{ "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": { "tags": [] }, "outputs": [], "source": [ "# standard python imports\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by initializing general simulation parameters. To streamline the setup and management of frequency-related values, we use the convenience class [FreqRange](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FreqRange.html). \n", "\n", "Note that the perfectly matched layer (PML) regions extend **outside** the user-defined simulation domain. As a result, the total computational domain is larger than the specified simulation region—unlike in some solvers where the PML occupies a portion of the domain itself." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "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", "# set frequency range\n", "freq_range = td.FreqRange(freq0=freq0, fwidth=fwidth)\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": { "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.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) objects. Each object consists of a [Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#geometry) and a [Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#mediums) 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": { "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=freq_range.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(geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, 1]), medium=material1)\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", ")" ] }, { "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": { "tags": [] }, "outputs": [], "source": [ "psource = td.PlaneWave(\n", " center=(0, 0, 1.5),\n", " direction=\"-\",\n", " size=(td.inf, td.inf, 0),\n", " source_time=freq_range.to_gaussian_pulse(),\n", " pol_angle=np.pi / 2,\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/latest/api/_autosummary/tidy3d.FieldMonitor.html) and [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html), which record the frequency-domain and time-domain fields, respectively. \n", "\n", "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_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/latest/api/_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", "[FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.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/latest/api/_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": { "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(\n", " center=[0, 0, -1], size=[20, 20, 0], freqs=freq_range.freqs(num_points=1), name=\"field1\"\n", ")\n", "freq_mnt2 = td.FieldMonitor(\n", " center=[0, 0, 0], size=[20, 0, 20], freqs=freq_range.freqs(num_points=1), name=\"field2\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation\n", "\n", "Now we can initialize the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_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.\n", "\n", "Tidy3D uses a hybrid floating-point precision by default as it is practically sufficient in almost all cases, double precision should only be explored in large simulations when very high accuracy is desired. Even then, it might not have a noticeable effect over other sources of numerical error like the finite grid. As this is a small example with a minimal cost, we will simply demonstrate you can leverage this option depending on your simulation goals." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "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", " precision=\"double\",\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": { "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",
       "│ │ │   attrs={},                                                                        │ │\n",
       "│ │ │   type='FieldTimeMonitor',                                                         │ │\n",
       "│ │ │   center=(0.0, 0.0, 0.0),                                                          │ │\n",
       "│ │ │   size=(0.0, 0.0, 0.0),                                                            │ │\n",
       "│ │ │   name='field_time',                                                               │ │\n",
       "│ │ │   interval_space=(1, 1, 1),                                                        │ │\n",
       "│ │ │   colocate=True,                                                                   │ │\n",
       "│ │ │   start=0.0,                                                                       │ │\n",
       "│ │ │   stop=None,                                                                       │ │\n",
       "│ │ │   interval=5,                                                                      │ │\n",
       "│ │ │   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                      │ │\n",
       "│ │ )                                                                                    │ │\n",
       "│ ╰──────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                          │\n",
       "│          attrs = {}                                                                      │\n",
       "│   bounding_box = Box(attrs={}, 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 = True                                                                    │\n",
       "│         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                    │\n",
       "│       geometry = Box(attrs={}, 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",
       "│                      attrs={},                                                           │\n",
       "│                      alpha=0.4,                                                          │\n",
       "│                      zorder=None,                                                        │\n",
       "│                      type='PlotParams',                                                  │\n",
       "│                      edgecolor='orange',                                                 │\n",
       "│                      facecolor='orange',                                                 │\n",
       "│                      fill=True,                                                          │\n",
       "│                      hatch=None,                                                         │\n",
       "│                      linewidth=3.0                                                       │\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",
       "
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"\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[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;92mTrue\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[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;33mattrs\u001b[0m = \u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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;92mTrue\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[33mattrs\u001b[0m=\u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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[33mzorder\u001b[0m=\u001b[3;35mNone\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[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[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",
