{
 "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:20.724919Z",
     "iopub.status.busy": "2023-04-08T03:28:20.724578Z",
     "iopub.status.idle": "2023-04-08T03:28:21.894007Z",
     "shell.execute_reply": "2023-04-08T03:28:21.893326Z"
    }
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3d imports\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Far Field for a Uniformly Illuminated Aperture <a name=\"setup\"></a>\n",
    "\n",
    "First, we will consider the simple case of an aperture in a perfect electric conductor sheet illuminated by a plane wave. The far fields in this case are known analytically, which allows for a straightforward comparison to `Tidy3D`'s field projection functionality. We will show how to compute the far fields both on your local machine, and on the server. The geometry is shown below.\n",
    "\n",
    "<img src=\"img/far_field_aperture.png\" width=350>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometry setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:21.896444Z",
     "iopub.status.busy": "2023-04-08T03:28:21.896087Z",
     "iopub.status.idle": "2023-04-08T03:28:21.916064Z",
     "shell.execute_reply": "2023-04-08T03:28:21.915503Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:21.918477Z",
     "iopub.status.busy": "2023-04-08T03:28:21.918316Z",
     "iopub.status.idle": "2023-04-08T03:28:21.935876Z",
     "shell.execute_reply": "2023-04-08T03:28:21.935323Z"
    }
   },
   "outputs": [],
   "source": [
    "# bandwidth in Hz\n",
    "fwidth = f0 / 10.0\n",
    "\n",
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=f0, fwidth=fwidth)\n",
    "\n",
    "# place the source to the left, propagating in the +y direction\n",
    "offset_src = -0.3\n",
    "source = td.PlaneWave(\n",
    "    center=(0, offset_src, -0),\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    source_time=gaussian,\n",
    "    direction=\"+\",\n",
    "    pol_angle=np.pi / 2,\n",
    ")\n",
    "\n",
    "# Simulation run time\n",
    "run_time = 50 / fwidth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create monitor\n",
    "\n",
    "First, we'll see how to do field projections using your machine after you've downloaded near fields from a `Tidy3D` simulation.\n",
    "\n",
    "We create a surface [FieldMonitor](../_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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:21.937711Z",
     "iopub.status.busy": "2023-04-08T03:28:21.937555Z",
     "iopub.status.idle": "2023-04-08T03:28:21.954683Z",
     "shell.execute_reply": "2023-04-08T03:28:21.954124Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:21.956619Z",
     "iopub.status.busy": "2023-04-08T03:28:21.956464Z",
     "iopub.status.idle": "2023-04-08T03:28:22.241803Z",
     "shell.execute_reply": "2023-04-08T03:28:22.241306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[monitor_near],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim.plot(x=0, ax=ax1)\n",
    "sim.plot(y=0, ax=ax2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:22.243717Z",
     "iopub.status.busy": "2023-04-08T03:28:22.243552Z",
     "iopub.status.idle": "2023-04-08T03:28:45.290405Z",
     "shell.execute_reply": "2023-04-08T03:28:45.289826Z"
    }
   },
   "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\">[20:28:22] </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\">'fdve-87041967-6219-46d4-a291-51392d47023av1'</span>.      <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:22]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_1'\u001b[0m with task_id \u001b[32m'fdve-87041967-6219-46d4-a291-51392d47023av1'\u001b[0m.      \u001b]8;id=250919;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=85051;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d7861951f2a49c789232f01af559ad3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:28:23] </span>status = queued                                                                            <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:23]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=755068;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=522054;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b0b3439257743c1a7adbbc6976163dc",
       "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\">[20:28:25] </span>status = preprocess                                                                        <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:25]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=859338;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=376407;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:28:32] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:32]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit  \u001b]8;id=820966;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=10175;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                                                               \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>starting up solver                                                                         <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=226079;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=791766;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\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>running solver                                                                             <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=635105;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=248283;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6d89fd914c6478bafa11e55c6f5b3b2",
       "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\">[20:28:39] </span>early shutoff detected, exiting.                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">314</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:39]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                                           \u001b]8;id=35900;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=194036;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\u001b\\\u001b[2m314\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">331</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=674100;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=655143;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41266184e5214ed3b6e4d1626d71b0c0",
       "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\">[20:28:44] </span>status = success                                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">338</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:44]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=800326;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=127691;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\u001b\\\u001b[2m338\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6957a90640d4b3385f790b90790438b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:28:45] </span>loading SimulationData from data/aperture_1.hdf5                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:45]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/aperture_1.hdf5                                           \u001b]8;id=547705;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=440015;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(\n",
    "    sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Far field points <a name=\"farfield1\"></a>\n",
    "Now, we'll define the set of observation angles far away from the source at which we'd like to measure the far fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:45.685639Z",
     "iopub.status.busy": "2023-04-08T03:28:45.685275Z",
     "iopub.status.idle": "2023-04-08T03:28:45.703735Z",
     "shell.execute_reply": "2023-04-08T03:28:45.703175Z"
    }
   },
   "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](../_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](../_autosummary/tidy3d.FieldProjector)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:45.705604Z",
     "iopub.status.busy": "2023-04-08T03:28:45.705469Z",
     "iopub.status.idle": "2023-04-08T03:28:48.627582Z",
     "shell.execute_reply": "2023-04-08T03:28:48.626994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6b23aeb0963421cb1931cd099cec348",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:48.684888Z",
     "iopub.status.busy": "2023-04-08T03:28:48.684742Z",
     "iopub.status.idle": "2023-04-08T03:28:48.712416Z",
     "shell.execute_reply": "2023-04-08T03:28:48.711894Z"
    }
   },
   "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](../_autosummary/tidy3d.FieldProjector.html), and also compute the root mean squared error between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:48.714314Z",
     "iopub.status.busy": "2023-04-08T03:28:48.714177Z",
     "iopub.status.idle": "2023-04-08T03:28:49.224706Z",
     "shell.execute_reply": "2023-04-08T03:28:49.224152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.79 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_field_plot(phi, theta, vals1, vals2):\n",
    "    n_plots = 2\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(8, 3.8))\n",
    "    im1 = ax[0].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals1),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im2 = ax[1].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals2),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    ax[0].set_title(\"Analytic\")\n",
    "    ax[1].set_title(\"Field projection\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$\\phi$ (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](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html) monitor as an input to the `Tidy3D` `Simulation` object as one of its `monitors`. Now, we no longer need to provide a separate near-field [FieldMonitor](../_autosummary/tidy3d.FieldMonitor.html) - the near fields will automatically be recorded based on the size and location of the [FieldProjectionAngleMonitor](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:49.227512Z",
     "iopub.status.busy": "2023-04-08T03:28:49.227340Z",
     "iopub.status.idle": "2023-04-08T03:28:49.247794Z",
     "shell.execute_reply": "2023-04-08T03:28:49.247224Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:28:49.249888Z",
     "iopub.status.busy": "2023-04-08T03:28:49.249725Z",
     "iopub.status.idle": "2023-04-08T03:29:12.421605Z",
     "shell.execute_reply": "2023-04-08T03:29:12.420897Z"
    }
   },
   "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\">[20:28:49] </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\">'fdve-98ec4841-46dc-4e73-b637-169cb1210535v1'</span>.      <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:49]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_2'\u001b[0m with task_id \u001b[32m'fdve-98ec4841-46dc-4e73-b637-169cb1210535v1'\u001b[0m.      \u001b]8;id=387648;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=570602;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a5f9231ed654ec3b2dc9715f7f4873f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:28:50] </span>status = queued                                                                            <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:50]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=345214;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=641358;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd871ba214b2414e9652d147dae822af",
       "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\">[20:28:52] </span>status = preprocess                                                                        <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:52]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=8847;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=691755;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:28:59] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:28:59]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit  \u001b]8;id=804901;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=558073;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                                                               \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>starting up solver                                                                         <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=859746;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=652609;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\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>running solver                                                                             <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=660514;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=845205;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49860f137f3e413bb994c7aff72e77c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:06] </span>status = postprocess                                                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">331</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:06]\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=280734;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=664170;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "277c5c934567495d9919c21f0e4d13de",
       "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\">[20:29:11] </span>status = success                                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">338</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:11]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=642559;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=928702;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\u001b\\\u001b[2m338\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07a6163e75ba47fe9fdaa12b93f11dff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:12] </span>loading SimulationData from data/aperture_2.hdf5                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:12]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/aperture_2.hdf5                                           \u001b]8;id=822763;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=333882;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data2 = web.run(\n",
    "    sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the projected fields are already contained in the returned `sim_data2` object - all we have to do is access it as follows, and then plot and compare to analytical results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:12.805622Z",
     "iopub.status.busy": "2023-04-08T03:29:12.805454Z",
     "iopub.status.idle": "2023-04-08T03:29:13.212457Z",
     "shell.execute_reply": "2023-04-08T03:29:13.211859Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.78 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the computed projected fields\n",
    "projected_field_data_server = sim_data2[monitor_far.name]\n",
    "\n",
    "# plot Etheta\n",
    "Etheta_proj_server = projected_field_data_server.Etheta.isel(f=0, r=0)\n",
    "make_field_plot(phi_proj, theta_proj, Etheta_analytic, Etheta_proj_server)\n",
    "\n",
    "# print the normalized RMSE\n",
    "print(\n",
    "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj_server.values) * 100:.2f} %\"\n",
    ")\n",
    "\n",
    "plt.show()\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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:13.214512Z",
     "iopub.status.busy": "2023-04-08T03:29:13.214344Z",
     "iopub.status.idle": "2023-04-08T03:29:13.232608Z",
     "shell.execute_reply": "2023-04-08T03:29:13.232136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection took 2.90 s\n",
      "Server-side field projection took 0.71 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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:13.234678Z",
     "iopub.status.busy": "2023-04-08T03:29:13.234517Z",
     "iopub.status.idle": "2023-04-08T03:29:14.033550Z",
     "shell.execute_reply": "2023-04-08T03:29:14.033040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1260x380 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 430x380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_cart_plot(phi, theta, vals1, vals2, vals3):\n",
    "    n_plots = 3\n",
    "    fig, ax = plt.subplots(1, n_plots, tight_layout=True, figsize=(12.6, 3.8))\n",
    "    im1 = ax[0].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals1),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im2 = ax[1].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals2),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    im3 = ax[2].pcolormesh(\n",
    "        phi * 180 / np.pi,\n",
    "        theta * 180 / np.pi,\n",
    "        np.real(vals3),\n",
    "        cmap=\"RdBu\",\n",
    "        shading=\"auto\",\n",
    "    )\n",
    "    fig.colorbar(im1, ax=ax[0])\n",
    "    fig.colorbar(im2, ax=ax[1])\n",
    "    fig.colorbar(im3, ax=ax[2])\n",
    "    ax[0].set_title(\"Ex\")\n",
    "    ax[1].set_title(\"Ey\")\n",
    "    ax[2].set_title(\"Ez\")\n",
    "    for _ax in ax:\n",
    "        _ax.set_xlabel(\"$\\phi$ (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](../_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](../_autosummary/tidy3d.FieldProjectionAngleMonitor), the fields are projected to a distance of 1m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:14.035537Z",
     "iopub.status.busy": "2023-04-08T03:29:14.035373Z",
     "iopub.status.idle": "2023-04-08T03:29:14.438587Z",
     "shell.execute_reply": "2023-04-08T03:29:14.438086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized root mean squared error: 4.79 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x380 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# new projection distance\n",
    "r_proj_new = 20 * wavelength\n",
    "\n",
    "# re-project our far field data above to this new distance\n",
    "reprojected_field_data = projected_field_data.renormalize_fields(r_proj_new)\n",
    "\n",
    "# now all the fields stored in 'projected_field_data' correspond to this new distance\n",
    "# compare to the analytical fields at this new distance\n",
    "analytic_field_data_new = analytic_fields_aperture(\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](../_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "However, in some cases we may want to project to intermediate distances where the far field approximation is no longer valid. `Tidy3D`'s field projection functionality allows doing this very easily: simply flip a switch when defining the [FieldProjectionAngleMonitor](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html)! The resulting computations will be a bit slower, but the results will be significantly more accurate.\n",
