{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0667fa3c",
   "metadata": {},
   "source": [
    "# Defining spatially-varying sources\n",
    "\n",
    "In this example we illustrate how to incorporate a source with a custom spatial dependence in a Tidy3D simulation. We will illustrate this both using data taken from a monitor from another Tidy3D simulation and using a simple array defined from scratch. This notebook will cover both custom \"field\" sources and custom \"current\" sources.\n",
    "\n",
    "The custom field source can be used to inject a specific (E, H) field distribution on a plane, e.g. coming from another simulation. Internally, we use the equivalence principle to compute the actual source currents (all sources in FDTD have to be converted to current sources). Because of this, the custom field source will only produce reliable results if the provided fields decay by the edges of the source plane, or if they extend through the simulation boundaries, and are well-matched to those boundaries.\n",
    "\n",
    "The custom current source is simpler, and can be used to simply provide a specific electric current and magnetic current distribution within a volume. The component labels `Ex, Ey, Ez, Hx, Hy, Hz` in the dataset are directly assigned as electric/magnetic current amplitudes `Jx, Jy, Jz, Mx, My, Mz`.\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "590771ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f985512",
   "metadata": {},
   "source": [
    "### Starting simulation with an in-built source\n",
    "\n",
    "We will first run a simulation with a [GaussianBeam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianBeam.html) source propagating in empty space. The definition of the source below is for a converging Gaussian beam (negative `waist_distance` parameter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdd2a272",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Free-space wavelength (in um) and frequency (in Hz)\n",
    "lambda0 = 1.55\n",
    "freq0 = td.C_0 / lambda0\n",
    "fwidth = freq0 / 10\n",
    "\n",
    "# Simulation size and run time\n",
    "sim_size = [10, 10, 10]\n",
    "run_time = 20 / fwidth\n",
    "\n",
    "# Grid specification\n",
    "grid_spec = td.GridSpec.auto(wavelength=lambda0)\n",
    "\n",
    "# In-built GaussianBeam source\n",
    "pulse = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "waist_radius = 2\n",
    "src_pos = -4\n",
    "gaussian_source = td.GaussianBeam(\n",
    "    source_time=pulse,\n",
    "    center=(src_pos, 0, 0),\n",
    "    size=(0, 10, 10),\n",
    "    waist_radius=waist_radius,\n",
    "    waist_distance=-8,\n",
    "    direction=\"+\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635a5b42",
   "metadata": {},
   "source": [
    "We will use monitors to record the fields at the beam waist along the propagation direction, and inject those in another simulation using a [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html). The best way to achieve this is to take into account the numerical grid used in the FDTD algorithm. Specifically, we need to record the raw fields at their Yee grid locations in order to achieve the highest accuracy in injecting the recorded data. For this purpose, we need to set `colocate=False` in the monitor definition. Furthermore, it is best to set a slightly nonzero size in the propagation direction. A monitor with a zero size will interpolate the fields to the exact monitor location along the zero-size dimension, which loses a bit of information. On the other hand, a monitor with a small but nonzero size along the propagation direction will store the fields exactly as they are in the simulation. We will illustrate the difference in the results below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d21c681b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monitor for propagation\n",
    "mnt_xy = td.FieldMonitor(center=(0, 0, 0), size=(10, 10, 0), freqs=[freq0], name=\"field_xy\")\n",
    "\n",
    "# Monitors for forward and backward flux\n",
    "mnt_flux_pos = src_pos - 0.5\n",
    "mnt_flux_f = td.FluxMonitor(\n",
    "    center=(-mnt_flux_pos, 0, 0), size=(0, td.inf, td.inf), freqs=[freq0], name=\"flux_f\"\n",
    ")\n",
    "mnt_flux_b = td.FluxMonitor(\n",
    "    center=(mnt_flux_pos, 0, 0), size=(0, td.inf, td.inf), freqs=[freq0], name=\"flux_b\"\n",
    ")\n",
    "\n",
    "# Monitor to be used as custom source, 0D along x\n",
    "mnt_yz_1 = td.FieldMonitor(\n",
    "    center=(-src_pos, 0, 0), size=(0, 8, 8), freqs=[freq0], colocate=False, name=\"yz_zero_size_x\"\n",
    ")\n",
    "\n",
    "# Monitor to be used as custom source, small nonzero along x\n",
    "mnt_yz_2 = td.FieldMonitor(\n",
    "    center=(-src_pos, 0, 0),\n",
    "    size=(1e-5, 8, 8),\n",
    "    freqs=[freq0],\n",
    "    colocate=False,\n",
    "    name=\"yz_nonzero_size_x\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9773710c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    sources=[gaussian_source],\n",
    "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b, mnt_yz_1, mnt_yz_2],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")\n",
    "sim.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffcf9d6d",
