{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0667fa3c",
   "metadata": {},
   "source": [
    "## Importing custom source data\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "590771ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:03:51.676941Z",
     "iopub.status.busy": "2023-02-03T05:03:51.676653Z",
     "iopub.status.idle": "2023-02-03T05:03:52.355750Z",
     "shell.execute_reply": "2023-02-03T05:03:52.355406Z"
    }
   },
   "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\">[23:03:52] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> This version of Tidy3D was pip installed from the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d-beta'</span> repository on   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">103</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         PyPI. Future releases will be uploaded to the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d'</span> repository. From now on, <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         please use <span style=\"color: #008000; text-decoration-color: #008000\">'pip install tidy3d'</span> instead.                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[23:03:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on   \u001b]8;id=969208;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=676435;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m               \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m         please use \u001b[32m'pip install tidy3d'\u001b[0m instead.                                        \u001b[2m               \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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Using client version: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.9</span>.0rc1                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1                                                  \u001b]8;id=959706;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=116020;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n"
   ]
  },
  {
   "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/v1.9.0rc2/_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": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:03:52.357660Z",
     "iopub.status.busy": "2023-02-03T05:03:52.357500Z",
     "iopub.status.idle": "2023-02-03T05:03:52.360107Z",
     "shell.execute_reply": "2023-02-03T05:03:52.359821Z"
    }
   },
   "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",
    ")\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/v1.9.0rc2/_autosummary/tidy3d.CustomFieldSource.html). The best way to achieve this is to use a monitor with a slightly nonzero size in the propagation direction. This is because of the details associated with the Yee grid used in the FDTD algorithm. 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 related to the numerical grid. 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": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:03:52.361572Z",
     "iopub.status.busy": "2023-02-03T05:03:52.361474Z",
     "iopub.status.idle": "2023-02-03T05:03:52.364346Z",
     "shell.execute_reply": "2023-02-03T05:03:52.364076Z"
    }
   },
   "outputs": [],
   "source": [
    "# Monitor for propagation\n",
    "mnt_xy = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(10, 10, 0), freqs=[freq0], name=\"field_xy\"\n",
    ")\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], 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), size=(1e-5, 8, 8), freqs=[freq0], name=\"yz_nonzero_size_x\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9773710c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:03:52.365821Z",
     "iopub.status.busy": "2023-02-03T05:03:52.365706Z",
     "iopub.status.idle": "2023-02-03T05:03:52.479347Z",
     "shell.execute_reply": "2023-02-03T05:03:52.479083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at z=0.00'}, xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffcf9d6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:03:52.480848Z",
     "iopub.status.busy": "2023-02-03T05:03:52.480734Z",
     "iopub.status.idle": "2023-02-03T05:04:19.843654Z",
     "shell.execute_reply": "2023-02-03T05:04:19.843289Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'bd4e366b-500e-4855-b2dd-33e066807881'</span>.                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[23:03:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'free space gaussian'\u001b[0m with task_id                                   \u001b]8;id=579627;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734190;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         \u001b[32m'bd4e366b-500e-4855-b2dd-33e066807881'\u001b[0m.                                           \u001b[2m             \u001b[0m\n"
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     "data": {
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       "\u001b[?25l"
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     "output_type": "display_data"
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       "model_id": "4c5764acb72f4456a234d29481715d68",
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       "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\">\n",
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       "\n",
       "\u001b[?25h"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:03:55] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = queued                                                                   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">262</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:03:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                                                   \u001b]8;id=623953;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=588419;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#262\u001b\\\u001b[2m262\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[?25l"
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       "Output()"
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:02] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Maximum FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                      <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">253</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                      \u001b]8;id=660270;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=15364;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\u001b[0m\u001b]8;;\u001b\\\n"
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    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:04] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = preprocess                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:04]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                                               \u001b]8;id=401847;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=532911;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#274\u001b\\\u001b[2m274\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:08] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> starting up solver                                                                <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">278</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=270738;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=225137;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\u001b[0m\u001b]8;;\u001b\\\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:11] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> running solver                                                                    <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">284</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=145983;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=596948;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
      ]
     },
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:15] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> early shutoff detected, exiting.                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">295</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[23:04:15]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=222306;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=487321;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\u001b[0m\u001b]8;;\u001b\\\n"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = postprocess                                                              <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">301</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                                              \u001b]8;id=474382;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=687872;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#301\u001b\\\u001b[2m301\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "Output()"