    "\n",
    "**Note**: when the far field approximations are turned off, we can no longer simply use `renormalize_fields` to re-project the fields at a new distance. Instead, we would need to re-run the [FieldProjector](../_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "Below, we will demonstrate this feature by looking at fields only a few wavelengths away from the aperture. Note that our analytical results also made far field approximations, so here we'll make our simulation domain a bit larger and measure the actual fields on a monitor, so that we can compare these actual fields to those computed by the [FieldProjector](../_autosummary/tidy3d.FieldProjector.html).\n",
    "\n",
    "Also, this time we'll use the [FieldProjectionCartesianMonitor](../_autosummary/tidy3d.FieldProjectionCartesianMonitor), which is the counterpart to the [FieldProjectionAngleMonitor](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html) where the observation grid is defined in Cartesian coordinates, not angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:14.440625Z",
     "iopub.status.busy": "2023-04-08T03:29:14.440456Z",
     "iopub.status.idle": "2023-04-08T03:29:14.651993Z",
     "shell.execute_reply": "2023-04-08T03:29:14.651500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# project fields only a few wavelengths away from the aperture\n",
    "r_proj_intermediate = 4 * wavelength\n",
    "\n",
    "# create a field monitor to measure these fields at the intermediate projection distance,\n",
    "# so that we have something to which we can compare the 'FieldProjector' results\n",
    "monitor_intermediate = td.FieldMonitor(\n",
    "    center=[0, offset_mon + r_proj_intermediate, 0],\n",
    "    size=[td.inf, 0, td.inf],\n",
    "    freqs=[f0],\n",
    "    name=\"inter_field\",\n",
    ")\n",
    "\n",
    "# make a larger simulation along y to accommodate the plane at which the intermediate fields need to be measured\n",
    "shift = 1.2 * r_proj_intermediate\n",
    "sim_size3 = [sim_size[0], sim_size[1] + shift, sim_size[2]]\n",
    "# move the sim center\n",
    "sim_center = [0, (sim_size[1] + shift) / 2 - sim_size[1] / 2, 0]\n",
    "sim3 = td.Simulation(\n",
    "    size=sim_size3,\n",
    "    center=sim_center,\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[\n",
    "        monitor_near,\n",
    "        monitor_intermediate,\n",
    "    ],  # provide both near field and intermediate field monitors\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim3.plot(x=0, ax=ax1)\n",
    "sim3.plot(y=0, ax=ax2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the new simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:14.654667Z",
     "iopub.status.busy": "2023-04-08T03:29:14.654494Z",
     "iopub.status.idle": "2023-04-08T03:29:31.761933Z",
     "shell.execute_reply": "2023-04-08T03:29:31.761356Z"
    }
   },
   "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\">[20:29:14] </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\">'fdve-faa2e7eb-1547-4bf7-8fd7-b3de396de288v1'</span>.      <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:14]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_3'\u001b[0m with task_id \u001b[32m'fdve-faa2e7eb-1547-4bf7-8fd7-b3de396de288v1'\u001b[0m.      \u001b]8;id=233464;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=654183;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "779a6c5b884949bfad27f5454c166038",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:15] </span>status = queued                                                                            <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:15]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=122751;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=520395;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f76d7ad145a4e29a92076c8228a5ebf",
       "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\">[20:29:17] </span>status = preprocess                                                                        <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:17]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=625916;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=488856;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:21] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:21]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit  \u001b]8;id=756482;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=964216;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                                                               \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>starting up solver                                                                         <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=621737;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=389666;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\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>running solver                                                                             <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=265211;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=287507;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "edc649a22db64a3ba067f380d90b73d3",
       "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\">[20:29:27] </span>early shutoff detected, exiting.                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">314</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:27]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                                           \u001b]8;id=255563;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=253079;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\u001b\\\u001b[2m314\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">331</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=810118;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=602997;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa5f8cfc0205494fbf730e9e9d63d6f5",
       "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\">[20:29:30] </span>status = success                                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">338</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:30]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=543480;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=336610;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\u001b\\\u001b[2m338\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4670b9534ed24e5890485342c0e683bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:31] </span>loading SimulationData from data/aperture_3.hdf5                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:31]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/aperture_3.hdf5                                           \u001b]8;id=630874;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=827822;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data3 = web.run(\n",
    "    sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the [FieldProjectionCartesianMonitor](../_autosummary/tidy3d.FieldProjectionCartesianMonitor.html), this time turning off the far field approximations, and then run the [FieldProjector](../_autosummary/tidy3d.FieldProjector.html) again.\n",
    "\n",
    "The [FieldProjectionCartesianMonitor](../_autosummary/tidy3d.FieldProjectionCartesianMonitor.html)'s xy observation grid is defined in a local coordinate system whose z axis points in the direction along which we want to project fields, in this case the +y axis. The mapping between local and global coordinates is as follows:\n",
    "* `proj_axis=0`: local x = global y, local y = global z\n",
    "* `proj_axis=1`: local x = global x, local y = global z\n",
    "* `proj_axis=2`: local x = global x, local y = global y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:32.041175Z",
     "iopub.status.busy": "2023-04-08T03:29:32.041043Z",
     "iopub.status.idle": "2023-04-08T03:29:47.810306Z",
     "shell.execute_reply": "2023-04-08T03:29:47.809813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f2250602092442e811043a992f765fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:48.070297Z",
     "iopub.status.busy": "2023-04-08T03:29:48.070168Z",
     "iopub.status.idle": "2023-04-08T03:29:54.720295Z",
     "shell.execute_reply": "2023-04-08T03:29:54.719746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f7be4aae57a445e8fe8a45df3609896",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:54.840982Z",
     "iopub.status.busy": "2023-04-08T03:29:54.840718Z",
     "iopub.status.idle": "2023-04-08T03:29:54.858594Z",
     "shell.execute_reply": "2023-04-08T03:29:54.858106Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Client-side field projection *with approximations on* took 6.63 s\n",
      "Client-side field projection *with approximations off* took 15.75 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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:54.860405Z",
     "iopub.status.busy": "2023-04-08T03:29:54.860270Z",
     "iopub.status.idle": "2023-04-08T03:29:54.879789Z",
     "shell.execute_reply": "2023-04-08T03:29:54.879229Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:54.881569Z",
     "iopub.status.busy": "2023-04-08T03:29:54.881435Z",
     "iopub.status.idle": "2023-04-08T03:29:56.435010Z",
     "shell.execute_reply": "2023-04-08T03:29:56.434519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation: 0.64 %\n",
      "Normalized RMSE for |E|, with far field approximation: 24.03 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAErCAYAAACirgCsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9eZgkRZn+GxGZWdXTcyA3gwgsIMitXMt4oCsuIqCsAsKjMKCAiig4LnLIjTLigYOijLgiroocHqyuCsIsrCjsTw4PWAEvrkVnuGSO7q7KzIj4/ZEZEV9UZXZXd9fMdM/E+zwzHRWZlRWZlflVfPG93/sxrbVGQEBAQEBAQEBAQEBAwJQDX9sDCAgICAgICAgICAgICKhGcNgCAgICAgICAgICAgKmKILDFhAQEBAQEBAQEBAQMEURHLaAgICAgICAgICAgIApiuCwBQQEBAQEBAQEBAQETFEEhy0gICAgICAgICAgIGCKIjhsAQEBAQEBAQEBAQEBUxTBYQsICAgICAgICAgICJiiCA5bQEBAQEBAQEBAQEDAFEVw2AICAqYsttlmGxx//PFrexjjxp133gnGGO6888419pnXXnstGGN4/PHHe973vvvum/Dn3XLLLdhzzz3RbDbBGMOLL7444WP1gnvvvRfz5s3D4OAgGGP4zW9+gwsvvBCMsQkd7/jjj8c222wz5n6PP/44GGO49tprJ/Q56xKm6rXo9bsMCAgImK4IDltAQMC4YCb75l+z2cTLX/5ynHrqqVi2bNnaHt64cOmll+Lmm29e28NYbfjyl7+8WibXzz//PI466igMDAzgS1/6Er75zW9icHCw759jkGUZjjzySLzwwgv4/Oc/j29+85vYeuutV9vnBUw9/PWvf8WFF16I3/zmN2t7KAEBAQFrHNHaHkBAQMD0xMUXX4xtt90WrVYLv/jFL3DVVVfhJz/5CR566CHMmDGjL5/x6KOPgvPVt6506aWX4ogjjsDhhx++2j5jTeHYY4/F0UcfjUajYfu+/OUvY+ONN+57lPLee+/FypUrcckll+DAAw/s67Gr8Oc//xlPPPEEvvrVr+LEE0+0/eeeey7OOuus1f75AQW23nprjIyMII7jNf7Zf/3rX3HRRRdhm222wZ577ult++pXvwql1BofU0BAQMCaQnDYAgICJoSDDz4Ye++9NwDgxBNPxEYbbYTLL78c//Ef/4Fjjjmm8j1DQ0PjisRQ5yNgdAghIIRYI5/1zDPPAAA22GCDvh1ztHuj7vOiKEIUhZ+xKrRaLSRJ0tcFDxNRn2pYGw5kQEBAwJpEoEQGBAT0Bf/0T/8EAHjssccAFHklM2fOxJ///Ge85S1vwaxZs/Cud70LQDE5/+hHP4qtttoKjUYDO+64Iz772c9Ca+0dsyqH7cUXX8Tpp59u37v99tvjsssu61phV0rhiiuuwG677YZms4lNNtkEb37zm23eFmMMQ0ND+MY3vmHpnfSznn76abznPe/BZptthkajgV122QXXXHNN13n/3//9Hw4//HAMDg5i0003xUc+8hG02+0JX8dXvepVePvb3+717bbbbmCM4Xe/+53tu+GGG8AYw8MPPwygO4dtm222wf/+7//iv//7v+35vf71r/eO2263sWDBAmyyySYYHBzEv/zLv+DZZ58ddXyvf/3rMX/+fADAPvvs4123u+66C0ceeSRe9rKXodFoYKuttsJHPvIRjIyMeMcY7d7oxPHHH48DDjgAAHDkkUd651GXw/atb30Le+21FwYGBrDhhhvi6KOPxlNPPTXqeQHFvXX88cdjzpw52GCDDTB//vyec/NeeOEF/Ou//it22203zJw5E7Nnz8bBBx+M3/72t95+Jr/xhhtuwDnnnIPNN98cg4ODeOtb39o1xte//vXYddddcf/992PevHkYGBjAtttui8WLF1ce8/rrr8e5556LLbfcEjNmzMCKFSsAADfddJO9HhtvvDHe/e534+mnn7bvv+CCC8A5x5IlS7zjnnzyyUiSxJ5DVQ6b+S6ffPJJHHrooZg5cya23HJLfOlLXwIAPPjgg/inf/onDA4OYuutt8Z111037ut25513Yp999gEAnHDCCfZ+NuOoymHr1cYwxnDqqafi5ptvxq677mqf9VtuucXbb+XKlTj99NOxzTbboNFoYNNNN8Wb3vQmPPDAAwgICAhY3QhLkwEBAX3Bn//8ZwDARhttZPvyPMdBBx2E17zmNfjsZz+LGTNmQGuNt771rbjjjjvw3ve+F3vuuSduvfVWnHHGGXj66afx+c9/vvYzhoeHccABB+Dpp5/G+973PrzsZS/D3XffjbPPPht/+9vfsGjRIrvve9/7Xlx77bU4+OCDceKJJyLPc9x11134n//5H+y999745je/iRNPPBH77rsvTj75ZADAdtttBwBYtmwZ/vEf/9FO5jbZZBP89Kc/xXvf+16sWLECp59+OgBgZGQEb3zjG/Hkk0/iwx/+MObOnYtvfvOb+K//+q8JX8fXvva1+M53vmNfv/DCC/jf//1fcM5x1113YffddwdQOEebbLIJXvGKV1QeZ9GiRfjQhz6EmTNn4uMf/zgAYLPNNvP2+dCHPoSXvOQluOCCC/D4449j0aJFOPXUU3HDDTfUju/jH/84dtxxR1x99dWWFmuu20033YTh4WF84AMfwEYbbYRf/epX+OIXv4j/+7//w0033eQdp+reqML73vc+bLnllrj00kvx4Q9/GPvss0/XeVB88pOfxHnnnYejjjoKJ554Ip599ll88YtfxOte9zr8+te/ro0Kaq3xtre9Db/4xS/w/ve/H694xSvwgx/8wDqnY+Evf/kLbr75Zhx55JHYdtttsWzZMnzlK1/BAQccgN///veYO3du1zgZYzjzzDPxzDPPYNGiRTjwwAPxm9/8BgMDA3a/v//973jLW96Co446CscccwxuvPFGfOADH0CSJHjPe97jHfOSSy5BkiT413/9V7TbbSRJgmuvvRYnnHAC9tlnHyxcuBDLli3DFVdcgV/+8pf2epx77rn40Y9+hPe+97148MEHMWvWLNx666346le/iksuuQR77LHHqOcupcTBBx+M173udfj0pz+Nb3/72zj11FMxODiIj3/843jXu96Ft7/97Vi8eDGOO+447L///th22217vm6veMUrcPHFF+P888/HySefjNe+9rUAgHnz5tV+l+OxMb/4xS/w/e9/H6eccgpmzZqFL3zhC3jHO96BJ5980tqz97///fjud7+LU089FTvvvDOef/55/OIXv8DDDz+MV73qVT3cIQEBAQGTgA4ICAgYB77+9a9rAPr222/Xzz77rH7qqaf09ddfrzfaaCM9MDCg/+///k9rrfX8+fM1AH3WWWd577/55ps1AP2JT3zC6z/iiCM0Y0z/6U9/sn1bb721nj9/vn19ySWX6MHBQf2HP/zBe+9ZZ52lhRD6ySef1Fpr/V//9V8agP7whz/cNX6llG0PDg56xzd473vfq7fYYgv93HPPef1HH320njNnjh4eHtZaa71o0SINQN944412n6GhIb399ttrAPqOO+7oOvZYuOmmmzQA/fvf/15rrfUPf/hD3Wg09Fvf+lb9zne+0+63++6763/5l3+xr8338thjj9m+XXbZRR9wwAFdn2H2PfDAA73r8ZGPfEQLIfSLL7446hjN+++9916v31wXioULF2rGmH7iiSdsX929UYc77rhDA9A33XST13/BBRdo+jP2+OOPayGE/uQnP+nt9+CDD+ooirz++fPn66233tq+Nvflpz/9aduX57l+7WtfqwHor3/966OOsdVqaSml1/fYY4/pRqOhL7744q5z2XLLLfWKFSts/4033qgB6CuuuML2HXDAARqA/tznPmf72u223nPPPfWmm26q0zT1jvkP//AP3neQpqnedNNN9a677qpHRkZs/3/+539qAPr888/3rlGSJPrEE0/Uf//73/WWW26p9957b51lmXc+ndfCfJeXXnqp7fv73/+uBwYGNGNMX3/99bb/kUce0QD0BRdcMO7rdu+999Z+D3XfZS82BoBOksTr++1vf6sB6C9+8Yu2b86cOfqDH/xg12cHBAQErAkESmRAQMCEcOCBB2KTTTbBVltthaOPPhozZ87ED37wA2y55Zbefh/4wAe81z/5yU8ghMCHP/xhr/+jH/0otNb46U9/WvuZN910E1772tfiJS95CZ577jn778ADD4SUEj//+c8BAN/73vfAGMMFF1zQdYyxZOC11vje976Hww47DFpr73MOOuggLF++3NKgfvKTn2CLLbbAEUccYd8/Y8YMG7GbCEz0wJzLXXfdhX322QdvetObcNdddwEoqHsPPfSQ3XeiOPnkk73r8drXvhZSSjzxxBMTOh6NDA0NDeG5557DvHnzoLXGr3/96679O++NyeL73/8+lFI46qijvO9t8803xw477IA77rij9r0/+clPEEWRNyYhBD70oQ/19NmNRsPmi0kp8fzzz2PmzJnYcccdK2lzxx13HGbNmmVfH3HEEdhiiy3wk5/8xNsviiK8733vs6+TJMH73vc+PPPMM7j//vu9fefPn+99B/fddx+eeeYZnHLKKV7u2SGHHIKddtoJP/7xj23frrvuiosuugj/9m//hoMOOgjPPfccvvGNb/ScI0jFYDbYYAPsuOOOGBwcxFFHHWX7d9xxR2ywwQb4y1/+YvvGe916wXhtzIEHHmijxACw++67Y/bs2d44N9hgA/y///f/8Ne//nVCYwoICAiYDAIlMiAgYEL40pe+hJe//OWIogibbbYZdtxxxy6BgyiK8NKXvtTre+KJJzB37lxvsgrAUvtGcxb++Mc/4ne/+x022WSTyu1GnOLPf/4z5s6diw033HDc5/Xss8/ixRdfxNVXX42rr7561M954oknsP3223c5gTvuuOO4P9dgs802ww477IC77roL73vf+3DXXXfhDW94A173utfhQx/6EP7yl7/g4YcfhlJq0g7by172Mu/1S17yEgAFDW8iePLJJ3H++efjhz/8Ydcxli9f7r2uujcmiz/+8Y/QWmOHHXao3D6aOMUTTzyBLbbYAjNnzvT6e/0uTc7kl7/8ZTz22GOQUtptlCZs0DlGxhi23377rjp6c+fO7RJjefnLXw6gyCn7x3/8R9tvaIb0nOrOYaeddsIvfvELr++MM87A9ddfj1/96le49NJLsfPOO9edrgeTI0oxZ84cvPSlL+16NubMmePdG+O9br1gvDam8zkAimeBjvPTn/405s+fj6222gp77bUX3vKWt+C4447DP/zDP0xojAEBAQHjQXDYAgICJoR9993XqkTWga6e9wNKKbzpTW/Cxz72scrtZiI72c8AgHe/+921+Usmj2x14TWveQ2WLFmCkZER3H///Tj//POx6667YoMNNsBdd92Fhx9+GDNnzsQrX/nKSX1Onaqk7hBm6AVSSrzpTW/CCy+8gDPPPBM77bQTBgcH8fTTT+P444/vEoXp970BFN8dYww//elPK8+t0xnrJy699FKcd955eM973oNLLrkEG264ITjnOP3009eY5DyNrk0Ef/nLX/DHP/4RQCEW0ivq7qNe7q+pcN16GedRRx2F1772tfjBD36An/3sZ/jMZz6Dyy67DN///vdx8MEHr5FxBgQErL8IDltAQMAaxdZbb43bb78dK1eu9FbAH3nkEbu9Dttttx1WrVo1Zu2v7bbbDrfeeiteeOGFUaNsVfTITTbZBLNmzYKUcszP2XrrrfHQQw9Ba+0d69FHHx31fWPhta99Lb7+9a/j+uuvh5QS8+bNA+ccr3nNa6zDNm/evDFl/Meif/YTDz74IP7whz/gG9/4Bo477jjbf9ttt62xMWy33XbQWmPbbbcdt/O+9dZbY8mSJVi1apXn2PX6XX73u9/FG97wBnzta1/z+l988UVsvPHGXfsbx8hAa40//elPXYsBf/3rX7tKHvzhD38AgC5lxKpzMudgVFwNHn30Ue9ZU0rh+OOPx+zZs3H66afbGoWdiqX9Rq/XbTz38mRszGjYYostcMopp+CUU07BM888g1e96lX45Cc/GRy2gICA1Y6QwxYQELBG8Za3vAVSSlx55ZVe/+c//3kwxkad/Bx11FG45557cOutt3Zte/HFF5HnOQDgHe94B7TWuOiii7r2o6vmg4ODXbLtQgi84x3vwPe+9z089NBDXe+nsvdvectb8Ne//hXf/e53bd/w8HAtlbJXGKrjZZddht133x1z5syx/UuWLMF9993XEx2y6vxWF4zzSK+v1hpXXHHFGvl8AHj7298OIQQuuuiiriih1hrPP/987Xvf8pa3IM9zXHXVVbZPSokvfvGLPX22EKLrM2+66SZPPp/i3//937Fy5Ur7+rvf/S7+9re/dd3/eZ7jK1/5in2dpim+8pWvYJNNNsFee+016pj23ntvbLrppli8eLFXauKnP/0pHn74YRxyyCG27/LLL8fdd9+Nq6++GpdccgnmzZuHD3zgA3juuefGPvlJoNfrZhzWXu7nydiYKkgpuyi9m266KebOnTupEh4BAQEBvSJE2AICAtYoDjvsMLzhDW/Axz/+cTz++OPYY4898LOf/Qz/8R//gdNPP91L/u/EGWecgR/+8Ic49NBDcfzxx2OvvfbC0NAQHnzwQXz3u9/F448/jo033hhveMMbcOyxx+ILX/gC/vjHP+LNb34zlFI2H+zUU08FAOy11164/fbbcfnll2Pu3LnYdtttsd9+++FTn/oU7rjjDuy333446aSTsPPOO+OFF17AAw88gNtvvx0vvPACAOCkk07ClVdeieOOOw73338/tthiC3zzm9+slKi/88478YY3vAEXXHABLrzwwlGv0fbbb4/NN98cjz76qCd68brXvQ5nnnkmAPTksO2111646qqr8IlPfALbb789Nt10065IS7+w0047YbvttsO//uu/4umnn8bs2bPxve99b8L5cBPBdttth0984hM4++yz8fjjj+Pwww/HrFmz8Nhjj+EHP/gBTj75ZPzrv/5r5XsPO+wwvPrVr8ZZZ52Fxx9/HDvvvDO+//3vd03U63DooYfi4osvxgknnIB58+bhwQcfxLe//e3aHKcNN9wQr3nNa3DCCSdg2bJlWLRoEbbffnucdNJJ3n5z587FZZddhscffxwvf/nLccMNN+A3v/kNrr766jELRsdxjMsuuwwnnHACDjjgABxzzDFW1n+bbbbBRz7yEQDAww8/jPPOOw/HH388DjvsMABFXb8999wTp5xyCm688caersFE0Ot122677bDBBhtg8eLFmDVrFgYHB7Hffvt15e0Bk7MxVVi5ciVe+tKX4ogjjsAee+yBmTNn4vbbb8e9996Lz33uc5M6/4CAgICesGZFKQMCAqY76iTdOzF//nw9ODhYuW3lypX6Ix/5iJ47d66O41jvsMMO+jOf+YwnMa91t6y/ee/ZZ5+tt99+e50kid544431vHnz9Gc/+1krc651Icn+mc98Ru+00046SRK9ySab6IMPPljff//9dp9HHnlEv+51r9MDAwMagPdZy5Yt0x/84Af1VlttpeM41ptvvrl+4xvfqK+++mpvPE888YR+61vfqmfMmKE33nhjfdppp+lbbrmlS9b/Rz/6kQagFy9ePOp1MzjyyCM1AH3DDTfYvjRN9YwZM3SSJJ5Mu9bVsv5Lly7VhxxyiJ41a5YGYCX+675DIw8/VjmCuvf//ve/1wceeKCeOXOm3njjjfVJJ51kJdI7peDr7o0q9Crrb/C9731Pv+Y1r9GDg4N6cHBQ77TTTvqDH/ygfvTRR70xUCl4rbV+/vnn9bHHHqtnz56t58yZo4899lj961//umdZ/49+9KN6iy220AMDA/rVr361vueee/QBBxzglVYw5/Kd73xHn3322XrTTTfVAwMD+pBDDvFKH2hdyPrvsssu+r777tP777+/bjabeuutt9ZXXnllT9fH4IYbbtCvfOUrdaPR0BtuuKF+17veZctv5Hmu99lnH/3Sl760q5zDFVdc4d2DdbL+Vd+lGXsntt56a33IIYeM+7pprfV//Md/6J133llHUeSNo+q77NXGAKiU66e2p91u6zPOOEPvscceetasWXpwcFDvscce+stf/nLX+wICAgJWB5jWE8guDwgICFgD2GqrrXDQQQfh3/7t39b2UCaNj33sY/jOd76DP/3pT2g0Gmt7OAFrCSbSetNNN3nlIKrw+te/Hs8991wlNTcgICAgYP1ByGELCAiYksiyDM8//3ylYMN0xB133IHzzjsvOGsBAQEBAQEB40LIYQsICJhyuPXWW3H99ddjZGQEb3zjG9f2cPqCe++9d20PISAgICAgIGAaIjhsAQEBUw6f+tSn8Kc//Qmf/OQn8aY3vWltDycgICAgICAgYK0h5LAFBAQEBAQEBAQEBARMUYQctoCAgICAgICAgICAgCmK4LAFBAQEBAQEBAQEBARMUQSHLSAgICAgICAgICAgYIoiOGwBAQEBAQEBAQEBAQFTFMFhCwgICAgICAgICAgImKIIDltAQEBAQEBAQEBAQMAURXDYAgICAgICAgICAgICpiiCwxYQEBAQEBAQEBAQEDBFERy2gICAgICAgICAgICAKYrgsAUEBAQEBAQEBAQEBExRBIctICAgICAgICAgICBgiiI4bAEBAQEBAQEBAQEBAVMUwWELCAgICAgICAgICAiYoggOW0BAQEBAQEBAQEBAwBRFcNgCAgICAgICAgICAgKmKILDFhAQEBAQEBAQEBAQMEURHLaAgICAgICAgICAgIApiuCwBQQEBAQEBAQEBAQETFEEhy0gICAgICAgICAgIGCKIjhsAQEBAQEBAQEBAQEBUxTBYQsICAgICAgICAgICJiiCA5bQEBAQEBAQEBAQEDAFEVw2AICAgICAgICAgICAqYogsMWEBAQEBAQEBAQEBAwRREctgBce+21YIzV/vuf//mftT3EgICAAA/BbgUEBExHBNsVMBFEa3sAAVMHF198Mbbddtuu/u23334tjCYgICBgbAS7FRAQMB0RbFfAeBActgCLgw8+GHvvvffaHkZAQEBAzwh2KyAgYDoi2K6A8SBQIgN6wgUXXADOOZYsWeL1n3zyyUiSBL/97W/X0sgCAgICuqG1xjbbbIO3ve1tXdtarRbmzJmD973vfWthZAEBAQGj4/Wvf30tZfLaa69d28MLWAsIEbYAi+XLl+O5557z+hhj2GijjXDuuefiRz/6Ed773vfiwQcfxKxZs3Drrbfiq1/9Ki655BLssccea2nUAQEB6zNGs1vvfve78elPfxovvPACNtxwQ7v9Rz/6EVasWIF3v/vda3q4AQEBAQBGt10f//jHceKJJ3rbvvWtb+HWW2/FpptuuiaHGTBFwLTWem0PImDt4tprr8UJJ5xQua3RaKDVagEAHnroIey111447rjj8JnPfAa77rortthiC9xzzz2IouD7BwQErDn0Yrf+8Ic/YMcdd8RVV12F97///Xb72972Nvzud7/DX/7yFzDG1tSQAwICAnqec1HcfffdeP3rX49jjz0WX/va11b3EAOmIMIsO8DiS1/6El7+8pd7fUII2951111x0UUX4eyzz8bvfvc7PPfcc/jZz34WnLWAgIC1htHs1stf/nLst99++Pa3v20dthdeeAE//elP8bGPfSw4awEBAWsNY825DJYuXYojjjgCe+65J7785S+vqeEFTDGEmXaAxb777jtmAuwZZ5yB66+/Hr/61a9w6aWXYuedd15DowsICAjoxlh267jjjsOpp56KJ554AltvvTVuuukmZFmGY489dg2OMiAgIMBHL3OuPM9x1FFHQUqJ73//+2g0GmtodAFTDUF0JGBc+Mtf/oI//vGPAIAHH3xwLY8mICAgYHQcffTRiOMY3/72twEUeSB77703dtxxx7U8soCAgIDRccYZZ+Cee+7BjTfeiJe+9KVrezgBaxHBYQvoGUopHH/88Zg9ezbOOeccfOc738H3v//9tT2sgICAgFpsuOGGOOSQQ/Dtb38bTzzxBH75y1+G6FpAQMCUx/XXX49Fixbhs5/9LA444IC1PZyAtYzgsAX0jMsvvxx33303rr76alxyySWYN28ePvCBD3SpHAUEBARMJRx77LH4/e9/jzPOOANCCBx99NFre0gBAQEBtXjooYdw4okn4t3vfjdOO+20tT2cgCmAkMMWYPHTn/4UjzzySFf/vHnz0G63cd555+H444/HYYcdBqBQOtpzzz1xyimn4MYbb1zTww0ICAgY1W79wz/8AwDgkEMOwUYbbYSbbroJBx98cJDFDggIWOsYzXYZFcnXve51+Na3vtW13di2gPUHwWELsDj//PMr+//t3/4NX/nKV7Dxxhtj0aJFtn+HHXbAwoULcdppp+HGG2/EUUcdtYZGGhAQEFCgzm59/etft5OaJEnwzne+E1/+8pcDHTIgIGBKYDTb9eyzz2JoaAgnn3xy5fbgsK1/CHXYAgICAgLWeXzkIx/B1772NSxduhQzZsxY28MJCAgICAjoGSGHLSAgICBgnUar1cK3vvUtvOMd7wjOWkBAQEDAtEOgRAYEBAQErJN45plncPvtt+O73/0unn/++ZC8HxAQEBAwLREctoCAgICAdRK///3v8a53vQubbropvvCFL2DPPfdc20MKCAgICAgYN0IOW0BAQEBAQEBAQEBAwBRFyGELCAgICAgICAgICAiYoggOW0BAQEBAQEBAQEBAwBTFepnDppTCX//6V8yaNQuMsbU9nIB1CFprrFy5EnPnzgXno6+HtFotpGlauz1JEjSbzX4PMWCaItitgNWJXm3XWHYLCLYrwCHYrYDVifVpzrVeOmx//etfsdVWW63tYQSsw3jqqafw0pe+tHZ7q9XCwKwNgXykdp/NN98cjz322JQ2IAFrDsFuBawJjGa7erFbQLBdAQ7BbgWsCawPc6710mGbNWsWAOAvv/0VZs2aCaZyoNReYUoCWhU7mr9VYNz7qxlzfVxA8/LS8giai65+xQRk+ZmZ1FCl9EuuNGT5QgJ2H6VIW9vhQsFpxlD5GLqQxcHsa8EBXr4Qpo8xiLIv5oDg5XbOEJdtriWYzNw1ksUqBZMpoKTrV3nZn7t+rfxrWTdQey25u5bCv5YAiutZXlMtEtcvosrrK5W7vlJpSNI2o9IAVLn/aCo8ZrScMQh7CgyiHO7wqlXYc+eX23usDmmaAvkI4l2PAUTcvYPMsPSh7yBN0ylrPALWLMw99edHHy7a1k6RO1YrMPNaK/s8Fv3ErtXZu7LNtCqMjjk+7Sef5dpjaFd1rqxXPeuMAbyin0eFfQWK5968l0f+PhV2VjPm7KmGbRt7oLWzB0o7G6vh21sQe6vJ/lV2eLyXg4N5/aXJLdpmmzl9Vm+D/PcVMLbcvpf5r8mhsXLlSuz08h1GtV1j2i0g2K4AD+Z++q8jD8TMOIKW3Q+HUgpauf5y6gAtnY0x71NS236lNWD7ld1HSw1V7qO1hiL93nFKG1e07cTA21+Rh9nOKbTftuOm+5Lzk94xKi7SBCAYbbsXguzDab833+t+n9mXCQZud+BgZg4oGFhpn7lgYKJ7f8YYeDkZYmQfLrgdAGcMTHQfp3iP6XfnwDirjJyZ963KcvzTTbevF3Ou9dJhMz9as2c0MHtGs3TYaiYtBJo6aZ0OG4+IExER5yKyN4cSMXIzeVAaWXl4rjQyZSZQGqzsZ9q1JTE6TDsjUifyqeEewIhz61AITp0z45gBEXd9pp2IGoctb4Hl5RMlo8JpA4rrmJcPrmLWYmnFgLx4oaUEmZ94YMJMuGBnEyxyMxjNrZUprjEAzTlQvk9HceHAAcWkrWznGu66k0lbZ5s6bG5C1zHGuokT8/fvlfrBG4NgZswEWo5OOQpY/2DuqVmzZmH2rJnuZutyxozRyMF07PrpAop15IR7r5S+7aPHLN+rFbmvqXlUdHpSAfqDy4X94acOGBj3bax5rhn37amxszxyx+URWdipXhjrXKwxpyCJjaX2wFxeqbVnD4xzponNkAqVE7s6VE2UAH9BjTps1O4Iup1MvBhpU+fN7O/bLHc/0f7iPWPbrjq7BQTbFeDD3E8z4wiDIrIehaLOGOOkX0Nr42yxrj4FQJdLEoWjVx5DcShl3gcoXc7NpIJMy2dWjeK8mbbzASE1I3bAzQtUR9tAluPtdtC6+9223kGdNGn7GGn7/fTBZtYmMK9t3mdsjZCAMHPTXIO5dTAwYeapADfzVMkgEuO8cXDt5oC8tOfFZzrnjcHMKxmYJs6bmfdx7pxGwPYbGKeQYn2Yc62XDltAwFQBYwKMLicZqIq+gICAgCmAWrsFBNsVEBAwZTGd51zrtcPG8jZYHvsr1IosG2vtcVfM4q9dBaZt5qI+4JGN7mgRQ5VLFJnUNtKTShdVy0mELZdAVo4hk7qjTSl+3eF4wK3AcMYsHSbmCrEwbY5GxO3pAUACBlauGnNyDE1WhoqO7hV3pvIisgaAZW27cq/TFnRWrljkGXSelW+VbjWeXmuArJa7B4pFMRAVUQIWJ7aPRQ17fXVUrpbIDEwUn6njpj3BSMTg5Wp9RpfDlIYyq9XQHheyjnJqrobUdPVbQ5YbUjW+JTMRx2BRxWoPC+URA2pgKI1V0TCV2+fKp3rnPj1Smra2zy+0AqR5ThV5TmURGQf8Z7YiqqZJX9ePoqUsuugZCL2ZcQ5mqCqM2yePCUAbGwP3mDIAmldHeXqFJFRzGoHTlPKknB2QStuInNKa0Cb9fu8zyGtRsQpcF2ErImWOBVH8Javp3P08KRJt08zZcMYYwF1E0PwmKA2YK6xQROTGY3Hq7Fbx+cF2BXRDSw2IjsiaiXSTiYwm1EYXDVNQqpvuqFLlqI8keqZS6d6rfIqjTKU7jp1HuWc/VRp11EcXefMjaFX0yNH6OjFK8o1FnZyGR4dk9LWupEEW/cbG6K730XbCmY22iZyBl5QvJhhEUtpt4ailNDLGE2G/XyYdVRIJwE2EFACHiZxyco4KirwyLRNpU1LZ41VRbEfDdJ5zrecOWwqWp97kB3BUyMIZI/kV3purKJEmlyqBLh2KXANpXhwvVxppeXOlxElLpUaaF+22lGjlzklrl8ZIkv2V1tZ40XtVMIAbCiNzdMZYcNtuRs4YNSMTruYuNA5nPBSh/IhOp5ZOEEtnDHkbOm0V16DdgirbyDPrvOk88yeCZALo0aSow2bajabtY0nZbgyAx6XzFjWc86YkEJeTPJVYB491cJctvUnC5sl0GlUvZ8XQABggjZPL3O2RjdNhY7xmtadu9TogoIRHgzROl1LOAVPSc8ZsW8p6Jy0vn1MpnXOmpHPElPKcstGokBrw7mNGHTaSb8bi4pnUXLjlIdLWgKXlaGKHtOZujIz0o/rZ8WhM5tja5aRp+DRISpU0djCTzn5mHRM+mifrPrPeHviOWmm3FaUwutxYrtyCm+DE0YKhi7tJGLgbi+AovDmg+CkrNwjO3D69MYk81NotINiugJ6gO34rTV5a4ZBpr09mxDHzqIwKMlN2X9MvMzmmkzaaY5aq6v6qtnld/DXPJsi27v2qtvWCqgUfQRyNYjtdfGeV+zhHjTppsO2Eu/HRY1hHTjN7rbudNzdGbfPciLuZAlq49+pyWzH35Hbs5irS91oHkE/AaJWYznOu9dphCwhY2+BRXDilHdA9rbkFBAQErHnU2S0g2K6AgICpi+k851qvHTYms0JIo1PF0CRKauUS2WlAmibHG688ShwNMmogM+F1SaJqHW0TSRvOpI2ktXKFdinQ0c6Vjdhk0rW9FRulPSUwKigSlysTMWeWBpkp0bWqw0mSumCAZGbVtuOCGQoWpUHmmRUd0WkLamSoaLeGodsjtt9SIrPURuToSr1WykXYQFbjo9ijQgIAS5pgjQHXNtG2gUHwZKA8Xm5pVDpyojJcKyRltM07NQ0btaSRcU1W5el1824XkrCbjTM8P51XewLWIjrVGikNktCVa1VbzbORZ35UzaMulyvXeepRIS09EhhdbIQLAKVQkRDQXLp+k7HOyftJm0UgHHTlokTMt9UuqjY+UBNoVtyVdhF3v63tc50pZSNvmVJeVI3S1FWHja6Cp+CoqA0v/saC28tk7LoS2ou2xcZMKAbF3Oq+XYkvuI92H0sY0ZT6rrsZJGMgRNgCJgJKhyxeu0hapQqkpUz6CpAyk137ylRZxo5MlTuG1JDlXEtqXRs9G09UjbYVxqY/jkWL7GXaUDyvndE0uq3Y7ve7SBon+9JoWlWfGQ+NttF9pNZIjGidJnaMRMwAgMkyepY4MRStNARhQSgbSWNj0iOpEEnnvdQr+jXnklLiwgsvxLe+9S0sXboUc+fOxfHHH49zzz13tdUbXK8dtoCAtQ0uavjUo5WUCAgICFiLqLNbQLBdAQEBUxf9mnNddtlluOqqq/CNb3wDu+yyC+677z6ccMIJmDNnDj784Q/3a7ge1m+HTaZF3lWH+AXNpaL5bJ7kNJXwR5HDpsv8qEzDRtJauUZargS0SYRtVZpjOHMRNhNtGyHtVi5t/luaKyc5TWq1AX7dNNNOIo6Bklccc0441u48HTeZQZRJIpLDlgpQumOVgNZ9Miv3Kvfy1nRruHjvyJDrHxkqVukByFYKlZWr+1JBSSMX7ueyGZEQJjh4XFxjnpi/TZfP1hy0ETaetsCaM4r24OxCptyMmzyMZuUmFg17roLBCpBoVgiJFC/canynjDeFySvJx5vDFifgcbfxUNMgPB+wlkFy2Bh5NllVP81nk5kf8SaRNNMPGmGTVChIdtnLemTWlmpF8lKFstE2FiXWNrAosZ+jlQDjTlRFa8J68M5fkPbY6CyDorxt/rNu89k8YRInCqV0wXwA/KjaaKJQFDTflYpFKW5yaZXXX/QxxOU1VVpa6fJYONsPxax+k2bknKCtTLqGdrXtJoA6uwUE2xUwOjpz14DuvDUqMGL+VuenKcjUiI6QNulXXlStXlxkPBG2zqhalVz/eCJsne818GT8O97m12HrbndGyky/gt/fub0qAtfZn3Bmr5dgQJKb685gFPNlisp2ES0r7DyNtGnpSImcRtXgcpjtvkpPOI+tX3Ouu+++G29729twyCGHAAC22WYbfOc738GvfvWrCY2rF6zXDpsaGepS8iyoO0bwAo6WQ6AZd1RJcxeKGHl5gxV0x+Jmbktl2yOZwnAZyl+V5lhZJsKOZNL2j2QSI6Y/lRgu21I5x8+8NrAOG2NISurjjETY48xsRoT2I7oeRs4cZVLqIpl9VFCVOZVDtwvHTLWGHA2yNQRd0iNlu418qNhHZhlkq3Te0twlGleJj6Bw0owakGgW1zpqtmxbNIbABgaLY6Qt8HLyB6Vsf1fhbnPdGEdk6jURClSnYayiRlEhAsBRmkayMepRdaAuPF9LNwoIqEJVHUmV20UL+gxoIgLUpeBq2sRJs04cUDpyPd7jXDjaM1dgpZOmkThBkTx1q51KQpcUGsYloMgCmX1+RTcdtOtadDhlFfMj2qcr+hU0sZmd7WKfTCpSdFtXOm+Ao3WNBmXFopwjF3Pm9bsxFp8TawaQKY5x6jTIpFAxsHIxiTFm6d4TERqhGI0SGWxXQC+gdEavX/miIoBx6FTX++pokDJVteIi1HlLibAQFQ6pcuqok2aOVfXezu2j9Y+F0felG1lXv9S+YiSIjJOx4ObqF/sW7YLqaI5NHTOGhJtrCkuJBNx1THi5ESUNsvyZ6WwbZ4xxR35UUnnqkYKYEUtvraBGjhf9mnPNmzcPV199Nf7whz/g5S9/OX7729/iF7/4BS6//PIJj20s1CmFBgQErAGIKIaIkop/1Qn9Y+FLX/oSttlmGzSbTey3335jrvbcdNNN2GmnndBsNrHbbrvhJz/5Se2+73//+8EYw6JFiyY0toCAgHUD9XZr4rYrICAgYHVjrDnXihUrvH/tdrvyOGeddRaOPvpo7LTTTojjGK985Stx+umn413vetdqG/t6HWFDTiTpS2ilwGLTFn4ollIiCRUSAJSIkZYUx0JcxNAaNUbK/uXtDKvKqNeqdm6jaivaOVa1iojVqlZuo2ojmURaCpCkuaql20WEBplEwr53oMxIl0pDNov3clLbx4iSZErZyFEiGKEFaWizKkNX7k0dKJSr9Ua0IMugSkqkbreQDxfRtmxoBPlwEWHLRxwlUmY5VEokxSvAOAdPii9ElNRI0UwQlRG2eHAA0WDxQInB1AkoZJmNtnGQ9SfGve8xKmmWkjOUSrPg0KiKtlNqFJX0Li8QAKCdj5MS2ccI2w033IAFCxZg8eLF2G+//bBo0SIcdNBBePTRR7Hpppt27X/33XfjmGOOwcKFC3HooYfiuuuuw+GHH44HHngAu+66q7fvD37wA/zP//wP5s6dO+5xBaxm0Oi0V5ONtvNquiOV7KeRN0p9JFE17zmtirRRGX/6WklXZ1FJt35LoulaSTBF9sfUmPh3SvxTUBpkZx9QRAvoKnmVzD9nDFKaVWxtS7Nkyu3vSgAQ9gFzkX3OXPQfAmAl9ZExZ8M1WWQnZNIJIUTYAvoNGm2jkTW73dIkiQCJV2PNtWmNxN5rrNVTHDtRV3uNbnft8c0JJgLzGYKxLrqjG4+Lmo0WoJLUUHRG5nRVVM1F5Oh5M6mdiIh0giJaOmaAFsyx2qW2tEQhhLsfRH/tyVhzrq222srrv+CCC3DhhRd27X/jjTfi29/+Nq677jrssssu+M1vfoPTTz8dc+fOxfz58/s6ZoP122ELCFjL4FECXpW8r8dHrQSAyy+/HCeddBJOOOEEAMDixYvx4x//GNdccw3OOuusrv2vuOIKvPnNb8YZZ5wBALjkkktw22234corr8TixYvtfk8//TQ+9KEP4dZbb7V87YCAgPUXtXYLmJDtCggICFgTGGvO9dRTT2H27Nm2u9HoVhUHgDPOOMNG2QBgt912wxNPPIGFCxcGh211QGUtqNRwaV3ems2joFeHMReZ4YIUyS5WgVOpbQQslRpt6fLWlreLle1VqcRyE0lLcywfKfqXD2dYafrbOUbSot3Olc1Dk0rbFSaaE8E5s8mXVGhkRiIq5aQFd6srrbw4n4bgNpFeKu7yNTpz2czqOinCS4UKdNqyOWxyZBjZUNHOh1vIyhy2vJVCtoqIGI22UY56cYnL70AwWzjRRNV4HCFqNuzx4vJ4SZYjLsfCOiJ2Zl2akQibFhFYXnyPcdS010twBmYk/jsvAcllsWUWyHVujVNqlrGa1Z4ytLtixQqvv9FoVBqQNE1x//334+yzz7Z9nHMceOCBuOeeeyo/+5577sGCBQu8voMOOgg333yzfa2UwrHHHoszzjgDu+yyS8/nFbAaUZFX6+VtVQmQ0CiZlC4qnmfVeWtKVQuNjJW/pqQTZFLKK4RtjjFqZIYIPvWEqmvRA0wEvTc5bQZlilVrVlsM24ukqe4V57r3+f3MJdLx8jUA0Dxaz8SUK9Kc2G0NJy6i3eEEo6IrvmFjXT2jo85umW0BAeOBqnkQaS4b3Ye2dU27H1L6vcAX9/Cf1wLuySoKUVcfp248Y6VrVcv71/dPBtXRO1dKgObCafKCtpXUEKRtxKU4OFjFQOn+/cBYc67Zs2d7DlsdhoeHwbn/+yOEgOpZlGv8WK8dNpjJCufu8VIcjFJxrJPmhEa0iBwVsvySc6kqa6ytSnNLg1zeyq3ztnw4w/Lhov3iSIZVraK9spVbJy3PpK0fUrAQncNmaDOMMTvEPOKVqpKAT5s0VMm8dC4yomqmQGiQdaACBqR2E3JXY022Uisukg0Rh21oBHnZn7dyyNRMIn2VKOOkccHsQyyS4n1RM4JslsIlWe6pTpowetwxsTQPKOfchtgZj6zjzUUMUT58gtS244qSkOiPgVPqVIRyMV6VSB7H1WprujinXsPzzz33HKSU2Gyzzbz+zTbbDI888kjlZy9durRy/6VLl9rXl112GaIoWm0ytQETRK+OChXlsA5YDa1xNBpkx35dqKSYODVdcOHXWRSi9n1dx6Q0ZvN6NHSssljnrG73in21ZnAiudXPNK1fKUEnYozUPtM1lCm/09ZZ83iLmhgfY4+0Tbin+0pV0CIBIAazQkmiBzdsIjWDau0WYG1XQAAFEwxccMgxFn30GJ6UrnHclPR/n13bf/9EHbVi/aSKKmheU/jOEqUs1o2lF5+kbp/O43bu2+m8mdcTFbGgjpl5bY5tzpUzZr8TIdz3I7j7DuvWdrTUqMxLIbCL+uNUdxxrztUrDjvsMHzyk5/Ey172Muyyyy749a9/jcsvvxzvec97xnWc8WD9dtgCAtYyxuJT9xqeXx24//77ccUVV+CBBx5YbYUgAwICph9CDltAQMB0RL90A774xS/ivPPOwymnnIJnnnkGc+fOxfve9z6cf/75/RpqF9Zrh01nZZI9F1ZoBEr4K9BmX7rKS2uulRGVTLlaHy3p5PtXphKr2oQGWUbVnl+VWkrki8OpFR1pt3PkZTZnnklIK3vvKJEALA2ScQYhjCyqv4+rySbQJpG3lETWAF+unlJo6i8cETPIsyLKhoJepUyELc0gy8iXSnNHg2ylSIeKffKRHLK8TjKVXs0Ve55ehK2sK9eMEA0Ux46zHCozlC7liSIkhprFOZRZ0Y9i8PK7Y+R7ZCJBHBX13HLOwM0KUIefYhiPSjsqU0bq4mXjpESKKAKPuh9Dpoq+XsPzG2+8MYQQWLZsmde/bNkybL755pXv2XzzzUfd/6677sIzzzyDl73sZXa7lBIf/ehHsWjRIjz++ONjjiug//DqQQIFjdAsWtfUI+sSCyGvzfPbM8aIitE6lrb2Gom2sTix1EfGBZhRFeTCj7wZ6jLj9kHUHaJBFj1GHO3Cg1kFBmw9MsYct4Axt0ZOhZo4A2LyUZbOKDh59snqOwcxqI5OWbUi3ivoMWwZgo5om6i4HIpQlzwhJvjn2Avq7BbgbFdAQBXYePjIJapqt/UKSlk0r8lozCfUtindDyR6NFqNtO4+nxbZD9TZkKooHN2XV/R3RuNc6ae69tif2SvsdzsOf2miNdiAsedcvWLWrFlYtGjRGlXNDrL+AQFrEazMQaz6Nx4kSYK99toLS5YssX1KKSxZsgT7779/5Xv2339/b38AuO222+z+xx57LH73u9/hN7/5jf03d+5cnHHGGbj11lvHeaYBAQHrCkazW5OZTAUEBASsTkxnu7V+L4XRIrCdFbRLeKvZNmIT2eLauSflX0bYMoWV7e4C2S8MpXh+VZF7tXwkw/OriqjTqlaOdhltyzOFvIw65al0MrUdK0w2h40DqlxO1eQcGJdIy2LYI2luxUhobpuVpa1ZvepcCGI1q/eeOIEpcJjlUKV4St5qQ9p2jnykuy0z6eWweZ9rCmeXS9sylYhTGplTle8zSMhKP6K4WOFHEVVjpvB5lIBHBd1QMJAcNt0VZTOwxTJJDmA23hw2wW2E1ENV3xhYsGAB5s+fj7333hv77rsvFi1ahKGhIasaedxxx2HLLbfEwoULAQCnnXYaDjjgAHzuc5/DIYccguuvvx733Xcfrr76agDARhtthI022sj7jDiOsfnmm2PHHXcc9/gC+gQScXJ9zG3r+8fxmiwufx+Lqrw1LuxzB85tsWwWxSTyFgO037O9hN1g+5lrA167CpystNvLxRiYyfdiRKyDadsuUn5tUhpK81xG2sp+EtYqIm1E3IPks3mr9TXoY369B85YJbWZszKiOI7PrbVbwIRsV8C6D7YG74tC3l57rwt0RtIMmH1NpfF94RLY93ZGyZzcPe2re8hXr2MwVj5b1T51ETa6b5H71r1P97HWrOMz3vuqn3OuNY312mHTqqDQdXFXLV2Hu0kAj6DL2mvgwopLmAl6rpzDNpxJS4kcziRWlIIiq1q5R4NcWdIj03aOrHTwsnZunUCZK48SacC4o68wzoDE9bO8dDQEswIkOaHsSdJWhBJpjEudklkXrCBBh/occZ60rfWkXO21VHo0SNPOR3LnsClfEcom2Zu6cpmCKmmj1JHVUnsPLy/rtvE4QhwXtCudNKGTgvrIkyaYLJw0nadgUeFMC55AMGe8TTK/4OSZ7mCRmeH2fP1KFKIx3QZuIjlj73znO/Hss8/i/PPPx9KlS7HnnnvilltuscIiTz75pKdqNG/ePFx33XU499xzcc4552CHHXbAzTff3FWDLWBqQYEBzNkm1oOT5jldXABc2jYTriabcbC6UuhpPTV6nIp2nZNmHbModsejCyjEeQN3wk4QwrU7KZGctHuAExUp/zLaZhDcPMgMoO1yWIJzaxsyQivljFtauWDc2oNMKktblJSW1FGrzfZ32AJeQVly9EwnekLpjL3MOxh5LwPAlAQbSwG04/11K9Ih3zWgDkww+9vJBYOsaFe+j6y2MMJxLN7n2vaJzDtEgOgCSkU9sqJWG6UtuvdWK0yOfY+vifprVejFYRqLNuk7bNXbfUeu204VIjPE5pE2VYMcK7I15jHGyejv55xrTWO9dtgCAtY2RJ9Xe0499VSceuqpldvuvPPOrr4jjzwSRx55ZM/HD3lrAQEBtXYLmBYr1QEBAesn+j3nWpMIDlsnOHeJ7yK2EvCaSMBrkSDLy8gakfI3wh7DmcQIibAZQREq3798OENaipGkI7ltU0qkypVdfdVktYYxZldiecRtVI0xZW/EQhekO6pmXq8uaE/MgETbiKCIactUWll/mUnI1O1fFWEz20VGqJfk2IBbdWGcexE2UdZwY0kTqlFE2NjAIFgpNMKiJiCL7yaKGpYSKZijMQnuiw/0A3Xc6enApw5YO1BaQzPuR9aIQAcjlEEv8mSjYBl5m4Auo20sTqwACVMSUIRuaJ7rDnn+LgESTzjEUR/BuR9VsyJAiRMdYa50CrhwjAbGne3lkaOmi8iPtlW0GQOYpUEyMK1tGwAEiHy0omIhGkwbu6rBlKMyWhsgGTgr7ZDWNvImtfZqn9Hou6wQIAFntaIInNge89queHfYCF9YwIzdnR5t02vAGQNTqp7yXoHRcj6C7QqogrEbRo69Tt6fRuHqoih2TqBotIaDw8wh/GgQpTh20xzHkv6nkbfxzJ16fw56iYqtjojdeIVL6qmSxPYYu0Jq6LKaKBkFq4nAVYET58r7PeoB03nOFRy2gIC1CBFxiKjC4Mipv9oTEBCwfqLWbgHBdgUEBExZTOc517R02H7+85/jM5/5DO6//3787W9/ww9+8AMcfvjhkzsokZmmBVv9Fd9itTjXrkCykfJPpfby1kx7+XCGF4dNDluGlWW0LSN5a3kmbd5anknkZdRJSdUlNlIMlcFksrMOuf9e0Lky20+Md7XDRMeU1DZqJlPl5YLJsp6hWc3WHQ8WJ6s4tuB23AJPipV7nkQQZZslTbAyh003h8DLNssb0GU+G5cZovJ7F9zltXDJyKqSW21y8Yrxo7MmMO0PWLfQL7tV5L4T4ZFR8tlsPxcAL54fFsdue57aKJjOU7cmLIQn908tho2OAUQMxMj3i0pbymKanyZqo2rmvZq76BmNqoHx6shblRBLBzgDOlNPJFgRZSt3sNE47ZgInDGrGyLgSnswBsS6GFcmNWLuImwmny3mjOQHu2MWecNuHHW5r51S+4IBsbFxDIjL6xUL5uW2VV0KDneJeEfkzSvV0gPq7JbZFrDuoJ/zLSa4i+Jz93utpXT550q6KFy5LxfcznMU6DFcVK34vxQoSwBGWD02Uk3LX3jFtfszJxqPYNBkxDl6jbZNtEh4FeoKcVMbRfPWmOA2UsYF8+aGdp5WFlO3xxLd+9A2jYLZ7fn4ktim85xrWjpsQ0ND2GOPPfCe97wHb3/72yd8HMa5qw9UMZnwaJBRYmt2UYERIzrSzhVx2BSGyvaqVm7rsK1s5RgpHba0LZG1XdtXhjSiI3kh6mHHW4xRQYDx0Z/E4qZ0dBpB2gbco9m4dk/wJmhlm0zkioeVk7Z5iOuPbxUxiQiKTxUqX6QAUF2V3pyTiDlEUjpdcYSspERGg8NgjYFiLK0hsIHBoh0PgGWFaqeOmlakJOLESeMasTFAOfPoSuMVG7HnJFgNn3rqh+cDxod+2S2lC+ERUSW+oRihbitLAQTjTlCEiI6wKIHOU9s20Eo6p6oDltpIf4Cr6I7UNhDVR92p9FjngFEnzezfQYP0VHxp3TYC85wqrR0NkMiq2Dps2k2EuHbPvdROwEBqeG1zeSPimGkNZLxa0Mk4cnTCRR25Onh2u2zGnNu0C8GYbVPqoyC2nRHHk8E54UzJYtDjsGG1dqv40J6PY/ClL30Jn/nMZ7B06VLsscce+OIXv4h99923ct9rr73WKt8aNBoNtFqtcX9uwNjol93infQ4Ms/2hES4ExIx+2qp7XyGA9DCtVV5V3Moe+8pyazDVry/TKeAm2eMt7x7PZWv+znonOfUvdfu38NCt66oD2ygKjwz3dFXp6Jd9/6x4AuAUOeKdW/nnDhyxKnzHLDuuWlnf+W+48w9m85zrmnpsB188ME4+OCD1/YwAgImDS44eEV4XufTYLknYFwIditgXUGd3QLGb7tuuOEGLFiwAIsXL8Z+++2HRYsW4aCDDsKjjz6KTTfdtPI9s2fPxqOPPmpfTweFt+mKYLcC1iVM5znXtHTY+oZyBZjFsV1NZnFciI0A0FFso2paJFCsrGUmpY2s1Un5G6GRla3cCo2sao1Ng8wzaaNqqlz57kRXGQJvW7m6QaJqUUeELSlv1ioxjc4kdQqPdmXLHfjRSV5GtZhwK/qMV0fbRoNLBtZdfYCCanevCDHBwZPy3IYyiKRYcaWiI/FQCzwZKs6nOQO6WbR5YwaYLCMNecvWZIs5Q1RxHZsRt7LeSjNINc4IZYlCmrv7PTpMQAJqYOr+cVJyhKky4txJjyzvI80jF1GhB1OSROeUle1n6Iiu8W4aJBP+s198PImMkUiaZtwJOJFIWleUjNN9qgVF/KhaNy20DsVzVkbBSFTNCDoxgETjXL8As8EnqWClw7V2kfUi2uYicsZmaBK1U1pDmuoIHZE3uvg9WrSek0gaZUbEgth4Qok0NdZcu8LmKwWm8nHJ+tfZLWD8tuvyyy/HSSedZKNmixcvxo9//GNcc801OOussyrfwxjD5ptvPq7PCVj7YJzbSA+lOUpJhERkdxSOCQZeRtIUFERZV1ZJDSad0IiNnvGCYeAwXmGK7v1rZekFr4yg0ehRVRSol3lQHTqjZ4oYkKoUGZpyUv3+jmjcKNG8sUCvHY2qURqknad2zAfrxEm4cPt3fc5o9SAqMJ3nXOuFw9Zut9Fut+3rFStWrMXRBAQ4sKh6tUfVJfQHrDcIditgqqLObgHOdnXer41GA41Gw+tL0xT3338/zj77bNvHOceBBx6Ie+65p/bzV61aha233hpKKbzqVa/CpZdeil122WWipxPQRwS7FTCVMZ3nXOuFw7Zw4UJcdNFFXf0sLoq2siixQhQsaVpxEYgE2uR1iBhpuUKRKSc20sqclP9w2R5KcxthW9XOMZzSotguV82Ki+TKrpCoPLURNi/pX/hRNbNSyjizijci4nblgpFIWhJxJMK1zUpsTHPYzKptr1K0dnXflUEoCuKWY4ljG23jSQxhJPZF6vLMEgFZXjOeSbfC1pGe1kmvTpXLKeGpsoV/Gc8h4uLzs0SAD5Uy/c0U+VARbcuGRpzE/8AwWKuIsLH2IFgZVWNRAywvfnCiaMCKCeScIS/HLgVDs7y+ikgFx3x8qzScs65iuaY/YP1Gnd0CijiRNlEtlXv5W4xGo8r8MBdb8vOXtBJg5ldgtAhLTWFsTxgEvvhHp+y+X/zaRf56iqTZD2feay+qVhNhowunnfaNQbuxAKCL0ybQpbS7doozG3lTgI2sa61t5E0qsj+xDUVErtxHa3L83iNsgB/FtzlsXq6tY08wuNgCZU9wRvL4jODIOERH6uyW2QYAW221ldd/wQUX4MILL/T6nnvuOUgpsdlmm3n9m222GR555JHK4++444645pprsPvuu2P58uX47Gc/i3nz5uF///d/8dKXvrTncwhYPai1W4RlA5RCI2a+IopsNNO28vzCRdUMiq1EYkS4aJu514to0tgTcJp75edcdUfHiihRdb/t887HHbc6D6t6fHQcdbllnfloVLyN7kNF69w+3ZE2LXVtlK7zmL1itGtbdU254N71c7oHHJ2S+961G2cO23Sec60XDtvZZ5+NBQsW2NcrVqzAVlttZRUDWdIEi0vHLGpAG9EREUOXdbokj5GVzkUqlUeFNH+HidDISkuJzDDccjXWMlJvTUpHiZR50a+VtI6aVtKjP5o2veE4Y3aeQgsCRrEgDptAgzhvRlksKveNOyiR5rbtegzIxMrSroQAjDhBlFjhAp5E1kkTcQRZOm9RM0I+UpyryBR4eW0obZIzRWoauY939Eg3/cyUAsrjMcGQl8fjgkPEpYLbUNs6j9FQE1GzcMzEwBB0c0ZxrZtD4I2izfK2pUdGUQNJaTgyBUt1ijm3jlyD+NLD4zUeJAG3sz9g/Uad3TJ1Fa1SF3GGmHATb60dDdJpo5Vt8/zSSXrnhL2mzlutYEjHWGopjlQB0nPSxueM9UJhYR3OT+c7CuXICnqMdntTB6pw6oiTxk0/cwqQglIoNZRRpVOaOHJUpMQ5cloDigqiVMzXqhxQxuALkJT7eIJTpJ8B7vtWElB58a9H1Nktsw0AnnrqKcyePdv2d0bXJor9998f+++/v309b948vOIVr8BXvvIVXHLJJX35jICJo85uAcXvslTGMWOW8sgFs8w2LpgVEmFGqZU4aQAgDL2a1mDlxH7Fdc6Q30/F0HyFwnJ+xTuofMSJoMIZnc6bJ8LB+Zjqh73CpzW6tnG2OuvgUifNqmwSemSVSrdS2ruuiuzbSaWk20dD1bU17UqV7xpqZKXYyHgpkdN4zrVeOGxVVIyAgKmA6bzaE7B6EexWwFRFLxG22bNnew5bFTbeeGMIIbBs2TKvf9myZT3nqMVxjFe+8pX405/+1NP+AasXwW4FTGVM5znXtHTYVq1a5Rnnxx57DL/5zW+w4YYb4mUve1nPx2GiEBthcexqc5Gomo6b0CVNLpUK7byMsCltqZArS1rjqlRiBREXWT5SRGhGUonUyvfnvtBIGZGTeW4FRrSUnpR/5bhJpXYeuagajziixEXSZpSJuQOxwIBpJ8JS+Qx9LxbMRt2K+jxj3Lh0VTxKnGBLFIM1imsnmi1LPRTNBCIrI18jKaKB4raTqbRRMJFIyMytdmGURRtJaqkU1NRyxW4k92qA8KG0PDaHaBZjTFcOI7LjWmXHyxoDTuI/aoCVtFgmEiRxEXnLlLL1WqTWaGhu22bVvTFOHjQXvGvVz/QHrFvol91SKKX9jQQ9j5xgBI1+c4HumHSx4mtXYkej31m1ihr5fBIp8yJpJOpWRYPspDuaCBcdShctsGqYZB+vFlCd+SIRRBpZHOtnWqAj2mfKIzDu0RqN1DgVLJE1NMj6yJsTL1HQYyrt268ITmhEMIwiQGJok8yKNTCtCsGZ8VAia+yW2dYrkiTBXnvthSVLltjaXkopLFmyBKeeempPx5BS4sEHH8Rb3vKWnj83oHf0bb5FRMiAIgJk660pUofNxtcAI76vufIk/g1odIePQoHsR/SMJ8KOnZOyRTyOXFqI6esQWqPXoN8Rtu6omomUKRJJk1DlHExL1RWRU6SclFba0iD9KN3YUbhe0PldUBpklUhJ53sAQsvvaPf6+dN1zjUtHbb77rsPb3jDG+xrE36fP38+rr322rU0qoCA8WM6r/YEjA/BbgWsK+glwtYrFixYgPnz52PvvffGvvvui0WLFmFoaMiqRh533HHYcsstsXDhQgDAxRdfjH/8x3/E9ttvjxdffBGf+cxn8MQTT+DEE0+c3EkFVCLYrYB1CdN5zjUtHbbXv/71dgVzMmCNBlijCdYcrIyq6aiJvFy1SaWyQiPtXGNlWqxWVEn5vziS2Ry2ESrlT4RG8lRC5k5oRGVlhK2iULZpi6jMCSO5aiJiiMooVRRz2x5IBJKoaM9IXIQtEZxE1kykjduC0IyRhXDG3OpzR2FaK3oSxTZvrRBwKSJZPIoRDRTXUbZSyFaZEzaQ2JUemUorOiIzCZE6rnRsC8wqT9q/6HP5bJR2LHJl8+MY51aAJE0yRM1CREQkEVIS+YtmFKIjqjkENrSi3KfpzilvQJSlHRIRuQibYtD263HfU2Ocqz2R4IiqonLTYLUnYHzol93SpQiGOZJmzNoKrSMrwlOXt6a1Aky+B42qdOaM1eSZ2agZ57CFrmvy1rQdl4uaUXEPXZYosOdWc870p5T+rho7pKFJ7i3zo2yaRJJ0R2SxJurWGW3y7KDZhVwXQfLvaL6eApX+Z17kjea8STssXVk2gF4XKoxiS7AAXh5ylQCJYM5eMq3JtVBg5b9eUWu3zIeOA+985zvx7LPP4vzzz8fSpUux55574pZbbrFCJE8++SQ4sat///vfcdJJJ2Hp0qV4yUtegr322gt33303dt5553F9bkBv6Nt8ixfiEeab1IrDsmOk61dw/bTPCJGAMyumITrE2Az8wsvVkZs6eXkeRySqFrmoWhLb+5D2M8EhTDkj0mc+R8SuRArt96JEPTwzXnTNi565fpllXr+JoMk09yJyqpy/Khthy0i0LXcieFneUx4cjcgZUMGTTnR+P8DoUc7O3EAa4WSjfE4VpvOca1o6bP0CawwWzppIoGPjpDWAcrKuowbSvLgZUqk9oZFVlgpZ3PjLRzK8OGwokZl13tKWU4bM2o4GmWcSMh0BAKiMKEN2CI3YsXLhKUAaWdIoFrYtIm4dsxmJwABx3kx/I+KWtkcpkY5O4wxn14JDlfpczovadcVgnNpmowmRF9dDNBNHj8xyROU1y1tO1TFqRpDlNZWZe0BFzRyCCpCk9oFVQKkmzISjR4pEoL0itW1hREeaw9apTBoroMqx84FBsNKBZ1EbKOmRSSNGXqrCScFhplZKAzPKaz0SjW+VhtbLo1DTYLUnYO2goEPCTvgFY37tNe7MukeJ1EYho+ah6lBj9BwzUvPNOWduH+OYUXqfVE55TMONl366T4P0h+M7ZmUfnJPGGayoiCY0QKWJIAuII6ZJnTna521X/nbTT2GuNaqpkoxQRRnjEOW1iYndVGCkDpu7NtSp61ShtNcJ1TAjYUT11xMaofWHtHSqoBNUiayyW8DEbNepp55aS4G88847vdef//zn8fnPf37cnxGw9sGEq8PWWZONKj8q4qqZPhDnjBE6XmUNtEk6ZkYwjQkObsTTksi2meDWEfPf6xw3O4+LYqfyTevWooPOV1dflyzie/XRTD8RqkOekbmkc8xomzp1bvE89yiTsmxP1pHzT2Pi35ndp8rJHUf9SGB6z7nWa4ctIGBtI4k44orVHjYNaoIEBASsn6izW0CwXQEBAVMX03nOtV47bFrENrpmaq/puAkdDwAAMk3qrUmNVklhXJVKjwoJFEIjq9pGyj+38v1pm1AiqdBIOmJpkLT2GgXjwiWzcuESxgWHiFw7MlGqWDihkSTCrGZUtgWaJT2yKTgapm0jbdyuSNPE9FpQ+g+PbESSJ02oMqrG0hZ4VtZBG0jt6o3KcqjU1UdzUTUFUY5dpAqq7Bc565L4l1p7bX+xplyRG8ndStpQZtvRQISoWUQ282aCbKhoRzOGwJolPXJoJYSJFEYxdEmJ5CJGo7xPivpKxTETsjDWrJETroOYxqs9AWsHxf2vIYgAjlnNZVp5UTUDzbmNHpn9gIoI0Vjy/DxyYhlaO0ENQ20mdGWt/aiaJ9DRw3laiiMVzoBjrlDqX3FOxd9adWatXeTMSNjLnPTJGvpkfeSpjjJuI2xcuGssoiL6hiIKZ8aueQQduwilJGUDpFlEpqUE/FOy18KARiF9ARLY2mtMkfOWeSnt3/tKdZ3dAoLtCqhGlwiHVB1CI4ZGrDrokQAjv7FaKogK+fy6Wl+dYiG2PmwcE9qii57RkkRF20XS7D5xRFJBYjsHMikh4MJv2wibs6WMCxuVt68r4M0NlXJ9NsKmXIRNSehyDua18xS6nI/p3J+PAUX0TJbbi7brt1G4LCeRuczRKgn1slO8xA67q0Zeec4dNMS6Ugmd+01GuGU6z7mmvksZELAOoxExS1P1/0194xEQELB+ot5uBdsVEBAwddHPOdfTTz+Nd7/73dhoo40wMDCA3XbbDffdd99qGHWB9TvCFjeKf5EvNCJ5sTLSyhTaOREaKSNlK9sumra8zFt7cTjDi8NFxGzVcIZ2KX6RtXMr65+1c+RpkWSlstTmsNXJ+Hf2c5LDRqX8RRkpK4RGynaNlH8j4l7umvlLcx5oIrsHKu1t81vcSpKOYltEmyVNMJvD1nJ5a80U3AiQNBNb6FrErtA1FzlZNXHXwEXVOgtqm7+uU0gFrMrK47lCie2kbfPmeBLZ3Lqo2QBLVrixm3y2qGFz2JSIEDWK804EtyvhFPE4V2nq+NRyGqz2BKwdaDjhEcBIwZfbeOSiZ9wVzoZW3vKcF+HyxDKE7TMRNppvJaV2bUUi3cpFwWlulibPJs1Rq9Mw8CJFNq8WnmS9WZ8VjAGcxBNNkV1SuJpGyhgtDi1z0ie7tjOtXEHWOmESAhqpZIzbfJtCoKk7/49x4fKAST/nApHJCxTCXleXF0gEWypHYq9GcY24s+eCM5K35kRHmBUd6T15f7QctmC7AurQnbdGYfLVXD6bucdotAbC5fOb4wB+JIYWq67LT+NxZKNtgrSjZsOLtnEzp2k0vUiaLWfUGHDzHhM9o3MhIdz7uG9ju0SMKsCr8mlJPq5WCiBloZCbSJrLZ0OeQbdHbD8nkTcA0O2WZUfRqFreapPct9xF1bK8Os8tzr2SAb4wSTn2mPvfJ8zl8L9TKjRCI6Ru//7msI3Xbv3973/Hq1/9arzhDW/AT3/6U2yyySb44x//iJe85CXjOs54sF47bAEBaxuJcGqeHmrUrwICAgLWNmrtFhBsV0BAwJRFv+Zcl112Gbbaait8/etft33bbrvtZIc3KtZvh00k5b8YiEtZ/6iBtFwJoMqQrVy5fLVUYkVHhG35SOopQ5octqwlbTtP28hHVgEo8tZkXi3lLyq4zJwUyxYRd8qQwkn5F/L9EWmbXDVhlSEbEYm2lTdooSTmJKHHSmHzZL5FBCbLleLGgF25UXlmI2w8aUI0i2sQNRPIMqql0gxRs7gG+UgOnhDOeXkt64p421V+uvDG6HayKr4qs6sxIhZomzIIAy2I5rAdl4m2eUW0BwbBSgVRlsW2oHaSzCAKbi5iULfqXIc6PvV4jxOwfsEoRQJlXpOJKHFmo99MK1uUetSQFslHNftLpa3UvFQKOY2gkXZm2ybq1hlVc/1O3r4+kuNyaRkpNaJ9mfry7TEnpyWcrVC6I4+NSvibyJosc21VDsi07OuItplIZZ6R1exRVnON3eYuZ6bIUykjZswpx2kRWVuieVTbz0nuIABEEfeibnWK1pVy/zSHr8ccvTqMlsMWbFdAFTpz2GguE+2n+WwWHVMiRvPVSFTNyusLDl7mntHoGY8juw8neWvRgIuqibLcU7EhccrXUWz7Ge2vzGEjarrC5bFqoiCrOyJstRMvai+rcmxJPiqTsrBdZX9lDlvagiaRNaCwcSwt2iJPbb9oujJM+Ujby32TJNpmo3BxDmU+Ryq7jxYKTLrz61SQ7ERV3pq/vf85bKZvxYoVXn+j0UCj0eja/4c//CEOOuggHHnkkfjv//5vbLnlljjllFNw0kknjWs848F67bDpKIGOGl7ttUzDOmmpVGhJIzSSWwn/4Uw6KuSIo0S2ynbazpGW9Mm0nSNrFTd/PrLK0iAlERrRUrqkVHQ7cMVf57AVjBsj5c8sDTKJuCflb/qblAbJmUeFBIpJkLl/OeDJ+psfew2aSF8tTsCiuKBFAoWULaES8NIYRM0G8tJJ462UUA+EFR2hFEYmGIR90CntCmXb0bIE82mTVACbDxlZf24dw2hFBBEXQiMpLT0wMAO6rMmmkya4cdhEApYX58G5QGImVnBIxjlZSQRHUmVwpkFNkIC1A6U1tNZExIO5hQul3YKP5mNK+Gvm6oFpDUjpxEOsM6ZAHDbnyGXKp0cCQCY1spIekxH6JJWuVx0OG7fOGKHvEYeN1omMNYfmmr652L/DgfUI3R31xgC4SY1M7cISUzmQF5R1nWfQpSgUlcqGUqTd4bxViAkwLhw1itSpZCKG5sVnsSgmVMkIWpSfSwSdQKiUnDrYhjI2mj+undCIV9aA1qQbp6x/rd0Cgu0K6AlMcE+839DqilplRX+dQAWlyFFHsBcnLSrL+tCUCJ40nTOWND26o3XM6D5RDJg0GhFberMrcxJ5Qk0QTsDJE3OiTloNJdJ7Lsnik7VhdCGqjuItc3BpnLe2deSsw5a2oMs5mm6PQDedeJxz3lq2nq5Kc+Qtk96TQ8blAnuWQ6XF+cks95xzK1giFTSp1zSaCElxWTihvU7etow159pqq6287gsuuAAXXnhh1+5/+ctfcNVVV2HBggU455xzcO+99+LDH/4wkiTB/PnzJz3OKqzXDltAwNpGHHHrWFPoaSAxGxAQsH6izm4BwXYFBARMXYw153rqqacwe/Zs218VXQOKouN77703Lr30UgDAK1/5Sjz00ENYvHhxcNhWB7RIiiibSKzQSJorF2FTGq1Srno4kxgu2ytaGVaWlL1VrWIlYrjlompZm1Ai2ylkmewp0xHkFcWyAYCpMroUJ1Blfycx0q5EC+4VFXQRNkd9TCJupfxjzjwp/9jQA630MyOUI2Ylobvk/T3REUP/iQBhQvARWFwj8W9EPJLUJv0KsvIlYm5FVUTsKEVVMLLmQPEd+bRIV4DWxAoFUxCGYjmU2Uhe1kyRlaUPoqEW4sEyErpqlaNEtobA2mU7ali6EniEqBy75Nyu7o9XdCTiDFEQHQkYB2wwjbw2hZVpxIwz5klF0yiMjXwpXSkYkkv3XNFIWi7hRdBMqRPal9lomyI0yO7ImoG1aySiHwvHCmhGGnFJP24IIIkcfdsE26SiNMg6apFL1Kcrz6ykROqsbRPzkWdQ5YozaLRNSVe8to4eSSiR4MLaRI91ECXeaj0jq/VmNZ6JBNqsipelRSAiIvgkrY22q/aAv1Kv1JhFwru29YA6uwUE2xVQD1aTJ8RRXRCezgN4BQ2ORtWKYtbdgiJRM/Goj7VRNRNJazQ7ompFmScWNywTS4nIipFpEUN3RMK1SLwIG6WaOwEhInuvUR8lL2eCnMGaNiGYJyxUGUWXqbNzeWpp4JBpQf8GwBslo4DYPj/a1oJOinYxjyvaKm3Z65uPtMFjI1KSOjq44JAkwmaokoxE2wBA8dHtTmcUFSjuC3sv5fmo7+/EWHOu2bNnew5bHbbYYgvsvPPOXt8rXvEKfO973xvXeMaDsBQWELAWkZSrPVX/JoIvfelL2GabbdBsNrHffvvhV7/61aj733TTTdhpp53QbDax22674Sc/+YndlmUZzjzzTOy2224YHBzE3Llzcdxxx+Gvf/3rhMYWEBCwbmA0uzVR2xUQEBCwutEvu/XqV78ajz76qNf3hz/8AVtvvXU/h+thPY+wlYWzo4ZbFVbaFstOpfYKZJscNlok+8VhkrdW5rB5QiMjq5C3CqGRbGQVVG6KZWfeWBgvV0W4sN+KF4FjNIeNkRw27kXVaD6byfvolPKnuWvmrwmmMeby2WrBuJOg9ST+Zb3Ef7liwzu45TbRl+SwMZLDRothUrECs/qfKr+QbOZJ/5stbv0uXpXZ8gEiFogGys9fMYR4sFhVi2aMIJpR5rA1Z0CXBbV5Y4bNYdOCCJDETRvhqFt1roNgNQmwYym/VOCGG27AggULsHjxYuy3335YtGgRDjroIDz66KPYdNNNu/a/++67ccwxx2DhwoU49NBDcd111+Hwww/HAw88gF133RXDw8N44IEHcN5552GPPfbA3//+d5x22ml461vfulprjQSMDb8QNYkpKe2iLiDa/6gWKaFRNZqrVhdVa+UK7dy1jd1s59K+L5MuVy0jOXF1MI94LDiJsCk0SgOVKW2Fksp3AAA4mF1xlCR/VZFonqB5azK3eR/2r8ygs3KVuT0C3SpEiHTaslG1IkmfFKO10bZRVobNKnPsVtq7pMBbpiBvZ/6MibblLtqmytV8RfLdaLRNScKA6LAdNO+lIp9vQqIjNXbLbAsIGA9oPlvddtqukukvom0kqkZk+m1+OhEU8aJqA4NeVI2XDBtEReknAFCiYGMBZSQ8Mu2keA1Alcl3Ujs7WJRCKQWMaC4vqA13BpKaSvokccbso83hRDIYo2wpgbi0GSJqgmsTbcsskwAk2mb6WJKCJwPl9jbUSDHn0QmJto0MWY0CFsfgkZvTmdw2JjgUjbaRCJuJtqk0szlsRRH0ahEaew36nBPbrznXRz7yEcybNw+XXnopjjrqKPzqV7/C1VdfjauvvrpfQ+3Ceu2wQcSAiJFrN1HJCaWnlSnisCnrpK1s5ZYKOdIy1EfZITRS/PBTGqRMR6yj1lVjrXz4VZ6CGwpNB6jDZkUaOREdER0OW2nUYsG9BH76oAOFUbBCI6glFNGB2AmJV0uEMUvdoQn2OkqsceNxBBGbiUoEnjg6AyNCI0zUj0JqWKdawbW9fZi2ddIEA0Q5xiSXEESAJB4qPj9qtpENFQYoHmwhHy6+MzY4DNYqjBdrD9rJFIsyqy5HayeNlw1Ux6dWE1ilvvzyy3HSSSfhhBNOAAAsXrwYP/7xj3HNNdfgrLPO6tr/iiuuwJvf/GacccYZAIBLLrkEt912G6688kosXrwYc+bMwW233ea958orr8S+++6LJ598Ei972cvGPcaAycPQZ6gDZig3SgOciPN0vs/0UyfNOmbK2b7RnLS27O73KZGlym6uHN2SUC8BX0nQ3P+CM5sMHnOGrKRxN6Jqb09wQJR2qHDSjKPagSpKoKmxlhMqUGvYTk7UyJCXhA+ifmuT5zNHxelMnKe0IDOJZFFSKMoBQGvYThB10gRLDWV8FOcNKOyooWWrvNJ5q6vnxDopkZ2Kc+Nw3EbLYZuI7QpY98Er0hzohH4sVUD3TIlKJ40nEaKmm2dEA8WzI5qJTclgjSZYs0xxII4ZS5ouDSIZsA4YFaQrROpK9UgRQ5X7pFIjN86ZMgtX1UJNtB6l1v4i9FiUcQCemjcVaKJ1dM2iccydnY14A0mphM5lZqnWZgGa5aldgGZZXNSfBaDTEWjjvEWJdeQQuYUowYVHWcyNmJLgyIlIiT0fzm0NN80JxXwUjHZvVN1Xo6Ffc6599tkHP/jBD3D22Wfj4osvxrbbbotFixbhXe9617iOMx6s3w5bQMBaRr9k/dM0xf3334+zzz7b9nHOceCBB+Kee+6pfM8999yDBQsWeH0HHXQQbr755trPWb58ORhj2GCDDcY1voCAgHUHQdY/ICBgOqKfpZQOPfRQHHroof0YVk+YlMOWZRmWLl2K4eFhbLLJJthwww37Na41AlWukkiaKK80SnYPWrmySfXDmcRIWtZha+dWdCTPnNCIazuhkTwdsW2VZ672miR0RyHAbJ0f7uT+layt+2MFOjizq8wNsnJApbA5YzbaxqnACKl51CUwgopoUVXImNZxEpFLxOcCzNQmEcJLtudWaCSyqyNFEmn1CkdnzTUqOuK33XsECRuOSG3FSBKukZTfXdTKka0qVprywRj5UPE9ZUNNJLOK7ykaGYKeMas4v3YLvOmoBDo3QgAJoqg4p3iUyGAVElKWgUJF46sJ8txzz0FKic0228zr32yzzfDII49UfvbSpUsr91+6dGnl/q1WC2eeeSaOOeaYnpJypyqmu93qhAJQ3t7grJp+WETliLhIRS01GmFrZc72taVjGtCoWjuXtuyJsY1prpCW26XStm2ObxB59qtoU0r3QCIIxbGzDEDxN+YccfnoSO7Oz0TaLKh8fQclUueZi6SlLUcFag1ZOWuV+nLWktQZMpE1ukrsCSUkkasBmcSOAt4kdZwaAzbCppMmeF6u9GcZWKO0N40Z7hzM+YjEozsamhHrrO9UdS16WNUeDXV2C3C2K6D/mO62q/M3nkr5V+5PompmH875mJL9gtZVJcIhvDnDiYg0Z7gIW3PQRs9U3LBtTdoQsROnkwpZWow9V65eL61LaeaRUmuPieDKoijHeqgrqEhAy54UTCnXNmkxgjGYmtCCMZsKkwiGVJrIm0Biyh+YSGKUuWgbKV/EeARumFKtIcus0kJAG3YDF/48rmwbmwkAigiQUGaCEtzZ0B6ibVX3w3ht2VhzrqmMcXMXVq5ciauuugoHHHAAZs+ejW222QaveMUrsMkmm2DrrbfGSSedhHvvvXd1jDUgYJ2DmbBW/QOKmiBz5syx/xYuXLhWxpllGY466ihorXHVVVetlTFMBsFuBQT0D6PZrRBh6y+C7QoI6B+ms90aV4Tt8ssvxyc/+Ulst912OOyww3DOOedg7ty5GBgYwAsvvICHHnoId911F/75n/8Z++23H774xS9ihx12WF1jnzRMXkWuHMc4Vy4foy1pvoaLsI2kuW3npdR/ninkZZ/KU1cguz0CWa7g5u0RL3pmwJQAt/Lb8Zjj5kSAhN5otB0L5hL4ueM4F/3mOOXnM9pmY+ZhacaLVVzAEyChK7tFMdhynyh2ifdx7OV32FUzwYnQSO/rCDSfrTPC5l4riPJyJ5xhQJQRhZEcmYmUjuSWb523UlsUMk5btui3Tlu2sC6TDTBVRrpk6vjc40xcjWuKOJrVqF5rgmy88cYQQmDZsmVe/7Jly7D55ptXvmfzzTfvaX/jrD3xxBP4r//6r2kXXVvX7JaBlfLXDJrkrdFouY06AV4ehbRRNT/CRkWW2oRdQNs0quZsYvG3TSJsqfRz2CiozTL3f0IElNJcQTZNxMxJgQvmSmdkSkFqbs9bjZV9q5WVszaRNp2lpIjsSLGKjCLBXraLZz0favlFYst8DJXlbnW4I2Helx03wkoREUVoQDTLXNpmy0bb+MCgFaZiA4NgZZsbqe7moJ+HZkQQdGRFRIqivSTHmJy/HZ9W9TriPaDObgHOdgVMHuuq7TIwc4G6CLWRbmeC2ygOT6JRnqlSXIREsDkVFBkYBDc5bAODNnKtowZ0QtplvpeOGlBlPlcqNdJMubY0ETZnQ1OS32tER9pSWfGlQpTJCTEZ9oDqKE9EYUg7nDOvBEpM8n1Nu0H0CpqRe0ZTyWCCSongdo6blAIlSRK7OSgp+g0eWWYVY9zuo7mAMt8H4KJt6C5HBQC1wvtpXlnOAai/JyaLseZcUxnjctjuvfde/PznP8cuu+xSuX3ffffFe97zHixevBhf//rXcdddd01p46HLf4om4RN1n0y6hy7NFYYJ7cfQH81fmSso8yC2Ryz1UeWpo0EqaX+MtZJejSRTe40r5dMly304Z5YGyYhHVeew0XC4IA86Y77YCFAKjXQ4cWPC0CA7nDd3cFcng3FHidQdCaqd9TWKNvP6O52gYnJm2rqDKlleFwYk5SFTBcQlZyxVGiPlToOpgiq/06yVIy+dN5VmXrKsndDlbnIHKR39kybwj3ORhjPmJRXTfqD3miBJkmCvvfbCkiVLcPjhhxdjVwpLlizBqaeeWvme/fffH0uWLMHpp59u+2677Tbsv//+9rVx1v74xz/ijjvuwEYbbTSOs5saWNfsFlD80JvnQumO+44qjhHKDX1OqsRFhjNJKOCOBjmSSa+9qnxORlJpbeJIRimRznmrctjoSmYj8utIDpQKrp0OHl18ikvqX5wzNMrFl0Qwxw7UGtpYty5xjdLJNc902rLUR9qW7TbyUoQoGxqx9kC2Upswr9JsVOER89fUiRJEITdvplYgIWo2EA2Wx8kzJ36gFHj53ZgjMymdgh2tpRYlgDbywhoondw6ARJPYGQCKpF1dstsC+gP1kXbVYXOCTl11ICJ0SCrBEV4cxBscHbZP+AUIOMZVlwEcRMqLmiTOTjanpOmu9qFEJOjjAOF7aOUcjunVH67yj7W2UpvQZ77QnJWEVxwq6jbiEhbCNvOlUJe1rWkfxvl4k+UROAV8zvOyVyPC0vPo9aDk9edjttoTlvncbSUfXXSKMaac01ljMth+853vtPTfo1GA+9///snNKCAgPUJxQS021DkEwjPL1iwAPPnz8fee++NfffdF4sWLcLQ0JBVjTzuuOOw5ZZbWlrlaaedhgMOOACf+9zncMghh+D666/HfffdZ2VpsyzDEUccgQceeAD/+Z//CSmlzW/bcMMNkSTVaqZTDcFuBQT0F3V2C5iY7QqoRrBdAQH9RT/nXGsa67VKpKFEUoqQJJTITPkS1Sbc3c4VZLl6okyCvVSQZcV1rSSUqeFDImadIiKmzbjwomp1oIulhvbUGWEzq9Wc1JrgpE5HUbvIvzEZY5XJjAx05Z4mspOxcg4o5r82g7VhddoWTuaa+/LXNHLIexTvoDSCzraLKLjVm5TUl8qUgiwjAypVkIbSmuVWhlZlOWCiollmazBx5Wo6aZnDcC5ZjUhMHQQRgensHy/e+c534tlnn8X555+PpUuXYs8998Qtt9xihUWefPJJTwJ33rx5uO6663DuuefinHPOwQ477ICbb74Zu+66KwDg6aefxg9/+EMAwJ577ul91h133IHXv/714x5jQP9AVzKr2G1dQiP2vncJ8ZnyS5dQSqRXg7LlalAawaWRTGKkfE6GK0RH8lxB1STTG7bAiBdhk0gTU8fIp4ZTqnfD0Iuko/YozYk4Sc2zQ6XrzTOdZ66dtqAMfX2ohcyKELWQDZeRt1bb2gaZ5TZCr2R1lIoLDp6Yuo8uwiaaDcgyGpA3U8SlkEk8mCOybAtpmRdMOgaG+SQ+4J+pCbAxuNIGjKmxBUgmgDq7ZbYFBIwFJkSX+Jpru6gO4Iv3eFL+tK5rEvlCPlU0yMHZYGW9MR03oOKSBpkMEKGRAbTLRyOVTjAkVX5UjdpNKk4HFHNEU5uyJQlNPPfbVRG2KnEmoF6gibabgqNhy6FwzCgZC81IYUap0DQjFoRKzsrPZ1BlXyI4Gkl5vUgJJwUnekGfcBpVAwpmgN2mnCgTJ9+1sZdMushpHT2y7h6ZKPo551rTmJTDtmTJEixZsgTPPPMMVIdSyzXXXDOpgQUErA+IuKOuUmQTXO059dRTaymQd955Z1ffkUceiSOPPLJy/2222cZO+NclBLsVEDA51NktYOK2K2BsBNsVEDA59HvOtSYxYYftoosuwsUXX4y9994bW2yxRaUs/FSH0q4IrZmWKpAih4pUq1faWw1x6shlToTSlXL8VRG1KvirSyT3y4h1kJuJkXy2iBOZfroaw0ASVMfH2R3zu6xbse3s5+Q87DkRyWsizco4t2IjnJ6rYDbptvNvZ7sTkqy4+2UAzHZAlS+UVNBlW0tlE161VNDl6jeUdDkjVYVnJwCam9jZH9B/rAt2qwpVgSzdmd9p7ZpfFJuuDldF2JYPZzbCtrKVY1Xb5LDlLofNijBJyz4oUsZcUr0B566MSB4xpEZ0JBGV8taUOZAIjnaZw9aItI2qSZK31vUkkmfWRMWVLZxNZP3bTr4/b7WRmRy24ZYt+ZG3UuQjhT2QmSIRNl2Zw8YFAyuNlEgEomYpkJDmUGlm21S8xLRjdMs4K7iogxbCrXQz7iL+cCvgRWHtyUn4V6HObpltAf3Humi7qiImVGDEmx+QckCmn5bK4EkTLO6W8veKYscNP2/NRNuiJnSZt9ZWsFG1ltRo5y5vrY6BMJz5ETZfpE7aqNpwKi1Tq3NOKWvk6U3Zpi42VXkNZiTCK4cykJiomrDnMSPmyGTRn0mNrIy8mQicFICJ3WvNYBRKGuU1MbDRfcCKHGFg0DGLlHIRNiVdhE0qCFICxRQ+B1xRbSa4lxdn7GA/omoU03nONWGHbfHixbj22mtx7LHH9nM8axxGdIRWn7fKPSS6UJcUao/TQx0NxoWvDkkcGs6dQ0P7qcNmDDTno6hEkn0MCnpk+ZlUdKRCaKTnW9bjZ7oEVRABEqsARcRVQM/JEx0RdmJD6ZFcdNeNE4TCWYS3ddnuVIoc+2y07P7eKL1JUaqqqhYa6XLexoG4ZrWnbvU6YHJYV+wWhda6VhnSOjQdapC+k+acNzoJWT5cOBSrWjleHDHtzFEiU4l26bzlqaOGG5q40traRWofGbVfgiMqJw2aUNMBN1FJhF+fzdbMJMn8Wlc7rf6FopTIUnSEqESqPEM+UipDtlLnvA2NWOctb+XIR4pzzlq5pVFrqT1b4mwZswtRIub2vdFAjnjQ0a4VmdhQGGKotbZcQJcTGI+ixIWjQUZEbESTvag9nGTkvM5umW0B/ce6aLsoKA2yU4yMCbegywSHKIV8eBxBlMrJLI4dDTJpVqpE6ojUWKNtQoNs5woto/CYa6uKO5RKrErrHLayRi+hjlNBJuOwjaS5daJoncpeRUeMHRScWUXdgSRy9jEWmFE6bDObEbJygShTwqs1TOvFAcBgIuAvDxkxI+47bcZOEcEjppVVsVVKWfo2nS8J1bC2TZBFKa44tPleFVnMdyPosomTxXSec03YYUvTFPPmzevnWAIC1jtMZ8Wi6YhgtwICJo+gErnmEWxXQMDkMZ3nXBN22E488URcd911OO+88/o5nikBSVYfVY8rkazDO2dVdEAlwKOkeh8j3x8lXttEyjhjEEZQRDBPCptG2OhNV0UXrFtEmDC9omd6JHlNImxu9ZnWVuEQiYs4mnNKbMkC186IvHnCu2uxmb+dUTo7zF7ETXqktU4EseC2hkpnf0D/sa7aLZpraEteqM6SFyYyRUuXaJscP0Lqra1q5Xa1+MWRDKtaRRTqxeEMw6Z2YSaRtv3yJipXkJb64lPGbdScM2selOAuCkfqrY1whkZUfE4ScTTS4g0jqcRgGZFT5JyU1kTW3782jEa/O6nqSlohIUmiapLUYizqMhZjSVdltp2P5E60iFAitdQeW8AIKIlYgJe1RuJUOjp2KmtXkU2EISaiTbS+paVrR5mV3NaSA6ykGcHQIoFuguXEUWe3zLaA/mNdtV2jwRclK+c/HUIjKEsGIUps+SDWaHrRNhdJa3ry/aad6WoaZEsqDJWRslWpxMqSUbAqdVG1oTT3ImsAsLKde3V7vZJQRKjO2D5F0my6r0Hxl3OGYcpMsHPA3EbVBpIII5mwn2UFTpqRZ/OrWGKwU1M/0sZLsZI4arhajx7DSIMlpThSljl6ZJ5Cm/mulOBJyWogAiRaKhdFlbzv0bQqTOc514Qdtlarhauvvhq33347dt99d8Sxr+p1+eWXT3pwAQHrOuoSYKNpEJ6fjgh2KyBg8hhNdCTYrtWDYLsCAiaP6TznmrDD9rvf/c5KfT/00EPetumeDEujMFa4o4NLbFY9TNRLCG6jZzxKwMvkV56nEDRCUyFRKqIEwvCt4wRRmQjL48RGoKJEQJhoVMS9qFqD8JpdMUVGxu6He1frfcm6I2lU1p9FMVj5Q8O4k+f1om2JsBG2aCCCGCpXksrVlwHBkJalBAp5WiO7Wx1hSzhDTKJ0JjqXcAZRrtaLxOXQcbLSwoVw5wEXFa3FOHPYaA5iZ39A/7Eu2C3O6ukbCi7CpElbaZcb0ZYSbVOiRCq3spwrKyhC5ftp3tpwK0da7pO1pY2s2b8pjbDpytxexpm1ZTLSNgpH2QyMMQyTFWRTUDvNlZ+LYUWhxrhoJayYkMlhyzObz6bS3BbF9nLYRjKkq4p90qHU5qHlrRzS5O5lsjIftshhcxG2aKCwd1q6864SKzFtKroAABEX0CSioImNtYJOPPJWwm27OFD3RTG5x3VsiQrU2S2zLaD/mO62q1PVkqJTaKQzh42TPi6Ei7ZFsWMwRbETGolisEYxp0LUsM+MFomN+mgRIy+jSZ1Fsavy1la2c9telTrxpeXDzj46+5l5gkzGPspc2ci6zF2ETRIb0JnvayBIbr+IXOS+HXGMpCbCJm20Lc2dbU9zBTmj285WIoErlg1A8GJ/Jjhi4WyPZR5FOZgq8wgbGVhZJgWNAbDStiJPwcvvQPG8mFehYFhwZUSUeFceG+ALkFRhtPuqCtN5zjVhh+2OO+7o5zimJDgjCoVElSeJuOeoAQCPXPK8bAwgMrV9iLPGo9hNGJQk1McYojQ0cXMmROmwRUkDUfnwccHAyWdW1eBIIu5ER7w6bKxLaAQgCeuTvE9NgjvzC8XZCQATjvKpuXOAeBJV1lMRsYCIXaK+meQ0U1cPxc2NXFK9YI4i1SlMMlB+kQPCteOGgEi6nUQ6LiZcHRKM5axNADGrSYCdBj/A0xHrqt2iP1lONAm+Omq5U0GJJCqRtu2UzYZTiZFykrGS0CPTdo6spEFm7XwMSqSurMPGOYM0izOSQyv3XBlHlAuGkdTZtZlNN/EwKmtKaU8gSsGdt/1Yrd0iinKUSPpX5Ub1MXM11lpt5FYxMvdokNlQsX/WcnXYZKoqJ0KcUZVIV/dRpvUOnoGII8jSJkojspC07IIXogTaLH7l7reFqbygRQKFDTbn39meBOrsltkW0H+sq7arCqxKJZIu7praXVwUzgMKJ83QIxmhR2oeAcI5abpsq6hh6Ymp1EgVcdiI6iOlQa4q7cPyEaec++JwZh21lS0n1JQS5dy8PF6eSa92L1UbrxOuq6KSFwGCchEnFlaZV9I6mB0qlAaVdEi4hXxap4wzQEgn6iZKCilXEhAl7Vol0Lw4bxbFYMYhjjL3HUQJUNK3eRIV9W1RzPucGiQHk4bWvfqokdN5zjWpOmwvvvgivva1r+Hhhx8GAOyyyy54z3vegzlz5vRlcAEB6zqmcwLsdEWwWwEBk0MQHVk7CLYrIGBymM5zrgk7bPfddx8OOuggDAwMYN999wVQcKg/+clP4mc/+xle9apX9W2QawKcRKAoDdKIftBIVoNE00wELIq5bSs5w1IfGRfIyxUHlaeVlEgeJRDlPlFzJqKBmQCAuBEhaRRfUdKIkDTKCvaJk26lkteJ4HbloBAgKT6nS2hjohepF1St3HLhRanMqguPYojErBxHEM1i9UY0U8Qzi+shMwVZrk4ZKsHMlf4KUVKG7P3Im0+JdBE2jsHI0S1tXaSma/M4hkhc5M/WASHRwVoK0ThXrmMBS2Pt7A/oP9Y1uwX40TVaU5JK3Wvtaq9J7WSdcyKNP5K6CNuqdm6FRkZSiYzQIE07JW2P8kPYBdTeGfDYCStp7fJwivqSxf484hCRSdQXLnk+V3aFOOsQVRlTH0prEllz1EizmqvS3K78qtSPqlEaZEb6U3PeGnaFnkIwQMjyNyRXEGXUUFVE1wCfDsaHRlw7diILJnmf5akVHdGJdFSkKPbKjGja9uT+J446u2W2BfQf66LtohTgXvt4HLkIXBzb+USn6AjKaJCOYie8EyVASevLlaNBUqn7VNLyJsoyDYYz6VHGXyzLniwfSQl93NjJ3EXVUkodVy4aRkRHKBtBd1DDzRy0sxyKYXmpXEHlZu6pbVkiv86b/7xT9hVQ2CkjuiGYtM+24BxReQwhHT0yEbGNbGqZucimaoA1iH2y0c8MsGyA1NozmWbuu83c+JjglVTxfkTepvOca8IO20c+8hG89a1vxVe/+lVEUXGYPM9x4okn4vTTT8fPf/7zvg0yIGBdBSf0g87+gP4j2K2AgMmjzm6ZbQH9R7BdAQGTx3Sec00qwkYNBwBEUYSPfexj2HvvvfsyuDUBBl+Eg8MX7miSvDVXkDC2Kyp5GfVK0shbGQFmFdvjxIqRqDytLJzNo8TmsEVJA3EZVYsbEeLy+HEjslG9opp9sQ+tch9zhki4dmwScxkDL+NqHC4cTBOVzTXoNe9yrLw1cBKB4sLymlkcO6nXKLYrLVEzgWyWUbWBBHG5uq8GY1Kc1q2u8FIEIOEMI7Iqt60zwlaMpSk4GnOKz2nMbiApI3nJzBhR+fnxYBOibPMkcvLAsRNM6SocPsGckFiwmtWeqW88piPWBbvV652h4KJOnVL+JoctUxqt3ElOU/lp2079HIyU5LA5sZEyD6w94kfYKuydylOvvInb7laTeSQRlXmshTx1+Tm02Kz2c9gMdGeojW6zctIkh62MqmmlrOiIzFxR7IxG1Ujh7DSTGJE0um/yBd1H05IiCWcYqAkDMiJM4vJ3U6iSdWDy6aJmAyotinizRtNG1XSe+RL/ZURBK9Wbmv84bVid3TLbAvqPdcF2jRYh6RTcASoEwGAk/rtZL0W0LbH9NqrGI2jh8tlU2c4yhdKseaIjrVyhlbscNidA4nJ5V7ZykreWe5E1oJt9kKfOfpoIm5KKlD3xbaV3XUj5J2sfSYRN5sLmDUupoJS7P1aR4wjuxOmoaB1QPLNRactjztAoxyg4g2BOFyAqr1EUx+CytDciBpORvb5OYC6x8yWdku+JC09AxuazCVdEWysn948aUZGJRtum85xrwg7b7Nmz8eSTT2KnnXby+p966inMmjVr0gNbk2C0ThfX1tGJBbcOUDMSGDC1LmKBDWYUN6KZPHQmjBqnKI8FZLOY8BeqQN0TGBFxK14SxQJx0zlpjWbxOY2BCLPKz5zVjDGzdOpmNiM7rkYkLCUyFhzmfo8Fs+2qRYTOn+kxnTbGHO8KcHRH8vxoxoHSYLIotrWDmEdbGIBoFpOPeHDAoyNVPYwueV9YdceolWMgdXQsWkPPfqeRq+sWNSMkM8trOruB5kua5ecniGcPFvs3E0TlRIknTZLUnDgaAI/s+elJJPBPZz71dMS6Yrfqbg+lNagpckIczrmh9J9MuiT1kVRaQY+R1NURKiYcNIGe9hc/2vlIMTVQeWodNlXjsHEuKicnjDn1SEXU1HJaT4hQIpXy6wnV5NI7UBqgco6bsTUyzaFSoxgprQKkSpUVF6E0yBGpyWIRRnHYUPZrSF28mEmGxQWz9qkQPzJCSClEWQvOLCDlrbZbTMpSq3bJ8swXVKHnauslqUqnTDNulSR7RchhW/NYV2xXFTiv/g1ldcqRxGGzlEiSesGiGFoYobMIEOVUV8SOJtghypSWtdfauXIqurmjR46k0lODpAIj1FEDCsfNiDP5lEgJmZfzHJIiY2wmgEqbCRQL+zSNRhknSWkr3FT3GK8CPCdNkFQf89fMe9uRU5eMOUciiHBVab+k0uBGMVJE1jlmQhCxF18dXNPvTFR8r7L6HuCcj1sJcjRM5znXhGea73znO/He974XN9xwA5566ik89dRTuP7663HiiSfimGOO6ecYAwLWWRhFpqp/Af1HsFsBAZPHaHYr2K7Vg2C7AgImj9Vhtz71qU+BMYbTTz+9fwOtwIQjbJ/97GfBGMNxxx2HvFwxiOMYH/jAB/CpT32qbwNcnSjqGRVeK5W9N6HRhuBolisQM2KBmaUoBV3lNX+fB5FfjTii2IXDZblyY+Rcu8YRcYioeG8UC0t9jBsCjYFitWJmM8IGM4qV1TkDMWY1XYRtRmwibG68lBLJmDs/3tHuKxj3qZL2onKwsi4d8szWR2F5Bj5QRLUipSDN6nZHdM1E1miEzQiEFLWQnLR2FUTSEWEbLCNscxqIB0t65AazkMyaAQBIZs1ANFiOcWDQUSKTpls9YsxF1rg77/FG2+h309kf0H+sG3bL0Jsdqu58rR0bsFgdddG2jCTbm+hVO1eerL+h7uSZ8uqs2WhbmkG2R4rjp+VfEmHTsoYSKYT3w2NWjVUUedQempzfJjRIukJuooY9s2PMSi0RHzERNi2VjfLLTDoJftpOlRUXGZEKxuSMSOUJoBgUEwEzXmb3KfpKgZURUg+SUCKjZmRrwZm/aiCxY0SeuXpy5FozGmGrU2KhdspE3sZhu+rsltkW0H+sC7arFzDOu4RHuvrM3KarxquhREbk91k4NgyPkJvIvdLIrfiSE2WilMg2KXWyqpVbAZJOBgKNrBV/HSshJdRxlafWZtIUGa0kFGm78xbghEpO02h0mUajVFJZPsW/fq5MSiPKbWTNUDmTiNv0n1bO0RCq3FchLyNfOSfXSwFRGbVkhAapGbd0bBbT0gqudp4m0dSC3tpNhWWSrzZp/37Pue6991585Stfwe677z65gfWACTtsSZLgiiuuwMKFC/HnP/8ZALDddtthxowZfRtcQMC6DsEBUTFPquoLmDyC3QoImDzq7JbZFtB/BNsVEDB59HPOtWrVKrzrXe/CV7/6VXziE5+Y/ODGwIQctizL8OY3vxmLFy/GDjvsgN12263f41ojYIyV/wAOl1hpcg6aEbfRq0wqZGUyZ1XhQcEZVraKfVfFAlm7aFMZV00KvQJupZxxZhNIo9iVDGgkwkbS5sxIbN7aBjNiG+0bjIUdYzPiaJDSA6ZNw720oLSLKjIrZOALkfhLDjZ6RnMhyMqsZtyJkPAIWrjVbBOlgpLFCjAAlrm8Cy4lktnF/lwIm2zcWVwbAJLBzJPWNqvfmkjaFscxPGm3gh0NuAhbPNhENFisUiWzZqCxQZFZkswehBgscgJ4c4aNArJG063UiajyGowXHE4QprM/oL9YV+zWWDAmxstlI7ltktihVPr5YSkRIKHFsL3Cr0YGP0+9yBrQLTpSBR4lKGNEiACorHxvlFh5aplrrwA3ZTTYHDbtRIZUD7L+TCuyou1sk5Kuz0r8S00i9y6fLVMuwpYqeIJHVbL+gLa2l+a2jUgFUdqMOFXWnvFEIBpw7IFowImgFGPJHRMhzyBo3popVSClsx5a2fy0sVL8xoM6u2W2BfQX64Lt0lJNqvIvI1E1WmrHtBkRIKE59JpE2xSYy1tT2kbmpdY2it8mEf2WdKyDTgZC2zINlJejVvRJpER0xNpJYh9HYyP4523EVoQVaxIkP7gQrBuoeJ9jfQnB0Rbl2COOpCyZMlDOi0ZS6bQQcoV25FgXM2JzvRhhNzAoGPE6olfQEdmkY9ekTb/LyWK8kbh+zrk++MEP4pBDDsGBBx44dR22OI7xu9/9rt9jWWvgjCRkKo2yTBcammOGKm6oTAo/mbxDZWdGIjCzaWpxZJ7amq08X0OJjCJujzOQCOtozWzG9oGa1Yysk0ZpkDOIwzYjFpYS2Yy4nSjE3DmhjJyreVQMNXQi8J00DmhTX0nZ0Dgi5eoCJU2wcoLElbJULg5HjeJxBF6qYIpmYpPsk5IWlA210MiM2EFqJ19GGKATPBEQpI6RqfcWz2giHjSiIwOIZxcrlWJwFtiM2cV4BwbBmjPs2I3aFAgNYKLOGlBcsqqVnT7YsYAOrCt2i6E7+bjOWaELRH79sm4HiNINcyL64dETc4U8LYQwvMkHofkoQ9OrER2hkFyAG5GSPIWUJYVGaSvkpGocNllz0l1WdhRBjWKMhBJJ6JG6PH9NnDfqmHW2sxrREfvpivYzqzApmIIoj1+InTj6pbFpTt1SeeO14il55iiRgKN8dlxy67x1qNxqFDTvXlFnt8y2gP5iXbFd4wVVE7R9tB5qZ41XQ7uji6iENuktXBHRkUz6QkyGHknnbyOZtAtaIx1CIibdhapB2npo6YhHHTeLWypzlEhfdITUHyMPE48SZweUdIs1BJzPsONinEEINx6TdjOScsxIpD0nAJhBal1msfJUhA19PhFOdEQROrqg3wG57oxzIjTSIRRDxVQMJV5wrxbb6sJYc64VK1Z4/Y1GA41Go2v/66+/Hg888ADuvffe1THMSkzYtL773e/G1772tX6OJSBgvQMb5V9A/xHsVkDA5DGa3Qq2a/Ug2K6AgMljLLu11VZbYc6cOfbfwoULu47x1FNP4bTTTsO3v/1tNEsV+DWBCQeo8zzHNddcg9tvvx177bUXBgcHve2XX375pAe3umEiSwxOejnmzFJHJIeNXknlKIKCwQp6NCNTmy3HzJLWMjIQ25D6SCYrV4eBGplV4eq9JRG3YeqZzcgmhTYigRmxE0NpEGEUJzrCbaSw4Ow6SiSlQgL+D6y5HqOCcdjlYg1QaqClTXLhKDhaAeVKDwPAy+i9Fm5lRscxtJHnjRLESbEiFc1oIh4spP/zkWJlv7FBbusSqTT3VpxpeNyTji0/JxpISO23hhUXEQMzwErqI2/OABucXbYHLSVSi8TWkNMiKWiR5noQgZXxQDD33Xf2B/Qf64LdGg005qS1E+OQCl7NMlVhk9LcUYGUIvvkytITlVTeqrAk0bHib+bL+pcrwkwIF/Uh4HHSQQtyUT3T1grVETZC7ayLtnVdH0ohhE+D9KNX2kbYZCZdW8MKjUjtImyZ9imRTlzE/baAmM1UaV/uvzyozKRXd9LST03Jkyz3x2uur5K+kEp5Hkwr3w5Pgg1AUWe3zLaA/mNdt10UvCIE0ilEAviiI14URwgXRSZRHxpVU9pRIjOlPAaCiSrRCBu1j3murJiclI4yTkWTDBNBZalPg8yczaxjI/jnaCT7FbiZI3VsN/vkXIDxYk7Dybh45Mabk/Pw6fBlVI1EG6XSNtooFffsrYm2xZ1pMWKM6Ccde8d3ar731SM3UmCsOddTTz2F2bNn2/6q6Nr999+PZ555Bq961atsn5QSP//5z3HllVei3W5DiG5GyWQxYYftoYcesoP9wx/+4G1jwWAHBPQEjuowd2AVrR4EuxUQMHnU2S2M0h8wOQTbFRAweYw155o9e7bnsFXhjW98Ix588EGv74QTTsBOO+2EM888c7U4a8AkHLY77rijn+NYKzBh0ELq3kS73EUpglvF1zir4eT+Y8HQKJMyTaRrViLQKiX427myBWg7SwCMGWEj+WxNwRHbqBpHwxbxdoIizYijIUxEjnl5a+UuZbV6d36mbRYZ6PmPG4zbFRPGuC1QqeEidRqJzyc3UTgR2yRa3R6BTpyUvm4XUTWdtsDLCFtcrkzptEXy1sYXYeNJZFepWKPpS/aXMrmMCo0kTei46NdxA1oY2WDhrSpNdOWafved/QH9x7pgt8aCqpCXoAIdgIsASeWS7T1BD6lsXoaSfj6ZWSHWSnptoIy6Zd2iI1pJt1LMpS30qrIUqrQBKk9tVE+TCJ+mUv5kJbyX8/ZQk8tmhUY6REcUbRuRFpL/lyptV4KLlXvT9iNtgjzKZsE5VRpJ+YynSmPArPpLDWlW6zNl21WlB7RSrkxBcQLl346VetPfR0+qzm6ZbQH9x7puu6oiaJWgOWxdB+HdbcL80cqJE2k4m6G0iyopKspEolGSlECRkrAOcpfjq0iuL42k0by1sdgIXadkcvtrzllScQ8uII0wiVCQgoyRRP9Sck5AOWc150AYGBm5FgoaupzVFSVjSvvMiY5B1fXvxGjfn3fe5XfWZ3n/fsy5Zs2ahV133dXrGxwcxEYbbdTV309MQrNn3QEjtXI61TfMS8E5oUFyzIiNik6pIql0R6Kma9MaGbJiTiEYwI0QCGOIuXEMOWm7umqxYJb6SJ2xmDObTEnVLoUnOuLanPTZNsau+K4Z1dNRMDMBzSOb1F68Lh0mmfv0BCNGonLb5nEDkCU9IG1Bl6IiOk9trSEY+kBG+ggViCbrFh9FDAap1YLSoLE4BjPtKHb14ZImEBVhcC1ijwaJUnRER4k7j0nQjDzKVEd/QEAVzN1mFll0j3RAAzohoc4PdYboPooKgJDaQTKnSfOlciGh9lCaD00yl1lKaD7V+wOxPS/6+f549Zj1h+ovgqMdGvhCI46SCTgnTBLHl9KrqCNH+wvb7Pqz8r0JoVBKzWw7kZo4jcprF9fCHy86KJ7jgrHzWo9b6bbObpltAQETBXXc6KJrJTqUCA10Z5qCUYkkoiNKkzqVhDIuNeyCu08Zl9ap6RRFom3At5Mqp06ao0F2Lm7VUiKVWeiqdnKYEFCkPpv9rFhUjlETx9OIqEgVufMkNSUpbVRreNfOmkdqO0h9WgZ4Cp714yffceb6Oudz/cJ0nnNNeKa5cOFCXHPNNV3911xzDS677LJJDSogYH2BKy3R/S+g/wh2KyBg8hjNbgXbtXoQbFdAwOSxuuzWnXfeiUWLFvVnkDWYcITtK1/5Cq677rqu/l122QVHH300zjzzzEkNbE2AM1ZGk7QLNynXZszRByOuIXmZtKmcuAet42FWaDKpKxNbzWv6+QZmAYmTmmmcMUvDpGPhpE0pjt37oOzvjKC5frOvzYsnY+q6f80qilbeKgqIOL9bdIksBUkz7uhIUgDaSHdLsMisWOVgqqzOlAzaSB1XuTtOVVRNuvpDdpsBXdEx0T4h/GibTViOoG2NNWGpj5rI92seWcon3R9c+DLZ4wCvWe0JrKLVg3XBbgG9U5ipvVEVkbhOEQ8awaIrso6qWB0R0+R5rI6YkbFzUbuP69fWZOiOKFo+0ahaDapWcpXStqyBlp100u5oW/EaFf2OHO7TJjuPQyJ75HM7x9hZesA7D0qpGk/EjbH6uhA1qLNbZltA/7Eu2C4tpRcJmyhq63fV1Id1JSu0o/JpX4jJq+9IbWJFCRQvYkXmfvbZVLKS7kjtXR0bYTQow1hQEloZyqC0/SpPwePEjkVpN8a6MimAX9LFE3OiTAtS67K4hiXDg0bVvGhb9XfEOO9LTcg6CulYmM5zrglH2JYuXYotttiiq3+TTTbB3/72t0kNqhd86UtfwjbbbINms4n99tsPv/rVr1b7ZwYE9BtGqbTq30Qw3ufipptuwk477YRms4nddtsNP/nJT7ztWmucf/752GKLLTAwMIADDzwQf/zjHyc2uCmAYLcCAiaP0ezWRGxXv+3WuohguwICJo9+z7nWJCYcYdtqq63wy1/+Ettuu63X/8tf/hJz586d9MBGww033IAFCxZg8eLF2G+//bBo0SIcdNBBePTRR7Hpppv2fBzGin9FRlbh83PBwMuVBqEZdOnSKs3cKijh78qK1Rq6ekAXg+sWMeliOb1paD5ZMU6zP4mY1fV3HqtDaMTs4z53lMha14BdxMzLZ2MASqlXDeFOmCb7CwWydA5t2kq5/DetvPd6/Z3HI21WJypAVnq62lQ4hBTCtvuJqDJ5eSqKjoz3ubj77rtxzDHHYOHChTj00ENx3XXX4fDDD8cDDzxgE2c//elP4wtf+AK+8Y1vYNttt8V5552Hgw46CL///e/XaP2RfmFdsFu9gopwdAphjCWD3/ko0Ty5qogYfd0ZdatqmxVh0fFeO/ZxRtF63n0CeRE0ktWZg1wVJeuOutF+Rt7HuvZXHR+gq5Ke6XaSv8tIH3M7jPr+iaKfoiOrw26ti1ifbFcVbB5bRW56V7sG3tys1j76z5yJPOUe+4DM91Q3C8D7TM8murIovbARuo5lInVEuGm099LSKHS8BnlHpM2g7nq4a8Z6i5LVfT+W8bTmNWWns9DbhB22k046CaeffjqyLMM//dM/AQCWLFmCj33sY/joRz/atwFW4fLLL8dJJ52EE044AQCwePFi/PjHP8Y111yDs846a9zHo1RCrQFRxks7KUSaSm103K11if8TYe9U3TedFKjKfbqOM/oNOCnKbo0TNOYEoWsmSJLobaPz4nYfs845Gwu11EVP3YhV93e+vw81jRi6vzfU9I2F8T4XV1xxBd785jfjjDPOAABccskluO2223DllVdi8eLF0Fpj0aJFOPfcc/G2t70NAPDv//7v2GyzzXDzzTfj6KOPnsAo1y7WJbvVL3QKefQKSklR46Hg0WMo5R2nbuKhaiYW/YSW0lKaOtHp8AITqxXkjsMqBajqx6Yq22sLdXYLo/TXod92a11FsF01qKNHEtStT9Wlq3RSBu1xxrA/ljoue3PA+g1N6JejLXpVnUfVOfvXpOZYvdixHr6jNYV+zrnWNCbssJ1xxhl4/vnnccoppyBNC65us9nEmWeeibPPPrtvA+xEmqa4//77vc/gnOPAAw/EPffcU/medruNdrttX69YsWK1jS8gYDwYa7Wn815tNBqVhRwn8lzcc889WLBggdd30EEH4eabbwYAPPbYY1i6dCkOPPBAu33OnDnYb7/9cM8990xLhy3YrYCAyaOXCFsvtmt12K11FdPFdgW7FTCVsV5G2BhjuOyyy3Deeefh4YcfxsDAAHbYYYfKyWQ/8dxzz0FKic0228zr32yzzfDII49UvmfhwoW46KKLuvqZ1mAdywM0ciNGiQb1EgXqPF6vqIwCdfZVhcc6o0Fjpij2vuqhewzHaYxOS1DojFhV7NND9HKiC+11zySNYNbRRotto1NHe71O9hgV96DpBwoaDMUFF1yACy+8sGv/iTwXS5curdx/6dKldrvpq9tnumFdsFsBAWsbdXbLbAN6s12rw26tq5gutivYrYCpjLHmXFMZk67DNnPmTOyzzz79GMtqw9lnn+2tyK1YsaLrxyQgYG2AUXXMjn4AeOqppzB79mzbv7p/nNcXBLsVEDBx1Nktsw0Itmt1YarbrmC3AqYyxppzTWWMy2FbsGABLrnkEgwODnZREjpx+eWXT2pgddh4440hhMCyZcu8/mXLlmHzzTevfE8djcyiU9jCSKdqXyADMu/ur9uXHNugF1GMrkrxVcIWjHWIZXRL2XYVQqXvrfis8eZm0cUIynOuElyhETKl3T5F4cpSMpYcs75/9Yq60JII5j2iS8ilFKThDK7WOpt4PiC9bzr7AcyePdub9NRhIs/F5ptvPur+5u+yZcs8dbJly5Zhzz33HHNMUwXrrN3qEygVZDy0ECrPzcuE8vHyCRjn3nFYjXAAL8e1OmkrTAjwmiR4QR5wIwk9kayMquP0NrbuYsJrFXV2y2xDb7ZrdditdQnT0XatKbtl0YOQUN3vM32U/DJL1faGjWF/jJ1iQtTastUJxoW1p3yUsVadR9U5V5We6jpWL3ZsNRXBnhDGmHNNZYzLYfv1r3+NLMtsuw6rs3BmkiTYa6+9sGTJEhx++OEAAKUUlixZglNPPXV8Byu/OEadNJUDxtOWxBNX0raZTAFJ9geK9xjFnzyrrBOmy+NYjFEnDFwUtcKAQqHQ1P1iHBCkNpipH9ahdGhrhnU4ctY5Mx4HY05hjNZMq3HcOp21SgesxjGTHQqbmrT9mnbd+2TS1QPpR527WDC7j2DM7iMYQ2TanFXWvFNaE8OmwSeaskpqynX1jwMTeS72339/LFmyBKeffrrtu+2227D//vsDALbddltsvvnmWLJkiXXQVqxYgf/3//4fPvCBD4xrfGsT65zd6hH0nux0FMQY59rNwCZUYF7vYDHuT1RM4j3tZ1xYB6/zvXbs43TMet59Asnv1EHqdLKsbWB0m6vDRq+1/x1UO2684wPYGF6du6a8q698Mer7J4w6u2W29YjVYbfWJayvtqsKVmynru5qD/edp4xdax/9a2l+5yNObSBZ7OWjO3K+7eOuLQSYGtupo3bTOGPFMXnX8bvf68ZIx2sQ1TimddeDXrOe7ri674fUlVzj6NOca21gXA7bHXfcUdle01iwYAHmz5+PvffeG/vuuy8WLVqEoaEhq2AUEDBdUPCpq1Qwx8+nHuu5OO6447Dlllti4cKFAIDTTjsNBxxwAD73uc/hkEMOwfXXX4/77rsPV199dTEGxnD66afjE5/4BHbYYQcr6z937lz7wz0dEOxWQEB/UWe3zLbxoN92a11CsF0BAf1FP+dcaxqTymFrtVr43e9+h2eeeQaKrHowxnDYYYdNenB1eOc734lnn30W559/PpYuXYo999wTt9xyS1dS7FhgJLpGI2WsrFAPmRXRNAAsT8FksdKFvA2dtord28VfnWfQ5ft0lgGmTaJtQCFj3TUOziujaixOANOOYrAoKftjsKSogcVEbCNvLHJt8Mh9LhfQJNrGTETOjoDDkJkYCD1SK582WUGD1PCpjy4y5kfVrGSsJrWLFJDZfm33aeXKRtMyRdvlX6lsW2ntJL87njdv5bpcQYo5Q1yumHe2G2VYrRlxxOXqVSQ0YrPCphliczk4cyfOmXc1xwUa0e3sHyfGei6efPJJcLISP2/ePFx33XU499xzcc4552CHHXbAzTff7NUy+tjHPoahoSGcfPLJePHFF/Ga17wGt9xyy7SswWYw3e0WUDxbvayq0yhzVZkPqpjVqZ7lVmeZo/p0RMpoG6hfNe5cBa46ht/PCCnAH3c0zujbWGAVUTfOmY12McE8W0IjZoJp0m+PCGMPin3cdr9Nj2M+i9uoGo2uMVK3yNA2O6mRlFraS00qi4lMVOrsltk2DqwOu7WuYrrbLu8enQTMPKrLEni1XMt9tPIm48ZuMqatTeTM2T7a9hk2ZB/OnH1kjqljn00uwMv5Gs9TKC8ypspjCOgentMqZoJne4Xr51Fi+7ngJOXDjZd32HzAMIxcH2fdbZr64TEuqANUlxpEUDUHngjsvZSPMzLWxznXmsaEHbZbbrkFxx57LJ5//vmubYwxSLl6w4unnnrqGg3HBwSsFvSZTz3ac3HnnXd29R155JE48sgja4/HGMPFF1+Miy++eELjmWoIdisgoA/oIYdtPOi33VoXEWxXQEAfsL7ksFF86EMfwlFHHYXzzz9/QivEUwpaWe+a5SlQRtJY3gLLinoiLBtxUbWRIejWcPHW9kjxN23Z7brdgsqLY6g0tysKWiqPs8vISqlZQeVJ5FZTk2YRZTNtE1VrkHbSBGsMFG3VsPlsiGJAFO/VOrIrIDYCB4AZ4RIewaXQKz+frQJ1eWud+Wk0J81Ez/KOqFqaF+22lGjlxRvauXJtqdAu21l5wJZU9nhprryCj7ToY1XkIIk4kvK6NyKOJmk3omLFZkbMMSMu2s2IQ5b7JJ2LYWbFSuseM2+7wVQOJqenYtF0xLpgt4wFqSp30Qt4xwqqgeDcW0G2K6tkNZmu4IoogcrSsr9gAmglvVXjquiZiJPaKJ23f8XndwqkjDfXzV0Eszrti3m46Bb3IntVUTUaJUs4kFrTritzQARjiEk7savbsG0mmB0TF9xrF9eiQ3yEMDPGDXr/jOaAVaDObpltAf3HumC7ekFVkfjaXCflClRrKe3chWnl7CO5tzkREfPExUiOl2CwcwR/7iCQlCycNvcjVrQNFHbS2cYEnGoaVJ0GKXjdCSciQqJ2UUzaiW17kb2aMTLO7Hkk5ZzHO0/BnbASye1nDN61cyn8NKrm0w3NOelRcl6rvuPVmds2nedcE3bYli1bhgULFkxvw0FFR4jqo6VBZm2wrHDI1NBKqNZQ8bahFVDGYRsp+uTIMPKhwmHLW22orPjy85F03A5b1CwfuDhCNFCoLUUzqMM2AN6cUbQHBsFKR5EPDNp9Cie0/KwogUb5QIOQ90qaJNPK9bHx/fBTGqTqoDuadq60ddJyWdAcgYL6OJwVD3GbtIczhXYZ5m5JhVWt4lqOpEVfmisMm7Ycn8M2EAtLfRxIBGY2i2swmESYERuHkTsaZiwwywpecWew4OhNSrOJEiKn9WrPdMQ6YbfGgEsM9wV4fFpf+ZdQgT3Kj+DgkXEW/B9+bhaRUmHbKneTEwM6CelUgrT2Lk48Og8nkwrb9ug63HPaqs+7BjULUIw4Q85BYs5JIuJECaFIJZwhVdR5Q9lmlaIjne81+yfUORYMouRdi5jbNv2t4HFptzlRCC5OoPzbYcMnILQyJvocYQsYG+u67TJzoyqKsofRBG+qFLqJCjhjEVi54MLgbAZnzKY+0MWqJOKefTSOjhAcwtiHiEOU/VKW2yWHKm2jVhJCEcelAkxJaF6zjVIiy4UxHiXW9grisPE4sWOh4+KRG68Q3J1Hec6NyPVxxrwUEmub4MTpGCPUUi2raZB1dmC0749gtTlt03jONWFLfsQRR1RSFQICAsYBY7yq/gX0HcFuBQT0AaPZrWC7VguC7QoI6AOmsd2acITtyiuvxJFHHom77roLu+22G+I49rZ/+MMfnvTgVju0Lv8pGyJlMrWiI0ymUGUETQ2vgBpaWbxteAVk2c5WFJG2bGgEWRlhy4ZbkK2CSqnSHLKMtqm0+obgiVtxjgYS8PJaxoNNiDLaFg82EQ8W1MdoRsuKmrA8A2sOFgdSCqxc0eYDJCGXk3puIFRIuipihUY0LOmqTtYfHfXP7OXslOl30TaTF5opP5Lm2tJrm6jaqlaOlSbCVm5f1cosTXIklY5umVevkERk9SiJOGaU3MaZzRizWlHZjjBnRlyOMbIr5IV8f3kZGUNmLg1IBI9cj/Gy1BgVvOnoD+g/1gm7NQpojIkxV86CUn7oCmonXdisJnMS4RJkdZYLJ0vNo8SuHCsSWbOf37Fq7N4XV1J4itpF5WcSSiLj1dFyLzm+R0oy66AQMhJVo0yHgppYHjsWrp0X9EcASJWjNULBLn9KXS0uEhMaZMIpJZJBJNx+lkgcXdNG+cqoGo8jf7yCnI89N3ce462v2Svq7JbZFtB/rOu2i0KV0RVOTrEq4qKVcvebcuWZICVYVDKbSESFluYp6H7FD3ZMIveFGBlJoSBzB2Mfo4gjj5ytispIuMyLv0pw6KRRjtHRNkXHs2HsUcFQMLRywsIi0UZPUCROICglsoy2RUnDi6SZcQnBIcrxRuQ86LmZdixctFFwZgXYfNooYWwoX+DFlLwqnCDHXKsSG+n8TtUakPmfznOuCTts3/nOd/Czn/0MzWYTd955p68aw9g6ZTwCAlYbpnF4fjoi2K2AgD4gUCLXOILtCgjoA6bxnGvCDtvHP/5xXHTRRTjrrLO8ROhpCVI4Gyq38v06bTlxkZEh6OEVAIB8xXKkK4rIW/vFVQCAdOUw8qEi3y0baiEdKo6Rj+SQmUk41d4KgpNnZhClyEU0ECEu86rSZox4sMhJk7MHIVtFVC1Jc0Rl1E5ISeRCXFK6FiSBP3cRNsa441Fb3WzH8a4TGqmDIkIjCk6+X2sns58pV+i6RQRFaFRtVTvHiraLqr04nNn+5cPFeZtIWzuVyMv35ZmCLI+nla4snM04s1zuKOaIGyaqlmNVea3npImXC2cgGIj0v4Ioc/wEc/upSciMM5XXrPZM/QTY6Yh1wW6ZGDgdvZ27dUR4/eLxbgW5SqqaSjtHEbey9sXqbLnKGnFEVSvHpfCRN07FYVaNvTHV5V9EiV0dZrxehpqOtwpd3+ooNs0rQNshmU/l9U3UK8kkiZIBUpMxlKY97hgWzVsbEO69pt0UHLw8Pk9chE0krt8WzPWigE50hEWxXzy3RoSk0r6XOT7jqUNUZ7fMtoD+Y12wXeOFicAoKWHuaK0kiarVRHE8XQI3vyvyUbtFg2hUKRaurA+NPA3EAiOlSMdAIiyjR+YashxnVEbKtRK23JBIfNtonlPJhc39ZVzU57dR+f6o226KxoD9DC44IjOXTFwechQL2z+QCCs2MlD2eRE2zjtKH3VH1QrREcMuIN8Bue6aRjy9tvTOVRlhkjVURHs6z7km7LClaYp3vvOd09twmJuL1uyQrkaDbrccJbI1bGmQ6Yoh66i5vyuRDRUPX3t52zlsrRyypELKtPqGFAm3P9JRM0I0UHwtyWBsxUtUlkOlZVsqJOVN3uigxeiSJqEJRYY1OCCLtmbEeTMPVmftjNIpYVp5P/DUGTIKTJq2tVOwk9o5aVJrS2Fs5071kYqODGUSy0sn7cXhDMtHTDu19Mh22Ze1qcMmIUulSVVDiSySb8uJaCwQN9x7jZAJddIAOrnliIW71oZKILXT06RU0HFDEXGYzv6AvmOdsFtjgJEfVQOqjkaphIngHbQY9wOelj/meaYI5UfYBRIeJXaiQH+AjT3S0v9hphOPyEwwPOctshMMETHPeaty2Lg3gWBjCrVqRhwcItDBheujoiPOcRKWshiPcCQljar465bLzORParrg44uLGGdvQHBHiUy4XaSLm5EVGomaEURJhbR/kwgiMcIDsa8SaR054qwxPu5FuJ5QZ7fMtoC+Y7rbrs66geOFFW9TyokZEWEj6sgxraCN8rfKnUoktCe4ZCiRgjnxpUbufuebOcdAaQdGUmnbaS6QGodNKkgzvzIL1l2PQKnkzQVkqSzOuICOnDCJqhAmqau9xqPELpLxKLHOWNKIEJm5JHHSooSjUfbPSIQ9DyrA5ilmk7Zb3KN124praa61V8fYOmy+gidIm36Xk8W476tpPOea8BM0f/583HDDDf0cS0DA+odpnAA7HRHsVkBAHxBER9Y4gu0KCOgDprHdmnCETUqJT3/607j11lux++67dyXAXn755ZMe3OqGrdCuFKnUrqFzQok0tdVGhqxsf7pyGOnKgirZfrGIurVXtNH6e8u201UVEbZcda2+AmVSP4mwJTOLaylTCV3yCs1fA0PdEUnsJLKjBNrUQ4oSoKwDwpKmU8MgEre6z5xdpd3H0LbWTso/U9qrt2YpkURcZPlIhhcNDXI4Q3ukjLC1TIQtR9YiETZpqAmqckWfiiZEsbDROZUruyL2PHxhAxN1aEauDEBbKiv3nwjmzhWEFjrOUBsj30dnf0D/sS7YLXOPjXWHFNLLRbuI9BAJa+HokeZeb0RuNXlGImz0OYo5VF7SeOhqsnbXzjx3jAsok0ivqiNstJ6QSAYcJVL40tOWhkki25S2KQgtp+dF1k7p+w7REW4jWsLS1L12wpGUtmxAcLhvgRNGAaGherL+IBE2hoTQ4A2rgrZFIqzolCClXswYEcVgpb1nlALPBaG714QdO+3LeOuw1dgtsy2g/1gXbFcvKCJohl9c0QfYaAgVHdF5Bm3nPH6kByTaFpW1aCMORIYCLrSlQTYjjnYpz9+OBAaS4jgzm5FlB6UJaZN5RB2MgFPOmSc0YqKDKk89yqcBpTnzKHE12aIEIirPw4ukCcSNoh03IiQNF20ztn0giSwV0pQ1GkiErUPbjDiahB5ZdiPizF0vzryomnF2GKlprLPUzqU9uiqJZGmpvCibrcm2GqNd/ZpzLVy4EN///vfxyCOPYGBgAPPmzcNll12GHXfcsV9D7cKEHbYHH3wQr3zlKwEADz30kLeNjcVNCQgIKEB+TLr6A/qOYLcCAvqAOrtltgX0HcF2BQT0AX2ac/33f/83PvjBD2KfffZBnuc455xz8M///M/4/e9/j8HBwT4N1seEHbY77rijn+NYq6CrAkzl0GUiqM5TK5+v0hbyUqpftlJkpeiIyVtr/b2F9opie3tFG+3lZb9UGClXDVKlQQNlNrchYxgoV7ObRKSERti88QoOnjiZZ1tINRmBLgtn6zwFU6adgZWrStCiu8izUj451kSJan4DOheTqtYltC7k/AEgk5q0lS2i3c5dDtmqVo5VpejIylaGlWU+W3skR6uMtplIW9bOkZX75mnbcsJVnlav6BO+d5Q0oHS3EALjDC8OGz53ZMfVyiXyMqJQjL2M5im3oq71JH4sdU0oXodJz+rAumK3tK4OnnDGoGEiPX5hWBONirmfYJ+QPAZTkH4giTCQOGaAKu2QlMrmjPqryjOL7e0R99zVRNiolL9oDCAqc7KiRHiFXo3oSWdZDpvDRvLZinMc46LRXC6ST8csWyECN/lhJG+NE/GPaCBCYm2yhDGcgjnb3mnjK0VHYuGias0IUbOqnSBqNsoxmL6GbbM4sRE2dOazVcn51xUOn0hErM5umW0Bfce6YruqoJSy0XUKm+skXYRNyw4BizKKw4j4hc4zICqjPiKHLss2QWY2B1Qw7VhOjCEp89wbiqORO9bBDFXmrSUKs8pnUyrtiZStGuXcCtkAJ4Bm2FEqiqwwie6wlf77id00to8wEKJYWLGTIke/1EBoCCKwFmFWszjvmY3InodlVMSOSURz9ZOIedeI5v8hLaNnkoh4SEmiau570nnmf2ey4nutiaqpfkfb+jTnuuWWW7zX1157LTbddFPcf//9eN3rXjeZEdZiwg7bOoEOwRHTp7OSEplllhKp0tyqNGZDLeRl21Af01Wpc9iWp1hROl0jUmGk/AWvddg4s/vMjDRmrnQ7ucmEsJMGMZJCNIvPF60UcSlMgjyzDqbOMut4MtX0FZMI/dO7FugWGqmDowCSQ2hN6Foaqpw4Kq09SmQuXdsk7g6nEiOlqMoIUYEsnDPXBgrxkXykMJF5axWkcaoJxaC4ds5hi8w+2QCAWW6f0hhFicBI7Oq8zSwNXZortMh4DSWyODdmr8FENUd0njkD19EfEFCFXu81DgZWKqLFgiFWpZOmmKd82iw5LyORtDUKRzLXTnNl1W1lLkal/zCifKaJIIA3rtjVE4qS2NF5YmHFTaLYOW9UDCUiFE5BnFCqhtkVbaDbhPD+ggu34MW5J/JhaOpxM4JK3QRDeYtoxfklnCGtuC6dDpudZA1ESAaLCVQymHRQIssJZTMhjlpJiUwi8HJRjkUxYGrYRTFYSZFjUexseK8CFeOkRNbZLbMtIKAKTPBaNUDTT/eh9diomqClYJPco845j3EiNFH+hsrBy3YsIuTG6RLMUpllxNEun9NMCTtfGUwiz0lzbXc+1HGzdSwFhyiFy0TErWiTlI5KqRVx3si8jJGamVQ5V3gOG/cokcaGxo3IUh5nNSM7p5nVdP2DpX1pCo4ZcTclMhEMiaXPw1Lpuczs9WUy8wMe1klL7Vya5ohp4rAp8vugpE97HUs10oqNjFPccaw514oVK7z+RqOBRqMx5nGXL18OANhwww3HN6BxYFyiI08++eS4Dv7000+Pa/+AgPUO0zgBdrog2K2AgD4jiI6sEQTbFRDQZ4xht7baaivMmTPH/lu4cOHYh1QKp59+Ol796ldj1113XW1DH1eEbZ999sHhhx+OE088Efvss0/lPsuXL8eNN96IK664AieffPL0KeZIZe290G2ZFJrlkEZWP8uQlwIZ9G9eUvaGcolV5SrKqtxF2KTWnuy9rQHCHFWGgq/KXC2kmFu557gZQZb0TJU2IcvQtMozsAp6QNcP6GpMCq9ae5da20icVNpSIlOp/n975x9sWVXd+e/e+5xz73uvf9nSdEtsJMhUwIwy2oQfxknQJsI4Y5mEWMOMsQAtSKw0CcJEYaKCmWFGkWiUQUkcgqaA0jFEU4YZZhBJ/EELVFMmgzRUcEKgWhvQ5mf3u+/82Hv+OPvH2uee8969/d7r92t9ql69ffc999xz77t3vb32Wuu7fIQtr7RPx5rJK+Q2qpbPlMhnQiokAJTTL6Ec1HtZxeAlkhJZROIHvl8R2fVPdRUib2odSiddTqJ6MyW5rlL7na+K9HkzpP/cXK9/NkxRhF2oxjyzMKxqu4V61819o4UAhAljlyYoBHxRvRLap0QmSiJVVkQjC1LV68qwmzxT6tBzsCO6FnZ+NarE9SIyrcdLkhakErI7nLaPs46UyLqfHOxrmlvWv35DgtgIYCNTTso/C+nlMgupiVVeoSrCjj9NU3f2ucq1l/uPXqsQvp8bbQ+Q9BNkU3V0LF2Xkmhb6qNp6dSE78PpUiNVP/ORNCRhLKTy0TaQ1i2mOfbvhZzX/4Euu+XuYxaG1W67aFSNzolGZNhU2guQ6KL0kWdTFEBi1zxlELkwM4MgyCMTQNloW5kDTnQkS330qDK1IAgAVCTaVBmDolL+GPf/f7gNkLVn9hwvSYHcRtWkEr5diUwq336ojrDZ16RNZ+YC7UfpvsJKNXushXGWBUERmga5aTL18y7a5l7nRKowac8xmSpktA+b6zupgugI8pDNJXTp150oZ2BmrFAfzfgqg70wWvt2VUB7JK05t5A92uZacz355JPYsGGDnx8luvY7v/M7eOihh/Cd73xnwa6zjbEctocffhjXXHMNfuVXfgX9fh87duzAMcccg36/j2effRYPP/wwfvCDH+ANb3gDrr32WrztbW9brOtmmNUBbSjZnGcWBLZbDLPAdNktdx+zILDtYpgFZo4114YNGyKHbS527dqFv/7rv8a3vvUtvPKVr1yoq2xlLIft5S9/OT75yU/immuuwR133IHvfOc7+Kd/+idMT0/jqKOOwrve9S6cffbZixoSPKLQItYq7AronDbDdr+1r7WaroyPqk1XBgPdJTpid2ukQNyAtR5lUkDZqF01pf0ub1VUoYl2UUbXpVq6yZuqitoWeGjd2uG9Q9EpdWMuSPyHfG9NGmrnjUgWbUTpdqF0ZVDa97jMrehLPo3CRtiqmWlUts6w6qhhU27nGbHgQZlNRLtdFb0WV7ND/mbjSvaPgqk68qkr3qVeKNaC3QrWI9RzGYQovhbwTWLrejYnYW1QWgnrIlHIs+HvoytQB5pCIySyZgv2y1xD+iLy9l1jIUNTbJnIsDucqajpK20x4HZ8s0T6sZRxDVsQWCECJEIEsQ0pEQlz2N/S7sSrNPU796rfQ2JtbJVXqPK4pgYAhBKo7A61zLoEogTJkmgIjdhxNpWGaNtUH8nUhL2GbFjWP+vXbVpQt2sRzrYlob2LkQngavRIhM3fXgC67Ja7j1kY1oLtaoOKjQCAUWH9JZXy6x+ZVCSKk/pIjyHRNpGUMJWNBskEsGNZKiSy/v5kCqhsja9WQGVtUmVUayYNgEjwiLYEAupa20M2AjadS6iEtEhxGVekHUBFa7fIEwr6HEoSextEmWiWwkSmfO3xun4a1a1tnAzRtnXWzq2zx67vJaGGLZU+8pjJUMOWSAFZ1mswUeWhLrDKQ71gGeoIQWvFytw7STovQw1bUcbCMkdgs2eh1lzGGFxyySX46le/ir/5m7/Bz/7szy7UJXZyWKIjExMT+I3f+A38xm/8xkJfz7IgUutxH7Kh4kj7RXMOW1H5ovNcGz8ujInm20RH6jlt56R39iaUQd8uFMrpEtVUcBLdwoE6kkYTwZSy8P1JhnYUFjElkhq3kD4QxlS4o9LG9zKJinhL7Q1YSV6rtj1Wynzaj6t8gNKmRDaVloQOPUx875Mi9+mRusxRVal/Tve20HQHmgYJIOqj18a4epFd6lBdilHM4bNa7Ja2qTmqJQdQonbOgDo10h0iRVhM9JTyqq2VNih8gb2B7rWooJn4++CfixTBRymR3mEDUUEznak9bT2EMrLwmMgSolomg8KlDP3kRu3DJkgqpP+dOGXIxIuOJP0M2qaaJ0UZLaJcOqdUElXm/j8kRPnM+DRIoWTksMkspLWn67IwnqrTbpKpCaSTtUOWTvWRWuctmax/ix5x2KLxBKBS9yJD+mNj3Eqb+NYczKZqx7Zr4VkNtkvOIoBjKk06GpJ5GTZKhB9XkHZhr8vCb7igLGDsWgBJ6lPzRJIGlWyZA6XbrEl8TzajZHDYABhjryLs9Q6/HhGcNNrLEqjtlFP+ns5LX+6Rlxpli+hInRLZ/jxhvykWHUnIc1FbOUGcMCcusq6feIGRyVRFjhpQO25Tdq6viMNGREcSaO+kiTKHcE5wWQDWkTMzA5KWOh3GVQVdkpRIIiBD17JUPVI3nPYuZvtctbFQa67f+Z3fwW233Ya/+qu/wvr167F//34AwMaNGzExMTHWuUZlbatEMsxSUxb1T9s8wzDMcqTLbrn7GIZhliMLtOb63Oc+BwA488wzo/mbb74ZF1xwwWFe3Oyww9YC7S4POp6FZjqM25WuTNyfh0o/h9RHeElZGoXLqciF1lH6pYuqVUUZ7VBEkbS28HLXNs5hRt1MR8SpmUZQtUbewi5+XlY+BUtXxqcnahOEC6hceCQd7qKgjT5sLvXRVCQ9tLGLMptEuT8PiWS0RTXmg6napc/b5himCyplL913Ugr/RTRC+MhbqhBJ/PeSUFTv7Na6fjJUWO9wkbrpXEY7vkAjndmElMhmmk+0U+wjbHFqz4TdEZ4g6ZETmfItCaQIPYIkER1pfkUNEd2gPY3qByqINKQd+hTEovQpkbooo11e6aNnAjK3565M9D9A0GOcwEoqfauAZCJBatMgk37m0yDTyT6yDZP1eGoCyWSIpgGA6E/V0TS0SPm7KIJSXljByIT0YSNvzDzTu7vslruPYRaCKF1OhTQ67dpzSAlpRUegFExpo20zAxj7HTdpGr7vJEXaCAlhx2k6AZ3QxHKHbI2yKRLdT5WIRJHc70O2TdB0prDO2sTpvIyyinIydtBxM+3StzSRwkfzJrIk9NJMaUpkiLBNpsqnPNKxi7RNZQp95aKEAn372nqJROrsajEDFDZqWc5A+PHAt78y+aBVdARlEcp48jLOCpujD9tCs1Brrq6172LCDhvDLCXUqDXnGYZhliNddsvdxzAMsxxZwWuuw3bYXnzxRaxfv37uA5czriCbFmaLlsJ01IWuYTycMytaZPmbNOufaOTN3achGtE5O1+R3WpSQ1fvFtCizSA64tGV370QtFbB/jaNxuGjQF+J2xCiQiPGwNfJ0BowrcNriptPmjif24Sd+WZ0zJDXo/Vs0bO5d0xoUa+jrZgYsMIGdpeaSqZLEWrX5JgRuLpId/hryM1nF4dVYbcaNBtFu9vSGBhfZxEavQPwjVEp2hhoQzIKJoefSwnhJax7SRkK6+1ucllUPjpudNiF1M3aN3ddSYhAZVmIpK3vJ5HoiBtnitSwKel3uen3sRNq510NW5r5ejaZpEgm6loyXZR+RzghO79C5aEWL1PQuathM1EUzrcKULGsv2sVkJBoXtLvIZkarltL109CTkzV5+vb31kfol//YeTElK9hMyrzrymOqsnhkKN7w+axQdxlt9x9zMKzGm0XxQtRYLhBsJGh1klIjcrW6gslUc3U9VNKKkDW0R1IBWPXbFpKSBdZFzK09CBjAOiltu4ooY1S4K9GZiGiT+Xu67GTwXdRL4VpX8MWsocO5UksaBY14m5fe9GWAVTcxIkvTWYqRNiIrewnNKomWyNs7nemBHpWOKqvQvSuJwFR1HWBohxAONERMjb5AHr6oB9H0Tb7d6pmZkhWWBE1RjctNWxa6zlr1w6XlbzmOmyH7V/+y3+JO++8E9u2bVvI6zmiGCF8nxoROWyuylOFlBMlQ8G6kv6fsCT/mENPoJA6V/82ftwlWkFTBmNHzl5rFTs9moSR27rG1/PBkRFtzsuc6ZHt6aDdaZCm9Xbdh82Nw/0Vcd5K0rOJpk+1Omz0dc4SxqaprTQVKh63CCE0DGMQNhBe3EBC+OXvSIvFLkxHo1nDaUWLwWqwW220ff40BJRflQtAuk0hIHVF9Un4nnZmB0/GKmhuXPdHK/0YAPIy9HIrSQ/Doet1amqNHmtU4cwVxNNFSC+RfjGREpVIJcL3ccgVlcG2u7RBpyCLJI1EPJT9p53kZeuCQSjphUmqoiQOW7stlUpC2mtXaRKrUBIFyNQ7bCENUk5MBUfN/qZOmsj6QNJzTxRSImUSxlQlkzJf4akuu+XuYxac1Wi72lSd2wRIjNJ+A6U+1qZg54UfCzUIAhRSwfix9OmRkMqv9ZpumaOXTlinDRDCQDpBOGmdQlilXRU2jnpJfYyzTTOlwozdxBr029Wwc9LfkqZBlmScNDaPY9srh8Z9JX2Key8JTlo/kVGfNbdhR9Uge2Su50xmMQ1R2tTHfBqyOGTnZ2AK67BNHwxO2sx0GBc5tB3XfYydAIlGRZTNadprm9BI22dkXqzgNddha/y+/vWvx2mnnYZHHnkkmv/+97/PvUAYZkRMWdodn+ZPOfeDmbFhu8Uw86fbbrHtWizYdjHM/FnJa67DjrDdfPPNuOqqq/CmN70JX/va13D00UfjQx/6EG6//faVYzykqn9U2JE0MvFF6CJNAStcIcnuqEwTXzzuZJpVqnw4XAmNzBXmV2hE3upxZcLY3TcXuoojT4BLgwzRNt+rRJNdBCrrX2tt2yd1JzNhLtLUDemThuzVR9eEOOLmOrppkHROg6gPW94m5U/SIN1Tj4JQCrDfMyGVj8JFkTSlfHRUppmfV0kS9YNSRJqX9n0K6Q4h9aHebQu7+24sx80z0h27PYssjX3gwAFccskl+PrXvw4pJc4991x8+tOfxrp16zofMxgMcPnll+NLX/oSZmZmcPbZZ+Ozn/0stm7dCgD4u7/7O3zsYx/Dd77zHfzkJz/Bcccdh9/+7d/G7/3e7y3qaxmHVWG3LPSr2pb1pgRgrJhRHT5zObxAyIeTsO156ls+zTeOXrmMAbqbO51XOORFQoJsdV7W45mOHWSaZtyLdooVJtKQBkklqftJkJx2oiOpCt/HuvfaHEY0Som0Nj5JYdw460PaFJ5kKjZAfkc/TfzusMoLb28BtKZECiUhU9fnjUbYMh9hoymRqteDIGmQISVyMlyjnUPSg7FS/ibJYqERJ7jQIeUvjJ5fT7Yuu+XuYxac1WS7gOHsmLZsGf+Nykv/3aGtjABEAiTCpUQCIcJGzhPZTHJbA34NJIz26ZEqkZD2e6Iq43vnKhFsT09pzNhrd1GsmVJjYNsHzFTatzIqdDxus4+ziY4on4YZ0sHpuKekj571EjJWcVSNyva73z0i3y9z2x6hCKmPsjhUC48AMPk0zMEX6vducBCGpkS6Nkv5oFVohIo4GR0ipzQNci5Bo3lF2pZozbUQzEt05KMf/Sh6vR5+5Vd+BVVVYefOndi9ezdOPfXUhbo+hlnVmKKAKYaNj8v9Xize9a534cc//jHuuusuFEWBCy+8EBdffDFuu+22zse8//3vxx133IGvfOUr2LhxI3bt2oVf//Vfx3e/+10AwJ49e3D00Ufjlltuwfbt23Hvvffi4osvhlIKu3btWtTXMw5stxhmfnTZLXcfsziw7WKY+bFUa66F4LAdtqeeegr/5b/8F3z+85/Ha17zGjzyyCO44IILVpbhoIIjLsdZKdJUlRSkZ4nfEZVZ6ovHU/t7JlNQNto2UQpMVyE3uLC73JWpJfzd2EFr3iTiaJuLUsVNBUkkqqNoE7oKTQt1kDGtRUdcYVwQHxFtAiSzQIVG3CMMYtERKt9fkCa8caHt8A6TIfVsmtSwOaKImVTQc9SqqSTzEv8qyaCsLLZUIaqWpMpLitNamiyRUa66r2eLJMWJnMO4tSFLsNuzd+9e3HnnnXjggQdwyimnAACuv/56vO1tb8N1112HY445Zugxzz//PG666SbcdttteMtb3gKg3vE96aST8L3vfQ+