{ "cells": [ { "cell_type": "markdown", "id": "9e129b4e", "metadata": {}, "source": [ "# Data Plotting\n", "\n", "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FVizData.ipynb).\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": { "execution": { "iopub.execute_input": "2022-07-21T20:54:18.005458Z", "iopub.status.busy": "2022-07-21T20:54:18.005327Z", "iopub.status.idle": "2022-07-21T20:54:18.973030Z", "shell.execute_reply": "2022-07-21T20:54:18.972540Z" }, "tags": [] }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Simulation.html) so we have data to plot.\n", "\n", "We will add each possible type of monitor into the simultion to explore their output data separately." ] }, { "cell_type": "code", "execution_count": 2, "id": "61c49575", "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:18.975056Z", "iopub.status.busy": "2022-07-21T20:54:18.974940Z", "iopub.status.idle": "2022-07-21T20:54:18.981558Z", "shell.execute_reply": "2022-07-21T20:54:18.981216Z" }, "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,.5,.5), size=(1.5,1.4,1.5)),\n", " medium=td.Medium(permittivity=2),\n", " name='scatterer'\n", " ), \n", " ],\n", " sources=[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", " 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", " 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", " td.FluxMonitor(\n", " size=(0,3,3),\n", " center=(2,0,0),\n", " freqs=freqs, \n", " name='flux'),\n", " td.FluxTimeMonitor(\n", " size=(0,3,3),\n", " center=(2,0,0),\n", " interval=10,\n", " 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", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "004348be", "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:18.982984Z", "iopub.status.busy": "2022-07-21T20:54:18.982879Z", "iopub.status.idle": "2022-07-21T20:54:18.994398Z", "shell.execute_reply": "2022-07-21T20:54:18.994140Z" } }, "outputs": [ { "data": { "text/html": [ "
[16:54:18] INFO     Auto meshing using wavelength 1.4990 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
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<Figure size 1008x288 with 3 Axes>\n",
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jT+7wsnZ0Z8aPGhpT52/4+Fnxz0IlWZnbx/T44fh2je95HvNDjdLy87wHWiNdi0+O363x0+K/0Fhzz8YAUVKOusvBYIB+v1/a6a1ohw+HQwCA67ql7OjOjN/3seNIg+dv+Php8U/Jyi7kTI8fjm/P+J7nsZGpWVJ+LlOX4pPjd2/8suK/82dmiIiIiIiom6w4/BKduoo6vrLfHDA69ea6LgCUehquE+OPHbPnb/j4afHPo6/Jyt4+pscPx7dn/Pvvv5/5oWZJ+bmMS+AjXYpPjt+98cuqzzt/ZmbyHoH4NXqe5809/uQ1hGk3TVk7/tjBnoML5s7f8PGz4r+qd+I1XZnbx/T44fh2jd/r9ZgfapSWnz3PMzJ+OD7Hn0Va/BfR6WYm7WbnshJG2s1QZe1w48ePGpmlNTPnb/j40+KfBUuysraP6fHD8e0cn/mhHln5udfrGRs/HJ/j55EV/0V0tpmZtmrTvAlj2qoO8+7wTowfNTJ938z5Gzx+nvhnwZJu3u1jevxwfLvHZ36o1rT83Ov1jI4fjs/xs1Sxqmonm5m8G6powpi2oyNFd3hnxk9pZIyZv6Hj541/FizZim4f0+OH43N8gPmhKnnzs+nxw/E5fpKq3h7CqDv8RGRqUvV9H+Px+PBGynp+dJnZ0tISlpeXsbi4CMfJ7u9838fq6iqWlpYwGo1y7URrxz/oYOSFz9+3z7z5GzD+5H1fs8R/pN/vY3V1Fb6f3njaIinHxLdP2v71PA/79u0zLn44PsePS7qPNE/8U7LJfJI3P0f5BDArfjg+x48roz7Jy6hmxnEcHHvssandYt6OMrJv3z68/vWvL3uaFPnCT61//j1/0Nw8Ouzyyy/Htm3bAMz3zrzRz9qu1+th165dG74/bdsyl1AXxPNJ3Dy5xWbxfDLLNmQ+oS4oqz7Jw6jDLCKSekqLyZZsNm/8R6eMKdm0U+o8q0VdVtZNv7ZifUI2qyP+jWpmACQmVCYKsllZ8c+/nWxpBd1oNCr9vauI2iYr/ikd6xOyWV3xb1wzM5lQmSjIZr7vM/5rlJZ/iq6NT2SStPindMzPZKs66xOj7pmJRAl1OBwCAFzXZaIgK41GI/5HWbOk/MOj02SLpPinZL7vMz+TteqsT4w7M0NERERERAQY2sxEp7Zd14Xrurwpkaw1GAwY/zVLyj9cAIBskRT/lMxxHOZnslad9YlxzczkPTJcZYVs5jgO479GafmHCwCQDdLin9IxP5Ot6qxPjGtmkm4mYkNDNisr/vm3ky1tsZHBYMAFAKjzsuKf0rE+IZvVFf9GNTOqmnozERMG2Wze+OeqRNmmrZrId0anLuOqofNhfUI2qyP+jfofWEQyEykTBtmsaPzHCxXaiIUc2YzxXw7WJ2SzquPfqGYmDyYMstms8c9CJRu3D9mM8V8u1idksyrjv3PNDJB/g3EFIuqivPHPQiUbtw/ZjPFfDTY0ZLOq4r+TzQwwfYONRiOuQESdlSf+Waik4/YhmzH+qzUtP/NAK3VZFQ1NY82MiOwUketF5N9E5HYR+aWyXyNtg0WJmisQUZdNi38bCpUiecam7UM0ifGfrOyaJSs/80ArdV1W/BfR5JmZMYBfUdVTAbwAwC+IyKllv8jkBosnaq5ARF2XFf+WFCoz5xnLtg/RYb7vM/7TlV6zpOVnHmglG6TFfxH9kueWm6o+CODB8PMnROQOACcA+LeyXyvaYMPhEADgui4GgwGvVyUrpMW/DWbNM77vs5Aja41GI8Z/iqpqlqT8zNqEbJEU/0W04tSEiJwE4GwANyY8drGI7BaR3fv37699bkTUDWl5Jp5jVlZWGpkbEZmDNQtRuzTezIjIFgAfAvBmVd03+biqXqGq56rquVu2bCn0GtGpK9d14bouVxEhqzD+s/NMPMds3brVyu1DBARHSRn/2cquWZLyMxcAIFskxX8RjTYzIrKAIClcrarXVPEak/cIxK/RY8KgrsuKf1sKllnzjG3bhyjiOA7jP0PZNUtafuYCAGSDtPgvosnVzATAXwG4Q1X/pIrXSLvZmQmDbDAt/m0oWIrkGZu2D9Ekxn+ysmuWrPzMBQCo67Liv4gmz8x8L4CfBvAiEbk5/PjRsgaftmoTEwZ1WZ74t6RgKZRnLNo+RBsw/hOVVrNMy89caZW6rIpVVZtczezzAKSKsfNuKCYM6qK88R8vWLq6gtE8ecaG7UOUhvH/ZGXVLBYuj090WFXx37lqnomCbDZr/PMIbDZuH7IZ479crE/IZlXGv1HNjKpmJlQmCrJZ0fiPFyy0EQs6shnjvxysT8hmVce/Uc2MiKQmVCYKstm88T/PKiI2mFbQcWVE6jI2NPNhfUI2qyP+jWpmgORlU5koyGZlxT//drKlFXSj0YgrI1LnZcU/pWN9QjarK/6Na2YmEyoTBdnM933Gf43S8g9XRiQbpMU/pWN+JlvVWZ80tprZPKKEOhwOAQCu6zJRkJVGoxH/o6xZUv7h0WmyRVL8UzLf95mfyVp11ifGnZkhIiIiIiICDG1molPbruvCdV3elEjWGgwGjP+aJeUfLgBAtkiKf0rmOA7zM1mrzvrEuGZm8h4ZrrJCNnMch/Ffo7T8wwUAyAZp8U/pmJ/JVnXWJ8Y1M0k3E7GhIZuVFf/828mWttjIYDDgAgDUeVnxT+lYn5DN6op/o5oZVU29mYgJg2w2b/xzVaJs01ZNdByjUinRTLhq6HxYn5DN6oh/o1YzE5HMRBrfYEy6ZJui8R8vVGgjFnJkM8Z/OequT64ZXnP48wvcCyp9LeqQa5+//vlLbyxt2Krj36hmJg82NGSzWeOfhUq2OrcPiw8qpKLiA2B+KBvrE7JZlfHfyWsj8p7S4gpE1EV545+FSjZuH7IZ478avOSMbFZV/HeymQGmb7DRaMQViKiz8sQ/C5V03D5kM8Z/tablZx5opS6roqHpbDMDpG+wKFFzBSLqsmnxz0IlGbcP2YzxX4+s/MwDrdR1WfFfRKebGWDjBosnaq5ARF2XFf8sVJJx+5CtfN9n/NcoLT/zQCvZIC3+i+jcAgBJog02HA4BAK7rYjAY8HpVskJa/NNGvu+zkCNrjUYjxn/NkvIzaxOyRVL8F8FTE0REREREZCQrzsxEp66ijo/vqUE2SYt/Hn3dyHEcbh+y1mAwYPzXLCk/Ly0tNTwronokxX8RnT8zM3mPQPwaPa4YQl2XFf+8lCEZtw/ZynEcxn+N0vIzFwAgG6TFfxGdPjOTdrNztMGWl5cbnF13vOHKzyR+/8L+Q4c/v+rO5Oe0xWUXvbjpKZRuWvzzCGwybp/ZpP39m6yL+SAvxn89svIzFwCgrsuK/yI628xMW7WJCYO6LE/8s2BJx+1DNmP8V2tafrZ1pdUuHhgB7D44kqSKVVU7+ReTd0PZmjCo2/LGPy85y8btQzZj/FeDy+OTzaqK/85V80wUZLNZ458FSzZuH7IZ479crE/IZlXGv1HNjKpmJlQmCrJZ0fiPFyy0EQs6shnjvxysT8hmVcd/o82MiFwpIo+IyG05n5+aUJkoyGbzxv88q4i02aw5Js20go4rI1KXsaEJFM0nrE/IZnXEf9NnZq4CcP4sP5CUUJkoyGZlxX9H/3auwow5Jk1aQTcajbiUKnVeVvxb5CrMmE9Yn5DN6or/RpsZVf0cgJmubZlMqEwUZDPf9xn/GYrkmCxp+YcrI5IN0uLfFkXyCfMz2arO+sTIpZmjhDocDgEArusyUZCVRqMR/6OsWVL+sezoNFksKf5pnYhcDOBiADjqqKOYn8laddYnTV9mNpWIXCwiu0Vk9/79+5ueDhF1DHMMEZVFVa9Q1XNV9dzNmzc3PR0iK7S+mYknhi1btgBYvwbPdV24rmv9TYlkr8FgwPifU1KOyZKUf7gAANkiKf4pmeM4zM9krTrrk9Y3M5Mm75HhKitkM8dxGP81Sss/XACAbJAW/5SO+ZlsVWd90vTSzB8A8C8AniUi94nI66b9TNLNRGxoyGZlxX8X/3aK5Jg0aYuNDAYDLgBAnZcV/7Yokk9Yn5DN6or/RhcAUNWfnPH5qTcTxTcYb7gj28wb/11dlWjWHJNm2qqJjmPcSW6i3LhqaKBoPmF9QjarI/6N+h9YRDI3Ao+AkM2Kxn+8UKGNWMiRzRj/5WB9QjarOv6NambyYMIgm80a/yxUsnH7kM0Y/+VifUI2qzL+O9fMAPk3GFcgoi7KG/8sVLJx+5DNGP/VYENDNqsq/o1808w8pl2jNxqNrF+B6A1XfqbpKVBF8sQ/C5WNVsc+brnnUYjvoT8+hHF/Ex59eN/Un+t5q3P/PT1y9B2HP7/uMf5tNsmk3PjWhfX4fNec8z76sSfgPT6aOf5pNtPyMw+0UpdVcQ9NJ8/MRNI6wKiQ4wpE1GXT4p+NTLJ4IadOr+npENWK8V+PrPxs+4FW6r6s+C+is2dmIpMdILC+vDNP8VLXZcU/G5kkykKO7KWKvsf4L0t0pjeL+ILH730Q4/4mAEB/fAi+0+eZ3g6x/UxvlqT4L6LzzQywXtANh0MAgOu6GAwGbGbICmnxTxsJwEKOrCXqMf5rpk4P4/4m9McHAQDj/hIc9RqeFVE9kuK/iE5fZkZERERERN1lxZmZ6B4B13UB4EmX3BB1XVr88+zMRgrwMjOylkqP8V+z9XuUgiPS0WVmRDZIiv8iOn9mZvJm5/g9BFwxhLouK/55mWUSCU95H4L4vNSDLCOM/zpNLrYQXXIjvMyMLJAW/0V0uplJW7UpKui4Ygh12bT4Z0OTbP0aXhZ0ZB/Gfz3SVo1TpwcVnhWjbsuK/yI628xMW352MBhwaWbqrDzxz4YmHQs6shnjv1pTl78WqX9SRDWpYvn3TjYzed9Hw3E6+euT5fLGPxuabCzoyGaM/2rwfXzIZlXFf+eqeb4hINls1vhnQ5ONBR3ZjPFfLjYyZLMq49+oZkZVMwsuNjJks6LxH29oaCMWdGQzxn852MiQzaqOf6OaGRFJPYLMRoZsNm/8Rw0NJZta0KnWPymimrChmQ8bGbJZHfFvVDMDIPGSGDYyZLOy4p9/O9nSCjrxPS6lSp2XFf+Ujo0M2ayu+DeumZm8xp+NDNnM933Gf40mC7ooUXMpVbJBWvxTGmUjQ/bS+uLfyLeZjRqa4XAIAHBdl4UcWWk0GrGRqdl6QXcQADDuL8HhmRmyRFL8UzIB2MiQtUS92uLfuDMzREREREREgKHNTHRpmeu6cF2Xy8qStQaDAeO/ZuvXAC9h3F8KLrPhAgBkicT4p0QKcNEEspZKr7b4N66ZmbxHhu+TQTZzHIfxX6PJmxmjS264AADZIC3+KY1wFTiyl9QX/8Y1M0k3O7OhIZuVFf/828mWtiqLOj0uAECdlxX/lI7LWpPN6op/o5oZVU292ZkNDdls3viPznhSsqnLS4rUPymimnB54fmwoSGb1RH/RjUzIpK5ahMbGrJZ0fiPX7pJG5lWyD04uh1fXr4aw9VvQtVvejqUYBsew/Oda/FDvWuansphT5M78YNHfhrHL9wLwXrcmBb/bWVqQ3Pt8B24Yc//wD0Hb8Kaf7Dp6dCErdiLc53P4PULb8fLen/d9HQO+4HeR/Azx7wX5xxxI7Y6y5XHv5FLM2eJF3RcspZsM2v8832asplYyN178EbcsfIJfP3Ap7Aom3HS0vdi19ILcPTCyRAx6vhVp2zDY3iOsxvP7X0Wx8q9EChWsYjrvQuanhoA4DnOv+IF2z6PM/2vYE0XcfuBM/C1g8/Bowd2YK1/hDHx32bxgs6UnDJc/SYEDh44dCsgwPGD78QpSz+A4wbfiQWHy3I3YSv24lnOV/Dc3mdxvPw7AKAHLzg90ZI++Xj5No5ZfAgvWrgWP7TtU3hs7OKWA+fgroPPxMFR+UuWd66ZAfIXdL7Po5bUPXnjn41MNhMbmciCLGFRNsPTNdy58kk2Ng05EkM82/ny4QZGAXjo4wC2QOCjj7Wmp/gkq/4AI92EHsZ47uYbcfYRN2F1xybcfvAM3HnoNDy4dgLUrAs6WsfEhmZRNkNE4KuPBw7dwsamAUkNjI8+DmILAMEi2req4NhfwCEsAVBs7+/Bi7aFjc2ai9ufOA1fWzsd+7ScK0IabWZE5HwAfwqgB+C9qvqHZY09raAbjUZYXV0t6+WIWiVP/NvSyBTJM000MqqKldU9UFUsrt1XeJxVf+Xw5z1ZQE8WAICNTU2yGpjgbRTXOVAcI8X3NQAsYBWAD8CZa6wl2R+sJQzA0x4O+ktQEfTEw3M334SzNu8+fMbG1samrJplakNTwlLv+w4+jOVDDwefH5jn8rD1uTjiYCBbAICNTQ2mNTCTNuMJnOtcP9drbpHHIVD46M011nZ5LPaVYE0H4aEbxfaFPXjhjhvwQr0ew/ExuOXgOfjGoWfjCf/Iwq/XWDMjIj0AfwHgJQDuA/AlEfmYqv5bWa+RVtBFhdzSEv/gqLumxb8ljczMeaapMzKPrXwbu++5Bo700Pc+VHgchaInixu+z8amOrM0MBGFAwce3rDwm3O9tisPQuDBw8JcYwkU43EfUIWoQkUAkeD30KBUCM7YbGxsbFB2zZLW0IjvlbLU+20PXoflgw/CEQc97/OFxxEk50A2NtXY2MAIfPRSG5jIGhZxpAzxsv58980cLY8AUPjozzmWYlUXE6Yca2zUx1G9x9bP2Ixd3LJyVqFXy9XMiMhnAPyxqn4i9r0rVPXiQq8aeB6Au1T1W+F4fwfgFQBKa2aAjQUdsL68MxcJoK7Liv82NTIV5Rhg5jyjjV3+4fljOOKg7yxiwa+2AJjW2Jy97dVwuNz0VKc5X8QLex/L3cA8mYQFynwUCoFA4WCE+eKmP9HITEprbH5xrlctl4i8CcD7VXVvyUPPXLP0ZQ075a70EXvAWHo4MF7CERKcPTngLWHBWcOP9f9grsl+1bsfD8gqBo4DB+O5xgKeyH44FiqeKvaPPoubRzfgqxC8cusJ+Kntu+Z8fTuc4tyObbIXCgc+AB89RBt3/kyRT7T4hwMPR8iU/T7FfhyBflbsCaAqWPUX4MCD23sY5237v4VeK+8huJMB/KqIvCP2vXMLveK6EwDcG/v6vvB7TyIiF4vIbhHZvX///kIvFBV0w+EQw+GwdYUcUZUMif8qcgyQI8/Ec8yBlRVjrmMvi4M+HPShqjjkL+OxtW9BwfsJ83iKPABXHoQPBwJgjEXka2TaK62RmeShhzF6EMx/SVTJnorgrMkHReR8kdLWTZ+5ZllZOTB10L7j4Yj+QaysHYGVtSNwRP+gsSu9+6o4FJ5VciA4fbANpw62NjwrcyzrDhzQoG1xNGppWvf3VSqRoHFy4MMRHyLFft+8l5k9DuDFAP5MRP4PgJ8q9GoFqOoVAK4AgF27dnV7rxLZ63G0IMccf8KJjeYYz/egOoL6xasZhaIvg8NnXhKfo4o1PRg0LQK4i0/HKUsvxImbzsamXvHrlm1zg3cB/tl7GU6WO3Bm7//hNOdLEIzQg4dDOCI8sppGsQnTi91pJBxL4GETVqY8O5tijDHS4wZQLMpqWGQBD60dj1sPnAXgfXO9bplU9TdE5DcB/DCAnwXwbhH5IIC/UtVv1vD6h/PJzhOPbSyfHNnr4+7VETzPg6PF84kHxRbpIa0n9FVxUL3DJfdpg2140eZj8NylHdjidHKNqco8psfhMT0OCziEI2UPduARLMkBQBW+OPDhIO1gSXAxmlfCfWyCoIHSYIW0wqMoejKGpuZARV/G6IkHVcHja9tx5/Kz8dDa8QBmv7wtb6SJqo4B/DcRuRDA5wEcNfOrPdn9AHbGvj4x/F7ponsEXNcFAL6nBlklLf5bdnamihwDzJhnFGjsMrOnbD0FZ+98GVSB7U88q/A43zzwWTw0um1DM8MGphoeFnCXnoG7xmfgI/i53I1NcDmH4B/GPz/X61/QuwwOPPjo4xqv+FhnOP+C05wvwPP6QWF6uHhNbmC+OXoWVvzo4pf2NDMAoKoqIg8BeAjAGEEu+QcRuU5V31Zw2JlrlrEu4F59Ruag8Xv0AODg2iH4Th9XjS8pOM3A4kmfw5lesMKVu/ecwuN85OE3Y4StkFgRreoHuUQUUOCYwXPw9KUfwPGbzsTA2YJ/B/DvwXVSNKcjMTy8pPsxcn9wbxv6WMUmxBubRRzCY3os3r/2y3O93hsWLkXwP6HgsrV3Fh7n5f0r8R34CkaI1xmKBVlDH2MoBHu9o3Hrypn41v6TMcTxAIL/f4vI28y85/BUVK8SkVsB/EKhV1z3JQDPFJGTESSEVwN4zZxjbpB0s3N0DwEXAKCuy4r/ljU0VeQYYOY8I40tm+pID8duC5qYY8YvKDzOw6P1S/jZwNRr1sbGg4Pb/OL7GgB+pPf+w5/PM9YJ4c3GKgJRHwuyBie85CO5gWknEfklAD8DYAjgvQDeqqprEqxs8Q0ARZuZ0muWpMVGxv1N6Hnz38/bdxbRd4KFQDb33LnHm9bAUDWW4eKL/vn4on/+lMYGGKOPZcy3r73Ymdl5xlpDtAjNkxuYZW87bj1wNr5+6Dl4fG17YvwXkauZUdXLJ77+MoCLCr3i+hhjEXkjgGsRLHN4parePs+Yk9JWbYruIVheXi7z5YhaZVr8t6mhqSLHhOPMnGdMfB+ISWMdAb4AAhy9+HQ8nQ1M7aY1Nn2sYRXN/+3FLcgaNjmHACgeHj0Vtx48B3etPqf1DcyEHQAuUNW7499UVV9EfqzooGXXLGmrJqrTg/rtyjmrWGED0wJZjU2wRHu7LDojQPGkBmavdzSA7PgvotELGsOViz4x9YkFTFt+djAYYHFx4/KlRF2QJ/7b1tBUpUieMbmhOXrxmXjq+F42MC2S1tgsoj0rat6vJ+Oe1V245eA5+OboWTgwXlq//Mmg1bpV9R0Zj90x59il1CxTl39v0QoAZ239TziidzQbmJZJamz2Y3vT0zpst/dDWH5iAXeufufhBiZSxdsfdPLurLzvo+E4BmVoopzyxr9NDU0RpjY0Tz/i+/H0I76/6WlQinhj0ya3+S/Ag48O4PXCg3wOjIz/tmvqfayKOm3ry5ueAk0RNTZtcpeeib37/fV8Eqoq/jtXzdv0hoBEk2aN/3hDw/dd2ije0Ig//xvZEZmE8V8u0xoZojJVGf9GNTOqmllwsZEhmxWN/3hDQxuxoCObMf7LwUaGbFZ1/Bt1mZmIpBZrbGTsddlFL256Co2bN/6jhoaSTb3kTPkWWNRdpl5y2RZsZMhmdcS/UWdmACReEsNGhmxWVvzzbydb2hFq8T2I8og1dVtW/FM6NjJks7ri37hmZvIafzYyZDPf9xn/NZos6KJErcIihbovLf4pjbKRIXtpffFv1GVmkaihGQ6HAADXdVnIkZVGoxEbmZqtF3QHAQDj/hIcnpkhSyTFPyUTgI0MWUvUqy3+jTszQ0REREREBBjazESXlrmuC9d1uawsWWswGDD+a7Z+DfASxv3gjQW5AADZIjH+KZECXAWOrKXSqy3+jWtmJu+R4ftkkM0cx2H812jyZsbokhsuAEA2SIt/SiNc1prsJfXFv3HNTNLNzmxoyGZlxT//drKlrcqiTo8LAFDnZcU/peP79JDN6op/o5oZVU292ZkNDdls3viPznhSsqnLS4rUPymimnB54fmwoSGb1RH/RjUzIpK5ahMbGrJZ0fiPX7pJG7GQI5sx/svBhoZsVnX8G9XM5MGGhmw2a/zzfZqysZAjmzH+y8WGhmxWZfx3rpkB8hd0vu/XOCuieuSNfzYy2VjIkc0Y/9VgQ0M2qyr+O9nMANMLutFohNXV1QZmRlS9PPHPRiYdCzmyGeO/WlMLOi71Th1WRUPT2WYGSC/ookJucXGxwdkRVWta/LORScZCjmzG+K9HWkEnvsel3qnzsuK/iE43M8DGgi5eyDlO5399slxW/LORSaIs5Mheyviv02RBFzWSXOqdbJAW/0X0S55bK0UF3XA4BAC4rovBYMAFAsgKafFPGwnAQo6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}, "metadata": { "needs_background": "light" }, "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": { "execution": { "iopub.execute_input": "2022-07-21T20:54:19.169848Z", "iopub.status.busy": "2022-07-21T20:54:19.169726Z", "iopub.status.idle": "2022-07-21T20:54:19.223646Z", "shell.execute_reply": "2022-07-21T20:54:19.223339Z" }, "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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) objects.\n", "\n", "Since these will be a short and simple simulations, we can use the [web.run](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.webapi.run.html) function to do them all in one line." ] }, { "cell_type": "code", "execution_count": 6, "id": "bedb64a3", "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:19.225526Z", "iopub.status.busy": "2022-07-21T20:54:19.225402Z", "iopub.status.idle": "2022-07-21T20:55:48.539210Z", "shell.execute_reply": "2022-07-21T20:55:48.538814Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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[16:54:21] INFO     Uploaded task 'straight waveguide' with task_id             webapi.py:120\n",
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       "Output()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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[16:55:39] INFO     downloading file \"output/monitor_data.hdf5\" to              webapi.py:574\n",
       "                    \"data/simulation.hdf5\"                                                   \n",
       "
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      ],
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    {
     "data": {
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       "
[16:55:48] INFO     loading SimulationData from data/simulation.hdf5            webapi.py:398\n",
       "
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      ],
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       "\u001b[2;36m[16:55:48]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/simulation.hdf5            \u001b]8;id=620922;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=514121;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#398\u001b\\\u001b[2m398\u001b[0m\u001b]8;;\u001b\\\n"
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       "                    sources.                                                                 \n",
       "
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      ],
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Auto meshing using wavelength \u001b[1;36m1.4990\u001b[0m defined from        \u001b]8;id=7780;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=400632;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#472\u001b\\\u001b[2m472\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         sources.                                                 \u001b[2m                \u001b[0m\n"
      ]
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     "metadata": {},
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   ],
   "source": [
    "sim0_data = web.run(simulation0, task_name='straight waveguide', path='data/simulation.hdf5')\n",
    "sim_data = web.run(simulation, task_name='scattered waveguide', path='data/simulation.hdf5')"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.096233Z",
     "iopub.status.busy": "2022-07-21T20:55:49.096141Z",
     "iopub.status.idle": "2022-07-21T20:55:49.098180Z",
     "shell.execute_reply": "2022-07-21T20:55:49.097921Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation domain Nx, Ny, Nz: [176, 152, 152]\n",
      "Applied symmetries: (0, 0, 0)\n",
      "Number of computational grid points: 4.2214e+06.\n",
      "Using subpixel averaging: True\n",
      "Number of time steps: 7.2850e+03\n",
      "Automatic shutoff factor: 1.00e-05\n",
      "Time step (s): 5.4916e-17\n",
      "\n",
      "\n",
      "Compute source modes time (s):     0.4161\n",
      "Compute monitor modes time (s):    0.8458\n",
      "Rest of setup time (s):            3.3141\n",
      "\n",
      "Running solver for 7285 time steps...\n",
      "- Time step    242 / time 1.33e-14s (  3 % done), field decay: 1.00e+00\n",
      "- Time step    291 / time 1.60e-14s (  4 % done), field decay: 1.00e+00\n",
      "- Time step    582 / time 3.20e-14s (  8 % done), field decay: 1.00e+00\n",
      "- Time step    874 / time 4.80e-14s ( 12 % done), field decay: 5.58e-04\n",
      "- Time step   1165 / time 6.40e-14s ( 16 % done), field decay: 9.01e-07\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "\n",
      "Solver time (s):                   1.1244\n",
      "Post-processing time (s):          0.7983\n",
