{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "id": "b819d6fb",
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   },
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   "source": [
    "# Slotline Modes\n",
    "\n",
    "This notebook builds a boxed slotline cross-section (substrate + air + two PEC slot conductors), solves eigenmodes with `WaveguideModeSolver`, and plots mode fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760fab3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:16.663734Z",
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     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5217c2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:17.175475Z",
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     "duration": 0.012575,
     "end_time": "2026-08-04T16:23:17.184888+00:00",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_slotline_mesh(\n",
    "    box_w=8.0,\n",
    "    h_sub=1.0,\n",
    "    h_air=3.0,\n",
    "    slot_gap=0.5,\n",
    "    metal_t=0.06,\n",
    "    lc_bulk=0.18,\n",
    "    lc_slot=0.04,\n",
    "    filename=None,\n",
    "):\n",
    "    gmsh.initialize()\n",
    "    gmsh.option.setNumber(\"General.Verbosity\", 0)\n",
    "    gmsh.model.add(\"slotline_modes\")\n",
    "\n",
    "    sub = gmsh.model.occ.addRectangle(-box_w / 2, -h_sub, 0, box_w, h_sub)\n",
    "    air = gmsh.model.occ.addRectangle(-box_w / 2, 0.0, 0, box_w, h_air)\n",
    "\n",
    "    left_w = (box_w - slot_gap) / 2\n",
    "    right_w = left_w\n",
    "    left_metal = gmsh.model.occ.addRectangle(-box_w / 2, 0.0, 0, left_w, metal_t)\n",
    "    right_metal = gmsh.model.occ.addRectangle(slot_gap / 2, 0.0, 0, right_w, metal_t)\n",
    "\n",
    "    _, outmap = gmsh.model.occ.fragment([(2, sub), (2, air), (2, left_metal), (2, right_metal)], [])\n",
    "    gmsh.model.occ.remove(list(outmap[2]) + list(outmap[3]), recursive=True)\n",
    "    gmsh.model.occ.synchronize()\n",
    "\n",
    "    all_surfs = [t for _, t in gmsh.model.getEntities(2)]\n",
    "    substrate_surfs = []\n",
    "    air_surfs = []\n",
    "    for tag in all_surfs:\n",
    "        _, cy, _ = gmsh.model.occ.getCenterOfMass(2, tag)\n",
    "        if cy < -1e-9:\n",
    "            substrate_surfs.append(tag)\n",
    "        else:\n",
    "            air_surfs.append(tag)\n",
    "\n",
    "    gmsh.model.addPhysicalGroup(2, substrate_surfs, tag=1, name=\"substrate\")\n",
    "    gmsh.model.addPhysicalGroup(2, air_surfs, tag=2, name=\"air\")\n",
    "\n",
    "    bnd = gmsh.model.getBoundary([(2, t) for t in substrate_surfs + air_surfs], oriented=False, combined=False)\n",
    "    edge_tags = sorted({abs(t) for _, t in bnd})\n",
    "\n",
    "    left_slot_pec = []\n",
    "    right_slot_pec = []\n",
    "    open_edges = []\n",
    "\n",
    "    for et in edge_tags:\n",
    "        ex, ey, _ = gmsh.model.occ.getCenterOfMass(1, et)\n",
    "        on_metal_y = (-1e-6 <= ey <= metal_t + 1e-6)\n",
    "        if on_metal_y and ex < -slot_gap / 2 + 1e-6:\n",
    "            left_slot_pec.append(et)\n",
    "        elif on_metal_y and ex > slot_gap / 2 - 1e-6:\n",
    "            right_slot_pec.append(et)\n",
    "        else:\n",
    "            open_edges.append(et)\n",
    "\n",
    "    if left_slot_pec:\n",
    "        gmsh.model.addPhysicalGroup(1, left_slot_pec, tag=1, name=\"slot_pec_left\")\n",
    "    if right_slot_pec:\n",
    "        gmsh.model.addPhysicalGroup(1, right_slot_pec, tag=2, name=\"slot_pec_right\")\n",
    "    if open_edges:\n",
    "        gmsh.model.addPhysicalGroup(1, open_edges, tag=3, name=\"open_boundary\")\n",
    "\n",
    "    for _, ptag in gmsh.model.getEntities(0):\n",
    "        x, y, _ = gmsh.model.getValue(0, ptag, [])\n",
    "        near_slot = abs(x) <= (slot_gap + 1.0) and -0.2 <= y <= (metal_t + 0.4)\n",
    "        gmsh.model.mesh.setSize([(0, ptag)], lc_slot if near_slot else lc_bulk)\n",
    "\n",
    "    gmsh.model.mesh.generate(2)\n",
    "    return write_and_finalize_gmsh(filename, prefix=\"wg_slotline_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb5efc7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:17.431566Z",
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     "start_time": "2026-08-04T16:23:17.427674+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "eps_sub = 4.1\n",
    "eps_air = 1.0\n",
    "mu_r = 1.0\n",
    "omega = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f988b03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:17.440642Z",
     "iopub.status.busy": "2026-08-04T16:23:17.440483Z",
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    },
