{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e129b4e",
   "metadata": {},
   "source": [
    "# Performing visualization of simulation data\n",
    "\n",
    "This notebook is a tutorial on working with `Tidy3D` output data.\n",
    "\n",
    "We will cover:\n",
    "\n",
    "- Accessing data.\n",
    "\n",
    "- Manipulating data.\n",
    "\n",
    "- Visualizing data.\n",
    "\n",
    "First we import the packages we'll need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a22293a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1c1323",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "### Creating Simulation\n",
    "\n",
    "First, let's make a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) so we have data to plot.\n",
    "\n",
    "We will add each possible type of monitor into the simulation to explore their output data separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61c49575",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# simulation parameters\n",
    "Lx, Ly, Lz = 5, 5, 5\n",
    "min_steps_per_wvl = 32\n",
    "\n",
    "# monitor parameters\n",
    "freq0 = 2e14\n",
    "freqs = np.linspace(1e14, 3e14, 11)\n",
    "num_modes = 3\n",
    "\n",
    "simulation = td.Simulation(\n",
    "    size=(Lx, Ly, Lz),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),\n",
    "    run_time=4e-13,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    structures=[\n",
    "        td.Structure(\n",
    "            geometry=td.Box(center=(0, 0, 0), size=(10001, 1.4, 1.5)),\n",
    "            medium=td.Medium(permittivity=2),\n",
    "            name=\"waveguide\",\n",
    "        ),\n",
    "        td.Structure(\n",
    "            geometry=td.Box(center=(0, 0.5, 0.5), size=(1.5, 1.4, 1.5)),\n",
    "            medium=td.Medium(permittivity=2),\n",
    "            name=\"scatterer\",\n",
    "        ),\n",
    "    ],\n",
    "    sources=[\n",
    "        td.ModeSource(\n",
    "            source_time=td.GaussianPulse(freq0=freq0, fwidth=6e13),\n",
    "            center=(-2.0, 0.0, 0.0),\n",
    "            size=(0.0, 3, 3),\n",
    "            direction=\"+\",\n",
    "            mode_spec=td.ModeSpec(),\n",
    "            mode_index=0,\n",
    "        )\n",
    "    ],\n",
    "    monitors=[\n",
    "        td.FieldMonitor(\n",
    "            fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "            size=(td.inf, 0, td.inf),\n",
    "            center=(0, 0, 0),\n",
    "            freqs=freqs,\n",
    "            name=\"field\",\n",
    "        ),\n",
    "        td.FieldTimeMonitor(\n",
    "            fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "            size=(td.inf, 0, td.inf),\n",
    "            center=(0, 0, 0),\n",
    "            interval=200,\n",
    "            name=\"field_time\",\n",
    "        ),\n",
    "        td.FluxMonitor(size=(0, 3, 3), center=(2, 0, 0), freqs=freqs, name=\"flux\"),\n",
    "        td.FluxTimeMonitor(size=(0, 3, 3), center=(2, 0, 0), interval=10, name=\"flux_time\"),\n",
    "        td.ModeMonitor(\n",
    "            size=(0, 3, 3),\n",
    "            center=(2, 0, 0),\n",
    "            freqs=freqs,\n",
    "            mode_spec=td.ModeSpec(num_modes=num_modes),\n",
    "            name=\"mode\",\n",
    "        ),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "004348be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "monitor field requires 7.06e+06 bytes of storage.\n",
      "monitor field_time requires 1.06e+07 bytes of storage.\n",
      "monitor flux requires 4.40e+01 bytes of storage.\n",
      "monitor flux_time requires 2.65e+03 bytes of storage.\n",
      "monitor mode requires 7.92e+02 bytes of storage.\n"
     ]
    }
   ],
   "source": [
    "tmesh = simulation.tmesh\n",
    "\n",
    "total_size_bytes = 0\n",
    "for monitor in simulation.monitors:\n",
    "    monitor_grid = simulation.discretize(monitor)\n",
    "    num_cells = np.prod(monitor_grid.num_cells)\n",
    "    monitor_size = monitor.storage_size(num_cells=num_cells, tmesh=tmesh)\n",
    "    print(f\"monitor {monitor.name} requires {monitor_size:.2e} bytes of storage.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3748bef",
   "metadata": {},
   "source": [
    "### Visualize Geometry\n",
    "\n",
    "We've created a simple waveguide with a defect / scattering region defined using a [Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html) geometry.\n",
    "\n",
    "A modal source is injected from the -x side of the simulation and we measure the mode amplitudes and flux at the +x side.\n",
    "\n",
    "We've also placed a couple field monitors to visualize the field patterns.\n",
    "\n",
    "Let's take a look at the geometry from a few cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f7e72a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))\n",
    "simulation.plot(x=0.0, ax=ax1)\n",
    "simulation.plot(y=0.01, ax=ax2)\n",
    "simulation.plot(z=0.01, ax=ax3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db68e728",
   "metadata": {},
   "source": [
    "### Make normalization Simulation\n",
    "\n",
    "For purposes of demonstration, let's create another simulation without the scatterer, so we can compare what the data should look like for just the straight waveguide case.\n",
    "\n",
    "This is as simple as making a copy of the original simulation and removing the scatterer from the list of structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf9aedee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get rid of scatterer for normalization\n",
    "simulation0 = simulation.copy(update=dict(structures=[simulation.structures[0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653a3b32",
   "metadata": {},
   "source": [
    "### Running simulations\n",
    "\n",
    "Now we will run both simulations and load them into [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) objects.\n",
    "\n",
    "Since these will be a short and simple simulations, we can use the [web.run](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.webapi.run.html) function to do them all in one line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bedb64a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:19 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'straight waveguide'</span> with task_id                    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4ea25ca1-4f87-4349-812b-00b165a5690f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:19 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'straight waveguide'\u001b[0m with task_id                    \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-4ea25ca1-4f87-4349-812b-00b165a5690f'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">87-4349-812b-00b165a5690f'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=856630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=648642;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=856630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=179950;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=856630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32m-4ea25ca1-4f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=856630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[32m87-4349-812b-00b165a5690f'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=738739;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "244115fb2ab34d0b8a3226cc1c77bfaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:22 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:22 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:24 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:24 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "113b97b2fbd9416fb2390b555dd6c783",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:31 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:31 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:36 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:36 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "985db802435d47d2a770218bfc7b2cfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:41 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:41 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m12\u001b[0m%, exiting.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:42 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:42 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2673b51a6c1f4ce7b233d4f10354a16f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:44 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:44 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:47 CEST </span>status = running                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:47 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = running                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:51 CEST </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:51 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:54 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:54 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:18:56 CEST </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">87-4349-812b-00b165a5690f'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:18:56 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=201840;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622027;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=201840;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=586849;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=201840;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34m-4ea25ca1-4f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=201840;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea25ca1-4f87-4349-812b-00b165a5690f\u001b\\\u001b[4;34m87-4349-812b-00b165a5690f'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a8de34b22ea4280b46ba8fca0952b95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:07 CEST </span>loading simulation from data/simulation0.hdf5                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:07 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation0.hdf5                     \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: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                      </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more  \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                    \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                      \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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'scattered waveguide'</span> with task_id                   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'scattered waveguide'\u001b[0m with task_id                   \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d7-4a78-9967-f761ef3ed72f'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=478215;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=422930;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=478215;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=367585;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=478215;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32m-4ee239cc-e3\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=478215;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[32md7-4a78-9967-f761ef3ed72f'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=880154;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f5f7d818a9149e68bc1443a89ce01ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:10 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:10 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:11 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:11 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62ae34e5620940d5b63b99a9e91c90a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:25 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:25 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:26 CEST </span>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:26 CEST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0543cab474994faeadde340a4e76250c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:28 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:28 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ebfb21d537a45d3a870787a1f23aafe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:31 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:31 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:33 CEST </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">d7-4a78-9967-f761ef3ed72f'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:33 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=638261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=313174;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=638261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=808411;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=638261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34m-4ee239cc-e3\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=638261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ee239cc-e3d7-4a78-9967-f761ef3ed72f\u001b\\\u001b[4;34md7-4a78-9967-f761ef3ed72f'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e15ee28f26ad4fb38cc1ce093dd65f50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:19:42 CEST </span>loading simulation from data/simulation.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:19:42 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation.hdf5                      \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: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                      </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more  \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                    \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                      \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim0_data = web.run(\n",
