{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68529042",
   "metadata": {},
   "source": [
    "# Simulating the Beer-Lambert Law with Tidy3D\n",
    "\n",
    "In this tutorial, we demonstrate how to translate the absorption coefficient parameter ($\\alpha$ [$\\text{cm}^{-1}$]) into a Tidy3D [Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html) and calculate volumetric power absorption using a 3D [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) and [PermittivityMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "472042de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4278210",
   "metadata": {},
   "source": [
    "## Creating a Lossy Medium\n",
    "\n",
    "Tidy3D follows the $e^{-\\omega t}$ convention, in which losses are represented by a positive imaginary part of the refractive index, here denoted as $k$:\n",
    "\n",
    "$\\tilde{n} = n + i k$\n",
    "\n",
    "We can relate $k$ to the absorption coefficient from the Beer-Lambert law:\n",
    "\n",
    "$I = I_0 e^{-\\alpha z}$\n",
    "\n",
    "where:\n",
    "\n",
    "$k = \\frac{\\lambda \\alpha}{4\\pi} \\times 10^{-4}$ (using $\\lambda$ in microns and $\\alpha$ in cm⁻¹)\n",
    "\n",
    "An important consideration is that Tidy3D uses microns (µm) as distance units, while $\\alpha$ is generally given in cm⁻¹.\n",
    "\n",
    "With the $k$ value, we can define a lossy medium using the [`td.Medium.from_nk`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html#tidy3d.Medium.from_nk) method.\n",
    "\n",
    "Let’s consider a medium with a real refractive index of 3 and $\\alpha = 0.5$ $µ\\text{m}^{-1}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da50caf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the central wavelength and frequency\n",
    "wvl0 = 1.5\n",
    "freq0 = td.C_0 / wvl0\n",
    "\n",
    "n = 3\n",
    "alpha = 0.5  # absorption in 1/µm\n",
    "k = alpha * wvl0 / (4 * np.pi)\n",
    "medium = td.Medium.from_nk(n=n, k=k, freq=freq0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883fd7d7",
   "metadata": {},
   "source": [
    "### Relating *k* with Conductivity\n",
    "\n",
    "Alternatively, we can relate the complex refractive index with the complex permittivity and calculate the conductivity $\\sigma$.\n",
    "\n",
    "$\\tilde{n} = n + i\\kappa$\n",
    "\n",
    "$\\varepsilon_r = \\tilde{n}^2 = (n^2 - \\kappa^2) + i(2n\\kappa)$\n",
    "\n",
    "$\\varepsilon'' = 2n\\kappa$\n",
    "\n",
    "$\\sigma(\\omega) = \\omega \\varepsilon_0 \\varepsilon'' = 2\\omega \\varepsilon_0 n \\kappa$\n",
    "\n",
    "$\\sigma(\\omega) = \\frac{4\\pi \\varepsilon_0 c}{\\lambda} n \\kappa$ [S/µm]\n",
    "\n",
    "Again, following Tidy3D’s unit convention, the **conductivity is in units of S/µm.**\n",
    "\n",
    "With the conductivity value, we can create the same medium using the regular [td.Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html) class.\n",
    "\n",
    "As we can see, the two methods are equivalent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f349ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "medium  conductivity: 0.003982\n",
      "medium2 conductivity: 0.003982\n"
     ]
    }
   ],
   "source": [
    "# Equivalent conductivity-based definition to verify consistency\n",
    "# Note that epsilon is the vacuum permittivity, not the relative permittivity\n",
    "sigma = 4 * np.pi * td.EPSILON_0 * td.C_0 * k * n / wvl0\n",
    "medium2 = td.Medium(permittivity=n**2, conductivity=sigma)\n",
    "\n",
    "print(f\"medium  conductivity: {medium.conductivity:.6f}\")\n",
    "print(f\"medium2 conductivity: {medium2.conductivity:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af02210",
   "metadata": {},
   "source": [
    "## Simulating the Beer-Lambert Law\n",
    "\n",
    "With the lossy medium defined, we can use it as the background medium in a simulation and calculate the absorbed power using the following definition:\n",
    "\n",
    "$P_{abs} = \\tfrac{1}{2} \\, \\omega \\, \\varepsilon_0 \\, \\varepsilon'' \\, |E|^2 = \\tfrac{1}{2} \\, \\sigma \\, |E|^2$\n",
    "\n",
    "First, let’s define the simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c47af143",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_size = (1, 1, 11)\n",
    "sim_center = (0, 0, 4.5)\n",
    "\n",
    "run_time = 1e-12\n",
    "min_steps_per_wvl = 30\n",
    "\n",
