{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "# Performing parallel / batch processing of simulations\n", "\n", "\"diagram\"\n", "\n", "Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n", "\n", "In this notebook, we will show an example of using tidy3d to evaluate device performance over a set of many design parameters.\n", "\n", "This example will also provide a walkthrough of Tidy3D's [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) and [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) features for managing both individual simulations and sets of simulations.\n", "\n", "> Note: as of version `2.10`, the [tidy3d.web.run](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.run.run.html) unifies the submission of a single simulation as well as any nested combination of lists, tuples, and dictionaries of them, handling the same functionality as the batch, with a simpler syntax. As such, it could be a good alternative for parameter scan depending on how your script is set up.\n", "\n", "Additionally, we will show how to do this problem using `tidy3d.plugins.design`, which provides convenience methods for defining and running parameter scans, which has a full tutorial [here](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/).\n", "\n", "For demonstration, we look at the splitting ratio of a directional coupler as we vary the coupling length between two waveguides. The sidewall of the waveguides is slanted, deviating from the vertical direction by `sidewall_angle`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "# standard python imports\n", "import os\n", "\n", "import gdstk\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# tidy3D imports\n", "import tidy3d as td\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "First we set up some global parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "# wavelength / frequency\n", "lambda0 = 1.550 # all length scales in microns\n", "freq0 = td.constants.C_0 / lambda0\n", "fwidth = freq0 / 10\n", "\n", "# Permittivity of waveguide and substrate\n", "wg_n = 3.48\n", "sub_n = 1.45\n", "mat_wg = td.Medium(permittivity=wg_n**2)\n", "mat_sub = td.Medium(permittivity=sub_n**2)\n", "\n", "# Waveguide dimensions\n", "\n", "# Waveguide height\n", "wg_height = 0.22\n", "# Waveguide width\n", "wg_width = 0.45\n", "# Waveguide separation in the beginning/end\n", "wg_spacing_in = 8\n", "# Reference plane where the cross section of the device is defined\n", "reference_plane = \"bottom\"\n", "# Angle of the sidewall deviating from the vertical ones, positive values for the base larger than the top\n", "sidewall_angle = np.pi / 6\n", "# Total device length along propagation direction\n", "device_length = 100\n", "# Length of the bend region\n", "bend_length = 16\n", "# space between waveguide and PML\n", "pml_spacing = 1\n", "# resolution control: minimum number of grid cells per wavelength in each material\n", "grid_cells_per_wvl = 16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define waveguide bends and coupler\n", "\n", "Here is where we define our directional coupler shape programmatically in terms of the geometric parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "def tanh_interp(max_arg):\n", " \"\"\"Interpolator for tanh with adjustable extension\"\"\"\n", " scale = 1 / np.tanh(max_arg)\n", " return lambda u: 0.5 * (1 + scale * np.tanh(max_arg * (u * 2 - 1)))\n", "\n", "\n", "def make_coupler(\n", " length,\n", " wg_spacing_in,\n", " wg_width,\n", " wg_spacing_coup,\n", " coup_length,\n", " bend_length,\n", "):\n", " \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n", " # bend interpolator\n", " interp = tanh_interp(3)\n", " delta = wg_width + wg_spacing_coup - wg_spacing_in\n", " offset = lambda u: wg_spacing_in + interp(u) * delta\n", "\n", " coup = gdstk.RobustPath(\n", " (-0.5 * length, 0),\n", " (wg_width, wg_width),\n", " wg_spacing_in,\n", " simple_path=True,\n", " layer=1,\n", " datatype=[0, 1],\n", " )\n", " coup.segment((-0.5 * coup_length - bend_length, 0))\n", " coup.segment(\n", " (-0.5 * coup_length, 0),\n", " offset=[lambda u: -0.5 * offset(u), lambda u: 0.5 * offset(u)],\n", " )\n", " coup.segment((0.5 * coup_length, 0))\n", " coup.segment(\n", " (0.5 * coup_length + bend_length, 0),\n", " offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n", " )\n", " coup.segment((0.5 * length, 0))\n", " return coup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Simulation and Submit Job\n", "\n", "The following function creates a tidy3d simulation object for a set of design parameters.\n", "\n", "Note that the simulation has not been run yet, just created." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [], "source": [ "def make_sim(coup_length, wg_spacing_coup, domain_field=False):\n", " \"\"\"Make a simulation with a given length of the coupling region and\n", " distance between the waveguides in that region. If ``domain_field``\n", " is True, a 2D in-plane field monitor will be added.