{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d97a572",
   "metadata": {},
   "source": [
    "# Reflection spectrum of twisted bilayer photonic crystal slab\n",
    "\n",
    "**author: Dun Wang, University of Pennsylvania**\n",
    "\n",
    "Twisted bilayer photonic crystal (TBPC) structures have attracted significant attention due to their unique resonance properties and strong connection to phenomena observed in 2D materials. The twist angle introduces new coupling effects and Moiré patterns, leading to rich optical responses. Studying their reflection spectrum provides insight into these resonances and their potential applications in photonics.\n",
    "\n",
    "<img src=\"img/tbfc.png\" alt=\"diagram\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "380ae5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfea9d0",
   "metadata": {},
   "source": [
    "## Define Study Parameters\n",
    "We now define global parameters for the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f8052cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(12345)\n",
    "\n",
    "# period\n",
    "a = 0.5\n",
    "\n",
    "# thickness of each slab\n",
    "H = 0.2\n",
    "\n",
    "# diameter of the air holes, at A or B sublattice\n",
    "d_A = 0.2\n",
    "d_B = 0.2\n",
    "\n",
    "# space between the slabs and PMLs\n",
    "h_space = 2\n",
    "\n",
    "# inter-layer spacing\n",
    "h_layer = 0.13\n",
    "\n",
    "# alpha: twist angle for commensurate Moiré pattern (I,I+1) rot_angle: rotate the moire lattice vectors in alignment with xy axis\n",
    "I = 1\n",
    "rot_angle = np.arccos((1.5 * I + 0.5) / np.sqrt(3 * I**2 + 3 * I + 1)) - np.pi / 6\n",
    "alpha = np.arccos(\n",
    "    (3 * I**2 + 3 * I + 0.5) / (3 * I**2 + 3 * I + 1)\n",
    ")  # I=1 corresponds to the largest commensurate angle 21.79 degrees\n",
    "\n",
    "# dt= strength of Wu-Hu perturbation\n",
    "dt = 0.015\n",
    "\n",
    "# light incident angle\n",
    "theta = 0\n",
    "phi = 0\n",
    "\n",
    "freqs = np.linspace(2e14, 3.5e14, 2000)  # frequency range\n",
    "freq0 = (freqs[0] + freqs[-1]) / 2\n",
    "wl0 = td.C_0 / freq0\n",
    "fwidth = np.abs(freqs[0] - freqs[-1])\n",
    "pulse_width = fwidth\n",
    "run_time = 600 / pulse_width\n",
    "\n",
    "n_air = 1\n",
    "n_medium = 2.02\n",
    "n_bg = 1.46\n",
    "SiN = td.Medium(permittivity=n_medium**2)\n",
    "SiO2 = td.Medium(permittivity=n_bg**2)\n",
    "Air = td.Medium(permittivity=n_air**2)\n",
    "\n",
    "N = 30  # max number of unit cells allowed\n",
    "# Simulation size is set as one moire unit cell after perturbation\n",
    "sim_size = (\n",
    "    np.sqrt(3 * I**2 + 3 * I + 1) * np.sqrt(3) * a,\n",
    "    np.sqrt(3) * np.sqrt(3 * I**2 + 3 * I + 1) * np.sqrt(3) * a,\n",
    "    2 * h_space + 2 * H + h_layer,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d7a032",
   "metadata": {},
   "source": [
    "## Define Geometry\n",
    "\n",
    "The geometry consists of etched holes twisted in two SiN layers, separated by air.\n",
    "For defining the rotated holes, we define an auxiliary function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b38ee32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first SiN slab (bottom layer)\n",
    "slab1 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, 0),\n",
    "        size=(td.inf, td.inf, H),\n",
    "    ),\n",
    "    medium=SiN,\n",
    "    name=\"slab1\",\n",
    ")\n",
    "\n",
    "\n",
    "# air spacer between slabs\n",
    "spacer = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, H / 2 + h_layer / 2),\n",
    "        size=(td.inf, td.inf, h_layer),\n",
    "    ),\n",
    "    medium=Air,\n",
    "    name=\"spacer\",\n",
    ")\n",
    "\n",
    "# second SiN slab (top layer)\n",
    "slab2 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, H + h_layer),\n",
    "        size=(td.inf, td.inf, H),\n",
    "    ),\n",
    "    medium=SiN,\n",
    "    name=\"slab2\",\n",
    ")\n",
    "\n",
    "# air substrate/superstrate\n",
    "sub = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, -h_space - H / 2),\n",
    "        size=(td.inf, td.inf, h_space * 2),\n",
    "    ),\n",
    "    medium=Air,\n",
    "    name=\"sub\",\n",
    ")\n",
    "\n",
    "\n",
    "layers = [sub, slab1, spacer, slab2]\n",
    "\n",
    "\n",
    "# Add holes in one layer with angle theta w.r.t x axis, at height h, lateral shift shift, Wu-Hu perturbation dt\n",
    "def hex_holes(a, d_A, d_B, theta, h, shift, dt):\n",
    "    holes = []\n",
    "    A1 = np.array(\n",
    "        [\n",
    "            3 * a / 2 * np.cos(theta) - np.sqrt(3) * a / 2 * np.sin(theta),\n",
