{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-00",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Directional coupler\n",
    "\n",
    "Directional couplers are fundamental components in photonic integrated circuits, enabling efficient splitting and combining of optical signals. By placing two parallel waveguides in close proximity, these passive devices exploit evanescent field coupling to control how light is distributed between channels. It is also possible to control the output by adjusting the relative phase between the inputs, enabling flexible power splitting ratios and switching behavior. Their design and compatibility with silicon-on-insulator technology have made them a popular choice for applications ranging from wavelength-division multiplexing to optical switching and signal processing.\n",
    "\n",
    "An important consideration is how fabrication variations might affect device performance. Hence, one common approach is to sweep over different parameters and see how the quantities of interest  behave.\n",
    "\n",
    "In this notebook, we will demonstrate how to set up a simple directional coupler simulation, illustrating the common steps of the workflow:\n",
    "\n",
    "[1](#1). Importing the geometry using [Photonforge](https://www.flexcompute.com/photonforge/landing-page/). For the process of this geometry, please follow the steps presented in [this](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSCreation) example.\n",
    "\n",
    "[2](#2). Placing the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) and [ModeMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) to excite the first TE mode and  analyze the transmittance and losses on both arms\n",
    "\n",
    "[3](#3). Carry out the post-process analysis to find the 50/50 splitting frequency and efficiency.\n",
    "\n",
    "[4](#4). Create a parametric sweep over different values of the sidewall angle and its effect on the splitting frequency and efficiency, and create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object to run in parallel.\n",
    "\n",
    "[5](#5). Use the `Simulation.updated_copy` method to easily create a simulation with two sources at the input ports with a $\\pi$/2 phase difference.\n",
    "\n",
    "<img src=\"img/DirectionalCoupler.png\" alt=\"Schematic of the directional coupler\" width=\"500\"/>\n",
    "\n",
    "For instructions on how to calculate the full S-matrix, please refer to our [SMatrix](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.Port.html) tutorial. Additionally, if you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-01",
   "metadata": {},
   "source": [
    "First, we import the relevant packages. We will use [Photonforge](https://www.flexcompute.com/photonforge/) for importing the geometry. To install it, uncomment the `!pip install photonforge` line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# install photonforge\n",
    "# !pip install photonforge\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import photonforge as pf\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-03",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Importing the Geometry <a name=\"1\"></a>\n",
    "\n",
    "We will use Photonforge to load the GDS file and extract polygon vertices, which are then extruded along the z-direction (`axis=2`) into [`PolySlab`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html) geometries with a given sidewall angle. For detailed instructions, please refer to [this](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSImport) tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell-04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gds_path = \"misc/coupler.gds\"\n",
    "\n",
    "components = pf.load_layout(gds_path)\n",
    "top_level = pf.find_top_level(*components.values())[0]\n",
    "structures_dict = top_level.get_structures()\n",
    "\n",
    "# Define waveguide height and fabrication parameters\n",
    "wg_height = 0.22\n",
    "dilation = 0.02\n",
    "sidewall_angle = np.pi / 6\n",
    "reference_plane = \"bottom\"\n",
    "\n",
    "substrate_geo = td.GeometryGroup(\n",
    "    geometries=[\n",
    "        td.PolySlab(vertices=p.to_polygon().vertices, axis=2, slab_bounds=(-4, 0))\n",
    "        for p in structures_dict.get((0, 0), [])\n",
    "    ]\n",
    ")\n",
    "\n",
    "arms_geo = td.GeometryGroup(\n",
    "    geometries=[\n",
    "        td.PolySlab(\n",
    "            vertices=p.to_polygon().vertices,\n",
    "            axis=2,\n",
    "            slab_bounds=(0, wg_height),\n",
    "            sidewall_angle=sidewall_angle,\n",
    "            dilation=dilation,\n",
    "            reference_plane=reference_plane,\n",
    "        )\n",
    "        for p in structures_dict.get((1, 0), [])\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-05",
   "metadata": {},
   "source": [
    "### Set up Structures\n",
    "\n",
    "Next, we assign material properties to the imported geometries and create the [`td.Structures`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/structures.html) with each of those material properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cell-06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Permittivity of waveguide and substrate\n",
    "wg_n = 3.48\n",
    "sub_n = 1.45\n",
    "medium_wg = td.Medium(permittivity=wg_n**2)\n",
    "medium_sub = td.Medium(permittivity=sub_n**2)\n",
    "\n",
    "# Substrate\n",
    "substrate = td.Structure(geometry=substrate_geo, medium=medium_sub)\n",
