{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Edge coupler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: The cost of running the entire notebook is higher than 1 FlexCredit.\n", "\n", "Fiber-to-chip couplers are essential components in photonic integrated circuits (PICs) that need to couple in/out light using optical fibers. However, the huge mismatch between the mode sizes of a single mode fiber ($\\approx 10\\mu m$ in diameter) and a silicon wire waveguide (220 x 500 nm) can cause significant coupling loss if they are coupled directly. For this reason, the design of the fiber-to-chip coupling devices is critical for the whole system's performance and needs to be carefully accomplished. There are two main categories of fiber-to-chip optical coupling devices: [grating couplers](../notebooks/GratingCoupler.html), where the optical fiber is positioned perpendicularly to the chip plane; and edge couplers, which allow in-plane light coupling by the direct alignment between the fiber and the integrated waveguide facets. \n", "\n", "In this notebook, we will show an example of using Tidy3D to evaluate the performance of edge couplers built using inverted taper mode transformers of linear, quadratic, and exponential profiles. We will also see how to set up a [Gaussian beam](../_autosummary/tidy3d.GaussianBeam.html) to simulate the field launched by a lensed fiber and the use of [Batch](../_autosummary/tidy3d.web.container.Batch.html) simulations to perform parameter sweeps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by importing the necessary modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Tidy3D imports\n", "import tidy3d as td\n", "import tidy3d.web as web\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation Setup\n", "\n", "The inverted taper structure is made using an SOI platform consisting of a silicon (Si) substrate, a 3 $\\mu m$ thick silicon dioxide (SiO2) layer, and a 220 nm thick silicon layer covered by 3 $\\mu m$ thick silicon dioxide. As shown in the image below, the inverted taper is defined at the 220 nm thick Si layer, and its main geometric parameters are also defined in the figure. An optical fiber mode with a Gaussian profile (2.5 $\\mu m$ spot-size) is focused at the taper tip and coupled into the output waveguide. \n", "\n", "\"Inverted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of the simulation wavelength and materials." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "wl_c = 1.55 # Central wavelength.\n", "wl_bw = 0.100 # Wavelength bandwidth.\n", "wl_n = 101 # Number of wavelength points to compute the output data.\n", "\n", "mat_si = td.Medium(permittivity=3.48**2) # Taper and substrate material.\n", "mat_sio2 = td.Medium(permittivity=1.44**2) # Box and cladding material.\n", "mat_air = td.Medium(permittivity=1.00) # External medium material.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of the taper size, lensed fiber parameters, and SOI structure." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "taper_l = 25 # Taper length.\n", "taper_w_in = 0.19 # Taper tip width.\n", "taper_w_out = 0.50 # Taper output width.\n", "taper_t = 0.22 # Taper thickness.\n", "\n", "# Spot size of the gaussian mode launched by the lensed fiber at the taper tip.\n", "spot_size = 2.5\n", "\n", "box_thick = 3.0 # Silicon dioxide box layer.\n", "clad_thick = 3.0 # Silicon dioxide layer covering the taper.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculated parameters and constants used in the simulation set up." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Extra space around the taper at x,y directions.\n", "pad_x = 1 * wl_c\n", "pad_y = 1.5 * wl_c\n", "\n", "# Wavelength and frequency values.\n", "wl_range = np.linspace(wl_c - wl_bw / 2, wl_c + wl_bw / 2, wl_n)\n", "freq_c = td.C_0 / wl_c\n", "freq_range = td.C_0 / wl_range\n", "freq_width = 0.5 * (np.max(freq_range) - np.min(freq_range))\n", "run_time = 30 / freq_width\n", "\n", "# Large number to be used in replacement of td.inf when necessary.\n", "_inf = 1e3\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function to create the inverted taper geometry based on the work of [G. Ren et al. (2013)](https://www.sciencedirect.com/science/article/abs/pii/S0030401811006389)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# List of inverted tapers names.\n", "tap_names = [\"linear\", \"quadratic\", \"exponential\"]\n", "\n", "\n", "def get_taper(\n", " taper_shape=\"linear\",\n", " init_coord=[pad_x, taper_w_in / 2],\n", " end_coord=[pad_x + taper_l, taper_w_out / 2],\n", "):\n", " \"\"\"Return a polygon composed of the taper vertices given the desired shape.\"\"\"\n", "\n", " x0 = init_coord[0]\n", " x1 = end_coord[0]\n", " y0 = init_coord[1]\n", " y1 = end_coord[1]\n", " x = np.linspace(x0, x1, 41)\n", "\n", " tap_name = \"linear_tap\"\n", " if taper_shape == \"quadratic\":\n", " alpha = ((x - x0) / (x1 - x0)) ** 2\n", " tap_name = \"quadratic_tap\"\n", " elif taper_shape == \"exponential\":\n", " alpha = np.expm1((x - x0) / (x1 - x0)) / np.expm1(1)\n", " tap_name = \"exponential_tap\"\n", " elif taper_shape == \"linear\":\n", " alpha = (x - x0) / (x1 - x0)\n", " else:\n", " print(\n", " \"taper_shape is neither 'linear', 'quadratic', or 'exponential'!\\n\"\n", " + \"Linear taper shape returned!\"\n", " )\n", " alpha = (x - x0) / (x1 - x0)\n", "\n", " y = y0 + alpha * (y1 - y0)\n", " upper_bound = [[xv, yv] for xv, yv in zip(x, y)] + [[_inf, y1]]\n", " lower_bound = [[_inf, -y1]] + [[xv, -yv] for xv, yv in zip(x[::-1], y[::-1])]\n", " taper_polygon = upper_bound + lower_bound\n", "\n", " # Inverted taper structure using a PolySlab.