{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dispersive materials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction / Setup\n",
    "\n",
    "Here we show to to model dispersive materials in Tidy3D with an example showing transmission spectrum of a multilayer stack of slabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:19.491483Z",
     "iopub.status.busy": "2023-03-27T20:31:19.490896Z",
     "iopub.status.idle": "2023-03-27T20:31:20.694726Z",
     "shell.execute_reply": "2023-03-27T20:31:20.694111Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let us define some basic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.697099Z",
     "iopub.status.busy": "2023-03-27T20:31:20.696810Z",
     "iopub.status.idle": "2023-03-27T20:31:20.716002Z",
     "shell.execute_reply": "2023-03-27T20:31:20.715432Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Wavelength and frequency range\n",
    "lambda_range = (0.5, 1.5)\n",
    "lam0 = np.sum(lambda_range) / 2\n",
    "freq_range = (td.constants.C_0 / lambda_range[1], td.constants.C_0 / lambda_range[0])\n",
    "Nfreq = 333\n",
    "\n",
    "# frequencies and wavelengths of monitor\n",
    "monitor_freqs = np.linspace(freq_range[0], freq_range[1], Nfreq)\n",
    "monitor_lambdas = td.constants.C_0 / monitor_freqs\n",
    "\n",
    "# central frequency, frequency pulse width and total running time\n",
    "freq0 = monitor_freqs[Nfreq // 2]\n",
    "freqw = 0.3 * (freq_range[1] - freq_range[0])\n",
    "t_stop = 100 / freq0\n",
    "\n",
    "# Thicknesses of slabs\n",
    "t_slabs = [0.5, 0.2, 0.4, 0.3]  # um\n",
    "\n",
    "# Grid resolution (cells per um)\n",
    "res = 150\n",
    "\n",
    "# space between slabs and sources and PML\n",
    "spacing = 1 * lambda_range[-1]\n",
    "\n",
    "# simulation size\n",
    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + sum(t_slabs))\n"
   ]
  },
  {
   "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, lossless, dispersionless dielectric defined by a real-valued relative permittivity.\n",
    "2. Lossy 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 pre-defined library of materials.\n",
    "\n",
    "More complicated dispersive materials [can also be defined](api.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](notebooks/Fitting.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.717926Z",
     "iopub.status.busy": "2023-03-27T20:31:20.717761Z",
     "iopub.status.idle": "2023-03-27T20:31:20.735313Z",
     "shell.execute_reply": "2023-03-27T20:31:20.734819Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# simple, lossless, dispersionless material (either epsilon or n)\n",
    "mat1 = td.Medium(permittivity=4.0)\n",
    "\n",
    "# lossy material with n & k values at a specified frequency or wavelength\n",
    "mat2 = td.Medium.from_nk(n=3.0, k=0.1, freq=freq0)\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=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]\n"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.737306Z",
     "iopub.status.busy": "2023-03-27T20:31:20.737130Z",
     "iopub.status.idle": "2023-03-27T20:31:20.754471Z",
     "shell.execute_reply": "2023-03-27T20:31:20.753961Z"
    },
    "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\n"
   ]
  },
  {
   "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 direciton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.756830Z",
     "iopub.status.busy": "2023-03-27T20:31:20.756673Z",
     "iopub.status.idle": "2023-03-27T20:31:20.773531Z",
     "shell.execute_reply": "2023-03-27T20:31:20.773077Z"
    },
    "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=freq0, fwidth=freqw),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -Lz / 2 + spacing),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    ")\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": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.775442Z",
     "iopub.status.busy": "2023-03-27T20:31:20.775273Z",
     "iopub.status.idle": "2023-03-27T20:31:20.791972Z",
     "shell.execute_reply": "2023-03-27T20:31:20.791438Z"
    },
    "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=monitor_freqs,\n",
    "    name=\"flux\",\n",
    ")\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": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.793827Z",
     "iopub.status.busy": "2023-03-27T20:31:20.793694Z",
     "iopub.status.idle": "2023-03-27T20:31:20.810663Z",
     "shell.execute_reply": "2023-03-27T20:31:20.810141Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is time to define the simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.812707Z",
     "iopub.status.busy": "2023-03-27T20:31:20.812375Z",
     "iopub.status.idle": "2023-03-27T20:31:20.836445Z",
     "shell.execute_reply": "2023-03-27T20:31:20.835948Z"
    },
    "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=40),\n",
    "    structures=slabs,\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=t_stop,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\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": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:20.838359Z",
     "iopub.status.busy": "2023-03-27T20:31:20.838223Z",
     "iopub.status.idle": "2023-03-27T20:31:21.063622Z",
     "shell.execute_reply": "2023-03-27T20:31:21.063213Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot(x=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use `Simulation.plot_eps()` to vizualize 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 frequences 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": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:21.065508Z",
     "iopub.status.busy": "2023-03-27T20:31:21.065336Z",
     "iopub.status.idle": "2023-03-27T20:31:21.515301Z",
     "shell.execute_reply": "2023-03-27T20:31:21.514769Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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[0], freq_range[1])\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()\n"
   ]
  },
  {
   "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 interst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:21.517283Z",
     "iopub.status.busy": "2023-03-27T20:31:21.517080Z",
     "iopub.status.idle": "2023-03-27T20:31:21.902813Z",
     "shell.execute_reply": "2023-03-27T20:31:21.902292Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "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, [-8e-16, -8e-16], [8e-16, 8e-16], alpha=0.4, color=\"g\", label=\"mesure\"\n",
    ")\n",
    "ax2.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:21.904707Z",
     "iopub.status.busy": "2023-03-27T20:31:21.904518Z",
     "iopub.status.idle": "2023-03-27T20:31:59.189940Z",
     "shell.execute_reply": "2023-03-27T20:31:59.188963Z"
    },
    "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\">[14:28:36] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'dispersion'</span> with task_id                               <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-88f2ed24-bc1c-4325-8ce8-5c96128fc25bv1'</span>.                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:28:36]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dispersion'\u001b[0m with task_id                               \u001b]8;id=868053;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=511819;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-88f2ed24-bc1c-4325-8ce8-5c96128fc25bv1'\u001b[0m.                       \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df549a8c9e764517ad240ac67de01d5c",
