{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Source normalization\n",
    "\n",
    "In this notebook, we will exemplify the three different source normalization schemes used for [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) (as well as [GaussianBeam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianBeam.html) and [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)), [TFSF](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.TFSF.html), and [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html) sources, and how each relates to the magnitude of the electric field, $|E|$. We will also discuss the importance of monitor placement when there are forward and backward fields of nearly equal amplitude.\n",
    "\n",
    "You can find more information about source normalization on [this](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/How-are-results-normalized.html) page.\n",
    "\n",
    "## Table of contents\n",
    "1. [Simulation setup](#setup)\n",
    "2. [Plane wave and TFSF normalization](#pw_tfsf)\n",
    "3. [Custom source normalization](#customSource)\n",
    "4. [Placing a normalization monitor](#normalization)\n",
    "5. [Point dipole normalization](#dipole)\n",
    "6. [Current source normalization](#currentSource)"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup <a name=\"setup\"></a>\n",
    "\n",
    "We will first define global variables and an auxiliary function for returning the simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global variables\n",
    "freq0 = td.C_0 / 1.5\n",
    "fwidth = 0.1 * freq0\n",
    "source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auxiliary function\n",
    "def get_sim(\n",
    "    size=1,\n",
    "    source_type=\"PW\",\n",
    "    theta_degree=0,\n",
    "    min_steps_per_wvl=20,\n",
    "    bloch_medium=td.Medium(permittivity=1),\n",
    "    pol_angle_degree=0,\n",
    "    structures=[],\n",
    "):\n",
    "    \"\"\"\n",
    "    Creates and returns a Tidy3D simulation object with either a Plane Wave (PW) or TFSF source.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    size : float\n",
    "        x and y size of the simulation domain.\n",
    "    source_type : str\n",
    "        Type of source to use: \"PW\" (Plane Wave) or \"TFSF\" (Total-Field Scattered-Field).\n",
    "    theta_degree : float\n",
    "        Incident angle θ in degrees (for both PW and TFSF).\n",
    "    min_steps_per_wvl : int\n",
    "        Minimum number of grid steps per wavelength.\n",
    "    bloch_medium : td.Medium\n",
    "        Medium used for Bloch boundary condition (only for PW source).\n",
    "    pol_angle_degree : float\n",
    "        Polarization angle in degrees.\n",
    "    structures : list\n",
    "        List of structures.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    sim : td.Simulation\n",
    "        Configured Tidy3D simulation object.\n",
    "    \"\"\"\n",
    "\n",
    "    # angle to radian\n",
    "    theta = np.deg2rad(theta_degree)\n",
    "    pol_angle = np.deg2rad(pol_angle_degree)\n",
    "\n",
    "    # flux monitor\n",
    "    flux_mon = td.FluxMonitor(\n",
    "        name=\"flux_mon\",\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, 0),\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "\n",
    "    # field monitor\n",
    "    field_mon = td.FieldMonitor(\n",
    "        name=\"field_mon\",\n",
    "        size=flux_mon.size,\n",
    "        center=flux_mon.center,\n",
    "        freqs=flux_mon.freqs,\n",
    "    )\n",
    "\n",
    "    # plane wave source\n",
    "    if source_type == \"PW\":\n",
    "        sim_size = (size, size, 4)\n",
    "\n",
    "        source = td.PlaneWave(\n",
    "            center=(0, 0, -1),\n",
    "            size=(td.inf, td.inf, 0),\n",
    "            direction=\"+\",\n",
    "            source_time=source_time,\n",
    "            angle_theta=theta,\n",
    "            pol_angle=pol_angle,\n",
    "        )\n",
    "\n",
    "        # bloch boundaries.\n",
    "        bloch_x = td.Boundary.bloch_from_source(\n",
    "            source=source, domain_size=size, axis=0, medium=bloch_medium\n",
    "        )\n",
    "\n",
    "        bloch_y = td.Boundary.bloch_from_source(\n",
    "            source=source, domain_size=size, axis=1, medium=bloch_medium\n",
    "        )\n",
    "\n",
    "        bspec = td.BoundarySpec(x=bloch_x, y=bloch_y, z=td.Boundary.pml())\n",
    "\n",
    "        grid_spec = td.GridSpec.auto(wavelength=1.5, min_steps_per_wvl=min_steps_per_wvl)\n",
    "\n",
    "    # TFSF source\n",
    "    elif source_type == \"TFSF\":\n",
    "        sim_size = (size + 2, size + 2, 4)\n",
    "\n",
    "        source = td.TFSF(\n",
    "            center=(0, 0, 0),\n",
    "            size=(size, size, 2),\n",
    "            direction=\"+\",\n",
    "            source_time=source_time,\n",
    "            injection_axis=2,\n",
    "            angle_theta=theta,\n",
    "            pol_angle=pol_angle,\n",
    "        )\n",
    "\n",
    "        mesh_override = td.MeshOverrideStructure(\n",
    "            geometry=td.Box(center=source.center, size=source.size),\n",
    "            dl=(0.02,) * 3,\n",
    "        )\n",
    "        grid_spec = td.GridSpec.auto(\n",
    "            wavelength=1.5, min_steps_per_wvl=min_steps_per_wvl, override_structures=[mesh_override]\n",
    "        )\n",
    "        bspec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
    "\n",
    "    else:\n",
    "        return 'source_type must be \"PW\" or \"TFSF\"'\n",
    "\n",
    "    # creating the simulation object\n",
    "    sim = td.Simulation(\n",
    "        size=sim_size,\n",
    "        boundary_spec=bspec,\n",
    "        grid_spec=grid_spec,\n",
    "        run_time=8e-12 / np.cos(theta),\n",
    "        sources=[source],\n",
    "        monitors=[flux_mon, field_mon],\n",
    "        structures=structures,\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plane wave and TFSF normalization <a name=\"pw_tfsf\"></a>\n",
    "\n",
    "There is an important difference between the normalization of the `TFSF` and `PlaneWave` sources:\n",
    "\n",
    "- The `PlaneWave` source is normalized to inject **1 W of total power**, regardless of the source plane size.\n",
    "\n",
    "$$\n",
    "P_{pw} = 1~\\text{W} = \\text{constant}\n",
    "$$\n",
    "\n",
    "- The `TFSF` source is normalized to inject **1 W/µm² of intensity**. Therefore, the **total power depends on the source plane area**.\n",
    "\n",
    "$$\n",
    "I_{TFSF} = \\frac{1}{\\text{Area}}~\\frac{\\text{W}}{\\mu\\text{m}^2}\n",
    "$$\n",
    "\n",
    "$$\n",
    "P_{TFSF} = \\text{Area}~[\\text{W}]\n",
    "$$\n",
    "\n",
    "Relating the field intensity to the electric field amplitude $|E|_0$ via the **Poynting theorem**:\n",
    "\n",
    "$$\n",
    "I = |\\langle\\vec{S}\\rangle| = \\frac{1}{2}cn\\epsilon_0 |E_0|^2~\\frac{\\text{W}}{\\mu\\text{m}^2}\n",
    "$$\n",
    "\n",
    "we see that an advantage of the `TFSF` normalization is that $|E_0|$ **does not depend on the source size**.\n",
    "\n",
    "On the other hand, for the `PlaneWave` source, $|E_0|$ is **proportional to $\\frac{1}{\\sqrt{(S_{area})}}$**, where $S_{area}$ is the source area.\n",
    "\n",
    "As a result, the `TFSF` source is often more convenient when calculating **cross sections** and other quantities that depend on $|E_0|$.\n",
    "\n",
    "We can perform a simple test to illustrate this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a batch of simulations for plane wave and TFSF sources with different sizes\n",
    "sizes = [0.5, 1, 1.5, 2, 2.5, 3]\n",
    "sims_PW = {str(i): get_sim(size=i, source_type=\"PW\") for i in sizes}\n",
    "sims_TFSF = {str(i): get_sim(size=i, source_type=\"TFSF\") for i in sizes}\n",
    "\n",
    "batch_PW = web.Batch(simulations=sims_PW, folder_name=\"plane_wave_normalization\")\n",
    "batch_TFSF = web.Batch(simulations=sims_TFSF, folder_name=\"TFSF_normalization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_pw = list(sims_PW.values())[0]\n",
    "sim_tfsf = list(sims_TFSF.values())[0]\n",
    "\n",
    "# create side-by-side subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), gridspec_kw={\"width_ratios\": [3, 1]})\n",
    "\n",
    "# plot fields at y = 0\n",
    "sim_pw.plot(y=0, ax=ax1)\n",
    "ax1.set_title(\"Plane Wave Source\")\n",
    "ax1.set_aspect(0.3)\n",
    "\n",
    "sim_tfsf.plot(y=0, ax=ax2)\n",
    "ax2.set_title(\"TFSF Source\")\n",
    "\n",
    "fig.tight_layout(pad=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e53eb89006c74954a4b7537c5d4d0c0e",
       "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\">14:41:49 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:49 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\u001b[0m tasks.                       \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\">14:42:45 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.150</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:42:45 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.150\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13abb32c5c204e18833fe7d603259de6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:42:57 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:42:57 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "237d313c918048868c5336fcb7790377",
       "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\">14:43:13 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:43:13 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\u001b[0m tasks.                       \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\">14:44:07 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.369</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:07 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.369\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "530d8642b4204c3d9b02bdbbde37612c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:19 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:19 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# running the batch\n",
    "batch_data_PW = batch_PW.run()\n",
    "batch_data_TFSF = batch_TFSF.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plane wave field\n",
    "Ex_PW = np.array(\n",
    "    [batch_data_PW[str(s)][\"field_mon\"].Ex.sel(x=0, y=0, method=\"nearest\").squeeze() for s in sizes]\n",
    ")\n",
    "Ey_PW = np.array(\n",
    "    [batch_data_PW[str(s)][\"field_mon\"].Ey.sel(x=0, y=0, method=\"nearest\").squeeze() for s in sizes]\n",
    ")\n",
    "Ez_PW = np.array(\n",
    "    [batch_data_PW[str(s)][\"field_mon\"].Ez.sel(x=0, y=0, method=\"nearest\").squeeze() for s in sizes]\n",
    ")\n",
    "E_PW = np.sqrt(np.abs(Ex_PW) ** 2 + np.abs(Ey_PW) ** 2 + np.abs(Ez_PW) ** 2)\n",
    "\n",
    "# plane wave flux\n",
    "flux_PW = [batch_data_PW[str(s)][\"flux_mon\"].flux.values.squeeze() for s in sizes]\n",
    "\n",
    "# analytical |E| field for the plane wave\n",
    "E0_PW = [np.sqrt(2 / (td.C_0 * 1 * td.EPSILON_0 * s**2)) for s in sizes]\n",
    "\n",
    "# TFSF field\n",
    "Ex_TFSF = np.array(\n",
    "    [\n",
    "        batch_data_TFSF[str(s)][\"field_mon\"].Ex.sel(x=0, y=0, method=\"nearest\").squeeze()\n",
    "        for s in sizes\n",
    "    ]\n",
    ")\n",
    "Ey_TFSF = np.array(\n",
    "    [\n",
    "        batch_data_TFSF[str(s)][\"field_mon\"].Ey.sel(x=0, y=0, method=\"nearest\").squeeze()\n",
    "        for s in sizes\n",
    "    ]\n",
    ")\n",
    "Ez_TFSF = np.array(\n",
    "    [\n",
    "        batch_data_TFSF[str(s)][\"field_mon\"].Ez.sel(x=0, y=0, method=\"nearest\").squeeze()\n",
    "        for s in sizes\n",
    "    ]\n",
    ")\n",
    "E_TFSF = np.sqrt(np.abs(Ex_TFSF) ** 2 + np.abs(Ey_TFSF) ** 2 + np.abs(Ez_TFSF) ** 2)\n",
    "\n",
    "# TFSF flux\n",
    "flux_TFSF = [batch_data_TFSF[str(s)][\"flux_mon\"].flux.values.squeeze() for s in sizes]\n",
    "\n",
    "# analytical |E| field for the TFSF source\n",