\n", " \"\"\"\n", "\n", " # Geometry must be placed in GDS cells to import into Tidy3D\n", " coup_cell = gdstk.Cell(\"Coupler\")\n", "\n", " substrate = gdstk.rectangle(\n", " (-device_length / 2, -wg_spacing_in / 2 - 10),\n", " (device_length / 2, wg_spacing_in / 2 + 10),\n", " layer=0,\n", " )\n", " coup_cell.add(substrate)\n", "\n", " # Add the coupler to a gdstk cell\n", " gds_coup = make_coupler(\n", " device_length,\n", " wg_spacing_in,\n", " wg_width,\n", " wg_spacing_coup,\n", " coup_length,\n", " bend_length,\n", " )\n", " coup_cell.add(gds_coup)\n", "\n", " # Substrate\n", " (oxide_geo,) = td.PolySlab.from_gds(\n", " gds_cell=coup_cell,\n", " gds_layer=0,\n", " gds_dtype=0,\n", " slab_bounds=(-10, 0),\n", " reference_plane=reference_plane,\n", " axis=2,\n", " )\n", "\n", " oxide = td.Structure(geometry=oxide_geo, medium=mat_sub)\n", "\n", " # Waveguides (import all datatypes if gds_dtype not specified)\n", " coupler1_geo, coupler2_geo = td.PolySlab.from_gds(\n", " gds_cell=coup_cell,\n", " gds_layer=1,\n", " slab_bounds=(0, wg_height),\n", " sidewall_angle=sidewall_angle,\n", " reference_plane=reference_plane,\n", " axis=2,\n", " )\n", "\n", " coupler1 = td.Structure(geometry=coupler1_geo, medium=mat_wg)\n", "\n", " coupler2 = td.Structure(geometry=coupler2_geo, medium=mat_wg)\n", "\n", " # Simulation size along propagation direction\n", " sim_length = 2 + 2 * bend_length + coup_length\n", "\n", " # Spacing between waveguides and PML\n", " sim_size = [\n", " sim_length,\n", " wg_spacing_in + wg_width + 2 * pml_spacing,\n", " wg_height + 2 * pml_spacing,\n", " ]\n", "\n", " # source\n", " src_pos = -sim_length / 2 + 0.5\n", " msource = td.ModeSource(\n", " center=[src_pos, wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " direction=\"+\",\n", " mode_spec=td.ModeSpec(),\n", " mode_index=0,\n", " )\n", "\n", " mon_in = td.ModeMonitor(\n", " center=[(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"in\",\n", " )\n", " mon_ref_bot = td.ModeMonitor(\n", " center=[(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"reflect_bottom\",\n", " )\n", " mon_top = td.ModeMonitor(\n", " center=[-(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"top\",\n", " )\n", " mon_bot = td.ModeMonitor(\n", " center=[-(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n", " size=[0, 3, 2],\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", " name=\"bottom\",\n", " )\n", " monitors = [mon_in, mon_ref_bot, mon_top, mon_bot]\n", "\n", " if domain_field:\n", " domain_monitor = td.FieldMonitor(\n", " center=[0, 0, wg_height / 2],\n", " size=[td.inf, td.inf, 0],\n", " freqs=[freq0],\n", " name=\"field\",\n", " )\n", " monitors.append(domain_monitor)\n", "\n", " # initialize the simulation\n", " sim = td.Simulation(\n", " size=sim_size,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=grid_cells_per_wvl),\n", " structures=[oxide, coupler1, coupler2],\n", " sources=[msource],\n", " monitors=monitors,\n", " run_time=50 / fwidth,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " )\n", "\n", " return sim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspect Simulation\n", "\n", "Let's create and inspect a single simulation to make sure it was defined correctly before doing the full scan. The sidewalls of the waveguides deviate from the vertical direction by 30 degrees. We also add an in-plane field monitor to have a look at the field evolution in this one simulation. We will not use such a monitor in the batch to avoid storing unnecessarily large amounts of data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Length of the coupling region\n", "coup_length = 10\n", "\n", "# Waveguide separation in the coupling region\n", "wg_spacing_coup = 0.10\n", "\n", "sim = make_sim(coup_length, wg_spacing_coup, domain_field=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize geometry\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))\n", "sim.plot(z=wg_height / 2 + 0.01, ax=ax1)\n", "sim.plot(x=0.1, ax=ax2)\n", "ax2.set_xlim([-3, 3])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create and Submit Job\n", "\n", "The [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) object provides an interface for managing simulations.\n", "\n", "`job = Job(simulation)` will create a job and upload the simulation to our server to run.\n", "\n", "Then, one may call various methods of `job` to monitor progress, download results, and get information.\n", "\n", "For more information, refer to the API reference." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
12:13:44 -03 Created task 'CouplerVerify' with task_id                          \n",
       "             'fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001B[2;36m12:13:44 -03\u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'CouplerVerify'\u001B[0m with task_id \n", "\u001B[2;36m \u001B[0m\u001B[32m'fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a7\n",
       "             2-4261-a06c-b6575e0f33fe'.                                         \n",
       "
\n" ], "text/plain": [ "\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at \n", "\u001B[2;36m \u001B[0m\u001B]8;id=145950;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=624063;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=145950;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=4405;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=145950;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32m-d5f43711-3a7\u001B[0m\u001B]8;;\u001B\\\n", "\u001B[2;36m \u001B[0m\u001B]8;id=145950;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[32m2-4261-a06c-b6575e0f33fe'\u001B[0m\u001B]8;;\u001B\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
