Near2FarSphereRCS.ipynb

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    "# Scattering cross-section calculation of a dielectric sphere\n",
    "\n",
    "This tutorial will show you how to compute the radar cross section (RCS) for a dielectric sphere by sampling scattered near fields on a closed surface surrounding the sphere, and transforming them to observation points far away.\n",
    "\n",
    "This example demonstrates the usefulness of the near field to far field transformation for reducing the simulation size needed for structures involving lots of empty space.\n",
    "\n",
    "To obtain the scattered field, we will use a [total-field/scattered-field source (TFSF)](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.TFSF.html), and measure the scattered fields in the region surrounding the sphere. For a more detailed demonstration on TFSF, please refer to the [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/TFSF/).\n",
    "\n",
    "Then, we'll show how to use a [near-field to far-field transformation](https://www.flexcompute.com/tidy3d/examples/notebooks/FieldProjections/) in `Tidy3D` to compute the RCS for the sphere either on the cloud during the simulation run, or on your local machine afterwards.\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
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    "# 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": {},
   "source": [
    "## Define Simulation Parameters\n",
    "\n",
    "We first need to define our simulation parameters and the structure."
   ]
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    "# radius and location of the sphere\n",
    "radius = 0.5\n",
    "center = [0, 0, 0]\n",
    "\n",
    "# permittivity of the sphere\n",
    "epsr = 4\n",
    "\n",
    "# free space central wavelength\n",
    "wavelength = (2.0 * radius) / 2.0\n",
    "f0 = td.C_0 / wavelength\n",
    "\n",
    "# distance between the surface of the sphere and the start of the PML layers along each cartesian direction\n",
    "buffer_PML = 3 * wavelength\n",
    "\n",
    "# distance between the sphere and the near field monitor along each cartesian direction\n",
    "buffer_mon = 1 * wavelength\n",
    "\n",
    "# distance between the sphere and the TFSF source region along each cartesian direction\n",
    "buffer_tfsf = 0.5 * wavelength\n",
    "\n",
    "# Define material properties\n",
    "air = td.Medium(permittivity=1)\n",
    "diel = td.Medium(permittivity=epsr)\n",
    "\n",
    "# resolution control\n",
    "min_steps_per_wvl = 20\n",
    "\n",
    "# create the sphere\n",
    "sphere = td.Structure(geometry=td.Sphere(center=center, radius=radius), medium=diel)\n",
    "geometry = [sphere]\n",
    "\n",
    "# define PML layers on all sides\n",
    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "\n",
    "# set the domain size in x, y, and z\n",
    "domain_size = buffer_PML + 2 * radius + buffer_PML\n",
    "\n",
    "# construct simulation size array\n",
    "sim_size = (domain_size, domain_size, domain_size)\n"
   ]
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   "source": [
    "## Create Source\n",
    "\n",
    "For our incident field, we create a total-field scattered-field region injecting a plane wave propagating in the z direction, from below the sphere, polarized in the x direction."
   ]
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    "# 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 total-field region around the sphere, injecting a plane wave from the bottom in the +z direction\n",
    "source_size = [2 * radius + 2 * buffer_tfsf] * 3\n",
    "source = td.TFSF(\n",
    "    center=center,\n",
    "    size=source_size,\n",
    "    source_time=gaussian,\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    injection_axis=2,\n",
    ")\n",
    "\n",
    "# Simulation run time (s)\n",
    "run_time = 2e-12\n"
   ]
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   "source": [
    "## Create Monitors\n",
    "\n",
    "Next, we define the monitors that will capture the near field data.\n",
    "\n",
    "First, we create a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) completely enclosing the sphere, using the `.surfaces()` method to extract the 6 planar surfaces surrounding the volume. This cuts down on the data required vs computing the full volume because only the fields on the enclosing surface are required to get the far field information.\n",
    "\n",
    "The near fields will be captured on these surfaces; after the simulation, they will be used to compute far fields on your local machine.\n",
    "\n",
    "We will also turn off server-side field colocation and store the near fields directly on the Yee grid. The fields are colocated during the field projection computation as needed."
   ]
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    "# create a set of surface monitors around the sphere for local computation of far fields\n",
    "mon_size = 2 * radius + 2 * buffer_mon\n",
    "monitors_near = td.FieldMonitor.surfaces(\n",
    "    center=center, size=[mon_size] * 3, freqs=[f0], name=\"near_field\", colocate=False\n",
    ")\n"
   ]
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   "source": [
    "Next, we'll make a [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) which is used for computing far-fields directly on the server during the simulation run with `Tidy3D`'s full hardware optimization, making it extremely fast. Note that in this case, `surfaces()` must not be used because the solver already knows to use only surface tangential fields to compute the far fields.\n",
    "\n",
    "With this approach, the near-field data associated with the monitor is used to compute the far fields, and only the far field data is downloaded and returned to the user. Then, we'll show how to easily and quickly retrieve various quantities such as power and radar cross section from the computed far fields.\n",
    "\n",
    "Note that for server-side calculation of far fields, this is the only monitor required. The monitor's `size` and `center` fields specify the location where near fields will be sampled, while its `theta` and `phi` fields specify the far-field observation angles. Therefore, all the information needed is contained within [FieldProjectionAngleMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) (one can also use [FieldProjectionCartesianMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html) or [FieldProjectionKSpaceMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionKSpaceMonitor.html) to specify the far-field observation grid in different coordinate systems)."
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    "# set the far-field observation angles of interest\n",
    "num_theta = 300\n",
    "num_phi = 2\n",
    "thetas = np.linspace(0, np.pi, num_theta)\n",
    "phis = np.linspace(0, np.pi / 2, num_phi)\n",
    "\n",
    "# create the far field monitor for server-side computation of far fields\n",
    "monitor_far = td.FieldProjectionAngleMonitor(\n",
    "    center=center,\n",
    "    size=[mon_size, mon_size, mon_size],\n",
    "    freqs=[f0],\n",
    "    name=\"far_field\",\n",
    "    custom_origin=center,\n",
    "    phi=list(phis),\n",
    "    theta=list(thetas),\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 the far field approximations can be invoked to speed up the calculation\n",
    ")\n"
   ]
