{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tidy3D first walkthrough\n", "\n", "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FSimulation.ipynb).\n", "\n", "Our first tutorial focuses on illustrating the basic setup, run, and analysis of a ``Tidy3D`` simulation. In this example, we will simulate a plane wave impinging on dielectric slab with a triangular pillar made of a lossy dielectric sitting on top. First, we import everything needed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:32.207234Z", "iopub.status.busy": "2022-07-21T20:26:32.206834Z", "iopub.status.idle": "2022-07-21T20:26:33.182981Z", "shell.execute_reply": "2022-07-21T20:26:33.182456Z" }, "tags": [] }, "outputs": [], "source": [ "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import h5py\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we initialize some general simulation parameters. We note that the PML layers extend **beyond** the simulation domain, making the total simulation size larger - as opposed to some solvers in which the PML is covering part of the user-defined simulation domain." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.184904Z", "iopub.status.busy": "2022-07-21T20:26:33.184745Z", "iopub.status.idle": "2022-07-21T20:26:33.187530Z", "shell.execute_reply": "2022-07-21T20:26:33.187267Z" }, "tags": [] }, "outputs": [], "source": [ "# Simulation domain size (in micron)\n", "sim_size = [4, 4, 4]\n", "\n", "# Central frequency and bandwidth of pulsed excitation, in Hz\n", "freq0 = 2e14\n", "fwidth = 1e13\n", "\n", "# # pad the z direction (out of plane) with PML but leave it off x and y for periodic boundary conditions.\n", "# boundary_spec=td.BoundarySpec.pml(x=False, y=False, z=True)\n", "\n", "# apply a PML in all directions\n", "boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The run time of a simulation depends a lot on whether there are any long-lived resonances. In our example here, there is no strong resonance. Thus, we do not need to run the simulation much longer than after the sources have decayed. We thus set the run time based on the source bandwidth." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.188878Z", "iopub.status.busy": "2022-07-21T20:26:33.188749Z", "iopub.status.idle": "2022-07-21T20:26:33.190461Z", "shell.execute_reply": "2022-07-21T20:26:33.190238Z" }, "tags": [] }, "outputs": [], "source": [ "# Total time to run in seconds\n", "run_time = 2/fwidth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Structures and materials\n", "\n", "Next, we initialize the simulated structure. The structure consists of two [Structure](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Structure.html) objects. Each object consists of a [Geometry](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.components.geometry.Geometry.html) and a [Medium](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.components.medium.AbstractMedium.html) to define the spatial extent and material properties, respectively. Note that the size of any object (structure, source, or monitor) can extend beyond the simulation domain, and is truncated at the edges of that domain. \n", "\n", "Note: For best results, structures that intersect with the PML or simulation edges should extend extend all the way through. In many such cases, an \"infinite\" size `td.inf` can be used to define the size along that dimension." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.191846Z", "iopub.status.busy": "2022-07-21T20:26:33.191732Z", "iopub.status.idle": "2022-07-21T20:26:33.195046Z", "shell.execute_reply": "2022-07-21T20:26:33.194785Z" }, "tags": [] }, "outputs": [], "source": [ "# Lossless dielectric specified directly using relative permittivity\n", "material1 = td.Medium(permittivity=6.)\n", "\n", "# Lossy dielectric defined from the real and imaginary part of the refractive index\n", "material2 = td.Medium.from_nk(n=1.5, k=0.0, freq=freq0)\n", "# material2 = td.Medium(permittivity=2.)\n", "\n", "\n", "# Rectangular slab, extending infinitely in x and y with medium `material1`\n", "box = td.Structure(\n", " geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, 1]),\n", " medium=material1\n", ")\n", "\n", "# Triangle in the xy-plane with a finite extent in z\n", "equi_tri_verts = [[-1/2, -1/4],\n", " [1/2, -1/4],\n", " [0, np.sqrt(3)/2 - 1/4]]\n", "\n", "poly = td.Structure(\n", " geometry=td.PolySlab(\n", " vertices=(2*np.array(equi_tri_verts)).tolist(),\n", " # vertices=equi_tri_verts, \n", " slab_bounds=(.5, 1.0),\n", " axis=2),\n", " medium=material2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sources\n", "\n", "Next, we define a source injecting a normal-incidence plane-wave from above. The time dependence of the source is a Gaussian pulse. A source can be added to multiple simulations. After we add the source to a specific simulation, such that the total run time is known, we can use in-built plotting tools to visualize its time- and frequency-dependence, which we will show below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.196447Z", "iopub.status.busy": "2022-07-21T20:26:33.196319Z", "iopub.status.idle": "2022-07-21T20:26:33.198445Z", "shell.execute_reply": "2022-07-21T20:26:33.198215Z" }, "tags": [] }, "outputs": [], "source": [ "psource = td.PlaneWave(\n", " center=(0,0,1.5),\n", " direction='-',\n", " size=(td.inf, td.inf, 0),\n", " source_time = td.GaussianPulse(\n", " freq0=freq0,\n", " fwidth=fwidth),\n", " pol_angle=np.pi/2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Monitors\n", "\n", "Finally, we can also add some monitors that will record the fields that we request during the simulation run. \n", "\n", "The two monitor types for measuring fields are [FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) and [FieldTimeMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldTimeMonitor.html), which record the frequency-domain and time-domain fields, respectively. \n", "\n", "[FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) objects operate by running a discrete Fourier transform of the fields at a given set of frequencies to perform the calculation \"in-place\" with the time stepping. [FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) objects are useful for investigating the steady-state field distribution in 2D or even 3D regions of the simulation.\n", "\n", "[FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) objects are best used to monitor the time dependence of the fields at a single point, but they can also be used to create \"animations\" of the field pattern evolution. Because spatially large [FieldMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html) objects can lead to a very large amount of data that needs to be stored, an optional start and stop time can be supplied, as well as an `interval` specifying the amount of time steps between each measurement (default of 1)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.199854Z", "iopub.status.busy": "2022-07-21T20:26:33.199752Z", "iopub.status.idle": "2022-07-21T20:26:33.202241Z", "shell.execute_reply": "2022-07-21T20:26:33.202007Z" }, "tags": [] }, "outputs": [], "source": [ "# measure time domain fields at center location, measure every 5 time steps\n", "time_mnt = td.FieldTimeMonitor(center=[0, 0, 0], size=[0, 0, 0], interval=5, name='field_time')\n", "\n", "# measure the steady state fields at central frequency in the xy plane and the xz plane.\n", "freq_mnt1 = td.FieldMonitor(center=[0, 0, -1], size=[20, 20, 0], freqs=[freq0], name='field1')\n", "freq_mnt2 = td.FieldMonitor(center=[0, 0, 0], size=[20, 0, 20], freqs=[freq0], name='field2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation\n", "\n", "Now we can initialize the [Simulation](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Simulation.html) with all the elements defined above. A nonuniform simulation grid is generated automatically based on a given minimum number of cells per wavelength in each material (10 by default), using the frequencies defined in the source." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.203715Z", "iopub.status.busy": "2022-07-21T20:26:33.203610Z", "iopub.status.idle": "2022-07-21T20:26:33.206707Z", "shell.execute_reply": "2022-07-21T20:26:33.206474Z" }, "tags": [] }, "outputs": [], "source": [ "# Initialize simulation\n", "sim = td.Simulation(size=sim_size,\n", " grid_spec = td.GridSpec.auto(min_steps_per_wvl=20),\n", " structures=[box, poly], \n", " sources=[psource],\n", " monitors=[time_mnt, freq_mnt1, freq_mnt2],\n", " run_time=run_time,\n", " boundary_spec=boundary_spec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the simulation monitors just to make sure everything looks right." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.208204Z", "iopub.status.busy": "2022-07-21T20:26:33.208036Z", "iopub.status.idle": "2022-07-21T20:26:33.242000Z", "shell.execute_reply": "2022-07-21T20:26:33.241745Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