\n" ], "text/plain": [ "\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mTask folder: \u001B]8;id=856254;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001B\\\u001B[32m'default'\u001B[0m\u001B]8;;\u001B\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7707a164d0c94b2ba11bfed72900a601", "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": [
       "
12:13:48 -03 Maximum FlexCredit cost: 0.588. Minimum cost depends on task       \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:13:48 -03\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.588\u001B[0m. Minimum cost depends on task       \n",
       "\u001B[2;36m             \u001B[0mexecution details. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed  \n",
       "\u001B[2;36m             \u001B[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:13:49 -03 status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:13:49 -03\u001B[0m\u001B[2;36m \u001B[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or              \n",
       "\u001B[2;36m             \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI.      \n",
       "\u001B[2;36m             \u001B[0mTerminating the Python script will not stop the job running on the \n",
       "\u001B[2;36m             \u001B[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d71c2e51e3b94dcb85f8ec1a25213e44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:14:10 -03 status = preprocess                                                \n",
       "
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      ],
      "text/plain": [
       "\u001B[2;36m12:14:10 -03\u001B[0m\u001B[2;36m \u001B[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:14:14 -03 starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:14:14 -03\u001B[0m\u001B[2;36m \u001B[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0362a79483d94bc3ab052e7dfaeda7d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:14:41 -03 early shutoff detected at 52%, exiting.                            \n",
       "
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      ],
      "text/plain": [
       "\u001B[2;36m12:14:41 -03\u001B[0m\u001B[2;36m \u001B[0mearly shutoff detected at \u001B[1;36m52\u001B[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
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      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "
             status = postprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6ae3ec629f848b3b56ff83b7461194c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:14:44 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:14:44 -03\u001B[0m\u001B[2;36m \u001B[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:14:46 -03 View simulation result at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a7\n",
       "             2-4261-a06c-b6575e0f33fe'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:14:46 -03\u001B[0m\u001B[2;36m \u001B[0mView simulation result at                                          \n",
       "\u001B[2;36m             \u001B[0m\u001B]8;id=743781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=348474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=743781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=31302;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=743781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34m-d5f43711-3a7\u001B[0m\u001B]8;;\u001B\\\n",
       "\u001B[2;36m             \u001B[0m\u001B]8;id=743781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d5f43711-3a72-4261-a06c-b6575e0f33fe\u001B\\\u001B[4;34m2-4261-a06c-b6575e0f33fe'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Output()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "
12:15:01 -03 loading simulation from data/sim_data.hdf5                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:15:01 -03\u001B[0m\u001B[2;36m \u001B[0mloading simulation from data/sim_data.hdf5                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create job, upload sim to server to begin running\n",
    "job = web.Job(simulation=sim, task_name=\"CouplerVerify\", verbose=True)\n",
    "\n",
    "# download the results and load them into a simulation\n",
    "sim_data = job.run(path=\"data/sim_data.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postprocessing\n",
    "\n",
    "The following function takes a completed simulation (with data loaded into it) and computes the quantities of interest.\n",
    "\n",
    "For this case, we measure both the total transmission in the right ports and also the ratio of power between the top and bottom ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def measure_transmission(sim_data):\n",
    "    \"\"\"Constructs a \"row\" of the scattering matrix when sourced from top left port\"\"\"\n",
    "\n",
    "    input_amp = sim_data[\"in\"].amps.sel(direction=\"+\")\n",
    "\n",
    "    amps = np.zeros(4, dtype=complex)\n",
    "    directions = (\"-\", \"-\", \"+\", \"+\")\n",
    "    for i, (monitor, direction) in enumerate(zip(sim_data.simulation.monitors[:4], directions)):\n",
    "        amp = sim_data[monitor.name].amps.sel(direction=direction)\n",
    "        amp_normalized = amp / input_amp\n",
    "        amps[i] = np.squeeze(amp_normalized.values)\n",
    "\n",
    "    return amps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode amplitudes in each port:\n",