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    "Let's also create another near-field monitor where the fields are automatically downsampled based on given sampling rates. The rationale is that we may be able to approximate the far fields fairly well even if the resolution of near-fields is not very fine. This downsampled is achieved by supplying the `interval_space` field in the [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html), and can be useful for further reducing the amount of data that needs to be downloaded from the server, while still leading to accurate far fields, as shown below.\n",
    "\n",
    "Here, we downsample by a factor of 2 along the x and y dimensions, and a factor of 3 along z.\n",
    "\n",
    "Note that the downsampling feature can be useful if downloading near fields to compute far fields on your local machine, but is unnecessary if you choose to use server-side far field computations.\n",
    "\n",
    "Like before, we also turn off the server-side field colocation."
   ]
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    "# create a set of surface monitors around the sphere\n",
    "monitors_downsampled = td.FieldMonitor.surfaces(\n",
    "    center=center,\n",
    "    size=[mon_size, mon_size, mon_size],\n",
    "    freqs=[f0],\n",
    "    name=\"near_field_downsampled\",\n",
    "    colocate=False,\n",
    "    interval_space=(\n",
    "        2,\n",
    "        2,\n",
    "        3,\n",
    "    ),  # these are the (x, y, z) factors by which fields are downsampled\n",
    ")\n"
   ]
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   "source": [
    "## Create Simulation\n",
    "\n",
    "Now we can put everything together and define the simulation object.\n",
    "\n",
    "Note that for best results, it is recommended that the FDTD grid is made uniform inside the TFSF box along the directions perpendicular to the injection axis. So, we'll add a mesh override region slightly larger than the TFSF box itself, whose mesh size is set based on the permittivity of the sphere."
   ]
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    "# create the phantom override structure for the mesh based on the TFSF source size\n",
    "mesh_override = td.Structure(\n",
    "    geometry=td.Box(center=source.center, size=[i * 1.1 for i in source.size]),\n",
    "    medium=diel,\n",
    ")\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_wvl=min_steps_per_wvl, override_structures=[mesh_override]\n",
    ")\n",
    "\n",
    "# collect together all monitors\n",
    "monitors = monitors_near + [monitor_far] + monitors_downsampled\n",
    "\n",
    "# create the simulation object\n",
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=geometry,\n",
    "    sources=[source],\n",
    "    monitors=monitors,\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Visualize Geometry\n",
    "\n",
    "Let's take a look and make sure everything is defined properly."
   ]
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00ENM07S6TMSxWKzvXDVEtJB9IvsUBLJP4UIOVR/84Q9/YJdddhlbWlpimqaxY489lu3evbspcZ6fwWKsNg35hBNOYIqisM2bN7N3vOMddQ/j73//e/aWt7yFHXfccSwWi7H5+Xn2spe9jN19993uNvfccw+7+OKL2bZt25iqqmzbtm3sP//n/8x+85vftG170P0Mw2Cf+MQn2EknncQ0TWNzc3PsjDPOYNdcc01TSParX/0qO/30093tzjvvvLqw/sGDB9mrXvUqNjMzw4DOifPuuOMO93jz8/NtE+c1EsRgMVYLQz//+c9n8Xicbdu2jf31X/81+973vtfUnmKxyP7Lf/kvLJPJMHRInOeFG7qPf/zjgbbvhptvvpkdf/zxTFVVdtxxx7HPfe5zTcMfrQxWpVJhH/jAB9iWLVuYpmnsrLPOYt/97nd963nwwQfZueeey2KxGFtaWmK7d+9m+Xw+9PMhwoXsE9mnTpB9CheBsQ7xX4IgeuZv//Zv8b73vQ9PP/00jjrqqGE3hyAIwoXsU7iQQ0UQEcEYw6mnnoqFhQU3Jw5BEMQoQPYpfChtAkGETKlUwj/90z/h3nvvxaOPPoq77rpr2E0iCIIAQPYpSihCRRAh8/TTT2PXrl3IZDJ45zvfieuuu27YTSIIggBA9ilKyKEiCIIgCILoE8pDRRAEQRAE0SfkUBEEQRAEQfTJVInSHcfB/v37MTMz09Mq4wRBRANjDIVCAdu2bWu7aO4kQ/aJIEaToPZpqhyq/fv3j8yCmQRBNLN3717s2LFj2M0YCmSfCGK06WSfpsqhmpmZAQD89V//dd0K64ZhuOslqarq+3bIP19cXISiKKG3zTAMdx2pTCYDVVVDr6MTjDEcPnwYAKBpGmZnZwfeBgBYX19318haWloayts63Y8Ngt4P0zSxsrICAF3fs0qlgk9+8pPuMzqN8HM/77zzoCgKkskkALhr0gG1Ndssy0KxWASApu0SiQQymQyA2lp7nHQ6HWpZMplEqVRCsVhEIpGAKIqh1+FXBsA9dwDuGnCGYbjn7f0skUiEVtbqM8MwUCwWEYvF6sqjqKtVWbFYdCMn8XgcgiB0VVcqlQLQ372ZmZlBoVBAsViELMuIxWKB9g37O6IoCkzTRLFYrDsv27Zx6NAhd7tWz5dfmWEY+Jd/+ZeO9mmqHCpu5OPxuHuhdV2HIAgQRRGMMQiCAE3TmsJ6juPAcRyk0+nQO1fDMFAqldwO0zRNzM7ODrQTdxwHq6urSCaT0DQN1WoVoigOvIMrFAqQJAnz8/PQdR2maWJhYWGgw0B0Pzbo5n7w6yaKYs/3a5qHuvi5K4oCVVURi8UA1L5/HFmumWxVVV17xb+Xuq5D0zT3ZdEwDHe/MMts24Zt20gkEu7fg6iXw69HpVKBqqqQJAmWZUEUxbqXXcuyoGla3b79lPl9Zpqm22eoqgrHcdx2h11XqzLeN3FHybZtxONxqKoauC7+Xevn3jiOg1gsBsYYTNOEKIqB9g3zO2IYBkRRRCqVgmma7nWJx+OwbRuyLEMQBAiC4Pt8tSsDOtunqXKoGtF1HZZlQZZlaJoGy7JgGIZrmAbRiRuGgZWVFSiKgoWFBQDA6uoqVlZWsLi4OJBOnHfepmm6dRYKBdfrH1QnzutMp9OYmZlxr83q6urAnCq6HxuMwv2YRpLJJGKxWFNE0rIsJBIJ9+3ftu26MsYYZmdnMT8/33TMsMps20axWMT8/DzS6TSy2SyKxSJs20Y6nXYdvrDrbaRcLiMejyORSECSJEiS5Npx7zPq96z0U+b9jI9spFIp6LqORCKBcrkMSZJc5yasulqV2baNcrmMVCqFTZs2AahdG+5UBa2Lf9d6vTc8GpROp5HP56HrOiqVCuLxeN1okN++YX1HdF0HYwxbt25FPB5HLBZDsViEJEmYm5uDbdvI5XJuAMUv4t+qLOio1NRaxEZnCqi9/fE3P13X4ThOpG1o7Lz5m/3CwgIURcHKykrT21nY+HXeQO2h4w9HoVCItA1Ac+cN1N7CFxcXYZomVldX6X5M2f0gNrAsy7VX3LhzJ4JHBKJOKcidKUmSkE6n3bf1VCoFSZKQz+dhWVakbQA2HAbuTAG1qF4sFkO1Wo38GQVqtqJarSIWi7nPKHekuJMTNbweSZLqnBZ+XSqVCmzbjrwdfg41j1iWy2VUKpXI2+DnwEmShFQqBdu2kc/nB2KvpjJCxcO0XmeKw78QUUeq/DpvDu/Eo46MtOq8ObwjjToy4td5c3gnHnVkhO7HBqNwP6aZUqlUN+SQy+VcZ4p31I1OtWmaKJfLEATBdTLW19ebjt1rmW3bOHDggOs0ZLPZpu2KxSLW19dh27bbhn7rbSwrl8vI5/NuZ83hmhfLsrC6ugpN03wdK682ptsyb3skSYKmadB1Hbqu123vOA5KpRKq1WrfdbXa3nEcVCoVd1jNb7tqtYrDhw8jHo+7z2i7uhrPMUjZgQMHXOeW26XG7dbX12GaZpM989uul3bouo7l5WXEYjEIgoC1tbW67WzbdssKhQJEUawbuvN7QW0s87uXfkylJXQcx9eZ4kQdqWrXeXOijox06rw5UUdG2nXenKgjI3Q/NhiF+0Fs4I1MNTopHEmSoCgKGGORRAO8kalW4mlgI1LFI0hh4x3KauXEK4riOjpRRMtM04RhGNA0reUwEHdyuNMTNo3OVCtisRhEUUSlUonkGeWRKW+ksBEeqYoqcsgjU95IYSM8UsW1ZlEylREqURRbOlOcxkhVWBGJIJ23t51RREaCdt6cqCIjQTpvTlSREbofG/R7P4hw4BqqRCLhOgXcHrXSeDiOA8aYqyfis584vepUeLRpfn6+ru5W+87Nzbn7pVKp0PQxfDZhKpWqm+XnxfudNQzDjVS1ivR2W8Y1U5lMxre9fscQRbHJEe1HQ2XbNkqlEtLpdJNz2+q43iHSVtt1q6HiTko8HnedqU77cscnLA0V75czmUyd49pq33Q67Tp1iqKQhiosgl6csCNV3XTenLAjI9123pywIyPddN6csCMjdD82GIX7QWzgjUw1Cr1bIQgCksmkqxnpl0bNVNA28EhVsVjsO0rEGHMjIfy4QeAz3MKKjHg1U4E7V1EMVVPFj8OPGxQeQSqXy6E8o17NVND7wSNVlUollKgdj0zxGXxB4C8bjDFYlhWJvZrKCFU3cGOm6zps2+75JvTSeXPCioz02nlzwoqM9NJ5c8KKVNH92CCs+5HL5eA4Tk/3g5yxDfL5vHsNZVkOpPFwHAfFYhGCIGB2dhbr6+u+OYeC6lTW1tZcwfPs7Cyy2WzXGpdyuYy9e/e2jCoFOV6xWEQul0MikXCP4Xc9/LRB3JHikSpFUXrSUJmm6eppG/VSQY7BNVXFYtH3ex5EQ+Ud5nMcp+kaBGlTpVJxr2GnZ7TVvfEOu+bz+a6/E6Zp4sCBA+4wYND9vBw+fNh1brmDFlQH6NXaAbXny+/akYYqQryRKt5pdEM/nTen38hIv503p9/ISD+dN6ffyAjdjw3CvB+WZcG27a5nm1mWVSfCnna4xjNoZKoR7+ymXiIj3tlj3URCGuGRER7R6JYgGp1OeDVVvXzHvM5UrwmdvZqqoB2zl6CaqU5w7Vmvmiq/2ZXdoqqqG6nqJXKo63rT7Mpu4RPSeKQqzNmxY+NQXX/99TjrrLMwMzODTZs24ZJLLsETTzwxsPr5FGU+iyToFzKMzpvTayceVufN6bUTD6Pz5vTqVNH92CDs+zE3N9f18DjP/TbuhGmfuO6mH3qdwh9UgB6UXoXqfPtuhvla0atQPQxnitOrUD0sZ4rTq1A9DOeW06tQPYgAPSiiKEbiVI3NkN/999+P3bt346yzzoJlWfibv/kbXHjhhfj3f/93N1181IiiiLm5Oayvrwcabgqz8/a2oZvhprA7b063w01hdt6cbof/6H5sEMX9UBQFsizXdUTtrjF3przZvseVMO1TOp32TewJdBalNyb2nJ2dbamD6leAHrSsW6E6F6Dv3Lmz7ezGRjqJvbsRqpumCVmWkUqlfL+b/STsDCpU70WAHrSsMdrUTpTeiwA9aFnQ5J/dCtD9yizLakrsyR1sHrXqV5Q+Ng7Vd7/73br/b731VmzatAkPPfQQXvKSlwysHYqiBOrEo+i8OUE78ag6b07QTjyKzpsT1Kmi+7FBlPdDFEWoqtrRqfI6U5qmDSQBYZSMin1qpDG5YSv8MqCHBReqF4tFFItFzM3N+Q5nNgrQex3ybIVXqM7/94PP7G7lTPUDF5SXy+W2a/j1KkAPSpA2AM0Z0MOEz7TvFEHlkalMJuMOF4YFH1rnk0BaaUCDRrDGxqFqhAvO2i1PwJOucfgXgjHW9RgyfwM0TdOdcpnNZrG8vIy5ubm6RGGmaSKbzUKWZaTT6ciyB3PDx9vg9aIZY8hms7Asy31DjGJoRdM0JBIJN/lg49s4F2OmUqmWyfbCgO5HjWHeD56tWxAEKIrizoxSVbXOSNm27TpT/BpFneV70PRjn0qlEgzDcG2UV9Ddqow7I97r6BXlplIplEoldwFlAK4Tm81m3c47mUxiZWWlbl++XRhl5XIZf/jDH5BMJuvOwbIslEolrK+vI5FI1B3DT6jeCE922amsWq1CURQUi0V3zUTvdoZhwDRNmKbZckZa0LralXFhOc/g7d2uUYDeeP5h1M+pVCooFAquI+G9X3xWoKqqTZMSwvxO2LaNbDYLwzBcB5Zvc/jwYbecO+S91slfGoCak8+vQ6lUqlttoFGo3s2w+Vg6VI7j4L3vfS9e+MIX4uSTT2653fXXX49rrrmmqbwfh4rP9gNqRsowDOTzeaiq6t4knvhNVdU6gxkF8XjcXZSWd1yMsbpFIvmXKSokSXJX5OZfSGBDbJxMJiFJUk+CzG6g+1FjWPeDv+HxkLqiKG6dfFFSx3HchWx5u/izNSmEYZ/4D//f+5lfGf/xCvy9Lw6O40DTNFQqFei6DlEU3cVjDcOAoigQRdHdx7uv3/F6LVMUBYqiQNd195lgjLmLf/MFhh3H8T2e3/8A3HUNg5RxTRQ/d74dr1dRFN99e6mrXRlPxspfOvjzwZ9ZrnUKo65WZbIsu9eBfyd4P8efUX5dovpOiKLoztjztsNxHNi27drRfutstDONzxK3R96JNfyZCrpo+1g6VLt378Zjjz2GH/3oR223u/LKK7Fnzx73/3w+j507d2JpaSlwXhUOH8J45Stf6TvOShDTzvr6Or7zne/UiXi90cFUKoVcLod4PN4URQx7OGGY9GufkskkNE1z7Yz3OrUq4x3liSeeiJ07dwIAlpeX3W34wrnjXgbAXUbESy6XQyaTibRskuvikdRh3NdB1VmpVPDggw+6zqrfs8QjuYZhIBaLoVwuIxaLBdZBjp1D9a53vQvf+ta38MADD2DHjh1tt9U0zTcjOg/1dott2y1XcycIAu7ivfz5UlUViqLg8OHDWF9fdzVvjTqFfmdRjQph2CeuFeF2xivKblXGIyw7d+7E8ccfD6DWOXC2b98+EWUAfDu3eDyOxcXFSMsmua7NmzcDGM59HVSd+Xwejz76KBRFcSdfAP7P0jPPPAMAmJubw1FHHTV5Q36MMbz73e/GN7/5Tdx3333YtWvXsJtEEAQBgOwTQRBj5FDt3r0bt99+O+666y7MzMzg4MGDAGrh7jDycxAEET58dqWqqkin01hdXQ11LcZRIUz7lM/n6yYLBM0Cnc/nsby87L6ZHzp0qGm/SShbWVlp2sZvVmLYZZNel5dh3Neoj18sFpHP55tScTSK14vFIiqVCjRNQ7FYxN69ewPbqrGxaDfddBPW19fx0pe+FFu3bnV/7rjjjmE3jSAIHxpTVWiaNrFr/5F9IojxpjGpbS/JcccmQjVJs4AIYtJplfcrrLUYRw2yTwQxvnCnKZPJIJVKuVErnsdtdXU10HHGxqEiCGI8ME0T6+vrLZOo+jlVxAbpdLpOlO6lXRZo0zSxadMmV5TrZVLK2uEntg67bFLrahSle4m6bFB18mTGjaJ0HpnKZDLYuXNnU4qE+fl538kjfoz/qyFBECOD4zjIZrMdM9I3rsVIER6CIAaNZVnuMF8qlWqZbyro8kcUoeqCSdJ8EEQUGIYBWZYDDeV5I1XezMTTzsrKSl2ePBKlkyh9EHV5mQZROo9M8eV32mXnD7rcDUWoAmKaZmTLphDEpCAIAubm5gLrorhTFdVyQOOIYRhkawgiQhoF6GFBDlUADMNANpsNnH6eIKYVvoRGt/u0yhQ9jaiqimq1GvkySQQxjTiOg3K57A7zhQkN+XWAz1aSZTn0lccJYtLo9aVjUjKlhwHPJK+qalMOKxKlt2bShOKDrGtaROmpVAq6rrcVoDcyPz8fWJIwlRGqoBfHO/W7ce0xgiCIqNA0DYlEAuVyOXCkijSeBNEe27bdReqj6M+nMkJVKpVQKBQwMzPTcpvGPDqk8SAIYhB4M6UbhoHl5WXEYrG6CHmjaNYwDORyORKlR1g26XV5mVRRerFYhKZpbQXofmVBRelT6VAlk0l3dXs/p6pVUkKCIIhBomkaYrEYqtVqy21s20apVKIIOkF0IOq+fGodKlEUfZ0qcqYIghgleGSKC9W9SQb5bCVRFMlWEUQHon7pmEqHCthworxOFTlTBEEMm1aZ0nVdrxOqc2dqfn4eiUQChw8fJlF6xGWTWte0iNL9MqV76VeUPrUOFVDvVBmGAV3XyZkiCGIk0TQN8Xgc5XIZ5XIZlmVBkiSk02nYtj3s5hHE1DPVDhWwEZniGgVypgiCGCZeUTrQLJAtl8tYW1sDUItmZbNZypQ+oUJxEqVHlymdE6Yofeo9Bx6Z4tASGARBjCq2bdfNOKaM6gQxOkx1hKpRM1UqldrO/iMIghgW3uUy0um0G1nXdR2yPNWmnCBGgql9Cv0E6H5CdYIgiEHiJ0r3CtD5MB+wIVTXNA3pdJpE6RGXTWpdJEqvQaL0HjBNE6VSyVeATk4VQRCjRGNkyjv1mwvVi8UiZUoniCEzlQ5VLpfD7OxsSwE6OVUEQQwLryjdtm0cOHAAkiQhkUi4kalG0ayu68hmsyRKj7Bs0uvyMqmi9FwuB1VVIxOlT6VDJctyx9l8jU6VN5keQRBE1HgjU4lEou22mqbVdRIEQTRj2zYMw2hadDwsptKhymQygVIjeJ2qRCJBBosgiIHQjTPFoXQvBNEeSZJgGEZkfflUOlTdpJ/nTlUul0MymYyqSQQx1TDGht2EkSGZTMK27SYBOtBaNGtZFkzTJFF6xGWTWte0iNIzmQwcx3GjVI2RKhKlD4CZmRlYlkU5XwiiA94cSUFxHAe5XC78xowp5XIZiUSiSYBOEET/8OHxcrkc+rHJoQpIMpmkt2iC6ACfQcsX9O2E4zhYXV3tyRGbVEqlEjRNaylA9yujTOmTKRQnUXq0mdLX19dhmmaTvaJM6QOAkucRRHsURUGxWEShUOi4LXemTNNEJpOJvnFjQlDNFEEQvcNTjlSr1dBGn8hDIAgiNGRZRiqV6phyxOtMLS4uBn4DJAiCCAtN0xCLxdy1fFsRdHSKHCqCIEIlmUxCluWWTlWjM6WqKjlUHvwypXNIlN6aSROKD7KuaRGlt8qUrus6KpWKr1B9bm4ucASLHCqCIEKnVXJcP2eKIAhimPA8k35C9Xw+H3gVAnKoCIKIhEanKplMkjMVAG+mdIBE6SRKH0xdXqZFlA40P0teoTqfcRs0bxU5VARBRIbXqcrn8xAEgZwpgiBGEh6pKhQKqFarEAQBqVQq8JAfzfIjCCJSvAlxNU0jZ4ogiJHFu8xcYzSrExShIggiMrhmShAEaJqGarWKQqFAC463gUTprcvaMWlC8UHWNe2idM7c3Jy7nSzLME0T8Xg88Fq+5FARBBEJfgL0QqHQMaUCQRDEMMjn87BtG6lUCpIkQdd1lMvlwCsWkENFEETotJrN12r2H7EBidLbl5EonUTpvZR1EqVzAXoqlUKxWKzbN+jSWORQEQQRKoyxtrP5yKkiCGKUKJfLdZEpL5qm0ZAfQRCDhzGGbDYLx3HazuZrdKoIgiCGAXem2qVHIIcqAmhxZIJoj2EYsCwLmzZt6jibz+tUBdUoTAPJZBKpVIpE6SRKH1hd0yhK5wJ0PszXSqg+Pz+PUqnUVO4HpU0ICGMstAUUCWJSYYxhbm4ucGqEmZkZpNPpwAZrGuBvzARBREejAD0MKEIVAMdxkM1mIYrkfxJEO1RVhaIoXe0zMzODQqEQUYvGj1Kp5L68SZJEonQSpQ+kLi+TLkrXdb2lAB1ofr6CrjVKHkIH+Gwly7IoISFBdKDXlw5v8s9ph2s5isUiRaoIIkQYY20F6P0yVg7VAw88gFe/+tXYtm0bBEHAnXfe2dNxTNMMtJ136vfc3BxFqAiCaElY9gnozakijSdBtMdxHDiO09X6fN0wVkN+pVIJp556Kt7ylrfgNa95Tc/HyeVySKfTbSNOjXl0AIz32yJjgKPX/i4+Ndy2EPWkdtV+ixpA4uyxJSz7xDOlc9Hs2tpaR9GsaZqoVCrjK0rftg1wTMA2AX25VjZr1T4sW/UbV3zscNXGYtKOtmyC69o8U7vGftd8+5al2h+MufZpXEXpqVTKXf6qm0kfQTWeY+VQveIVr8ArXvGKvo8jyzJWVlZaTuv2S0o49oJ0Rwee+t+1v3/3leG2hajnuLfWfu+6DJBiw20L0TNh2SeOIAhIp9NYX19HsVh0tVGNMMZQKBTGO0LlmMDB7wMrOeDQ/bWy3BHbvNJge7M+Iwx5CyjK0ZZNcl35I7pHv2ueuLj22zkZkLrTR44akiRFEpnijJVD1S26rkPXdfd/nvMmk8nANE1fp6pVhmeCIIgwaWWfGjOl27aNcrnsCvcbherFYhGmaULX9fEVpdsmsJLDoZV8k8N0qOH/lXxDxApAthh92aTX5aXumh/K1X7Zh5ocqnETpReLRVSrVd9M6V56FaVPtEN1/fXX45prrmkqFwQBCwsLWF1drXOqJtqZKj61EZnK/ar2W50DxIn+CowujgUYR2bc8Puy9GJg9sThtYkYKK3skx+JRALlcrkpUsWXy0gmk4G1oSOJvlyLTGVNoHqkIzRSgCACVsMQVeP/AGA5gCVGWzbJdVlHHAyT/zYB+4gTwSOG6QUgvtm/nQSACXeorrzySuzZs8f9P5/PY+fOnQBqs5G8TtXCwgLy+fxkOlMEQYwcrewT11A16jlmZ2chSRJs20YymUS1WkUikXBtmuM446uhmrU2hpvKR3Q68xIgBh+eWUw3bxt22aTWtTlTK9u+cMQlYA5gHbkPi0fuS2YBSISfAHRYiT2nXkPVLZ3W4OFO1crKipvbZGlpafKdKXWu9nvHxYDWOkMvESH6CvDsXcNuBTFEulkjjOPVVAG1HF6yLMOygg3ljAXykRQaS+cCyiwgNixPJDbnDYJYBjKJaMsmua6FI1HPTenab2cfsPawf5uIlky0Q0W0gA/zaYtAYttw2zLN0HAr4UOjhqpRz1EoFFw9leM4btRqrBN7li1gxahpd6wj4vqsAGgSDhXqIyorpeYIS7YsAYoUadlE16UeKdNqvw+tC8D6kfug1r6Lh4xVIF5vs8ZNQ8UTe5KGCrUL8uSTT7r/P/XUU3jkkUcwPz+Po446quvjeZN2Li4uIp/Pt539RxAE0Yqw7VOrOhzHQTKZhK7rKJfLmJ2dDeXYBEH0x1hlqvzFL36B008/HaeffjoAYM+ePTj99NNx1VVXdX2sRgG6pmlYWFiAoihYWVkZ/zQJBEEMlDDtkx+NGZ4pozpBjBZjFaF66UtfGkquFcaY72y+RqE6RaoIgghKWPbJj1bLZXCnqlQqwXGcSOomCCIYYxWhCotcLtdyNh93qihSRRDEKMCdqVbLZaRSKYiiSA4VQXQg6mdkrCJUYWFZFrZt29Yy+uQXqSIIghgEPFEnUHOm8vk84vE4yuWy61BxUTrHtm2USiWsra25C03zmcteRrasYgNZs5a0k+eZWi0CqoSVbP2svux6uelY64Vm0XDYZZNel5eVXBnIH7kPcu27uGJmgVjDBIEQvxNRf+dKpRIKhQIURYEsb7g+jc+SX1m1Wm3axo+pdKgymUzHobxGp4qEnwRBDBIemYrH44EWZp+mxdt//ujTsOyNaINhWFDV+u4s7LJJrUuWRFz4ouf51j+JVCoV96UjbKbSoVKUYOsReZ2qbDbbci2tSYUxBotNUH6bASMLMgRa7JjogUqlgng87mZID8I0fdcs28HzT90FBzWnKl+sIJ2K120TdllUxx1WXSJECIKAnz7ye9+6JxFRFCGKIiqVSpMeMQym0qHqBu5ULS8vT52eymIWHi0/OuxmjC2nJE6BIoz3YqLd0qtGIWgm4mmgWq1CluWWmimihgMHa9YaAKBoGbCs+uGssMuiOu6w6pqX5yFhur5fgiAgHo+7KUcSiRYJVXtkemLEfSCKIubm5qbqDZAgesEwjK7XlCsUCuRQeXAcB/F4nJwpgogI/nyVy+VQheoUoQqIIAhTnULhmeozw27C2HBULJwkjuOIIAjIZrNQFCXQ81IoFJDP5yPTNIwjjuOgUqm4mig/Z7OxzHEclMtl5HI5xOO1IZ1sNtu038iWVW0gbyFbtGoL+AJArgwoUpMIfb1QgWFYyBcrKFq1UYOVQh4lu747q5StUMuiOu6g6wJqy8vIcgUiJBiG1XSNs/kKUDxyH+Sa7CNr5d1M6u52IX4nov7OVSoVd2KHKIpIJpOoVCooFouIxWJ1GsTG50vX9abj+0EOVRdQhIog2qOqKmzbDpTHjTtT6XSapvx7SKVS0DQNMzMzTZ+1KrNtG5ZlIZPJ+M5KHvmypA0Uj3RHVq1jW8wkANVft6qqMtKpuDt8VbJlJFLN3VnYZVEdd5B1pVK1ZzItxyEJElRVxtxsbehrce7I9TYLgHPkPqSPHCOVBmK1dWDDvP+D+s6VSiUkEgnIsgxRFDEzM+M+Tzwq3Or5Cqq7JoeK6Iqt6jZIAo0UN2IzBweM/cNuxtARBAFzc3Mdl3HyOlMzMzO+62kRRLckxSTc117RQEps+O71UxbVcQdQFwNQcmhY3Y94PI5KpdJWqB5UP00OFdEVkiBCJIeKaIMgCG1XHGh0pggiLARsjCQIgtA0qtBPWRjHGFpdEWXwnxS4U+UnVDcMI7BDRT0jQRCh02rFAXKmCIIYRfyE6qZpolqtBtZPU4SKIIhIaEyOq2kaqtUqOVMdKJfLsKyN/G8kSu8sSq+JrQGIhhuVKZebZ5v2UxbGMYZVF2MMZcdCpWyhKNWu2bSL0jneZ8krVJckCbZtI5PJBNZ4kkNFEERkcKfqwIEDqFariMVi5Ex1IJFIkCgdvYnSU6JaN/TFBdhe+imL6rhR18UYAxyzroxE6f7PEn+eAECWZczPzwdeeoaG/AiCiBTvG6Cu61OXIJcgiPHBa59s23adqyCQQ0UQRGR4NVNbt25t0lQRBEGMCoZhoFqtQtM0pFIpiKLYVfJPcqgIgoiERgF6K6E6QRDEsOEC9Fgs5uad8grVg0AaKoIgQqfVbL5GoXqn5J/TCInSSZQe9nFJlN5elG5ZFgzDQCaTga7rTUL1oBoqcqgIggiVUqmEcrnccjafn1NFbECidBKlh31cEqW3FqWbpgnGGDKZDObn55v2pUzpxMTAHIaSYaFUNVHWTeiWDdN2YDu1RHWCAMiiCFWWEFMkJDUFqZgCVaGFZYeBZVkolUrIZDJtZ/M1OlUUpSLGEcYAy7FRNW04FQMOY3AcB8wBKmUTOrMhCgIkSYQkiDAsG47DIIq0jNkoYBgGdF2HpmmBnaZ2kEPVBd4wPBEdzGHIVQysFipYL+uu89QKHTZKen3IO67KmE/FsJCKI6aSczUoTNNEKpUKlBrB61TlcrnoGzcmcFEsMZowBuiGDd2yaw4SAypVkwd0XGwG2A5gg8G0bQA2KhUTlgDIkoCYIiNGL35DwytAD8OZAsihCkypVIJpth7rJvrHdhiW18s4tF6GYQWfqupHxbCwb62IfWtFzCY0bJ1LIh2nKEjUKIqCZDIZeHvuVPnphKYVx3FQqVQoX9eIwRygbFrIlarQnP46YMtmKNomilUTjulAi8tQJJojNij4MF8sFoOu66EdlxyqABQKBRSLxa46CiI4jAEr+TL2rhVh2cGmp3bDelnHelnHbELDUYsziKv0tY8KWe7+2oqiiEwmE35jxpR4PA7btn3XFSMGD2NA1azJDhwGdAiYd41u2sgWdcRUCSlNoeHAiDEMA5ZlIZVKQVVVcqj6hXWxUCSfrdRqFWqiP6qGjd8v51CsRh/9Wy/reGyvju1zKWzNpEBrPI8OrRaHnUYEQahzqojhYdkM+YoOy45+ceGqYcMwbSRjCuKKXFvpmQgVx3FgmiZisVgkus2pdKhyuRzS6XTd1Ek/vFO/+TpkRHhki1X8fnm9o0YqTBgDnl0rYr1i4DlbMhRmJ0aOcrkMVVURj8dRKpXc3Dhe/NImFItFSpsQYtoE3axpMxvfv/Vqs5Y2zLJSyYR2ZIJNpUJpE8Iq42v0iaIIURRRKBQABEtLEjSKNZUOlWVZWF1dxcLCQkunqjGPDiUhDJcD2RL2rhaGVn+hYuDf967i+G3zJFonRgpv2oRUKoXDhw9DFEXE4/GWaRMqlQpisRilTQgjbQIDSroNWwRicX+tVDzRXB52mSk4iMVkSpsQUlmpVHKH+dqt5edXFlS0PpWv55lMBqZpYnV11TelfKukhEQ4PLtWHKozxdEtG/++bxUVg2ZvEqOJJEmIx+OuUN0PwzBgGEbHiDsRAAYUqiaqxvAnIFk2Q76swxlgBH/SifoZmconUFEULC4u+jpV5ExFy8FsCfvXisNuhotlO3hifxaG2d+sQoKICh6d4tnQvfDlMvhbN9EfBd0aqRcsmwG5chUBl5IjhszUPoGqqjY5VeRMRUu2pOOZEYhMNWJYNn5zIDdQLRdBdAN3qrxCddM0oet6ZALbaaNqWKjow49MNWLZQL6qN2m5iNFjKjVUHO5Urays4MCBAwBAzlRE6KaN3x9aH3YzWlI2TPzhcB7Hbp4ddlOIKafdWn5cqM6dKj7tm9by60+UbtsM65UNp8VPPM6JWpTu91kFgFFxkNAkEqVHsJZfIyRK7xFVVetm8FGuqQhgODKbb7Tj1iuFCuZSMcwlKUs1MTw6reWnqqqb+oVnpae1/HoXpTN2RADeIED3E4q3+yzsMr/PtHjtGpEovfuyVmv5eSFRep8UCgV3WrIgCC2F6kTvrBQqKFTGY5bkHw7naeiPGFkMw4BpmpBlGYIgtBSqE8HRLXsgeabCoFAxaOhvhJlqh8qrmVpYWGgpVCd6x3EY9o6QCL0ThmXjUI6WQSFGDy5Aj8ViiMViLYXqRHAcByOpm2qFZbO+l+UiomNqHSo/AbqfUJ3oj8P5CswxMwAHcmWKUhEjhZ8AvVGo3s0KEESNsmGFvpRM1FR8ko0So8FUaqhKpRIYY74CdK9QvVPyT6I9jAEHxzDaYzsOVvIVbM7QOmrE4GkUpa+vr8MwDFeAruu6r1DdMAwSpXchSmcMyJWqPQvFoyzrtH02V4GqbAjESZQeTJReKpVcDRWHROl9UiqVsGXLlpaz+fycKqJ78mUd+phFpzjL+TI5VMRQ8IrSDcOAJEnIZDJQFKWlaDaRSCCbzZIovQtRetWwoTkKHNafUDyqsnafiZqIVKL+3EiU3r6sVCohFotB0zQSpYdJMpnsmBqhcfiPwunds1Yc37UPK4aFij46Cf6I6cMwDFSrVWia1tGgi6JIC0x3SdUa3+fbMB3KoN4jUU7kmFqHKghepyqbzZJT1QWMAdlysDDpqJItja9DSIw3XgF64LdjkiYEhjHAtMZbI2uMefuHgSAIbZdx6hd6AjvAnSrLsmiB5C6oGBYse7wf+HFJ9TBK9PrSYZrjM9MqaigDevRYjj32wm7DHk85xTBpt4xTGEylhqpbVFXF3Nycr3iN8Kc0RlORW1HULYABoJGUwBiGgVgs1vU+uVwumgaNIYVCAYlEwleAzmkso0zp3YnSq4btpksYR1E6ABhVE+IRn4pE6d1lSgeAfD6PYrHoO5t/akTpn//85/GpT30KBw8exKmnnoobb7wRZ599duT1KopCb4tdMAn6I9txYFh23Wwaoj2MMWSzWWzatCnQEJRhGFhZWYEsj50p8iUM+zQzM+NmQPf7zK+MMqV3J0p3KgYcz2M9bqJ0XpZMquCnS6L09mWNmdITiQTK5TIqlYr7EjJVovQ77rgDe/bswdVXX41f/vKXOPXUU/Hyl78cy8vLA6mfNArBqY7p7L5GJuU8BoWqqrAsK1AeN+5MKYqCTCYzmAZGSFj2KajxJnpnUnIMkq63dyRJQiKRCFVTNVYewmc/+1lcccUVePOb34znPe95+OIXv4hEIoGvfvWrw24a0YA1IeP7464DGzSiKGJubq5jclyvM7WwsDARM9TIPo0P9pgsNdMJm5F96gdJklxNVTunKqgD3pNDdf755+Oaa65pKs9mszj//PN7OWRHDMPAQw89hAsuuMAtE0URF1xwAX7605/67qPrOvL5fN0PMRgmxQ+hjOndoyhK2xUHGp2psCO/ZJ+ITkzKU00Bqv7pJFTnKxEEoSfhwn333YdHH30UDz/8ML7+9a+7aQgMw8D999/fyyE7srKyAtu2sXnz5rryzZs34/HHH/fd5/rrr/c1rMQgoCd9mmm14kDUzhQw/vapMVM6idLDF6VXyqa75My4itIBQAFgSBKJ0rsUpftlSnccp0mo7jgOSqVS3fPYjp6VoHfffTfe9ra34fnPfz7++Z//Gcccc0yvh4qMK6+8Env27HH/z+fz2Llz5xBbND1MwhAOAEzIaQyFRqcqnU5jdXU1UmeKM872yZspvRESpdfoV5SuOza8o37jKkpPJVUocs3JIVF6+7JGUbrfs+QVqmuahkqlgnQ6HdhW9WzRtm7divvvvx+nnHIKzjrrLNx33329HioQi4uLkCQJhw4dqis/dOgQtmzZ4ruPpmlIp9N1P8RgUMTJ8EQUmojQF9yp8s7mG8T6mGSfiHYIE2KfIJB9ChMuVOfDfHw2YFB6uhv8DUDTNNx+++34q7/6K1x00UX4whe+0MvhAqGqKs444wzcc889bpnjOLjnnnvwghe8ILJ6id7gb03jzqScxzRB9onohDghDpU0GacxMfQ05Nc4VfOjH/0oTjzxRFx++eWhNKoVe/bsweWXX44zzzwTZ599Nm644QaUSiW8+c1vjrReonti6mQ4IjHKQdUXPDKlqqo75OfVVEUB2SeiE5IoARjvmTOiAIikSQgVHpmSJMkd8uORqiD05FA99dRTWFpaqit77WtfixNOOAG/+MUvejlkIF7/+tfj8OHDuOqqq3Dw4EGcdtpp+O53v9skBCWGT1Id/1w6cVWemDfZYeAnQPcTqofNuNsnEqVHL0o3TBuV6nhnSldkEUWxdv4kSg9HlF4qlSCKoptGgQvVIxWlH3300b7lJ510Ek466aReDhmYd73rXXjXu94VaR1E/6Ti459VfmYCzmFYtJrN12r2X5iMu30iUXr0ovSEw2B7vnbjKEpPxhQktY0unETp7cvaidK5A5VOp5FIJFAoFNzPEomEr8PmBynaiEiQRGHsHZLZuDbsJowlpmm2TY3AnapOyT8JIipEUcC4yyNVmbrvMODOVCsBOheqB4HuCBEZ88nuFskdJURBwGxivB3CYeA4DrLZbMfUCI1OFS2hQQwaVR5fWYIkADLNQO4b27ZdZ4oPlfsRedqEaYTepLtjPhUb2zxOC6kY6ad6wDCMwKkRvE5VLpcbTAPHANNs1sMQ4ZMY44kzmqqMrW0dFbypEdo5U91ADlVATNOEYRjDbsZYocgi5lPhfFEbeaxwCH//h5/CisjJ3TSbjOS4k44gCJibmwv8RsedqqCiz2nANE1yqgaAKApQlWi6wMObSnjq1CxYRCtGjLMzOArwSRx8Lb+wIIcqAIZhIJvNTkz270GyLRONY/L/cnuxr5rHb8sroR97NqEhGet5EYGpRlXVrp8TVVWRyWSiadAYoigKdF2nF7gB4BV1hwUDw/KmIkoZE6VU+I6xpkgUPe8DrpnqRhsVFOo1OuDN8KyqpKnplrgmY3EmjpVC65W8u2XFKOGpShZVx8K/5Q/ixNSm0I4NADvmm2dSEcHo9aVDUcZXzxI2pmlCFEWsrq5C0zRfx4rSJvSXNsGLbTqoNJT3kzahnDZRjpuwJYblmSKk5ZnA+3b6TBAAFSKKxfrvBKVNCJY2oVgswrIsSJKEZDKJQqEQKC2JrutN2/gxlQ5VqVTC7Oxsx+28U7/T6XTgi0rUs3MhhWxJhx3S8NyjhUMwHBtJScGvS8soWDpm5HBm5G2aTVB0ihgqPG2CpmmoVqsANhzOdmkTDMOgtAkB0yZ4cRwGHQ4a50X0mjbh8PYSIAGSJSK/VcfRhzKQHDHQvp3qSsYUMNMJdF7eMkqbUEubEI/H4TgOkslkV2lJgr7wTeWQX6lUqssz4UdjHh0a7usdRZZw1GI4UR+HMfwyvx+iICAuKqg6Fn5VPNR5xwCosoSdFJ0iRgRVVRGLxaDrekdNleM4NFOyR0RRQDIWToTUFh2sLVQgOAIkS4Cp2FjPhPMiLksCEiq97PVLmJqpRqbSoUomk8jn8y2dqlZJCYneWZqJYz7VfxqFpypZrBolxEXZXXbhl+v7Q+lMnrM5A4kWxyJGCFVVoWlaW6eKz1YieieuyKEI1NczVZiKDckSIbCaLVlb6P/eiEJN20nv9f0RdV8+lZ5CMplEOp32darImYoIAdi1aRYJrb83wf8vfxAWY5CPrLIeF2Xs1/M4oLePOHZi11IaqTjpeIjRQ1EU16lq1FNx7ZQoimSr+kAQgHRMg9TnJVxdrA1BikecKdEWkc/oMNT+ZrHOxFVIJETvm6hHmqY2fsjHSvP5vPs/OVPRIokCjt+awb/vW4Nu2l3vX7FNPFY8CFUU3QdDFSRUHAv/X+EgtsXSPbVr+3wKS7PhzvYgiF5ptZafbdt1QnXHcVCtVpFKpdyFXEmU3r0o3VumMBHlit6TKN3QbKzPVCFYAhyHgTkMgglYmoNDqRIWn030JEpPxhSYug1Tt3s+LxKld17LzwuJ0nvA61QZhgFd18mZihhFlnDi9nk8sT+LitHdW9uvS4dRtA3MSBviS0EQIEHAI/kDuGDhOV1nD94+n8L2OX/hK0EMg3Zr+XGhum3XOtdUKoVNmzbRWn59iNIby1IpFfuXC1DbRKz9ROTr23UwmUExJAiiAAc1fZYtMOS3VbFjLd1y31ai9GRMweJcs+aHROndl7Vby49DovQ+mZmZQSwWQ7VaBWOMnKkBoMoSTty+0PVaf4+s7wcYIAn19ycuKchZ1a5yUglHhiC3z6cAiqQTY4KqqlAUBZZlgTEWqcB2WhFFAemEBrkLPSUDw+piCYAAocGgSJaASsLqKidVTTOlIqZQAs9xYuo9Bx6Z4viF/4jwkSUBJ2ydw9ZMsOgQzz0VE5uDqrIggjGGf8sfDHQsVZZw4rYFLKWpMyLGCx6J4lBG9WgQRQFzyRjiARN/VtIWKgkLktXshImOACYyrM0HE6fLEjCXjEEjZ2rsmOohv0bNVKlUqtNUEdEiiAJ2LqYwl9Tw1OE8KkbrzoHnnpptkW9KFaVAOak2zyawY2GGBJ7E2OFdLiOVSsE0TVeoLknU+YaNIAAzMQUxWUS+bMBuM5E4t6kCJjKIZnOMQoAAwRGwtljBgthaqykIQEJVkNBkms03pkytQ+UnQPcTqhPRk4orOHnHPA4XKtifLcGw6gXr3txTrWZpxEQZeVvHr4qH8PzMUU2fZ5Ix7JxPBX7jJIhh4SdK9wrQ4/F4k1BdlmVUq1USpfcpSm9VpgoSdNtGRTebROSOyJBbrAKWAObAXb+POQw8lbFoCDBUG2szZYjr9TZMr1rQFAkJVQazHJSsjZmcYZ0DidJJlB4ZpmmiVCr5CtDJqRoOgihg02wCSzNxrJWqWM5XUKjUHnxv7qlWeHNSnTO7E4IgQJZEzKdi2DybQJwS4hFjQqMona89xgXojfAZfgBIlB6CKL1V2QwAxuJYy1UgKgLMIyGrtfky7BiDYtWvscdF6TUE2IKD8g4TcbMmcBYFQFMVMMNGOt06qh7GOZAofTCi9KnsZXK5HGZnZ1sK0MmpGh6CKGBhJo6FmTgMy0aupOO7TzwBGxu5p1oRFxUcMAowNQunLGxCOq5CoKE9YoyxbRuVSgWiKLYUoKuqCtu23WVqiOgQhNrixKmUWluyxrLx1Kbadee5p1oh2SKKCwakwwLSUCCLEgQBKFrhLMlFdMYJafmzVkylQyXLcsfZfI1OlaaFs1YcERxVlpBIyHiseAgpRUFSVcFY7W2LyxkE1IycAAEJyDisl/Hb6gpelNw5zKYTRN/wDOjtnCmOqqo0O3nAiKIAJBjyszoUR6zNCjximBhqtkkUj8z5EwAJIqqyjcJSFQtr/a8aQXSP4zgwDAOxWDTXfyqfwEwmE8j4zMzMuBnVafbfcPjXw88iZ1TBABRNAxXbgCgAsihAFgVIogCHMRQtA0XLhM0YfnjgKZhO94lDCWJU8ArQg6ZGIIdq8BxKl2BKDhgAS7RhSw4EoTacx38zkdU+Ex04AsO+2QI2XgmJQSKKIkzTjGx27FRGqLpJP88jVblcDslkMqomES1YN6pIKaq7EnzJMqAIElTPrKasUUVcliFAQFqpRRILpoF5jdIijAu0sO8GxWIRjuM0CdC9NJZxB4xE6dGI0lt9VsjokE0BjgMwETAkG7BqEXPHqX2nddWGbNfaJBsiDNjIl3RITOyqrn7KSJReE6VXq1V3ggdQW9aJROkDZmZmBpZlNa2jRUTPfzz6RPzZUScAAJ4u5vBXP/uXprc7BobT5rfgo6edB6AmUBdp3vFQ8M5OC4rjOMjlcuE3ZkwRRRGJRMJXgN5KNEuZ0qMXpft9dnJuE07KbUKxaMDYbOMXRx2AKAoQjyT3lEQBggBsL6Rx4oFFFIsGZlIqhKTge7x2dfVTRqL0elG6bduQZblJykOi9AGRTCbpLXoICIIA+YiBlDo4Sd0uO0OED59Bq6rBsuA7joPV1dWeHLFJJYhmihgNeF50EQKEdqJ0VttG9MmkTgwevuB4tVqFZVmBHaZOUA/UBbJM/uc4whgjZ3hAKIqCYrGIQqHQcVvuTJmmiUwmE33jxoSoBLPEiMIAklQNHlVVEYvFoOt6aJoq8hCIiefg//wtzDUd6XOWkDptHnKmuzUEieDIsoxUKtUx5YjXmVpcXHTzKBHEtHH+mgJ5XcCzM8DBFINBSe8Hhqqq0DQtsEaqE+RQEROPuaKj+vsC9D+UsPpPe5F43ixmzlxE4nkZiCoFacMmmUxCluWWTlWjM6WqKjlUHvwypTdCovTREKV7y4yKDcYYbMbAGFxROmMMpmmjWDR8902YKtK6gLkKcPxhYH/MwTNxG4dUB+UKidLDKmuVKd0wDNi27eo4/YTq6+vrTcf3gxwqYioQYxKkGQVO1Ubxl2soPZyFNKsgdeYCZv5kAdrRya5mfxLtaZUc18+ZIuppzJTuhUTpNUZFlO5FiosQBAGSZ1JMTZQuQDmSDNRvX1EUYAqAIQKKI2BXVcIxuoSKDPxekbGqiCiogFd6RaL07ss6ZUo3TdNXqC5JUuBheHKoiLEjZ+iQhI0Zl5sOSzjxKQGrz+713d458vYqiAKkhAwpUVszyy5ayN19AOv3H4K6LU5DgiHT6FQlk0lypoiJR1dsCAxwWC0P1bFrMTz/mSQ2FQQYhgxVr39xEz1Zik0JMI+UxUzgJEMCqwjIacwdEiSiwU+oXqlUEI/HkUi0XtTaCzlURFfYbHjLJCxocZy1uK1u8VAA+JMnFJz0mIy1x/b778gYxIRUn25BFiCl5Zpg3XCgP1PC4SNDgvHnzSJ1xnxXQ4I2o0Sifnidqnw+D0EQyJkiIoMB4Enr/Caj9FPWafu4LmOxGId9xEOyLAeyLOL8383h9ANJAAIcJjWldGGMwWzQTTkCoMuAYzOogoD5qoD5KnDiqoC9qozDArASZ3CEYOdAiUSDwe3S6uoqdF2HIAhIJBKBRevkUBFdccBo4bQMiLc877imMuP3OVhPFMBmW+0lwIYDu9U6TgqAWaG2OnzVQv6hwyj8cgWYFSGdnoB8WgLCToWGBHskmUzWLeFEzhQRFSVnQ/tSdix3mCuMsk7bJwwZxz+d3igrWkikZMzlVTAAZdmGYwNio/NkA60yvjgADNGGIQICqw0JHlORcEwVKMkMf0iaeCZloWwbXZ0D0RqvfZIkCZIkkUNFEN0iiAKQECAkRDCLAUUH1g8LsH9UhLBDhXbFIoQ4idi7gWumBEFww+mFQoEWHCeILmECYEgMDhhkEUhYAk5e13BCXsUBWcWPU1V0WJ+ZCEC5XK5p4SSp64Te5FARgTgqdtSwm9CSw7KDHMpQxHAiH8xhcKza2yQkQE6qSD1nAQupnRCVzg7Vn6T+JJR2jDt+AvRCodAxpQJBdEtSSiAl1T//gmSEWtbrMVShlsxThAQIzM2ivrGhT1nbzxgUR4CEmpNlSgJyqoiUlKoTrrdq05Ky5F8XgXK5DNu2EY/H3XX/+DI1QSCHipgY2iXv7DRcx7VUTrk29VnUJKROnUfqrAUkT8pA1Cg5TDe0ms3XavYfsQGlTeg+bULYKRLCXF/PNBUwJsGxWS2VQoOZ4ukV/HA/Y4DMAIUJAAMskeHpmINnYjYOag6KFROJUn2271ZtWpfrU5RMe9oE/ixVq1XEYjHE4/GmNC7FYrHp+H6QQ0W0RBZknJI4ZdjN6Mi/a1WUxFWwXAuNlMMgxVWoKa35I8uBWdZhmw5kWULq6AXsPPc4bD3jaMQX/KdsE+1hjLWdzUdOVXsobUKwtAmZVBJp1JzHvFxBOlW/XE/YZb0eI1mpQDJMzKAmDm9eeobBgghban7pc2BDYwJEVstiUNIk/EFjKC0mYMoiZAA7AOSV4G2anakvo7QJNWcrFothaWmpyeGamZkJvEoKOVRESwRBgCKEs8ZRlDz3Fadi8Y+2tvz83//xFygdzruGjDkMZsWAVTVr2p7ZOHaccyx2nH0s5o7dVNNSET3BGEM2m4XjOG1n8zU6VQTRLYIgQEItYiJCgiTUR0/CLuv1GM8uxpGdqT0HpYqBZLz+mTh6fwkqGBy4U/YgO4DoMDgATEXESlrBalpFSZOwXqxiVlEg+dQVpE1EPZVKBY7jIB6PQ5L8r0/QiTTkUHUBrQc3msQzSWw/c1fLz5/4p4drYXLdhFkywBiDHFOw7YxjsOP5x2HLqTsha6PvOI4DhmHAsixs2rSpoxHyOlU0g3IDsjOdkSURP33k9+7/hmFBVeu7s7DLojrupjXd1UPxnFSOAOiKiLwIOHEFrAhgfzT1y9L0TrQpl8uuMyW2mmrZBeRQBYQx1pXanxgtzIoBiAJmj5rHUS/8I2w7cxcSNKQXOowxzM3NBX6j407VwYMHo2zWWMFz38zONucBaVXGh/zm5+exefPmpm1GvmzGAvJHXmqsWpRg80IK0NJN2wLAhS96Xt3/2fWyO2wVVVlUxz32oQOImw4cWUQ1oSC3GEchE4OlSgM7Lz7Ut3nxyPU21gHnyH2YO3Jf0nNAvDacFub9H9R3rlgsIpVKQZZlNx0CH17nkalWzxdFqELEcRxks9lQPFhi8Gw94xgsHr8VO86hIb2oUVUVitJdtG9mZgaFQiGiFo0foiiiXC5jdna25RAEMTmszijQ4iry83FUEzJA0drIKZfLSCQSSKVSgQXnQSCHqgN8tpJlWUilKKIxjpx4MaUxGBS9vnQkk8mQWzK+bN68GY7jQJIkpNP1EZr5+fmm7efn52FZFkzTxKZNm7B9+/ambUa+bNYCckeiAEfShm/flAZimaZtW+EKqiMsi+K4K8+tlaUA+NU4iPPikantmzO1Aj3rRqi2Lx65L5kFIFGL/oR5/wf1ncvn85iZmYFhGEgkEti5c6cbreK0er78Ztr6MZUhl6BZT71Tv+fm5ihCRRBE5AiCgFQqBUmSkM/nYdvBljUi7RVBtMe2bTiO4w79hc3YRKiuu+46fPvb38YjjzwCVVWRy+V6PlYul0M6nW47LtqYRwdAYMM28jhHctzoK8NtxzRTXdm4D8TYE6Z9yufzMAwDs7OzKBaLyOVySCQSdW/S6+vrdftYloVcLofl5WU3kn7o0KGmY49sWdkCVgwcypoAf+Hdvw+Q8zi0Wj8ks5Krz5kEHMmbZBYiLZvouowj3ye9lrvp0KFDwPqR+6DUtMOHjFUgXu8yhPmdiPo7VywWkc/noaoqisWi+zw1Pkt+ZY15qVoxNg6VYRi49NJL8YIXvABf+cpX+jqWLMtYWVlpOa3bLynhRAnSjSMJz569CxDH5iswWTjWxn1IbBtuW4i+CdM+eUmlUigUCq7moxWlUmlyIlT2kc5r7WFAEIFcw4ts3ufFtui4eZMiK5vkuo4M77m/182N+zBBiKIY6UjT2PSm11xzDQDg1ltvDbyPruvQdd39n+e8yWQyME3T16lqleGZIAiiFWHap0YSiQTK5XJLoXqxWITjOCRJIIgORP2MjI1D1QvXX3+9a+i8CIKAhYUFrK6u1jlVE+1MpXYBx7219vfvwnuDJvqAR6Ze/q/DbQcxFFrZp3Q6jXg8XieQ5cN/jUJ1PhU8mUxidXV1fEXpW5aAxMXAoRxw6P5aGRdDKw2jA7KPBla2NjJ6R1U2wXXx1Ah+13z7K75c+2Pfvo2yMRWlp9NpKIoCSZJaCtD9ykiUDuDKK6/E+vq6+7N37173M1EUsbCwAEVRsLKyAl3XJ9eZIghi5Ghnn/zwCtUty0KxWIRt20in05RegSBGgKFGqD784Q/jE5/4RNttfv3rX+OEE07o6fiapkHTmtdv43CnamVlBSsrNYH20tLSZDpTogbsuqz299KLh9sWghgDhmWfuCid4xXIzs7OolAouKL3ZDLpzgTM5/PjK0pnDHBOxiH7EJBeqJVlar8PGat1+62YPgvhWvnaWnMRlk10XenaGn2+1/xIZCrq78SgROmNqRImRpT+/ve/H29605vabnPssccOpjGTjiAAUqz29+yJw20LQYwBZJ8GiCAAklL7iR/JdH0k51HjzDLEfKJxmuQu3BtZ2STXdSQDestrTgRiqFdtaWkJS0tLQ6vfm7RzcXER+Xy+7ew/giCmh2HbJz+4AD2ZTELXdVeoThDE8BkbN/SZZ57B2toannnmGdi2jUceeQQA8JznPKenDOZ+AnQ/oTpBEEQnwrRPfqJ0YEOAnk6n3RmBXKieTCbHO1N6wLJ28HyBUZZNal18/bth3NdJEqWPjUN11VVX4X/9r//l/n/66acDAO6991689KUv7epYjDFfATrXVJFTRRBEN4Rpn/zwCtC9GZ65UL1QKMBxnL7rIQiid8bGobr11lu7yvHSjlwuB1VVfR0mcqoIguiWMO1Toyj9wIEDsG0biUTCjUz5CdVLpdL4itK7KOMTiLxksz5C9ZDLJr0uL8O4r4MQpfO+PypR+kSnTWgF10y1cpQaUypMVJZ0giDGBh6Zalx6ppFkMglBEAbYMoIYPxzHiTSSO5UOVSaT6Rh1anSqgi6oTBAEEQZBnSmglqyYMqUTRHsEQUC5XI5sXd6xGfILE0VRAm3nHf7LZrM9id8JgiC6YWZmBo7jNAnQgdaiWcuySJQ+YULxQdY1LaL0TCbjjjg16hEBypQeOdypkmWZhv4IogO9htODGqxpgL9B+xl8giB6RxAEJJPJuhUHwoSe1gCIooi5ubmWi5cSBFHDMAwoitLVRA4uqCZqrK+v19mbIKLZsc+U3kUZidJJlN5LWWOm9GKxiPX1ddi23TSkTqL0iBEEgWb7EUQHBEFANpsNHM0tFArI5/NIJpMRt2x8CKKZIgiiP3jKkTA1VRSh6gKaRUMQ7VFVFbZtB0o5wp2pdDpNOZQ8ZDIZ38SeAGmo2jFpuqZB1jUtGqrGxJ5zc7VleGzbRiqVIg0VQRCjgyAImJub65hyxOtMzczMDLiVBEEQNXvFI1XFYrGlpkrX9UDHowgVQRChIghC2+S45Ey1pzGxJ2moSEM1iLq8TIOGisOfpXK5jL179yKVSqFYLLqf67qOXC7XdHw/KEJFEETotEqOS84UQRCjCNcu8vxvQG2STaVSgaZpgY5BDhVBEJHQ6FStrq6SM0UQxMjiFapXKhVUq1XE4/HADhUN+REEERncqTpw4ACq1SpisRg5Ux1Ip9MkSm9R1o5JE4oPsq5pFaU3woXqXEs1MzODrVu3kiidIIjRwGuMdF2nBLkEQYws1WrV/duyrK6Sf1KEiiCIyPBqppLJZEuhOrEBidLbl5EonUTpvZR1EqUDtRe+SqUC0zShqqorVA+aF44iVARBREKjAL2VUJ0gCGLYcAF6PB53X/a4UL1cLgc6BjlUBEGETqvZfORUEQQxahiG0VKAnkqlIIrBXCUa8iMIIlRKpRLK5XLL2XzcqfIO/xEbkCi9dVk7Jk0oPsi6plmUrus6GGNIp9PYunVr077z8/OB5QkUoSIIIjQsy0KxWOyYGqExUmWa5gBbOdowxobdBIKYCiqVijvM1y41QtBl5yhC1QXdqP0JYhoxTROpVCpQagRvpCpoJuJp4NChQ3WRKBKlkyh9EHV5mQZRuleALghC3ey+xuerUqk0Hd8PilAFpFQq0Vs0QXRAURQkk8nA23OnSpbp3Y7jOE5gESxBEN3jJ0APA3KoAlAoFFAsFqEoyrCbQhAjTS+OkSiKyGQy4TdmTEkkErBtu249MYIgwqGdAL1fpvK1sBuNAp+txFPSEwQRPkE1CtNAJpOBqqqQJAmSJGF2dtb9jETprZk0ofgg65oWUXoqlUK1WsXc3FxLAbpfGWVKb0Mul4PjOB23a0xKSBAEMQgkSUI6ne4qUhXEphHENGNZFmKxWOiRKc5URqgsy8Lq6ioWFhZa5pdozKND+XIIghgE3kzptm0jl8uhUCggkUi42zSKZsvlMrLZLInSIyyb9Lq8TKoovVwuQ1XVOj10kEkfJEpvQyaTgWmaWF1d9X2ra5WUkCAIYpBIkuRqqloJ1XVdR7VaJUkCQXQgaILOno8f6dFHFEVRsLi46OtUkTNFEMQo4XWqGof/+GylWCwWeWdBEER7pnLIDwBUVcXi4iJWVlbc4b9SqUTOFEEQQ6VVpvTZ2dk6oTrP8Lx161YoioLl5WUSpUdcNql1TYso3S9TuhcSpfcBd6pM08SBAwfImSIIYmTxCtVzuZybRycejw+7aQRBYIojVBxVVaFpmpsllWbzEQQxTLyidKBZIFsqlVAoFADU0k2sra1RpvQJFYqTKD26TOkcEqWHSKFQQLVaRSwWgyAILYXqBEEQw0bXdZimCUVRIAgCZVQniBFiqh0qrwB9YWGhpVCdIAhi2HiXy4jH43VCdVpQmSCGz9QO+fnN5vMTqtPMGYIgBomfKN0rQI/H41hbWwOwIVQHgJmZGRKlR1w2qXWRKL0GidJ7oN1sPq9QnSJVBEEMG13XWwrQuVDdcRzYtj2kFhLEeBB1JHcqI1SlUglbtmxpOZvPL1JFEAQxCLyidF3Xsby87Go8eWSqUTRrWRaJ0iMum/S6vEyqKD2Xy0HTNBKlh0kymeyYGqExUkUaBYIgBgmPTMViMaiq2nZbSZJInkAQHWCMRTqRYyqfwKCpEbxOVTabJaeKIIiB4BWgd3KmOORQEUR7RFGE4ziROVVTOeTXDdypWl5epgWSCaIDvb50eBcrnXY0TQOAJgE60Fo0a1kWTNMkUXrEZZNa17SI0jOZDERRRKVScfWHXkiUPgBUVcXc3BxFqAiiA4ZhdP2cGIaBXC4XTYPGEF3XKQM6QUSEJElIpVJuMtwwoQhVQBRFCRx6J4hphTGGbDaLTZs2BRqCMgwDKysrkGUyRRzDMFCtVlsK0P3KKFP6ZArFSZQeXab0VCqF9fV1lMtlJBKJum0nWpT+9NNP461vfSt27dqFeDyO4447DldfffXAh+BIo0AQ7VFVFZZlBUo5wp0pRVGQyWQG08AICNs+0YsbQUSPN1IVlqZqLF4LH3/8cTiOgy996Ut4znOeg8ceewxXXHEFSqUSPv3pTw+7eQRBHEEURczNzWF9fb1tclyvM7WwsOCuTTeOkH0iiPFEkiQkEgmUy2WUy2XMzs76bhc0x9tYOFQXXXQRLrroIvf/Y489Fk888QRuuukmMlgEMWIoitJ2xYFGZ2rcI79h2ye/TOkcEqW3ZtKE4oOsa1pE6a0ypc/OzqJYLPoK1dPpNKrVatMx/RgLh8qP9fV1X+PiRdd16Lru/s8FaKZpdh2ON00TlmX56hkIgqg9k7xjVxQFs7OzyGazWF5extzcHARBcFOQyLKMdDoNy7IATN4sv37sk23bsG3bvTbet+NWZbZtw3EcVCoV9zjFYrHp2ONeBsB3xlWlUmkqD7tskuvi13gY93VQdZZKpboVBRqfpXg87tony7IgCAJs20Yulwv80jeWDtWTTz6JG2+8sePb3/XXX49rrrmmqfzw4cOBp0FyHMcBYwzf+c536gS0juPAMAwIggBFUdy/VVWFIAhd1dErjDF3dpWqqjBN0/17kG//3s4UgPv3IAXHdD82GPT9sCwLuVwOgiC458k7ef72xw2VLMs4ePCgu2+UyfYGTb/26eDBg1AUxZ356O0YWpUxxlAsFvHggw/i0UcfBYC6GUz8rTuXy8FxHAiCgNnZWXdxZVEUIQiCu53fvmGVzczMuCJ6URTBGEMqlYIkSXXn5bdv47lz/ITFncocx4HjOKhWq0gkEnAcB6IoQhRF3337qatVmeM4KJVKSCQSEATBnSHL70eYdbUqY4whkUiAMYZMJgPHcdx7I4pi5N8J73fTtm03SmRZFsrlsmtL+q3TcRwsLy8DAARB8H2WkskkbNtGqVSCKIqwbdv9vgZhqA7Vhz/8YXziE59ou82