{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "id": "e81d95c6",
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   "source": [
    "# Differential Microstrip Modes\n",
    "\n",
    "This notebook builds a boxed differential microstrip cross-section (substrate + air + ground plane + two PEC strips), solves eigenmodes, and plots the first fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "beb0233b",
   "metadata": {
    "execution": {
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    "tags": []
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   "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": "e1c45668",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:27.979932Z",
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     "duration": 0.012503,
     "end_time": "2026-08-04T14:29:27.989305+00:00",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_differential_microstrip_mesh(\n",
    "    box_w=8.0,\n",
    "    h_sub=1.0,\n",
    "    h_air=3.0,\n",
    "    strip_w=0.8,\n",
    "    strip_gap=0.5,\n",
    "    strip_t=0.06,\n",
    "    lc_bulk=0.18,\n",
    "    lc_strip=0.05,\n",
    "    filename=None,\n",
    "):\n",
    "    gmsh.initialize()\n",
    "    gmsh.option.setNumber(\"General.Verbosity\", 0)\n",
    "    gmsh.model.add(\"differential_microstrip_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",
    "    x1 = -(strip_gap / 2 + strip_w)\n",
    "    x2 = strip_gap / 2\n",
    "    strip_p = gmsh.model.occ.addRectangle(x1, 0.0, 0, strip_w, strip_t)\n",
    "    strip_n = gmsh.model.occ.addRectangle(x2, 0.0, 0, strip_w, strip_t)\n",
    "\n",
    "    _, outmap = gmsh.model.occ.fragment([(2, sub), (2, air), (2, strip_p), (2, strip_n)], [])\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",
    "    strip_p_edges = []\n",
    "    strip_n_edges = []\n",
    "    ground_edges = []\n",
    "    open_edges = []\n",
    "\n",
    "    c1 = x1 + strip_w / 2\n",
    "    c2 = x2 + strip_w / 2\n",
    "    for et in edge_tags:\n",
    "        ex, ey, _ = gmsh.model.occ.getCenterOfMass(1, et)\n",
    "        on_strip_y = (-1e-6 <= ey <= strip_t + 1e-6)\n",
    "\n",
    "        if on_strip_y and abs(ex - c1) <= strip_w / 2 + 1e-6:\n",
    "            strip_p_edges.append(et)\n",
    "        elif on_strip_y and abs(ex - c2) <= strip_w / 2 + 1e-6:\n",
    "            strip_n_edges.append(et)\n",
    "        elif abs(ey + h_sub) <= 1e-6:\n",
    "            ground_edges.append(et)\n",
    "        else:\n",
    "            open_edges.append(et)\n",
    "\n",
    "    if ground_edges:\n",
    "        gmsh.model.addPhysicalGroup(1, ground_edges, tag=1, name=\"ground_plane\")\n",
    "    if strip_p_edges:\n",
    "        gmsh.model.addPhysicalGroup(1, strip_p_edges, tag=2, name=\"strip_p\")\n",
    "    if strip_n_edges:\n",
    "        gmsh.model.addPhysicalGroup(1, strip_n_edges, tag=3, name=\"strip_n\")\n",
    "    if open_edges:\n",
    "        gmsh.model.addPhysicalGroup(1, open_edges, tag=4, name=\"open_boundary\")\n",
    "\n",
    "    for _, ptag in gmsh.model.getEntities(0):\n",
    "        x, y, _ = gmsh.model.getValue(0, ptag, [])\n",
    "        near_pair = (abs(x) <= (strip_gap / 2 + strip_w + 0.8) and -0.1 <= y <= strip_t + 0.5)\n",
    "        gmsh.model.mesh.setSize([(0, ptag)], lc_strip if near_pair else lc_bulk)\n",
    "\n",
    "    gmsh.model.mesh.generate(2)\n",
    "    return write_and_finalize_gmsh(filename, prefix=\"wg_diffms_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67e95f8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:27.994151Z",
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     "start_time": "2026-08-04T14:29:27.991314+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "eps_sub = 4.0\n",
    "eps_air = 1.0\n",
    "mu_r = 1.0\n",
    "omega = 16.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5578b59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:28.001781Z",
     "iopub.status.busy": "2026-08-04T14:29:28.001647Z",
     "iopub.status.idle": "2026-08-04T14:29:34.976736Z",
     "shell.execute_reply": "2026-08-04T14:29:34.976160Z"
    },
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     "duration": 6.978404,
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     "start_time": "2026-08-04T14:29:27.999202+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mesh file: /tmp/wg_diffms_jcjvk8ne.msh\n",
      "Groups to render transparent: ['air_none', 'air_plastic_enclosure']\n",
      "\n",
      "Mesh loaded successfully with 2 cell blocks\n",
      "Found 5826 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>"
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     },
     "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_diffms_jcjvk8ne_modes/config.json\n",
       "&gt;&gt; /home/runner/.cache/palacetoolkit/runtime/palace-cpu-v0.17.0/bin/palace-x86_64.bin /tmp/wg_diffms_jcjvk8ne_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: 4\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          3016        3016        3016        3016\n",
       " edges             8843        8843        8843        8843\n",
       " elements          5826        5826        5826        5826\n",
       " neighbors            0           0           0\n",
       "\n",
       "            minimum     maximum\n",
       " h         0.003452    0.026072\n",
       " kappa            1     2.16017\n",
       "\n",
       "Estimated current per-rank memory usage is: Min. 46.1M, Max. 46.1M, Avg. 46.1M, Total 46.1M\n",
       "Estimated current per-node memory usage is: Min. 46.1M, Max. 46.1M, Avg. 46.1M, Total 46.1M\n",
       "\n",
       "Configuring 2D waveguide mode analysis at f = 7.639e-01 GHz (omega = 1.280886e+02)\n",
       " ND space: 29338 DOFs, H1 space: 11859 DOFs, total: 41197\n",
       " Auto kn_target = 2.686809e+02 (from max(mu_r) * max(epsilon_r) = 4.000000e+00)\n",
       "\n",
       "Solving GEP for 8 propagation mode(s)...\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.088364e-01\n",
