{ "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 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": { "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", "# 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": { "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](../_autosummary/tidy3d.Structure.html) objects. Each object consists of a [Geometry](../_autosummary/tidy3d.components.geometry.Geometry.html) and a [Medium](../_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": { "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": { "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](../_autosummary/tidy3d.FieldMonitor.html) and [FieldTimeMonitor](../_autosummary/tidy3d.FieldTimeMonitor.html), which record the frequency-domain and time-domain fields, respectively. \n", "\n", "[FieldMonitor](../_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](../_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](../_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](../_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(\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](../_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": { "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": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 <class 'tidy3d.components.monitor.FieldTimeMonitor'> \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\n",
       "\u2502 :class:`Monitor` that records electromagnetic fields in the time domain.       \u2502\n",
       "\u2502                                                                                \u2502\n",
       "\u2502 \u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e \u2502\n",
       "\u2502 \u2502 FieldTimeMonitor(                                                          \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   type='FieldTimeMonitor',                                               \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   center=(0.0, 0.0, 0.0),                                                \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   size=(0.0, 0.0, 0.0),                                                  \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   name='field_time',                                                     \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   start=0.0,                                                             \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   stop=None,                                                             \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   interval=5,                                                            \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'),                           \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   interval_space=(1, 1, 1),                                              \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   colocate=False                                                         \u2502 \u2502\n",
       "\u2502 \u2502 )                                                                          \u2502 \u2502\n",
       "\u2502 \u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f \u2502\n",
       "\u2502                                                                                \u2502\n",
       "\u2502   bounding_box = Box(type='Box', center=(0.0, 0.0, 0.0), size=(0.0, 0.0, 0.0)) \u2502\n",
       "\u2502         bounds = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0))                            \u2502\n",
       "\u2502         center = (0.0, 0.0, 0.0)                                               \u2502\n",
       "\u2502       colocate = False                                                         \u2502\n",
       "\u2502         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                          \u2502\n",
       "\u2502       geometry = Box(type='Box', center=(0.0, 0.0, 0.0), size=(0.0, 0.0, 0.0)) \u2502\n",
       "\u2502       interval = 5                                                             \u2502\n",
       "\u2502 interval_space = (1, 1, 1)                                                     \u2502\n",
       "\u2502           name = 'field_time'                                                  \u2502\n",
       "\u2502    plot_params = PlotParams(                                                   \u2502\n",
       "\u2502                      alpha=0.4,                                                \u2502\n",
       "\u2502                      edgecolor='orange',                                       \u2502\n",
       "\u2502                      facecolor='orange',                                       \u2502\n",
       "\u2502                      fill=True,                                                \u2502\n",
       "\u2502                      hatch=None,                                               \u2502\n",
       "\u2502                      linewidth=3.0,                                            \u2502\n",
       "\u2502                      type='PlotParams'                                         \u2502\n",
       "\u2502                  )                                                             \u2502\n",
       "\u2502           size = (0.0, 0.0, 0.0)                                               \u2502\n",
       "\u2502          start = 0.0                                                           \u2502\n",
       "\u2502           stop = None                                                          \u2502\n",
       "\u2502           type = 'FieldTimeMonitor'                                            \u2502\n",
       "\u2502      zero_dims = [0, 1, 2]                                                     \u2502\n",
       "\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\n",
       "
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\u001b[0m\u001b[33mstop\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33minterval\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33minterval\u001b[0m = \u001b[1;36m5\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field_time'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mstart\u001b[0m = \u001b[1;36m0.0\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mstop\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldTimeMonitor'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 <class 'tidy3d.components.monitor.FieldMonitor'> \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\n",