    "            3 * a / 2 * np.sin(theta) + np.sqrt(3) * a / 2 * np.cos(theta),\n",
    "            0,\n",
    "        ]\n",
    "    )\n",
    "    A2 = np.array(\n",
    "        [\n",
    "            3 * a / 2 * np.cos(theta) + np.sqrt(3) * a / 2 * np.sin(theta),\n",
    "            3 * a / 2 * np.sin(theta) - np.sqrt(3) * a / 2 * np.cos(theta),\n",
    "            0,\n",
    "        ]\n",
    "    )\n",
    "    Rot = np.array(\n",
    "        [\n",
    "            [np.cos(theta), -np.sin(theta), 0],\n",
    "            [np.sin(theta), np.cos(theta), 0],\n",
    "            [0, 0, 1],\n",
    "        ]\n",
    "    )\n",
    "    t1 = Rot @ np.array([0, -a / np.sqrt(3) + dt, 0])\n",
    "    t2 = Rot @ np.array(\n",
    "        [-(a / np.sqrt(3) - dt) * np.sqrt(3) / 2, -(a / np.sqrt(3) - dt) / 2, 0]\n",
    "    )\n",
    "    t3 = Rot @ np.array(\n",
    "        [-(a / np.sqrt(3) - dt) * np.sqrt(3) / 2, (a / np.sqrt(3) - dt) / 2, 0]\n",
    "    )\n",
    "    t4 = Rot @ np.array([0, a / np.sqrt(3) - dt, 0])\n",
    "    t5 = Rot @ np.array(\n",
    "        [(a / np.sqrt(3) - dt) * np.sqrt(3) / 2, (a / np.sqrt(3) - dt) / 2, 0]\n",
    "    )\n",
    "    t6 = Rot @ np.array(\n",
    "        [(a / np.sqrt(3) - dt) * np.sqrt(3) / 2, -(a / np.sqrt(3) - dt) / 2, 0]\n",
    "    )\n",
    "\n",
    "    for i in np.arange(-N, N + 1, 1):\n",
    "        for j in np.arange(-N, N + 1, 1):\n",
    "            r1 = np.array([0, shift, h]) + i * A1 + j * A2 + t1\n",
    "            tol = 0.1\n",
    "            if (np.abs(r1[0]) <= np.min([N * a, sim_size[0] / 2 + tol])) & (\n",
    "                np.abs(r1[1]) <= np.min([N * a, sim_size[1] / 2 + tol])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_A / 2, center=tuple(r1), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "            r2 = np.array([0, shift, h]) + i * A1 + j * A2 + t2\n",
    "            if (np.abs(r2[0]) <= np.min([N * a, sim_size[0] / 2 + tol])) & (\n",
    "                np.abs(r2[1]) <= np.min([N * a, sim_size[1] / 2 + tol])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_B / 2, center=tuple(r2), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "            r3 = np.array([0, shift, h]) + i * A1 + j * A2 + t3\n",
    "            if (np.abs(r3[0]) <= np.min([N * a, sim_size[0] / 2 + tol])) & (\n",
    "                np.abs(r3[1]) <= np.min([N * a, sim_size[1] / 2 + tol])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_A / 2, center=tuple(r3), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "            r4 = np.array([0, shift, h]) + i * A1 + j * A2 + t4\n",
    "            if (np.abs(r4[0]) <= np.min([N * a, sim_size[0] / 2 + tol])) & (\n",
    "                np.abs(r4[1]) <= np.min([N * a, sim_size[1] / 2 + tol])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_B / 2, center=tuple(r4), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "            r5 = np.array([0, shift, h]) + i * A1 + j * A2 + t5\n",
    "            if (np.abs(r5[0]) <= np.min([N * a, sim_size[0] / 2 + tol])) & (\n",
    "                np.abs(r5[1]) <= np.min([N * a, sim_size[1] / 2 + tol])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_A / 2, center=tuple(r5), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "            r6 = np.array([0, shift, h]) + i * A1 + j * A2 + t6\n",
    "            if (np.abs(r6[0]) <= np.min([N * a, sim_size[0] / 2 + 0.1])) & (\n",
    "                np.abs(r6[1]) <= np.min([N * a, sim_size[1] / 2 + 0.1])\n",
    "            ):\n",
    "                holes.append(\n",
    "                    td.Structure(\n",
    "                        geometry=td.Cylinder(\n",
    "                            radius=d_B / 2, center=tuple(r6), length=H, axis=2\n",
    "                        ),\n",
    "                        medium=Air,\n",
    "                    )\n",
    "                )\n",
    "\n",
    "    return holes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dd0baf",
   "metadata": {},
   "source": [
    "## Define Sources, Monitors, and the Simulation Object\n",
    "\n",
    "The source is a circularly polarized source, defined by two plane waves with orthogonal polarizations and a π/2 phase difference.\n",
    "\n",
    "We will also place [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html)s to record transmitted and reflected fields, and [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)s for visualization.\n",
    "\n",
    "Finally, we define a `makesim` function to generate the model as a function of the source’s incident angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "918f8de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def circular_source(pol, theta, phi):\n",