       "  1 (restart 0) KSP residual norm 1.057448e-12\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.716e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.685008e-03\n",
       "  1 (restart 0) KSP residual norm 5.923799e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.042e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.813401e-03\n",
       "  1 (restart 0) KSP residual norm 9.065954e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.238e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.209174e-03\n",
       "  1 (restart 0) KSP residual norm 7.593204e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.250e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.023241e-02\n",
       "  1 (restart 0) KSP residual norm 1.340666e-13\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.310e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.968402e-03\n",
       "  1 (restart 0) KSP residual norm 3.448584e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.328e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.023687e-03\n",
       "  1 (restart 0) KSP residual norm 8.686022e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.626e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.795438e-03\n",
       "  1 (restart 0) KSP residual norm 4.898632e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.453e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.727597e-03\n",
       "  1 (restart 0) KSP residual norm 1.049995e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.078e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.341111e-03\n",
       "  1 (restart 0) KSP residual norm 1.053706e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.501e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.171952e-03\n",
       "  1 (restart 0) KSP residual norm 5.807309e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.955e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.354116e-03\n",
       "  1 (restart 0) KSP residual norm 9.002139e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.648e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.026614e-04\n",
       "  1 (restart 0) KSP residual norm 9.591385e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.592e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.440658e-03\n",
       "  1 (restart 0) KSP residual norm 2.265304e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.572e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.486959e-04\n",
       "  1 (restart 0) KSP residual norm 7.509155e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.848e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.307377e-03\n",
       "  1 (restart 0) KSP residual norm 1.823863e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.395e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.436604e-03\n",
       "  1 (restart 0) KSP residual norm 2.100550e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.462e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.660807e-03\n",
       "  1 (restart 0) KSP residual norm 2.237252e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.408e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.632452e-03\n",
       "  1 (restart 0) KSP residual norm 3.376406e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.283e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.225459e-03\n",
       "  1 (restart 0) KSP residual norm 2.051210e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.674e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.814157e-03\n",
       "  1 (restart 0) KSP residual norm 2.344969e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.293e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.703417e-03\n",
       "  1 (restart 0) KSP residual norm 3.724891e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.378e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.041051e-03\n",
       "  1 (restart 0) KSP residual norm 3.106138e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.522e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.273776e-03\n",
       "  1 (restart 0) KSP residual norm 2.767089e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.217e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.013579e-04\n",
       "  1 (restart 0) KSP residual norm 3.924135e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.525e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.225467e-03\n",
       "  1 (restart 0) KSP residual norm 1.007270e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.219e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.312400e-03\n",
       "  1 (restart 0) KSP residual norm 1.120090e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.535e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.933692e-04\n",
       "  1 (restart 0) KSP residual norm 6.866541e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.551e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.439139e-04\n",
       "  1 (restart 0) KSP residual norm 9.559151e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.133e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.197447e-04\n",
       "  1 (restart 0) KSP residual norm 8.605641e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.691e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.128923e-04\n",
       "  1 (restart 0) KSP residual norm 2.395278e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.908e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.486589e-04\n",
       "  1 (restart 0) KSP residual norm 1.447742e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.152e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.069319e-04\n",
       "  1 (restart 0) KSP residual norm 9.366047e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.848e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.450273e-04\n",
       "  1 (restart 0) KSP residual norm 5.684568e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.648e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.283564e-04\n",
