![\"diagram\"](\"img/splitter.png\")
\n",
"\n",
"Note: the cost of running the entire notebook is larger than 1 FlexUnit.\n",
"\n",
"In this notebook, we will show an example of using tidy3d to evaluate device performance over a set of many design parameters.\n",
"\n",
"This example will also provide a walkthrough of Tidy3D's [Job](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Job.html) and [Batch](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Batch.html) features for managing both individual simulations and sets of simulations.\n",
"\n",
"For demonstration, we look at the splitting ratio of a directional coupler as we vary the coupling length between two waveguides. The sidewall of the waveguides is slanted, deviating from the vertical direction by `sidewall_angle`."
]
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"[18:07:40] WARNING This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on __init__.py:103\n", " PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on, \n", " please use 'pip install tidy3d' instead. \n", "\n" ], "text/plain": [ "\u001b[2;36m[18:07:40]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on \u001b]8;id=791511;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=601819;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m please use \u001b[32m'pip install tidy3d'\u001b[0m instead. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
INFO Using client version: 1.9.0rc1 __init__.py:121\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1 \u001b]8;id=676415;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=73718;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import gdstk\n", "\n", "# tidy3D imports\n", "import tidy3d as td\n", "from tidy3d import web\n", "\n", "# set tidy3d to only print error information to reduce verbosity\n", "td.config.logging_level = \"error\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "First we set up some global parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T00:07:40.785224Z", "iopub.status.busy": "2023-02-03T00:07:40.785059Z", "iopub.status.idle": "2023-02-03T00:07:40.787860Z", "shell.execute_reply": "2023-02-03T00:07:40.787575Z" }, "tags": [] }, "outputs": [], "source": [ "# wavelength / frequency\n", "lambda0 = 1.550 # all length scales in microns\n", "freq0 = td.constants.C_0 / lambda0\n", "fwidth = freq0 / 10\n", "\n", "# Permittivity of waveguide and substrate\n", "wg_n = 3.48\n", "sub_n = 1.45\n", "mat_wg = td.Medium(permittivity=wg_n**2)\n", "mat_sub = td.Medium(permittivity=sub_n**2)\n", "\n", "# Waveguide dimensions\n", "\n", "# Waveguide height\n", "wg_height = 0.22\n", "# Waveguide width\n", "wg_width = 0.45\n", "# Waveguide separation in the beginning/end\n", "wg_spacing_in = 8\n", "# Reference plane where the cross section of the device is defined\n", "reference_plane = \"bottom\"\n", "# Angle of the sidewall deviating from the vertical ones, positive values for the base larger than the top\n", "sidewall_angle = np.pi / 6\n", "# Total device length along propagation direction\n", "device_length = 100\n", "# Length of the bend region\n", "bend_length = 16\n", "# space between waveguide and PML\n", "pml_spacing = 1\n", "# resolution control: minimum number of grid cells per wavelength in each material\n", "grid_cells_per_wvl = 16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define waveguide bends and coupler\n", "\n", "Here is where we define our directional coupler shape programmatically in terms of the geometric parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T00:07:40.789326Z", "iopub.status.busy": "2023-02-03T00:07:40.789226Z", "iopub.status.idle": "2023-02-03T00:07:40.793059Z", "shell.execute_reply": "2023-02-03T00:07:40.792771Z" }, "tags": [] }, "outputs": [], "source": [ "def tanh_interp(max_arg):\n", " \"\"\"Interpolator for tanh with adjustable extension\"\"\"\n", " scale = 1 / np.tanh(max_arg)\n", " return lambda u: 0.5 * (1 + scale * np.tanh(max_arg * (u * 2 - 1)))\n", "\n", "def make_coupler(\n", " length, wg_spacing_in, wg_width, wg_spacing_coup, coup_length, bend_length, npts_bend=30\n", "):\n", " \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n", " # bend interpolator\n", " interp = tanh_interp(3)\n", " delta = wg_width + wg_spacing_coup - wg_spacing_in\n", " offset = lambda u: wg_spacing_in + interp(u) * delta\n", "\n", " coup = gdstk.RobustPath(\n", " (-0.5 * length, 0),\n", " (wg_width, wg_width),\n", " wg_spacing_in,\n", " simple_path=True,\n", " layer=1,\n", " datatype=[0, 1],\n", " )\n", " coup.segment((-0.5 * coup_length - bend_length, 0))\n", " coup.segment(\n", " (-0.5 * coup_length, 0), offset=[lambda u: -0.5 * offset(u), lambda u: 0.5 * offset(u)]\n", " )\n", " coup.segment((0.5 * coup_length, 0))\n", " coup.segment(\n", " (0.5 * coup_length + bend_length, 0),\n", " offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n", " )\n", " coup.segment((0.5 * length, 0))\n", " return coup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Simulation and Submit Job\n", "\n", "The following function creates a tidy3d simulation object for a set of design parameters.