       "\u2502 :class:`Monitor` that records electromagnetic fields in the frequency domain.                  \u2502\n",
       "\u2502                                                                                                \u2502\n",
       "\u2502 \u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e \u2502\n",
       "\u2502 \u2502 FieldMonitor(                                                                              \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   type='FieldMonitor',                                                                   \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   center=(0.0, 0.0, -1.0),                                                               \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   size=(20.0, 20.0, 0.0),                                                                \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   name='field1',                                                                         \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   freqs=(200000000000000.0,),                                                            \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   apodization=ApodizationSpec(                                                           \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   start=None,                                                                        \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   end=None,                                                                          \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   width=None,                                                                        \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   type='ApodizationSpec'                                                             \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   ),                                                                                     \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'),                                           \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   interval_space=(1, 1, 1),                                                              \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   colocate=False                                                                         \u2502 \u2502\n",
       "\u2502 \u2502 )                                                                                          \u2502 \u2502\n",
       "\u2502 \u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f \u2502\n",
       "\u2502                                                                                                \u2502\n",
       "\u2502    apodization = ApodizationSpec(start=None, end=None, width=None, type='ApodizationSpec')     \u2502\n",
       "\u2502   bounding_box = Box(type='Box', center=(0.0, 0.0, -1.0), size=(20.0, 20.0, 0.0))              \u2502\n",
       "\u2502         bounds = ((-10.0, -10.0, -1.0), (10.0, 10.0, -1.0))                                    \u2502\n",
       "\u2502         center = (0.0, 0.0, -1.0)                                                              \u2502\n",
       "\u2502       colocate = False                                                                         \u2502\n",
       "\u2502         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                          \u2502\n",
       "\u2502          freqs = (200000000000000.0,)                                                          \u2502\n",
       "\u2502       geometry = Box(type='Box', center=(0.0, 0.0, -1.0), size=(20.0, 20.0, 0.0))              \u2502\n",
       "\u2502 interval_space = (1, 1, 1)                                                                     \u2502\n",
       "\u2502           name = 'field1'                                                                      \u2502\n",
       "\u2502    plot_params = PlotParams(                                                                   \u2502\n",
       "\u2502                      alpha=0.4,                                                                \u2502\n",
       "\u2502                      edgecolor='orange',                                                       \u2502\n",
       "\u2502                      facecolor='orange',                                                       \u2502\n",
       "\u2502                      fill=True,                                                                \u2502\n",
       "\u2502                      hatch=None,                                                               \u2502\n",
       "\u2502                      linewidth=3.0,                                                            \u2502\n",
       "\u2502                      type='PlotParams'                                                         \u2502\n",
       "\u2502                  )                                                                             \u2502\n",
       "\u2502           size = (20.0, 20.0, 0.0)                                                             \u2502\n",
       "\u2502           type = 'FieldMonitor'                                                                \u2502\n",
       "\u2502      zero_dims = [2]                                                                           \u2502\n",
       "\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\n",
       "
\n" ], "text/plain": [ "\u001b[34m\u256d\u2500\u001b[0m\u001b[34m\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \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 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u001b[0m\u001b[34m\u2500\u256e\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[36m:class:`Monitor` that records electromagnetic fields in the frequency domain.\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[1;35mFieldMonitor\u001b[0m\u001b[1m(\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'field1'\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mapodization\u001b[0m=\u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[1m)\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mfreqs\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field1'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldMonitor'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mzero_dims\u001b[0m = \u001b[1m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 <class 'tidy3d.components.monitor.FieldMonitor'> \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\n",
