{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Using automatic nonuniform meshing\n",
        "\n",
        "Here we demonstrate how to use the automatic nonuniform grid generation\n",
        "available in `Tidy3D`. We show different possible ways to discretize the\n",
        "air-slot waveguide first introduced in [this\n",
        "reference](https://opg.optica.org/ol/abstract.cfm?uri=ol-29-11-1209).\n",
        "\n",
        "If you are new to the finite-difference time-domain (FDTD) method, we\n",
        "highly recommend going through our\n",
        "[FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
      ],
      "id": "d563be10-786f-4a1f-8333-3797c64cf018"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# basic imports\n",
        "from __future__ import annotations\n",
        "\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "# tidy3d imports\n",
        "import tidy3d as td\n",
        "\n",
        "td.config.simulation.use_local_subpixel = True"
      ],
      "id": "f6fb6b90"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "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())"
      ],
      "id": "13ea2743"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define a helper function to show us the various grids as we go along\n",
        "this example."
      ],
      "id": "e4144b13-e1c4-4f28-846b-3381eaa28143"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "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"
      ],
      "id": "18133ba7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Uniform grid\n",
        "\n",
        "The most standard way to define a simulation is to use a constant grid\n",
        "size in each of the three directions. This can be achieved simply using\n",
        "`td.GridSpec.uniform(dl=...)` as shown below."
      ],
      "id": "95e1c018-e708-4c10-9cd7-db1feacb3d53"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 8.3"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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/Qi17xtry5EZwcLDCwsKUlpamnJwcJScnF3omDQAA4LuysrIs3rnWs2dPp8wb\nHBysm266qcgx9u6DOnbsqICAAOXk5OjQoUNKSkqyeDfz448/rk8//VRnz57V66+/rs8//1xdu3ZV\nixYt1LRpUzVo0KDYvagz5rCVM7Ly752//PJLLV26VFLRe+cdO3aYxw4fPmzR8Pa1vXNx95nLFbd3\nDg8PV+nSpXX+/Hmn7p1XrVqldevWmXvnjIwMi/+HJ0+eNP+bm5vr1s9UzrN9+3b16dNHWVlZatOm\njT777LMim4ExMTHsme3IZc98iTv3zBEREebvj4yMDJ0/f14RERHF1gEoGTS8AVy1UlJSzD/0SpUq\npSpVqjhl3qioqGKbcmfOnDEv23Kat5CQEFWpUkV79+416/M3LytXrqyEhAQ99NBD+v3333XkyBFN\nmzZN06ZNk3TpVa233Xab7r///kJP0+2MOWzljKzjx4+bl/M3H22VkpJi8X3+P9bdfToma5x9n7mc\nLafiznsXfk5OTrFjizNjxgy98MILOnLkiM01Z8+edWvDu1SpUpo1a5aaNWtm/u6IiYnRzJkzPeLJ\nhDz5T0uf95ENxck/ztHNmbNz3XU9Lp/H2qnEXamwd/LnyX92DHvG2vo4LlWqlNLS0iRJaWlpNLwB\nALgKnTlzxjytdUxMjNP+HihTpkyxDRd790GlS5dWhQoVdPToUbM+f/OyevXq5jtON2zYoEOHDunj\njz/Wxx9/LOnS9evUqZMeeOCBQpulzpjDVs7Iyr93zjsVuT18fe9c3H3mcq7eO//73//WSy+9ZPH/\nsSiGYejcuXPFfoxYSTt+/Li6du2qlJQU1atXT/PmzbN6yml3YM9sfT57sGf+f6VKlTLfbZ+WlkbD\nG3Ajz3l2FgBcLD093bxs66l/bGHLH/D5s239wzB/oy9/fZ7rr79e69ev1y+//KIXXnhBbdq0Mf/4\nTUlJ0ezZs9W9e3e1atWq0CajM+aw1ZVm5T91kyMu/xyykro/OEtJ3Gfc5c0339SgQYPsvg/Z+orl\nkrR7926LV0TnvXrfk+R/csbW2zj/OEdfne3sXHddD0kWp+12xpNU3iz/447TmQMAcHXytb1zkyZN\n9Oeff2r16tV6/vnn1apVK/N6JScn6+uvv1aXLl0UHx+vEydOWM1wxhy2utIs9s7F89S98yuvvKLB\ngwfb3OzO4+69c1pamrp3764DBw6oQoUKWrRokdvfBZwfe2br89mDPfP/Y88MeA4egQCuWvn/sEtJ\nSVFOTk6JfWb15fK/Ijg5OdmmmqSkJPNyUa/UvfXWW81Tqufk5Gjjxo1auHChPvnkEx08eFBr1qzR\nHXfcod9//73Qd6U6Yw5bOZqV/zZ49913Va9ePbtyGzdubPF9/vvD6dOnHbgmJask7zOulJiYqNGj\nR5vfV6hQQV27dlXDhg1VoUIFhYWFWTzx9dxzz1mcTs+dDh06pCFDhlj87PDhwxoyZIjmzJlTbP2e\nPXs0YsQIp62nWrVqmjRpUoGf538sHDhwQFlZWcU+mbh7927zcv369R1aj7Nz3XU9JMtXuue9u9lZ\n8p920Bs46x0AAADAe+XfKyUlJbn0M0pLch/UqlUrtWrVStKlhsWGDRu0YMECffLJJzpy5IhWrVql\nnj17au3atYVeX2fMYStHs/LfBhMmTFCtWrXsym3SpInF9+ydXWPLli164403zO8rVapk7p3Lly+v\nUqVKWeyPnnzySe3Zs8cdS7WQk5Oju+++W3/88YfCwsI0f/58Va9e3aba7du367nnnnPaWurUqaP3\n33+/wM/z7zX37t1r0/OBzt4zOyPXXddDYs+cJzc3V5mZmeb3nvaGCOBqQ8MbwFWrVKlSKleunE6d\nOqWcnBz9/vvvuvnmm12Snf/06Rs3blSXLl2KHH/ixAkdO3ZM0qXPCK9UqZJNOQEBAbrxxht14403\nasSIEbr55pu1Y8cObdiwQUuXLlXnzp1dMoet7MmqWrWqeTk6OvqKP+epWrVq5uXffvvNJZ8bZQ9X\n3WdK2jfffGO++rVLly769ttvizxNuTM3u1ciNzdXAwcONJ8wueuuu7Rq1SodP35c3377raZPn16g\nGX65lJQULViwwGlratiwodWfx8XFKSgoSNnZ2crOztbatWvVtm3bQuc5efKkdu7caX5/3XXXObQe\nZ+e663pIlz52IY+978jJzs4u8tSctj7p5gmSk5OVlZUl6dLG3ZbTNwIAAN8THR2tqKgonT17Vunp\n6dq8eXOBFxCXlCpVqpifQb1x40a1a9euyPEHDhww/94KCgpS+fLlbcoJDAzUTTfdpJtuukkjRozQ\nTTfdpD179ui3337TTz/9VOTfoc6cw1b2ZFWtWtX8GLAyZco4Ze+cd2r03377TR07dryi+ZzNVfeZ\nkvb111+b767v1auXvvrqK4WEhBQ6/oknnnDV0or0+OOPa/78+fL39zc/EsxWSUlJTt0zF/Z5zQ0a\nNJC/v79yc3N14cKFYp8PPHTokPbt22d+7+he09m57roe0qU98x9//CHJvj2zLWcf8KY984kTJ8wG\nfdmyZd1+enfgascpzQFc1fJvAqdPn+6y3FtuucW8/NVXXxU7/r///a95uWHDhg69yy4iIkL9+/c3\nv9+wYYNb5nBWVnx8vHn5888/v+K8/PeF//znP2aTxxH5m13OOrWTO+4zJSH/K86feeaZIpvdhw8f\n1q5du4qdsyRu78uNGTNGP/30k6RLjdhPP/1U//nPf8x3TowYMcKi2epOwcHBFk/q5L8vWDNjxgxz\ng1ajRg3FxcV5RK67rock1a5d27x88OBBu2rzP4Fgzbp16xxakzscOnTIvJz/NgEAAFcXPz8/tW7d\n2vzeW/bON9xwg0PNh+joaPXt29f83pF9rzPmcFZW/r3zZ599dsV5+ffOn3322RWdPtvT9s6O3mdK\nQv6987PPPltks3vv3r3F7kOkkt87v/vuu5oyZYok6YMPPlCPHj2cnuEM4eHh5tkSpOL3ml988YV5\nuX79+hZvmHBnrruuh+T4njknJ6fY8eyZATiKhjeAq9rAgQPNy5999pn+97//uSS3R48e5mmGNm/e\nbG4IrDl48KDGjBljft+nT58CY6ZNm2bTKYTyf+7T5adTc8YctnJGVs+ePc0m7po1azRu3Dibss+c\nOWN1E5B/vkOHDmn48OE2zWdN/ubylX5eWx5n32fcJf+pqYr6jKnc3Fw99thjBT4vzpqSuL3z++WX\nX/Taa69JutSEnTVrlsLDw9WpUyc99dRTki6d9nnAgAFFvlCiTp06+uGHH5z2NXny5EKz8r9Y5Isv\nvtDmzZutjjt58qTeffdd8/u77rrLrtumpHPddT3yn7Zx27ZtdtUW9SRidna2PvjgA4fX5Wpbtmwx\nLxf27ggAAHB1yL93njp1qpYsWeKS3F69epl7wXXr1hX5t9bevXv19ttvm99b2wd9/PHHFh/ZUpii\n9qLOmMNWzsjq27ev+VnbK1as0EcffWRT9qlTpzRr1qwCP+/Tp485319//aVnn33WpvmsKYm9nLPv\nM+5i69754sWLeuyxx2yasyT3zt98842ef/55SdLTTz+tYcOG2T1Hw4YNnbpnnjBhQqFZ+fean3zy\nSaEfpXb06FGL06I7c8/sjFx3XY+S2jNnZmba/DvKE7BnBjyMAQBXsdzcXKNZs2aGJEOSERgYaIwa\nNco4c+ZMgbHnzp0zZs+ebXz44YdW5woICDAkGRUqVLApe8CAAWZuQECAMXbsWCM9Pd1izIoVK4zq\n1aub40qXLm0cP368wFy1atUyKlSoYLz88svGzp07Cxw/f/688cEHHxiBgYHmXIsXL3b6HLZyVtZr\nr71mHpdkDBw40Ni2bVuBcbm5ucamTZuMp556yoiOjjY6d+5sdV2vvPKKxXw9evQwtm7dWmDcxYsX\njZ9++sl45plnjJycnALHP/roI3OO22+/3cjKyir2Nhk2bJhZ88MPP1gd48z7jGEYRs+ePc1x69ev\nL3aNFSpUMCQZISEhxY4tzNixY83MChUqGAkJCQXGbNmyxbjtttss/l9IMg4dOmR1Tkdub1ulpKRY\n3J7jxo2zOJ6ZmWk0bdrUPD5ixAinZV+JzMxMo0aNGua6KleuXOC23rZtm9G4cWNzTEREhHHixAmr\n8x04cMDo1q2b+VXYOGfnOns+Wx09etScr379+sWOr1atmsVjc8KECQXuhwcOHDC6d+9ucZ+uUqWK\n1d8h9jw2Z8yYYY59/vnnixybnp5ujq1Vq1ax1+upp54yx//73/8udjwAAPBdOTk5xvXXX2/+bRAU\nFGS89tprRnJycoGxZ8+eNb7++mtj8uTJBY5lZ2ebc1SrVs2m7N69e1vs2ceNG2dkZGRYjPnxxx+N\nKlWqmOOioqKM06dPF5ircuXKRqVKlYzRo0cbiYmJBY6fO3fOeO+99wx/f39zrsv//nTGHLZyVtaL\nL75o8Xfogw8+aHUvnpuba/z555/G8OHDjcjISKNnz55W1/Xcc89ZzNe3b19jx44dBcZdvHjRSEhI\nMJ599lmr84wbN86co2fPnsbFixeLuUUMY/DgwWbN0qVLrY5x5n3GMAyjc+fO5rgtW7YUu8aoqChz\nTkeNHj3azLzmmmuMn3/+ucCYDRs2GO3atSuwdz516pTVOR25vW3x888/GyEhIYYko1+/fkZubq5T\n5i1JFy5csPj/X61aNWPNmjUWYzZv3mw0bNjQHBMTE2P1+ULDMIzdu3db7Jmt/W4siVxnz2erffv2\nmfM1adKk2PF5zyflPSYnT55sZGdnF5gz/2OtqH2rPY/J6dOnm2NHjx5d5Njk5GRzbMOGDYu9XkOH\nDjXHf/nll8WOB1CyaHgDuOr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htvuJQxdepU49prrzWWLFli9Z3o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\nnTvXK71eDAveRUTed4pf7Bz8eT/V8N9PZl9M3ut17NmzR+XKldPPP/+sB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          }
        }
      ],
      "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",
        "ax = plot_sim_grid(sim_uniform)"
      ],
      "id": "61feb250"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: `Tidy3D` is warning us that our Simulation does not contain\n",
        "sources. In this case, since we are using the simulation as a\n",
        "demonstration of the meshing process and are not running any\n",
        "simulations, we may safely ignore this warning throughout this notebook.\n",
        "\n",
        "## Quasi-uniform grid\n",
        "\n",
        "In many simulations, we aim to have a meshing that is almost uniform\n",
        "everywhere, but locally adjust to structure boundaries or snapping\n",
        "points. This can be achieved simply using\n",
        "`td.GridSpec.quasiuniform(dl=...)` as shown below."