    "    # define a circularly polarized plane wave incident at angle theta phi\n",
    "    plane_wave_x = td.PlaneWave(\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=pulse_width),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, 1.5 * H + h_layer + 0.5 * h_space),\n",
    "        direction=\"-\",\n",
    "        angle_theta=theta,\n",
    "        angle_phi=phi,\n",
    "        pol_angle=0,\n",
    "    )\n",
    "\n",
    "    # determine the phase difference given the polarization\n",
    "    if pol == \"LCP\":\n",
    "        phase = -np.pi / 2\n",
    "    elif pol == \"RCP\":\n",
    "        phase = np.pi / 2\n",
    "    else:\n",
    "        raise ValueError(\"pol must be `LCP` or `RCP`\")\n",
    "\n",
    "    # define a plane wave polarized in the y direction with a phase difference\n",
    "    plane_wave_y = td.PlaneWave(\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=pulse_width, phase=phase),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, 1.5 * H + h_layer + 0.5 * h_space),\n",
    "        direction=\"-\",\n",
    "        angle_theta=theta,\n",
    "        angle_phi=phi,\n",
    "        pol_angle=np.pi / 2,\n",
    "    )\n",
    "\n",
    "    return [plane_wave_x, plane_wave_y]\n",
    "\n",
    "\n",
    "# In this simulation we only use the reflection monitor monitor_r. The others can be used to record the field distributions.\n",
    "monitor_r = td.FluxMonitor(\n",
    "    center=[0, 0, 1.5 * H + h_layer + 0.9 * h_space],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=freqs,\n",
    "    name=\"R\",\n",
    ")\n",
    "monitor_t = td.FluxMonitor(\n",
    "    center=[0, 0, -0.5 * H - 0.9 * h_space],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=freqs,\n",
    "    name=\"T\",\n",
    ")\n",
    "monitor_fz = td.FieldMonitor(\n",
    "    center=[0, 0, H / 2 + h_layer / 2], size=[td.inf, td.inf, 0], freqs=freqs, name=\"Fz\"\n",
    ")\n",
    "monitor_fx = td.FieldMonitor(\n",
    "    center=[0, 0, 0], size=[0, td.inf, td.inf], freqs=freqs, name=\"Fx\"\n",
    ")\n",
    "\n",
    "\n",
    "# Make the simulation. j is a label used in the batch\n",
    "def makesim(pol: str, theta: float, phi: float, dt: float, j: int):\n",
    "    source = circular_source(pol, theta, phi)\n",
    "    structures = (\n",
    "        layers\n",
    "        + hex_holes(a, d_A, d_B, -rot_angle, 0, 0, 0)\n",
    "        + hex_holes(a, d_A, d_B, -rot_angle + alpha, H + h_layer, 0, dt)\n",
    "    )\n",
    "\n",
    "    # Bloch boundaries from source.\n",
    "    bloch_x = td.Boundary.bloch_from_source(\n",
    "        source=source[0], domain_size=sim_size[0], axis=0, medium=Air\n",
    "    )\n",
    "\n",
    "    bloch_y = td.Boundary.bloch_from_source(\n",
    "        source=source[0], domain_size=sim_size[1], axis=1, medium=Air\n",
    "    )\n",
    "\n",
    "    bspecs = td.BoundarySpec(x=bloch_x, y=bloch_y, z=td.Boundary.pml())\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=sim_size,\n",
    "        center=(0, 0, H / 2 + h_layer / 2),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=8, wavelength=wl0),\n",
    "        structures=structures,\n",
    "        medium=Air,\n",
    "        sources=source,\n",
    "        monitors=[monitor_r],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=bspecs,\n",
    "        shutoff=1e-5,\n",
    "    )\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fb6e61",
   "metadata": {},
   "source": [
    "## Make a Batch of Simulations With Different Incident Angles\n",
    "\n",
    "Now, we create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) of simulations with different incident angles to run them all in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49b81b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = {}\n",
    "angle_max = 25 * np.pi / 180\n",
    "N_angle = 100\n",
    "\n",
    "# High symmetry points in K space are defined for convenience\n",
    "a_M = sim_size[1]\n",
    "K_plus = np.array([1 / (np.sqrt(3) * a_M), 1 / (3 * a_M)])\n",
    "K_minus = np.array([1 / (np.sqrt(3) * a_M), -1 / (3 * a_M)])\n",
    "K_up = np.array([0, 2 / (3 * a_M)])\n",
    "K_down = np.array([0, -2 / (3 * a_M)])\n",
    "M_plus = np.array([1 / (np.sqrt(3) * a_M), 0])\n",
    "M_minus = np.array([-1 / (np.sqrt(3) * a_M), 0])\n",
    "Gamma = np.array([0, 0])\n",
    "k_start = Gamma\n",
    "k_end = (\n",
    "    1 / wl0 * np.sin(angle_max)\n",
    ")  # k_end can be set either as one special point in K space or with a maximum incident angle angle_max\n",
    "\n",
