{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "id": "3e4d4e4c",
   "metadata": {
    "papermill": {
     "duration": 0.002887,
     "end_time": "2026-08-04T14:42:58.669417+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:58.666530+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Hollow Rectangular Waveguide Modes\n",
    "\n",
    "This notebook solves a PEC rectangular waveguide with `WaveguideModeSolver`, compares against analytic TE/TM modes, and plots the first fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfef1c6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:42:58.675324Z",
     "iopub.status.busy": "2026-08-04T14:42:58.675096Z",
     "iopub.status.idle": "2026-08-04T14:42:59.187982Z",
     "shell.execute_reply": "2026-08-04T14:42:59.187115Z"
    },
    "papermill": {
     "duration": 0.516876,
     "end_time": "2026-08-04T14:42:59.188643+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:58.671767+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import palacetoolkit as ptk\n",
    "\n",
    "import gmsh\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from palacetoolkit.mode_solver import WaveguideModeSolver\n",
    "from palacetoolkit.utils import write_and_finalize_gmsh\n",
    "from palacetoolkit.viz import view_mesh\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "902f3519",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:42:59.194790Z",
     "iopub.status.busy": "2026-08-04T14:42:59.194435Z",
     "iopub.status.idle": "2026-08-04T14:42:59.201375Z",
     "shell.execute_reply": "2026-08-04T14:42:59.200688Z"
    },
    "papermill": {
     "duration": 0.010657,
     "end_time": "2026-08-04T14:42:59.201887+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:59.191230+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def _set_transfinite_rect(surf_tag, nx, ny):\n",
    "    bnd = gmsh.model.getBoundary([(2, surf_tag)], oriented=True)\n",
    "    lines = [abs(t) for _, t in bnd]\n",
    "    for line in lines:\n",
    "        pts = gmsh.model.getBoundary([(1, line)], oriented=False)\n",
    "        c0 = gmsh.model.getValue(0, pts[0][1], [])\n",
    "        c1 = gmsh.model.getValue(0, pts[1][1], [])\n",
    "        dx = abs(c1[0] - c0[0])\n",
    "        dy = abs(c1[1] - c0[1])\n",
    "        n = (nx + 1) if dx > dy else (ny + 1)\n",
    "        gmsh.model.mesh.setTransfiniteCurve(line, n)\n",
    "    gmsh.model.mesh.setTransfiniteSurface(surf_tag)\n",
    "\n",
    "\n",
    "def make_rectangular_mesh(a, b, nx, ny, structured=True, lc=None, filename=None):\n",
    "    gmsh.initialize()\n",
    "    gmsh.option.setNumber(\"General.Verbosity\", 0)\n",
    "    gmsh.model.add(\"hollow_waveguide\")\n",
    "\n",
    "    gmsh.model.occ.addRectangle(0, 0, 0, a, b, tag=1)\n",
    "    gmsh.model.occ.synchronize()\n",
    "\n",
    "    gmsh.model.addPhysicalGroup(2, [1], tag=1, name=\"domain\")\n",
    "    bnd = gmsh.model.getBoundary([(2, 1)], oriented=False)\n",
    "    bnd_tags = [abs(t) for _, t in bnd]\n",
    "    gmsh.model.addPhysicalGroup(1, bnd_tags, tag=1, name=\"PEC\")\n",
    "\n",
    "    if structured:\n",
    "        _set_transfinite_rect(1, nx, ny)\n",
    "        gmsh.model.mesh.setRecombine(2, 1)\n",
    "    else:\n",
    "        _lc = lc if lc is not None else max(a / nx, b / ny)\n",
    "        gmsh.option.setNumber(\"Mesh.CharacteristicLengthMin\", 0.8 * _lc)\n",
    "        gmsh.option.setNumber(\"Mesh.CharacteristicLengthMax\", 1.2 * _lc)\n",
    "\n",
    "    gmsh.model.mesh.generate(2)\n",
    "    return write_and_finalize_gmsh(filename, prefix=\"wg_rect_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae6533ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:42:59.206975Z",
     "iopub.status.busy": "2026-08-04T14:42:59.206804Z",
     "iopub.status.idle": "2026-08-04T14:42:59.221991Z",
     "shell.execute_reply": "2026-08-04T14:42:59.221307Z"
    },
    "papermill": {
     "duration": 0.01835,
     "end_time": "2026-08-04T14:42:59.222539+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:59.204189+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def analytic_kn(a, b, omega, m_max=5, n_max=5, mu=1.0, eps=1.0):\n",
