{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bc813f9",
   "metadata": {},
   "source": [
    "# Local permittivity with subpixel averaging\n",
    "\n",
    "This notebook demonstrates how to use local subpixel averaging in Tidy3D to obtain a more accurate permittivity profile, and more accurate results when running a [ModeSimulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSimulation.html) locally.\n",
    "\n",
    "For more information about subpixel averaging, refer to [this lecture](https://www.flexcompute.com/fdtd101/Lecture-10-Introduction-to-subpixel-averaging/).\n",
    "\n",
    "> The local subpixel averaging feature requires Tidy3D extras. You can install it with:\n",
    "> ```\n",
    "> pip install \"tidy3d[extras]\"\n",
    "> ```\n",
    "\n",
    "The notebook builds a three-layer dielectric stack and evaluates the local permittivity along a vertical line using [`Simulation.epsilon()`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html). The result is then compared against a full FDTD run using a [`PermittivityMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html).\n",
    "\n",
    "**Key concepts:**\n",
    "\n",
    "- Without subpixel averaging, each interface is represented as a staircase on the Yee grid.\n",
    "- With subpixel averaging, the local effective permittivity near a dielectric interface is smoothed to better represent the true filling fraction inside each cell.\n",
    "- The largest difference between the two approaches appears near the waveguide boundaries; deep inside homogeneous regions both methods agree closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773ce387",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "\n",
    "td.config.logging_level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb083a3d",
   "metadata": {},
   "source": [
    "## Geometry and Physical Parameters\n",
    "\n",
    "A three-layer dielectric stack is embedded in a silica-like background ([`Medium`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html) with n = 1.44). The stack is oriented along z with three 100 nm layers of increasing refractive index (n = 2, 3, 4). The line probe runs along z through the stack center and is used to query the local permittivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb0880d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Central wavelength and frequency used for all epsilon queries and monitors\n",
    "wavelength = 1.55\n",
    "freq0 = td.C_0 / wavelength\n",
    "\n",
    "# Background medium\n",
    "cladding_medium = td.Medium(permittivity=1.44**2)\n",
    "\n",
    "# Layer media defined by refractive index\n",
    "bottom_layer_medium = td.Medium(permittivity=2.0**2)\n",
    "middle_layer_medium = td.Medium(permittivity=3.0**2)\n",
    "top_layer_medium = td.Medium(permittivity=4.0**2)\n",
    "\n",
    "# Lateral dimensions in micrometers\n",
    "waveguide_width = 0.50\n",
    "waveguide_length = 1e3\n",
    "\n",
    "# Vertical stack definition in micrometers\n",
    "layer_thickness = 0.10\n",
    "stack_thickness = 3 * layer_thickness\n",
    "\n",
    "# Simulation region dimensions in micrometers\n",
    "sim_size_x = 4.0\n",
    "sim_size_y = 2.0\n",
    "sim_size_z = 4.0\n",
    "\n",
    "# Mesh target\n",
    "min_steps_per_wvl = 10\n",
    "\n",
    "# Three stacked layers along z\n",
    "bottom_layer_box = td.Box(\n",
    "    center=(0.0, 0.0, -layer_thickness),\n",
    "    size=(waveguide_length, waveguide_width, layer_thickness),\n",
    ")\n",
    "\n",
    "middle_layer_box = td.Box(\n",
    "    center=(0.0, 0.0, 0.0),\n",
    "    size=(waveguide_length, waveguide_width, layer_thickness),\n",
    ")\n",
    "\n",
    "top_layer_box = td.Box(\n",
    "    center=(0.0, 0.0, layer_thickness),\n",
    "    size=(waveguide_length, waveguide_width, layer_thickness),\n",
    ")\n",
    "\n",
    "bottom_layer = td.Structure(\n",
    "    geometry=bottom_layer_box,\n",
    "    medium=bottom_layer_medium,\n",
    "    name=\"bottom_layer_n2\",\n",
    ")\n",
    "\n",
    "middle_layer = td.Structure(\n",
    "    geometry=middle_layer_box,\n",
    "    medium=middle_layer_medium,\n",
    "    name=\"middle_layer_n3\",\n",
    ")\n",
    "\n",
    "top_layer = td.Structure(\n",
    "    geometry=top_layer_box,\n",
    "    medium=top_layer_medium,\n",
    "    name=\"top_layer_n4\",\n",
    ")\n",
    "\n",
    "stack_structures = [bottom_layer, middle_layer, top_layer]\n",
    "\n",
    "# Box used for the local epsilon extraction.\n",
    "# It is a line along z located at x=0 and y=0, crossing the stack vertically.\n",
    "line_box = td.Box(\n",
    "    center=(0.0, 0.0, 0.0),\n",
    "    size=(0.0, 0.0, 1.0),\n",
    ")\n",
    "\n",
    "layer_boundaries = np.array(\n",
    "    [\n",
    "        -stack_thickness / 2,\n",
    "        -stack_thickness / 2 + layer_thickness,\n",
    "        -stack_thickness / 2 + 2 * layer_thickness,\n",
    "        stack_thickness / 2,\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cf60d9",
   "metadata": {},
   "source": [
    "## Helper to Build Simulations\n",
    "\n",
    "The helper function keeps the geometry fixed and only toggles the `subpixel` flag and `td.config.simulation.use_local_subpixel`. When `include_source_and_monitor=True`, a dummy [`PlaneWave`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) source and a [`PermittivityMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html) are added to enable the FDTD run in Section 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72301639",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_simulation(include_source_and_monitor=False):\n",
    "    monitors = []\n",
    "    sources = []\n",
    "\n",
    "    if include_source_and_monitor:\n",
    "        source_time = td.GaussianPulse(\n",
    "            freq0=freq0,\n",
    "            fwidth=0.2 * freq0,\n",
    "        )\n",
    "\n",
    "        plane_wave = td.PlaneWave(\n",
    "            center=(0.0, 0.0, -0.6),\n",
    "            size=(sim_size_x, sim_size_y, 0.0),\n",
    "            source_time=source_time,\n",
    "            direction=\"+\",\n",
    "        )\n",
    "\n",
    "        permittivity_monitor = td.PermittivityMonitor(\n",
    "            center=line_box.center,\n",
    "            size=line_box.size,\n",
    "            freqs=[freq0],\n",
    "            name=\"eps_line_monitor\",\n",
    "        )\n",
    "\n",
    "        sources = [plane_wave]\n",
    "        monitors = [permittivity_monitor]\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=(sim_size_x, sim_size_y, sim_size_z),\n",
    "        center=(0.0, 0.0, 0.0),\n",
    "        medium=cladding_medium,\n",
    "        structures=stack_structures,\n",
    "        sources=sources,\n",
    "        monitors=monitors,\n",
    "        grid_spec=td.GridSpec.auto(\n",
    "            wavelength=wavelength,\n",
    "            min_steps_per_wvl=min_steps_per_wvl,\n",
    "        ),\n",
    "        run_time=1e-14,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(td.PML()),\n",
    "    )\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631c6788",
   "metadata": {},
   "source": [
    "## Local Epsilon without and with Local Subpixel Averaging\n",
    "\n",
    "Two [`Simulation`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) objects are created, with and without local subpixel.\n",
    "\n",
    "**`coord_key` options:**\n",
    "\n",
    "- `\"centers\"` — samples at cell centers; returns the average of the diagonal permittivity components.\n",
    "- `\"Ex\"`, `\"Ey\"`, `\"Ez\"` — samples at the corresponding Yee-grid field locations; returns `eps_xx`, `eps_yy`, `eps_zz`, respectively.\n",
    "\n",
    "Here `coord_key=\"Ex\"` is used so the local epsilon query is sampled at the same Yee-grid location as `PermittivityMonitor.eps_xx`, enabling a direct comparison in Section 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bea4d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local epsilon without local subpixel averaging\n",
    "td.config.simulation.use_local_subpixel = False\n",
    "sim_no_subpixel = make_simulation(include_source_and_monitor=False)\n",
    "\n",
    "eps_line_no_subpixel = sim_no_subpixel.epsilon(\n",
    "    box=line_box,\n",
    "    coord_key=\"Ex\",\n",
    "    freq=freq0,\n",
    ")\n",
    "\n",
    "# Local epsilon with local subpixel averaging\n",
    "td.config.simulation.use_local_subpixel = True\n",
    "\n",
    "sim_with_subpixel = make_simulation(include_source_and_monitor=False)\n",
    "\n",
    "eps_line_with_subpixel = sim_with_subpixel.epsilon(\n",
    "    box=line_box,\n",
    "    coord_key=\"Ex\",\n",
    "    freq=freq0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9516a9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_line_profile(eps_data):\n",
    "    # For a line box, keep the non-singleton spatial axis.\n",
    "    # The returned object can carry tensor components such as eps_xx, eps_yy, eps_zz.\n",
    "    if hasattr(eps_data, \"eps_xx\"):\n",
    "        data = eps_data.eps_xx\n",
    "    else:\n",
    "        data = eps_data\n",
    "\n",
    "    squeezed = data.squeeze(drop=True)\n",
    "\n",
    "    for axis_name in (\"x\", \"y\", \"z\"):\n",
    "        if axis_name in squeezed.coords and squeezed.coords[axis_name].size > 1:\n",
    "            coord = squeezed.coords[axis_name].values\n",
    "            values = np.real(squeezed.values)\n",
    "            return axis_name, coord, values\n",
    "\n",
    "    raise RuntimeError(\"Could not identify the varying spatial coordinate in the epsilon data.\")\n",
    "\n",
    "\n",
    "axis_no_subpixel, coord_no_subpixel, values_no_subpixel = extract_line_profile(eps_line_no_subpixel)\n",
    "\n",