    "    simulation0,\n",
    "    task_name=\"straight waveguide\",\n",
    "    path=\"data/simulation0.hdf5\",\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "sim_data = web.run(\n",
    "    simulation,\n",
    "    task_name=\"scattered waveguide\",\n",
    "    path=\"data/simulation.hdf5\",\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72401eff",
   "metadata": {},
   "source": [
    "### Inspecting Log\n",
    "\n",
    "Now let's take a look at the log to see if everything looks ok (fields decayed, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e528e33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:19:20] INFO: Auto meshing using wavelength 1.4990 defined from sources.     \n",
      "           INFO: Auto meshing using wavelength 1.4990 defined from sources.     \n",
      "           USER: Simulation domain Nx, Ny, Nz: [176, 152, 152]                  \n",
      "           USER: Applied symmetries: (0, 0, 0)                                  \n",
      "           USER: Number of computational grid points: 4.2214e+06.               \n",
      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
      "           timestep_reduction=0.3, edge_singularity_correction=False),          \n",
      "           pmc=Staircasing(attrs={}, type='Staircasing'),                       \n",
      "           lossy_metal=SurfaceImpedance(attrs={}, type='SurfaceImpedance',      \n",
      "           timestep_reduction=0.0, edge_singularity_correction=False),          \n",
      "           type='SubpixelSpec')                                                 \n",
      "           USER: Number of time steps: 6.6230e+03                               \n",
      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
      "           USER: Time step (s): 6.0408e-17                                      \n",
      "           USER:                                                                \n",
      "                                                                                \n",
      "           USER: Mode solver at f=2.0000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:21] USER: Compute source modes time (s):     1.1316                      \n",
      "[11:19:22] USER: Rest of setup time (s):            0.3972                      \n",
      "[11:19:23] USER: Mode solver at f=1.2000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=1.0000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=1.4000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=2.0000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=1.6000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=2.6000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=3.0000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=2.2000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=1.8000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=2.4000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:23] USER: Mode solver at f=2.8000000000e+14 with plane size (89, 89),    \n",
      "           direction: +                                                         \n",
      "[11:19:24] WARNING: Mode '2' appears to undergo a discontinuous change between  \n",
      "           frequencies '160000000000000.0' and '180000000000000.0' (overlap:    \n",
      "           '0.00').                                                             \n",
      "[11:19:25] USER: Compute monitor modes time (s):    1.9568                      \n",
      "           USER: Solver time (s):                   0.7280                      \n",
      "           USER: Time-stepping speed (cells/s):     1.09e+10                    \n",
      "[11:19:26] USER: Post-processing time (s):          0.8220                      \n",
      "\n",
      " ====== SOLVER LOG ====== \n",
      "\n",
      "Processing grid and structures...\n",
      "Building FDTD update coefficients...\n",
      "Solver setup time (s):             0.2240\n",
      "\n",
      "Running solver for 6623 time steps...\n",
      "- Time step    220 / time 1.33e-14s (  3 % done), field decay: 1.00e+00\n",
      "- Time step    264 / time 1.59e-14s (  4 % done), field decay: 1.00e+00\n",
      "- Time step    529 / time 3.20e-14s (  8 % done), field decay: 1.00e+00\n",
      "- Time step    794 / time 4.80e-14s ( 12 % done), field decay: 8.03e-04\n",
      "- Time step   1059 / time 6.40e-14s ( 16 % done), field decay: 1.36e-06\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "Time-stepping time (s):            0.4125\n",
      "Data write time (s):               0.0891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sim_data.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4a54c3",
   "metadata": {},
   "source": [
    "## Post-Processing\n",
    "\n",
    "Now that the simulations have run and their data are loaded into the `sim_data` and `sim0_data` variables, we will explore how to access, manipulate, and visualize the data.  \n",
    "\n",
    "### Accessing Data\n",
    "\n",
    "#### Original Simulation\n",
    "\n",
    "The [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) objects store a copy of the original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html), so it can be recovered if the [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) is loaded in a new session and the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) is no longer in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c3ef22b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5.0, 5.0, 5.0)\n"
     ]
    }
   ],
   "source": [
    "print(sim_data.simulation.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e26ff6",
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    "#### Monitor Data\n",
    "\n",
    "More importantly, the [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) contains a reference to the data for each of the monitors within the original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
    "This data can be accessed directly using the `name` given to the monitors initially.\n",
    "\n",
    "For example, our [FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) was named `'flux'` while our [FluxTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxTimeMonitor.html) was named `'flux_time'`, each with a `.flux` attribute storing the raw data. Therefore, we can access the data as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58af7e8c",
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    "tags": []
   },
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   "source": [
    "# get the flux data from the monitor name\n",
    "flux_data = sim_data[\"flux\"].flux\n",
    "flux_time_data = sim_data[\"flux_time\"].flux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9f3a94",
   "metadata": {},
   "source": [
    "### Structure of Data\n",
    "\n",
    "Now that we have the data loaded, let's inspect it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d672a805",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.FluxDataArray (f: 11)&gt; Size: 44B\n",
       "array([0.59199595, 0.77460945, 0.8931856 , 0.9529229 , 0.97218543,\n",
       "       0.97665787, 0.97956353, 0.98391604, 0.9880339 , 0.99017555,\n",
       "       0.99026334], dtype=float32)\n",
       "Coordinates:\n",
       "  * f        (f) float64 88B 1e+14 1.2e+14 1.4e+14 ... 2.6e+14 2.8e+14 3e+14\n",
       "Attributes:\n",
       "    units:      W\n",
       "    long_name:  flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.FluxDataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>f</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5f20f1c7-dd0b-4c1d-9d2b-b94906ef71d1' class='xr-array-in' type='checkbox' checked><label for='section-5f20f1c7-dd0b-4c1d-9d2b-b94906ef71d1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.592 0.7746 0.8932 0.9529 0.9722 ... 0.9839 0.988 0.9902 0.9903</span></div><div class='xr-array-data'><pre>array([0.59199595, 0.77460945, 0.8931856 , 0.9529229 , 0.97218543,\n",
       "       0.97665787, 0.97956353, 0.98391604, 0.9880339 , 0.99017555,\n",
       "       0.99026334], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-1e468a5a-4422-4838-9899-471890ce7bef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e468a5a-4422-4838-9899-471890ce7bef' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>f</span></div><div class='xr-var-dims'>(f)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+14 1.2e+14 ... 2.8e+14 3e+14</div><input id='attrs-c3ed62cc-c5ee-4432-913c-d26d8942fe56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3ed62cc-c5ee-4432-913c-d26d8942fe56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2ed8ccf-e29c-4502-b42d-2dd3ad266916' class='xr-var-data-in' type='checkbox'><label for='data-d2ed8ccf-e29c-4502-b42d-2dd3ad266916' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>frequency</dd></dl></div><div class='xr-var-data'><pre>array([1.0e+14, 1.2e+14, 1.4e+14, 1.6e+14, 1.8e+14, 2.0e+14, 2.2e+14, 2.4e+14,\n",
       "       2.6e+14, 2.8e+14, 3.0e+14])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-baec3f7a-2d3a-4581-9287-79c4536b23a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-baec3f7a-2d3a-4581-9287-79c4536b23a1' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>f</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0a3a3a2a-0f38-4747-ab27-0f215ffd77ae' class='xr-index-data-in' type='checkbox'/><label for='index-0a3a3a2a-0f38-4747-ab27-0f215ffd77ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000000000000.0, 120000000000000.0, 140000000000000.0,\n",
       "       160000000000000.0, 180000000000000.0, 200000000000000.0,\n",
       "       220000000000000.0, 240000000000000.0, 260000000000000.0,\n",
       "       280000000000000.0, 300000000000000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;f&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4506fbac-046d-4a51-b868-a28bd796ce77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4506fbac-046d-4a51-b868-a28bd796ce77' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W</dd><dt><span>long_name :</span></dt><dd>flux</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.FluxDataArray (f: 11)> Size: 44B\n",
       "array([0.59199595, 0.77460945, 0.8931856 , 0.9529229 , 0.97218543,\n",
       "       0.97665787, 0.97956353, 0.98391604, 0.9880339 , 0.99017555,\n",
       "       0.99026334], dtype=float32)\n",
       "Coordinates:\n",
       "  * f        (f) float64 88B 1e+14 1.2e+14 1.4e+14 ... 2.6e+14 2.8e+14 3e+14\n",
       "Attributes:\n",
       "    units:      W\n",
       "    long_name:  flux"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flux_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f11714",
   "metadata": {},
   "source": [
    "The data is stored as a [DataArray](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html) object using the [xarray](https://xarray.pydata.org/en/stable/) package.\n",
    "You can think of it as a dataset where the flux data is stored as a large, multi-dimensional array (like a numpy array) and the coordinates along each of the dimensions are specified so it is easy to work with.\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f27c9e1d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of flux dataset = (11,)\n",
      ".\n",
      "frequencies in dataset = <bound method Mapping.values of Coordinates:\n",
      "  * f        (f) float64 88B 1e+14 1.2e+14 1.4e+14 ... 2.6e+14 2.8e+14 3e+14> \n",
      ".\n",
      "flux values in dataset = [0.59199595 0.77460945 0.8931856  0.9529229  0.97218543 0.97665787\n",
      " 0.97956353 0.98391604 0.9880339  0.99017555 0.99026334]\n",
      ".\n"
     ]
    }
   ],
   "source": [
    "# flux values (W) as numpy array\n",
    "print(f\"shape of flux dataset = {flux_data.shape}\\n.\")\n",