\n", "\n", "Note that the simulation has not been run yet, just created." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T00:07:40.794612Z", "iopub.status.busy": "2023-02-03T00:07:40.794528Z", "iopub.status.idle": "2023-02-03T00:07:40.800537Z", "shell.execute_reply": "2023-02-03T00:07:40.800272Z" }, "tags": [] }, "outputs": [], "source": [ "def make_sim(coup_length, wg_spacing_coup, domain_field=False):\n", " \"\"\"Make a simulation with a given length of the coupling region and\n", " distance between the waveguides in that region. If ``domain_field``\n", " is True, a 2D in-plane field monitor will be added.\n", " \"\"\"\n", "\n", " # Geometry must be placed in GDS cells to import into Tidy3D\n", " coup_cell = gdstk.Cell(\"Coupler\")\n", "\n", " substrate = gdstk.rectangle(\n", " (-device_length / 2, -wg_spacing_in / 2 - 10),\n", " (device_length / 2, wg_spacing_in / 2 + 10),\n", " layer=0,\n", " )\n", " coup_cell.add(substrate)\n", "\n", " # Add the coupler to a gdstk cell\n", " gds_coup = make_coupler(\n", " device_length,\n", " wg_spacing_in,\n", " wg_width,\n", " wg_spacing_coup,\n", " coup_length,\n", " bend_length,\n", " )\n", " coup_cell.add(gds_coup)\n", "\n", " # Substrate\n", " oxide_geo, = td.PolySlab.from_gds(\n", " gds_cell=coup_cell,\n", " gds_layer=0,\n", " gds_dtype=0,\n", " slab_bounds=(-10, 0),\n", " reference_plane=reference_plane,\n", " axis=2,\n", " )\n", "\n", " oxide = td.Structure(geometry=oxide_geo, medium=mat_sub)\n", "\n", " # Waveguides (import all datatypes if gds_dtype not specified)\n", " coupler1_geo, coupler2_geo = td.PolySlab.from_gds(\n", " gds_cell=coup_cell,\n", " gds_layer=1,\n", " slab_bounds=(0, wg_height),\n", " sidewall_angle=sidewall_angle,\n", " reference_plane=reference_plane,\n", " axis=2,\n", " )\n", "\n", " coupler1 = td.Structure(geometry=coupler1_geo, medium=mat_wg)\n", "\n", " coupler2 = td.Structure(geometry=coupler2_geo, medium=mat_wg)\n", "\n", " # Simulation size along propagation direction\n", " sim_length = 2 + 2 * bend_length + coup_length\n", "\n", " # Spacing between waveguides and PML\n", " sim_size = [\n", " sim_length,\n", " wg_spacing_in + wg_width + 2 * pml_spacing,\n", " wg_height + 2 * pml_spacing,\n", " ]\n", "\n", " # source\n", " src_pos = -sim_length / 2 + 0.5\n", " msource = td.ModeSource(\n", " center=[src_pos, wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " direction=\"+\",\n", " mode_spec=td.ModeSpec(),\n", " mode_index=0,\n", " )\n", "\n", " mon_in = td.ModeMonitor(\n", " center=[(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"in\",\n", " )\n", " mon_ref_bot = td.ModeMonitor(\n", " center=[(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"refect_bottom\",\n", " )\n", " mon_top = td.ModeMonitor(\n", " center=[-(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"top\",\n", " )\n", " mon_bot = td.ModeMonitor(\n", " center=[-(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"bottom\",\n", " )\n", " monitors = [mon_in, mon_ref_bot, mon_top, mon_bot]\n", "\n", " if domain_field == True:\n", " domain_monitor = td.FieldMonitor(\n", " center=[0, 0, wg_height / 2],\n", " size=[td.inf, td.inf, 0],\n", " freqs=[freq0],\n", " name=\"field\",\n", " )\n", " monitors.append(domain_monitor)\n", "\n", " # initialize the simulation\n", " sim = td.Simulation(\n", " size=sim_size,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=grid_cells_per_wvl),\n", " structures=[oxide, coupler1, coupler2],\n", " sources=[msource],\n", " monitors=monitors,\n", " run_time=20 / fwidth,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " )\n", "\n", " return sim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspect Simulation\n", "\n", "Let's create and inspect a single simulation to make sure it was defined correctly before doing the full scan. The sidewalls of the waveguides deviate from the vertical direction by 30 degree. We also add an in-plane field monitor to have a look at the fields evolution in this one simulation. We will not use such a monitor in the batch to avoid storing unnecesarrily large amounts