       "│ │ │   attrs={},                                                                                        │ │\n",
       "│ │ │   type='FieldMonitor',                                                                             │ │\n",
       "│ │ │   center=(0.0, 0.0, -1.0),                                                                         │ │\n",
       "│ │ │   size=(20.0, 20.0, 0.0),                                                                          │ │\n",
       "│ │ │   name='field1',                                                                                   │ │\n",
       "│ │ │   interval_space=(1, 1, 1),                                                                        │ │\n",
       "│ │ │   colocate=True,                                                                                   │ │\n",
       "│ │ │   freqs=array([2.e+14]),                                                                           │ │\n",
       "│ │ │   apodization=ApodizationSpec(                                                                     │ │\n",
       "│ │ │   │   attrs={},                                                                                    │ │\n",
       "│ │ │   │   start=None,                                                                                  │ │\n",
       "│ │ │   │   end=None,                                                                                    │ │\n",
       "│ │ │   │   width=None,                                                                                  │ │\n",
       "│ │ │   │   type='ApodizationSpec'                                                                       │ │\n",
       "│ │ │   ),                                                                                               │ │\n",
       "│ │ │   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                                      │ │\n",
       "│ │ )                                                                                                    │ │\n",
       "│ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                                          │\n",
       "│     apodization = ApodizationSpec(attrs={}, start=None, end=None, width=None, type='ApodizationSpec')    │\n",
       "│           attrs = {}                                                                                     │\n",
       "│    bounding_box = Box(attrs={}, 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 = True                                                                                   │\n",
       "│          fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                                   │\n",
       "│           freqs = array([2.e+14])                                                                        │\n",
       "│ frequency_range = (np.float64(200000000000000.0), np.float64(200000000000000.0))                         │\n",
       "│        geometry = Box(attrs={}, 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",
       "│                       attrs={},                                                                          │\n",
       "│                       alpha=0.4,                                                                         │\n",
       "│                       zorder=None,                                                                       │\n",
       "│                       type='PlotParams',                                                                 │\n",
       "│                       edgecolor='orange',                                                                │\n",
       "│                       facecolor='orange',                                                                │\n",
       "│                       fill=True,                                                                         │\n",
       "│                       hatch=None,                                                                        │\n",
       "│                       linewidth=3.0                                                                      │\n",
       "│                   )                                                                                      │\n",
       "│            size = (20.0, 20.0, 0.0)                                                                      │\n",
       "│            type = 'FieldMonitor'                                                                         │\n",
       "│       zero_dims = [2]                                                                                    │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "
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\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;92mTrue\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[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m2.e+14\u001b[0m\u001b[1m]\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[33mattrs\u001b[0m=\u001b[1m{\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[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[33mattrs\u001b[0m=\u001b[1m{\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;33mattrs\u001b[0m = \u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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;92mTrue\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[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m2.e+14\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfrequency_range\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;35mnp.float64\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mnp.float64\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m\u001b[1m)\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[33mattrs\u001b[0m=\u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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[33mzorder\u001b[0m=\u001b[3;35mNone\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[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[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",
       "│ │ │   attrs={},                                                                                        │ │\n",
       "│ │ │   type='FieldMonitor',                                                                             │ │\n",
       "│ │ │   center=(0.0, 0.0, 0.0),                                                                          │ │\n",
       "│ │ │   size=(20.0, 0.0, 20.0),                                                                          │ │\n",
       "│ │ │   name='field2',                                                                                   │ │\n",
       "│ │ │   interval_space=(1, 1, 1),                                                                        │ │\n",
       "│ │ │   colocate=True,                                                                                   │ │\n",
       "│ │ │   freqs=array([2.e+14]),                                                                           │ │\n",
