{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling dispersive materials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction / Setup\n",
    "\n",
    "Here we show how to model dispersive materials in `Tidy3D` with an example showing transmission spectrum of a multilayer stack of slabs.\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. For simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define basic simulation-related parameters. To simplify the initialization and management of frequency-related variables, we use the convenience class [FreqRange](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FreqRange.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wavelength interval and number of sampled frequencies\n",
    "wvl_min = 0.5\n",
    "wvl_max = 1.5\n",
    "Nfreq = 333\n",
    "\n",
    "# initialize frequency range, wavelengths of monitor\n",
    "freq_range = td.FreqRange.from_wvl_interval(wvl_min=wvl_min, wvl_max=wvl_max)\n",
    "monitor_lambdas = freq_range.wvls(num_points=Nfreq, spacing=\"uniform_freq\")[::-1]\n",
    "\n",
    "# central frequency, frequency pulse width and total running time\n",
    "t_stop = 100 / freq_range.freq0\n",
    "\n",
    "# Thicknesses of slabs\n",
    "t_slabs = [0.5, 0.2, 0.4, 0.3]  # um\n",
    "\n",
    "# Grid resolution (min steps per wavelength in a material)\n",
    "res = 40\n",
    "\n",
    "# space between slabs and sources and PML\n",
    "spacing = wvl_max\n",
    "\n",
    "# simulation size\n",
    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + sum(t_slabs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Materials (4 Ways)\n",
    "\n",
    "Here, we will illustrate defining materials in four different ways:\n",
    "\n",
    "1. Simple, lossy dielectric defined by a real-valued relative permittivity, and DC conductivity.\n",
    "2. **Active** material defined by real and imaginary part of the refractive index ($n$) and ($k$) at a given frequency. Values are exact only at that frequency, so this approach is only good for narrow-band simulations.\n",
    "3. Simple, lossless dispersive material (one-pole fitting) defined by the real part of the refractive index $n$ and the dispersion $\\mathrm{d}n/\\mathrm{d}\\lambda$ at a given frequency. The dispersion must be negative. This is a convenient approach to incorporate weakly dispersive materials in your simulations, as the values can be taken directly from [refractiveindex.info](https://refractiveindex.info/)\n",
    "4. Dispersive material imported from our predefined library of materials.\n",
    "\n",
    "More complicated dispersive materials [can also be defined](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#dispersive-mediums) through dispersive models like Lorentz, Sellmeier, Debye, or Drude, if the model parameters are known. Finally, arbitrary dispersion data can also be fit, which is a the subject of [this tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Fitting/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# simple, lossy material\n",
    "mat1 = td.Medium(permittivity=4.0, conductivity=0.005)\n",
    "\n",
    "# active material with n & k values at a specified frequency or wavelength\n",
    "# note: negative k value corresponds to a gain medium; it is only allowed\n",
    "#       when `allow_gain` is set to be True\n",
    "mat2 = td.Medium.from_nk(n=3.0, k=-0.1, freq=freq_range.freq0, allow_gain=True)\n",
    "\n",
    "# weakly dispersive material defined by dn_dwvl at a given frequency\n",
    "mat3 = td.Sellmeier.from_dispersion(n=2.0, dn_dwvl=-0.1, freq=freq_range.freq0)\n",
    "\n",
    "# dispersive material from tidy3d library\n",
    "mat4 = td.material_library[\"BK7\"][\"Zemax\"]\n",
    "\n",
    "# put all together\n",
    "mat_slabs = [mat1, mat2, mat3, mat4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Simulation\n",
    "Now we set everything else up (structures, sources, monitors, simulation) to run the example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define the multilayer stack structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "slabs = []\n",
    "slab_position = -Lz / 2 + 2 * spacing\n",
    "for t, mat in zip(t_slabs, mat_slabs):\n",
    "    slab = td.Structure(\n",
    "        geometry=td.Box(\n",
    "            center=(0, 0, slab_position + t / 2),\n",
    "            size=(td.inf, td.inf, t),\n",
    "        ),\n",
    "        medium=mat,\n",
    "    )\n",
    "    slabs.append(slab)\n",
    "    slab_position += t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must now define the excitation conditions and field monitors. We will excite the slab using a normally incident (along z) planewave, polarized along the x direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Here we define the planewave source, placed just in advance (towards negative z) of the slab\n",
    "source = td.PlaneWave(\n",
    "    source_time=td.GaussianPulse(freq0=freq_range.freq0, fwidth=0.3 * freq_range.fwidth),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -Lz / 2 + spacing),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the field monitor, placed just past (towards positive z) of the stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We are interested in measuring the transmitted flux, so we set it to be an oversized plane.\n",
    "monitor = td.FluxMonitor(\n",
    "    center=(0, 0, Lz / 2 - spacing),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=freq_range.freqs(num_points=Nfreq, spacing=\"uniform_freq\"),\n",
    "    name=\"flux\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, define the boundary conditions to use PMLs along z and the default periodic boundaries along x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is time to define the simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sim = td.Simulation(\n",
    "    center=(0, 0, 0),\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=res),\n",
    "    structures=slabs,\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=t_stop,\n",
    "    boundary_spec=boundary_spec,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot The Structure\n",
    "\n",