    "    modes = []\n",
    "    k0_sq = omega**2 * mu * eps\n",
    "\n",
    "    for m in range(0, m_max + 1):\n",
    "        for n in range(0, n_max + 1):\n",
    "            if m == 0 and n == 0:\n",
    "                continue\n",
    "            kc_sq = (m * np.pi / a) ** 2 + (n * np.pi / b) ** 2\n",
    "            kn = np.sqrt((k0_sq - kc_sq) + 0j)\n",
    "            modes.append((f\"TE{m}{n}\", np.sqrt(kc_sq), kn))\n",
    "\n",
    "    for m in range(1, m_max + 1):\n",
    "        for n in range(1, n_max + 1):\n",
    "            kc_sq = (m * np.pi / a) ** 2 + (n * np.pi / b) ** 2\n",
    "            kn = np.sqrt((k0_sq - kc_sq) + 0j)\n",
    "            modes.append((f\"TM{m}{n}\", np.sqrt(kc_sq), kn))\n",
    "\n",
    "    modes.sort(key=lambda x: -x[2].real)\n",
    "    return modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3be79dcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:42:59.382925Z",
     "iopub.status.busy": "2026-08-04T14:42:59.382719Z",
     "iopub.status.idle": "2026-08-04T14:42:59.387680Z",
     "shell.execute_reply": "2026-08-04T14:42:59.387027Z"
    },
    "papermill": {
     "duration": 0.163897,
     "end_time": "2026-08-04T14:42:59.388535+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:59.224638+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 8 analytic modes:\n",
      "  TE10  : kc=  1.5708, kn= +5.268611 +0.000000j\n",
      "  TE01  : kc=  3.1416, kn= +4.511769 +0.000000j\n",
      "  TE20  : kc=  3.1416, kn= +4.511769 +0.000000j\n",
      "  TE11  : kc=  3.5124, kn= +4.229499 +0.000000j\n",
      "  TM11  : kc=  3.5124, kn= +4.229499 +0.000000j\n",
      "  TE21  : kc=  4.4429, kn= +3.238280 +0.000000j\n",
      "  TM21  : kc=  4.4429, kn= +3.238280 +0.000000j\n",
      "  TE30  : kc=  4.7124, kn= +2.831793 +0.000000j\n"
     ]
    }
   ],
   "source": [
    "a, b = 2.0, 1.0\n",
    "mu, eps = 1.0, 1.0\n",
    "c0 = 1.0 / np.sqrt(mu * eps)\n",
    "\n",
    "fc_te10 = c0 * np.pi / a / (2 * np.pi)\n",
    "f_op = 3.5 * fc_te10\n",
    "omega = 2 * np.pi * f_op\n",
    "\n",
    "analytic = analytic_kn(a, b, omega, m_max=4, n_max=4, mu=mu, eps=eps)\n",
    "print(\"Top 8 analytic modes:\")\n",
    "for name, kc, kn in analytic[:8]:\n",
    "    print(f\"  {name:6s}: kc={kc:8.4f}, kn={kn.real:+10.6f}{kn.imag:+10.6f}j\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b2cd9cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:42:59.393853Z",
     "iopub.status.busy": "2026-08-04T14:42:59.393672Z",
     "iopub.status.idle": "2026-08-04T14:43:00.618236Z",
     "shell.execute_reply": "2026-08-04T14:43:00.617683Z"
    },
    "papermill": {
     "duration": 1.228283,
     "end_time": "2026-08-04T14:43:00.619075+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:42:59.390792+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mesh file: /tmp/wg_rect_rmard364.msh\n",
      "Groups to render transparent: ['air_none', 'air_plastic_enclosure']\n",
      "\n",
      "Mesh loaded successfully with 2 cell blocks\n",
      "Found 1024 triangles total\n",
      "Physical group tags in mesh: {1: 'domain'}\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<details open class=\"ptk-scroll-output\"><summary><strong>Palace simulation output</strong></summary><pre style=\"--ptk-output-visible-lines:20; --ptk-output-line-height:1.35;\">  Running: /home/runner/.cache/palacetoolkit/runtime/palace-cpu-v0.17.0/bin/palace --serial /tmp/wg_rect_rmard364_modes/config.json\n",
       "&gt;&gt; /home/runner/.cache/palacetoolkit/runtime/palace-cpu-v0.17.0/bin/palace-x86_64.bin /tmp/wg_rect_rmard364_modes/config.json\n",
       "\n",
       "_____________     _______\n",
       "_____   __   \\____ __   /____ ____________\n",
       "____   /_/  /  __ ` /  /  __ ` /  ___/  _ \\\n",
       "___   _____/  /_/  /  /  /_/  /  /__/  ___/\n",
       "  /__/     \\___,__/__/\\___,__/\\_____\\_____/\n",
       "\n",
       "Git changeset ID: v0.17.0-272-gb22f654ab\n",
       "Running with 1 MPI process, 1 OpenMP thread\n",
