{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic nonuniform grid\n",
    "\n",
    "Here we demonstrate how to use the automatic nonuniform grid generation available in Tidy3D. We will show different possible ways to discretize the air-slot waveguide first introduced in [this reference](https://opg.optica.org/ol/abstract.cfm?uri=ol-29-11-1209)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:18.542957Z",
     "iopub.status.busy": "2023-02-03T04:23:18.542476Z",
     "iopub.status.idle": "2023-02-03T04:23:20.138075Z",
     "shell.execute_reply": "2023-02-03T04:23:20.137575Z"
    },
    "scrolled": true
   },
   "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\">[22:23:19] </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[22:23:19]\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=98845;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=497926;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"
    },
    {
     "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=134166;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=849705;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basic imports\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# tidy3d imports\n",
    "import tidy3d as td\n",
    "from tidy3d.plugins import ModeSolver\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.140740Z",
     "iopub.status.busy": "2023-02-03T04:23:20.140300Z",
     "iopub.status.idle": "2023-02-03T04:23:20.144984Z",
     "shell.execute_reply": "2023-02-03T04:23:20.144602Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# basic parameters (note, all length units are microns)\n",
    "nm = 1e-3\n",
    "wavelength = 1550 * nm\n",
    "freq0 = td.C_0 / wavelength\n",
    "\n",
    "# waveguide parameters\n",
    "slot_width = 50 * nm\n",
    "box_length = 300 * nm\n",
    "box_height = 180 * nm\n",
    "\n",
    "# materials\n",
    "si_med = td.Medium(permittivity=3.48**2)\n",
    "sub_med = td.Medium(permittivity=1.46**2)\n",
    "\n",
    "# structures\n",
    "box1 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, -box_length / 2 - slot_width / 2, 0),\n",
    "        size=(td.inf, box_length, box_height),\n",
    "    ),\n",
    "    medium=si_med,\n",
    ")\n",
    "box2 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, box_length / 2 + slot_width / 2, 0),\n",
    "        size=(td.inf, box_length, box_height),\n",
    "    ),\n",
    "    medium=si_med,\n",
    ")\n",
    "\n",
    "# boundaries\n",
    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a helper function to show us the various grids as we go along this exmaple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.147078Z",
     "iopub.status.busy": "2023-02-03T04:23:20.146902Z",
     "iopub.status.idle": "2023-02-03T04:23:20.150697Z",
     "shell.execute_reply": "2023-02-03T04:23:20.150302Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Plot simulation and overlay grid in the yz and xy planes\n",
    "def plot_sim_grid(sim):\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "    sim.plot(x=0, ax=ax[0])\n",
    "    sim.plot_grid(x=0, ax=ax[0], lw=0.4, colors=\"r\")\n",
    "    ax[0].set_xlim(-0.6, 0.6)\n",
    "    ax[0].set_ylim(-0.4, 0.4)\n",
    "    sim.plot(z=0, ax=ax[1])\n",
    "    sim.plot_grid(z=0, ax=ax[1], lw=0.4, colors=\"r\")\n",
    "    ax[1].set_xlim(-0.6, 0.6)\n",
    "    ax[1].set_ylim(-0.4, 0.4)\n",
    "    print(f\"Total number of grid points (millions): {sim.num_cells / 1e6:1.2}\")\n",
    "\n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform grid\n",
    "\n",
    "The most standard way to define a simulation is to use a constant grid size in each of the three directions. This can be achieved simply using `td.GridSpec.uniform(dl=...)` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.152631Z",
     "iopub.status.busy": "2023-02-03T04:23:20.152500Z",
     "iopub.status.idle": "2023-02-03T04:23:20.420160Z",
     "shell.execute_reply": "2023-02-03T04:23:20.419736Z"
    },
    "scrolled": false
   },
   "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\">[22:23:20] </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[22:23:20]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                                                     \u001b]8;id=72175;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=428434;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#494\u001b\\\u001b[2m494\u001b[0m\u001b]8;;\u001b\\\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 8.3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot: title={'center': 'cross section at x=0.00'}, xlabel='y', ylabel='z'>,\n",
       "       <AxesSubplot: title={'center': 'cross section at z=0.00'}, xlabel='x', ylabel='y'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_uniform = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.uniform(dl=20 * nm),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "plot_sim_grid(sim_uniform)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatic nonuniform grid\n",
    "\n",
    "By default, Tidy3D uses a more advanced meshing, using an algorithm to automatically define a nonuniform grid in all three directions. The resolution of this grid is specified using the desired minimum steps per wavelength in each material (`min_steps_per_wvl = 10` by default). This specification therefore requires a target wavelength, which can be either provided directly to `grid_spec`, or inferred from any sources present in the simulation. The two simulations below will have the same grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.422419Z",
     "iopub.status.busy": "2023-02-03T04:23:20.422240Z",
     "iopub.status.idle": "2023-02-03T04:23:20.673601Z",
     "shell.execute_reply": "2023-02-03T04:23:20.673186Z"
    },
    "scrolled": false
   },
   "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=961111;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=483176;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"
    },
    {