       "  1 (restart 0) KSP residual norm 1.071643e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.502e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 4.489377e-04\n",
       "  1 (restart 0) KSP residual norm 1.099634e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.449e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.133939e-04\n",
       "  1 (restart 0) KSP residual norm 6.652838e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.867e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.811841e-04\n",
       "  1 (restart 0) KSP residual norm 1.083210e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.852e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.660016e-04\n",
       "  1 (restart 0) KSP residual norm 1.676355e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.010e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.353758e-05\n",
       "  1 (restart 0) KSP residual norm 6.817871e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.289e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.159493e-04\n",
       "  1 (restart 0) KSP residual norm 1.879174e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.621e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.199073e-04\n",
       "  1 (restart 0) KSP residual norm 2.327192e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.941e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.677250e-05\n",
       "  1 (restart 0) KSP residual norm 7.279866e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.390e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.461089e-05\n",
       "  1 (restart 0) KSP residual norm 5.691503e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.016e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.770092e-05\n",
       "  1 (restart 0) KSP residual norm 7.858359e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.043e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.586863e-05\n",
       "  1 (restart 0) KSP residual norm 1.090191e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.137e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.640522e-05\n",
       "  1 (restart 0) KSP residual norm 2.415420e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.505e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.407181e-04\n",
       "  1 (restart 0) KSP residual norm 2.727164e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.133e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.100179e-05\n",
       "  1 (restart 0) KSP residual norm 9.615928e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.187e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.168383e-04\n",
       "  1 (restart 0) KSP residual norm 2.481754e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.145e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.275324e-05\n",
       "  1 (restart 0) KSP residual norm 2.323545e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.505e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.107876e-04\n",
       "  1 (restart 0) KSP residual norm 1.228089e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.109e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.170745e-05\n",
       "  1 (restart 0) KSP residual norm 9.894419e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.079e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.504654e-05\n",
       "  1 (restart 0) KSP residual norm 9.408094e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.898e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.240819e-05\n",
       "  1 (restart 0) KSP residual norm 1.398004e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.696e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.050161e-04\n",
       "  1 (restart 0) KSP residual norm 4.046445e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.853e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.069173e-04\n",
       "  1 (restart 0) KSP residual norm 2.654177e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.482e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.366095e-04\n",
       "  1 (restart 0) KSP residual norm 2.636229e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.930e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.547727e-04\n",
       "  1 (restart 0) KSP residual norm 3.160927e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.241e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.917720e-04\n",
       "  1 (restart 0) KSP residual norm 4.030574e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.381e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 5.551546e-05\n",
       "  1 (restart 0) KSP residual norm 1.108145e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.996e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.961368e-04\n",
       "  1 (restart 0) KSP residual norm 3.450393e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.759e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.232255e-05\n",
       "  1 (restart 0) KSP residual norm 3.884537e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.208e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.202992e-04\n",
       "  1 (restart 0) KSP residual norm 3.516714e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.923e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.488132e-05\n",
       "  1 (restart 0) KSP residual norm 2.508528e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.955e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.152185e-04\n",
       "  1 (restart 0) KSP residual norm 3.603465e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.128e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.074978e-05\n",
       "  1 (restart 0) KSP residual norm 3.968854e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.373e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.096270e-04\n",
       "  1 (restart 0) KSP residual norm 4.508562e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.113e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.965179e-05\n",