    "print(f\"frequencies in dataset = {flux_data.coords.values} \\n.\")\n",
    "print(f\"flux values in dataset = {flux_data.values}\\n.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e4f0da",
   "metadata": {},
   "source": [
    "So we can see the dataset stores a 1D array of flux values (Watts) and a 1D array of frequency values (Hz).\n",
    "\n",
    "### Manipulating Data\n",
    "\n",
    "We can now do some useful operations with this data, including:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8774d777",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at central frequency, flux is 9.77e-01 W\n"
     ]
    }
   ],
   "source": [
    "# Selecting data at a specific coordinate\n",
    "flux_central_freq = flux_data.sel(f=freq0)\n",
    "print(f\"at central frequency, flux is {float(flux_central_freq):.2e} W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c2defca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at 4th frequency, flux is 9.72e-01 W\n"
     ]
    }
   ],
   "source": [
    "# Selecting data at a specific index\n",
    "flux_4th_freq = flux_data.isel(f=4)\n",
    "print(f\"at 4th frequency, flux is {float(flux_4th_freq):.2e} W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e63cfd62",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at an intermediate (not stored) frequency, flux is 9.79e-01 W\n"
     ]
    }
   ],
   "source": [
    "# Interpolating data at a specific coordinate\n",
    "flux_interp_freq = flux_data.interp(f=213.243523142e12)\n",
    "print(f\"at an intermediate (not stored) frequency, flux is {float(flux_interp_freq):.2e} W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1cff990",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# arithmetic operations with numbers\n",
    "flux_times_2_plus_1 = 2 * flux_data + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87f3cc32",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# operations with dataset (in this case, dividing by the normalization flux)\n",
    "flux_transmission = flux_data / sim0_data[\"flux\"].flux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86475df",
   "metadata": {},
   "source": [
    "There are many more things you can do with the xarray package that are not covered here, so it is best to take a look at [their documentation](http://xarray.pydata.org/en/stable/) for more details.\n",
    "\n",
    "### Plotting Data\n",
    "\n",
    "These datasets have built-in plotting methods, which can be handy for quickly producing customizable plots.\n",
    "\n",
    "Let's plot the flux data to get a feeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c2c6760",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(10, 3))\n",
    "flux_data.plot(ax=ax1)\n",
    "ax1.set_title(\"flux vs. frequency\")\n",
    "ax1.set_xlim(150e12, 250e12)\n",
    "\n",
    "# plot the ratio of flux with scatterer to without scatterer with dashed line signifying unity transmission.\n",
    "flux_transmission.plot(ax=ax2, color=\"k\")\n",
    "ax2.plot(flux_data.f, np.ones_like(flux_data.f), \"--\")\n",
    "ax2.set_ylabel(\"flux(f) / flux_normalize(f)\")\n",
    "ax2.set_xlabel(\"frequency (Hz)\")\n",
    "ax2.set_title(\"transmission spectrum\")\n",
    "ax2.set_xlim(150e12, 250e12)\n",
    "ax2.set_ylim(-0.1, 1.1)\n",
    "\n",
    "# plot the time dependence of the flux\n",
    "flux_time_data.plot(color=\"crimson\", ax=ax3)\n",
    "ax3.set_title(\"flux vs. time\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca03826c",
   "metadata": {},
   "source": [
    "Conveniently, xarray adds a lot of the plot labels and formatting automatically, which can save some time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7f17db",
   "metadata": {},
   "source": [
    "### Complex, Multi-dimensional Data\n",
    "\n",
    "While the flux data is 1D and quite simple, the same approaches apply to more complex data, such as mode amplitude and field data, which can be complex-valued and multi-dimensional.\n",
    "\n",
    "Let's use the [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) data as an example.  We'll access the data using its name `'mode'` in the original simulation and print out some of the metadata to examine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28bd7aaf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 11, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_data = sim_data[\"mode\"]\n",
    "mode_data.amps.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877d5042",
   "metadata": {},
   "source": [
    "As we can see, the data is complex-valued and contains three dimensions.\n",
    "- a propagation direction for the mode, which is just positive or negative  (+/-)\n",
    "- an index into the list of modes returned by the solver given our initial [ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html) specifications.\n",
    "- the frequency of the mode\n",
    "\n",
    "Let's plot the data just to get a feeling for it, using `.sel()` to split the forward and backward propagating modes onto separate plot axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa9bb765",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QYrFmzRqjGv3oo49qFExjJIRAriYPN3IycSP7Fm7mZCI7P8fgX/fLTt8u0OB24Z2kQSd0db4/1ior2KitYKO2Lpm2LjNdsbx8mbXKqviLuUIFtUpV7gu+Uu9Xf7VSJR0NUCoa1CNXiBoE9u1EROaXkpICF5eKp6M2Rvy2ZqRCbRFu5WTiRk4GbuRk4GZORnHCkJtZMl1SVrLsZk4mNEUFZo1JIVdU/qVemrbSTwJK6tqoykyrrcrUt660vrVSzV/oiYiIiIzg7e1t6RDqjFGJRUZGBrZs2YLff/8dFy5cQF5eHjw8PBAUFISIiAh0797dXHGahRACOfl5uJFzCzdzMpCefScZKC4r816SPGTmZddoWzYqK7g6uMDN3gmONg7V/rp/5wt/5UcGbMt94Vfx13wiqoXG1rcTUePWu3dvdO7cGQqFAuvWrYNarcbbb7+NoUOHYvz48fjqq6/g5eWFDz74AI899pi03m+//YY33ngDf/31F1xdXTF8+HC8/fbbUCqLv0fl5uZi7Nix+Oabb+Dg4IDJkydX2LZGo8GMGTPw5ZdfIiMjA506dcKCBQvQu3fvKuMteyrU+fPn4e/vj6+//hoffPAB9u3bhzZt2iAuLg6hoaHSOmvXrkVMTAzS09MRERGBsLCwCu1+++23mDt3Lk6cOAFfX18MHz4cM2bMgFKpxFtvvYW4uDgcPXpUumXvgAEDkJeXh4SEBLP9UGzQN9KrV68iJiYGX3zxBXx9fREcHIzAwEDY2Njg5s2b2LlzJxYtWoSWLVti9uzZGDx4sFmCNaUeMYOQWZRbo+sHZDIZXOyc4GbvDDd7Z7jaO8HNwQWudiXv9k5ws3eBq70z3Byc4WrvDFu1tRn2goio5hpj305ETcO6devw5ptvYv/+/di4cSPGjh2LLVu2YODAgZg+fTqWLFmCYcOG4eLFi7C1tcWVK1fQv39/jBgxAp9//jlOnTqF0aNHw9raGnPmzAEAvPHGG/jtt9/w7bffwtPTE9OnT8ehQ4cQGBgobXf8+PE4ceIENmzYAF9fX2zZsgX9+vXD0aNH0aZNG4PjnzFjBhYtWoQ2bdpgxowZGDJkCM6ePQulUol9+/Zh1KhRiI2NRWRkJOLj4zF79my99X///XdERUVh+fLl6NmzJ86dO4cxY8YAAGbPno0ZM2YgPj4eL774IrZs2YKVK1diz549+Ouvv8x79okwgKenp3jjjTfE8ePHq6yTl5cn1q9fLx588EGxcOFCQ5q1iMzMTAFAeIzsIrxeChFeL4WIVhN6iS7TnhKPvjNcPLt8ohi3eraI2bRELN2+Rny+a4vYdminSDpzWJxJSRbp2bdEkbbI0rtBRGSw0n4vMzNTr7wx9e11qarPk4hM4/bt2+LEiRPi9u3blS7v1auXCAsLk+aLioqEnZ2dGDZsmFSWkpIiAIikpCQhhBDTp08X7dq1EzqdTqqzcuVKYW9vL7RarcjOzhZqtVps2rRJWn7jxg1hY2MjJk6cKIQQ4sKFC0KhUIgrV67oxfPII4+IadOmVbk/AMSWLVuEEEIkJycLAOLTTz+Vlh8/flwAECdPnhRCCDFkyBDRv39/vTYGDx4snJyc9LY5f/58vTr//e9/hY+PjzR/7tw54eDgIKZMmSJsbGzEF198UWWM1X3mxvR5Bh2xOHHixF2ffGhjY4MhQ4ZgyJAhuHHjRq2Snbqw9fU4tPBpzqMJRNRkNca+nYiahvvvv1+aVigUcHNzQ+fOnaUyLy8vAMVPkwaAkydPIjQ0VO95DT169EBOTg4uX76MW7duoaCgACEhIdJyV1dXtGvXTpo/evQotFot2rZtqxeLRqMx+gnhZeP38fGRYm3fvj1OnjyJgQMH6tUPDQ1FfHy8NP/XX39h9+7deOedd6QyrVaL/Px85OXlwdbWFvfccw8WLVqEl156CYMHD8bQoUONirEmDEos3NzcoNFoYGVlZVCjDeHx65382vK2iUTUpDXGvp2ImgaVSqU3L5PJ9MpKEwidznR30MzJyYFCocDBgwcrPJ3a3t7eqLZqG2tOTg7mzp2Lp59+usIya+s7P5jv2rULCoUC58+fR1FRkXQ9ibkYfJKVk5MT+vTpg7feegu///679Lh1IiJquNi3E1FT0KFDByQlJek9fHf37t1wcHBA8+bN0bp1a6hUKuzbt09afuvWLZw5c0aaDwoKglarxbVr13DvvffqvUx556cOHTroxQEAe/fu1Zt/4IEHcPr06Qpx3HvvvdI1FBs3bsQ333yDxMREXLx4EfPmzTNZjFUxOLGIi4tDy5YtsXr1avTq1QvOzs549NFHERsbi71790Kr1ZozTiIiMgP27UTUFLzyyiu4dOkSJkyYgFOnTuHbb7/F7NmzER0dDblcDnt7e4waNQpvvPEGfv31Vxw7dgwjRozQu9C5bdu2eO655xAVFYVvvvkGycnJ2L9/P2JjY7Ft2zaTxfrqq68iPj4eixYtwj///IMVK1bonQYFADExMfj8888xd+5cHD9+HCdPnsSGDRswc+ZMAMDly5cxduxYLFiwAGFhYVizZg3mz59fIUExNYMTixEjRmDt2rU4f/48zp49iw8++AC+vr6Ii4tDjx494OLiggEDBpgzViIiMjH27UTUFDRr1gzbt2/H/v37ERAQgJdffhmjRo2SvogDwMKFC9GzZ0888cQTCA8PR1hYGLp06aLXzpo1axAVFYXXX38d7dq1Q2RkJP7880+0aNHCZLE++OCDWLVqFZYtW4aAgAD8/PPPenECQEREBH744Qf8/PPP6NatGx588EEsWbIELVu2hBACI0aMQHBwMMaPHy/VHzt2LJ5//nnk5OSYLNYK7np59138+++/YsaMGcLR0VHI5fLaNmd2vJsHETU1Nen3atu3r1ixQrRs2VJYWVmJ4OBgsW/fvmrrb9q0SbRr105YWVmJTp06iW3btukt//rrr8Wjjz4qXF1dBQBx+PBhveU3btwQ48ePF23bthXW1tbCz89PTJgwQWRkZOjVA1Dh9eWXXxq1bxxHiMzrbneFItMz1V2hjL6R7cWLF7Fu3TqMHDkS/v7+uP/++7Fv3z5MnjwZO3fuNF3GQ0REdcaUffvGjRsRHR2N2bNn49ChQwgICEBERIR0d5by9uzZgyFDhmDUqFE4fPgwIiMjERkZiWPHjkl1cnNzERYWhgULFlTaxtWrV3H16lUsWrQIx44dw9q1axEfH49Ro0ZVqLtmzRqkpKRIr8jISKP2j4iIKicTosxVLNV44YUXkJiYiJs3b6JHjx7o2bMnevXqhW7dupn9CnNTysrKgpOTEzIzM3lXKCJqEqrr98zRt4eEhKBbt25YsWIFgOI7nfj5+WHChAmYOnVqhfqDBw9Gbm4ufvjhB6nswQcfRGBgIOLi4vTqlj619vDhw3oPrarM5s2b8fzzzyM3N1fal7JPwK0pjiNE5pWfn4/k5GT4+/vr3eGIzKe6z9yYPs/gUWPt2rVo0aIFZsyYgUceeQRBQUF69wImIqKGx9R9e0FBAQ4ePIhp06ZJZXK5HOHh4UhKSqp0naSkJERHR+uVRUREYOvWrTWOA4A0CJZPkMaNG4cXX3wR99xzD15++WWMHDmS4xkRkQkYnFicPHkSO3fuRGJiIhYvXgyNRoOwsDD06tULvXv3xgMPPGDeR4QTEZHJmbpvT09Ph1arlR5OVcrLywunTp2qdJ3U1NRK66emphq/Q2XimDdvHsaMGaNX/tZbb+Hhhx+Gra0tfv75Z7zyyivIycnBq6++WmVbGo0GGo1Gms/KyqpxXEREjZnBiUW7du3Qrl07vPzyywCKn9j622+/YefOnVi0aBHy8/MRFhamdyibiIjqt8bYt2dlZWHAgAHo2LEj5syZo7ds1qxZ0nRQUBByc3OxcOHCahOL2NhYzJ0711zhEhE1GjW+OKJjx45wc3ODi4sLXFxcsGHDBvz444+mjI2IiOpYbft2d3d3KBQKpKWl6ZWnpaVV+QApb29vo+pXJzs7G/369YODgwO2bNlS4em85YWEhGDevHnVPoF82rRpeqdqZWVlwc/Pz+jYiIgaO6MSi2vXriExMVE6bH7mzBmo1WoEBwfjtddeQ58+fcwVJxERmYkp+3a1Wo0uXbogISFBukBap9MhISFBup96eaGhoUhISMCkSZOksh07diA0NNSo/cjKykJERASsrKzw3XffGXTR55EjR+Di4lJlUgEAVlZW1S4nIqJiBicWHTp0wJkzZ6BUKtGtWzc888wz6N27N3r06MEr9omIGihz9O3R0dEYPnw4unbtiuDgYCxduhS5ubkYOXIkACAqKgrNmjVDbGwsAGDixIno1asXFi9ejAEDBmDDhg04cOAAPvnkE6nNmzdv4uLFi7h69SoA4PTp0wCKj3Z4e3sjKysLffv2RV5eHv73v/8hKytLuhbCw8MDCoUC33//PdLS0vDggw/C2toaO3bswPz58zF58uQaf35ERHSHwYlFZGQk+vTpg7CwMNja2pozJiIiqiPm6NsHDx6M69evIyYmBqmpqQgMDER8fLx0gfbFixf1Lgjv3r071q9fj5kzZ2L69Olo06YNtm7dik6dOkl1vvvuOykxAYBnn30WADB79mzMmTMHhw4dwr59+wAA9957r148ycnJaNWqFVQqFVauXInXXnsNQgjce++9eP/99zF69GiT7DcRUVNn8HMsGgvef5yImhr2e6bFz5PIvPgci7pnqudYGHQPwXfffRe3b982KLB9+/Zh27ZtBtUlIiLLYd9ORFR/rVy5Eq1atYK1tTVCQkKwf/9+S4d0VwYlFidOnECLFi3wyiuv4Mcff8T169elZUVFRfj777/x4Ycfonv37hg8eDAcHBzMFjAREZkG+3Yiovpp48aNiI6OxuzZs3Ho0CEEBAQgIiIC165ds3Ro1TIosfj888/xyy+/oLCwEEOHDoW3tzfUajUcHBxgZWWFoKAgrF69GlFRUTh16hQeeughgwMwNhtbunQp2rVrBxsbG/j5+eG1115Dfn6+wdsjIqJi5uzbiYio5kqv/xo5ciQ6duyIuLg42NraYvXq1ZYOrVoGX7wdEBCAVatW4eOPP8bff/+NCxcu4Pbt23B3d0dgYCDc3d2N3nhpNhYXF4eQkBAsXboUEREROH36NDw9PSvUX79+PaZOnYrVq1eje/fuOHPmDEaMGAGZTIb333/f6O0TETV15ujbiYjqIyEE8gos82O0rdoaMpnMoLoFBQU4ePAgpk2bJpXJ5XKEh4cjKSnJXCGahNEPyJPL5QgMDERgYGCtN142GwOAuLg4bNu2DatXr8bUqVMr1N+zZw969OiBoUOHAgBatWqFIUOGSHcCISKimjFl305EVB/lFeSj9UTLPHPt3LKdsLOyMahueno6tFqtdCe9Ul5eXjh16pQ5wjMZg06FKkuhUFR6fteNGzegUCgMbqc0GwsPD78TzF2yse7du+PgwYPS6VL//vsvtm/fjv79+xu5F0REVJap+nYiImq6jD5iUdXdaTUaDdRqtcHt1CQbGzp0KNLT0xEWFgYhBIqKivDyyy9j+vTpVW5Ho9FAo9FI86UPTCIiojtM1bcTEdVXtmprnFu202LbNpS7uzsUCgXS0tL0ytPS0uDt7W3q0EzK4MRi+fLlAACZTIZPP/0U9vb20jKtVotdu3ahffv2po+wjMTERMyfPx8ffvghQkJCcPbsWUycOBHz5s3DrFmzKl0nNjYWc+fONWtcREQNVX3o24mI6oJMJjP4dCRLUqvV6NKlCxISEhAZGQkA0Ol0SEhIwPjx4y0b3F0YnFgsWbIEQPGvWnFxcXqHxtVqNVq1aoW4uDiDN1yTbGzWrFkYNmwYXnzxRQBA586dkZubizFjxmDGjBl6T3ItNW3aNERHR0vzWVlZ8PPzMzhOIqLGzNR9OxER1V50dDSGDx+Orl27Ijg4GEuXLkVubq50XXJ9ZXBikZycDADo06cPtmzZAmdn51ptuCbZWF5eXoXkoXQQrOowvpWVFaysrGoVKxFRY2Xqvp2IiGpv8ODBuH79OmJiYpCamorAwEDEx8dXuISgvjHqGovCwkJcvHgRKSkpJhl87paNRUVFoVmzZoiNjQUAPPHEE3j//fcRFBQknQo1a9YsPPHEE7y4kIiohkzdtxMRUe2NHz++3p/6VJ5RiYVKpTLpw+julo1dvHhR7wjFzJkzIZPJMHPmTFy5cgUeHh544okn8M4775gsJiKipsbUfTsRETVNMlHVOURVmD9/Ps6cOYNPP/0USqXRN5WyuKysLDg5OSEzMxOOjo6WDoeIyOwM6fcaet9elziOEJlXfn4+kpOT4e/vD2trw++mRDVX3WduTJ9n9Ojx559/IiEhAT///DM6d+4MOzs7veXffPONsU0SEZGFsW8nIqLaMjqxcHZ2xv/93/+ZIxYiIrIQ9u1ERFRbRicWa9asMUccRERkQezbiYiotio++IGIiIiIiMhIRh+xCAoKgkwmM6juoUOHjA6IiIjqHvt2IiKqLaMTi379+uHDDz9Ex44dERoaCgDYu3cvjh8/jrFjx8LGpv4/Kp2IiPSxbyciotoyOrG4fv06Xn31VcybN0+vfPbs2bh06RJWr15tsuCIiKhusG8nIqLaMvoai82bNyMqKqpC+fPPP4+vv/7aJEEREVHdYt9ORES1ZXRiYWNjg927d1co3717Nx9iQkTUQLFvJyKqH3bt2oUnnngCvr6+kMlk2Lp1q6VDMpjRp0JNmjQJY8eOxaFDhxAcHAwA2LdvH1avXo1Zs2aZPEAiIjI/9u1ERPVDbm4uAgIC8MILL+Dpp5+2dDhGMTqxmDp1Ku655x4sW7YM//vf/wAAHTp0wJo1azBo0CCTB0hEDZcQAkVFRdBqtZYOpVFTKBRQKpUG39WpMqbu21euXImFCxciNTUVAQEB+OCDD6SEpTKbN2/GrFmzcP78ebRp0wYLFixA//79peXffPMN4uLicPDgQdy8eROHDx9GYGCgXhv5+fl4/fXXsWHDBmg0GkRERODDDz+El5eXVOfixYsYO3Ysdu7cCXt7ewwfPhyxsbFQKo0eDomIzOKxxx7DY489ZukwaqRGPemgQYOYRBBRtQoKCpCSkoK8vDxLh9Ik2NrawsfHB2q1usZtmKpv37hxI6KjoxEXF4eQkBAsXboUEREROH36NDw9PSvU37NnD4YMGYLY2Fg8/vjjWL9+PSIjI3Ho0CF06tQJQPEveGFhYRg0aBBGjx5d6XZfe+01bNu2DZs3b4aTkxPGjx+Pp59+WjrFS6vVYsCAAfD29saePXuQkpKCqKgoqFQqzJ8/v9b7TUT1nBBAoYXGJJUtUIsffxoKmRBCGLPCpUuXIJPJ0Lx5cwDA/v37sX79enTs2BFjxowxS5CmlJWVBScnJ2RmZsLR0dHS4RA1SjqdDv/88w8UCgU8PDygVqtr9Ws6VU0IgYKCAly/fh1arRZt2rSBXK5/+Zwh/Z4p+/aQkBB069YNK1asAFD878HPzw8TJkzA1KlTK9QfPHgwcnNz8cMPP0hlDz74IAIDAxEXF6dX9/z58/D3969wxCIzMxMeHh5Yv349nnnmGQDAqVOn0KFDByQlJeHBBx/Ejz/+iMcffxxXr16VjmLExcVhypQpuH79usFJGccRIvPKz89HcnIy/P39TXuNV0Eu8JaP6dozRkwKoLYzejWZTIYtW7YgMjLS9DGVUd1nbkyfZ/QRi6FDh2LMmDEYNmwYUlNTER4ejk6dOuGLL75AamoqYmJijG2SiBqZgoIC6cukra2tpcNp9GxsbKBSqXDhwgUUFBTUaCA2Vd9eUFCAgwcPYtq0aVKZXC5HeHg4kpKSKl0nKSkJ0dHRemURERFGXbB48OBBFBYWIjw8XCpr3749WrRoISUWSUlJ6Ny5s96pURERERg7diyOHz+OoKCgStvWaDTQaDTSfFZWlsFxERE1JUYnFseOHZPOk920aRM6d+6M3bt34+eff8bLL7/MxIKIJOV/OSfzqe1nbaq+PT09HVqtVu/LOwB4eXnh1KlTla6Tmppaaf3U1FSD409NTYVarYazs3OV7VS1ndJlVYmNjcXcuXMNjoWI6imVbfGRA0ttuwkwOrEoLCyElZUVAOCXX37Bk08+CaD4l6GUFAv9sYiIqFbYt1dt2rRpekdUsrKy4OfnZ8GIiKhGZLIanY5EhjP6J6777rsPcXFx+P3337Fjxw7069cPAHD16lW4ubmZPEAioprq3bs3Jk2aVKs2THEP8bVr11b4Jb2+MVXf7u7uDoVCgbS0NL3ytLQ0eHt7V7qOt7e3UfWraqOgoAAZGRlVtlPVdkqXVcXKygqOjo56LyIic8nJycGRI0dw5MgRAEBycjKOHDmCixcvWjYwAxidWCxYsAAff/wxevfujSFDhiAgIAAA8N1331V7K0EiooYoJSWlwd72zxim6tvVajW6dOmChIQEqUyn0yEhIQGhoaGVrhMaGqpXHwB27NhRZf3KdOnSBSqVSq+d06dP4+LFi1I7oaGhOHr0KK5du6a3HUdHR3Ts2NHgbRERmdOBAwcQFBQkXfcVHR2NoKCgBnG5gdGnQvXu3Rvp6enIysqCi4uLVD5mzBhepElEjY4xv5o3ZKbs26OjozF8+HB07doVwcHBWLp0KXJzczFy5EgAQFRUFJo1a4bY2FgAwMSJE9GrVy8sXrwYAwYMwIYNG3DgwAF88sknUps3b97ExYsXcfXqVQDFSQNQ/Pfx9vaGk5MTRo0ahejoaLi6usLR0RETJkxAaGgoHnzwQQBA37590bFjRwwbNgzvvfceUlNTMXPmTIwbN046DYyIyNJ69+4NI2/aWm/U6Go/hUKhN/AAQKtWrSq9PzkRUVm9e/fGhAkTMGnSJLi4uMDLywurVq2Svng6ODjg3nvvxY8//qi33m+//Ybg4GBYWVnBx8cHU6dORVFRkbQ8NzcXUVFRsLe3h4+PDxYvXlxh2xqNBpMnT0azZs1gZ2eHkJAQJCYmVhtv2VOhzp8/D5lMhm+++QZ9+vSBra0tAgICKtztaO3atWjRogVsbW0xcOBA3Lhxo0K73377LR544AFYW1vjnnvuwdy5c6X9eeutt+Dr66u33oABA9CnTx/odLpq460NU/XtgwcPxqJFixATE4PAwEAcOXIE8fHx0oXSFy9e1Ltuo3v37li/fj0++eQTBAQE4KuvvsLWrVulZ1gAxUdOgoKCMGDAAADAs88+i6CgIL3b0S5ZsgSPP/44/u///g8PPfQQvL298c033+jt3w8//ACFQoHQ0FA8//zziIqKwltvvWXU/hERURWEAYKCgsTNmzcNqSqEEKJHjx7i8uXLBtevS5mZmQKAyMzMtHQoRI3W7du3xYkTJ8Tt27crLOvVq5dwcHAQ8+bNE2fOnBHz5s0TCoVCPPbYY+KTTz4RZ86cEWPHjhVubm4iNzdXCCHE5cuXha2trXjllVfEyZMnxZYtW4S7u7uYPXu21O7YsWNFixYtxC+//CL+/vtv8fjjjwsHBwcxceJEqc6LL74ounfvLnbt2iXOnj0rFi5cKKysrMSZM2eq3BcAYsuWLUIIIZKTkwUA0b59e/HDDz+I06dPi2eeeUa0bNlSFBYWCiGE2Lt3r5DL5WLBggXi9OnTYtmyZcLZ2Vk4OTlJbe7atUs4OjqKtWvXinPnzomff/5ZtGrVSsyZM0cIIURRUZEIDQ0VkZGRQgghVqxYIZydncWFCxdq9JlX1e81pr69LnEcITKv6vozMo+ajCGVMSixkMlkYufOneKvv/4y6GVnZyfOnTtn/F7VAQ4IROZ3t8QiLCxMmi8qKhJ2dnZi2LBhUllKSooAIJKSkoQQQkyfPl20a9dO6HQ6qc7KlSuFvb290Gq1Ijs7W6jVarFp0yZp+Y0bN4SNjY2UWFy4cEEoFApx5coVvXgeeeQRMW3atCr3pbLE4tNPP5WWHz9+XAAQJ0+eFEIIMWTIENG/f3+9NgYPHqyXWDzyyCNi/vz5enX++9//Ch8fH2n+3LlzwsHBQUyZMkXY2NiIL774osoYhajZoNCY+va6xHGEyLyYWNQ9UyUWBl9j8cgjjxh8vhefsEtE1bn//vulaYVCATc3N3Tu3FkqKz1lpvQi25MnTyI0NFSvb+nRowdycnJw+fJl3Lp1CwUFBQgJCZGWu7q6ol27dtL80aNHodVq0bZtW71YNBqN0Xe0Kxu/j4+PFGv79u1x8uRJDBw4UK9+aGgo4uPjpfm//voLu3fvxjvvvCOVabVa5OfnIy8vD7a2trjnnnuwaNEivPTSSxg8eDCGDh1qVIyGYt9ORESmYlBikZycbHTDzZs3N3odImoaVCqV3rxMJtMrK/0Ca8rrCXJycqBQKHDw4EEoFAq9Zfb29ka1VdtYc3JyMHfuXDz99NMVlpV9avauXbugUChw/vx5FBUVQak0+n4b1WLfTkREpmTQKNWyZUtzx0FEVKUOHTrg66+/hhBC+iK/e/duODg4oHnz5nB1dYVKpcK+ffvQokULAMCtW7dw5swZ9OrVCwAQFBQErVaLa9euoWfPnmaNdd++fXple/fu1Zt/4IEHcPr0adx7771VtrNx40Z88803SExMxKBBgzBv3jyTP/2ZfTsREZmSaX/+IiIyg1deeQVLly7FhAkTMH78eJw+fRqzZ89GdHQ05HI57O3tMWrUKLzxxhtwc3ODp6cnZsyYAbn8zo3v2rZti+eeew5RUVFYvHgxgoKCcP36dSQkJOD++++X7jZUW6+++ip69OiBRYsW4amnnsJPP/2kdxoUAMTExODxxx9HixYt8Mwzz0Aul+Ovv/7CsWPH8Pbbb+Py5csYO3YsFixYgLCwMKxZswaPP/44HnvsMenWqURERPVNjW43S0RUl5o1a4bt27dj//79CAgIwMsvv4xRo0Zh5syZUp2FCxeiZ8+eeOKJJxAeHo6wsDB06dJFr501a9YgKioKr7/+Otq1a4fIyEj8+eef0lEOU3jwwQexatUqLFu2DAEBAfj555/14gSAiIgI/PDDD/j555/RrVs3PPjgg1iyZAlatmwJIQRGjBiB4OBgjB8/Xqo/duxYPP/888jJyTFZrERERKYkE4ZetddIZGVlwcnJCZmZmXB0dLR0OESNUn5+PpKTk+Hv7693zQCZT3WfOfs90+LnSWReHEPqnqnGEIsfsVi5ciVatWoFa2trhISEYP/+/dXWz8jIwLhx4+Dj4wMrKyu0bdsW27dvr6NoiYiIiIioMkYnFpcuXcLly5el+f3792PSpEn45JNPjN74xo0bER0djdmzZ+PQoUMICAhARESEdIvJ8goKCvDoo4/i/Pnz+Oqrr3D69GmsWrUKzZo1M3rbRER0hyn7diIiqrnY2Fh069YNDg4O8PT0RGRkJE6fPm3psAxidGIxdOhQ7Ny5EwCQmpqKRx99FPv378eMGTPw1ltvGdXW+++/j9GjR2PkyJHo2LEj4uLiYGtri9WrV1daf/Xq1bh58ya2bt2KHj16oFWrVujVqxcCAgKM3Q0iIirDlH07ERHV3G+//YZx48Zh79692LFjBwoLC9G3b1/k5uZaOrS7MjqxOHbsGIKDgwEAmzZtQqdOnbBnzx588cUXWLt2rcHtFBQU4ODBgwgPD78TjFyO8PBwJCUlVbrOd999h9DQUIwbNw5eXl7o1KkT5s+fD61WW+V2NBoNsrKy9F5ERKTPVH07ERHVTnx8PEaMGIH77rsPAQEBWLt2LS5evIiDBw9aOrS7Mvp2s4WFhbCysgIA/PLLL3jyyScBAO3bt0dKSorB7aSnp0Or1UpP2C3l5eWFU6dOVbrOv//+i19//RXPPfcctm/fjrNnz+KVV15BYWEhZs+eXek6sbGxJr/3OxFRY2Oqvp2IqL4SQqCoqOofo81JqVRIz2EyVmZmJgDA1dXVlCGZhdGJxX333Ye4uDgMGDAAO3bswLx58wAAV69ehZubm8kDLEun08HT0xOffPIJFAoFunTpgitXrmDhwoVVJhbTpk1DdHS0NJ+VlQU/Pz+zxklE1NBYsm8nIqoLRUVafLZui0W2PWr4QKhUxj8+TqfTYdKkSejRowc6depkhshMy+g9XLBgAQYOHIiFCxdi+PDh0vUN3333nXQY3RDu7u5QKBRIS0vTK09LS4O3t3el6/j4+EClUkGhUEhlHTp0QGpqKgoKCqBWqyusY2VlJf0KR0RElTNV305ERKYzbtw4HDt2DH/88YelQzGI0YlF7969kZ6ejqysLLi4uEjlY8aMga2trcHtqNVqdOnSBQkJCYiMjARQnJUlJCRID4Uqr0ePHli/fj10Op30RN0zZ87Ax8en0qSCiIgMY6q+nYiovlIqFRg1fKDFtm2s8ePH44cffsCuXbvQvHlzM0RlejV6joUQAgcPHsTHH3+M7OxsAMWJgrGDT3R0NFatWoV169bh5MmTGDt2LHJzczFy5EgAQFRUFKZNmybVHzt2LG7evImJEyfizJkz2LZtG+bPn49x48bVZDeIiKgMU/XtRET1kUwmg0qltMjLmOsrhBAYP348tmzZgl9//RX+/v5m/FRMy+gjFhcuXEC/fv1w8eJFaDQaPProo3BwcMCCBQug0WgQFxdncFuDBw/G9evXERMTg9TUVAQGBiI+Pl66oPvixYvSkQkA8PPzw08//YTXXnsN999/P5o1a4aJEydiypQpxu4GERGVYcq+nYiIam7cuHFYv349vv32Wzg4OCA1NRUA4OTkBBsbGwtHVz2jj1hMnDgRXbt2xa1bt/R2buDAgUhISDA6gPHjx+PChQvQaDTYt28fQkJCpGWJiYkVbnMYGhqKvXv3Ij8/H+fOncP06dP1rrkgIqoPVq5ciVatWsHa2hohISHYv3+/pUOqlqn7diIiqpmPPvoImZmZ6N27N3x8fKTXxo0bLR3aXRl9xOL333/Hnj17KlzT0KpVK1y5csVkgRERNVQbN25EdHQ04uLiEBISgqVLlyIiIgKnT5+Gp6enpcOrFPt2IqL6QQhh6RBqzOgjFjqdrtIH0l2+fBkODg4mCYqIqCF7//33MXr0aIwcORIdO3ZEXFwcbG1tsXr1akuHViX27UREVFtGJxZ9+/bF0qVLpXmZTIacnBzMnj0b/fv3N2VsREQSIQRyNbct8jLm16OCggIcPHgQ4eHhUplcLkd4eDiSkpLM8dGYBPt2IiKqLaNPhVq8eDEiIiLQsWNH5OfnY+jQofjnn3/g7u6OL7/80hwxEhEhryAfrSf2sci2zy3bCTsrwy6YS09Ph1arlW5CUcrLywunTp0yR3gmwb6diIhqy+gjFs2bN8dff/2F6dOn47XXXkNQUBDeffddHD58uN6eO0xERNUzR99u7AXsmzdvRvv27WFtbY3OnTtj+/btesuFEIiJiYGPjw9sbGwQHh6Of/75R1qemJgImUxW6evPP/8EAJw/f77S5Xv37q3RPhIR0R3GP1scgFKpxPPPP2/qWIiIqmSrtsa5ZTsttm1Dubu7Q6FQIC0tTa88LS0N3t7epg7NpEzZtxt7AfuePXswZMgQxMbG4vHHH8f69esRGRmJQ4cOoVOnTgCA9957D8uXL8e6devg7++PWbNmISIiAidOnIC1tTW6d++OlJQUvXZnzZqFhIQEdO3aVa/8l19+wX333SfNu7m5mWS/ich0GvJFzA2NqT5rgxKL7777zuAGn3zyyRoHQ0RUFZlMZvDpSJakVqvRpUsXJCQkIDIyEkDxhdEJCQkYP368ZYMrx5x9e9kL2AEgLi4O27Ztw+rVqzF16tQK9ZctW4Z+/frhjTfeAADMmzcPO3bswIoVKxAXFwchBJYuXYqZM2fiqaeeAgB8/vnn8PLywtatW/Hss89CrVbrJW+FhYX49ttvMWHChAoPp3Jzc6v3iR5RU6VSqQAAeXl59f65DY1FXl4egDuffU0ZlFiUDo6lZDJZhcymtNOu7K4iRERNSXR0NIYPH46uXbsiODgYS5cuRW5urvQlu74wV99eegH7tGnTpLK7XcCelJSE6OhovbKIiAhs3boVAJCcnIzU1FS9i+KdnJwQEhKCpKQkPPvssxXa/O6773Djxo1KP/cnn3wS+fn5aNu2Ld58803+KEZUjygUCjg7O+PatWsAAFtbW6OeXE2GE0IgLy8P165dg7Ozc62fDWdQYqHT6aTpX375BVOmTMH8+fMRGhoKoHhAmDlzJubPn1+rYIiIGoPBgwfj+vXriImJQWpqKgIDAxEfH1/hgm5LM1ffXpML2FNTUyutX/rE2dL36uqU99lnnyEiIgLNmzeXyuzt7bF48WL06NEDcrkcX3/9NSIjI7F169YqkwuNRgONRiPNZ2VlVVqPiEyn9IhiaXJB5uXs7GySo7hGX2MxadIkxMXFISwsTCqLiIiAra0txowZg5MnT9Y6KCKihm78+PH17tSn6jS2vv3y5cv46aefsGnTJr1yd3d3vSMj3bp1w9WrV7Fw4cIqE4vY2FjMnTvXrPESkT6ZTAYfHx94enqisLDQ0uE0aiqVqtZHKkoZnVicO3cOzs7OFcqdnJxw/vx5E4RERER1zZR9e00uYPf29q62ful7WloafHx89OoEBgZWaG/NmjVwc3Mz6BSnkJAQ7Nixo8rl06ZN00tGsrKy4Ofnd9d2iaj2FAqFyb70kvkZfbvZbt26ITo6Wm8ASEtLwxtvvIHg4GCTBkdERHXDlH172QvYS5VewF56mlV5oaGhevUBYMeOHVJ9f39/eHt769XJysrCvn37KrQphMCaNWsQFRVl0IWIR44c0UtWyrOysoKjo6Pei4iIKjL6iMXq1asxcOBAtGjRQvrF5tKlS2jTpo10kR0RETUspu7b73YBe1RUFJo1a4bY2FgAwMSJE9GrVy8sXrwYAwYMwIYNG3DgwAF88sknAIpPi5g0aRLefvtttGnTRrrdrK+vb4WL0H/99VckJyfjxRdfrBDXunXroFarERQUBAD45ptvsHr1anz66adG7yMREekzOrG499578ffff2PHjh3SRXgdOnRAeHg4r9gnImqgTN233+0C9osXL0Iuv3PQvHv37li/fj1mzpyJ6dOnSwlN6TMsAODNN99Ebm4uxowZg4yMDISFhSE+Ph7W1vrPGfnss8/QvXt3tG/fvtLY5s2bhwsXLkCpVKJ9+/bYuHEjnnnmGaP3kYiI9MlEE3v6SFZWFpycnJCZmcnD2URmkp+fj+TkZPj7+1f40kfmUd1nzn7PtPh5ElFTYkyfZ/QRi7feeqva5TExMcY2SUREFsa+nYiIasvoxGLLli1684WFhUhOToZSqUTr1q05+BARNUDs24mIqLaMTiwOHz5coSwrKwsjRozAwIEDTRIUERHVLfbtRERUW0bfbrYyjo6OmDt3LmbNmmWK5oiIqB5g305ERMYwSWIBAJmZmcjMzDRVc0REVA+wbyciIkMZfSrU8uXL9eaFEEhJScF///tfPPbYYyYLjIioodq1axcWLlyIgwcPIiUlBVu2bKnwrIX6hn07ERHVltGJxZIlS/Tm5XI5PDw8MHz4cEybNs1kgRERNVS5ubkICAjACy+8gKefftrS4RiEfTsREdWW0YlFcnKyOeIgImo0HnvssQb3Kz/7diIiqi2jr7F44YUXkJ2dXaE8NzcXL7zwgkmCIiKqQAigINcyrybwHFH27UREVFtGP3lboVAgJSUFnp6eeuXp6enw9vZGUVGRSQM0NT4xlcj8zPLk7YJc4C0f07RlrJgUQG1Xo1VlMlmdXGNR2ydvN/S+vS5xHCGipsQsT97OysqCEAJCCGRnZ+sNXFqtFtu3b68wIBERUf3Gvp2IiEzF4MTC2dkZMpkMMpkMbdu2rbBcJpNh7ty5Jg2OiEiisi0+cmCpbTdS7NuJiMhUDE4sdu7cCSEEHn74YXz99ddwdXWVlqnVarRs2RK+vr5mCZKICDJZjU9HoqqxbyciIlMxOLHo1asXgOI7h7Ro0QIymcxkQaxcuRILFy5EamoqAgIC8MEHHyA4OPiu623YsAFDhgzBU089ha1bt5osHiKi2sjJycHZs2el+eTkZBw5cgSurq5o0aKFBSOryJx9OxERNS0GJRZ///03OnXqBLlcjszMTBw9erTKuvfff79RAWzcuBHR0dGIi4tDSEgIli5dioiICJw+fbra83rPnz+PyZMno2fPnkZtj4jI3A4cOIA+ffpI89HR0QCA4cOHY+3atRaKqiJz9u1ERNT0GHRXKLlcjtTUVHh6ekIul0Mmk6Gy1WQyGbRarVEBhISEoFu3blixYgUAQKfTwc/PDxMmTMDUqVMrXUer1eKhhx7CCy+8gN9//x0ZGRkGH7Hg3TyIzM8sd4WiatXkrlDm7NsbM44jRNSUmPyuUMnJyfDw8JCmTaWgoAAHDx7Ue6qrXC5HeHg4kpKSqlzvrbfegqenJ0aNGoXff/+92m1oNBpoNBppPisrq/aBExE1Aubq24mIqGkyKLFo2bJlpdO1lZ6eDq1WCy8vL71yLy8vnDp1qtJ1/vjjD3z22Wc4cuSIQduIjY3lHU2IiCphrr6diIiaJoMSi++++87gBp988skaB3M32dnZGDZsGFatWgV3d3eD1pk2bZp0fjNQfMTCz8/PXCESETUY9aVvJyKixsGgxMLQJ8Yaex6uu7s7FAoF0tLS9MrT0tLg7e1dof65c+dw/vx5PPHEE1KZTqcDACiVSpw+fRqtW7fWW8fKygpWVlYGx0RE1FSYq28nIqKmSW5IJZ1OZ9DL2IFHrVajS5cuSEhI0NtWQkICQkNDK9Rv3749jh49iiNHjkivJ598En369MGRI0d4JIKIyAjm6tuJiKhpMvg5FuYSHR2N4cOHo2vXrggODsbSpUuRm5uLkSNHAgCioqLQrFkzxMbGwtraGp06ddJb39nZGQAqlBMRERERUd0x6IhFeQkJCXj88cfRunVrtG7dGo8//jh++eWXGgUwePBgLFq0CDExMQgMDMSRI0cQHx8vXdB98eJFpKSk1KhtIiIynCn7dqD44aetWrWCtbU1QkJCsH///mrrb968Ge3bt4e1tTU6d+6M7du36y0XQiAmJgY+Pj6wsbFBeHg4/vnnH706rVq1gkwm03u9++67enX+/vtv9OzZE9bW1vDz88N7771X430kIqI7jE4sPvzwQ/Tr1w8ODg6YOHEiJk6cCEdHR/Tv3x8rV66sURDjx4/HhQsXoNFosG/fPoSEhEjLEhMTq32g1Nq1a/nUbSKiWjJ131768NPZs2fj0KFDCAgIQEREBK5du1Zp/T179mDIkCEYNWoUDh8+jMjISERGRuLYsWNSnffeew/Lly9HXFwc9u3bBzs7O0RERCA/P1+vrbfeegspKSnSa8KECdKyrKws9O3bFy1btsTBgwexcOFCzJkzB5988onR+0hEROUIIzVr1kx88MEHFcpXrFghfH19jW2uzmVmZgoAIjMz09KhEDVat2/fFidOnBC3b9+2dChNRnWfuSH9nqn79uDgYDFu3DhpXqvVCl9fXxEbG1tp/UGDBokBAwbolYWEhIiXXnpJCCGETqcT3t7eYuHChdLyjIwMYWVlJb788kuprGXLlmLJkiVVxvXhhx8KFxcXodFopLIpU6aIdu3aGbxvHEeIqCkxps8z+ohFRkYG+vXrV6G8b9++yMzMrHWiQ0REdc+UfXvpw0/Dw8Olsrs9/DQpKUmvPgBERERI9ZOTk5GamqpXx8nJCSEhIRXafPfdd+Hm5oagoCAsXLgQRUVFett56KGHoFar9bZz+vRp3Lp1y6j9JCIifUYnFk8++SS2bNlSofzbb7/F448/bpKgiIgaqtjYWHTr1g0ODg7w9PREZGQkTp8+bemw7sqUfXt1Dz9NTU2tdJ3U1NRq65e+363NV199FRs2bMDOnTvx0ksvYf78+XjzzTfvup2y2yhPo9EgKytL70VERBUZfVeojh074p133kFiYqJ0S9i9e/di9+7deP3117F8+XKp7quvvmq6SImIGoDffvsN48aNQ7du3VBUVITp06ejb9++OHHiBOzs7CwdXpUaS99e9oGo999/P9RqNV566SXExsbW+JlGsbGxmDt3rqlCJCJqtIxOLD777DO4uLjgxIkTOHHihFTu7OyMzz77TJqXyWT1evAhIjKH+Ph4vfm1a9fC09MTBw8exEMPPWShqO7OlH27sQ8/BQBvb+9q65e+p6WlwcfHR69OYGBglbGEhISgqKgI58+fR7t27arcTtltlDdt2jS9hCUrK4vPTSIiqoTRiUVycrI54iAiqpYQAkVFlnlQm1KpgEwmq9G6pdcnuLq6mjIkkzNl31724aelT/cuffjp+PHjK10nNDQUCQkJmDRpklS2Y8cO6eiJv78/vL29kZCQICUSWVlZ2LdvH8aOHVtlLEeOHIFcLoenp6e0nRkzZqCwsBAqlUraTrt27eDi4lJpG1ZWVjU+2kFE1JRY/AF5RESGKCrS4rN1Fa8BqAujhg+ESmV8d6nT6TBp0iT06NGjyT3E05iHnwLAxIkT0atXLyxevBgDBgzAhg0bcODAAek2sDKZDJMmTcLbb7+NNm3awN/fH7NmzYKvr6+UvCQlJWHfvn3o06cPHBwckJSUhNdeew3PP/+8lDQMHToUc+fOxahRozBlyhQcO3YMy5Ytw5IlS+r+QyIiamSMHimFEPjqq6+wc+dOXLt2DTqdTm/5N998Y7LgiIgasnHjxuHYsWP4448/LB3KXZm6bx88eDCuX7+OmJgYpKamIjAwsMLDT+XyO/cP6d69O9avX4+ZM2di+vTpaNOmDbZu3aqXkL355pvIzc3FmDFjkJGRgbCwMMTHx8Pa2hpA8ZGFDRs2YM6cOdBoNPD398drr72mdxqTk5MTfv75Z4wbNw5dunSBu7s7YmJiMGbMGKM/MyIi0icTQghjVpg4cSI+/vhj9OnTB15eXhVOD1izZo1JAzS1rKwsODk5ITMzE46OjpYOh6hRys/PR3JyMvz9/aUvfbXV0E6FGj9+PL799lvs2rUL/v7+Zorsjuo+c0P6vYbet9cljiNE1JQY0+cZfcTiv//9L7755hv079+/xgESERlLJpPV6HSkuiaEwIQJE7BlyxYkJibWSVJhCuzbiYiotowepZ2cnHDPPfeYIxYiogZv3LhxWL9+Pb799ls4ODhIz0ZwcnKCjY2NhaOrGvt2IiKqLaMfkDdnzhzMnTsXt2/fNkc8REQN2kcffYTMzEz07t0bPj4+0mvjxo2WDq1a7NuJiKi2jD5iMWjQIHz55Zfw9PREq1atpNv1lTp06JDJgiMiamiMvGyt3mDfTkREtWV0YjF8+HAcPHgQzz//fKUX+BERUcPDvp2IiGrL6MRi27Zt+OmnnxAWFmaOeIiIyALYtxMRUW0ZfY2Fn58fb69HRNTIsG8nIqLaMjqxWLx4Md58802cP3/eDOEQEZElsG8nIqLaMvpUqOeffx55eXlo3bo1bG1tK1zgd/PmTZMFR0REdYN9OxER1ZbRicXSpUvNEAYRNUYN9Q5JDVFtP2v27UREVFs1uisUEVF1Sn/tzsvLq9cPhWtM8vLyAKDCkQZDsW8nIqLaMjqxKCs/Px8FBQV6Zbz4j4gUCgWcnZ1x7do1AICtrS1vX2omQgjk5eXh2rVrcHZ2hkKhqHWb7NuJiKgmjE4scnNzMWXKFGzatAk3btyosFyr1ZokMCJq2Ly9vQFASi7IvJydnaXPvCbYtxMRUW0ZnVi8+eab2LlzJz766CMMGzYMK1euxJUrV/Dxxx/j3XffNUeMRNQAyWQy+Pj4wNPTE4WFhZYOp1FTqVS1PlLBvp2IiGrL6MTi+++/x+eff47evXtj5MiR6NmzJ+699160bNkSX3zxBZ577jlzxElEDZRCoTDJ6TlkXuzbiYiotox+jsXNmzdxzz33ACg+57b0FoRhYWHYtWuXaaMjIqI6wb6diIhqy+jE4p577kFycjIAoH379ti0aROA4l+7nJ2dTRocERHVDfbtRERUW0YnFiNHjsRff/0FAJg6dSpWrlwJa2trvPbaa3jjjTdMHiAREZkf+3YiIqotmajlU5UuXLiAgwcP4t5778X9999fozZWrlyJhQsXIjU1FQEBAfjggw8QHBxcad1Vq1bh888/x7FjxwAAXbp0wfz586usX15WVhacnJyQmZnJ2ycSUZNQk37PFH17Y8VxhIiaEmP6vFo9xwIAWrZsiZYtW9Z4/Y0bNyI6OhpxcXEICQnB0qVLERERgdOnT8PT07NC/cTERAwZMgTdu3eHtbU1FixYgL59++L48eNo1qxZbXaFiIhK1LZvJyKipqfWRyxqKyQkBN26dcOKFSsAADqdDn5+fpgwYQKmTp161/W1Wi1cXFywYsUKREVF3bU+f2kioqaG/Z5p8fMkoqbEmD7P6GssTKmgoAAHDx5EeHi4VCaXyxEeHo6kpCSD2sjLy0NhYSFcXV3NFSYREREREd1FrU+Fqo309HRotVp4eXnplXt5eeHUqVMGtTFlyhT4+vrqJSdlaTQaaDQaaT4rK6vmARMRERERUaUsesSitt59911s2LABW7ZsgbW1daV1YmNj4eTkJL38/PzqOEoioqZp5cqVaNWqFaytrRESEoL9+/dXW3/z5s1o3749rK2t0blzZ2zfvl1vuRACMTEx8PHxgY2NDcLDw/HPP/9Iy8+fP49Ro0bB398fNjY2aN26NWbPno2CggK9OjKZrMJr7969pt15IqImqEaJxblz5zBz5kwMGTIE165dAwD8+OOPOH78uFHtuLu7Q6FQIC0tTa88LS0N3t7e1a67aNEivPvuu/j555+rvWPJtGnTkJmZKb0uXbpkVIxERE2Fqfp24M6NOWbPno1Dhw4hICAAERERUrvl7dmzB0OGDMGoUaNw+PBhREZGIjIyUroDIAC89957WL58OeLi4rBv3z7Y2dkhIiIC+fn5AIBTp05Bp9Ph448/xvHjx7FkyRLExcVh+vTpFbb3yy+/ICUlRXp16dLF6H0kIqJyhJESExOFjY2NCA8PF2q1Wpw7d04IIURsbKz4v//7P2ObE8HBwWL8+PHSvFarFc2aNROxsbFVrrNgwQLh6OgokpKSjN5eZmamACAyMzONXpeIqCEypN8zR98+btw4aV6r1QpfX98q+/ZBgwaJAQMG6JWFhISIl156SQghhE6nE97e3mLhwoXS8oyMDGFlZSW+/PLLKuN47733hL+/vzSfnJwsAIjDhw8bvU+lOI4QUVNiTJ9n9BGLqVOn4u2338aOHTugVqul8ocffrhGh5Kjo6OxatUqrFu3DidPnsTYsWORm5uLkSNHAgCioqIwbdo0qf6CBQswa9YsrF69Gq1atUJqaipSU1ORk5Nj9LaJiKiYKfv2mtyYIykpqcK1chEREVL95ORkpKam6tVxcnJCSEhItTf7yMzMrPTmHk8++SQ8PT0RFhaG7777rtr90Wg0yMrK0nsREVFFRicWR48excCBAyuUe3p6Ij093egABg8ejEWLFiEmJgaBgYE4cuQI4uPjpQu6L168iJSUFKn+Rx99hIKCAjzzzDPw8fGRXosWLTJ620REVMyUfXt1N+ZITU2tdJ3U1NRq65e+G9Pm2bNn8cEHH+Cll16Syuzt7bF48WJs3rwZ27ZtQ1hYGCIjI6tNLnitHhGRYYy+K5SzszNSUlLg7++vV3748OEaP6Bu/PjxGD9+fKXLEhMT9ebPnz9fo20QEVHVzNG3W9KVK1fQr18//Oc//8Ho0aOlcnd3d0RHR0vz3bp1w9WrV7Fw4UI8+eSTlbY1bdo0vXWysrKYXBARVcLoIxbPPvsspkyZgtTUVMhkMuh0OuzevRuTJ0826AF1RERU/5iyb6/JjTm8vb2rrV/6bkibV69eRZ8+fdC9e3d88sknd403JCQEZ8+erXK5lZUVHB0d9V5ERFSR0YnF/Pnz0b59e/j5+SEnJwcdO3bEQw89hO7du2PmzJnmiJGIiMzMlH27Wq1Gly5dkJCQIJXpdDokJCQgNDS00nVCQ0P16gPAjh07pPr+/v7w9vbWq5OVlYV9+/bptXnlyhX07t0bXbp0wZo1ayCX332YO3LkCHx8fIzaRyIiqsjoU6HUajVWrVqFWbNm4dixY8jJyUFQUBDatGljjviIiKgOmLpvj46OxvDhw9G1a1cEBwdj6dKlFW7M0axZM8TGxgIAJk6ciF69emHx4sUYMGAANmzYgAMHDkhHHGQyGSZNmoS3334bbdq0gb+/P2bNmgVfX19ERkYCuJNUtGzZEosWLcL169eleEqPaqxbtw5qtRpBQUEAgG+++QarV6/Gp59+WqP9JCKiO2r85O0WLVqgRYsWpoyFiIgszFR9++DBg3H9+nXExMQgNTUVgYGBFW7MUfZoQvfu3bF+/XrMnDkT06dPR5s2bbB161Z06tRJqvPmm28iNzcXY8aMQUZGBsLCwhAfHy89IHXHjh04e/Yszp49i+bNm+vFI4SQpufNm4cLFy5AqVSiffv22LhxI5555pla7zMRUVMnE2V72yqUvWjtbt5///1aBWRuWVlZcHJyQmZmJs+TJaImoap+rzH17XWJ4wgRNSXG9HkGHbE4fPiw3vyhQ4dQVFSEdu3aAQDOnDkDhULRJJ5cqtPpUFBQWPwqLLozXfLSSMv0y8vOq1RKeLi5wMPDBR7uLvBwd4WtrbWld42Imhj27UREZEoGJRY7d+6Upt9//304ODhg3bp1cHFxAQDcunULI0eORM+ePc0TpRmk37iF7Jx8/WSgikSg7HxRkbbW2y4sLMKFvBRcuHTn+Ry2ttbwdHeFu3tpsuHCZIOIzKox9u11KTsnDw4ODpDJZJYOhYioXjDoVKiymjVrhp9//hn33XefXvmxY8fQt29fXL161aQBmlrp4Zz3l6+FjY1tjdtRKhVQq1VQq1TF76Wv8vNqFaxK3lUqJdRqFfLzNbiefkt6ZWRkobK/gp2tTXGS4eFSnHC4MdkgIuMZchi7offtdansOOLg6ABvT3fpByF3dxc42Nsy2SCiRsPkp0KVb7zsnTZKXb9+HdnZ2cY2ZzF2NjZwcnaskASUJgZWahVUaqVUZlWmjkqtgsKAWxhWyckB3l7u0mxhYRHSb2QgvUyycSsjC7l5t5F78TbOX7wzoNvZ2UgDWOnLxobJBhHVTmPp2+uSVqdFUaEWl6+k4fKVO8/XsLJSw93NWUo0PNxd4Ohgx2SDiBo9oxOLgQMHYuTIkVi8eDGCg4MBAPv27cMbb7yBp59+2uQBmsuzg/rVm4vuVColfLzd4eNdMdkoTjRuIj39Fm5lZCM39zZyc2/j/IU7yYa9nU3J4OVaJtmwssSuEFED1Vj69rp0RHsU58+lwFnpCB9bL/jaesHTxh0aTQGuXL2GK1evSXXVapWUbJQmHE6O9kw2iKhRMfpUqLy8PEyePBmrV69GYWEhAECpVGLUqFFYuHAh7OzszBKoqTTku3kUFBTixs2SZON6yWlUmZX/kmhvb6t3gbi7uwtsrJlsEDVFhvR7Db1vr0tlP08bO1sc+Pcodh7fi8QTe3H80ll42rjB19YLPrZeaGbvAy8bd8hlFY9yq1VKuLm5wMPdWTqy4eToALmcyQYR1R/GfHc2OrEolZubi3PnzgEAWrdu3WAGnYacWFSmoKCwzJGNW0i/W7JR7jQqayYbRI2eMf1eQ+3b61J1n+f1rBv47eT+4kTj5H7cyL4FuUwOT2s3+Nh5ob1ba7Ry8oO1sKr02jqVSgk319LTqIrfnZ0cmWwQkcXUSWIBAJcvXwaACg8iqs8aW2JRGSnZuH4T128UJxyZmTmV1nUoTTY8XOHuVppsqOs4YiIyJ2P7vYbYt9clQz9PnU6Ho5fOIPHEXuw8vhd//nsUWl3xnQXlkMHH3gs9WnVFB/d74aR0RF5OfqV3HlQqFWWSDRd4urvAxcWRp1ERUZ0wa2Kh0+nw9ttvY/HixcjJKf6y6uDggNdffx0zZszQe5JqfdQUEovKaAoKpYvDS98zs6pINhzsKhzZsLJiskHUUBnS7zX0vr0u1XQcybqdgz9OHcDOkkTj8s1UveW+zp54tH1PBDW7D55WbsjOykP6jQwUFhZVaEutVsHbq/jaPG8vd3h6uEChUNR634iIyjNrYjFt2jR89tlnmDt3Lnr06AEA+OOPPzBnzhyMHj0a77zzTs0jrwNNNbGojEZToHca1fX0W8iqItlwdLDTe8YGkw2ihsOQfq+h9+11yRTjiBACZ9MuIPH4Pvx6IglJZw4jv1AjLZfL5HjA/z707hCCUP8guFu54caNTOmGHuWPbCgUcnh6uErJhpeXO6zUqlrtJxERYObEwtfXF3FxcXjyySf1yr/99lu88soruHLlivER1yEmFtUrTTauXS++E9X1G7eQlZVbaV1HRzt4uJUcmvdwhbubM5MNonrIkH6vofftdckc48jtgnzsO/uXdDTjTEqy3nIXO0c81CEYvTs+iO5tHoC1sELatRtISU1Hamo6budrKrTp5uoEH28PKdmws7MxSaxE1LSYNbGwtrbG33//jbZt2+qVnz59GoGBgbh9+7bxEdchJhbG02gK9I5qpKffQlZ2VcmGvd5RDXd3F/5qRmRhhvR7Db1vr0t1MY5cuZlWfG3GiX3YdXI/sm7rH01WK1Vo7uqNFu6+8HP1gZ+TL9xVrlAVqZGfU4DcnIp/LwcHO+nUKR9vdzg78anhRHR3Zk0sQkJCEBISguXLl+uVT5gwAX/++Sf27t1rfMR1iImFaeTnFyD9xi29hCO7mmTDsyTJcHdzho21FVRqFVRKJVQqJRQKOQc3IjMypN9r6H17XarrcaRIW4TD50/g1+PFRzOOXjotXQReFTcbFwR4dcA9Ti3hoXaDWqghg34/a22tLkkyio9quLs71+7hr0TUKJk1sfjtt98wYMAAtGjRAqGhoQCApKQkXLp0Cdu3b0fPnj1rHnkdYGJhPvn5GlxPz5Ae6Hc9/Rayc/Luup5cJoNSpYRapYRKpYJKpSz3UpUsuzNfOi2tU5KkqNRKKBUKJipEZRjS7zX0vr0uWXocKdQWIeXWNVy6kYJLN1JwseS99JVy6xp0Qqe3jpVcjeb2Pmhh3wwtHZqjmZ0PVHL9Z+TK5ICDky2a+XjhnhbN4e3lDpXK6OfoElEjY/bbzV69ehUrV67EqVOnAAAdOnTAK6+8Al9f35pFXIcsPSA0NbfzNVKScT39Fm7eykSBphCFhUUo0lb/i1tNyWQoSTSMT1KUSiXU6pIy5Z16TFSoITO032vIfXtdqu/jSKG2CFdLEo+L6VelhONyyfvVjGvFt7u19ZISjRb2vrBR6l+DoRM65IhcCLUO9s628PX2QCvvZvBz84GbvTP7RaImos6eY9EQ1fcBoSnR6QQKi4pQWFicaJR9FVQoKyyzrFxZ0Z165lI+0SibpFR3tEWtKpOkqIvflUoF5HKe/kV1h/2eaTX0z7OgqBApt67h4o07Scel9BTcysgCNDI4yZ3Qwr4ZnK0q7lv67Zu4mHMFKflp0Kq0cHVxKr7Gw63k5V787mrnxD6OqJEwps+r0THO/Px8/P3337h27Rp0Ov3DreXvKEJUFblcBiu1ymQXdwshKiQapQlIQTVJSlFViUxRkfRkXClxMeH1q0qlouRVfPqWUqmAQqGotLz0Vby8ODkpu0yvvNwyJjFkKPbtTYNaqUJLj2Zo6dGs0uUFRYW4cjMV/169hEtXUpF5KxciH7CGNdxtXOFu4wqgMwAguyAHFy5dQfzJP3Ax+zLSbqdDQMDOyrYk2fC+k3S4+cDPzRd+bj5wseMD/ogaI6MTi/j4eERFRSE9Pb3CMplMBq2ZTm8huhuZTAa1WgW1CROVIq0WhQVlk5XCckdN9MuqS1IKC4qgK3OAsKhIW3Iv+gKTxFsdJjF0N+zbqZRaqYK/px/8Pf2AwDvl+fkFSLuWjktX03D5SioyMnLgoLZHJ9d26OTaDgCg0RbgYs6V4lfWZexM2YciUfFosr21bbmEo/jVoiTxcLLlHauIGiKjT4Vq06YN+vbti5iYGHh5eZkrLrNp6IewqeESQkCnK05WioqKoC1JLIrnS19F0rxWb1lxeVXraLVaFOqtc+doS12rNolRKKBQKqCQy6FQKKBQyovLFAooFPJK35UGlvHuYlUzpN9r6H17XeI4UqyoSItr128WP0sjrfhV4ZRUGVCoKMStogwkZ13CsWuncCkj5a5tO1jbVUw63H3R3NUbbg4ucLC2g52VDZ8IT1QHzHqNhaOjIw4fPozWrVvXKkhL4YBATUHtkhgtirRVr1OfkpjyFAp5maSlJOmQV5/AVJW4yBXycsvKLpeXS5CKy+rrERtD+r2G3rfXJenzvHENjq4elg6n3tDpBG7ezEBKWrr04L682/kV6jk7O8DeyQZaVRFuaTNxJStV73qP61k3DdqeTCaDvZUtHGzsYG9tBwdrO/1pazvY29jBwdq2zHTFevbWtlAqePcroqqY9RqLZ555BomJiRx8iOoxmUwGhUIGhUJu9gcUGpPEaLU6aPXetSgq0kGrK1NWVDKtK1lWUq/sukVlysoqrqMDzHgh/91UdiSlNFFRyIuTD4WiOCmRlyQjpdN3lhe/l53Wq1P+vZJ6ZacLCgrvGjf79hqIvQdwdARs3QBbV8DOrWS65GVXybSNC9BIv8TK5bLi5xW5u6DzfW0ghEBWdi5SU4sTjZS068jMzEFGRjYyMrKl9Vrat8SDrbvAp4c7vL3dYWWrxtVbaXdup1vmzlaXbqQgIzcLRTothBDIzs9Fdn7lz1Ayho3ausaJSdlpK5W61rEQNWRGH7HIy8vDf/7zH3h4eKBz585QqfS/tLz66qsmDdDUeMSCqPEoTWqkJKWKxEWnK1lWpJ+kFGnvnrjo168sOdLdPVALu307D9Gvjqi232vofXtdksaRyfZwtDLyCJVMBlg7G5CEuN6ZtnYuXq8RuH07Xzp1KiU1Hek3MlD+a4iVlRreXm7Sg/s83F2gUNw55UkIAU1RAbJv5yKnJLGoOJ2H7PxcZN3OKS6/XbxMfzoP+YUak+6flVJdJgGxLU44bEqPjFQ+XT5psbexg43Kql4e/aSmyaynQn322Wd4+eWXYW1tDTc3N71/+DKZDP/++2/Noq4j0oczqwUcbRQAZCUdtqz46UDSdJl3WUmHJpNVsry6dcqWQX+ZTFHyXvYlA+Tly8q+FGXWr+Ill5erU8V2qlxfcfc65duRV7aNarYlV9yJS15mXu+9XF152XJFyX4qyrVVJnaiOiKEKDnCop+4FJVLcLRaHXS64nq6kvo6nU4qLztdvo40XYP1dEIYlFiYo29fuXIlFi5ciNTUVAQEBOCDDz5AcHBwlfU3b96MWbNm4fz582jTpg0WLFiA/v37633Ws2fPxqpVq5CRkYEePXrgo48+Qps2baQ6N2/exIQJE/D9999DLpfj//7v/7Bs2TLY29tLdf7++2+MGzcOf/75Jzw8PDBhwgS8+eabBu+XNI6knoejohDIuwHk3QRyb5RM3yg3nV78fjvDuA+wlFyhn4TYOAMqm5KXNaAseVfZlpu3AZTWZeqWnS+pp7Qu7k8tpLCwCGnXbkjJRtq1GyU3tbhDoZDD08MNPt7u8PF2h62tTclwLNN/AZDJZZChXHlldUvKCrVFUhJSdQJS3XTxurmauz8M1hgKuUI6TatsMmJnZQu1UgmVUgW1QgWlQlFmWlm8TKGCWlkyryiuq1KUlitL6qn05qtcR6mCWqGEQs4HzzZlZk0svL298eqrr2Lq1Kkmu2jK1INPdWr1SxM1DBWSF0UlyUiZ6arKq2vHqPbLJz7lE6dKkk295Kp8cllZElgmUTNn+xXWNaB9JnsWJYTArVsZcHNzrXZQMHXfvnHjRkRFRSEuLg4hISFYunQpNm/ejNOnT8PT07NC/T179uChhx5CbGwsHn/8caxfvx4LFizAoUOH0KlTJwDAggULEBsbi3Xr1sHf3x+zZs3C0aNHceLECVhbWwMAHnvsMaSkpODjjz9GYWEhRo4ciW7dumH9+vUAiseAtm3bIjw8HNOmTcPRo0fxwgsvYOnSpRgzZoxB+1bjI9/aIuD2rUoSj0qSkNybxe8FOYa3X1NK6yoSEttKEpSS8vIJijGJTTX9gVanQ3p6BlJTryOl5ILw/Hzz3jnvbsmHDLI7CYu8JIEpVxeykiOoQgcBUZzcCx20Qluc9AstinTa4lM8tVoU6YpQpNOiUFtU8ipEobYIBdpCCKErbgsCQgiIMu86IaCDTtqWDsU/HgihK5kXZZaVxCN00no6qe2SspJ4daKy+ZK2i7dYfE2ZXCGddqmUK4uvOZPLoFCUJihyKBQlz25SqqCSF0+XJjKq0mUK/cTlTrKjvJPgSAnRnUSq9BRPpVwpTSvkCshld6aVckXJspJTTmV3pqV9KLOMCdPdmTWxcHV1xZ9//mmy83DNMfhUR/pw/tkHR3s7AALFV56Wexe6SpahZFlVy8u+66pYhpJl5V668mUCENrK60rLy65bWV1Rxba0d68j1atmedk2dAbGKrQldbV3Yi+dl9rQ3tknvbol79Tw3PUImBFHyKotq2k75Zebop2ydcrUhayKNsof7aykTtmjpOXL5VW3n5WbB6euz1Q7KJi6bw8JCUG3bt2wYsUKAIBOp4Ofnx8mTJiAqVOnVqg/ePBg5Obm4ocffpDKHnzwQQQGBiIuLg5CCPj6+uL111/H5MmTAQCZmZnw8vLC2rVr8eyzz+LkyZPo2LEj/vzzT3Tt2hVA8W10+/fvj8uXL8PX1xcfffQRZsyYgdTUVKjVxefDT506FVu3bpWeOH43dXpKbWE+cLvc0ZC8W0BRPlB4u/glTZe8F5WdLrss7868znLXIeklMmqbO4lIJYmNUFojQ+aMlEInpBbaIy3fGgU6WfGwgtL3klfptJChntxPokkrTU7unsjcmS5OjHTlEiP9ZcV/YyG1U3a+bBJW4b0kMSu/DCiT65YklEC597KJJ8omlqXryCCXySArSVTkMpRMF9dVyOXF83JADjlk8uL68tLkRi4rToBKp+UyyGUKyOWyO9fRyeUl8cjKbKNkmyVJr1w6cldcJpfivLPN0ljvLCtOmktvPlKabMnlMmn+dm4eIntEmCexeO211+Dh4YHp06cbs1qVTD343A2vsWjgSpMUvSREq5+MlC7XS1hEubqGJDdlE5ry7VYVQ5kEq0IM1SV4JTGWTxKrStjKr6urJBmti3Xry+2gqFpZGgGnRTnV9num7NsLCgpga2uLr776CpGRkVL58OHDkZGRgW+//bbCOi1atEB0dDQmTZoklc2ePRtbt27FX3/9hX///RetW7fG4cOHERgYKNXp1asXAgMDsWzZMqxevRqvv/46bt26JS0vKiqCtbU1Nm/ejIEDByIqKgpZWVnYunWrVGfnzp14+OGHcfPmTbi4uFSITaPRQKO5cy5+VlYW/Pz8GvY4oi3ST0CqTEjKJy8NJ5EpTjjkxQmIrOQdcgiZ7E455FUv05suU6fK9sqvX9U6ZaZlikpecggoitsqLSuNU1a8HV1Jma6kXO+9pF0dZMXtQAadtF7xcl1JvLoyMd8pK5mXyu/sQ/k6PBrdNBhyOm0po29NodVq8d577+Gnn37C/fffX+ECv/fff9/gtgoKCnDw4EFMmzZNKpPL5QgPD0dSUlKl6yQlJSE6OlqvLCIiQm+QKKuyAYEasNLTcuQKS0dCAO4cwasqKSlz1EunBVCaPBl4dM2geoaWGVNHmLCt0n1H1e2ifLmopLySdUqTwgrrl2s3rwDAnmr/lKbs29PT06HVais8D8PLy6vKowKpqamV1k9NTZWWl5ZVV6f8kW6lUglXV1e9Ov7+/hXaKF1WWWIRGxuLuXPnVr3DDZFCCSgcACuHutmeXiKTV/xudCJTUr/aH0Hu/PuX6bSQVfl/taofT+7yf73a5U3vxxYp2ZAVJzE6lE9+ZHeSIJm8XDKkKFNXLiVnVa4nK74utkJyVibBQ4Vld5JEVJXklcyjZF8gk1Xarg7FdfQT0+Kjx6UJHcotK1sX0uek37bQa7fssjLbKLMc5cpK/w6lZdK8rOy8HAIoOaptXkYnFkePHkVQUBAA4NixY3rLjD1PzRyDT3mNckAgqi9KT8mB+TsrqoWsLOB1p2qrmLJvb2ymTZum94NW6RELMkJdJzKWUvaHgQpJRxU/ElR5KnEVpxmjzKnV0nSZ07UrvN+tfk2WFy+TAVDUpE1Dt1HpckMYWNccbRrVrrFtlvncatim9OcrmdGbl8rLbkmGrNw86P+kXzWjE4udO3cau4pFcUAgIro7U/bt7u7uUCgUSEtL0ytPS0uDt7d3pet4e3tXW7/0PS0tDT4+Pnp1Sk+N8vb2xrVr1/TaKCoqws2bN/XaqWw7ZbdRnpWVFaysrKrcXyIJf2yhek5W7t0QVllZACYZVNei//LNMfiUZ2VlBUdHR70XERGZj1qtRpcuXZCQkCCV6XQ6JCQkIDQ0tNJ1QkND9eoDwI4dO6T6/v7+8Pb21quTlZWFffv2SXVCQ0ORkZGBgwcPSnV+/fVX6HQ6hISESHV27dqFwsJCve20a9eu0tOgiIjIcBZNLMwx+BARkeVFR0dj1apVWLduHU6ePImxY8ciNzcXI0eOBABERUXpXV83ceJExMfHY/HixTh16hTmzJmDAwcOYPz48QCKT8eaNGkS3n77bXz33Xc4evQooqKi4OvrK10g3qFDB/Tr1w+jR4/G/v37sXv3bowfPx7PPvssfH19AQBDhw6FWq3GqFGjcPz4cWzcuBHLli2rcO0eERHVgLCwDRs2CCsrK7F27Vpx4sQJMWbMGOHs7CxSU1OFEEIMGzZMTJ06Vaq/e/duoVQqxaJFi8TJkyfF7NmzhUqlEkePHjVoe5mZmQKAyMzMNMv+EBHVN5bq9z744APRokULoVarRXBwsNi7d6+0rFevXmL48OF69Tdt2iTatm0r1Gq1uO+++8S2bdv0lut0OjFr1izh5eUlrKysxCOPPCJOnz6tV+fGjRtiyJAhwt7eXjg6OoqRI0eK7OxsvTp//fWXCAsLE1ZWVqJZs2bi3XffNWq/OI4QUVNiTJ9n9O1mzWHFihXSA/ICAwOxfPly6bB179690apVK6xdu1aqv3nzZsycOVN6QN57771n/APyGvJtAomIjMB+z7T4eRJRU2LWB+Q1dBwQiKipYb9nWvw8iagpMabP420LiIiIiIio1oy+3WxDV3qAhg/KI6KmorS/a2IHqM2G4wgRNSXGjCFNLrHIzs4GAD7LgoianOzsbDg5Vf+gPLo7jiNE1BQZMoY0uWssdDodrl69CgcHB4s8Tbb0AX2XLl1qkufmNuX9b8r7DjTt/bf0vgshkJ2dDV9fX8jlPAO2tiw5jlj635KlNeX9b8r7DjTt/bf0vhszhjS5IxZyuRzNmze3dBhN/mF9TXn/m/K+A017/y257zxSYTr1YRxpyv+PgKa9/01534Gmvf8NYQzhT1dERERERFRrTCyIiIiIiKjWmFjUMSsrK8yePRtWVlaWDsUimvL+N+V9B5r2/jflfSfTaur/lpry/jflfQea9v43pH1vchdvExERERGR6fGIBRERERER1RoTCyIiIiIiqjUmFkREREREVGtMLExs165deOKJJ+Dr6wuZTIatW7fedR2NRoMZM2agZcuWsLKyQqtWrbB69WrzB2tiNdn3L774AgEBAbC1tYWPjw9eeOEF3Lhxw/zBmlhsbCy6desGBwcHeHp6IjIyEqdPn77reps3b0b79u1hbW2Nzp07Y/v27XUQrenVZP9XrVqFnj17wsXFBS4uLggPD8f+/fvrKGLTqenfvtSGDRsgk8kQGRlpviCpweAYwjGEYwjHkIY8hjCxMLHc3FwEBARg5cqVBq8zaNAgJCQk4LPPPsPp06fx5Zdfol27dmaM0jyM3ffdu3cjKioKo0aNwvHjx7F582bs378fo0ePNnOkpvfbb79h3Lhx2Lt3L3bs2IHCwkL07dsXubm5Va6zZ88eDBkyBKNGjcLhw4cRGRmJyMhIHDt2rA4jN42a7H9iYiKGDBmCnTt3IikpCX5+fujbty+uXLlSh5HXXk32vdT58+cxefJk9OzZsw4ipYaAYwjHEI4hHEMa9BgiyGwAiC1btlRb58cffxROTk7ixo0bdRNUHTFk3xcuXCjuuecevbLly5eLZs2amTGyunHt2jUBQPz2229V1hk0aJAYMGCAXllISIh46aWXzB2e2Rmy/+UVFRUJBwcHsW7dOjNGZn6G7ntRUZHo3r27+PTTT8Xw4cPFU089VTcBUoPBMWRLtXU4hnAMKYtjyFN1E+Bd8IiFhX333Xfo2rUr3nvvPTRr1gxt27bF5MmTcfv2bUuHZnahoaG4dOkStm/fDiEE0tLS8NVXX6F///6WDq3WMjMzAQCurq5V1klKSkJ4eLheWUREBJKSkswaW10wZP/Ly8vLQ2FhoVHr1EeG7vtbb70FT09PjBo1qi7CokaKYwjHkLI4hnAMsTSlpQNo6v7991/88ccfsLa2xpYtW5Ceno5XXnkFN27cwJo1aywdnln16NEDX3zxBQYPHoz8/HwUFRXhiSeeMOoUgPpIp9Nh0qRJ6NGjBzp16lRlvdTUVHh5eemVeXl5ITU11dwhmpWh+1/elClT4OvrW2GgbEgM3fc//vgDn332GY4cOVJ3wVGjxDGEY0hZHEM4hlgaj1hYmE6ng0wmwxdffIHg4GD0798f77//PtatW9fof3E6ceIEJk6ciJiYGBw8eBDx8fE4f/48Xn75ZUuHVivjxo3DsWPHsGHDBkuHYhE12f93330XGzZswJYtW2BtbW3G6MzLkH3Pzs7GsGHDsGrVKri7u9dhdNQYcQzhGNLYcAxp4GOIpc/FasxgwDmiUVFRonXr1nplJ06cEADEmTNnzBideRmy788//7x45pln9Mp+//13AUBcvXrVjNGZz7hx40Tz5s3Fv//+e9e6fn5+YsmSJXplMTEx4v777zdTdOZnzP6XWrhwoXBychJ//vmnGSMzP0P3/fDhwwKAUCgU0ksmkwmZTCYUCoU4e/ZsHUVM9R3HkC3V1uEYwjFECI4h9W0M4RELC+vRoweuXr2KnJwcqezMmTOQy+Vo3ry5BSMzv7y8PMjl+v8EFQoFAEAIYYmQakwIgfHjx2PLli349ddf4e/vf9d1QkNDkZCQoFe2Y8cOhIaGmitMs6nJ/gPAe++9h3nz5iE+Ph5du3Y1c5TmYey+t2/fHkePHsWRI0ek15NPPok+ffrgyJEj8PPzq6PIqTHgGMIxpCyOIQ1PoxtDLJfTNE7Z2dni8OHDUkb5/vvvi8OHD4sLFy4IIYSYOnWqGDZsmF795s2bi2eeeUYcP35c/Pbbb6JNmzbixRdftNQu1Jix+75mzRqhVCrFhx9+KM6dOyf++OMP0bVrVxEcHGypXaixsWPHCicnJ5GYmChSUlKkV15enlRn2LBhYurUqdL87t27hVKpFIsWLRInT54Us2fPFiqVShw9etQSu1ArNdn/d999V6jVavHVV1/prZOdnW2JXaixmux7efXpjh5kWRxDOIZwDOEY0pDHECYWJrZz504BoMJr+PDhQojiP36vXr301jl58qQIDw8XNjY2onnz5iI6OlrvH1RDUZN9X758uejYsaOwsbERPj4+4rnnnhOXL1+u++BrqbL9BiDWrFkj1enVq5f0WZTatGmTaNu2rVCr1eK+++4T27Ztq9vATaQm+9+yZctK15k9e3adx18bNf3bl1WfBgWyLI4hHEM4hnAMachjiEyIBna8kIiIiIiI6h1eY0FERERERLXGxIKIiIiIiGqNiQUREREREdUaEwsiIiIiIqo1JhZERERERFRrTCyIiIiIiKjWmFgQEREREVGtMbEgIiIiIqJaY2JBDYIQAmPGjIGrqytkMhmOHDli6ZDqpREjRkAmk0Emk2Hr1q0mbTsxMVFqOzIy0qRtN2a7du3CE088AV9f3xr9XfLz8zFixAh07twZSqXyrp/97t27oVQqERgYWOOYiRobjiGG4RhSPzWkcYSJBTUI8fHxWLt2LX744QekpKSgU6dOlg6p3urXrx9SUlLw2GOPSWVVdUQjRowwuIPv3r07UlJSMGjQIBNF2jTk5uYiICAAK1eurNH6Wq0WNjY2ePXVVxEeHl5t3YyMDERFReGRRx6p0baIGiuOIYbjGFL/NKRxhIkFNQjnzp2Dj48PunfvDm9vbyiVygp1CgoKLBBZ/WNlZQVvb29YWVmZtF21Wg1vb2/Y2NiYtN3G7rHHHsPbb7+NgQMHVrpco9Fg8uTJaNasGezs7BASEoLExERpuZ2dHT766COMHj0a3t7e1W7r5ZdfxtChQxEaGmrKXSBq8DiGGI5jSP3TkMYRJhZU740YMQITJkzAxYsXIZPJ0KpVKwBA7969MX78eEyaNAnu7u6IiIgAABw7dgyPPfYY7O3t4eXlhWHDhiE9PV1qLzc3F1FRUbC3t4ePjw8WL16M3r17Y9KkSVKdyn6dcXZ2xtq1a6X5S5cuYdCgQXB2doarqyueeuopnD9/Xi/uyMhILFq0CD4+PnBzc8O4ceNQWFgo1dFoNJgyZQr8/PxgZWWFe++9F5999hmEELj33nuxaNEivRiOHDkCmUyGs2fP1u5DrcT58+elw9RlX7179zb5tuiO8ePHIykpCRs2bMDff/+N//znP+jXrx/++ecfo9pZs2YN/v33X8yePdtMkRI1TBxD7uAY0jjVp3GEiQXVe8uWLcNbb72F5s2bIyUlBX/++ae0bN26dVCr1di9ezfi4uKQkZGBhx9+GEFBQThw4ADi4+ORlpamd+j1jTfewG+//YZvv/0WP//8MxITE3Ho0CGjYiosLERERAQcHBzw+++/Y/fu3bC3t0e/fv30fvXauXMnzp07h507d2LdunVYu3at3sASFRWFL7/8EsuXL8fJkyfx8ccfw97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      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 3))\n",
