{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dispersive materials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction / Setup\n", "\n", "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FDispersion.ipynb).\n", "\n", "Here we show to to model dispersive materials in Tidy3D with an example showing transmission spectrum of a multilayer stack of slabs." ] }, { "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", "\n", "import tidy3d as td\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us define some basic parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Wavelength and frequency range\n", "lambda_range = (0.5, 1.5)\n", "lam0 = np.sum(lambda_range)/2\n", "freq_range = (td.constants.C_0/lambda_range[1], td.constants.C_0/lambda_range[0])\n", "Nfreq = 333\n", "\n", "# frequencies and wavelengths of monitor\n", "monitor_freqs = np.linspace(freq_range[0], freq_range[1], Nfreq)\n", "monitor_lambdas = td.constants.C_0 / monitor_freqs\n", "\n", "# central frequency, frequency pulse width and total running time\n", "freq0 = monitor_freqs[Nfreq // 2]\n", "freqw = 0.3 * (freq_range[1] - freq_range[0])\n", "t_stop = 100 / freq0\n", "\n", "# Thicknesses of slabs\n", "t_slabs = [0.5, 0.2, 0.4, 0.3] # um\n", "\n", "# Grid resolution (cells per um)\n", "res = 150\n", "\n", "# space between slabs and sources and PML\n", "spacing = 1 * lambda_range[-1]\n", "\n", "# simulation size\n", "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4*spacing + sum(t_slabs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining Materials (4 Ways)\n", "\n", "Here, we will illustrate defining materials in four different ways:\n", "\n", "1. Simple, lossless, dispersionless dielectric defined by a real-valued relative permittivity.\n", "2. Lossy material defined by real and imaginary part of the refractive index ($n$) and ($k$) at a given frequency. Values are exact only at that frequency, so this approach is only good for narrow-band simulations.\n", "3. Simple, lossless dispersive material (one-pole fitting) defined by the real part of the refractive index $n$ and the dispersion $\\mathrm{d}n/\\mathrm{d}\\lambda$ at a given frequency. The dispersion must be negative. This is a convenient approach to incorporate weakly dispersive materials in your simulations, as the values can be taken directly from [refractiveindex.info](https://refractiveindex.info/)\n", "4. Dispersive material imported from our pre-defined library of materials.\n", "\n", "More complicated dispersive materials [can also be defined](https://docs.simulation.cloud/en/latest/api.html#dispersive-mediums) through dispersive models like Lorentz, Sellmeier, Debye, or Drude, if the model parameters are known. Finally, arbitrary dispersion data can also be fit, which is a the subject of [this tutorial](https://docs.simulation.cloud/en/latest/notebooks/Fitting.html)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "# simple, lossless, dispersionless material (either epsilon or n)\n", "mat1 = td.Medium(permittivity=4.0)\n", "\n", "# lossy material with n & k values at a specified frequency or wavelength\n", "mat2 = td.Medium.from_nk(n=3.0, k=0.1, freq=freq0)\n", "\n", "# weakly dispersive material defined by dn_dwvl at a given frequency\n", "mat3 = td.Sellmeier.from_dispersion(n=2.0, dn_dwvl=-0.1, freq=freq0)\n", "\n", "# dispersive material from tidy3d library\n", "mat4 = td.material_library['BK7']['Zemax']\n", "\n", "# put all together\n", "mat_slabs = [mat1, mat2, mat3, mat4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Simulation\n", "Now we set everything else up (structures, sources, monitors, simulation) to run the example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define the multilayer stack structure." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [], "source": [ "slabs = []\n", "slab_position = -Lz/2 + 2*spacing\n", "for t, mat in zip(t_slabs, mat_slabs):\n", " slab = td.Structure(\n", " geometry=td.Box(\n", " center=(0, 0, slab_position + t/2),\n", " size=(td.inf, td.inf, t),\n", " ),\n", " medium=mat,\n", " )\n", " slabs.append(slab)\n", " slab_position += t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We must now define the excitation conditions and field monitors. We will excite the slab using a normally incident (along z) planewave, polarized along the x direciton." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Here we define the planewave source, placed just in advance (towards negative z) of the slab\n", "source = td.PlaneWave(\n", " source_time = td.GaussianPulse(\n", " freq0=freq0,\n", " fwidth=freqw\n", " ),\n", " size=(td.inf, td.inf, 0),\n", " center=(0, 0, -Lz/2+spacing),\n", " direction='+',\n", " pol_angle=0,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we define the field monitor, placed just past (towards positive z) of the stack." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [], "source": [ "# We are interested in measuring the transmitted flux, so we set it to be an oversized plane.