    "\n",
    "# Waveguides\n",
    "arms = td.Structure(geometry=arms_geo, medium=medium_wg)\n",
    "\n",
    "structures = [substrate, arms]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-07",
   "metadata": {},
   "source": [
    "## Set up Simulation <a name=\"2\"></a>\n",
    "\n",
    "Now we set up the simulation. First, we define the wavelength of interest and set the runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cell-08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wavelength range\n",
    "wl1 = 1.4\n",
    "wl2 = 1.7\n",
    "\n",
    "# Transforming in frequency\n",
    "f1 = td.C_0 / wl2\n",
    "f2 = td.C_0 / wl1\n",
    "\n",
    "fwidth = (f2 - f1) / 2\n",
    "freq0 = f1 + fwidth\n",
    "wl0 = td.C_0 / freq0\n",
    "\n",
    "# Frequencies to analyze\n",
    "num_freqs = 21\n",
    "freqs = [freq0] if num_freqs == 1 else np.linspace(freq0 - fwidth, freq0 + fwidth, num_freqs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-09",
   "metadata": {},
   "source": [
    "Now, we add the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html), which excites the first order TE mode (`mode_index = 0`). To analyze the transmittance, we will place [ModeMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) at the end of both output arms. These monitors will carry out mode decomposition and analyze the fraction of power transmitted in each mode. Since we are only interested in the first order TE mode, the number of modes analyzed can be just 1.\n",
    "\n",
    "We will also add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to visualize the field profile in the xy-plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell-10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source\n",
    "source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "source = td.ModeSource(\n",
    "    center=(-8, -2, 0), size=(0, 2, 1), source_time=source_time, mode_index=0, direction=\"+\"\n",
    ")\n",
    "\n",
    "# Field monitor\n",
    "field_mon = td.FieldMonitor(\n",
    "    name=\"field_mon\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, wg_height / 2),\n",
    "    freqs=freqs,\n",
    ")\n",
    "\n",
    "# Mode monitor\n",
    "mode_spec = td.ModeSpec(num_modes=1)\n",
    "mode_mon_up = td.ModeMonitor(\n",
    "    center=(8, 2, 0), size=(0, 2, 1), freqs=freqs, name=\"mode_mon_up\", mode_spec=mode_spec\n",
    ")\n",
    "\n",
    "mode_mon_down = td.ModeMonitor(\n",
    "    center=(8, -2, 0), size=mode_mon_up.size, freqs=freqs, name=\"mode_mon_down\", mode_spec=mode_spec\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-11",
   "metadata": {},
   "source": [
    "Finally, we define the grid with our [AutoGrid](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AutoGrid.html) object, which  automatically creates a non-uniform mesh, and place everything together in the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# GridSpec\n",
    "min_steps_per_wvl = 15\n",
    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl)\n",
    "\n",
    "# Simulation size\n",
    "sim_size = (17, 7, wg_height + 2)\n",
    "sim_center = list(arms_geo.bounding_box.center)\n",
    "sim_center[2] = 0\n",
    "\n",
    "### Initialize and visualize simulation ###\n",
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=sim_center,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=structures,\n",
    "    run_time=5e-12,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    sources=[source],\n",
    "    monitors=[field_mon, mode_mon_up, mode_mon_down],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-13",
   "metadata": {},
   "source": [
    "### Plot Simulation Geometry\n",
    "\n",
    "It is a good practice to check the simulation and see if everything is correctly set. That can be easily achieved with the `Simulation.plot` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell-14",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 5))\n",
    "\n",
    "sim.plot(z=wg_height / 2, lw=1, ax=ax1)\n",
    "sim.plot(x=0.1, lw=1, ax=ax2)\n",
    "\n",
    "ax2.set_xlim([-3, 3])\n",
    "_ = ax2.set_ylim([-1, 1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-15",
   "metadata": {},
   "source": [
    "We can also see the 3D model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell-16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-17",
   "metadata": {},
   "source": [
    "Before running the simulation, we can check the **maximum** possible Flexcredit cost. If the field decays before the runtime, the simulation will auto shut-off and we will only be billed for the Flexcredits of the effective runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cell-18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:09:25 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'directional coupler'</span> with resource_id                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:09:25 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'directional coupler'\u001b[0m with resource_id                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-45a0-b8ed-9ed9c6cfdeec'</span></a>.                                         \n",
