"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_end = sim_data[\"movie\"].Ex.coords[\"t\"][-1] # end time of the animation\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"def animate(i):\n",
" t = t_end * i / frames # time at each frame\n",
" sim_data[\"movie\"].Ex.sel(t=t, method=\"nearest\").plot(\n",
" x=\"x\", y=\"z\", ax=ax, vmin=-20, vmax=20, add_colorbar=False, cmap=\"RdBu\"\n",
" )\n",
" ax.set_aspect(0.7)\n",
"\n",
"# create animation\n",
"ani = animation.FuncAnimation(fig, animate, frames=frames)\n",
"plt.close()\n",
"\n",
"# display the animation\n",
"HTML(ani.to_jshtml())"
]
},
{
"cell_type": "markdown",
"id": "a0c8e122",
"metadata": {},
"source": [
"## Activity 2\n",
"\n",
"Modify the first activity to calculate the reflectance of DBR devices for N = [2, 4, 6, 8] layers and compare the simulated and analytical results. \n"
]
},
{
"cell_type": "markdown",
"id": "160dd4e6",
"metadata": {},
"source": [
"We will start by defining a function to create the DBR structure based on the number of pairs."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5a675999",
"metadata": {},
"outputs": [],
"source": [
"def build_DBR(N):\n",
" # N is the number of repeated pairs of low/high refractive index material.\n",
"\n",
" z_0 = 0 # z coordinate of the layer (modify as needed).\n",
" inf_eff = 10 # effective infinity in this model.\n",
"\n",
" DBR = [] # holder for the DBR structure.\n",
"\n",
" # build the substrate structure.\n",
" sub = td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
" ),\n",
" medium=medium_s,\n",
" )\n",
" DBR.append(sub)\n",
"\n",
" # build the first layer.\n",
" layer_1 = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_2)),\n",
" medium=medium_2,\n",
" )\n",
" DBR.append(layer_1)\n",
" z_0 = t_2\n",
"\n",
" # include the N pairs of layers alternatively.\n",
" for i in range(2 * N):\n",
" if i % 2 == 0:\n",
" DBR.append(\n",
" td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-inf_eff, -inf_eff, z_0),\n",
" rmax=(inf_eff, inf_eff, z_0 + t_1),\n",
" ),\n",
" medium=medium_1,\n",
" )\n",
" )\n",
" z_0 = z_0 + t_1\n",
" else:\n",
" DBR.append(\n",
" td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-inf_eff, -inf_eff, z_0),\n",
" rmax=(inf_eff, inf_eff, z_0 + t_2),\n",
" ),\n",
" medium=medium_2,\n",
" )\n",
" )\n",
" z_0 = z_0 + t_2\n",
"\n",
" return DBR"
]
},
{
"cell_type": "markdown",
"id": "baac9cd7",
"metadata": {},
"source": [
"We will define another function to build the simulation."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5c2a6a65",
"metadata": {},
"outputs": [],
"source": [
"def make_DBR(N):\n",
"\n",
" # build the DBR layers using the previously defined function\n",
" DBR = build_DBR(N)\n",
"\n",
" thickness = t_2 + N * (t_1 + t_2) # MODIFY this value to include the DBR multilayer thickness.\n",
" src_z_pos = thickness + lda0 / 4 # source position.\n",
" freq0 = td.C_0 / lda0 # source frequency.\n",
" fwidth = 0.5 * freq0 # width of the frequency distribution.\n",
"\n",
" plane_wave = td.PlaneWave(\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" size=(td.inf, td.inf, 0),\n",
" center=(0, 0, src_z_pos),\n",
" direction=\"-\",\n",
" pol_angle=0,\n",
" )\n",
"\n",
" freqs = freq0 * np.linspace(0.5, 1.5, 1001) # frequency range of interest.\n",
"\n",
" # Flux monitor to measure the reflectance.\n",
" flux_monitor = td.FluxMonitor(\n",
" center=(0, 0, src_z_pos + lda0 / 4),\n",
" size=(td.inf, td.inf, 0),\n",
" freqs=freqs,\n",
" name=\"R\",\n",
" )\n",
"\n",
" # Field monitor to visualize the EM field distribution.\n",
" field_xz = td.FieldMonitor(\n",
" center=(0, 0, 0),\n",
" size=(td.inf, 0, td.inf),\n",
" freqs=[freq0],\n",
" name=\"field_xz\",\n",
" )\n",
"\n",
" Lz = thickness + 2 * lda0 # Domain size in z-direction.\n",
"\n",
" sim = td.Simulation(\n",
" size=(0, 0, Lz), # simulation domain size.\n",
" center=(0, 0, thickness / 2),\n",
" grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=lda0),\n",
" structures=DBR,\n",
" sources=[plane_wave],\n",
" monitors=[flux_monitor, field_xz],\n",
" run_time=1e-12,\n",
" boundary_spec=td.BoundarySpec(\n",
" x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
" ), # pml is applied in the z-direction.\n",
" shutoff=1e-7,\n",
" ) # early shutoff level is decreased to 1e-7 to increase the simulation accuracy.\n",
"\n",
" return sim"
]
},
{
"cell_type": "markdown",
"id": "2410bb98",
"metadata": {},
"source": [
"Then, run a parameter sweep using a `Batch` object to submit and run all the simulations at once."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "696b0a08",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"15:40:39 -03 Created task 'N=2.00' with task_id \n",
" 'fdve-c557cd06-209d-45fc-9f54-863bff55b33a' and task_type 'FDTD' . \n",
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c557cd06-209 d-45fc-9f54-863bff55b33a' \n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:40:40 -03 Created task 'N=4.00' with task_id \n",
" 'fdve-0774499b-604a-426f-9643-25d0f6167e74' and task_type 'FDTD' . \n",
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0774499b-604 a-426f-9643-25d0f6167e74' \n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:40:42 -03 Created task 'N=6.00' with task_id \n",
" 'fdve-cc9eb736-d818-4b18-9f11-13225e5246cc' and task_type 'FDTD' . \n",
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-cc9eb736-d81 8-4b18-9f11-13225e5246cc' \n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:40:43 -03 Created task 'N=8.00' with task_id \n",
" 'fdve-85ee1ae1-33ac-407f-8ab7-c33b83ce88c2' and task_type 'FDTD' . \n",
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-85ee1ae1-33a c-407f-8ab7-c33b83ce88c2' \n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:40:48 -03 Started working on Batch. \n",
"15:40:52 -03 Maximum FlexCredit cost: 0.100 for the whole batch. \n",
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
" Batch has completed. \n",
"15:41:13 -03 Batch complete. \n",
"\n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Ns = np.array([2, 4, 6, 8]) # collection of N for the parameter sweep.\n",