\n", " taper = td.Structure(\n", " geometry=td.PolySlab(\n", " vertices=taper_polygon, axis=2, slab_bounds=(-taper_t / 2, taper_t / 2)\n", " ),\n", " medium=mat_si,\n", " name=tap_name,\n", " )\n", " return taper\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function to create the simulation object. Since the box and cladding oxide layers are considerably thick, the silicon substrate and the upper air layer will be removed from the simulation, so being possible to take advantage of the symmetry along the z-axis. In addition, we will insert the Gaussian source directly into the taper tip to reduce the simulation cost further." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def get_simulations(\n", " tap_length=taper_l,\n", " tap_w_in=taper_w_in,\n", " tap_w_out=taper_w_out,\n", " tap_t=taper_t,\n", " tap_names=tap_names,\n", "):\n", " \"\"\"Return a dict of simulation objects for the content of 'tap_names'.\"\"\"\n", "\n", " size_x = tap_length + 2 * pad_x\n", " size_y = tap_w_out + 2 * pad_y\n", " size_z = box_thick + clad_thick + tap_t\n", "\n", " # Gaussian source focused at the taper tip.\n", " # The Gaussian source must be placed at a plane within a uniform medium.\n", " gauss_source = td.GaussianBeam(\n", " center=(0.99 * pad_x, 0, 0),\n", " size=(0, size_y, size_z),\n", " source_time=td.GaussianPulse(freq0=freq_c, fwidth=freq_width),\n", " pol_angle=0,\n", " direction=\"+\",\n", " num_freqs=7,\n", " waist_radius=spot_size / 2,\n", " )\n", "\n", " # Field monitor to visualize the fields throughout the length of the taper.\n", " field_monitor_xy = td.FieldMonitor(\n", " center=(size_x / 2, 0, 0),\n", " size=(size_x, size_y, 0),\n", " freqs=[freq_c],\n", " name=\"field_xy\",\n", " )\n", "\n", " # Field monitor to visualize the Gaussian input fields.\n", " field_input = td.FieldMonitor(\n", " center=(pad_x, 0, 0),\n", " size=(0, size_y, size_z),\n", " freqs=[freq_c],\n", " name=\"field_input\",\n", " )\n", "\n", " # Field monitor to visualize the fields at the output waveguide.\n", " field_output = td.FieldMonitor(\n", " center=(size_x - pad_x / 2, 0, 0),\n", " size=(0, size_y, size_z),\n", " freqs=[freq_c],\n", " name=\"field_output\",\n", " )\n", "\n", " # Mode monitor to get the power coupled into the fundamental TE mode.\n", " mode_spec = td.ModeSpec(num_modes=1)\n", " mode_monitor = td.ModeMonitor(\n", " center=(size_x - pad_x / 2, 0, 0),\n", " size=(0, size_y, size_z),\n", " freqs=freq_range,\n", " mode_spec=mode_spec,\n", " name=\"mode_0\",\n", " )\n", "\n", " # Silicon dioxide box + cladding layers\n", " sio2_medium = td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)\n", " ),\n", " medium=mat_sio2,\n", " )\n", "\n", " # Simulation definition\n", " sim_tap = []\n", " for t_name in tap_names:\n", " taper_poly = get_taper(\n", " t_name,\n", " init_coord=[pad_x, tap_w_in / 2],\n", " end_coord=[size_x - pad_x, tap_w_out / 2],\n", " )\n", "\n", " sim_tap.append(\n", " td.Simulation(\n", " center=(size_x / 2, 0, 0),\n", " size=(size_x, size_y, size_z),\n", " medium=mat_air,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=15, wavelength=wl_c),\n", " structures=[sio2_medium, taper_poly],\n", " sources=[gauss_source],\n", " normalize_index=0,\n", " monitors=[field_monitor_xy, field_input, field_output, mode_monitor],\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML(num_layers=20)),\n", " symmetry=(0, -1, 1),\n", " run_time=run_time,\n", " )\n", " )\n", " sims = {sim_name: sim for sim_name, sim in zip(tap_names, sim_tap)}\n", " return sims\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison between inverted tapers with different shapes.\n", "\n", "We start our analysis by comparing the response of inverted tapers with 3 different geometries: linear, quadratic, and exponential. Then, before running the simulations, we will visualize the shapes of the inverted tapers and examine the simulation set up to see if everything is correct." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Get the list of simulation objects.\n", "sim_tap = get_simulations(tap_length=taper_l)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Structure visualization.\n", "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(8, 6))\n", "for sim_s, tap_n, ax in zip(sim_tap.values(), sim_tap.keys(), axs):\n", " sim_s.plot_structures(z=0, ax=ax)\n", " ax.set_xlim(0, 2 * pad_x + taper_l)\n", " ax.set_ylim(-1.5 * taper_w_out / 2, 1.5 * taper_w_out / 2)\n", " ax.set_aspect(\"auto\") # Used to better visualize the shapes.\n", " ax.set_title(tap_n)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Simulation set up visualization.\n", "fig = plt.figure(tight_layout=True, figsize=(8, 6))\n", "gs = fig.add_gridspec(2, 2)\n", "ax1 = fig.add_subplot(gs[0, :])\n", "ax2 = fig.add_subplot(gs[1, 0])\n", "ax3 = fig.add_subplot(gs[1, 1])\n", "sim_tap[\"linear\"].plot(z=0, ax=ax1)\n", "sim_tap[\"linear\"].plot(x=pad_x, ax=ax2)\n", "sim_tap[\"linear\"].plot(x=pad_x + taper_l + pad_x / 2, ax=ax3)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will run the simulations using a batch object." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[14:18:39] Created task 'linear' with task_id 'fdve-0c4fea65-4cb9-4273-98c3-18c6102a5331v1'.          webapi.py:139\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:18:39]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'linear'\u001b[0m with task_id \u001b[32m'fdve-0c4fea65-4cb9-4273-98c3-18c6102a5331v1'\u001b[0m. \u001b]8;id=213236;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=518273;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "140bdf2617854c3ca9a0d56bc6acda4e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:18:41] Created task 'quadratic' with task_id 'fdve-b79892a6-6cee-437d-8ce1-6fb874cf98ddv1'.       