    "Let's now plot the permittivity profile to confirm that the structure was defined correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we use the `Simulation.plot()` method to plot the materials only, which assigns a different color to each slab without knowledge of the material properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot(x=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use `Simulation.plot_eps()` to visualize the permittivity of the stack. However, because the stack contains dispersive materials, we need to specify the `freq` of interest as an argument to the plotting tool.  Here we show the permittivity at the lowest and highest frequencies in the range of interest.  Note that in this case, the real part of the permittivity (being plotted) only changes slightly between the two frequencies on the dispersive material.  However, for other materials with more dispersion, the effect can be much more prominent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the permittivity at a few frequencies\n",
    "freqs_plot = freq_range.freqs(num_points=2)\n",
    "fig, axes = plt.subplots(1, len(freqs_plot), tight_layout=True, figsize=(12, 4))\n",
    "for ax, freq_plot in zip(axes, freqs_plot):\n",
    "    sim.plot_eps(x=0, freq=freq_plot, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at the source to make sure it's defined correctly over our frequency range of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check probe and source\n",
    "ax1 = sim.sources[0].source_time.plot(times=np.linspace(0, sim.run_time, 1001))\n",
    "ax1.set_xlim(0, 1e-13)\n",
    "ax2 = sim.sources[0].source_time.plot_spectrum(times=np.linspace(0, sim.run_time, 1001))\n",
    "ax2.fill_between(\n",
    "    freq_range.freqs(num_points=2),\n",
    "    [-8e-16, -8e-16],\n",
    "    [8e-16, 8e-16],\n",
    "    alpha=0.4,\n",
    "    color=\"g\",\n",
    "    label=\"measure\",\n",
    ")\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the simulation\n",
    "\n",
    "We will submit the simulation to run as a new project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "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\">11:20:43 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'dispersion'</span> with task_id                             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-9845de83-abeb-4625-a73f-77dab9b2052e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:20:43 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dispersion'\u001b[0m with task_id                             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-9845de83-abeb-4625-a73f-77dab9b2052e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \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>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abe</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4625-a73f-77dab9b2052e'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=433352;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=296544;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=433352;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=531184;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=433352;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32m-9845de83-abe\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=433352;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[32mb-4625-a73f-77dab9b2052e'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=495515;https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1e4378f82c0465d98f72ed97a198ff3",
       "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\">11:20:45 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.104</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:20:45 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.104\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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\">11:20:46 EDT </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:20:46 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3be76d3b7d2f4025991abba282d8db0d",
       "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\">11:20:58 EDT </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:20:58 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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": {
      "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\">11:21:02 EDT </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:02 EDT\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98ca00ee1548443a8fee92503e83a80b",
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:21:10 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:10 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m28\u001b[0m%, exiting.                            \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": {
      "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>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36d14cc0347443adbd6eb4dd5a063739",
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:21:13 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:13 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {
      "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\">11:21:15 EDT </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abe</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4625-a73f-77dab9b2052e'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:15 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=909758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=390110;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=909758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=156158;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=909758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34m-9845de83-abe\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=909758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9845de83-abeb-4625-a73f-77dab9b2052e\u001b\\\u001b[4;34mb-4625-a73f-77dab9b2052e'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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      "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\">             </span>loading simulation from data/sim_data.hdf5                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/sim_data.hdf5                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"dispersion\", path=\"data/sim_data.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postprocess and Plot\n",
    "\n",
    "Once the simulation has completed, we can download the results and load them into the simulation object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we compute the transmitted flux and plot the transmission spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve the power flux through the monitor plane.\n",
    "transmission = sim_data[\"flux\"].flux\n",
    "plt.plot(monitor_lambdas, transmission, color=\"k\")\n",
    "plt.xlabel(\"wavelength (um)\")\n",