"sims = {f\"N={N:.2f}\": make_DBR(N) for N in Ns} # construct simulations for each N from Ns.\n",
"\n",
"# Batch simulation.\n",
"batch = web.Batch(simulations=sims, verbose=True)\n",
"batch_results = batch.run(path_dir=\"data\")"
]
},
{
"cell_type": "markdown",
"id": "deb37a5a",
"metadata": {},
"source": [
"Finally, we get the simulation results and calculate the analytical reflectance values."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "31a1ba2e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df75395f302d4922a2cc7a17513460a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:41:36 -03 loading simulation from \n",
" data/fdve-c557cd06-209d-45fc-9f54-863bff55b33a .hdf5 \n",
"\n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:41:38 -03 loading simulation from \n",
" data/fdve-0774499b-604a-426f-9643-25d0f6167e74 .hdf5 \n",
"\n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:41:41 -03 loading simulation from \n",
" data/fdve-cc9eb736-d818-4b18-9f11-13225e5246cc .hdf5 \n",
"\n",
" \n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"15:41:43 -03 loading simulation from \n",
" data/fdve-85ee1ae1-33ac-407f-8ab7-c33b83ce88c2 .hdf5 \n",
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n",
"\n",
"for i, N in enumerate(Ns):\n",
" sim_data = batch_results[f\"N={N:.2f}\"]\n",
" R = sim_data[\"R\"].flux # extract reflectance data from the flux monitor\n",
" R_c = (\n",
" ((n_s * n_0 * n_1 ** (2 * N)) - (n_2 ** (2 * N + 2)))\n",
" / ((n_s * n_0 * n_1 ** (2 * N)) + (n_2 ** (2 * N + 2)))\n",
" ) ** 2\n",
" ax.plot(wl_range, R, label=f\"N={N}\")\n",
" ax.scatter(lda0, R_c, marker=\"*\", s=100)\n",
"\n",
"ax.set_title(\"Reflectance of DBR of different number of periods\")\n",
"ax.set_xlabel(\"Wavelength (um)\")\n",
"ax.set_ylabel(\"Reflectance\")\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d5a38740",
"metadata": {},
"source": [
"## References\n",
"[1] Born M, Wolf E, Bhatia AB, et al. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light. 7th ed. Cambridge: Cambridge University Press; 1999. doi:10.1017/CBO9781139644181"
]
}
],
"metadata": {
"description": "This notebook demonstrates how to model distributed Bragg reflector and cavity in Tidy3D FDTD.",
"feature_image": "./img/dbr_schematic.png",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"keywords": "distributed bragg reflector, DBR, cavity, Tidy3D, FDTD",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.1"
},
"title": "Distributed Bragg Reflector and Cavity | Flexcompute",
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"09a6392e66064180940d99f350a67ac9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
"border_right": null,
"border_top": 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,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"0f58e3a1357c42c3b22815068f47a6e4": {
"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_58602acbc14b44f5840f3ed061c0dabd",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "solver progress (field decay = 2.57e-06) ━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \n \n",
"text/plain": "solver progress (field decay = 2.57e-06) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"tabbable": null,
"tooltip": null
}
},
"10a4452f1a1648e3b1852bb0dd223173": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
"border_right": null,
"border_top": 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,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"13eee602b074403f83c70f533a1e8573": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
"border_right": null,
"border_top": 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,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"18d89640c3b44101ba37506ae4f42f80": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
"border_right": null,
"border_top": 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,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"1d0fb3e19a344abb9e54125a799731dd": {
"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_6c53fe54bfca442a9521a09a4ef0ec0d",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 111.9/111.9 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 9.1/9.1 kB • ? • 0:00:00 \n🚶 Starting 'dbr_cavity'... \n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 108.6/108.6 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 9.3/9.3 kB • ? • 0:00:00 \n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 113.6/113.6 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 9.1/9.1 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 17.0/17.0 kB • ? • 0:00:00 \n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 110.2/110.2 kB • ? • 0:00:00 \nN=2.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \nN=3.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \nN=4.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \nN=5.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \nN=10.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 \n \n",
"text/plain": "N=2.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=3.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=4.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=5.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=10.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"tabbable": null,
"tooltip": null
}
},
"8fdd2cf4d9c1471f943af89a8a4e0bc6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "2.0.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "2.0.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border_bottom": null,
"border_left": null,
"border_right": null,
"border_top": 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,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"8fde6f97af6a4a078403e84e43e3e21a": {
"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_2edd86b37cab403a81740fceb08aff35",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━ 100.0% • 29.0/29.0 MB • 17.5 MB/s • 0:00:00 \n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 122.7/122.7 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 9.0/9.0 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 9.1/9.1 kB • ? • 0:00:00 \n🚶 Finishing 'dbr_cavity'... \n ![\"Schematic](\"DBR.png\")