webapi.py:139\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:18:41]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'quadratic'\u001b[0m with task_id \u001b[32m'fdve-b79892a6-6cee-437d-8ce1-6fb874cf98ddv1'\u001b[0m. \u001b]8;id=855593;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=626328;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f19cc4327a1841049a92349a3c09b6e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:18:43] Created task 'exponential' with task_id 'fdve-3ebbc177-ac49-4aad-adfe-887d05429781v1'.     webapi.py:139\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:18:43]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'exponential'\u001b[0m with task_id \u001b[32m'fdve-3ebbc177-ac49-4aad-adfe-887d05429781v1'\u001b[0m. \u001b]8;id=220679;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=684543;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb431e227ce44275b495bbd9569733c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Estimated FlexCredit cost for linear = 0.12\n", "Estimated FlexCredit cost for quadratic = 0.12\n", "Estimated FlexCredit cost for exponential = 0.12\n", "Total estimated FlexCredit cost for batch = 0.35\n" ] } ], "source": [ "# Initialize a simulation batch.\n", "batch = web.Batch(simulations=sim_tap, verbose=True)\n", "\n", "# Get the estimated simulation cost.\n", "tot_cost = 0\n", "for bat in batch.get_info().values():\n", " sim_name = bat.taskName\n", " cost = web.estimate_cost(bat.taskId)\n", " tot_cost += cost\n", " print(f\"Estimated FlexCredit cost for {sim_name} = {cost:.2f}\")\n", "print(f\"Total estimated FlexCredit cost for batch = {tot_cost:.2f}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the batch and store all of the data in the `data_taper` dir." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[14:18:47] Started working on Batch.                                                               container.py:402\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:18:47]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch. \u001b]8;id=85837;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=443496;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\u001b\\\u001b[2m402\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8813adee5b4d48e98ebdfbc24843a9ec", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:19:47] Batch complete.                                                                         container.py:436\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:19:47]\u001b[0m\u001b[2;36m \u001b[0mBatch complete. \u001b]8;id=989470;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=320077;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\u001b\\\u001b[2m436\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_taper = batch.run(path_dir=\"data/data_taper\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inverted taper results\n", "Now we will see the field intensity and the modal coupling efficiency for the 3 inverted tapers." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "97ecf655583b47b8812712973802d802", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:19:50] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_taper/fdve-0c4fea65-4cb9-4273-98c3-18c6102a5331v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:19:50]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=772758;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=461711;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_taper/fdve-\u001b[93m0c4fea65-4cb9-4273-98c3-18c6102a5331\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f1636bd05f6b4d34b3b0d9cd882bff29", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:19:59] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_taper/fdve-b79892a6-6cee-437d-8ce1-6fb874cf98ddv1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:19:59]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=425812;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=890348;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_taper/fdve-\u001b[93mb79892a6-6cee-437d-8ce1-6fb874cf98dd\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "608c8905847c471885ed2346f587d5f1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:09] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_taper/fdve-3ebbc177-ac49-4aad-adfe-887d05429781v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:09]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=509679;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=545724;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_taper/fdve-\u001b[93m3ebbc177-ac49-4aad-adfe-887d05429781\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim_tap_results = {tl: sn for tl, sn in batch_taper.items()}\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(10, 8))\n", "for tap_n, sim_data, ax in zip(sim_tap_results.keys(), sim_tap_results.values(), axs):\n", " sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n", " ax.set_title(tap_n)\n", " ax.set_aspect(\"auto\") # Used to better visualize the shapes.\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bellow, we have the $|E_{y}|$ field recorded by the monitors positioned at the taper tip and taper output of the quadratic structure. We can clearly visualize the Gaussian profile corresponding to the fields launched by a lensed fiber at the taper tip. In contrast, the output field corresponds to the transverse electric polarization of the fundamental output waveguide mode." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n", "sim_tap_results[\"quadratic\"].plot_field(\n", " \"field_input\", \"Ey\", f=freq_c, val=\"abs\", ax=ax1\n", ")\n", "ax1.set_title(\"|Ey| at taper tip\")\n", "ax1.collections[-1].set_clim(0, 20)\n", "ax1.set_xlim(-2, 2)\n", "ax1.set_ylim(-2, 2)\n", "\n", "sim_tap_results[\"quadratic\"].plot_field(\n", " \"field_output\", \"Ey\", f=freq_c, val=\"abs\", ax=ax2\n", ")\n", "ax2.set_title(\"|Ey| at taper output\")\n", "ax2.collections[-1].set_clim(0, 50)\n", "ax2.set_xlim(-2, 2)\n", "ax2.set_ylim(-2, 2)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The modal coupling efficiencies shown below reveal that the quadratic taper profile has the lowest insertion losses." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "power_tap_25 = []\n", "for sim_data in sim_tap_results.values():\n", " mode_amps = sim_data[\"mode_0\"]\n", " coeffs_f = mode_amps.amps.sel(direction=\"+\")\n", " power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n", " power_0_db = 10 * np.log10(power_0)\n", " power_tap_25.append(power_0_db)\n", "\n", "plot_colors = (\"black\", \"red\", \"blue\")\n", "fig, ax1 = plt.subplots(1, figsize=(8, 4))\n", "for data, color, label in zip(power_tap_25, plot_colors, sim_tap_results.keys()):\n", " ax1.plot(\n", " wl_range,\n", " data,\n", " color=color,\n", " linestyle=\"solid\",\n", " linewidth=1.0,\n", " label=label,\n", " )\n", "ax1.set_xlim([wl_range[0], wl_range[-1]])\n", "ax1.set_xlabel(\"Wavelength (um)\")\n", "ax1.set_ylabel(\"Power (dB)\")\n", "ax1.set_title(\"Coupling Efficiency\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inverted Taper Length Sweep\n", "Before, we simulated the inverted tapers with a short length of only 25 $\\mu m$ to improve simulation time. Nevertheless, the designs can be improved by making the inverted taper larger in length so that its adiabaticity is increased. In the following sections, we increase the taper length to 50 $\\mu m$ and 100 $\\mu m$ (other values can be added to the sweep at a higher simulation cost)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[14:20:13] Created task 'sim_50_linear' with task_id 'fdve-a991273f-5479-431e-b237-b46591ecf261v1'.   webapi.py:139\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:13]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_50_linear'\u001b[0m with task_id \u001b[32m'fdve-a991273f-5479-431e-b237-b46591ecf261v1'\u001b[0m. \u001b]8;id=705670;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=458325;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cd2b96061bb748ae8176685ecf62851c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:14] Created task 'sim_50_quadratic' with task_id                                               webapi.py:139\n",
       "           'fdve-4e01bb58-5916-4c68-b332-14df193011efv1'.                                                          \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:14]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_50_quadratic'\u001b[0m with task_id \u001b]8;id=994243;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=723449;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-4e01bb58-5916-4c68-b332-14df193011efv1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "43afe2a8b8b545a18d69494d23fde398", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:16] Created task 'sim_50_exponential' with task_id                                             webapi.py:139\n",
       "           'fdve-08d598bf-32d8-4ae5-bf8f-ab5c4e7d925dv1'.                                                          \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:16]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_50_exponential'\u001b[0m with task_id \u001b]8;id=405763;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=608981;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-08d598bf-32d8-4ae5-bf8f-ab5c4e7d925dv1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7446cc6a8d5f48068189789da8dc814d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:18] Created task 'sim_100_linear' with task_id 'fdve-f5d5981f-4d19-4881-8ea9-a1154588d919v1'.  webapi.py:139\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:18]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_100_linear'\u001b[0m with task_id \u001b[32m'fdve-f5d5981f-4d19-4881-8ea9-a1154588d919v1'\u001b[0m. \u001b]8;id=431533;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=544747;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd4b913e076e4e09bff5608d65da2752", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:19] Created task 'sim_100_quadratic' with task_id                                              webapi.py:139\n",
       "           'fdve-dd2ef482-688a-4b74-a513-b484d47de3c5v1'.                                                          \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:19]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_100_quadratic'\u001b[0m with task_id \u001b]8;id=932376;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=207706;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-dd2ef482-688a-4b74-a513-b484d47de3c5v1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ebac90a7e8a4297b582072994831691", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:20:21] Created task 'sim_100_exponential' with task_id                                            webapi.py:139\n",
