{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tidy3D first walkthrough\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": "2023-02-03T06:00:26.360305Z", "iopub.status.busy": "2023-02-03T06:00:26.360009Z", "iopub.status.idle": "2023-02-03T06:00:27.038883Z", "shell.execute_reply": "2023-02-03T06:00:27.038522Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[00:00:26] WARNING This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on __init__.py:103\n", " PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on, \n", " please use 'pip install tidy3d' instead. \n", "\n" ], "text/plain": [ "\u001b[2;36m[00:00:26]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on \u001b]8;id=566003;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=418241;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m please use \u001b[32m'pip install tidy3d'\u001b[0m instead. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
INFO Using client version: 1.9.0rc1 __init__.py:121\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1 \u001b]8;id=625744;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=87606;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import h5py\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", "from tidy3d import web\n" ] }, { "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": "2023-02-03T06:00:27.040612Z", "iopub.status.busy": "2023-02-03T06:00:27.040460Z", "iopub.status.idle": "2023-02-03T06:00:27.042805Z", "shell.execute_reply": "2023-02-03T06:00:27.042547Z" }, "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())\n" ] }, { "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": "2023-02-03T06:00:27.044174Z", "iopub.status.busy": "2023-02-03T06:00:27.044070Z", "iopub.status.idle": "2023-02-03T06:00:27.045859Z", "shell.execute_reply": "2023-02-03T06:00:27.045603Z" }, "tags": [] }, "outputs": [], "source": [ "# Total time to run in seconds\n", "run_time = 2 / fwidth\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Structures and materials\n", "\n", "Next, we initialize the simulated structure. The structure consists of two [Structure](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.Structure.html) objects. Each object consists of a [Geometry](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.components.geometry.Geometry.html) and a [Medium](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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": "2023-02-03T06:00:27.047218Z", "iopub.status.busy": "2023-02-03T06:00:27.047108Z", "iopub.status.idle": "2023-02-03T06:00:27.049911Z", "shell.execute_reply": "2023-02-03T06:00:27.049630Z" }, "tags": [] }, "outputs": [], "source": [ "# Lossless dielectric specified directly using relative permittivity\n", "material1 = td.Medium(permittivity=6.0)\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]), medium=material1\n", ")\n", "\n", "# Triangle in the xy-plane with a finite extent in z\n", "equi_tri_verts = [[-1 / 2, -1 / 4], [1 / 2, -1 / 4], [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=(0.5, 1.0),\n", " axis=2,\n", " ),\n", " medium=material2,\n", ")\n" ] }, { "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": "2023-02-03T06:00:27.051218Z", "iopub.status.busy": "2023-02-03T06:00:27.051124Z", "iopub.status.idle": "2023-02-03T06:00:27.053150Z", "shell.execute_reply": "2023-02-03T06:00:27.052855Z" }, "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(freq0=freq0, fwidth=fwidth),\n", " pol_angle=np.pi / 2,\n", ")\n" ] }, { "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.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.FieldMonitor.html) and [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.FieldTimeMonitor.html), which record the frequency-domain and time-domain fields, respectively. \n", "\n", "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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": "2023-02-03T06:00:27.054479Z", "iopub.status.busy": "2023-02-03T06:00:27.054388Z", "iopub.status.idle": "2023-02-03T06:00:27.056582Z", "shell.execute_reply": "2023-02-03T06:00:27.056310Z" }, "tags": [] }, "outputs": [], "source": [ "# measure time domain fields at center location, measure every 5 time steps\n", "time_mnt = td.FieldTimeMonitor(\n", " center=[0, 0, 0], size=[0, 0, 0], interval=5, name=\"field_time\"\n", ")\n", "\n", "# measure the steady state fields at central frequency in the xy plane and the xz plane.\n", "freq_mnt1 = td.FieldMonitor(\n", " center=[0, 0, -1], size=[20, 20, 0], freqs=[freq0], name=\"field1\"\n", ")\n", "freq_mnt2 = td.FieldMonitor(\n", " center=[0, 0, 0], size=[20, 0, 20], freqs=[freq0], name=\"field2\"\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation\n", "\n", "Now we can initialize the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_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": "2023-02-03T06:00:27.058030Z", "iopub.status.busy": "2023-02-03T06:00:27.057935Z", "iopub.status.idle": "2023-02-03T06:00:27.060516Z", "shell.execute_reply": "2023-02-03T06:00:27.060254Z" }, "tags": [] }, "outputs": [], "source": [ "# Initialize simulation\n", "sim = td.Simulation(\n", " 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,\n", ")\n" ] }, { "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": "2023-02-03T06:00:27.061844Z", "iopub.status.busy": "2023-02-03T06:00:27.061751Z", "iopub.status.idle": "2023-02-03T06:00:27.093455Z", "shell.execute_reply": "2023-02-03T06:00:27.093184Z" }, "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( │ │\n", "│ │ │ type='FieldTimeMonitor', │ │\n", "│ │ │ center=(0.0, 0.0, 0.0), │ │\n", "│ │ │ size=(0.0, 0.0, 0.0), │ │\n", "│ │ │ name='field_time', │ │\n", "│ │ │ start=0.0, │ │\n", "│ │ │ stop=None, │ │\n", "│ │ │ interval=5, │ │\n", "│ │ │ fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'), │ │\n", "│ │ │ interval_space=(1, 1, 1), │ │\n", "│ │ │ colocate=False │ │\n", "│ │ ) │ │\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( │\n", "│ alpha=0.4, │\n", "│ edgecolor='orange', │\n", "│ facecolor='orange', │\n", "│ fill=True, │\n", "│ hatch=None, │\n", "│ linewidth=3.0, │\n", "│ type='PlotParams' │\n", "│ ) │\n", "│ size = (0.0, 0.0, 0.0) │\n", "│ start = 0.0 │\n", "│ stop = None │\n", "│ type = 'FieldTimeMonitor' │\n", "│ zero_dims = [0, 1, 2] │\n", "╰────────────────────────────────────────────────────────────────────────────────╯\n", "\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\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\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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldTimeMonitor'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'field_time'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mstart\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mstop\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33minterval\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\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 \u001b[3;33mzero_dims\u001b[0m = \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m]\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( │ │\n", "│ │ │ type='FieldMonitor', │ │\n", "│ │ │ center=(0.0, 0.0, -1.0), │ │\n", "│ │ │ size=(20.0, 20.0, 0.0), │ │\n", "│ │ │ name='field1', │ │\n", "│ │ │ freqs=(200000000000000.0,), │ │\n", "│ │ │ apodization=ApodizationSpec( │ │\n", "│ │ │ │ start=None, │ │\n", "│ │ │ │ end=None, │ │\n", "│ │ │ │ width=None, │ │\n", "│ │ │ │ type='ApodizationSpec' │ │\n", "│ │ │ ), │ │\n", "│ │ │ fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'), │ │\n", "│ │ │ interval_space=(1, 1, 1), │ │\n", "│ │ │ colocate=False │ │\n", "│ │ ) │ │\n", "│ ╰────────────────────────────────────────────────────────────────────────────────────────────╯ │\n", "│ │\n", "│ apodization = ApodizationSpec(start=None, end=None, width=None, type='ApodizationSpec') │\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( │\n", "│ alpha=0.4, │\n", "│ edgecolor='orange', │\n", "│ facecolor='orange', │\n", "│ fill=True, │\n", "│ hatch=None, │\n", "│ linewidth=3.0, │\n", "│ type='PlotParams' │\n", "│ ) │\n", "│ size = (20.0, 20.0, 0.0) │\n", "│ type = 'FieldMonitor' │\n", "│ zero_dims = [2] │\n", "╰────────────────────────────────────────────────────────────────────────────────────────────────╯\n", "\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\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\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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[2;32m│ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'field1'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mapodization\u001b[0m=\u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\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;33mapodization\u001b[0m = \u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m\u001b[1m)\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\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 \u001b[3;33mzero_dims\u001b[0m = \u001b[1m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m]\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( │ │\n", "│ │ │ type='FieldMonitor', │ │\n", "│ │ │ center=(0.0, 0.0, 0.0), │ │\n", "│ │ │ size=(20.0, 0.0, 20.0), │ │\n", "│ │ │ name='field2', │ │\n", "│ │ │ freqs=(200000000000000.0,), │ │\n", "│ │ │ apodization=ApodizationSpec( │ │\n", "│ │ │ │ start=None, │ │\n", "│ │ │ │ end=None, │ │\n", "│ │ │ │ width=None, │ │\n", "│ │ │ │ type='ApodizationSpec' │ │\n", "│ │ │ ), │ │\n", "│ │ │ fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'), │ │\n", "│ │ │ interval_space=(1, 1, 1), │ │\n", "│ │ │ colocate=False │ │\n", "│ │ ) │ │\n", "│ ╰────────────────────────────────────────────────────────────────────────────────────────────╯ │\n", "│ │\n", "│ apodization = ApodizationSpec(start=None, end=None, width=None, type='ApodizationSpec') │\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( │\n", "│ alpha=0.4, │\n", "│ edgecolor='orange', │\n", "│ facecolor='orange', │\n", "│ fill=True, │\n", "│ hatch=None, │\n", "│ linewidth=3.0, │\n", "│ type='PlotParams' │\n", "│ ) │\n", "│ size = (20.0, 0.0, 20.0) │\n", "│ type = 'FieldMonitor' │\n", "│ zero_dims = [1] │\n", "╰────────────────────────────────────────────────────────────────────────────────────────────────╯\n", "\n" ], "text/plain": [ "\u001b[34m╭─\u001b[0m\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\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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[2;32m│ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'field2'\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mapodization\u001b[0m=\u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ │ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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'Hz'\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \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[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\u001b[0m \u001b[2;32m│ \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m│\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[32m│\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;33mapodization\u001b[0m = \u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m\u001b[1m)\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\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[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\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 \u001b[3;33mzero_dims\u001b[0m = \u001b[1m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m]\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()\n" ] }, { "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": "2023-02-03T06:00:27.094768Z", "iopub.status.busy": "2023-02-03T06:00:27.094682Z", "iopub.status.idle": "2023-02-03T06:00:27.199540Z", "shell.execute_reply": "2023-02-03T06:00:27.199286Z" }, "tags": [] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "