM5QYkEIIYQQzlBiQQghhBDOUGJBCCGEEM5QYkEIIYQQzlBiQQghhBDOUGJBCCGEEM5QYkEIIYQQzlBiQQghhBDOUGJBCE8aNGiANWvWvPd2/2/n3kKa7OM4gH9dOzRzq5YOxOyggRATT7AOCIV0uCgdBEFRpLig7mrZig4QGLKLmEUGeVNEdBEWdABLDCIbSgdoRis0U0YH6sae1ua2Fu7fxfv2vD2v863enibp9wNe/P/P73/Yc/X12X/P8PAwrFYrgsHguDUrV67E7t271dvc3zZt2gSv16v6vEQ0eTFYEE2AWCyGgYEBlJSUfLe2qakJDocDCxYs+P0b+5fDhw+jqakJoVAo7WsT0Z+JwYJoAgQCAQghYLPZ/rMuGo3izJkzcDqdadqZks1mQ2FhIS5cuDAh6xPRn4fBgiiNent7UVVVhcrKSiSTScybNw8nTpwYt/7GjRswGAxYunSp3DcyMoJt27YhKysLubm5Kb+qSCaT8Hg8WLhwIYxGI0pKSnD58mVFTTgcxpYtWzBjxgzk5ubi+PHjKb9Sqa6uxsWLF3/pcxPR1MFgQZQmg4ODWLFiBaqqqlBTU4MNGzagoaEBLpcLvb29Kcf4fD5UVFQo+txuN7q6unDt2jV0dnbizp07ePTokaLG4/Hg/PnzaG1txdOnT+FyubB161Z0dXXJNXv27EF3dzeuX7+OW7duwefzjZkHAOx2Ox48eIBPnz79+k0goslPEFFarFq1StTV1QkhhLDb7cLr9YrR0VFhNpvFyZMnU45xOByivr5ebofDYaHX60VbW5vcNzw8LIxGo9i1a5cQQoh4PC4yMzNFT0+PYi6n0yk2b94shBDi48ePQqfTiUuXLsnXP3z4IDIzM+V5vnr8+LEAIILB4P/+7EQ0dWgnOtgQTQXv3r3D7du30dPTg9HRUTx58gQejwcajQbTpk2DXq9POS4Wi2H69Olye3BwEIlEAkuWLJH7LBYLioqK5PaLFy8QjUaxevVqxVyJRAJlZWUAgKGhIXz+/Bl2u12+PnPmTMU8XxmNRgB/nfcgIvoeBguiNLh37x6SySRKS0vR39+PWCyG0tJSBINBSJKE5cuXpxyXnZ0NSZJ+aq1IJAIAaG9vR15enuKawWD46b2/f/8eAJCTk/PTY4lo6uEZC6I0SCQSAIB4PA6/34/58+fDYrGgtbUVNpsNxcXFKceVlZXh2bNncruwsBA6nQ7379+X+yRJwvPnz+X24sWLYTAY8PLlSyxatEjxl5+fDwAoKCiATqfDw4cP5XGhUEgxz1eBQABz585Fdnb2r90EIpoS+MSCKA2WLVsGrVaLxsZGRCIRFBQU4NSpU2hpacHdu3fHHbd27VocOHAAkiRh9uzZyMrKgtPphNvtxpw5c2C1WnHo0CFoNP/8j2AymbB37164XC4kk0lUVlYiFAqhu7sbZrMZtbW1MJlMqK2thdvthsVigdVqxZEjR6DRaJCRkaHYg8/nw5o1a37bvSGiyYXBgigN8vPzcfbsWezfvx9v376FVqtFNBpFR0fHmF99fKu4uBjl5eVoa2vDjh07AADHjh1DJBJBdXU1TCYTGhoaxrzA6ujRo8jJyYHH48HQ0BBmzZqF8vJyHDx4UK5pbm7Gzp07sX79epjNZuzbtw+vXr1SnOmIx+O4evUqOjo6VL4jRDRZZQghxERvgmgqsVgsOHfuHGpqan6ovr29HW63G4FAQPFkQm0jIyPIy8uD1+uVX8h1+vRpXLlyBZ2dnb9tXSKaXPjEgiiNXr9+DUmSvvvGzW+tW7cOAwMDePPmjXxGQg1+vx99fX2w2+0IhUJobGwEADgcDrlGp9OhpaVFtTWJaPLjEwuiNLp58yY2btyIcDg85ixDuvn9fmzfvh39/f3Q6/WoqKhAc3PzuAdJiYh+BIMFERERqYY/NyUiIiLVMFgQERGRahgsiIiISDUMFkRERKQaBgsiIiJSDYMFERERqYbBgoiIiFTDYEFERESqYbAgIiIi1TBYEBERkWoYLIiIiEg1XwDggSxYFAoyZAAAAABJRU5ErkJggg==\n",
      "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(\n",
    "    1, 1, tight_layout=True, figsize=(7, 5), subplot_kw={\"projection\": \"polar\"}\n",
    ")\n",
    "ax.grid(False)\n",
    "# im = ax.pcolormesh(np.squeeze(phi), np.squeeze(theta) * 180 / np.pi, np.abs(Etheta), cmap='RdBu', shading='auto')\n",
    "im = ax.pcolormesh(\n",
    "    np.squeeze(phi),\n",
    "    np.squeeze(theta) * 180 / np.pi,\n",
    "    np.abs(Etheta),\n",
    "    cmap=\"RdBu\",\n",
    "    shading=\"auto\",\n",
    ")\n",
    "fig.colorbar(im, ax=ax)\n",
    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
    "\n",
    "label_position = ax.get_rlabel_position()\n",
    "_ = ax.text(\n",
    "    np.radians(label_position - 8),\n",
    "    ax.get_rmax() / 1.3,\n",
    "    \"$\\\\theta$ (deg)\",\n",
    "    rotation=label_position,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Far Field for a Finite-Sized Structure\n",
    "The above examples are very useful when simulating thin structure with a large extent in the lateral direction, such as a metasurface or metalens. If the structure is small enough, we may instead want to enclose it in a closed surface, which now serves as an equivalent surface in the spirit of the equivalence principle, without having to worry about whether the fields decay at the monitor's edges or not. To learn more, see the [sphere radar cross section](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/Near2FarSphereRCS.html) and [plasmonic nanoparticle](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/PlasmonicNanoparticle.html) case studies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes <a name=\"notes\"></a>\n",
    "* Since field projections rely on the surface equivalence principle, we have assumed that the tangential near fields recorded on the near field monitor serve as equivalent sources which generate the correct far fields. However, this requires that the field strength decays nearly to zero near the edges of the near-field monitor, which may not always be the case. For example, if we had used a larger aperture compared to the full simulation size in the transverse direction, we may expect a degradation in accuracy of the field projections.\n",
    "Despite this limitation, the field projections are still remarkably accurate in realistic scenarios. For realistic case studies further demonstrating the accuracy of the field projections, see our [metalens](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/Metalens.html) and [zone plate](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/ZonePlateFieldProjection.html) case studies.\n",
    "* The field projections make use of the analytical homogeneous medium Green's function, which assumes that the fields are propagating in a homogeneous medium. Therefore, one should use PMLs / absorbers as boundary conditions in the part of the domain where fields are projected. For far field projections in the context of perdiodic boundary conditions, see the [diffraction efficiency example](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/GratingEfficiency.html) which demonstrates the use of a [DiffractionMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.DiffractionMonitor).\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": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "05f425767b13425eb47111d84a047186": {
      "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_5a80e02e94cc4a15bed61bfba7261d9a",
       "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\">% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m\n% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "09cced4348514e72853d94b0de9ee0fb": {
