{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
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   "source": [
    "# GDSII import\n",
    "\n",
    "<img src=\"img/splitter.png\" alt=\"diagram\" width=\"400\"/>\n",
    "\n",
    "In Tidy3D, complex structures can be defined or imported from GDSII files via the third-party [gdstk](https://heitzmann.github.io/gdstk/) package. In this tutorial, we will first illustrate how to use the package to define a structure, then we will save this to file, and then we will read that file and import the structures in a simulation.\n",
    "\n",
    "Note that this tutorial requires gdstk, so grab it with `pip install gdstk` before running the tutorial or uncomment the cell line below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-02-03T01:42:32.490752Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<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\">[19:42:32] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> This version of Tidy3D was pip installed from the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d-beta'</span> repository on   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">103</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         PyPI. Future releases will be uploaded to the <span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d'</span> repository. From now on, <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         please use <span style=\"color: #008000; text-decoration-color: #008000\">'pip install tidy3d'</span> instead.                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[19:42:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on   \u001b]8;id=467878;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=198439;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m               \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m         please use \u001b[32m'pip install tidy3d'\u001b[0m instead.                                        \u001b[2m               \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "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><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Using client version: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.9</span>.0rc1                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1                                                  \u001b]8;id=121716;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=739439;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
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   ],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gdstk\n",
    "import os\n",
    "\n",
    "# tidy3d import\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a beam splitter with gdstk\n",
    "\n",
    "First, we will construct an integrated beam splitter as in the title image in this notebook using `gdstk`. If you are only interested in importing an already existing GDSII file, just jump ahead to the next section.\n",
    "\n",
    "We first define some structural parameters. We consider the sidewall of the device to be slanted, deviating from the vertical sidewall by `sidewall_angle`. `sidewall_angle>0` corresponds to a typical fabrication scenario where the base of the device is larger than the top.\n",
    "\n",
    "We define the device by constructing the cross section of the device. The cross section can be supplied at the `top`, `bottom`, or the `middle` of the device, specified by the parameter `reference_plane`. Here we choose to define the cross section on the base. On the base, the two arms of the device start at a distance `wg_spacing_in` apart, then come together at a coupling distance `wg_spacing_coup` for a certain length `coup_length`, and then split again into separate ports. In the coupling region, the field overlap results in energy exchange between the two waveguides. Here, we will only see how to define, export, and import such a device using `gdstk`. When importing the device, we can optionally dilate or erode its cross section via `dilation`. In a later example we will simulate the device and study the frequency dependence of the transmission into each of the ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.493752Z",
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     "shell.execute_reply": "2023-02-03T01:42:32.496162Z"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "### Length scale in micron.\n",
    "\n",
    "# Waveguide width\n",
    "wg_width = 0.45\n",
    "# Waveguide separation in the beginning/end\n",
    "wg_spacing_in = 8\n",
    "# Length of the coupling region\n",
    "coup_length = 10\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",
    "# Reference plane where the cross section of the device is defined\n",
    "reference_plane = \"bottom\"\n",
    "# Length of the bend region\n",
    "bend_length = 16\n",
    "# Waveguide separation in the coupling region\n",
    "wg_spacing_coup = 0.10\n",
    "# Total device length along propagation direction\n",
    "device_length = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Geometry\n",
    "To create the device, we will use the `RobustPath` class to define two parallel waveguide with a variable distance between them.  First, we define a helper fuction that will give the shape of the waveguides in the S-bends that bring them together and then apart.  The distance between them (described by the `offset` parameter in `RobustPath`) will follow a hyperbolic tangent function, so we create a helper function that simple produces a normalized version of _tanh_ in the unit interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-02-03T01:42:32.645919Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11dd334c0>"
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     "execution_count": 1,