       "           'fdve-9752d3f1-11fa-4eb2-a219-ab72291b9deav1'.                                                          \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:21]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_100_exponential'\u001b[0m with task_id \u001b]8;id=984983;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=676635;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-9752d3f1-11fa-4eb2-a219-ab72291b9deav1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4470657ef0de4f3882e4ed2fa29b0c17", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Estimated FlexCredit cost for sim_50_linear = 0.22\n", "Estimated FlexCredit cost for sim_50_quadratic = 0.22\n", "Estimated FlexCredit cost for sim_50_exponential = 0.22\n", "Estimated FlexCredit cost for sim_100_linear = 0.42\n", "Estimated FlexCredit cost for sim_100_quadratic = 0.42\n", "Estimated FlexCredit cost for sim_100_exponential = 0.42\n", "Total estimated FlexCredit cost for batch = 1.93\n" ] } ], "source": [ "# Taper lengths on the sweep.\n", "taper_sweep = [50, 100]\n", "sim_sweep = [get_simulations(tap_length=t_l) for t_l in taper_sweep]\n", "\n", "# Make a dictionary of {task name : simulation}\n", "sims = {\n", " f\"sim_{tap_l}_{sim_name}\": sim\n", " for sim_l, tap_l in zip(sim_sweep, taper_sweep)\n", " for sim, sim_name in zip(sim_l.values(), sim_l.keys())\n", "}\n", "# Initialize a batch and run them all\n", "batch = web.Batch(simulations=sims, verbose=True)\n", "\n", "# Get the estimated simulation cost.\n", "tot_cost = 0\n", "for bat in batch.get_info().values():\n", " sim_name = bat.taskName\n", " cost = web.estimate_cost(bat.taskId)\n", " tot_cost += cost\n", " print(f\"Estimated FlexCredit cost for {sim_name} = {cost:.2f}\")\n", "print(f\"Total estimated FlexCredit cost for batch = {tot_cost:.2f}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the batch and store all of the data in the `data_sweep` dir." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[14:20:29] Started working on Batch.                                                               container.py:402\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:20:29]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch. \u001b]8;id=124000;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=689137;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\u001b\\\u001b[2m402\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f85d3b51226b4e1abf9ce9ab6c09b3c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:22:31] Batch complete.                                                                         container.py:436\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:22:31]\u001b[0m\u001b[2;36m \u001b[0mBatch complete. \u001b]8;id=21004;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=771935;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\u001b\\\u001b[2m436\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_sweep = batch.run(path_dir=\"data/data_sweep\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the simulation log corresponding to the longest inverted taper to inspection." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8c64973732764a53a7a95484f4f499af", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:22:34] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-dd2ef482-688a-4b74-a513-b484d47de3c5v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:22:34]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=370633;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=579890;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93mdd2ef482-688a-4b74-a513-b484d47de3c5\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Simulation domain Nx, Ny, Nz: [3477, 126, 136]\n", "Applied symmetries: (0, -1, 1)\n", "Number of computational grid points: 1.5829e+07.\n", "Using subpixel averaging: True\n", "Number of time steps: 8.9104e+04\n", "Automatic shutoff factor: 1.00e-05\n", "Time step (s): 5.3908e-17\n", "\n", "\n", "Compute source modes time (s): 0.3339\n", "Compute monitor modes time (s): 28.5612\n", "Rest of setup time (s): 5.7972\n", "\n", "Running solver for 89104 time steps...\n", "- Time step 2364 / time 1.27e-13s ( 2 % done), field decay: 1.00e+00\n", "- Time step 3564 / time 1.92e-13s ( 4 % done), field decay: 1.00e+00\n", "- Time step 7128 / time 3.84e-13s ( 8 % done), field decay: 7.76e-01\n", "- Time step 10692 / time 5.76e-13s ( 12 % done), field decay: 5.40e-01\n", "- Time step 14256 / time 7.69e-13s ( 16 % done), field decay: 4.18e-01\n", "- Time step 17820 / time 9.61e-13s ( 20 % done), field decay: 3.55e-01\n", "- Time step 21384 / time 1.15e-12s ( 24 % done), field decay: 3.02e-01\n", "- Time step 24949 / time 1.34e-12s ( 28 % done), field decay: 1.31e-05\n", "- Time step 28513 / time 1.54e-12s ( 32 % done), field decay: 1.40e-07\n", "Field decay smaller than shutoff factor, exiting solver.\n", "\n", "Solver time (s): 42.5757\n", "\n" ] } ], "source": [ "print(batch_sweep[\"sim_100_quadratic\"].log)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter sweep results\n", "\n", "Now, we will get the batch results and plot the modal coupling efficiency at the central wavelength as a function of the inverted taper length." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4f04375411a446ec99d66aa57ade61ea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:22:36] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-a991273f-5479-431e-b237-b46591ecf261v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:22:36]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=868704;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=30705;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93ma991273f-5479-431e-b237-b46591ecf261\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "badb5bafbe86499eafe8a596357751de", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:22:38] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-4e01bb58-5916-4c68-b332-14df193011efv1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:22:38]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=756247;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=990038;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93m4e01bb58-5916-4c68-b332-14df193011ef\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2121e101d204777a10ce57d4ede1eb2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:22:50] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-08d598bf-32d8-4ae5-bf8f-ab5c4e7d925dv1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:22:50]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=662965;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=767478;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93m08d598bf-32d8-4ae5-bf8f-ab5c4e7d925d\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d88512dbac094a76a85a03c18c96a340", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:23:04] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-f5d5981f-4d19-4881-8ea9-a1154588d919v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:04]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=827384;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=824833;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93mf5d5981f-4d19-4881-8ea9-a1154588d919\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:23:04] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-dd2ef482-688a-4b74-a513-b484d47de3c5v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:04]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=274250;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=493;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93mdd2ef482-688a-4b74-a513-b484d47de3c5\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17c664bbba5340469f95da696b7ded03", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:23:18] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-9752d3f1-11fa-4eb2-a219-ab72291b9deav1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:18]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=238030;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=642039;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93m9752d3f1-11fa-4eb2-a219-ab72291b9dea\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coup_eff_tl = np.zeros((len(tap_names), len(taper_sweep) + 1))\n", "for i in range(len(tap_names)):\n", " coup_eff_tl[i, 0] = power_tap_25[i].sel(f=freq_c)\n", "\n", "sims = {\n", " f\"sim_{tap_l}_{sim_name}\": sim\n", " for sim_l, tap_l in zip(sim_sweep, taper_sweep)\n", " for sim, sim_name in zip(sim_l.values(), sim_l.keys())\n", "}\n", "\n", "for j, tl in enumerate(taper_sweep):\n", " for i, sn in enumerate(tap_names):\n", " data_sim = batch_sweep[f\"sim_{tl}_{sn}\"]\n", " mode_amps = data_sim[\"mode_0\"]\n", " coeffs_f = mode_amps.amps.sel(direction=\"+\", f=freq_c)\n", " power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n", " power_0_db = 10 * np.log10(power_0)\n", " coup_eff_tl[i, j + 1] = power_0_db\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown below, the longer the taper, the higher the modal coupling efficiency, as is expected due to the increased adiabaticity. However, the quadratic shape profile seems more advantageous for this case as it achieved a steady state coupling loss of $\\lt$ 0.6 dB for a 50 $\\mu m$ taper length. In contrast, the exponential shape needs double this value, and an even longer taper length would be necessary for the linear profile to obtain similar coupling loss. Detailed discussions about the effects of taper profile over the coupling efficiency, bandwidth, footprint size, or taper/fiber misalignments can be found in the literature [[1](https://www.mdpi.com/2076-3417/10/4/1538),[2](https://opg.optica.org/prj/fulltext.cfm?uri=prj-7-2-201&id=404538),[3](https://www.sciencedirect.com/science/article/abs/pii/S0030401811006389)]. \n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, figsize=(6, 3))\n", "lin_style = [\"ok\", \"sr\", \"hb\"]\n", "taper_lengths = [taper_l] + taper_sweep[:]\n", "for ce, ls, lab in zip(coup_eff_tl, lin_style, tap_names):\n", " ax.plot(taper_lengths, ce, ls, label=lab)\n", "ax.set_xlim([taper_lengths[0] - 2, taper_lengths[-1] + 2])\n", "ax.set_xlabel(\"Taper Length ($\\mu m$)\")\n", "ax.set_ylabel(\"Coupling Efficiency (dB)\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will visualize the field distribution and the wavelength-dependent coupling coefficients for these longer inverted taper structures." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[14:23:18] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-f5d5981f-4d19-4881-8ea9-a1154588d919v1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:18]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=158559;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=239808;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93mf5d5981f-4d19-4881-8ea9-a1154588d919\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:23:18] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-4e01bb58-5916-4c68-b332-14df193011efv1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:18]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=943125;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=817246;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93m4e01bb58-5916-4c68-b332-14df193011ef\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[14:23:18] loading SimulationData from                                                                webapi.py:512\n",
