{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wpV1wpKNrWQa",
        "outputId": "8b8682eb-f9eb-4bcb-ee44-4779accde4fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "se-CJM v5.9\n",
            "Navier-Stokes Atemporal Structural Persistence\n",
            "============================================================\n",
            "Physical system         : 3D incompressible Navier-Stokes\n",
            "Initial condition       : Taylor-Green vortex\n",
            "Grid                    : 20^3\n",
            "Snapshot time           : 4.0\n",
            "Re range                : 50.0 - 400.0\n",
            "CJM trajectory input    : NONE\n",
            "CJM previous-time input : NONE\n",
            "STEP 2 modification     : NONE\n",
            "Structural readout      : M_J, D_J, F_J + full E texture\n",
            "Hypercomputation claim  : NONE\n",
            "============================================================\n",
            "\n",
            "[Independent snapshot 1/15] Re = 50.0\n",
            "[Independent snapshot 2/15] Re = 75.0\n",
            "[Independent snapshot 3/15] Re = 100.0\n",
            "[Independent snapshot 4/15] Re = 125.0\n",
            "[Independent snapshot 5/15] Re = 150.0\n",
            "[Independent snapshot 6/15] Re = 175.0\n",
            "[Independent snapshot 7/15] Re = 200.0\n",
            "[Independent snapshot 8/15] Re = 225.0\n",
            "[Independent snapshot 9/15] Re = 250.0\n",
            "[Independent snapshot 10/15] Re = 275.0\n",
            "[Independent snapshot 11/15] Re = 300.0\n",
            "[Independent snapshot 12/15] Re = 325.0\n",
            "[Independent snapshot 13/15] Re = 350.0\n",
            "[Independent snapshot 14/15] Re = 375.0\n",
            "[Independent snapshot 15/15] Re = 400.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x540 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            "RESULT SUMMARY\n",
            "============================================================\n",
            "M_J = mean(E) range     : 0.3801 - 0.5123\n",
            "D_J = std(E) range      : 0.3275 - 0.4525\n",
            "F_J fragmentation range : 0.2000 - 0.8000\n",
            "encoded AND3 range      : 0.0427 - 0.2307\n",
            "viscous-control range   : 0.0976 - 0.3239\n",
            "Critical point planted  : NO\n",
            "Synthetic input         : NO\n",
            "Sequential CJM input    : NO\n",
            "STEP-2 retuning         : NO\n",
            "Singularity claim       : NONE\n",
            "Runtime                 : 24.7 seconds\n",
            "Saved figure            : secjm_v59_navier_atemporal_texture.png\n",
            "============================================================\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Re': 50.0,\n",
              "  'J_mean': 0.4940644689279006,\n",
              "  'D_J': 0.45253898497180384,\n",
              "  'F_J_count': 4,\n",
              "  'F_J': 0.2,\n",
              "  'energy_median': 0.4987336560127002,\n",
              "  'raw_density': 0.2307142857142857,\n",
              "  'enstrophy': 0.36140104332255635,\n",
              "  'omega_max': 1.822196858611924,\n",
              "  'high_k': 0.03381923818808271,\n",
              "  'positive_stretch': 0.594125,\n",
              "  'viscous_control': 0.323875,\n",
              "  'energy': array([3.69537724e-01, 3.02883910e-04, 2.73191401e-03, 9.98851679e-01,\n",
              "         1.00000000e+00, 6.27929588e-01, 1.00000000e+00, 9.98851679e-01,\n",
              "         2.73191401e-03, 0.00000000e+00, 2.48649457e-01, 3.02883910e-04,\n",
              "         2.73191401e-03, 9.98851679e-01, 1.00000000e+00, 6.27929588e-01,\n",
              "         1.00000000e+00, 9.98851679e-01, 2.73191401e-03, 3.02883910e-04])},\n",
              " {'Re': 75.0,\n",
              "  'J_mean': 0.48606367827879005,\n",
              "  'D_J': 0.4155783262612654,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.5084944542229429,\n",
              "  'raw_density': 0.13166666666666665,\n",
              "  'enstrophy': 0.5062010527161717,\n",
              "  'omega_max': 2.3828755118846536,\n",
              "  'high_k': 0.05148861970953687,\n",
              "  'positive_stretch': 0.55025,\n",
              "  'viscous_control': 0.232125,\n",
              "  'energy': array([0.91603222, 0.        , 0.04058929, 1.        , 0.50849445,\n",
              "         0.93271011, 0.50849445, 1.        , 0.04058929, 0.        ,\n",
              "         0.74816795, 0.        , 0.04058929, 1.        , 0.50849445,\n",
              "         0.93271011, 0.50849445, 1.        , 0.03590748, 0.        ])},\n",
              " {'Re': 100.0,\n",
              "  'J_mean': 0.5123132160072499,\n",
              "  'D_J': 0.4309755295410545,\n",
              "  'F_J_count': 10,\n",
              "  'F_J': 0.5,\n",
              "  'energy_median': 0.6166074676982247,\n",
              "  'raw_density': 0.11892857142857143,\n",
              "  'enstrophy': 0.614067216097807,\n",
              "  'omega_max': 2.803997592163015,\n",
              "  'high_k': 0.06349285902802541,\n",
              "  'positive_stretch': 0.55,\n",
              "  'viscous_control': 0.208,\n",
              "  'energy': array([0.71972537, 0.0021324 , 0.0125139 , 0.98285145, 0.92657114,\n",
              "         0.61660747, 0.92657114, 1.        , 0.0125139 , 0.        ,\n",
              "         0.56471211, 0.00443645, 0.0125139 , 0.98285145, 0.92657114,\n",
              "         0.61660747, 0.92657114, 1.        , 0.0125139 , 0.        ])},\n",
              " {'Re': 125.0,\n",
              "  'J_mean': 0.4366233583386105,\n",
              "  'D_J': 0.36725709602989315,\n",
              "  'F_J_count': 12,\n",
              "  'F_J': 0.6,\n",
              "  'energy_median': 0.4999999999994401,\n",
              "  'raw_density': 0.08916666666666667,\n",
              "  'enstrophy': 0.6968814267720004,\n",
              "  'omega_max': 3.12470775794033,\n",
              "  'high_k': 0.07198874754271635,\n",
              "  'positive_stretch': 0.524625,\n",
              "  'viscous_control': 0.166625,\n",
              "  'energy': array([1.        , 0.06623567, 0.        , 0.86839333, 0.51132905,\n",
              "         0.48867095, 0.51132905, 0.88090291, 0.        , 0.05745724,\n",
              "         0.94254276, 0.06623567, 0.        , 0.88090291, 0.51132905,\n",
              "         0.48867095, 0.51132905, 0.88090291, 0.        , 0.06623567])},\n",
              " {'Re': 150.0,\n",
              "  'J_mean': 0.4461859482682253,\n",
              "  'D_J': 0.38051020850439815,\n",
              "  'F_J_count': 12,\n",
              "  'F_J': 0.6,\n",
              "  'energy_median': 0.5675994437567642,\n",
              "  'raw_density': 0.08071428571428571,\n",
              "  'enstrophy': 0.7622481449134696,\n",
              "  'omega_max': 3.3586164453268506,\n",
              "  'high_k': 0.07827753353445248,\n",
              "  'positive_stretch': 0.519,\n",
              "  'viscous_control': 0.148,\n",
              "  'energy': array([0.73105749, 0.0312608 , 0.        , 1.        , 0.58046017,\n",
              "         0.55473872, 0.58046017, 0.98579914, 0.        , 0.0312608 ,\n",
              "         0.65703926, 0.03892343, 0.        , 1.        , 0.58046017,\n",
              "         0.55473872, 0.58046017, 0.98579914, 0.        , 0.0312608 ])},\n",
              " {'Re': 175.0,\n",
              "  'J_mean': 0.4659850241985638,\n",
              "  'D_J': 0.3671609034512313,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.4655217965514138,\n",
              "  'raw_density': 0.07142857142857142,\n",
              "  'enstrophy': 0.8150658348784265,\n",
              "  'omega_max': 3.5356611937443128,\n",
              "  'high_k': 0.08310920255344223,\n",
              "  'positive_stretch': 0.51575,\n",
              "  'viscous_control': 0.13775,\n",
              "  'energy': array([0.65557346, 0.        , 0.136299  , 0.99425721, 0.4655218 ,\n",
              "         0.75851997, 0.4655218 , 0.99425721, 0.136299  , 0.        ,\n",
              "         0.73929578, 0.        , 0.136299  , 1.        , 0.4655218 ,\n",
              "         0.75851997, 0.4655218 , 0.99425721, 0.1540355 , 0.        ])},\n",
              " {'Re': 200.0,\n",
              "  'J_mean': 0.4866462492310551,\n",
              "  'D_J': 0.4023666074807425,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.386066138247132,\n",
              "  'raw_density': 0.07285714285714286,\n",
              "  'enstrophy': 0.8585884359404584,\n",
              "  'omega_max': 3.6738573905911767,\n",
              "  'high_k': 0.0869339290154782,\n",
              "  'positive_stretch': 0.50625,\n",
              "  'viscous_control': 0.13225,\n",
              "  'energy': array([0.97594616, 0.        , 0.13763067, 0.99729374, 0.38606614,\n",
              "         0.83314685, 0.38606614, 0.99437238, 0.11566005, 0.01726814,\n",
              "         1.        , 0.        , 0.13763067, 0.99729374, 0.38606614,\n",
              "         0.83314685, 0.38606614, 0.99437238, 0.13763067, 0.01726814])},\n",
              " {'Re': 225.0,\n",
              "  'J_mean': 0.4472583509505256,\n",
              "  'D_J': 0.3819354973172818,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.38711377035688016,\n",
              "  'raw_density': 0.06583333333333333,\n",
              "  'enstrophy': 0.8950482755999524,\n",
              "  'omega_max': 3.7845011222691456,\n",
              "  'high_k': 0.09003541743896556,\n",
              "  'positive_stretch': 0.504875,\n",
              "  'viscous_control': 0.122875,\n",
              "  'energy': array([0.92260545, 0.        , 0.11597391, 1.        , 0.38711377,\n",
              "         0.51663397, 0.38711377, 0.99050019, 0.13800415, 0.        ,\n",
              "         0.94531153, 0.        , 0.11597391, 1.        , 0.38711377,\n",
              "         0.51663397, 0.38711377, 0.99707072, 0.13800415, 0.        ])},\n",
              " {'Re': 250.0,\n",
              "  'J_mean': 0.43825066766683446,\n",
              "  'D_J': 0.38789420497454186,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.4099991761616441,\n",
              "  'raw_density': 0.06535714285714286,\n",
              "  'enstrophy': 0.9260225297231353,\n",
              "  'omega_max': 3.8749674342777314,\n",
              "  'high_k': 0.09260052634671995,\n",
              "  'positive_stretch': 0.500125,\n",
              "  'viscous_control': 0.122125,\n",
              "  'energy': array([0.91612303, 0.        , 0.06613428, 0.99253137, 0.40999918,\n",
              "         0.4866456 , 0.40999918, 1.        , 0.0846596 , 0.        ,\n",
              "         0.93361145, 0.0153405 , 0.06613428, 1.        , 0.40999918,\n",
              "         0.4866456 , 0.40999918, 0.99253137, 0.0846596 , 0.        ])},\n",
              " {'Re': 275.0,\n",
              "  'J_mean': 0.46455346806484127,\n",
              "  'D_J': 0.39592475839539104,\n",
              "  'F_J_count': 12,\n",
              "  'F_J': 0.6,\n",
              "  'energy_median': 0.5536460613571677,\n",
              "  'raw_density': 0.06833333333333333,\n",
              "  'enstrophy': 0.9526548138511589,\n",
              "  'omega_max': 3.9502490643922066,\n",
              "  'high_k': 0.09475707526773411,\n",
              "  'positive_stretch': 0.508375,\n",
              "  'viscous_control': 0.133875,\n",
              "  'energy': array([0.91858079, 0.        , 0.04534746, 0.95360459, 0.56692179,\n",
              "         0.54037033, 0.56692179, 0.96988   , 0.03506061, 0.        ,\n",
              "         1.        , 0.        , 0.04534746, 0.96988   , 0.56692179,\n",
              "         0.54037033, 0.56692179, 0.96988   , 0.03506061, 0.        ])},\n",
              " {'Re': 300.0,\n",
              "  'J_mean': 0.43191040292499966,\n",
              "  'D_J': 0.35236040245754746,\n",
              "  'F_J_count': 12,\n",
              "  'F_J': 0.6,\n",
              "  'energy_median': 0.5933224806148157,\n",
              "  'raw_density': 0.06523809523809523,\n",
              "  'enstrophy': 0.9757934603137065,\n",
              "  'omega_max': 4.013834685789226,\n",
              "  'high_k': 0.09659535598854489,\n",
              "  'positive_stretch': 0.50675,\n",
              "  'viscous_control': 0.12875,\n",
              "  'energy': array([0.90053901, 0.        , 0.03076841, 0.67566921, 0.70270316,\n",
              "         0.51097576, 0.70270316, 0.70270316, 0.03985428, 0.        ,\n",
              "         1.        , 0.00691482, 0.03076841, 0.70270316, 0.70270316,\n",
              "         0.51097576, 0.70270316, 0.67566921, 0.03985428, 0.        ])},\n",
              " {'Re': 325.0,\n",
              "  'J_mean': 0.40699159161760645,\n",
              "  'D_J': 0.3440605832120488,\n",
              "  'F_J_count': 8,\n",
              "  'F_J': 0.4,\n",
              "  'energy_median': 0.4630454646466466,\n",
              "  'raw_density': 0.062380952380952384,\n",
              "  'enstrophy': 0.9960804598445212,\n",
              "  'omega_max': 4.068229956899938,\n",
              "  'high_k': 0.09818092353952788,\n",
              "  'positive_stretch': 0.50275,\n",
              "  'viscous_control': 0.12375,\n",
              "  'energy': array([0.99468052, 0.        , 0.05872796, 0.46304546, 0.73536311,\n",
              "         0.58365461, 0.73536311, 0.46304546, 0.04559438, 0.        ,\n",
              "         1.        , 0.        , 0.05872796, 0.46304546, 0.73536311,\n",
              "         0.58365461, 0.73536311, 0.43860855, 0.04559438, 0.        ])},\n",
              " {'Re': 350.0,\n",
              "  'J_mean': 0.3909328310869224,\n",
              "  'D_J': 0.32745256030713765,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.4099991761616441,\n",
              "  'raw_density': 0.0455952380952381,\n",
              "  'enstrophy': 1.0140102815556455,\n",
              "  'omega_max': 4.115278073926653,\n",
              "  'high_k': 0.09956250713087202,\n",
              "  'positive_stretch': 0.483625,\n",
              "  'viscous_control': 0.097625,\n",
              "  'energy': array([0.97383394, 0.06613428, 0.0153405 , 0.58505936, 0.40999918,\n",
              "         0.75386408, 0.40999918, 0.58505936, 0.0153405 , 0.06613428,\n",
              "         1.        , 0.06613428, 0.        , 0.63136114, 0.40999918,\n",
              "         0.75386408, 0.40999918, 0.58505936, 0.0153405 , 0.06613428])},\n",
              " {'Re': 375.0,\n",
              "  'J_mean': 0.4284411764370201,\n",
              "  'D_J': 0.34838988352661826,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.2422782090996629,\n",
              "  'raw_density': 0.042738095238095235,\n",
              "  'enstrophy': 1.02996976034332,\n",
              "  'omega_max': 4.15636356343611,\n",
              "  'high_k': 0.1007770749723361,\n",
              "  'positive_stretch': 0.484625,\n",
              "  'viscous_control': 0.098625,\n",
              "  'energy': array([0.97996083, 0.24227821, 0.        , 0.76045936, 0.21095938,\n",
              "         0.84825375, 0.21095938, 0.77729161, 0.02861384, 0.24227821,\n",
              "         1.        , 0.24227821, 0.        , 0.76045936, 0.21095938,\n",
              "         0.84825375, 0.21095938, 0.72396683, 0.02861384, 0.24227821])},\n",
              " {'Re': 400.0,\n",
              "  'J_mean': 0.38014573199160917,\n",