of data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T00:07:40.801883Z", "iopub.status.busy": "2023-02-03T00:07:40.801794Z", "iopub.status.idle": "2023-02-03T00:07:40.829168Z", "shell.execute_reply": "2023-02-03T00:07:40.828900Z" }, "tags": [] }, "outputs": [], "source": [ "# Length of the coupling region\n", "coup_length = 10\n", "\n", "# Waveguide separation in the coupling region\n", "wg_spacing_coup = 0.10\n", "\n", "sim = make_sim(coup_length, wg_spacing_coup, domain_field=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T00:07:40.830624Z", "iopub.status.busy": "2023-02-03T00:07:40.830528Z", "iopub.status.idle": "2023-02-03T00:07:40.991923Z", "shell.execute_reply": "2023-02-03T00:07:40.991671Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(-3.0, 3.0)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"model_id": "655321a9de134ebcae915f09091e402b",
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"text/plain": [
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},
"metadata": {},
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},
{
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{
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{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m job \u001b[38;5;241m=\u001b[39m web\u001b[38;5;241m.\u001b[39mJob(simulation\u001b[38;5;241m=\u001b[39msim, task_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouplerVerify\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# download the results and load them into a simulation\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m sim_data \u001b[38;5;241m=\u001b[39m \u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/sim_data.hdf5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py:70\u001b[0m, in \u001b[0;36mJob.run\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;124;03m\"\"\"run :class:`Job` all the way through and return data.\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \n\u001b[1;32m 58\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m Dictionary mapping task name to :class:`.SimulationData` for :class:`Job`.\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstart()\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmonitor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload(path\u001b[38;5;241m=\u001b[39mpath)\n",
"File \u001b[0;32m~/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py:130\u001b[0m, in \u001b[0;36mJob.monitor\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmonitor\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 123\u001b[0m \u001b[38;5;124;03m\"\"\"Monitor progress of running :class:`Job`.\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \n\u001b[1;32m 125\u001b[0m \u001b[38;5;124;03m Note\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;124;03m call :meth:`Job.load`.\u001b[39;00m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 130\u001b[0m \u001b[43mweb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmonitor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtask_id\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py:293\u001b[0m, in \u001b[0;36mmonitor\u001b[0;34m(task_id)\u001b[0m\n\u001b[1;32m 291\u001b[0m new_description \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m% done (field decay = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield_decay\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2e\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 292\u001b[0m progress\u001b[38;5;241m.\u001b[39mupdate(pbar_pd, completed\u001b[38;5;241m=\u001b[39mperc_done, description\u001b[38;5;241m=\u001b[39mnew_description)\n\u001b[0;32m--> 293\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m perc_done \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m perc_done \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m100\u001b[39m:\n\u001b[1;32m 295\u001b[0m log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mearly shutoff detected, exiting.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# create job, upload sim to server to begin running\n",
"job = web.Job(simulation=sim, task_name=\"CouplerVerify\")\n",
"\n",
"# download the results and load them into a simulation\n",
"sim_data = job.run(path=\"data/sim_data.hdf5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Postprocessing\n",
"\n",
"The following function takes a completed simulation (with data loaded into it) and computes the quantities of interest.\n",
"\n",
"For this case, we measure both the total transmission in the right ports and also the ratio of power between the top and bottom ports."