      "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_55b05d60514d437eb3e3e5ed528e101d",
       "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>\nComputing 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></pre>\n",
          "text/plain": "\r\u001b[2KComputing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nComputing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "0cfe1059b5be48de8aaf15d783518f67": {
      "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
      }
     },
     "1255d4f916c24fef94bb80b4ec2be7af": {
      "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_270abd0fab08476b93c0fcfe67b10ae9",
       "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.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.4 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.4 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "143d746ae53d41c084218516a9fd6da5": {
      "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_3005c1e2b717453c900c8d66adc4a2c7",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_3'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_3'...\u001b[0m\n\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_3'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "2058a477ad7e4035825646e705468ba1": {
      "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
      }
     },
     "235cef8d45e7436cad6c774474742fb1": {
      "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
      }
     },
     "270abd0fab08476b93c0fcfe67b10ae9": {
      "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
      }
     },
     "290ea1b414514680afb74dd7a2e83628": {
      "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_31a50144fa434c38b134923c10eab924",
       "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\">% done (field decay = 9.27e-06) <span style=\"color: #f92672; text-decoration-color: #f92672\">━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  4%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n% done (field decay = 9.27e-06) <span style=\"color: #f92672; text-decoration-color: #f92672\">━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  4%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 9.27e-06) \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  4%\u001b[0m \u001b[36m-:--:--\u001b[0m\n% done (field decay = 9.27e-06) \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  4%\u001b[0m \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "2b2ea235cb1d44d0b490a1bd60f38e32": {
      "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
      }
     },
     "2c7193f05377480e99294861ce466970": {
      "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_3f167ec5448448a0acc7cc2e171a5100",
       "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.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/3.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/3.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/3.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/3.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "3005c1e2b717453c900c8d66adc4a2c7": {
      "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
      }
     },
     "31440cbe12554116830dff34da12838c": {
      "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_e5350d5f87184771a2f569c407290034",
       "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\">% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m\n% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "3188fd10f56a4a3ea4706e2cabbfdab0": {
      "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_d36d68b906c443baa1cdefee36268457",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_4'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_4'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_4'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "31a50144fa434c38b134923c10eab924": {
      "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
      }
     },
     "3846bb2b73fc43e295e50f797917b03e": {
      "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
      }
     },
     "3b2011f7415a4a26bc02e3101aea1457": {
      "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
      }
     },
     "3dfd44714d0449c186022b10a73b23d9": {
      "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
      }
     },
     "3f167ec5448448a0acc7cc2e171a5100": {
      "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
      }
     },
     "4034c69d3c89492eaf09e2d315a19189": {
      "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_9fc94bee7e0e45a9bbadc30461e0c8fb",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'kspace_monitor'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'kspace_monitor'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'kspace_monitor'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "4d37e2423cbf43078d33761fdb41527d": {
      "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
      }
     },
     "51b7b47067354da4ba4d87ce3606d55a": {
      "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
      }
     },
     "55b05d60514d437eb3e3e5ed528e101d": {
      "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
      }
     },
     "57c0399822424b099d18b32664997d5c": {
      "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_4d37e2423cbf43078d33761fdb41527d",
       "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>\nComputing 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></pre>\n",
          "text/plain": "\r\u001b[2KComputing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nComputing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "58ff7b355be44b72aa4e0f513fd55865": {