\n", "monitor = td.FluxMonitor(\n", " center = (0, 0, Lz/2 - spacing),\n", " size = (td.inf, td.inf, 0),\n", " freqs = monitor_freqs,\n", " name='flux',\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, define the boundary conditions to use PMLs along z and the default periodic boundaries along x and y" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "boundary_spec = td.BoundarySpec.pml(z=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it is time to define the simulation object." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [], "source": [ "sim = td.Simulation(\n", " center = (0, 0, 0),\n", " size = sim_size,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=40),\n", " structures = slabs,\n", " sources = [source],\n", " monitors = [monitor],\n", " run_time = t_stop,\n", " boundary_spec = boundary_spec\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot The Structure\n", "\n", "Let's now plot the permittivity profile to confirm that the structure was defined correctly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we use the Simulation.plot() method to plot the materials only, which assigns a different color to each slab without knowledge of the material properties." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sim.plot(x=0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we use Simulation.plot_eps() to vizualize the permittivity of the stack. However, because the stack contains dispersive materials, we need to specify the freq of interest as an argument to the plotting tool. Here we show the permittivity at the lowest and highest frequences in the range of interest. Note that in this case, the real part of the permittivity (being plotted) only changes slightly between the two frequencies on the dispersive material. However, for other materials with more dispersion, the effect can be much more prominent." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the permittivity at a few frequencies\n", "freqs_plot = (freq_range[0], freq_range[1])\n", "fig, axes = plt.subplots(1, len(freqs_plot), tight_layout=True, figsize=(12, 4))\n", "for ax, freq_plot in zip(axes, freqs_plot):\n", " sim.plot_eps(x=0, freq=freq_plot, ax=ax)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also take a look at the source to make sure it's defined correctly over our frequency range of interst." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n" 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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Check probe and source\n", "ax1 = sim.sources[0].source_time.plot(times=np.linspace(0, sim.run_time, 1001))\n", "ax1.set_xlim(0, 1e-13)\n", "ax2 = sim.sources[0].source_time.plot_spectrum(times=np.linspace(0, sim.run_time, 1001))\n", "ax2.fill_between(freq_range, [-8e-16, -8e-16], [8e-16, 8e-16], alpha=0.4, color='g', label=\"mesure\")\n", "ax2.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the simulation\n", "\n", "We will submit the simulation to run as a new project." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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[09:41:52] INFO     loading SimulationData from data/sim_data.hdf5                                    webapi.py:397\n",
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           INFO     Auto meshing using wavelength 0.7500 defined from sources.                     grid_spec.py:473\n",
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[09:42:28] INFO     loading SimulationData from data/sim_data.hdf5                                    webapi.py:397\n",
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(monitor_lambdas, transmission, label='with structure')\n", "plt.plot(monitor_lambdas, transmission_norm, label='no structure')\n", "plt.plot(monitor_lambdas, transmission / transmission_norm, 'k--', label='normalized')\n", "plt.legend()\n", "plt.xlabel('wavelength (um)')\n", "plt.ylabel('fraction of transmitted power (normalized)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that since the flux monitor already takes the source power into account, the normalizing run has no visible effect on the results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analytical Comparison\n", "\n", "We will use a transfer matrix method (TMM) [code](https://github.com/sbyrnes321/tmm) to compare tidy3d transmission to a semi-analytical result." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [], "source": [ "# import TMM package\n", "import tmm" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# prepare list of thicknesses including air boundaries\n", "d_list = [np.inf] + t_slabs + [np.inf]\n", "\n", "# convert the complex permittivities at each frequency to refractive indices\n", "n_list1 = np.sqrt(mat1.eps_model(monitor_freqs))\n", "n_list2 = np.sqrt(mat2.eps_model(monitor_freqs))\n", "n_list3 = np.sqrt(mat3.eps_model(monitor_freqs))\n", "n_list4 = np.sqrt(mat4.eps_model(monitor_freqs))\n", "\n", "# loop through wavelength and record TMM computed transmission\n", "transmission_tmm = [] \n", "for i, lam in enumerate(monitor_lambdas):\n", " \n", " # create list of refractive index at this wavelength including outer material (air)\n", " n_list = [1, n_list1[i], n_list2[i], n_list3[i], n_list4[i], 1]\n", "\n", " # get transmission at normal incidence\n", " T = tmm.coh_tmm('s', n_list, d_list, 0, lam)['T']\n", " transmission_tmm.append(T)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(monitor_lambdas, transmission_tmm, label='TMM')\n", "plt.plot(monitor_lambdas, transmission / transmission_norm, 'k--', label='Tidy3D')\n", "plt.xlabel('wavelength ($\\mu m$)')\n", "plt.ylabel('Transmitted')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0931d55584eb4b138e49501c545a1e51": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "17d68637c23d486ab94e925abc9e810a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5a990cf9aa5348a2a78a59a75c0dfe3d", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 1.43e-06) ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━  28% 0:00:52\n
\n", "text/plain": "% done (field decay = 1.43e-06) \u001b[38;2;249;38;114m━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 28%\u001b[0m \u001b[36m0:00:52\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "34824e4fb20e4a94bd06261f4a284e76": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_576bd4bcc4a749848d56f68329791ec3", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/25.0 kB • ? • -:--:--\n
\n", "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/25.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "39fd371e8a024f9d8da675e673dc29b6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_b1906cf2458d4104b8ea6e3a6d06892f", "msg_id": "", "outputs": [ { "data": { "text/html": "
🏃  Finishing 'docs_dispersion_norm'...\n
\n", "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'docs_dispersion_norm'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "3dfd2e297dae4634b13e9da838d0afe0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "576bd4bcc4a749848d56f68329791ec3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "58dd92a48f2a438ab9efb0543a2fa603": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5a990cf9aa5348a2a78a59a75c0dfe3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5d52d4dcd6be40948320708070a3d4bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "68df34a8844e480e8771277131f028de": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_3dfd2e297dae4634b13e9da838d0afe0", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 1.88e-06) ━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  16% -:--:--\n
\n", "text/plain": "% done (field decay = 1.88e-06) \u001b[38;2;249;38;114m━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 16%\u001b[0m \u001b[36m-:--:--\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "6b985344f7474c1c953b002e4b76b50b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9eeacee2b3274cf1b71648a2556a2c87", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/19.7 kB • ? • -:--:--\n
\n", "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/19.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "7a61d39714ec46f3a03a17b2f1965cac": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_5d52d4dcd6be40948320708070a3d4bc", "msg_id": "", "outputs": [ { "data": { "text/html": "
🚶  Starting 'dispersion'...\n
\n", "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'dispersion'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "950931dfa40d4366be0b18ed8f1d3252": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9eeacee2b3274cf1b71648a2556a2c87": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ae410281962e479890990705e31fe8d4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_df8bebae58de427387701f6dbe7b1cbc", "msg_id": "", "outputs": [ { "data": { "text/html": "
🚶  Starting 'docs_dispersion_norm'...\n
\n", "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mStarting 'docs_dispersion_norm'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b1906cf2458d4104b8ea6e3a6d06892f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b1e3bb5952114ed29c0be3accf10b0f7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0931d55584eb4b138e49501c545a1e51", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/23.6 kB • ? • -:--:--\n
\n", "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/23.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b87a202ad72749acaac913b4b68be0dc": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_950931dfa40d4366be0b18ed8f1d3252", "msg_id": "", "outputs": [ { "data": { "text/html": "
🚶  Finishing 'dispersion'...\n
\n", "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'dispersion'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d74adba472de4dbb9adb15e8887f33f4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_58dd92a48f2a438ab9efb0543a2fa603", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/15.9 kB • ? • -:--:--\n
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