{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 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. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "# standard python imports\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", "import tidy3d.web as web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Simulation Parameters\n", "\n", "We first need to define our simulation parameters and the structure." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 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)" ] }, { "cell_type": "markdown", "metadata": {}, "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." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "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 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" ] }, { "cell_type": "markdown", "metadata": {}, "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." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "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)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "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." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 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", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize Geometry\n", "\n", "Let's take a look and make sure everything is defined properly." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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()" ] }, { "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": {}, "outputs": [ { "data": { "text/html": [ "
16:02:18 CEST Created task 'sphereRCS' with task_id                             \n",
       "              'fdve-46956f80-9f33-4490-909b-1a2e4297569e' and task_type 'FDTD'. \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m16:02:18 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sphereRCS'\u001b[0m with task_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-46956f80-9f33-4490-909b-1a2e4297569e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              View task using web UI at                                         \n",
       "              'https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f\n",
       "              33-4490-909b-1a2e4297569e'.                                       \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", "\u001b[2;36m \u001b[0m\u001b]8;id=73172;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=630670;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=73172;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=956150;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=73172;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32m-46956f80-9f\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=73172;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[32m33-4490-909b-1a2e4297569e'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Task folder: 'default'.                                           \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=316145;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "96bee9af91f2409d848359a1b5f245db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
16:02:21 CEST Maximum FlexCredit cost: 0.412. Minimum cost depends on task      \n",
       "              execution details. Use 'web.real_cost(task_id)' to get the billed \n",
       "              FlexCredit cost after a simulation run.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:21 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.412\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
16:02:22 CEST status = queued                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:22 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
              To cancel the simulation, use 'web.abort(task_id)' or             \n",
       "              'web.delete(task_id)' or abort/delete the task in the web UI.     \n",
       "              Terminating the Python script will not stop the job running on the\n",
       "              cloud.                                                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08cd75714efe4ab49d7cec5feed4d25f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
16:02:36 CEST status = preprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:36 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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16:02:41 CEST starting up solver                                                \n",
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       "\u001b[2;36m16:02:41 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
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       "Output()"
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16:03:33 CEST early shutoff detected at 84%, exiting.                           \n",
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       "\u001b[2;36m16:03:33 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m84\u001b[0m%, exiting.                           \n"
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       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
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16:03:38 CEST status = success                                                  \n",
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16:03:40 CEST View simulation result at                                         \n",
       "              'https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f\n",
       "              33-4490-909b-1a2e4297569e'.                                       \n",
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       "\u001b[2;36m16:03:40 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=314716;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=567271;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=314716;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=979070;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=314716;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34m-46956f80-9f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=314716;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46956f80-9f33-4490-909b-1a2e4297569e\u001b\\\u001b[4;34m33-4490-909b-1a2e4297569e'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
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16:03:52 CEST loading simulation from data/sphereRCS.hdf5                       \n",
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       "\u001b[2;36m16:03:52 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/sphereRCS.hdf5                       \n"
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   "source": [
    "sim_data = web.run(sim, task_name=\"sphereRCS\", path=\"data/sphereRCS.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local near-to-far: 2.4700496196746826 s\n",
      "Local near-to-far with downsampling: 1.877598524093628 s\n",
      "Server-side near-to-far: 0.0086 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\")"
   ]
  },
  {
   "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",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "far_fields_server = sim_data[monitor_far.name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {
    "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)"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "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(thetas, to_db(RCS_server_phi90), \"--g\", label=\"$\\\\phi = \\\\pi/2$, 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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",
  "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",
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   "version": "3.11.12"
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       "diff": [
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           "op": "addrange",
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  },
  "title": "Scattering Cross-section of a Sphere | Flexcompute",
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        {
         "data": {
          "text/html": "
🚶  Finishing 'sphereRCS'...\n
\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'sphereRCS'...\u001b[0m\n"
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━ 100.0% • 8.6/8.6 MB • 40.9 MB/s • 0:00:00\n
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