{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd45434d",
   "metadata": {
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     "duration": 0.001332,
     "end_time": "2026-08-04T14:29:19.960486+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:19.959154+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Rectangular Dielectric Waveguide Modes\n",
    "\n",
    "This notebook solves a dielectric core inside cladding, classifies guided modes, and plots transverse and longitudinal fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c33a4d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:19.963576Z",
     "iopub.status.busy": "2026-08-04T14:29:19.963361Z",
     "iopub.status.idle": "2026-08-04T14:29:20.485245Z",
     "shell.execute_reply": "2026-08-04T14:29:20.484669Z"
    },
    "papermill": {
     "duration": 0.524898,
     "end_time": "2026-08-04T14:29:20.486417+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:19.961519+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from palacetoolkit.mode_solver import WaveguideModeSolver, ModeMetrics\n",
    "from palacetoolkit.utils import write_and_finalize_gmsh\n",
    "from palacetoolkit.viz import view_mesh\n",
    "\n",
    "import gmsh\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "216a1ceb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:20.489337Z",
     "iopub.status.busy": "2026-08-04T14:29:20.489093Z",
     "iopub.status.idle": "2026-08-04T14:29:20.495959Z",
     "shell.execute_reply": "2026-08-04T14:29:20.495253Z"
    },
    "papermill": {
     "duration": 0.008994,
     "end_time": "2026-08-04T14:29:20.496553+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:20.487559+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def _set_lc_for_dielectric(a_core, b_core, lc_core, lc_clad):\n",
    "    pts = gmsh.model.getEntities(0)\n",
    "    for _, ptag in pts:\n",
    "        coord = gmsh.model.getValue(0, ptag, [])\n",
    "        in_core = (abs(coord[0]) <= a_core / 2 + 1e-10 and abs(coord[1]) <= b_core / 2 + 1e-10)\n",
    "        gmsh.model.mesh.setSize([(0, ptag)], lc_core if in_core else lc_clad)\n",
    "\n",
    "\n",
    "def make_dielectric_waveguide_mesh(a_core, b_core, a_clad, b_clad, nx_core=16, ny_core=8, nx_clad=8, ny_clad=8, filename=None):\n",
    "    gmsh.initialize()\n",
    "    gmsh.option.setNumber(\"General.Verbosity\", 0)\n",
    "    gmsh.model.add(\"dielectric_waveguide\")\n",
    "\n",
    "    clad = gmsh.model.occ.addRectangle(-a_clad / 2, -b_clad / 2, 0, a_clad, b_clad)\n",
    "    core = gmsh.model.occ.addRectangle(-a_core / 2, -b_core / 2, 0, a_core, b_core)\n",
    "    _, outmap = gmsh.model.occ.fragment([(2, clad)], [(2, core)])\n",
    "    gmsh.model.occ.synchronize()\n",
    "\n",
    "    core_surfs = [tag for _, tag in outmap[1]]\n",
    "    clad_surfs = [tag for _, tag in outmap[0] if tag not in core_surfs]\n",
    "\n",
    "    gmsh.model.addPhysicalGroup(2, core_surfs, tag=1, name=\"core\")\n",
    "    gmsh.model.addPhysicalGroup(2, clad_surfs, tag=2, name=\"cladding\")\n",
    "\n",
    "    all_surfs = [(2, t) for t in core_surfs + clad_surfs]\n",
    "    outer_bnd = gmsh.model.getBoundary(all_surfs, oriented=False, combined=True)\n",
    "    pec_tags = [abs(tag) for _, tag in outer_bnd]\n",
    "    gmsh.model.addPhysicalGroup(1, pec_tags, tag=1, name=\"PEC\")\n",
    "\n",
    "    lc_core = min(a_core / nx_core, b_core / ny_core)\n",
    "    lc_clad = max(lc_core * max(nx_core / nx_clad, ny_core / ny_clad), 1.5 * lc_core)\n",
    "    _set_lc_for_dielectric(a_core, b_core, lc_core, lc_clad)\n",
    "\n",
    "    gmsh.model.mesh.generate(2)\n",
    "    return write_and_finalize_gmsh(filename, prefix=\"wg_diel_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b05177fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:20.499204Z",
     "iopub.status.busy": "2026-08-04T14:29:20.499037Z",
     "iopub.status.idle": "2026-08-04T14:29:20.502719Z",
     "shell.execute_reply": "2026-08-04T14:29:20.502114Z"
    },
    "papermill": {
     "duration": 0.005761,
     "end_time": "2026-08-04T14:29:20.503296+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:20.497535+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Guided-mode window: 5.4414 < kn < 10.8828\n"
     ]
    }
   ],
   "source": [
    "a_core, b_core = 1.0, 0.5\n",
    "a_clad, b_clad = 4.0, 3.0\n",
    "eps_core, eps_clad = 4.0, 1.0\n",
    "mu = 1.0\n",
    "\n",
    "k0_cutoff_approx = np.pi / a_core / np.sqrt(eps_core - eps_clad)\n",
    "omega = 3.0 * k0_cutoff_approx\n",
    "\n",
    "kn_min = omega * np.sqrt(mu * eps_clad)\n",
    "kn_max = omega * np.sqrt(mu * eps_core)\n",
    "print(f\"Guided-mode window: {kn_min:.4f} < kn < {kn_max:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce38bb09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:20.506012Z",
     "iopub.status.busy": "2026-08-04T14:29:20.505879Z",
     "iopub.status.idle": "2026-08-04T14:29:24.825373Z",
     "shell.execute_reply": "2026-08-04T14:29:24.824581Z"
    },
    "papermill": {
     "duration": 4.321516,
     "end_time": "2026-08-04T14:29:24.825862+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:20.504346+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mesh file: /tmp/wg_diel_n4a6evn6.msh\n",