    "# The angular scan proceeds with a fixed step size in wavevector k\n",
    "for i in range(N_angle):\n",
    "    k_i = k_start + i / N_angle * (k_end - k_start)\n",
    "    if np.linalg.norm(k_i) != 0:\n",
    "        theta = np.arcsin(np.linalg.norm(k_i) * wl0)\n",
    "        phi = np.arcsin(k_i[1] / np.linalg.norm(k_i)) + np.pi * (k_i[0] < 0)\n",
    "    else:\n",
    "        theta = 0\n",
    "        phi = 0\n",
    "    sims[f\"sim_{i}\"] = makesim(\"RCP\", theta, phi, dt, i)\n",
    "\n",
    "# Create the Batch object\n",
    "batch = web.Batch(simulations=sims, folder_name=\"data\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b3a51b",
   "metadata": {},
   "source": [
    "We can now plot one simulation to inspect the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8991eba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the first simulation\n",
    "sims[\"sim_0\"].plot_3d()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ccd7b85",
   "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\">10:08:57 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.821</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:08:57 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.821\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# estimate the maximum cost for the whole Batch\n",
    "_ = batch.estimate_cost()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad097d9",
   "metadata": {},
   "source": [
    "## Run the Batch of Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2f2dc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d149769461141dfba4b39abc05c75a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:10:02 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span> tasks.                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:10:02 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m100\u001b[0m tasks.                     \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\">10:13:03 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.821</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:13:03 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.821\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "912bd2660e1d429689e70d57e5b2580b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:18:52 -03 </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:18:52 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67632bdc657b492da5e02211dd083558",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f99ece3",
   "metadata": {},
   "source": [
    "## Postprocessing\n",
    "\n",
    "Finally, we can analyze the reflection of the bilayer as a function of the incident angle.\n",
    "\n",
    "Since we are dealing with resonant structures, the fields do not always fully decay by the end of the simulation. For this reason, we add the following line to suppress the related warning messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65dd8054",
   "metadata": {},
   "outputs": [],
   "source": [
    "td.config.logging_level = \"ERROR\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80ed1a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collect all reflection data in flux_sig\n",
    "flux_sig = np.zeros((0, len(freqs)))\n",
    "for i in range(N_angle):\n",
    "    flux_sig = np.vstack(\n",
    "        (flux_sig, batch_data[f\"sim_{i}\"].monitor_data[\"R\"].flux.squeeze())\n",
    "    )\n",
    "\n",
    "\n",
    "# plot the angle-resolved reflection spectrum\n",
    "angle_max = np.arcsin(k_end * wl0)  # enter the proper maximum angle\n",
    "K, F = np.meshgrid(k_end * np.arange(N_angle) / N_angle, freqs)\n",
    "flux_plot = flux_sig / 2\n",
    "\n",
    "\n",
    "plt.figure(figsize=(5, 8))\n",
    "plt.pcolormesh(K.T, F.T, flux_plot, shading=\"nearest\", cmap=\"viridis\")\n",
    "plt.colorbar(label=\"Intensity\")\n",
    "\n",
    "plt.rcParams[\"text.usetex\"] = True\n",
    "plt.xlabel(\"$k_x (\\mu m^{-1})$\")\n",
    "plt.ylabel(\"frequency(Hz)\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Photonic crystals"
  ],
  "description": "This notebook demonstrates how to simulate the reflection spectrum of twisted bilayer photonic crystal slabs using Tidy3D.",
  "feature_image": "./img/tbfc.png",
  "features": [
   "2D material"
  ],
  "kernelspec": {
   "display_name": "td2100rc2",
   "language": "python",
   "name": "python3"
  },
  "keywords": "twisted bilayer photonic crystals, moiré resonances, reflection spectrum, 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": "3.11.13"
  },
  "title": "How to model twisted bilayer photonic crystals using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}