    "# Broadband plane wave incident along +z\n",
    "source_time = td.GaussianPulse(freq0=freq0, fwidth=0.2 * freq0)\n",
    "source = td.PlaneWave(\n",
    "    center=(0, 0, 0),\n",
    "    size=(sim_size[0], sim_size[1], 0),\n",
    "    source_time=source_time,\n",
    "    direction=\"+\",\n",
    ")\n",
    "\n",
    "# Periodic lateral boundaries and absorbing PML along propagation\n",
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    ")\n",
    "\n",
    "# 2D slice to inspect field decay through the bulk\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, 0, td.inf), freqs=[freq0], name=\"field_monitor\"\n",
    ")\n",
    "\n",
    "# Bounding box enclosing the integration region for absorbed power\n",
    "abs_monitor_geometry = td.Box.from_bounds(rmin=(-1e3, -1e3, 0), rmax=(1e3, 1e3, 1e3))\n",
    "\n",
    "# Store volume fields for absorption post-processing\n",
    "abs_field_monitor = td.FieldMonitor(\n",
    "    center=abs_monitor_geometry.center,\n",
    "    size=abs_monitor_geometry.size,\n",
    "    freqs=[freq0],\n",
    "    name=\"abs_field_monitor\",\n",
    ")\n",
    "\n",
    "# Matching permittivity sample to extract the imaginary part\n",
    "abs_permittivity_monitor = td.PermittivityMonitor(\n",
    "    center=abs_field_monitor.center,\n",
    "    size=abs_field_monitor.size,\n",
    "    freqs=abs_field_monitor.freqs,\n",
    "    name=\"abs_permittivity_monitor\",\n",
    ")\n",
    "\n",
    "\n",
    "# Automatic mesh obeying the min steps per wavelength target\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_wvl=min_steps_per_wvl,\n",
    "    override_structures=[],\n",
    ")\n",
    "\n",
    "# Assemble the simulation object with the lossy background medium\n",
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=sim_center,\n",
    "    grid_spec=grid_spec,\n",
    "    medium=medium,\n",
    "    sources=[source],\n",
    "    monitors=[field_monitor, abs_field_monitor, abs_permittivity_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6001c9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual check of the structure cross-section at y = 0\n",
    "sim.plot(y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e6f1b9",
   "metadata": {},
   "source": [
    "Now we can run the simulation and process the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba081018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:48:19 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'absorption_calculation'</span> with resource_id             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-593f57f7-1490-438d-91a9-76268f2b65e8'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:48:19 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'absorption_calculation'\u001b[0m with resource_id             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-593f57f7-1490-438d-91a9-76268f2b65e8'\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-593f57f7-1490-438d-91a9-76268f2b65e8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-149</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0-438d-91a9-76268f2b65e8'</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=862571;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=70059;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=862571;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=867890;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=862571;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32m-593f57f7-149\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=862571;https://tidy3d.simulation.cloud/workbench?taskId=fdve-593f57f7-1490-438d-91a9-76268f2b65e8\u001b\\\u001b[32m0-438d-91a9-76268f2b65e8'\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-f9e994c1-6315-4b44-af3a-c2cb1435b23a\" 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=975916;https://tidy3d.simulation.cloud/folders/folder-f9e994c1-6315-4b44-af3a-c2cb1435b23a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a0979e840fc4252b9e23c1504b41608",
       "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\">18:48:22 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.026</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;36m18:48:22 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.026\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\">18:48:23 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:48:23 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b877fe4a713a4618a3469f35fa451110",