    "E0_TFSF = [np.sqrt(2 / (td.C_0 * 1 * td.EPSILON_0)) for s in sizes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the results\n",
    "fig, Ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "Ax[0].plot(sizes, flux_PW)\n",
    "Ax[0].plot(sizes, [1 for i in sizes], \"o\")\n",
    "Ax[0].set_ylim(0.9, 1.1)\n",
    "Ax[0].set_title(\"Plane Wave flux\")\n",
    "Ax[0].set_xlabel(\"Plane size µm$^2$\")\n",
    "Ax[0].set_ylabel(\"Flux\")\n",
    "\n",
    "Ax[1].plot(sizes, flux_TFSF)\n",
    "Ax[1].plot(sizes, [s**2 for s in sizes], \"o\")\n",
    "Ax[1].set_title(\"TFSF flux\")\n",
    "Ax[1].set_xlabel(\"Plane size µm$^2$\")\n",
    "Ax[1].set_ylabel(\"Flux\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the figure above, the flux of the `PlaneWave` does not vary with the plane size, whereas it does for the `TFSF`.\n",
    "\n",
    "Below, we illustrate the behavior of the electric field amplitude $|E_0|$, which exhibits the opposite trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, Ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "Ax[0].plot(sizes, E_PW)\n",
    "Ax[0].plot(sizes, E0_PW, \"o\")\n",
    "\n",
    "Ax[0].set_title(\"Plane Wave |E|\")\n",
    "Ax[0].set_xlabel(\"Plane size µm$^2$\")\n",
    "Ax[0].set_ylabel(\"|E| (V/µm)\")\n",
    "\n",
    "Ax[1].plot(sizes, E_TFSF)\n",
    "Ax[1].plot(sizes, E0_TFSF, \"o\")\n",
    "Ax[1].set_title(\"TFSF |E|\")\n",
    "Ax[1].set_xlabel(\"Plane size µm$^2$\")\n",
    "Ax[1].set_ylabel(\"|E| (V/µm)\")\n",
    "Ax[1].set_ylim(20, 30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Field Source <a name=\"normalization\"></a>\n",
    "\n",
    "The [`CustomFieldSource`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) object allows users to inject a planar field distribution directly into the simulation. More information on how to set up a `CustomFieldSource` can be found [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/how-do-i-set-a-custom-field-source.html).\n",
    "\n",
    "For this type of source, **no automatic normalization** is applied, so the total power is determined by the source definition.\n",
    "\n",
    "To illustrate this behavior, we will consider a simple x-polarized plane wave source. The fields are defined as:\n",
    "\n",
    "$E_x = E_0e^{i(kz + \\phi)}e^{-\\omega t }$\n",
    "\n",
    "$H_y = \\frac{E_0}{\\eta_0}e^{i(kz + \\phi)}e^{-\\omega t }$\n",
    "\n",
    "As a result, the total power radiated by the source is determined by the value of $E_0$, as discussed in the previous section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x and y coordinates\n",
    "x = np.linspace(-0.5, 0.5, 1001)\n",
    "y = x\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "# central wavelength and E_0 definition\n",
    "wl0 = td.C_0 / freq0\n",
    "k = 2 * np.pi / wl0\n",
    "E0 = 1\n",
    "\n",
    "# field data definition\n",
    "Ex_data = E0 * np.ones(X.shape) * np.exp(-1j * k)\n",
    "Hy_data = (E0 / td.ETA_0) * np.ones(X.shape) * np.exp(-1j * k)\n",
    "\n",
    "# FieldDataset definition\n",
    "coords = {\"x\": x, \"y\": y, \"z\": [-1], \"f\": [freq0]}\n",
    "\n",
    "Ex = td.ScalarFieldDataArray(Ex_data[..., None, None], coords=coords)\n",
    "Hy = td.ScalarFieldDataArray(Hy_data[..., None, None], coords=coords)\n",
    "\n",
    "dataset = td.FieldDataset(Ex=Ex, Hy=Hy)\n",
    "\n",
    "# creating the CustomFieldSource\n",
    "custom_src = td.CustomFieldSource(\n",
    "    source_time=source_time,\n",
    "    center=(0, 0, -1),\n",
    "    size=(1, 1, 0),\n",
    "    field_dataset=dataset,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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\">14:44:22 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_custom_source'</span> with resource_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:22 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_custom_source'\u001b[0m with resource_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e'\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-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-49af-9e32-28c5332cea3e'</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=51645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=194918;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=51645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=554441;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=51645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32m-8a94e6c0-7fe\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=51645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[32m5-49af-9e32-28c5332cea3e'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=757968;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd1fed67b195440fb9aefcc151e1b10a",
       "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\">14:44:32 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:32 UTC\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:34 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:34 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">14:44:48 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:48 UTC\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:49 UTC </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:49 UTC\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd0ff984ec1d4598ae1bb35fda502b4f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:50 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:50 UTC\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:51 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:51 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">5-49af-9e32-28c5332cea3e'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=578842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=239693;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=578842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=61995;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=578842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34m-8a94e6c0-7fe\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=578842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a94e6c0-7fe5-49af-9e32-28c5332cea3e\u001b\\\u001b[4;34m5-49af-9e32-28c5332cea3e'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03cdeb49853c4a5a8b7c5cdfc5bad4af",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:44:53 UTC </span>Loading results from simulation_data.hdf5                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:44:53 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from simulation_data.hdf5                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run the simulation with the custom source\n",
    "sim = get_sim()\n",
    "\n",
    "xz_mon = td.FieldMonitor(size=(td.inf, 0, td.inf), center=(0, 0, 0), name=\"xz_mon\", freqs=[freq0])\n",
    "\n",
    "sim_custom_source = sim.updated_copy(sources=[custom_src], monitors=list(sim.monitors) + [xz_mon])\n",
    "\n",
    "\n",
    "sim_data_custom_source = web.run(sim_custom_source, \"sim_custom_source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = sim_data_custom_source[\"flux_mon\"].flux\n",
    "E = np.sqrt(\n",
    "    np.abs(sim_data_custom_source[\"field_mon\"].Ex) ** 2\n",
    "    + np.abs(sim_data_custom_source[\"field_mon\"].Ey) ** 2\n",
    "    + np.abs(sim_data_custom_source[\"field_mon\"].Ez) ** 2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, no additional normalization is applied, and the total flux closely matches the analytical expression for the given value of $E_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flux:\t\t\t0.00134 [W]\n",
      "Flux analytic:\t\t0.00133 [W]\n",
      "E0:\t\t\t1.00918 [V/µm]\n"
     ]
    }
   ],
   "source": [
    "# analytic flux\n",
    "analytic_flux = (1 / 2) * td.C_0 * 1 * td.EPSILON_0 * abs(E0) ** 2\n",
    "print(f\"Flux:\\t\\t\\t{flux[0]:.5f} [W]\")\n",
    "print(f\"Flux analytic:\\t\\t{analytic_flux:.5f} [W]\")\n",
    "print(f\"E0:\\t\\t\\t{E.max():.5f} [V/µm]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monitor Placement <a name=\"normalization\"></a>\n",
    "\n",
    "When dealing with forward and backward waves, it is important to carefully consider the monitor type and its position, specially at moderate resolutions (around 10 steps per wavelength). When using a `DiffractionMonitor`, as demonstrated below, the error increases when the monitor needs to resolve forward and backward propagating fields with similar magnitudes.\n",
    "\n",
    "Therefore, for highly reflective structures, the correct placement of the `DiffractionMonitor` is **behind** the source, where the forward-propagating field dominates. This helps minimize errors in the normalization process. \n",
    "\n",
    "Here, we illustrate this issue using a plane wave incident on a dielectric interface at oblique incidence, varying the angle of incidence until the condition for total internal reflection (TIR) is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# medium for the dielectric interface\n",
    "medium = td.Medium(permittivity=3.5**2)\n",
    "\n",
    "# defining FluxMonitors and DiffractionMonitors for computing the flux\n",
    "structure = td.Structure(\n",
    "    geometry=td.Box.from_bounds(rmin=(-100, -100, -100), rmax=(100, 100, 0)), medium=medium\n",
    ")\n",
    "# flux monitor behind the source\n",
    "flux_mon_back = td.FluxMonitor(\n",
    "    name=\"flux_mon_back\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -1.5),\n",
    "    freqs=[freq0],\n",
    ")\n",
    "# flux monitor in front of the source\n",
    "flux_mon_front = td.FluxMonitor(\n",
    "    name=\"flux_mon_front\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -0.5),\n",
    "    freqs=[freq0],\n",
    ")\n",
    "\n",
    "# diffraction monitor behind the source\n",
    "diff_mon_back = td.DiffractionMonitor(\n",
    "    name=\"diff_mon_back\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -1.5),\n",
    "    freqs=[freq0],\n",
    "    normal_dir=\"-\",\n",
    ")\n",
    "# diffraction monitor in front of the source\n",
    "diff_mon_front = td.DiffractionMonitor(\n",
    "    name=\"diff_mon_front\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -0.5),\n",
    "    freqs=[freq0],\n",
    "    normal_dir=\"-\",\n",
    ")\n",
    "\n",
    "# normalization monitor in front of the source\n",
    "norm_monitor = td.DiffractionMonitor(\n",
    "    name=\"norm_monitor\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, -0.5),\n",
    "    freqs=[freq0],\n",
    ")\n",
    "\n",
    "monitors = [flux_mon_back, flux_mon_front, diff_mon_back, diff_mon_front, norm_monitor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create simulations for different angles until total internal reflection\n",
    "thetas2 = [0, 5, 10, 15, 20, 25]\n",
    "\n",
    "sims_PW_ref = {}\n",
    "\n",
    "for t in thetas2:\n",
    "    sim = get_sim(\n",
    "        source_type=\"PW\",\n",
    "        theta_degree=t,\n",
    "        bloch_medium=medium,\n",
    "        pol_angle_degree=90,\n",
    "        structures=[structure],\n",
    "        min_steps_per_wvl=10,\n",
    "    )\n",
    "    sims_PW_ref[str(t)] = sim.updated_copy(monitors=monitors)\n",
    "\n",
    "batch_PW_ref = web.Batch(simulations=sims_PW_ref, folder_name=\"plane_wave_ref\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the simulation setup\n",
    "sims_PW_ref[\"20\"].plot(y=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ceaa195497f14365825bf3fe2e8e6661",
       "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\">14:45:06 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:45:06 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\u001b[0m tasks.                       \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\">14:45:57 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.150</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:45:57 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.150\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c18e1fa4a1f47aa8ed807542c853770",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:46:09 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:46:09 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# running the batch\n",