vf/1rnHDCCe7/+/btw0UXXYRLL70UV1xxRdt9r7zySuzZs8f9P5/PY+fOnXUGvxsYY9A0ze2gOIqiYG1tDdVqFbIsY35+fmCdNycWi2Ftbc3VoszPzze1cxCUSiX3C5pKpQInUQ0Tuh8bDPJ+mKbpdgT8+eL/G4bhitT9HMtB358gDNM+8R+g/tp0KpNl2f2eeR1nXqaqqpvYsFqtQpIkN++Vdzu/fcMqU1UVjDHkcjk3SsCXA/EOrfjt6/c/UNPCNJYHKTMMo+7Fg08I8Nu337palem67l4HURTr0mWEXVer89I0zf1OAPXPaNTfCe930zAMd5Zr4woB/dZp23bds+L3LHF7xZ1tfs2Cruk7VIfq/e9/P970pje13ebYY491/96/fz9e9rKX4dxzz8XNN9/c8fiaptUZC06vDpXjOC3TJ/DjSZIERVEGrgtxHKfuxg8rzYOu6+65y7I8tBlLdD9qDPp+8GfLe729xsjvc14+agzLPgH1NqrR4PuV8WssiqK7Tpl3vbLGslb3Ici+YZXx6BQv4z/t9vVeg8ayxvIgZZIkuZ2rd/kev337ratdGb+fjZ9HUZffefFzb7w2fr/DLvP+5veDf4/DrhPYcKZaPUveaJTX8QrCUB2qpaUlLC0tBdp23759eNnLXoYzzjgDt9xyy8gIWbnAVlVVpNNprK6utp3dFAWO42B1dRWWZWFxcRH5fB4rKytYXFwcaCdeKBRc4R+wEXLttG5imND92GAU7odlWTAMA6IoukOwuq5D07SReYZbMSz7lEwmEYvFfGcctSrjkoTZ2dm2YnY+xDc3N4etW7eiUCigWCy6uau6EcL3WjY3N4d8Po9kMolkMulGyoJkrW6H3/e6XRkfls9kMkilUqhWq9A0zX1G2z0n3dbVqowPL6XTaTdKxGeehV1XuzJJkpBMJrFjxw6Uy2Wsra25w7BRfyf4/7qugzGGdDqNTCbjygD4S0e/dTZKEvyepWQy6WoSuUQhkUi0fPFpZLQt2hH27duHl770pTjqqKPw6U9/GocPH8bBgwfrNBjDoHG2kqZpdQsqBw0T9gPvvE3TxOLiIjRNw8LCAhRFwcrKysBydXk775mZGczMzCCdTiOfzw9sSjzdjw1G4X5wZ0oQBGia5kbIGGPQdX0g92MQjKp9asSyLFfLlkqloCgK0um062QNinw+D9u2kUqlIMuy23Hz8kHgHVbi0eNYLIZqtTqwZ5TnPxJFEYlEwnWkwsyLFIRyuezeD/6d4Hq2Qd2PSqXirl2paRri8TgSiQQqlUrdxI0o4RNB+NA5Hx5ljAWeNDMWovQf/OAHePLJJ/Hkk09ix44ddZ8NazmYVlO/+dp/raaMh0lj583frERRxMLCAlZXVwcSGWnsvDn870FERuh+bDAK96PRmfIOOwIYq0hVJ8K2T6VSqc6A+znAjWWO46BYLLpDV0B9tmfuNHFRcuNnhUKh7phBsrP3UsbrT6VSdU4cn7aey+Vc56LV8fyuh98ko1Zlpmm63z1d1+u240lp2zkS3dTVqoxP1uDDtd5z4kL1YrHo+9IRRv2cSqUCx3GgaZrrbAMbzh5vlyRJkX0ndF1HpVKBaZoQBKFOR2eaJgqFAmKxWN918megcRivUCjAcRw3MiXLctN1CurgjoUle9Ob3gTGmO/PMOiUR4d34lFGRlp13hzeiUcdGWnVeXMGERmh+7HBKNwP27Z9nSnOpEWqRs0+NcI7ksahJI43MhJlpMobCWnURQFwy/l2UWBZlutM+U0SURQFmqbBMIzIUnl4nSm/9Rp5OZ+BGBXcmYrH4773gzu2UUaqDMNwI1N+L5neyGFUkSruTPHIlJ+uMOjM6LFwqEYJ0zQDJSWMshPv1Hlzou7EO3XenCg78aBJIul+bBDl/fCmrWgXfWp0qkbF+Zg0eKSBD/O1ImqnijtJjdGnRvjnUThVPP9gK2eKw4cAdV0P3anq5ExxvE5V0HXkusHrTLWLEEfpVPFhVz7M1wruVEUx/Nc4zNfqWgQVpo/FkN+o4DgOstmsq4vpNFQRxXBT0M6bE9VwU9DOmxPFcFO3GbfpfmwQxf3gyW87OVMc7/DfoNflHGVisRiSyWTfonTuHGUyGTcdgxc/8S53tMMSqnMBOh/mCyJybpe1uh1BBeh+9fvtq2lak1A9SF2tyrwC9MZIYbvnj894C0uoLkmSq1Hizi3/XrW7h2EK1SuViqvX2rp1a6D94vF4X0L1RlG6V4DOnalWz1fQlDcUoQoI92RlWe6qIw4zMtJt580JOzLSbefNCTMy0uvyJXQ/Ngj7fmSz2cDOFGeYqTVGFT4E0Q+NAvTAb9hHMtiHFanyCtDbRaYaCVOo3ihAD0qYQvVGAXpQ+PZhCdUrlUqgSKEXnvA1rEhVpVJBuVzuGJlqJEyhetDIVLdQhCoA3qnfmUym64sfRmSk186bE1ZkpNfOmxNGZKTfteDofmwQ5v3gM2O6zSnlTUZJoK7DkGW5a1E6gJYC9KCC3jCE6q0E6EGP10qo3o0ovZ0AvdO+HC5U50OFYQrQW+3rd4x+hep8xQKenqEdrb4T/QrVDcOAoiiIx+OoVqt1GrGgx+pVqO59SWgnQAeav2NBtWwUoeqAd7aSN/Fbt/QTGem38+b0Gxnpt/Pm9BMZCWthXbofG4R1P3p52eCM+0y/MOEiWMuyuo5UcceqlQC9mzb0o6nqJEAPSj9C9U4C9KBwoXovmqqgmqlO9CtU55qpWCzW17PWj6YqqGaqE/0I1XkKhLAjU5yptGJBH4pWU797pZdOPKzOm9NrJx5W583ppRMPy5ni0P3YYBTuB7GB16kK2nExxgIJ0LtpQy9OVVABelB6EaoHFaAHxetUBXVyw3KmOL0K1YMK0IPSi1PlHXbtx5ni9CJU54uOR+VMAVM65Oc4jvvm0opGZwoIJ+dVN8NNYXfenG6Hm8LuvDndDDdF1XnT/dhgFO4HUZ8p3TRNrK2tQZZlyLLcUjTL16OLxWKBBehBy4IK1XsRoAct48N/7Ry1fgToQcsMw3CH/9oJ1bsVoHeblT2oUL1RgC6Kou927UTp/QrVvQJ0TdNaZkrvpSyoUD2dTiOXy2FmZqajAN2vLKhTPlUOFXeITNN0NQZ+F8pxHDeKpaoqbNt2983n86G88aiqilwuh1KphEwm02QAvYuHZjIZN5NsmHA9wDPPPINMJuN7XqVSCaVSCclkEo7j+I5V94sgCDh48CAKhYLv4r2maSKXy0GWZSQSiUjyJ9H92KDX+2GapntNutVR8f2mOX0CP3cepeT337Ztd+FpXuYd9uGRLNM0kUgk3A7G+/3kOpFeyniWe754rncpDu994wtgS5LknkM/9TaWiaKIarWK9fV1JBKJOide13V3aSPDMNxFoBuHx/h2vZR5yefzUFXVdaq82/PFpy3Lctvca11+n3nvM78fftutr68jFoshkUjANE1XT+ZXFz+Pbr4TkiS50X2/7wQArK2tuf/zyFrj8fr9bgqCgFwu566N2bidbduoVqvuOn3e9VUB+C7I3VjGv8+d7JPApsiCPfvss9i5c+ewm0EQRAv27t3blG18WiD7RBCjTSf7NFUOleM42L9/P2ZmZrp+g87n89i5cyf27t3bVW6UUYPOY7SYlPMA+jsXxhgKhQK2bds2tcOHZJ/oPEaNSTkPYDD2aaqG/ERR7PvtN51Oj/0XC6DzGDUm5TyA3s/FT78wTZB92oDOY7SYlPMAorVP0/kqSBAEQRAEESLkUBEEQRAEQfQJOVQB0TQNV199dSg5NIYJncdoMSnnAUzWuYwbk3Lt6TxGi0k5D2Aw5zJVonSCIAiCIIgooAgVQRAEQRBEn5BDRRAEQRAE0SfkUBEEQRAEQfQJOVQEQRAEQRB9Qg5Vlzz99NN461vfil27diEej+O4447D1Vdf7a71M05cd911OPfcc5FIJJDJZIbdnK74/Oc/j2OOOQaxWAznnHMO/t//+3/DblLXPPDAA3j1q1+Nbdu2QRAE3HnnncNuUtdcf/31OOusszAzM4NNmzbhkksuwRNPPDHsZk0tZJ9GA7JPo8Gg7RM5VF3y+OOPw3EcfOlLX8KvfvUrfO5zn8MXv/hF/M3f/M2wm9Y1hmHg0ksvxTve8Y5hN6Ur7rjjDuzZswdXX301fvnLX+LUU0/Fy1/+ciwvLw+7aV1RKpVw6qmn4vOf//ywm9Iz999/P3bv3o2f/exn+MEPfgDTNHHhhRe6C5QSg4Xs0/Ah+zQ6DNw+MaJvPvnJT7Jdu3YNuxk9c8stt7DZ2dlhNyMwZ599Ntu9e7f7v23bbNu2bez6668fYqv6AwD75je/Oexm9M3y8jIDwO6///5hN4U4AtmnwUL2aXSJ2j5RhCoE1tfXMT8/P+xmTAWGYeChhx7CBRdc4JaJoogLLrgAP/3pT4fYMgKoPQsA6HkYIcg+DQ6yT6NN1PaJHKo+efLJJ3HjjTfibW9727CbMhWsrKzAtm1s3ry5rnzz5s04ePDgkFpFAIDjOHjve9+LF77whTj55JOH3RwCZJ8GDdmn0WUQ9okcqiN8+MMfhiAIbX8ef/zxun327duHiy66CJdeeimuuOKKIbW8nl7OgyDCYPfu3Xjsscfwf/7P/xl2UyYOsk8E0R+DsE9yZEceM97//vfjTW96U9ttjj32WPfv/fv342UvexnOPfdc3HzzzRG3Ljjdnse4sbi4CEmScOjQobryQ4cOYcuWLUNqFfGud70L3/rWt/DAAw9gx44dw27OxEH2aTwg+zSaDMo+kUN1hKWlJSwtLQXadt++fXjZy16GM844A7fccgtEcXQCfd2cxziiqirOOOMM3HPPPbjkkksA1EK599xzD971rncNt3FTCGMM7373u/HNb34T9913H3bt2jXsJk0kZJ/GA7JPo8Wg7RM5VF2yb98+vPSlL8XRRx+NT3/60zh8+LD72bi9gTzzzDNYW1vDM888A9u28cgjjwAAnvOc5yCVSg23cW3Ys2cPLr/8cpx55pk4++yzccMNN6BUKuHNb37zsJvWFcViEU8++aT7/1NPPYVHHnkE8/PzOOqoo4bYsuDs3r0bt99+O+666y7MzMy4OpHZ2VnE4/Eht276IPs0fMg+jQ4Dt0+RzB2cYG655RYGwPdn3Lj88st9z+Pee+8ddtM6cuONN7KjjjqKqarKzj77bPazn/1s2E3qmnvvvdf3+l9++eXDblpgWj0Lt9xyy7CbNpWQfRoNyD6NBoO2T8KRSgmCIAiCIIgeGZ3BdYIgCIIgiDGFHCqCIAiCIIg+IYeKIAiCIAiiT8ihIgiCIAiC6BNyqAiCIAiCIPqEHCqCIAiCIIg+IYeKIAiCIAiiT8ihIgiCIAiC6BNyqAiCIAiCIPqEHCqCIAiCIIg+IYeKIAiCIAiiT8ihIsaKw4cPY8uWLfj4xz/ulv3kJz+Bqqq45557htgygiCmGbJNBC2OTIwd3/nOd3DJJZfgJz/5CY4//nicdtppuPjii/HZz3522E0jCGKKIds03ZBDRYwlu3fvxt13340zzzwTjz76KH7+859D07RhN4sgiCmHbNP0Qg4VMZZUKhWcfPLJ2Lt3Lx566CGccsopw24SQRAE2aYphjRUxFjyu9/9Dvv374fjOHj66aeH3RyCIAgAZJumGYpQEWOHYRg4++yzcdppp+H444/HDTfcgEcffRSbNm0adtMIgphiyDZNN+RQEWPHBz/4QfzjP/4j/u3f/g2pVArnnXceZmdn8a1vfWvYTSMIYooh2zTd0JAfMVbcd999uOGGG3DbbbchnU5DFEXcdtttePDBB3HTTTcNu3kEQUwpZJsIilARBEEQBEH0CUWoCIIgCIIg+oQcKoIgCIIgiD4hh4ogCIIgCKJPyKEiCIIgCILoE3KoCIIgCIIg+oQcKoIgCIIgiD4hh4ogCIIgCKJPyKEiCIIgCILoE3KoCIIgCIIg+oQcKoIgCIIgiD4hh4ogCIIgCKJP/n8iS8G0VeilUQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(7, 3))\n",
    "sim.plot(y=0, ax=ax[0])\n",
    "sim.plot(y=0, ax=ax[1])\n",
    "sim.plot_grid(y=0, ax=ax[1])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Simulations\n",
    "\n",
    "Now we can run the simulation over time and measure the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T21:45:06.764322Z",
     "iopub.status.busy": "2023-08-18T21:45:06.764174Z",
     "iopub.status.idle": "2023-08-18T21:47:15.742211Z",
     "shell.execute_reply": "2023-08-18T21:47:15.741599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:45:06] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sphereRCS'</span> with task_id                   <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#188\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1'</span>.          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:45:06]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sphereRCS'\u001b[0m with task_id                   \u001b]8;id=970488;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=497787;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>View task using web UI at                               <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#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-</span></a> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">2f80db90-6695-4b3b-a074-cd037ae0e318v1'</span></a>.                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=421578;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=335256;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=817758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=730791;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=817758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=246511;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=817758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=817758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2f80db90-6695-4b3b-a074-cd037ae0e318v1\u001b\\\u001b[32m2f80db90-6695-4b3b-a074-cd037ae0e318v1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