╭────────────────── <class 'tidy3d.components.monitor.FieldTimeMonitor'> ───────────────────╮\n",
       "│ :class:`Monitor` that records electromagnetic fields in the time domain.                  │\n",
       "│                                                                                           │\n",
       "│ ╭───────────────────────────────────────────────────────────────────────────────────────╮ │\n",
       "│ │ FieldTimeMonitor(type='FieldTimeMonitor', center=(0.0, 0.0, 0.0), size=(0.0, 0.0,     │ │\n",
       "│ │ 0.0), name='field_time', start=0.0, stop=None, interval=5, fields=('Ex', 'Ey', 'Ez',  │ │\n",
       "│ │ 'Hx', 'Hy', 'Hz'), interval_space=(1, 1, 1), colocate=False)                          │ │\n",
       "│ ╰───────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                           │\n",
       "│   bounding_box = Box(type='Box', center=(0.0, 0.0, 0.0), size=(0.0, 0.0, 0.0))            │\n",
       "│         bounds = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))                                       │\n",
       "│         center = (0.0, 0.0, 0.0)                                                          │\n",
       "│       colocate = False                                                                    │\n",
       "│         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                     │\n",
       "│       geometry = Box(type='Box', center=(0.0, 0.0, 0.0), size=(0.0, 0.0, 0.0))            │\n",
       "│       interval = 5                                                                        │\n",
       "│ interval_space = (1, 1, 1)                                                                │\n",
       "│           name = 'field_time'                                                             │\n",
       "│    plot_params = PlotParams(alpha=0.4, edgecolor='orange', facecolor='orange', fill=True, │\n",
       "│                  hatch=None, linewidth=3.0, type='PlotParams')                            │\n",
       "│           size = (0.0, 0.0, 0.0)                                                          │\n",
       "│          start = 0.0                                                                      │\n",
       "│           stop = None                                                                     │\n",
       "│           type = 'FieldTimeMonitor'                                                       │\n",
       "╰───────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "
\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\u001b[34m───────────────── \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'tidy3d.components.monitor.FieldTimeMonitor'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m ──────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[36m:class:`Monitor` that records electromagnetic fields in the time domain.\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╭───────────────────────────────────────────────────────────────────────────────────────╮\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[1;35mFieldTimeMonitor\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldTimeMonitor'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'field_time'\u001b[0m, \u001b[33mstart\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mstop\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33minterval\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[33mfields\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[33minterval_space\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m\u001b[1m)\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╰───────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounding_box\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounds\u001b[0m = \u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcenter\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfields\u001b[0m = \u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mgeometry\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33minterval\u001b[0m = \u001b[1;36m5\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33minterval_space\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field_time'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m\u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33msize\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mstart\u001b[0m = \u001b[1;36m0.0\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mstop\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldTimeMonitor'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m╰───────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
╭──────────────────── <class 'tidy3d.components.monitor.FieldMonitor'> ─────────────────────╮\n",
       "│ :class:`Monitor` that records electromagnetic fields in the frequency domain.             │\n",
       "│                                                                                           │\n",
       "│ ╭───────────────────────────────────────────────────────────────────────────────────────╮ │\n",
       "│ │ FieldMonitor(type='FieldMonitor', center=(0.0, 0.0, -1.0), size=(20.0, 20.0, 0.0),    │ │\n",
       "│ │ name='field1', freqs=(200000000000000.0,), fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy',      │ │\n",
       "│ │ 'Hz'), interval_space=(1, 1, 1), colocate=False)                                      │ │\n",
       "│ ╰───────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                           │\n",
       "│   bounding_box = Box(type='Box', center=(0.0, 0.0, -1.0), size=(20.0, 20.0, 0.0))         │\n",
       "│         bounds = ((-10.0, -10.0, -1.0), (10.0, 10.0, -1.0))                               │\n",
       "│         center = (0.0, 0.0, -1.0)                                                         │\n",
       "│       colocate = False                                                                    │\n",
       "│         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                     │\n",
       "│          freqs = (200000000000000.0,)                                                     │\n",
       "│       geometry = Box(type='Box', center=(0.0, 0.0, -1.0), size=(20.0, 20.0, 0.0))         │\n",
       "│ interval_space = (1, 1, 1)                                                                │\n",
       "│           name = 'field1'                                                                 │\n",
       "│    plot_params = PlotParams(alpha=0.4, edgecolor='orange', facecolor='orange', fill=True, │\n",
       "│                  hatch=None, linewidth=3.0, type='PlotParams')                            │\n",
       "│           size = (20.0, 20.0, 0.0)                                                        │\n",
       "│           type = 'FieldMonitor'                                                           │\n",
       "╰───────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "
\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\u001b[34m─────────────────── \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'tidy3d.components.monitor.FieldMonitor'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m ────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[36m:class:`Monitor` that records electromagnetic fields in the frequency domain.\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╭───────────────────────────────────────────────────────────────────────────────────────╮\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[1;35mFieldMonitor\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[33mname\u001b[0m=\u001b[32m'field1'\u001b[0m, \u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[33mfields\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[33minterval_space\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m\u001b[1m)\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╰───────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounding_box\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounds\u001b[0m = \u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m-10.0\u001b[0m, \u001b[1;36m-10.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m10.0\u001b[0m, \u001b[1;36m10.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcenter\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfields\u001b[0m = \u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfreqs\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mgeometry\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m-1.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33minterval_space\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field1'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m\u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33msize\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldMonitor'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m╰───────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