    "axis_with_subpixel, coord_with_subpixel, values_with_subpixel = extract_line_profile(\n",
    "    eps_line_with_subpixel\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26c323f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "ax.plot(coord_no_subpixel, values_no_subpixel, \"o-\", label=\"epsilon(box), no subpixel\")\n",
    "\n",
    "ax.plot(\n",
    "    coord_with_subpixel,\n",
    "    values_with_subpixel,\n",
    "    \"o-\",\n",
    "    label=\"epsilon(box), with subpixel\",\n",
    ")\n",
    "\n",
    "for boundary_index, boundary_position in enumerate(layer_boundaries):\n",
    "    label = \"layer boundary\" if boundary_index == 0 else None\n",
    "    ax.axvline(boundary_position, linestyle=\":\", alpha=0.5, label=label)\n",
    "\n",
    "ax.set_xlabel(f\"{axis_no_subpixel} (µm)\")\n",
    "ax.set_ylabel(\"Local permittivity\")\n",
    "ax.set_title(\"Local permittivity across the three-layer stack\")\n",
    "ax.legend()\n",
    "ax.set_xlim(-0.2, 0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446f8723",
   "metadata": {},
   "source": [
    "The curves agree inside each homogeneous layer and differ mainly at the dielectric boundaries.\n",
    "\n",
    "- The non-subpixel result transitions abruptly — each interface maps directly onto the nearest Yee cell edge.\n",
    "- The subpixel result transitions smoothly — it approximates the effective filling fraction at each interface.\n",
    "- The three 100 nm layers allow multiple interface transitions to be inspected in a single profile.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfdeee17",
   "metadata": {},
   "source": [
    "## FDTD Run with PermittivityMonitor\n",
    "\n",
    "A minimal FDTD job (`run_time = 1e-14` s) is run to sample the permittivity via a [`PermittivityMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html). The result is compared against the local epsilon computed in Section 3.\n",
    "\n",
    "> The very short `run_time` is intentional — only the discretized permittivity is needed, not converged field data. A field-decay warning is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41d5e211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:40:04 -03 </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d-4ec0-a767-419517e0af7d'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:40:04 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=391192;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=664462;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=391192;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=689641;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=391192;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32m-183ffa2a-eb1\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=391192;https://tidy3d.simulation.cloud/workbench?taskId=fdve-183ffa2a-eb1d-4ec0-a767-419517e0af7d\u001b\\\u001b[32md-4ec0-a767-419517e0af7d'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_monitor = make_simulation(include_source_and_monitor=True)\n",
    "\n",
    "sim_data = web.run(\n",
    "    sim_monitor,\n",
    "    task_name=\"subpixel_local_epsilon_dummy_monitor\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43fd2390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab the monitored permittivity directly along the z line.\n",
    "monitor_eps_xx = sim_data[\"eps_line_monitor\"].eps_xx.squeeze(drop=True)\n",
    "monitor_coord = monitor_eps_xx.coords[\"z\"].values\n",
    "monitor_values = np.real(monitor_eps_xx.values)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.plot(\n",
    "    coord_with_subpixel,\n",
    "    values_with_subpixel,\n",
    "    \"o-\",\n",
    "    label=\"local epsilon with subpixel\",\n",
    ")\n",
    "ax.plot(monitor_coord, monitor_values, \"s--\", label=\"PermittivityMonitor\")\n",
    "\n",
    "for boundary_index, boundary_position in enumerate(layer_boundaries):\n",
    "    label = \"layer boundary\" if boundary_index == 0 else None\n",
    "    ax.axvline(boundary_position, linestyle=\":\", alpha=0.5, label=label)\n",
    "\n",
    "ax.set_xlabel(\"z (µm)\")\n",
    "ax.set_ylabel(\"Permittivity\")\n",
    "ax.set_title(\"Local epsilon vs PermittivityMonitor for the three-layer stack\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c919def",
   "metadata": {},
   "source": [
    "As we can see, the local subpixel matches exactly the grid used in cloud computation.\n"
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to use local subpixel averaging to obtain accurate permittivity profiles near dielectric interfaces using Tidy3D.",
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "keywords": "subpixel averaging, permittivity, dielectric interfaces, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "title": "How to apply local subpixel averaging for accurate permittivity profiles using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