      "\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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) objects store a copy of the original [Simulation](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Simulation.html), so it can be recovered if the [SimulationData](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) is loaded in a new session and the [Simulation](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Simulation.html) is no longer in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c3ef22b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.099488Z",
     "iopub.status.busy": "2022-07-21T20:55:49.099342Z",
     "iopub.status.idle": "2022-07-21T20:55:49.101194Z",
     "shell.execute_reply": "2022-07-21T20:55:49.100944Z"
    },
    "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",
   "metadata": {},
   "source": [
    "#### Monitor Data\n",
    "\n",
    "More importantly, the [SimulationData](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) contains a reference to the data for each of the monitors within the original [Simulation](https://docs.simulation.cloud/projects/tidy3d/en/latest/_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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FluxMonitor.html) was named `'flux'` while our [FluxTimeMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FluxTimeMonitor.html) was named `'flux_time'`, so we can access the data as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58af7e8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.102467Z",
     "iopub.status.busy": "2022-07-21T20:55:49.102321Z",
     "iopub.status.idle": "2022-07-21T20:55:49.104731Z",
     "shell.execute_reply": "2022-07-21T20:55:49.104483Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the flux data from the monitor name\n",
    "flux_data = sim_data['flux']\n",
    "flux_time_data = sim_data['flux_time']"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.105963Z",
     "iopub.status.busy": "2022-07-21T20:55:49.105818Z",
     "iopub.status.idle": "2022-07-21T20:55:49.112032Z",
     "shell.execute_reply": "2022-07-21T20:55:49.111790Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Tidy3dDataArray (f: 11)>\n",
       "array([0.59744805, 0.7788942 , 0.8959934 , 0.95459837, 0.97313416,\n",
       "       0.977259  , 0.9800654 , 0.98429835, 0.98820335, 0.9901537 ,\n",
       "       0.99016523], dtype=float32)\n",
       "Coordinates:\n",
       "  * f        (f) float64 1e+14 1.2e+14 1.4e+14 1.6e+14 ... 2.6e+14 2.8e+14 3e+14\n",
       "Attributes:\n",
       "    units:      W\n",
       "    long_name:  flux
"
      ]
     },
     "execution_count": 1,
     "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.113374Z",
     "iopub.status.busy": "2022-07-21T20:55:49.113147Z",
     "iopub.status.idle": "2022-07-21T20:55:49.115762Z",
     "shell.execute_reply": "2022-07-21T20:55:49.115491Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of flux dataset = (11,)\n",
      ".\n",
      "frequencies in dataset =  \n",
      ".\n",
      "flux values in dataset = [0.59744805 0.7788942  0.8959934  0.95459837 0.97313416 0.977259\n",
      " 0.9800654  0.98429835 0.98820335 0.9901537  0.99016523]\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 datset 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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.116999Z",
     "iopub.status.busy": "2022-07-21T20:55:49.116850Z",
     "iopub.status.idle": "2022-07-21T20:55:49.119163Z",
     "shell.execute_reply": "2022-07-21T20:55:49.118914Z"
    },
    "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.120317Z",
     "iopub.status.busy": "2022-07-21T20:55:49.120168Z",
     "iopub.status.idle": "2022-07-21T20:55:49.122120Z",
     "shell.execute_reply": "2022-07-21T20:55:49.121888Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at 4th frequency, flux is 9.73e-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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.123291Z",
     "iopub.status.busy": "2022-07-21T20:55:49.123126Z",
     "iopub.status.idle": "2022-07-21T20:55:49.130240Z",
     "shell.execute_reply": "2022-07-21T20:55:49.129986Z"
    },
    "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.131452Z",
     "iopub.status.busy": "2022-07-21T20:55:49.131260Z",
     "iopub.status.idle": "2022-07-21T20:55:49.132987Z",
     "shell.execute_reply": "2022-07-21T20:55:49.132726Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# arithmetic opertions with numbers\n",
    "flux_times_2_plus_1 = 2*flux_data + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87f3cc32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.134180Z",
     "iopub.status.busy": "2022-07-21T20:55:49.134006Z",
     "iopub.status.idle": "2022-07-21T20:55:49.136061Z",
     "shell.execute_reply": "2022-07-21T20:55:49.135813Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# operations with dataset (in this case, dividing by the normlization flux)\n",
    "flux_transmission =  flux_data / sim0_data['flux']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86475df",
   "metadata": {},
   "source": [
    "There are mny more things you can do with the xarray package that are not covered here, so it is best to take a look at [their documention](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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.137294Z",
     "iopub.status.busy": "2022-07-21T20:55:49.137102Z",
     "iopub.status.idle": "2022-07-21T20:55:49.280115Z",
     "shell.execute_reply": "2022-07-21T20:55:49.279781Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 720x216 with 3 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 72\u001b[0m\u001b[1;36m0x216\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m3\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 muli-dimensional.\n",
    "\n",
    "Let's use the [ModeMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.ModeMonitor.html) data as an example.  We'll access the data using it's name `'mode'` in the original simulation and print out some of the metadata to examine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28bd7aaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.282275Z",
     "iopub.status.busy": "2022-07-21T20:55:49.282116Z",
     "iopub.status.idle": "2022-07-21T20:55:49.285780Z",
     "shell.execute_reply": "2022-07-21T20:55:49.285537Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
(2, 11, 3)\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m, \u001b[1;36m11\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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.simulation.cloud/projects/tidy3d/en/latest/_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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.287471Z",
     "iopub.status.busy": "2022-07-21T20:55:49.287343Z",
     "iopub.status.idle": "2022-07-21T20:55:49.413155Z",
     "shell.execute_reply": "2022-07-21T20:55:49.412842Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 576x216 with 2 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 576x216 with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.415231Z",
     "iopub.status.busy": "2022-07-21T20:55:49.415107Z",
     "iopub.status.idle": "2022-07-21T20:55:49.418183Z",
     "shell.execute_reply": "2022-07-21T20:55:49.417908Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sum abolute 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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) or [FieldTimeMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_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 dont get an xarray object, but rather a [FieldData](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldData.html) container holding all of the field components.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68111c23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.420004Z",
     "iopub.status.busy": "2022-07-21T20:55:49.419834Z",
     "iopub.status.idle": "2022-07-21T20:55:49.441019Z",
     "shell.execute_reply": "2022-07-21T20:55:49.440714Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[16:55:49] INFO     Auto meshing using wavelength 1.4990 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
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      "text/plain": [
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       "\u001b[2;36m           \u001b[0m         sources.                                                 \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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           INFO     Auto meshing using wavelength 1.4990 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
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      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Auto meshing using wavelength \u001b[1;36m1.4990\u001b[0m defined from        \u001b]8;id=639982;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=436404;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#472\u001b\\\u001b[2m472\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         sources.                                                 \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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 it's `data_dict` dictionary.\n",
    "\n",
    "Let's look at what field components are contined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43efb783",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.443221Z",
     "iopub.status.busy": "2022-07-21T20:55:49.442966Z",
     "iopub.status.idle": "2022-07-21T20:55:49.445020Z",
     "shell.execute_reply": "2022-07-21T20:55:49.444780Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Ex', 'Ey', 'Ez'])\n"
     ]
    }
   ],
   "source": [
    "print(field_data.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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.446632Z",
     "iopub.status.busy": "2022-07-21T20:55:49.446501Z",
     "iopub.status.idle": "2022-07-21T20:55:49.473616Z",
     "shell.execute_reply": "2022-07-21T20:55:49.473393Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Tidy3dDataArray (x: 178, y: 3, z: 154, f: 11)>\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",
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       "          -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",
       "         [ 2.32727562e-08+9.18843490e-09j,\n",
       "          -1.21154979e-08+1.27215616e-08j,\n",
       "          -6.54509202e-09-1.29552358e-09j, ...,\n",
       "           1.16099166e-08+5.80530024e-09j,\n",
       "          -5.11198817e-09+8.43979997e-09j,\n",
       "          -3.85548793e-09-6.05461148e-09j],\n",
       "         ...,\n",
       "         [-2.17748184e-08-8.35067215e-09j,\n",
       "...\n",
       "           2.99944292e-10+8.04604550e-10j],\n",
       "         ...,\n",
       "         [-1.24971744e-09+3.62627595e-10j,\n",
       "           6.41821596e-10+2.61034749e-09j,\n",
       "           9.29857649e-11-1.27113720e-09j, ...,\n",
       "           7.02323089e-10+3.60556279e-10j,\n",
       "          -2.08780160e-10-1.31877828e-10j,\n",
       "           2.28684716e-09-9.02492664e-11j],\n",
       "         [-8.27865831e-10+1.04162989e-09j,\n",
       "          -2.49292462e-11+4.41955583e-10j,\n",
       "          -8.23942442e-11-3.62977877e-11j, ...,\n",
       "           1.48374105e-10+1.00988128e-10j,\n",
       "           1.04744564e-11+1.36821152e-10j,\n",
       "           3.40443368e-10+2.55534260e-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]]]], dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 -2.913 -2.88 -2.847 -2.814 ... 2.814 2.847 2.88 2.913\n",
       "  * y        (y) float64 -0.03929 -0.007143 0.025\n",
       "  * z        (z) float64 -3.108 -3.061 -3.015 -2.968 ... 2.922 2.968 3.015 3.062\n",
       "  * f        (f) float64 1e+14 1.2e+14 1.4e+14 1.6e+14 ... 2.6e+14 2.8e+14 3e+14
"
      ]
     },
     "execution_count": 1,
     "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": [
    "### Centering 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.  For this, we provide an `.at_centers()` method of [SimulationData](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) to return the fields co-located at the yee grid centers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc53fa35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.475199Z",
     "iopub.status.busy": "2022-07-21T20:55:49.475067Z",
     "iopub.status.idle": "2022-07-21T20:55:49.670758Z",
     "shell.execute_reply": "2022-07-21T20:55:49.670416Z"
    },
    "tags": []
   },
   "outputs": [],