   "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\">13:06:40 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'free space gaussian'</span> with resource_id                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:40 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'free space gaussian'\u001b[0m with resource_id                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490'\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-8fe1408f-1abb-4eaf-90f5-4e261d621490\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1ab</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4eaf-90f5-4e261d621490'</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=910312;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=440420;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=910312;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=522387;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=910312;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32m-8fe1408f-1ab\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=910312;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8fe1408f-1abb-4eaf-90f5-4e261d621490\u001b\\\u001b[32mb-4eaf-90f5-4e261d621490'\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=862176;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": "e2339548e0e74165b922d2f542ef848c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:06:43 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;36m13:06:43 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\">13:06:45 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:45 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b4bf04e34ce4f7c9f7ee2586f889a0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:06:48 UTC </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:48 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"free space gaussian\", verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "061b02ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax)\n",
    "ax.set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax.set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "ax.set_title(\n",
    "    f\"Flux fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32705449",
   "metadata": {},
   "source": [
    "### Custom source from simulation data\n",
    "\n",
    "Now we can use the recorded data as an input to a [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) in another simulation. We will run two simulations to illustrate the difference between zero and nonzero size of the monitor along `x`. Note that we can create the source directly using the [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) method `to_source`. Note that we can center the source anywhere we want. The size of the source is by default taken from the [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) used to create it. However, the source size needs to be `0` along one of the directions, so in the case of the `nonzero_size_x` data, we have to manually reset it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ff5d0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_src_1 = sim_data[\"yz_zero_size_x\"].to_source(source_time=pulse, center=(0, 1, 0))\n",
    "custom_src_2 = sim_data[\"yz_nonzero_size_x\"].to_source(\n",
    "    source_time=pulse,\n",
    "    center=(0, 1, 0),\n",
    "    # size is by default taken from the data, but it must be reset to zero along x\n",
    "    size=(0, 8, 8),\n",
    ")\n",
    "sim_1 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    sources=[custom_src_1],\n",
    "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")\n",
    "sim_2 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    sources=[custom_src_2],\n",
    "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d0b3bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70a8b29963494b038aad27a6857ecdf4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:06:53 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:53 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\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\">13:06:56 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.050</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:56 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.050\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": "967d85e0a68f4745bef6051fcbfc66bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:07:00 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:00 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 = web.Batch(simulations={\"zero_x\": sim_1, \"nonzero_x\": sim_2}, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f22fc0f",
   "metadata": {},
   "source": [
    "Below we plot the injected field in the two simulations. As can be seen, there is a bit of backwards power injected in the simulation in which we used a `FieldMonitor` with size `0` along x, while the injection is extremely clean in the case where the size of the monitor was slightly larger than zero. As mentioned above, this is because the fields from the original simulation are captured exactly as they are on the numerical grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c3b481c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sim_data = batch_results[\"zero_x\"]\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[0])\n",
    "ax[0].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax[0].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    "title += \"\\nzero-size FieldData along x\"\n",
    "ax[0].set_title(title)\n",
    "sim_data = batch_results[\"nonzero_x\"]\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[1])\n",
    "ax[1].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax[1].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    "title += \"\\nnonzero-size FieldData along x\"\n",
    "ax[1].set_title(title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bb1a69",
   "metadata": {},
   "source": [
    "### Importing arbitrary fields\n",
    "\n",
    "Next we show how to import arbitrary fields. We create numpy arrays to emulate the same Gaussian beam input. Data can be imported from file in exactly the same way, once it is read into a numpy array. We create two types of datasets: the first one contains the `Ey` field only, while the second one contains `Ey` and `Hz`. We can provide any combination of field components to the custom source, as long as at least one of the tangential fields is defined. However, as we will see, providing both `E` and `H` is required to make the source directional.\n",