    "\n",
    "mode_data.amps.sel(direction=\"+\").abs.plot.line(x=\"f\", ax=ax1)\n",
    "mode_data.amps.sel(direction=\"-\").abs.plot.line(x=\"f\", ax=ax2)\n",
    "ax1.set_xlim(150e12, 250e12)\n",
    "ax2.set_xlim(150e12, 250e12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48ae969a",
   "metadata": {},
   "source": [
    "As before, we can manipulate the data using basic algebra or xarray built ins, for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5a2b68fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sum absolute value squared of the mode amplitudes to get powers\n",
    "mode_powers = abs(mode_data.amps) ** 2\n",
    "\n",
    "# select the powers at the central frequency only\n",
    "powers_central_freq = mode_powers.sel(f=freq0)\n",
    "\n",
    "# sum the powers over all of the mode indices\n",
    "powers_sum_modes = powers_central_freq.sum(\"mode_index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99570157",
   "metadata": {},
   "source": [
    "## Field Data\n",
    "\n",
    "Data from a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) or [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html) is more complicated as it contains data from several field components.\n",
    "\n",
    "As such, when we just grab the data by name, we don't get an xarray object, but rather a [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) container holding all of the field components.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68111c23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tidy3d.components.data.monitor_data.FieldData'>\n"
     ]
    }
   ],
   "source": [
    "field_data = sim_data[\"field\"]\n",
    "field0_data = sim0_data[\"field\"]\n",
    "print(type(field_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8d2385",
   "metadata": {
    "tags": []
   },
   "source": [
    "The field_data object contains data for each of the field components in its `data_dict` dictionary.\n",
    "\n",
    "Let's look at what field components are contained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43efb783",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['attrs', 'type', 'Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz', 'monitor', 'symmetry', 'symmetry_center', 'grid_expanded', 'grid_primal_correction', 'grid_dual_correction'])\n"
     ]
    }
   ],
   "source": [
    "print(field_data.dict().keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01b21ff",
   "metadata": {},
   "source": [
    "The individual field components, themselves, can be accessed conveniently using a \"dot\" syntax, and are stored similarly to the flux and mode data as xarray data arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acd7313a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.ScalarFieldDataArray (x: 177, y: 1, z: 153, f: 11)&gt; Size: 2MB\n",
       "array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j],\n",
       "         [-7.26815017e-08-1.93432790e-08j,\n",
       "           7.22568529e-08-3.66484549e-08j,\n",
       "           3.17547730e-08+5.06346520e-09j, ...,\n",
       "          -2.62116409e-08-2.60843418e-08j,\n",
       "           3.33986954e-08-3.85094054e-08j,\n",
       "           1.61420584e-08+2.12886224e-08j],\n",
       "         [-1.59778108e-06+2.88020658e-07j,\n",
       "           8.14004522e-07-9.83333507e-07j,\n",
       "           5.53362213e-07+8.00854139e-08j, ...,\n",
       "          -7.83388600e-07-2.75885611e-07j,\n",
       "           3.77692942e-07-8.47853187e-07j,\n",
       "           5.51245080e-07+4.02447967e-07j],\n",
       "         ...,\n",
       "         [ 7.92121213e-08+1.71917609e-08j,\n",
       "...\n",
       "         ...,\n",
       "         [ 2.29554131e-09+9.86773241e-09j,\n",
       "           1.34686440e-09-7.62740004e-09j,\n",
       "           1.87367277e-09+5.09031839e-09j, ...,\n",
       "          -8.77192208e-10-1.85117577e-09j,\n",
       "           1.84902349e-09+1.44861689e-09j,\n",
       "          -4.77616702e-09-7.74031561e-10j],\n",
       "         [-4.45794651e-10+1.44840112e-11j,\n",
       "          -5.39102270e-11-1.89234919e-10j,\n",
       "           3.20862531e-10+2.04376918e-10j, ...,\n",
       "           1.35644190e-10-1.70398695e-10j,\n",
       "           3.90925597e-10-1.48512841e-10j,\n",
       "          -4.35329939e-10-4.08092338e-10j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j]]]],\n",
       "      shape=(177, 1, 153, 11), dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 1kB -2.896 -2.863 -2.83 -2.797 ... 2.83 2.863 2.896\n",
       "  * y        (y) float64 8B 0.0\n",
       "  * z        (z) float64 1kB -3.061 -3.015 -2.968 -2.921 ... 2.968 3.015 3.062\n",
       "  * f        (f) float64 88B 1e+14 1.2e+14 1.4e+14 ... 2.6e+14 2.8e+14 3e+14\n",
       "Attributes:\n",
       "    long_name:  field value</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.ScalarFieldDataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 177</li><li><span class='xr-has-index'>y</span>: 1</li><li><span class='xr-has-index'>z</span>: 153</li><li><span class='xr-has-index'>f</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2176d294-c563-4d1a-8f0c-dd3111e1bbf9' class='xr-array-in' type='checkbox' checked><label for='section-2176d294-c563-4d1a-8f0c-dd3111e1bbf9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0j 0j (-0-0j) (-0-0j) 0j 0j ... 0j (-0-0j) (-0+0j) 0j -0j (-0-0j)</span></div><div class='xr-array-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j],\n",
       "         [-7.26815017e-08-1.93432790e-08j,\n",
       "           7.22568529e-08-3.66484549e-08j,\n",
       "           3.17547730e-08+5.06346520e-09j, ...,\n",
       "          -2.62116409e-08-2.60843418e-08j,\n",
       "           3.33986954e-08-3.85094054e-08j,\n",
       "           1.61420584e-08+2.12886224e-08j],\n",
       "         [-1.59778108e-06+2.88020658e-07j,\n",
       "           8.14004522e-07-9.83333507e-07j,\n",
       "           5.53362213e-07+8.00854139e-08j, ...,\n",
       "          -7.83388600e-07-2.75885611e-07j,\n",
       "           3.77692942e-07-8.47853187e-07j,\n",
       "           5.51245080e-07+4.02447967e-07j],\n",
       "         ...,\n",
       "         [ 7.92121213e-08+1.71917609e-08j,\n",
       "...\n",
       "         ...,\n",
       "         [ 2.29554131e-09+9.86773241e-09j,\n",
       "           1.34686440e-09-7.62740004e-09j,\n",
       "           1.87367277e-09+5.09031839e-09j, ...,\n",
       "          -8.77192208e-10-1.85117577e-09j,\n",
       "           1.84902349e-09+1.44861689e-09j,\n",
       "          -4.77616702e-09-7.74031561e-10j],\n",
       "         [-4.45794651e-10+1.44840112e-11j,\n",
       "          -5.39102270e-11-1.89234919e-10j,\n",
       "           3.20862531e-10+2.04376918e-10j, ...,\n",
       "           1.35644190e-10-1.70398695e-10j,\n",
       "           3.90925597e-10-1.48512841e-10j,\n",
       "          -4.35329939e-10-4.08092338e-10j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j]]]],\n",
       "      shape=(177, 1, 153, 11), dtype=complex64)</pre></div></div></li><li class='xr-section-item'><input id='section-9c0d3c8f-c4fa-4cf9-88e3-684611f4fb5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c0d3c8f-c4fa-4cf9-88e3-684611f4fb5f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.896 -2.863 -2.83 ... 2.863 2.896</div><input id='attrs-52e93cb4-16b5-4098-9737-ceb9dc94949d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52e93cb4-16b5-4098-9737-ceb9dc94949d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b75a4ac-a6af-403e-9d25-e9015dfdc130' class='xr-var-data-in' type='checkbox'><label for='data-7b75a4ac-a6af-403e-9d25-e9015dfdc130' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>um</dd><dt><span>long_name :</span></dt><dd>x position</dd></dl></div><div class='xr-var-data'><pre>array([-2.896226e+00, -2.863208e+00, -2.830189e+00, -2.797170e+00,\n",
       "       -2.764151e+00, -2.731132e+00, -2.698113e+00, -2.665094e+00,\n",
       "       -2.632075e+00, -2.599057e+00, -2.566038e+00, -2.533019e+00,\n",
       "       -2.500000e+00, -2.466981e+00, -2.433962e+00, -2.400943e+00,\n",
       "       -2.367925e+00, -2.334906e+00, -2.301887e+00, -2.268868e+00,\n",
       "       -2.235849e+00, -2.202830e+00, -2.169811e+00, -2.136792e+00,\n",
       "       -2.103774e+00, -2.070755e+00, -2.037736e+00, -2.004717e+00,\n",
       "       -1.971698e+00, -1.938679e+00, -1.905660e+00, -1.872642e+00,\n",
       "       -1.839623e+00, -1.806604e+00, -1.773585e+00, -1.740566e+00,\n",
       "       -1.707547e+00, -1.674528e+00, -1.641509e+00, -1.608491e+00,\n",
       "       -1.575472e+00, -1.542453e+00, -1.509434e+00, -1.476415e+00,\n",
       "       -1.443396e+00, -1.410377e+00, -1.377358e+00, -1.344340e+00,\n",
       "       -1.311321e+00, -1.278302e+00, -1.245283e+00, -1.212264e+00,\n",
       "       -1.179245e+00, -1.146226e+00, -1.113208e+00, -1.080189e+00,\n",
       "       -1.047170e+00, -1.014151e+00, -9.811321e-01, -9.481132e-01,\n",
       "       -9.150943e-01, -8.820755e-01, -8.490566e-01, -8.160377e-01,\n",
       "       -7.830189e-01, -7.500000e-01, -7.173913e-01, -6.847826e-01,\n",
       "       -6.521739e-01, -6.195652e-01, -5.869565e-01, -5.543478e-01,\n",
       "       -5.217391e-01, -4.891304e-01, -4.565217e-01, -4.239130e-01,\n",
       "       -3.913043e-01, -3.586957e-01, -3.260870e-01, -2.934783e-01,\n",
       "       -2.608696e-01, -2.282609e-01, -1.956522e-01, -1.630435e-01,\n",
       "       -1.304348e-01, -9.782609e-02, -6.521739e-02, -3.260870e-02,\n",
       "       -2.220446e-15,  3.260870e-02,  6.521739e-02,  9.782609e-02,\n",
       "        1.304348e-01,  1.630435e-01,  1.956522e-01,  2.282609e-01,\n",
       "        2.608696e-01,  2.934783e-01,  3.260870e-01,  3.586957e-01,\n",
       "        3.913043e-01,  4.239130e-01,  4.565217e-01,  4.891304e-01,\n",
       "        5.217391e-01,  5.543478e-01,  5.869565e-01,  6.195652e-01,\n",
       "        6.521739e-01,  6.847826e-01,  7.173913e-01,  7.500000e-01,\n",
       "        7.830189e-01,  8.160377e-01,  8.490566e-01,  8.820755e-01,\n",
       "        9.150943e-01,  9.481132e-01,  9.811321e-01,  1.014151e+00,\n",
       "        1.047170e+00,  1.080189e+00,  1.113208e+00,  1.146226e+00,\n",
       "        1.179245e+00,  1.212264e+00,  1.245283e+00,  1.278302e+00,\n",
       "        1.311321e+00,  1.344340e+00,  1.377358e+00,  1.410377e+00,\n",
       "        1.443396e+00,  1.476415e+00,  1.509434e+00,  1.542453e+00,\n",
       "        1.575472e+00,  1.608491e+00,  1.641509e+00,  1.674528e+00,\n",
       "        1.707547e+00,  1.740566e+00,  1.773585e+00,  1.806604e+00,\n",
       "        1.839623e+00,  1.872642e+00,  1.905660e+00,  1.938679e+00,\n",
       "        1.971698e+00,  2.004717e+00,  2.037736e+00,  2.070755e+00,\n",
       "        2.103774e+00,  2.136792e+00,  2.169811e+00,  2.202830e+00,\n",
       "        2.235849e+00,  2.268868e+00,  2.301887e+00,  2.334906e+00,\n",
       "        2.367925e+00,  2.400943e+00,  2.433962e+00,  2.466981e+00,\n",
       "        2.500000e+00,  2.533019e+00,  2.566038e+00,  2.599057e+00,\n",
       "        2.632075e+00,  2.665094e+00,  2.698113e+00,  2.731132e+00,\n",
       "        2.764151e+00,  2.797170e+00,  2.830189e+00,  2.863208e+00,\n",