     "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> Auto meshing using wavelength <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5500</span> defined from sources.                     <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#510\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Auto meshing using wavelength \u001b[1;36m1.5500\u001b[0m defined from sources.                     \u001b]8;id=749509;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=891096;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 0.53\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# provide wavelength directly\n",
    "sim_nonuniform_10 = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(wavelength=wavelength),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "# no grid_spec defined; using default with wavelength taken from source\n",
    "source = td.ModeSource(\n",
    "    center=(-2, 0, 0),\n",
    "    size=(0, 2, 2),\n",
    "    mode_index=0,\n",
    "    direction=\"+\",\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    ")\n",
    "sim_nonuniform_10 = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    "    sources=[source],\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default grid is somewhat coarse, but it is often good for initial simulations. We can easily refine it by requiring a higher number of steps per wavelength:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.675890Z",
     "iopub.status.busy": "2023-02-03T04:23:20.675716Z",
     "iopub.status.idle": "2023-02-03T04:23:20.980190Z",
     "shell.execute_reply": "2023-02-03T04:23:20.979758Z"
    },
    "scrolled": false
   },
   "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=410973;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=179579;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 2.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_nonuniform_20 = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "ax = plot_sim_grid(sim_nonuniform_20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the automatic grid conforms to the structures in the simulation, and the grid steps are high in the high-index regions. \n",
    "\n",
    "### Lower bound of grid size\n",
    "Sometimes the grid size in the automatically generated mesh is too small. A lower bound `dl_min` can be set to increase the minimal grid size. Note that the bound is soft, meaning that the actual grid size might be slightly smaller.\n",
    "\n",
    "The minimal step size along y-direction in the previous setup is,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.982528Z",
     "iopub.status.busy": "2023-02-03T04:23:20.982364Z",
     "iopub.status.idle": "2023-02-03T04:23:20.985096Z",
     "shell.execute_reply": "2023-02-03T04:23:20.984693Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimal grid size along y-direction = 16.67nm\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's set a lower bound of 25nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:20.987257Z",
     "iopub.status.busy": "2023-02-03T04:23:20.987092Z",
     "iopub.status.idle": "2023-02-03T04:23:21.234422Z",
     "shell.execute_reply": "2023-02-03T04:23:21.233946Z"
    },
    "scrolled": false
   },
   "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=716466;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=886507;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 1.9\n",
      "Minimal grid size along y-direction = 24.57nm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_nonuniform_20 = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        dl_min=25 * nm,\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "ax = plot_sim_grid(sim_nonuniform_20)\n",
    "print(\n",
    "    f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual minimal grid size along y-direction is 24.57nm, greater than 16.67nm when `dl_min` is not set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mesh override structures\n",
    "For this particular problem, we may want to do a final refinement around the slot, where we expect the fields to be strongest. This can be achieved by adding `override_structures` to the simulation `grid_spec`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`override_structures` is a list of Tidy3D structures each with an arbitrary geometry which are used exclusively for the meshing, added on top of any physical simulation structures.\n",
    "\n",
    "There are two types of Tidy3D structures that can be added the `override_structures` list. The first type defines a fictitious medium inside the override structure so that the grid size is decided by the minimum steps per wavelength in the medium; The second type is more straightforward: one can directly define the grid size along each axis inside the override structures.\n",
    "\n",
    "#### Override Structure -- option 1\n",
    "The first type is identical to the [Structure](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.Structure.html) object that consists of a [Geometry](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.components.geometry.Geometry.html) and a [Medium](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.components.medium.AbstractMedium.html). The grid step in the override_structure region is decided by the minimum steps per wavelength in this `medium`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:21.236773Z",
     "iopub.status.busy": "2023-02-03T04:23:21.236597Z",
     "iopub.status.idle": "2023-02-03T04:23:21.490697Z",
     "shell.execute_reply": "2023-02-03T04:23:21.490249Z"
    },
    "scrolled": false
   },
   "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\">[22:23:21] </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[22:23:21]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                                                     \u001b]8;id=530767;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=251929;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 4.0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a \"dummy\" box with refractive index 5 around the central location of the waveguide\n",