      "Groups to render transparent: ['air_none', 'air_plastic_enclosure']\n",
      "\n",
      "Mesh loaded successfully with 2 cell blocks\n",
      "Found 3122 triangles total\n",
      "Physical group tags in mesh: {1: 'core', 2: 'cladding'}\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_diel_n4a6evn6_modes/config.json\n",
       "&gt;&gt; /home/runner/.cache/palacetoolkit/runtime/palace-cpu-v0.17.0/bin/palace-x86_64.bin /tmp/wg_diel_n4a6evn6_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",
       "Added 48 boundary elements for material interfaces to the mesh\n",
       "\n",
       "Characteristic length and time scales:\n",
       " Lc = 4.000e+00 m, tc = 1.334e+01 ns\n",
       "Finished partitioning mesh into 1 subdomain\n",
       "\n",
       "Mesh curvature order: 1\n",
       "Mesh bounding box:\n",
       " (Xmin, Ymin) = (-2.000e+00, -1.500e+00) m\n",
       " (Xmax, Ymax) = (+2.000e+00, +1.500e+00) m\n",
       "\n",
       "Parallel Mesh Stats:\n",
       "\n",
       "                minimum     average     maximum       total\n",
       " vertices          1618        1618        1618        1618\n",
       " edges             4739        4739        4739        4739\n",
       " elements          3122        3122        3122        3122\n",
       " neighbors            0           0           0\n",
       "\n",
       "            minimum     maximum\n",
       " h        0.0121447   0.0356425\n",
       " kappa            1     2.03476\n",
       "\n",
       "Estimated current per-rank memory usage is: Min. 45.0M, Max. 45.0M, Avg. 45.0M, Total 45.0M\n",
       "Estimated current per-node memory usage is: Min. 45.0M, Max. 45.0M, Avg. 45.0M, Total 45.0M\n",
       "\n",
       "Configuring 2D waveguide mode analysis at f = 2.598e-01 GHz (omega = 2.178066e+01)\n",
       " ND space: 15722 DOFs, H1 space: 6357 DOFs, total: 22079\n",
       " Auto kn_target = 4.568750e+01 (from max(mu_r) * max(epsilon_r) = 4.000000e+00)\n",
       "\n",
       "Solving GEP for 10 propagation mode(s)...\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.484753e-01\n",
       "  1 (restart 0) KSP residual norm 1.074814e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.239e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.859058e-03\n",
       "  1 (restart 0) KSP residual norm 6.280013e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.991e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.616666e-03\n",
       "  1 (restart 0) KSP residual norm 6.041397e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.131e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.170070e-03\n",
       "  1 (restart 0) KSP residual norm 3.718077e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.055e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.088504e-03\n",
       "  1 (restart 0) KSP residual norm 2.014263e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.927e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.239904e-03\n",
       "  1 (restart 0) KSP residual norm 5.361130e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.506e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.986859e-03\n",
       "  1 (restart 0) KSP residual norm 3.173534e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.960e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.152904e-03\n",
       "  1 (restart 0) KSP residual norm 3.881413e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.426e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.265599e-03\n",
       "  1 (restart 0) KSP residual norm 3.267121e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.205e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.235858e-03\n",
       "  1 (restart 0) KSP residual norm 1.434801e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.417e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.185478e-03\n",
       "  1 (restart 0) KSP residual norm 3.889154e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.413e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.768621e-03\n",
       "  1 (restart 0) KSP residual norm 3.915059e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.784e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.515014e-03\n",
       "  1 (restart 0) KSP residual norm 1.015454e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.703e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.533081e-03\n",
       "  1 (restart 0) KSP residual norm 2.497288e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.068e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.992352e-03\n",
       "  1 (restart 0) KSP residual norm 2.998172e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.006e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.229685e-03\n",
       "  1 (restart 0) KSP residual norm 3.431405e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.561e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.872122e-03\n",
       "  1 (restart 0) KSP residual norm 2.191955e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.661e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.016679e-02\n",
       "  1 (restart 0) KSP residual norm 5.318105e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.231e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.369257e-02\n",
       "  1 (restart 0) KSP residual norm 6.497862e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.746e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.044957e-02\n",
       "  1 (restart 0) KSP residual norm 6.847077e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.552e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.181825e-03\n",
       "  1 (restart 0) KSP residual norm 4.785485e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.741e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.630570e-03\n",
       "  1 (restart 0) KSP residual norm 3.946235e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.952e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.352318e-02\n",
       "  1 (restart 0) KSP residual norm 8.157072e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.032e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.656475e-02\n",
       "  1 (restart 0) KSP residual norm 1.189433e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.181e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.736208e-02\n",
       "  1 (restart 0) KSP residual norm 9.995797e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.757e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.074781e-02\n",
       "  1 (restart 0) KSP residual norm 8.861067e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.245e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.960241e-03\n",
       "  1 (restart 0) KSP residual norm 1.769323e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.977e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.725999e-03\n",
       "  1 (restart 0) KSP residual norm 4.656915e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.133e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.665069e-03\n",
       "  1 (restart 0) KSP residual norm 3.662837e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.466e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.267297e-04\n",
       "  1 (restart 0) KSP residual norm 4.036379e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.459e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.344353e-03\n",
       "  1 (restart 0) KSP residual norm 3.342529e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.694e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.370372e-03\n",
       "  1 (restart 0) KSP residual norm 1.453192e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.131e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.860042e-03\n",
       "  1 (restart 0) KSP residual norm 2.011928e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.035e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.491008e-03\n",
       "  1 (restart 0) KSP residual norm 1.333563e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.354e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.305854e-03\n",
       "  1 (restart 0) KSP residual norm 1.375767e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.966e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.653836e-03\n",
       "  1 (restart 0) KSP residual norm 1.722915e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.492e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.962841e-03\n",
       "  1 (restart 0) KSP residual norm 1.168210e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.952e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.681891e-04\n",
       "  1 (restart 0) KSP residual norm 2.754885e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.884e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.097267e-04\n",
       "  1 (restart 0) KSP residual norm 5.592362e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.147e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.719594e-04\n",
       "  1 (restart 0) KSP residual norm 6.753570e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.745e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.422844e-04\n",
       "  1 (restart 0) KSP residual norm 3.408565e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.286e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.561666e-04\n",
       "  1 (restart 0) KSP residual norm 3.819631e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.821e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.824256e-04\n",
       "  1 (restart 0) KSP residual norm 3.769061e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.523e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.089639e-04\n",
       "  1 (restart 0) KSP residual norm 3.002673e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.931e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.080791e-04\n",
       "  1 (restart 0) KSP residual norm 3.713605e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.107e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.879597e-04\n",
       "  1 (restart 0) KSP residual norm 3.489712e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.935e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.317189e-04\n",
       "  1 (restart 0) KSP residual norm 3.577181e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.663e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.018717e-03\n",
       "  1 (restart 0) KSP residual norm 5.958251e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.849e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.125049e-04\n",
       "  1 (restart 0) KSP residual norm 2.544580e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.169e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.262688e-04\n",
       "  1 (restart 0) KSP residual norm 4.456794e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.394e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.177190e-04\n",
       "  1 (restart 0) KSP residual norm 4.545365e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.780e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.706729e-04\n",
       "  1 (restart 0) KSP residual norm 3.586815e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.621e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.327658e-04\n",
       "  1 (restart 0) KSP residual norm 3.619556e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.794e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.710151e-04\n",
       "  1 (restart 0) KSP residual norm 3.259615e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.920e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.787346e-04\n",
       "  1 (restart 0) KSP residual norm 2.815469e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.434e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.866842e-04\n",
       "  1 (restart 0) KSP residual norm 4.031916e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.284e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.471773e-04\n",
       "  1 (restart 0) KSP residual norm 5.319490e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.722e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.596068e-04\n",
       "  1 (restart 0) KSP residual norm 6.769026e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.911e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.221574e-04\n",
       "  1 (restart 0) KSP residual norm 3.533649e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.767e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.117839e-04\n",
       "  1 (restart 0) KSP residual norm 5.175738e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.272e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.886014e-04\n",
       "  1 (restart 0) KSP residual norm 4.865458e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.958e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.771106e-04\n",
       "  1 (restart 0) KSP residual norm 5.742507e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.204e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.931370e-04\n",
       "  1 (restart 0) KSP residual norm 8.034611e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.629e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.609524e-04\n",
       "  1 (restart 0) KSP residual norm 1.186328e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.115e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.145757e-04\n",
       "  1 (restart 0) KSP residual norm 1.476365e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.402e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.809494e-04\n",
       "  1 (restart 0) KSP residual norm 1.127440e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.344e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.449885e-04\n",
       "  1 (restart 0) KSP residual norm 7.573734e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.390e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.426387e-04\n",
       "  1 (restart 0) KSP residual norm 5.319069e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.312e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.219964e-04\n",
       "  1 (restart 0) KSP residual norm 2.322102e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.212e-14)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.809551e-04\n",
       "  1 (restart 0) KSP residual norm 9.213122e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.180e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.259335e-04\n",
       "  1 (restart 0) KSP residual norm 7.443264e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.189e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.175614e-04\n",
       "  1 (restart 0) KSP residual norm 5.944813e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.149e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.481640e-04\n",
       "  1 (restart 0) KSP residual norm 8.160494e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.821e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.225440e-04\n",
       "  1 (restart 0) KSP residual norm 1.103217e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.611e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.244116e-04\n",
       "  1 (restart 0) KSP residual norm 1.608991e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.068e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.245433e-04\n",
       "  1 (restart 0) KSP residual norm 1.996008e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.755e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.194902e-04\n",
       "  1 (restart 0) KSP residual norm 1.495185e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.414e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.663678e-04\n",
       "  1 (restart 0) KSP residual norm 1.399640e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.471e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.683569e-04\n",
       "  1 (restart 0) KSP residual norm 2.050244e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.668e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.269585e-04\n",
       "  1 (restart 0) KSP residual norm 9.987558e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.895e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.207022e-04\n",
       "  1 (restart 0) KSP residual norm 7.119509e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.692e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.066572e-04\n",
       "  1 (restart 0) KSP residual norm 7.103009e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.747e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.649699e-04\n",
       "  1 (restart 0) KSP residual norm 5.927233e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.275e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.120873e-04\n",
       "  1 (restart 0) KSP residual norm 7.562740e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.477e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.803954e-04\n",
       "  1 (restart 0) KSP residual norm 8.030047e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.384e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.692936e-04\n",
       "  1 (restart 0) KSP residual norm 8.449201e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.262e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.579284e-04\n",
       "  1 (restart 0) KSP residual norm 5.802063e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.267e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.107372e-04\n",
       "  1 (restart 0) KSP residual norm 1.304583e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.609e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.213143e-04\n",
       "  1 (restart 0) KSP residual norm 7.292532e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.399e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.789216e-04\n",
       "  1 (restart 0) KSP residual norm 7.630115e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.318e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.616887e-04\n",
       "  1 (restart 0) KSP residual norm 8.231664e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.244e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.148689e-04\n",
       "  1 (restart 0) KSP residual norm 7.189025e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.169e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.397369e-04\n",
       "  1 (restart 0) KSP residual norm 6.995983e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.094e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.960567e-04\n",
       "  1 (restart 0) KSP residual norm 9.921657e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.425e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.442326e-04\n",
       "  1 (restart 0) KSP residual norm 8.620427e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.584e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.041712e-04\n",
       "  1 (restart 0) KSP residual norm 9.655591e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.915e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.939500e-04\n",
       "  1 (restart 0) KSP residual norm 1.139094e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.918e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.426731e-04\n",
       "  1 (restart 0) KSP residual norm 9.409763e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.734e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.576681e-04\n",
       "  1 (restart 0) KSP residual norm 8.927051e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.601e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.818894e-04\n",
       "  1 (restart 0) KSP residual norm 1.247247e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.829e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.757240e-04\n",
       "  1 (restart 0) KSP residual norm 1.211924e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.794e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.653031e-04\n",
       "  1 (restart 0) KSP residual norm 1.259112e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.893e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.126320e-04\n",
       "  1 (restart 0) KSP residual norm 1.391116e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.524e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.952825e-04\n",
       "  1 (restart 0) KSP residual norm 7.973040e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.147e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.194026e-04\n",
       "  1 (restart 0) KSP residual norm 6.662553e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.589e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.316471e-04\n",
       "  1 (restart 0) KSP residual norm 1.226092e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.676e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.460103e-04\n",
       "  1 (restart 0) KSP residual norm 8.552658e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.566e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.141406e-04\n",
       "  1 (restart 0) KSP residual norm 8.882677e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.728e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.039156e-04\n",
       "  1 (restart 0) KSP residual norm 8.239874e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.635e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.290069e-04\n",
       "  1 (restart 0) KSP residual norm 7.070913e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.648e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.600346e-04\n",
       "  1 (restart 0) KSP residual norm 7.547683e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.641e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.231050e-04\n",
       "  1 (restart 0) KSP residual norm 9.871872e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.887e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.994737e-04\n",
       "  1 (restart 0) KSP residual norm 1.125951e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.254e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.845453e-04\n",
       "  1 (restart 0) KSP residual norm 1.087312e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.244e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.877405e-04\n",
       "  1 (restart 0) KSP residual norm 7.862581e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.028e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.385654e-04\n",
       "  1 (restart 0) KSP residual norm 8.656113e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.607e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.290246e-04\n",
       "  1 (restart 0) KSP residual norm 8.823287e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.210e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.364898e-04\n",
       "  1 (restart 0) KSP residual norm 1.129924e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.351e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.180753e-04\n",
       "  1 (restart 0) KSP residual norm 9.304776e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.505e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.568971e-04\n",
       "  1 (restart 0) KSP residual norm 1.046929e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.594e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.306659e-04\n",
       "  1 (restart 0) KSP residual norm 1.049409e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.263e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.566811e-04\n",
       "  1 (restart 0) KSP residual norm 6.848801e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.230e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.347082e-04\n",
       "  1 (restart 0) KSP residual norm 8.531966e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.161e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.871774e-04\n",
       "  1 (restart 0) KSP residual norm 9.621415e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.400e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.421508e-04\n",
       "  1 (restart 0) KSP residual norm 7.031422e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.590e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.584671e-04\n",
       "  1 (restart 0) KSP residual norm 6.352182e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.386e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.427396e-04\n",
       "  1 (restart 0) KSP residual norm 6.293245e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.421e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.029421e-04\n",
       "  1 (restart 0) KSP residual norm 6.741994e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.673e-13)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.099960e-04\n",
       "  1 (restart 0) KSP residual norm 6.457387e-17\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.575e-13)\n",
       " Found 10 converged eigenvalues (sigma = -2.087348e+03)\n",
       " eig 0: kn = 3.809699e+01-1.543033e-14i, n_eff = 1.749120e+00-7.084418e-16i\n",
       " eig 1: kn = 3.598998e+01-8.311159e-13i, n_eff = 1.652382e+00-3.815844e-14i\n",
       " eig 2: kn = 3.230410e+01+5.310536e-08i, n_eff = 1.483155e+00+2.438189e-09i\n",
       " eig 3: kn = 3.151213e+01+2.504690e-11i, n_eff = 1.446794e+00+1.149960e-12i\n",
       " eig 4: kn = 2.568911e+01-1.244308e-09i, n_eff = 1.179446e+00-5.712901e-11i\n",
       " eig 5: kn = 2.406211e+01-8.567109e-10i, n_eff = 1.104747e+00-3.933356e-11i\n",
       " eig 6: kn = 2.345727e+01+1.503863e-08i, n_eff = 1.076977e+00+6.904579e-10i\n",
       " eig 7: kn = 2.218599e+01+4.363269e-08i, n_eff = 1.018609e+00+2.003277e-09i\n",
       " eig 8: kn = 2.126291e+01+2.760824e-07i, n_eff = 9.762287e-01+1.267558e-08i\n",
       " eig 9: kn = 2.113315e+01-1.662714e-08i, n_eff = 9.702715e-01-7.633903e-10i\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,      +9.524247e+00,      -3.857582e-15,      +1.749120e+00,      -7.084418e-16,      +1.401206e-17,      +2.491397e-15\n",
       "     2,      +8.997494e+00,      -2.077790e-13,      +1.652382e+00,      -3.815844e-14,      +9.623663e-16,      +1.375988e-13\n",
       "     3,      +8.076025e+00,      +1.327634e-08,      +1.483155e+00,      +2.438189e-09,      +4.123021e-11,      +4.484389e-09\n",
       "     4,      +7.878032e+00,      +6.261724e-12,      +1.446794e+00,      +1.149960e-12,      +1.308830e-13,      +1.358467e-11\n",
       "     5,      +6.422277e+00,      -3.110769e-10,      +1.179446e+00,      -5.712901e-11,      +5.350426e-13,      +4.271270e-11\n",