      "\n",
      "\tmonitor     = \"in\"\n",
      "\tamplitude^2 = 0.00\n",
      "\tphase       = -2.12 (rad)\n",
      "\n",
      "\tmonitor     = \"reflect_bottom\"\n",
      "\tamplitude^2 = 0.00\n",
      "\tphase       = 1.14 (rad)\n",
      "\n",
      "\tmonitor     = \"top\"\n",
      "\tamplitude^2 = 0.95\n",
      "\tphase       = -0.32 (rad)\n",
      "\n",
      "\tmonitor     = \"bottom\"\n",
      "\tamplitude^2 = 0.03\n",
      "\tphase       = 1.25 (rad)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# monitor and test out the measure_transmission function the results of the single run\n",
    "amps_arms = measure_transmission(sim_data)\n",
    "print(\"mode amplitudes in each port:\\n\")\n",
    "for amp, monitor in zip(amps_arms, sim_data.simulation.monitors[:-1]):\n",
    "    print(f'\\tmonitor     = \"{monitor.name}\"')\n",
    "    print(f\"\\tamplitude^2 = {abs(amp) ** 2:.2f}\")\n",
    "    print(f\"\\tphase       = {(np.angle(amp)):.2f} (rad)\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 3))\n",
    "sim_data.plot_field(\"field\", \"Ey\", z=wg_height / 2, f=freq0, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D Parameter Scan\n",
    "\n",
    "Now we will scan through the coupling length parameter to see the effect on splitting ratio.\n",
    "\n",
    "To do this, we will create a list of simulations corresponding to each parameter combination.\n",
    "\n",
    "We will use this list to create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object, which has similar functionality to [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) but allows one to manage a *set* of jobs.\n",
    "\n",
    "First, we create arrays to store the input and output values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create variables to store parameters, simulation information, results\n",
    "Nl = 11\n",
    "\n",
    "ls = np.linspace(5, 12, Nl)\n",
    "split_ratios = np.zeros(Nl)\n",
    "efficiencies = np.zeros(Nl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Batch\n",
    "\n",
    "We now create our list of simulations and use them to initialize a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html).\n",
    "\n",
    "For more information, refer to the API reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# submit all jobs\n",
    "sims = {f\"l={l:.2f}\": make_sim(l, wg_spacing_coup) for l in ls}\n",
    "batch = web.Batch(simulations=sims, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monitor Batch\n",
    "\n",
    "Here we can perform real-time monitoring of how many of the jobs in the batch have completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1726b7d176d445ba88931ea73ebbd90",
       "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": [
       "
12:15:13 -03 Started working on Batch containing 11 tasks.                      \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:15:13 -03\u001B[0m\u001B[2;36m \u001B[0mStarted working on Batch containing \u001B[1;36m11\u001B[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:15:26 -03 Maximum FlexCredit cost: 6.251 for the whole batch.                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:15:26 -03\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m6.251\u001B[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
       "             Batch has completed.                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mUse \u001B[32m'Batch.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed FlexCredit cost after the\n",
       "\u001B[2;36m             \u001B[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c68a67e5b584bbd984349496297ef08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:16:44 -03 Batch complete.                                                    \n",
       "
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      ],
      "text/plain": [
       "\u001B[2;36m12:16:44 -03\u001B[0m\u001B[2;36m \u001B[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "822258da3ac545169dffc65483c25450",
       "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"
    }
   ],
   "source": [
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Visualize Results\n",
    "\n",
    "Finally, we can compute the output quantities and load them into the arrays we created initially.\n",
    "\n",
    "Then we may plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 11)\n",
      "[[-1.10353955e-02+1.61814435e-03j -9.04455738e-04+1.64353104e-04j\n",
      "  -5.57275648e-03-4.53279048e-04j -6.53363273e-04+1.78063348e-03j\n",
      "  -9.35706226e-03-1.24234920e-02j -2.86757404e-03+1.55956985e-03j\n",
      "  -2.04751816e-03+2.28606979e-03j -8.83556221e-03-3.10461617e-03j\n",
      "   1.13680282e-03-9.24590296e-04j -1.10006191e-02-9.66687551e-04j\n",
      "  -3.02976298e-03-7.60206417e-04j]\n",
      " [ 9.63807986e-03-5.97860610e-04j -1.58835058e-03+3.62786802e-04j\n",
      "   2.61942014e-04+1.46495188e-03j -4.55473477e-04+7.45351174e-04j\n",
      "   5.40159172e-03+1.02762774e-02j -1.11744009e-03-1.94196039e-03j\n",
      "  -1.82687266e-04+3.03443194e-03j  5.25204228e-03+2.18227003e-03j\n",
      "  -5.84591401e-03+1.02783366e-03j  6.75417572e-03+7.94890492e-03j\n",
      "  -1.20488584e-03-1.32813564e-03j]\n",
      " [ 4.30400058e-01+4.38699992e-01j  6.85579816e-01-2.36521733e-01j\n",