nn3463vOe90SPOf7447F792785V/+5bJx2FaF3UIcTZKiGW0L9xkbSRNKQNiPpjCI6r18TYcQkCJ87mhmAC2wpxG2CVJYD9Q7y75Go2rfQQbQWjyfkfq0CRJhmyA1F71EomeP7yex6EiQ9Q/1bDSqVst42+Odjc/6deNXAChzP1ZDNsfusmcJqha5/2a9WxxhS/xjpW/M3QstBPqZr0uLatTImIqPGGV1xpPUj6Oomkyiuj1PJC4lQ83G4UTaOMJ2xFkttqsNKukupByKsGgAyNtT1tpmFdqjapGgia78ukdq7dc+xmhIO06THlRqm9pLg8TWAdfj+phMKRTa1vBaOzAoQyStjrDV84U2flyRNZIm7ZyGXovLeohqdhEyDaTw4x5Zo/RJplAqhSvLQ9ZojO1+Sy8oQiX7ZyDyQ35sZuqxmT4IPTjox65uTZNxNchRDWo1xnJAREdotC0vo7o109BhcIgxm2PPygqOsB32u/CzP/uz+Na3voWvfOUr2LNnD26//XZcfPHF+MQnPrGQ18cwqxpdFp0/APDCCy9EPzNWEWs+7N69G5s2bfLOGgCcddZZkFLivvvua33Mnj17UBQFzjrrLD934okn4thjj8Xu3bs7n+v555/H5s2b533NCwXbLYaZP7PZLWe7mIWFbRfDzJ+VbLcOO8L2Z3/2ZzjvvPP87XPOOQf33HMP/s2/+Td4/PHHccMNNyzIBS4mxqlpqQyQthGzyiCs8pbI+mGHM+sj6dudFlp/MFEvoNN+gmKifjuzQYkJ5RphS7JzopHbTYPhCNvo160r7evZhmRR3W5FUQRlyLLw+dmmLOrXC/gdV2HC7lIUbetQidSGyHUTKX9tQuStqIyX9S9IA/B6fg5Z/zEaEgqpINPQINvPKwXlompZ30fVVDbho21JppCkLsImSXPeJJLG7dHdK7vTk0h4xciE5JYLPV7hqtFmaHfezQPA9u3bo/mrrroKV1999VjP0WT//v04+uijo7kkSbB582bs37+/8zFZlmHTpk3R/NatWzsfc++99+LLX/4y7rjjjnld70KyGuyWw0eLBJHyb9oRd4wBpN0dlkb4nTpJIlP12EalhUBqd5BTJXGoqL9biZLo2QhaX4XvjIuwURU02hgWaFc/61I+m8hUqFtLpK9t6yUqqt2garxz7j4KCaOsSmRlfxNZf/QmIMiurvI1sNLXz+i89JLiUXZDx26wzBJ/W2VpiLaRqJroTUTKjz6alvVDw+xeXcNmVAbjrldlRBlS+dcW1eo1Xr+vSRYSQgzbnVHpslvuPmbhWU22y9HWLLmrgTKtZ2ujOetWLxrw32sJ+IiO0FVd6w9A9EnmkS5J/X/pj+mrFKmtR02lRmbrqEptkNv1mItYTaQStpQXlTG+WXZdtwY/dl+VZgZCG0oKb99TKf36I5UhM0EJgcQli4nQeiBTwtvcVMJH3pS2TkpeBDXIYqZ1bAYHg3z/4JCvVdNkbPKBj6pVgxzloF4f1yqRITOhq26t62/v21ItQKRtrjXXcuawHTZqOBxveMMbcO+99+Jf/at/Na+LOlKUpv5JpfJF6KYqfCG36JF/mBNTUP2XANS9cgrbKyfp1x/UdF2K5GD9dvb7CXL7Rci1QOVTIkPuUjP8rVoK/GmqZJOoaNN+mCvyRTBlHlIiqwrGNgoUuh+cioqk0LQ5SUMy/W2HmEi+PwiNGBSuV5oxUaGtW8TVogRhcdclAU7THN3vMNa+xwoaqZHSLmxUbwJJNuHHac86bKlCaqXDk1RhvRM56JEeUCQdq59IbwxpHxYlAFnZvjDVeA6bLkroZNgxdikDTz75JDZs2ODne71e57muuOIKfPzjH5/1+fbu3TvW9R0uDz30EN7xjnfgqquuwlvf+tYj8pyjsBrsVhOJ4KgJtDhtqD+j2gTnzacP6qbD5s5JHKMyfNZ7SmMmcWk/wXlz8tFFFb73TdnqrmJ656TR9MhUik55avd9TFVos6FmdVpdeqCIhTlQp0Y6ZwlaxyIE1p5IKb0t0WWBxLVRmUVwhLaAcU6aSDLfJgZJFjlp7v+MpE4ake2n4iI+JZJK+askvLbZhEYcUrZrmo9Il91y9zELz2qwXTTtjS7OO51/13tNa4jKpjYTwQm6aSLJOTRZ/CutIUnfVue8iaoKQiNF4cV+pC79eslUfZjEjpMMKqm/mxMqRS+rv5N5ZZCpOPW70ECpwrpowtqsusWRvXYT9+bt2qim6e+KbNI5O1eLnoUxdcycnU1kEBqRVQH4lEf3Ow891ooBhF3PmHw66rEW9VtzKZEzAy/fXw1ylNPBSSut80ZFR6isP3XSZuu75lLMuxy6cZhrzbWcmZfoSBvHHXcc7r333oU+LcOsSqK6w8Y8AGzYsCFy2Gbj8ssvxwUXXDDrMccffzy2bduGp59+OpovyxIHDhzo7PGzbds25HmO5557LoqyPfXUU0OPefjhh7Fz505cfPHF+NCHPjTStS81bLcYZnS67Ja7jzlysO1imNGZa821nFlwhw0AXvayly3GaRecUhuU2kAlqQ97Q+Ve5ln2JiFtw1LTP4hk0kbVJqdJo9N6BzQblCim6sdVucaUzX3MtUFl3O4KoNwOTGM3xe2JZlJ0pkc6sRFTGTLW0LYhIU3X0UUJaXeJQKNtRR5SgGhqpNt91RqQs39wDcLmrEYQCDeG7jAR+dpKR7vuVJSApk+FrEwza3i6jp7ZiKiu/Jgik5SkPk5A2Qhb2p9EZqNqWU8h7dk0yH6Cdf36fVnfT7DOHjOZkt39VPpIQ0JSDBKBOu0UqNMpxkDnBSo1vCPu/qbjsGXLFmzZsmXO48444ww899xz2LNnD3bs2AEA+OY3vwmtNU477bTWx+zYsQNpmuLuu+/GueeeCwB49NFH8cQTT+CMM87wx/3gBz/AW97yFpx//vm45pprxn4NS8lKsVsuEuajarRp9CyRJqtIDWkMlIv6ixDtr6P79jsrDBLjol0SfdcYttQ+vaenNIrUFtbb/J+6qN5G2bO4wL4LZ+9SJf1ucqqET0VOiVT1ZCrRszvsNCWyFkyxr7P5BD7CRlIFfePsHkRv2N5JwKcemiT19lPpCqbI7XgWO+kibGnmI3UiSYPYSW8C8IJW3VE1n+boxEVU2h1Vo5FEihOuou/NkBhLRxplB112y93HHFlWgu3SWgcxHEvcIHmOdQeJtrkoncxCTxJD0uuSfhZFblTlom1EcKLMoV3m0cQUhBtn0yEtuSpgUhthKzMYZWXtVepLLvoq8xFwF7mpSFZRRSJpdI2kQctJiOgaec30myyFIBkQIXomBKKsLCqMJq2wkKhyiCKsB4XPCMrJ/bY+vpyJI2muETZtkJ0PYGZsVG1mJhYXsVle5SBvjarpvGiNqnX9/YWS4W/ftlYaM+q2kGuuI80CSq8wDDMuxtR1h0M/46pNjsFJJ52Ec845BxdddBHuv/9+fPe738WuXbtw3nnneYXIffv24cQTT8T9998PANi4cSPe+9734rLLLsM999yDPXv24MILL8QZZ5yB008/HUCdBvnmN78Zb33rW3HZZZdh//792L9/P5555plFey0Mwxx5Ou3WItsuhmGY+bAUa66FYlEibCuFUte5xqIy6Kkg5W+qerfTJHloYjqYgpiqU9PSQ9MoD9W7C9mG+v5qUKDcYGVLiwqVLdJf94IGFZid9js9w8Ij9Hc9Djd0ZbzQiKkMKlvkX+VV1JBQU8lpVxyfD+odXQCwXd3r12qjQVr5nVqYuT+4VGik3jGqx5UGiaoFKf8ZInEbRdjIbn1ValS+Li9E2GgzbUFr1OwOuUyySPbfHSPTzNetJf11SCbqhtBpL0Hat5L9E6mPtm2aTH1UbV0/wbrMRdiUj7BlSviG6LSGTZSDaKdqHHReRm0J6Pxicuutt2LXrl3YuXOnb5z9mc98xt9fFAUeffRRHDp0yM996lOf8sfSxtmOv/iLv8AzzzyDW265Bbfccouff9WrXoXHH398UV/PWqOW47djWs8gabRNRMGW8FUSfsdX0e+vCEI6qoKvve2Ktk1WBgNXTG/Fe+j3vqg0qW+du0ZDCZAIW4hmU/n+nlLIEhflRlTDpqL3o75hQKNqSdhd91EqDZEO2zuRpL4uQ6SZj6pBk8L4LhloGdqOQKo6ygYANMKWZCGqlqSdUTUXTXM7+FFUTSb+Q+CjbsCQlL+vXTPCZzEIIX2tmzgMWf8uu+XuY5hxMJWGni3CVmlIV8dU6aimiUZfXCTNVBqyCI22Kxs9SSZKKLv+kUXh23iIsiAiQH0fSarrS20boLQH47+nSRz1dllZ9nuoVIaMtNlw37VKGx9BozW92sSRNQrNnnBQO6+kgLC2VegSqEJUzWX8CBJVQ5X7WntXt2aKGWgiHOIjaTOD1qiaJuIi5XSQ7C8HebQGrUiETbeINQGY/e8O+7+uI5p/OCzVmmshWOMOW50SKQWgXPpN0vMfclP1Ia06l+xPwfTrMHGybh2Sg1ZsxKZGllM9pIP6cemg9A6VzitU09aRMxouqKlEEOioDHXYRLfQiBcXqbxAR1Vor75TkS+Izsuw4Oj1g6JPkvoCepE58ZEEkM55S0ByE+PnN8GJ8mIChr6OIDgwKHWUJjUog4rcjHfYKu+86Up7J60iC702pFQwxDHzPeZU3HtNtQiNpL3EC41kPYVJJzTST73oyIZ+isnUpWAp9O04I4W7mRJeYUlUuU+nQDVeWL12To+8YtHmzZtnbZJ93HHHRc4yAPT7fdxwww2damRXX331vBUsmbmhPdTcba8YCZIqY4wvqqePAwDp0uGkgG0ZCQPivEnhHa9EC6/wWEmDTFuBEWX8d8OnQhPRkaKiIkQkFaiZDu4cNqKCpoSI1CAjZUhSYK/IY0WH3QxvAOm36R2hoA4nRBALMWXh7STKImwKaU3GDYeNiiO5lEipfOpjpEhJUhtNQtIcZRIERmQSxLCIGqRPZZQJjHPYOsyFkAqwzrbQZciLFTKkp1XoVpbsoMtuufsYpo2myIRbuGuicB0fT1LmdPh8OgES2UiXc2NFFbMrDeVUXosSiV0vySyH6g/smKQl5/0odblVHKhrk4VuptC+iPZ+JWT4LgtJ1J/Q/f2L+ifa71apg3ic0YBzwIjCZT2281UZHLZyxm/a6xnijHknbTqU0HQ4aTov51SArIqy1Ulr/q2bgk1Nwqp5YViqNddCsKYdNoZZaqq8RNUiVVutgN0ehmHWJl12y93HMAyzHFnJa6417bDlpH+GDy+nPS9AYpIcxkZpxNQGCBexGhxCtqFOFQvFljl6Liw8XUJb0ZEq15iIKu7tDpDASD3ZfLEqSYnUJCVS05TIvIiKP5XtFSdnBjBul6gsvECGT42UCYyxO79GB6lbIkaiIb0cLU3y1CSlqqiofH9IjRqQNMjpvMJ00dKzqTQkJTLsdtS7IfFONpX1d7fdb1cILJMspEH2+5HQiBv3J1Jsmqxf96aJFBvteDJVPiVyXZagb3d9MiV9hC0V8EW6ohgESdxyvMbWK1mxiFka6lRHEYmL+Ai9FL7/Iv3+doVghBAk2pYgSULEzInqVNqg9LZK+EhZZkIUztmpTAHGhMJ7nxKpEbX/6IKKiLioWlRUL8M4JQJNTRGW+EUG0REvee9k/UFSkSrhjxUqyP2bkvS07EqDBEj0Lk6JpMIgLjMgSqmiu/Fd8/4c4f2vA6hdaab2JZsQwZQy8f/njNEhFVKI8SNsrBLJzBOaBtmMuJgWlaK4z6pdlxQhKiNkSInURQFZ2LY+aQJt0yNlmvj1kkwTSCtBn0zkkGm9plO9no+kYXAoRNiSlKQxk7YcaQph1x0+gu56/AKAUrHdIWI/0XeuK0OA2kszvDaDLkPac1WFyJsu/RoPZR6iXfkgtC1wEbay8BE2lHkkKOLTHUnq46hRNXcMTV2t72u3WyFbSkRpr6GoyL5V80iRXMlrrjXtsDHMUqPLErpoSQMol/9uD8Mwa5Muu+XuYxiGWY6s5DXXmnbYXBRIkIaxqjLoudzktA9j835lNhEk/ifXQ9masGxDiLBVNqe3t6HwoiNVQXK2nw+7CtPEm59N8tqhTZDy13lFIngVSls7lwxylFbmVg1yJDbaJrOBjw6aJIV2ctK+AD4NDZ9V2LnpuixDREdoDdtMVXkRgpmy8rVqg7LCtI0IHsorTNtdl0N5hdK9T5WGJgIkLsLWuYPc0Syb1q0lWf13pEIjaS9B5uvWEmycqHfGNk6m2OBERzIVNeqldWuusa8oB775JKoCcKIj5Xg1bFXREZ5fAU0cmaVDNiJKLooijCG1DSauc+jCR1qKINojJBK7Q6ylDHWq2ngxkkobZGQMxBLW2ojITtCvcmfNFdlkpvVs3j6LUKPXbBjbVpwfnVRIX0ti6H1+9zvUfMCkvnjftz8B4ubThKhZtZC+uW+0i0533aWK6l6ieV8Ho6JoGuDqexG/hhaoIIGTORCSRBCjNgDj71Z32S13H8M0MZUGEjVnZLbOJmrUzzfWAaIKX3JpG1QLFWqjhJSQLgKUJRADW/OWpVBpiLZJm0lTDXI/Tvo5ZDptj0/COqnX9zWlXS06fLZPkoY5pUIETpI62kaEbaSG91RfwGWCaQ34Rt9VqD+jtbdl4XUMDBGeA4m0aVfXRppcU5n+qiFq1ybTr/Myipp2/U3b6sUEMdwSEi6Pqy2aZiod6o3HjIyt5DXX2nbYqpAS6VDCQNm0oDSdgHaOTJVDTq0HEBdopvZ3NpiJCjKrIjhUNJURL9VfCpoWVPdqo9cQ/3aEok1DHELt0yPLQe7TIKtB7otCZZZAZsFho4qRgE2NdIsSXdaLFWBYdMT+1mRRNpsy5ICmQdprnC6qKD2ycqIjDZXINkeNpj62zcskg0xd77UeERcJQiO9iQTrXBrkZOZTIjcSlcj1vQTrbX+2XiLQS5xaHREaKWka5CCkR46pErmSw/PM0lCLjKDRe83eV5VBRENTBySkOgNhEUAXCUI2FvFO8UxIKJKyp+0zUyeszWGLew6JqOfQKOXdbWqXs/Uf8vOgjh/pN0ZSAh0G8KmBhrxmGF1vXrkxOb79YuO+ZsFJI+lQsyg8ulTJivwvMKX2Y++kGQNqGdx7Gjm6IGmh2ngPttIGSTMNklyv6UrJaoFTIpnDgaY+UvEJmgZJBch0a2pkLFLh1lqSLJiEFF5VUhalX9zLQY4qcw5b6p0BlSaQ1pErskFw6rIEKnX9Eg/6Y2SaBCcsTSNHzl5MPHZ9H4nDRsWJ/O0WNE3Ddu8X7SdH+tJBB4ctGpd5UA0v8yi1EWj08M0b4nVFcNgMSTn1zhv5O+q8Ig4bUcEkf8dOkZHK+L+hhrZOW30LjZFSslP0aC4Wes11ww034BOf+AT279+Pk08+Gddffz1OPfXUwzrXXHAfNoZZQrTN+R7+Wf67PQzDrE267RbbLoZhli8Lueb68pe/jMsuuwxXXXUVHnzwQZx88sk4++yz8fTTTy/Cla/xCJuT9QdCGk1emZCKk/agUldAWcBkNj1yauA7wUvbpy2dGqCYqqNYySBH5ntUZFEqoxtPHDKgOwduA6IyJpL1j+YrJ+VfocqtOEBeobRtA1SmfBpkmaWQB61kbZp4+VqRppHEPwCoXh8itWmgJshcd2HoznqHlP+g0l7Kfzqv8KJN25zOSxxyEcGiQukikZVGVQZZf99CQFdRsTEQ70RRARKZBin/JFVIUpcGGYRG0l7i5fvX9RKs6w/3W1uXKfRtlLVPe68JQOT1eycK0nutmPFjY+8flWYxLp1nmDacnH2UMkhEMWjReST37Jsntkv9DxXA+zS9EDESMoHwkTcR0vfSECEyCBEdbVw/tDja5qCmphlUp6mNNMJGxVZolHEoFbKJEKH3GJHWNlGaUUtLk2YaZFcakxcaaQgLkBQo935p0gtPG4SMDANv+zSCoBNtsdFlGXxBvhBQ0h0vwhsrRSRA4uW/VVJ/Vjp2+NvoslvuPoZpYipdR5wj8YnhlDmjac/ZcD8lRGw0icoEqGiFLCqfbieVhHClImnio3BVloQoHEmbFEr6qJrKQhROKEkib/Sx9n4pwzolSUmETTXKOKj96Pj+kQhb9N0iNt+vkUgapNHaR8HouCoKEimzcySSZio9Z7qjLsrOiOjcf7MQDY2FZAS0tWJ1QqSNnNL0SJIGKUhfvnFYyDXXJz/5SVx00UW48MILAQA33ngj7rjjDvzZn/0ZrrjiirHPNxdr2mFjmKVG5yWqoUStldHEkWGYtUmX3XL3MQzDLEfmWnO98MIL0Xyv10Ov1xs6Ps9z7NmzB1deeaWfk1LirLPOwu7duxf4qmvWtMM2XVSQRYXJVMEGfSCgoawXn1cGfStAgjSHKeux7E9F9WwAkM4MkFmJ2IoKkOSVrzdL8zTeOfIK8Bq5JjuiLVQm7FiYyqCyoiPloITK7O52XqH0QiMJqjQU1Lr6OpkMYDLXULuORpmigOjbHYyqhEladpmBSNaf1qlQKf8ZLzqiQ90akfI/ROrZqjIIjZRF5ce1lH+IsLVB69lc3ZrKJnxULUkV0l4YU6GRdX1Xw5Zi40Q9XpclWGffx8lUNYRGbG1MOeOLdEU5E9WtmUEdcXVyuKNidEfDUN6lZjpw0SRayyVoZMhF2KqSRNuqULweyf2Tz1lDOEMIm10QCVTEkTdBa7JgG3J7KXoiY2+CrL9BoyaLipF0vOa46TeZb422xTbUR9UAH4Yyxg0adX3K3d/x/WtG12apCQPqSFpoZxBseGUMiTjGkTT3zM33zEHfLy88A/gaNAEDaBeSNO6VQ5L3mv6doOWQAMJcdNktdx/DtBE3wm7Ws3VHaegcEIuRVS06X5IYCaGkj+hEkTdVxpE3ewyNvMlG5E3K4XmhpG/MTedCfVwaXQttQ0Dn56L53oX3iGRqkeiZIbVlVR7qz2i0Tfvo2fwiac2IaH3u7kwtU8V/H8CZZvu8UQWbhnIiTu4zQ5qkj8tca67t27dH81dddRWuvvrqoeN/8pOfoKoqbN26NZrfunUrHnnkkcO6trlY0w7bTGmQuAZD9j+1EPCph0oYpPbLppI+TFIv0E2aQ05tqMeDQ/b3FNKpOh2uFvyoF/ZpXiIbuLBz5VNfjNb+Q56RdBdatE+hqYcir7xxqXIZUiJT6Z03meZQ/drBLAc5lHUmVT/zSoamsGl8ZR6KU5N+ECQgCzsjQloOVYmshUa0HWsvOpJXOlKGzEl6pFOGLAsdVCJJH7Y6PdKF8ocdNpoGKZSCtAW/KpFI7OtPMuXHaU9hwjps6/sJNjknrZ/4NMjJVGG9Nbq9JKRBZkogceF4KjRSDGqlSABmcDD0MynG68Om88qnATTnGaYNJzji/uUJY0J6WzMNks5b501EqX+zpD87B0CS/mVCBpEOkvrnvo+GKDEKkgIIIUOqd9QPLYhdxOmRo8iSBKiTNqSd4a6XjIPyWkhDmu0ZmyqQ9Vy7kIo2oeC+aqSPe7VHHRyzOo2UPNZvjJm5stNJqqiAEO7/FkJ+pBbWaQOkEbCH1P36Gv3pulTq2uiyW+6+xeLAgQO45JJL8PWvfx1SSpx77rn49Kc/jXXr1nU+5swzz8Tf/u3fRnO/9Vu/hRtvvHHRrpMZxmgNY0RIfSRCI800SOqouTnnANBUu6aapKP5CZSKOkcuLZg6b8LPC1UEB4+Kl2Shv6IkjpdME5/y6Ockcczoc5P55n2j0JpOOtTDzjo9kVNXRSmPzf53TbGQrr9FOLcZ6e/QhlQSlet/rARki5PmZEeA2pSF57LHymB9zJj/K+Zacz355JPYsGGDn2+Lri0VK0505JprrsEb3/hGTE5OYtOmTUt9OQwzL7SujefQD+9SryrYbjGriU67tci2613vehd+8IMf4K677sJf//Vf41vf+hYuvvjiOR930UUX4cc//rH/ufbaaxftGlcbbLuY1cRca64NGzZEP10O21FHHQWlFJ566qlo/qmnnsK2bdsW5dpXXIQtz3O8853vxBlnnIGbbrppXucqTC1DLyuDGVH/sZRQyP1OpUEq6/l+0oOxghamyiF7kwAAOWlTI2emkdgoS0pER6rBDLIpK+Wfa98/rd7loDtG9XydGhlks9swVdjdqHKNKgsS/y7alvRLn5ZZpYlPiaz7jdhoUJ/I+ttUvyhdqgNNdo0LraOUyC4pfyc0kpcapX0PqJS/Jr3X2np0UASRyZVJBpXY/ilEaCRJpRcayXoJJmy0beNE5oVGNvRTnwa5PlPop/X+RSYFelZ0JFMSoqijqDSqJqrCp8OaIvQ1cXOjUhUGVYuMQFWMt2vELG8W0m5JuLRIuwusiyjF0UePGgIkvtdiMz3SMYu4hmhJ9xNChF5ipL8XjapFkTciykHPFyI9Ia1Quce0XIu/3DFk6JvnMNF4+Dxd0T4DGpyMe6LRFMdRImk0rTykiNaRtbbr8C8jkvB3abEGYbOeCo3UkTX3nG43utKIImxRS4cR6LJb7r7FYO/evbjzzjvxwAMP4JRTTgEAXH/99Xjb296G6667Dsccc0znYycnJxdtEbXaWSjbZbQGlBpKhfT3t6TYuXKSSJQkkogfL8ojGxEtQVIlx4vCydYoHL3fn0vKxrkXNsIGYChi5u4fVRhklOgZXXdSxn3fjQrnEErAuPdOG7iMB5oeCaX8c0SpkU50ZMwNooVac2VZhh07duDuu+/Gr/7qr9ZXrDXuvvtu7Nq1a6xzjcqKc9g++tGPAgC+8IUvLO2FMMwCYCoNI1vyqbmX0aqC7RazmuiyW+6+xWD37t3YtGmTd9YA4KyzzoKUEvfddx9+7dd+rfOxt956K2655RZs27YNb3/72/HhD38Yk5OTi3Kdqw22XcxqYiHXXJdddhnOP/98nHLKKTj11FPxx3/8xzh48KBXjVxoVpzDdjjMzMxgZibUFjkVmJlSQ9mIkNsAKLSA0vWNUhsvRpJIidQ2lzZJD8aKkQgr6y8GUxC2ni2ZOoTURrfKgxO+wLMYlEiLun5KV9oLh5gJA9jIWGU0QjVFvPPrNjeUITskpBauyitUNkpUDkok/dDwkDZCNC6a5n8XXl5VGh0K7hs7F74YHohqN1zdWllpX8+Wl9oLkORlaJZdFlVUq+afyoRm2Vqbzto1oK5bc2OVJD6qppREYl9/2kv8eLKfYL0VGlnfb5fyj4VGJOxDoXQRRdWEb0Q5E2oASSN115xyVKpc2795Y75gh22t02W3lBS2BsneYXSIpFUddWvNeVup3yoT3UabFLVUECWpZ6vv9EIgRpEat0bkLRRfJSHyFol4NMb+yUMUrhbaGC8K14xYaVKQS8VQ6mOtPTJo1JjNLhYySiSN1rNpY0DXCnPV79F6PSffr6RoFRqBNkTuP0TbFBDeL6ns32H05UCX3QKC7RpVbW1U9u/fj6OPPjqaS5IEmzdvxv79+zsf9+///b/Hq171KhxzzDH4+7//e3zwgx/Eo48+ir/8y7887GthuumyW0AzokajOPF4OAJE6t0aLQDazjHq4ptGuJqNt8N8iJ4J1T7v5+RwhE02Htf23BR6HW2Nw4Hh19es+XPHUOEP+l425+h7Csz/fQXi10fPJ6o4mknl+50jpSGhXLuGyniBLXe9Qo1XN0dZyDXXv/23/xbPPPMMPvKRj2D//v34F//iX+DOO+8cEiJZKNaEw/Zf/+t/9btElKLSKCoNJYCB+18nhP9wJNogsf8ES22QWCfNJBlM2q+Pd6mR/SmYfq0UqCYmkU7Vi/liaoDSOm/ZVO4LG5sCJKayKXgzxjtm+SypgZp8Qf05Uwk9kfh556SpvIh7cNhFmk/jqyq09h+aBZe2U+mwwCh0UD7LS428Cs5b3qIAWZUaFZ3vWKj4HiYWmWSh95qSkDZ9kQqN1CqR1jHLFNb1gkrkVOYcNukdtn4qQ781WTtt9YsaQDjHtspDv7WSpESWORFwGc9h4wgb00WX3QKs6IhzsIzx31vRUICM0iC9UxdSeKMePlp3O23EYTNeCY30FPKqrUF9UFRJLFaiaHqkEyYpomNAUihFhyMXC6DoMB+9QWSx0JLe2OqAzeKYtaU4UhXHioiFUKERQxw26phpY1rn6TW2Uf9/CmPX566ecCcgQiNCNK43vD5tHT/l3t9xVCJHiLCNqrZ2xRVX4OMf//isz7d3796Rr60JrXF77Wtfi1e84hXYuXMnfvjDH+LVr371YZ+XaafTblU2JbLFMYgESIij0ZaON4paYX3uw/8fGvVHs0QOXTSW0X3hHMPOXXR/y2NGZbjHWbuzFe6PVTZn65FWn2Mh3rvQI89QdU7ETpq/BuiQigpNlCQ1jHuvnIpvpcPzjNuHbYHXXLt27Vq0FMgmy0J05IorroAQYtaf+chkXnnllXj++ef9z5NPPrmAV88wh48uNapi+EeX7LAtd9huMWuVLrtFbdeTTz4ZfX5pvyLK5Zdfjr179876c/zxx2Pbtm14+umno8eWZYkDBw6MVZ922mmnAQAee+yxw3z1K5/FtF1st5jlzEpecy2LCNvll1+OCy64YNZjjj/++MM+f1cqRi2eEYRHAKCQIUpUaoNSu+iRQOakW5M+jLIS/yQ1UgxceuQhJK4/21TfC37ookRFImxUgIQic3dbg258jLIhQ3epaNqkj6pVoQeHcrvpugo762SHXhgd7Vobsjsbp/O4XWbjI2mVjt9HX5BPImyzIaTqnFNJ6JOSZLHQiBunPeVTH9f1U6wnfdhoGqQbZ0ogJVL+0tj0sSqHsOljoixCKtnMIEQoiyK8f7OllbWgKw0thg3F4Yb7mSPHUtktJQWkEL73mtBlHFWL+q2ZcEwVRIZcRBhahxRpWrA+y+dYkD5rQ3NJGiLiMkTgRJICVRAdge3xVvdRIamSNG3S2QAaVVNBsCSOthkfIOqSp6+jWvaYjqjaXOmLXTL9zcc6IaZmJM3NU0EpbYJgSRfK79YbuM3hVEpo6R5HXvOQ0AjIOGRGKJ9VUr/vpsXmdtFlt9x9QFBbm4stW7Zgy5Ytcx53xhln4LnnnsOePXuwY8cOAMA3v/lNaK29EzYK3//+9wEAr3jFK0Z+zGpjMW3XbKmvTcGRZiqkP64Z8WmkQbp1VNQDLDp3LJARC3TMvf5ooy2KBrSnNjajZ12P9ce3RPSazBb1antNw+/heI+fC9mIMoax8fe7pxSVhLTzRgnffkoTMX+hRLhmKfw1KRXGQgbxkcONAq7kNdeycNhGNdgMs9rQebsM9krY7VnrsN1i1ipddgtYPNt10kkn4ZxzzsFFF12EG2+8EUVRYNeuXTjvvPO8QuS+ffuwc+dO/Pmf/zlOPfVU/PCHP8Rtt92Gt73tbXj5y1+Ov//7v8f73/9+/NIv/RJe97rXLcp1rgTYdjFrlZW85loWDts4PPHEEzhw4ACeeOIJVFXld8tOOOGEWZtntlE3qQakNn6nku6CpjLshJY6iGsolcKktp6trIVIZNaH7NcRNtM/CDNTN9FOpwa+iXZVlEh9LZmepRO8rTXJa4GRJlKIOXdsaM53FG2L8phDVMiMGSEKtRvGj+nroRG2ikTVZiuRc2IFUgpo18ic7Pq6gl6pZFSrlvVcVC3x44l+EqJqvSA0MpUqTDoxklR5+f5UCi86kkjha9VEmQfhBl1GtT80qrYWd3uY0VlIuyUFIBHq1tBshE2FRsgx/jOqQ82qKfPWMUDsQxNaUyqd6Ij9vpZFqHEjETakaVTvJpJaBAhChu+RVIC0tk8mgA6CJt4OGB21EhA2ImcAwEaVmmIktG6NRtYAV3sW6tBo9MzZL4NQJqER6s1ohK2oTGQT2yNsRPqfRPvoNTZxAiP+f48AUrubXQs82boQYSBtvbUU8etwQThlAG3FSIx7z1D/7YQu434BczBKhG0xuPXWW7Fr1y7s3LnTN87+zGc+4+8vigKPPvooDh2qBcCyLMM3vvENr962fft2nHvuufjQhz60aNe42lgo26UrDSSqVRo+EiBpEcoA0KhbI2PX/Lmx5onbJtlzk+9ZFY1HfhmzMk5Zmhq3LQmhq+XT8HGH/RRD0NemhIArj65tlM32UILUrcmoJo02wnZ/GwmQujTjx1ob/2/GVAaiEfync+Pam5W85lpxDttHPvIRfPGLX/S3X//61wMA7rnnHpx55pmHfd6K/IPz/2BhUNl/ghVJj9RpCuUVI/v290ykGCldSuT6Aaq8XuTrvIR246JsFMsOf1iEIl3ryTdPqvbeIKMyn4LSuZgrtaeJK8wVUniHDJAQMpyHOnIAIBPZmQbphEZqNch6UbhpMsXGyXrcqQwpBRInNiPgHbZIrMEEUQZDFr3zoSo7FItWgPFgRmch7ZYAonTHoTRIeqyhTpr97BZFlAYZjauWNGk0bAZN32mk0RnSI9FICZFYO1nmwelK0qBQm2QQibvGoCQpZHDMaiEV65jJxGvnGpUE51QmMMalRw69ZfXpI4fMnoM4TpU20f+B2HkLY+qkOVXcZrqj71NZhbR2mj7ubju6Ut+dwxZSIoX3HlMl/fMDEkqQNE8z/CZQ8RT6XhiDsUVHuuwWsLi2a/Pmzbjttts67z/uuOMi8art27fjb//2bxftetYCC2m7moIjc/UVo7c1ccbCOWLHzN1X5brVOatMu6PWdIC6HJ1RHaVxGcV5W4zn7nretiWlEoLMG/9YJcIYpfFiIfUSOXRUk6Svml/XQkZpjrTfmv87kzEVHzlckZCVvOZaFqIj4/CFL3wBxpihn/k4awyzVOhcQ+dVy8/yNx7M6LDdYlYT3XaLbddqg20Xs5pYyWuuFRdhW2zqnceQyldJtzMjIhENqWxKj/1t0h5kZqX++1MwM3WETeYDL/GvixLaSu3XUv4kiuRCx0r66JlKK1RFvcs9FGGTIcLmCjhVpvx5aM8QQc4JjFbgergoImXrxkoKEkkLUTWVSL8barT0u8naxMIk/nj3viRxv7XUpkFmvcRH0tb1U2yacOMgNDJBImy9RIY0SCWQuveritMg4aIOVYFIAn0B0JVBLO0S5hmmDSlELN8PREJBIQ3SxMe0pDvTSLEpchJB1q3HQ1fh0yoVgIKMUd8mfdr890TKkDapq3B8FSJ5IklJpDAJ4SClos6U0dilR0avW/ixQXisMSYaA3bHXQ/vvle6fUwjaYXWUVQtToO070alQaX//dtIRUeIrZNSRL3gnDl1x0shfNNQqU2UGu+uMYXwNsUYEUXSuvrMSSrgMgJddsvdxzBNjNYAiaJ0HjeL+IijTaCEpkHSVOToe90YO6pGinLzviajfMQXKxo3F6NE62ikrO1xcfpj+xydbztGVCZE1SD9nboyvscapU5zJP3WZPfr0JWGcpkcY67FVvKaix02hllCqqJqbSNSddUPMQzDLDFddgtg28UwzPJlJa+51rTDRncGKL5eQTeK0E2oZzO2hi3UaGS+nk1MTEHYGjaRD5CsC3VrLsIGhBxcWoMmpYDM6p3OcrqELFR0rIPWsKk0RNjoWKauHiQ0oaXRNiq/LYggwCj42goh/FhKEUXVfANyKZBZcY8ykUSGl0TSsrBB3hRjCWIj9nwNKf8eiaRtmqz/HhsnUi80sqGfdkr5u7q1lNSwoaxIw+Eg3FBHHdpXKYJGFcbAaAPTstszSuuD+XDgwAFccskl+PrXv+6L9z/96U/PWkQ+GAxw+eWX40tf+hJmZmZw9tln47Of/Sy2bt06dOxPf/pTnHzyydi3bx+effZZbNq0aRFfzdrCCY501a21Ee1C0vpL2jibHjdbDZs/T5gTkhzrbQmJ0kkFkVpbqSvA2k3RfE43n4RSNAMyFjKOpLnHdghgDF2yq+Eit8M4rlWj47aommsJA8S1as2oWqsoU1eNTGXi/0k+6EUFRUJkTHv5/vAuVdoH4ToxxvjHGtR/s3Fk/bvslruPYdqIBEA6xq2P6xAiaZ6D3hfrEsw1nru2bbaoW9vlL6cIW3Od2zymvt/4+5oCI/VvapuGch382B1jKuNtq1Lhb6Vk+BsKRf62HeaHRuTCOURn9HUulmrNtRCsaYdNWmdDSdHquAFxkXZInRFebUtYxw1JDyatUx9lbxJy0hbVl6FHV9ahwEidqCLLfYpjkamo30hbCqWgKZGpQjJR/0mTfgKVuXEP0o5lmoT0yDmctGaKTFek3TtpIjhpWSKR2eeZyBLMWMnUPFWt6Y6y0kRJMr6/mRKZpAqJdWp7RAFy02SGjdZ5e/m6DJun6r/N+kxhnX3963sKfZtOmamGMqRLzdJlWGiShfHQ17nFSfPqdyOi88q3p4rmx+znNi7vete78OMf/xh33XUXiqLAhRdeiIsvvnjWgv73v//9uOOOO/CVr3wFGzduxK5du/Drv/7r+O53vzt07Hvf+1687nWvw759+xbzZaxNqOCInzPhvgV/uo70yOgY4pjReftbAL73m0izIHSC2jkDAFM0nLTUNVbTgBV/gih9HzZjZPTdFCQNso04HdD9pj3W4FNlNBlTcRGqBllog8KJHBj4MU2J1MZ4R406b7Pj7OkIh46BJq+VKrO4Xm3jrC+77Baw+LaLWZmYUXYSFohhh6orPTLc3zUO52j0T5zl+dzxo1zb4dItGDJ8fnqstZ7RfHe6Y9Mxc0TbaNH53euTI6RmLgTjio8s1ZprIVhxoiMMs5pwjnjbz2Kxd+9e3Hnnnfjv//2/47TTTsOb3vQmXH/99fjSl76EH/3oR62Pef7553HTTTfhk5/8JN7ylrdgx44duPnmm3Hvvffie9/7XnTs5z73OTz33HP4D//hPyzaa2AYZumYzW4tpu1iGIaZDyvZbq3pCFuqJFIloUTYDZCifRPIkNSZivRkc+IjIslgKivxn5WQE7YYvwxCFUJrpMSLp2mKMq3Po9JplP169zkZlFGEjcratsn6q0wh6buoWgbVr3vFySyBsumRKkshE3fN9rdStdR2fVFzyjtLEaJtqQopkXUvMxu9SqTvcZYlEpM2ClhF0bMqpDlqFYrgtfFpkEIIfzkqCRG2CXu+SL5/Isj3b5xMfVRtXS/B+iykRPbdNUqBlKRECt9jLaRBimbEwkfVpI+siRSAduMS41BojaJFh7xYxNYLu3fvxqZNm3DKKaf4ubPOOgtSStx33334tV/7taHH7NmzB0VR4KyzzvJzJ554Io499ljs3r0bp59+OgDg4Ycfxh/+4R/ivvvuw//7f/9v0V7DWmYoFZJ+Vjq+u0LKWCyEpDB6Gf5RdxjbjnNRZiJK0rkfW+RxeqT9yogEMJXr4VZF30En2V+P6etXZDw3prG7rRGiXjTKRMfakF6TJA2StgGYLQ2SRtsczV32cXoy0Z1rl8VN57qCGPQYjThjgL7GUeiyW+4+hunicFLPhBSH3VSs+bC2NMhRxgBZAw6JlMyeItkVVRv3JcVRd0PmZ0+DHI6gubzF8DrDOeLoWfs4Pqd7HYeTFSBmERfpwmjTmUI5F0ux5loo1rTDxjBLDU3LaM4DwAsvvBDN93o99Hq9eT3n/v37cfTRR0dzSZJg8+bN2L9/f+djsiwbqkXbunWrf8zMzAz+3b/7d/jEJz6BY489lh02hlmldNktdx/DMMxyZK4113JmTTtsCYmw0Dosiq9pABEjkUHiXyc2sqIyIHWS/SWQ2ajalIZs2ZHOpCLiH9ILhKg0gRrMAADSokQ5qKNtpgptAKgsrqTd5NMEyUS9c636PST9epxO9aH6bj4L0bTE1d+lQYBkhAibACB9EXx4DxMl/XgiU8ht3VpJdtQUESCZzqU/xmgzJG9dX06oi3NRtV4iMUGjZzaquHEy9TVs67IEG+38OlLD1idS/lR0RJrQIFtUZWiWrXXr7r2QqhZVgN3nok2BxyDXBklLAWxu34vt27dH81dddRWuvvrq1nNdccUV+PjHPz7r8+3du3es6xuHK6+8EieddBJ+8zd/c9Geg7GMWqtGv8u+ZrWK57z0vvLiIfQTSasYQCX557xE3V7PJlXIOiCR6iGc3aRCI1Bzv/ZmFG2Of8RxpMn+xtwRp2ZUqqturY2uiJokBf9SCiLuZB9HdqObUbW4TmX0XWtjTHeBcgdddsvdxzBNahGKue2WUAKYJdhf3+8ynASqlrESwke1yOH+NjD+Al1j7gbc4Xb7fbPVr3XbitmPab7WQJv1dfP2tsss67yqua+peTuyQ6p9LOYIxc11PxDWweOmMs615lrOrGmHra8k+kn8UXVCJE2MCR/5uj9bfYxPjUx6fpFvkjilru3LYKREahf3Mk2Q2PTFop95J60a5EisqqTOi85+E74nW5b61EfqvCWTfaRTEwAANTEJ0avHwvaNE0nqnTcjk7DIa/Rrc7dqoZb6dadS+v5l/URixjqwUyZOf3TUDptNT8yqVqeOQhUm3eMmUuVTLNf1g+jIhl7ot7aul2CdPWZjL8WEFTDoNYRG3FgUhe+9Bh3SsZqLP0F6fzixBIHgpMkxmy/SfjHNeQB48sknsWHDBj8/W3Tt8ssvxwUXXDDr8x1//PHYtm0bnn766Wi+LEscOHAA27Zta33ctm3bkOc5nnvuuSjK9tRTT/nHfPOb38T//b//F3/xF38BIKSfHXXUUfiDP/gDfPSjH5312pgRaXNYqM1y318hvHAQ3WCoU6CzcDr3MADGpfZi+F88GnOzpkZi2Bmbq/+j0RWEDqmVwAibH4cpsjLO/+amYmQXbo2hTdhwAkmJlEK0OoGR4