    "plt.ylabel(\"transmitted flux\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `Tidy3D`, results are normalized by default. In some cases, and largely depending on the required accuracy, a normalizing run may still be needed. Here, we show how to do such a normalizing run by simulating an empty simulation with the exact same source and monitor but none of the structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "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\">11:21:16 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'docs_dispersion_norm'</span> with task_id                   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:16 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'docs_dispersion_norm'\u001b[0m with task_id                   \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \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>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2ad</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-4a0a-b15f-d4b36e292654'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=628135;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=393233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=628135;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=331341;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=628135;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32m-ab02b48b-2ad\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=628135;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[32mf-4a0a-b15f-d4b36e292654'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=892814;https://tidy3d.simulation.cloud/folders/folder-f7b925a9-b2c3-4519-8f36-17122860e556\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39fc85d0d3134366ae5ee6118548a4c9",
       "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\">11:21:17 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:17 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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\">11:21:18 EDT </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:21:18 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f72938720ce4747bcd6ed894b671fea",
       "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\">13:00:39 EDT </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:00:39 EDT\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b33b40b07e3744bf959ade5b002fc500",
       "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\">13:00:40 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:00:40 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \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": {
      "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>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {
      "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>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2ad</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">f-4a0a-b15f-d4b36e292654'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=11428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=583892;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=11428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=962508;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=11428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34m-ab02b48b-2ad\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=11428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab02b48b-2adf-4a0a-b15f-d4b36e292654\u001b\\\u001b[4;34mf-4a0a-b15f-d4b36e292654'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbaa062d843b4c96a88ec4a0f5cc77a6",
       "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\">13:00:41 EDT </span>loading simulation from data/sim_data.hdf5                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:00:41 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/sim_data.hdf5                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_norm = sim.copy(update={\"structures\": []})\n",
    "\n",
    "sim_data_norm = web.run(\n",
    "    sim_norm,\n",
    "    task_name=\"docs_dispersion_norm\",\n",
    "    path=\"data/sim_data.hdf5\",\n",
    "    verbose=True,\n",
    ")\n",
    "transmission_norm = sim_data_norm[\"flux\"].flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(monitor_lambdas, transmission, label=\"with structure\")\n",
    "plt.plot(monitor_lambdas, transmission_norm, label=\"no structure\")\n",
    "plt.plot(monitor_lambdas, transmission / transmission_norm, \"k--\", label=\"normalized\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"wavelength (um)\")\n",
    "plt.ylabel(\"fraction of transmitted power (normalized)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that since the flux monitor already takes the source power into account, the normalizing run has no visible effect on the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analytical Comparison\n",
    "\n",
    "We will use a transfer matrix method (TMM) [code](https://github.com/sbyrnes321/tmm) to compare `Tidy3D`'s simulated transmission to a semi-analytical result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import TMM package\n",
    "import tmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare list of thicknesses including air boundaries\n",
    "d_list = [np.inf] + t_slabs + [np.inf]\n",
    "\n",
    "# convert the complex permittivities at each frequency to refractive indices\n",
    "n_list1 = np.sqrt(mat1.eps_model(freq_range.freqs(num_points=Nfreq)))\n",
    "n_list2 = np.sqrt(mat2.eps_model(freq_range.freqs(num_points=Nfreq)))\n",
    "n_list3 = np.sqrt(mat3.eps_model(freq_range.freqs(num_points=Nfreq)))\n",
    "n_list4 = np.sqrt(mat4.eps_model(freq_range.freqs(num_points=Nfreq)))\n",
    "\n",
    "# loop through wavelength and record TMM computed transmission\n",
    "transmission_tmm = []\n",
    "for i, lam in enumerate(monitor_lambdas):\n",
    "    # create list of refractive index at this wavelength including outer material (air)\n",
    "    n_list = [1, n_list1[i], n_list2[i], n_list3[i], n_list4[i], 1]\n",
    "\n",
    "    # get transmission at normal incidence\n",
    "    T = tmm.coh_tmm(\"s\", n_list, d_list, 0, lam)[\"T\"]\n",
    "    transmission_tmm.append(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(monitor_lambdas, transmission_tmm, label=\"TMM\")\n",
    "plt.plot(monitor_lambdas, transmission / transmission_norm, \"k--\", label=\"Tidy3D\")\n",
    "plt.xlabel(r\"wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Transmitted\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to model dispersive materials in Tidy3D for FDTD simulations.",
  "feature_imag": "",
  "feature_image": "N/A",
  "kernelspec": {
   "display_name": "test_examp_env",
   "language": "python",
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