       "     6,      +6.015528e+00,      -2.141777e-10,      +1.104747e+00,      -3.933356e-11,      +3.133029e-13,      +2.368748e-11\n",
       "     7,      +5.864317e+00,      +3.759657e-09,      +1.076977e+00,      +6.904579e-10,      +8.716940e-13,      +6.469070e-11\n",
       "     8,      +5.546497e+00,      +1.090817e-08,      +1.018609e+00,      +2.003277e-09,      +1.961581e-11,      +1.403574e-09\n",
       "     9,      +5.315726e+00,      +6.902060e-08,      +9.762287e-01,      +1.267558e-08,      +6.030342e-13,      +4.210705e-11\n",
       "    10,      +5.283288e+00,      -4.156786e-09,      +9.702715e-01,      -7.633903e-10,      +4.734449e-11,      +3.294936e-09\n",
       "\n",
       "\n",
       "Completed 0 iterations of adaptive mesh refinement (AMR):\n",
       " Indicator norm = 9.257e-03, global unknowns = 22079\n",
       " Max. iterations = 0, tol. = 1.000e-02\n",
       "\n",
       "Estimated peak per-rank memory usage is: Min. 188.9M, Max. 188.9M, Avg. 188.9M, Total 188.9M\n",
       "Estimated peak per-node memory usage is: Min. 188.9M, Max. 188.9M, Avg. 188.9M, Total 188.9M\n",
       "\n",
       "Elapsed Time Report (s)           Min.        Max.        Avg.\n",
       "==============================================================\n",
       "Initialization                   0.006       0.006       0.006\n",
       "  Mesh Preprocessing             0.017       0.017       0.017\n",
       "Operator Construction            0.041       0.041       0.041\n",
       "  Preconditioner                 1.511       1.511       1.511\n",
       "Eigenvalue Solve                 0.359       0.359       0.359\n",
       "Estimation                       0.018       0.018       0.018\n",
       "  Construction                   0.105       0.105       0.105\n",
       "  Solve                          0.280       0.280       0.280\n",
       "Postprocessing                   1.044       1.044       1.044\n",
       "Disk IO                          0.005       0.005       0.005\n",
       "--------------------------------------------------------------\n",
       "Total                            3.654       3.654       3.654\n",
       "\n",
       "Peak Memory                   Per-Node       Total   Total HWM\n",
       "==============================================================\n",
       "Initialization                    2.5M        2.5M        2.5M\n",
       "  Mesh Preprocessing              2.1M        2.1M        4.6M\n",
       "Operator Construction            28.4M       28.4M       33.0M\n",
       "  Preconditioner                 72.9M       72.9M      105.9M\n",
       "Eigenvalue Solve                 30.5M       30.5M      136.4M\n",
       "Estimation                        0.0K        0.0K      136.4M\n",
       "  Construction                   12.3M       12.3M      148.8M\n",
       "  Solve                           0.0K        0.0K      148.8M\n",
       "Postprocessing                    0.0K        0.0K      148.8M\n",
       "Disk IO                           2.1M        2.1M      150.9M\n",
       "--------------------------------------------------------------\n",
       "Total                           163.4M      163.4M      163.4M</pre></details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed modes:\n",
      "  Mode  1: kn= +9.524247 -0.000000j  [guided]\n"
     ]
    }
   ],
   "source": [
    "mesh_file = make_dielectric_waveguide_mesh(\n",
    "    a_core,\n",
    "    b_core,\n",
    "    a_clad,\n",
    "    b_clad,\n",
    "    nx_core=16,\n",
    "    ny_core=8,\n",
    "    nx_clad=8,\n",
    "    ny_clad=8,\n",
    ")\n",
    "view_mesh(mesh_file)\n",
    "\n",
    "solver = WaveguideModeSolver(\n",
    "    mesh_file=mesh_file,\n",
    "    order=2,\n",
    "    pec_bdr=[1],\n",
    "    materials=[\n",
    "        {\"attrs\": [1], \"eps_r\": eps_core, \"mu_r\": mu},\n",
    "        {\"attrs\": [2], \"eps_r\": eps_clad, \"mu_r\": mu},\n",
    "    ],\n",
    "    omega=omega,\n",
    ")\n",
    "results = solver.solve(num_modes=10, mode_idx=1, target=0.0, save=0, num_procs=4)\n",
    "\n",
    "print(\"Computed modes:\")\n",
    "for i in sorted(results):\n",
    "    kn = results[i].k_n\n",
    "    if kn_min < kn.real < kn_max:\n",
    "        mode_type = \"guided\"\n",
    "    elif abs(kn.imag) > 0.1 * abs(kn.real):\n",
    "        mode_type = \"evanescent\"\n",
    "    else:\n",
    "        mode_type = \"radiation/substrate\"\n",
    "    print(f\"  Mode {i:2d}: kn={kn.real:+10.6f}{kn.imag:+10.6f}j  [{mode_type}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3f8e9c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:24.830710Z",
     "iopub.status.busy": "2026-08-04T14:29:24.830570Z",
     "iopub.status.idle": "2026-08-04T14:29:24.833779Z",
     "shell.execute_reply": "2026-08-04T14:29:24.833095Z"
    },
    "papermill": {
     "duration": 0.006508,
     "end_time": "2026-08-04T14:29:24.834544+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:24.828036+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",
    "# For more details, see palacetoolkit.postpro_vtu utilities.\n",
    "print(\"Field visualization moved to pyvista-based VTU post-processing.\")"
   ]
  }
 ],
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