       "</pre>\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=746355;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=787448;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=746355;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=558474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=746355;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32m-e785aa79-451\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=746355;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e785aa79-451f-45a0-b8ed-9ed9c6cfdeec\u001b\\\u001b[32mf-45a0-b8ed-9ed9c6cfdeec'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-431883bc-afbb-4cc8-a95f-ddfeaeeb059a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=386102;https://tidy3d.simulation.cloud/folders/folder-431883bc-afbb-4cc8-a95f-ddfeaeeb059a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29b5fb4f12a24202b92d66238fc3db1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:09:29 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:09:29 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.241\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": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:09:30 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.241</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:09:30 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.241\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"
    }
   ],
   "source": [
    "# Estimating the cost\n",
    "task_id = web.upload(sim, \"directional coupler\")\n",
    "cost = web.estimate_cost(task_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-19",
   "metadata": {},
   "source": [
    "Finally, we can run the simulation and analyze the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cell-20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4e40-9247-be5f753094f4'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=901315;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=861270;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=901315;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=219937;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=901315;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32m-e928be0f-051\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=901315;https://tidy3d.simulation.cloud/workbench?taskId=fdve-e928be0f-051c-4e40-9247-be5f753094f4\u001b\\\u001b[32mc-4e40-9247-be5f753094f4'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, \"directional coupler\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-21",
   "metadata": {},
   "source": [
    "##  Postprocessing <a name=\"3\"></a>\n",
    "\n",
    "Now we would like to analyze the output of each arm as a function of wavelength, and find the frequency where the fields are equally distributed, and the losses.\n",
    "\n",
    "The monitor decomposes the fields into mode components, and since the source is [normalized](https://docs.flexcompute.com/projects/tidy3d/en/latest/faq/docs/faq/How-are-results-normalized.html), the transmittance of each component is simply $\\text{|amplitude|}^2$.\n",
    "\n",
    "For finding the point where the amplitudes of each arm are the same, we will just look at the signal change of the difference between both signals, which is the crossing point. For better resolution, we will interpolate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cell-22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crossing wavelength :1.53\n",
      "Total transmittance: 0.9910\n"
     ]
    }
   ],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "\n",
    "\n",
    "def postprocess(sim_data, plot=True):\n",
    "    transmission_up = sim_data[\"mode_mon_up\"].amps.sel(mode_index=0, direction=\"+\").abs ** 2\n",
    "    transmission_down = sim_data[\"mode_mon_down\"].amps.sel(mode_index=0, direction=\"+\").abs ** 2\n",
    "    freqs = transmission_up.f.values\n",
    "\n",
    "    diff = (transmission_up - transmission_down).values\n",
    "\n",
    "    # Interpolate difference to find zero crossing (sign change)\n",
    "    sign_change = np.where(np.diff(np.sign(diff)))[0]\n",
    "    if len(sign_change) == 0:\n",
    "        crossing_freq = freqs[np.argmin(abs(diff))]\n",
    "        print(\"No sign change found, showing closest point only.\")\n",
    "    else:\n",
    "        i = sign_change[0]\n",
    "        f_interp = interp1d(diff[i : i + 2], freqs[i : i + 2])\n",
    "        crossing_freq = f_interp(0)\n",
    "    amp_crossing = np.interp(crossing_freq, freqs, transmission_up)\n",
    "    if plot:\n",
    "        fig, ax = plt.subplots()\n",
    "\n",
    "        wavelengths = td.C_0 / freqs\n",
    "        ax.plot(wavelengths, 10 * np.log10(transmission_up), label=\"port 3\")\n",
    "        ax.plot(wavelengths, 10 * np.log10(transmission_down), label=\"port 4\")\n",
    "\n",
    "        ax.plot([td.C_0 / crossing_freq], [10 * np.log10(amp_crossing)], \"*\", color=\"black\")\n",
    "\n",
    "        ax.set_ylabel(\"Transmittance\")\n",
    "        ax.set_xlabel(\"Wavelength (µm)\")\n",
    "        ax.hlines(-3.01, 1, 1.6, colors=\"gray\", linestyles=\"dashed\")\n",
    "        ax.set_xlim(1.4, 1.6)\n",
    "        ax.set_ylim(-15, 0)\n",
    "        ax.legend()\n",
    "\n",
    "        plt.show()\n",
    "    return float(crossing_freq), 2 * amp_crossing\n",
    "\n",
    "\n",
    "cross_freq, total_transmittance = postprocess(sim_data)\n",
    "\n",
    "print(f\"Crossing wavelength :{td.C_0 / cross_freq:.2f}\")\n",
    "print(f\"Total transmittance: {total_transmittance:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-23",
   "metadata": {},
   "source": [
    "Now, we can easily plot the E field with the `SimulationData.plot_field` method. As we can see, the input field is equally split over the two output arms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cell-24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot field\n",