       "│ │ │   apodization=ApodizationSpec(                                                                     │ │\n",
       "│ │ │   │   attrs={},                                                                                    │ │\n",
       "│ │ │   │   start=None,                                                                                  │ │\n",
       "│ │ │   │   end=None,                                                                                    │ │\n",
       "│ │ │   │   width=None,                                                                                  │ │\n",
       "│ │ │   │   type='ApodizationSpec'                                                                       │ │\n",
       "│ │ │   ),                                                                                               │ │\n",
       "│ │ │   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                                      │ │\n",
       "│ │ )                                                                                                    │ │\n",
       "│ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────╯ │\n",
       "│                                                                                                          │\n",
       "│     apodization = ApodizationSpec(attrs={}, start=None, end=None, width=None, type='ApodizationSpec')    │\n",
       "│           attrs = {}                                                                                     │\n",
       "│    bounding_box = Box(attrs={}, 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 = True                                                                                   │\n",
       "│          fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                                   │\n",
       "│           freqs = array([2.e+14])                                                                        │\n",
       "│ frequency_range = (np.float64(200000000000000.0), np.float64(200000000000000.0))                         │\n",
       "│        geometry = Box(attrs={}, 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",
       "│                       attrs={},                                                                          │\n",
       "│                       alpha=0.4,                                                                         │\n",
       "│                       zorder=None,                                                                       │\n",
       "│                       type='PlotParams',                                                                 │\n",
       "│                       edgecolor='orange',                                                                │\n",
       "│                       facecolor='orange',                                                                │\n",
       "│                       fill=True,                                                                         │\n",
       "│                       hatch=None,                                                                        │\n",
       "│                       linewidth=3.0                                                                      │\n",
       "│                   )                                                                                      │\n",
       "│            size = (20.0, 0.0, 20.0)                                                                      │\n",
       "│            type = 'FieldMonitor'                                                                         │\n",
       "│       zero_dims = [1]                                                                                    │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "
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\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;92mTrue\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[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m2.e+14\u001b[0m\u001b[1m]\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[33mattrs\u001b[0m=\u001b[1m{\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[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[33mattrs\u001b[0m=\u001b[1m{\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;33mattrs\u001b[0m = \u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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;92mTrue\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[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m2.e+14\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", "\u001b[34m│\u001b[0m \u001b[3;33mfrequency_range\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;35mnp.float64\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mnp.float64\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m\u001b[1m)\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[33mattrs\u001b[0m=\u001b[1m{\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[33mattrs\u001b[0m=\u001b[1m{\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[33mzorder\u001b[0m=\u001b[3;35mNone\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[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[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()" ] }, { "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": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "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 grayscale.\n", "\n", "By default, sources are overlaid in green, monitors in yellow, and PML boundaries in gray." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 3, figsize=(13, 4))\n", "sim.plot_eps(z=0.75, freq=freq_range.freq0, ax=ax[0])\n", "sim.plot_eps(y=0.01, freq=freq_range.freq0, ax=ax[1])\n", "sim.plot_eps(x=0, freq=freq_range.freq0, ax=ax[2])\n", "plt.show()" ] }, { "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": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "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])\n", "plt.show()" ] }, { "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.flexcompute.com/projects/tidy3d/en/latest/api/index.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": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
13:09:06 UTC Created task 'Simulation' with resource_id                         \n",
       "             'fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd' and task_type 'FDTD'.  \n",
       "
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             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4\n",
       "             d-4abc-9bc5-d7e8a5b4fafd'.                                         \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", "\u001b[2;36m \u001b[0m\u001b]8;id=638218;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=840680;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=638218;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=472645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=638218;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32m-0f42a095-1a4\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=638218;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[32md-4abc-9bc5-d7e8a5b4fafd'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
