{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Defining spatially-varying sources\n",
        "\n",
        "In this example we illustrate how to incorporate a source with a custom\n",
        "spatial dependence in a Tidy3D simulation. We will illustrate this both\n",
        "using data taken from a monitor from another Tidy3D simulation and using\n",
        "a simple array defined from scratch. This notebook will cover both\n",
        "custom “field” sources and custom “current” sources.\n",
        "\n",
        "The custom field source can be used to inject a specific (E, H) field\n",
        "distribution on a plane, e.g. coming from another simulation.\n",
        "Internally, we use the equivalence principle to compute the actual\n",
        "source currents (all sources in FDTD have to be converted to current\n",
        "sources). Because of this, the custom field source will only produce\n",
        "reliable results if the provided fields decay by the edges of the source\n",
        "plane, or if they extend through the simulation boundaries, and are\n",
        "well-matched to those boundaries.\n",
        "\n",
        "The custom current source is simpler, and can be used to simply provide\n",
        "a specific electric current and magnetic current distribution within a\n",
        "volume. The component labels `Ex, Ey, Ez, Hx, Hy, Hz` in the dataset are\n",
        "directly assigned as electric/magnetic current amplitudes\n",
        "`Jx, Jy, Jz, Mx, My, Mz`.\n",
        "\n",
        "If you are new to the finite-difference time-domain (FDTD) method, we\n",
        "highly recommend going through our\n",
        "[FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
      ],
      "id": "37421718-1cfc-4c8a-97f0-4a3e94cfad06"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "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"
      ],
      "id": "7747a10f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Starting simulation with an in-built source\n",
        "\n",
        "We will first run a simulation with a\n",
        "[GaussianBeam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianBeam.html)\n",
        "source propagating in empty space. The definition of the source below is\n",
        "for a converging Gaussian beam (negative `waist_distance` parameter)."
      ],
      "id": "e7507c75-ed55-43e1-820d-7f2122580243"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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",
        ")"
      ],
      "id": "5a8b39c6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will use monitors to record the fields at the beam waist along the\n",
        "propagation direction, and inject those in another simulation using a\n",
        "[CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html).\n",
        "The best way to achieve this is to take into account the numerical grid\n",
        "used in the FDTD algorithm. Specifically, we need to record the raw\n",
        "fields at their Yee grid locations in order to achieve the highest\n",
        "accuracy in injecting the recorded data. For this purpose, we need to\n",
        "set `colocate=False` in the monitor definition. Furthermore, it is best\n",
        "to set a slightly nonzero size in the propagation direction. A monitor\n",
        "with a zero size will interpolate the fields to the exact monitor\n",
        "location along the zero-size dimension, which loses a bit of\n",
        "information. On the other hand, a monitor with a small but nonzero size\n",
        "along the propagation direction will store the fields exactly as they\n",
        "are in the simulation. We will illustrate the difference in the results\n",
        "below."
      ],
      "id": "507c7cce-9713-436b-94c2-22bfe02c44b7"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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",
        ")"
      ],
      "id": "d37acb4e"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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JLVmyRO3t7RozZozGjRunnXbaSTNmzCheobhULBbTrbfeqm984xu6//779fbb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0O/wAEAAPwsEfvobMh0Jm92/xfy/teY4XUgfv3rX+vCCy+UJO2777665557ql7Vtz8+9alPFU89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\n6KKLIm5nwIABAIDGxkZ8/vnnOOecc/QLRUREmuInr0RERCYxc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          }
        }
      ],
      "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()"
      ],
      "id": "025382ba"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:05 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-628fb3ff-f72b-49eb-85ac-30f4f1564fcd'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72b-49eb-85ac-30f4f1564fcd\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72b-49eb-85ac-30f4f1564fcd\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-49eb-85ac-30f4f1564fcd'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"7df30d61d2e942c08357c6ca2ca45855\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:06 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:08 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"6128278ff46449c58d8bd079cf361950\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:27 UTC </span>status = preprocess                                                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:31 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:32 UTC </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"24fece04f0e94a9d82d1cb748a8ff342\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>%, exiting.                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:33 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72b-49eb-85ac-30f4f1564fcd\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-628fb3ff-f72b-49eb-85ac-30f4f1564fcd\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-49eb-85ac-30f4f1564fcd'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"7142fb8352c04e98836a780ed32f3486\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:34 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "sim_data = web.run(sim, task_name=\"free space gaussian\", verbose=True)"
      ],
      "id": "6e03e8e0"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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RzenMM8/UE088IelA0aJ3796hxw4WNhzH0RlnnKH9+/fr9ddf16ZNm/Tbb7+pRYsWobYH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\nevWSJP3555+69tp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          }
        }
      ],
      "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()"
      ],
      "id": "c15ad4a3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Custom source from simulation data\n",
        "\n",
        "Now we can use the recorded data as an input to a\n",
        "[CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html)\n",
        "in another simulation. We will run two simulations to illustrate the\n",
        "difference between zero and nonzero size of the monitor along `x`. Note\n",
        "that we can create the source directly using the\n",
        "[FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html)\n",
        "method `to_source`. Note that we can center the source anywhere we want.\n",
        "The size of the source is by default taken from the\n",
        "[FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html)\n",
        "used to create it. However, the source size needs to be `0` along one of\n",
        "the directions, so in the case of the `nonzero_size_x` data, we have to\n",
        "manually reset it."
      ],
      "id": "2b6b405a-4b76-4b71-89ec-9b624419b7a8"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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",
        ")"
      ],
      "id": "2840b461"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"861219ca978c448e8682bb3cd99935e8\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:38 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:40 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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"48eb3aaf360d44d486e0d7f871ed36a3\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:25:55 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "batch = web.Batch(simulations={\"zero_x\": sim_1, \"nonzero_x\": sim_2}, verbose=True)\n",
        "batch_results = batch.run(path_dir=\"data\")"
      ],
      "id": "c967e4ac"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Below we plot the injected field in the two simulations. As can be seen,\n",
        "there is a bit of backwards power injected in the simulation in which we\n",
        "used a `FieldMonitor` with size `0` along x, while the injection is\n",
        "extremely clean in the case where the size of the monitor was slightly\n",
        "larger than zero. As mentioned above, this is because the fields from\n",
        "the original simulation are captured exactly as they are on the\n",
        "numerical grid."
      ],
      "id": "9bbb62a3-97ef-45d6-935f-dc93cd026805"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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DB7SWXb9+XTg6OgogfU7SEydOGMzr999/l/IqWLCgeP78eZY0mjdMdS2Xa/PmzbLaMgC0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\nV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9q3b6/27dvr8ccf12uvvaYJEybom2++0YED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\nadIkjRgxQlLutG6FFTDNmzfX8uXLlZ6erp07d0Ytjh9tAAAAA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          }
        }
      ],
      "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()"
      ],
      "id": "07bbbb80"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Importing arbitrary fields\n",
        "\n",
        "Next we show how to import arbitrary fields. We create numpy arrays to\n",
        "emulate the same Gaussian beam input. Data can be imported from file in\n",
        "exactly the same way, once it is read into a numpy array. We create two\n",
        "types of datasets: the first one contains the `Ey` field only, while the\n",
        "second one contains `Ey` and `Hz`. We can provide any combination of\n",
        "field components to the custom source, as long as at least one of the\n",
        "tangential fields is defined. However, as we will see, providing both\n",
        "`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*\n",
        "coordinates of the source, that is to say with the coordinate origin\n",
        "lying at the source center. This makes it easy to specify a source\n",
        "profile and use that anywhere in a simulation. We illustrate this below\n",
        "by defining the dataset to be centered around `(0, 0, 0)`, while the\n",
        "source is placed at a different location in the simulation."
      ],
      "id": "19848b57-21ba-4166-80bb-f4b999f69015"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "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",
        ")"
      ],
      "id": "34916c2b"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"ba5b50e5b6484a6ca319a3e13ff420bc\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:26:44 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:26:46 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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"2a1b88ecebd7492782614f3359484e29\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:14 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "batch = web.Batch(simulations={\"custom_E\": sim_3, \"custom_EH\": sim_4}, verbose=True)\n",
        "batch_results = batch.run(path_dir=\"data\")"
      ],
      "id": "f13d5d21"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As can be seen below, if we only provide the `E` field to the custom\n",
        "source (or only the `H` field), the source is not directional, but\n",
        "instead equal power is injected in both directions. We can make it\n",
        "directional by providing both fields, provided that they are set with\n",
        "the correct phase offset. In this example, multiplying one of the two\n",
        "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\n",
        "automatically normalized. This could however be done by hand if both the\n",
        "`E` and the `H` fields are known, for example using the Tidy3D\n",
        "[FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html)\n",
        "property `flux`."