      "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
      }
     },
     "5a80e02e94cc4a15bed61bfba7261d9a": {
      "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
      }
     },
     "5ca8f685dcf241fa86042f5e1ef97f1b": {
      "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_b056e2a391754a488b12a0fc1aceea36",
       "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.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/3.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/3.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/3.4 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/3.4 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7f7a3bc76f68426fba2cb52944f5b12f": {
      "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_2058a477ad7e4035825646e705468ba1",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_1'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_1'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'aperture_1'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7f8b96b6de4146c3842b2ca4c3399519": {
      "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
      }
     },
     "7fe5f2429c7449d589700490f199faa4": {
      "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
      }
     },
     "8013ae34ccc64cc88e70d7809969bfac": {
      "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_3b2011f7415a4a26bc02e3101aea1457",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'kspace_monitor'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'kspace_monitor'...\u001b[0m\n\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'kspace_monitor'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "93fd6c895212418d87bf3bb255fcee39": {
      "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
      }
     },
     "98605d33165c4eeaa9efabe57b17a5b6": {
      "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_a89965d4abff41cc98948a4a0cc9e857",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_3'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_3'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_3'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9b25e74d594c47a8b651b97272eba9e8": {
      "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_51b7b47067354da4ba4d87ce3606d55a",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_2'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_2'...\u001b[0m\n\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_2'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9dcdeee675d641478856c2de82f010b4": {
      "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_0cfe1059b5be48de8aaf15d783518f67",
       "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.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.5 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.5 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9fc94bee7e0e45a9bbadc30461e0c8fb": {
      "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
      }
     },
     "a89965d4abff41cc98948a4a0cc9e857": {
      "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
      }
     },
     "a965ddcb503b4eb7a396d0c9857b9d0b": {
      "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_7fe5f2429c7449d589700490f199faa4",
       "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\">% done (field decay = 1.32e-09) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n% done (field decay = 1.32e-09) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 1.32e-09) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m\n% done (field decay = 1.32e-09) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b0116cceab6241a3abe6a7bc800cd2cf": {
      "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_b87c5f1014f347bd84c03b90f343011a",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_4'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_4'...\u001b[0m\n\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_4'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b056e2a391754a488b12a0fc1aceea36": {
      "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
      }
     },
     "b1cbf85b4fae47739f87229556590f93": {
      "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
      }
     },
     "b715ce1e98b44c2391888d48030c4895": {
      "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_3dfd44714d0449c186022b10a73b23d9",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_1'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_1'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_1'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b87c5f1014f347bd84c03b90f343011a": {
      "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
      }
     },
     "bacc71d96e99411d82de90a88d4ed2f0": {
      "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
      }
     },
     "bf39aaec704048fa9e560c33992014bb": {
      "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
      }
     },
     "c44d8a6dd5fc4b5f87579a0538017f8c": {
      "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_bacc71d96e99411d82de90a88d4ed2f0",
       "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\">% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n% done (field decay = 2.75e-11) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">  8%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m\n% done (field decay = 2.75e-11) \u001b[38;2;249;38;114m━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m  8%\u001b[0m \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "c75f2547a9df4898ac162e5f5bc324ce": {
