{
"cells": [
{
"cell_type": "markdown",
"id": "e8df7288",
"metadata": {},
"source": [
"# Start Here\n",
"\n",
"Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FStartHere.ipynb).\n",
"\n",
"This is a basic Tidy3D script showing the FDTD simulation of a delectric cube in the presence of a point dipole."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1ea643db",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:14.211649Z",
"iopub.status.busy": "2022-07-21T20:34:14.211142Z",
"iopub.status.idle": "2022-07-21T20:34:15.195356Z",
"shell.execute_reply": "2022-07-21T20:34:15.194884Z"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# import the package and the web API\n",
"import tidy3d as td\n",
"import tidy3d.web as web"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0d73a70a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:15.197458Z",
"iopub.status.busy": "2022-07-21T20:34:15.197299Z",
"iopub.status.idle": "2022-07-21T20:34:15.202226Z",
"shell.execute_reply": "2022-07-21T20:34:15.201894Z"
},
"tags": []
},
"outputs": [],
"source": [
"# set up parameters of simulation (length scales are micrometers)\n",
"grid_cells_per_wvl = 30\n",
"pml = td.PML()\n",
"sim_size = (4, 4, 4)\n",
"lambda0 = 1.0\n",
"freq0 = td.C_0 / lambda0\n",
"fwidth = freq0 / 10.0\n",
"run_time = 12.0 / fwidth\n",
"\n",
"# create structure\n",
"dielectric = td.Medium.from_nk(n=2, k=0, freq=freq0)\n",
"square = td.Structure(\n",
" geometry=td.Box(center=(0, 0, 0), size=(1.5, 1.5, 1.5)),\n",
" medium=dielectric)\n",
"\n",
"# create source\n",
"source = td.UniformCurrentSource(\n",
" center=(-1.5, 0, 0),\n",
" size=(0, 0.4, 0.4),\n",
" source_time = td.GaussianPulse(\n",
" freq0=freq0,\n",
" fwidth=fwidth),\n",
" polarization='Ey')\n",
"\n",
"# create monitor\n",
"monitor = td.FieldMonitor(\n",
" fields=['Ex', 'Ey', 'Hz'],\n",
" center=(0, 0, 0),\n",
" size=(td.inf, td.inf, 0),\n",
" freqs=[freq0],\n",
" name='fields_on_plane')\n",
"\n",
"# Initialize simulation\n",
"sim = td.Simulation(size=sim_size,\n",
" grid_spec = td.GridSpec.auto(min_steps_per_wvl=grid_cells_per_wvl),\n",
" structures=[square],\n",
" sources=[source],\n",
" monitors=[monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML())\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "21f75a2a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:15.203930Z",
"iopub.status.busy": "2022-07-21T20:34:15.203782Z",
"iopub.status.idle": "2022-07-21T20:34:15.213708Z",
"shell.execute_reply": "2022-07-21T20:34:15.213457Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"[16:34:15] INFO Auto meshing using wavelength 1.0000 defined from grid_spec.py:472\n",
" sources. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:15]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Auto meshing using wavelength \u001b[1;36m1.0000\u001b[0m defined from \u001b]8;id=218748;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=729535;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": [
"simulation grid is shaped [192, 192, 192] for 7 million cells.\n"
]
}
],
"source": [
"print(f'simulation grid is shaped {sim.grid.num_cells} for {int(np.prod(sim.grid.num_cells)/1e6)} million cells.')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "52c4fd32",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:15.215105Z",
"iopub.status.busy": "2022-07-21T20:34:15.214998Z",
"iopub.status.idle": "2022-07-21T20:34:59.371823Z",
"shell.execute_reply": "2022-07-21T20:34:59.371522Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
" INFO Using Tidy3D credentials from stored file auth.py:74\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using Tidy3D credentials from stored file \u001b]8;id=445679;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/auth.py\u001b\\\u001b[2mauth.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=636393;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/auth.py#74\u001b\\\u001b[2m74\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:17] INFO Uploaded task 'quickstart' with task_id webapi.py:120\n",
" '873f64e9-3ccc-431f-8c9d-da84a2abf053'. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:17]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Uploaded task \u001b[32m'quickstart'\u001b[0m with task_id \u001b]8;id=234676;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=667821;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n",
"\u001b[2;36m \u001b[0m \u001b[32m'873f64e9-3ccc-431f-8c9d-da84a2abf053'\u001b[0m. \u001b[2m \u001b[0m\n"
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"model_id": "a1bcbc0d33594a9db64800b7417d5e7a",
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"text/plain": [
"Output()"
]
},
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},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