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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "fig, ax = plt.subplots(1, figsize=(7, 4))\n",
    "x = np.linspace(0, 1, 101)\n",
    "for max_arg in [2, 3, 4]:\n",
    "    s_bend = tanh_interp(max_arg)\n",
    "    ax.plot(x, s_bend(x), label=f\"tanh_interp({max_arg})\")\n",
    "ax.set_title(\"Tanh S-Bend\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `tanh_interp(3)` for our waveguides and put the whole geometry creation steps in convenience function. The [documentation for `RobustPath`](https://heitzmann.github.io/gdstk/geometry/gdstk.RobustPath.html) has more details on the use of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.649207Z",
     "iopub.status.busy": "2023-02-03T01:42:32.649004Z",
     "iopub.status.idle": "2023-02-03T01:42:32.654528Z",
     "shell.execute_reply": "2023-02-03T01:42:32.654142Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_coupler(\n",
    "    length, wg_spacing_in, wg_width, wg_spacing_coup, coup_length, bend_length, npts_bend=30\n",
    "):\n",
    "    \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n",
    "    # bend interpolator\n",
    "    interp = tanh_interp(3)\n",
    "    delta = wg_width + wg_spacing_coup - wg_spacing_in\n",
    "    offset = lambda u: wg_spacing_in + interp(u) * delta\n",
    "\n",
    "    coup = gdstk.RobustPath(\n",
    "        (-0.5 * length, 0),\n",
    "        (wg_width, wg_width),\n",
    "        wg_spacing_in,\n",
    "        simple_path=True,\n",
    "        layer=1,\n",
    "        datatype=[0, 1],\n",
    "    )\n",
    "    coup.segment((-0.5 * coup_length - bend_length, 0))\n",
    "    coup.segment(\n",
    "        (-0.5 * coup_length, 0), offset=[lambda u: -0.5 * offset(u), lambda u: 0.5 * offset(u)]\n",
    "    )\n",
    "    coup.segment((0.5 * coup_length, 0))\n",
    "    coup.segment(\n",
    "        (0.5 * coup_length + bend_length, 0),\n",
    "        offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n",
    "    )\n",
    "    coup.segment((0.5 * length, 0))\n",
    "    return coup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing to GDS cells\n",
    "\n",
    "Next, we construct the splitter and write it to a GDS cell. We add a rectangle for the substrate to layer 0, and in layer 1 we add two paths, one for the upper and one for the lower splitter arms, and set the path width to be the waveguide width defined above.\n",
    "\n",
    "We also store the cell in a library that is saved to a file, so that we can demosntrate how to load the geometry straight from a gds file. Alternatively, we could use the created cell directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.656720Z",
     "iopub.status.busy": "2023-02-03T01:42:32.656554Z",
     "iopub.status.idle": "2023-02-03T01:42:32.662375Z",
     "shell.execute_reply": "2023-02-03T01:42:32.661931Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a gds cell to add our structures to\n",
    "coup_cell = gdstk.Cell(\"Coupler\")\n",
    "\n",
    "# make substrate and add to cell\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",
    "\n",
    "coup_cell.add(substrate)\n",
    "\n",
    "# make coupler and add to the cell\n",
    "coup = make_coupler(\n",
    "    device_length, wg_spacing_in, wg_width, wg_spacing_coup, coup_length, bend_length\n",
    ")\n",
    "\n",
    "coup_cell.add(coup)\n",
    "\n",
    "# Create a library for the cell and save it, just so that we can demosntrate loading\n",
    "# geometry from a gds file\n",
    "gds_path = \"data/coupler.gds\"\n",
    "\n",
    "if os.path.exists(gds_path):\n",
    "    os.remove(gds_path)\n",
    "\n",
    "lib = gdstk.Library()\n",
    "lib.add(coup_cell)\n",
    "lib.write_gds(gds_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Loading a GDS file into Tidy3d\n",
    "\n",
    "We first load the file we just created into a gdstk library and grab its top level cell, containing our structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.664507Z",
     "iopub.status.busy": "2023-02-03T01:42:32.664336Z",
     "iopub.status.idle": "2023-02-03T01:42:32.666821Z",
     "shell.execute_reply": "2023-02-03T01:42:32.666251Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lib_loaded = gdstk.read_gds(gds_path)\n",
    "coup_cell_loaded = lib_loaded.top_level()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Geometries\n",
    "\n",
    "Then we can construct tidy3d \"PolySlab\" geometries from the GDS cell we just loaded, along with other information such as the axis, sidewall angle, and bounds of the \"slab\". When loading GDS cell as the cross section of the device, we can tune ``reference_plane`` to set the cross section to lie at ``bottom``, ``middle``, or ``top`` of the \"PolySlab\" with respect to the axis. E.g. if ``axis=1``, ``bottom`` refers to the negative side of the y-axis, and ``top`` refers to the positive side of the y-axis. Additionally, we can optionally dilate or erode the cross section by setting `dilation`. A negative `dilation` corresponds to erosion.\n",
    "\n",
    "Note, we have to keep track of the `gds_layer` and `gds_dtype` used to defined the GDS cell earlier, so we can load the right components.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.669060Z",
     "iopub.status.busy": "2023-02-03T01:42:32.668788Z",
     "iopub.status.idle": "2023-02-03T01:42:32.713571Z",
     "shell.execute_reply": "2023-02-03T01:42:32.713119Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define waveguide height\n",
    "wg_height = 0.22\n",
    "dilation = 0.02\n",
    "\n",
    "[substrate_geo] = td.PolySlab.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=0,\n",
    "    gds_dtype=0,\n",
    "    axis=2,\n",
    "    slab_bounds=(-430, 0),\n",
    "    reference_plane=reference_plane,\n",
    ")\n",
    "top_arm_geo, bot_arm_geo = td.PolySlab.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=1,\n",
    "    axis=2,\n",
    "    slab_bounds=(0, wg_height),\n",
    "    sidewall_angle=sidewall_angle,\n",
    "    dilation=dilation,\n",
    "    reference_plane=reference_plane,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we can also individually select the dtype by supplying `gds_dtype` to `PolySlab.from_gds`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.716127Z",
     "iopub.status.busy": "2023-02-03T01:42:32.715933Z",
     "iopub.status.idle": "2023-02-03T01:42:32.759641Z",
     "shell.execute_reply": "2023-02-03T01:42:32.759259Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "top_arm_geo = td.PolySlab.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=1,\n",
    "    gds_dtype=0,\n",
    "    axis=2,\n",
    "    slab_bounds=(0, wg_height),\n",
    "    sidewall_angle=sidewall_angle,\n",
    "    dilation=dilation,\n",
    "    reference_plane=reference_plane,\n",
    ")[0]\n",
    "bot_arm_geo = td.PolySlab.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=1,\n",
    "    gds_dtype=1,\n",
    "    axis=2,\n",
    "    slab_bounds=(0, wg_height),\n",
    "    sidewall_angle=sidewall_angle,\n",
    "    dilation=dilation,\n",
    "    reference_plane=reference_plane,\n",
    ")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the base and the top of the coupler waveguide arms to make sure it looks ok. The base of the device should be larger than the top due to a positive `sidewall_angle`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.761860Z",
     "iopub.status.busy": "2023-02-03T01:42:32.761689Z",
     "iopub.status.idle": "2023-02-03T01:42:32.969605Z",
     "shell.execute_reply": "2023-02-03T01:42:32.969184Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 5.0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 1, figsize=(15, 6))\n",
    "top_arm_geo.plot(z=0.0, ax=ax[0])\n",
    "bot_arm_geo.plot(z=0.0, ax=ax[0])\n",
    "ax[0].set_ylim(-5, 5)\n",
    "\n",
    "top_arm_geo.plot(z=wg_height, ax=ax[1])\n",
    "bot_arm_geo.plot(z=wg_height, ax=ax[1])\n",
    "ax[1].set_ylim(-5, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Structures\n",
    "\n",
    "To make use of these new geometries, we need to load them into a tidy3d.Simulation as td.Structures with material properties.\n",
    "\n",
    "We'll define the substrate and waveguide mediums and then link them up with the Polyslabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.972018Z",
     "iopub.status.busy": "2023-02-03T01:42:32.971824Z",
     "iopub.status.idle": "2023-02-03T01:42:32.975303Z",
     "shell.execute_reply": "2023-02-03T01:42:32.974896Z"
    },
    "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(\n",
    "    geometry=substrate_geo,  # td.Box(center=(0, 0, -td.inf/2), size=(td.inf, td.inf, td.inf)),\n",
    "    medium=medium_sub,\n",
    ")\n",
    "\n",
    "# Waveguides (import all datatypes if gds_dtype not specified)\n",
    "top_arm = td.Structure(geometry=top_arm_geo, medium=medium_wg)\n",
    "\n",
    "bot_arm = td.Structure(geometry=bot_arm_geo, medium=medium_wg)\n",
    "\n",
    "structures = [substrate, top_arm, bot_arm]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Simulation\n",
    "\n",
    "Now let's set up the rest of the Simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.977553Z",
     "iopub.status.busy": "2023-02-03T01:42:32.977410Z",
     "iopub.status.idle": "2023-02-03T01:42:32.984122Z",
     "shell.execute_reply": "2023-02-03T01:42:32.983726Z"
    },
    "tags": []
   },
   "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> No sources in simulation.                                                     <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">simulation.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#494\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">494</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                                                     \u001b]8;id=195468;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\u001b\\\u001b[2msimulation.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=737613;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#494\u001b\\\u001b[2m494\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulation size along propagation direction\n",
    "sim_length = 2 + 2 * bend_length + coup_length\n",
    "\n",
    "# Spacing between waveguides and PML\n",
    "pml_spacing = 1\n",
    "sim_size = (\n",
    "    np.ceil(sim_length),\n",
    "    np.ceil(wg_spacing_in + wg_width + 2 * pml_spacing),\n",
    "    np.ceil(wg_height + 2 * pml_spacing),\n",
    ")\n",
    "\n",
    "# grid size in each direction\n",
    "dl = 0.020\n",
    "\n",
    "### Initialize and visualize simulation ###\n",
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    structures=structures,\n",
    "    run_time=2e-12,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Simulation Geometry\n",
    "\n",
    "Let's take a look at the simulation all together with the PolySlabs added. Here the angle of the sidewall deviating from the vertical direction is 30 degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T01:42:32.986176Z",
     "iopub.status.busy": "2023-02-03T01:42:32.986017Z",
     "iopub.status.idle": "2023-02-03T01:42:33.213200Z",
     "shell.execute_reply": "2023-02-03T01:42:33.212771Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 1.0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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, edgecolor=\"k\", ax=ax1)\n",
    "sim.plot(x=0.1, lw=1, edgecolor=\"k\", ax=ax2)\n",
    "\n",
    "ax2.set_xlim([-3, 3])\n",
    "ax2.set_ylim([-1, 1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}