      "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_3846bb2b73fc43e295e50f797917b03e",
       "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>\nProcessing 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></pre>\n",
          "text/plain": "\r\u001b[2KProcessing surface monitor 'near_field'... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nProcessing surface monitor 'near_field'... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "ca39caa39af647cea79087dafe7f69f0": {
      "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_7f8b96b6de4146c3842b2ca4c3399519",
       "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.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">0.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">0.0/7.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/7.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "cbaf961b1bc44ff59f3634d26e8f45d1": {
      "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_93fd6c895212418d87bf3bb255fcee39",
       "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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">57.9%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">524.3/905.8 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">2.1 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:01</span>\n<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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">57.9%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">524.3/905.8 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">2.1 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:01</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m57.9%\u001b[0m • \u001b[32m524.3/905.8 kB\u001b[0m • \u001b[31m2.1 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m57.9%\u001b[0m • \u001b[32m524.3/905.8 kB\u001b[0m • \u001b[31m2.1 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "d36d68b906c443baa1cdefee36268457": {
      "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
      }
     },
     "d397257d14734c57b8c45588fe0770aa": {
      "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_58ff7b355be44b72aa4e0f513fd55865",
       "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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">58.1%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.0/1.8 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">3.0 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:01</span>\n<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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">58.1%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.0/1.8 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">3.0 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:01</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m58.1%\u001b[0m • \u001b[32m1.0/1.8 MB\u001b[0m • \u001b[31m3.0 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m58.1%\u001b[0m • \u001b[32m1.0/1.8 MB\u001b[0m • \u001b[31m3.0 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "dceb6348dffe4a14a0780ef007ac5bec": {
      "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_bf39aaec704048fa9e560c33992014bb",
       "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<span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_2'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_2'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_2'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e34a6a7638d54b5ca6b3ae693f2bcaf8": {
      "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_b1cbf85b4fae47739f87229556590f93",
       "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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">45.7%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/574.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">45.7%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/574.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m45.7%\u001b[0m • \u001b[32m262.1/574.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m45.7%\u001b[0m • \u001b[32m262.1/574.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e3c658e3309b414885de685cc821a99b": {
      "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_2b2ea235cb1d44d0b490a1bd60f38e32",
       "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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">45.6%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/574.9 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">45.6%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/574.9 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m45.6%\u001b[0m • \u001b[32m262.1/574.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m45.6%\u001b[0m • \u001b[32m262.1/574.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "e5350d5f87184771a2f569c407290034": {
      "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
      }
     },
     "f832b0cc06b94e8185ffa9883ffe1a9d": {
      "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_235cef8d45e7436cad6c774474742fb1",
       "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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">46.5%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/563.3 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n<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: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">46.5%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">262.1/563.3 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m46.5%\u001b[0m • \u001b[32m262.1/563.3 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m46.5%\u001b[0m • \u001b[32m262.1/563.3 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}