       "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\">[14:28:40] </span>status = queued                                                      <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:28:40]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                      \u001b]8;id=501400;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=461687;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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       "Output()"
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    },
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     "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\">[14:28:46] </span>status = preprocess                                                  <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m[14:28:46]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                  \u001b]8;id=691919;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=380691;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\u001b[0m\u001b]8;;\u001b\\\n"
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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\"></pre>\n"
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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\">[14:28:51] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.106</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get  <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>the billed FlexCredit cost after a simulation run.                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
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       "\u001b[2;36m[14:28:51]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.106\u001b[0m. 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  \u001b]8;id=901371;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=249516;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mthe billed FlexCredit cost after a simulation run.                   \u001b[2m             \u001b[0m\n"
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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\">           </span>starting up solver                                                   <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                   \u001b]8;id=716119;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=799379;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                       \u001b]8;id=635771;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=187916;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\u001b[2;36m[14:29:19]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                     \u001b]8;id=901184;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=151185;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#313\u001b\\\u001b[2m313\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\n"
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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\">           </span>status = postprocess                                                 <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#330\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">330</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                 \u001b]8;id=530253;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=412946;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#330\u001b\\\u001b[2m330\u001b[0m\u001b]8;;\u001b\\\n"
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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\">[14:29:22] </span>status = success                                                     <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#337\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">337</span></a>\n",
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       "\u001b[2;36m[14:29:22]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                     \u001b]8;id=745580;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=463688;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#337\u001b\\\u001b[2m337\u001b[0m\u001b]8;;\u001b\\\n"
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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\">[14:29:23] </span>loading SimulationData from data/sim_data.hdf5                       <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#509\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">509</span></a>\n",
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       "\u001b[2;36m[14:29:23]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/sim_data.hdf5                       \u001b]8;id=402986;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=865253;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#509\u001b\\\u001b[2m509\u001b[0m\u001b]8;;\u001b\\\n"
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   "source": [
    "sim_data = web.run(sim, task_name=\"dispersion\", path=\"data/sim_data.hdf5\", verbose=True)\n"
   ]
  },
  {
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   "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": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:59.839390Z",
     "iopub.status.busy": "2023-03-27T20:31:59.839213Z",
     "iopub.status.idle": "2023-03-27T20:31:59.960678Z",
     "shell.execute_reply": "2023-03-27T20:31:59.960130Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "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()\n"
   ]
  },
  {
   "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": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:31:59.964370Z",
     "iopub.status.busy": "2023-03-27T20:31:59.964191Z",
     "iopub.status.idle": "2023-03-27T20:32:16.225532Z",
     "shell.execute_reply": "2023-03-27T20:32:16.224898Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-a5cf8413-5c6f-4749-8295-9ffe281859d0v1'</span>.                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:29:23]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'docs_dispersion_norm'\u001b[0m with task_id                     \u001b]8;id=426657;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=854785;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n",
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       "Output()"
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    },
    {
     "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\"></pre>\n"
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     },
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     "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\">\n",
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       "\n"
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     "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\">[14:29:24] </span>status = queued                                                      <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m[14:29:24]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                      \u001b]8;id=70161;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=744501;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
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     "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\">[14:29:26] </span>status = preprocess                                                  <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:29:26]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                  \u001b]8;id=522613;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=268730;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#263\u001b\\\u001b[2m263\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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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\"></pre>\n"
      ],
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     },