╭──────────────────── <class 'tidy3d.components.monitor.FieldMonitor'> ─────────────────────╮\n",
       "│ :class:`Monitor` that records electromagnetic fields in the frequency domain.             │\n",
       "│                                                                                           │\n",
       "│ ╭───────────────────────────────────────────────────────────────────────────────────────╮ │\n",
       "│ │ FieldMonitor(type='FieldMonitor', center=(0.0, 0.0, 0.0), size=(20.0, 0.0, 20.0),     │ │\n",
       "│ │ name='field2', freqs=(200000000000000.0,), fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy',      │ │\n",
       "│ │ 'Hz'), interval_space=(1, 1, 1), colocate=False)                                      │ │\n",
       "│ ╰───────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                           │\n",
       "│   bounding_box = Box(type='Box', center=(0.0, 0.0, 0.0), size=(20.0, 0.0, 20.0))          │\n",
       "│         bounds = ((-10.0, 0.0, -10.0), (10.0, 0.0, 10.0))                                 │\n",
       "│         center = (0.0, 0.0, 0.0)                                                          │\n",
       "│       colocate = False                                                                    │\n",
       "│         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                     │\n",
       "│          freqs = (200000000000000.0,)                                                     │\n",
       "│       geometry = Box(type='Box', center=(0.0, 0.0, 0.0), size=(20.0, 0.0, 20.0))          │\n",
       "│ interval_space = (1, 1, 1)                                                                │\n",
       "│           name = 'field2'                                                                 │\n",
       "│    plot_params = PlotParams(alpha=0.4, edgecolor='orange', facecolor='orange', fill=True, │\n",
       "│                  hatch=None, linewidth=3.0, type='PlotParams')                            │\n",
       "│           size = (20.0, 0.0, 20.0)                                                        │\n",
       "│           type = 'FieldMonitor'                                                           │\n",
       "╰───────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "
\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\u001b[34m─────────────────── \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'tidy3d.components.monitor.FieldMonitor'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m ────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[36m:class:`Monitor` that records electromagnetic fields in the frequency domain.\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╭───────────────────────────────────────────────────────────────────────────────────────╮\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[1;35mFieldMonitor\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m20.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[33mname\u001b[0m=\u001b[32m'field2'\u001b[0m, \u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[33mfields\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[33minterval_space\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m\u001b[1m)\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m╰───────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounding_box\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m20.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mbounds\u001b[0m = \u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m-10.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m-10.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m10.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m10.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcenter\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfields\u001b[0m = \u001b[1m(\u001b[0m\u001b[32m'Ex'\u001b[0m, \u001b[32m'Ey'\u001b[0m, \u001b[32m'Ez'\u001b[0m, \u001b[32m'Hx'\u001b[0m, \u001b[32m'Hy'\u001b[0m, \u001b[32m'Hz'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfreqs\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mgeometry\u001b[0m = \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'Box'\u001b[0m, \u001b[33mcenter\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[33msize\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m20.0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33minterval_space\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field2'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m\u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33msize\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m20.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m20.0\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldMonitor'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m╰───────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for m in sim.monitors:\n", " m.help()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualization functions\n", "\n", "We can now use the some in-built plotting functions to make sure that we have set up the simulation as we desire.\n", "\n", "First, let's take a look at the source time dependence." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.243645Z", "iopub.status.busy": "2022-07-21T20:26:33.243500Z", "iopub.status.idle": "2022-07-21T20:26:33.329176Z", "shell.execute_reply": "2022-07-21T20:26:33.328901Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
<Figure size 432x288 with 1 Axes>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\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": [ "# Visualize source\n", "psource.source_time.plot(np.linspace(0, run_time, 1001))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's visualize the simulation. \n", "\n", "For this, we will plot three cross sections at `z=0.75`, `y=0`, and `x=0`, respectively. \n", "\n", "The relative permittivity of objects is plotted in greyscale.\n", "\n", "By default, sources are overlayed in green, monitors in yellow, and PML boundaries in grey." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.330909Z", "iopub.status.busy": "2022-07-21T20:26:33.330732Z", "iopub.status.idle": "2022-07-21T20:26:33.699627Z", "shell.execute_reply": "2022-07-21T20:26:33.699186Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[16:26:33] INFO     Auto meshing using wavelength 1.4990 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[16:26:33]\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=83777;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=463364;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": [ "
<AxesSubplot:title={'center':'cross section at x=0.00'}, xlabel='y', ylabel='z'>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m{\u001b[0m\u001b[32m'center'\u001b[0m\u001b[39m:\u001b[0m\u001b[32m'cross section at \u001b[0m\u001b[32mx\u001b[0m\u001b[32m=\u001b[0m\u001b[32m0\u001b[0m\u001b[32m.00'\u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m, \u001b[0m\u001b[33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'y'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'z'\u001b[0m\u001b[1m>\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
<Figure size 936x288 with 6 Axes>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 936x288 with \u001b[0m\u001b[1;36m6\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": [ "fig, ax = plt.subplots(1, 3, figsize=(13, 4))\n", "sim.plot_eps(z=0.75, freq=freq0, ax=ax[0]);\n", "sim.plot_eps(y=0.01, freq=freq0, ax=ax[1]);\n", "sim.plot_eps(x=0, freq=freq0, ax=ax[2]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we can also plot the structures with a fake color based on the material they are made of." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.701413Z", "iopub.status.busy": "2022-07-21T20:26:33.701222Z", "iopub.status.idle": "2022-07-21T20:26:33.851869Z", "shell.execute_reply": "2022-07-21T20:26:33.851538Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