              "  'D_J': 0.36820144221197076,\n",
              "  'F_J_count': 16,\n",
              "  'F_J': 0.8,\n",
              "  'energy_median': 0.17220854288217788,\n",
              "  'raw_density': 0.04404761904761905,\n",
              "  'enstrophy': 1.0442657622377032,\n",
              "  'omega_max': 4.192545711287917,\n",
              "  'high_k': 0.10185317670594375,\n",
              "  'positive_stretch': 0.48925,\n",
              "  'viscous_control': 0.103,\n",
              "  'energy': array([1.        , 0.        , 0.17220854, 0.70884696, 0.10253936,\n",
              "         0.8333556 , 0.10253936, 0.72970625, 0.13731694, 0.        ,\n",
              "         0.99713727, 0.        , 0.17220854, 0.74950246, 0.10253936,\n",
              "         0.8333556 , 0.10253936, 0.68691049, 0.17220854, 0.        ])}]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# ============================================================\n",
        "# Appendix 1: Python Code for Navier-Stokes Figure (se-CJM v5.9)\n",
        "# ============================================================\n",
        "# se-CJM (software-emulated CJM) implements a 4-step pipeline:\n",
        "#   (1) Navier-Stokes snapshot embedding -> logic-like literals (x1, x2)\n",
        "#   (2) CJM core engine                  -> plane-wise admissibility energy\n",
        "#   (3) Structural readout              -> dispersion and fragmentation\n",
        "#   (4) Visualization                   -> physical loss + CJM texture map\n",
        "#\n",
        "# This appendix presents the primary Navier-Stokes snapshot scan\n",
        "# used in Figure 1. Each Reynolds-number case is evaluated from one\n",
        "# static flow state u(x,t0), while the CJM discrimination stage does\n",
        "# not use previous or future flow states.\n",
        "#\n",
        "# Unlike the earlier scalar visualization, the present figure retains\n",
        "# the full STEP-2 output vector across structural planes. The resulting\n",
        "# texture map is used to visualize changes in internal admissibility\n",
        "# organization rather than reducing each snapshot to a single mean.\n",
        "#\n",
        "# The texture is not interpreted as evidence of singularity or\n",
        "# finite-time blow-up. It is used only as a structural visualization\n",
        "# of how a fixed CJM core organizes independent Navier-Stokes snapshots.\n",
        "# ============================================================\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "\n",
        "TINY = 1e-12\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# User settings\n",
        "# ==========================================\n",
        "N = 20\n",
        "\n",
        "RE_VALUES = np.arange(50.0, 401.0, 25.0)\n",
        "\n",
        "SNAPSHOT_TIME = 4.0\n",
        "DT = 0.02\n",
        "\n",
        "# Universal CJM core settings -- fixed across this scan\n",
        "CJM_GATE = \"AND3\"\n",
        "CJM_BETA = 34.0\n",
        "CJM_MODE = \"logistic\"\n",
        "\n",
        "SAVE_PATH = \"secjm_v59_navier_atemporal_texture.png\"\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Utility normalization\n",
        "# ==========================================\n",
        "def normalize01(y):\n",
        "    \"\"\"\n",
        "    Normalize an array to [0,1] for visualization.\n",
        "    \"\"\"\n",
        "    y = np.asarray(y, dtype=float)\n",
        "\n",
        "    return (\n",
        "        (y - np.min(y))\n",
        "        /\n",
        "        (np.max(y) - np.min(y) + TINY)\n",
        "    )\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Fourier grid for periodic Navier-Stokes flow\n",
        "# ==========================================\n",
        "def make_spectral_grid(N):\n",
        "    \"\"\"\n",
        "    Construct the Fourier grid on the periodic domain [0,2pi]^3.\n",
        "\n",
        "    Returns:\n",
        "      KX, KY, KZ : wave-number grids\n",
        "      K2         : squared wave-number magnitude\n",
        "      K2_SAFE    : zero-safe K2\n",
        "      K_MAG      : wave-number magnitude\n",
        "      DEALIAS    : 2/3-rule dealiasing mask\n",
        "      K_CUT      : dealiased cutoff\n",
        "    \"\"\"\n",
        "    k = np.fft.fftfreq(N, d=1.0 / N)\n",
        "\n",
        "    KX, KY, KZ = np.meshgrid(\n",
        "        k, k, k,\n",
        "        indexing=\"ij\"\n",
        "    )\n",
        "\n",
        "    K2 = (\n",
        "        KX**2\n",
        "        +\n",
        "        KY**2\n",
        "        +\n",
        "        KZ**2\n",
        "    )\n",
        "\n",
        "    K2_SAFE = K2.copy()\n",
        "    K2_SAFE[0, 0, 0] = 1.0\n",
        "\n",
        "    K_MAG = np.sqrt(K2)\n",
        "\n",
        "    K_CUT = N // 3\n",
        "\n",
        "    DEALIAS = (\n",
        "        (np.abs(KX) <= K_CUT)\n",
        "        &\n",
        "        (np.abs(KY) <= K_CUT)\n",
        "        &\n",
        "        (np.abs(KZ) <= K_CUT)\n",
        "    )\n",
        "\n",
        "    return (\n",
        "        KX, KY, KZ,\n",
        "        K2, K2_SAFE,\n",
        "        K_MAG,\n",
        "        DEALIAS,\n",
        "        K_CUT\n",
        "    )\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Divergence-free Fourier projection\n",
        "# ==========================================\n",
        "def project_divergence_free(\n",
        "    u_hat,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2_SAFE\n",
        "):\n",
        "    \"\"\"\n",
        "    Apply the Fourier-space Leray projection.\n",
        "\n",
        "    This enforces the incompressibility condition\n",
        "\n",
        "        div u = 0.\n",
        "    \"\"\"\n",
        "    k_dot_u = (\n",
        "        KX * u_hat[0]\n",
        "        +\n",
        "        KY * u_hat[1]\n",
        "        +\n",
        "        KZ * u_hat[2]\n",
        "    )\n",
        "\n",
        "    out = u_hat.copy()\n",
        "\n",
        "    out[0] -= KX * k_dot_u / K2_SAFE\n",
        "    out[1] -= KY * k_dot_u / K2_SAFE\n",
        "    out[2] -= KZ * k_dot_u / K2_SAFE\n",
        "\n",
        "    # Remove the mean-velocity mode\n",
        "    out[:, 0, 0, 0] = 0.0\n",
        "\n",
        "    return out\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Taylor-Green initial condition\n",
        "# ==========================================\n",
        "def taylor_green_initial_condition(\n",
        "    N,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2_SAFE,\n",
        "    DEALIAS\n",
        "):\n",
        "    \"\"\"\n",
        "    Standard three-dimensional Taylor-Green vortex:\n",
        "\n",
        "      u =  sin(x) cos(y) cos(z)\n",
        "      v = -cos(x) sin(y) cos(z)\n",
        "      w =  0\n",
        "\n",
        "    The field is projected and dealiased before integration.\n",
        "    \"\"\"\n",
        "    x = 2.0 * np.pi * np.arange(N) / N\n",
        "\n",
        "    X, Y, Z = np.meshgrid(\n",
        "        x, x, x,\n",
        "        indexing=\"ij\"\n",
        "    )\n",
        "\n",
        "    u = np.zeros(\n",
        "        (3, N, N, N),\n",
        "        dtype=float\n",
        "    )\n",
        "\n",
        "    u[0] = (\n",
        "        np.sin(X)\n",
        "        *\n",
        "        np.cos(Y)\n",
        "        *\n",
        "        np.cos(Z)\n",
        "    )\n",
        "\n",
        "    u[1] = (\n",
        "        -np.cos(X)\n",
        "        *\n",
        "        np.sin(Y)\n",
        "        *\n",
        "        np.cos(Z)\n",
        "    )\n",
        "\n",
        "    u[2] = 0.0\n",
        "\n",
        "    u_hat = np.fft.fftn(\n",
        "        u,\n",
        "        axes=(1, 2, 3)\n",
        "    )\n",
        "\n",
        "    u_hat = project_divergence_free(\n",
        "        u_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE\n",
        "    )\n",
        "\n",
        "    u_hat *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    return u_hat\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Spectral vorticity\n",
        "# ==========================================\n",
        "def curl_hat(\n",
        "    u_hat,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ\n",
        "):\n",
        "    \"\"\"\n",
        "    Compute Fourier-space vorticity\n",
        "\n",
        "        omega_hat = i k x u_hat.\n",
        "    \"\"\"\n",
        "    omega_hat = np.empty_like(u_hat)\n",
        "\n",
        "    omega_hat[0] = (\n",
        "        1j\n",
        "        *\n",
        "        (\n",
        "            KY * u_hat[2]\n",
        "            -\n",
        "            KZ * u_hat[1]\n",
        "        )\n",
        "    )\n",
        "\n",
        "    omega_hat[1] = (\n",
        "        1j\n",
        "        *\n",
        "        (\n",
        "            KZ * u_hat[0]\n",
        "            -\n",
        "            KX * u_hat[2]\n",
        "        )\n",
        "    )\n",
        "\n",
        "    omega_hat[2] = (\n",
        "        1j\n",
        "        *\n",
        "        (\n",
        "            KX * u_hat[1]\n",
        "            -\n",
        "            KY * u_hat[0]\n",
        "        )\n",
        "    )\n",
        "\n",
        "    return omega_hat\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Navier-Stokes nonlinear term\n",
        "# ==========================================\n",
        "def nonlinear_term_hat(\n",
        "    u_hat,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2_SAFE,\n",
        "    DEALIAS\n",
        "):\n",
        "    \"\"\"\n",
        "    Evaluate the projected rotational nonlinear term\n",
        "\n",
        "        P(u x omega).\n",
        "\n",
        "    The resulting field is dealiased and projected.\n",
        "    \"\"\"\n",
        "    omega_hat = curl_hat(\n",
        "        u_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ\n",
        "    )\n",
        "\n",
        "    u = np.fft.ifftn(\n",
        "        u_hat,\n",
        "        axes=(1, 2, 3)\n",
        "    ).real\n",
        "\n",
        "    omega = np.fft.ifftn(\n",
        "        omega_hat,\n",
        "        axes=(1, 2, 3)\n",
        "    ).real\n",
        "\n",
        "    cross = np.empty_like(u)\n",
        "\n",
        "    cross[0] = (\n",
        "        u[1] * omega[2]\n",
        "        -\n",
        "        u[2] * omega[1]\n",
        "    )\n",
        "\n",
        "    cross[1] = (\n",
        "        u[2] * omega[0]\n",
        "        -\n",
        "        u[0] * omega[2]\n",
        "    )\n",
        "\n",
        "    cross[2] = (\n",
        "        u[0] * omega[1]\n",
        "        -\n",
        "        u[1] * omega[0]\n",
        "    )\n",
        "\n",
        "    nonlinear_hat = np.fft.fftn(\n",
        "        cross,\n",
        "        axes=(1, 2, 3)\n",
        "    )\n",
        "\n",
        "    nonlinear_hat *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    nonlinear_hat = project_divergence_free(\n",
        "        nonlinear_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE\n",
        "    )\n",
        "\n",
        "    return nonlinear_hat\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Navier-Stokes right-hand side\n",
        "# ==========================================\n",
        "def navier_stokes_rhs(\n",
        "    u_hat,\n",
        "    nu,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2,\n",
        "    K2_SAFE,\n",
        "    DEALIAS\n",
        "):\n",
        "    \"\"\"\n",
        "    Evaluate the Fourier-space Navier-Stokes right-hand side:\n",
        "\n",
        "        du/dt = P(u x omega) - nu k^2 u.\n",
        "    \"\"\"\n",
        "    nonlinear_hat = nonlinear_term_hat(\n",
        "        u_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "        DEALIAS\n",
        "    )\n",
        "\n",
        "    viscous_hat = (\n",
        "        -nu\n",
        "        *\n",
        "        K2[None, :, :, :]\n",
        "        *\n",
        "        u_hat\n",
        "    )\n",
        "\n",
        "    return (\n",
        "        nonlinear_hat\n",
        "        +\n",
        "        viscous_hat\n",
        "    )\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# SSP-RK3 time integrator\n",
        "# ==========================================\n",
        "def rk3_step(\n",
        "    u_hat,\n",
        "    dt,\n",
        "    nu,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2,\n",
        "    K2_SAFE,\n",
        "    DEALIAS\n",
        "):\n",
        "    \"\"\"\n",
        "    Advance one conventional Navier-Stokes integration step\n",
        "    using a three-stage SSP-RK3 scheme.\n",
        "    \"\"\"\n",
        "    # Stage 1\n",
        "    f0 = navier_stokes_rhs(\n",
        "        u_hat,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS\n",
        "    )\n",
        "\n",
        "    u1 = (\n",
        "        u_hat\n",
        "        +\n",
        "        dt * f0\n",
        "    )\n",
        "\n",
        "    u1 = project_divergence_free(\n",
        "        u1,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE\n",
        "    )\n",
        "\n",
        "    u1 *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    # Stage 2\n",
        "    f1 = navier_stokes_rhs(\n",
        "        u1,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS\n",
        "    )\n",
        "\n",
        "    u2 = (\n",
        "        0.75 * u_hat\n",
        "        +\n",
        "        0.25\n",
        "        *\n",
        "        (\n",
        "            u1\n",
        "            +\n",
        "            dt * f1\n",
        "        )\n",
        "    )\n",
        "\n",
        "    u2 = project_divergence_free(\n",
        "        u2,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE\n",
        "    )\n",
        "\n",
        "    u2 *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    # Stage 3\n",
        "    f2 = navier_stokes_rhs(\n",
        "        u2,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS\n",
        "    )\n",
        "\n",
        "    u_new = (\n",
        "        (1.0 / 3.0) * u_hat\n",
        "        +\n",
        "        (2.0 / 3.0)\n",
        "        *\n",
        "        (\n",
        "            u2\n",
        "            +\n",
        "            dt * f2\n",
        "        )\n",
        "    )\n",
        "\n",
        "    u_new = project_divergence_free(\n",
        "        u_new,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE\n",
        "    )\n",
        "\n",
        "    u_new *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    return u_new\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Static physical snapshot generation\n",
        "# ==========================================\n",
        "def generate_snapshot(\n",
        "    Re,\n",
        "    grid\n",
        "):\n",
        "    \"\"\"\n",
        "    Generate one Navier-Stokes state u(x,t0) for a given Reynolds number.\n",
        "\n",
        "    Conventional temporal integration is used only to generate the\n",
        "    reference physical snapshot.\n",
        "\n",
        "    No intermediate flow state is sent to the CJM discrimination stage.\n",
        "    \"\"\"\n",
        "    (\n",
        "        KX, KY, KZ,\n",
        "        K2, K2_SAFE,\n",
        "        K_MAG,\n",
        "        DEALIAS,\n",
        "        K_CUT\n",
        "    ) = grid\n",
        "\n",
        "    nu = 1.0 / Re\n",
        "\n",
        "    u_hat = taylor_green_initial_condition(\n",
        "        N,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "        DEALIAS\n",
        "    )\n",
        "\n",
        "    n_steps = int(\n",
        "        np.round(\n",
        "            SNAPSHOT_TIME / DT\n",
        "        )\n",
        "    )\n",
        "\n",
        "    for _ in range(n_steps):\n",
        "        u_hat = rk3_step(\n",
        "            u_hat,\n",
        "            DT,\n",
        "            nu,\n",
        "            KX,\n",
        "            KY,\n",
        "            KZ,\n",
        "            K2,\n",
        "            K2_SAFE,\n",
        "            DEALIAS\n",
        "        )\n",
        "\n",
        "    return u_hat, nu\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Snapshot structural quantities\n",
        "# ==========================================\n",
        "def snapshot_structural_fields(\n",
        "    u_hat,\n",
        "    nu,\n",
        "    grid\n",
        "):\n",
        "    \"\"\"\n",
        "    Extract instantaneous structural quantities from one static snapshot.\n",
        "\n",
        "    Primary fields:\n",
        "      - vortex stretching: omega . S . omega\n",
        "      - viscous structural proxy: nu |grad omega|^2\n",
        "\n",
        "    Additional diagnostics:\n",
        "      - enstrophy\n",
        "      - maximum vorticity\n",
        "      - high-k energy fraction\n",
        "      - positive-stretch fraction\n",
        "      - viscous-control fraction\n",
        "\n",
        "    Note:\n",
        "      nu |grad omega|^2 is used as a positive local structural proxy\n",
        "      for viscous enstrophy loss. Pointwise, it is not identical to\n",
        "\n",
        "          nu omega . Delta omega.\n",
        "\n",
        "      On a periodic domain,\n",
        "\n",
        "          integral omega . Delta omega dV\n",
        "            = - integral |grad omega|^2 dV.\n",
        "\n",
        "      The local comparison used here is therefore a structural\n",
        "      heuristic rather than an exact pointwise enstrophy balance.\n",
        "    \"\"\"\n",
        "    (\n",
        "        KX, KY, KZ,\n",
        "        K2, K2_SAFE,\n",
        "        K_MAG,\n",
        "        DEALIAS,\n",
        "        K_CUT\n",
        "    ) = grid\n",
        "\n",
        "    Ks = [\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ\n",
        "    ]\n",
        "\n",
        "    omega_hat = curl_hat(\n",
        "        u_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ\n",
        "    )\n",
        "\n",
        "    omega = np.fft.ifftn(\n",
        "        omega_hat,\n",
        "        axes=(1, 2, 3)\n",
        "    ).real\n",
        "\n",
        "    # Velocity-gradient tensor\n",
        "    grad_u = np.empty(\n",
        "        (3, 3, N, N, N),\n",
        "        dtype=float\n",
        "    )\n",
        "\n",
        "    for i in range(3):\n",
        "        for j in range(3):\n",
        "            grad_u[i, j] = np.fft.ifftn(\n",
        "                1j\n",
        "                *\n",
        "                Ks[j]\n",
        "                *\n",
        "                u_hat[i]\n",
        "            ).real\n",
        "\n",
        "    # Strain tensor\n",
        "    strain = (\n",
        "        0.5\n",
        "        *\n",
        "        (\n",
        "            grad_u\n",
        "            +\n",
        "            np.swapaxes(\n",
        "                grad_u,\n",
        "                0,\n",
        "                1\n",
        "            )\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Vortex stretching: omega . S . omega\n",
        "    S_omega = np.einsum(\n",
        "        \"ijxyz,jxyz->ixyz\",\n",
        "        strain,\n",
        "        omega,\n",
        "        optimize=True\n",
        "    )\n",
        "\n",
        "    stretching = np.sum(\n",
        "        omega\n",
        "        *\n",
        "        S_omega,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    # Vorticity-gradient tensor\n",
        "    grad_omega = np.empty(\n",
        "        (3, 3, N, N, N),\n",
        "        dtype=float\n",
        "    )\n",
        "\n",
        "    for i in range(3):\n",
        "        for j in range(3):\n",
        "            grad_omega[i, j] = np.fft.ifftn(\n",
        "                1j\n",
        "                *\n",
        "                Ks[j]\n",
        "                *\n",
        "                omega_hat[i]\n",
        "            ).real\n",
        "\n",
        "    # Positive viscous structural proxy\n",
        "    viscous_dissipation = (\n",
        "        nu\n",
        "        *\n",
        "        np.sum(\n",
        "            grad_omega\n",
        "            *\n",
        "            grad_omega,\n",
        "            axis=(0, 1)\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Enstrophy and maximum vorticity\n",
        "    omega_sq = np.sum(\n",
        "        omega\n",
        "        *\n",
        "        omega,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    enstrophy = (\n",
        "        0.5\n",
        "        *\n",
        "        np.mean(\n",
        "            omega_sq\n",
        "        )\n",
        "    )\n",
        "\n",
        "    omega_max = np.max(\n",
        "        np.sqrt(\n",
        "            omega_sq\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # High-wave-number energy fraction\n",
        "    spectral_energy = np.sum(\n",
        "        np.abs(u_hat) ** 2,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    sphere_mask = (\n",
        "        DEALIAS\n",
        "        &\n",
        "        (K_MAG <= K_CUT)\n",
        "    )\n",
        "\n",
        "    high_mask = (\n",
        "        sphere_mask\n",
        "        &\n",
        "        (\n",
        "            K_MAG\n",
        "            >=\n",
        "            0.65 * K_CUT\n",
        "        )\n",
        "    )\n",
        "\n",
        "    high_k_fraction = (\n",
        "        np.sum(\n",
        "            spectral_energy[\n",
        "                high_mask\n",
        "            ]\n",
        "        )\n",
        "        /\n",
        "        (\n",
        "            np.sum(\n",
        "                spectral_energy[\n",
        "                    sphere_mask\n",
        "                ]\n",
        "            )\n",
        "            +\n",
        "            TINY\n",
        "        )\n",
        "    )\n",
        "\n",
        "    positive_stretch_fraction = np.mean(\n",
        "        stretching > 0.0\n",
        "    )\n",
        "\n",
        "    viscous_control_fraction = np.mean(\n",
        "        (stretching > 0.0)\n",
        "        &\n",
        "        (\n",
        "            viscous_dissipation\n",
        "            >=\n",
        "            stretching\n",
        "        )\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"stretching\": stretching,\n",
        "        \"viscous_dissipation\": viscous_dissipation,\n",
        "        \"enstrophy\": float(enstrophy),\n",
        "        \"omega_max\": float(omega_max),\n",
        "        \"high_k\": float(high_k_fraction),\n",
        "        \"positive_stretch_fraction\": float(positive_stretch_fraction),\n",
        "        \"viscous_control_fraction\": float(viscous_control_fraction),\n",
        "    }\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 1: INPUT MODULE (Navier-Stokes snapshot embedding)\n",
        "# ==========================================\n",
        "def snapshot_to_literal_matrix(\n",
        "    stretching,\n",
        "    viscous_dissipation\n",
        "):\n",
        "    \"\"\"\n",
        "    Convert one static Navier-Stokes snapshot into literals x1, x2.\n",
        "\n",
        "    Interpretation:\n",
        "      - x1 = 1 for positive local vortex stretching\n",
        "      - x2 = 1 where the viscous structural proxy is at least as\n",
        "             large as the positive stretching term\n",
        "      - axis 0 is spatial ordering, not time\n",
        "      - columns represent z-planes of the same static snapshot\n",
        "\n",
        "    A zero spacer separates neighboring y-rows so STEP 2's x1_prev\n",
        "    coupling does not cross artificial row boundaries.\n",
        "\n",
        "    Note:\n",
        "      The present encoding uses x-direction spatial adjacency.\n",
        "      This directional choice is an encoding decision and should be\n",
        "      tested against equivalent y- and z-direction encodings later.\n",
        "    \"\"\"\n",
        "    x1_columns = []\n",
        "    x2_columns = []\n",
        "\n",
        "    for z in range(N):\n",
        "        x1_rows = []\n",
        "        x2_rows = []\n",
        "\n",
        "        for y in range(N):\n",
        "            local_stretch = stretching[:, y, z]\n",
        "            local_viscous = viscous_dissipation[:, y, z]\n",
        "\n",
        "            # x1: positive vortex-stretching event\n",
        "            x1_line = (\n",
        "                local_stretch > 0.0\n",
        "            ).astype(np.uint8)\n",
        "\n",
        "            # x2: local viscous-control heuristic\n",
        "            x2_line = (\n",
        "                local_viscous\n",
        "                >=\n",
        "                np.maximum(\n",
        "                    local_stretch,\n",
        "                    0.0\n",
        "                )\n",
        "            ).astype(np.uint8)\n",
        "\n",
        "            x1_rows.extend(\n",
        "                x1_line.tolist()\n",
        "            )\n",
        "\n",
        "            x2_rows.extend(\n",
        "                x2_line.tolist()\n",
        "            )\n",
        "\n",
        "            # Spacer prevents artificial row-to-row adjacency\n",
        "            x1_rows.append(0)\n",
        "            x2_rows.append(0)\n",
        "\n",
        "        x1_columns.append(x1_rows)\n",
        "        x2_columns.append(x2_rows)\n",
        "\n",
        "    x1 = np.asarray(\n",
        "        x1_columns,\n",
        "        dtype=np.uint8\n",
        "    ).T\n",
        "\n",
        "    x2 = np.asarray(\n",
        "        x2_columns,\n",
        "        dtype=np.uint8\n",
        "    ).T\n",
        "\n",
        "    return x1, x2\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 2: CJM CORE ENGINE (kept reusable)\n",
        "# ==========================================\n",
        "def step2_cjm_core_engine(\n",
        "    x1,\n",
        "    x2,\n",
        "    gate=\"AND3\",\n",
        "    beta=34.0,\n",
        "    mode=\"logistic\"\n",
        "):\n",
        "    \"\"\"\n",
        "    Universal CJM core engine.\n",
        "\n",
        "    A. 3-literal clause interaction\n",
        "       clauses_i = f(x1_i, x2_i, x1_{i-1})\n",
        "\n",
        "    B. plane-wise admissibility density\n",
        "       mean over the spatial structural axis\n",
        "\n",
        "    C. Contrast mapping\n",
        "       normalized energy in [0,1]\n",
        "\n",
        "    This function is kept unchanged.\n",
        "    \"\"\"\n",
        "    x1_prev = np.roll(\n",
        "        x1,\n",
        "        shift=1,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    x1_prev[0, :] = 0\n",
        "\n",
        "    if gate == \"MAJ\":\n",
        "        clauses = (\n",
        "            (x1 + x2 + x1_prev) >= 2\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    elif gate == \"AND3\":\n",
        "        clauses = (\n",
        "            x1\n",
        "            &\n",
        "            x2\n",
        "            &\n",
        "            x1_prev\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    else:\n",
        "        clauses = (\n",
        "            x1\n",
        "            |\n",
        "            x2\n",
        "            |\n",
        "            x1_prev\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    sat_density = clauses.mean(\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    if mode == \"zscore\":\n",
        "        z = (\n",
        "            sat_density\n",
        "            -\n",
        "            sat_density.mean()\n",
        "        ) / (\n",
        "            sat_density.std()\n",
        "            +\n",
        "            TINY\n",
        "        )\n",
        "\n",
        "        energy = (\n",
        "            1\n",
        "            /\n",
        "            (\n",
        "                1\n",
        "                +\n",
        "                np.exp(-z)\n",
        "            )\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        center = np.median(\n",
        "            sat_density\n",
        "        )\n",
        "\n",
        "        energy = (\n",
        "            1\n",
        "            /\n",
        "            (\n",
        "                1\n",
        "                +\n",
        "                np.exp(\n",
        "                    -beta\n",
        "                    *\n",
        "                    (\n",
        "                        sat_density\n",
        "                        -\n",
        "                        center\n",
        "                    )\n",
        "                )\n",
        "            )\n",
        "        )\n",
        "\n",
        "    energy = (\n",
        "        energy\n",
        "        -\n",
        "        energy.min()\n",
        "    ) / (\n",
        "        energy.max()\n",
        "        -\n",
        "        energy.min()\n",
        "        +\n",
        "        TINY\n",
        "    )\n",
        "\n",
        "    return energy\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 3: Structural readout\n",
        "# ==========================================\n",
        "def evaluate_one_snapshot(\n",
        "    Re,\n",
        "    grid\n",
        "):\n",
        "    \"\"\"\n",
        "    Evaluate one independent Navier-Stokes snapshot.\n",
        "\n",
        "    Pipeline:\n",
        "      1) generate u(x,t0)\n",
        "      2) extract instantaneous structural fields\n",
        "      3) construct x1, x2\n",
        "      4) evaluate the fixed CJM core\n",
        "      5) retain the full energy vector E\n",
        "\n",
        "    Structural readouts:\n",
        "      M_J = mean(E)\n",
        "      D_J = std(E)\n",
        "      F_J = periodic median-crossing fragmentation rate\n",
        "\n",
        "    The complete vector E is retained for the texture map in Figure 1.\n",
        "    \"\"\"\n",
        "    u_hat, nu = generate_snapshot(\n",
        "        Re,\n",
        "        grid\n",
        "    )\n",
        "\n",
        "    fields = snapshot_structural_fields(\n",
        "        u_hat,\n",
        "        nu,\n",
        "        grid\n",
        "    )\n",
        "\n",
        "    x1, x2 = snapshot_to_literal_matrix(\n",
        "        fields[\"stretching\"],\n",
        "        fields[\"viscous_dissipation\"]\n",
        "    )\n",
        "\n",
        "    # STEP 2\n",
        "    energy = step2_cjm_core_engine(\n",
        "        x1,\n",
        "        x2,\n",
        "        gate=CJM_GATE,\n",
        "        beta=CJM_BETA,\n",
