{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Germanium resonator benchmark \n",
    "\n",
    "In this example, we reproduce the findings of Campione et al. (2016), which is linked [here](https://pubs.acs.org/doi/abs/10.1021/acsphotonics.6b00556?casa_token=v7Cq9VMW40UAAAAA:AWrBfYCHrwGQ9PYBYgdprrQ8X8i-nOairIplRs1Ejo2sbDmFT9nsV1M6UEXpvOfYSnwMjagD9IT97Ph2).\n",
    "\n",
    "This notebook was originally developed and written by Romil Audhkhasi (USC). \n",
    "\n",
    "The paper investigates the resonances of Germanium structures by measuring their transmission spectrum under varying geometric parameters.\n",
    "\n",
    "The paper uses a different commercial finite-difference time-domain software, which matches the result from Tidy3D.\n",
    "\n",
    "<!-- <img src=\"img/Ge_struct.png\" alt=\"diagram\" width=\"300\"/> -->\n",
    "\n",
    "To do this calculation, we use a broadband pulse and frequency monitor to measure the flux on the opposite side of the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8WfOAGgkyh3K",
    "outputId": "f02b3adf-9117-42b1-a69c-b5f4f3f0a6c5",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "gy7J23c5yh3S",
    "tags": []
   },
   "outputs": [],
   "source": [
    "Nfreq = 1000\n",
    "wavelengths = np.linspace(8, 12, Nfreq)\n",
    "freqs = td.constants.C_0 / wavelengths\n",
    "freq0 = freqs[len(freqs) // 2]\n",
    "freqw = freqs[0] - freqs[-1]\n",
    "\n",
    "# Define material properties\n",
    "n_BaF2 = 1.45\n",
    "n_Ge = 4\n",
    "BaF2 = td.Medium(permittivity=n_BaF2**2)\n",
    "Ge = td.Medium(permittivity=n_Ge**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mGFiHbvhyh3T",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# space between resonators and source\n",
    "spc = 8\n",
    "\n",
    "# geometric parameters\n",
    "Px = Py = P = 4.2\n",
    "h = 2.53\n",
    "L1 = 3.036\n",
    "L2 = 2.024\n",
    "w1 = w2 = w = 1.265\n",
    "\n",
    "# resolution (should be commensurate with periodicity)\n",
    "dl = P / 32\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Ol9GwSGUyh3U",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# total size in z and [x,y,z]\n",
    "Lz = spc + h + h + spc\n",
    "sim_size = [Px, Py, Lz]\n",
    "\n",
    "# BaF2 substrate\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, 0, -Lz / 2],\n",
    "        size=[td.inf, td.inf, 2 * (spc + h)],\n",
    "    ),\n",
    "    medium=BaF2,\n",
    "    name=\"substrate\",\n",
    ")\n",
    "\n",
    "# Define structure\n",
    "\n",
    "cell1 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[(L1 / 2) - L2, -w1 / 2, h / 2],\n",
    "        size=[L1, w1, h],\n",
    "    ),\n",
    "    medium=Ge,\n",
    "    name=\"cell1\",\n",
    ")\n",
    "\n",
    "cell2 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[-L2 / 2, w2 / 2, h / 2],\n",
    "        size=[L2, w2, h],\n",
    "    ),\n",
    "    medium=Ge,\n",
    "    name=\"cell2\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YO2yKasKyh3U",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=freq0, fwidth=freqw)\n",
    "\n",
    "# plane wave source\n",
    "source = td.PlaneWave(\n",
    "    source_time=gaussian,\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, Lz / 2 - spc + 2 * dl),\n",
    "    direction=\"-\",\n",
    "    pol_angle=0,\n",
    ")\n",
    "\n",
    "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
    "run_time = 8e-11\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4TZn_s8kyh3V",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# monitor fields on other side of structure (substrate side) at range of frequencies\n",
    "monitor = td.FluxMonitor(\n",
    "    center=[0.0, 0.0, -Lz / 2 + spc - 2 * dl],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=freqs,\n",
    "    name=\"flux\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Case Studies\n",
    "\n",
    "Here we define the two simulations to run\n",
    "\n",
    "- With no resonator (normalization)\n",
    "- With Ge resonator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fnpi5BiZyh3W",
    "outputId": "d25c2f44-d5a2-4241-b69b-0ffe5369ec5b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_spec = td.GridSpec(\n",
    "    grid_x=td.UniformGrid(dl=dl),\n",
    "    grid_y=td.UniformGrid(dl=dl),\n",
    "    grid_z=td.AutoGrid(min_steps_per_wvl=32),\n",
    ")\n",
    "\n",
    "# normalizing run (no Ge) to get baseline transmission vs freq\n",
    "# can be run for shorter time as there are no resonances\n",
    "sim_empty = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=[substrate],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time / 10,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n",
    "\n",
    "# run with Ge nanorod\n",
    "sim_actual = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    structures=[substrate, cell1, cell2],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "id": "T8YFcftUyh3X",
    "outputId": "93b60986-ceb3-4064-eb8d-71a75495c11d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Structure visualization in various planes\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 6))\n",
    "sim_actual.plot_eps(x=0, ax=ax1)\n",
    "sim_actual.plot_eps(y=-0.1, ax=ax2)\n",
    "sim_actual.plot_eps(z=0.1, ax=ax3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IG0QZ-PDyh3Y",