<AxesSubplot:title={'center':'cross section at x=0.00'}, xlabel='y', ylabel='z'>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m{\u001b[0m\u001b[32m'center'\u001b[0m\u001b[39m:\u001b[0m\u001b[32m'cross section at \u001b[0m\u001b[32mx\u001b[0m\u001b[32m=\u001b[0m\u001b[32m0\u001b[0m\u001b[32m.00'\u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m, \u001b[0m\u001b[33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'y'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'z'\u001b[0m\u001b[1m>\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
<Figure size 864x216 with 3 Axes>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 864x216 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": "iVBORw0KGgoAAAANSUhEUgAAAqMAAADgCAYAAAA3zG+DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3cklEQVR4nO3deZQcV30v8O+vu6dHYrR5PMY2spEFBoIwZjPYBPPCYl4cHoGwncfyCI6TJ5OQPHA4kIBfQk4CeSSchCwEkAAfQnBYjDcWO8Zgy7JlG294k4WxvEi29tZImr17uvr3/ugqTU2rl6ruqr51b38/5/Q5M73cvl33/m7/uupWXVFVEBERERGZkDNdASIiIiIaXExGiYiIiMgYJqNEREREZAyTUSIiIiIyhskoERERERnDZJSIiIiIjGEy6hARuU5EPmi6HkTUGeOVyB6M13QxGbWUiPyViHwrfJ+q/paq/nuf63GaiKiIFFJ+nzeKyC9FZEZEbhKRNS2e92wRmWq4qYh8zH/8dSJSa3icAwylatDi1X+v94nIDhGZFpGrRWS0zXNfKiL3+PF9j4i8NPTY6/2YPyIiT6ZdbyLGa0/xKiLydyJy0L/9nYhIp/dnMgqgHw1N3RORMQBXAvgLAKMA7gbw3WbPVdWdqrosuAF4MYAagCtCT9sdfk6/BxjqDeM1+0TkRQA2APgAgBMBzAD4UovnFgFcA+BbAI4D8O8ArvHvB4BpAJcC+HjK1aYUMF6zL+F4XQ/gdwC8BMCZAH4bwEUdK6Gqzt4AnIp6EnMAwEEAX/TvvwDAFgBf8O//DICVAL7pP3cHgP8LIOc//3QANwM4AqAE4Lv+/eKXsR/ABIAHAZzRoi4XAHgcwCSAJwC8P/TYhQC2ATgE4HoAa0KPvQjADQDGAewD8CkA5wOoAJgHMAXgfv+5mwD8gf93zv8MO/z6fRPASv+x0wAogA8C2Ol/pkvabMf/AeAX/md8CsBfhR7b6Zc15d9e3eT1h0OPT/vPPy1GO64HcFvo/xEAswB+LcJrPw3gptD/rwPwtOm+yVvTtmK8Go5XACeh/kV0fOi+l/vbeShGW/4tgP8M/f9cfxssb/Lc/w5gFwBpqOf5Dc87D8CTpvspb4zXrMSr/5xrAfxD6P/vALg0ZlsmFq8AbgOwPvTY7wO4o2MdTHfoFAMlD+B+vzOPAFgC4NxQx60C+BMABQBL/c50DYDlfmf6FYDf95//bQCX+B0wXM5vArgHwCo/cF4I4OQmdRnxO9oL/P9PBvAi/++3Adjuv7bgd/Db/MeWA9gD4GP++y4HcLb/2F8B+FbD+4SD5UK/3OcAWIb6oPEfDcHyVf+zvwRAGcALW2zL16G+hzGH+i+dfQB+p6GsQoxOvxnAEIBno56otrq9z3/NPwP4ckM5DwF4Z4f3EgCPAbig4bNU/M/wRNA/TPfXQb+B8ZqZeEX9y+0PQ/9/AcC/+n+f2yROw7dgW18D4M8ayp0C8Iom73cxgOsa7vsRgI813MdkNCM3MF6zFK8noZ4QvwHA+1FPypf7j/U9XlH/UXF26LGzAEx27FOmO3WKwfJq1H+FHdOIqAfLzobAqgBYF7rvIgCb/L+/CWAjgFMaynkD6kF1DvxfeS3qMuI3/DsBLG147Dr4Qen/n0N9z8QaAO8F8IsWZXYKlp8B+KPQYy9A/ZdeIdTBTwk9fieA90Tctv8E4Av+3x2DJfS6/wngSQAnxGzLrwP4XMN9WxBKMlu87rV+QC0L3XcSgHX+dl6LemK8wXR/HfQb4zU78erH6ZbQtt4L4FUx2/NnAD7UcN8uAK9r8ty/APCdhvsuQ2gPkX8fk9GM3Biv2YlX/3nvRH2vagl+ghmzPROLVwAeQkctATzP/wzSrg4uzxk9FcAOVa22ePyp0N9jqO+p2xG6bweA1f7fn0D9l9mdIrJVRC4EAFW9EcAXAfwbgP0islFEVjS+kapOoz7AfwjAHhH5sYj8mv/wGgD/LCKHReQw6ocLxH/vU1Hfs9eNZzX5PAXU54ME9ob+nkH9F94xRORs/wSCAyJyxP8cY3EqIyIvQ31bvV1VD8R5LeoJZeN2XYH6IZl2PgjgClWdCu5Q1b2q+rCq1lT1CdTb9p0x60PJY7xmJ16vAbBORNYCeBOAI6p6Z4zXA/Fittv4JnMYr9mJVwD4IepJ/yOqemvM1wLJxmvj4ysATKmfmbbicjL6FIBnt5k8Hd4wJdR/1awJ3fds1H8ZBAnM/1bVZ6H+i+5LInK6/9i/qOorUN/b9ny0mGSvqter6ptQP4TwS9R34Qf1vEhVV4VuS1X1Nv+x50SofzO7m3yeKuqHAOL6TwA/AHCqqq4E8BXUAzpKPSAizwRwNYAPq+ovQvc3O/M9fHu//9StqB/qCF43gvqclq1t3nMpgHejPrm6HYXbcWALxmtG4lVV5wB8D8D/Qv2Ehv8IHhOR13aI2df6T22M2ecAGEZ9T1ejrQDObDjj9ky0iW8yjvGakXj1fRb1ebEni8h7gzsNxeuisvy/O8ayy1/Cd6I+H+RzIjIiIktE5DXNnqiqHuqD72dFZLnULxv0p6ifLQYRebeInOI//RDqHaQmIq/0f9UMoX5izhzqZ24vIiInisjb/CSqjPovh+B5XwHwSf9sNojIShF5t//Yj1DvXB8VkWG/bmf7j+0DcJqItGrDbwO4WETWisgy1OdqfrfNL9l2lgMYV9U5EXkVgPeFHjvgf5amQe0PVt9H/ZDH98KPacOZ701ul/lPvQrAGSLyThFZAuAvATygqr9sU+e3o95WNzXU5/UiskbqTgXwOdT3BJFZjNcMxGvIN1E/3PpWhJJRVb2lQ8ze4j/1MgC/7X8ZjgD4awBXqmqzPS2bUD+093/87fbH/v03AoCI5Py4H6r/K0tk4cxdMoPxmpF4FZH/BuD3APwu6kcD/1VEVgNm4hX1seNPRWS1iDwL9Tm53+i4FTrNJbD5hvqvlatRP6OvBOBf/PsvAHBrw3OPQz04DqD+i+kvsXC239+j/ituCvXd+uv9+98I4AH//pLfoMua1ONkLJwteNhvzPD8mQ+gfqZgcDbdpaHHzkB9Psch1Hf7/7l///EAbvXvv1ePndOS8z/DU/5n+haA47TFPJTwa5vU/12oH4aYRD2Av4jQfBrUO+4B/7Od0/Da4L2msXBG4BSAZ8dsy/NQ/8U769f1tNBjXwHwlYbnXw/gb5qU86d+W8742+Zf0OSMQd4Yr4MYrw3lPArg5h7a832on2U7jfoPvtHQY9cB+FTo/5ehfrLKLIB7Abws9Njr/M8fvm0y3V8H/cZ4NR+vqB8CfxKh+agA/g7AT9BhjmaTeiQVr+K36bh/+/sodRH/xUREREeJyI2oX+7la6brQkRuYzJKRESLiMgrUb/+4qna/FAdEVFiXJ4zSkREMYnIvwP4KYCPMhElon7gnlEiIiIiMoZ7RomIiIjIGCajRERERGRMqwvWZtLIyIiuWrUq1mtEBPl8vqv3q9XqlyrL5dLJ2Vl+dsr3PA9xpqzs3r27pKonpFIxihXrrWLcpf7pcvlxYo9xl11RYzbqd3JW+mcswWVGFaj5+/qsqn/E8qPGbJx4tSoZXbVqFS6++GIAQLVaRblcBgAMDw+jUGj+UarVKtasWdP0sXbK5TJKpRIAYGxsDMPDw13WmuXbUP7evXtb9qFmPv7xj+/o/CzqVjjWO2kW4671T5fL37FjR+TYY9xlV9SYjfKdnKX+Gct0vXuW52soTReTL99nevtEjdk48WpVMhqoVquoVCpHN1ClUgGAWMlEO+VyGePj4xgbqy8POz4+jtHR0cQanOVnr3zP8xLrP2SWi/2T5dOgsL1/ludrGJ/0MPZMS+tvKH6tmzMaJKLFYhGFQgGFQgHFYhGVSgXVajcrcS0WNESw8YeHhzE6Oorx8fGje2JZvnvle56XSP8hs1ztnyyfBoHt/TNIREeX5+2sv8H4tS4ZDSeigaQS0saGCCTVICw/u+Xn8/nEftCQGS73T5YPxqYFBv77N0hEhxZSK6vqn2L5nViXjDYmooFeE9JWDRHotUFYfrbLz+fzie5hp/4y3X9YfrrlB0fEKNsG/vu3IRG1rv4plR+Fdclou3l93SaknRoi0G2DsHw7yk96ygf1h+d5meg/LD+d8sNTsyjbBv77t0kialX9I5bveV7k8qOyLhntJG5CEbUhAnEbnOXbVT4T0uyIsv2r1So8z8tM/2H5yZbfeI4AZRu/fwej/DTOsTCWjIrIqSJyk4g8LCJbReQjSZUdDoh2GXzchghEbXCWb2f5TEizodP2DxKVfD6fqf7D8pMpn4monaKOn7b3z0EuP8o5FnG/O03uGa0C+JiqrgNwDoAPi8i6pAoPAsLzvKYN0m1DBDo1OMu3u3wmpOa12/7hRKWbRS1s758uld9shwETUbt1Gj9t6p8s/1idzrHoZo63sWRUVfeo6r3+35MAtgFYneR7FAoF5PP5Yxqk14YItGpwlu9G+a0GVCan/dFu+yeRqNjeP10pv/GQHxNRN7SK36hzvDtxpf/bWn6U8TlWeV3XJEEichqAlwH4edJl5/P5ow0yOjoKINmLuIYbnOW7V37wZRj+lcezevun1fZPKlGxvX+6UH5wyC/ARNQdzeI3zhzvTlzo/zaXn+T4bDzaRWQZgCsAfFRVJ5o8vh7AeqC+3FjcvVLBYfqlS5fi8OHDAIClS5eiXC7HOuOsE5Zvd/mdzg4sFAqJr/RF0QTbO2jP4eHWy/92Ixiw01pej+W3l8/nkc/nU2tfMqsxfguFglX9k+W3l9T4bDTiRWQI9UT0MlW9stlzVHUjgI0AsGbNGo27zvzExAQuuuiiXqtKjtuwYQNWrFjR8vFyefFavURERJQMY8moiAiArwPYpqr/aKoeRJ0Ec2zCa/VS/wRzkIJf80nvoW7WvkkdxmL5nXmeB8/zUmtfMqtZ/JbLZWv6J8tvL6nx2eTZ9K8B8AEAbxCR+/zbm5N+k1qtlnSRNEAaJ3sHhzyoPxpPZkn6Kget2rfTZVFYfnLle56XWvuSWc3it9lJxd1yof/bXH6S47PJs+lvVVVR1TNV9aX+7dok36NcLvNkE+paq7MOk5xvQ621Oqs6qYSlXfsmMWCz/Gjl5/P5VNqXzGoVv+GTim3onyy/uSjjcxzOrcAUCBqCS8hRN5K6/AV1r91ZmVEXtmilU/v2OmCz/OjlN7tOLBNSu3W6PJdN/ZPlH8vzvEjjcxxOJqPhhsjlnPyIlCImotnQ6fIgnRa2aCVq+3Y7YLP8ZMpnQmqnqNeJtb1/DnL54ak1rdg0ZzQVTCSoF+w/2RFlMIs7By1u+8YdsFl+suUzIbVL3AULbO+fg1p+49SaJFiVjKpq2w3GRIJ6wf5jp6hz0Lpt36gDNstPp/xu56BR/3VzwXPb++ei8udbnzBtRf0jlt/NEsydWJWMikjLDcZEgnpRq9XYfyzWaUDtdXxg+WbL72YOGvVftytn2d4/j5Y/6TVNSK2pv8H8yqpkFEDTDcZElHpVqVTYf1IkIqeKyE0i8rCIbBWRjyT9Hq0G1KTGB5Zvtnxed7R/uo3XXtrI9v45PDyM0eX5YxJSq+pvML+yLhlt3GBMRCkJxWKR/SddVQAfU9V1AM4B8GERWZf0m6Q9PrB8s+VT3/QlXhvZ3j+Hh3ILCamN9TcYv1b+1Aw2WFprrdLg4VUX0qWqewDs8f+eFJFtAFYDeDjp90p7fGD5Zsun9PUzXhvZ3j/rCSnsrb+h+LUyGSUie4nIaQBeBuDnDfevB7AeAFatWhX57GnP8zAxMbHovlqtdvSQ4ezsbKxLl0TB8pMrv5vrxFL/tIpX/7HYMdssXhtlqX/GUvb88mFn/SOWn0bMWpmMBruO01prlQYPl43tDxFZBuAKAB9V1UXfSKq6EcBGAFizZo2uWbMmUpkTExO46KKLkq4q9cmGDRuwYsUK09WgJtrFK9BdzDodr9s3Lvx9+npz9UhZGjFr3bHJxjkMca+TRdRMpVJh/0mZiAyh/sV2mapeabo+RNQa45X6ybpktNleUCak1Ktiscj+kyIREQBfB7BNVf/RdH3IPozN/ukmXjtdB5yoHauSUVVteTieCSn1IpfLsf+k6zUAPgDgDSJyn397s+lKkR2CI2LUN7Hjtd11wIk6sWrOqIi0nRcaTkg5h5TiYv9Jj6reCkDSKJvzfd0WnppF/dFtvHL8HAxpjLlW7RmNgntIqRfsP9kQ9ZBfuVzmMpEO43VK7cLxczCkcY6Fc8kosDgguNeE4uKAal6UQ35BosJlIt3ERNROHD/dF+Uci7ht72QyCiwEBPeaUDc4oJoXda1kLlhgt2Y7DJiI2o3jp9s6nWPRzRxvp0fx4eFh7jWhrrVbq5fSZ3qtZOqPxkN+bF83tIpfHq10Q5TxOQ6nk1GAyzxSb1qt1Uv9wbXO3Rc+5Mf2dUuz+LXlaKXWajj0yCPY+tWv4umbbjJdnUxKcny26mx6IhOardVL/cO1zt0WHPJj+7qpMX5HRkYM16g1rdVw+NFHsXvzZjz105+iMjmJ6swMnnXuuTjl9a83Xb1MSmp8ZjJKREREA6lZAqqeh8LSpSguWwYRwcy+fdj+ve91LuzAg3jGCcvxrJeflnq9XcNkdMBUxidQHOU60HEEhx6CPaI8TJ8+Tz3sqeyp/13xUDlSQXFlff73rtIuFFcWkS/mAQDz1XlcWXJvtcIjs0fw6L5HAQBrx9bi+GXHG65ROg5VDsGb9Fq2L9khHLOL7m+I30OThzIRr1qeR/Xy21G75WHo1Bzg1YAlQ8BwHvUFqKaB6jR0qIbJx7Zi72NbOxQI1CpTqA0JvL9ci5Wr3D2Cdrh6GNOVaQCdx+eonJ9QycnSC7RWwxNfuhqTj+w0XRVrNM6BCQ5JUH+EB7p8MY98MY/iyiIqRyrwKp7p6qXq+gevx8ZNG/HVm7+KK+65wnR1UlObrw1k+w6CZvGbG8pI2lH1oPc/AR2fgng1oJgHhoJENEQEWFKsJ6ptbtWi4EBtDuPlWfzFPTdipjJj5nP1UZLjs9N7Rm2aLN0PB295AHO7D2L35Tfj+Z96P4Qnd7XVajI257P1R+NAFwgPeMGvcRd56qGQLyAnOad/VOeGcm3bl3tI7dQqfiWXykJsscnIEhQ//0Ho/iPw7nwUtZsegu6uH/XSoTwwPFRPTOfmIfkcZHX7PZ21+TmU8ntwaFUNAoGq9uNjGBNlfI7D2WQ0SCSWLl1quiqZUJ2ew94f3Q4AmNtdwsFbHsTYb7zEcK2yi2f1mpWXPPKTeaweW918+xeB8lC9jZYtXYZ3jL2j/5VM2X3PuA/35u5FPpfH2qVrnfyMALChuAErig1Th0Ltyxi0Q17yOLl4MgB//Jwcbxq/E4WJbPXlMQDrAFwATO/Zgz1btmDn9ddj6qmnAFXUqgWc9OpX45zPfKZtMdvHt+Nz3zgXy4aGMVwZxluPfytWLlnZj0/Qd1/OfRmzk7Mdx+c4nExGw4kErwlZt/dHt8Obngv9fxtWvfIFKDxjicFaZRMTUfNUteP2D6ZMHDlypI81o34JXzaGsWgPm8fPkZNPxunvehdOf9e7FiWmK57zHNNVy5RKpRJpfI7D6HFaEblURPaLyENJlWlzIKRlbs9BHLzlgUX3edNz2OfvKaUF7D/ZICKRtj8XtnAbV/Kxi0vjZ5CYvuGrX8W6Cy80XZ1MKRaLibev6UmD3wBwftQnq2qktapdCIQk7fr+zUCTOWelzQ9gbs9BAzXKJvYfO3FhC7eFE1LKNo6fgyGNMdfoKK6qmwFEHmFEJNJa1QyEBUceeAxT23Y0f7BWqyeqhFqtxv5DlFG8ikX2RZlaQ9SKdbsUuFZ1dLWqh91XbG77nKltO3Dkgcf6VKPsijIHhojMYWxmW9SpNUTNZD4ZFZH1InK3iNw9NTXFtapjKN14LyoHDnd83u4rN6NWHexr+qUxB4aIiIg6y3wyqqobVfUsVT1r2bJlABavhVoqlZiINjE/MY19/3VnpOdW9h9G6aZfpFyjbOO8QyIiIjP4DeyovddsQW0u+kVn9133c8xPur9iBFHWzc7PYv/0fszOzx69r1wtY//0fszMM0aJskRVsX96Pw7OLD4Z+MDMAZRmSoZqZR/Tl3b6NoDbAbxARJ4Wkd+P8rrwWuFjY2O87EeDmZ37MH7Hw7FeU5urYO81W1KqUfa5vMJNFqRxGTdXXXz9xTj7a2fjql9ehWKhiGK+iFt23oJzvnYOLrj6AtPVowHBmI3mrt134eyvnY0PXPUBKOqrLokAb77szTj7a2fj8UOPG66hHUyfTf9eVT1ZVYdU9RRV/Xqn17RaK5wJ6YJdl28CuliKbPz2rZjZuS/5ClmgUqmw/6TrG4hxGbdBduaJZ0IgWLVkFUaGRrCksATHLTkOAHDGM88wXDsaIN8AY7ajtavWIi95LCsuw/Kh+iIyK4eW4hlDz8BQbgirl682XEM7WHeYvtVa4UxI6w7d9UvMPLa7uxerYvflmxKtjy2KxSL7T4riXsZtkJ1/+vnISe6Yta0LuQLe8vy3GKqVeYzN/oobs52uA+6qE0ZOwJknnonp+elF90+WJ/Gm574JwwWezxKFVcuBtruOGZeOA2qVeey5+taeyph+bDcO3f0IjjvrBQnVyg65XG7g+49pIrIewHoAVl9T8v4/+kLPZRy3ChjPPY0lqK8uNY8qcshD/uYm3I/erg38ki9d3HP9+i04IkbZ0hizLo2fceL4VcMjuGvZQQwXFqZ7edUCznzcw/0/7H086JZNsW7VntFO1zEb9D2k+39yN+YPTfZczp6rbkGtUk2gRnYZ9P5jWrMrZ0Th4nzfN5VfgrIsxOCMVPD68hnI2TVkJyI8NYuypTFmB3X8PLvyfAgENX/OaBU1CAQvnV9ruGbpSGPMdW5kG9SEojI+if033J1IWfOHJrH/hrsSKcs2g9p/sibqIb9yuYxKJfpVI2zxmsoLkYMcPSEijzx+o/wiw7XqP15H2i6DOn6O6jI8r3oyZlH/ATmNKs6pPB9Fuw4+R5bGORbOJaPA4oBwca9JM3uu2gydT25v5oEb7kZlvPe9rDYa1AE1S9ot/RsIEpVisdjHmvXHKd7xONFbhTLmMY8qlmoRL6g+y3S1+oqJqJ0Gdfx809xL4B398Sh4w9yLDdcoPVHOsYjb9k4mo8BCQLi416TR1PZdOHzPrxIts1apYs/VtyRapk0GdUBNSzeXcWu3/cOJiqsLFgSH6l0/RN9shwETUfO6vfQiMJjj59mV5wMA5lFDzuFD9MDicyzajc+xykyqclk0PDzs5F6TMK2ldwb84bsfwdT2XamUbYNWA+qgDK5J6uYybu22/yAkKsGh+jxyTh+ibzzkNyjtm3XdxGxYq/h19WjlqC7DC7EKBzCH1+JEZw/RB6KMz3E4nYwC7i/zOH77Vsw+tT+18ndfvglai3/NUlc0BhzP6u2vVtt/EBKV4FD9Uh12+hB9+JDfILXvIGgWvy4frXwLToEAOB+DcW3RJMdnt1N3x3mzZez9YbqrJs0+tR/jd2zF8b8+uBfbDgKuVKov7TY2Nma4RoOl2fYflETlPbPnYlLmnD1EDywc8hvE9h0EjfE7MjJiuEbpORcn4s04Ba/A8Zju/HQnJDU+Mxm12L7rfo7qRPprVe/9wRasevnzkV/i9pQHoqw5r/wS01UgoohGMYzP4OUAMDDJaFLc/bntuPL+Qyhtuq8v71WdmMG+a+/oy3tlUXDoYWxsDGNjYzxM32fNtj/n7bqjVquxfR3WGL8uH6YfREmNz84no65Olt59xWZo1evb+5U23Yfy/sN9e7+saJwDExySoP5otf2ZsLijUqmwfR3VLH5dP6l4kCQ5PjudjLo6WXry4R2YePDxvr6nVj3svqK3ZQht02oyNuez9Ue77c+ExR3FYpHt66BW8ev6ScWDIsr4HIezvcLVC2KrV8OuKzYZee+JBx/H5LYdRt6733hWr3nttv8gLmzhqmbJCRNSu3H8dFswtabT+ByHk8moyxfELm2+H+U95uYs7v7+zVDP7S9/DqTmqWrH7T9IC1sMIiakduL46b7w1JpWBv7STo2B4NIgVp2axb4f3260DnN7DqK0+X6c8PqXGa1HWjiQZsO8V8Mj+yYiPTdfreAPL/1ZyjWK4awzTdegvSxtKwDHH5yEd7j1OC01weGn9qBaWNLHWlFclWoNDz65F4XqHKqFJTjQIn7zXsbitZUu4vjc/M6jf996agbGgZS28/FTFeydizY+R2XVbkNVjbRWtauJxN4f3Q5vxnxyve/Ht6M6NWu6Golzvf/YRWI8NcZzyTqay6NaWIJCdc50VagtPZqIai5vujKUphTGXKuSURGJtFa1i4nE7O4SDt76gOlqAAC8mTL2/sjsHtqkdZoDQ0TmBAkpZZcATESpa1YlowAGdq3q3ZdvAjK0LOfBWx/A7O6S6WokJsocGCIyh0lOtinYRtQ965LRQVyr+sh92zH1yFOmq7FYTesJsiMaLy9DRERxcLoMdc+6ZBRYvBZqqVRyOhGtzVex+8rNpqvR1NQjT+HIfdtNVyMRrl11gYiIyBb8Bs64Azfei0rpiOlqtLT7qs2ozVdNV4OIiIgsZWUyOihrVc8fmcL+/7rTdDXaqhw4ggM33mu6Gj3jhdOJiIjMsC4ZHaS1qvdcswW18rzpanS0/7/uxPyRKdPV6EmlUnGu/xAR9U92TrAl+1iXjA7KWtUzT+7FoZ8/bLoakdTK89hzzRbT1ehJsVh0qv8QuUZqnukqUBsCthF1z6pktN0Sga4lpLsu32TVD81DP38YMzv2mq5G13K5nFP9h8glUvN40fuMUwCF6hwTUuqK0WRURM4XkUdEZLuI/HmE50daq9r2hOLQndsw88Qe09WIR4Fd39tkuhY9caX/ZFW8eI/xS0wt+tVGsQWJKC96319xv58BObpSFhNSx6Uw5hpLRkUkD+DfAPwWgHUA3isi63ot1/aEwivPY8/Vt5quRldmntiDQ3duM12Nntjef7IqbrxHPeQnNQ+i/OJzVTgR5QXV+6fb7+fw0q1MSN0l6iXevoWObyryJwC+paqHEn1n4FUAtqvq4/77fAfA2wC0niipVWB6R8eChwGMLq1hfP9TWFrMAds3JlPjPpi8/zEsH34CONF0Tboze/O9WLn815EbsuyLo1wCputdPNx/RpfnMTxk1WyWnojIzwD8g6peG7pvo6qu77HoWPEeHPJrl4QEiUot13EYIwsxEY0mpZiN//3sCyekbDs3qeQjjc9xRPmWPRHAXSLyPX+3fVLLLKwGEF5W6Gn/vkVEZL2I3C0id09NTUcufHgoh9HleVSq9lyypzo1i6ltnZPtLPNmyph8+EnT1ehZ0H/GJz2U5+3pQwlYC+DPROTTofvOSqDcjvEejvWZ6em2e1gWHbpNbEgiI5oc8mMiGksaMRv7+3lmeuGKKtxD6jhpPyWjmzneHZNRVf2/AJ4H4OsALgDwqIj8rYg8N9Y7dUlVN6rqWap61rJlI7FeOzyUQ7Fgz16tw794FOrZn/hMbtuB6tSs6Wr0rFVC6nhyehjAGwGcKCI/FJGV/XrjcKw/Y2RZyy80JipuaTzkx/aN7TAyErOLHmuVkHKOtxOijM9xRDq+paoqInsB7AVQBXAcgO+LyA2q+olY77hgF4BTQ/+f4t/XmhSAkTWx3iTnTQCn93qEMX1Tjz6NXXdNA+hLjp+66t3PxWl/8BbT1YhueAMwsuLYuwGMLvWvbbt0FAAwPjHe58r1lahqFcAficgFAG5FPd57FT/ecewhP6Dz4XuyS/iQH8D27UIaMdtVvDZqFr+c4+2OJMfnjrsNReQjInIPgL8HsAXAi1X1DwG8AsA7Y9d+wV0Anicia0WkCOA9AH7QQ3nW0prWL+XkkCP3PoqpR582XY1EBCc1lUollEoljI6Omq5Smr4S/KGq30D9aMhPEii363hfGPBmUajOMlFxzdFDfmzfLqURs4l9PzfGrwrb1iVJjc9RjmGPAniHqv6mql6uqvMAoKo1AF3v+vJ/yf0xgOsBbAPwPVXd2m15Nhu/7UHMPX3AdDUSt+vyTdAaD8nYRFU3NPx/j6pemEC5jHeiFKQRs4xX6reOh+lV9dNtHuvpOj7+2X/Xdnyiw7zZMvb+4DbT1UjF3NMHMH7bQzj+3BebrkpPgiVox8bGANRXAaP4uo33hTlISwHwMK5zVFHw2L5Zk9T3c2P85j1eMs8lSY3P9pzd06VaLdsnm+y79g4nTvZpZe8Pb4M3a+/gEySiwcpfwSF76o/Gk1l4lq57RD22r6OaxS8P07sjyfHZ6WS0XC6jUqmYrkZL5X2HUNp0n+lqpKo6OYN9195huhpdaUxEA+1WAaPktDqrmgmLW1TybF8HtbwqAi/F5oQo43McziajQSJRLBZNV6WlXd+/2YlLOXVS2nQfyvuSXjMhXa0SUeoXbXu4Z9GAx0vF2K1JcsKE1G68PJfjNNr4HIeTyWg4kcjlsvkRJ7Y+gcmtT5iuRl+oV8Ou799suhqRMRE1T4COX2TBgMdLxbiJCamdmIi6Lzy1ppWBnzNqQyKhnofd399suhp9Nbn1CUxsfdJ0NTqyof8MAkW0wYxz0NzGhNQuTEQHQ+PUmiRYlYyqKsrl1ifD2JJIlG6+H+V9g3dG9u6MT0uwpf8MhhjzyjgHzWndzkGjfmt/6JYcksKYa1UyKiIYHx9vmpDakkhUp2ax78d2ntDTq/K+cZRuvs90NZqq1WpW9B+iQdTNHDTqryhTa4hasSoZBYDR0dFjElJbElEA2PuDLVZf6qhX+36czUtZVSoVK/oP0aBikpNtUafWEDUTaW36LAmu8xgknwCsSURnnz6Ag1seMl0No+oX+d+CU953numqLFIsFjPffwZJsZDDmc8+IdJzJyYm8JkL35hyjSgtGzZsx4oVK0xXg3pULOQjxazT8br9saN/vv90Rz8j0olZ6/aMAs3XCrchkdh1+SZehgbAwS0PYTZjy59m9aoLREREruM3cJ8c/sWjmH70adPVyAbVemJOREREA8+6w/RA87XCs753VL0aTv6dc01XI1OqU7MoLFtquhoAsr9sLBERkausS0abnawUnkOa1YT0uLNeYLoK1EalUkG5XM5s/yEiInKVdYfpW60V3uwse6KoisUi+w9RhjE2s63TdcCJ2rEqGVXVlns/mZBSL3K5HPsPUUYFR8Qou9pdB5yoE6uSURFpexiVCSn1gv3HTpzv67bw1CzKNo6fgyGNMdeqZDQKJhTUC/afbIh6yK9cLqNSqfShRmSCTQuaEMfPQRGcY5Ek55JRYHFAcK8JxcUB1bwoh/yCRKVYLPaxZtQvTETtxPHTfVHOsYjb9k4mo8BCQHCvCXWDA6p57bZ/OFHhggV2a7bDgImo3Th+uq3TORbdzPF2ehQfHh7mXhPqWqsBlYNrPCLybhHZKiI1ETkr6uvabX8mKu5oPOTH9jWr23ht1Cp+ebTSDVHG5zicTkYBLvNIvWkMOJ7V25WHALwDwOa4L2y1/ZmouCN8yI/tmwldx2ujZvHLo5XuSHJ8tu6i90T9FgRcqVQCgKMrf1E0qroNqM8D7Uaz7c9ExR3BIT+2bzb0Gq+NGuN3ZGQkkXIpG5Ian5mMElH2aBWY3rHw/3wN8Lz637O7gWroiEfZA7Zv7G/9KDnlElCT1u1LdmiM2bBw/JbL7sbr/p53JtuhXAKmDy383258jojJKFEHwaGHYI8oD9MfS0R+CuCkJg9doqrXRCxjPYD1ADB63Mqj95fnaxif9DC2Ig8AGJ/0MLocGB5iwuKCWg1s3z5LIl79cprGbFhj/B6Z9mLXl7IrqfHZ+WSUk6WpF83mwPDi28dS1fMSKGMjgI0AsObZqxVYGOhGl+ePDm6jy5mwuKRSrbF9+yyJePXLOSZmw5rFb7FwzNPIUp3G5ziMJKMi8m4AfwXghQBepap3p/E+nCxNvWg1GZvz2fpACigXTsL4xDhGn9mw/QGMLvXbZukoMFwGTl9vrq7Uk+LIlzG8atXR/xvbl/FmCSkAI2uO/lsul5vGb86bGIx4dfkzDm9AuTDccXyOw9TPzsTO1muFF8SmXvCs3uSIyNtF5GkArwbwYxG5Psrr2m1/LmzhjmZXPOF1Ks3pNl7DOH66rVarRRqf4zCyZzTps/UahQOBAxnFxYE0Wap6FYCrYr6m4/YPBrwjR470WkXKoHBCyljsn27iNYzjp/sqlUqk8TkO5ybkMBCoF+w/2SAikbY/F7ZwG/eQ2oXj52AoFouJt29qe0bTOFvvuOOOQ7lcbrkRGAjUC/YfO3FhC7eFE1LKNo6fgyGNMTe1ZDSVs/XWrNFWnZ2JBPWi0xwYIjKnmzlo1F9RptYQtWLdLgWuVU1piDIHhojMYWxmW9SpNUTNGElGezlbj2tVUxrSmANDREREnZk6m76ns/W4VjUljfMOiYiIzOA3MBEREREZY+VyoM3WCudheuoFL5xORERkhnV7RhvniPI6dJSESqXC/kNERGSAdcloq7XCmZBSL4rFIvsPUYYxNrNNVdlG1DWrktF21zFjQkq9yOVy7D9EGRUcEaPsEhGOn9Q1q5LRTtcxY0JKvWD/sRPn+7otPDWLso3j52BIY8y1KhmNggkF9YL9JxuiHvIrl8uoVCp9qBGZwOtI24Xj52BI4xwL55JRYHFAcK8JxcUB1bwoh/yCRKVYLPaxZtQvTETtxPHTfVHOsYjb9k4mo8BCQHCvCXWDA6p57bZ/OFHhggV2a7bDgImo3Th+uq3TORbdzPF2ehQfHh7mXhPqWqsBlYNrf7Tb/kxU3NF4yI/t64ZW8cujlW6IMj7H4XQyCnCZR+pNY8DxrN7+arX9mai4I3zIj+3rlmbxy6OV7khyfLZyBSaifgoCrlQqAcDRlb+oP5ptfyYq7ggO+bF93dQYvyMjI4ZrRElKanzmbkMiIiIiMoZ7Rok6CA49BHtEeZg+HhH5PIDfBlAB8BiA31PVw1Ff32z78zCuO2q1Gts3Q3qN10aN8XvkyJEkqkkZkdT47PyeUU6Wpl40zoEJDklQLDcAOENVzwTwKwCfjPrCVtufZ+m6o1KpsH2zpet4bdQsfnlSsTuSHJ+dTkY5WZp60WoyNvfYxKOqP1HVqv/vHQBOifK6dtufCYs7isUi2zdDuo3XRq3ilycVuyHK+ByHs72CF8SmXvCs3tRcCOC6KE9st/25sIU7miUnTEgzI3K8hnH8dFswtabT+ByHk3NGw4HAgYzi4kAan4j8FMBJTR66RFWv8Z9zCYAqgMtalLEewHoAWLlyJebm5rB3796271ur1TA1NYUvf/nLkfe41Go1VCoVFIvFjq+J81yW3135pVIJhw4davncnTt3olAocI9agpKIV/85R2N21apV2LFjB2q1GqrVKgqFQtP49TwPGzZsAGBH/4z13HJp4cHhDfbVP2L5Bw4caNm+3XIuGW1MJJiMUhxMRLujque1e1xELgDwFgBvVFVtUcZGABsBYPXq1Rr1qIaqYnZ2NlKbddO+cV7D8uOXf+jQIRQKrb+Kcrnc0S9MSkYS8eqXsyhmAaBaraJYLLZt0xUrVhz9O+v9M1b5+fzCAyMr2j83i/WPWH4+n088Hq36qamqkdaqZiJB3WD/SYeInA/gEwDeqqozSZefz+cjHdLttn2jHjJm+emUXygUUCwWOf+/T3qJ1+BHQ7tEtJHt/XNR+fOtpwxZUf+I5efDSXdCrEpGRSTSWtVMJCiuTnNgqCdfBLAcwA0icp+IfCXpN+g0oPY6PrB8s+UHCSn1RdfxGjcRDdjeP4+WP+k1TUitqb/B/MqqZBQA16qmVIQvL0PJUtXTVfVUVX2pf/tQGu+T9lr2LN9s+d0kORRfL/HaSxvZ3j+Hh4cxujx/TEJqVf0N5lfWJaNcq5rS0Hh5GbJT2uMDyzdbPrnN9v45PJRbSEhtrL/B+LXyp2awwbiWMSWFZ+q6I+3xgeWbLZ/cZnv/rCeksLf+huKX38BEREREZIyVe0aDXcdcy5iSwgunuyPt8YHlmy2f3GZ7/yzP1zA+6WHsmZbW31D8GtkzKiKfF5FfisgDInKViKyK+trGOQxRL0tA1E6lUmH/cUDa4wPLN1s+uc32/hkkoqPL83bW32D8mjpMfwOAM1T1TAC/AvDJqC9slqVzwKNeFYtF9h/LtZpsn9T4wPLNll+tVjs/iYzqpY1s75/lcnkhER1aSK2sqr/B/MpIMqqqP1HVoNfeAeCUiK9rubuYCSn1IpfLsf9YrNNZn72ODyzfbPnVapUXvbdApVLpKiG1vX8eLb8hEbWu/gbzqyzMGb0QwHdbPdi49m2UtarDaxmH18Ft9