       "Device configuration: omp,cpu\n",
       "Memory configuration: host-std\n",
       "libCEED backend: /cpu/self/xsmm/blocked\n",
       "\n",
       "\n",
       "Characteristic length and time scales:\n",
       " Lc = 2.000e+00 m, tc = 6.671e+00 ns\n",
       "Finished partitioning mesh into 1 subdomain\n",
       "\n",
       "Mesh curvature order: 1\n",
       "Mesh bounding box:\n",
       " (Xmin, Ymin) = (+0.000e+00, +0.000e+00) m\n",
       " (Xmax, Ymax) = (+2.000e+00, +1.000e+00) m\n",
       "\n",
       "Parallel Mesh Stats:\n",
       "\n",
       "                minimum     average     maximum       total\n",
       " vertices           561         561         561         561\n",
       " edges             1072        1072        1072        1072\n",
       " elements           512         512         512         512\n",
       " neighbors            0           0           0\n",
       "\n",
       "            minimum     maximum\n",
       " h          0.03125     0.03125\n",
       " kappa            1           1\n",
       "\n",
       "Estimated current per-rank memory usage is: Min. 44.1M, Max. 44.1M, Avg. 44.1M, Total 44.1M\n",
       "Estimated current per-node memory usage is: Min. 44.1M, Max. 44.1M, Avg. 44.1M, Total 44.1M\n",
       "\n",
       "Configuring 2D waveguide mode analysis at f = 2.625e-01 GHz (omega = 1.100319e+01)\n",
       " ND space: 4192 DOFs, H1 space: 2145 DOFs, total: 6337\n",
       " Auto kn_target = 1.154024e+01 (from max(mu_r) * max(epsilon_r) = 1.000000e+00)\n",
       "\n",
       "Solving GEP for 8 propagation mode(s)...\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.900686e-01\n",
       "  1 (restart 0) KSP residual norm 5.497017e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.892e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.557880e-02\n",
       "  1 (restart 0) KSP residual norm 2.026715e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.647e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.978490e-02\n",
       "  1 (restart 0) KSP residual norm 2.728975e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.162e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.156731e-01\n",
       "  1 (restart 0) KSP residual norm 3.795863e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.282e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.213735e-02\n",
       "  1 (restart 0) KSP residual norm 1.958863e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.649e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.193495e-02\n",
       "  1 (restart 0) KSP residual norm 2.481224e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.079e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.002334e-02\n",
       "  1 (restart 0) KSP residual norm 5.865397e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.515e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.264014e-01\n",
       "  1 (restart 0) KSP residual norm 6.966671e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.512e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.497191e-02\n",
       "  1 (restart 0) KSP residual norm 2.107648e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.244e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.318565e-02\n",
       "  1 (restart 0) KSP residual norm 3.722923e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.621e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.650705e-02\n",
       "  1 (restart 0) KSP residual norm 2.439935e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.478e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.939922e-02\n",
       "  1 (restart 0) KSP residual norm 9.687404e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.459e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.822965e-02\n",
       "  1 (restart 0) KSP residual norm 1.439697e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.472e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.466726e-02\n",
       "  1 (restart 0) KSP residual norm 6.900204e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.704e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.946027e-02\n",
       "  1 (restart 0) KSP residual norm 1.895608e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.741e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.115941e-03\n",
       "  1 (restart 0) KSP residual norm 5.550031e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.088e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.074433e-02\n",
       "  1 (restart 0) KSP residual norm 9.816185e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.136e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.607772e-03\n",
       "  1 (restart 0) KSP residual norm 1.537393e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.742e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.482659e-03\n",
       "  1 (restart 0) KSP residual norm 5.839469e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.884e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.637011e-03\n",
       "  1 (restart 0) KSP residual norm 1.732251e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.610e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.494313e-02\n",
       "  1 (restart 0) KSP residual norm 7.198537e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.817e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.821868e-02\n",
       "  1 (restart 0) KSP residual norm 1.149329e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.073e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.280587e-02\n",
       "  1 (restart 0) KSP residual norm 1.974346e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.657e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.403202e-03\n",
       "  1 (restart 0) KSP residual norm 2.573460e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.763e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.161792e-03\n",
       "  1 (restart 0) KSP residual norm 9.249421e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.222e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.504896e-02\n",
       "  1 (restart 0) KSP residual norm 3.907876e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.597e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.821418e-03\n",
       "  1 (restart 0) KSP residual norm 1.317862e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.264e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.043179e-03\n",
       "  1 (restart 0) KSP residual norm 9.882368e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.837e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.786422e-03\n",
       "  1 (restart 0) KSP residual norm 1.274962e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.137e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.831683e-03\n",
       "  1 (restart 0) KSP residual norm 1.566782e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.533e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.007629e-02\n",
       "  1 (restart 0) KSP residual norm 7.760764e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.702e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.308857e-03\n",
       "  1 (restart 0) KSP residual norm 2.780050e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.452e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.161542e-03\n",
       "  1 (restart 0) KSP residual norm 2.665133e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.233e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.111105e-03\n",
       "  1 (restart 0) KSP residual norm 2.774952e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.314e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.703013e-03\n",
       "  1 (restart 0) KSP residual norm 9.345132e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.457e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.840440e-03\n",
       "  1 (restart 0) KSP residual norm 1.104149e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.887e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.788175e-03\n",
       "  1 (restart 0) KSP residual norm 1.849452e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.633e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.968292e-03\n",