]
},
{
"cell_type": "code",
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"source": [
"def measure_transmission(sim_data):\n",
" \"\"\"Constructs a \"row\" of the scattering matrix when sourced from top left port\"\"\"\n",
"\n",
" input_amp = sim_data[\"in\"].amps.sel(direction=\"+\")\n",
"\n",
" amps = np.zeros(4, dtype=complex)\n",
" directions = (\"-\", \"-\", \"+\", \"+\")\n",
" for i, (monitor, direction) in enumerate(\n",
" zip(sim_data.simulation.monitors[:4], directions)\n",
" ):\n",
" amp = sim_data[monitor.name].amps.sel(direction=direction)\n",
" amp_normalized = amp / input_amp\n",
" amps[i] = np.squeeze(amp_normalized.values)\n",
"\n",
" return amps"
]
},
{
"cell_type": "code",
"execution_count": 9,
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"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"mode amplitudes in each port:\n",
"\n",
"\tmonitor = \"in\"\n",
"\tamplitude^2 = 0.00\n",
"\tphase = -0.18 (rad)\n",
"\n",
"\tmonitor = \"refect_bottom\"\n",
"\tamplitude^2 = 0.00\n",
"\tphase = -2.06 (rad)\n",
"\n",
"\tmonitor = \"top\"\n",
"\tamplitude^2 = 0.81\n",
"\tphase = 2.06 (rad)\n",
"\n",
"\tmonitor = \"bottom\"\n",
"\tamplitude^2 = 0.17\n",
"\tphase = -2.65 (rad)\n",
"\n"
]
}
],
"source": [
"# monitor and test out the measure_transmission function the results of the single run\n",
"amps_arms = measure_transmission(sim_data)\n",
"print(\"mode amplitudes in each port:\\n\")\n",
"for amp, monitor in zip(amps_arms, sim_data.simulation.monitors[:-1]):\n",
" print(f'\\tmonitor = \"{monitor.name}\"')\n",
" print(f\"\\tamplitude^2 = {abs(amp)**2:.2f}\")\n",
" print(f\"\\tphase = {(np.angle(amp)):.2f} (rad)\\n\")"
]
},
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"source": [
"# submit all jobs\n",
"sims = {f\"l={l:.2f}\": make_sim(l, wg_spacing_coup) for l in ls}\n",
"batch = web.Batch(simulations=sims)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor Batch\n",
"\n",
"Here we can perform real-time monitoring of how many of the jobs in the batch have completed."
]
},
{
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"(4, 11)\n",
"[[ 4.61903757e-04-3.02491013e-03j -1.76492290e-03-1.93131155e-03j\n",
" 1.51192371e-03-8.42259248e-04j 6.24655075e-04-3.53510876e-03j\n",
" -1.30511215e-03-5.56092026e-04j -1.97910271e-07-1.58548088e-03j\n",
" -6.10997873e-04-1.21667803e-03j 2.51348630e-03-2.68261448e-03j\n",
" -1.74108484e-03-9.25419904e-04j 2.70415809e-03-2.75713135e-03j\n",
" -1.45349968e-03-2.00091314e-03j]\n",
" [-1.32584887e-03-5.84756253e-04j -1.67244170e-03+9.38134945e-04j\n",
" -1.22111001e-03+1.63926002e-03j -1.73482547e-03+1.15987476e-03j\n",
" -1.14609934e-03-1.16893477e-04j -2.85371434e-03+2.19864195e-03j\n",
" 6.33296659e-04+7.68090523e-04j -4.54542595e-03+1.11685248e-03j\n",
" 1.44825955e-03+1.89316216e-03j -3.06304615e-03+8.07302639e-04j\n",
" -1.06741732e-03+8.34135854e-04j]\n",
" [-6.49056719e-01-3.22110390e-01j -5.82842879e-01+5.85609355e-01j\n",
" 3.74286731e-01+8.24815394e-01j 9.58552261e-01-6.42287299e-02j\n",
" 2.82465450e-01-9.47960619e-01j -7.95310568e-01-5.90397933e-01j\n",
" -8.06808581e-01+5.28963845e-01j 2.32159738e-01+8.82190740e-01j\n",
" 8.30079552e-01+8.37257217e-02j 3.18502709e-01-6.60627874e-01j\n",
" -4.18337818e-01-4.47732829e-01j]\n",
" [-3.01396004e-01+6.07863020e-01j 3.88921754e-01+3.88173917e-01j\n",
" 3.69797256e-01-1.67179244e-01j -1.65351864e-02-2.50688913e-01j\n",
" -8.58314801e-02-2.54214021e-02j 4.51525150e-02-5.99073311e-02j\n",
" -1.29646668e-01-1.99187133e-01j -3.81229943e-01+9.96459514e-02j\n",
" -5.47072505e-02+5.36804310e-01j 6.03151006e-01+2.91214000e-01j\n",
" 5.70751230e-01-5.33449609e-01j]]\n"
]
}
],
"source": [
"amps_batch = []\n",
"for task_name, sim_data in batch_results.items():\n",
" amps_arms_i = np.array(measure_transmission(sim_data))\n",
" amps_batch.append(amps_arms_i)\n",
"amps_batch = np.stack(amps_batch, axis=1)\n",
"print(amps_batch.shape) # (4, Nl)\n",
"print(amps_batch)"
]
},
{
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"source": [
"powers = abs(amps_batch) ** 2\n",
"power_top = powers[2]\n",
"power_bot = powers[3]\n",
"power_out = power_top + power_bot"
]
},
{
"cell_type": "code",
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↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "1efebcf0162b4a5db17c25d1c599953b": { "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_d2a23664a2c64d3c8247c8dbeb8d8828", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "213aebe5cf8f4581ac7c662e924b7cb5": { "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_65346bfd624e47989621c1dd8942e2d2", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "2273cdc71522452fa1e1a94e3c9f03ea": { "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_6e23f334433c413ab36e08cba4dfac42", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.0 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "257bad144ff2410cb841d997351f2424": { "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_19b42d05cff4468598fd8fd6ab2fa0c4", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "2716e32ff57547f0bc7a4195ef046c28": { "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_0455295ba48f47999b04b1ea369316a7", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 