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{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:20] INFO status = queued webapi.py:253\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:20]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m status = queued \u001b]8;id=536588;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=573055;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\u001b[0m\u001b]8;;\u001b\\\n"
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"version_minor": 0
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"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:23] INFO Maximum flex unit cost: 0.20 webapi.py:244\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Maximum flex unit cost: \u001b[1;36m0.20\u001b[0m \u001b]8;id=48797;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=162635;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#244\u001b\\\u001b[2m244\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:27] INFO status = preprocess webapi.py:265\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:27]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m status = preprocess \u001b]8;id=37274;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=280863;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#265\u001b\\\u001b[2m265\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:35] INFO starting up solver webapi.py:269\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m starting up solver \u001b]8;id=132880;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=78773;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:47] INFO running solver webapi.py:275\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:47]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m running solver \u001b]8;id=157610;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=564653;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#275\u001b\\\u001b[2m275\u001b[0m\u001b]8;;\u001b\\\n"
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},
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"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:53] INFO early shutoff detected, exiting. webapi.py:286\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m early shutoff detected, exiting. \u001b]8;id=539431;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=740502;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n"
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},
"metadata": {},
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{
"data": {
"text/html": [
"\n"
],
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},
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},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
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},
{
"data": {
"text/html": [
" INFO status = postprocess webapi.py:292\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m status = postprocess \u001b]8;id=24345;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=475726;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#292\u001b\\\u001b[2m292\u001b[0m\u001b]8;;\u001b\\\n"
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{
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},
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{
"data": {
"text/html": [
"[16:34:56] INFO status = success webapi.py:298\n",
"\n"
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"text/plain": [
"\u001b[2;36m[16:34:56]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m status = success \u001b]8;id=550373;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=93006;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#298\u001b\\\u001b[2m298\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:57] INFO downloading file \"output/monitor_data.hdf5\" to webapi.py:574\n",
" \"data/data.hdf5\" \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to \u001b]8;id=394435;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=701959;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#574\u001b\\\u001b[2m574\u001b[0m\u001b]8;;\u001b\\\n",
"\u001b[2;36m \u001b[0m \u001b[32m\"data/data.hdf5\"\u001b[0m \u001b[2m \u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "2fc09ae92f904b05885d9ebc11c4aced",
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"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:34:59] INFO loading SimulationData from data/data.hdf5 webapi.py:398\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:34:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m loading SimulationData from data/data.hdf5 \u001b]8;id=680444;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=893632;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#398\u001b\\\u001b[2m398\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" INFO Auto meshing using wavelength 1.0000 defined from grid_spec.py:472\n",