    "refine_box = td.Structure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n",
    "    medium=td.Medium(permittivity=5**2),\n",
    ")\n",
    "\n",
    "# Use the box as a grid refinement structure\n",
    "sim_nonuniform_20_refine = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=[refine_box],\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_20_refine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the override region is highlighted in the grid plotting. Now this looks pretty good - the grid size of the points around the slot region is `1550 / 20 / 5 = 15.5` nanometers, while overall the simulation has twice fewer total number of grid points (`4.0M`) compared to the simulation with a uniform mesh step of `20` nanometers (`8.3M`). One thing to note, however, is that the override structure made the grid along the x-dimension small, which may not be necessary. To avoid this, we can change that structure to have `size = 0` along that dimension. This would force the non-uniform grid to place a grid boundary at the `center` of the override structure along `x`, but apart from that it will not refine the grid step along `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:21.492941Z",
     "iopub.status.busy": "2023-02-03T04:23:21.492741Z",
     "iopub.status.idle": "2023-02-03T04:23:21.745015Z",
     "shell.execute_reply": "2023-02-03T04:23:21.744569Z"
    }
   },
   "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=857445;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=502616;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 3.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redefine the box with size 0 along x\n",
    "refine_box = td.Structure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(0, 0.4, 0.4)),\n",
    "    medium=td.Medium(permittivity=5**2),\n",
    ")\n",
    "\n",
    "# Use the box as a grid refinement structure\n",
    "sim_nonuniform_20_refine = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=[refine_box],\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_20_refine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Override Structure -- option 2\n",
    "The second type is the [MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.MeshOverrideStructure.html) object that consists of a [Geometry](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.components.geometry.Geometry.html), and a tuple `dl` specifying the grid sizes along x,y,z directions. We can override the grid sizes just along a few selected directions by setting the value to be `None` in the `dl` tuple along the other directions. E.g. if we only plan to refine the grid size along x-direction with grid size 0.01 micron, we can apply `dl=(0.01, None, None)`. In the following, we override the grid size along `y` and `z` to be 15.5nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:21.747239Z",
     "iopub.status.busy": "2023-02-03T04:23:21.747047Z",
     "iopub.status.idle": "2023-02-03T04:23:21.998894Z",
     "shell.execute_reply": "2023-02-03T04:23:21.998468Z"
    },
    "scrolled": false
   },
   "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=977117;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=315836;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 2.9\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "refine_box = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n",
    "    dl=[None, 15.5 * nm, 15.5 * nm],\n",
    ")\n",
    "\n",
    "# Use the box as a grid refinement structure\n",
    "sim_nonuniform_20_refine = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=[refine_box],\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_20_refine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for fun, we can also have a look at the modes of this structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:22.001244Z",
     "iopub.status.busy": "2023-02-03T04:23:22.001080Z",
     "iopub.status.idle": "2023-02-03T04:23:24.197616Z",
     "shell.execute_reply": "2023-02-03T04:23:24.197161Z"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/twhughes/.pyenv/versions/3.10.9/lib/python3.10/site-packages/numpy/linalg/linalg.py:2139: RuntimeWarning: divide by zero encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n",
      "/Users/twhughes/.pyenv/versions/3.10.9/lib/python3.10/site-packages/numpy/linalg/linalg.py:2139: RuntimeWarning: invalid value encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n"
     ]
    },
    {
     "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\">[22:23:22] </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[22:23:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                                                     \u001b]8;id=907866;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=825304;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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_modes = 3\n",
    "plane = td.Box(center=(0, 0, 0), size=(0, 2, 2))\n",
    "mode_spec = td.ModeSpec(num_modes=num_modes)\n",
    "ms = ModeSolver(\n",
    "    simulation=sim_nonuniform_20_refine, plane=plane, mode_spec=mode_spec, freqs=[freq0]\n",
    ")\n",
    "modes = ms.solve()\n",
    "fig, axs = plt.subplots(num_modes, 2, figsize=(12, 14))\n",
    "for mode_ind in range(num_modes):\n",
    "    ms.plot_field(\n",
    "        \"Ey\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 0], robust=False\n",
    "    )\n",
    "    ms.plot_field(\n",
    "        \"Ez\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1], robust=False\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mesh override with coarser grid size\n",
    "Sometimes, instead of refinement, we want to override a region with a coarser mesh. This can be achieved by setting `enforce=True` in [MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.MeshOverrideStructure.html).\n",
    "\n",
    "First, let's see the default behavior where `enforce=False` so that the override structure with coarser mesh will be ignored unless it fully covers the structure of fine mesh. In the following example, we try to override the mesh along x-direction be to 40nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:24.200624Z",