    "\n",
    "**Note**: the fields are assumed to be defined w.r.t. the *local* coordinates of the source, that is to say with the coordinate origin lying at the source center. This makes it easy to specify a source profile and use that anywhere in a simulation. We illustrate this below by defining the dataset to be centered around `(0, 0, 0)`, while the source is placed at a different location in the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a7393de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scalar gaussian field with the same waist_radius as the beam source above\n",
    "ys, zs = np.linspace(-4, 4, 101), np.linspace(-4, 4, 101)\n",
    "y_grid, z_grid = np.meshgrid(ys, zs)\n",
    "scalar_gaussian = np.exp(-(y_grid**2 + z_grid**2) / waist_radius**2)\n",
    "\n",
    "# Field dataset defining only the E-field\n",
    "dataset_E = td.FieldDataset(\n",
    "    Ey=td.ScalarFieldDataArray(\n",
    "        scalar_gaussian[None, ..., None],\n",
    "        coords={\n",
    "            \"x\": [0],\n",
    "            \"y\": ys,\n",
    "            \"z\": zs,\n",
    "            \"f\": [freq0],\n",
    "        },\n",
    "    )\n",
    ")\n",
    "\n",
    "# Field dataset defining both E and H\n",
    "dataset_EH = td.FieldDataset(\n",
    "    Ey=td.ScalarFieldDataArray(\n",
    "        scalar_gaussian[None, ..., None],\n",
    "        coords={\n",
    "            \"x\": [0],\n",
    "            \"y\": ys,\n",
    "            \"z\": zs,\n",
    "            \"f\": [freq0],\n",
    "        },\n",
    "    ),\n",
    "    Hz=td.ScalarFieldDataArray(\n",
    "        scalar_gaussian[None, ..., None] / td.ETA_0,\n",
    "        coords={\n",
    "            \"x\": [0],\n",
    "            \"y\": ys,\n",
    "            \"z\": zs,\n",
    "            \"f\": [freq0],\n",
    "        },\n",
    "    ),\n",
    ")\n",
    "\n",
    "custom_src_3 = td.CustomFieldSource(\n",
    "    source_time=pulse,\n",
    "    center=(-1, 1, 0),\n",
    "    size=(0, 8, 8),\n",
    "    field_dataset=dataset_E,\n",
    ")\n",
    "custom_src_4 = td.CustomFieldSource(\n",
    "    source_time=pulse,\n",
    "    center=(-1, 1, 0),\n",
    "    size=(0, 8, 8),\n",
    "    field_dataset=dataset_EH,\n",
    ")\n",
    "sim_3 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    sources=[custom_src_3],\n",
    "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")\n",
    "sim_4 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    sources=[custom_src_4],\n",
    "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e95a8ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1ef7819d6e14eeea6db627802e65dbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:07:07 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:07 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\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\">13:07:10 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.050</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:10 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.050\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": "243006abe40d443f9bfe2a7ae50e3c32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:07:16 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:16 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 = web.Batch(simulations={\"custom_E\": sim_3, \"custom_EH\": sim_4}, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ddc2022",
   "metadata": {},
   "source": [
    "As can be seen below, if we only provide the `E` field to the custom source (or only the `H` field), the source is not directional, but instead equal power is injected in both directions. We can make it directional by providing both fields, provided that they are set with the correct phase offset. In this example, multiplying one of the two fields by `-1` will make the source inject in the backwards direction.\n",
    "\n",
    "Note also that in the case of custom fields, the flux is not automatically normalized. This could however be done by hand if both the `E` and the `H` fields are known, for example using the Tidy3D [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) property `flux`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae1f2baf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sim_data = batch_results[\"custom_E\"]\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[0])\n",
    "ax[0].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax[0].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    "title += \"\\nOnly E field supplied\"\n",
    "ax[0].set_title(title)\n",
    "sim_data = batch_results[\"custom_EH\"]\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[1])\n",
    "ax[1].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax[1].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    "title += \"\\nE and H fields supplied\"\n",
    "ax[1].set_title(title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8381fb18",
   "metadata": {},
   "source": [
    "### Notes on `CustomFieldSource`\n",
    "\n",
    "- Only the field components tangential to the custom source plane are needed and used in the simulation. Due to the equivalence principle, these fully define the currents that need to be injected. This is not to say that the normal components of the data (`Ex`, `Hx` in our example) is lost or not injected. It is merely not needed as it can be uniquely obtained using the tangential components.\n",
    "- Source data can be imported from file just as shown here, after the data is imported as a numpy array using standard numpy functions like [loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html).\n",