      ],
      "id": "e984ce21-085c-439c-ba08-f965c866e627"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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6msGDB+vU9W01tcHkyZMtGsLOXtdNbroPZNTJ1Wq1OH36tM59nkPYUU7CHkiUrVJTU1Gi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\n1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5yuvvpq9e3bV6eeeqrtdjIyMvTNN9/ovffe0zfffKOZ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\nwQchF12FWbFiha688kq1bt1aW7ZscaVNAAAAwCm/6d2PG3OIl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          }
        }
      ],
      "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()"
      ],
      "id": "d4e524fb"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Notes on `CustomFieldSource`\n",
        "\n",
        "- Only the field components tangential to the custom source plane are\n",
        "  needed and used in the simulation. Due to the equivalence principle,\n",
        "  these fully define the currents that need to be injected. This is not\n",
        "  to say that the normal components of the data (`Ex`, `Hx` in our\n",
        "  example) is lost or not injected. It is merely not needed as it can be\n",
        "  uniquely obtained using the tangential components.\n",
        "- Source data can be imported from file just as shown here, after the\n",
        "  data is imported as a numpy array using standard numpy functions like\n",
        "  [loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html).\n",
        "- If the data is not coming from a Tidy3D simulation, the normalization\n",
        "  is likely going to be arbitrary and the directionality of the source\n",
        "  will likely not be perfect, even if both the `E` and `H` fields are\n",
        "  provided. An empty normalizing run may be needed to accurately\n",
        "  normalize results.\n",
        "\n",
        "## Custom Current Sources\n",
        "\n",
        "Alternatively, one might want to inject a raw electric and magnetic\n",
        "current distribution within the simulation. For this, we introduce a\n",
        "`CustomCurrentSource` object.\n",
        "\n",
        "The syntax is very similar to `CustomFieldSource`, except instead of a\n",
        "`field_dataset`, the source accepts a `current_dataset`. This dataset\n",
        "still contains E{x,y,z} and H{x,y,z} field components, which correspond\n",
        "to J and M components respectively.\n",
        "\n",
        "There are also fewer constraints on the data requirements for\n",
        "`CustomCurrentSource`. It can be volumetric or planar without requiring\n",
        "tangential components.\n",
        "\n",
        "Finally, note that the dataset is still defined w.r.t. the source\n",
        "center, just as in the case of the `CustomFieldSource`, and can then be\n",
        "placed anywhere in the simulation.\n",
        "\n",
        "To demonstrate, here we make a CustomCurrentSource using our field\n",
        "pattern and the relations $J = n\\times H$ and $M = -n\\times E$ that\n",
        "should recreate the unidirectional excitation (up to the numerical\n",
        "resolution of our source)."
      ],
      "id": "9a7b696d-3988-466a-8163-f4891ce7c9b5"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "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])"
      ],
      "id": "cd97d83d"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:16 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-63d22265-5e21-488d-beb8-7e5e0def78c7'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e21-488d-beb8-7e5e0def78c7\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e2</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e21-488d-beb8-7e5e0def78c7\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">1-488d-beb8-7e5e0def78c7'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"dd6ec0bc641246709d48efb54de31e8a\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:17 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:19 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"a1bd0dd4c22b436ca5abaf3b72b3887b\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:31 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"266c29f457cd465789e8ec7c0f6cd90d\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:32 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>%, exiting.                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:33 UTC </span>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"d7d3d998e46d486f9e33afa5db7f808b\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:35 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e21-488d-beb8-7e5e0def78c7\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e2</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-63d22265-5e21-488d-beb8-7e5e0def78c7\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">1-488d-beb8-7e5e0def78c7'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"728bf147760749019572aa1a5a14d6a4\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">10:27:58 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "sim_data_JM = web.run(sim_JM, task_name=\"custom current\")"
      ],
      "id": "c8603b1e"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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LYyMv5OAQAKza7XMpdcXibikNdUl5d5eyX4PwCbIJ+o6zJO2Yde5uFyxn4xXj1+3uZsz75xefnY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\n"
          }
        }
      ],
      "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()"
      ],
      "id": "44b65c5a"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}