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:17] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> status = success                                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:17]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                                                  \u001b]8;id=90163;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=476923;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#307\u001b\\\u001b[2m307\u001b[0m\u001b]8;;\u001b\\\n"
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    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h\r",
       "\u001b[1A\u001b[2K"
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     },
     "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\">[23:04:18] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Billed FlexUnit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>                                                       <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">311</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:18]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.025\u001b[0m                                                       \u001b]8;id=324272;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=456941;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to <span style=\"color: #008000; text-decoration-color: #008000\">\"simulation_data.hdf5\"</span>             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">593</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b[32m\"simulation_data.hdf5\"\u001b[0m             \u001b]8;id=736191;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=586669;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#593\u001b\\\u001b[2m593\u001b[0m\u001b]8;;\u001b\\\n"
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      },
      "text/plain": [
       "Output()"
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     },
     "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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\">[23:04:19] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from simulation_data.hdf5                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[23:04:19]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from simulation_data.hdf5                                  \u001b]8;id=435486;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=629636;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"free space gaussian\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "061b02ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:04:20.158448Z",
     "iopub.status.busy": "2023-02-03T05:04:20.158358Z",
     "iopub.status.idle": "2023-02-03T05:04:20.307879Z",
     "shell.execute_reply": "2023-02-03T05:04:20.307625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
    ")\n",
    "plt.show()\n"
   ]
  },
  {
   "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/v1.9.0rc2/_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/v1.9.0rc2/_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/v1.9.0rc2/_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": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:04:20.309599Z",
     "iopub.status.busy": "2023-02-03T05:04:20.309459Z",
     "iopub.status.idle": "2023-02-03T05:04:20.346182Z",
     "shell.execute_reply": "2023-02-03T05:04:20.345934Z"
    }
   },
   "outputs": [],
   "source": [
    "custom_src_1 = sim_data[\"yz_zero_size_x\"].to_source(source_time=pulse, center=(0, 0, 0))\n",
    "custom_src_2 = sim_data[\"yz_nonzero_size_x\"].to_source(\n",
    "    source_time=pulse,\n",
    "    center=(0, 0, 0),\n",
    "    size=(\n",
    "        0,\n",
    "        8,\n",
    "        8,\n",
    "    ),  # by default taken from the data, but it must be reset to zero along x\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",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d0b3bcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:04:20.347748Z",
     "iopub.status.busy": "2023-02-03T05:04:20.347669Z",
     "iopub.status.idle": "2023-02-03T05:04:45.870284Z",
     "shell.execute_reply": "2023-02-03T05:04:45.869881Z"
    }
   },
   "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\">           </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Created task <span style=\"color: #008000; text-decoration-color: #008000\">'zero_x'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'4399ee23-a277-40d0-abb3-7f7e5e215d85'</span>.        <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">120</span></a>\n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n",
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
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       "\n",
       "\u001b[?25h"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:30] </span>Started working on Batch.                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#361\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">361</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m[23:04:30]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                                               \u001b]8;id=830945;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=961328;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[?25l"
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       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:45] </span>Batch complete.                                                                         <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#382\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">382</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[23:04:45]\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                                         \u001b]8;id=223425;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=615605;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#382\u001b\\\u001b[2m382\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "batch = web.Batch(simulations={\"zero_x\": sim_1, \"nonzero_x\": sim_2})\n",
    "batch_results = batch.run(path_dir=\"data\")\n"
   ]
  },
  {
   "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 ppower 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": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:04:46.064995Z",
     "iopub.status.busy": "2023-02-03T05:04:46.064811Z",
     "iopub.status.idle": "2023-02-03T05:04:49.698937Z",
     "shell.execute_reply": "2023-02-03T05:04:49.698671Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">\"data/4399ee23-a277-40d0-abb3-7f7e5e215d85.hdf5\"</span>                                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
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       "Output()"
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    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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      ],
      "text/plain": [
       "\n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[23:04:47] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/<span style=\"color: #ffff00; text-decoration-color: #ffff00\">4399ee23-a277-40d0-abb3-7f7e5e215d85</span>.hdf5        <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
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\n",
      "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):1.2e}, bck={float(sim_data['flux_b'].flux):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):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
    "title += \"\\nnonzero-size FieldData along x\"\n",
    "ax[1].set_title(title)\n"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a7393de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T05:04:49.714999Z",
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   "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=(0, 0, 0),\n",
    "    size=(0, 8, 8),\n",
    "    field_dataset=dataset_E,\n",
    ")\n",
    "custom_src_4 = td.CustomFieldSource(\n",
    "    source_time=pulse,\n",
    "    center=(0, 0, 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",
    ")\n"
   ]
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    "batch = web.Batch(simulations={\"custom_E\": sim_3, \"custom_EH\": sim_4})\n",
    "batch_results = batch.run(path_dir=\"data\")\n"
   ]
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   "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/v1.9.0rc2/_autosummary/tidy3d.FieldData.html) property `flux`."
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\n",
      "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):1.2e}, bck={float(sim_data['flux_b'].flux):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):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
    "title += \"\\nE and H fields supplied\"\n",
    "ax[1].set_title(title)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8381fb18",
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   "source": [
    "### Final notes\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."
   ]
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