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      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:09:18 UTC Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:09:18 UTC\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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. This is the maximum possible cost, and can be lower in case of early shutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
13:09:24 UTC Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:09:24 UTC\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimate_cost = web.estimate_cost(task_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now start the task, and if we want to, continuously monitor its status and wait until the run is successful. The [monitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.webapi.monitor.html#tidy3d.web.monitor) function will keep running until either a `'success'` or `'error'` status is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
13:09:27 UTC status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:09:27 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed0893f234f24375b0514398278647f0",
       "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": [
       "
13:09:50 UTC starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:09:50 UTC\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc1e2643d291483bab52cc96070a926e",
       "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": [
       "
13:09:56 UTC status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:09:56 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View simulation result at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4\n",
       "             d-4abc-9bc5-d7e8a5b4fafd'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=821564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=827063;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=821564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=303451;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=821564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34m-0f42a095-1a4\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=821564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f42a095-1a4d-4abc-9bc5-d7e8a5b4fafd\u001b\\\u001b[4;34md-4abc-9bc5-d7e8a5b4fafd'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# web.start(task_id, solver_version=\"improve_python_overgap-0.0.0\")\n",
    "web.start(task_id)\n",
    "web.monitor(task_id, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the ``real_cost`` function once the task is complete to check the cost that was actually billed. It may take a few seconds before it is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
13:10:01 UTC Billed flex credit cost: 0.025.                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:10:01 UTC\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.025\u001b[0m.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Note: the task cost pro-rated due to early shutoff was below the   \n",
       "             minimum threshold, due to fast shutoff. Decreasing the simulation  \n",
       "             'run_time' should decrease the estimated, and correspondingly the  \n",
       "             billed cost of such tasks.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mNote: the task cost pro-rated due to early shutoff was below the   \n",
       "\u001b[2;36m             \u001b[0mminimum threshold, due to fast shutoff. Decreasing the simulation  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'run_time'\u001b[0m should decrease the estimated, and correspondingly the  \n",
       "\u001b[2;36m             \u001b[0mbilled cost of such tasks.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "time.sleep(4)\n",
    "real_cost = web.real_cost(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": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06cf76c9ce9743b7bbd518d6e262d69d",
       "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": [
       "
13:10:08 UTC Loading simulation from data/sim_data.hdf5                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:10:08 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from data/sim_data.hdf5                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:10:09 UTC WARNING: Simulation final field decay value of 0.282 is greater    \n",
       "             than the simulation shutoff threshold of 1e-05. Consider running   \n",
       "             the simulation again with a larger 'run_time' duration for more    \n",
       "             accurate results.                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:10:09 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.282\u001b[0m\u001b[31m is greater    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Warning messages were found in the solver log. For more   \n",
       "             information, check 'SimulationData.log' or use                     \n",
       "             'web.download_log(task_id)'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:09:44] INFO: Auto meshing using wavelength 1.4999 defined from sources.     \n",
      "           INFO: Auto meshing using wavelength 1.4999 defined from sources.     \n",
      "           INFO: Auto meshing using wavelength 1.4999 defined from sources.     \n",
      "           USER: Simulation domain Nx, Ny, Nz: [156, 156, 104]                  \n",
      "           USER: Applied symmetries: (0, 0, 0)                                  \n",
      "           USER: Number of computational grid points: 2.6462e+06.               \n",
      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
      "           timestep_reduction=0.3, edge_singularity_correction=True),           \n",
      "           pmc=Staircasing(attrs={}, type='Staircasing'),                       \n",
      "           lossy_metal=SurfaceImpedance(attrs={}, type='SurfaceImpedance',      \n",
      "           timestep_reduction=0.0, edge_singularity_correction=True),           \n",
      "           type='SubpixelSpec')                                                 \n",
      "           USER: Number of time steps: 3.4930e+03                               \n",
      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
      "           USER: Time step (s): 5.7275e-17                                      \n",
      "           USER:                                                                \n",
      "                                                                                \n",
      "[13:09:45] USER: Compute source modes time (s):     0.3307                      \n",
      "           WARNING: Provided source time dependence appears not to fully decay  \n",
      "           by the chosen run time, broadband source set up may not be accurate. \n",
      "           You may need to increase simulation run time.                        \n",
      "           WARNING: Provided source time dependence appears not to fully decay  \n",
      "           by the chosen run time, broadband source set up may not be accurate. \n",