    "sim_data.plot_field(\"field_mon\", \"E\", \"abs\", f=cross_freq)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-25",
   "metadata": {},
   "source": [
    "## Parametric Sweep <a name=\"4\"></a>\n",
    "\n",
    "Now, we will investigate how the sidewall angle changes the component performance. To do so, we will create a dictionary where each value is a simulation object with a different coupler sidewall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cell-26",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = {}\n",
    "sidewall_angles = [0, 5, 10, 15, 20, 25, 30]\n",
    "for s_angle in sidewall_angles:\n",
    "    arms_geo_sweep = td.GeometryGroup(\n",
    "        geometries=[\n",
    "            td.PolySlab(\n",
    "                vertices=p.to_polygon().vertices,\n",
    "                axis=2,\n",
    "                slab_bounds=(0, wg_height),\n",
    "                sidewall_angle=np.deg2rad(s_angle),\n",
    "                dilation=dilation,\n",
    "            )\n",
    "            for p in structures_dict[(1, 0)]\n",
    "        ]\n",
    "    )\n",
    "    arms_sweep = td.Structure(geometry=arms_geo_sweep, medium=medium_wg)\n",
    "    sims[str(s_angle)] = sim.updated_copy(structures=[substrate, arms_sweep])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-27",
   "metadata": {},
   "source": [
    "Next, we create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object to run all simulations in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cell-28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:09:33 -03 </span>Found all simulations in cache.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:09:33 -03\u001b[0m\u001b[2;36m \u001b[0mFound all simulations in cache.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = web.Batch(simulations=sims, folder_name=\"directional_coupler\")\n",
    "batch_data = batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-29",
   "metadata": {},
   "source": [
    "Now we can easily track the crossing frequency and transmittance using our defined post-processing function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cell-30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No sign change found, showing closest point only.\n",
      "No sign change found, showing closest point only.\n"
     ]
    }
   ],
   "source": [
    "freqs = []\n",
    "transmittance = []\n",
    "for data in batch_data.values():\n",
    "    f, t = postprocess(data, plot=False)\n",
    "    freqs.append(f)\n",
    "    transmittance.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cell-31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Use scatter for color mapping\n",
    "sc = ax.scatter(sidewall_angles, td.C_0 / np.array(freqs), c=transmittance, cmap=\"viridis\")\n",
    "plt.colorbar(sc, ax=ax, label=\"Transmittance\")\n",
    "\n",
    "ax.set_xlabel(\"Sidewall Angle\")\n",
    "ax.set_ylabel(\"Wavelength (µm)\")\n",
    "ax.set_title(\"Wavelength vs Sidewall Angle (color=Transmittance)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-32",
   "metadata": {},
   "source": [
    "## Creating a Simulation With Two Ports <a name=\"5\"></a>\n",
    "\n",
    "We can use the `updated_copy` method to easily create another simulation with a second source with a $\\pi$/2 phase difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cell-33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:09:37 -03 </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-788</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">9-40bd-af2e-d32ee9af83bb'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:09:37 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=544048;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=470302;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=544048;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=302796;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=544048;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32m-a3e455ca-788\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=544048;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3e455ca-7889-40bd-af2e-d32ee9af83bb\u001b\\\u001b[32m9-40bd-af2e-d32ee9af83bb'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "source_time2 = source_time.updated_copy(phase=np.pi / 2)\n",
    "source2 = source.updated_copy(\n",
    "    center=(source.center[0], -source.center[1], source.center[2]), source_time=source_time2\n",
    ")\n",
    "\n",
    "sim2 = sim.updated_copy(sources=[source, source2])\n",
    "\n",
    "sim_data2 = web.run(sim2, \"sources at 1 and 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-34",
   "metadata": {},
   "source": [
    "As we can see, in this configuration, all power is directed to the upper output at the crossing frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cell-35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot field\n",
    "sim_data2.plot_field(\"field_mon\", \"E\", \"abs\", f=cross_freq)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to import geometries from a gds file to Tidy3D for FDTD simulations.",
  "feature_image": "./img/DirectionalCoupler.png",
  "features": [
   "Parameter sweep"
  ],
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "keywords": "gds, import, 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": "3.11.7"
  },
  "title": "GDS File Import in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}