      ]
     },
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       "version_minor": 0
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "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\">[14:45:12] </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#361\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">361</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:45:12]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=340506;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=795006;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:45:21] </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#355\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">355</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:45:21]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=789551;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=888341;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#355\u001b\\\u001b[2m355\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:45:24] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.413</span>. Use                     <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#341\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">341</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\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[14:45:24]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.413\u001b[0m. Use                     \u001b]8;id=721842;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=943335;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\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[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \u001b[0m\n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </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#377\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">377</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=451366;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=852709;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#377\u001b\\\u001b[2m377\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#386\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">386</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=2411;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=516585;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#386\u001b\\\u001b[2m386\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or   <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#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>web UI. Terminating the Python script will not stop the <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>job running on the cloud.                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or   \u001b]8;id=916;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=106386;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
      ]
     },
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:47:07] </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#404\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">404</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:47:07]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=819309;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=678116;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#404\u001b\\\u001b[2m404\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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     },
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    },
    {
     "data": {
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       "</pre>\n"
      ],
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       "\n"
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     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                    <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#419\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">419</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=451793;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=685996;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#419\u001b\\\u001b[2m419\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
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       "Output()"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:47:14] </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#426\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">426</span></a>\n",
       "</pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:47:15] </span>loading SimulationData from data/sphereRCS.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#590\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">590</span></a>\n",
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       "\u001b[2;36m[14:47:15]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/sphereRCS.hdf5         \u001b]8;id=759809;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=315782;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n"
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   "source": [
    "sim_data = web.run(sim, task_name=\"sphereRCS\", path=\"data/sphereRCS.hdf5\", verbose=True)\n"
   ]
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   "source": [
    "## Setting Up the Local Near2Far Computation\n",
    "\n",
    "To set up the near-to-far transformation locally, we need to grab the fields on each surface of the near-field [FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) objects.\n",
    "\n",
    "So, we simply create a [FieldProjector](https://docs.simulation.cloud/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjector.html) object and pass in the surface monitors as shown below.  Note that we also need to pass the normal directions of each of the monitors in the list.\n",
    "\n",
    "In addition to storing the near field data, this object will compute the surface currents and provide various methods for projecting the far field quantities.\n",
    "\n",
    "We can optionally pass in the number of points per wavelength in the background medium with which to sample fields on the monitors. The default is 10 points per wavelength. This can reduce computation times significantly. By default, 10 points per wavelength are used.\n",
    "\n",
    "One can also pass in coordinates for the local origin of the set of monitors; the far-field observation points will be defined with respect to this origin. By default, the local origin is set to the average of the centers of all surface monitors passed in.\n",
    "\n",
    "To see the usefulness of downsampling the fields recorded on monitors, we'll also run the near-to-far transformation with downsampled fields to compare the RCS.\n",
    "\n",
    "Finally, to get a sense of performance, we'll measure the time it takes to compute the far fields locally, and compare it to the time it took to compute them on the server."