        "        mode=CJM_MODE\n",
        "    )\n",
        "\n",
        "    # Mean response\n",
        "    J_mean = float(\n",
        "        np.mean(\n",
        "            energy\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Structural dispersion\n",
        "    D_J = float(\n",
        "        np.std(\n",
        "            energy\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Periodic median-crossing fragmentation\n",
        "    energy_median = float(\n",
        "        np.median(\n",
        "            energy\n",
        "        )\n",
        "    )\n",
        "\n",
        "    above_median = (\n",
        "        energy\n",
        "        >\n",
        "        energy_median\n",
        "    )\n",
        "\n",
        "    fragmentation_count = int(\n",
        "        np.sum(\n",
        "            above_median\n",
        "            !=\n",
        "            np.roll(\n",
        "                above_median,\n",
        "                shift=1\n",
        "            )\n",
        "        )\n",
        "    )\n",
        "\n",
        "    fragmentation_rate = float(\n",
        "        fragmentation_count\n",
        "        /\n",
        "        max(\n",
        "            1,\n",
        "            len(energy)\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Direct encoded AND3 density before STEP 2 contrast mapping\n",
        "    x1_prev = np.roll(\n",
        "        x1,\n",
        "        shift=1,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    x1_prev[0, :] = 0\n",
        "\n",
        "    direct_clauses = (\n",
        "        x1\n",
        "        &\n",
        "        x2\n",
        "        &\n",
        "        x1_prev\n",
        "    ).astype(np.uint8)\n",
        "\n",
        "    raw_density = float(\n",
        "        np.mean(\n",
        "            direct_clauses\n",
        "        )\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"Re\": float(Re),\n",
        "        \"J_mean\": J_mean,\n",
        "        \"D_J\": D_J,\n",
        "        \"F_J_count\": fragmentation_count,\n",
        "        \"F_J\": fragmentation_rate,\n",
        "        \"energy_median\": energy_median,\n",
        "        \"raw_density\": raw_density,\n",
        "        \"enstrophy\": fields[\"enstrophy\"],\n",
        "        \"omega_max\": fields[\"omega_max\"],\n",
        "        \"high_k\": fields[\"high_k\"],\n",
        "        \"positive_stretch\": fields[\"positive_stretch_fraction\"],\n",
        "        \"viscous_control\": fields[\"viscous_control_fraction\"],\n",
        "        \"energy\": energy,\n",
        "    }\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Independent Reynolds-number snapshot sweep\n",
        "# ==========================================\n",
        "def step3_independent_snapshot_sweep():\n",
        "    \"\"\"\n",
        "    Evaluate each Reynolds-number state independently.\n",
        "\n",
        "    The CJM evaluation for Re_i does not require the CJM result\n",
        "    of Re_(i-1), Re_(i+1), or any earlier flow state.\n",
        "    \"\"\"\n",
        "    grid = make_spectral_grid(N)\n",
        "\n",
        "    results = []\n",
        "\n",
        "    for index, Re in enumerate(RE_VALUES):\n",
        "        print(\n",
        "            f\"[Independent snapshot \"\n",
        "            f\"{index + 1}/{len(RE_VALUES)}] \"\n",
        "            f\"Re = {Re:.1f}\"\n",
        "        )\n",
        "\n",
        "        result = evaluate_one_snapshot(\n",
        "            Re,\n",
        "            grid\n",
        "        )\n",
        "\n",
        "        results.append(\n",
        "            result\n",
        "        )\n",
        "\n",
        "    return results\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Reynolds-number cell edges for heatmap\n",
        "# ==========================================\n",
        "def parameter_edges(values):\n",
        "    \"\"\"\n",
        "    Construct plotting-cell edges around an ordered parameter array.\n",
        "    \"\"\"\n",
        "    values = np.asarray(\n",
        "        values,\n",
        "        dtype=float\n",
        "    )\n",
        "\n",
        "    if len(values) == 1:\n",
        "        return np.asarray(\n",
        "            [\n",
        "                values[0] - 0.5,\n",
        "                values[0] + 0.5\n",
        "            ]\n",
        "        )\n",
        "\n",
        "    mid = (\n",
        "        values[:-1]\n",
        "        +\n",
        "        values[1:]\n",
        "    ) / 2.0\n",
        "\n",
        "    first = (\n",
        "        values[0]\n",
        "        -\n",
        "        (\n",
        "            values[1]\n",
        "            -\n",
        "            values[0]\n",
        "        ) / 2.0\n",
        "    )\n",
        "\n",
        "    last = (\n",
        "        values[-1]\n",
        "        +\n",
        "        (\n",
        "            values[-1]\n",
        "            -\n",
        "            values[-2]\n",
        "        ) / 2.0\n",
        "    )\n",
        "\n",
        "    return np.concatenate(\n",
        "        (\n",
        "            [first],\n",
        "            mid,\n",
        "            [last]\n",
        "        )\n",
        "    )\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 4: Visualization\n",
        "# ==========================================\n",
        "def step4_plot(results):\n",
        "    \"\"\"\n",
        "    Plot the Navier-Stokes structural persistence figure.\n",
        "\n",
        "    Panel (a):\n",
        "      direct encoded AND3 density and physical viscous-control fraction\n",
        "\n",
        "    Panel (b):\n",
        "      complete STEP-2 admissibility-energy texture across independent\n",
        "      Reynolds-number snapshots and structural z-planes\n",
        "\n",
        "    Note:\n",
        "      STEP 2 normalizes each snapshot internally. Heatmap colors\n",
        "      therefore represent within-snapshot structural organization,\n",
        "      not an absolute energy magnitude shared across Reynolds numbers.\n",
        "    \"\"\"\n",
        "    Re = np.asarray(\n",
        "        [r[\"Re\"] for r in results]\n",
        "    )\n",
        "\n",
        "    raw = np.asarray(\n",
        "        [r[\"raw_density\"] for r in results]\n",
        "    )\n",
        "\n",
        "    viscous_control = np.asarray(\n",
        "        [r[\"viscous_control\"] for r in results]\n",
        "    )\n",
        "\n",
        "    # Complete STEP-2 energy vectors\n",
        "    energy_matrix = np.stack(\n",
        "        [\n",
        "            r[\"energy\"]\n",
        "            for r in results\n",
        "        ],\n",
        "        axis=1\n",
        "    )\n",
        "\n",
        "    fig, axes = plt.subplots(\n",
        "        1,\n",
        "        2,\n",
        "        figsize=(14, 5.4)\n",
        "    )\n",
        "\n",
        "    # -------------------- PANEL (a) --------------------\n",
        "    ax = axes[0]\n",
        "\n",
        "    # Both quantities are fractions in [0,1],\n",
        "    # so they are shown on their original scale.\n",
        "    ax.plot(\n",
        "        Re,\n",
        "        raw,\n",
        "        linewidth=2.5,\n",
        "        marker=\"o\",\n",
        "        label=\"Direct encoded AND3 density\"\n",
        "    )\n",
        "\n",
        "    ax.plot(\n",
        "        Re,\n",
        "        viscous_control,\n",
        "        linewidth=2.5,\n",
        "        linestyle=\"--\",\n",
        "        marker=\"s\",\n",
        "        label=\"Viscous-control fraction\"\n",
        "    )\n",
        "\n",
        "    ax.set_title(\n",
        "        \"(a) Loss of Instantaneous Structural Control\"\n",
        "    )\n",
        "\n",
        "    ax.set_xlabel(\n",
        "        \"Reynolds number\"\n",
        "    )\n",
        "\n",
        "    ax.set_ylabel(\n",
        "        \"Fraction\"\n",
        "    )\n",
        "\n",
        "    ax.set_ylim(\n",
        "        -0.02,\n",
        "        max(\n",
        "            0.36,\n",
        "            1.10\n",
        "            *\n",
        "            max(\n",
        "                np.max(raw),\n",
        "                np.max(viscous_control)\n",
        "            )\n",
        "        )\n",
        "    )\n",
        "\n",
        "    ax.grid(\n",
        "        linestyle=\"--\",\n",
        "        alpha=0.35\n",
        "    )\n",
        "\n",
        "    ax.legend()\n",
        "\n",
        "    # -------------------- PANEL (b) --------------------\n",
        "    ax = axes[1]\n",
        "\n",
        "    re_edges = parameter_edges(\n",
        "        Re\n",
        "    )\n",
        "\n",
        "    plane_edges = np.arange(\n",
        "        0.5,\n",
        "        N + 1.5,\n",
        "        1.0\n",
        "    )\n",
        "\n",
        "    texture = ax.pcolormesh(\n",
        "        re_edges,\n",
        "        plane_edges,\n",
        "        energy_matrix,\n",
        "        shading=\"flat\",\n",
        "        vmin=0.0,\n",
        "        vmax=1.0\n",
        "    )\n",
        "\n",
        "    ax.set_title(\n",
        "        \"(b) Fixed-Core CJM Admissibility Texture\"\n",
        "    )\n",
        "\n",
        "    ax.set_xlabel(\n",
        "        \"Reynolds number\"\n",
        "    )\n",
        "\n",
        "    ax.set_ylabel(\n",
        "        \"Structural plane index\"\n",
        "    )\n",
        "\n",
        "    ax.set_yticks(\n",
        "        np.arange(\n",
        "            1,\n",
        "            N + 1,\n",
        "            2\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # Colorbar shows relative within-snapshot organization\n",
        "    cbar = fig.colorbar(\n",
        "        texture,\n",
        "        ax=ax,\n",
        "        pad=0.02\n",
        "    )\n",
        "\n",
        "    cbar.set_label(\n",
        "        \"Relative CJM admissibility energy\"\n",
        "    )\n",
        "\n",
        "    fig.suptitle(\n",
        "        \"se-CJM v5.9: \"\n",
        "        \"Navier-Stokes Atemporal Structural Persistence\",\n",
        "        fontsize=13\n",
        "    )\n",
        "\n",
        "    fig.tight_layout()\n",
        "\n",
        "    fig.savefig(\n",
        "        SAVE_PATH,\n",
        "        dpi=300,\n",
        "        bbox_inches=\"tight\"\n",
        "    )\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# MASTER CONTROL (se-CJM v5.9-Navier)\n",
        "# ==========================================\n",
        "# Each Reynolds-number case is generated conventionally to one\n",
        "# static snapshot u(x,t0), after which CJM evaluation is performed\n",
        "# independently without sequential trajectory input.\n",
        "#\n",
        "# Atemporal discrimination here means removal of trajectory\n",
        "# dependence from the CJM evaluation stage. It does not imply\n",
        "# hypercomputation or computation beyond the Turing framework.\n",
        "# ==========================================\n",
        "def run_secjm_v59():\n",
        "    \"\"\"\n",
        "    Runs the se-CJM v5.9 Navier-Stokes pipeline:\n",
        "      1) static Navier-Stokes snapshot -> x1, x2\n",
        "      2) fixed CJM core -> energy vector E\n",
        "      3) structural readout -> M_J, D_J, F_J\n",
        "      4) plot physical loss and CJM admissibility texture\n",
        "\n",
        "    Note:\n",
        "      The experiment is a finite numerical structural probe.\n",
        "      No singularity or global-regularity result is claimed.\n",
        "    \"\"\"\n",
        "    start = time.time()\n",
        "\n",
        "    print(\"=\" * 60)\n",
        "    print(\"se-CJM v5.9\")\n",
        "    print(\"Navier-Stokes Atemporal Structural Persistence\")\n",
        "    print(\"=\" * 60)\n",
        "\n",
        "    print(\n",
        "        \"Physical system         : \"\n",
        "        \"3D incompressible Navier-Stokes\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Initial condition       : \"\n",
        "        \"Taylor-Green vortex\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Grid                    : \"\n",
        "        f\"{N}^3\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Snapshot time           : \"\n",
        "        f\"{SNAPSHOT_TIME}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Re range                : \"\n",
        "        f\"{RE_VALUES[0]:.1f} - {RE_VALUES[-1]:.1f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"CJM trajectory input    : NONE\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"CJM previous-time input : NONE\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"STEP 2 modification     : NONE\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Structural readout      : M_J, D_J, F_J + full E texture\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Hypercomputation claim  : NONE\"\n",
        "    )\n",
        "\n",
        "    print(\"=\" * 60)\n",
        "    print()\n",
        "\n",
        "    # STEP 1-3\n",
        "    results = step3_independent_snapshot_sweep()\n",
        "\n",
        "    # STEP 4\n",
        "    step4_plot(results)\n",
        "\n",
        "    elapsed = (\n",
        "        time.time()\n",
        "        -\n",
        "        start\n",
        "    )\n",
        "\n",
        "    J_mean = np.asarray(\n",
        "        [r[\"J_mean\"] for r in results]\n",
        "    )\n",
        "\n",
        "    D_J = np.asarray(\n",
        "        [r[\"D_J\"] for r in results]\n",
        "    )\n",
        "\n",
        "    F_J = np.asarray(\n",
        "        [r[\"F_J\"] for r in results]\n",
        "    )\n",
        "\n",
        "    raw = np.asarray(\n",
        "        [r[\"raw_density\"] for r in results]\n",
        "    )\n",
        "\n",
        "    viscous_control = np.asarray(\n",
        "        [r[\"viscous_control\"] for r in results]\n",
        "    )\n",
        "\n",
        "    print()\n",
        "    print(\"=\" * 60)\n",
        "    print(\"RESULT SUMMARY\")\n",
        "    print(\"=\" * 60)\n",
        "\n",
        "    print(\n",
        "        f\"M_J = mean(E) range     : \"\n",
        "        f\"{J_mean.min():.4f} - \"\n",
        "        f\"{J_mean.max():.4f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"D_J = std(E) range      : \"\n",
        "        f\"{D_J.min():.4f} - \"\n",
        "        f\"{D_J.max():.4f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"F_J fragmentation range : \"\n",
        "        f\"{F_J.min():.4f} - \"\n",
        "        f\"{F_J.max():.4f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"encoded AND3 range      : \"\n",
        "        f\"{raw.min():.4f} - \"\n",
        "        f\"{raw.max():.4f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"viscous-control range   : \"\n",
        "        f\"{viscous_control.min():.4f} - \"\n",
        "        f\"{viscous_control.max():.4f}\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Critical point planted  : NO\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Synthetic input         : NO\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Sequential CJM input    : NO\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"STEP-2 retuning         : NO\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        \"Singularity claim       : NONE\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Runtime                 : \"\n",