      "           You may need to increase simulation run time.                        \n",
      "           USER: Rest of setup time (s):            0.4530                      \n",
      "[13:09:48] USER: Compute monitor modes time (s):    0.0002                      \n",
      "[13:09:53] USER: Solver time (s):                   5.2400                      \n",
      "           USER: Time-stepping speed (cells/s):     2.06e+09                    \n",
      "           USER: Post-processing time (s):          0.4005                      \n",
      "\n",
      " ====== SOLVER LOG ====== \n",
      "\n",
      "Processing grid and structures...\n",
      "Building FDTD update coefficients...\n",
      "Solver setup time (s):             0.7297\n",
      "\n",
      "Running solver for 3493 time steps...\n",
      "- Time step    139 / time 7.96e-15s (  4 % done), field decay: 1.00e+00\n",
      "- Time step    279 / time 1.60e-14s (  8 % done), field decay: 1.00e+00\n",
      "- Time step    419 / time 2.40e-14s ( 12 % done), field decay: 1.00e+00\n",
      "- Time step    558 / time 3.20e-14s ( 16 % done), field decay: 1.00e+00\n",
      "- Time step    698 / time 4.00e-14s ( 20 % done), field decay: 1.00e+00\n",
      "- Time step    838 / time 4.80e-14s ( 24 % done), field decay: 1.00e+00\n",
      "- Time step    978 / time 5.60e-14s ( 28 % done), field decay: 1.00e+00\n",
      "- Time step   1117 / time 6.40e-14s ( 32 % done), field decay: 1.00e+00\n",
      "- Time step   1257 / time 7.20e-14s ( 36 % done), field decay: 1.00e+00\n",
      "- Time step   1397 / time 8.00e-14s ( 40 % done), field decay: 1.00e+00\n",
      "- Time step   1536 / time 8.80e-14s ( 44 % done), field decay: 1.00e+00\n",
      "- Time step   1676 / time 9.60e-14s ( 48 % done), field decay: 1.00e+00\n",
      "- Time step   1816 / time 1.04e-13s ( 52 % done), field decay: 1.00e+00\n",
      "- Time step   1956 / time 1.12e-13s ( 56 % done), field decay: 1.00e+00\n",
      "- Time step   2095 / time 1.20e-13s ( 60 % done), field decay: 1.00e+00\n",
      "- Time step   2235 / time 1.28e-13s ( 64 % done), field decay: 1.00e+00\n",
      "- Time step   2375 / time 1.36e-13s ( 68 % done), field decay: 1.00e+00\n",
      "- Time step   2514 / time 1.44e-13s ( 72 % done), field decay: 1.00e+00\n",
      "- Time step   2654 / time 1.52e-13s ( 76 % done), field decay: 8.45e-01\n",
      "- Time step   2780 / time 1.59e-13s ( 79 % done), field decay: 1.00e+00\n",
      "- Time step   2794 / time 1.60e-13s ( 80 % done), field decay: 1.00e+00\n",
      "- Time step   2934 / time 1.68e-13s ( 84 % done), field decay: 1.00e+00\n",
      "- Time step   3073 / time 1.76e-13s ( 88 % done), field decay: 9.61e-01\n",
      "- Time step   3213 / time 1.84e-13s ( 92 % done), field decay: 5.51e-01\n",
      "- Time step   3353 / time 1.92e-13s ( 96 % done), field decay: 3.05e-01\n",
      "- Time step   3492 / time 2.00e-13s (100 % done), field decay: 2.82e-01\n",
      "Time-stepping time (s):            4.4849\n",
      "Data write time (s):               0.0245\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": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "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], val=\"real\")\n",
    "sim_data.plot_field(\"field2\", \"Ey\", ax=ax[1], val=\"real\")\n",
    "plt.show()"
   ]
  },
  {
   "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](../notebooks/VizData.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.ScalarFieldDataArray (x: 157, y: 157, z: 1, f: 1)> Size: 394kB\n",
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       "\n",
       "        ...,\n",
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       "Coordinates:\n",
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       "  * y        (y) float64 1kB -2.367 -2.337 -2.306 -2.276 ... 2.295 2.325 2.354\n",
       "  * z        (z) float64 8B -1.0\n",
       "  * f        (f) float64 8B 2e+14\n",
       "Attributes:\n",
       "    long_name:  field value
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      ],
      "text/plain": [
       " Size: 394kB\n",
       "array([[[[ 0.00000000e+00-0.00000000e+00j]],\n",
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       "\n",
       "        ...,\n",
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       "\n",
       "        [[ 3.61339662e-08-4.11815663e-09j]],\n",
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       "       [[[ 0.00000000e+00-0.00000000e+00j]],\n",
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       "        [[-2.99871924e-08-2.24670746e-09j]],\n",
       "\n",
       "        [[-2.08066365e-07+1.14916445e-08j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-3.09183733e-08+8.88291692e-09j]],\n",
       "\n",
       "        [[-2.33044080e-09+5.34447572e-10j]],\n",
       "\n",
       "        [[ 0.00000000e+00-0.00000000e+00j]]]], shape=(157, 157, 1, 1))\n",
       "Coordinates:\n",
       "  * x        (x) float64 1kB -2.364 -2.333 -2.303 -2.273 ... 2.303 2.333 2.364\n",
       "  * y        (y) float64 1kB -2.367 -2.337 -2.306 -2.276 ... 2.295 2.325 2.354\n",
       "  * z        (z) float64 8B -1.0\n",
       "  * f        (f) float64 8B 2e+14\n",
       "Attributes:\n",
       "    long_name:  field value"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon1_data = sim_data[\"field1\"]\n",
    "mon1_data.Ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mon1_data.Ez.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.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html), which look largely like a delayed version of the source input, indicating that no resonant features were excited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_data = sim_data[\"field_time\"]\n",
    "fig, ax = plt.subplots(1)\n",
    "time_data.Ey.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": 21,
   "metadata": {
    "tags": []
   },
   "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(box=volume, coord_key=\"centers\")\n",
    "\n",
    "# at Ex locations in the yee cell\n",
    "eps_Ex = sim.epsilon(box=volume, coord_key=\"Ex\")"
   ]
  },
  {
   "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.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html).\n",
    "\n",
    "We can then plot or post-process this data as we wish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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       "
"
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     },
     "metadata": {},
     "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": "markdown",
   "metadata": {},
   "source": [
    "For simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook illustrates the basic setup, run, and analysis of a Tidy3D simulation",
  "feature_image": "",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
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  "title": "First Walkthrough in Tidy3D | Flexcompute",
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🏃  Starting 'Simulation'...\n
\n",
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 1.4/1.4 kB • ? • 0:00:00\n
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