{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Simulating the Beer-Lambert Law with Tidy3D\n",
        "\n",
        "In this tutorial, we demonstrate how to translate the absorption\n",
        "coefficient parameter ($\\alpha$ \\[$\\text{cm}^{-1}$\\]) into a Tidy3D\n",
        "[Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html)\n",
        "and calculate volumetric power absorption using a 3D\n",
        "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
        "and\n",
        "[PermittivityMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html)."
      ],
      "id": "4560e580-2803-49f5-8321-f3397c2aa111"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "0d53c9b6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Creating a Lossy Medium\n",
        "\n",
        "Tidy3D follows the $e^{-\\omega t}$ convention, in which losses are\n",
        "represented by a positive imaginary part of the refractive index, here\n",
        "denoted as $k$:\n",
        "\n",
        "$\\tilde{n} = n + i k$\n",
        "\n",
        "We can relate $k$ to the absorption coefficient from the Beer-Lambert\n",
        "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\n",
        "microns and $\\alpha$ in cm⁻¹)\n",
        "\n",
        "An important consideration is that Tidy3D uses microns (µm) as distance\n",
        "units, while $\\alpha$ is generally given in cm⁻¹.\n",
        "\n",
        "With the $k$ value, we can define a lossy medium using the\n",
        "[`td.Medium.from_nk`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html#tidy3d.Medium.from_nk)\n",
        "method.\n",
        "\n",
        "Let’s consider a medium with a real refractive index of 3 and\n",
        "$\\alpha = 0.5$ $µ\\text{m}^{-1}$."
      ],
      "id": "f092c98d-0435-4894-ae03-65199835e166"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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)"
      ],
      "id": "b20df076"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Relating *k* with Conductivity\n",
        "\n",
        "Alternatively, we can relate the complex refractive index with the\n",
        "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",
        "$\\sigma(\\omega) = \\frac{4\\pi \\varepsilon_0 c}{\\lambda} n \\kappa$\n",
        "\\[S/µm\\]\n",
        "\n",
        "Again, following Tidy3D’s unit convention, the **conductivity is in\n",
        "units of S/µm.**\n",
        "\n",
        "With the conductivity value, we can create the same medium using the\n",
        "regular\n",
        "[td.Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Medium.html)\n",
        "class.\n",
        "\n",
        "As we can see, the two methods are equivalent."
      ],
      "id": "18af9ee5-539f-43a8-b989-ae65219d93b6"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "medium  conductivity: 0.003982\n",
            "medium2 conductivity: 0.003982"
          ]
        }
      ],
      "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}\")"
      ],
      "id": "38b042e9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulating the Beer-Lambert Law\n",
        "\n",
        "With the lossy medium defined, we can use it as the background medium in\n",
        "a simulation and calculate the absorbed power using the following\n",
        "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."
      ],
      "id": "43f8952a-6139-4492-991d-ca49c13916f3"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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",
        ")"
      ],
      "id": "dfdc43ba"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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t5N4Ez6effqpdu3ZJkj0BNlAIdwCNqqioSPfcc4/988SJExuxNwiGsLAw3Xvv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\n7jrtNc1Ur6mOupo8ebLuvPNOOZ1OLVmyRDfddFO1/fDludenH0BTRrj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AAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "# Visual check of the structure cross-section at y = 0\n",
        "sim.plot(y=0)\n",
        "plt.show()"
      ],
      "id": "55d299e6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can run the simulation and process the data."
      ],
      "id": "bb8840ae-3dab-4e30-b428-ed529fbc0bdf"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">02:58:35 UTC </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-50374140-48e7-4388-8e64-05cd010bae1a'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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-50374140-48e7-4388-8e64-05cd010bae1a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-50374140-48e</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-50374140-48e7-4388-8e64-05cd010bae1a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">7-4388-8e64-05cd010bae1a'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"39909ce31ca64c56ba1f66dab85601a8\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">02:58:37 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.026</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">02:58:38 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"0782a42213b44fa68ae8db3d40fb38c0\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">02:58:39 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# Submit the simulation to Tidy3D Cloud\n",
        "sim_data = web.run(sim, task_name=\"absorption_calculation\")"
      ],
      "id": "ba82fe17"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Processing\n",
        "\n",
        "First, let’s plot the fields, where we can observe an exponential decay\n",
        "in the field intensity."
      ],
      "id": "01ed804a-18b5-4df2-b6c4-b5a8c2cba9ac"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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l2QUg7DVr1kxTpkzRFVdcIUnW/JhxcXH64IMP/HrhvVWrVn6ryylvv/12kedn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\nTLdu3ax6nn32WT9G+YepU6d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FFVu2UqVKkqTU1FSZpimjqFvtAQCo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MxV9QsOsCokwP8KPxk+es9Wi0VWL3ganCfSAQC4es6LN+2CS5GYgmpcqY5W7vjV1fbV+u81ssvj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\n7dmg9Qe2KyM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          }
        }
      ],
      "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()"
      ],
      "id": "a7f22816"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, we can calculate the volumetric absorbed power, using the following\n",
        "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",
        "$I = I_0 e^{-\\alpha z}$"
      ],
      "id": "6dee6854-5c3f-493b-8ccf-fe5f3f5af45b"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total absorbed power: 0.99\n",
            "Analytical absorbed power: 0.99"
          ]
        }
      ],
      "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}\")"
      ],
      "id": "5e9e7e96"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finally, we compute the cumulative integral of the volumetric absorbed\n",
        "power along the full z-direction and directly compare the resulting\n",
        "energy loss with the analytical prediction given by the Beer–Lambert\n",
        "law."
      ],
      "id": "ba2882ec-128f-4c97-88f3-e3ab5d030bd3"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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9e7V06VJJ0tlnn62//vWvNfpADR8+XH/7298kSUVFRVq8ePHB/kpo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\nQ3W+VrIsj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          }
        }
      ],
      "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()"
      ],
      "id": "c32abd0b"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}