     "iopub.status.busy": "2023-02-03T04:23:24.200451Z",
     "iopub.status.idle": "2023-02-03T04:23:24.454879Z",
     "shell.execute_reply": "2023-02-03T04:23:24.454462Z"
    },
    "scrolled": false
   },
   "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\">[22:23:24] </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[22:23:24]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                                                     \u001b]8;id=652731;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=528808;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 2.9\n",
      "Minimal grid size along x-direction = 22.22nm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "refine_box = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n",
    "    dl=[50 * nm, 15.5 * nm, 15.5 * nm],\n",
    ")\n",
    "\n",
    "# Use the box as a grid refinement structure\n",
    "sim_nonuniform_20_coarser = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=[refine_box],\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_20_coarser)\n",
    "print(\n",
    "    f\"Minimal grid size along x-direction = {min(sim_nonuniform_20_coarser.grid.sizes.x)*1e3:1.2f}nm\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The minimal grid size along x-direction is still 22nm, so the `override_structures` doesn't take effect. This is because, by default, the meshing chooses the step size based on the highest-index structure (real or override) at any given cross-section. However, this behavior can be overridden if we set `enforce=True` in an `MeshOverrideStructure`, in which case other structures are ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:24.457211Z",
     "iopub.status.busy": "2023-02-03T04:23:24.457022Z",
     "iopub.status.idle": "2023-02-03T04:23:24.703656Z",
     "shell.execute_reply": "2023-02-03T04:23:24.703254Z"
    }
   },
   "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=838183;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=767174;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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 1.4\n",
      "Minimal grid size along x-direction = 40.00nm\n"
     ]
    },
    {
     "data": {
      "image/png": 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79u1etc6cq/3p3LmzPv300yq/2Fi/fr0aNGig888/36vOmXP9N998owMHDjDXIyAW/4gJcXFxGjx4sP7+979X+Z+lJBljAuZzcnIUHx+vZ5991qvtCy+8oKKiIg0aNEiSVFxcrFOnTnllL7roIsXFxXnennXkyJEq9U//j9rXW7hOq0nu17/+tQ4ePKj58+dXaVtaWup5kvHgwYMVFxenhx9+uMok89PX17BhQx09etTvmE7r1q2b0tPTNXfuXK/X8N5772n79u2e6xOq03cQfjrG8vJyPf/881XaNmzYsFZvC3z99de1ZMkSPf7445owYYKGDx+uSZMm6YsvvvBqF8xn/q+88ko1bdpUc+bM8ao1Z84cNWjQwOv6FBYWaseOHV6fIb3++utVUVGhefPmeY6VlZXpxRdfVM+ePT2/+W/cuLFycnL08ssvq6SkxNN24cKF+uGHHzR06NAaXw8AiEbM98z3gRw6dEi//vWvddlll3l2DPAlmM/8X3fddapfv77XGI0xmjt3rlq0aKHevXt7jWPHjh06efKk59j111+vw4cP669//avnWGFhoV577TVde+21ns/4d+jQQe3atdO8efO83ok4Z84c1alTR9dff32NrgVilFNPGgTC7cCBAyYzM9M0aNDA3HPPPeaPf/yjeeihh0yHDh08e/eefvqvrycET5kyxUgyV199tXnuuefMXXfdZerWrWu6d+9uysvLjTHGvPHGG6ZFixbmnnvuMc8//7x59tlnTffu3U39+vXNunXrjDHG/Pa3vzVdunQxkyZNMvPnzzePPfaYadGihTn77LO99gY+U01yFRUVZuDAgaZOnTpm+PDhZtasWWbmzJnmtttuM02bNvV6Xb///e+NJNO7d28zbdo0M2vWLDNy5Eivp9Hecccdpk6dOuaRRx4xr7zyilm+fLkxxve+v6evXc+ePc3MmTPNxIkTTYMGDUzr1q299kY+ve+vv+sbSGFhoWnSpIlp1aqVefrpp8306dNNly5dTKdOnaqM58knnzSSzL333msWL15s3nrrLb91Dx8+bJo1a2auuOIKz9OPCwsLTUZGhunVq1eVJyQH4/T+v9dff72ZP3++GTlypJFkHnvsMa92p6/DmXsqDx061NSrV8/cf//95o9//KPp3bu3qVevnvnHP/7h1W7Tpk0mISHBdOnSxcyZM8c8+OCDJjEx0Vx99dUhvwYAiAbM98z3/lx//fWmbt26Zvr06WbhwoVeX5999lnAMdXE/fffbySZcePGmfnz55tBgwYZSWbRokVe7U7vhPD11197jp06dcpceumlplGjRmbq1Klm9uzZpkOHDiY5Odns2LHDK//3v//d1KlTx1x55ZVm3rx55u677zZxcXFm7NixIb8GuBuLf8SUvXv3mpEjR5q0tDSTkJBg2rRpY+68805TVlZmjAn8w4AxP271065dO1O/fn2TkZFhbr/9dq+J7quvvjK33HKLyc7ONomJiaZp06bmiiuuMB9++KGnzfLly811111nmjdvbuLj403z5s3NDTfcYL744ouAY69prry83DzxxBOmQ4cOJiEhwTRp0sR07drVTJ06tcq2dQsWLDBdunTxtOvbt69ZtmyZ53x+fr4ZNGiQSU5ONpI82wD5+mHAGGOWLFniqde0aVMzYsQIc+DAAa82ofwwYIwxa9asMZdeeqlJSkoyzZs3N//n//wf8/7771cZzw8//GBuvPFGk5qaaiQF3AboV7/6lUlOTjZ79uzxOv63v/3NSDJPPPFEteOqiXnz5pkLLrjAxMfHm+zsbDNjxgyvrZaM8b/4Ly0tNb/73e9MZmamSUhIMN27d/ds+3Om1atXm969e5vExESTlpZm7rzzTlNcXGzJawCAaMB8z3zvS9++fWu0RW+wKioqzB/+8AfTqlUrEx8fbzp06GBefvnlKu18Lf6NMebIkSNmzJgx5qyzzjINGjQwffv29ft39I033jCdO3c2CQkJ5uyzzzaTJk3y/HIK8KeOMdW8/wkAAAAAAEQ1PvMPAAAAAIDLsfgHAAAAAMDlWPwDAAAAAOByUbf4nz17tlq3bq3ExET17NlTGzZsqFHu1VdfVZ06dTR48GB7BwgAAELCXA8AgPWiavG/ZMkS5ebmasqUKfr000/VqVMn9e/fXwUFBQFze/bs0e9+9zv16dMnTCMFAADBYK4HAMAeUfW0/549e6p79+567rnnJEmVlZVq2bKl7rrrLk2YMMFnpqKiQpdffrluueUWrV69WkePHtWbb74ZxlEDAICaYq4HAMAe9ZweQE2Vl5dr06ZNmjhxoudYXFyccnJytG7dOr+5hx9+WOnp6RozZoxWr15dbT9lZWUqKyvzfF9ZWakjR47orLPOUp06dUJ7EQAAWMAYo5KSEjVv3lxxcVH1Jr6AmOsBAPiRHXN91Cz+CwsLVVFRoYyMDK/jGRkZ2rFjh8/Mxx9/rBdeeEFbtmypcT95eXmaOnVqKEMFACAs9u/fr7PPPtvpYViGuR4AAG9WzvVRs/ivrZKSEt10002aP3++mjVrVuPcxIkTlZub6/m+qKhI55xzjvbv36+UlJTgB7R794//zM4OvoaVdUKtVZtsdW1DOe9UNlLHFWlZq46HMga3Zt06rmDb2pG3uo6FtYo/+0wtL79cycnJoY8pioVlrg/2zyxack70SS42c070SS42c070aUOuuLhYLVu2tHSuj5rFf7NmzVS3bl0dPnzY6/jhw4eVmZlZpf3u3bu1Z88eXXvttZ5jlZWVkqR69epp586dyvZxkRMSEpSQkFDleEpKSmiL/9N/aKHUsLJOqLVqk62ubSjnncpG6rgiLWvV8VDG4NasW8cVbFs78lbXsbJWo0aS5Lq3qEfkXB/sn1m05Jzok1xs5pzok1xs5pzo08aclXN91HxQMD4+Xl27dtXy5cs9xyorK7V8+XL16tWrSvt27dpp69at2rJli+frF7/4ha644gpt2bJFLVu2DOfwAQBANZjrAQCwT9Tc+Zek3NxcjRo1St26dVOPHj00c+ZMHTt2TKNHj5YkjRw5Ui1atFBeXp4SExPVsWNHr3xqaqokVTleI7t3/+c3M8HYuzf4rB11Qq1Vm2x1bUM571TWztpuylp1PJQxuDVrZ20nxxVsWzvyVtexstb+/dbUiUCOzvUAALhYVC3+hw0bpm+//VaTJ09Wfn6+OnfurKVLl3oeDLRv3z5XPfUYAIBYw1wPAIA9omrxL0njx4/X+PHjfZ5btWpVwOxLL70UfMfZ2dZ83rNt29BrWFkn1Fq1yVbXNpTzTmXtrO2mrFXHQxmDW7N21nZyXMG2tSNvdR0rapWUWDOOCOXYXA8AgIvxq3MAAAAAAFyOxT8AAAAAAC7H4h8AAAAAAJeLus/8AwAA2MLXzj7B7tAQLTkn+iQXmzkn+iQXmzkn+rQjZ8PzfbjzDwAAAACAy3HnHwAAQAq8s0+wOzRES86JPsnFZs6JPsnFZs6JPq3MFRcHVysA7vwDAAAAAOByLP4BAAAAAHA5Fv8AAAAAALgci38AAAAAAFyOxT8AAAAAAC7H0/4BAAAkafduKTnZ+1gk7flsR86JPsnFZs6JPsnFZs6JPu3IlZQEVzMA7vwDAAAAAOBy3PkHAACQpOxsKSXF97lI2PPZzpwTfZKLzZwTfZKLzZwTfVqZKy4OrlYA3PkHAAAAAMDlWPwDAAAAAOByvO2/pnw9BKg2QnlghR11Qq1Vm2x1bUM571TWztpuylp1PJQxuDVrZ20nxxVsWzvyVtexstb+/dbUAQAAMYM7/wAAAAAAuBx3/msq0EOAaiOUB1bYUSfUWrXJVtc2lPNOZe2s7aasVcdDGYNbs3bWdnJcwba1I291HStq2bD9D8RWf+TI2Zlzok9ysZlzos8o2eqPxT8AAICkx97coITEJK9jTb87LEk6ctbBWtWKlpwTfZKLzZwTfZKLzZwTfdqRKztRWqtaNcHiHwAAQFJhepbikxr6PPdtRougakZLzok+ycVmzok+ycVmzok+rcyVlx4LqlYgfOYfAAAAAACXY/EPAAAAAIDLsfgHAAAAAMDlom7xP3v2bLVu3VqJiYnq2bOnNmzY4LftX//6V3Xr1k2pqalq2LChOnfurIULF4ZxtAAAoLaY6wEAsF5ULf6XLFmi3NxcTZkyRZ9++qk6deqk/v37q6CgwGf7pk2b6sEHH9S6dev0//7f/9Po0aM1evRovf/++2EeOQAAqAnmegAA7BFVT/ufPn26xo4dq9GjR0uS5s6dq3feeUcLFizQhAkTqrT/2c9+5vX9b3/7W/3v//6vPv74Y/Xv3z8cQwYAALXg5FzfrOCQ363+aitack70SS42c070SS42c070aUfOjq3+oubOf3l5uTZt2qScnBzPsbi4OOXk5GjdunXV5o0xWr58uXbu3KnLL7/cb7uysjIVFxd7fQEAAPsx1wMAYJ+oufNfWFioiooKZWRkeB3PyMjQjh07/OaKiorUokULlZWVqW7dunr++ed11VVX+W2fl5enqVOnWjZuAABQM07P9YXpWYpPaugzEwl7PtuZc6JPcrGZc6JPcrGZc6JPK3PlpceCqhVI1Nz5D1ZycrK2bNmijRs36rHHHlNubq5WrVrlt/3EiRNVVFTk+dq/f3/4BgsAAGqNuR4AgOpFzZ3/Zs2aqW7dujp82PtzEYcPH1ZmZqbfXFxcnNq2bStJ6ty5s7Zv3668vLwqnxE8LSEhQQkJCZaNGwAA1AxzPQAA9omaO//x8fHq2rWrli9f7jlWWVmp5cuXq1evXjWuU1lZqbKyMjuGCAAAQsBcDwCAfaLmzr8k5ebmatSoUerWrZt69OihmTNn6tixY54nAo8cOVItWrRQXl6epB8/09etWzdlZ2errKxM7777rhYuXKg5c+Y4+TIAAIAfzPUAANgjqhb/w4YN07fffqvJkycrPz9fnTt31tKlSz0PBtq3b5/i4v7zZoZjx47pjjvu0IEDB5SUlKR27drp5Zdf1rBhw5x6CQAAIAAn53q2+iNHzr6cE32Si82cE31Gy1Z/UbX4l6Tx48dr/PjxPs+d+XCfRx99VI8++qg1He/eLSUnB5/fu9eacVhVJ9RatclW1zaU805l7aztpqxVx0MZg1uzdtZ2clzBtrUjb3UdK2u5/AF1js31AAC4WNQt/gEAAOzAVn/RMVZy0Z1zok9ysZlzos9I3+qPxX9NZWdLKSmh1/n304gjpk6otWqTra5tKOedytpZ201Zq46HMga3Zu2s7eS4gm1rR97qOlbUKimxZhwAACBmRM3T/gEAAAAAQHBY/AMAAAAA4HIs/gEAAAAAcDkW/wAAAAAAuBwP/AMAAJDUrOCQEhKTvI5F0p7PduSc6JNcbOac6JNcbOac6NOOXNmJ0qBqBsKdfwAAAAAAXI47/wAAAJIK07MUn9TQ57lI2PPZzpwTfZKLzZwTfZKLzZwTfVqZKy89FlStQLjzDwAAAACAy7H4BwAAAADA5Vj8AwAAAADgciz+AQAAAABwORb/AAAAAAC4HE/7BwAAkNSs4JASEpO8jkXSns925Jzok1xs5pzok1xs5pzo045c2YnSoGoGwp1/AAAAAABcjjv/AAAAkgrTsxSf1NDnuUjY89nOnBN9kovNnBN9kovNnBN9WpkrLz0WVK1AuPMPAAAAAIDLcee/pnbvlpKTg8/v3WvNOKyqE2qt2mSraxvKeaeydtZ2U9aq46GMwa1ZO2s7Oa5g29qRt7qOlbX277emDgAAiBnc+QcAAAAAwOW4819T2dlSSkroddq2Db2GlXVCrVWbbHVtQznvVNbO2m7KWnU8lDG4NWtnbSfHFWxbO/JW17GiVkmJNeMAAAAxg8U/AACA2OqPHDk7c070SS42c070yVZ/AAAAAAAgInDnHwAAQGz1Fy1jJRfdOSf6JBebOSf6ZKs/i82ePVutW7dWYmKievbsqQ0bNvhtO3/+fPXp00dNmjRRkyZNlJOTE7A9AABwHnM9AADWi6rF/5IlS5Sbm6spU6bo008/VadOndS/f38VFBT4bL9q1SrdcMMNWrlypdatW6eWLVvq6quv1sGDB8M8cgAAUBPM9QAA2COqFv/Tp0/X2LFjNXr0aF144YWaO3euGjRooAULFvhsv2jRIt1xxx3q3Lmz2rVrpz/96U+qrKzU8uXLwzxyAABQE8z1AADYI2oW/+Xl5dq0aZNycnI8x+Li4pSTk6N169bVqMbx48d18uRJNW3a1G+bsrIyFRcXe30BAAD7MdcDAGCfqHngX2FhoSoqKpSRkeF1PCMjQzt27KhRjQceeEDNmzf3+qHiTHl5eZo6dWpIYwU0fbp04oSUmur7/NGjP/7T1/lA56Ita9XxUMYQLdmkJOmxx3zXAGKEk3P905I6/t8XFFevvtfxBsd/kCQdb9CoRv1HW86JPsnFZs6JPsnFZs6JPu3IVZ46Kd/veQte1Cz+Q/X444/r1Vdf1apVq5SYmOi33cSJE5Wbm+v5vri4WC1btgzHEOEmJ05I//M/Utu2vs/v2vXjP32dD3Qu2rJWHQ9lDNGS/cn/dwAEJ9S5/v/0GaiExCSvtqf3YD5ylvcvJKoTLTkn+iQXmzkn+iQXmzkn+rQjV3aiVFr5Vq3qVSdqFv/NmjVT3bp1dfjwYa/jhw8fVmZmZsDstGnT9Pjjj+vDDz/UxRdfHLBtQkKCEhISQh4vAACoHeZ6AADsEzWL//j4eHXt2lXLly/X4MGDJcnzQJ/x48f7zT355JN67LHH9P7776tbt25hGi0AAKgtp+f6wvQsxSc19HkuEvZ8tjPnRJ/kYjPnRJ/kYjPnRJ9W5spLjwVVK5CoWfxLUm5urkaNGqVu3bqpR48emjlzpo4dO6bRo0dLkkaOHKkWLVooLy9PkvTEE09o8uTJWrx4sVq3bq38/HxJUqNGjdSoUe0/OwIAAOzFXA8AgD2iavE/bNgwffvtt5o8ebLy8/PVuXNnLV261PNgoH379iku7j8bGMyZM0fl5eW6/vrrvepMmTJFDz30UDiHDgAAaoC5HgAAe0TV4l+Sxo8f7/etf6tWrfL6fs+ePdZ1vHu3lJwcfH7vXmvGYVWdUGvVJltd21DOO5Wt7nxxcfB9R+P18HfOquOhjCFaskeP/ucBgJE0rnDXDratHXmr61hZa/9+a+pEKMfmegAAXCyu+iYAAAAAACCaRd2df8dkZ0spKaHX8bcdmFN1Qq1Vm2x1bUM571TW3/mUFKlVq+h8TXZkrToeyhgiPZua6q7rEWrtYNvakbe6jhW1SkqsGQe8NCs45Herv9qKlpwTfZKLzZwTfZKLzZwTfdqRKztRGlTNQLjzDwAAAACAy3HnHwAAQGz1Fy1jJRfdOSf6JBebOSf6jPSt/rjzDwAAAACAy7H4BwAAAADA5Vj8AwAAAADgciz+AQAAAABwORb/AAAAAAC4HE/7BwAAkNSs4JASEpO8jkXSns925Jzok1xs5pzok1xs5pzo045c2YnSoGoGwp1/AAAAAABcjjv/AAAAkgrTsxSf1NDnuUjY89nOnBN9kovNnBN9kovNnBN9WpkrLz0WVK1AWPzX1O7dUnJy8Pm9e60Zh1V1Qq1Vm2x1bUM571S2uvPFxcH3HY3Xw985q46HMoZoyR49Ku3aFXnjCnftYNvakbe6jpW19u+3pg4AAIgZvO0fAAAAAACX485/TWVnSykpoddp2zb0GlbWCbVWbbLVtQ3lvFNZf+dTUqRWraLzNdmRtep4KGOI9GxqqruuR6i1g21rR97qOlbUKimxZhwAACBmcOcfAAAAAACX484/AACA2OqPHDk7c070SS42c070yVZ/AAAAAAAgInDnHwAAQGz1Fy1jJRfdOSf6JBebOSf6jPSt/rjzDwAAAACAy7H4BwAAAADA5Vj8AwAAAADgciz+AQAAAABwOR74V1O7d0vJycHn9+61ZhxW1Qm1Vm2y1bUN5bxT2erOFxcH33c0Xg9/56w6HsoYoiV79Ki0a1fkjSvctYNta0fe6jpW1tq/35o6AAAgZrD4BwAAkNSs4JASEpO8jkXSns925Jzok1xs5pzok1xs5pzo045c2YnSoGoGEnWL/9mzZ+upp55Sfn6+OnXqpFmzZqlHjx4+227btk2TJ0/Wpk2btHfvXs2YMUP33HNPcB1nZ0spKcEP/LS2bUOvYWWdUGvVJltd21DOO5X1dz4lRWrVKjpfkx1Zq46HMoZIz6amuut6hFo72LZ25K2uY0WtkhJrxhGhHJvrAQBwsaha/C9ZskS5ubmaO3euevbsqZkzZ6p///7auXOn0tPTq7Q/fvy42rRpo6FDh+ree+91YMQAAKA2nJzrC9OzFJ/U0Oe5SNjz2c6cE32Si82cE32Si82cE31amSsvPRZUrUCi6oF/06dP19ixYzV69GhdeOGFmjt3rho0aKAFCxb4bN+9e3c99dRTGj58uBISEsI8WgAAUFvM9QAA2CNqFv/l5eXatGmTcnJyPMfi4uKUk5OjdevWWdZPWVmZiouLvb4AAID9mOsBALBP1Cz+CwsLVVFRoYyMDK/jGRkZys/Pt6yfvLw8NW7c2PPVsmVLy2oDAAD/mOsBALBP1Cz+w2XixIkqKiryfO1nOyUAAFyFuR4AEIui5oF/zZo1U926dXX4sPd2CIcPH1ZmZqZl/SQkJPj+zODu3VJycvCF3bbftJV7dbtxL/Pi4uD7jsbr4e+cVcdDGUO0ZI8elXbtirxxhbt2sG3tyFtdx8paLl2sOj3Xs9UfOXL25Zzok1xs5pzok63+LBYfH6+uXbtq+fLlGjx4sCSpsrJSy5cv1/jx450dHGw3871PdeSsg1WOn/4Pxte56s6Hkq3u/OA9BfrIz5jtHJeT2Xt+fonPXG35+7OuyRj8nYuG7IAvvtHS19ZG3Lj8Za368wZ+irkeAAD7RM3iX5Jyc3M1atQodevWTT169NDMmTN17NgxjR49WpI0cuRItWjRQnl5eZJ+fHDQ559/7vn3gwcPasuWLWrUqJHa1naP5ezsH/duD5Xb9pu2cq/uAOePnHUw4NYZ1W2rYVfW3/kTiQ105KwMx8blSLZVq9rvU+/jeHV/1gHHUM25SM4eb9DIb5uIfE2B/rxPC3Teyv932J23uo4VtUpKrBlHBHJyrmerv+gYK7nozjnRJ7nYzDnRZ6Rv9RdVi/9hw4bp22+/1eTJk5Wfn6/OnTtr6dKlngcD7du3T3Fx/3mMwTfffKMuXbp4vp82bZqmTZumvn37atWqVeEePgAAqAZzPQAA9oiqxb8kjR8/3u9b/86c5Fu3bi1jTBhGBQAArMJcDwCA9XjaPwAAAAAALsfiHwAAAAAAl4u6t/07hq3+gs9asG2Yv20wqttWI9D5ULLVnU88cTzovp16TSFnff05BrHVX8xdt39rcPwHpR0+6Pe8U+Pye96urSyDbWtH3uo6VtZy6VZ/AADAPiz+AQAAJDUrOKSExCSvY5G057MdOSf6JBebOSf6JBebOSf6tCNXdqI0qJqBsPivKbb6Cz3LVn9hGxdb/UVflq3+gqwTjrzVdayo5eKt/gAAgD1Y/AMAAEgqTM9SfFJDn+ciYc9nO3NO9EkuNnNO9EkuNnNO9Gllrrz0WFC1AuGBfwAAAAAAuByLfwAAAAAAXI7FPwAAAAAALsdn/muKrf6Cz7LVX9jGxVZ//kV6lq3+QmxrR97qOlbWYqs/AABQSyz+AQAAxFZ/5MjZmXOiT3KxmXOiT7b6cxu