      ],
      "id": "feccfb83-4a4f-4dc5-82ed-5d34e7869799"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 8.4"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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8uOuxv97M9dVVV11V4vGCH9Typq0nb25YrVZVqlRJmZmZcjgcSklJKXYnDQAA\nEL7sdnuhK9f69u3rl36tVquuvvrqEtt4Ow+64YYbZLFY5HA4dODAAZ06darQ1cyPPvqoPvzwQ50+\nfVqvvPKKPvroI/Xs2VNt27ZVq1at1Lx581Lnov7ow1P+yCo4d/7kk0+0cOFCSSXPnbdu3eo6dvDg\nwUIL3uE2dy7tNXOu0ubOMTExio2NVUZGhl/nzsuWLdPq1atdc+fs7OxCf4bHjh1z/dfpdAb0nsr5\ntmzZov79+8tut6tDhw6aNm1aiYuBiYmJzJm9yGXOfFYg58xxcXGufz+ys7OVkZGhuLi4UusAlA0W\nvAFUWKmpqa5f9KKjo1WnTh2/9Fu5cuVSF+VOnjzpeuzJNm82m0116tTR7t27XfUFFy9r166tpUuX\n6t5779Vvv/2mQ4cO6f3339f7778v6eynWm+88UbdfffdxW7T7Y8+POWPrD///NP1uODio6dSU1ML\nfV/wl/VAb8fkjr9fM+fyZCvu/KvwHQ5HqW1LM336dD3zzDM6dOiQxzWnT58O6IJ3dHS0Zs6cqdat\nW7v+7UhMTNSMGTOC4s2EfAW3pc+/ZUNpCrbzdXLm79xAPY9z+3G3lXh5Ku5K/nwFd8fwpq2nf4+j\no6OVmZkpScrMzGTBGwCACujkyZOuba0TExP99vtA1apVS11w8XYeFBsbqxo1aujw4cOu+oKLl/Xq\n1XNdcbp27VodOHBA7733nt577z1JZ59ft27ddM899xS7WOqPPjzlj6yCc+f8rci9Ee5z59JeM+cq\n77nz//3f/+m5554r9OdYEsMwlJaWVuptxMran3/+qZ49eyo1NVVNmzbVN99843bL6UBgzuy+P28w\nZ/7/oqOjXVfbZ2ZmsuANBFDwvDsLAOUsKyvL9djTrX884ckv8AWzPf3FsOBCX8H6fJdeeqnWrFmj\nlStX6plnnlGHDh1cv/ympqbqiy++UO/evdW+fftiFxn90Yenzjer4NZNvjj3PmRl9Xrwl7J4zQTK\nmDFjNGTIEK9fQ55+Yrks7dy5s9AnovM/vR9MCr454+k5LtjO109n+zs3UM9DUqFtu/3xJlUoK/j3\nju3MAQComMJt7nzllVfq999/1/Lly/X000+rffv2rueVkpKizz77TD169FCnTp109OhRtxn+6MNT\n55vF3Ll0wTp3fvHFFzV06FCPF7vzBXrunJmZqd69e2vfvn2qUaOG5s2bF/CrgAtizuy+P28wZ/7/\nmDMDwYO/gQAqrIK/2KWmpsrhcJTZPavPVfATwSkpKR7VnDp1yvW4pE/qXnfdda4t1R0Oh9atW6e5\nc+fqgw8+0P79+7VixQrdfPPN+u2334q9KtUffXjK16yC5+C1115T06ZNvcq9/PLLC31f8PVw4sQJ\nH55J2SrL10x52rFjh5KTk13f16hRQz179lSLFi1Uo0YNVapUqdAbX0899VSh7fQC6cCBAxo2bFih\nnx08eFDDhg3TrFmzSq3ftWuXhg8f7rfx1K1bV++++26Rnxf8u7Bv3z7Z7fZS30zcuXOn63GzZs18\nGo+/cwP1PKTCn3TPv7rZXwpuOxgK/HUFAAAACF0F50qnTp0q13uUluU8qH379mrfvr2kswsWa9eu\n1Zw5c/TBBx/o0KFDWrZsmfr27atVq1YV+3z90YenfM0qeA4mTJighg0bepV75ZVXFvqeuXP52Lhx\no0aPHu36vlatWq658wUXXKDo6OhC86PHH39cu3btCsRQC3E4HLr99tv1v//9T5UqVdLs2bNVr149\nj2q3bNmip556ym9jady4sd58880iPy8419y9e7dH7wf6e87sj9xAPQ+JOXM+p9OpnJwc1/fBdkEE\nUNGw4A2gwoqOjlb16tV1/PhxORwO/fbbb7rmmmvKJbvg9unr1q1Tjx49Smx/9OhRHTlyRNLZe4TX\nqlXLoxyLxaKrrrpKV111lYYPH65rrrlGW7du1dq1a7Vw4UJ17969XPrwlDdZF198setxQkLCed/n\nqW7duq7Hv/76a7ncN8ob5fWaKWuff/6569OvPXr00JdfflniNuX+nOyeD6fTqcGDB7veMLntttu0\nbNky/fnnn/ryyy81derUIovh50pNTdWcOXP8NqYWLVq4/XmTJk0UGRmp3Nxc5ebmatWqVerYsWOx\n/Rw7dkzbtm1zfd+yZUufxuPv3EA9D+nsbRfyeXtFTm5ubolbc3r6plswSElJkd1ul3R24u7J9o0A\nACD8JCQkqHLlyjp9+rSysrK0YcOGIh8gLit16tRx3YN63bp16ty5c4nt9+3b5/p9KzIyUhdccIFH\nOREREbr66qt19dVXa/jw4br66qu1a9cu/frrr/rpp59K/D3Un314ypusiy++2HUbsKpVq/pl7py/\nNfqvv/6qG2644bz687fyes2Utc8++8x1dX2/fv306aefymazFdv+n//8Z3kNrUSPPvqoZs+eLbPZ\n7LolmKdOnTrl1zlzcfdrbt68ucxms5xOp86cOVPq+4EHDhzQnj17XN/7Otf0d26gnod0ds78v//9\nT5J3c2ZPdh8IpTnz0aNHXQv01apVC/j27kBFx5bmACq0gpPAqVOnllvutdde63r86aefltr+v//9\nr+txixYtfLrKLi4uTrfeeqvr+7Vr1wakD39lderUyfX4o48+Ou+8gq+F//znP65FHl8UXOzy19ZO\ngXjNlIWCnzj/17/+VeJi98GDB7V9+/ZS+yyL832uUaNG6aeffpJ0diH2ww8/1H/+8x/XlRPDhw8v\ntNgaSFartdCbOgVfC+5Mnz7dNUGrX7++mjRpEhS5gXoektSoUSPX4/3793tVW/ANBHdWr17t05gC\n4cCBA67HBc8JAACoWEwmk66//nrX96Eyd77iiit8WnxISEjQgAEDXN/7Mu/1Rx/+yio4d542bdp5\n5xWcO0+bNu28ts8Otrmzr6+ZslBw7vzkk0+WuNi9e/fuUuchUtnPnV977TVNnjxZkvTWW2+pT58+\nfs/wh5iYGNduCVLpc82PP/7Y9bhZs2aFLpgIZG6gnofk+5zZ4XCU2p45MwBfseANoEIbPHiw6/G0\nadP03XfflUtunz59XNsMbdiwwTUhcGf//v0aNWqU6/v+/fsXafP+++97tIVQwfs+nbudmj/68JQ/\nsvr27etaxF2xYoVef/11j7JPnjzpdhJQsL8DBw7oscce86g/dwouLp/v/dry+fs1EygFt6Yq6R5T\nTqdTjzzySJH7xblTFue7oJUrV+rll1+WdHYRdubMmYqJiVG3bt00YsQISWe3fR40aFCJH5Ro3Lix\nvv/+e799TZo0qdisgh8W+fjjj7Vhwwa37Y4dO6bXXnvN9f1tt93m1bkp69xAPY+C2zZu3rzZq9qS\n3kTMzc3VW2+95fO4ytvGjRtdj4u7OgIAAFQMBefOU6ZM0YIFC8olt1+/fq654OrVq0v8XWv37t16\n9dVXXd+7mwe99957hW7ZUpyS5qL+6MNT/sgaMGCA617bixcv1jvvvONR9vHjxzVz5swiP+/fv7+r\nvz/++ENPPvmkR/25UxZzOX+/ZgLF07lzXl6eHnnkEY/6LMu58+eff66nn35akvTEE0/o4Ycf9rqP\nFi1a+HXOPGHChGKzCs41P/jgg2JvpXb48OFC26L7c87sj9xAPY+ymjPn5OR4/G9UMGDODAQZAwAq\nMKfTabRu3dqQZEgyIiIijBdeeME4efJkkbZpaWnGF198Ybz99ttu+7JYLIYko0aNGh5lDxo0yJVr\nsViMsWPHGllZWYXaLF682KhXr56rXWxsrPHnn38W6athw4ZGjRo1jOeff97Ytm1bkeMZGRnGW2+9\nZURERLj6mj9/vt/78JS/sl5++WXXcUnG4MGDjc2bNxdp53Q6jfXr1xsjRowwEhISjO7du7sd14sv\nvliovz59+hibNm0q0i4vL8/46aefjH/961+Gw+Eocvydd95x9XHTTTcZdru91HPy8MMPu2q+//57\nt238+ZoxDMPo27evq92aNWtKHWONGjUMSYbNZiu1bXHGjh3ryqxRo4axdOnSIm02btxo3HjjjYX+\nLCQZBw4ccNunL+fbU6mpqYXO5+uvv17oeE5OjtGqVSvX8eHDh/st+3zk5OQY9evXd42rdu3aRc71\n5s2bjcsvv9zVJi4uzjh69Kjb/vbt22f06tXL9VVcO3/n+rs/Tx0+fNjVX7NmzUptX7du3UJ/NydM\nmFDkdbhv3z6jd+/ehV7TderUcftviDd/N6dPn+5q+/TTT5fYNisry9W2YcOGpT6vESNGuNr/3//9\nX6ntAQBA+HI4HMall17q+t0gMjLSePnll42UlJQibU+fPm189tlnxqRJk4ocy83NdfVRt25dj7Jv\nueWWQnP2119/3cjOzi7U5ocffjDq1Knjale5cmXjxIkTRfqqXbu2UatWLSM5OdnYsWNHkeNpaWnG\nG2+8YZjNZldf5/7+6Y8+POWvrGeffbbQ76F///vf3c7FnU6n8fvvvxuPPfaYER8fb/Tt29ftuJ56\n6qlC/Q0YMMDYunVrkXZ5eXnG0qVLjSeffNJtP6+//rqrj759+xp5eXmlnBHDGDp0qKtm4cKFbtv4\n8zVjGIbRvXt3V7uNGzeWOsbKlSu7+vRVcnKyK/PCCy80fv755yJt1q5da3Tu3LnI3Pn48eNu+/Tl\nfHvi559/Nmw2myHJGDhwoOF0Ov3Sb1k6c+ZMoT//unXrGitWrCjUZsOGDUaLFi1cbRITE92+X2gY\nhrFz585Cc2Z3/zaWRa6/+/PUnj17XP1deeWVpbbPfz8p/+/kpEmTjNzc3CJ9Fvy7VtK81Zu/k1On\nTnW1TU5OLrFtSkqKq22LFi1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LT0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\n"
          }
        }
      ],
      "source": [
        "sim_quasiuniform = td.Simulation(\n",
        "    size=[5, 3, 3],\n",
        "    grid_spec=td.GridSpec.quasiuniform(dl=20 * nm),\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_quasiuniform)"
      ],
      "id": "e1a80d2d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The grids look quite uniform. Compared to the uniform grid figure, here\n",
        "the grid is aligned with the edges of the two waveguides’ boundaries.\n",
        "\n",
        "## Automatic nonuniform grid\n",
        "\n",
        "By default, `Tidy3D` uses a more advanced meshing, using an algorithm to\n",
        "automatically define a nonuniform grid in all three directions. The\n",
        "resolution of this grid is specified using two parameters: - Minimum\n",
        "steps per wavelength in each material (`min_steps_per_wvl = 10` by\n",
        "default). This specification therefore requires a target wavelength,\n",
        "which can be either provided directly to `grid_spec`, or inferred from\n",
        "any sources present in the simulation. The two simulations below will\n",
        "have the same grid; - Minimum steps per longest edge length of\n",
        "simulation domain bounding box (`min_steps_per_sim_size=10` by default).\n",
        "This specification is relevant when the entire simulation domain is\n",
        "subwavelength, e.g. in radio-frequency simulations."