    "batch_data_PW_ref = batch_PW_ref.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for calculating the analytical Fresnel reflection coefficient for s-polarized light\n",
    "def fresnel_rs(n1, n2, theta_i_deg):\n",
    "    theta_i = np.radians(theta_i_deg)\n",
    "    theta_t = np.arcsin(n1 * np.sin(theta_i) / n2 + 0j)\n",
    "    r_s = (n1 * np.cos(theta_i) - n2 * np.cos(theta_t)) / (\n",
    "        n1 * np.cos(theta_i) + n2 * np.cos(theta_t)\n",
    "    )\n",
    "\n",
    "    return abs(r_s) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computing the reflection\n",
    "reflection_analytic = fresnel_rs(3.5, 1, thetas2)\n",
    "\n",
    "flux_front = np.array([batch_data_PW_ref[str(t)][\"flux_mon_front\"].flux for t in thetas2]).flatten()\n",
    "flux_back = np.array([batch_data_PW_ref[str(t)][\"flux_mon_back\"].flux for t in thetas2]).flatten()\n",
    "\n",
    "diff_front = np.array(\n",
    "    [batch_data_PW_ref[str(t)][\"diff_mon_front\"].power.sum() for t in thetas2]\n",
    ").flatten()\n",
    "diff_back = np.array(\n",
    "    [batch_data_PW_ref[str(t)][\"diff_mon_back\"].power.sum() for t in thetas2]\n",
    ").flatten()\n",
    "\n",
    "norm = np.array([batch_data_PW_ref[str(t)][\"norm_monitor\"].power.sum() for t in thetas2]).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, when TIR occurs, the error for the `DiffractionMonitor` placed in front of the source increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the results\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(thetas2, 1 - flux_front, \"o\", label=\"flux monitor - front\")\n",
    "ax.plot(thetas2, -flux_back, \"o\", label=\"flux monitor - behind\")\n",
    "\n",
    "ax.plot(thetas2, diff_front, \"*\", label=\"Diff monitor - front\")\n",
    "\n",
    "ax.plot(thetas2, diff_back, \"^\", label=\"Diff monitor - behind\")\n",
    "\n",
    "ax.plot(thetas2, reflection_analytic, label=\"analytical\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.set_ylabel(\"R\")\n",
    "ax.set_xlabel(\"$\\\\theta$ (degrees)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the relative error across the different monitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# define consistent colors\n",
    "colors = {\n",
    "    \"diff_front\": \"tab:blue\",\n",
    "    \"diff_back\": \"tab:orange\",\n",
    "    \"flux_front\": \"tab:green\",\n",
    "    \"flux_back\": \"tab:red\",\n",
    "}\n",
    "\n",
    "\n",
    "def plot_errors(ax):\n",
    "    ax.plot(\n",
    "        thetas2,\n",
    "        abs(reflection_analytic - diff_front),\n",
    "        \"-\",\n",
    "        label=\"diffraction monitor front\",\n",
    "        color=colors[\"diff_front\"],\n",
    "    )\n",
    "    ax.plot(\n",
    "        thetas2,\n",
    "        abs(reflection_analytic - diff_back),\n",
    "        \"--\",\n",
    "        label=\"diffraction monitor back\",\n",
    "        color=colors[\"diff_back\"],\n",
    "    )\n",
    "    ax.plot(\n",
    "        thetas2,\n",
    "        abs(reflection_analytic - (1 - flux_front)),\n",
    "        \":\",\n",
    "        label=\"flux monitor front\",\n",
    "        color=colors[\"flux_front\"],\n",
    "    )\n",
    "    ax.plot(\n",
    "        thetas2,\n",
    "        abs(reflection_analytic - (-flux_back)),\n",
    "        \"-.\",\n",
    "        label=\"flux monitor back\",\n",
    "        color=colors[\"flux_back\"],\n",
    "    )\n",
    "    ax.set_xlabel(\"$\\\\theta$ (degrees)\")\n",
    "    ax.legend()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "plot_errors(axes[0])\n",
    "axes[0].set_ylabel(\"absolute error\")\n",
    "axes[1].set_ylim(-0.005, 0.029)\n",
    "\n",
    "plot_errors(axes[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown, the `DiffractionMonitor` exhibits high error when used for normalization in the presence of forward- and backward-propagating fields of equal magnitude. In contrast, the `FluxMonitor` maintains an error below 1% under the same conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "ax1 = axes[0]\n",
    "ax1.plot(\n",
    "    thetas2, norm, \"--o\", label=\"normalization - DiffractionMonitor\", color=colors[\"diff_front\"]\n",
    ")\n",
    "ax1.plot(\n",
    "    thetas2,\n",
    "    flux_front - flux_back,\n",
    "    \"--*\",\n",
    "    label=\"normalization - FluxMonitor\",\n",
    "    color=colors[\"flux_front\"],\n",
    ")\n",
    "ax1.plot(thetas2, [1 for _ in thetas2], label=\"Analytical normalization\", color=colors[\"flux_back\"])\n",
    "ax1.set_xlabel(\"$\\\\theta$ (degrees)\")\n",
    "ax1.set_ylabel(\"Normalization\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = axes[1]\n",
    "ax2.plot(\n",
    "    thetas2, norm, \"--o\", label=\"normalization - DiffractionMonitor\", color=colors[\"diff_front\"]\n",
    ")\n",
    "ax2.plot(\n",
    "    thetas2,\n",
    "    flux_front - flux_back,\n",
    "    \"--*\",\n",
    "    label=\"normalization - FluxMonitor\",\n",
    "    color=colors[\"flux_front\"],\n",
    ")\n",
    "ax2.plot(thetas2, [1 for _ in thetas2], label=\"Analytical normalization\", color=colors[\"flux_back\"])\n",
    "ax2.set_xlabel(\"$\\\\theta$ (degrees)\")\n",
    "ax2.set_ylim(0.99, 1.01)\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increasing resolution\n",
    "\n",
    "Now, we investigate the relative error for the `DiffractionMonitor` and `FluxMonitor` as a function of the resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create simulations for different angles until total internal reflection\n",
    "resolutions_pw = [10, 20, 30, 40]\n",
    "sims_PW_res = {}\n",
    "\n",
    "for r in resolutions_pw:\n",
    "    sim = get_sim(\n",
    "        source_type=\"PW\",\n",
    "        theta_degree=20,\n",
    "        bloch_medium=medium,\n",
    "        pol_angle_degree=90,\n",
    "        structures=[structure],\n",
    "        min_steps_per_wvl=r,\n",
    "    )\n",
    "    sims_PW_res[str(r)] = sim.updated_copy(monitors=monitors)\n",