2+gs9y1Z/YRsXW/1FX5at/oKsE4681XWsqMVWfwAAoJZY/AMAAIit/qJlrOSiO+dEn+RiM+dEn2z1BwAAAAAAHMXiHwAAAAAAl2PxDwAAAACAy/GZ/5piq7/gs2z1F7ZxsdWff5GeZau/ENvakbe6jpW12OoPAADUEnf+AQAAAABwOe781xRb/YWeZau/sI2Lrf6iL8tWf0HWCUfe6jpW1GKrP1s0KzikhMQkr2ORtOezHTkn+iQXmzkn+iQXmzkn+rQjV3aiNKiagbD4BwAAkPTg4B5KOfMjfqc/qtGqVe2KRUvOiT7JxWbOiT7JxWbOiT5tyBWXlOjJh2tXrjos/gEAAKTA7/IL9t0a0ZJzok9ysZlzok9ysZlzok8rc8XFwdUKgM/8AwAAAADgciz+AQAAAABwOd72X1Ns9Rd8lq3+wjYutvrzL9KzbPUXYls78lbXsbIWW/0BAIBairrF/+zZs/XUU08pPz9fnTp10qxZs9SjRw+/7V977TX9/ve/1549e3TeeefpiSee0MCBA8M4YgAAUBuOzfW+ftEf7C9soiXnRJ/kYjPnRJ/kYjPnRJ925GzY2SeqFv9LlixRbm6u5s6dq549e2rmzJnq37+/du7cqfT09Crt165dqxtuuEF5eXm65pprtHjxYg0ePFiffvqpOnbsWLvO2eov9Cxb/YVtXGz1F31ZtvoLsk448lbXsaJWGLb6GzVqlMaMGaPLL7/c9r5+ytG5HgAAFwtq8X/llVeqb9++mjJlitfx77//XkOGDNGKFSssGdyZpk+frrFjx2r06NGSpLlz5+qdd97RggULNGHChCrtn3nmGQ0YMED333+/JOmRRx7RsmXL9Nxzz2nu3Lm2jBEAADcoKipSTk6OWrVqpdGjR2vUqFFq0SLwL5Os4Ohcz9P+yZGzP+dEn+RiM+dEn2582v+qVav03HPPafDgwTp27JjneHl5uf7xj39YNrifKi8v16ZNm5STk+M5FhcXp5ycHK1bt85nZt26dV7tJal///5+20tSWVmZiouLvb4AAIg1b775pg4ePKjbb79dS5YsUevWrfXzn/9cr7/+uk6ePGlLn8z1AADYJ+in/X/44YfKz8/XpZdeqj179lg4JN8KCwtVUVGhjIwMr+MZGRnKz8/3mcnPz69Ve0nKy8tT48aNPV8tW7YMffAAAEShtLQ05ebm6rPPPtP69evVtm1b3XTTTWrevLnuvfdeffnll5b2x1wPAIB9gl78Z2Vl6R//+Icuuugide/eXatWrbJwWM6ZOHGiioqKPF/7eaIyACDGHTp0SMuWLdOyZctUt25dDRw4UFu3btWFF16oGTNmOD28WmOuBwDEoqA+81+nTh1JUkJCghYvXqxHH31UAwYM0AMPPGDp4H6qWbNmqlu3rg4f9t6C6vDhw8rMzPSZyczMrFV76cfXlJCQUPUEW/0Fn2Wrv7CNi63+/Iv0LFv9hdjWjrzVdaysFYbF6smTJ/XWW2/pxRdf1AcffKCLL75Y99xzj2688Ual/Ptz8W+88YZuueUW3XvvvZb0GZFzfSQ9+dmOnBN9kovNnBN9kovNnBN9RsnT/oO682+M8fp+0qRJWrRokZ5++mlLBuVLfHy8unbtquXLl3uOVVZWavny5erVq5fPTK9evbzaS9KyZcv8tgcAAD/KysrS2LFj1apVK23YsEGffPKJbrvtNs/CX5KuuOIKpaamWtYncz0AAPYJ6s7/119/rbS0NK9jQ4YMUbt27fTJJ59YMjBfcnNzNWrUKHXr1k09evTQzJkzdezYMc8TgUeOHKkWLVooLy9PkvTb3/5Wffv21dNPP61Bgwbp1Vdf1SeffKJ58+bVvnO2+gs9y1Z/YRsXW/1FX5at/oKsE4681XWsqBWGrf5mzJihoUOHKjEx0W+b1NRUff3115b2G7FzfSQ8+dnOnBN9kovNnBN9kovNnBN9RvjT/oNa/Ldq1crn8Q4dOqhDhw4hDSiQYcOG6dtvv9XkyZOVn5+vzp07a+nSpZ4H/ezbt09xcf95M0Pv3r21ePFiTZo0Sf/zP/+j8847T2+++Sb7/gIAUI2bbrrJkX6Z6wEAsEdQi38njR8/XuPHj/d5ztdDB4cOHaqhQ4faPCoAAGAV5noAAKwX9NP+AQAAAABAdIi6O/8AAAC24Gn/5MjZl3OiT3KxmXOizyh52j+L/5piq7/gs2z1F7ZxsdWff5GeZau/ENvakbe6jpW12JfeFo+9uUEJiUlex07/93DkrIO+In5FS86JPsnFZs6JPsnFZs6JPu3IlZ0orVWtmmDxDwAAIKkwPUvxSQ19nqtu5wx/oiXnRJ/kYjPnRJ/kYjPnRJ9W5spLjwVVKxAW/zXFVn+hZ9nqL2zjYqu/6Muy1V+QdcKRt7qOFbXCsNUfAABwFx74BwAAAACAy7H4BwAAAADA5Vj8AwAAAADgcnzmHwAAQFKzgkN+n/ZfW9GSc6JPcrGZc6JPcrGZc6JPO3I87d9JbPUXfJat/sI2Lrb68y/Ss2z1F2JbO/JW17GyFlv9AQCAWmLxDwAAILb6i5axkovunBN9kovNnBN9stWfW7DVX+hZtvoL27jY6i/6smz1F2SdcOStrmNFLbb6AwAAtcQD/wAAAAAAcDkW/wAAAAAAuByLfwAAAAAAXI7FPwAAAAAALscD/2qKrf6Cz7LVX9jGxVZ//kV6lq3+QmxrR97qOlbWYqs/WzQrOKSExCSvY5G057MdOSf6JBebOSf6JBebOSf6tCNXdqI0qJqBcOcfAAAAAACX485/TbHVX+hZtvoL27jY6i/6smz1F2SdcOStrmNFLbb6s0Vhepbikxr6PBcJez7bmXOiT3KxmXOiT3KxmXOiTytz5aXHgqoVCHf+AQAAAABwORb/AAAAAAC4HIt/AAAAAABcjsU/AAAAAAAuxwP/AAAAxFZ/5MjZmXOiT3KxmXOiz2jZ6i9qFv9HjhzRXXfdpb///e+Ki4vTkCFD9Mwzz6hRo0Z+M/PmzdPixYv16aefqqSkRN9//71SU1ODG8Du3VJycnBZyX37TVu5V3cNzvv7D8OpfdCrO5944njs7Vfv68/R359tgOMxd93+rcHxH5R2+GDEjcvv+UD/3QZ7LpS2duStrmNlrf37rakTYRyf6wEAcLGoWfyPGDFChw4d0rJly3Ty5EmNHj1a48aN0+LFi/1mjh8/rgEDBmjAgAGaOHFiGEcLq93z80t+3FrsTKd/kPZ1rrrzoWSrO7/pLV3ib8x2jsvprAX8/lnXdAzRmv1ylXoP7R154wqUBSzm9FzPVn/RMVZy0Z1zok9ysZlzos9I3+ovKhb/27dv19KlS7Vx40Z169ZNkjRr1iwNHDhQ06ZNU/PmzX3m7rnnHknSqlWrQh9EdraUkhJ6HbftN23lXt127hluV9bf+ZQU+/dBj6asVcdDGUOkZ1NT3XU9Qq0dbFs78lbXsaJWSYk144ggETHXAwDgYlHxwL9169YpNTXV88OAJOXk5CguLk7r16+3tK+ysjIVFxd7fQEAAHsx1wMAYK+oWPzn5+crPT3d61i9evXUtGlT5efnW9pXXl6eGjdu7Plq2bKlpfUBAEBVzPUAANjL0cX/hAkTVKdOnYBfO3bsCOuYJk6cqKKiIs/Xfpc+VAkAgHBgrgcAIDI4+pn/++67TzfffHPANm3atFFmZqYKCgq8jp86dUpHjhxRZmampWNKSEhQQkKCpTUBAIhVzPUAAEQGRxf/aWlpSktLq7Zdr169dPToUW3atEldu3aVJK1YsUKVlZXq2bOn3cP8EVv9BZ+1YKu/iMtWd7642L6t0KIpa9XxUMYQLdmjR6VduyJvXOGuHWxbO/JW17GyVhTdqY6mub5ZwSElJCZ5HYukPZ/tyDnRJ7nYzDnRJ7nYzDnRpx25shOlQdUMJCo+89++fXsNGDBAY8eO1YYNG7RmzRqNHz9ew4cP9zz99+DBg2rXrp02bNjgyeXn52vLli3a9e8fqLdu3aotW7boyJEjjrwOAADgG3M9AAD2ioqt/iRp0aJFGj9+vPr166e4uDgNGTJEzz77rOf8yZMntXPnTh0/ftxzbO7cuZo6darn+8svv1yS9OKLL1b7FsQq2Oov9Cxb/YVvXJGWZas/tvqrbTbYtnbkra5jRS0XbvUnOT/XF6ZnKT6poc9zkbDns505J/okF5s5J/okF5s5J/q0MldeeiyoWoFEzeK/adOmWrx4sd/zrVu3ljHG69hDDz2khx56yOaRAQAAKzDXAwBgn6h42z8AAAAAAAgei38AAAAAAFyOxT8AAAAAAC4XNZ/5BwAAsBNb/ZEjZ1/OiT7JxWbOiT7Z6g8AAAAAAEQE7vwDAACIrf6iZazkojvnRJ/kYjPnRJ+RvtUfd/4BAAAAAHA57vzX1O7dUnJy8Pm9e60Zh1V1Qq1Vm2x1bUM571S2uvPFxcH3HY3Xw985q46HMoZoyR49Ku3aFXnjCnftYNvakbe6jpW19u+3pg4AAIgZ3PkHAAAAAMDluPNfU9nZUkpK6HXatg29hpV1Qq1Vm2x1bUM571TW3/mUFKlVq+h8TXZkrToeyhgiPZua6q7rEWrtYNvakbe6jhW1SkqsGQcAAIgZ3PkHAAAAAMDluPMPAAAgqVnBISUkJnkdi6Q9n+3IOdEnudjMOdEnudjMOdGnHbmyE6VB1QyEO/8AAAAAALgcd/4BAAAkFaZnKT6poc9zkbDns505J/okF5s5J/okF5s5J/q0MldeeiyoWoFw5x8AAAAAAJdj8Q8AAAAAgMux+AcAAAAAwOVY/AMAAAAA4HI88A8AAEBs9UeOnJ05J/okF5s5J/pkqz8AAAAAABARuPMPAAAgtvqLlrGSi+6cE32Si82cE31G+lZ/LP5ravduKTk5+PzevdaMw6o6odaqTba6tqGcdypb3fni4uD7jsbr4e+cVcdDGUO0ZI8elXbtirxxhbt2sG3tyFtdx8pa+/dbUwcAAMQM3vYPAAAAAIDLcee/prKzpZSU0Ou0bRt6DSvrhFqrNtnq2oZy3qmsv/MpKVKrVtH5muzIWnU8lDFEejY11V3XI9Tawba1I291HStqlZRYMw4AABAzoubO/5EjRzRixAilpKQoNTVVY8aM0Q8//BCw/V133aULLrhASUlJOuecc3T33XerqKgojKMGAAA1xVwPAIB9ombxP2LECG3btk3Lli3T22+/rY8++kjjxo3z2/6bb77RN998o2nTpulf//qXXnrpJS1dulRjxowJ46gBAEBNMdcDAGCfqHjb//bt27V06VJt3LhR3bp1kyTNmjVLAwcO1LRp09S8efMqmY4dO+r//t//6/k+Oztbjz32mH7zm9/o1KlTqlcvKl46AAAxIRLm+mYFh5SQmOR1LJL2fLYj50Sf5GIz50Sf5GIz50SfduTKTpQGVTOQqLjzv27dOqWmpnp+GJCknJwcxcXFaf369TWuU1RUpJSUlIA/DJSVlam4uNjrCwAA2Iu5HgAAe0XF7e/8/Hylp6d7HatXr56aNm2q/Pz8GtUoLCzUI488EvDtg5KUl5enqVOnBj1WQJKUmCj94Q8/PsTNl6NHf/ynr/OBzkVb1qrjoYwhWrJJSVXPAzHE6bn+aUkdV7+ruHr1vY43OP7jMweON2hUozFEW86JPsnFZs6JPsnFZs6JPu3IVZ46qSdrVa16ji7+J0yYoCeeeCJgm+3bt4fcT3FxsQYNGqQLL7xQDz30UMC2EydOVG5urle2ZcuWIY8BMeb03yF/T/Q+vZ+7r/OBzkVb1qrjoYwhmrOAC0TTXH/LkDGKT2ro1Tbt8EFJ0rcZLWo1nmjJOdEnudjMOdEnudjMOdGnHbny0mPSyrdqVa86ji7+77vvPt18880B27Rp00aZmZkqKCjwOn7q1CkdOXJEmZmZAfMlJSUaMGCAkpOT9cYbb6h+/foB2yckJCghIaFG4wcAAIEx1wMAEBkcXfynpaUpLS2t2na9evXS0aNHtWnTJnXt2lWStGLFClVWVqpnz55+c8XFxerfv78SEhL01ltvKTEx0bKxAwCA6jHXAwAQGaLigX/t27fXgAEDNHbsWG