       "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\">18:48:29 -03 </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:48:29 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Submit the simulation to Tidy3D Cloud\n",
    "sim_data = web.run(sim, task_name=\"absorption_calculation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccea87ef",
   "metadata": {},
   "source": [
    "## Data Processing\n",
    "\n",
    "First, let’s plot the fields, where we can observe an exponential decay in the field intensity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af10eacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Field snapshot and intensity decay along z\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sim_data.plot_field(\"field_monitor\", \"Ex\", \"real\", ax=ax[0])\n",
    "(np.abs(sim_data[\"field_monitor\"].Ex) ** 2).sel(x=0, method=\"nearest\").plot(ax=ax[1])\n",
    "ax[1].set_ylabel(\"|Ex|²\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c1792e",
   "metadata": {},
   "source": [
    "Now, we can calculate the volumetric absorbed power, using the following relation:\n",
    "\n",
    "$P_{abs} = \\tfrac{1}{2} \\, \\omega \\, \\varepsilon_0 \\, \\varepsilon'' \\, |E|^2$\n",
    "\n",
    "And compare with the analytical results, from Beer-Lambert law:\n",
    "\n",
    "$I = I_0 e^{-\\alpha z}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3046045a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total absorbed power: 0.99\n",
      "Analytical absorbed power: 0.99\n"
     ]
    }
   ],
   "source": [
    "# Extract field components sampled in the absorption volume\n",
    "Ex = sim_data[\"abs_field_monitor\"].Ex.squeeze(drop=True)\n",
    "Ey = sim_data[\"abs_field_monitor\"].Ey.squeeze(drop=True)\n",
    "Ez = sim_data[\"abs_field_monitor\"].Ez.squeeze(drop=True)\n",
    "\n",
    "x = sim_data[\"abs_field_monitor\"].Ex.x\n",
    "y = sim_data[\"abs_field_monitor\"].Ey.y\n",
    "z = sim_data[\"abs_field_monitor\"].Ez.z\n",
    "\n",
    "# Interpolate permittivity onto the same grid to access eps''\n",
    "eps_xx = sim_data[\"abs_permittivity_monitor\"].eps_xx.interp_like(\n",
    "    Ex, method=\"linear\", kwargs={\"fill_value\": \"extrapolate\"}\n",
    ")\n",
    "\n",
    "E_square = np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2\n",
    "omega = 2 * np.pi * freq0\n",
    "\n",
    "# Compute volumetric absorbed power density and integrate over the box\n",
    "P_abs_volumetric = 0.5 * omega * td.EPSILON_0 * eps_xx.imag * E_square\n",
    "P_abs = P_abs_volumetric.integrate(\"x\").integrate(\"y\").integrate(\"z\").squeeze()\n",
    "\n",
    "print(f\"Total absorbed power: {P_abs:.2f}\")\n",
    "print(f\"Analytical absorbed power: {1 - np.exp(-10 * alpha):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef5af59e",
   "metadata": {},
   "source": [
    "Finally, we compute the cumulative integral of the volumetric absorbed power along the full z-direction and directly compare the resulting energy loss with the analytical prediction given by the Beer–Lambert law."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b0ae634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative absorption along z to compare with Beer-Lambert prediction\n",
    "I_z = P_abs_volumetric.integrate(\"x\").integrate(\"y\").squeeze()\n",
    "from scipy.integrate import cumulative_trapezoid\n",
    "\n",
    "C_z = cumulative_trapezoid(I_z, z, initial=0.0)  # [W] absorbed up to each z\n",
    "\n",
    "P_abs = P_abs_volumetric.integrate(\"x\").integrate(\"y\").squeeze()\n",
    "I_analytical = np.exp(-z * alpha)\n",
    "I = 1 - C_z\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(z, I, \"o\", label=\"Simulated\")\n",
    "ax.plot(z, I_analytical, label=\"Analytical\")\n",
    "\n",
    "ax.set_xlabel(\"z (µm)\")\n",
    "ax.set_ylabel(\"I/I0\")\n",
    "ax.set_title(\"Absorption\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to simulate optical absorption based on the Beer-Lambert law using Tidy3D.",
  "kernelspec": {
   "display_name": "td210rc1",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Beer-Lambert law, absorption coefficient, power absorption, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  },
  "title": "How to model absorption using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}