       "  1 (restart 0) KSP residual norm 6.746055e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 7.525e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.099989e-04\n",
       "  1 (restart 0) KSP residual norm 6.925234e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.296e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.705788e-04\n",
       "  1 (restart 0) KSP residual norm 6.429244e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.769e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.534700e-04\n",
       "  1 (restart 0) KSP residual norm 6.379339e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.517e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.878533e-05\n",
       "  1 (restart 0) KSP residual norm 1.310992e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.664e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.515016e-04\n",
       "  1 (restart 0) KSP residual norm 3.260304e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.296e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.671827e-05\n",
       "  1 (restart 0) KSP residual norm 1.867384e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.153e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.046606e-04\n",
       "  1 (restart 0) KSP residual norm 1.038741e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.925e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.256156e-05\n",
       "  1 (restart 0) KSP residual norm 4.851667e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.242e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.203497e-04\n",
       "  1 (restart 0) KSP residual norm 2.738957e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.276e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.251496e-05\n",
       "  1 (restart 0) KSP residual norm 1.835081e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.224e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.308395e-04\n",
       "  1 (restart 0) KSP residual norm 1.307197e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.991e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.023857e-04\n",
       "  1 (restart 0) KSP residual norm 1.470549e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.436e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.508518e-04\n",
       "  1 (restart 0) KSP residual norm 2.292991e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.520e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.339324e-04\n",
       "  1 (restart 0) KSP residual norm 2.433975e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.040e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.816602e-05\n",
       "  1 (restart 0) KSP residual norm 8.239543e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.209e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.937770e-04\n",
       "  1 (restart 0) KSP residual norm 1.892050e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 9.764e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.655641e-05\n",
       "  1 (restart 0) KSP residual norm 9.150431e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.195e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.009874e-04\n",
       "  1 (restart 0) KSP residual norm 1.969877e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.951e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.677761e-05\n",
       "  1 (restart 0) KSP residual norm 8.512287e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.109e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.607073e-05\n",
       "  1 (restart 0) KSP residual norm 1.800862e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.875e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.036851e-04\n",
       "  1 (restart 0) KSP residual norm 2.109674e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.035e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.040499e-04\n",
       "  1 (restart 0) KSP residual norm 2.532076e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.434e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.107591e-04\n",
       "  1 (restart 0) KSP residual norm 1.459142e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.317e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.953242e-04\n",
       "  1 (restart 0) KSP residual norm 3.878125e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.985e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.769770e-05\n",
       "  1 (restart 0) KSP residual norm 6.044702e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.929e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.774365e-04\n",
       "  1 (restart 0) KSP residual norm 3.663812e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.065e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.358073e-05\n",
       "  1 (restart 0) KSP residual norm 2.913208e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.113e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.006120e-04\n",
       "  1 (restart 0) KSP residual norm 1.125884e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.119e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.695960e-05\n",
       "  1 (restart 0) KSP residual norm 2.009039e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.072e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.967297e-05\n",
       "  1 (restart 0) KSP residual norm 1.853346e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.067e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.359673e-05\n",
       "  1 (restart 0) KSP residual norm 2.349406e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.510e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.458354e-05\n",
       "  1 (restart 0) KSP residual norm 2.065996e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.184e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.423877e-04\n",
       "  1 (restart 0) KSP residual norm 1.903936e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.337e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.594554e-05\n",
