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        "# Band structure calculation of hexagonal lattices\n",
        "\n",
        "In this notebook, we demonstrate the approach for simulating band\n",
        "diagrams of periodic structures defined in hexagonal lattices. We will\n",
        "reproduce the TM band diagram calculation presented in the paper by\n",
        "`Tsung-li Liu, Kasey J. Russell, Shanying Cui, and Evelyn L. Hu, \"Two-dimensional hybrid photonic/plasmonic crystal cavities\", Optics Express, (2014).`\n",
        "[DOI:10.1364/OE.22.008219](https://doi.org/10.1364/oe.22.008219).\n",
        "\n",
        "For calculating band diagrams of periodic structures in a **square**\n",
        "lattice, please refer to our [band diagrams for a photonic crystal\n",
        "slab](https://www.flexcompute.com/tidy3d/examples/notebooks/Bandstructure/)\n",
        "example notebook.\n",
        "\n",
        "Due to the rectangular nature of the FDTD simulation domain, a primitive\n",
        "hexagonal unit cell **cannot** be represented directly; therefore, it is\n",
        "necessary to use a rectangular supercell of size $(a \\times \\sqrt{3}a$),\n",
        "constructed from two primitive real-space lattice vectors. However, this\n",
        "supercell enlarges the real-space period and reduces the Brillouin zone,\n",
        "which introduces artificial band folding.\n",
        "\n",
        "To suppress these folded modes, we apply **matching dipoles**. For each\n",
        "dipole placed in the supercell, a second dipole is added at the position\n",
        "shifted by the primitive lattice vector $\\vec{r}$ of the true hexagonal\n",
        "cell. The second dipole is driven with a Bloch phase factor\n",
        "\n",
        "$$e^{-i 2\\pi\\,\\vec{b}\\cdot\\vec{r}}$$\n",
        "\n",
        "where $\\vec{b}$ is the Bloch wavevector. This enforces the correct Bloch\n",
        "periodicity of the primitive cell, ensuring that only the physical\n",
        "(unfolded) Bloch modes are excited, and allowing the band diagram to be\n",
        "computed using the standard high-symmetry points of the hexagonal\n",
        "Brillouin zone.\n",
        "\n",
        "This notebook is structured as follows:\n",
        "\n",
        "[1)](#1) Definition of the K-points for the hexagonal lattice  \n",
        "[2)](#2) Supercell definition and base simulation setup  \n",
        "[3)](#3) Function for creating matching dipoles for a given Bloch\n",
        "vector  \n",
        "[4)](#4) Band diagram calculation"
      ],
      "id": "e7e744fc-c63e-413f-abb9-1be1b9753543"
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        "<center>"
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<img src=\"./img/HexagonalSupercell.png\" width=500 />"
      ],
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        "</center>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from typing import Callable\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "from tidy3d import web\n",
        "\n",
        "# Defining a random seed for reproducibility\n",
        "np.random.seed(12)"
      ],
      "id": "13ab4ed1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## K-points Definition <a name=\"1\"></a>\n",
        "\n",
        "For a hexagonal lattice, we can define the two real-space lattice\n",
        "vectors as  \n",
        "$a_1 = (a,\\,0)$ and  \n",
        "$a_2 = a(\\cos(\\pi/3),\\,\\sin(\\pi/3))$.\n",
        "\n",
        "To transform these vectors into reciprocal space, we use the standard\n",
        "definition, where $a_3$ is simply (0,0,1).\n",
        "\n",
        "$b_i = 2\\pi\\, \\frac{a_j \\times a_k}{a_1 \\cdot (a_2 \\times a_3)}$\n",
        "\n",
        "Hence, the vectors in the reciprocal space are:\n",
        "\n",
        "\\$ b_1 = (1,-) \\$  \n",
        "\\$ b_2 = (0,) \\$\n",
        "\n",
        "For band calculations, we only need to sweep points in the irreducible\n",
        "Brillouin zone (IBZ), which in reduced reciprocal coordinates\n",
        "corresponds to the triangular region defined by the points\n",
        "\n",
        "- $\\Gamma = (0,\\,0)$  \n",
        "- $M = \\left(0,\\,\\tfrac{1}{\\sqrt{3}}\\right)$  \n",
        "- $K = \\left(\\tfrac{1}{3},\\,\\tfrac{1}{\\sqrt{3}}\\right)$.\n",
        "\n",
        "The code below illustrates the real and reciprocal spaces."
      ],
      "id": "87c1f48d-00b2-4eef-ab4f-e71d0cfe03f8"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
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ZYRgF3uf9m5WVJUl2z7l15NixY/r222+1detWHT58WKmpqcrOzraVZ2Rk2N4n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\nOHFC6enpTsv//fdfzZgxw62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eHou1MPCGvpecnKwh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          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# -------------------------------------------------\n",
        "# Lattice definitions\n",
        "# -------------------------------------------------\n",
        "a = 1.0  # lattice constant\n",
        "\n",
        "# Real-space lattice vectors\n",
        "a1 = np.array([a, 0.0])\n",
        "a2 = a * np.array([np.cos(np.pi / 3), np.sin(np.pi / 3)])\n",
        "\n",
        "# Reciprocal-space lattice vectors\n",
        "b1 = (2 * np.pi / a) * np.array([1.0, -1.0 / np.sqrt(3)])\n",
        "b2 = (2 * np.pi / a) * np.array([0.0, 2.0 / np.sqrt(3)])\n",
        "\n",
        "# -------------------------------------------------\n",
        "# Generate lattice points\n",
        "# -------------------------------------------------\n",
        "N = 5  # number of points in each direction (odd, so we get symmetry around 0)\n",
        "\n",
        "# Real space points\n",
        "pts_real = []\n",
        "for i in range(-(N // 2), N // 2 + 1):\n",
        "    for j in range(-(N // 2), N // 2 + 1):\n",
        "        pts_real.append(i * a1 + j * a2)\n",
        "pts_real = np.array(pts_real)\n",
        "\n",
        "# Reciprocal space points\n",
        "pts_rec = []\n",
        "for i in range(-(N // 2), N // 2 + 1):\n",
        "    for j in range(-(N // 2), N // 2 + 1):\n",
        "        pts_rec.append(i * b1 + j * b2)\n",
        "pts_rec = np.array(pts_rec)\n",
        "\n",
        "\n",
        "# -------------------------------------------------\n",
        "# IBZ path in reciprocal space\n",
        "# (given in reduced coordinates; convert to Cartesian k)\n",
        "# -------------------------------------------------\n",
        "Gamma = 2 * np.pi / a * np.array([0.0, 0.0])\n",
        "M = 2 * np.pi / a * np.array([0.0, 1.0 / np.sqrt(3)])\n",
        "K = 2 * np.pi / a * np.array([1.0 / 3.0, 1.0 / np.sqrt(3)])\n",
        "\n",
        "ibz_path = np.vstack([Gamma, M, K, Gamma])\n",
        "\n",
        "# -------------------------------------------------\n",
        "# Plot\n",
        "# -------------------------------------------------\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
        "\n",
        "\n",
        "# --- Real space ---\n",
        "ax1.scatter(pts_real[:, 0], pts_real[:, 1], marker=\"o\", s=200, color=\"black\")\n",
        "ax1.quiver(0, 0, a1[0], a1[1], angles=\"xy\", scale_units=\"xy\", scale=1)\n",
        "ax1.quiver(0, 0, a2[0], a2[1], angles=\"xy\", scale_units=\"xy\", scale=1)\n",
        "ax1.set_title(\"Real-space lattice and vectors\")\n",
        "ax1.set_xlabel(\"x\")\n",
        "ax1.set_ylabel(\"y\")\n",
        "ax1.set_aspect(\"equal\")\n",
        "ax1.grid(True)\n",
        "\n",
        "# Supercell corners in real space\n",
        "W = a / 2\n",
        "H = (a * np.sqrt(3)) / 2\n",
        "\n",
        "# Rectangle corners (closed loop)\n",
        "rect = np.array([[-W, -H], [W, -H], [W, H], [-W, H], [-W, -H]])\n",
        "\n",
        "# Draw rectangle\n",
        "ax1.plot(rect[:, 0], rect[:, 1], \"--\")\n",
        "\n",
        "# Fill it\n",
        "ax1.fill(rect[:, 0], rect[:, 1], alpha=0.15)\n",
        "\n",
        "\n",
        "# --- Reciprocal space ---\n",