       "  1 (restart 0) KSP residual norm 4.595231e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.699e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.529073e-03\n",
       "  1 (restart 0) KSP residual norm 1.900979e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.387e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.537451e-03\n",
       "  1 (restart 0) KSP residual norm 2.448530e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.593e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.872380e-03\n",
       "  1 (restart 0) KSP residual norm 1.067211e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.700e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.265849e-03\n",
       "  1 (restart 0) KSP residual norm 1.264539e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.990e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.541261e-03\n",
       "  1 (restart 0) KSP residual norm 2.194153e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.634e-13)\n",
       " Found 9 converged eigenvalues (sigma = -1.331771e+02)\n",
       " eig 0: kn = 1.054516e+01+4.081167e-15i, n_eff = 9.583738e-01+3.709077e-16i\n",
       " eig 1: kn = 9.032807e+00+4.113589e-14i, n_eff = 8.209265e-01+3.738543e-15i\n",
       " eig 2: kn = 9.032807e+00-1.818667e-14i, n_eff = 8.209265e-01-1.652855e-15i\n",
       " eig 3: kn = 8.468885e+00-2.138248e-12i, n_eff = 7.696756e-01-1.943299e-13i\n",
       " eig 4: kn = 8.468885e+00+7.028803e-14i, n_eff = 7.696756e-01+6.387971e-15i\n",
       " eig 5: kn = 6.489462e+00+5.015378e-14i, n_eff = 5.897802e-01+4.558115e-15i\n",
       " eig 6: kn = 6.489462e+00+4.320984e-12i, n_eff = 5.897802e-01+3.927030e-13i\n",
       " eig 7: kn = 5.678270e+00-3.672850e-14i, n_eff = 5.160568e-01-3.337988e-15i\n",
       " eig 8: kn = 3.401550e-11-2.689935e+00i, n_eff = 3.091423e-12-2.444688e-01i\n",
       "\n",
       "Computing solution error estimates and performing postprocessing\n",
       "\n",
       "     m,       Re{kn} (1/m),       Im{kn} (1/m),          Re{n_eff},          Im{n_eff},      Error (Bkwd.),       Error (Abs.)\n",
       "     1,      +5.272582e+00,      +2.040583e-15,      +9.583738e-01,      +3.709077e-16,      +2.591505e-16,      +1.286903e-13\n",
       "     2,      +4.516404e+00,      +2.056794e-14,      +8.209265e-01,      +3.738543e-15,      +1.096654e-15,      +2.322803e-13\n",
       "     3,      +4.516404e+00,      -9.093337e-15,      +8.209265e-01,      -1.652855e-15,      +3.281985e-15,      +6.951510e-13\n",
       "     4,      +4.234442e+00,      -1.069124e-12,      +7.696756e-01,      -1.943299e-13,      +3.855242e-15,      +6.857024e-13\n",
       "     5,      +4.234442e+00,      +3.514402e-14,      +7.696756e-01,      +6.387971e-15,      +2.817445e-13,      +5.011175e-11\n",
       "     6,      +3.244731e+00,      +2.507689e-14,      +5.897802e-01,      +4.558115e-15,      +2.183201e-14,      +2.623639e-12\n",
       "     7,      +3.244731e+00,      +2.160492e-12,      +5.897802e-01,      +3.927030e-13,      +2.265411e-14,      +2.722434e-12\n",
       "     8,      +2.839135e+00,      -1.836425e-14,      +5.160568e-01,      -3.337988e-15,      +6.537755e-16,      +7.091175e-14\n",
       "\n",
       "\n",
       "Completed 0 iterations of adaptive mesh refinement (AMR):\n",
       " Indicator norm = 1.631e-03, global unknowns = 6337\n",
       " Max. iterations = 0, tol. = 1.000e-02\n",
       "\n",
       "Estimated peak per-rank memory usage is: Min. 104.0M, Max. 104.0M, Avg. 104.0M, Total 104.0M\n",
       "Estimated peak per-node memory usage is: Min. 104.0M, Max. 104.0M, Avg. 104.0M, Total 104.0M\n",
       "\n",
       "Elapsed Time Report (s)           Min.        Max.        Avg.\n",
       "==============================================================\n",
       "Initialization                   0.001       0.001       0.001\n",
       "  Mesh Preprocessing             0.002       0.002       0.002\n",
       "Operator Construction            0.015       0.015       0.015\n",
       "  Preconditioner                 0.092       0.092       0.092\n",