      "   5.80353209e-02-8.17359704e-01j -7.99786258e-01-4.03020412e-01j\n",
      "  -7.27283483e-01+6.11388491e-01j  2.85865338e-01+9.40989626e-01j\n",
      "   9.87902019e-01+1.03040603e-01j  4.98659601e-01-8.43784578e-01j\n",
      "  -5.57930954e-01-7.60418327e-01j -8.60686222e-01+1.99562982e-01j\n",
      "  -1.36299042e-01+7.92573720e-01j]\n",
      " [ 5.57536346e-01-5.42707403e-01j -2.18383866e-01-6.40458253e-01j\n",
      "  -5.57344922e-01-4.17021811e-02j -1.93682498e-01+3.80456642e-01j\n",
      "   1.83605737e-01+2.20308048e-01j  1.33568602e-01-4.02348960e-02j\n",
      "  -1.47241085e-03+1.37878655e-02j  1.41021758e-01+8.34878416e-02j\n",
      "   2.51292451e-01-1.85527773e-01j -1.03031466e-01-4.40936770e-01j\n",
      "  -5.73371370e-01-9.70873365e-02j]]\n"
     ]
    }
   ],
   "source": [
    "amps_batch = []\n",
    "for task_name, sim_data in batch_results.items():\n",
    "    amps_arms_i = np.array(measure_transmission(sim_data))\n",
    "    amps_batch.append(amps_arms_i)\n",
    "amps_batch = np.stack(amps_batch, axis=1)\n",
    "print(amps_batch.shape)  # (4, Nl)\n",
    "print(amps_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "powers = abs(amps_batch) ** 2\n",
    "power_top = powers[2]\n",
    "power_bot = powers[3]\n",
    "power_out = power_top + power_bot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ls, 100 * power_top, label=\"Top\")\n",
    "plt.plot(ls, 100 * power_bot, label=\"Bottom\")\n",
    "plt.plot(ls, 100 * power_out, label=\"Top + Bottom\")\n",
    "plt.xlabel(\"Coupling length (µm)\")\n",
    "plt.ylabel(\"Power ratio (%)\")\n",
    "plt.ylim(0, 100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Remarks\n",
    "\n",
    "Batches provide some other convenient functionality for managing large numbers of jobs.\n",
    "\n",
    "For example, one can save the batch information to file and load the batch at a later time, if needing to disconnect from the service while the jobs are running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save batch metadata\n",
    "batch.to_file(\"data/batch_data.json\")\n",
    "\n",
    "# load batch metadata into a new batch\n",
    "loaded_batch = web.Batch.from_file(\"data/batch_data.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more reference, refer to our documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the `design` Plugin\n",
    "\n",
    "In `Tidy3D` version `2.6.0`, we introduced a `Design` plugin, which allows users to programmatically define and run their parameter scans while also providing a convenient container for managing the resulting data.\n",
    "\n",
    "For more details, please refer to our [full tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/) on the `Design` plugin.\n",
    "\n",
    "We import the plugin through `tidy3d.plugins.design` below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tidy3d.plugins.design as tdd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define our sweep dimensions as `tdd.ParameterFloat` instances and give them each a range.\n",
    "\n",
    "> Note: we need to ensure that the `name` arguments match the function argument names in our `make_sim()` function, which we will use to construct the simulations for the parameter scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_spc = tdd.ParameterFloat(name=\"wg_spacing_coup\", span=(0.1, 0.15), num_points=3)\n",
    "param_len = tdd.ParameterFloat(name=\"coup_length\", span=(5, 12), num_points=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we will do a grid search over these points, which we can define a `tdd.MethodGrid` and then combine everything into a `tdd.DesignSpace`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodGrid()\n",
    "design_space = tdd.DesignSpace(\n",
    "    parameters=[param_spc, param_len],\n",
    "    method=method,\n",
    "    task_name=\"ParameterScan_Notebook\",\n",
    "    path_dir=\"./data\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the sweep, we need to pass the design space our evaluation function. Here we will define it through pre and post processing functions, which enables the `design` plugin to take advantage of parallelism to perform `Batch` processing under the hood.\n",
    "\n",
    "The functions `make_sim` and `measure_transmission` already almost work perfectly as pre and post processing functions. We'll just wrap `measure_transmission` to return a dictionary of the amplitudes, so that the dictionary keys can be used to label the outputs in the resulting dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_post(*args, **kwargs):\n",
    "    \"\"\"Post processing function, but label the outputs using a dictionary.\"\"\"\n",
    "    a, b, c, d = measure_transmission(*args, **kwargs)\n",
    "    return dict(input=a, reflect_bottom=b, top=c, bottom=d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we pass our pre-processing and post-processing functions to the `DesignSpace.run_batch()` method to get our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
12:17:02 -03 Running 9 Simulations                                              \n",
       "
\n"
      ],
      "text/plain": [