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     "start_time": "2026-08-04T16:23:17.437592+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mesh file: /tmp/wg_slotline_v2qk_x4z.msh\n",
      "Groups to render transparent: ['air_none', 'air_plastic_enclosure']\n",
      "\n",
      "Mesh loaded successfully with 2 cell blocks\n",
      "Found 5224 triangles total\n",
      "Physical group tags in mesh: {1: 'substrate', 2: 'air'}\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_slotline_v2qk_x4z_modes/config.json\n",
       "&gt;&gt; /home/runner/.cache/palacetoolkit/runtime/palace-cpu-v0.17.0/bin/palace-x86_64.bin /tmp/wg_slotline_v2qk_x4z_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",
       "\u001b[38;2;255;255;000m--&gt; Warning!\u001b[0m\n",
       "One or more external boundary attributes has no associated boundary condition!\n",
       "&quot;PMC&quot;/&quot;ZeroCharge&quot; condition is assumed!\n",
       "\n",
       "Boundary attribute list: 3\n",
       "\n",
       "\n",
       "Characteristic length and time scales:\n",
       " Lc = 8.000e+00 m, tc = 2.669e+01 ns\n",
       "Finished partitioning mesh into 1 subdomain\n",
       "\n",
       "Mesh curvature order: 1\n",
       "Mesh bounding box:\n",
       " (Xmin, Ymin) = (-4.000e+00, -1.000e+00) m\n",
       " (Xmax, Ymax) = (+4.000e+00, +3.000e+00) m\n",
       "\n",
       "Parallel Mesh Stats:\n",
       "\n",
       "                minimum     average     maximum       total\n",
       " vertices          2765        2765        2765        2765\n",
       " edges             7988        7988        7988        7988\n",
       " elements          5224        5224        5224        5224\n",
       " neighbors            0           0           0\n",
       "\n",
       "            minimum     maximum\n",
       " h       0.00292176    0.026074\n",
       " kappa            1     2.31839\n",
       "\n",
       "Estimated current per-rank memory usage is: Min. 45.6M, Max. 45.6M, Avg. 45.6M, Total 45.6M\n",
       "Estimated current per-node memory usage is: Min. 45.6M, Max. 45.6M, Avg. 45.6M, Total 45.6M\n",
       "\n",
       "Configuring 2D waveguide mode analysis at f = 4.775e-02 GHz (omega = 8.005538e+00)\n",
       " ND space: 26424 DOFs, H1 space: 10753 DOFs, total: 37177\n",
       " Auto kn_target = 1.700117e+01 (from max(mu_r) * max(epsilon_r) = 4.100000e+00)\n",
       "\n",
       "Solving GEP for 8 propagation mode(s)...\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.145665e-01\n",
       "  1 (restart 0) KSP residual norm 2.182764e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.017e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.762598e-03\n",
       "  1 (restart 0) KSP residual norm 9.795242e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.003e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.191016e-02\n",
       "  1 (restart 0) KSP residual norm 3.462617e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.085e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.596788e-02\n",
       "  1 (restart 0) KSP residual norm 2.144417e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.258e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.390447e-03\n",
       "  1 (restart 0) KSP residual norm 8.028338e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.086e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.439197e-03\n",
       "  1 (restart 0) KSP residual norm 2.139547e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.535e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.002538e-03\n",
       "  1 (restart 0) KSP residual norm 1.995751e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.494e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.016097e-02\n",
       "  1 (restart 0) KSP residual norm 9.890434e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.906e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.552247e-02\n",
       "  1 (restart 0) KSP residual norm 1.629125e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.050e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.756288e-02\n",
       "  1 (restart 0) KSP residual norm 3.522681e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.006e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.205492e-02\n",
       "  1 (restart 0) KSP residual norm 2.018605e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.675e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.475711e-03\n",
       "  1 (restart 0) KSP residual norm 2.350458e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.773e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.904484e-02\n",
       "  1 (restart 0) KSP residual norm 2.286182e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.200e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.328567e-02\n",