      ],
      "id": "8efb81a9-2b29-43ba-8680-4746c33890e8"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 0.53"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "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)"
      ],
      "id": "c1706331"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The default grid is somewhat coarse, but it is often good for initial\n",
        "simulations. We can easily refine it by requiring a higher number of\n",
        "steps per wavelength:"
      ],
      "id": "4cc5a6b5-1f9d-4f9e-b764-78b2fc004435"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 2.3"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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lJR4v+EGtsoz15c0Nu92uKlWqKDMzUy6XS8nJycXupAEAAM5eTqez0JlrAwcO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\n27Zt21If1+jRoz3jP/roo1L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          }
        }
      ],
      "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)"
      ],
      "id": "43a4936f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Notice how the automatic grid conforms to the structures in the\n",
        "simulation, and the grid steps are high in the high-index regions.\n",
        "\n",
        "### Lower bound of grid size\n",
        "\n",
        "Sometimes the grid size in the automatically generated mesh is too\n",
        "small. A lower bound `dl_min` can be set to increase the minimal grid\n",
        "size. Note that the bound is soft, meaning that the actual grid size\n",
        "might be slightly smaller.\n",
        "\n",
        "The minimal step size along y-direction in the previous setup is,"
      ],
      "id": "81565415-2494-4ae9-8cb0-eb3ced8c9031"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimal grid size along y-direction = 16.67nm"
          ]
        }
      ],
      "source": [
        "print(f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y) * 1e3:1.2f}nm\")"
      ],
      "id": "32ec4609"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let’s set a lower bound of 25nm."
      ],
      "id": "60aa5310-6831-4f38-b27a-e0e718e2b796"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 1.9\n",
            "Minimal grid size along y-direction = 24.57nm"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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15cWN4OBghYWFKTExUenp6YqJiXF6Jg0AAFB0paamZvvkWu/evd2SGxwcrBtv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\nAViLBW8Axd7evXuN6OjobE+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          }
        }
      ],
      "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(f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y) * 1e3:1.2f}nm\")"
      ],
      "id": "cc658b10"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The actual minimal grid size along y-direction is 24.57nm, greater than\n",
        "16.67nm when `dl_min` is not set.\n",
        "\n",
        "### Grid snapping points\n",
        "\n",
        "Sometimes we want to make sure that grids are placed at certain\n",
        "locations. This can be achieved by adding `snapping_points` to the\n",
        "simulation `grid_spec`. `snapping_points` is a list of coordinates\n",
        "defining the positions of the points. Note that the snapping of grids is\n",
        "not enforced if they are too close among themselves or to structure\n",
        "boundaries.\n",
        "\n",
        "In the previous example, there is no grid point at `(0, -0.4, 0.2)`.\n",
        "Let’s add a snapping point there to see the effects. As illustrated\n",
        "below, the snapping point is visualized as a red dot. Now grid\n",
        "boundaries pass through this point."
      ],
      "id": "4df695ad-d1f9-427c-8efe-ea858ee88adc"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 2.0"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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TArK8r4/1Yk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\nEhPeRERERERERERERERERERUJ/0/5zUF/61THY8AAAAASUVORK5CYII=\n"
          }
        }
      ],
      "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",
        "        snapping_points=[\n",
        "            (0, -0.4, 0.2),\n",
        "        ],\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)"
      ],
      "id": "91c26d4e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Mesh override structures\n",
        "\n",
        "For this particular problem, we may want to do a final refinement around\n",
        "the slot, where we expect the fields to be strongest. This can be\n",
        "achieved by adding `override_structures` to the simulation `grid_spec`.\n",
        "\n",
        "`override_structures` is a list of `Tidy3D` structures each with an\n",
        "arbitrary geometry which are used exclusively for the meshing, added on\n",
        "top of any physical simulation structures.\n",
        "\n",
        "There are two types of `Tidy3D` structures that can be added the\n",
        "`override_structures` list. The first type defines a fictitious medium\n",
        "inside the override structure so that the grid size is decided by the\n",
        "minimum steps per wavelength in the medium; The second type is more\n",
        "straightforward: one can directly define the grid size along each axis\n",
        "inside the override structures.\n",
        "\n",
        "#### Override Structure – option 1\n",
        "\n",
        "The first type is identical to the\n",
        "[Structure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html)\n",
        "object that consists of a\n",
        "[Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#geometry)\n",
        "and a\n",
        "[Medium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#mediums).\n",
        "The grid step in the override_structure region is decided by the minimum\n",
        "steps per wavelength in this `medium`."