       "  1 (restart 0) KSP residual norm 4.503679e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.930e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.445135e-04\n",
       "  1 (restart 0) KSP residual norm 1.733802e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.200e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.068099e-04\n",
       "  1 (restart 0) KSP residual norm 2.008271e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.880e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.591917e-05\n",
       "  1 (restart 0) KSP residual norm 2.520378e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.628e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.837073e-05\n",
       "  1 (restart 0) KSP residual norm 1.949305e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.206e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.700028e-05\n",
       "  1 (restart 0) KSP residual norm 4.876095e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.027e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.276339e-05\n",
       "  1 (restart 0) KSP residual norm 4.418520e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 5.339e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.245609e-04\n",
       "  1 (restart 0) KSP residual norm 3.385633e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.718e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 3.063464e-04\n",
       "  1 (restart 0) KSP residual norm 2.119235e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.918e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.454319e-05\n",
       "  1 (restart 0) KSP residual norm 6.546494e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.014e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 2.323007e-04\n",
       "  1 (restart 0) KSP residual norm 2.842927e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.224e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.606931e-05\n",
       "  1 (restart 0) KSP residual norm 2.998878e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.122e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.135330e-04\n",
       "  1 (restart 0) KSP residual norm 2.490585e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.194e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.260276e-04\n",
       "  1 (restart 0) KSP residual norm 8.364139e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.637e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.992643e-05\n",
       "  1 (restart 0) KSP residual norm 2.730015e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.036e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.781139e-04\n",
       "  1 (restart 0) KSP residual norm 2.994913e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.681e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.046534e-04\n",
       "  1 (restart 0) KSP residual norm 1.924447e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.839e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 6.972532e-05\n",
       "  1 (restart 0) KSP residual norm 1.037333e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.488e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.958399e-05\n",
       "  1 (restart 0) KSP residual norm 2.032675e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.041e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.493220e-05\n",
       "  1 (restart 0) KSP residual norm 4.025715e-16\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.740e-12)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.967210e-05\n",
       "  1 (restart 0) KSP residual norm 2.055351e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.580e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.710727e-05\n",
       "  1 (restart 0) KSP residual norm 3.227134e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 3.705e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.930938e-05\n",
       "  1 (restart 0) KSP residual norm 2.299432e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.899e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.914606e-05\n",
       "  1 (restart 0) KSP residual norm 2.536730e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.559e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.436524e-04\n",
       "  1 (restart 0) KSP residual norm 3.639788e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 2.534e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 7.497969e-05\n",
       "  1 (restart 0) KSP residual norm 4.705547e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 6.276e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.408716e-04\n",
       "  1 (restart 0) KSP residual norm 1.967739e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.397e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.056343e-04\n",
       "  1 (restart 0) KSP residual norm 4.369313e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 4.136e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.669148e-05\n",
       "  1 (restart 0) KSP residual norm 1.731308e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.997e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 9.372570e-05\n",
       "  1 (restart 0) KSP residual norm 1.178106e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.257e-10)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 8.639282e-05\n",
       "  1 (restart 0) KSP residual norm 7.365468e-15\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 8.526e-11)\n",
       "  Residual norms for GMRES solve\n",
       "  0 (restart 0) KSP residual norm 1.501833e-04\n",
       "  1 (restart 0) KSP residual norm 2.365713e-14\n",
       "GMRES solver converged in 1 iteration (avg. reduction factor: 1.575e-10)\n",
       " Found 8 converged eigenvalues (sigma = -7.218945e+04)\n",
       " eig 0: kn = 2.559608e+02-1.806622e-10i, n_eff = 1.998310e+00-1.410447e-12i\n",
       " eig 1: kn = 2.558666e+02+9.385745e-09i, n_eff = 1.997575e+00+7.327540e-11i\n",
       " eig 2: kn = 2.558326e+02-1.068329e-08i, n_eff = 1.997310e+00-8.340545e-11i\n",
       " eig 3: kn = 2.557573e+02+4.177738e-10i, n_eff = 1.996722e+00+3.261600e-12i\n",