xrb14pl+b29plQqIZ/Pcy1sC3meF2leU3hAjTMHKurlTVh+OuUHiSgvep994ZWyol5z1Pb+uaj8auv8xIr6Ryzf87zEr/ubWjIqIj8FcFKThy5R1Wv851wCoArgslblNK59G2UDhNcyzufzi9bBbdTNdbTivIbl21F+eH3scP/hhbbNqVarHbd/tVqF53mR+0/cATtu/2T5yZYfTkQZi9kXtFHUhNT2/nlM+R12Cme+/hHL37lzZ6TxOY7Udv2o6nmqekaTW5CIXgDgLQDer6qa5HuH1zL2PK/l87q9oGvUXdYs387yw/2H89TM6bT9g0Qln89nqv+w/GTKZyJqp6jjp+39c5DLz+fzkcbnOEydTX8+gE8AeKuqzqTxHkFAeJ7XtEG6bYhApwZn+XaXz4TUvHbbP5yo5PP52GXb3j9dKr/ZDgMmonbrNH7a1D9Z/rHy+Xyk8TkOU5PivghgOYAbROQ+EflKGm9SKBSQz+ePaZBeGyLQqsFZvhvltxpQmZz2R7vtn0SiYnv/dKV8z/NSaV8yq1X8Rp3j3Ykr/d/W8qOMz7HK67omPVDV0/v1Xvl8ftEcCiDZi7g2ztFg+W6V3zgHqvFvSler7Z9UomJ7/3Sh/OCQX4CJqDuaxW+cOd6duND/bS4/yfF5IKI9aBBb13Jl+WbLD4Iq+HWZZNnUWbPtn2SiYnv/tL38fD6PfD6fWvuSWY3xWygUrOqfLL+9pMZnXruGiIiIiIwZiJ+fwRwJW9dyZflmyw/mwATl8TB9f7Xa/kntPbO9f9pevud58DwvtfYls5rFb7lctqZ/svz2khqfnd8z2jhZutNZZnE1TgZm+W6V33gyRTBpm/qj1fZP6ioHtvdPF8r3PC+19iWzmsVvs5OKu+VC/7e5/CTHZ6eT0VYXxE6qQVqdlcby3Si/1Vm93GPTH+22fxIJi+3905Xy8/l8Ku1LZrWK3/BJxTb0T5bfXJTxOQ5nk9FOF8TutUE6XR6B5dtdPi8vY1677R91YYtWbO+fLpXf7DqxTEjt1mn8tKl/svxjeZ4XaXyOw8lkNOoFsbttkKjX6WL5dpbPRDQbOm3/TgtbtGJ7/xyU8pmQ2inq+Gl7/xzk8sNTa1qJ+90pCa/EmSoROQBgR8yXjQEopVCduLJSDyA7dclKPYD4dVmjqiekVZlBFzPWs9SP0sDPt4Bxl1ExYpb92X5RP2PkeLUqGe2GiNytqmexHguyUpes1APIVl0oHtfbjp+PXOJ6e7v++YB0PqOTh+mJiIiIyA5MRomIiIjImEFIRjearoAvK/UAslOXrNQDyFZdKB7X246fj1zienu7/vmAFD6j83NGiYiIiCi7BmHPKBERERFllPPJqIh8XkR+KSIPiMhVIrLKYF3eLSJbRaQmIn0/205EzheRR0Rku4j8eb/fP1SPS0Vkv4g8ZKoOfj1OFZGbRORhv10+YrI+1J0sxXjSshKzaWD8DS5XY5bx2j3nk1EANwA4Q1XPBPArAJ80WJeHALwDwOZ+v7GI5AH8G4DfArAOwHtFZF2/6+H7BoDzDb13WBXAx1R1HYBzAHzY4Dah7mUpxhOTsZhNA+NvcDkXs4zX3jifjKrqT1Q1WL7jDgCnGKzLNlV9xNDbvwrAdlV9XFUrAL4D4G0mKqKqmwGMm3jvhnrsUdV7/b8nAWwDsNpsrSiuLMV4wjITs2lg/A0uR2OW8doD55PRBhcCuM50JQxZDeCp0P9PgwP/USJyGoCXAfi54apQb1yK8YGJWcbfQHMlZhmvPXBi4W0R+SmAk5o8dImqXuM/5xLUdzNfZroulC0isgzAFQA+qqoTputDx8pSjFOyGH9uYsy6Ka14dSIZVdXz2j0uIhcAeAuAN2rK17LqVBeDdgE4NfT/Kf59A01EhlAPrMtU9UrT9aHmshTjfeR8zDL+3DWAMct47YHzh+lF5HwAnwDwVlWdMV0fg+4C8DwRWSsiRQDvAfADw3UySkQEwNcBbFPVfzRdH+qOwzHudMwy/gaXozHLeO2lfDd+kLQmItsBDAM46N91h6p+yFBd3g7gXwGcAOAwgPtU9Tf7+P5vBvBPAPIALlXVz/brvRvq8W0ArwMwBmAfgE+r6tcN1ONcALcAeBBAzb/7U6p6bb/rQt3LUownLSsxmwbG3+ByNWYZrz2U73oySkRERETZ5fxheiIiIiLKLiajRERERGQMk1EiIiIiMobJKBEREREZw2SUiIiIiIxhMkpERERExjAZJSIiIiJjmIwOCBF5pYg8ICJLRGRERLaKyBmm60VEx2K8EtlFRP5aRD4a+v+zIvIRg1WyCi96P0BE5DMAlgBYCuBpVf1/hqtERC0wXonsISKnAbhSVV8uIjkAjwJ4laoebP9KApiMDhR/vdy7AMwB+HVV9QxXiYhaYLwS2UVEbgDwCQAnAvgDVX2X4SpZo2C6AtRXxwNYBmAI9T0u02arQ0RtMF6J7PI1ABcAOAnApWarYhfuGR0gIvIDAN8BsBbAyar6x4arREQtMF6J7OIfzXgQ9R+Qz+PRjOi4Z3RAiMjvAphX1f8UkTyA20TkDap6o+m6EdFijFci+6hqRURuAnCYiWg83DNKRERE1CP/xKV7AbxbVR81XR+b8NJORERERD0QkXUAtgP4GRPR+LhnlIiIiIiM4Z5RIiIiIjKGySgRERERGcNklIiIiIiMYTJKRERERMYwGSUiIiIiY5iMEhEREZEx/x/LPy9zg5uwrQAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 3, figsize=(12, 3))\n", "sim.plot(z=0.75, ax=ax[0]);\n", "sim.plot(y=0.01, ax=ax[1]);\n", "sim.plot(x=0, ax=ax[2]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running through the web API\n", "\n", "Now that the simulation is constructed, we can run it using the [web](https://docs.simulation.cloud/projects/tidy3d/en/latest/api.html#submitting-simulations) API of ``Tidy3D``. First, we submit the project. Note that we can give it a custom name." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:26:33.853624Z", "iopub.status.busy": "2022-07-21T20:26:33.853484Z", "iopub.status.idle": "2022-07-21T20:26:37.400804Z", "shell.execute_reply": "2022-07-21T20:26:37.399439Z" }, "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=926919;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=291088;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": [ "
[16:26:36] INFO     Uploaded task 'Simulation' with task_id                     webapi.py:120\n",
       "                    'c8502647-47fc-419e-a467-1ac40887b9d3'.                                  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[16:26:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Uploaded task \u001b[32m'Simulation'\u001b[0m with task_id \u001b]8;id=301463;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=493141;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'c8502647-47fc-419e-a467-1ac40887b9d3'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6088a533b82147818d911073f948d0e9", "version_major": 2, "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"
    }
   ],
   "source": [
    "task_id = web.upload(sim, task_name='Simulation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task is still in draft status and will not run until we call the start function. Before that, we may want to check the estimated cost of the task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:26:37.416061Z",
     "iopub.status.busy": "2022-07-21T20:26:37.415900Z",
     "iopub.status.idle": "2022-07-21T20:26:42.964565Z",
     "shell.execute_reply": "2022-07-21T20:26:42.964207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[16:26:42] WARNING  Could not get estimated cost! It will be reported in        webapi.py:527\n",
       "                    preprocessing upon simulation run.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:26:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Could not get estimated cost! It will be reported in        \u001b]8;id=445677;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=629682;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#527\u001b\\\u001b[2m527\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         preprocessing upon simulation run.                          \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max flex unit cost:  None\n"
     ]
    }
   ],
   "source": [
    "print(\"Max flex unit cost: \", web.estimate_cost(task_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now start the task, and if we want to, continously monitor its status, and wait until the run is successful. The [monitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.webapi.monitor.html#tidy3d.web.webapi.monitor) function will keep running until either a `'success'` or `'error'` status is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:26:42.966324Z",
     "iopub.status.busy": "2022-07-21T20:26:42.966170Z",
     "iopub.status.idle": "2022-07-21T20:27:48.598889Z",
     "shell.execute_reply": "2022-07-21T20:27:48.597365Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[16:26:44] INFO     status = queued                                             webapi.py:253\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:26:44]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                             \u001b]8;id=287491;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=640437;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "179cdbe078204bdc986fa9f846eedd01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
[16:26:49] INFO     Maximum flex unit cost: 0.20                                webapi.py:244\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:26:49]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum flex unit cost: \u001b[1;36m0.20\u001b[0m                                \u001b]8;id=843821;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=322078;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": [
       "
[16:26:53] INFO     status = preprocess                                         webapi.py:265\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:26:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                         \u001b]8;id=680929;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=958131;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": [
       "
[16:27:03] INFO     starting up solver                                          webapi.py:269\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                          \u001b]8;id=523619;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=803462;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": [
       "
[16:27:14] INFO     running solver                                              webapi.py:275\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                              \u001b]8;id=631561;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=378967;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#275\u001b\\\u001b[2m275\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7590b7efcda40909df4d8bf7224b122",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