       "\u2502 :class:`Monitor` that records electromagnetic fields in the frequency domain.                  \u2502\n",
       "\u2502                                                                                                \u2502\n",
       "\u2502 \u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e \u2502\n",
       "\u2502 \u2502 FieldMonitor(                                                                              \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   type='FieldMonitor',                                                                   \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   center=(0.0, 0.0, 0.0),                                                                \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   size=(20.0, 0.0, 20.0),                                                                \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   name='field2',                                                                         \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   freqs=(200000000000000.0,),                                                            \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   apodization=ApodizationSpec(                                                           \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   start=None,                                                                        \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   end=None,                                                                          \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   width=None,                                                                        \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   \u2502   type='ApodizationSpec'                                                             \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   ),                                                                                     \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   fields=('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz'),                                           \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   interval_space=(1, 1, 1),                                                              \u2502 \u2502\n",
       "\u2502 \u2502 \u2502   colocate=False                                                                         \u2502 \u2502\n",
       "\u2502 \u2502 )                                                                                          \u2502 \u2502\n",
       "\u2502 \u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f \u2502\n",
       "\u2502                                                                                                \u2502\n",
       "\u2502    apodization = ApodizationSpec(start=None, end=None, width=None, type='ApodizationSpec')     \u2502\n",
       "\u2502   bounding_box = Box(type='Box', center=(0.0, 0.0, 0.0), size=(20.0, 0.0, 20.0))               \u2502\n",
       "\u2502         bounds = ((-10.0, 0.0, -10.0), (10.0, 0.0, 10.0))                                      \u2502\n",
       "\u2502         center = (0.0, 0.0, 0.0)                                                               \u2502\n",
       "\u2502       colocate = False                                                                         \u2502\n",
       "\u2502         fields = ('Ex', 'Ey', 'Ez', 'Hx', 'Hy', 'Hz')                                          \u2502\n",
       "\u2502          freqs = (200000000000000.0,)                                                          \u2502\n",
       "\u2502       geometry = Box(type='Box', center=(0.0, 0.0, 0.0), size=(20.0, 0.0, 20.0))               \u2502\n",
       "\u2502 interval_space = (1, 1, 1)                                                                     \u2502\n",
       "\u2502           name = 'field2'                                                                      \u2502\n",
       "\u2502    plot_params = PlotParams(                                                                   \u2502\n",
       "\u2502                      alpha=0.4,                                                                \u2502\n",
       "\u2502                      edgecolor='orange',                                                       \u2502\n",
       "\u2502                      facecolor='orange',                                                       \u2502\n",
       "\u2502                      fill=True,                                                                \u2502\n",
       "\u2502                      hatch=None,                                                               \u2502\n",
       "\u2502                      linewidth=3.0,                                                            \u2502\n",
       "\u2502                      type='PlotParams'                                                         \u2502\n",
       "\u2502                  )                                                                             \u2502\n",
       "\u2502           size = (20.0, 0.0, 20.0)                                                             \u2502\n",
       "\u2502           type = 'FieldMonitor'                                                                \u2502\n",
       "\u2502      zero_dims = [1]                                                                           \u2502\n",
       "\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\n",
       "