        "        f\"{elapsed:.1f} seconds\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Saved figure            : \"\n",
        "        f\"{SAVE_PATH}\"\n",
        "    )\n",
        "\n",
        "    print(\"=\" * 60)\n",
        "\n",
        "    return results\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# Execution\n",
        "# ==========================================\n",
        "run_secjm_v59()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
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      "metadata": {
        "id": "VtepXTVBoqaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Appendix 2: Conceptual Pseudocode for Navier Filter\n",
        "# ============================================================\n",
        "# This appendix provides conceptual pseudocode (not executable Python)\n",
        "# for the CJM-Navier Filter. The purpose is not numerical simulation\n",
        "# of the Navier--Stokes equations, but structural screening of a supplied\n",
        "# flow state before deeper CJM stages.\n",
        "#\n",
        "# The Navier Filter is treated as an optional preprocessing-style gate:\n",
        "#   (1) Flow-state signalization         -> instantaneous flow profile\n",
        "#   (2) Vorticity--strain encoding       -> structural indicators\n",
        "#   (3) 3SAT translation                 -> finite logical profile\n",
        "#   (4) Persistence screening            -> residual structural support\n",
        "#   (5) Admissibility scoring            -> Navier structural score\n",
        "#   (6) Navier decision                  -> PASS / BORDERLINE / REJECT\n",
        "#\n",
        "# In recursive CJM usage, the input may be:\n",
        "#   - a Navier--Stokes flow snapshot u(x,t0)\n",
        "#   - a Navier structural indicator profile Theta_N\n",
        "#   - a Navier-encoded 3SAT formula F\n",
        "#   - a SAT-reduced instance F\n",
        "#   - or an already encoded structural object Sigma_N\n",
        "#\n",
        "# The filter does not replace temporal Navier--Stokes evolution and is\n",
        "# not a formal reduction of the full regularity problem to 3SAT.\n",
        "# ============================================================\n",
        "\n",
        "DEFINE TINY_THRESHOLD = small positive constant\n",
        "DEFINE TAU_HIGH = high admissibility threshold\n",
        "DEFINE TAU_LOW  = low admissibility threshold\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 1: INPUT MODULE (flow-state signalization)\n",
        "# ==========================================\n",
        "PROCEDURE step1_input_navier_filter(input_instance):\n",
        "\n",
        "    # Interpret the input according to its current physical or logical form\n",
        "    IF input_instance is FlowSnapshot u(x,t0):\n",
        "        U <- ExtractInstantaneousFlowState(u(x,t0))\n",
        "        Sigma_N <- BuildNavierStructuralRepresentation(U)\n",
        "\n",
        "    ELSE IF input_instance is NavierIndicatorProfile Theta_N:\n",
        "        Sigma_N <- LiftToNavierStructuralRepresentation(Theta_N)\n",
        "\n",
        "    ELSE IF input_instance is Navier3SATFormula F:\n",
        "        Sigma_N <- LiftToNavierStructuralRepresentation(F)\n",
        "\n",
        "    ELSE IF input_instance is SATReducedInstance F:\n",
        "        Sigma_N <- LiftToNavierStructuralRepresentation(F)\n",
        "\n",
        "    ELSE:\n",
        "        Sigma_N <- input_instance\n",
        "\n",
        "    # Preserve only Navier-persistence-relevant information\n",
        "    Preserve:\n",
        "        - instantaneous velocity-field structure\n",
        "        - incompressibility consistency\n",
        "        - spatial neighborhood relations\n",
        "        - vorticity organization\n",
        "        - strain organization\n",
        "        - Reynolds-number metadata\n",
        "        - clause/indicator coupling structure\n",
        "\n",
        "    RETURN Sigma_N\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 2: VORTICITY--STRAIN INDICATOR ENCODING\n",
        "# ==========================================\n",
        "PROCEDURE step2_vorticity_strain_encoding(Sigma_N):\n",
        "\n",
        "    # Construct instantaneous physical indicators\n",
        "    omega <- ComputeVorticity(Sigma_N)\n",
        "    S <- ComputeStrainTensor(Sigma_N)\n",
        "\n",
        "    # Vortex-stretching production\n",
        "    P_omega <- Dot(\n",
        "        omega,\n",
        "        S * omega\n",
        "    )\n",
        "\n",
        "    # Positive viscous structural proxy\n",
        "    D_omega <- nu * GradientNormSquared(omega)\n",
        "\n",
        "    # Convert continuous indicators into structural literals\n",
        "    x1 <- Indicator(\n",
        "        P_omega > 0\n",
        "    )\n",
        "\n",
        "    x2 <- Indicator(\n",
        "        D_omega >= max(P_omega, 0)\n",
        "    )\n",
        "\n",
        "    Theta_N <- BuildNavierIndicatorProfile(\n",
        "        P_omega,\n",
        "        D_omega,\n",
        "        x1,\n",
        "        x2\n",
        "    )\n",
        "\n",
        "    RETURN Theta_N\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 3: 3SAT TRANSLATION\n",
        "# ==========================================\n",
        "PROCEDURE step3_translate_to_3sat(Theta_N):\n",
        "\n",
        "    # Translate the finite indicator profile into a logical structure\n",
        "    # compatible with the common CJM interface\n",
        "    F_N <- TranslateTo3SAT(Theta_N)\n",
        "\n",
        "    # Local AND3 interaction used in the present probe\n",
        "    FOR each structural index i:\n",
        "        C_i <-\n",
        "            x1[i]\n",
        "            AND\n",
        "            x2[i]\n",
        "            AND\n",
        "            x1[AdjacentPrevious(i)]\n",
        "    END FOR\n",
        "\n",
        "    AttachClauses(F_N, {C_i})\n",
        "\n",
        "    # AdjacentPrevious(i) denotes spatial adjacency\n",
        "    # within the same snapshot, not a previous time state.\n",
        "\n",
        "    RETURN F_N\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 4: STRUCTURAL-PERSISTENCE SCREENING\n",
        "# ==========================================\n",
        "PROCEDURE step4_persistence_screening(F_N, Theta_N):\n",
        "\n",
        "    # Evaluate whether distributed structural support remains organized\n",
        "    # within the supplied instantaneous flow state\n",
        "    control_fraction <- MeasureViscousControlFraction(Theta_N)\n",
        "\n",
        "    clause_density <- MeasureAND3Density(F_N)\n",
        "\n",
        "    spatial_persistence <- MeasurePersistentOrganization(F_N)\n",
        "\n",
        "    fragmentation <- MeasureStructuralFragmentation(F_N)\n",
        "\n",
        "    Check for:\n",
        "        - broad loss of viscous-control support\n",
        "        - concentration into localized residual regions\n",
        "        - fragmentation of admissible spatial organization\n",
        "        - persistence of coherent structural bands\n",
        "        - excessive direction-dependent encoding sensitivity\n",
        "\n",
        "    PersistenceProfile <- Combine(\n",
        "        control_fraction,\n",
        "        clause_density,\n",
        "        spatial_persistence,\n",
        "        fragmentation\n",
        "    )\n",
        "\n",
        "    RETURN PersistenceProfile\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 5: ADMISSIBILITY SCORING\n",
        "# ==========================================\n",
        "PROCEDURE step5_admissibility_scoring(\n",
        "    F_N,\n",
        "    PersistenceProfile\n",
        "):\n",
        "\n",
        "    # Pass the filtered structural profile through the fixed CJM core\n",
        "    E <- FixedCJMCore(F_N)\n",
        "\n",
        "    # Preserve the full admissibility texture\n",
        "    M_J <- Mean(E)\n",
        "    D_J <- StandardDeviation(E)\n",
        "    F_J <- MedianCrossingFragmentation(E)\n",
        "\n",
        "    NavierScore <-\n",
        "          w1 * PersistenceProfile.control_fraction\n",
        "        + w2 * PersistenceProfile.clause_density\n",
        "        + w3 * PersistenceProfile.spatial_persistence\n",
        "        - w4 * PersistenceProfile.fragmentation\n",
        "\n",
        "    RETURN\n",
        "        NavierScore,\n",
        "        E,\n",
        "        M_J,\n",
        "        D_J,\n",
        "        F_J\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# STEP 6: NAVIER FILTER DECISION\n",
        "# ==========================================\n",
        "PROCEDURE step6_navier_filter_decision(\n",
        "    NavierScore,\n",
        "    E\n",
        "):\n",
        "\n",
        "    # The decision concerns structural admissibility only.\n",
        "    # It is not a declaration of smoothness, blow-up, or turbulence.\n",
        "    IF NavierScore >= TAU_HIGH:\n",
        "        verdict <- PASS\n",
        "\n",
        "    ELSE IF NavierScore >= TAU_LOW:\n",
        "        verdict <- BORDERLINE\n",
        "\n",
        "    ELSE:\n",
        "        verdict <- REJECT\n",
        "    END IF\n",
        "\n",
        "    RETURN verdict, NavierScore, E\n",
        "\n",
        "\n",
        "# ==========================================\n",
        "# MASTER CONTROL (CJM-Navier Filter)\n",
        "# ==========================================\n",
        "PROCEDURE run_CJM_Navier_Filter(\n",
        "    input_instance,\n",
        "    filter_enabled = TRUE\n",
        "):\n",
        "\n",
        "    # The Navier Filter is optional within the common CJM architecture\n",
        "    IF filter_enabled == FALSE:\n",
        "        ForwardToDeeperCJM(input_instance)\n",
        "        RETURN BYPASS\n",
        "    END IF\n",
        "\n",
        "    # STEP 1\n",
        "    Sigma_N <- step1_input_navier_filter(\n",
        "        input_instance\n",
        "    )\n",
        "\n",
        "    # STEP 2\n",
        "    Theta_N <- step2_vorticity_strain_encoding(\n",
        "        Sigma_N\n",
        "    )\n",
        "\n",
        "    # STEP 3\n",
        "    F_N <- step3_translate_to_3sat(\n",
        "        Theta_N\n",
        "    )\n",
        "\n",
        "    # STEP 4\n",
        "    PersistenceProfile <- step4_persistence_screening(\n",
        "        F_N,\n",
        "        Theta_N\n",
        "    )\n",
        "\n",
        "    # STEP 5\n",
        "    NavierScore, E, M_J, D_J, F_J <-\n",
        "        step5_admissibility_scoring(\n",
        "            F_N,\n",
        "            PersistenceProfile\n",
        "        )\n",
        "\n",
        "    # STEP 6\n",
        "    verdict, NavierScore, E <-\n",
        "        step6_navier_filter_decision(\n",
        "            NavierScore,\n",
        "            E\n",
        "        )\n",
        "\n",
        "    # Output to deeper CJM stages\n",
        "    IF verdict == PASS:\n",
        "        ForwardToDeeperCJM(\n",
        "            F_N,\n",
        "            E\n",
        "        )\n",
        "\n",
        "    ELSE IF verdict == BORDERLINE:\n",
        "        FlagForReencodingOrAuxiliaryReview(\n",
        "            F_N,\n",
        "            E\n",
        "        )\n",
        "\n",
        "    ELSE:\n",
        "        RejectAsStructurallyInadmissible(\n",
        "            F_N\n",
        "        )\n",
        "    END IF\n",
        "\n",
        "    RETURN\n",
        "        verdict,\n",
        "        NavierScore,\n",
        "        E,\n",
        "        M_J,\n",
        "        D_J,\n",
        "        F_J,\n",
        "        PersistenceProfile"
      ],
      "metadata": {
        "id": "n2CW9tK3mSVC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# se-CJM v5.9-R2\n",
        "# Navier-Stokes Two-Mode Robustness + Structural Susceptibility\n",
        "# Google Colab-ready\n",
        "# ============================================================\n",
        "#\n",
        "# Updates from v5.9-R:\n",
        "#   1) Keeps the original Taylor-Green baseline and frozen CJM core.\n",
        "#   2) Adds TWO independent divergence-free perturbation modes.\n",
        "#   3) Tests eps = 1%, 2%, 3% over Re = 275,300,325,350,375.\n",
        "#   4) Computes finite CJM structural susceptibility:\n",
        "#          chi_J = 1 - corr(E0, E_eps)\n",
        "#   5) Computes relative physical flow-field difference at t0:\n",
        "#          ||u_eps - u0||_2 / ||u0||_2\n",
        "#   6) Keeps the original 3% Mode-1 full-Re robustness test.\n",
        "#   7) Displays ONE two-panel paper figure with plt.show().\n",
        "#      NO PNG files are written.\n",
        "#\n",
        "# IMPORTANT:\n",
        "#   - STEP 2 CJM core is frozen.\n",
        "#   - Solver/grid/t0/dt/gate/beta/normalization are not retuned.\n",
        "#   - The present literals are a finite logic-like structural interface,\n",
        "#     NOT a formal proof-theoretic 3SAT reduction of Navier-Stokes.\n",
        "# ============================================================\n",
        "\n",
        "import csv\n",
        "import time\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "TINY = 1e-12\n",
        "\n",
        "# ============================================================\n",
        "# USER SETTINGS\n",
        "# ============================================================\n",
        "\n",
        "N = 20\n",
        "RE_VALUES = np.arange(50.0, 401.0, 25.0)\n",
        "SNAPSHOT_TIME = 4.0\n",
        "DT = 0.02\n",
        "\n",
        "# Full-Re robustness check: original v5.9-R experiment\n",
        "FULL_ROBUSTNESS_MODE = 1\n",
        "FULL_ROBUSTNESS_EPS = 0.03\n",
        "\n",
        "# Focused two-mode susceptibility sweep\n",
        "FOCUSED_RE_VALUES = np.array([275.0, 300.0, 325.0, 350.0, 375.0])\n",
        "PERTURB_EPS_VALUES = np.array([0.01, 0.02, 0.03])\n",
        "PERTURB_MODES = (1, 2)\n",
        "\n",
        "# Universal CJM core settings -- frozen\n",
        "CJM_GATE = \"AND3\"\n",
        "CJM_BETA = 34.0\n",
        "CJM_MODE = \"logistic\"\n",
        "\n",
        "# CSV output is kept for reproducibility.\n",
        "# Set to False if you want screen-only output.\n",
        "SAVE_CSV = True\n",
        "ROBUSTNESS_CSV = \"secjm_v59R2_full_robustness.csv\"\n",
        "SUSCEPTIBILITY_CSV = \"secjm_v59R2_two_mode_susceptibility.csv\"\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# UTILITIES\n",
        "# ============================================================\n",
        "\n",
        "def make_spectral_grid(N):\n",
        "    k = np.fft.fftfreq(N, d=1.0 / N)\n",
        "    KX, KY, KZ = np.meshgrid(k, k, k, indexing=\"ij\")\n",
        "\n",
        "    K2 = KX**2 + KY**2 + KZ**2\n",
        "    K2_SAFE = K2.copy()\n",
        "    K2_SAFE[0, 0, 0] = 1.0\n",
        "\n",
        "    K_CUT = N // 3\n",
        "    DEALIAS = (\n",
        "        (np.abs(KX) <= K_CUT)\n",
        "        & (np.abs(KY) <= K_CUT)\n",
        "        & (np.abs(KZ) <= K_CUT)\n",
        "    )\n",
        "\n",
        "    return KX, KY, KZ, K2, K2_SAFE, DEALIAS\n",
        "\n",
        "\n",
        "def project_divergence_free(u_hat, KX, KY, KZ, K2_SAFE):\n",
        "    k_dot_u = KX * u_hat[0] + KY * u_hat[1] + KZ * u_hat[2]\n",