        "ax2.scatter(pts_rec[:, 0], pts_rec[:, 1], marker=\"o\", s=200, color=\"black\")\n",
        "ax2.quiver(0, 0, b1[0], b1[1], angles=\"xy\", scale_units=\"xy\", scale=1)\n",
        "ax2.quiver(0, 0, b2[0], b2[1], angles=\"xy\", scale_units=\"xy\", scale=1)\n",
        "\n",
        "# IBZ path Γ–M–K–Γ\n",
        "ax2.plot(ibz_path[:, 0], ibz_path[:, 1], marker=\"o\")\n",
        "\n",
        "# Label the special points\n",
        "ax2.text(Gamma[0], Gamma[1], r\"$\\Gamma$\", ha=\"right\", va=\"bottom\")\n",
        "ax2.text(M[0], M[1], r\"$M$\", ha=\"right\", va=\"bottom\")\n",
        "ax2.text(K[0], K[1], r\"$K$\", ha=\"left\", va=\"bottom\")\n",
        "\n",
        "ax2.set_title(\"Reciprocal-space lattice, vectors and IBZ path\")\n",
        "ax2.set_xlabel(r\"$k_x$\")\n",
        "ax2.set_ylabel(r\"$k_y$\")\n",
        "ax2.set_aspect(\"equal\")\n",
        "ax2.grid(True)\n",
        "\n",
        "ax1.set_xlim(-1.1, 1.1)\n",
        "ax1.set_ylim(-1.1, 1.1)\n",
        "\n",
        "ax2.set_xlim(-3.1 * np.pi / a, 3.1 * np.pi / a)\n",
        "ax2.set_ylim(-3.1 * np.pi / a, 3.1 * np.pi / a)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "d91a714d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup <a name=\"2\"></a>\n",
        "\n",
        "Next, we will define the global parameters to be used in the simulation.\n",
        "Since we can express everything in terms of the lattice constant, we\n",
        "will set it to 1 and then scale the frequency accordingly during\n",
        "post-processing."
      ],
      "id": "8bfdeca2-8e9e-4d2e-bb3a-8f92818d1f21"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Source polarization\n",
        "polarization = \"Ez\"\n",
        "\n",
        "theta = np.pi / 6\n",
        "sizeZ = 6\n",
        "\n",
        "# Option to add or not add matching dipoles\n",
        "matchingDipoles = True\n",
        "\n",
        "# K-points of the Brillouin zone of the hexagonal lattice\n",
        "kPoints = [(0, 0), (0, 1 / (np.sqrt(3))), (1 / 3, 1 / np.sqrt(3)), (0, 0)]\n",
        "\n",
        "# Source frequency and width\n",
        "freq0 = 200e12\n",
        "fwidth = 150e12\n",
        "\n",
        "runTime = 2e-12\n",
        "\n",
        "rodMaterial = td.Medium(permittivity=2.5**2)\n",
        "\n",
        "# Lattice constant defined as 1. Then we just scale the results\n",
        "latticeConstant = 1\n",
        "\n",
        "radius = 0.28 * latticeConstant\n",
        "height = 1 * latticeConstant\n",
        "\n",
        "\n",
        "# Lattice vectors\n",
        "y = latticeConstant * np.cos(theta)\n",
        "x = latticeConstant * np.sin(theta)\n",
        "a1 = np.array([x, y, 0])\n",
        "a2 = np.array([-x, y, 0])\n",
        "\n",
        "\n",
        "# Creating the supercell\n",
        "centerRod = td.Cylinder(center=(0, 0, 0), radius=radius, length=height)\n",
        "\n",
        "geometry = centerRod\n",
        "geometry += centerRod.updated_copy(center=tuple(-a1))\n",
        "geometry += centerRod.updated_copy(center=tuple(a2))\n",
        "geometry += centerRod.updated_copy(center=tuple(-a2))\n",
        "geometry += centerRod.updated_copy(center=tuple(a1))\n",
        "\n",
        "\n",
        "structure = td.Structure(geometry=geometry, medium=rodMaterial)\n",
        "\n",
        "size = (2 * x, 2 * y, sizeZ)"
      ],
      "id": "e21e3b64"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To ensure periodicity, we will use a\n",
        "[MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MeshOverrideStructure.html)\n",
        "to create a custom grid with a fixed number of grid points in x and y."