       "           data/data_sweep/fdve-9752d3f1-11fa-4eb2-a219-ab72291b9deav1.hdf5                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m[14:23:18]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from \u001b]8;id=76409;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=533809;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mdata/data_sweep/fdve-\u001b[93m9752d3f1-11fa-4eb2-a219-ab72291b9dea\u001b[0mv1.hdf5 \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "final_sim_data = [\n", " batch_sweep[\"sim_100_linear\"],\n", " batch_sweep[\"sim_50_quadratic\"],\n", " batch_sweep[\"sim_100_exponential\"],\n", "]\n", "\n", "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(10, 8))\n", "for sim_data, tap_n, ax in zip(final_sim_data, tap_names, axs):\n", " sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n", " ax.set_title(tap_n)\n", " ax.set_aspect(\"auto\") # Used to better visualize the shapes.\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_colors = (\"black\", \"red\", \"blue\")\n", "fig, ax1 = plt.subplots(1, figsize=(8, 4))\n", "for sim_data, color, label in zip(final_sim_data, plot_colors, tap_names):\n", " mode_amps = sim_data[\"mode_0\"]\n", " coeffs_f = mode_amps.amps.sel(direction=\"+\")\n", " power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n", " power_0_db = 10 * np.log10(power_0)\n", "\n", " ax1.plot(\n", " wl_range,\n", " power_0_db,\n", " color=color,\n", " linestyle=\"solid\",\n", " linewidth=1.0,\n", " label=label,\n", " )\n", "ax1.set_xlim([wl_range[0], wl_range[-1]])\n", "ax1.set_xlabel(\"Wavelength (um)\")\n", "ax1.set_ylabel(\"Power (dB)\")\n", "ax1.set_title(\"Coupling Efficiency\")\n", "plt.legend()\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" }, "vscode": { "interpreter": { "hash": "2b879493f2cfdd3ec87baca7755ab31685687aa86c1a92ec9eb7010d3f638e67" } }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00fbcfa8974749ef80548123d4686e08": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "062ca02f98c44904bc504d5ce1a651c8": { "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_4bda1c90cc8d4ed69eadcb5f74e1e2df", "msg_id": "", "outputs": [ { "data": { "text/html": "
sim_50_linear: status = success       \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nsim_50_quadratic: status = success    \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nsim_50_exponential: status = success  \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nsim_100_linear: status = success      \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nsim_100_quadratic: status = success   \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nsim_100_exponential: status = success \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\n
\n", "text/plain": "sim_50_linear: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nsim_50_quadratic: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nsim_50_exponential: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nsim_100_linear: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nsim_100_quadratic: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nsim_100_exponential: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "07df39b17cc246878897f3473dd7f29f": { "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_f1ae54f235fd42958aa225c74f4ab1de", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 4.4/4.4 MB \u2022 35.2 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m4.4/4.4 MB\u001b[0m \u2022 \u001b[31m35.2 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0f4754189e20469eb79d45d0949d8aeb": { "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_d471d0c69b1045f68c8ee8d189f8068e", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 10.3/10.3 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m10.3/10.3 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "22f26ff09a42442189f5f8d389dec130": { "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_da56950343d747d0b0a951be02d9d13f", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.7/8.7 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.7/8.7 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "296c3913cbba4f1ebf68a0f40489c3db": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "32b906c79a8c4dd39407164ffdc641e9": { "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_65b9e9d9c54d408a95727f5655ba343d", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.3/8.3 MB \u2022 33.5 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.3/8.3 MB\u001b[0m \u2022 \u001b[31m33.5 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "36860f056df24a878c5ccf9800fe74bc": { "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_d47b10ae0f104f72892a933b44d2599a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 2.5/2.5 MB \u2022 27.5 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m2.5/2.5 MB\u001b[0m \u2022 \u001b[31m27.5 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "4007a6ca87314c96b4817ba19bfdf11a": { "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_4712110243da422893c16740a03688ac", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 4.4/4.4 MB \u2022 30.8 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m4.4/4.4 MB\u001b[0m \u2022 \u001b[31m30.8 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "4712110243da422893c16740a03688ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4bda1c90cc8d4ed69eadcb5f74e1e2df": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4e4de29e304d44759ba9cbcb2f85c7ef": { "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_296c3913cbba4f1ebf68a0f40489c3db", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.6/8.6 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.6/8.6 