      ],
      "id": "f9a6335c-56d0-4c0b-8a19-7cc9b7b74996"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 4.0"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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OjpKSkgpdSQMAAASuzMxMpyvX+vTp45W44eHhuuKKK4osU9xx0LXXXquQkBDl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\nvNC47rrrjKuvvtqoX7++ERY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D4tPRadMGGCJkyYsNdi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          }
        }
      ],
      "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)"
      ],
      "id": "f366a69a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note that the override region is highlighted in the grid plotting. Now\n",
        "this looks pretty good - the grid size of the points around the slot\n",
        "region is `1550 / 20 / 5 = 15.5` nanometers, while overall the\n",
        "simulation has twice fewer total number of grid points (`4.0M`) compared\n",
        "to the simulation with a uniform mesh step of `20` nanometers (`8.3M`).\n",
        "One thing to note, however, is that the override structure made the grid\n",
        "along the x-dimension small, which may not be necessary. To avoid this,\n",
        "we can change that structure to have `size = 0` along that dimension.\n",
        "This would force the non-uniform grid to place a grid boundary at the\n",
        "`center` of the override structure along `x`, but apart from that it\n",
        "will not refine the grid step along `x`."
      ],
      "id": "bf416065-6cbc-4477-9627-4deab19b7237"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 3.0"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5p9T93h/UKk/bQN7csNlsqlmzpnJzc+V0OpWenl7iShoAAODvy+Fw+Ny5NmjQ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\n0/6DDz4osz2A8GLCG0C1t23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W3atOZ9f/rTn/TEE0/oqKOOiqjGqlWr9Nprr7W5b/Xq1Vq9enXzv7t169bmgne8egWAaLB7PRrx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\nwkJp3Dhje2iJfmJTM5I5ycY+G2qOceGPM3Nvknxjx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          }
        }
      ],
      "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)"
      ],
      "id": "aed33202"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Override Structure – option 2\n",
        "\n",
        "The second type is the\n",
        "[MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MeshOverrideStructure.html)\n",
        "object that consists of a\n",
        "[Geometry](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#geometry),\n",
        "and a tuple `dl` specifying the grid sizes along x,y,z directions. We\n",
        "can override the grid sizes just along a few selected directions by\n",
        "setting the value to be `None` in the `dl` tuple along the other\n",
        "directions. E.g. if we only plan to refine the grid size along\n",
        "x-direction with grid size 0.01 micron, we can apply\n",
        "`dl=(0.01, None, None)`. In the following, we override the grid size\n",
        "along `y` and `z` to be 15.5nm."
      ],
      "id": "5dae8487-660e-4236-8f94-98d2f0df0ab2"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 2.9"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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Vk5OjxMTEAlfSAAAA567MzEynM9d69+7tlbyhoaG66qqrCm1T3HHQ9ddfr6Cg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\ngoKCjIsvvti44YYbjGuvvda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          }
        }
      ],
      "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)"
      ],
      "id": "87361e63"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Just for fun, we can also have a look at the modes of this structure."
      ],
      "id": "71508734-6297-4eb2-9b59-3c45fbdefeaa"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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eQxFAEA2OVkZ2frn//8p0444QSNGTNGDz/8sMLhnfP3CAA+gvwFObAvupqe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\neib77LPP9Oqrr6bl2K655hq98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AAAAAAFqWYsd9GvqA1qkgD55IUu/evfXJJ5/oxBNP1IgRI9SlSxf1798/bRDivvvu04AB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\nxgc5JGnkyJGaNGm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          }
        }
      ],
      "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",
        "\n",
        "mode_simulation = td.ModeSimulation.from_simulation(\n",
        "    simulation=sim_nonuniform_20_refine,\n",
        "    plane=plane,\n",
        "    mode_spec=mode_spec,\n",
        "    freqs=[freq0],\n",
        ")\n",
        "sim_data = mode_simulation.run_local()\n",
        "modes = sim_data.modes\n",
        "\n",
        "fig, axs = sim_data.plot_field_components(\n",
        "    field_names=(\"Ey\", \"Ez\"),\n",
        "    mode_indices=tuple(range(num_modes)),\n",
        "    val=\"abs\",\n",
        "    f=freq0,\n",
        "    robust=False,\n",
        "    figsize=(12, 14),\n",
        ")\n",
        "plt.show()"
      ],
      "id": "3b3342c9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Mesh override with coarser grid size\n",
        "\n",
        "Sometimes, instead of refinement, we want to override a region with a\n",
        "coarser mesh. This can be achieved by setting `enforce=True` in\n",
        "[MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MeshOverrideStructure.html).\n",
        "\n",
        "First, let’s see the default behavior where `enforce=False` so that the\n",
        "override structure with coarser mesh will be ignored unless it fully\n",
        "covers the structure of fine mesh. In the following example, we try to\n",
        "override the mesh along x-direction be to 40nm."
      ],
      "id": "032bb89f-addc-420b-9d7d-ca261178c7e3"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 2.9\n",
            "Minimal grid size along x-direction = 22.22nm"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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Vk5OjxMTEAlfSAAAA567MzEynM9d69+7tlbyhoaG66qqrCm1T3HHQ9ddfr6Cg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\ngoKCjIsvvti44YYbjGuvvda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          }
        }
      ],
      "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",
        ")"
      ],
      "id": "2eed2947"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The minimal grid size along x-direction is still 22nm, so the\n",
        "`override_structures` doesn’t take effect. This is because, by default,\n",
        "the meshing chooses the step size based on the highest-index structure\n",
        "(real or override) at any given cross-section. However, this behavior\n",
        "can be overridden if we set `enforce=True` in an\n",
        "`MeshOverrideStructure`, in which case other structures are ignored."
      ],
      "id": "9a2ea7b2-600d-4c91-b16d-84195607a0f3"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 1.4\n",
            "Minimal grid size along x-direction = 40.00nm"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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X/aJWedr68+GG3W5XbGyssrOz5XQ6lZaWVuJMGgAA4NTlcDi8rlzr379/QPq1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\nPO0//PDDMtsDCC4K3gCqvC1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YqCHi4uJE2bJl88WLjIy0yFytVW5urpgyZYqwsbFR2+edO3cWz549M0qsly9fismTJ2vcj8hk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\npP8H2m8Ij6HDyhwAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "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",
        ")"
      ],
      "id": "76aed728"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now the minimal grid size along x-direction is target 40nm. When\n",
        "multiple override structures with `enforce=True` overlap, the grid size\n",
        "in the intersection region is decided by the last such structure in the\n",
        "list.\n",
        "\n",
        "## Combination of uniform and nonuniform grid\n",
        "\n",
        "Finally, we note that `Tidy3D` allows another easy way to handle the `x`\n",
        "direction in our example, namely applying different grid specifications\n",
        "along each of the three simulation directions. For example, we can set a\n",
        "fixed grid size of `40nm` along the propagation direction, where we\n",
        "expect the field dependence to be smooth, while still using the refined\n",
        "nonuniform mesh in the other two directions. This achieves the same\n",
        "effect as in the plot above."