3YsEFr1qzR+PHjNXz4cM/Tfw8ePKh27dppw4YNkn78YeDqq6/WsWPH9MILL6i4uFj5+fnKz89XRUWFky8HAACcgbkeAAB7RcUD/yRp0aJFGj9+vPr166e4uDgNGTJEzz77rOf8yZMntXPnTh0/flyS9Omnn3qeDtz2jM/Tfv3112rdunXYxg4AAKrHXA8AgH2iZvHftGlTLV682O/51q1byxjj+f5nP/uZ1/cAACCyOT3XNys4pIRE7503ImnPZztyTvRJLjZzTvRJLjZzTvRpR67sRGlQNQOJirf9AwAAAACA4EXNnX/H7d4tJScHn9+715pxWFUn1Fq1yVbXNpTzTmXtrO2mrFXHQxmDW7N21nZyXMG2tSNvdR0ra+3fb00deClMz6qy1d9pwWw1FU05J/okF5s5J/okF5s5J/q0MldeeiyoWoFw5x8AAAAAAJfjzn9NZWdLKSmh1znjgUSO1wm1Vm2y1bUN5bxTWTtruylr1fFQxuDWrJ21nRxXsG3tyFtdx4paJSXWjAMAAMQM7vwDAAAAAOByLP4BAAAAAHA53vYPAAAgtvojR87OnBN9kovNnBN9stUfAAAAAACICNz5BwAAEFv9RctYyUV3zok+ycVmzok+2eoPAAAAAAA4isU/AAAAAAAux+IfAAAAAACXY/EPAAAAAIDLsfgHAAAAAMDleNo/AACApGYFh5SQmOR1LJL2fLYj50Sf5GIz50Sf5GIz50SfduTKTpQGVTMQ7vwDAAAAAOBy3PkHAACQVJiepfikhj7PRcKez3bmnOiTXGzmnOiTXGzmnOjTylx56bGgagXCnX8AAAAAAFyOxT8AAAAAAC7H4h8AAAAAAJfjM/81tXu3lJwcfH7vXmvGYVWdUGvVJltd21DOO5W1s7abslYdD2UMbs3aWdvJcQXb1o681XWsrLV/vzV1AABAzGDxDwAAILb6I0fOzpwTfZKLzZwTfUbLVn8s/msqO1tKSQm9Ttu2odewsk6otWqTra5tKOedytpZ201Zq46HMga3Zu2s7eS4gm1rR97qOlbUKimxZhwAACBmsPgHAAAQW/1Fy1jJRXfOiT7JxWbOiT7Z6s8iR44c0YgRI5SSkqLU1FSNGTNGP/zwQ8DMrbfequzsbCUlJSktLU3XXXedduzYEaYRAwCA2mCuBwDAPlGz+B8xYoS2bdumZcuW6e2339ZHH32kcePGBcx07dpVL774orZv3673339fxhhdffXVqqioCNOoAQBATTHXAwBgn6h42//27du1dOlSbdy4Ud26dZMkzZo1SwMHDtS0adPUvHlzn7mf/sDQunVrPfroo+rUqZP27Nmj7OzssIwdAABUj7keAAB7RcWd/3Xr1ik1NdXzw4Ak5eTkKC4uTuvXr69RjWPHjunFF1/Uueeeq5YtW/ptV1ZWpuLiYq8vAABgL+Z6AADsFRWL//z8fKWnp3sdq1evnpo2bar8/PyA2eeff16NGjVSo0aN9N5772nZsmWKj4/32z4vL0+NGzf2fAX64QEAAFiDuR4AAHs5+rb/CRMm6IknngjYZvv27SH1MWLECF111VU6dOiQpk2bpl//+tdas2aNEhMTfbafOHGicnNzPd8XFxfzQwEAAEGKprm+WcEhJSQmebWNpD2f7cg50Se52Mw50Se52Mw50acdubITpUHVDMTRxf99992nm2++OWCbNm3aKDMzUwUFBV7HT506pSNHjigzMzNg/vRv9c877zxdeumlatKkid544w3dcMMNPtsnJCQoISGhVq8DAAD4xlwPAEBkcHTxn5aWprS0tGrb9erVS0ePHtWmTZvUtWtXSdKKFStUWVmpnj171rg/Y4yMMSorKwt6zAAAoOaiaa4vTM9SfFJDn+ciYc9nO3NO9EkuNnNO9EkuNnNO9Gllrrz0WFC1AomKz/y3b99eAwYM0NixY7VhwwatWbNG48eP1/Dhwz1P/z148KDatWunDRs2SJK++uor5eXladOmTdq3b5/Wrl2roUOHKikpSQMHDnTy5QAAgDMw1wMAYK+oWPxL0qJFi9SuXTv169dPAwcO1GWXXaZ58+Z5zp88eVI7d+7U8ePHJUmJiYlavXq1Bg4cqLZt22rYsGFKTk7W2rVrqzxQCAAAOI+5HgAA+zj6tv/aaNq0qRYvXuz3fOvWrWWM8XzfvHlzvfvuu+EYGgAAsABzPQAA9omaO/8AAAAAACA4UXPnHwAAwE5s9UeOnH05J/okF5s5J/pkqz8AAIAo8uDgHkpJTvY+uHfvj/9s1ap2xaIl50Sf5GIz50Sf5GIz50SfNuSKS0r05MO1K1cdFv8AAACSlJ0tpaT4Pte2bXA1oyXnRJ/kYjPnRJ/kYjPnRJ9W5oqLg6sVAIv/mtq9WzrzbkBtnP6tTqisqhNqrdpkq2sbynmnsnbWdlPWquOhjMGtWTtrOzmuYNvakbe6jpW19u+3pg4AAIgZPPAPAAAAAACX485/TQV6K2BthPK2FTvqhFqrNtnq2oZy3qmsnbXdlLXqeChjcGvWztpOjivYtnbkra5jRa2SEmvGAQAAYgZ3/gEAAAAAcDkW/wAAAAAAuBxv+wcAAJB8P9w32Ic0RkvOiT7JxWbOiT7JxWbOiT7tyNnwET/u/AMAAAAA4HLc+QcAAJACP9w3EvZ8tjPnRJ/kYjPnRJ/kYjPnRJ9W5oqLg6sVAHf+AQAAAABwORb/AAAAAAC4HIt/AAAAAABcjsU/AAAAAAAux+IfAAAAAACX42n/AAAAkrR7t5Sc7H0skvZ8tiPnRJ/kYjPnRJ/kYjPnRJ925EpKgqsZAHf+AQAAAABwOe78AwAASFJ2tpSS4vtcJOz5bGfOiT7JxWbOiT7JxWbOiT6tzBUXB1crAO78AwAAAADgciz+AQAAAABwORb/AAAAAAC4HJ/5rylfTwCujVCeVmlHnVBr1SZbXdtQzjuVtbO2m7JWHQ9lDG7N2lnbyXEF29aOvNV1rKy1f781dQAAQMzgzj8AAAAAAC4XNXf+jxw5orvuukt///vfFRcXpyFDhuiZZ55Ro0aNqs0aYzRw4EAtXbpUb7zxhgYPHlz7AQR6AnBthPK0SjvqhFqrNtnq2oZy3qmsnbXdlLXqeChjcGvWztpOjivYtnbkra5jRS0b9v6NBI7P9QAAuFjU3PkfMWKEtm3bpmXLluntt9/WRx99pHHjxtUoO3PmTNWpU8fmEQIAgFAw1wMAYJ+ouPO/fft2LV26VBs3blS3bt0kSbNmzdLAgQM1bdo0NW/e3G92y5Ytevrpp/XJJ58oKysrXEMGAAC1wFwPAIC9omLxv27dOqWmpnp+GJCknJwcxcXFaf369frlL3/pM3f8+HHdeOONmj17tjIzM2vUV1lZmcrKyjzfFxUVSZKKi4tDeAX6z1s0I6VOqLVqk62ubSjnncpG6rgiLWvV8VDG4NasW8cVbFs78lbXsbBW8Q8/SPrxre5uERFz/WefSWd+xOD0wxVr+1GLaMk50Se52Mw50Se52Mw50acNOTvm+qhY/Ofn5ys9Pd3rWL169dS0aVPl5+f7zd17773q3bu3rrvuuhr3lZeXp6lTp1Y53rJly5oPGACAMPjuu+/UuHFjp4dhiYiY6y+/vOYDBgAgDKyc6x1d/E+YMEFPPPFEwDbbt28PqvZbb72lFStWaPPmzbXKTZw4Ubm5uZ7vjx49qlatWmnfvn2u+QHLCsXFxWrZsqX279+vFCsehOgiXBvfuC7+cW1847r4V1RUpHPOOUdNmzZ1eijVYq6Pbvx36BvXxT+ujW9cF9+4Lv7ZMdc7uvi/7777dPPNNwds06ZNG2VmZqqgoMDr+KlTp3TkyBG/b/FbsWKFdu/erdTUVK/jQ4YMUZ8+fbRq1SqfuYSEBCUkJFQ53rhxY/5C+pCSksJ18YNr4xvXxT+ujW9cF//i4iL/ub3M9e7Af4e+cV3849r4xnXxjevin5VzvaOL/7S0NKWlpVXbrlevXjp69Kg2bdqkrl27Svpxwq+srFTPnj19ZiZMmKD//u//9jp20UUXacaMGbr22mtDHzwAAKgWcz0AAJEhKj7z3759ew0YMEBjx47V3LlzdfLkSY0fP17Dhw/3PP334MGD6tevn/785z+rR48eyszM9Hmn4JxzztG5554b7pcAAAACYK4HAMBekf9+wX9btGiR2rVrp379+mngwIG67LLLNG/ePM/5kydPaufOnTp+/Lil/SYkJGjKlCk+3x4Yy7gu/nFtfOO6+Me18Y3r4p9brw1zfeTh2vjGdfGPa+Mb18U3rot/dlybOsZN+wQBAAAAAIAqoubOPwAAAAAACA6LfwAAAAAAXI7FPwAAAAAALsfiHwAAAAAAl2Px78ORI0c0YsQIpaSkKDU1VWPGjNEPP/xQbW7dunW68sor1bBhQ6WkpOjyyy9XaWlpGEYcHsFeF0kyxujnP/+56tSpozfffNPegTqgttfmyJEjuuuuu3TBBRcoKSlJ55xzju6++24VFRWFcdTWmz17tlq3bq3ExET17NlTGzZsCNj+tddeU7t27ZSYmKiLLrpI7777bphGGn61uTbz589Xnz591KRJEzVp0kQ5OTnVXstoVdu/M6e9+uqrqlOnjgYPHmzvAB1U22tz9OhR3XnnncrKylJCQoLOP/98V/83FSrmet+Y6/1jrv8Rc71/zPW+Mdf7F/a53qCKAQMGmE6dOpl//vOfZvXq1aZt27bmhhtuCJhZu3atSUlJMXl5eeZf//qX2bFjh1myZIk5ceJEmEZtv2Cuy2nTp083P//5z40k88Ybb9g7UAfU9tps3brV/OpXvzJvvfWW2bVrl1m+fLk577zzzJAhQ8I4amu9+uqrJj4+3ixYsMBs27bNjB071qSmpprDhw/7bL9mzRpTt25d8+STT5rPP//cTJo0ydSvX99s3bo1zCO3X22vzY033mhmz55tNm/ebLZv325uvvlm07hxY3PgwIEwj9xetb0up3399demRYsWpk+fPua6664Lz2DDrLbXpqyszHTr1s0MHDjQfPzxx+brr782q1atMlu2bAnzyKMHc71vzPX+Mdcz1wfCXO8bc71/Tsz1LP7P8PnnnxtJZuPGjZ5j7733nqlTp445ePCg31zPnj3NpEmTwjFERwR7XYwxZvPmzaZFixbm0KFDrvyBIJRr81N/+ctfTHx8vDl58qQdw7Rdjx49zJ133un5vqKiwjRv3tzk5eX5bP/rX//aDBo0yOtYz549za233mrrOJ1Q22tzplOnTpnk5GTzv//7v3YN0RHBXJdTp06Z3r17mz/96U9m1KhRrv2BoLbXZs6cOaZNmzamvLw8XEOMasz1vjHX+8dc/yPmev+Y631jrvfPibmet/2fYd26dUpNTVW3bt08x3JychQXF6f169f7zBQUFGj9+vVKT09X7969lZGRob59++rjjz8O17BtF8x1kaTjx4/rxhtv1OzZs5WZmRmOoYZdsNfmTEVFRUpJSVG9evXsGKatysvLtWnTJuXk5HiOxcXFKScnR+vWrfOZWbdunVd7Serfv7/f9tEqmGtzpuPHj+vkyZNq2rSpXcMMu2Cvy8MPP6z09HSNGTMmHMN0RDDX5q233lKvXr105513KiMjQx07dtQf/vAHVVRUhGvYUYW53jfmev+Y65nrA2Gu94253j+n5noW/2fIz89Xenq617F69eqpadOmys/P95n56quvJEkPPfSQxo4dq6VLl+qSSy5Rv3799OWXX9o+5nAI5rpI0r333qvevXvruuuus3uIjgn22vxUYWGhHnnkEY0bN86OIdqusLBQFRUVysjI8DqekZHh9xrk5+fXqn20CubanOmBBx5Q8+bNq/wAFc2CuS4ff/yxXnjhBc2fPz8cQ3RMMNfmq6++0uuvv66Kigq9++67+v3vf6+nn35ajz76aDiGHHWY631jrvePuZ65PhDmet+Y6/1zaq6PmcX/hAkTVKdOnYBfO3bsCKp2ZWWlJOnWW2/V6NGj1aVLF82YMUMXXHCBFixYYOXLsJyd1+Wtt97SihUrNHPmTGsHHSZ2XpufKi4u1qBBg3ThhRfqoYceCn3gcJXHH