      ],
      "id": "cc752aa3-f3a0-4430-b36b-1c1a13640c97"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "grid = td.MeshOverrideStructure(\n",
        "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, td.inf)),\n",
        "    dl=(size[0] / 40, size[1] / 60, 0.1),\n",
        ")\n",
        "\n",
        "grid_spec = td.GridSpec.auto(\n",
        "    override_structures=[grid],\n",
        ")"
      ],
      "id": "312f16c0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we will define the sources and monitors.\n",
        "\n",
        "We will place 5 sources randomly distributed in the lower-left unit cell\n",
        "region, and 5\n",
        "[FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html)\n",
        "objects randomly distributed in the supercell."
      ],
      "id": "8fd04978-4cf1-4498-9131-ed713678479c"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "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\">04:58:56 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval'</span><span style=\"color: #800000; text-decoration-color: #800000\"> field was left as its default      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value, which will set it to </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> internally. A value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> means that  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the data will be sampled at every time step, which may potentially </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">produce more data than desired, depending on the use case. To      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">reduce data storage, one may downsample the data by setting        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval &gt; 1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or by choosing alternative </span><span style=\"color: #008000; text-decoration-color: #008000\">'start'</span><span style=\"color: #800000; text-decoration-color: #800000\"> and </span><span style=\"color: #008000; text-decoration-color: #008000\">'stop'</span><span style=\"color: #800000; text-decoration-color: #800000\"> values</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">for the time sampling. If you intended to use the highest          </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">resolution time sampling, you may suppress this warning by         </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">explicitly setting </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval=1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in the monitor.                    </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval'</span><span style=\"color: #800000; text-decoration-color: #800000\"> field was left as its default      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value, which will set it to </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> internally. A value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> means that  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the data will be sampled at every time step, which may potentially </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">produce more data than desired, depending on the use case. To      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">reduce data storage, one may downsample the data by setting        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval &gt; 1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or by choosing alternative </span><span style=\"color: #008000; text-decoration-color: #008000\">'start'</span><span style=\"color: #800000; text-decoration-color: #800000\"> and </span><span style=\"color: #008000; text-decoration-color: #008000\">'stop'</span><span style=\"color: #800000; text-decoration-color: #800000\"> values</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">for the time sampling. If you intended to use the highest          </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">resolution time sampling, you may suppress this warning by         </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">explicitly setting </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval=1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in the monitor.                    </span>\n",
              "</pre>"
            ]
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        },
        {
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          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval'</span><span style=\"color: #800000; text-decoration-color: #800000\"> field was left as its default      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value, which will set it to </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> internally. A value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> means that  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the data will be sampled at every time step, which may potentially </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">produce more data than desired, depending on the use case. To      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">reduce data storage, one may downsample the data by setting        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval &gt; 1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or by choosing alternative </span><span style=\"color: #008000; text-decoration-color: #008000\">'start'</span><span style=\"color: #800000; text-decoration-color: #800000\"> and </span><span style=\"color: #008000; text-decoration-color: #008000\">'stop'</span><span style=\"color: #800000; text-decoration-color: #800000\"> values</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">for the time sampling. If you intended to use the highest          </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">resolution time sampling, you may suppress this warning by         </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">explicitly setting </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval=1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in the monitor.                    </span>\n",
              "</pre>"
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        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval'</span><span style=\"color: #800000; text-decoration-color: #800000\"> field was left as its default      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value, which will set it to </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> internally. A value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> means that  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the data will be sampled at every time step, which may potentially </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">produce more data than desired, depending on the use case. To      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">reduce data storage, one may downsample the data by setting        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval &gt; 1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or by choosing alternative </span><span style=\"color: #008000; text-decoration-color: #008000\">'start'</span><span style=\"color: #800000; text-decoration-color: #800000\"> and </span><span style=\"color: #008000; text-decoration-color: #008000\">'stop'</span><span style=\"color: #800000; text-decoration-color: #800000\"> values</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">for the time sampling. If you intended to use the highest          </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">resolution time sampling, you may suppress this warning by         </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">explicitly setting </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval=1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in the monitor.                    </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval'</span><span style=\"color: #800000; text-decoration-color: #800000\"> field was left as its default      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">value, which will set it to </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> internally. A value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> means that  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the data will be sampled at every time step, which may potentially </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">produce more data than desired, depending on the use case. To      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">reduce data storage, one may downsample the data by setting        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval &gt; 1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or by choosing alternative </span><span style=\"color: #008000; text-decoration-color: #008000\">'start'</span><span style=\"color: #800000; text-decoration-color: #800000\"> and </span><span style=\"color: #008000; text-decoration-color: #008000\">'stop'</span><span style=\"color: #800000; text-decoration-color: #800000\"> values</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">for the time sampling. If you intended to use the highest          </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">resolution time sampling, you may suppress this warning by         </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">explicitly setting </span><span style=\"color: #008000; text-decoration-color: #008000\">'interval=1'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in the monitor.                    </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# Sources\n",
        "Nsources = 5\n",
        "\n",
        "sourceTime = [\n",
        "    td.GaussianPulse(freq0=freq0, fwidth=fwidth, phase=i)\n",
        "    for i in 2 * np.pi * np.random.random(2 * Nsources)\n",
        "]\n",
        "\n",
        "\n",
        "# Random positions for the sources\n",
        "posySource = np.random.uniform(-0.8, -0.2, Nsources)\n",
        "posxSource = np.random.uniform(-0.2, -0.1, Nsources)\n",
        "\n",
        "\n",
        "# Monitors\n",
        "Nmonitors = 5\n",
        "\n",
        "posyMon = np.random.uniform(-0.8, 0.8, Nmonitors)\n",
        "posxMon = np.random.uniform(-0.2, 0.2, Nmonitors)\n",
        "\n",
        "monitors = [\n",
        "    td.FieldTimeMonitor(center=(posxMon[i], posyMon[i], 0), name=f\"mon{i}\", size=(0, 0, 0), start=0)\n",
        "    for i in range(Nmonitors)\n",
        "]"
      ],
      "id": "6441312d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Defining the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) and Matching Dipoles <a name=\"3\"></a>\n",
        "\n",
        "Finally, we define a function to generate the\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
        "object as a function of the parameters below."
      ],
      "id": "6b8db043-30e1-4d11-bdfa-4104a3524fb4"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Function to return a simulation for each Bloch vector\n",
        "def getSim(pol, bloch_x, bloch_y, matchingDipoles=True):\n",
        "    symmetry = [0, 0, -1] if pol == \"Ez\" else [0, 0, 1]\n",
        "\n",
        "    boundary_spec = td.BoundarySpec(\n",
        "        x=td.Boundary.bloch(bloch_x * size[0]),\n",
        "        y=td.Boundary.bloch(bloch_y * size[1]),\n",
        "        z=td.Boundary.pml(),\n",
        "    )\n",
        "\n",
        "    bVector = np.array([bloch_x, bloch_y, 0])\n",
        "\n",
        "    # Creating the sources\n",
        "    sources = [\n",
        "        td.PointDipole(\n",
        "            center=(posxSource[i], posySource[i], 0), polarization=pol, source_time=sourceTime[i]\n",
        "        )\n",
        "        for i in range(len(posxSource))\n",
        "    ]\n",
        "\n",
        "    # Creating the matching dipoles\n",
        "    matching_dipoles = []\n",
        "    if matchingDipoles:\n",
        "        for i, sc in enumerate(sources):\n",
        "            # Coordinates of the matching dipole\n",
        "            px = sc.center[0] + 0.5\n",
        "            py = sc.center[1] + np.sqrt(3) / 2\n",
        "\n",
        "            # Adjust the phase\n",
        "            phase = sc.source_time.phase\n",
        "            r_vec = np.array((px, py, 0)) - np.array(sc.center)\n",
        "            deltaPhase = 2 * np.pi * bVector.dot(r_vec)\n",
        "\n",
        "            source_time = sc.source_time.updated_copy(phase=phase + deltaPhase)\n",
        "\n",
        "            sp = sc.updated_copy(center=(px, py, 0), source_time=source_time)\n",
        "            matching_dipoles.append(sp)\n",
        "\n",
        "    sim = td.Simulation(\n",
        "        size=size,\n",
        "        structures=[structure],\n",
        "        sources=sources + matching_dipoles,\n",
        "        monitors=monitors,\n",
        "        run_time=runTime,\n",
        "        boundary_spec=boundary_spec,\n",
        "        grid_spec=grid_spec,\n",
        "        symmetry=symmetry,\n",
        "        medium=td.Medium(),\n",
        "        shutoff=False,\n",
        "    )\n",
        "    return sim"
      ],
      "id": "5cf3f223"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can create a simulation object and inspect the permittivity to\n",
        "confirm that the periodicity is preserved."