       "  1 (restart 0) KSP residual norm 4.153619e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.248e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.425635e-02\n",
       "  1 (restart 0) KSP residual norm 4.317416e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.755e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.259366e-02\n",
       "  1 (restart 0) KSP residual norm 5.320950e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.355e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.411787e-02\n",
       "  1 (restart 0) KSP residual norm 5.768640e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.392e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.414558e-02\n",
       "  1 (restart 0) KSP residual norm 4.718546e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.954e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.584877e-02\n",
       "  1 (restart 0) KSP residual norm 1.898426e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.198e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.403778e-03\n",
       "  1 (restart 0) KSP residual norm 3.033498e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.226e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.216212e-03\n",
       "  1 (restart 0) KSP residual norm 6.923114e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.327e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.117613e-02\n",
       "  1 (restart 0) KSP residual norm 1.934591e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.731e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.173702e-02\n",
       "  1 (restart 0) KSP residual norm 1.105686e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.087e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.442944e-02\n",
       "  1 (restart 0) KSP residual norm 1.390508e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.637e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.461311e-03\n",
       "  1 (restart 0) KSP residual norm 8.445615e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.307e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.498132e-03\n",
       "  1 (restart 0) KSP residual norm 2.895418e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.862e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.419824e-03\n",
       "  1 (restart 0) KSP residual norm 3.032697e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.724e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.318140e-03\n",
       "  1 (restart 0) KSP residual norm 2.648442e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.142e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.792684e-03\n",
       "  1 (restart 0) KSP residual norm 9.764883e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.686e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.641383e-03\n",
       "  1 (restart 0) KSP residual norm 1.085537e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.635e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.517444e-03\n",
       "  1 (restart 0) KSP residual norm 6.141123e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.359e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.162475e-03\n",
       "  1 (restart 0) KSP residual norm 1.693222e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.354e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.237916e-03\n",
       "  1 (restart 0) KSP residual norm 4.877262e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.179e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.928856e-03\n",
       "  1 (restart 0) KSP residual norm 3.694241e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.261e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.352703e-03\n",
       "  1 (restart 0) KSP residual norm 4.084166e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.218e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.712324e-03\n",
       "  1 (restart 0) KSP residual norm 8.378083e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.089e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.734105e-03\n",
       "  1 (restart 0) KSP residual norm 5.708268e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.292e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.427189e-03\n",
       "  1 (restart 0) KSP residual norm 1.173842e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.836e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.590368e-03\n",
       "  1 (restart 0) KSP residual norm 1.731610e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.685e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.717899e-03\n",
       "  1 (restart 0) KSP residual norm 1.910043e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.028e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.570737e-03\n",
