{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tailoring directional scattering in silicon nanodisks\n",
    "\n",
    "The manipulation of light at the nanoscale is essential for advancing modern photonic technologies. High-index all-dielectric nanoparticles have emerged as a low-loss alternative to plasmonic structures, offering strong electric and magnetic resonances without the significant absorption losses typical of metals. A key advantage of these dielectric systems is the ability to exploit the interference between optically induced electric and magnetic modes, enabling precise control over light scattering directionality. In particular, silicon nanodisks can be engineered to achieve spectral overlap between electric and magnetic dipole resonances by adjusting their aspect ratio. This overlap leads to suppressed backscattering and enhanced forward scattering.\n",
    "\n",
    "In this notebook, we reproduce the key results presented in `Isabelle Staude, Andrey E. Miroshnichenko, Manuel Decker, Nche T. Fofang, Sheng Liu, Edward Gonzales, Jason Dominguez, Ting Shan Luk, Dragomir N. Neshev, Igal Brener, and Yuri Kivshar, \"Tailoring Directional Scattering through Magnetic and Electric Resonances in Subwavelength Silicon Nanodisks\", ACS NANO, (2013).` [DOI: https://doi.org/10.1021/nn402736f](https://doi.org/10.1021/nn402736f).\n",
    "\n",
    "We begin by performing a multipole decomposition of a single silicon nanodisk to identify its electric and magnetic resonances. By varying the aspect ratio of the structure, we highlight the potential for spectral overlap between these contributions to the extinction cross-section. Next, we simulate a periodic array of nanodisks with different aspect ratios and demonstrate that backscattering is strongly suppressed when the electric and magnetic dipole resonances are spectrally aligned.\n",
    "\n",
    "<img src=\"img/cylinder.png\" width=\"400\" alt=\"Schematic of the experiment\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multipole Decomposition\n",
    "\n",
    "First, we will sweep the disk aspect ratio and analyze the position of the electric and magnetic dipole resonance. To carry out the multipole decomposition, we will follow the methods described in [this](https://www.flexcompute.com/tidy3d/examples/notebooks/MultipoleExpansion/) example notebook. \n",
    "\n",
    "From the example above we will copy the function to carry out the multipole decomposition, and create a function to carry out the decomposition for a given `SimulationData` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for carrying out multipole decomposition\n",
    "def ME(Ex, Ey, Ez, x, y, z, eps_xx, eps_yy, eps_zz, freqs, eps_medium):\n",
    "    EModulus = 2 / (td.C_0 * td.EPSILON_0 * eps_medium)\n",
    "\n",
    "    # first we create the 4-dimensional (x,y,z,f) datasets\n",
    "    X, Y, Z, Freqs = np.meshgrid(x, y, z, freqs, indexing=\"ij\")\n",
    "\n",
    "    # defining the angular frequencies\n",
    "    omega = 2 * np.pi * freqs\n",
    "\n",
    "    # import libraries to carry out integration and Bessel functions\n",
    "    from scipy.integrate import trapezoid as trapz\n",
    "    from scipy.special import spherical_jn as jn\n",
    "\n",
    "    # function for calculating the current density\n",
    "    J = lambda Ei, epsilon: 1j * (2 * np.pi * Freqs) * td.EPSILON_0 * (epsilon - eps_medium) * Ei\n",
    "\n",
    "    # function for calculating volume integral\n",
    "    integrate = lambda Data: trapz(trapz(trapz(Data, x=x, axis=0), x=y, axis=0), x=z, axis=0)\n",
    "\n",
    "    # defining the current densities\n",
    "    Jx = J(Ex, eps_xx)\n",
    "    Jy = J(Ey, eps_yy)\n",
    "    Jz = J(Ez, eps_zz)\n",
    "\n",
    "    # r vector\n",
    "    r = np.sqrt(X**2 + Y**2 + Z**2)\n",
    "\n",
    "    # wavevector\n",
    "    k = omega / td.C_0\n",
    "\n",
    "    # dot product k.r\n",
    "    kr = k * r\n",
    "\n",
    "    # dot product r.J\n",
    "    rj = X * Jx + Y * Jy + Z * Jz\n",
    "\n",
    "    # function for calculating dipole moments\n",
    "    P = lambda Ji, Xi: (\n",
    "        (1j / omega)\n",
    "        * (\n",
    "            integrate(Ji * jn(0, kr))\n",
    "            + ((k**2) / 2) * integrate((3 * rj * Xi - Ji * r**2) * jn(2, kr) / kr**2)\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # dipole moments\n",
    "    Px = P(Jx, X)\n",
    "    Py = P(Jy, Y)\n",
    "    Pz = P(Jz, Z)\n",
    "\n",
    "    Ed = np.abs(Px) ** 2 + np.abs(Py) ** 2 + np.abs(Pz) ** 2\n",
    "\n",
    "    # cross product for calculating magnetic moments\n",
    "    rXJ_x = Y * Jz - Z * Jy\n",
    "    rXJ_y = Z * Jx - X * Jz\n",
    "    rXJ_z = X * Jy - Y * Jx\n",
    "\n",
    "    # dipole magnetic moments\n",
    "    Mx = (3 / 2) * integrate(rXJ_x * jn(1, kr) / kr)\n",
    "    My = (3 / 2) * integrate(rXJ_y * jn(1, kr) / kr)\n",
    "    Mz = (3 / 2) * integrate(rXJ_z * jn(1, kr) / kr)\n",
    "\n",
    "    Md = np.abs(Mx) ** 2 + np.abs(My) ** 2 + np.abs(Mz) ** 2\n",
    "\n",
    "    # auxiliary lists for calculating quadrupoles\n",
    "    coords = [X, Y, Z]\n",
    "    crossProd = [rXJ_x, rXJ_y, rXJ_z]\n",
    "    Js = [Jx, Jy, Jz]\n",
    "\n",
    "    # electric quadrupole\n",
    "    Eq = np.zeros(Px.shape)\n",
    "    for alpha in range(3):\n",
    "        for beta in range(3):\n",
    "            ra = coords[alpha]\n",
    "            rb = coords[beta]\n",
    "            ja = Js[alpha]\n",
    "            jb = Js[beta]\n",
    "\n",
    "            delta = 0 if alpha != beta else 1\n",
    "\n",
    "            integrand1 = (3 * (ra * jb + rb * ja) - 2 * rj * delta) * (jn(1, kr) / kr)\n",
    "            integrand2 = ((5 * ra * rb * rj) - (ra * jb + rb * ja) * r**2 - (r**2 * rj * delta)) * (\n",
    "                jn(3, kr) / kr**3\n",
    "            )\n",
    "\n",
    "            Eq_ab = (3j / omega) * (integrate(integrand1) + (2 * k**2) * integrate(integrand2))\n",
    "\n",
    "            Eq += (1 / 120) * np.abs(k * Eq_ab) ** 2\n",
    "\n",
    "    # magnetic quadrupole\n",
    "    Mq = np.zeros(Px.shape)\n",
    "    for alpha in range(3):\n",
    "        for beta in range(3):\n",
    "            ra = coords[alpha]\n",
    "            rb = coords[beta]\n",
    "\n",
    "            rxja = crossProd[alpha]\n",
    "            rxjb = crossProd[beta]\n",
    "\n",
    "            Mq_ab = 15 * integrate((ra * rxjb + rb * rxja) * jn(2, kr) / kr**2)\n",
    "            Mq += (1 / 120) * (np.abs(k * Mq_ab) ** 2) / td.C_0**2\n",
    "\n",
    "    # constants\n",
    "    k = (2 * np.pi * freqs) / td.C_0\n",
    "    const = (k**4) / (6 * np.pi * ((eps_medium * td.EPSILON_0) ** 2) * EModulus)\n",
    "\n",
    "    return Ed * const, Md * const / (td.C_0 / np.sqrt(eps_medium)) ** 2, Eq * const, Mq * const"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also define an auxiliary function for calling the ME function for a given SimulationData object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_me(sim_data):\n",
    "    freqs = sim_data[\"fieldMon\"].Ex.f.values\n",
    "    eps_medium = sim_data.simulation.medium.permittivity\n",
    "\n",
    "    # electric field and permittivity data\n",
    "    E = sim_data[\"fieldMon\"]\n",
    "    Eps = sim_data[\"epsMon\"]\n",
    "\n",
    "    eps_xx = Eps.eps_xx\n",
    "    eps_yy = Eps.eps_yy\n",
    "    eps_zz = Eps.eps_zz\n",
    "\n",
    "    Ex = E.Ex\n",
    "    Ey = E.Ey\n",
    "    Ez = E.Ez\n",
    "\n",
    "    # spatial components\n",
    "    x, y, z = eps_xx.x, eps_xx.y, eps_xx.z\n",
    "\n",
    "    # calling the function.\n",
    "\n",
    "    Ed, Md, Eq, Mq = ME(\n",
    "        Ex.values,\n",
    "        Ey.values,\n",
    "        Ez.values,\n",
    "        x.values,\n",
    "        y.values,\n",
    "        z.values,\n",
    "        eps_xx.values,\n",
    "        eps_yy.values,\n",
    "        eps_zz.values,\n",
    "        freqs,\n",
    "        eps_medium,\n",
    "    )\n",
    "\n",
    "    return Ed, Md, Eq, Mq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "Next we will define a function for creating the `Simulation` object. The model consists in a single unit cell of a Si cylinder, with the needed monitors for calculating the multipole expansion;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global variables\n",
    "height = 0.22\n",
    "\n",
    "index_disk = 3.5\n",
    "index_medium = 1.5\n",
    "\n",
    "disk_medium = td.Medium(permittivity=index_disk**2)\n",
    "background_medium = td.Medium(permittivity=index_medium**2)\n",
    "\n",
    "run_time = 5e-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_me_sim(radius):\n",
    "    # defining source and monitor frequency\n",
    "    wl2 = 1.7\n",
    "    wl1 = 0.8\n",
    "\n",
    "    freq2 = td.C_0 / wl1\n",
    "    freq1 = td.C_0 / wl2\n",
    "\n",
    "    freq0 = freq1 + (freq2 - freq1) / 2\n",
    "    fwidth = (freq2 - freq1) / 2\n",
    "    wl = td.C_0 / freq0\n",
    "\n",
    "    freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 101)\n",