2.50e-05) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n% done (field decay = 2.50e-05) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n", "text/plain": "\r\u001b[2K% done (field decay = 2.50e-05) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n% done (field decay = 2.50e-05) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "36c6abcc96914b79aaf98d8f431631d0": { "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 } }, "3d377537a632490da21496739edda4de": { "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_d55689d70bcb40648c12fef9b475cfa9", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "40415788178a4bf1aa2154e7316b446b": { "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_8aecb89c4cf5470ca00c184f6d7bb6c8", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "41b81d127339491eafbf75fd6f331945": { "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 } }, "46a38646ba524c3c9889bc827f75f66c": { "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 } }, "48d2789ddb9648d489d3eb446f6d6cf9": { "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_36c6abcc96914b79aaf98d8f431631d0", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.5 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.5 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "4d260ee237234ea699e33222ee05f4b3": { "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 } }, "4db4e8d0c7534804bd403dc33c694a3b": { "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_d36539bd77ff494fb1ffe128915f2f78", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.1 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/14.1 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.1 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/14.1 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "5343107b52f74e66bfd397c634446d0b": { "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 } }, "55efcded6fa242d0ad62cc0c19e28470": { "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 } }, "65346bfd624e47989621c1dd8942e2d2": { "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 } }, "671d269b11724174b859da9e7dd0fc3b": { "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_55efcded6fa242d0ad62cc0c19e28470", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.9 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.9 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "6e23f334433c413ab36e08cba4dfac42": { "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 } }, "70634829923747f1962c50daaffd8b1c": { "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_fba4aab781e74c47a58cefa909bca627", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.8 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.8 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "707e1567796e482796c14123549957b4": { "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 } }, "76495f535d2c4d33a876ee1fb1838951": { "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_8a987435d7d84e438a068f3f233e5e9f", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.7 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.7 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "7b5001755ff246fdb52e5e30aef60602": { "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_cb3e123e8b664fe59f5e126c28213396", "msg_id": "", "outputs": [ { "data": { "text/html": "
🏃 Starting 'CouplerVerify'...\n🏃 Starting 'CouplerVerify'...\n", "text/plain": "\r\u001b[2K\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'CouplerVerify'...\u001b[0m\n\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'CouplerVerify'...\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "836e9587ef0344e192693906e11329bf": { "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 } }, "8a987435d7d84e438a068f3f233e5e9f": { "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 } }, "8aa56e05bd4b465f9e69ad60991012b7": { "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_46a38646ba524c3c9889bc827f75f66c", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.7 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.7 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "8aecb89c4cf5470ca00c184f6d7bb6c8": { "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 } }, "8cfabb54359e44efa3147fe12336b17b": { "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_836e9587ef0344e192693906e11329bf", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.5 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.5 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.5 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "909cba18ea3f48acbf643c847551cea2": { "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 } }, "95136260078649a9be04e20420e9eb68": { "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 } }, "98d241b007d64a37b3e8efba4757129a": { "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_95136260078649a9be04e20420e9eb68", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.8 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.8 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a33a5bcf2832467ea1d91efe08dae439": { "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_07def0464afe447fa2568677a6ed8806", "msg_id": "", "outputs": [ { "data": { "text/html": "
↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/13.8 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/13.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "a661f5d53394414dbae5294272ffc630": { "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_707e1567796e482796c14123549957b4", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "afc83efa37cf40b5948b4937bf7869b6": { "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 } }, "b583722627894af5b56ba277affbf19e": { "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_5343107b52f74e66bfd397c634446d0b", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 98.2% • 27.8/28.4 MB • 9.1 MB/s • 0:00:01\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 98.2% • 27.8/28.4 MB • 9.1 MB/s • 0:00:01\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m \u001b[35m98.2%\u001b[0m • \u001b[32m27.8/28.4 MB\u001b[0m • \u001b[31m9.1 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m \u001b[35m98.2%\u001b[0m • \u001b[32m27.8/28.4 MB\u001b[0m • \u001b[31m9.1 MB/s\u001b[0m • \u001b[36m0:00:01\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "b701b2e3607442a6a96db6ad0cc8767b": { "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_4d260ee237234ea699e33222ee05f4b3", "msg_id": "", "outputs": [ { "data": { "text/html": "
l=5.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=5.70: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=6.40: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.10: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.80: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=8.50: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.20: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.90: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=10.60: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=11.30: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=12.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=5.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=5.70: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=6.40: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.10: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.80: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=8.50: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.20: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.90: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=10.60: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=11.30: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=12.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n", "text/plain": "\r\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2Kl=5.00: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=5.70: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=6.40: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=7.10: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=7.80: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=8.50: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=9.20: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=7.10: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=7.80: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=8.50: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=9.20: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=9.90: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nl=10.60: status = success \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m 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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.6 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.6 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "c4f6c3f82ea5491680f5b001fed4890d": { "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 } }, "cb3e123e8b664fe59f5e126c28213396": { "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 } }, "cd9161f7b410459387fa734a9ad751dc": { "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 } }, "cf0ffe359e3c4d38a9adf91d7fc62923": { "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_cd9161f7b410459387fa734a9ad751dc", "msg_id": "", "outputs": [ { "data": { "text/html": "
↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.7 kB • ? • -:--:--\n↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0% • 0.0/92.7 kB • ? • -:--:--\n", "text/plain": "\r\u001b[2K\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m\n\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m0.0%\u001b[0m • \u001b[32m0.0/92.7 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m-:--:--\u001b[0m" }, "metadata": {}, "output_type": "display_data" } ], "tabbable": null, "tooltip": null } }, "d2a23664a2c64d3c8247c8dbeb8d8828": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", 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