\n" ], "text/plain": [ "\u001b[34m\u256d\u2500\u001b[0m\u001b[34m\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \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 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u001b[0m\u001b[34m\u2500\u256e\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[36m:class:`Monitor` that records electromagnetic fields in the frequency domain.\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u256d\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256e\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[1;35mFieldMonitor\u001b[0m\u001b[1m(\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'FieldMonitor'\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'field2'\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mfreqs\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mapodization\u001b[0m=\u001b[1;35mApodizationSpec\u001b[0m\u001b[1m(\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mstart\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mend\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mwidth\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u2502 \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'ApodizationSpec'\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[1m)\u001b[0m, \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \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\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[2;32m\u2502 \u001b[0m\u001b[33mcolocate\u001b[0m=\u001b[3;91mFalse\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[32m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[32m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mcolocate\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mfreqs\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m200000000000000.0\u001b[0m,\u001b[1m)\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'field2'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mplot_params\u001b[0m = \u001b[1;35mPlotParams\u001b[0m\u001b[1m(\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33malpha\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33medgecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfacecolor\u001b[0m=\u001b[32m'orange'\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mfill\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mhatch\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mlinewidth\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[33mtype\u001b[0m=\u001b[32m'PlotParams'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[1m)\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\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\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mtype\u001b[0m = \u001b[32m'FieldMonitor'\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2502\u001b[0m \u001b[3;33mzero_dims\u001b[0m = \u001b[1m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m \u001b[34m\u2502\u001b[0m\n", "\u001b[34m\u2570\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u256f\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": 10, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize source\n", "psource.source_time.plot(np.linspace(0, run_time, 1001))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's visualize the simulation. \n", "\n", "For this, we will plot three cross sections at `z=0.75`, `y=0`, and `x=0`, respectively. \n", "\n", "The relative permittivity of objects is plotted in greyscale.\n", "\n", "By default, sources are overlayed in green, monitors in yellow, and PML boundaries in grey." ] }, { "cell_type": "code", "execution_count": 12, "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=freq0, ax=ax[0])\n", "sim.plot_eps(y=0.01, freq=freq0, ax=ax[1])\n", "sim.plot_eps(x=0, freq=freq0, ax=ax[2])\n", "plt.show()\n" ] }, { "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": 13, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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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()\n" ] }, { "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](../api.html#submitting-simulations) API of ``Tidy3D``. First, we submit the project. Note that we can give it a custom name." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [], "source": [ "task_id = web.upload(sim, task_name=\"Simulation\")\n" ] }, { "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": 15, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max flex unit cost: 0.025\n" ] } ], "source": [ "print(\"Max flex unit cost: \", web.estimate_cost(task_id))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now start the task, and if we want to, continously monitor its status, and wait until the run is successful. The [monitor](../_autosummary/tidy3d.web.webapi.monitor.html#tidy3d.web.webapi.monitor) function will keep running until either a `'success'` or `'error'` status is returned." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[13:03:51] status = queued                                                                            webapi.py:269\n",
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[13:03:53] status = preprocess                                                                        webapi.py:263\n",
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[13:03:57] Maximum FlexCredit cost: 0.025. Use 'web.real_cost(task_id)' to get the billed FlexCredit cost webapi.py:286\n",
       "           after a simulation run.                                                                                 \n",
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       "\u001b[2;36m[13:03:57]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. 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 FlexCredit cost \u001b]8;id=27349;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=954724;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mafter a simulation run.                                                                    \u001b[2m             \u001b[0m\n"
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           starting up solver                                                                         webapi.py:290\n",
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           running solver                                                                             webapi.py:300\n",
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=407029;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=87774;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
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[13:04:05] early shutoff detected, exiting.                                                           webapi.py:313\n",
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           status = postprocess                                                                       webapi.py:330\n",
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=12386;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=182179;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#330\u001b\\\u001b[2m330\u001b[0m\u001b]8;;\u001b\\\n"
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[13:04:09] status = success                                                                           webapi.py:337\n",