yWIKi513kSY82ncSiB1tp+q9QrhPw5CxO2MRIdjZtAiqjQLXXbL3ccw41A7W8PzfjMa0qdSVlVY/FcapHcXWeiXJnLMnBNBP7NNR+5waXPIxlWUHPX8lPD62iw1QMVA6vdgvOed7TnjufY0SdHhoDWdOOkDFx2bWAusvjTXmms5s6YdNoZZanJjoFpWj7k1Hhs2bIgcttnYsmULtmzZMudxZ5xxBp577jns2bMHO3bsAFA7W1prnHbaaa2P2bFjB9I0xd13341zzz0XAPDoo4/iiSeewBlnnAEAuP322zE9Pe0f88ADD+A973kPvv3tb+PVr371SK+BYZjlT5fdcvcxDMMsR+Zacy1n1rTDlirho0OOelfTpUnGx9MePn4nxe68lkIgTWz0w5A0OhPSgiRC3zadhqiWSDOo3kEAQDLVR2UjbOVgxo91paFz0sMt6oEUUiudlL/qZ1BWyCSZ6kPZyIzo9SH7k/Z5g2CKEyAxZFeepkcKhN1ZIUwkOuJ2eVOpMWkjWdoY6J69FpLWOFkqHLJCKnmpyPsYp006MiXDY23EbCILoiMTpK/aRKq8uMi6LCHzEv1E+PPRlEhZ1UIjQpeA671mutIgpe9VJNIgkFC/93a+GE8atjLt2R+LmU990kkn4ZxzzsFFF12EG2+8EUVRYNeuXTjvvPNwzDHHAAD27duHnTt34s///M9x6qmnYuPGjXjve9+Lyy67DJs3b8aGDRtwySWX4IwzzvCCI02n7Cc/+Yl/Pu7DtohI6avhjZBe9h5ShgixTIJ8vq58WmSU2kt7n1WVb3NhSgSBHdqfbY6IWZTq2Bj7FGxCfS2H189wMVFCQNuMAm3iKJmPjsnQI4hG0mTdOC06Vxs0quZIid2kwk5xG5VwXve/rO6N6S5L+L8pfer5ykN32S13H8OMi283pCsfdXEplKYyXpyiPs5mC1QS0h2jyLhqRn3qD2UmBUISDJWjbxfX6Ioi1+cMxzc/8zR6R1MWF4uuKBeda5Pw7zoXlfXvGmcynC+cm0T6lYjF8WSIpPlyILLWEyTaVt9eHBH7pVhzLRRr2mFjmKUm16a1b8pi7/bceuut2LVrF3bu3OkbZ3/mM5/x9xdFgUcffRSHDh3yc5/61Kf8sbRxNsMwa4suuwWsjJ1qhmHWJku15loI1rTDNpFKTKYKzb57vq+0EK3F28aQyBrcrmqQ+FdJ44QuSiWkl9SHlCHalvVh+lP1MYODUDMDAECSD3xUTRelbxCoq6o1wiZVEDKRaeKFRkSSQUzY82d9X8Mm3XP2+oCsPwpGkRq2DiTi3RpXB6iNiiRzaU1FEBqpfHSs0iY0I29E2PwuclTDVv/uJ8o/50QaxpOp8hG+dVniI2l13Zp7LKlhgwZshA1VHkRHmtE1EXb9Q5QijOtD7N94TAFH3bHbs9j1r5s3b561SfZxxx03JIHe7/dxww034IYbbhjpOc4888yhczCLhI+KGz82Mgl7wEb7yBtt7m5gI1sAUOZEGET6VhW1MIn9lGrp69xGuywSMXNROhUiaSLJoqiaj7yR44eEkLrGfq6ZNVH/rtrmTFz14eaNETAizNG2L9L+s0+VDP8IIOrbAKQ2/vtbmdFqMGitmpqlho1mNCiJkN2g4ro1miXir528vvnSZbfcfQwzF1IJuAp/QwrYpBRemkS6+5UJY23841QGVHUS0tDYhdIyiSiq5iJDuQYy6WrOBBGdCBW8mRTxmsYeQeTGouNpzVh7FXBzfpg2gZQ2uqJq7c2w4+iYbDm2a9wWSctke7RNCgGV0YwvF0mT0byziZFoHh03DBWN1M2XpVpzLQRr2mFLpUQqJZQ0kbZEVL9PxrQvjy8kN+EL6gRIRNKLU05oiqFdkEiZwBDxD5PXTpqZmIKZqeuAZD6Asb3BTJEDbkyK/Y3WUTG/IGqFrteRSNIgMNKbgHApkb1+eH7rsEEm4UU3FkPuOySE8DsUSsL3W6saxZzhCw0U2jpMSoY+RlRJSZsoFdIZhlSGhZArtu8lEj0rQNJPJHoqOGzOecuIuEhznLrXURaRMqRflBodOW2GONzwofxG7yu3SFbjpUTm2vgFYDTPjg4zF8SuhM+ojj6vVHTELy+EDHZCKqC0qxwZ5o2ugnpkmQfnDUCkDjBb7zYqOKLidMfw/GHjKuptKMl3Skhvk8w4jlsLXnTDpSwi/PMWAlH6oBsrWadCArCpkfVzFVq3Omk0JTJFu/hSfV5SfN+y+EqV9DZXEYctpEfKKA3SnUJJYsJBxpEYiRhDR3KYLrsFsO1iZselwdF+bEIJ4gyF1EZN7ncoKLhvrYasnTPUzlrbWFQGmRU3q0xQA2w6Yw4q+z7bODg9GDpPU9Cka9w21+WTDCszzu6w0fmmk6YadmVchy2eF75HLk2DVJn0a1OVycjpUqnbvAt9hIUSQR2cOHVtYiSH07vNcaTXXI8//jj+03/6T/jmN7+J/fv345hjjsFv/uZv4g/+4A+QZdncJyCsaYeNYZaaOp962FCshHxqhmHWJl12y93HMAyzHDnSa65HHnkEWmv8yZ/8CU444QQ89NBDuOiii3Dw4EFcd911Y51rTTtsiap/jBFRONQ52lQSOYq0kWPc7mnRcPgzK0Aio+hLAmEjWUImEMrJ+vdgirr3mpmZDtG2soCx6ZHQlY+2gRT+D6VGkp1zl/ok0tB7TWb9KLJWX1cG40RHZBKibY1da7rjrOyOc2JCKJm2SFBEfrqXGBQ2dajQoXeR1qZ1Z4n2H6JF9i6Slirhn6unFDIvKBKeM5Pt8v2JAERZv9eiykN0QVchqkZTIoWA20szMvHH+PfIvU8uApCOlxPJETbmsKC9yYT00SuDpKV03oqRuLYjVRUiSWl4LLT2GQCoQoRNJGmI6msdR9Vmi7ABDVl/ElWjdipNwzgJ4ygzQZIxsUtmxMhak0iAw313YWCMi+TRMZD6lyHJ91UGcRERonBSh3deG+NtUpO4j5pLKQq72JKIitComhcakSFln/ZhU5La6lhEy71bo0j9zwZH2JjDQSrZ6CNrvycIQiLS30YkKEJ7uNVRtjrTxVkglcViI9qOBVlkyCrUL+Vk/UFl7+soXBjPFW2rb7tMK7u2qZ85mnOPo+mR44qRtIkWdfZAQxwRo8fMFWHriqrRlEhnk4QSUFmImLm/qUoViZ5JqNRF2GQcVXORN9kQKSGPbUuF9AIlbf0gZuFIr7nOOeccnHPOOf728ccfj0cffRSf+9zn2GFjmJVEaQyKlt2ekhc9DMMsU7rslruPYRhmObIc1lzPP/88Nm/ePPbj1rTDlsg6IqMNqU8jf0hjhurXPW5n1e1OVhqQLR+CVKaQqasjSby4B6SCUUQUxEXksgnARoBMWdS1awBQFqGejUprN5tpt9SJ1I2xQ7TNRdb8tSRpGNPdelIDUzdmDXUfNH/b7SAbIyHTkNfs6iuKyvgatopE2NqkcN1zuR3nuoYt7C4DceF9osLzu78nUEfbEnIOaayMeVnUkTUA0KWPOnRJ+dcPch+CIOtf36Q1Nm7be7xGvitZYpZZWnx9GhC+vyhhrFkX9BhdBuskJOB2oo0JYiRGx9E2b2OqsIupQ5uStugara8VTWl+GmFrERcRUgI26wBChii2UlGrkTAWUeRtrmibFKHcP2RRCAg7K4i9VxCAdP8URByR087GhZpdTZpruxYAjriut2WnOIq0hdtUIrsrquavl0j50x10QaN0CJE8gfkJkLCsPzNfLdrNJAAAEklJREFUhBSNOjZaxRaPFEItk6lMiNLl4XxGCd9aoz6/sfM6emxl2wr1K7oWMT5KVqsIws43o2o0aibIvDvPcPSsbY68C/Z1zk2XdWvWrHXVsM0mTDJbJC2qg5sjqiYkERTJFJknkbRMRs2yu+rW2mT951O75phrzfXCCy9E871eD73emGpys/DYY4/h+uuvHzu6Bqxxhy2z6jaVCQ6YQUiPHPpqtXxWjD82fOFpfqUxBolzLpJeSJ2TSXAcyjQ4b7qEqOyHo6ognUOhy/aUyFnCwX5RlGRBmESlYSHUlgbZTEWKXr/w74NvmWJfOwBkEChcllYqkdqFTaXqAv36ren+b962UEnVcF88JQQSsoCJHbYw7xw2WRVeDVLoEqBCI87xbjhrQawBrfNRahZZXJqmQugc5NpAcEokMw4+BdelQZIkG5kAImiY+RReIfznVZggTFJvVJA0SJey3ewl2ZKC3ZYa2fnvtNmHjapHtjpj0qdnGiH9dRmZ+HNFacmHSd3Sye0UwdvuCtZpAwBpIFy6oxRQbiFIerJVOnbU5kp1ilKXOhw2IeCdM3cITZmkKfuR6Ahx3qhipBTh7yNFsOdufpylUJfdAth2Me1QNUCg7i3rFuD032wzPdLNUSESpxiJDBBVcMacGJgkzpupNKrcpVbGzptT3laVgamcGIkhzhY6xiKap85b/dsMzYV5env296xJe4+1YQfNzce3h+fnEh2RQpC/G+mf1nDS2hQgqZNGxUWaapCjpEE2HbW4X9t4Ttxca67t27dH81dddRWuvvrqoeOvuOIKfPzjH5/1ufbu3YsTTzzR3963bx/OOeccvPOd78RFF1001nUDa9xhY5ilpjKmowCWFz0MwyxPuuyWu49hGGY5Mtea68knn8SGDRv8fFd07fLLL8cFF1ww63Mdf/zxfvyjH/0Ib37zm/HGN74Rf/qnf3oYV77GHbZECSRKQGgiImLgU2TMGI67BkB0nVvHRgooZVMfZUVShBIf9UGZR9E246JBugSSYdn5KDIUiWXEktd+51qquteae140dq0VSdvsQInw3uh6G7h+GpiQImqAykcT6+ibv0zyXYlSjchucpTeQyJr7vkViaoFGewQjZOmgihddLIKqY+6rCNrdr47DZKkO7bMRylbQoQImxpPprXo2O0peNHDzIYIu9CAjCJmgoiRGP+5D33YjNEhkqbL+jYAyDiq5tOtjfHznfam63sUXe/w7SidmKQ4RvMyiexXmI+j3G1jIepUx3osIBqp7NIYYueDLZNkN10YEaXMOzGSOpXeRf+jOCfm+vq22T3/FpDoWFuELUTJ4tTH+HGw1yVao2ruNqKrHp0uuwWw7WK6EUr4SBaNkmhonx5J+7M5G6eU8IIiojIwNpJGz6cr3RptMyQyZCpNjjc+Y0CTCJupNJQfmygrqCvy1rwfECPNz4e5pPy75tsibACidEdJIl1RhM33/G2Phs0nqubO5eb9HDknxT9uPM2ROddcGzZsiBy2LrZs2YItW7aM9Jz79u3Dm9/8ZuzYsQM333wzpBx+PaOwJh0218z3pRdfBFCns7i5CjQ9shv6T9Pdpv8k/RdCktoC2XAooh5gbpyH9CJdhXldhjoSuiiLFk0dntCcDluoIzEqg2tgYqSCsTUlWqjQP400vC40NVxBdYnWdBgzXBvYdpldDpvP5nQOG2KHzR075LBp6rC5Gpwymg8LURMvVruIHDbX2Dc4bC8etAqfIy5apqFbDXg+UlY7s5Zwn6kXrd0Kn1fyATLaOyW101WSefL5ds4I3bQgaZBibIdtDA8FOAyHzXkswWEzMomPIcqTfgNFCG+rKhNUfX3ae2Szgi0zIIpvBv6fQe2whePbap/HfTtGcdh8dmbTYfOP63bY/PPM4bC5z9UotqvLbgFsu5gYv94qnErt8AdH6+CwAaQlKlGUjBytipRYEIfNUEfLHmOMIemRptthc8+vNbTbZNfjOWzRseT1DStFzh8VjYljRs7f1t9xToeNlKRAGLgtH4mw4SUh/HztXtuxFpDGpd6LkEpupN/ll0JAWE0DqQWEJvbPz4fXIKSArLodNv+5WqZrrn379uHMM8/Eq171Klx33XV45pln/H3btm0b61xr0mFz/5je8PM/t8RXwqxWfvrTn2Ljxo2d92dZhm3btuHW/fs6j9m2bdvYjRWZ1YuzW6/+uZOW+EqY1cyLL77YabtGsVsA2y4m4OzWW77yjSW+EmY1M5vdApZuzXXXXXfhsccew2OPPYZXvvKV0X2jOpkOYcZ9xCpAa41HH30Ur3nNa4byVZlhXnjhBWzfvp3fqxF4/vnnceyxx+LZZ5/Fpk2bZj12MBggz/PO+7MsQ7/fX+ArZFYqbLfGg+3W6Lj36uGHH8bP/dzPzZqyM5fdAth2MQG2W+PBdmt0xrFbwMpfc63JCJuUEj/zMz8DYPR8VYbfq3EYJUe53+8va+PALC/Ybh0e/F6Nzs/8zM/MabvYbjHjwHbr8OD3anRGsVvAyrddh1f5xjAMwzAMwzAMwyw67LAxDMMwDMMwDMMsU9asw9br9XDVVVctaAfz1Qq/V6PD7xWzmPDna3T4vRodfq+YxYQ/X6PD79XorLX3ak2KjjAMwzAMwzAMw6wE1myEjWEYhmEYhmEYZrnDDhvDMAzDMAzDMMwyhR02hmEYhmEYhmGYZQo7bACuueYavPGNb8Tk5OSczY7XGjfccAOOO+449Pt9nHbaabj//vuX+pKWJd/61rfw9re/HccccwyEEPja17621JfErHLYbnXDdms02G4xRxq2W7PDtmtu1qrdYocNQJ7neOc734n3ve99S30py4ovf/nLuOyyy3DVVVfhwQcfxMknn4yzzz4bTz/99FJf2rLj4MGDOPnkk3HDDTcs9aUwawS2W+2w3RodtlvMkYbtVjdsu0ZjrdotVokkfOELX8Cll16K5557bqkvZVlw2mmn4Rd+4Rfw3/7bfwMAaK2xfft2XHLJJbjiiiuW+OqWL0IIfPWrX8Wv/uqvLvWlMGsAtlsxbLcOD7ZbzJGE7dYwbLvGZy3ZLY6wMa3keY49e/bgrLPO8nNSSpx11lnYvXv3El4ZwzBMO2y3GIZZibDtYuaCHTamlZ/85Ceoqgpbt26N5rdu3Yr9+/cv0VUxDMN0w3aLYZiVCNsuZi5WrcN2xRVXQAgx688jjzyy1JfJMAzjYbvFMMxKg+0Wwyw+yVJfwGJx+eWX44ILLpj1mOOPP/7IXMwK5KijjoJSCk899VQ0/9RTT2Hbtm1LdFUMs7phuzU/2G4xzJGH7db8YdvFzMWqddi2bNmCLVu2LPVlrFiyLMOOHTtw9913+2JOrTXuvvtu7Nq1a2kvjmFWKWy35gfbLYY58rDdmj9su5i5WLUO2zg88cQTOHDgAJ544glUVYXvf//7AIATTjgB69atW9qLW0Iuu+wynH/++TjllFNw6qmn4o//+I9x8OBBXHjhhUt9acuOl156CY899pi//Y//+I/4/ve/j82bN+PYY49dwitjVitst9phuzU6bLeYIw3brW7Ydo3GmrVbhjHnn3++ATD0c8899yz1pS05119/vTn22GNNlmXm1FNPNd/73veW+pKWJffcc0/rZ+j8889f6ktjVilst7phuzUabLeYIw3brdlh2zU3a9VucR82hmEYhmEYhmGYZcqqVYlkGIZhGIZhGIZZ6bDDxjAMwzAMwzAMs0xhh41hGIZhGIZhGGaZwg4bwzAMwzAMwzDMMoUdNoZhGIZhGIZhmGUKO2wMwzAMwzAMwzDLFHbYGIZhGIZhGIZhlinssDEMwzAMwzAMwyxT2GFjGIZhGIZhGIZZprDDxjAMwzAMwzAMs0xhh22V8dOf/hRHH300Hn/88Xmd58wzz8Sll166INc0X8477zz80R/90VJfBsMwiwjbLoZhVhpst5gjhTDGmKW+CGbhuOyyy/Diiy/i85///LzOc+DAAaRpivXr1y/QlR0+Dz30EH7pl34J//iP/4iNGzcu9eUwDLMIsO1iGGalwXaLOVJwhG0VcejQIdx0001473vfO+9zbd68eV6GI8/zeV+D45//83+OV7/61bjlllsW7JwMwywf2HYxDLPSYLvFHEnYYVvGvPKVr8RnP/vZaO7ee+/F5OQk/umf/mno+P/5P/8ner0eTj/99Gj+zDPPxCWXXIJLL70UL3vZy7B161Z8/vOfx8GDB3HhhRdi/fr1OOGEE/C//tf/ih5Dw/Naa1x77bU44YQT0Ov1cOyxx+Kaa66Jjt+1axcuvfRSHHXUUTj77LMBADMzM/jd3/1dHH300ej3+3jTm96EBx54IHrc7/7u7+IDH/gANm/ejG3btuHqq68eem1vf/vb8aUvfWms949hmKWBbVeAbRfDrAzYbgXYbi0/2GFbxpx22mnRF80Yg0svvRTvf//78apXvWro+G9/+9vYsWNH67m++MUv4qijjsL999+PSy65BO973/vwzne+E2984xvx4IMP4q1vfSve/e5349ChQ62Pv/LKK/Gxj30MH/7wh/Hwww/jtttuw9atW4eeI8syfPe738WNN94IAPjABz6A22+/HV/84hfx4IMP4oQTTsDZZ5+NAwcORI+bmprCfffdh2uvvRZ/+Id/iLvuuis696mnnor7778fMzMzo715DMMsGWy7Amy7GGZlwHYrwHZrGWKYZcu1115rfv7nf97f/uIXv2i2bdtmXnzxxdbj3/GOd5j3vOc9Q/O//Mu/bN70pjf522VZmqmpKfPud7/bz/34xz82AMzu3bv9Y37v937PGGPMCy+8YHq9nvn85z/fea2//Mu/bF7/+tdHcy+99JJJ09Tceuutfi7Pc3PMMceYa6+9tvXajDHmF37hF8wHP/jBaO7v/u7vDADz+OOPd14DwzDLA7ZdAbZdDLMyYLsVYLu1/OAI2zLm9NNPx969e/HSSy/h4MGD+I//8T/iP//n/4x169a1Hj89PY1+v9963+te9zo/Vkrh5S9/OV772tf6Obdz8/TTTw89du/evZiZmcHOnTtnvd7mTtMPf/hDFEWBX/zFX/RzaZri1FNPxd69e1uvDQBe8YpXDF3HxMQEAHTuRjEMs3xg2xVg28UwKwO2WwG2W8uPZKkvgOlmx44dkFLiwQcfxDe+8Q1s2bIFF154YefxRx11FJ599tnW+9I0jW4LIaI5IQSAOm+6ifvizsXU1NRIx41ybc3rcOH8LVu2HNZzMAxz5GDbFWDbxTArA7ZbAbZbyw+OsC1jJicn8drXvha33347rrvuOnzqU5+ClN1/ste//vV4+OGHF/w6/tk/+2eYmJjA3XffPdbjXv3qV/v8akdRFHjggQfwmte8ZqxzPfTQQ3jlK1+Jo446aqzHMQxz5GHbFWDbxTArA7ZbAbZbyw+OsC1zTj/9dFx//fV4xzvegTPPPHPWY88++2xceeWVePbZZ/Gyl71swa6h3+/jgx/8ID7wgQ8gyzL84i/+Ip555hn84Ac/mFXOdmpqCu973/vw+7//+9i8eTOOPfZYXHvttTh06NDYMrjf/va38da3vnW+L4VhmCME264atl0Ms3Jgu1XDdmv5wQ7bMufkk09Gmqb4xCc+Meexr33ta/GGN7wB/+N//A/81m/91oJex4c//GEkSYKPfOQj+NGPfoRXvOIV+O3f/u05H/exj30MWmu8+93vxosvvohTTjkF//t//++xjNtgMMDXvvY13HnnnfN5CQzDHEHYdrHtYpiVBtsttlvLFWGMMUt9EUw3b37zm/GGN7wBf/RHfzTS8XfccQd+//d/Hw899NCsofyVxOc+9zl89atfxf/5P/9nqS+FYZgRYdvFtothVhpst9huLVc4wrYM0VrjmWeewU033YR/+Id/wF/91V+N/Nh//a//Nf7hH/4B+/btw/bt2xfxKo8caZri+uuvX+rLYBhmDth2xbDtYpjlD9utGLZbyxOOsC1D/uZv/gZvectbcOKJJ+Lmm2/GaaedttSXxDAMMydsuxiGWWmw3WJWAuywMQzDMAzDMAzDLFNWR8ItwzAMwzAMwzDMKoQdNoZhGIZhGIZhmGUKO2wMwzAMwzAMwzDLFHbYGIZhGIZhGIZhlinssDEMwzAMwzAMwyxT2GFjGIZhGIZhGIZZprDDxjAMwzAMwzAMs0xhh41hGIZhGIZhGGaZwg4bwzAMwzAMwzDMMoUdNoZhGIZhGIZhmGUKO2wMwzAMwzAMwzDLlP8PXk+Swyu7ucIAAAAASUVORK5CYII=",
      "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:56.437087Z",
     "iopub.status.busy": "2023-04-08T03:29:56.436908Z",
     "iopub.status.idle": "2023-04-08T03:29:56.644623Z",
     "shell.execute_reply": "2023-04-08T03:29:56.644087Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim4 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=[0, 0, 0],\n",
    "    grid_spec=td.GridSpec.uniform(dl=wavelength / min_cells_per_wvl),\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=[monitor_intermediate_proj],  # only need to supply the projection monitor\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
    "sim4.plot(x=0, ax=ax1)\n",
    "sim4.plot(y=0, ax=ax2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:29:56.646605Z",
     "iopub.status.busy": "2023-04-08T03:29:56.646436Z",
     "iopub.status.idle": "2023-04-08T03:30:21.018029Z",
     "shell.execute_reply": "2023-04-08T03:30:21.017380Z"
    }
   },
   "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\">[20:29:56] </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\">'fdve-41d3bfcd-296b-4066-8166-94c22f233a7ev1'</span>.      <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:56]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'aperture_4'\u001b[0m with task_id \u001b[32m'fdve-41d3bfcd-296b-4066-8166-94c22f233a7ev1'\u001b[0m.      \u001b]8;id=838731;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=410585;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ba252d1005e456987d43b3e4a550103",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:57] </span>status = queued                                                                            <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:29:57]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=106109;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=736187;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e261f9e37164495e93c00571ef696732",
       "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\">[20:30:00] </span>status = preprocess                                                                        <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:00]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=800023;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=383787;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:30:07] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:07]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit  \u001b]8;id=449734;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=870557;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                                                               \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>starting up solver                                                                         <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=336379;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=986604;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\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>running solver                                                                             <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=882270;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=15252;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a99307abc4248d5a819ef256dd3023c",
       "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\">[20:30:15] </span>early shutoff detected, exiting.                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">314</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:15]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                                           \u001b]8;id=947516;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=880055;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\u001b\\\u001b[2m314\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">331</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=230771;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=529994;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a91faa7b4e7a4ceb9ba5e1936e79c4f4",
       "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\">[20:30:20] </span>status = success                                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">338</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:20]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=704604;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=846230;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\u001b\\\u001b[2m338\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa040771f61b4986bc531e63bee3564d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:30:20] </span>loading SimulationData from data/aperture_4.hdf5                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:20]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/aperture_4.hdf5                                           \u001b]8;id=374711;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=524702;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run the simulation\n",
    "sim_data4 = web.run(\n",
    "    sim4, task_name=\"aperture_4\", path=\"data/aperture_4.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:30:21.459302Z",
     "iopub.status.busy": "2023-04-08T03:30:21.459117Z",
     "iopub.status.idle": "2023-04-08T03:30:22.557639Z",
     "shell.execute_reply": "2023-04-08T03:30:22.557016Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized RMSE for |E|, no far field approximation, computed on the server: 0.91 %\n",
      "\n",
      "Client-side field projection *without approximations* took 15.75 s\n",
      "Server-side field projection *without approximations* took 1.04 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the projected fields as before and plot them\n",
    "projected_field_data_server = sim_data4[monitor_intermediate_proj.name]\n",
    "\n",
    "# plot the actual measured fields from the previous simulation\n",
    "fields_meas = sim_data3[monitor_intermediate.name].colocate(x=xs, z=ys)\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_meas.Ex.isel(f=0, y=0),\n",
    "    fields_meas.Ey.isel(f=0, y=0),\n",
    "    fields_meas.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Measured fields\")\n",
    "\n",
    "# projected field without approximations computed on the server\n",
    "fields_proj_noapprox = projected_field_data_server.fields_cartesian\n",
    "make_cart_plot(\n",
    "    ys,\n",
    "    xs,\n",
    "    fields_proj_noapprox.Ex.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ey.isel(f=0, y=0),\n",
    "    fields_proj_noapprox.Ez.isel(f=0, y=0),\n",
    ")\n",
    "plt.suptitle(\"Projected, no approximations, computed on the server\")\n",
    "\n",
    "# RMSE\n",
    "Emag_proj_server = np.sqrt(\n",
    "    np.abs(fields_proj_noapprox.Ex) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ey) ** 2\n",
    "    + np.abs(fields_proj_noapprox.Ez) ** 2\n",
    ")\n",
    "print(\n",
    "    f\"Normalized RMSE for |E|, no far field approximation, computed on the server: {rmse(Emag_meas.values, Emag_proj_server.values) * 100:.2f} %\\n\"\n",
    ")\n",
    "\n",
    "# use the simulation log to find the time taken for server-side computations\n",
    "server_time = float(\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](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html) and [FieldProjectionCartesianMonitor](../_autosummary/tidy3d.FieldProjectionCartesianMonitor.html), one can also define the far field observation grid in reciprocal space using [FieldProjectionKSpaceMonitor](../_autosummary/tidy3d.FieldProjectionKSpaceMonitor.html).\n",
    "\n",
    "To demonstrate, we'll compute the far field associated with a Gaussian beam propagating at an angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:30:22.559708Z",
     "iopub.status.busy": "2023-04-08T03:30:22.559536Z",
     "iopub.status.idle": "2023-04-08T03:30:22.578335Z",
     "shell.execute_reply": "2023-04-08T03:30:22.577772Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:30:22.580182Z",
     "iopub.status.busy": "2023-04-08T03:30:22.579992Z",
     "iopub.status.idle": "2023-04-08T03:30:22.715150Z",
     "shell.execute_reply": "2023-04-08T03:30:22.714568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:30:22.717141Z",
     "iopub.status.busy": "2023-04-08T03:30:22.716991Z",
     "iopub.status.idle": "2023-04-08T03:30:40.627016Z",
     "shell.execute_reply": "2023-04-08T03:30:40.626407Z"
    }
   },
   "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\">[20:30:22] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'kspace_monitor'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'fdve-d3aa5946-9aed-4b02-9bfe-0693d3fb4456v1'</span>.  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:22]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'kspace_monitor'\u001b[0m with task_id \u001b[32m'fdve-d3aa5946-9aed-4b02-9bfe-0693d3fb4456v1'\u001b[0m.  \u001b]8;id=281913;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=731939;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36b12e9b2fc349638699ec89f4916fe7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:30:23] </span>status = queued                                                                            <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:23]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=779220;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=599162;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c83745833ece41abb041a7a6ec293634",
       "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\">[20:30:25] </span>status = preprocess                                                                        <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:25]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=546161;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=958556;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:30:30] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.045</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit  <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:30]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.045\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit  \u001b]8;id=776236;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=616835;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                                                               \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>starting up solver                                                                         <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=858498;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=892355;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\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>running solver                                                                             <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=861547;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=991557;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1eab002fbe24be99faec94d666ea901",
       "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\">[20:30:37] </span>early shutoff detected, exiting.                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">314</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:37]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                                           \u001b]8;id=666916;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=255870;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#314\u001b\\\u001b[2m314\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">331</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=629495;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=730867;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73fffe309ddb426b8bc4925f80a7a48f",
       "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\">[20:30:39] </span>status = success                                                                           <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">338</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:39]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=297232;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=2904;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#338\u001b\\\u001b[2m338\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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4a603f424f14354b574d5433f3ba4a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:30:40] </span>loading SimulationData from data/kspace_monitor.hdf5                                       <a href=\"file:///home/momchil/Drive/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:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[20:30:40]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/kspace_monitor.hdf5                                       \u001b]8;id=727780;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=922838;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data5 = web.run(\n",
    "    sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\", verbose=True\n",
    ")\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": {
    "execution": {
     "iopub.execute_input": "2023-04-08T03:30:40.961746Z",
     "iopub.status.busy": "2023-04-08T03:30:40.961606Z",
     "iopub.status.idle": "2023-04-08T03:30:41.315422Z",
     "shell.execute_reply": "2023-04-08T03:30:41.314906Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_188683/2062883249.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": "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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the computed projected fields\n",
    "far_data = sim_data5[monitor_far.name]\n",
    "\n",
    "# We can compute the theta and phi angles associated with the given reciprocal coordinates\n",
    "coords = far_data.coords_spherical\n",
    "theta = coords[\"theta\"]\n",
    "phi = coords[\"phi\"]\n",
    "\n",
    "# plot\n",
    "Etheta = far_data.Etheta.isel(f=0, r=0)\n",
    "fig, ax = plt.subplots(\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](../notebooks/Near2FarSphereRCS.html) and [plasmonic nanoparticle](../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](../notebooks/Metalens.html) and [zone plate](../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](../notebooks/GratingEfficiency.html) which demonstrates the use of a [DiffractionMonitor](../_autosummary/tidy3d.DiffractionMonitor.html).\n",
    "* Server-side field projections will add to the monetary cost of the simulation. However, typically the far field projections have a very small computation cost compared to the FDTD simulation itself, so the increase in monetary cost should be negligibly small in most cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "07a6163e75ba47fe9fdaa12b93f11dff": {
      "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_2bab94977bec43daaec7c2c316cc4381",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">538.0/538.0 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">24.1 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m538.0/538.0 kB\u001b[0m • \u001b[31m24.1 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "0ba252d1005e456987d43b3e4a550103": {
      "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_418942ad8b8547ee9bb9b784fdeb7ae0",
       "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: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">14.6/14.6 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m14.6/14.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "0df5577a368841a19336793ce9b5ffa2": {
      "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
      }
     },
     "0f2250602092442e811043a992f765fd": {
      "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_9237303c6ef845239cec10dec1c53df7",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Computing projected fields <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Computing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "19ee63ab2e7747a3915a55665b05720e": {
      "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
      }
     },
     "1e8a06087af84c4fbe5577dba8ad0970": {
      "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
      }
     },
     "1f76d7ad145a4e29a92076c8228a5ebf": {
      "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_586fe5f58c4741c1970d3238e95b718e",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_3'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_3'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "251322449fa045cfb1081b29f72579f2": {
      "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
      }
     },
     "277c5c934567495d9919c21f0e4d13de": {
      "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_df6ef6cb4618485691325be091b091cd",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_2'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'aperture_2'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "2a99307abc4248d5a819ef256dd3023c": {
      "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_51a12df7701c450088ab1cbfd5c5e49b",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "2bab94977bec43daaec7c2c316cc4381": {
      "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
      }
     },
     "36b12e9b2fc349638699ec89f4916fe7": {
      "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_251322449fa045cfb1081b29f72579f2",
       "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: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">13.6/13.6 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m13.6/13.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "41266184e5214ed3b6e4d1626d71b0c0": {
      "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_4f601e732d8949d3867a7c082027f9d7",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_1'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'aperture_1'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "418942ad8b8547ee9bb9b784fdeb7ae0": {
      "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
      }
     },
     "43a43a1645d3401a9049798e5e3b29ca": {
      "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
      }
     },
     "4670b9534ed24e5890485342c0e683bb": {
      "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_dfa076bd7dce4c9ba49a638564e36826",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">1.7/1.7 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">6.2 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m1.7/1.7 MB\u001b[0m • \u001b[31m6.2 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "49860f137f3e413bb994c7aff72e77c4": {
      "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_717491fd4739498f8df9d89081bb3d6b",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "4f359d578a154048b4f15e87ed93ff57": {
      "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
      }
     },
     "4f601e732d8949d3867a7c082027f9d7": {
      "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
      }
     },
     "51a12df7701c450088ab1cbfd5c5e49b": {
      "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
      }
     },
     "586fe5f58c4741c1970d3238e95b718e": {
      "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
      }
     },
     "5f826014cc774c179d6634618a6d9305": {
      "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
      }
     },
     "67f0ed2cdbcc4ffd8bac20035b92d981": {
      "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
      }
     },
     "6b69e92601d54ba9a5661f88ff6a8c1b": {
      "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
      }
     },
     "6f7be4aae57a445e8fe8a45df3609896": {
      "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_edb8029d828f43a88e7b9ceab27c5b3a",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Computing projected fields <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Computing projected fields \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "717491fd4739498f8df9d89081bb3d6b": {
      "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
      }
     },
     "73fffe309ddb426b8bc4925f80a7a48f": {
      "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_4f359d578a154048b4f15e87ed93ff57",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'kspace_monitor'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'kspace_monitor'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "779a6c5b884949bfad27f5454c166038": {
      "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_67f0ed2cdbcc4ffd8bac20035b92d981",
       "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: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">8.2/8.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m8.2/8.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "793cd5d3bc784c90b298c05836cba0a3": {
      "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
      }
     },
     "7b0b3439257743c1a7adbbc6976163dc": {
      "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_7d8e4a50550845fa93024f728fd7b738",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_1'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_1'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "7bb22fcdcbce40feb983fa1047958480": {
      "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
      }
     },
     "7d8e4a50550845fa93024f728fd7b738": {
      "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
      }
     },
     "7e8a922965c04467b74b7039748cefb1": {
      "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
      }
     },
     "84d81bcdbfcb4f40bd8eb194d4dd4e27": {
      "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
      }
     },
     "8a5f9231ed654ec3b2dc9715f7f4873f": {
      "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_b7072487c6fa4074a04e5067d0bf0b2f",
       "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: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">14.4/14.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m14.4/14.4 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "8d7861951f2a49c789232f01af559ad3": {
      "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_95c01b6416ec4b078ec56a45a35962ee",
       "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: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">7.3/7.3 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m7.3/7.3 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "9237303c6ef845239cec10dec1c53df7": {
      "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
      }
     },
     "95c01b6416ec4b078ec56a45a35962ee": {
      "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
      }
     },
     "a91faa7b4e7a4ceb9ba5e1936e79c4f4": {
      "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_5f826014cc774c179d6634618a6d9305",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_4'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'aperture_4'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b7072487c6fa4074a04e5067d0bf0b2f": {
      "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
      }
     },
     "bee0bdd0ef2e49ccb99a51f81a5f78bc": {
      "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
      }
     },
     "c83745833ece41abb041a7a6ec293634": {
      "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_793cd5d3bc784c90b298c05836cba0a3",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'kspace_monitor'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'kspace_monitor'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "d4a603f424f14354b574d5433f3ba4a9": {
      "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_19ee63ab2e7747a3915a55665b05720e",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">537.7/537.7 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">27.2 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m537.7/537.7 kB\u001b[0m • \u001b[31m27.2 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "d6d89fd914c6478bafa11e55c6f5b3b2": {
      "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_7e8a922965c04467b74b7039748cefb1",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "df6ef6cb4618485691325be091b091cd": {
      "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
      }
     },
     "dfa076bd7dce4c9ba49a638564e36826": {
      "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
      }
     },
     "e261f9e37164495e93c00571ef696732": {
      "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_1e8a06087af84c4fbe5577dba8ad0970",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_4'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_4'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "edb8029d828f43a88e7b9ceab27c5b3a": {
      "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
      }
     },
     "edc649a22db64a3ba067f380d90b73d3": {
      "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_0df5577a368841a19336793ce9b5ffa2",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "f1eab002fbe24be99faec94d666ea901": {
      "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_43a43a1645d3401a9049798e5e3b29ca",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 0.00e+00) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 0.00e+00) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "f4c6d5ae3e9e46d29c70604bea0dcfce": {
      "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
      }
     },
     "f6957a90640d4b3385f790b90790438b": {
      "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_6b69e92601d54ba9a5661f88ff6a8c1b",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">868.4/868.4 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">6.2 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m868.4/868.4 kB\u001b[0m • \u001b[31m6.2 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "f6b23aeb0963421cb1931cd099cec348": {
      "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_bee0bdd0ef2e49ccb99a51f81a5f78bc",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Processing surface monitor 'near_field'... <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "Processing surface monitor 'near_field'... \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "fa040771f61b4986bc531e63bee3564d": {
      "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_84d81bcdbfcb4f40bd8eb194d4dd4e27",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">538.8/538.8 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">21.4 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m538.8/538.8 kB\u001b[0m • \u001b[31m21.4 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "fa5f8cfc0205494fbf730e9e9d63d6f5": {
      "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_f4c6d5ae3e9e46d29c70604bea0dcfce",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'aperture_3'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'aperture_3'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "fd871ba214b2414e9652d147dae822af": {
      "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_7bb22fcdcbce40feb983fa1047958480",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'aperture_2'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'aperture_2'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}