      ],
      "id": "71622d60-ea41-45e3-bb6d-c61ce60af633"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">00:09:29 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Override structures take no effect along x-axis. If       </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to apply override structures to this axis, use </span><span style=\"color: #008000; text-decoration-color: #008000\">'AutoGrid'</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">or </span><span style=\"color: #008000; text-decoration-color: #008000\">'QuasiUniformGrid'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of grid points (millions): 1.4"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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X/aJWedr68+GG3W5XbGyssrOz5XQ6lZaWVuJMGgAA4NTlcDi8rlzr379/QPq1\n2+26+OKLS21T3nHQ5ZdfLpvNJqfTqV27dunIkSNeVzPfddddevvtt5WRkaH/+7//07vvvqvevXur\nXbt2uuCCC9S6desyx6KB6MNfgYhVdOz84Ycfav78+ZJKHzuvX7/es2337t1eBe9Tbexc1jlzorLG\nznFxcYqPj1dWVlZAx85LlizRihUrPGPn3Nxcr//DAwcOeP51uVym3lO50Lp16zRw4EA5HA516tRJ\n77zzTqnFwOTkZMbM5YjLmNnNzDFzQkKC5+9Hbm6usrKylJCQUOZ+AIKDgjeAKis9Pd3zRi8mJkb1\n69cPSL/VqlUrsyh3+PBhz7I/07xFRUWpfv362rp1q2f/osXLevXqafHixbr11lv1yy+/aM+ePXrz\nzTf15ptvSnJ/q/WKK67QTTfdVOI03YHow1+BiPXXX395losWH/2Vnp7u9XPRN+tmT8fkS6DPmRP5\nMxV34VX4TqezzLZlmTp1qh566CHt2bPH730yMjJMLXjHxMRo2rRpuuiiizx/O5KTk/XBBx+ExYcJ\nhYpOS194y4ayFG1X0cFZoOOa9TxO7MfXVOKhVNKV/IWKzo5Rnrb+/h7HxMQoOztbkpSdnU3BGwCA\nKujw4cOeaa2Tk5MD9n6gRo0aZRZcyjsOio+PV+3atbV3717P/kWLl40aNfJccbpy5Urt2rVLb7zx\nht544w1J7ufXo0cP3XzzzSUWSwPRh78CEavo2LlwKvLyONXHzmWdMycK9dj5v//9rx5++GGv/8fS\nGIaho0ePlnkbsWD766+/1Lt3b6Wnp6tly5aaMWOGzymnzcCY2Xd/5cGY+biYmBjP1fbZ2dkUvAET\nhc+nswAQYjk5OZ5lf6f+8Yc/b+CLxvb3jWHRQl/R/QudffbZ+vnnn7Vs2TI99NBD6tSpk+fNb3p6\nuj799FP17dtXHTp0KLHIGIg+/HWysYpO3VQRJ96HLFjnQ6AE45wxy1NPPaWhQ4eW+xzy9xvLwbR5\n82avb0QXfns/nBT9cMbfY1y0XUW/nR3ouGY9D0le03YH4kOqyqzo7x3TmQMAUDWdamPn888/X7/9\n9puWLl2qBx98UB06dPA8r7S0NH388cfq1auXunTpov379/uMEYg+/HWysRg7ly1cx86PPfaYhg0b\n5nexu5DZY+fs7Gz17dtXO3bsUO3atTVnzhzTrwIuijGz7/7KgzHzcYyZgfDBbyCAKqvoG7v09HQ5\nnc6g3bP6REW/EZyWlubXPkeOHPEsl/ZN3csuu8wzpbrT6dTvv/+u2bNn66233tLOnTv1ww8/6Kqr\nrtIvv/xS4lWpgejDXxWNVfQYPPvss2rZsmW54p577rlePxc9Hw4dOlSBZxJcwTxnQmnTpk1KTU31\n/Fy7dm317t1bbdq0Ue3atRUbG+v1wdcDDzzgNZ2emXbt2qXhw4d7rdu9e7eGDx+u6dOnl7n/li1b\nNGrUqIDl07BhQ7366qvF1hf9XdixY4ccDkeZHyZu3rzZs9yqVasK5RPouGY9D8n7m+6FVzcHStFp\nByuDQF0BAAAAKq+iY6UjR46E9B6lwRwHdejQQR06dJDkLlisXLlSs2bN0ltvvaU9e/ZoyZIl6t+/\nv5YvX17i8w1EH/6qaKyix+DFF19U06ZNyxX3/PPP9/qZsXNorFmzRk8++aTn57p163rGzqeddppi\nYmK8xkf33HOPtmzZYkaqXpxOp6699lr9+uuvio2N1cyZM9WoUSO/9l23bp0eeOCBgOXSvHlzvfDC\nC8XWFx1rbt261a/PAwM9Zg5EXLOeh8SYuZDL5VJeXp7n53C7IAKoaih4A6iyYmJiVKtWLR08eFBO\np1O//PKLLrnkkpDELjp9+u+//65evXqV2n7//v3at2+fJPc9wuvWretXHJvNpgsvvFAXXnihRo0a\npUsuuUTr16/XypUrNX/+fPXs2TMkffirPLEaNGjgWU5KSjrp+zw1bNjQs/zTTz+F5L5R5RGqcybY\nPvnkE8+3X3v16qXPPvus1GnKAznYPRkul0s33HCD5wOTf/zjH1qyZIn++usvffbZZ5oyZUqxYviJ\n0tPTNWvWrIDl1KZNG5/rW7RoocjISOXn5ys/P1/Lly9X586dS+znwIED2rBhg+fns846q0L5BDqu\nWc9Dct92oVB5r8jJz88vdWpOfz90CwdpaWlyOByS3AN3f6ZvBAAAp56kpCRVq1ZNGRkZysnJ0erV\nq4t9gThY6tev77kH9e+//66uXbuW2n7Hjh2e91uRkZE67bTT/IoTERGhiy++WBdffLFGjRqliy++\nWFu2bNFPP/2k7777rtT3oYHsw1/lidWgQQPPbcBq1KgRkLFz4dToP/30ky6//PKT6i/QQnXOBNvH\nH3/subp+wIAB+uijjxQVFVVi+5EjR4YqtVLdddddmjlzpqxWq+eWYP46cuRIQMfMJd2vuXXr1rJa\nrXK5XDp27FiZnwfu2rVL27Zt8/xc0bFmoOOa9Twk95j5119/lVS+MbM/sw9UpjHz/v37PQX6mjVr\nmj69O1DVMaU5gCqt6CBwypQpIYt76aWXepY/+uijMtv/73//8yy3adOmQlfZJSQk6JprrvH8vHLl\nSlP6CFSsLl26eJbffffdk45X9Fx47733PEWeiiha7ArU1E5mnDPBUPQb5/fdd1+pxe7du3dr48aN\nZfYZjON9ovHjx+u7776T5C7Evv3223rvvfc8V06MGjXKq9hqJrvd7vWhTtFzwZepU6d6BmiNGzdW\nixYtwiKuWc9Dkpo1a+ZZ3rlzZ7n2LfoBgi8rVqyoUE5m2LVrl2e56DEBAABVi8ViUceOHT0/V5ax\n83nnnVeh4kNSUpIGDRrk+bki495A9BGoWEXHzu+8885Jxys6dn7nnXdOavrscBs7V/ScCYaiY+f7\n77+/1GL31q1byxyHSMEfOz/77LN6/fXXJUkvvfSS+vXrF/AYgRAXF+eZLUEqe6z5/vvve5ZbtWrl\ndcGEmXHNeh5SxcfMTqezzPaMmQFUFAVvAFXaDTfc4Fl+55139NVXX4Ukbr9+/TzTDK1evdozIPBl\n586dGj9+vOfngQMHFmvz5ptv+jWFUNH7Pp04nVog+vBXIGL179/fU8T94Ycf9Nxzz/kV+/Dhwz4H\nAUX727Vrl+6++26/+vOlaHH5ZO/XVijQ54xZik5NVdo9plwul+68885i94vzJRjHu6hly5bpiSee\nkOQuwk6bNk1xcXHq0aOHRo8eLck97fOQIUNK/aJE8+bN9fXXXwfs8dprr5UYq+iXRd5//32tXr3a\nZ7sDBw7o2Wef9fz8j3/8o1zHJthxzXoeRadtXLt2bbn2Le1DxPz8fL300ksVzivU1qxZ41ku6eoI\nAABQNRQdO0+ePFnz5s0LSdwBAwZ4xoIrVqwo9b3W1q1b9cwzz3h+9jUOeuONN7xu2VKS0saigejD\nX4GINWjQIM+9thcuXKhXXnnFr9gHDx7UtGnTiq0fOHCgp78///xT999/v1/9+RKMsVygzxmz+Dt2\nLigo0J133ulXn8EcO3/yySd68MEHJUn33nuv7rjjjnL30aZNm4COmV988cUSYxUda7711lsl3kpt\n7969XtOiB3LMHIi4Zj2PYI2Z8/Ly/P4bFQ4YMwNhxgCAKszlchkXXXSRIcmQZERERBiPPvqocfjw\n4WJtjx49anz66afGyy+/7LMvm81mSDJq167tV+whQ4Z44tpsNmPChAlGTk6OV5uFCxcajRo18rSL\nj483/vrrr2J9NW3a1Khdu7bxyCOPGBs2bCi2PSsry3jppZeMiIgIT19z584NeB/+ClSsJ554wrNd\nknHDDTcYa9euLdbO5XIZq1atMkaPHm0kJSUZPXv29JnXY4895tVfv379jD/++KNYu4KCAuO7774z\n7rvvPsPpdBbb/sorr3j6uPLKKw2Hw1HmMbnjjjs8+3z99dc+2wTynDEMw+jfv7+n3c8//1xmjrVr\n1zYkGVFRUWW2LcmECRM8MWvXrm0sXry4WJs1a9YYV1xxhdf/hSRj165dPvusyPH2V3p6utfxfO65\n57y25+XlGRdccIFn+6hRowIW+2Tk5eUZjRs39uRVr169Ysd67dq1xrnnnutpk5CQYOzfv99nfzt2\n7DD69OnjeZTULtBxA92fv/bu3evpr1WrVmW2b9iwodfv5osvvljsPNyxY4fRt29fr3O6fv36Pv+G\nlOd3c+rUqZ62Dz74YKltc3JyPG2bNm1a5vMaPXq0p/1///vfMtsDAIBTl9PpNM4++2zPe4PIyEjj\niSeeMNLS0oq1zcjIMD7++GPjtddeK7YtPz/f00fDhg39ip2SkuI1Zn/uueeM3NxcrzbffPONUb9+\nfU+7atWqGYcOHSrWV7169Yy6desaqampxqZNm4ptP3r0qDFx4kTDarV6+jrx/Wcg+vBXoGKNHTvW\n633oLbfc4nMs7nK5jN9++824++67jcTERKN///4+83rggQe8+hs0aJCxfv36Yu0KCgqMxYsXG/ff\nf7/Pfp577jlPH/379zcKCgrKOCKGMWzYMM8+8+fP99kmkOeMYRhGz549Pe3WrFlTZo7VqlXz9FlR\nqampnpinn3668f333xdrs3LlSqNr167Fxs4HDx702WdFjrc/vv/+eyMqKsqQZAwePNhwuVwB6TeY\njh075vX/37BhQ+OHH37warN69WqjTZs2njbJyck+Py80DMPYvHmz15jZ19/GYMQNdH/+2rZtm6e/\n888/v8z2hZ8nFf5Ovvbaa0Z+fn6xPov+rpU2bi3P7+SUKVM8bVNTU0ttm5aW5mnbpk2bMp/XiBEj\nPO0//PDDMtsDCC4K3gCqvC1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YqCHi4uJE2bJl88WLjIy0yFytVW5urpgyZYqwsbFR2+edO3cWz549M0qsly9fismTJ2vcj8hk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\npP8H2m8Ij6HDyhwAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "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)"
      ],
      "id": "885470f7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: the 2nd warning is just letting us know that we are using uniform\n",
        "grid along x-axis even though the override structures have some extent\n",
        "in these dimensions. We can ignore as this is intended."
      ],
      "id": "fedcbae8-69d1-44c6-9efb-f99d76d1183f"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}