    "outputId": "332212da-3f04-4920-e593-801457a5e339",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:59:22] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'norm'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4a05e377-cae3-4124-a983-b24a59c41805v1'</span>.            <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:59:22]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'norm'\u001b[0m with task_id \u001b[32m'fdve-4a05e377-cae3-4124-a983-b24a59c41805v1'\u001b[0m.            \u001b]8;id=751659;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=886351;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1fee9a131604b05ac894748685d8d90",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:59:24] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'actual'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'fdve-cc594121-15ca-400c-925a-a364e615af34v1'</span>.          <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:59:24]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'actual'\u001b[0m with task_id \u001b[32m'fdve-cc594121-15ca-400c-925a-a364e615af34v1'\u001b[0m.          \u001b]8;id=691329;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=446272;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c83f5203b39a42538fcf5ae26f21d1a3",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[14:59:27] </span>Started working on Batch.                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">402</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:59:27]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                                               \u001b]8;id=24450;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=830259;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\u001b\\\u001b[2m402\u001b[0m\u001b]8;;\u001b\\\n"
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     },
     "metadata": {},
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       "model_id": "e1827408b1324d309f72609ed632bd34",
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      "text/plain": [
       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:03:08] </span>Batch complete.                                                                         <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">436</span></a>\n",
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       "\u001b[2;36m[15:03:08]\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                                         \u001b]8;id=991322;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=899679;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\u001b\\\u001b[2m436\u001b[0m\u001b]8;;\u001b\\\n"
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      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# run all simulations, take about 2-3 minutes each with some download time\n",
    "batch = web.Batch(simulations={\"norm\": sim_empty, \"actual\": sim_actual}, verbose=True)\n",
    "batch_data = batch.run(path_dir=\"data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalizing run computes the transmitted flux for an air -> SiO2 interface, which is just below unity due to some reflection.\n",
    "\n",
    "While not technically necessary for this example, since this transmission can be computed analytically, it is often a good idea to run a normalizing run so you can accurately measure the *change* in output when the structure is added.  For example, for multilayer structures, the normalizing run displays frequency dependence, which would make it prudent to include in the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "722f09aeb48a40a1930f8f8fc97245b0",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:03:12] </span>loading SimulationData from data/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">cc594121-15ca-400c-925a-a364e615af34</span>v1.hdf5          <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">512</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[15:03:12]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/fdve-\u001b[93mcc594121-15ca-400c-925a-a364e615af34\u001b[0mv1.hdf5          \u001b]8;id=387965;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=180216;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:03:13] </span>loading SimulationData from data/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">4a05e377-cae3-4124-a983-b24a59c41805</span>v1.hdf5          <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">512</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[15:03:13]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/fdve-\u001b[93m4a05e377-cae3-4124-a983-b24a59c41805\u001b[0mv1.hdf5          \u001b]8;id=567474;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=928256;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data = batch.load(path_dir=\"data\")\n",
    "transmission = batch_data[\"actual\"][\"flux\"].flux / batch_data[\"norm\"][\"flux\"].flux\n",
    "reflection = 1 - transmission\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "x10TjU0uyh3a",
    "outputId": "b74cd9e6-f5a4-468f-f1d0-90c8d9861ed9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot transmission, compare to paper results, look similar\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
    "plt.plot(wavelengths, reflection, \"k\", label=\"R\")\n",
    "plt.plot(wavelengths, transmission, \"r--\", label=\"T\")\n",
    "plt.xlabel(\"wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Magnitude\")\n",
    "plt.xlim([8.8, 12])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.legend()\n",
    "plt.show()\n"
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