       "\u001B[2;36m12:17:02 -03\u001B[0m\u001B[2;36m \u001B[0mRunning \u001B[1;36m9\u001B[0m Simulations                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = design_space.run(fn=make_sim, fn_post=fn_post)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a `pandas.DataFrame` of the results and print out the first 5 elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "  \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "    \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "      \n",
       "    \n",
       "  \n",
       "
wg_spacing_coupcoup_lengthinputreflect_bottomtopbottomamp_squared_top
00.1005.0-0.011035+0.001618j0.009638-0.000598j0.430400+0.438700j0.557536-0.542707j0.377702
10.1255.0-0.013190-0.001773j0.009348-0.001238j0.278187+0.191785j0.532314-0.765999j0.114169
20.1505.0-0.005330+0.002088j0.007897+0.000475j0.072539+0.035940j0.450558-0.880867j0.006554
30.1008.5-0.002868+0.001560j-0.001117-0.001942j0.285865+0.940990j0.133569-0.040235j0.967181
40.1258.50.001674+0.002439j-0.005892-0.002392j0.432173+0.715344j0.459917-0.277823j0.698491
\n",
       "
"
      ],
      "text/plain": [
       "   wg_spacing_coup  coup_length               input      reflect_bottom  \\\n",
       "0            0.100          5.0 -0.011035+0.001618j  0.009638-0.000598j   \n",
       "1            0.125          5.0 -0.013190-0.001773j  0.009348-0.001238j   \n",
       "2            0.150          5.0 -0.005330+0.002088j  0.007897+0.000475j   \n",
       "3            0.100          8.5 -0.002868+0.001560j -0.001117-0.001942j   \n",
       "4            0.125          8.5  0.001674+0.002439j -0.005892-0.002392j   \n",
       "\n",
       "                  top              bottom  amp_squared_top  \n",
       "0  0.430400+0.438700j  0.557536-0.542707j         0.377702  \n",
       "1  0.278187+0.191785j  0.532314-0.765999j         0.114169  \n",
       "2  0.072539+0.035940j  0.450558-0.880867j         0.006554  \n",
       "3  0.285865+0.940990j  0.133569-0.040235j         0.967181  \n",
       "4  0.432173+0.715344j  0.459917-0.277823j         0.698491  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = results.to_dataframe()\n",
    "\n",
    "# take absolute value squared of output 2 to get powers to the top port\n",
    "df[\"amp_squared_top\"] = df[\"top\"].map(lambda x: abs(x) ** 2)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can also use `DataFrame` visualization methods, documented [here](https://pandas.pydata.org/docs/user_guide/visualization.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"wg_spacing_coup\", y=\"coup_length\", c=\"amp_squared_top\", s=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in this approach to parameter sweeps, we highly recommend checking out our `Design` plugin [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/) for a deep dive and also the [documentation](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) for `pandas.DataFrame`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Scan using Signac\n",
    "\n",
    "For users who might need more structure to their parameter scans, the open source tool \"[signac](https://signac.io)\" is a fantastic resource. Here we will reproduce some of the previous parameter scan using signac to give an introduction to how to apply it to Tidy3D projects. You can see detailed tutorials and examples at their [documentation page](https://docs.signac.io/en/latest/tutorial.html).\n",
    "\n",
    "After importing the package, we need to define a `project`, which also tells `signac` to store our parameter scan data in a local directory of our choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import signac\n",
    "\n",
    "# make the project and give it a path to save the data into\n",
    "project = signac.init_project(path=\"data/coupler\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `siganc` (and more generally in parameter sweeps), it is very useful to have a single function to define the inputs and outputs of your parameter scan. In our case, we have a single input (coupling length `l`) and two outputs: coupling efficiency (`eff`) and the split ratio (`ratio`). We write a function to link these inputs and outputs through a Tidy3D simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute(l: float):\n",
    "    \"\"\"compute efficiency and split ratio as a function of the coupling length\"\"\"\n",
    "    sim = make_sim(l, wg_spacing_coup)\n",
    "    task_name = f\"SWEEP_l={l:.3f}\"\n",
    "    output_file = f\"{task_name}.hdf5\"\n",
    "    sim_data = web.run(sim, task_name=task_name, verbose=False, path=output_file)\n",
    "    amps_arms = np.array(measure_transmission(sim_data))\n",
    "    powers = np.abs(amps_arms) ** 2\n",
    "    efficiency = np.sum(powers)\n",
    "    ratio_0 = powers[2] / efficiency\n",
    "    return efficiency, ratio_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `signac` `project` contains a set of `job` objects, which each define a specific data point of our parameter scan. As such, we define another function that wraps `compute` but instead simply accepts a `job` instance, which makes our lives easier for managing parameter sweeps in `signac`.\n",
    "\n",
    "This function basically will open a job, grab its inputs (`l` value in our case), compute the output quantities, and save them to both the `job.document` record and also the `.txt` files storing the parameter scan results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_transmission(job):\n",