        "\n",
        "    out = u_hat.copy()\n",
        "    out[0] -= KX * k_dot_u / K2_SAFE\n",
        "    out[1] -= KY * k_dot_u / K2_SAFE\n",
        "    out[2] -= KZ * k_dot_u / K2_SAFE\n",
        "\n",
        "    out[:, 0, 0, 0] = 0.0\n",
        "    return out\n",
        "\n",
        "\n",
        "def rms_speed(u):\n",
        "    return float(np.sqrt(np.mean(np.sum(u * u, axis=0))))\n",
        "\n",
        "\n",
        "def relative_spectral_l2(u_hat_ref, u_hat_test):\n",
        "    \"\"\"\n",
        "    Relative velocity-field displacement at the sampling time.\n",
        "\n",
        "    Parseval's theorem means the common FFT normalization cancels in\n",
        "    this ratio, so it can be evaluated directly in Fourier space.\n",
        "    \"\"\"\n",
        "    numerator = np.linalg.norm((u_hat_test - u_hat_ref).ravel())\n",
        "    denominator = np.linalg.norm(u_hat_ref.ravel()) + TINY\n",
        "    return float(numerator / denominator)\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# STEP 1A -- ORIGINAL TAYLOR-GREEN INITIAL CONDITION\n",
        "# ============================================================\n",
        "\n",
        "def taylor_green_real(N):\n",
        "    x = 2.0 * np.pi * np.arange(N) / N\n",
        "    X, Y, Z = np.meshgrid(x, x, x, indexing=\"ij\")\n",
        "\n",
        "    u = np.zeros((3, N, N, N), dtype=float)\n",
        "    u[0] = np.sin(X) * np.cos(Y) * np.cos(Z)\n",
        "    u[1] = -np.cos(X) * np.sin(Y) * np.cos(Z)\n",
        "    u[2] = 0.0\n",
        "\n",
        "    return u, X, Y, Z\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# STEP 1B -- TWO DIVERGENCE-FREE SYMMETRY-BREAKING MODES\n",
        "# ============================================================\n",
        "\n",
        "def perturbation_real(N, mode):\n",
        "    \"\"\"\n",
        "    Return an RMS-normalized divergence-free perturbation field.\n",
        "\n",
        "    Mode 1:\n",
        "        psi1 = sin(2x + pi/7) sin(3y) cos(2z)\n",
        "        du1  = (d_y psi1, -d_x psi1, 0)\n",
        "\n",
        "    Mode 2:\n",
        "        psi2 = sin(3x + pi/5) sin(2y + pi/9) cos(z)\n",
        "        du2  = (d_y psi2, -d_x psi2, 0)\n",
        "\n",
        "    Both are analytically divergence-free.\n",
        "    \"\"\"\n",
        "    u_tg, X, Y, Z = taylor_green_real(N)\n",
        "    delta_u = np.zeros_like(u_tg)\n",
        "\n",
        "    if mode == 1:\n",
        "        phi = np.pi / 7.0\n",
        "\n",
        "        delta_u[0] = (\n",
        "            3.0\n",
        "            * np.sin(2.0 * X + phi)\n",
        "            * np.cos(3.0 * Y)\n",
        "            * np.cos(2.0 * Z)\n",
        "        )\n",
        "\n",
        "        delta_u[1] = (\n",
        "            -2.0\n",
        "            * np.cos(2.0 * X + phi)\n",
        "            * np.sin(3.0 * Y)\n",
        "            * np.cos(2.0 * Z)\n",
        "        )\n",
        "\n",
        "    elif mode == 2:\n",
        "        phi_x = np.pi / 5.0\n",
        "        phi_y = np.pi / 9.0\n",
        "\n",
        "        delta_u[0] = (\n",
        "            2.0\n",
        "            * np.sin(3.0 * X + phi_x)\n",
        "            * np.cos(2.0 * Y + phi_y)\n",
        "            * np.cos(Z)\n",
        "        )\n",
        "\n",
        "        delta_u[1] = (\n",
        "            -3.0\n",
        "            * np.cos(3.0 * X + phi_x)\n",
        "            * np.sin(2.0 * Y + phi_y)\n",
        "            * np.cos(Z)\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        raise ValueError(f\"Unknown perturbation mode: {mode}\")\n",
        "\n",
        "    # Normalize perturbation RMS to Taylor-Green RMS.\n",
        "    delta_u *= rms_speed(u_tg) / (rms_speed(delta_u) + TINY)\n",
        "    return delta_u\n",
        "\n",
        "\n",
        "def initial_condition_hat(\n",
        "    N,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2_SAFE,\n",
        "    DEALIAS,\n",
        "    perturb_mode=None,\n",
        "    eps=0.0,\n",
        "):\n",
        "    u_tg, _, _, _ = taylor_green_real(N)\n",
        "\n",
        "    if perturb_mode is None or eps == 0.0:\n",
        "        u = u_tg\n",
        "    else:\n",
        "        delta_u = perturbation_real(N, perturb_mode)\n",
        "        u = u_tg + eps * delta_u\n",
        "\n",
        "    u_hat = np.fft.fftn(u, axes=(1, 2, 3))\n",
        "    u_hat = project_divergence_free(u_hat, KX, KY, KZ, K2_SAFE)\n",
        "    u_hat *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    return u_hat\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# NAVIER-STOKES SOLVER -- SAME NUMERICAL STRUCTURE\n",
        "# ============================================================\n",
        "\n",
        "def curl_hat(u_hat, KX, KY, KZ):\n",
        "    omega_hat = np.empty_like(u_hat)\n",
        "\n",
        "    omega_hat[0] = 1j * (KY * u_hat[2] - KZ * u_hat[1])\n",
        "    omega_hat[1] = 1j * (KZ * u_hat[0] - KX * u_hat[2])\n",
        "    omega_hat[2] = 1j * (KX * u_hat[1] - KY * u_hat[0])\n",
        "\n",
        "    return omega_hat\n",
        "\n",
        "\n",
        "def nonlinear_term_hat(u_hat, KX, KY, KZ, K2_SAFE, DEALIAS):\n",
        "    omega_hat = curl_hat(u_hat, KX, KY, KZ)\n",
        "\n",
        "    u = np.fft.ifftn(u_hat, axes=(1, 2, 3)).real\n",
        "    omega = np.fft.ifftn(omega_hat, axes=(1, 2, 3)).real\n",
        "\n",
        "    cross = np.empty_like(u)\n",
        "    cross[0] = u[1] * omega[2] - u[2] * omega[1]\n",
        "    cross[1] = u[2] * omega[0] - u[0] * omega[2]\n",
        "    cross[2] = u[0] * omega[1] - u[1] * omega[0]\n",
        "\n",
        "    nonlinear_hat = np.fft.fftn(cross, axes=(1, 2, 3))\n",
        "    nonlinear_hat *= DEALIAS[None, :, :, :]\n",
        "    nonlinear_hat = project_divergence_free(\n",
        "        nonlinear_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "    )\n",
        "\n",
        "    return nonlinear_hat\n",
        "\n",
        "\n",
        "def navier_stokes_rhs(\n",
        "    u_hat,\n",
        "    nu,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2,\n",
        "    K2_SAFE,\n",
        "    DEALIAS,\n",
        "):\n",
        "    nonlinear_hat = nonlinear_term_hat(\n",
        "        u_hat,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "        DEALIAS,\n",
        "    )\n",
        "\n",
        "    viscous_hat = -nu * K2[None, :, :, :] * u_hat\n",
        "    return nonlinear_hat + viscous_hat\n",
        "\n",
        "\n",
        "def rk3_step(\n",
        "    u_hat,\n",
        "    dt,\n",
        "    nu,\n",
        "    KX,\n",
        "    KY,\n",
        "    KZ,\n",
        "    K2,\n",
        "    K2_SAFE,\n",
        "    DEALIAS,\n",
        "):\n",
        "    f0 = navier_stokes_rhs(\n",
        "        u_hat,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS,\n",
        "    )\n",
        "\n",
        "    u1 = u_hat + dt * f0\n",
        "    u1 = project_divergence_free(u1, KX, KY, KZ, K2_SAFE)\n",
        "    u1 *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    f1 = navier_stokes_rhs(\n",
        "        u1,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS,\n",
        "    )\n",
        "\n",
        "    u2 = 0.75 * u_hat + 0.25 * (u1 + dt * f1)\n",
        "    u2 = project_divergence_free(u2, KX, KY, KZ, K2_SAFE)\n",
        "    u2 *= DEALIAS[None, :, :, :]\n",
        "\n",
        "    f2 = navier_stokes_rhs(\n",
        "        u2,\n",
        "        nu,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2,\n",
        "        K2_SAFE,\n",
        "        DEALIAS,\n",
        "    )\n",
        "\n",
        "    u_new = (\n",
        "        (1.0 / 3.0) * u_hat\n",
        "        + (2.0 / 3.0) * (u2 + dt * f2)\n",
        "    )\n",
        "\n",
        "    u_new = project_divergence_free(\n",
        "        u_new,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "    )\n",
        "\n",
        "    u_new *= DEALIAS[None, :, :, :]\n",
        "    return u_new\n",
        "\n",
        "\n",
        "def generate_snapshot(\n",
        "    Re,\n",
        "    grid,\n",
        "    perturb_mode=None,\n",
        "    eps=0.0,\n",
        "):\n",
        "    KX, KY, KZ, K2, K2_SAFE, DEALIAS = grid\n",
        "    nu = 1.0 / Re\n",
        "\n",
        "    u_hat = initial_condition_hat(\n",
        "        N,\n",
        "        KX,\n",
        "        KY,\n",
        "        KZ,\n",
        "        K2_SAFE,\n",
        "        DEALIAS,\n",
        "        perturb_mode=perturb_mode,\n",
        "        eps=eps,\n",
        "    )\n",
        "\n",
        "    n_steps = int(np.round(SNAPSHOT_TIME / DT))\n",
        "\n",
        "    for _ in range(n_steps):\n",
        "        u_hat = rk3_step(\n",
        "            u_hat,\n",
        "            DT,\n",
        "            nu,\n",
        "            KX,\n",
        "            KY,\n",
        "            KZ,\n",
        "            K2,\n",
        "            K2_SAFE,\n",
        "            DEALIAS,\n",
        "        )\n",
        "\n",
        "    return u_hat, nu\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# STRUCTURAL FIELDS -- SAME DEFINITIONS\n",
        "# ============================================================\n",
        "\n",
        "def snapshot_structural_fields(u_hat, nu, grid):\n",
        "    KX, KY, KZ, K2, K2_SAFE, DEALIAS = grid\n",
        "    Ks = [KX, KY, KZ]\n",
        "\n",
        "    omega_hat = curl_hat(u_hat, KX, KY, KZ)\n",
        "    omega = np.fft.ifftn(\n",
        "        omega_hat,\n",
        "        axes=(1, 2, 3),\n",
        "    ).real\n",
        "\n",
        "    grad_u = np.empty(\n",
        "        (3, 3, N, N, N),\n",
        "        dtype=float,\n",
        "    )\n",
        "\n",
        "    for i in range(3):\n",
        "        for j in range(3):\n",
        "            grad_u[i, j] = np.fft.ifftn(\n",
        "                1j * Ks[j] * u_hat[i]\n",
        "            ).real\n",
        "\n",
        "    strain = 0.5 * (\n",
        "        grad_u + np.swapaxes(grad_u, 0, 1)\n",
        "    )\n",
        "\n",
        "    S_omega = np.einsum(\n",
        "        \"ijxyz,jxyz->ixyz\",\n",
        "        strain,\n",
        "        omega,\n",
        "        optimize=True,\n",
        "    )\n",
        "\n",
        "    stretching = np.sum(\n",
        "        omega * S_omega,\n",
        "        axis=0,\n",
        "    )\n",
        "\n",
        "    grad_omega = np.empty(\n",
        "        (3, 3, N, N, N),\n",
        "        dtype=float,\n",
        "    )\n",
        "\n",
        "    for i in range(3):\n",
        "        for j in range(3):\n",
        "            grad_omega[i, j] = np.fft.ifftn(\n",
        "                1j * Ks[j] * omega_hat[i]\n",
        "            ).real\n",
        "\n",
        "    viscous_dissipation = (\n",
        "        nu\n",
        "        * np.sum(\n",
        "            grad_omega * grad_omega,\n",
        "            axis=(0, 1),\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # NOTE:\n",
        "    # This is a fraction of ALL grid points satisfying BOTH conditions.\n",
        "    # It is not conditionalized by the number of stretching-positive points.\n",
        "    viscous_control_fraction = np.mean(\n",
        "        (stretching > 0.0)\n",
        "        & (viscous_dissipation >= stretching)\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"stretching\": stretching,\n",
        "        \"viscous_dissipation\": viscous_dissipation,\n",
        "        \"viscous_control_fraction\": float(\n",
        "            viscous_control_fraction\n",
        "        ),\n",
        "    }\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# FINITE LOGIC-LIKE STRUCTURAL INTERFACE\n",
        "# ============================================================\n",
        "\n",
        "def snapshot_to_literal_matrix(\n",
        "    stretching,\n",
        "    viscous_dissipation,\n",
        "):\n",
        "    x1_columns = []\n",
        "    x2_columns = []\n",
        "\n",
        "    for z in range(N):\n",
        "        x1_rows = []\n",
        "        x2_rows = []\n",
        "\n",
        "        for y in range(N):\n",
        "            local_stretch = stretching[:, y, z]\n",
        "            local_viscous = viscous_dissipation[:, y, z]\n",
        "\n",
        "            x1_line = (\n",
        "                local_stretch > 0.0\n",
        "            ).astype(np.uint8)\n",
        "\n",
        "            x2_line = (\n",
        "                local_viscous\n",
        "                >= np.maximum(local_stretch, 0.0)\n",
        "            ).astype(np.uint8)\n",
        "\n",
        "            x1_rows.extend(x1_line.tolist())\n",
        "            x2_rows.extend(x2_line.tolist())\n",
        "\n",
        "            # Prevent x-row wrap coupling.\n",
        "            x1_rows.append(0)\n",
        "            x2_rows.append(0)\n",
        "\n",
        "        x1_columns.append(x1_rows)\n",
        "        x2_columns.append(x2_rows)\n",
        "\n",
        "    x1 = np.asarray(\n",
        "        x1_columns,\n",
        "        dtype=np.uint8,\n",
        "    ).T\n",
        "\n",
        "    x2 = np.asarray(\n",
        "        x2_columns,\n",
        "        dtype=np.uint8,\n",
        "    ).T\n",
        "\n",
        "    return x1, x2\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# STEP 2 -- UNIVERSAL CJM CORE ENGINE\n",
        "# FROZEN FORMULA; only safer argument validation was added.\n",
        "# ============================================================\n",
        "\n",
        "def step2_cjm_core_engine(\n",
        "    x1,\n",
        "    x2,\n",
        "    gate=\"AND3\",\n",
        "    beta=34.0,\n",
        "    mode=\"logistic\",\n",
        "):\n",
        "    x1_prev = np.roll(\n",
        "        x1,\n",
        "        shift=1,\n",
        "        axis=0,\n",
        "    )\n",
        "    x1_prev[0, :] = 0\n",
        "\n",
        "    if gate == \"MAJ\":\n",
        "        clauses = (\n",
        "            (x1 + x2 + x1_prev) >= 2\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    elif gate == \"AND3\":\n",
        "        clauses = (\n",
        "            x1 & x2 & x1_prev\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    elif gate == \"OR3\":\n",
        "        clauses = (\n",
        "            x1 | x2 | x1_prev\n",
        "        ).astype(np.uint8)\n",
        "\n",
        "    else:\n",
        "        raise ValueError(\n",
        "            f\"Unknown CJM gate '{gate}'. \"\n",
        "            \"Use 'AND3', 'MAJ', or 'OR3'.\"\n",
        "        )\n",
        "\n",
        "    sat_density = clauses.mean(axis=0)\n",
        "\n",
        "    if mode == \"zscore\":\n",
        "        z = (\n",
        "            sat_density - sat_density.mean()\n",
        "        ) / (\n",
        "            sat_density.std() + TINY\n",
        "        )\n",
        "\n",
        "        energy = 1.0 / (\n",
        "            1.0 + np.exp(-z)\n",
        "        )\n",
        "\n",
        "    elif mode == \"logistic\":\n",
        "        center = np.median(sat_density)\n",
        "\n",
        "        energy = 1.0 / (\n",
        "            1.0\n",
        "            + np.exp(\n",
        "                -beta * (sat_density - center)\n",
        "            )\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        raise ValueError(\n",
        "            f\"Unknown CJM mode '{mode}'. \"\n",
        "            \"Use 'logistic' or 'zscore'.\"\n",
        "        )\n",
        "\n",
        "    energy = (\n",
        "        energy - energy.min()\n",
        "    ) / (\n",
        "        energy.max()\n",
        "        - energy.min()\n",
        "        + TINY\n",
        "    )\n",
        "\n",
        "    return energy\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# INDEPENDENT SNAPSHOT DISCRIMINATION\n",
        "# ============================================================\n",
        "\n",
        "def evaluate_one_snapshot(\n",
        "    Re,\n",
        "    grid,\n",
        "    perturb_mode=None,\n",
        "    eps=0.0,\n",
        "):\n",
        "    u_hat, nu = generate_snapshot(\n",
        "        Re,\n",
        "        grid,\n",
        "        perturb_mode=perturb_mode,\n",
        "        eps=eps,\n",
        "    )\n",
        "\n",
        "    fields = snapshot_structural_fields(\n",
        "        u_hat,\n",
        "        nu,\n",
        "        grid,\n",
        "    )\n",
        "\n",
        "    x1, x2 = snapshot_to_literal_matrix(\n",
        "        fields[\"stretching\"],\n",
        "        fields[\"viscous_dissipation\"],\n",
        "    )\n",
        "\n",
        "    energy = step2_cjm_core_engine(\n",
        "        x1,\n",
        "        x2,\n",
        "        gate=CJM_GATE,\n",
        "        beta=CJM_BETA,\n",
        "        mode=CJM_MODE,\n",
        "    )\n",
        "\n",
        "    x1_prev = np.roll(\n",
        "        x1,\n",
        "        shift=1,\n",
        "        axis=0,\n",
        "    )\n",
        "    x1_prev[0, :] = 0\n",
        "\n",
        "    direct_clauses = (\n",
        "        x1 & x2 & x1_prev\n",
        "    ).astype(np.uint8)\n",
        "\n",
        "    return {\n",
        "        \"Re\": float(Re),\n",
        "        \"eps\": float(eps),\n",
        "        \"mode\": perturb_mode,\n",
        "        \"raw_density\": float(\n",
        "            np.mean(direct_clauses)\n",
        "        ),\n",
        "        \"viscous_control\": fields[\n",
        "            \"viscous_control_fraction\"\n",
        "        ],\n",
        "        \"energy\": energy,\n",
        "        \"u_hat\": u_hat,\n",
        "    }\n",
        "\n",
        "\n",
        "def run_sweep(\n",
        "    grid,\n",
        "    re_values,\n",
        "    perturb_mode=None,\n",
        "    eps=0.0,\n",
        "    label=None,\n",
        "):\n",
        "    if label is None:\n",
        "        if perturb_mode is None or eps == 0.0:\n",
        "            label = \"Original TG\"\n",
        "        else:\n",
        "            label = (\n",
        "                f\"Mode {perturb_mode}, \"\n",
        "                f\"eps={100*eps:.1f}%\"\n",
        "            )\n",
        "\n",
        "    results = []\n",
        "\n",
        "    for index, Re in enumerate(re_values):\n",
        "        print(\n",
        "            f\"[{label} {index + 1}/{len(re_values)}] \"\n",
        "            f\"Re = {Re:.1f}\"\n",
        "        )\n",
        "\n",
        "        results.append(\n",
        "            evaluate_one_snapshot(\n",
        "                Re,\n",
        "                grid,\n",
        "                perturb_mode=perturb_mode,\n",
        "                eps=eps,\n",
        "            )\n",
        "        )\n",
        "\n",
        "    return results\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# ROBUSTNESS / SUSCEPTIBILITY METRICS\n",
        "# ============================================================\n",
        "\n",
        "def safe_pearson(a, b):\n",
        "    a = np.asarray(a, dtype=float)\n",
        "    b = np.asarray(b, dtype=float)\n",
        "\n",
        "    if (\n",
        "        np.std(a) < TINY\n",
        "        or np.std(b) < TINY\n",
        "    ):\n",
        "        return np.nan\n",
        "\n",
        "    return float(\n",
        "        np.corrcoef(a, b)[0, 1]\n",
        "    )\n",
        "\n",
        "\n",
        "def compare_one_pair(base, pert):\n",
        "    E0 = base[\"energy\"]\n",
        "    E1 = pert[\"energy\"]\n",
        "\n",
        "    corr = safe_pearson(E0, E1)\n",
        "    chi_j = (\n",
        "        np.nan\n",
        "        if not np.isfinite(corr)\n",
        "        else 1.0 - corr\n",
        "    )\n",
        "\n",
        "    band0 = E0 > 0.5\n",
        "    band1 = E1 > 0.5\n",
        "\n",
        "    band_agreement = float(\n",
        "        np.mean(band0 == band1)\n",
        "    )\n",
        "\n",
        "    union = np.logical_or(\n",
        "        band0,\n",
        "        band1,\n",
        "    ).sum()\n",
        "\n",
        "    if union == 0:\n",
        "        band_jaccard = 1.0\n",
        "    else:\n",
        "        band_jaccard = float(\n",
        "            np.logical_and(\n",
        "                band0,\n",
        "                band1,\n",
        "            ).sum()\n",
        "            / union\n",
        "        )\n",
        "\n",
        "    flow_rel_l2 = relative_spectral_l2(\n",
        "        base[\"u_hat\"],\n",
        "        pert[\"u_hat\"],\n",
        "    )\n",
        "\n",
        "    energy_l2 = float(\n",
        "        np.linalg.norm(E1 - E0)\n",
        "        / np.sqrt(E0.size)\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"Re\": base[\"Re\"],\n",
        "        \"mode\": pert[\"mode\"],\n",
        "        \"eps\": pert[\"eps\"],\n",
        "        \"correlation\": corr,\n",
        "        \"chi_J\": chi_j,\n",
        "        \"energy_RMS_difference\": energy_l2,\n",
        "        \"flow_relative_L2\": flow_rel_l2,\n",
        "        \"band_agreement\": band_agreement,\n",
        "        \"band_jaccard\": band_jaccard,\n",
        "        \"base_raw\": base[\"raw_density\"],\n",
        "        \"pert_raw\": pert[\"raw_density\"],\n",
        "        \"base_viscous\": base[\"viscous_control\"],\n",
        "        \"pert_viscous\": pert[\"viscous_control\"],\n",
        "    }\n",
        "\n",
        "\n",
        "def compare_sweeps(base_results, pert_results):\n",
        "    if len(base_results) != len(pert_results):\n",
        "        raise ValueError(\"Base and perturbed sweeps must have equal length.\")\n",
        "\n",
        "    return [\n",
        "        compare_one_pair(base, pert)\n",
        "        for base, pert in zip(base_results, pert_results)\n",
        "    ]\n",
        "\n",
        "\n",
        "def result_lookup(results):\n",
        "    return {\n",
        "        float(r[\"Re\"]): r\n",
        "        for r in results\n",
        "    }\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# CSV OUTPUT\n",
        "# ============================================================\n",
        "\n",
        "def write_csv(path, rows):\n",
        "    if not rows:\n",
        "        return\n",
        "\n",
        "    fieldnames = list(rows[0].keys())\n",
        "\n",
        "    # Remove array-valued internal objects if ever present.\n",
        "    fieldnames = [\n",
        "        f for f in fieldnames\n",
        "        if f not in {\"energy\", \"u_hat\"}\n",
        "    ]\n",
        "\n",
        "    with open(\n",
        "        path,\n",
        "        \"w\",\n",
        "        newline=\"\",\n",
        "        encoding=\"utf-8\",\n",
        "    ) as f:\n",
        "        writer = csv.DictWriter(\n",
        "            f,\n",
        "            fieldnames=fieldnames,\n",
        "        )\n",
        "        writer.writeheader()\n",
        "\n",
        "        for row in rows:\n",
        "            writer.writerow(\n",
        "                {\n",
        "                    key: row[key]\n",
        "                    for key in fieldnames\n",
        "                }\n",
        "            )\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# SINGLE PAPER FIGURE -- TWO PANELS, SCREEN ONLY\n",
        "# ============================================================\n",
        "\n",
        "def show_paper_figure(rows, full_robustness):\n",
        "    \"\"\"\n",
        "    Display ONE publication-oriented figure with two side-by-side panels.\n",
        "\n",
        "    (a) Finite CJM structural susceptibility:\n",
        "            chi_J = 1 - corr(E0, E_eps)\n",
        "\n",
        "    (b) Relative physical flow-field difference:\n",
        "            ||u_eps - u0||_2 / ||u0||_2\n",
        "\n",
        "    No PNG is written. The figure is displayed only with plt.show().\n",
        "    \"\"\"\n",
        "\n",
        "    fig, axes = plt.subplots(\n",
        "        1,\n",
        "        2,\n",
        "        figsize=(13.2, 4.9),\n",
        "    )\n",
        "\n",
        "    ax1, ax2 = axes\n",
        "\n",
        "    # Different markers distinguish perturbation amplitudes.\n",
        "    marker_map = {\n",
        "        0.01: \"o\",\n",
        "        0.02: \"s\",\n",
        "        0.03: \"^\",\n",
        "    }\n",
        "\n",
        "    # Different line styles distinguish the two perturbation modes.\n",
        "    linestyle_map = {\n",
        "        1: \"-\",\n",
        "        2: \"--\",\n",
        "    }\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # (a) Structural susceptibility\n",
        "    # --------------------------------------------------------\n",
        "    for mode in PERTURB_MODES:\n",
        "        for eps in PERTURB_EPS_VALUES:\n",
        "            selected = sorted(\n",
        "                [\n",
        "                    r for r in rows\n",
        "                    if (\n",
        "                        r[\"mode\"] == mode\n",
        "                        and np.isclose(r[\"eps\"], eps)\n",
        "                    )\n",
        "                ],\n",
        "                key=lambda r: r[\"Re\"],\n",
        "            )\n",
        "\n",
        "            Re = np.asarray(\n",
        "                [r[\"Re\"] for r in selected]\n",
        "            )\n",
        "\n",
        "            chi = np.asarray(\n",
        "                [r[\"chi_J\"] for r in selected]\n",
        "            )\n",
        "\n",
        "            ax1.plot(\n",
        "                Re,\n",
        "                chi,\n",
        "                marker=marker_map[float(eps)],\n",
        "                linestyle=linestyle_map[mode],\n",
        "                label=(\n",
        "                    f\"Mode {mode}, \"\n",
        "                    f\"{100*eps:.0f}%\"\n",
        "                ),\n",
        "            )\n",
        "\n",
        "    ax1.set_xlabel(\"Reynolds number\")\n",
        "    ax1.set_ylabel(\n",
        "        r\"$\\chi_J=1-\\rho(E_0,E_\\varepsilon)$\"\n",
        "    )\n",
        "    ax1.set_title(\n",
        "        \"(a) CJM structural susceptibility\"\n",
        "    )\n",
        "    ax1.grid(alpha=0.3)\n",
        "\n",
        "    # Full-Re 3% Mode-1 robustness statistic retained in the figure.\n",
        "    valid_corr = np.asarray(\n",
        "        [\n",
        "            r[\"correlation\"]\n",
        "            for r in full_robustness\n",
        "            if np.isfinite(r[\"correlation\"])\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    if valid_corr.size:\n",
        "        ax1.text(\n",
        "            0.03,\n",
        "            0.96,\n",
        "            (\n",
        "                \"Mode 1, 3% full sweep\\n\"\n",
        "                f\"median $\\\\rho$ = \"\n",
        "                f\"{np.median(valid_corr):.4f}\"\n",
        "            ),\n",
        "            transform=ax1.transAxes,\n",
        "            va=\"top\",\n",
        "        )\n",
        "\n",
        "    ax1.legend(\n",
        "        fontsize=8,\n",
        "        ncol=2,\n",
        "    )\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # (b) Physical flow-field difference\n",
        "    # --------------------------------------------------------\n",
        "    for mode in PERTURB_MODES:\n",
        "        for eps in PERTURB_EPS_VALUES:\n",
        "            selected = sorted(\n",
        "                [\n",
        "                    r for r in rows\n",
        "                    if (\n",
        "                        r[\"mode\"] == mode\n",
        "                        and np.isclose(r[\"eps\"], eps)\n",
        "                    )\n",
        "                ],\n",
        "                key=lambda r: r[\"Re\"],\n",
        "            )\n",
        "\n",
        "            Re = np.asarray(\n",
        "                [r[\"Re\"] for r in selected]\n",
        "            )\n",
        "\n",
        "            diff = np.asarray(\n",
        "                [\n",
        "                    r[\"flow_relative_L2\"]\n",
        "                    for r in selected\n",
        "                ]\n",
        "            )\n",
        "\n",
        "            ax2.plot(\n",
        "                Re,\n",
        "                diff,\n",
        "                marker=marker_map[float(eps)],\n",
        "                linestyle=linestyle_map[mode],\n",
        "                label=(\n",
        "                    f\"Mode {mode}, \"\n",
        "                    f\"{100*eps:.0f}%\"\n",
        "                ),\n",
        "            )\n",
        "\n",
        "    ax2.set_xlabel(\"Reynolds number\")\n",
        "    ax2.set_ylabel(\n",
        "        r\"$\\|u_\\varepsilon-u_0\\|_2/\\|u_0\\|_2$\"\n",
        "    )\n",
        "    ax2.set_title(\n",
        "        \"(b) Physical flow-field displacement\"\n",
        "    )\n",
        "    ax2.grid(alpha=0.3)\n",
        "    ax2.legend(\n",
        "        fontsize=8,\n",
        "        ncol=2,\n",
        "    )\n",
        "\n",
        "    fig.suptitle(\n",
        "        \"CJM Robustness and Structural Susceptibility\",\n",
        "        fontsize=13,\n",
        "    )\n",
        "\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# TABLE / TEXT SUMMARY\n",
        "# ============================================================\n",
        "\n",
        "def print_full_robustness_summary(comparison):\n",
        "    correlations = np.asarray(\n",
        "        [\n",
        "            r[\"correlation\"]\n",
        "            for r in comparison\n",
        "            if np.isfinite(r[\"correlation\"])\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    agreements = np.asarray(\n",
        "        [r[\"band_agreement\"] for r in comparison]\n",
        "    )\n",
        "\n",
        "    jaccards = np.asarray(\n",
        "        [r[\"band_jaccard\"] for r in comparison]\n",
        "    )\n",
        "\n",
        "    min_row = min(\n",
        "        comparison,\n",
        "        key=lambda r: r[\"correlation\"],\n",
        "    )\n",
        "\n",
        "    print()\n",
        "    print(\"=\" * 72)\n",
        "    print(\"FULL-Re MODE-1 3% ROBUSTNESS SUMMARY\")\n",
        "    print(\"=\" * 72)\n",
        "    print(\n",
        "        f\"Median E-vector correlation : \"\n",
        "        f\"{np.median(correlations):.4f}\"\n",
        "    )\n",
        "    print(\n",
        "        f\"Minimum E-vector correlation: \"\n",
        "        f\"{np.min(correlations):.4f} \"\n",
        "        f\"(Re={min_row['Re']:.0f})\"\n",
        "    )\n",
        "    print(\n",
        "        f\"Median band agreement       : \"\n",
        "        f\"{np.median(agreements):.4f}\"\n",
        "    )\n",
        "    print(\n",
        "        f\"Median band Jaccard         : \"\n",
        "        f\"{np.median(jaccards):.4f}\"\n",
        "    )\n",
        "    print(\"=\" * 72)\n",
        "\n",
        "\n",
        "def print_susceptibility_table(rows):\n",
        "    print()\n",
        "    print(\"=\" * 100)\n",
        "    print(\"TWO-MODE STRUCTURAL SUSCEPTIBILITY\")\n",
        "    print(\"=\" * 100)\n",
        "    print(\n",
        "        f\"{'Mode':>4} {'eps':>6} {'Re':>6} \"\n",
        "        f\"{'corr(E)':>10} {'chi_J':>10} \"\n",
        "        f\"{'flow_rel_L2':>14} {'E_RMS_diff':>12}\"\n",
        "    )\n",
        "    print(\"-\" * 100)\n",
        "\n",
        "    for row in sorted(\n",
        "        rows,\n",
        "        key=lambda r: (\n",
        "            r[\"mode\"],\n",
        "            r[\"eps\"],\n",
        "            r[\"Re\"],\n",
        "        ),\n",
        "    ):\n",
        "        print(\n",
        "            f\"{row['mode']:>4} \"\n",
        "            f\"{100*row['eps']:>5.1f}% \"\n",
        "            f\"{row['Re']:>6.0f} \"\n",
        "            f\"{row['correlation']:>10.4f} \"\n",
        "            f\"{row['chi_J']:>10.4f} \"\n",
        "            f\"{row['flow_relative_L2']:>14.6f} \"\n",
        "            f\"{row['energy_RMS_difference']:>12.6f}\"\n",
        "        )\n",
        "\n",
        "    print(\"=\" * 100)\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# MASTER\n",
        "# ============================================================\n",
        "\n",
        "def run_secjm_v59R2():\n",
        "    start = time.time()\n",
        "\n",
        "    print(\"=\" * 76)\n",
        "    print(\"se-CJM v5.9-R2\")\n",
        "    print(\"Navier-Stokes Two-Mode Robustness + Structural Susceptibility\")\n",
        "    print(\"=\" * 76)\n",
        "    print(f\"Grid                     : {N}^3\")\n",
        "    print(\n",
        "        f\"Full Re sweep             : \"\n",
        "        f\"{RE_VALUES[0]:.0f} - {RE_VALUES[-1]:.0f}\"\n",
        "    )\n",
        "    print(\n",
        "        f\"Focused Re values         : \"\n",
        "        f\"{FOCUSED_RE_VALUES.tolist()}\"\n",
        "    )\n",
        "    print(\n",
        "        f\"Perturbation amplitudes   : \"\n",
        "        f\"{[float(e) for e in PERTURB_EPS_VALUES]}\"\n",
        "    )\n",
        "    print(f\"Snapshot time            : {SNAPSHOT_TIME}\")\n",
        "    print(f\"dt                       : {DT}\")\n",
        "    print(\"Perturbation modes       : 1, 2\")\n",
        "    print(\"Perturbations div-free   : YES (analytic)\")\n",
        "    print(\"STEP 2 modification      : NONE\")\n",
        "    print(\"CJM gate/beta/mode       : AND3 / 34 / logistic\")\n",
        "    print(\"PNG output               : NONE (screen display only)\")\n",
        "    print(\"=\" * 76)\n",
        "\n",
        "    grid = make_spectral_grid(N)\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 1) Original full Taylor-Green baseline\n",
        "    # --------------------------------------------------------\n",
        "    base_full = run_sweep(\n",
        "        grid,\n",
        "        RE_VALUES,\n",
        "        perturb_mode=None,\n",
        "        eps=0.0,\n",
        "        label=\"Original TG\",\n",
        "    )\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 2) Original v5.9-R robustness test:\n",
        "    #    Mode 1, eps=3%, full Re sweep\n",
        "    # --------------------------------------------------------\n",
        "    mode1_3_full = run_sweep(\n",
        "        grid,\n",
        "        RE_VALUES,\n",
        "        perturb_mode=FULL_ROBUSTNESS_MODE,\n",
        "        eps=FULL_ROBUSTNESS_EPS,\n",
        "        label=\"Mode 1, eps=3%\",\n",
        "    )\n",
        "\n",
        "    full_robustness = compare_sweeps(\n",
        "        base_full,\n",
        "        mode1_3_full,\n",
        "    )\n",
        "\n",
        "    # Lookup tables allow reuse of already-computed snapshots.\n",
        "    base_lookup = result_lookup(base_full)\n",
        "    mode1_3_lookup = result_lookup(mode1_3_full)\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 3) Focused two-mode susceptibility sweep\n",
        "    # --------------------------------------------------------\n",
        "    susceptibility_rows = []\n",
        "\n",
        "    for mode in PERTURB_MODES:\n",
        "        for eps in PERTURB_EPS_VALUES:\n",
        "            print()\n",
        "            print(\n",
        "                f\"--- Focused susceptibility: \"\n",
        "                f\"Mode {mode}, eps={100*eps:.1f}% ---\"\n",
        "            )\n",
        "\n",
        "            for Re in FOCUSED_RE_VALUES:\n",
        "                base = base_lookup[float(Re)]\n",
        "\n",
        "                # Reuse Mode-1 / 3% result from the full sweep.\n",
        "                if (\n",
        "                    mode == FULL_ROBUSTNESS_MODE\n",
        "                    and np.isclose(\n",
        "                        eps,\n",
        "                        FULL_ROBUSTNESS_EPS,\n",
        "                    )\n",
        "                ):\n",
        "                    pert = mode1_3_lookup[float(Re)]\n",
        "\n",
        "                else:\n",
        "                    pert = evaluate_one_snapshot(\n",
        "                        Re,\n",
        "                        grid,\n",
        "                        perturb_mode=mode,\n",
        "                        eps=eps,\n",
        "                    )\n",
        "\n",
        "                susceptibility_rows.append(\n",
        "                    compare_one_pair(\n",
        "                        base,\n",
        "                        pert,\n",
        "                    )\n",
        "                )\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 4) Summaries\n",
        "    # --------------------------------------------------------\n",
        "    print_full_robustness_summary(\n",
        "        full_robustness\n",
        "    )\n",
        "\n",
        "    print_susceptibility_table(\n",
        "        susceptibility_rows\n",
        "    )\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 5) Optional CSV output\n",
        "    # --------------------------------------------------------\n",
        "    if SAVE_CSV:\n",
        "        write_csv(\n",
        "            ROBUSTNESS_CSV,\n",
        "            full_robustness,\n",
        "        )\n",
        "        write_csv(\n",
        "            SUSCEPTIBILITY_CSV,\n",
        "            susceptibility_rows,\n",
        "        )\n",
        "\n",
        "        print()\n",
        "        print(f\"Saved CSV: {ROBUSTNESS_CSV}\")\n",
        "        print(f\"Saved CSV: {SUSCEPTIBILITY_CSV}\")\n",
        "\n",
        "    # --------------------------------------------------------\n",
        "    # 6) ONE SCREEN-ONLY PAPER FIGURE\n",
        "    # --------------------------------------------------------\n",
        "\n",
        "    show_paper_figure(\n",
        "        susceptibility_rows,\n",
        "        full_robustness,\n",
        "    )\n",
        "\n",
        "    elapsed = time.time() - start\n",
        "\n",
        "    print()\n",
        "    print(\"=\" * 76)\n",
        "    print(\n",
        "        f\"Total runtime: {elapsed:.1f} seconds\"\n",
        "    )\n",
        "    print(\"=\" * 76)\n",
        "\n",
        "    return {\n",
        "        \"base_full\": base_full,\n",
        "        \"mode1_3_full\": mode1_3_full,\n",
        "        \"full_robustness\": full_robustness,\n",
        "        \"susceptibility\": susceptibility_rows,\n",
        "    }\n",
        "\n",
        "\n",
        "# ============================================================\n",
        "# RUN\n",
        "# ============================================================\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    results = run_secjm_v59R2()\n"
      ],
      "metadata": {
        "id": "bfVdzOLkSSam",
        "outputId": "737611b0-5efa-4c8f-b416-1e896b8e49c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================================\n",
            "se-CJM v5.9-R2\n",
            "Navier-Stokes Two-Mode Robustness + Structural Susceptibility\n",
            "============================================================================\n",
            "Grid                     : 20^3\n",
            "Full Re sweep             : 50 - 400\n",
            "Focused Re values         : [275.0, 300.0, 325.0, 350.0, 375.0]\n",
            "Perturbation amplitudes   : [0.01, 0.02, 0.03]\n",
            "Snapshot time            : 4.0\n",
            "dt                       : 0.02\n",
            "Perturbation modes       : 1, 2\n",
            "Perturbations div-free   : YES (analytic)\n",
            "STEP 2 modification      : NONE\n",
            "CJM gate/beta/mode       : AND3 / 34 / logistic\n",
            "PNG output               : NONE (screen display only)\n",
            "============================================================================\n",
            "[Original TG 1/15] Re = 50.0\n",
            "[Original TG 2/15] Re = 75.0\n",
            "[Original TG 3/15] Re = 100.0\n",
            "[Original TG 4/15] Re = 125.0\n",
            "[Original TG 5/15] Re = 150.0\n",
            "[Original TG 6/15] Re = 175.0\n",
            "[Original TG 7/15] Re = 200.0\n",
            "[Original TG 8/15] Re = 225.0\n",
            "[Original TG 9/15] Re = 250.0\n",
            "[Original TG 10/15] Re = 275.0\n",
            "[Original TG 11/15] Re = 300.0\n",
            "[Original TG 12/15] Re = 325.0\n",
            "[Original TG 13/15] Re = 350.0\n",
            "[Original TG 14/15] Re = 375.0\n",
            "[Original TG 15/15] Re = 400.0\n",
            "[Mode 1, eps=3% 1/15] Re = 50.0\n",
            "[Mode 1, eps=3% 2/15] Re = 75.0\n",
            "[Mode 1, eps=3% 3/15] Re = 100.0\n",
            "[Mode 1, eps=3% 4/15] Re = 125.0\n",
            "[Mode 1, eps=3% 5/15] Re = 150.0\n",
            "[Mode 1, eps=3% 6/15] Re = 175.0\n",
            "[Mode 1, eps=3% 7/15] Re = 200.0\n",
            "[Mode 1, eps=3% 8/15] Re = 225.0\n",
            "[Mode 1, eps=3% 9/15] Re = 250.0\n",
            "[Mode 1, eps=3% 10/15] Re = 275.0\n",
            "[Mode 1, eps=3% 11/15] Re = 300.0\n",
            "[Mode 1, eps=3% 12/15] Re = 325.0\n",
            "[Mode 1, eps=3% 13/15] Re = 350.0\n",
            "[Mode 1, eps=3% 14/15] Re = 375.0\n",
            "[Mode 1, eps=3% 15/15] Re = 400.0\n",
            "\n",
            "--- Focused susceptibility: Mode 1, eps=1.0% ---\n",
            "\n",
            "--- Focused susceptibility: Mode 1, eps=2.0% ---\n",
            "\n",
            "--- Focused susceptibility: Mode 1, eps=3.0% ---\n",
            "\n",
            "--- Focused susceptibility: Mode 2, eps=1.0% ---\n",
            "\n",
            "--- Focused susceptibility: Mode 2, eps=2.0% ---\n",
            "\n",
            "--- Focused susceptibility: Mode 2, eps=3.0% ---\n",
            "\n",
            "========================================================================\n",
            "FULL-Re MODE-1 3% ROBUSTNESS SUMMARY\n",
            "========================================================================\n",
            "Median E-vector correlation : 0.9745\n",
            "Minimum E-vector correlation: 0.7998 (Re=325)\n",
            "Median band agreement       : 1.0000\n",
            "Median band Jaccard         : 1.0000\n",
            "========================================================================\n",
            "\n",
            "====================================================================================================\n",
            "TWO-MODE STRUCTURAL SUSCEPTIBILITY\n",
            "====================================================================================================\n",
            "Mode    eps     Re    corr(E)      chi_J    flow_rel_L2   E_RMS_diff\n",
            "----------------------------------------------------------------------------------------------------\n",
            "   1   1.0%    275     0.9931     0.0069       0.011258     0.058532\n",
            "   1   1.0%    300     0.9234     0.0766       0.011603     0.141545\n",
            "   1   1.0%    325     0.8463     0.1537       0.011906     0.195505\n",
            "   1   1.0%    350     0.9800     0.0200       0.012174     0.072157\n",
            "   1   1.0%    375     0.9660     0.0340       0.012413     0.112555\n",
            "   1   2.0%    275     0.9275     0.0725       0.022515     0.150792\n",
            "   1   2.0%    300     0.8884     0.1116       0.023205     0.179435\n",
            "   1   2.0%    325     0.7664     0.2336       0.023811     0.269224\n",
            "   1   2.0%    350     0.9698     0.0302       0.024347     0.098584\n",
            "   1   2.0%    375     0.9381     0.0619       0.024826     0.123476\n",
            "   1   3.0%    275     0.9803     0.0197       0.033772     0.079928\n",
            "   1   3.0%    300     0.8664     0.1336       0.034806     0.196119\n",
            "   1   3.0%    325     0.7998     0.2002       0.035715     0.222063\n",
            "   1   3.0%    350     0.9393     0.0607       0.036520     0.124285\n",
            "   1   3.0%    375     0.9307     0.0693       0.037237     0.134311\n",
            "   2   1.0%    275     0.9874     0.0126       0.012554     0.074371\n",
            "   2   1.0%    300     0.9609     0.0391       0.012831     0.100232\n",
            "   2   1.0%    325     0.9172     0.0828       0.013073     0.137933\n",
            "   2   1.0%    350     0.9888     0.0112       0.013285     0.048925\n",
            "   2   1.0%    375     0.9270     0.0730       0.013472     0.134699\n",
            "   2   2.0%    275     0.9802     0.0198       0.025107     0.090666\n",
            "   2   2.0%    300     0.9383     0.0617       0.025661     0.125536\n",
            "   2   2.0%    325     0.8360     0.1640       0.026144     0.197979\n",
            "   2   2.0%    350     0.8612     0.1388       0.026568     0.176433\n",
            "   2   2.0%    375     0.8408     0.1592       0.026944     0.194727\n",
            "   2   3.0%    275     0.9454     0.0546       0.037659     0.144246\n",
            "   2   3.0%    300     0.9024     0.0976       0.038490     0.152959\n",
            "   2   3.0%    325     0.6718     0.3282       0.039213     0.268788\n",
            "   2   3.0%    350     0.6442     0.3558       0.039849     0.274486\n",
            "   2   3.0%    375     0.5936     0.4064       0.040413     0.305108\n",
            "====================================================================================================\n",
            "\n",
            "Saved CSV: secjm_v59R2_full_robustness.csv\n",
            "Saved CSV: secjm_v59R2_two_mode_susceptibility.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1320x490 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================================\n",
            "Total runtime: 141.6 seconds\n",
            "============================================================================\n"
          ]
        }
      ]
    }
  ]
}