   "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": [
    "The centered field data is stored as an xarray [Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html), which provies 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": 25,
   "id": "98ba6150",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.672814Z",
     "iopub.status.busy": "2022-07-21T20:55:49.672628Z",
     "iopub.status.idle": "2022-07-21T20:55:49.680586Z",
     "shell.execute_reply": "2022-07-21T20:55:49.680305Z"
    },
    "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": 26,
   "id": "4a5dc0c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:49.682325Z",
     "iopub.status.busy": "2022-07-21T20:55:49.682175Z",
     "iopub.status.idle": "2022-07-21T20:55:50.085255Z",
     "shell.execute_reply": "2022-07-21T20:55:50.084959Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.SimulationData.html) object as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fdf17ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:50.087003Z",
     "iopub.status.busy": "2022-07-21T20:55:50.086875Z",
     "iopub.status.idle": "2022-07-21T20:55:50.313304Z",
     "shell.execute_reply": "2022-07-21T20:55:50.312888Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 432x288 with 2 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data.plot_field('field', 'Ex', y=0., time=5e-14, freq=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": 28,
   "id": "12b2b76d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:50.315433Z",
     "iopub.status.busy": "2022-07-21T20:55:50.315301Z",
     "iopub.status.idle": "2022-07-21T20:55:50.428491Z",
     "shell.execute_reply": "2022-07-21T20:55:50.428190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 576x360 with 2 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 576x360 with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 29,
   "id": "3b49c1f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:50.431014Z",
     "iopub.status.busy": "2022-07-21T20:55:50.430881Z",
     "iopub.status.idle": "2022-07-21T20:55:50.649750Z",
     "shell.execute_reply": "2022-07-21T20:55:50.649373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 936x216 with 5 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 936x216 with \u001b[0m\u001b[1;36m5\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "f7c6c3e7",
   "metadata": {},
   "source": [
    "## Interactive Data Visualization GUI\n",
    "\n",
    "Tidy3d recently introduced a python-based graphical user interface for visualizing the contents of .hdf5 files in an interactive fashion.\n",
    "\n",
    "To use, we must import the `SimulationDataApp` plugin, load it from the file, and then call `.run()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d345c1a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:55:50.651476Z",
     "iopub.status.busy": "2022-07-21T20:55:50.651333Z",
     "iopub.status.idle": "2022-07-21T20:55:50.864558Z",
     "shell.execute_reply": "2022-07-21T20:55:50.864267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[16:55:50] INFO     Auto meshing using wavelength 1.4990 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
       "
\n"
      ],
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    "from tidy3d.plugins.webplots import SimulationDataApp\n",
    "\n",
    "app = SimulationDataApp.from_file('data/simulation.hdf5', mode='jupyter')\n",
    "\n",
    "app.run()"
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    "## 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.simulation.cloud/en/latest/_autosummary/tidy3d.SimulationData.html).\n",
    "\n",
    "* [Xarray documentation](http://xarray.pydata.org)."
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 99.1% • 27.4/27.7 MB • 3.7 MB/s • 0:00:01\n
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🏃  Finishing 'straight waveguide'...\n
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↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/11.1 kB • ? • -:--:--\n
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 98.5% • 16.8/17.0 MB • 3.7 MB/s • 0:00:01\n
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🚶  Finishing 'scattered waveguide'...\n
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