    "    l = job.statepoint()[\"l\"]\n",
    "    print(f\"Computing transmission of job: [l={l:.1f}]\", job)\n",
    "    eff, ratio = compute(**job.statepoint())\n",
    "    job.document[\"eff\"] = float(eff)\n",
    "    job.document[\"ratio\"] = float(ratio)\n",
    "    with open(job.fn(\"eff.txt\"), \"w\") as file:\n",
    "        file.write(str(float(eff)) + \"\\n\")\n",
    "    with open(job.fn(\"ratio.txt\"), \"w\") as file:\n",
    "        file.write(str(float(ratio)) + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, we can start our parameter scan, we simply loop through our desired `l` values, construct a `job` for each, and pass it to our `compute_transmission` function.\n",
    "\n",
    "> Note: If intermediate simulation results are needed, it should be possible to modify `compute` to return also the `SimulationData` associated with our task, which we could then store in the `job.document` or elsewhere.\n",
    "\n",
    "We will compute only 3 values between our ranges of 5 and 12 to save time.\n",
    "\n",
    "> Note, the `job` instances each have their own unique ID, similar to Tidy3D `task_id`, this is what is being printed below with each job being computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing transmission of job: [l=5.0] abb6bcf88ee5474914d016b3c1fcc7b3\n",
      "Computing transmission of job: [l=8.5] c261dda788d2af5c1645cb17feb7854a\n",
      "Computing transmission of job: [l=12.0] bc0f33313f39cc49b60566dcb7f169f9\n"
     ]
    }
   ],
   "source": [
    "for l in np.linspace(5, 12, 3):\n",
    "    statepoint = {\"l\": float(l)}\n",
    "    job = project.open_job(statepoint)\n",
    "    compute_transmission(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at our project and what data we've computed and stored so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'l': 5.0} {'eff': 0.9832976442130552, 'ratio': 0.3841175609995743}\n",
      "{'l': 6.75} {'eff': 0.9833838376103111, 'ratio': 0.751857333206374}\n",
      "{'l': 8.5} {'eff': 0.9866555611285913, 'ratio': 0.980261507486976}\n",
      "{'l': 5.875} {'eff': 0.9833562149702543, 'ratio': 0.5732702117271236}\n",
      "{'l': 11.125} {'eff': 0.986340240858416, 'ratio': 0.8231648161148162}\n",
      "{'l': 9.375} {'eff': 0.9874191639991634, 'ratio': 0.9974339517501508}\n",
      "{'l': 7.625} {'eff': 0.985429791095107, 'ratio': 0.8940999676792584}\n",
      "{'l': 10.25} {'eff': 0.9870420732050952, 'ratio': 0.9414113143923678}\n",
      "{'l': 12.0} {'eff': 0.9849441385745585, 'ratio': 0.6566367125564069}\n"
     ]
    }
   ],
   "source": [
    "for job in project:\n",
    "    print(job.statepoint(), job.document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`signac` also provides the ability to create a \"[linked view](https://docs.signac.io/en/latest/examples/notebooks/signac_102_Exploring_Data.html#1.2-Exploring-Data)\", which gives us a human-readable filesystem for looking at our jobs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.25  11.125  12.0  5.0  5.875  6.75  7.625  8.5  9.375\n",
      "eff.txt  ratio.txt  signac_job_document.json  signac_statepoint.json\n",
      "0.9832976442130552\n"
     ]
    }
   ],
   "source": [
    "project.create_linked_view(prefix=\"data/coupler/view\")\n",
    "\n",
    "# let's print out some info stored in the directory we just created\n",
    "!ls data/coupler/view/l/\n",
    "!ls data/coupler/view/l/5.0/job/\n",
    "!cat data/coupler/view/l/5.0/job/eff.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also initialize many data points to compute later, and feed them to the `compute_transmission` function with a very basic \"caching\" implemented by checking if our inputs already exist in the record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialize abb6bcf88ee5474914d016b3c1fcc7b3\n",
      "initialize 07222394eb1a1b59f9e869ef871a3c29\n",
      "initialize c261dda788d2af5c1645cb17feb7854a\n",
      "initialize bdbbc3c4500db5773fa3cd350b6195ed\n",
      "initialize bc0f33313f39cc49b60566dcb7f169f9\n"
     ]
    }
   ],
   "source": [
    "def init_statepoints(n):\n",
    "    for l in np.linspace(5, 12, n):\n",
    "        sp = {\"l\": float(l)}\n",
    "        job = project.open_job(sp)\n",
    "        job.init()\n",
    "        print(\"initialize\", job)\n",
    "\n",
    "\n",
    "# make 5 points between 5 and 12, note, 3 have already been computed\n",
    "init_statepoints(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After initializing our statepoints (input values), they are stored in our project and we can loop through them and compute any that don't have records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - skipping job: abb6bcf88ee5474914d016b3c1fcc7b3 \n",
      " - skipping job: 07222394eb1a1b59f9e869ef871a3c29 \n",
      " - skipping job: c261dda788d2af5c1645cb17feb7854a \n",
      " - skipping job: dca27bcde6b7bfede0d701809749e789 \n",
      " - skipping job: 9af2b1986858046540a9da68dbf4f425 \n",
      " - skipping job: c7a1813a4fd1fd835898df062c3262c9 \n",
      " - skipping job: b22f487baf2db695e6b15444d42e31ff \n",
      " - skipping job: bdbbc3c4500db5773fa3cd350b6195ed \n",
      " - skipping job: bc0f33313f39cc49b60566dcb7f169f9 \n"
     ]
    }
   ],
   "source": [
    "for job in project:\n",
    "    if \"eff\" not in job.document or \"ratio\" not in job.document:\n",