   ]
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   "source": [
    "import time\n",
    "\n",
    "# first, we construct the classes which compute far fields locally on your machine\n",
    "n2f = td.FieldProjector.from_near_field_monitors(\n",
    "    sim_data=sim_data,\n",
    "    near_monitors=monitors_near,  # only supply the non-downsampled surface monitors as sources\n",
    "    normal_dirs=[\"-\", \"+\", \"-\", \"+\", \"-\", \"+\"],\n",
    "    pts_per_wavelength=10,\n",
    ")\n",
    "\n",
    "# do the same for the downsampled monitors\n",
    "n2f_downsampled = td.FieldProjector.from_near_field_monitors(\n",
    "    sim_data=sim_data,\n",
    "    near_monitors=monitors_downsampled,  # only supply the downsampled surface monitors as sources\n",
    "    normal_dirs=[\"-\", \"+\", \"-\", \"+\", \"-\", \"+\"],\n",
    "    pts_per_wavelength=10,\n",
    ")\n",
    "\n",
    "# now, we retrieve the far fields in all three cases by passing in our far field monitor from before\n",
    "start = time.time()\n",
    "far_fields = n2f.project_fields(monitor_far)\n",
    "end = time.time()\n",
    "n2f_time = end - start\n",
    "\n",
    "start = time.time()\n",
    "far_fields_downsampled = n2f_downsampled.project_fields(monitor_far)\n",
    "end = time.time()\n",
    "n2f_downsampled_time = end - start\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Performance comparison\n",
    "\n",
    "We can see below that the local computation of far fields takes about the same time with and without downsampling, because the fields are anyway resampled on the near-field box prior to the computation. The real benefit of downsampling is reducing the amount of data that is stored and downloaded.\n",
    "\n",
    "The server-side computation is extremely fast, as expected, and requires downloading no near-field data."
   ]
  },
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     "name": "stdout",
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     "text": [
      "Local near-to-far: 1.1843602657318115 s\n",
      "Local near-to-far with downsampling: 1.1581153869628906 s\n",
      "Server-side near-to-far: 0.0826 s\n"
     ]
    }
   ],
   "source": [
    "# use the simulation log to find the time taken for server-side computations\n",
    "n2f_server_time = float(\n",
    "    sim_data.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
    ")\n",
    "\n",
    "print(f\"Local near-to-far: {n2f_time} s\")\n",
    "print(f\"Local near-to-far with downsampling: {n2f_downsampled_time} s\")\n",
    "print(f\"Server-side near-to-far: {n2f_server_time} s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Far Field Data for the Server-Side Computations\n",
    "\n",
    "Recall that we also computed scattered far fields on the server; let's extract that data too."
   ]
  },
  {
   "cell_type": "code",
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    "far_fields_server = sim_data[monitor_far.name]\n"
   ]
  },
  {
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   "source": [
    "## Compute the RCS\n",
    "\n",
    "Now that we have the far fields computed in three different ways (locally, locally with downsampling, and remotely on the server), various far field quantities can be extracted.\n",
    "\n",
    "For this example, we use `FieldProjectionAngleData.radar_cross_section` to get the RCS at the previously-specified `theta,phi` points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T21:47:20.656944Z",
     "iopub.status.busy": "2023-08-18T21:47:20.656758Z",
     "iopub.status.idle": "2023-08-18T21:47:20.680415Z",
     "shell.execute_reply": "2023-08-18T21:47:20.679814Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the RCS for the local, local downsampled and server-side cases\n",
    "RCS = np.real(far_fields.radar_cross_section.sel(f=f0).values)\n",
    "RCS_downsampled = np.real(far_fields_downsampled.radar_cross_section.sel(f=f0).values)\n",
    "RCS_server = np.real(far_fields_server.radar_cross_section.sel(f=f0).values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Results\n",
    "Now we can plot the RCS and compare it to the analytical RCS computed via the Mie series. The analytical results are stored in txt files which can be downloaded from our documentation [repo](https://github.com/flexcompute/tidy3d-notebooks/tree/develop/misc).\n",
    "\n",
    "The results match very well! As expected, there are minor deviations due to the FDTD discretization.\n",
    "\n",
    "Notice that the downsampled monitors also yield fairly accurate results with less than an eighth of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T21:47:20.682611Z",
     "iopub.status.busy": "2023-08-18T21:47:20.682467Z",
     "iopub.status.idle": "2023-08-18T21:47:21.183814Z",
     "shell.execute_reply": "2023-08-18T21:47:21.183273Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_db(val):\n",
    "    return 10.0 * np.log10(val)\n",
    "\n",
    "\n",
    "RCS_phi0 = RCS[0, :, 0]\n",
    "RCS_phi90 = RCS[0, :, 1]\n",
    "\n",
    "RCS_downsampled_phi0 = RCS_downsampled[0, :, 0]\n",
    "RCS_downsampled_phi90 = RCS_downsampled[0, :, 1]\n",
    "\n",
    "RCS_server_phi0 = RCS_server[0, :, 0]\n",
    "RCS_server_phi90 = RCS_server[0, :, 1]\n",
    "\n",
    "# ------ import analytical data from disk ------\n",
    "\n",
    "mie_file_id = \"2lambda_epsr4\"\n",
    "mie_filename_phi0 = \"./misc/mie_bRCS_phi0_\" + mie_file_id + \".txt\"\n",
    "mie_filename_phi90 = \"./misc/mie_bRCS_phi90_\" + mie_file_id + \".txt\"\n",
    "\n",
    "mie_data_phi0 = np.loadtxt(mie_filename_phi0, delimiter=\"\\t\", skiprows=2)\n",
    "mie_theta_phi0 = np.squeeze(mie_data_phi0[:, [0]])\n",
    "mie_phi0 = np.squeeze(mie_data_phi0[:, [1]])\n",
    "\n",
    "mie_data_phi90 = np.loadtxt(mie_filename_phi90, delimiter=\"\\t\", skiprows=2)\n",
    "mie_theta_phi90 = np.squeeze(mie_data_phi90[:, [0]])\n",
    "mie_phi90 = np.squeeze(mie_data_phi90[:, [1]])\n",
    "\n",
    "# ------ plot for phi = 0 ------\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.5, 5))\n",
    "\n",
    "ax.plot(mie_theta_phi0, to_db(mie_phi0), \"-k\", label=\"$\\\\phi = 0$, Mie\")\n",
    "ax.plot(thetas, to_db(RCS_phi0), \"--b\", label=\"$\\\\phi = 0$, near2far local\")\n",
    "ax.plot(\n",
    "    thetas,\n",
    "    to_db(RCS_downsampled_phi0),\n",
    "    \"--r\",\n",
    "    label=\"$\\\\phi = 0$, near2far local downsampled\",\n",
    ")\n",
    "ax.plot(thetas, to_db(RCS_server_phi0), \"--g\", label=\"$\\\\phi = 0$, near2far server\")\n",
    "ax.set(\n",
    "    xlabel=\"$\\\\theta$ (degrees)\",\n",
    "    ylabel=\"Bistatic RCS (dBs$\\\\mu$m)\",\n",
    "    yscale=\"linear\",\n",
    "    xscale=\"linear\",\n",
    ")\n",
    "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
    "plt.legend(loc=\"best\", prop={\"size\": 14})\n",
    "plt.tight_layout()\n",
    "\n",
    "# ------ plot for phi = pi/2 ------\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.5, 5))\n",
    "\n",
    "ax.plot(mie_theta_phi90, to_db(mie_phi90), \"-k\", label=\"$\\\\phi = \\\\pi/2$, Mie\")\n",
    "ax.plot(thetas, to_db(RCS_phi90), \"--b\", label=\"$\\\\phi = \\\\pi/2$, near2far local\")\n",
    "ax.plot(\n",
    "    thetas,\n",
    "    to_db(RCS_downsampled_phi90),\n",
    "    \"--r\",\n",
    "    label=\"$\\\\phi = \\\\pi/2$, near2far local downsampled\",\n",
    ")\n",
    "ax.plot(\n",
    "    thetas, to_db(RCS_server_phi90), \"--g\", label=\"$\\\\phi = \\\\pi/2$, near2far server\"\n",
    ")\n",
    "ax.set(\n",
    "    xlabel=\"$\\\\theta$ (degrees)\",\n",
    "    ylabel=\"Bistatic RCS (dBs$\\\\mu$m)\",\n",
    "    yscale=\"linear\",\n",
    "    xscale=\"linear\",\n",
    ")\n",
    "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
    "plt.legend(loc=\"best\", prop={\"size\": 14})\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Optical scattering and far-field radiation"
  ],
  "description": "This notebook demonstrates how to calculate the scattering cross-section of a sphere in Tidy3D FDTD.",
  "feature_image": "./img/sphere_rcs_feature_image.png",
  "features": [
   "Far field projection",
   "Total-field scattered-field"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "scattering cross-section, Mie scattering, total-field scattered-field, TFSF, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "key": 0,
         "length": 1,
         "op": "removerange"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ],
   "remote_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "12"
          },
          {
           "key": 5,
           "length": 1,
           "op": "removerange"
          }
         ],
         "key": 0,
         "op": "patch"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ]
  },
  "title": "Scattering Cross-section of a Sphere | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "03c6ae697f6b47a0b8c6e9df2a38d119": {
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       "_view_count": null,
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       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_5b6c9daeae23490d9494b7ad668a9b93",
       "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 'sphereRCS'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'sphereRCS'...\u001b[0m\n"
         },
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