       " eig 4: kn = 2.555874e+02-2.387020e-09i, n_eff = 1.995396e+00-1.863569e-11i\n",
       " eig 5: kn = 2.554697e+02-2.275356e-10i, n_eff = 1.994476e+00-1.776392e-12i\n",
       " eig 6: kn = 2.551943e+02+1.574730e-09i, n_eff = 1.992326e+00+1.229407e-11i\n",
       " eig 7: kn = 2.550083e+02-2.417824e-09i, n_eff = 1.990874e+00-1.887618e-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,      +3.199509e+01,      -2.258278e-11,      +1.998310e+00,      -1.410447e-12,      +2.732631e-13,      +5.852930e-11\n",
       "     2,      +3.198333e+01,      +1.173218e-09,      +1.997575e+00,      +7.327540e-11,      +6.307833e-13,      +1.341430e-10\n",
       "     3,      +3.197908e+01,      -1.335411e-09,      +1.997310e+00,      -8.340545e-11,      +1.049239e-12,      +2.225598e-10\n",
       "     4,      +3.196967e+01,      +5.222172e-11,      +1.996722e+00,      +3.261600e-12,      +1.803224e-13,      +3.803311e-11\n",
       "     5,      +3.194843e+01,      -2.983775e-10,      +1.995396e+00,      -1.863569e-11,      +5.227205e-13,      +1.088641e-10\n",
       "     6,      +3.193371e+01,      -2.844195e-11,      +1.994476e+00,      -1.776392e-12,      +1.275197e-13,      +2.632849e-11\n",
       "     7,      +3.189929e+01,      +1.968413e-10,      +1.992326e+00,      +1.229407e-11,      +5.437875e-13,      +1.100528e-10\n",
       "     8,      +3.187604e+01,      -3.022279e-10,      +1.990874e+00,      -1.887618e-11,      +1.302644e-13,      +2.601612e-11\n",
       "\n",
       "\n",
       "Completed 0 iterations of adaptive mesh refinement (AMR):\n",
       " Indicator norm = 5.024e-03, global unknowns = 41197\n",
       " Max. iterations = 0, tol. = 1.000e-02\n",
       "\n",
       "Estimated peak per-rank memory usage is: Min. 302.6M, Max. 302.6M, Avg. 302.6M, Total 302.6M\n",
       "Estimated peak per-node memory usage is: Min. 302.6M, Max. 302.6M, Avg. 302.6M, Total 302.6M\n",
       "\n",
       "Elapsed Time Report (s)           Min.        Max.        Avg.\n",
       "==============================================================\n",
       "Initialization                   0.010       0.010       0.010\n",
       "  Mesh Preprocessing             0.028       0.028       0.028\n",
       "Operator Construction            0.070       0.070       0.070\n",
       "  Preconditioner                 3.153       3.153       3.153\n",
       "Eigenvalue Solve                 0.625       0.625       0.625\n",
       "Estimation                       0.027       0.027       0.027\n",
       "  Construction                   0.093       0.093       0.093\n",
       "  Solve                          0.459       0.459       0.459\n",
       "Postprocessing                   1.539       1.539       1.539\n",
       "Disk IO                          0.009       0.009       0.009\n",
       "--------------------------------------------------------------\n",
       "Total                            6.276       6.276       6.276\n",
       "\n",
       "Peak Memory                   Per-Node       Total   Total HWM\n",
       "==============================================================\n",
       "Initialization                    2.4M        2.4M        2.4M\n",
       "  Mesh Preprocessing              3.2M        3.2M        5.6M\n",
       "Operator Construction            43.9M       43.9M       49.5M\n",
       "  Preconditioner                148.1M      148.1M      197.6M\n",
       "Eigenvalue Solve                 50.8M       50.8M      248.4M\n",
       "Estimation                        0.0K        0.0K      248.4M\n",
       "  Construction                   13.8M       13.8M      262.1M\n",
       "  Solve                           0.0K        0.0K      262.1M\n",
       "Postprocessing                    0.0K        0.0K      262.1M\n",
       "Disk IO                           2.5M        2.5M      264.7M\n",
       "--------------------------------------------------------------\n",
       "Total                           277.3M      277.3M      277.3M</pre></details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed differential microstrip modes:\n",
      "  Mode  1: kn=+31.995094 -0.000000j\n"
     ]
    }
   ],
   "source": [
    "mesh_file = make_differential_microstrip_mesh(\n",
    "    box_w=8.0,\n",
    "    h_sub=1.0,\n",
    "    h_air=3.0,\n",
    "    strip_w=0.8,\n",
    "    strip_gap=0.5,\n",
    "    strip_t=0.06,\n",
    ")\n",
    "view_mesh(mesh_file)\n",
    "\n",
    "# Ground and both strips are PEC; top/sides remain non-PEC.\n",
    "pec_bdr = [1, 2, 3]\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 differential microstrip 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": "efdc675a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:34.984471Z",
     "iopub.status.busy": "2026-08-04T14:29:34.984298Z",
     "iopub.status.idle": "2026-08-04T14:29:34.987445Z",
     "shell.execute_reply": "2026-08-04T14:29:34.986721Z"
    },
    "papermill": {
     "duration": 0.007286,
     "end_time": "2026-08-04T14:29:34.987974+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:34.980688+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": "c2433917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T14:29:34.994365Z",
     "iopub.status.busy": "2026-08-04T14:29:34.994222Z",
     "iopub.status.idle": "2026-08-04T14:29:34.997296Z",
     "shell.execute_reply": "2026-08-04T14:29:34.996508Z"
    },
    "papermill": {
     "duration": 0.006978,
     "end_time": "2026-08-04T14:29:34.997785+00:00",
     "exception": false,
     "start_time": "2026-08-04T14:29:34.990807+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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   "input_path": "docs/examples/differential_microstrip_modes.ipynb",
   "output_path": "docs/examples/differential_microstrip_modes.ipynb",
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   "start_time": "2026-08-04T14:29:26.793015+00:00",
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