       "        2.896226e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-bfaa181f-91f7-4bc5-9770-d3581007c9fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfaa181f-91f7-4bc5-9770-d3581007c9fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19e9f8ed-8499-4172-b18b-8e61a861459b' class='xr-var-data-in' type='checkbox'><label for='data-19e9f8ed-8499-4172-b18b-8e61a861459b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>um</dd><dt><span>long_name :</span></dt><dd>y position</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.061 -3.015 ... 3.015 3.062</div><input id='attrs-c288e208-2487-49f2-a22b-5f30e350085e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c288e208-2487-49f2-a22b-5f30e350085e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c38bb02f-c73d-41d1-bb12-bcda8134a34e' class='xr-var-data-in' type='checkbox'><label for='data-c38bb02f-c73d-41d1-bb12-bcda8134a34e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>um</dd><dt><span>long_name :</span></dt><dd>z position</dd></dl></div><div class='xr-var-data'><pre>array([-3.061435, -3.014649, -2.967862, -2.921076, -2.87429 , -2.827504,\n",
       "       -2.780717, -2.733931, -2.687145, -2.640359, -2.593572, -2.546786,\n",
       "       -2.5     , -2.453214, -2.406428, -2.359641, -2.312855, -2.266069,\n",
       "       -2.219283, -2.172496, -2.12571 , -2.078924, -2.032138, -1.985351,\n",
       "       -1.938565, -1.891779, -1.844993, -1.798206, -1.75142 , -1.704634,\n",
       "       -1.657848, -1.611062, -1.564275, -1.517489, -1.470703, -1.423917,\n",
       "       -1.37713 , -1.330344, -1.283558, -1.236772, -1.189985, -1.143199,\n",
       "       -1.096413, -1.049627, -1.00284 , -0.956054, -0.909268, -0.862482,\n",
       "       -0.816166, -0.783083, -0.75    , -0.71875 , -0.6875  , -0.65625 ,\n",
       "       -0.625   , -0.59375 , -0.5625  , -0.53125 , -0.5     , -0.46875 ,\n",
       "       -0.4375  , -0.40625 , -0.375   , -0.34375 , -0.3125  , -0.28125 ,\n",
       "       -0.25    , -0.217742, -0.185484, -0.153226, -0.120968, -0.08871 ,\n",
       "       -0.056452, -0.024194,  0.008065,  0.040323,  0.072581,  0.104839,\n",
       "        0.137097,  0.169355,  0.201613,  0.233871,  0.266129,  0.298387,\n",
       "        0.330645,  0.362903,  0.395161,  0.427419,  0.459677,  0.491935,\n",
       "        0.524194,  0.556452,  0.58871 ,  0.620968,  0.653226,  0.685484,\n",
       "        0.717742,  0.75    ,  0.78125 ,  0.8125  ,  0.84375 ,  0.875   ,\n",
       "        0.90625 ,  0.9375  ,  0.96875 ,  1.      ,  1.03125 ,  1.0625  ,\n",
       "        1.09375 ,  1.125   ,  1.15625 ,  1.1875  ,  1.21875 ,  1.25    ,\n",
       "        1.283123,  1.329407,  1.375778,  1.422621,  1.469463,  1.516306,\n",
       "        1.563149,  1.609991,  1.656834,  1.703676,  1.750519,  1.797361,\n",
       "        1.844204,  1.891047,  1.937889,  1.984732,  2.031574,  2.078417,\n",
       "        2.125259,  2.172102,  2.218945,  2.265787,  2.31263 ,  2.359472,\n",
       "        2.406315,  2.453157,  2.5     ,  2.546843,  2.593685,  2.640528,\n",
       "        2.68737 ,  2.734213,  2.781055,  2.827898,  2.874741,  2.921583,\n",
       "        2.968426,  3.015268,  3.062111])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>f</span></div><div class='xr-var-dims'>(f)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+14 1.2e+14 ... 2.8e+14 3e+14</div><input id='attrs-4e167af2-f427-40d1-8167-2fd7f570b4f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e167af2-f427-40d1-8167-2fd7f570b4f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57f31e83-caef-4c9c-a608-312e4c858ce9' class='xr-var-data-in' type='checkbox'><label for='data-57f31e83-caef-4c9c-a608-312e4c858ce9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>frequency</dd></dl></div><div class='xr-var-data'><pre>array([1.0e+14, 1.2e+14, 1.4e+14, 1.6e+14, 1.8e+14, 2.0e+14, 2.2e+14, 2.4e+14,\n",
       "       2.6e+14, 2.8e+14, 3.0e+14])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1fde2182-e65a-4c1e-9e64-e504839cc3dd' class='xr-section-summary-in' type='checkbox'  ><label for='section-1fde2182-e65a-4c1e-9e64-e504839cc3dd' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4a7d2bf0-7459-40e9-bcad-844c014739b6' class='xr-index-data-in' type='checkbox'/><label for='index-4a7d2bf0-7459-40e9-bcad-844c014739b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-2.8962264150943415,  -2.863207547169813, -2.8301886792452846,\n",
       "        -2.797169811320756, -2.7641509433962277, -2.7311320754716992,\n",
       "       -2.6981132075471708, -2.6650943396226423,  -2.632075471698114,\n",
       "       -2.5990566037735854,\n",
       "       ...\n",
       "        2.5990566037736107,  2.6320754716981476,  2.6650943396226845,\n",
       "        2.6981132075472214,  2.7311320754717583,   2.764150943396295,\n",
       "         2.797169811320832,   2.830188679245369,   2.863207547169906,\n",
       "         2.896226415094443],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=177))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-eec11d84-3dab-4c7f-ae2a-71d255213204' class='xr-index-data-in' type='checkbox'/><label for='index-eec11d84-3dab-4c7f-ae2a-71d255213204' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-191e09d8-c057-4b4c-acc8-7786deb7f33b' class='xr-index-data-in' type='checkbox'/><label for='index-191e09d8-c057-4b4c-acc8-7786deb7f33b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-3.0614348319608258, -3.0146485959640903,  -2.967862359967355,\n",
       "       -2.9210761239706193,  -2.874289887973884, -2.8275036519771484,\n",
       "        -2.780717415980413, -2.7339311799836774,  -2.687144943986942,\n",
       "       -2.6403587079902064,\n",
       "       ...\n",
       "        2.6405277146874875,  2.6873702862499833,   2.734212857812479,\n",
       "         2.781055429374975,   2.827898000937471,  2.8747405724999666,\n",
       "        2.9215831440624624,  2.9684257156249583,   3.015268287187454,\n",
       "          3.06211085874995],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;, length=153))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>f</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a2fd9eb3-6f17-4180-bb2d-4d32f852c9d9' class='xr-index-data-in' type='checkbox'/><label for='index-a2fd9eb3-6f17-4180-bb2d-4d32f852c9d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000000000000.0, 120000000000000.0, 140000000000000.0,\n",
       "       160000000000000.0, 180000000000000.0, 200000000000000.0,\n",
       "       220000000000000.0, 240000000000000.0, 260000000000000.0,\n",
       "       280000000000000.0, 300000000000000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;f&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-206c3b54-ffa0-4f56-847f-a89ef175f499' class='xr-section-summary-in' type='checkbox'  checked><label for='section-206c3b54-ffa0-4f56-847f-a89ef175f499' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>field value</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.ScalarFieldDataArray (x: 177, y: 1, z: 153, f: 11)> Size: 2MB\n",
       "array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j],\n",
       "         [-7.26815017e-08-1.93432790e-08j,\n",
       "           7.22568529e-08-3.66484549e-08j,\n",
       "           3.17547730e-08+5.06346520e-09j, ...,\n",
       "          -2.62116409e-08-2.60843418e-08j,\n",
       "           3.33986954e-08-3.85094054e-08j,\n",
       "           1.61420584e-08+2.12886224e-08j],\n",
       "         [-1.59778108e-06+2.88020658e-07j,\n",
       "           8.14004522e-07-9.83333507e-07j,\n",
       "           5.53362213e-07+8.00854139e-08j, ...,\n",
       "          -7.83388600e-07-2.75885611e-07j,\n",
       "           3.77692942e-07-8.47853187e-07j,\n",
       "           5.51245080e-07+4.02447967e-07j],\n",
       "         ...,\n",
       "         [ 7.92121213e-08+1.71917609e-08j,\n",
       "...\n",
       "         ...,\n",
       "         [ 2.29554131e-09+9.86773241e-09j,\n",
       "           1.34686440e-09-7.62740004e-09j,\n",
       "           1.87367277e-09+5.09031839e-09j, ...,\n",
       "          -8.77192208e-10-1.85117577e-09j,\n",
       "           1.84902349e-09+1.44861689e-09j,\n",
       "          -4.77616702e-09-7.74031561e-10j],\n",
       "         [-4.45794651e-10+1.44840112e-11j,\n",
       "          -5.39102270e-11-1.89234919e-10j,\n",
       "           3.20862531e-10+2.04376918e-10j, ...,\n",
       "           1.35644190e-10-1.70398695e-10j,\n",
       "           3.90925597e-10-1.48512841e-10j,\n",
       "          -4.35329939e-10-4.08092338e-10j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00-0.00000000e+00j,\n",
       "          -0.00000000e+00-0.00000000e+00j]]]],\n",
       "      shape=(177, 1, 153, 11), dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 1kB -2.896 -2.863 -2.83 -2.797 ... 2.83 2.863 2.896\n",
       "  * y        (y) float64 8B 0.0\n",
       "  * z        (z) float64 1kB -3.061 -3.015 -2.968 -2.921 ... 2.968 3.015 3.062\n",
       "  * f        (f) float64 88B 1e+14 1.2e+14 1.4e+14 ... 2.6e+14 2.8e+14 3e+14\n",
       "Attributes:\n",
       "    long_name:  field value"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_data.Ex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51efd903",
   "metadata": {},
   "source": [
    "In this case, the dimensions of the data are the spatial locations on the yee lattice (x,y,z) and the frequency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebeb2dfe",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Colocating Field Data\n",
    "\n",
    "For many advanced plots and other manipulations, it is convenient to have the field data co-located at the same positions, since they are naturally defined on separate locations on the yee lattice. There are several convenience methods that can be used for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc53fa35",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">the solver run. For most accurate results when colocating to      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">custom coordinates set </span><span style=\"color: #008000; text-decoration-color: #008000\">'Monitor.colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to use the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">raw data on the Yee grid and avoid double interpolation. Note: the</span>\n",
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">raw data on the Yee grid and avoid double interpolation. Note: the</span>\n",
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       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Colocating data that has already been colocated during   \u001b[0m\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Colocate data to the Yee grid centers\n",
    "field0_data_centered = sim0_data.at_centers(\"field\").interp(f=200e12)\n",
    "field_data_centered = sim_data.at_centers(\"field\").interp(f=200e12)\n",
    "\n",
    "# Colocate data to Yee grid boundaries\n",
    "# Note: by default, data is already colocated there, unless `colocate=False` is set in the monitor.\n",
    "# Here, at_boundaries just converts the Tidy3D data into an xarray Dataset.\n",
    "field_data_boundaries = sim_data.at_boundaries(\"field\").interp(f=200e12)\n",
    "\n",
    "# Colocate to custom coordinates\n",
    "# Note: some or all of the x/y/z coordinates can be provided\n",
    "field_data_colocate_z = sim_data[\"field\"].colocate(z=np.linspace(-1, 1, 21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "28b3434c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">the solver run. For most accurate results when colocating to      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">custom coordinates set </span><span style=\"color: #008000; text-decoration-color: #008000\">'Monitor.colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to use the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">raw data on the Yee grid and avoid double interpolation. Note: the</span>\n",
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       "\u001b[2;36m              \u001b[0m\u001b[31mthe solver run. For most accurate results when colocating to      \u001b[0m\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\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Colocating data that has already been colocated during   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">the solver run. For most accurate results when colocating to      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">custom coordinates set </span><span style=\"color: #008000; text-decoration-color: #008000\">'Monitor.colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to use the   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">raw data on the Yee grid and avoid double interpolation. Note: the</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">default value was changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D version </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">.      </span>\n",