      ],
      "id": "dc3c13f2-6f20-446b-8af2-51c562ef775a"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: RF simulations and functionality will require new license </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in an upcoming release. All RF-specific classes are   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">now available within the sub-package </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.rf'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains sources defined for RF wavelengths.                    </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "sim = getSim(polarization, 0, 0)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Left: epsilon\n",
        "eps = sim.epsilon(td.Box(center=(0, 0, 0), size=(99, 99, 0)))\n",
        "ax1.imshow(abs(eps).squeeze().T)\n",
        "ax1.set_title(\"epsilon\")\n",
        "\n",
        "# Right: structure\n",
        "sim.plot(z=0, ax=ax2)\n",
        "ax2.set_title(\"structure (z=0)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "a5b8473d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can interpolate points around the K-points and run a\n",
        "[Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html)\n",
        "simulation to execute the simulations in parallel."
      ],
      "id": "dc09664e-290b-40fa-83c6-6929cea94ad4"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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Ku2CqDQ2NUMLwKWroU/n3fwAqxiFhUl5eniTd9NvVfHx8rv986dKlKu0rODhY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PLChQumX79+jvnesGGDyz7eeustx7pJSkpyav/qq6/MF1984XLOjTHmyJEj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\n"
          }
        }
      ],
      "source": [
        "# Creating K points\n",
        "pointsPerZone = 10\n",
        "\n",
        "createPoints = lambda init, end: [\n",
        "    init + (end - init) * i / pointsPerZone for i in range(pointsPerZone)\n",
        "]\n",
        "\n",
        "KX = []\n",
        "KY = []\n",
        "\n",
        "kx1, ky1 = kPoints[0]\n",
        "for k in kPoints[1:]:\n",
        "    kx2, ky2 = k\n",
        "    KX += createPoints(kx1, kx2)\n",
        "    KY += createPoints(ky1, ky2)\n",
        "    kx1, ky1 = kx2, ky2\n",
        "\n",
        "# Visualizing the K-points\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(KX, KY, \"-o\")\n",
        "\n",
        "\n",
        "sims = {}\n",
        "for i in range(len(KX)):\n",
        "    for pol in [polarization]:\n",
        "        sims[f\"s{i}{pol}\"] = getSim(pol, KX[i], KY[i])\n",
        "\n",
        "plt.show()"
      ],
      "id": "722c31b6"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"6c803fb58d69426b9951f6d76a49c700\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "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\"></pre>"
            ]
          }
        },
        {
          "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\">04:59:17 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span> tasks.                      \n",
              "</pre>"
            ]
          }
        },
        {
          "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\">04:59:43 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.750</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"22c84834505d4623a70743a2475dbd8b\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "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\">04:59:57 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "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\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "# Running the simulations\n",
        "\n",
        "batch = web.Batch(simulations=sims, folder_name=\"hexagonalBandDiagram\")\n",
        "batch_data = batch.run(path_dir=\"bandDiagram\")"
      ],
      "id": "9421efa3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Band Diagram Calculation <a name=\"4\"></a>\n",
        "\n",
        "Now we can sum the signals from all monitors and use the\n",
        "[ResonanceFinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html)\n",
        "plugin to track the resonances for each simulation.\n",
        "\n",
        "To ensure that we compute **weakly confined modes above the light\n",
        "cone**, we first analyze the source decay time and only use the data\n",
        "immediately after the decay."