    "\n",
    "batch_PW_res = web.Batch(simulations=sims_PW_res, folder_name=\"plane_wave_ref\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef3cfb02849d46f29623393d3a9dd8db",
       "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": {
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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\">14:46:25 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:46:25 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m4\u001b[0m tasks.                       \n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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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\">14:47:06 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.892</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:47:06 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.892\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "064da9d613184a02adf812c83fe54424",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:47:39 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:47:39 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data_PW_res = batch_PW_res.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computing the reflection\n",
    "flux_front_res = np.array(\n",
    "    [batch_data_PW_res[str(r)][\"flux_mon_front\"].flux for r in resolutions_pw]\n",
    ").flatten()\n",
    "diff_front_res = np.array(\n",
    "    [batch_data_PW_res[str(r)][\"diff_mon_front\"].power.sum() for r in resolutions_pw]\n",
    ").flatten()\n",
    "\n",
    "flux_back_res = np.array(\n",
    "    [batch_data_PW_res[str(r)][\"flux_mon_back\"].flux for r in resolutions_pw]\n",
    ").flatten()\n",
    "diff_back_res = np.array(\n",
    "    [batch_data_PW_res[str(r)][\"diff_mon_back\"].power.sum() for r in resolutions_pw]\n",
    ").flatten()\n",
    "\n",
    "error_flux_front = [abs(i - reflection_analytic[-1]) for i in (1 - flux_front_res)]\n",
    "error_diff_front = [abs(i - reflection_analytic[-1]) for i in diff_front_res]\n",
    "\n",
    "error_flux_back = [abs(i - reflection_analytic[-1]) for i in (-flux_back_res)]\n",
    "error_diff_back = [abs(i - reflection_analytic[-1]) for i in diff_back_res]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error for the `DiffractionMonitor` placed in front of the source rapidly decreases as the resolution increases, but it remains slightly higher than that of the `FluxMonitor`. In contrast, the `DiffractionMonitor` placed behind the source shows less than 1% error even at modest resolutions, becoming very similar to the `FluxMonitor` at slightly higher resolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "# plot errors for monitors in front of the source\n",
    "ax1.plot(resolutions_pw, error_diff_front, \"--o\", label=\"error - DiffractionMonitor\")\n",
    "ax1.plot(resolutions_pw, error_flux_front, \"--*\", label=\"error - FluxMonitor\")\n",
    "ax1.set_title(\"Monitors in Front of Source\")\n",
    "ax1.set_xlabel(\"Resolution\")\n",
    "ax1.set_ylabel(\"Error\")\n",
    "ax1.legend()\n",
    "\n",
    "# plot errors for monitors behind the source\n",
    "ax2.plot(resolutions_pw, error_diff_back, \"--o\", label=\"error - DiffractionMonitor\")\n",
    "ax2.plot(resolutions_pw, error_flux_back, \"--*\", label=\"error - FluxMonitor\")\n",
    "ax2.set_title(\"Monitors Behind Source\")\n",
    "ax2.set_xlabel(\"Resolution\")\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, the `DiffractionMonitor` is effective for calculating diffraction orders and flux when positioned appropriately. However, caution is needed in setups with high reflectance, where its location becomes critical.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dipole Source <a name=\"dipole\"></a>\n",
    "\n",
    "The [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html) follows a different normalization scheme compared to other sources. Its spectral power is governed by the analytical expression for a infinitesimal antenna with a fixed current density, which is **not constant with respect to frequency.**\n",
    "\n",
    "The frequency dependence of the radiated power for a 3D electric current dipole follows:\n",
    "\n",
    "$$P(\\nu) = \\frac{\\mu_0 n}{12\\pi c} \\omega^2$$\n",
    "where:\n",
    "- $\\mu_0$ is the permeability of free space,\n",
    "- $n$ is the refractive index of the medium,\n",
    "- $c$ is the speed of light,\n",
    "- $\\omega$ is the angular frequency.\n",
    "\n",
    "For a magnetic current dipole or lower-dimensional sources (2D and 1D), the equation differs slightly. You can find more details in our [documentation](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/How-are-results-normalized.html).\n",
    "\n",
    "\n",
    "Unlike sources like plane waves or total-field scattered-field (TFSF) sources, which are typically normalized through their power or amplitude across frequency, the dipole source spectrum reflects the physical reality of how an oscillating dipole behaves.\n",
    "\n",
    "To illustrate, we will create a model with a `PointDipole` with $E_z$ polarization, placed at the center of the simulation domain, surrounded by `FluxMonitor` to compute the irradiated flux.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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\">14:47:43 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'point_dipole'</span> with resource_id                       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:47:43 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'point_dipole'\u001b[0m with resource_id                       \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </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-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4380-b88c-b90b1b27e595'</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=826242;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=88486;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=826242;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=546561;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=826242;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32m-5ab0aa0f-e45\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=826242;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[32mc-4380-b88c-b90b1b27e595'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=38766;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0daade2dd8ce4b94bc457c37f3692a8d",