       "  1 (restart 0) KSP residual norm 2.073143e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.320e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.293851e-03\n",
       "  1 (restart 0) KSP residual norm 6.278596e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.737e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.017922e-03\n",
       "  1 (restart 0) KSP residual norm 7.230997e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.396e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.690088e-03\n",
       "  1 (restart 0) KSP residual norm 9.985685e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.712e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.007386e-03\n",
       "  1 (restart 0) KSP residual norm 1.288091e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.283e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.583587e-03\n",
       "  1 (restart 0) KSP residual norm 1.220376e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.724e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.967318e-03\n",
       "  1 (restart 0) KSP residual norm 3.405670e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.731e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.339312e-03\n",
       "  1 (restart 0) KSP residual norm 4.099676e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.753e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.904494e-03\n",
       "  1 (restart 0) KSP residual norm 6.868373e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.365e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.855106e-03\n",
       "  1 (restart 0) KSP residual norm 7.641722e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.574e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.670217e-03\n",
       "  1 (restart 0) KSP residual norm 7.241076e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.712e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.356130e-03\n",
       "  1 (restart 0) KSP residual norm 5.517568e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.644e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.462211e-03\n",
       "  1 (restart 0) KSP residual norm 8.901400e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.995e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.438896e-03\n",
       "  1 (restart 0) KSP residual norm 1.188306e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.185e-10)\n",
       " Found 8 converged eigenvalues (sigma = -2.890398e+02)\n",
       " eig 0: kn = 1.340833e+01-6.588649e-13i, n_eff = 1.674882e+00-8.230114e-14i\n",
       " eig 1: kn = 1.028741e+01-7.565536e-11i, n_eff = 1.285036e+00-9.450377e-12i\n",
       " eig 2: kn = 9.745884e+00-1.565874e-11i, n_eff = 1.217393e+00-1.955989e-12i\n",
       " eig 3: kn = 8.250051e+00-5.765115e-09i, n_eff = 1.030543e+00-7.201408e-10i\n",
       " eig 4: kn = 8.174035e+00+3.347938e-11i, n_eff = 1.021047e+00+4.182027e-12i\n",
       " eig 5: kn = 7.983505e+00+6.257888e-09i, n_eff = 9.972478e-01+7.816948e-10i\n",
       " eig 6: kn = 6.777021e+00-2.212932e-11i, n_eff = 8.465416e-01-2.764251e-12i\n",
       " eig 7: kn = 5.995814e+00+3.177611e-10i, n_eff = 7.489583e-01+3.969266e-11i\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,      +1.676041e+00,      -8.235812e-14,      +1.674882e+00,      -8.230114e-14,      +2.165548e-16,      +3.752408e-12\n",
       "     2,      +1.285926e+00,      -9.456920e-12,      +1.285036e+00,      -9.450377e-12,      +1.025203e-16,      +1.059433e-12\n",
       "     3,      +1.218235e+00,      -1.957343e-12,      +1.217393e+00,      -1.955989e-12,      +3.978801e-15,      +3.881822e-11\n",
       "     4,      +1.031256e+00,      -7.206393e-10,      +1.030543e+00,      -7.201408e-10,      +3.450612e-15,      +2.956458e-11\n",
       "     5,      +1.021754e+00,      +4.184922e-12,      +1.021047e+00,      +4.182027e-12,      +1.120797e-15,      +9.548959e-12\n",
       "     6,      +9.979382e-01,      +7.822360e-10,      +9.972478e-01,      +7.816948e-10,      +3.433407e-15,      +2.885229e-11\n",
       "     7,      +8.471277e-01,      -2.766165e-12,      +8.465416e-01,      -2.764251e-12,      +1.435721e-16,      +1.118129e-12\n",
       "     8,      +7.494768e-01,      +3.972013e-11,      +7.489583e-01,      +3.969266e-11,      +3.796683e-14,      +2.840274e-10\n",
       "\n",
       "\n",
       "Completed 0 iterations of adaptive mesh refinement (AMR):\n",
       " Indicator norm = 1.951e-02, global unknowns = 37177\n",
       " Max. iterations = 0, tol. = 1.000e-02\n",
       "\n",
       "Estimated peak per-rank memory usage is: Min. 274.1M, Max. 274.1M, Avg. 274.1M, Total 274.1M\n",