    "- If the data is not coming from a Tidy3D simulation, the normalization is likely going to be arbitrary and the directionality of the source will likely not be perfect, even if both the `E` and `H` fields are provided. An empty normalizing run may be needed to accurately normalize results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56095046-4658-4997-a771-4f56b15cd7ca",
   "metadata": {},
   "source": [
    "## Custom Current Sources\n",
    "\n",
    "Alternatively, one might want to inject a raw electric and magnetic current distribution within the simulation.\n",
    "For this, we introduce a `CustomCurrentSource` object.\n",
    "\n",
    "The syntax is very similar to `CustomFieldSource`, except instead of a `field_dataset`, the source accepts a `current_dataset`. This dataset still contains E{x,y,z} and H{x,y,z} field components, which correspond to J and M components respectively.\n",
    "\n",
    "There are also fewer constraints on the data requirements for `CustomCurrentSource`. It can be volumetric or planar without requiring tangential components.\n",
    "\n",
    "Finally, note that the dataset is still defined w.r.t. the source center, just as in the case of the `CustomFieldSource`, and can then be placed anywhere in the simulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95e2dc8-75c7-40b5-b258-8b88ccd0c5ef",
   "metadata": {},
   "source": [
    "To demonstrate, here we make a CustomCurrentSource using our field pattern and the relations $J = n\\times H$ and $M = -n\\times E$ that should recreate the unidirectional excitation (up to the numerical resolution of our source)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce52f7a1-6a9a-4e57-92a2-6df73ecc83e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_JM = td.FieldDataset(\n",
    "    Ey=td.ScalarFieldDataArray(\n",
    "        -scalar_gaussian[None, ..., None] / td.ETA_0,\n",
    "        coords={\n",
    "            \"x\": [0],\n",
    "            \"y\": ys,\n",
    "            \"z\": zs,\n",
    "            \"f\": [freq0],\n",
    "        },\n",
    "    ),\n",
    "    Hz=td.ScalarFieldDataArray(\n",
    "        -scalar_gaussian[None, ..., None],\n",
    "        coords={\n",
    "            \"x\": [0],\n",
    "            \"y\": ys,\n",
    "            \"z\": zs,\n",
    "            \"f\": [freq0],\n",
    "        },\n",
    "    ),\n",
    ")\n",
    "\n",
    "custom_src_JM = td.CustomCurrentSource(\n",
    "    source_time=pulse,\n",
    "    center=(-1, 1, 0),\n",
    "    size=(0, 8, 8),\n",
    "    current_dataset=dataset_JM,\n",
    ")\n",
    "\n",
    "sim_JM = sim_4.updated_copy(sources=[custom_src_JM])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb7d6f21-277d-4a9c-90f1-cb132d188ee6",
   "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\">13:07:20 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'custom current'</span> with resource_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-6135ea77-f634-44a7-b414-38c8adefda1b'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:20 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'custom current'\u001b[0m with resource_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-6135ea77-f634-44a7-b414-38c8adefda1b'\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-6135ea77-f634-44a7-b414-38c8adefda1b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f63</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4-44a7-b414-38c8adefda1b'</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=824599;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=797266;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=824599;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=170659;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=824599;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32m-6135ea77-f63\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=824599;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6135ea77-f634-44a7-b414-38c8adefda1b\u001b\\\u001b[32m4-44a7-b414-38c8adefda1b'\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=759767;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": "f3c8ba672d734bcf85465edf5f229bf8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:07:24 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;36m13:07:24 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\">13:07:26 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:26 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0baf4e44b02848af945b109e8f1a93aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:07:29 UTC </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:07:29 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_JM = web.run(sim_JM, task_name=\"custom current\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0bab85e7-fbe6-4fd0-8efc-944f785627d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax)\n",
    "ax.set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
    "ax.set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
    "ax.set_title(\n",
    "    f\"Flux fwd={float(sim_data['flux_f'].flux.item()):1.2e}, bck={float(sim_data['flux_b'].flux.item()):1.2e}\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7581bc6-fe54-4717-9647-f2d3f4cd05c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to define complex, spatially-varying sources in Tidy3D FDTD.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "custom source, 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"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "key": 0,
         "length": 1,
         "op": "removerange"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ],
   "remote_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "12"
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       "op": "patch"
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   ]
  },
  "title": "Defining Spatially-varying Sources in Tidy3D | Flexcompute",
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