       "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\">14:47:52 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.283</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;36m14:47:52 UTC\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.283\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\">14:47:54 UTC </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:47:54 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">14:48:03 UTC </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:03 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:48:08 UTC </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:08 UTC\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:48:09 UTC </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:09 UTC\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94ff3332a5ee4e7e843633d7bbdae713",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:48:11 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:11 UTC\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">c-4380-b88c-b90b1b27e595'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=124865;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=118471;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=124865;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=459881;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=124865;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34m-5ab0aa0f-e45\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=124865;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5ab0aa0f-e45c-4380-b88c-b90b1b27e595\u001b\\\u001b[4;34mc-4380-b88c-b90b1b27e595'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "140a9b32f61d4d689c8bcbea079c4f77",
       "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\">14:48:13 UTC </span>Loading results from simulation_data.hdf5                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:13 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from simulation_data.hdf5                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dipole source\n",
    "dipole = td.PointDipole(center=(0, 0, 0), source_time=source_time, polarization=\"Ez\")\n",
    "\n",
    "freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 101)\n",
    "\n",
    "# flux monitor around the dipole source\n",
    "flux_mon = td.FluxMonitor(\n",
    "    name=\"flux_mon\",\n",
    "    size=(1, 1, 1),\n",
    "    center=(0, 0, 0),\n",
    "    freqs=freqs,\n",
    ")\n",
    "sim_pd = get_sim(size=3, source_type=\"TFSF\")\n",
    "sim_pd = sim_pd.updated_copy(size=(3, 3, 3), sources=[dipole], monitors=[flux_mon])\n",
    "sim_data_pd = web.run(sim_pd, \"point_dipole\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the results\n",
    "flux = [(td.MU_0 / (12 * np.pi * td.C_0)) * (2 * np.pi * f) ** 2 for f in freqs]\n",
    "wvls = td.C_0 / freqs\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(wvls, flux, \"o\", label=\"Power - analytical\")\n",
    "ax.plot(wvls, sim_data_pd[\"flux_mon\"].flux, label=\"Power - simulation\")\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Wavelength (µm)\")\n",
    "ax.set_ylabel(\"Flux\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Current Source <a name=\"currentSource\"></a> \n",
    "\n",
    "For a finite-size current source, the analytical expression becomes more complex, hence a normalization run might be necessary in some cases. \n",
    "\n",
    "However, an approximation for small-area current dipoles can be derived, as described in Chapter 4.5 of the book `Balanis, C. A. (2005). Antenna Theory: Analysis and Design (3rd ed.). Wiley‑Interscience`:\n",
    "\n",
    "$$\n",
    "P = \\eta \\frac{I^2}{8\\pi^2} \\int\\!\\!\\int \\frac{\\left[\\cos\\left(\\frac{kl}{2} \\cos\\theta\\right) - \\cos\\left(\\frac{kl}{2}\\right)\\right]^2}{\\sin(\\theta)} \\, d\\theta \\, d\\phi\n",
    "$$\n",
    "\n",
    "To compare the emitted flux with the analytical expression, we need to compute the total current using Faraday's Law of Induction.\n",
    "\n",
    "To illustrate this, we sweep across a range of source areas and compute both the analytical and simulated power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for calculating the current\n",
    "def faraday_law(monitor):\n",
    "    Hx = monitor.Hx\n",
    "    Hy = monitor.Hy\n",
    "\n",
    "    # integrate over a closed circuit\n",
    "    current = -(\n",
    "        -Hx.sel(y=Hx.y.max()).squeeze().integrate(\"x\")\n",
    "        + -Hy.sel(x=Hy.x.min()).squeeze().integrate(\"y\")\n",
    "        + Hx.sel(y=Hx.y.min()).squeeze().integrate(\"x\")\n",
    "        + Hy.sel(x=Hy.x.max()).squeeze().integrate(\"y\")\n",
    "    )\n",
    "\n",
    "    return current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create and run the simulation batch\n",
    "sims_cs = {}\n",
    "source_sizes = [\n",
    "    0.2,\n",
    "    0.4,\n",
    "    0.6,\n",
    "    0.8,\n",
    "    1,\n",
    "]\n",
    "for i in source_sizes:\n",
    "    current_source = td.UniformCurrentSource(\n",
    "        center=(0, 0, 0),\n",
    "        source_time=source_time,\n",
    "        polarization=\"Ez\",\n",
    "        size=(i, i, 1),\n",
    "        current_amplitude_definition=\"density\",\n",
    "    )\n",
    "\n",
    "    # flux monitor around the dipole source\n",
    "    flux_mon = td.FluxMonitor(\n",
    "        name=\"flux_mon\",\n",
    "        size=(2, 2, 2),\n",
    "        center=(0, 0, 0),\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "\n",
    "    # field monitor for calculating the current\n",
    "    current_monitor = td.FieldMonitor(\n",