[16:27:18] INFO     early shutoff detected, exiting.                            webapi.py:286\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:18]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                            \u001b]8;id=996338;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=558449;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\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": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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=347961;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=268394;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#292\u001b\\\u001b[2m292\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb658b3484bf490cb3a69f2d964cb1fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
[16:27:24] INFO     status = preprocess                                         webapi.py:298\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:24]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = preprocess                                         \u001b]8;id=594735;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=624423;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": [
       "
[16:27:31] INFO     status = running                                            webapi.py:298\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:31]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = running                                            \u001b]8;id=661386;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=556101;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": [
       "
[16:27:41] INFO     status = postprocess                                        webapi.py:298\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                        \u001b]8;id=339485;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=221767;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": [
       "
[16:27:48] INFO     status = success                                            webapi.py:298\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:48]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                            \u001b]8;id=171525;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=7268;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"
    }
   ],
   "source": [
    "web.start(task_id)\n",
    "web.monitor(task_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and analyzing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a successful run, we can download the results and load them into our simulation model. We use the `download_results` function from our web API, which downloads a single `hdf5` file containing all the monitor data, a log file, and a `json` file defining the original simulation (same as what you'll get if you run `sim.to_json()` on the current object). Optionally, you can provide a folder in which to store the files. In the example below, the results are stored in the `data/` folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:49.071216Z",
     "iopub.status.busy": "2022-07-21T20:27:49.071119Z",
     "iopub.status.idle": "2022-07-21T20:27:52.561477Z",
     "shell.execute_reply": "2022-07-21T20:27:52.561173Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[16:27:49] INFO     downloading file \"output/monitor_data.hdf5\" to              webapi.py:574\n",
       "                    \"data/sim_data.hdf5\"                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:49]\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=389363;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=828033;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/sim_data.hdf5\"\u001b[0m                                        \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e2826d08872498e805bc2b74c27480b",
       "version_major": 2,
       "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": [
       "
[16:27:52] INFO     loading SimulationData from data/sim_data.hdf5              webapi.py:398\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:27:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/sim_data.hdf5              \u001b]8;id=411620;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=176592;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 1.4990 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;36m1.4990\u001b[0m defined from        \u001b]8;id=330536;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=913163;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 domain Nx, Ny, Nz: [156, 157, 104]\n",
      "Applied symmetries: (0, 0, 0)\n",
      "Number of computational grid points: 2.6629e+06.\n",
      "Using subpixel averaging: True\n",
      "Number of time steps: 3.8430e+03\n",
      "Automatic shutoff factor: 1.00e-05\n",
      "Time step (s): 5.2068e-17\n",
      "\n",
      "\n",
      "Compute source modes time (s):     0.0182\n",
      "Compute monitor modes time (s):    0.0177\n",
      "Rest of setup time (s):            3.1161\n",
      "\n",
      "Running solver for 3843 time steps...\n",
      "- Time step    153 / time 7.97e-15s (  4 % done), field decay: 1.00e+00\n",
      "- Time step    307 / time 1.60e-14s (  8 % done), field decay: 1.00e+00\n",
      "- Time step    461 / time 2.40e-14s ( 12 % done), field decay: 1.00e+00\n",
      "- Time step    614 / time 3.20e-14s ( 16 % done), field decay: 1.00e+00\n",
      "- Time step    768 / time 4.00e-14s ( 20 % done), field decay: 1.00e+00\n",
      "- Time step    922 / time 4.80e-14s ( 24 % done), field decay: 1.00e+00\n",
      "- Time step   1076 / time 5.60e-14s ( 28 % done), field decay: 1.00e+00\n",
      "- Time step   1229 / time 6.40e-14s ( 32 % done), field decay: 1.00e+00\n",
      "- Time step   1383 / time 7.20e-14s ( 36 % done), field decay: 1.00e+00\n",
      "- Time step   1528 / time 7.96e-14s ( 39 % done), field decay: 1.00e+00\n",
      "- Time step   1537 / time 8.00e-14s ( 40 % done), field decay: 1.00e+00\n",
      "- Time step   1690 / time 8.80e-14s ( 44 % done), field decay: 1.00e+00\n",
      "- Time step   1844 / time 9.60e-14s ( 48 % done), field decay: 7.18e-01\n",
      "- Time step   1998 / time 1.04e-13s ( 52 % done), field decay: 2.58e-01\n",
      "- Time step   2152 / time 1.12e-13s ( 56 % done), field decay: 1.12e-01\n",
      "- Time step   2305 / time 1.20e-13s ( 60 % done), field decay: 4.16e-02\n",
      "- Time step   2459 / time 1.28e-13s ( 64 % done), field decay: 9.96e-03\n",
      "- Time step   2613 / time 1.36e-13s ( 68 % done), field decay: 2.08e-03\n",
      "- Time step   2766 / time 1.44e-13s ( 72 % done), field decay: 4.92e-04\n",
      "- Time step   2920 / time 1.52e-13s ( 76 % done), field decay: 1.17e-04\n",
      "- Time step   3074 / time 1.60e-13s ( 80 % done), field decay: 2.51e-05\n",
      "- Time step   3228 / time 1.68e-13s ( 84 % done), field decay: 4.93e-06\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "\n",
      "Solver time (s):                   1.9618\n",
      "Post-processing time (s):          0.2476\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sim_data = web.load(task_id, path='data/sim_data.hdf5')\n",
    "\n",
    "# Show the output of the log file\n",
    "print(sim_data.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization functions\n",
    "\n",
    "Finally, we can now use the in-built visualization tools to examine the results. Below, we plot the `y`-component of the field recorded by the two frequency monitors (this is the dominant component since the source is `y`-polarized)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:52.580852Z",
     "iopub.status.busy": "2022-07-21T20:27:52.580754Z",
     "iopub.status.idle": "2022-07-21T20:27:53.006953Z",
     "shell.execute_reply": "2022-07-21T20:27:53.006583Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
           INFO     Auto meshing using wavelength 1.4990 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;36m1.4990\u001b[0m defined from        \u001b]8;id=495266;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=920655;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": [
       "
<AxesSubplot:title={'center':'cross section at y=0.03'}, xlabel='x', ylabel='z'>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m{\u001b[0m\u001b[32m'center'\u001b[0m\u001b[39m:\u001b[0m\u001b[32m'cross section at \u001b[0m\u001b[32my\u001b[0m\u001b[32m=\u001b[0m\u001b[32m0\u001b[0m\u001b[32m.03'\u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m, \u001b[0m\u001b[33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'x'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'z'\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
<Figure size 720x288 with 4 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 72\u001b[0m\u001b[1;36m0x288\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\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": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sim_data.plot_field('field1', 'Ey', z=-1.0, ax=ax[0], freq=freq0, val='real');\n",
    "sim_data.plot_field('field2', 'Ey', y=0.025, ax=ax[1], freq=freq0, val='real');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monitor data\n",
    "The raw field data can be accessed through indexing by monitor name directly.\n",
    "\n",
    "For plenty of discussion on accessing and manipulating data, refer to the [data visualization tutorial](https://docs.simulation.cloud/projects/tidy3d/en/latest/notebooks/VizData.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:53.008625Z",
     "iopub.status.busy": "2022-07-21T20:27:53.008488Z",
     "iopub.status.idle": "2022-07-21T20:27:53.030239Z",
     "shell.execute_reply": "2022-07-21T20:27:53.029978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Tidy3dDataArray (x: 158, y: 159, z: 3, f: 1)>\n",
       "array([[[[ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j]],\n",
       "\n",
       "        [[ 3.7634109e-07+1.37481010e-07j],\n",
       "         [ 2.9865240e-07+8.58909104e-08j],\n",
       "         [ 2.5552725e-07+1.10070140e-07j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 3.6546808e-07+3.04554493e-09j],\n",
       "         [ 3.4347167e-07-3.49294282e-08j],\n",
       "         [ 2.3089351e-07-6.35356301e-08j]],\n",
       "\n",
       "        [[-6.5183743e-09+3.24995426e-08j],\n",
       "         [-7.0129853e-09-2.15622880e-08j],\n",
       "...\n",
       "         [ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j]],\n",
       "\n",
       "        [[ 2.3872553e-09-9.49622816e-08j],\n",
       "         [ 1.4518856e-08+5.62374609e-08j],\n",
       "         [-3.2043413e-08-4.26943316e-08j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-3.5053148e-08-5.51855024e-08j],\n",
       "         [-2.6551559e-08-8.72122499e-08j],\n",
       "         [-6.8542029e-08-3.96390583e-08j]],\n",
       "\n",
       "        [[ 8.9492671e-09+8.48070059e-08j],\n",
       "         [-1.8611086e-08+2.36543691e-08j],\n",
       "         [ 9.7731476e-09-1.98321928e-08j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j],\n",
       "         [ 0.0000000e+00+0.00000000e+00j]]]], dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 -2.379 -2.348 -2.318 -2.288 ... 2.288 2.318 2.348 2.379\n",
       "  * y        (y) float64 -2.39 -2.36 -2.33 -2.3 ... 2.266 2.295 2.325 2.354\n",
       "  * z        (z) float64 -1.108 -1.034 -0.9598\n",
       "  * f        (f) float64 2e+14
"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon1_data = sim_data['field1']\n",
    "mon1_data.Ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:53.031766Z",
     "iopub.status.busy": "2022-07-21T20:27:53.031619Z",
     "iopub.status.idle": "2022-07-21T20:27:53.125863Z",
     "shell.execute_reply": "2022-07-21T20:27:53.125493Z"
    }
   },
   "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 = mon1_data.Ez.interp(z=-1).real.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this raw data for example to also plot the time-domain fields recorded in the [FieldTimeMonitor](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.FieldMonitor.html), which look largely like a delayed version of the source input, indicating that no resonant features were excited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:53.127525Z",
     "iopub.status.busy": "2022-07-21T20:27:53.127377Z",
     "iopub.status.idle": "2022-07-21T20:27:53.279043Z",
     "shell.execute_reply": "2022-07-21T20:27:53.278497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 432x288 with 1 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\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": [
    "time_data = sim_data['field_time']\n",
    "fig, ax = plt.subplots(1)\n",
    "time_data.Ey.interp(x=0, y=0, z=0).plot()\n",
    "ax.set_ylabel('$E_y(t)$ [V/m]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permittivity data\n",
    "\n",
    "We can also query the relative permittivity in the simulation within a volume parameterized by a `td.Box`. The method `Simulation.epsilon(box, coord_key)` returns the permittivity within the specified volume.\n",
    "\n",
    "The `coord_key` specifies at what locations in the yee cell to evaluate the permittivity at (eg. `'centers'`, `'Ey'`, `'Hz'`, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:53.280635Z",
     "iopub.status.busy": "2022-07-21T20:27:53.280514Z",
     "iopub.status.idle": "2022-07-21T20:27:53.770766Z",
     "shell.execute_reply": "2022-07-21T20:27:53.770275Z"
    }
   },
   "outputs": [],
   "source": [
    "volume = td.Box(center=(0,0,0.75), size=(5, 5, 0))\n",
    "\n",
    "# at Yee cell centers\n",
    "eps_centers = sim.epsilon(\n",
    "    box=volume,\n",
    "    coord_key='centers'\n",
    ")\n",
    "\n",
    "# at Ex locations in the yee cell\n",
    "eps_Ex = sim.epsilon(\n",
    "    box=volume,\n",
    "    coord_key='Ex'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return an xarray DataArray containing the complex-valued permittivity values at the Yee cell centers and the \"Ex\" within the [box](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.Box.html).\n",
    "\n",
    "We can then plot or post-process this data as we wish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:27:53.772838Z",
     "iopub.status.busy": "2022-07-21T20:27:53.772674Z",
     "iopub.status.idle": "2022-07-21T20:27:53.951787Z",
     "shell.execute_reply": "2022-07-21T20:27:53.951438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 720x288 with 4 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 72\u001b[0m\u001b[1;36m0x288\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\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=(10, 4))\n",
    "\n",
    "eps_centers.real.plot(x='x', y='y', cmap='Greys', ax=ax1)\n",
    "eps_Ex.real.plot(x='x', y='y', cmap='Greys', ax=ax2)\n",
    "ax1.set_title('epsilon_r at centers')\n",
    "ax2.set_title('epsilon_r at Ex locations')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "179cdbe078204bdc986fa9f846eedd01": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_618f41beb3514ecd87bf3b5f84957262",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
🏃  Starting 'Simulation'...\n
\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'Simulation'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "3827739cc6514e84ae3bce98d2459324": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4e2826d08872498e805bc2b74c27480b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_74233f68bdaa4208a8f84b36c63321ef",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━ 93.7% • 6.0/6.4 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;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━\u001b[0m \u001b[35m93.7%\u001b[0m • \u001b[32m6.0/6.4 MB\u001b[0m • \u001b[31m3.7 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "6088a533b82147818d911073f948d0e9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_3827739cc6514e84ae3bce98d2459324",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/9.2 kB • ? • -:--:--\n
\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/9.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "618f41beb3514ecd87bf3b5f84957262": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "66edee11d89848099fef543d94893224": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "74233f68bdaa4208a8f84b36c63321ef": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "923674de8b1a496cb9e10c8210d3b929": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "eb658b3484bf490cb3a69f2d964cb1fd": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_66edee11d89848099fef543d94893224",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
🚶  Finishing 'Simulation'...\n
\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'Simulation'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f7590b7efcda40909df4d8bf7224b122": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_923674de8b1a496cb9e10c8210d3b929",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
% done (field decay = 4.93e-06) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━  84% -:--:--\n
\n",
          "text/plain": "% done (field decay = 4.93e-06) \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━\u001b[0m \u001b[35m 84%\u001b[0m \u001b[36m-:--:--\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}