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "525bac0aa0054a0fa2200ac1bf25b082": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "59ba52e7460546008b804a01a61fdbb4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "65b9e9d9c54d408a95727f5655ba343d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6ba9a362e5db4348834afb908c0775c4": { "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_59ba52e7460546008b804a01a61fdbb4", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.3/8.3 MB \u2022 35.7 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.3/8.3 MB\u001b[0m \u2022 \u001b[31m35.7 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "6e7342758eaa4409b9dd48bde720d82a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6f8b77ccf54742f1a7c665c8c3a0a916": { "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_83e6d9fc8ce4401196a3000d0f347b48", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.3/8.3 MB \u2022 37.4 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.3/8.3 MB\u001b[0m \u2022 \u001b[31m37.4 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "74f53585dddf4b6eaa41fd14610d38c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7746306e4a7b4ae89c213ad41794e989": { "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_f498c42be37e454cbe84d5e1d7b7487a", "msg_id": "", "outputs": [ { "data": { "text/html": "
linear: status = success      \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nquadratic: status = success   \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\nexponential: status = success \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\n
\n", "text/plain": "linear: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nquadratic: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nexponential: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "7e43f11a54384f9cb9ed6cabc4f06ab8": { "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_74f53585dddf4b6eaa41fd14610d38c5", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.7/8.7 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.7/8.7 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "83e6d9fc8ce4401196a3000d0f347b48": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "901673e663214a0f8a5904351f92dfc9": { "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_a886e5294df34306846817f7f2d39a01", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 2.5/2.5 MB \u2022 23.3 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m2.5/2.5 MB\u001b[0m \u2022 \u001b[31m23.3 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "924e6bf171b4484f8bac72593d6dc93d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "98919aa42050473398ed859c805e3b70": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a886e5294df34306846817f7f2d39a01": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ab074efc0fe846efb22dda349c44962c": { "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_98919aa42050473398ed859c805e3b70", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 9.3/9.3 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m9.3/9.3 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b36685e13b5d4763b19ffa35fb42cb2e": { "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_525bac0aa0054a0fa2200ac1bf25b082", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 9.8/9.8 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m9.8/9.8 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "b3be7343f3444ba8a9fc856939acd85a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cf585c1a7a4c4a2bb7107319d201f7b5": { "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_6e7342758eaa4409b9dd48bde720d82a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 9.3/9.3 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m9.3/9.3 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d024206e284643b0a7614e03c359c446": { "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_00fbcfa8974749ef80548123d4686e08", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 8.6/8.6 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m8.6/8.6 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "d471d0c69b1045f68c8ee8d189f8068e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d47b10ae0f104f72892a933b44d2599a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "da56950343d747d0b0a951be02d9d13f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e89d651e0b4645c09241811aedc97537": { "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_924e6bf171b4484f8bac72593d6dc93d", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 2.5/2.5 MB \u2022 17.0 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m2.5/2.5 MB\u001b[0m \u2022 \u001b[31m17.0 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "f07d14c86f1541a38bb2620302cf0661": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f1ae54f235fd42958aa225c74f4ab1de": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f3fe13eec8344ebe8acbe06f05311174": { "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_b3be7343f3444ba8a9fc856939acd85a", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 9.7/9.7 kB \u2022 ? \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m9.7/9.7 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "f498c42be37e454cbe84d5e1d7b7487a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f51508af145d4fb1a429e6260422e75c": { "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_f07d14c86f1541a38bb2620302cf0661", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2193 monitor_data.hdf5 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 4.4/4.4 MB \u2022 32.0 MB/s \u2022 0:00:00\n
\n", "text/plain": "\u001b[1;32m\u2193\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m4.4/4.4 MB\u001b[0m \u2022 \u001b[31m32.0 MB/s\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }