Autograd13Metasurface.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Diffractive metasurface inverse design with topology optimization\n",
    "\n",
    "In this tutorial, we will use inverse design and topology optimization to design a diffractive metasurface that generates a desired intensity pattern when light is transmitted through it. We use the `autograd` feature from Tidy3D to perform gradient based optimization of a mask to minimize the difference between the measured and target intensity distribution.\n",
    "\n",
    "With Tidy3D's `autograd` feature, we can optimize objective functions that involve arbitrary functions over measured field patterns. We define our metasurface using an arbitrary permittivity distribution as a function of (x,y) and minimize the loss function with respect to this pattern. We also include a penalty for small feature sizes.\n",
    "\n",
    "<img src=\"img/adjoint_13.png\" width=400 alt=\"Schematic of the diffractive metasurface\">\n",
    "\n",
    "If you are unfamiliar with inverse design, we also recommend our [intro to inverse design tutorials](https://www.flexcompute.com/tidy3d/learning-center/inverse-design/) and our [primer on automatic differentiation with tidy3d](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin1Intro/). For another example of metalens adjoint optimization in Tidy3D, see [this example](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin7Metalens/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91836104-8a1e-47ac-91ab-0c4324293ffe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import autograd\n",
    "import autograd.numpy as anp\n",
    "\n",
    "import tidy3d as td\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c57a46c-641a-4d48-896a-e436e6095243",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "The setup is simple and similar to other examples, such as our [metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/). Our structure consists of a slab of dielectric in the xy plane sitting on a substrate. A plane wave is incident from below (-z). We define the slab using a custom medium, which gives us full control of the permittivity value at each point in space.\n",
    "\n",
    "### Set Global Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b9ed675-6b49-452f-a827-c4edb63c9371",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# wavelength and source properties\n",
    "wavelength = 1.0\n",
    "freq0 = td.C_0 / wavelength\n",
    "fwidth = freq0 / 10\n",
    "run_time = 100 / fwidth\n",
    "\n",
    "# permittivity of the mask and substrate\n",
    "permittivity = 2.0\n",
    "\n",
    "# side length on x and y\n",
    "length = 20\n",
    "\n",
    "# thickess of the metalens, enough to apply a relative phase shift of just over pi\n",
    "k0 = 2 * np.pi / wavelength\n",
    "delta_n = np.sqrt(permittivity) - 1\n",
    "thickness = 4 / k0 / delta_n\n",
    "\n",
    "# distances between PML and source / monitor\n",
    "buffer = 1.5 * wavelength\n",
    "\n",
    "# distances between source / monitor and the mask\n",
    "dist_src = 1.5 * wavelength\n",
    "dist_mnt = 6.1 * wavelength\n",
    "\n",
    "# resolution\n",
    "min_steps_per_wvl = 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3c7abb1-6d18-4a0b-a5ed-35adeb459dc6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# total z size and the center of the slab\n",
    "Lz = buffer + dist_src + thickness + dist_mnt + buffer\n",
    "z_center_slab = -Lz/2 + buffer + dist_src + thickness / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1f2c4d4-bf35-41fc-a97c-3429aa7f4a6a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# resolution of the design region\n",
    "dl_design_region = 2 * wavelength / min_steps_per_wvl / np.sqrt(permittivity)\n",
    "\n",
    "# number of pixel cells in the design region (in x and y)\n",
    "nx = ny = int(length / dl_design_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efd2b8d-408a-490e-a877-d3dc7520cf8e",
   "metadata": {},
   "source": [
    "### Define Simulation Components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2db7723-a7e2-4517-bfad-df1bee96afd0",
   "metadata": {},
   "source": [
    "We start with defining some \"static\" components, which don't depend on our design parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f609d704-2f5e-4af3-8be2-678a0b44f961",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# substrate of the same permittivity as the mask\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-td.inf, -td.inf, -1000),\n",
    "        rmax=(+td.inf, +td.inf, z_center_slab-thickness/2)\n",
    "    ),\n",
    "    medium=td.Medium(permittivity=permittivity)\n",
    ")\n",
    "\n",
    "# plane wave\n",
    "src = td.PlaneWave(\n",
    "    center=(0, 0, -Lz/2 + buffer),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    ")\n",
    "\n",
    "# monitor we use to measure the intensity pattern above the device\n",
    "mnt_out = td.FieldMonitor(\n",
    "    center=(0, 0, +Lz/2 - buffer),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq0],\n",
    "    colocate=False,\n",
    "    name=\"output\",\n",
    ")\n",
    "\n",
    "# monitor we use to inspect the field pattern from the side for visualization\n",
    "mnt_side = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    freqs=[freq0],\n",
    "    name=\"side\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5aff19-0a41-48bb-9296-f705417eb223",
   "metadata": {},
   "source": [
    "Next we define the mask as a function of our design parameters using topology optimization + filtering and thresholding methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85ca201b-2ef0-4eb6-9220-5bdde41f0493",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tidy3d.plugins.autograd.functions import rescale\n",
    "from tidy3d.plugins.autograd.invdes import make_filter_and_project, get_kernel_size_px\n",
    "\n",
    "radius = 0.20\n",
    "beta = 50\n",
    "\n",
    "radius = 0.120\n",
    "beta = 50\n",
    "\n",
    "filter_size = get_kernel_size_px(radius, dl_design_region)\n",
    "filter_project = make_filter_and_project(filter_size)\n",
    "\n",
    "def get_eps(params: anp.ndarray, beta: float) -> anp.ndarray: \n",
    "    \"\"\"Get the permittivity values (1, permittivity) array as a funciton of the parameters (0, 1)\"\"\"\n",
    "    density = filter_project(params, beta)\n",
    "    eps = rescale(density, 1, permittivity)\n",
    "    return eps.reshape((nx, ny, 1, 1))\n",
    "    \n",
    "def make_slab(params: anp.ndarray, beta: float) -> td.Structure:\n",
    "    \"\"\"make the phase mask as a function of the parameters for a given `beta` value.\"\"\"\n",
    "\n",
    "    # construct the coordinates\n",
    "    x0_max = +length / 2 - dl_design_region / 2\n",
    "    y0_max = +length / 2 - dl_design_region / 2\n",
    "    coords_x = np.linspace(-x0_max, x0_max, nx).tolist()\n",
    "    coords_y = np.linspace(-y0_max, y0_max, ny).tolist()\n",
    "    coords = dict(x=coords_x, y=coords_y, z=[z_center_slab], f=[freq0])\n",
    "\n",
    "    # construct the data array for the permittivity\n",
    "    eps_values = get_eps(params, beta)\n",
    "    eps_data_array = td.ScalarFieldDataArray(eps_values, coords=coords)\n",
    "\n",
    "    # construct the permittiviy dataset\n",
    "    field_components = {f\"eps_{dim}{dim}\": eps_data_array for dim in \"xyz\"}\n",
    "    eps_dataset = td.PermittivityDataset(**field_components)\n",
    "\n",
    "    # construct the phase mask slab\n",
    "    custom_medium = td.CustomMedium(eps_dataset=eps_dataset)\n",
    "    box = td.Box(center=(0, 0, z_center_slab), size=(td.inf, td.inf, thickness))\n",
    "    return td.Structure(geometry=box, medium=custom_medium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be4d9460-f52e-44e3-ad01-1f7faa3b56c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_sim(params: anp.ndarray, beta: float, pml_xy:bool=False) -> td.Simulation:\n",
    "    \"\"\"The `autogradSimulation` as a function of the design parameters.\"\"\"\n",
    "\n",
    "    slab = make_slab(params, beta)\n",
    "    \n",
    "    # put a mesh override structure to ensure uniform dl across the slab\n",
    "    design_region_mesh = td.MeshOverrideStructure(\n",
    "        geometry=slab.geometry,\n",
    "        dl=[dl_design_region] * 3,\n",
    "        enforce=True,\n",
    "    )\n",
    "\n",
    "    return td.Simulation(\n",
    "        size=(length, length, Lz),\n",
    "        grid_spec=td.GridSpec.auto(\n",
    "            min_steps_per_wvl=min_steps_per_wvl,\n",
    "            override_structures=[design_region_mesh]\n",
    "        ),\n",
    "        boundary_spec=td.BoundarySpec.pml(x=pml_xy, y=pml_xy, z=True),\n",
    "        structures=[substrate, slab],\n",
    "        monitors=[mnt_side, mnt_out],\n",
    "        sources=[src],\n",
    "        run_time=run_time,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb0c957-b748-4dd4-9fbc-9311d0fd487d",
   "metadata": {},
   "source": [
    "Let's make a simulation with some random starting parameters to inpsect our setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8e87837c-a990-415f-a121-28ed117b7d74",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "params0 = np.random.random((nx, ny))\n",
    "beta0 = 1.0\n",
    "\n",
    "symmetrize = True\n",
    "\n",
    "# symmetrize the starting parameters (optional)\n",
    "if symmetrize:\n",
    "    params0 += np.fliplr(params0)\n",
    "    params0 += np.flipud(params0)\n",
    "    params0 /= 4.0\n",
    "\n",
    "sim = make_sim(params=params0, beta=beta0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84b13caf-9509-47aa-8478-5a2149b37b6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "ax1 = sim.plot_eps(x=0, ax=ax1)\n",
    "ax2 = sim.plot_eps(z=z_center_slab, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c11ef7-1046-41da-9236-a922aa8f1220",
   "metadata": {},
   "source": [
    "## Define Objective\n",
    "\n",
    "We'll design this phase mask to give a transmitted intensity distribution of our choice.\n",
    "\n",
    "### Define Target Intensity\n",
    "\n",
    "In this case, we'll try to reproduce the Flexcompute logo, so let's make a function to generate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2a121cec-97a8-49b7-a41a-cbeff7b21cf1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-python\n",
      "  Downloading opencv_python-4.10.0.82-cp37-abi3-macosx_11_0_arm64.whl.metadata (20 kB)\n",
      "Requirement already satisfied: numpy>=1.21.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from opencv-python) (1.26.4)\n",
      "Downloading opencv_python-4.10.0.82-cp37-abi3-macosx_11_0_arm64.whl (54.8 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: opencv-python\n",
      "Successfully installed opencv-python-4.10.0.82\n"
     ]
    }
   ],
   "source": [
    "# !python3.11 -m pip install opencv-python\n",
    "import cv2\n",
    "import xarray as xr\n",
    "\n",
    "logo_fname = \"misc/logo.png\"\n",
    "\n",
    "def get_logo() -> np.ndarray:\n",
    "    \"\"\"Get the Flexcompute logo from file, load it into a numpy array, rescale it to (0, 1).\"\"\"\n",
    "    im = cv2.imread(logo_fname, cv2.IMREAD_GRAYSCALE).astype(float)\n",
    "    im -= np.min(im)\n",
    "    im /= np.max(im)\n",
    "    return im\n",
    "\n",
    "def intensity_desired_fn_logo(xs:list, ys:list, rescale:float=0.5) -> np.ndarray:\n",
    "    \"\"\"Return the 'value' of the flexcompute logo as a function of (x,y) with some rescaling.\"\"\"\n",
    "    logo_values = get_logo()\n",
    "\n",
    "    # some rotations to get the logo in the right orientation for the final intensity pattern\n",
    "    logo_values = np.rot90(np.rot90(np.rot90(logo_values)))\n",
    "\n",
    "    # re-interpolate the logo data at the supplied x,y points using xarray\n",
    "    nx, ny = logo_values.shape\n",
    "    xs_logo = np.linspace(rescale * min(xs), rescale * max(xs), nx)\n",
    "    ys_logo = np.linspace(rescale * min(ys), rescale * max(ys), ny)    \n",
    "    logo_dataarray = xr.DataArray(logo_values, coords=dict(x=xs_logo, y=ys_logo))\n",
    "    logo_interp = logo_dataarray.interp(x=xs, y=ys)\n",
    "\n",
    "    # handle any nans for out of bounds (replace with 0)\n",
    "    return np.nan_to_num(logo_interp.values, nan=np.min(logo_interp))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d061e3cf-529f-445c-9b71-4fab1bfe58e6",
   "metadata": {},
   "source": [
    "Let's test this function out by plotting our target intensity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "070e845f-41a1-449d-9d98-6294b5f794ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xs = ys = np.linspace(-length/2, length/2, nx)\n",
    "intensity_desired = intensity_desired_fn_logo(xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fdf5eded-d61b-4259-a1aa-38f637ee28a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(xs, ys, intensity_desired.T, cmap='magma')\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('desired intensity pattern')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f986be-efc7-4be6-9192-1d87bb7e8f33",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Compare Measurement to Target\n",
    "\n",
    "Next we need a way to compare the measured intensity pattern to this target intensity pattern.\n",
    "\n",
    "We'll come up with a figure of merit for the closeness of our objective.\n",
    "\n",
    "First, let's run a simulation with an empty mask to figure out what the average intensity should be at the measurement plane (for normalization later).\n",
    "\n",
    "> Note: Although Tidy3D normalizes field values by default, in this case doing a normalization run is useful as we're injecting from a substrate, which will affect the results. In the new caching feature of 2.6, these simulations will not use credits or much time when run after the first time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a2306cdb-3eb9-4553-b740-5a8a803650c5",
   "metadata": {
    "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\">14:40:14 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'normalization'</span> with task_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:14 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'normalization'\u001b[0m with task_id                          \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa'\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-62c10ddb-f686-4dd6-8685-f814110fdfaa\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f68</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">6-4dd6-8685-f814110fdfaa'</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=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=345283;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=517697;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m-62c10ddb-f68\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m6-4dd6-8685-f814110fdfaa'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1ef335f8f03492da595a384a328513e",
       "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\">14:40:15 EDT </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:15 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\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": "",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:40:19 EDT </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:19 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"></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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:40:20 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.130</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:20 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.130\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">14:40:21 EDT </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:21 EDT\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
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    },
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       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:40:29 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:29 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "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\">\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\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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      "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:40:30 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:30 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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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\">14:40:31 EDT </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f68</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">6-4dd6-8685-f814110fdfaa'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:40:31 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=958935;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=454364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m-62c10ddb-f68\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m6-4dd6-8685-f814110fdfaa'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
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       "model_id": "091abe56d6ef4938bebf93d6287a9c42",
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      "text/plain": [
       "Output()"
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     },
     "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"
      ],
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     },
     "metadata": {},
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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\">\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\">             </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tidy3d.web as web\n",
    "\n",
    "params_empty = np.zeros_like(params0)\n",
    "\n",
    "sim_empty = make_sim(params_empty, beta=100)\n",
    "sim_data_norm = web.run(sim_empty, task_name=\"normalization\", verbose=True)\n",
    "intensity_norm = sim_data_norm.get_intensity(mnt_out.name)\n",
    "intensity_norm_mean = anp.mean(intensity_norm.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "88edc61c-17c0-477c-a82c-64aeb950cc1a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average intensity of '1.81' (a.u.) measured without any device.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average intensity of '{intensity_norm_mean:.2f}' (a.u.) measured without any device.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e5f0a1-e604-4f33-a02d-53dd71d59569",
   "metadata": {},
   "source": [
    "Next let's write our loss function over the measured intensity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3a5a34bc-9f22-4338-9215-5d177da445c5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_intensities(sim_data: td.SimulationData) -> tuple[anp.ndarray, np.ndarray]:\n",
    "    \"\"\"Convenience function to grab the (unnormalized) intensity patterns from the data.\"\"\"\n",
    "\n",
    "    # first, grab the dataset storing the intensity values and coordinates\n",
    "    mnt_out_name = mnt_out.name\n",
    "    intensity_dataset = sim_data.get_intensity(mnt_out.name)\n",
    "    xs = intensity_dataset.coords[\"x\"]\n",
    "    ys = intensity_dataset.coords[\"y\"]\n",
    "\n",
    "    # the \"measured\" values are just the raw data\n",
    "    intensity_measured = anp.squeeze(intensity_dataset.values)\n",
    "\n",
    "    # the \"desired\" or \"target\" values are the logo function evaluated at the data coords\n",
    "    intensity_desired = intensity_desired_fn_logo(xs, ys)\n",
    "\n",
    "    return intensity_measured, intensity_desired\n",
    "\n",
    "# range within which to consider intensity as part of the objective function\n",
    "# eg. if the measured intensity is above int_max, we just consider it at the target value of 1.0\n",
    "intensity_range = int_min, int_max = (0.0, 1.0)\n",
    "\n",
    "def intensity_diff_fn(sim_data: td.SimulationData) -> float:\n",
    "    \"\"\"Returns a measure for the amount of difference between desired and target intensity patterns.\"\"\"\n",
    "\n",
    "    intensity_measured, intensity_desired = get_intensities(sim_data)\n",
    "    \n",
    "    # normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\n",
    "    intensity_measured *= np.mean(intensity_desired) / intensity_norm_mean\n",
    "\n",
    "    # apply the \"capping\" within intensity_range (optional)\n",
    "    int_range_magnitude = abs(int_max - int_min)\n",
    "    intensity_measured = anp.minimum(intensity_measured, int_max)\n",
    "    intensity_measured = anp.maximum(intensity_measured, int_min)\n",
    "    intensity_desired = int_range_magnitude * intensity_desired + int_min\n",
    "    \n",
    "    # take the elementwise difference\n",
    "    difference = intensity_measured - intensity_desired\n",
    "    \n",
    "    # normalized by the 'worst case' (difference if measured was exact inverse of the target)\n",
    "    difference_denominator = int_range_magnitude * np.ones_like(intensity_desired)\n",
    "    \n",
    "    # return the normalized norm of the difference\n",
    "    return anp.linalg.norm(difference) / anp.linalg.norm(difference_denominator)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3611df44-6810-4db7-93ea-96a44ac8e9e8",
   "metadata": {},
   "source": [
    "### Fabrication Constraints\n",
    "\n",
    "If desired, we can add a fabrication constraint penalty to the figure of merit.\n",
    "\n",
    "As in other notebooks, we can consider a simple penalty based on whether the structure changes upon dilation and erosion with a given distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "92a2f4c7-1a46-47d2-ba92-1920f6a6f406",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n",
    "\n",
    "def penalty_fn(params, beta):\n",
    "    processed_params = filter_project(params, beta=beta)\n",
    "    \n",
    "    penalty = ErosionDilationPenalty(pixel_size=dl_design_region, length_scale=radius)\n",
    "    return penalty.evaluate(processed_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83169dac-977c-4a37-8581-a1eb2af506d7",
   "metadata": {},
   "source": [
    "### Loss Function\n",
    "\n",
    "Finally, we can throw all of this into a loss function to minimize. \n",
    "\n",
    "We will use a very small weight on our penalty function as it turns out to not be super important in this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ac5a5c88-4247-4eeb-b8a3-96e727e3fd7d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def loss_fn(params: anp.ndarray, beta: float) -> tuple[float, dict]:\n",
    "    \"\"\"Loss function for the design, the difference in intensity + the feature size penalty.\"\"\"\n",
    "    \n",
    "    # construct and run the simulation\n",
    "    sim = make_sim(params, beta=beta)\n",
    "    sim_data = web.run(sim, task_name=\"phase_mask_example\", verbose=False)\n",
    "    \n",
    "    # grab the respective and total losses\n",
    "    return intensity_diff_fn(sim_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1399efcd-21fe-41e9-b586-0b7a16c6bc71",
   "metadata": {},
   "source": [
    "Before optimizing, let's test out our loss function to ensure we can run it and get the gradient of it with respect to the starting parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7458767b-a95c-4b07-a809-9c7ec3167ab5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "interp only works for a numeric type array. Given object.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[41], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m loss_fn_val_grad \u001b[38;5;241m=\u001b[39m autograd\u001b[38;5;241m.\u001b[39mvalue_and_grad(loss_fn)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# call this on our initial parmaeters\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m val, grad \u001b[38;5;241m=\u001b[39m \u001b[43mloss_fn_val_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta0\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/differential_operators.py:138\u001b[0m, in \u001b[0;36mvalue_and_grad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalue_and_grad\u001b[39m(fun, x):\n\u001b[1;32m    136\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a function that returns both value and gradient. Suitable for use\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;124;03m    in scipy.optimize\"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue_and_grad only applies to real scalar-output \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    141\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctions. Try jacobian, elementwise_grad or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    142\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mholomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[40], line 9\u001b[0m, in \u001b[0;36mloss_fn\u001b[0;34m(params, beta)\u001b[0m\n\u001b[1;32m      6\u001b[0m sim_data \u001b[38;5;241m=\u001b[39m web\u001b[38;5;241m.\u001b[39mrun(sim, task_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mphase_mask_example\u001b[39m\u001b[38;5;124m\"\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# grab the respective and total losses\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mintensity_diff_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_data\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[38], line 25\u001b[0m, in \u001b[0;36mintensity_diff_fn\u001b[0;34m(sim_data)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintensity_diff_fn\u001b[39m(sim_data: td\u001b[38;5;241m.\u001b[39mSimulationData) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m     23\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a measure for the amount of difference between desired and target intensity patterns.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     intensity_measured, intensity_desired \u001b[38;5;241m=\u001b[39m \u001b[43mget_intensities\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;66;03m# normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\u001b[39;00m\n\u001b[1;32m     28\u001b[0m     intensity_measured \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(intensity_desired) \u001b[38;5;241m/\u001b[39m intensity_norm_mean\n",
      "Cell \u001b[0;32mIn[38], line 6\u001b[0m, in \u001b[0;36mget_intensities\u001b[0;34m(sim_data)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# first, grab the dataset storing the intensity values and coordinates\u001b[39;00m\n\u001b[1;32m      5\u001b[0m mnt_out_name \u001b[38;5;241m=\u001b[39m mnt_out\u001b[38;5;241m.\u001b[39mname\n\u001b[0;32m----> 6\u001b[0m intensity_dataset \u001b[38;5;241m=\u001b[39m \u001b[43msim_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_intensity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmnt_out\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m xs \u001b[38;5;241m=\u001b[39m intensity_dataset\u001b[38;5;241m.\u001b[39mcoords[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      8\u001b[0m ys \u001b[38;5;241m=\u001b[39m intensity_dataset\u001b[38;5;241m.\u001b[39mcoords[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:333\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData.get_intensity\u001b[0;34m(self, field_monitor_name)\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_intensity\u001b[39m(\u001b[38;5;28mself\u001b[39m, field_monitor_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m xr\u001b[38;5;241m.\u001b[39mDataArray:\n\u001b[1;32m    320\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"return `xarray.DataArray` of the intensity of a field monitor at Yee cell centers.\u001b[39;00m\n\u001b[1;32m    321\u001b[0m \n\u001b[1;32m    322\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m        Data is interpolated to the center locations on Yee grid.\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 333\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_scalar_field\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfield_monitor_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfield_monitor_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfield_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mabs^2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m    335\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:230\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._get_scalar_field\u001b[0;34m(self, field_monitor_name, field_name, val, phase)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"return ``xarray.DataArray`` of the scalar field of a given monitor at Yee cell centers.\u001b[39;00m\n\u001b[1;32m    211\u001b[0m \n\u001b[1;32m    212\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    Data is interpolated to the center locations on Yee grid.\u001b[39;00m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    229\u001b[0m field_monitor_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_field_monitor(field_monitor_name)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_scalar_field_from_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    231\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfield_monitor_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfield_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfield_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphase\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:270\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._get_scalar_field_from_data\u001b[0;34m(self, field_monitor_data, field_name, val, phase)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m Tidy3dKeyError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPoynting component \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 270\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_at_boundaries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfield_monitor_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    272\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_phase(data\u001b[38;5;241m=\u001b[39mdataset, phase\u001b[38;5;241m=\u001b[39mphase)\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m field_name \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;66;03m# Gather vector components\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:104\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._at_boundaries\u001b[0;34m(self, monitor_data)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m monitor_data\u001b[38;5;241m.\u001b[39mpackage_colocate_results(monitor_data\u001b[38;5;241m.\u001b[39mfield_components)\n\u001b[1;32m    103\u001b[0m \u001b[38;5;66;03m# colocate to monitor grid boundaries\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmonitor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mat_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolocation_boundaries\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/monitor_data.py:312\u001b[0m, in \u001b[0;36mAbstractFieldData.at_coords\u001b[0;34m(self, coords)\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(ncoords \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m ncoords \u001b[38;5;129;01min\u001b[39;00m coord_lens):\n\u001b[1;32m    310\u001b[0m         xyz_kwargs[dim] \u001b[38;5;241m=\u001b[39m coords_dim\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolocate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mxyz_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/dataset.py:140\u001b[0m, in \u001b[0;36mAbstractFieldDataset.colocate\u001b[0;34m(self, x, y, z)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m coord_data\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    133\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m DataError(\n\u001b[1;32m    134\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolocate given \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoords_supplied\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    135\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata only has one coordinate at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    136\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTherefore, can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt colocate along this dimension. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    137\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msupply \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=None to skip it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    138\u001b[0m             )\n\u001b[0;32m--> 140\u001b[0m     centered_fields[field_name] \u001b[38;5;241m=\u001b[39m \u001b[43mfield_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msupplied_coord_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbounds_error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m# combine all centered fields in a dataset\u001b[39;00m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpackage_colocate_results(centered_fields)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/xarray/core/dataarray.py:2319\u001b[0m, in \u001b[0;36mDataArray.interp\u001b[0;34m(self, coords, method, assume_sorted, kwargs, **coords_kwargs)\u001b[0m\n\u001b[1;32m   2193\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Interpolate a DataArray onto new coordinates\u001b[39;00m\n\u001b[1;32m   2194\u001b[0m \n\u001b[1;32m   2195\u001b[0m \u001b[38;5;124;03mPerforms univariate or multivariate interpolation of a DataArray onto\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2316\u001b[0m \u001b[38;5;124;03m  * y        (y) int64 24B 11 13 15\u001b[39;00m\n\u001b[1;32m   2317\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muifc\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2319\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   2320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterp only works for a numeric type array. Given \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2321\u001b[0m     )\n\u001b[1;32m   2322\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_temp_dataset()\u001b[38;5;241m.\u001b[39minterp(\n\u001b[1;32m   2323\u001b[0m     coords,\n\u001b[1;32m   2324\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2327\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcoords_kwargs,\n\u001b[1;32m   2328\u001b[0m )\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n",
      "\u001b[0;31mTypeError\u001b[0m: interp only works for a numeric type array. Given object."
     ]
    }
   ],
   "source": [
    "# construct a funciton of `params` and `beta` that returns the loss value, gradient, and the aux_data\n",
    "loss_fn_val_grad = autograd.value_and_grad(loss_fn)\n",
    "\n",
    "# call this on our initial parmaeters\n",
    "val, grad = loss_fn_val_grad(params0, beta=beta0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea8e2fa9-e672-4b78-a8ce-d6369d84d412",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# penalty = aux_data[\"penalty\"]\n",
    "# intensity_diff = aux_data[\"intensity_diff\"]\n",
    "\n",
    "print(f\"initial loss value = {val:.3f}\")\n",
    "# print(f\" - penalty contribution = {penalty:.3f}\")\n",
    "# print(f\" - intensity difference contribution = {intensity_diff:.3f}\")\n",
    "print(f\"gradient shape = {grad.shape:}\")\n",
    "print(f\"norm of gradient = {anp.linalg.norm(grad):.3e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d28f684-f441-4844-8ace-b5f711a9f3aa",
   "metadata": {},
   "source": [
    "Looks good! We get a reasonable loss value, our gradient has the expected shape, and it's non-zero (which can often indicate some issue concerning the flow of the gradient through the objective function.\n",
    "\n",
    "> Note: we passed `has_aux=True`, which means that we tell `autograd` that the 2nd return value (`aux_data`) should be ignored in the gradient calculation. In other words, it tells `autograd` to only consider the gradient w.r.t. the first output (`total_loss`).\n",
    "\n",
    "Let's also visualize the fields for another sanity check, which we can grab from the `aux_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6fdffd3-6947-43e0-ac78-341016bc8561",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sim_data = aux_data[\"sim_data\"]\n",
    "sim_data.plot_field(field_monitor_name=\"side\", field_name=\"Ex\", val=\"real\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f150383-ebdc-42c8-a020-c94da1a587a1",
   "metadata": {},
   "source": [
    "## Optimize Device\n",
    "\n",
    "Now we are finally ready to optimize our device.\n",
    "\n",
    "As in the other tutorials, we use the implementation of \"Adam Optimization\" from `optax`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ed00dc-0e76-488b-aaa5-ee7a4a15d9cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import optax\n",
    "\n",
    "# hyperparameters\n",
    "num_steps = 35\n",
    "learning_rate = 0.75\n",
    "\n",
    "# initialize adam optimizer with starting parameters\n",
    "params = params0.copy()\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "opt_state = optimizer.init(params)\n",
    "\n",
    "# store history\n",
    "history = dict(loss=[], params=[], betas=[], penalty=[], intensity_diff=[], sim_data=[])\n",
    "\n",
    "# gradually increase the binarization strength over iteration\n",
    "beta_increment = 0.5\n",
    "beta = beta0\n",
    "\n",
    "for i in range(num_steps):\n",
    "    print(f\"step = ({i + 1} / {num_steps})\")\n",
    "\n",
    "    # compute gradient and current loss funciton value\n",
    "    loss, gradient = loss_fn_val_grad(params, beta=beta)\n",
    "    # penalty = aux_data[\"penalty\"]\n",
    "    # intensity_diff = aux_data[\"intensity_diff\"]\n",
    "    \n",
    "    # save history\n",
    "    history[\"loss\"].append(loss)\n",
    "    history[\"params\"].append(params)\n",
    "    history[\"betas\"].append(beta)\n",
    "    # history[\"penalty\"].append(penalty)\n",
    "    # history[\"intensity_diff\"].append(intensity_diff)\n",
    "    history[\"sim_data\"].append(aux_data[\"sim_data\"])\n",
    "    \n",
    "    # log some output\n",
    "    print(f\"\\tloss = {loss:.3e}\")\n",
    "    # print(f\"\\t\\tpenalty = {penalty:.3e}\")\n",
    "    # print(f\"\\t\\tintensity difference = {intensity_diff:.3e}\")\n",
    "    print(f\"\\tbeta = {beta:.2f}\")\n",
    "    print(f\"\\t|gradient| = {np.linalg.norm(gradient):.3e}\")    \n",
    "    \n",
    "    # compute and apply updates to the optimizer based on gradient (+1 sign to minimize loss_fn)\n",
    "    updates, opt_state = optimizer.update(+gradient, opt_state, params)\n",
    "    params = optax.apply_updates(params, updates)\n",
    "\n",
    "    # cap the parameters between their bounds\n",
    "    params = anp.minimum(params, 1.0)\n",
    "    params = anp.maximum(params, 0.0)\n",
    "    \n",
    "    # update the beta value\n",
    "    beta += beta_increment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f4797d-db68-47d3-9f5d-2c97d602a682",
   "metadata": {},
   "source": [
    "## Analyze Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e552cf-70e7-45fc-9969-d3a93ff00454",
   "metadata": {
    "tags": []
   },
   "source": [
    "First, let's plot the objective function history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb24f819-69ac-4669-97af-ad5d4b266368",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.plot(history[\"loss\"], label=\"total loss\")\n",
    "# plt.plot(history[\"penalty\"], linestyle=\"-.\", label=\"penalty contribution\")\n",
    "# plt.plot(history[\"intensity_diff\"], linestyle=\"--\", label=\"intensity contribution\")\n",
    "plt.plot(np.zeros_like(history[\"loss\"]), linestyle=\":\", color=\"k\", label=\"no loss\")\n",
    "\n",
    "plt.xlabel(\"iteration number\")\n",
    "plt.ylabel(\"loss value\")\n",
    "\n",
    "plt.title(\"loss function over optimization\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b34678-1d12-4c62-8ce0-f51ab7eaf656",
   "metadata": {},
   "source": [
    "Next let's plot the final device pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60477482-5eca-4047-93bd-3751cbccdf45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the final parameters, construct the final simulation\n",
    "params_final = history[\"params\"][-1]\n",
    "beta_final = history[\"betas\"][-1]\n",
    "sim_final = make_sim(params_final, beta=beta_final)\n",
    "\n",
    "# convert to regular `td.Simulation`\n",
    "sim_final.plot_eps(z=z_center_slab, monitor_alpha=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5196e34-ab65-4a40-a742-de3149c577ff",
   "metadata": {},
   "source": [
    "Let's run this simulation to see the final field patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489932b3-5676-4dca-b7ca-ddb3ac9546c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sim_data_final = web.run(sim_final, task_name='Inspect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d743ed6e-4e4e-4847-940b-1cfc7c541111",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "\n",
    "for (ax, name) in zip(axes, (\"output\", \"side\")):\n",
    "    sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb141236-58ba-460e-9b33-52615bde2174",
   "metadata": {
    "tags": []
   },
   "source": [
    "Finally, we can create a nice figure combining everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a284bac-e2e8-453b-9715-573980cc8e28",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "f, ((ax0, ax1), (ax2, ax3), (ax4, ax5)) = plt.subplots(3, 2, figsize=(9, 10), tight_layout=True)\n",
    "\n",
    "# target intensity\n",
    "im = ax0.imshow(np.rot90(intensity_desired), cmap=\"magma\")\n",
    "ax0.set_aspect(\"equal\")\n",
    "ax0.set_xlabel('x')\n",
    "ax0.set_ylabel('y')\n",
    "ax0.set_title('target intensity (normalized)')\n",
    "plt.colorbar(im, ax=ax0)\n",
    "\n",
    "# optimization progress\n",
    "ax1.plot(history[\"loss\"], label=\"total loss\")\n",
    "# ax1.plot(history[\"penalty\"], linestyle=\"-.\", label=\"penalty contribution\")\n",
    "# ax1.plot(history[\"intensity_diff\"], linestyle=\"--\", label=\"intensity contribution\")\n",
    "ax1.plot(np.zeros_like(history[\"loss\"]), linestyle=\":\", color=\"k\", label=\"no loss\")\n",
    "ax1.set_xlabel(\"iteration number\")\n",
    "ax1.set_ylabel(\"loss value\")\n",
    "ax1.set_title(\"loss function over optimization\")\n",
    "ax1.legend()\n",
    "\n",
    "# ax1.plot(history[\"loss\"])\n",
    "# ax1.set_xlabel(\"iterations\")\n",
    "# ax1.set_ylabel(\"loss function\")\n",
    "# ax1.set_title('optimization progress')\n",
    "\n",
    "# final device (top and sides)\n",
    "sim_final.plot_eps(z=z_center_slab, ax=ax2)\n",
    "ax2.set_title(\"final design\")\n",
    "sim_final.plot_eps(x=0, ax=ax3)\n",
    "ax3.set_title(\"cross section\")\n",
    "\n",
    "# final fields\n",
    "vmin = None\n",
    "vmax = None\n",
    "for (ax, name) in zip((ax4, ax5), (\"output\", \"side\")):\n",
    "    sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", vmin=vmin, vmax=vmax, ax=ax)\n",
    "\n",
    "# plt.savefig('phase_mask.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2be21825-20fe-4d08-91e6-c802b94b009d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d3792e-8e84-4414-8022-e9be437afdbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Nanophotonics",
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates how to perform inverse design optimization of a diffractive metasurface in Tidy3D FDTD.",
  "feature_image": "./img/adjoint_13.png",
  "features": [
   "Adjoint inverse design"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "inverse design, adjoint optimization, 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": "Metasurface Inverse Design with Topology Optimization"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}