      ],
      "id": "620f12db-0ca9-4cf6-9e98-cc704bcdd0cf"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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3CJ/gWWpqaiTJ0q0WXTbDUpJSUlK0YsUKffXVV1q0aJGys7MVGhqq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\nl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          }
        }
      ],
      "source": [
        "sim.sources[0].source_time.plot(times=np.linspace(0, 0.2e-13, 1001), val=\"abs\")\n",
        "plt.show()"
      ],
      "id": "79bcee72"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "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\">05:03:07 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:08 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:09 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:10 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:11 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:12 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:13 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:14 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:15 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:16 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:17 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:18 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:19 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:20 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:21 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:22 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:23 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:24 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:25 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:26 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:27 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:28 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "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\">05:03:29 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# Sum the signals for all monitors\n",
        "def getSignal(sim_data, polarization, t_start=1e-14, t_end=2e-12):\n",
        "    signal = 0\n",
        "    for i in range(Nmonitors):\n",
        "        signal += sim_data[f\"mon{i}\"].field_components[polarization].squeeze()\n",
        "    return signal.where(signal.t > t_start, drop=True).where(signal.t < t_end, drop=True)\n",
        "\n",
        "\n",
        "from tidy3d.plugins.resonance import ResonanceFinder\n",
        "\n",
        "# Analyzing resonances\n",
        "dic = {}\n",
        "for i in range(len(KX)):\n",
        "    for pol in [polarization]:\n",
        "        resonance_finder = ResonanceFinder(freq_window=(0e12, 200e12))\n",
        "        sim_data = batch_data[f\"s{i}{pol}\"]\n",
        "        signal = getSignal(sim_data, pol)\n",
        "\n",
        "        resonance_data = resonance_finder.run_raw_signal(signal, sim_data.simulation.dt)\n",
        "        df = resonance_data.to_dataframe()\n",
        "        dic[f\"s{i}{pol}\"] = df"
      ],
      "id": "47425301"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finally, we can plot the band diagram. To match the results with the\n",
        "[reference\n",
        "paper](https://www.flexcompute.com/tidy3d/examples/notebooks/Bandstructure/),\n",
        "we will multiply the frequencies by four, since the original lattice\n",
        "constant is 0.25 µm."
      ],
      "id": "ac17fe30-14fe-40e9-a4d7-929fca733a78"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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U5Kg4IuJQx49b0wpHjYLiYquQ9euvkJ8PrVrBGWfA738P//d/sH07/OEPUN3K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\nWiQ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          }
        }
      ],
      "source": [
        "# Scale factor\n",
        "scaleFactor = 4\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "for name, df in dic.items():\n",
        "    pos = int(name[1:-2])\n",
        "    df = df[df[\"error\"] < 2]\n",
        "    sct = ax.scatter(x=np.repeat(pos, len(df)), y=df.index * scaleFactor * 1e-12, color=\"black\")\n",
        "\n",
        "# Light cone overlay (air background; ± branches)\n",
        "idx = np.arange(len(KX))\n",
        "# Convert reduced coordinates to |k| in m^-1\n",
        "k_mag = (2 * np.pi / latticeConstant) * np.sqrt(np.array(KX) ** 2 + np.array(KY) ** 2)\n",
        "# Frequency in THz: f = c |k| / (2π)\n",
        "f_light_thz = (td.constants.C_0 * k_mag / (2 * np.pi)) / 1e12\n",
        "ax.plot(idx, scaleFactor * f_light_thz, \"r--\", label=\"light cone\")\n",
        "ax.fill_between(idx, scaleFactor * f_light_thz, 800, color=\"red\", alpha=0.1)\n",
        "\n",
        "ax.set_ylim(0, 800)\n",
        "ax.set_xlim(0, len(KX))\n",
        "ax.set_ylabel(\"Frequency (THz)\")\n",
        "ax.set_xlabel(\"K-point index\")\n",
        "ax.set_title(\"TM band diagram\")\n",
        "\n",
        "ax.legend(loc=\"upper left\", fontsize=\"small\")\n",
        "\n",
        "plt.show()"
      ],
      "id": "e736084b"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
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}