       "Eigenvalue Solve                 0.041       0.041       0.041\n",
       "Estimation                       0.003       0.003       0.003\n",
       "  Construction                   0.008       0.008       0.008\n",
       "  Solve                          0.008       0.008       0.008\n",
       "Postprocessing                   0.155       0.155       0.155\n",
       "Disk IO                          0.001       0.001       0.001\n",
       "--------------------------------------------------------------\n",
       "Total                            0.594       0.594       0.594\n",
       "\n",
       "Peak Memory                   Per-Node       Total   Total HWM\n",
       "==============================================================\n",
       "Initialization                    2.4M        2.4M        2.4M\n",
       "  Mesh Preprocessing              1.3M        1.3M        3.7M\n",
       "Operator Construction            16.1M       16.1M       19.8M\n",
       "  Preconditioner                 24.7M       24.7M       44.4M\n",
       "Eigenvalue Solve                 11.2M       11.2M       55.6M\n",
       "Estimation                        0.0K        0.0K       55.6M\n",
       "  Construction                    7.9M        7.9M       63.5M\n",
       "  Solve                           0.0K        0.0K       63.5M\n",
       "Postprocessing                    0.0K        0.0K       63.5M\n",
       "Disk IO                           2.3M        2.3M       65.8M\n",
       "--------------------------------------------------------------\n",
       "Total                            78.6M       78.6M       78.6M</pre></details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Numerical modes:\n",
      "  Mode 1: kn= +5.272582 +0.000000j\n"
     ]
    }
   ],
   "source": [
    "from palacetoolkit.mode_solver import ModeMetrics\n",
    "\n",
    "mesh_file = make_rectangular_mesh(\n",
    "    a,\n",
    "    b,\n",
    "    nx=32,\n",
    "    ny=16,\n",
    "    structured=True,\n",
    ")\n",
    "view_mesh(mesh_file)\n",
    "solver = WaveguideModeSolver(\n",
    "    mesh_file=mesh_file,\n",
    "    order=2,\n",
    "    pec_bdr=[1],\n",
    "    materials=[{\"attrs\": [1], \"eps_r\": eps, \"mu_r\": mu}],\n",
    "    omega=omega,\n",
    ")\n",
    "results = solver.solve(num_modes=8, mode_idx=1, target=0.0, save=0, num_procs=4)\n",
    "\n",
    "print(\"\\nNumerical modes:\")\n",
    "for i in sorted(results):\n",
    "    mode = results[i]\n",
    "    print(f\"  Mode {i}: kn={mode.k_n.real:+10.6f}{mode.k_n.imag:+10.6f}j\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb43ec99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:43:00.911853Z",
     "iopub.status.busy": "2026-08-04T14:43:00.911647Z",
     "iopub.status.idle": "2026-08-04T14:43:00.914863Z",
     "shell.execute_reply": "2026-08-04T14:43:00.914242Z"
    },
    "papermill": {
     "duration": 0.293585,
     "end_time": "2026-08-04T14:43:00.915480+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:43:00.621895+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field visualization moved to pyvista-based VTU post-processing.\n"
     ]
    }
   ],
   "source": [
    "# Field visualization is no longer available through the solver.\n",
    "# Palace writes VTU field output files when save > 0 in solver.solve().\n",
    "# Load the VTU files with pyvista for field visualization:\n",
    "#   import pyvista as pv\n",
    "#   mesh = pv.read(\"mode_1.vtu\")\n",
    "#   mesh.plot(scalars=\"Ez\", cmap=\"hot\")\n",
    "#\n",
    "# For more details, see palacetoolkit.postpro_vtu utilities.\n",
    "print(\"Field visualization moved to pyvista-based VTU post-processing.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3.607882,
   "end_time": "2026-08-04T14:43:01.577425+00:00",
   "environment_variables": {},
   "exception": null,
   "input_path": "docs/examples/hollow_waveguide_modes.ipynb",
   "output_path": "docs/examples/hollow_waveguide_modes.ipynb",
   "parameters": {},
   "start_time": "2026-08-04T14:42:57.969543+00:00",
   "version": "2.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}