    "        compute_transmission(job)\n",
    "    else:\n",
    "        print(f\" - skipping job: {job} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we used `td.web.Batch` in our original example, `signac` lets us also leverage parallel processing tools in python to perform something similar.\n",
    "\n",
    "Let's initialize 9 total statepoints now (5 have already been computed) and feed them to a `ThreadPool`. We notice that the jobs will be computed in parallel depending on how many threads are available on your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialize abb6bcf88ee5474914d016b3c1fcc7b3\n",
      "initialize dca27bcde6b7bfede0d701809749e789\n",
      "initialize 07222394eb1a1b59f9e869ef871a3c29\n",
      "initialize b22f487baf2db695e6b15444d42e31ff\n",
      "initialize c261dda788d2af5c1645cb17feb7854a\n",
      "initialize c7a1813a4fd1fd835898df062c3262c9\n",
      "initialize bdbbc3c4500db5773fa3cd350b6195ed\n",
      "initialize 9af2b1986858046540a9da68dbf4f425\n",
      "initialize bc0f33313f39cc49b60566dcb7f169f9\n"
     ]
    }
   ],
   "source": [
    "init_statepoints(9)\n",
    "\n",
    "from multiprocessing.pool import ThreadPool\n",
    "\n",
    "\n",
    "# make a convenience function to just call compute_transmission only for uncomputed jobs\n",
    "def compute_transmission_cached(job):\n",
    "    if \"eff\" not in job.document or \"ratio\" not in job.document:\n",
    "        compute_transmission(job)\n",
    "\n",
    "\n",
    "with ThreadPool() as pool:\n",
    "    pool.map(compute_transmission_cached, list(project))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'l': 5.0} {'eff': 0.9832976442130552, 'ratio': 0.3841175609995743}\n",
      "{'l': 6.75} {'eff': 0.9833838376103111, 'ratio': 0.751857333206374}\n",
      "{'l': 8.5} {'eff': 0.9866555611285913, 'ratio': 0.980261507486976}\n",
      "{'l': 5.875} {'eff': 0.9833562149702543, 'ratio': 0.5732702117271236}\n",
      "{'l': 11.125} {'eff': 0.986340240858416, 'ratio': 0.8231648161148162}\n",
      "{'l': 9.375} {'eff': 0.9874191639991634, 'ratio': 0.9974339517501508}\n",
      "{'l': 7.625} {'eff': 0.985429791095107, 'ratio': 0.8940999676792584}\n",
      "{'l': 10.25} {'eff': 0.9870420732050952, 'ratio': 0.9414113143923678}\n",
      "{'l': 12.0} {'eff': 0.9849441385745585, 'ratio': 0.6566367125564069}\n"
     ]
    }
   ],
   "source": [
    "for job in project:\n",
    "    print(job.statepoint(), job.document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can consolidate and plot our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = np.array([job.statepoint()[\"l\"] for job in project])\n",
    "effs = np.array([job.document[\"eff\"] for job in project])\n",
    "ratios = np.array([job.document[\"ratio\"] for job in project])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ls, 100 * ratios, label=\"split ratio\")\n",
    "plt.scatter(ls, 100 * effs, label=\"efficiency\")\n",
    "plt.xlabel(\"Coupling length (µm)\")\n",
    "plt.ylabel(\"value (%)\")\n",
    "plt.ylim(0, 100)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information, we highly recommend you visit `signac` [documentation page](https://docs.signac.io/en/latest), which includes explanations about all of the many other features not covered here, which could assist you in your parameter scans using Tidy3D."
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to perform parallel/batch processing of simulations in Tidy3D FDTD.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "develop",
   "language": "python",
   "name": "python3"
  },
  "keywords": "parallel processing, batch, parameter scan, sweep, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  },
  "title": "Performing Parameter Scan in Tidy3D | Flexcompute",
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.1/3.1 kB • ? • 0:00:00\n
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.1/3.1 kB • ? • 0:00:00\n
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━ 100.0% • 28.3/28.3 MB • 24.9 MB/s • 0:00:00\n
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solver progress (field decay = 2.31e-05) ━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n
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l=5.00: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=5.70: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=6.40: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.10: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=7.80: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=8.50: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.20: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=9.90: status = success  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=10.60: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=11.30: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nl=12.00: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.1/3.1 kB • ? • 0:00:00\n
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.0/3.0 kB • ? • 0:00:00\n
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↓ monitor_data.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 82.4/82.4 kB • ? • 0:00:00\n
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↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.1/3.1 kB • ? • 0:00:00\n
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