    "\n",
    "    # defining the cylinder structure\n",
    "    geometry = td.Cylinder(radius=radius, length=height)\n",
    "    disk = td.Structure(geometry=geometry, medium=disk_medium)\n",
    "\n",
    "    # monitor and simulation size\n",
    "    lx = 2 * radius\n",
    "    ly = 2 * radius\n",
    "    lz = height\n",
    "\n",
    "    Lx = wl2 + lx\n",
    "    Ly = Lx\n",
    "    Lz = wl2 + lz\n",
    "\n",
    "    # adding PMLs\n",
    "    boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
    "\n",
    "    # TFSF source\n",
    "    source_TFSF = td.TFSF(\n",
    "        center=(geometry.center[0], geometry.center[1], -wl / 16),\n",
    "        size=(\n",
    "            lx * 1.3,\n",
    "            ly * 1.3,\n",
    "            lz + wl / 8 + 0.1,\n",
    "        ),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        injection_axis=2,\n",
    "    )\n",
    "\n",
    "    # since the structure is smaller than the target wavelength,\n",
    "    # a finer mesh is required to ensure accurate results\n",
    "    structure_override = td.MeshOverrideStructure(\n",
    "        geometry=td.Box(center=source_TFSF.center, size=source_TFSF.size),\n",
    "        dl=(0.02,) * 3,\n",
    "    )\n",
    "\n",
    "    mesh_override = [structure_override]\n",
    "\n",
    "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=15, override_structures=mesh_override)\n",
    "\n",
    "    # field monitor for calculating the fields\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=geometry.center,\n",
    "        fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "        size=(lx * 1.2, ly * 1.2, lz * 1.2),\n",
    "        freqs=freqs,\n",
    "        name=\"fieldMon\",\n",
    "        colocate=False,\n",
    "    )\n",
    "\n",
    "    # permittivity monitor\n",
    "    permittivity_monitor = td.PermittivityMonitor(\n",
    "        center=field_monitor.center,\n",
    "        size=field_monitor.size,\n",
    "        freqs=field_monitor.freqs,\n",
    "        name=\"epsMon\",\n",
    "    )\n",
    "\n",
    "    sim_me = td.Simulation(\n",
    "        size=(Lx, Ly, Lz),\n",
    "        boundary_spec=boundary_spec,\n",
    "        grid_spec=grid_spec,\n",
    "        sources=[source_TFSF],\n",
    "        monitors=[field_monitor, permittivity_monitor],\n",
    "        structures=[disk],\n",
    "        run_time=run_time,\n",
    "        medium=background_medium,\n",
    "    )\n",
    "\n",
    "    return sim_me"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the model to make sure everything is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": [
    "sim = get_me_sim(0.5)\n",
    "sim.plot_3d()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the Electric and Magnetic Components\n",
    "\n",
    "Next, we perform a parameter sweep over the disk diameter to vary the aspect ratio of the structure, aiming to identify the condition where the electric and magnetic dipole resonances spectrally overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_list = np.linspace(0.2, 0.6, 10) / 2\n",
    "sims_me = {str(r): get_me_sim(r) for r in r_list}\n",
    "batch_me = web.Batch(simulations=sims_me, folder_name=\"cylinders_me\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22ab891feed948ebb025cb2f0895b13f",
       "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\">14:02:03 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:03 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m10\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\">14:03:27 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.338</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:27 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.338\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    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \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    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1715c4caf324b05b46656f9627aeff7",
       "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\">14:03:45 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:45 UTC\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"
    }
   ],
   "source": [
    "batch_data_me = batch_me.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carry out multipole decomposition for each simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "MDs = []\n",
    "EDs = []\n",
    "\n",
    "for r in r_list:\n",
    "    sim_data = batch_data_me[str(r)]\n",
    "    Ed, Md, Eq, Mq = run_me(sim_data)\n",
    "\n",
    "    EDs.append(Ed)\n",
    "    MDs.append(Md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we analyze the position of the resonance peak of the electric and magnetic dipole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_ed = 1e15\n",
    "freq_md = 1e15\n",
    "\n",
    "max_ed = []\n",
    "max_md = []\n",
    "\n",
    "freqs = sim.monitors[0].freqs\n",
    "\n",
    "for i in range(len(MDs)):\n",
    "    # electric and magnetic dipole contributions\n",
    "    Ed = np.array(EDs[i])\n",
    "    Md = np.array(MDs[i])\n",
    "\n",
    "    # ignore the data to the lower frequencies of the last resonance to make sure we will follow the same resonance\n",
    "    Ed[freqs > freq_ed] = 0\n",
    "    Md[freqs > freq_md] = 0\n",
    "\n",
    "    idx_ed = np.argmax(Ed)\n",
    "    idx_md = np.argmax(Md)\n",
    "\n",
    "    freq_ed = freqs[idx_ed]\n",
    "    freq_md = freqs[idx_md]\n",
    "\n",
    "    max_ed.append(td.C_0 / freqs[idx_ed])\n",
    "    max_md.append(td.C_0 / freqs[idx_md])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the electric and magnetic components are at the same frequency for a disk diameter of around 0.29 µm. The results match very well the ones presented on figure 1 of the reference [paper](https://doi.org/10.1021/nn402736f)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(2 * r_list, max_ed, label=\"electric dipole\", color=\"blue\")\n",
    "ax.plot(2 * r_list, max_md, label=\"magnetic dipole\", color=\"red\")\n",
    "ax.set_xlabel(\"wavelength (µm)\")\n",
    "ax.set_ylabel(\"disk diameter\")\n",
    "ax.set_ylim(0.85, 1.6)\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# electric and magnetic contributions to the diameter in which the resonances are close in frequency\n",
    "idx = np.argmin(abs(r_list - 0.29 / 2))\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(td.C_0 / freqs, EDs[idx], label=\"electric dipole\", color=\"blue\")\n",
    "ax.plot(td.C_0 / freqs, np.array(MDs[idx]), label=\"magnetic dipole\", color=\"red\")\n",
    "ax.set_xlabel(\"wavelength (µm)\")\n",
    "ax.set_ylabel(\"Scattering cross-section (a.u.)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backscattering suppression\n",
    "\n",
    "Now, we can simulate a periodic array of disks, and analyze the spectral transmittance and reflectance. We will define another auxiliary function to return the simulation object, now with a [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) and [Periodic Boundary conditions](https://docs.flexcompute.com/projects/tidy3d/en/v2.7.6/faq/docs/faq/how-do-i-set-the-periodic-boundary-condition.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sim(diameter, min_steps_per_wvl=15):\n",
    "    # defining frequencies\n",
    "    wl2 = 1.65\n",
    "    wl1 = 1.25\n",
    "\n",
    "    freq2 = td.C_0 / wl1\n",
    "    freq1 = td.C_0 / wl2\n",
    "\n",
    "    freq0 = freq1 + (freq2 - freq1) / 2\n",
    "    fwidth = (freq2 - freq1) / 2\n",
    "    wl = td.C_0 / freq0\n",
    "\n",
    "    freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 101)\n",
    "\n",
    "    source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "\n",
    "    radius = diameter / 2\n",
    "    lattice_constant = 0.8\n",
    "\n",
    "    disk = td.Structure(geometry=td.Cylinder(radius=radius, length=height), medium=disk_medium)\n",
    "\n",
    "    sim_size = (lattice_constant, lattice_constant, height + 3 * wl)\n",
    "\n",
    "    source = td.PlaneWave(\n",
    "        source_time=source_time,\n",
    "        pol_angle=0,\n",
    "        angle_theta=0,\n",
    "        center=(0, 0, -sim_size[2] / 2 + 1),\n",
    "        direction=\"+\",\n",
    "        size=(td.inf, td.inf, 0),\n",
    "    )\n",
    "\n",
    "    # flux monitor\n",
    "    flux_mon = td.FluxMonitor(\n",
    "        name=\"flux_mon\",\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, sim_size[2] / 2 - 1),\n",
    "        freqs=freqs,\n",
    "    )\n",
    "\n",
    "    # field monitor\n",
    "    field_mon = td.FieldMonitor(name=\"field_mon\", size=(0, 0, 0), center=(0, 0, 0), freqs=freqs)\n",
    "\n",
    "    # grid spec\n",
    "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl)\n",
    "\n",
    "    boundary_spec = td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    )\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=sim_size,\n",
    "        center=(0, 0, 0),\n",
    "        grid_spec=grid_spec,\n",
    "        structures=[disk],\n",
    "        sources=[source],\n",
    "        monitors=[flux_mon, field_mon],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=boundary_spec,\n",
    "        medium=background_medium,\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sweep over multiple disk diameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "005b2471f5994b0480c88ff0c86ae658",
       "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\">14:05:05 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:05 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m9\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\">14:06:19 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.225</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:19 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.225\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    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \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    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a280f318c3b24ed99ed66a62a9c71a02",
       "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\">14:06:37 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:37 UTC\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"
    }
   ],
   "source": [
    "diameters = np.linspace(0.4, 0.6, 9)\n",
    "\n",
    "batch = web.Batch(simulations={str(i): get_sim(i) for i in diameters})\n",
    "batch_data = batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we analyze the transmitted flux over all simulations, and also the points of maximum for the electric and magnetic fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = []\n",
    "E_max = []\n",
    "H_max = []\n",
    "\n",
    "for d in diameters:\n",
    "    sim_data = batch_data[str(d)]\n",
    "    flux = sim_data[\"flux_mon\"].flux\n",
    "\n",
    "    # extract E field components\n",
    "    Ex = sim_data[\"field_mon\"].Ex\n",
    "    Ey = sim_data[\"field_mon\"].Ey\n",
    "    Ez = sim_data[\"field_mon\"].Ez\n",
    "\n",
    "    # extract H field components\n",
    "    Hx = sim_data[\"field_mon\"].Hx\n",
    "    Hy = sim_data[\"field_mon\"].Hy\n",
    "    Hz = sim_data[\"field_mon\"].Hz\n",
    "\n",
    "    # calculate |E| and |H| (magnitude)\n",
    "    E = np.sqrt(np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2)\n",
    "    H = np.sqrt(np.abs(Hx) ** 2 + np.abs(Hy) ** 2 + np.abs(Hz) ** 2)\n",
    "\n",
    "    idx_e = E.argmax()\n",
    "    E_max.append(float(td.C_0 / E.f[idx_e]))\n",
    "\n",
    "    idx_m = H.argmax()\n",
    "    H_max.append(float(td.C_0 / H.f[idx_m]))\n",
    "\n",
    "    flux = flux.assign_coords(wl=td.C_0 / flux.coords[\"f\"])\n",
    "    flux = flux.swap_dims({\"f\": \"wl\"})\n",
    "    flux = flux.drop_vars(\"f\")\n",
    "\n",
    "    T.append(flux)\n",
    "\n",
    "T = np.array(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By visualizing the transmitted and reflected flux, we observe a suppression of backscattering when the magnetic and electric field resonances overlap. The results closely match figure 4 of the reference [paper](https://doi.org/10.1021/nn402736f)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "freqs = E.f\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize=(10, 5))\n",
    "pm = ax[0].pcolormesh(diameters, td.C_0 / freqs, T.T, shading=\"auto\", cmap=\"jet\")\n",
    "pm2 = ax[1].pcolormesh(diameters, td.C_0 / freqs, 1 - T.T, shading=\"auto\", cmap=\"jet\")\n",
    "\n",
    "for axes, p in zip(ax, [pm, pm2]):\n",
    "    divider = make_axes_locatable(axes)\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    plt.colorbar(p, cax=cax)\n",
    "\n",
    "\n",
    "for a in ax:\n",
    "    x_lim = a.get_xlim()\n",
    "\n",
    "    a.plot(diameters, E_max, color=\"white\")\n",
    "    a.plot(diameters, H_max, color=\"white\")\n",
    "\n",
    "    a.set_xlim(x_lim)\n",
    "    a.set_xlabel(\"disk diameter (µm)\")\n",
    "\n",
    "ax[0].set_ylabel(\"wavelength (µm)\")\n",
    "ax[1].tick_params(left=False, labelleft=False)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to simulate directional scattering in silicon nanodisks by manipulating their electric and magnetic resonances using Tidy3D.",
  "feature_image": "./img/cylinder.png",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Directional scattering, Silicon nanodisks, Electric and magnetic resonances, 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": "3.11.2"
  },
  "title": "How to model directional scattering using nanodisks in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}