    "        name=\"current_monitor\", size=(3, 3, 0), center=(0, 0, 0), freqs=[freq0]\n",
    "    )\n",
    "\n",
    "    sim_cs = get_sim(size=3, source_type=\"TFSF\")\n",
    "    sim_cs = sim_cs.updated_copy(\n",
    "        size=(4.5, 4.5, 4.5), sources=[current_source], monitors=[flux_mon, current_monitor]\n",
    "    )\n",
    "    sims_cs[str(i)] = sim_cs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_cs.plot(x=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc907839f20b48bc95506952e37bd5a5",
       "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\">14:48:24 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:48:24 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \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\">14:49:06 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.122</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:49:06 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m2.122\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c26bb89555e463f9aca1c1bff8d48e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:49:16 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:49:16 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_cs = web.Batch(simulations=sims_cs, folder_name=\"UniformCurrentSource\")\n",
    "batch_cs_data = batch_cs.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "currents = []\n",
    "fluxes = []\n",
    "\n",
    "\n",
    "for i in source_sizes:\n",
    "    sim_data = batch_cs_data[str(i)]\n",
    "    fluxes.append(sim_data[\"flux_mon\"].flux)\n",
    "    currents.append(faraday_law(sim_data[\"current_monitor\"]))\n",
    "\n",
    "# calculate the analytical power\n",
    "\n",
    "from scipy.integrate import quad\n",
    "\n",
    "wl0 = td.C_0 / freq0\n",
    "kl = 2 * np.pi * wl0 * 1\n",
    "func = lambda theta: (np.cos((kl / 2) * np.cos(theta)) + np.cos(kl / 2)) ** 2 / np.sin(theta)\n",
    "integral = 2 * np.pi * quad(func, 0, np.pi)[0]\n",
    "\n",
    "analytic_power = [((td.ETA_0 * np.abs(I) ** 2) / (8 * np.pi**2)) * integral for I in currents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the computed flux follows the analytical expression for small-area sources, but the error increases for larger areas where the approximation no longer holds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(source_sizes, fluxes)\n",
    "ax.plot(source_sizes, analytic_power, \"o\")\n",
    "\n",
    "ax.set_xlabel(\"Source plane size [µm]\")\n",
    "ax.set_ylabel(\"Power [W]\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Dimensional Current Source\n",
    "\n",
    "For 2D and 1D sources, a normalization is applied to ensure that the emitted power remains independent of the simulation resolution. \n",
    "\n",
    "This can be illustrated by simulating a line source at different resolutions and comparing the emitted power in each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d7bcfee87204bfcb11ad1a51fdc5a8b",
       "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\">14:49:29 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:49:29 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \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\">14:50:11 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.569</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:11 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m2.569\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8b7f287143245379aef580f91f712b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:50:21 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:21 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resolutions = [10, 20, 30, 40, 50]\n",
    "sims_cs_0 = {}\n",
    "\n",
    "for i in resolutions:\n",
    "    current_source = td.UniformCurrentSource(\n",
    "        center=(0, 0, 0),\n",
    "        source_time=source_time,\n",
    "        polarization=\"Ez\",\n",
    "        size=(0, 0, 1),\n",
    "        current_amplitude_definition=\"density\",\n",
    "    )\n",
    "\n",
    "    sim_cs_0 = get_sim(size=3, source_type=\"TFSF\", min_steps_per_wvl=i)\n",
    "    sim_cs_0 = sim_cs_0.updated_copy(\n",
    "        size=(4.5, 4.5, 4.5), sources=[current_source], monitors=[flux_mon, current_monitor]\n",
    "    )\n",
    "    sims_cs_0[str(i)] = sim_cs_0\n",
    "\n",
    "batch_cs_0 = web.Batch(simulations=sims_cs_0, folder_name=\"UniformCurrentSource_0\")\n",
    "batch_cs_data_0 = batch_cs_0.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "currents_0 = []\n",
    "fluxes_0 = []\n",
    "\n",
    "for i in resolutions:\n",
    "    sim_data = batch_cs_data_0[str(i)]\n",
    "    fluxes_0.append(sim_data[\"flux_mon\"].flux)\n",
    "    currents_0.append(faraday_law(sim_data[\"current_monitor\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the results are close to the approximated analytical expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "analytic_power_0 = [((td.ETA_0 * np.abs(I) ** 2) / (8 * np.pi**2)) * integral for I in currents_0]\n",
    "\n",
    "ax.plot(resolutions, fluxes_0)\n",
    "ax.plot(resolutions, analytic_power_0, \"o\")\n",
    "\n",
    "ax.set_xlabel(\"Resolution\")\n",
    "ax.set_ylabel(\"Power [W]\")\n",
    "\n",
    "ax.set_ylim(120, 150)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, although an approximate expression can be derived for the power emitted by a `UniformCurrentSource`, most practical cases do not satisfy the approximation assumptions. Therefore, if accurate normalization is required, a normalization run should be performed to directly compute the emitted power under the simulation setup.\n"
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates the different source normalization schemes and their relation to the magnitude of the electric field using Tidy3D.",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "source normalization, TFSD, plane wave, simulation setup, 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": "3.11.2"
  },
  "title": "Understanding source normalization in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}