" sources. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Auto meshing using wavelength \u001b[1;36m1.0000\u001b[0m defined from \u001b]8;id=414387;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=103211;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"
}
],
"source": [
"# run the simulation, download the data.\n",
"data = web.run(sim, task_name='quickstart', path='data/data.hdf5')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bfe889fc",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:59.626961Z",
"iopub.status.busy": "2022-07-21T20:34:59.626825Z",
"iopub.status.idle": "2022-07-21T20:34:59.628689Z",
"shell.execute_reply": "2022-07-21T20:34:59.628432Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulation domain Nx, Ny, Nz: [192, 192, 192]\n",
"Applied symmetries: (0, 0, 0)\n",
"Number of computational grid points: 7.3014e+06.\n",
"Using subpixel averaging: True\n",
"Number of time steps: 1.3924e+04\n",
"Automatic shutoff factor: 1.00e-05\n",
"Time step (s): 2.8750e-17\n",
"\n",
"\n",
"Compute source modes time (s): 0.0085\n",
"Compute monitor modes time (s): 0.0174\n",
"Rest of setup time (s): 3.4423\n",
"\n",
"Running solver for 13924 time steps...\n",
"- Time step 556 / time 1.60e-14s ( 4 % done), field decay: 1.00e+00\n",
"- Time step 923 / time 2.65e-14s ( 6 % done), field decay: 1.00e+00\n",
"- Time step 1113 / time 3.20e-14s ( 8 % done), field decay: 9.57e-01\n",
"- Time step 1670 / time 4.80e-14s ( 12 % done), field decay: 1.71e-01\n",
"- Time step 2227 / time 6.40e-14s ( 16 % done), field decay: 4.05e-02\n",
"- Time step 2784 / time 8.00e-14s ( 20 % done), field decay: 1.84e-02\n",
"- Time step 3341 / time 9.61e-14s ( 24 % done), field decay: 8.94e-03\n",
"- Time step 3898 / time 1.12e-13s ( 28 % done), field decay: 4.25e-03\n",
"- Time step 4455 / time 1.28e-13s ( 32 % done), field decay: 3.17e-03\n",
"- Time step 5012 / time 1.44e-13s ( 36 % done), field decay: 2.33e-03\n",
"- Time step 5569 / time 1.60e-13s ( 40 % done), field decay: 1.80e-03\n",
"- Time step 6126 / time 1.76e-13s ( 44 % done), field decay: 1.09e-03\n",
"- Time step 6683 / time 1.92e-13s ( 48 % done), field decay: 7.57e-04\n",
"- Time step 7240 / time 2.08e-13s ( 52 % done), field decay: 3.30e-04\n",
"- Time step 7797 / time 2.24e-13s ( 56 % done), field decay: 2.27e-04\n",
"- Time step 8354 / time 2.40e-13s ( 60 % done), field decay: 8.50e-05\n",
"- Time step 8911 / time 2.56e-13s ( 64 % done), field decay: 1.18e-04\n",
"- Time step 9468 / time 2.72e-13s ( 68 % done), field decay: 6.03e-05\n",
"- Time step 10025 / time 2.88e-13s ( 72 % done), field decay: 9.70e-05\n",
"- Time step 10582 / time 3.04e-13s ( 76 % done), field decay: 4.02e-05\n",
"- Time step 11139 / time 3.20e-13s ( 80 % done), field decay: 5.61e-05\n",
"- Time step 11696 / time 3.36e-13s ( 84 % done), field decay: 2.18e-05\n",
"- Time step 12253 / time 3.52e-13s ( 88 % done), field decay: 3.46e-05\n",
"- Time step 12810 / time 3.68e-13s ( 92 % done), field decay: 8.46e-06\n",
"Field decay smaller than shutoff factor, exiting solver.\n",
"\n",
"Solver time (s): 6.1692\n",
"Post-processing time (s): 0.1168\n",
"\n",
"\n"
]
}
],
"source": [
"# see the log\n",
"print(data.log)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eecc9790",
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:34:59.630064Z",
"iopub.status.busy": "2022-07-21T20:34:59.629850Z",
"iopub.status.idle": "2022-07-21T20:34:59.903943Z",
"shell.execute_reply": "2022-07-21T20:34:59.903494Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" INFO Auto meshing using wavelength 1.0000 defined from grid_spec.py:472\n",
" sources. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Auto meshing using wavelength \u001b[1;36m1.0000\u001b[0m defined from \u001b]8;id=586675;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=428445;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"
},
{
"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": {
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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the fields stored in the monitor\n",
"ax = data.plot_field('fields_on_plane', 'Ey', freq=freq0, z=0)\n",
"_ = ax.set_title('Ey(x,y)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e68a0f2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"widgets": {
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"state": {
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"_view_module_version": "1.0.0",
"_view_name": "OutputView",
"layout": "IPY_MODEL_ec86238c32a241b8ad505ea4400489e0",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 86.1% • 2.4/2.7 MB • 3.7 MB/s • 0:00:01\n
\n",
"text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━\u001b[0m \u001b[35m86.1%\u001b[0m • \u001b[32m2.4/2.7 MB\u001b[0m • \u001b[31m3.7 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m\n"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
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