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   ],
   "source": [
    "web.start(task_id)\n",
    "web.monitor(task_id, verbose=True)\n"
   ]
  },
  {
   "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": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Billed flex unit cost:  0.025\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "time.sleep(4)\n",
    "print(\"Billed flex unit cost: \", web.real_cost(task_id))\n"
   ]
  },
  {
   "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": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation domain Nx, Ny, Nz: [156, 157, 104]\n",
      "Applied symmetries: (0, 0, 0)\n",
      "Number of computational grid points: 2.6629e+06.\n",
      "Using subpixel averaging: True\n",
      "Number of time steps: 3.4930e+03\n",
      "Automatic shutoff factor: 1.00e-05\n",
      "Time step (s): 5.7275e-17\n",
      "\n",
      "\n",
      "Compute source modes time (s):     0.1525\n",
      "Compute monitor modes time (s):    0.0023\n",
      "Rest of setup time (s):            3.0354\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   1389 / time 7.96e-14s ( 39 % 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: 8.05e-01\n",
      "- Time step   1816 / time 1.04e-13s ( 52 % done), field decay: 3.46e-01\n",
      "- Time step   1956 / time 1.12e-13s ( 56 % done), field decay: 1.51e-01\n",
      "- Time step   2095 / time 1.20e-13s ( 60 % done), field decay: 5.29e-02\n",
      "- Time step   2235 / time 1.28e-13s ( 64 % done), field decay: 1.21e-02\n",
      "- Time step   2375 / time 1.36e-13s ( 68 % done), field decay: 2.31e-03\n",
      "- Time step   2514 / time 1.44e-13s ( 72 % done), field decay: 4.89e-04\n",
      "- Time step   2654 / time 1.52e-13s ( 76 % done), field decay: 1.11e-04\n",
      "- Time step   2794 / time 1.60e-13s ( 80 % done), field decay: 2.39e-05\n",
      "- Time step   2934 / time 1.68e-13s ( 84 % done), field decay: 4.75e-06\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "\n",
      "Solver time (s):                   1.8709\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)\n"
   ]
  },
  {
   "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": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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()\n"
   ]
  },
  {
   "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": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.ScalarFieldDataArray (x: 158, y: 159, z: 1, f: 1)>\n",
       "array([[[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 9.8921271e-07+3.6848229e-07j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 9.9903627e-07+1.4422510e-07j]],\n",
       "\n",
       "        [[ 6.1203380e-08-8.3397850e-10j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]],\n",
       "\n",
       "\n",
       "       [[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 9.8921271e-07+3.6848229e-07j]],\n",
       "...\n",
       "        [[-8.2269871e-08-2.9938814e-08j]],\n",
       "\n",
       "        [[-1.8088322e-08+3.3552048e-08j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]],\n",
       "\n",
       "\n",
       "       [[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[-9.9369956e-08+6.6969790e-09j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-8.2269871e-08-2.9938814e-08j]],\n",
       "\n",
       "        [[-1.8088322e-08+3.3552048e-08j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]]], dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 -2.379 -2.348 -2.318 -2.288 ... 2.288 2.318 2.348 2.379\n",
       "  * y        (y) float64 -2.39 -2.36 -2.33 -2.3 ... 2.266 2.295 2.325 2.354\n",
       "  * z        (z) float64 -1.0\n",
       "  * f        (f) float64 2e+14\n",
       "Attributes:\n",
       "    long_name:  field value
"
      ],
      "text/plain": [
       "\n",
       "array([[[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 9.8921271e-07+3.6848229e-07j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 9.9903627e-07+1.4422510e-07j]],\n",
       "\n",
       "        [[ 6.1203380e-08-8.3397850e-10j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]],\n",
       "\n",
       "\n",
       "       [[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 9.8921271e-07+3.6848229e-07j]],\n",
       "...\n",
       "        [[-8.2269871e-08-2.9938814e-08j]],\n",
       "\n",
       "        [[-1.8088322e-08+3.3552048e-08j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]],\n",
       "\n",
       "\n",
       "       [[[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]],\n",
       "\n",
       "        [[-9.9369956e-08+6.6969790e-09j]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-8.2269871e-08-2.9938814e-08j]],\n",
       "\n",
       "        [[-1.8088322e-08+3.3552048e-08j]],\n",
       "\n",
       "        [[ 0.0000000e+00+0.0000000e+00j]]]], dtype=complex64)\n",
       "Coordinates:\n",
       "  * x        (x) float64 -2.379 -2.348 -2.318 -2.288 ... 2.288 2.318 2.348 2.379\n",
       "  * y        (y) float64 -2.39 -2.36 -2.33 -2.3 ... 2.266 2.295 2.325 2.354\n",
       "  * z        (z) float64 -1.0\n",
       "  * f        (f) float64 2e+14\n",
       "Attributes:\n",
       "    long_name:  field value"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon1_data = sim_data[\"field1\"]\n",
    "mon1_data.Ex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mon1_data.Ez.real.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this raw data for example to also plot the time-domain fields recorded in the [FieldTimeMonitor](../_autosummary/tidy3d.FieldMonitor.html), which look largely like a delayed version of the source input, indicating that no resonant features were excited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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()\n"
   ]
  },
  {
   "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": 23,
   "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\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return an xarray DataArray containing the complex-valued permittivity values at the Yee cell centers and the \"Ex\" within the [box](../_autosummary/tidy3d.Box.html).\n",
    "\n",
    "We can then plot or post-process this data as we wish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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   ],
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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