39cr776qt544w0lJiY6PRzHlJSU6KabbtL8+fPVrFkzp4cTcSorK5Wenq558+apa9euGjZsmB588EHNnTvX6aGFFXO9b8z1/jHXIxIw1/+IuT4wK+b66HvPUZDuu+8+3XzzzQHbtGnTRpmZmSooKPA6furUKR05csTvW9mysrIkSRdeeKHX8fbt22vfvn3BDzoM7LwuK1as0O7du5Wamup1fMiQIerTp49WrVoVwsjtZ+e1Oa2kpEQDBgxQcnKy3njjDdWvXz/UYTuiWbNmqlu3rg4fPux1/PDhw36vQWZmZq3aR6tgrs1p06ZN0+OPP64PP/xQF198sZ3DDLvaXpfdu3drz549uvbaaz3HTi/G6tWrp507dyo7O9veQYdJMH9nsrKyVL9+fdWtW9dzrH379srPz1d5ebni4+NtHXOkYK73jbneP+b6mmOu94+53jfmev8cm+uDflqAS51+oMsnn3ziOfb+++8HfKBLZWWlad68eZWHAHXu3NlMnDjR1vGGSzDX5dChQ2br1q1eX5LMM888Y7766qtwDd12wVwbY4wpKioyl156qenbt685duxYOIZqqx49epjx48d7vq+oqDAtWrQI+BCga665xutYr169XPsQoNpcG2OMeeKJJ0xKSopZt25dOIboiNpcl9LS0ir/P7nuuuvMlVdeabZu3WrKysrCOXTb1fbvzMSJE02rVq1MRUWF59jMmTNNVlaW7WONRsz1vjHX+8dc/yPmev+Y631jrvfPibmexb8PAwYMMF26dDHr1683H3/8sTnvvPO8tnI5cOCAueCCC8z69es9x2bMmGFSUlLMa6+9Zr788kszadIkk5iYaHbt2uXES7BFMNflTHLhE4CNqf21KSoqMj179jQXXXSR2bVrlzl06JDn69SpU069jJC8+uqrJiEhwbz00kvm888/N+PGjTOpqakmPz/fGGPMTTfdZCZMmOBpv2bNGlOvXj0zbdo0s337djNlyhRXb/9Tm2vz+OOPm/j4ePP66697/d0oKSlx6iXYorbX5UxufgJwba/Nvn37THJyshk/frzZuXOnefvtt016erp59NFHnXoJEY+53jfmev+Y65nrA2Gu94253j8n5noW/z5899135oYbbjCNGjUyKSkpZvTo0V7/IX799ddGklm5cqVXLi8vz5x99tmmQYMGplevXmb16tVhHrm9gr0uP+XWHwhqe21WrlxpJPn8+vrrr515ERaYNWuWOeecc0x8fLzp0aOH+ec//+k517dvXzNq1Civ9n/5y1/M+eefb+Lj402HDh3MO++8E+YRh09trk2rVq18/t2YMmVK+Adus9r+nfkpN/9AYEztr83atWtNz549TUJCgmnTpo157LHHonaBEQ7M9b4x1/vHXP8j5nr/mOt9Y673L9xzfR1jjKn+wwEAAAAAACBaxczT/gEAAAAAiFUs/gEAAAAAcDkW/wAAAAAAuByLfwAAAAAAXI7FPwAAAAAALsfiHwAAAAAAl2PxDwAAAACAy7H4BwAAAADA5Vj8AwAAAADgciz+AQAAAABwORb/AGz15z//WWeddZbKysq8jg8ePFg33XSTQ6MCAABW+fbbb5WZmak//OEPnmNr165VfHy8li9f7uDIAPxUHWOMcXoQANyrtLRUWVlZmj9/voYOHSpJKigoUIsWLfTBBx/oiiuucHiEAAAgVO+++64GDx6stWvX6oILLlDnzp113XXXafr06U4PDcC/sfgHYLs77rhDe/bs0bvvvitJmj59umbPnq1du3apTp06Do8OAABY4c4779SHH36obt26aevWrdq4caMSEhKcHhaAf2PxD8B2mzdvVvfu3bV37161aNFCF198sYYOHarf//73Tg8NAABYpLS0VB07dtT+/fu1adMmXXTRRU4PCcBP8Jl/ALbr0qWLOnXqpD//+c/atGmTtm3bpptvvtnpYQEAAAvt3r1b33zzjSorK7Vnzx6nhwPgDNz5BxAWc+bM0cyZM3XVVVfpyy+/1Pvvv+/0kAAAgEXKy8vVo0cPde7cWRdccIFmzpyprVu3Kj093emhAfg3Fv8AwqKoqEjNmzfXqVOn9Oc//1nDhg1zekgAAMAi999/v15//XV99tlnatSokfr27avGjRvr7bffdnpoAP6Nt/0DCIvGjRtryJAhatSokQYPHuz0cAAAgEVWrVqlmTNnauHChUpJSVFcXJwWLlyo1atXa86cOU4PD8C/cecfQNj069dPHTp00LPPPuv0UAAAAICYwuIfgO2+//57rVq1Stdff70+//xzXXDBBU4PCQAAAIgp9ZweAAD369Kli77//ns98cQTLPwBAAAAB3DnHwAAAAAAl+OBfwAAAAAAuByLfwAAAAAAXI7FPwAAAAAALsfiHwAAAAAAl2PxDwAAAACAy7H4BwAAAADA5Vj8AwAAAADgciz+AQAAAABwORb/AAAAAAC43P8HoAfz96zLe8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "refine_box = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n",
    "    dl=[40 * nm, None, None],\n",
    "    enforce=True,\n",
    ")\n",
    "\n",
    "# Use the box as a grid refinement structure\n",
    "sim_nonuniform_20_coarser = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=[refine_box],\n",
    "    ),\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_20_coarser)\n",
    "print(\n",
    "    f\"Minimal grid size along x-direction = {min(sim_nonuniform_20_coarser.grid.sizes.x)*1e3:1.2f}nm\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the minimal grid size along x-direction is target 40nm. When multiple override structures with `enforce=True` overlap, the grid size in the intersection region is decided by the last such structure in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combination of uniform and nonuiform grid\n",
    "\n",
    "Finally, we note that Tidy3D allows another easy way to handle the `x` direction in our example, namely applying different grid specifications along each of the three simulation directions. For example, we can set a fixed grid size of `40nm` along the propagation direction, where we expect the field dependence to be smooth, while still using the refined nonuniform mesh in the other two directions. This achieves the same effect as in the plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T04:23:24.705993Z",
     "iopub.status.busy": "2023-02-03T04:23:24.705828Z",
     "iopub.status.idle": "2023-02-03T04:23:24.952541Z",
     "shell.execute_reply": "2023-02-03T04:23:24.952092Z"
    },
    "scrolled": false
   },
   "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=682642;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=941497;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"
    },
    {
     "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> Override structures take no effect along x-axis. If intending to apply         <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">grid_spec.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#519\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">519</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         override structures to this axis, use <span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span>.                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Override structures take no effect along x-axis. If intending to apply         \u001b]8;id=370275;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=934456;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#519\u001b\\\u001b[2m519\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         override structures to this axis, use \u001b[32m'AutoGrid'\u001b[0m.                              \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of grid points (millions): 1.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_spec = td.GridSpec(\n",
    "    grid_x=td.UniformGrid(dl=0.04),\n",
    "    grid_y=td.AutoGrid(min_steps_per_wvl=20),\n",
    "    grid_z=td.AutoGrid(min_steps_per_wvl=20),\n",
    "    wavelength=wavelength,\n",
    "    override_structures=[refine_box],\n",
    ")\n",
    "\n",
    "sim_nonuniform_yz_uniform_x = td.Simulation(\n",
    "    size=[5, 3, 3],\n",
    "    grid_spec=grid_spec,\n",
    "    medium=sub_med,\n",
    "    structures=[box1, box2],\n",
    "    boundary_spec=boundary_spec,\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "ax = plot_sim_grid(sim_nonuniform_yz_uniform_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}