       "Estimated peak per-node memory usage is: Min. 274.1M, Max. 274.1M, Avg. 274.1M, Total 274.1M\n",
       "\n",
       "Elapsed Time Report (s)           Min.        Max.        Avg.\n",
       "==============================================================\n",
       "Initialization                   0.009       0.009       0.009\n",
       "  Mesh Preprocessing             0.025       0.025       0.025\n",
       "Operator Construction            0.068       0.068       0.068\n",
       "  Preconditioner                 1.212       1.212       1.212\n",
       "Eigenvalue Solve                 0.270       0.270       0.270\n",
       "Estimation                       0.023       0.023       0.023\n",
       "  Construction                   0.114       0.114       0.114\n",
       "  Solve                          0.419       0.419       0.419\n",
       "Postprocessing                   1.405       1.405       1.405\n",
       "Disk IO                          0.008       0.008       0.008\n",
       "--------------------------------------------------------------\n",
       "Total                            3.820       3.820       3.820\n",
       "\n",
       "Peak Memory                   Per-Node       Total   Total HWM\n",
       "==============================================================\n",
       "Initialization                    2.1M        2.1M        2.1M\n",
       "  Mesh Preprocessing              3.0M        3.0M        5.1M\n",
       "Operator Construction            40.7M       40.7M       45.8M\n",
       "  Preconditioner                125.3M      125.3M      171.1M\n",
       "Eigenvalue Solve                 49.3M       49.3M      220.4M\n",
       "Estimation                        0.0K        0.0K      220.4M\n",
       "  Construction                   13.3M       13.3M      233.7M\n",
       "  Solve                           0.0K        0.0K      233.7M\n",
       "Postprocessing                    0.0K        0.0K      233.7M\n",
       "Disk IO                           2.3M        2.3M      236.0M\n",
       "--------------------------------------------------------------\n",
       "Total                           248.6M      248.6M      248.6M</pre></details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed slotline modes:\n",
      "  Mode  1: kn= +1.676041 -0.000000j\n"
     ]
    }
   ],
   "source": [
    "mesh_file = make_slotline_mesh(\n",
    "    box_w=8.0,\n",
    "    h_sub=1.0,\n",
    "    h_air=3.0,\n",
    "    slot_gap=0.5,\n",
    "    metal_t=0.06,\n",
    ")\n",
    "view_mesh(mesh_file)\n",
    "\n",
    "# Only slot metal boundaries are PEC. Open boundaries stay non-PEC.\n",
    "pec_bdr = [1, 2]\n",
    "\n",
    "solver = WaveguideModeSolver(\n",
    "    mesh_file=mesh_file,\n",
    "    order=2,\n",
    "    pec_bdr=pec_bdr,\n",
    "    materials=[\n",
    "        {\"attrs\": [1], \"eps_r\": eps_sub, \"mu_r\": mu_r},\n",
    "        {\"attrs\": [2], \"eps_r\": eps_air, \"mu_r\": mu_r},\n",
    "    ],\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(\"Computed slotline modes:\")\n",
    "for i in sorted(results):\n",
    "    kn = results[i].k_n\n",
    "    print(f\"  Mode {i:2d}: kn={kn.real:+10.6f}{kn.imag:+10.6f}j\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7d0795a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:22.074101Z",
     "iopub.status.busy": "2026-08-04T16:23:22.073885Z",
     "iopub.status.idle": "2026-08-04T16:23:22.077587Z",
     "shell.execute_reply": "2026-08-04T16:23:22.076791Z"
    },
    "papermill": {
     "duration": 0.00781,
     "end_time": "2026-08-04T16:23:22.078116+00:00",
     "exception": false,
     "start_time": "2026-08-04T16:23:22.070306+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mesh visualization moved to pyvista-based VTU post-processing.\n"
     ]
    }
   ],
   "source": [
    "# Mesh visualization moved to pyvista-based VTU post-processing.\n",
    "# For more details, see palacetoolkit.postpro_vtu utilities.\n",
    "print(\"Mesh visualization moved to pyvista-based VTU post-processing.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6991688d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T16:23:22.085191Z",
     "iopub.status.busy": "2026-08-04T16:23:22.084990Z",
     "iopub.status.idle": "2026-08-04T16:23:22.189596Z",
     "shell.execute_reply": "2026-08-04T16:23:22.188893Z"
    },
    "papermill": {
     "duration": 0.109069,
     "end_time": "2026-08-04T16:23:22.190267+00:00",
     "exception": false,
     "start_time": "2026-08-04T16:23:22.081198+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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   "display_name": ".venv",
   "language": "python",
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