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      "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\">[14:29:31] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get  <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>the billed FlexCredit cost after a simulation run.                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:29:31]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. 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  \u001b]8;id=168;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=724336;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mthe billed FlexCredit cost after a simulation run.                   \u001b[2m             \u001b[0m\n"
      ]
     },
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    },
    {
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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\">           </span>starting up solver                                                   <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                   \u001b]8;id=926779;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=546826;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
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    {
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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\">           </span>running solver                                                       <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#300\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">300</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                       \u001b]8;id=409535;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=327328;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
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    {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
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    },
    {
     "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\">[14:29:37] </span>early shutoff detected, exiting.                                     <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#313\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">313</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:29:37]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                     \u001b]8;id=630070;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=585744;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#313\u001b\\\u001b[2m313\u001b[0m\u001b]8;;\u001b\\\n"
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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\"></pre>\n"
      ],
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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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
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    },
    {
     "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                                                 <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#330\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">330</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                 \u001b]8;id=385662;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=356578;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#330\u001b\\\u001b[2m330\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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       "Output()"
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     },
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    {
     "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\">[14:29:39] </span>status = success                                                     <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#337\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">337</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[14:29:39]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                     \u001b]8;id=395931;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=460452;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#337\u001b\\\u001b[2m337\u001b[0m\u001b]8;;\u001b\\\n"
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       "Output()"
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    {
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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\"></pre>\n"
      ],
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     },
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    {
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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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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     },
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    },
    {
     "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\">[14:29:40] </span>loading SimulationData from data/sim_data.hdf5                       <a href=\"file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/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:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#509\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">509</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:29:40]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/sim_data.hdf5                       \u001b]8;id=280352;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=99985;file:///home/weiliang/Documents/tidy3d-core/tidy3d_frontend/tidy3d/web/webapi.py#509\u001b\\\u001b[2m509\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "sim_norm = sim.copy(update={\"structures\": []})\n",
    "\n",
    "sim_data_norm = web.run(\n",
    "    sim_norm, task_name=\"docs_dispersion_norm\", path=\"data/sim_data.hdf5\", verbose=True,\n",
    ")\n",
    "transmission_norm = sim_data_norm[\"flux\"].flux\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:32:16.502944Z",
     "iopub.status.busy": "2023-03-27T20:32:16.502807Z",
     "iopub.status.idle": "2023-03-27T20:32:16.642196Z",
     "shell.execute_reply": "2023-03-27T20:32:16.641690Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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()\n"
   ]
  },
  {
   "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 transmission to a semi-analytical result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:32:16.644244Z",
     "iopub.status.busy": "2023-03-27T20:32:16.644096Z",
     "iopub.status.idle": "2023-03-27T20:32:16.672548Z",
     "shell.execute_reply": "2023-03-27T20:32:16.671369Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import TMM package\n",
    "import tmm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:32:16.676376Z",
     "iopub.status.busy": "2023-03-27T20:32:16.676085Z",
     "iopub.status.idle": "2023-03-27T20:32:16.752973Z",
     "shell.execute_reply": "2023-03-27T20:32:16.752448Z"
    }
   },
   "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(monitor_freqs))\n",
    "n_list2 = np.sqrt(mat2.eps_model(monitor_freqs))\n",
    "n_list3 = np.sqrt(mat3.eps_model(monitor_freqs))\n",
    "n_list4 = np.sqrt(mat4.eps_model(monitor_freqs))\n",
    "\n",
    "# loop through wavelength and record TMM computed transmission\n",
    "transmission_tmm = []\n",
    "for i, lam in enumerate(monitor_lambdas):\n",
    "\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T20:32:16.754998Z",
     "iopub.status.busy": "2023-03-27T20:32:16.754849Z",
     "iopub.status.idle": "2023-03-27T20:32:16.883441Z",
     "shell.execute_reply": "2023-03-27T20:32:16.882905Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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    "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(\"wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Transmitted\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
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