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      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Colocating data that has already been colocated during   \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mthe solver run. For most accurate results when colocating to      \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mcustom coordinates set \u001b[0m\u001b[32m'Monitor.colocate'\u001b[0m\u001b[31m to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to use the   \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mraw data on the Yee grid and avoid double interpolation. Note: the\u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mdefault value was changed to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D version \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m.      \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field0_data_centered = sim0_data.at_centers(\"field\").interp(f=200e12)\n",
    "field_data_centered = sim_data.at_centers(\"field\").interp(f=200e12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd0d9e9",
   "metadata": {},
   "source": [
    "All these methods return an xarray [Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html), which provides similar functionality as the [DataArray](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html) objects, but is aware of all of the field data and provides more convenience methods, as we will explore in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89af5c1f",
   "metadata": {},
   "source": [
    "### Plotting Fields\n",
    "\n",
    "#### Simple \n",
    "Plotting fields requires a little bit of manipulation to get them in the right shape, but then it is straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98ba6150",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the field data on the y=0 plane at frequency 200THz\n",
    "Ez_data = field_data.Ez.isel(y=0).interp(f=2e14)\n",
    "Ez0_data = field0_data.Ez.isel(y=0).interp(f=2e14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4a5dc0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# amplitude plot rz Ex(x,y) field on plane\n",
    "_, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True, figsize=(10, 6))\n",
    "\n",
    "Ez0_data.real.plot(x=\"x\", y=\"z\", ax=ax1)\n",
    "Ez_data.real.plot(x=\"x\", y=\"z\", ax=ax2)\n",
    "\n",
    "abs(Ez0_data).plot(x=\"x\", y=\"z\", cmap=\"magma\", ax=ax3)\n",
    "abs(Ez_data).plot(x=\"x\", y=\"z\", cmap=\"magma\", ax=ax4)\n",
    "\n",
    "ax1.set_title(\"without scatterer (real Ez)\")\n",
    "ax2.set_title(\"with scatterer (real Ez)\")\n",
    "ax3.set_title(\"without scatterer (abs Ez)\")\n",
    "ax4.set_title(\"with scatterer (abs Ez)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1807646a",
   "metadata": {},
   "source": [
    "#### Advanced\n",
    "\n",
    "More advanced plotting is available and is best done with the centered field data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516493a1",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Structure Overlay\n",
    "\n",
    "One can overlay the structure permittivity by calling `plot_fields` from the [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) object as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8fdf17ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data.plot_field(\"field\", \"Ex\", y=0.0, f=200e12, eps_alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2936a5",
   "metadata": {},
   "source": [
    "##### Quiver Plots\n",
    "\n",
    "Quiver plotting can be done through the centered field Dataset.\n",
    "We first should spatially downsample the data to be quivered to reduce clutter on the plot, which can be done by `.sel()` with `slice(None, None, skip)` range to select every `skip` data point along that axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12b2b76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# downsample the field data for more clear quiver plotting\n",
    "field0_data_resampled = field0_data_centered.sel(x=slice(None, None, 7), z=slice(None, None, 7))\n",
    "\n",
    "# quiver plot of \\vec{E}_{x,y}(x,y) on plane with Ez(x,y) underlying.\n",
    "f, ax = plt.subplots(figsize=(8, 5))\n",
    "field0_data_centered.isel(y=0).Ez.real.plot.imshow(x=\"x\", y=\"z\", ax=ax, robust=True)\n",
    "field0_data_resampled.isel(y=0).real.plot.quiver(\"x\", \"z\", \"Ex\", \"Ez\", ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27630dee",
   "metadata": {},
   "source": [
    "##### Stream Plots\n",
    "Stream plots can be another useful feature, although they do not take the amplitude into account, so use with caution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffde187e",
   "metadata": {},
   "source": [
    "##### Time Lapse Fields\n",
    "\n",
    "For time-domain data, it can be convenient to see the fields at various snapshots of the simulation.  For this, it is convenient to supply the `row` and `col` keyword arguments to plot, which expand all plots on a row or column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b49c1f1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field_time_data = sim_data[\"field_time\"]\n",
    "\n",
    "# select the data and downsample and window the time coordinate to reduce number of plots.\n",
    "time_data = field_time_data.Ez.isel(y=0).sel(t=slice(1e-14, 5e-14, 1))\n",
    "\n",
    "time_data.plot(x=\"x\", y=\"z\", col=\"t\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4104ee6d-8118-4498-a943-00b4c1b48d4a",
   "metadata": {},
   "source": [
    "## Export data to csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2256ecf5",
   "metadata": {},
   "source": [
    "In many cases, you might want to save the monitor data as a .csv file for future use. This can be done in multiple ways. Here we demonstrate two methods as examples. \n",
    "\n",
    "For saving a 2D array such as a field plot at a specific frequency or time instance, we can first convert the [xarray.DataArray](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html) to a [numpy.array](https://numpy.org/doc/stable/reference/generated/numpy.array.html) using the [to_numpy()](https://docs.xarray.dev/en/latest/generated/xarray.DataArray.to_numpy.html) method and then using numpy's [savetxt()](https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html) method to save the 2D numpy array as a .csv file. The spatial coordinates can be saved as separate .csv files in the same way. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "211a8503",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save Ez to a numpy array\n",
    "Ez = Ez0_data.real.squeeze().to_numpy()\n",
    "\n",
    "# save x coordinate to a numpy array\n",
    "x_array = Ez0_data.real.coords[\"x\"].to_numpy()\n",
    "\n",
    "# save z coordinate to a numpy array\n",
    "z_array = Ez0_data.real.coords[\"z\"].to_numpy()\n",
    "\n",
    "# save the numpy arrays to .csv files\n",
    "np.savetxt(\"./data/Ez.csv\", Ez, delimiter=\",\")\n",
    "np.savetxt(\"./data/x.csv\", x_array)\n",
    "np.savetxt(\"./data/z.csv\", z_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f284c8",
   "metadata": {},
   "source": [
    "If the data you want to save is higher dimensional such as the field distribution at multiple time instances, you can consider export the [xarray.DataArray](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html) to a csv file using its [to_dataframe()](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.to_dataframe.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6c4e1eab-9ff3-47bd-bd45-b66e428d238a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = time_data.to_dataframe(\"value\")\n",
    "df.to_csv(\"./data/time_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff38ffe5-b6d1-4de2-a626-0a7aaebfa3dd",
   "metadata": {},
   "source": [
    "It will stack the array coordinates to generate a 2D table. Import the csv file to check the content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "329574aa-ce1b-4d09-9a21-556bd7c795ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>y</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.896226</td>\n",
       "      <td>-3.061435</td>\n",
       "      <td>1.208153e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.896226</td>\n",
       "      <td>-3.061435</td>\n",
       "      <td>2.416306e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.896226</td>\n",
       "      <td>-3.061435</td>\n",
       "      <td>3.624459e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.896226</td>\n",
       "      <td>-3.061435</td>\n",
       "      <td>4.832612e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.896226</td>\n",
       "      <td>-3.014649</td>\n",
       "      <td>1.208153e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108319</th>\n",
       "      <td>2.896226</td>\n",
       "      <td>3.015268</td>\n",
       "      <td>4.832612e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108320</th>\n",
       "      <td>2.896226</td>\n",
       "      <td>3.062111</td>\n",
       "      <td>1.208153e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108321</th>\n",
       "      <td>2.896226</td>\n",
       "      <td>3.062111</td>\n",
       "      <td>2.416306e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108322</th>\n",
       "      <td>2.896226</td>\n",
       "      <td>3.062111</td>\n",
       "      <td>3.624459e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108323</th>\n",
       "      <td>2.896226</td>\n",
       "      <td>3.062111</td>\n",
       "      <td>4.832612e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108324 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               x         z             t    y  value\n",
       "0      -2.896226 -3.061435  1.208153e-14  0.0    0.0\n",
       "1      -2.896226 -3.061435  2.416306e-14  0.0    0.0\n",
       "2      -2.896226 -3.061435  3.624459e-14  0.0    0.0\n",
       "3      -2.896226 -3.061435  4.832612e-14  0.0    0.0\n",
       "4      -2.896226 -3.014649  1.208153e-14  0.0    0.0\n",
       "...          ...       ...           ...  ...    ...\n",
       "108319  2.896226  3.015268  4.832612e-14  0.0    0.0\n",
       "108320  2.896226  3.062111  1.208153e-14  0.0    0.0\n",
       "108321  2.896226  3.062111  2.416306e-14  0.0    0.0\n",
       "108322  2.896226  3.062111  3.624459e-14  0.0    0.0\n",
       "108323  2.896226  3.062111  4.832612e-14  0.0    0.0\n",
       "\n",
       "[108324 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"./data/time_data.csv\")\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67dd2c51",
   "metadata": {},
   "source": [
    "## Load data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67c5c66",
   "metadata": {},
   "source": [
    "When running the simulations, we have specified the path to save the simulation data, i.e. `path=\"data/simulation0.hdf5\"`. It means the simulation data is saved as a .hdf5 file, which can be loaded into a [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html) object in the future for analysis. Here we demonstrate loading the .hdf5 files using the `from_file` method. If you performed a mode solving simulation instead of a FDTD simulation, [ModeSolverData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSolverData.html) can be loaded in a similar fashion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f0a7064",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_data0 = td.SimulationData.from_file(\"data/simulation0.hdf5\")\n",
    "sim_data = td.SimulationData.from_file(\"data/simulation.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1fb0f4f",
   "metadata": {},
   "source": [
    "After the simulation data is loaded, you can perform postprocessing as discussed above such as plotting the field distribution by using the `plot_field` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f23d0f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "sim_data0.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", f=2e14, ax=ax1)\n",
    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", f=2e14, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52001cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "This hopefully gives some sense of what kind of data post-processing and visualization can be done in `Tidy3D`.\n",
    "\n",
    "For more detailed information and reference, the best places to check out are\n",
    "\n",
    "* API reference for [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html).\n",
    "\n",
    "* [Xarray documentation](http://xarray.pydata.org).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167de7b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to perform visualization of simulation data in Tidy3D FDTD.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "result visualization, data plotting, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "key": 0,
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