{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0084f067",
   "metadata": {},
   "source": [
    "# NA Inversion Example (updated physics)\n",
    "\n",
    "This notebook demonstrates kinematic inversion using the Neighbourhood Algorithm (NA) with the updated physical implementation:\n",
    "\n",
    "$$M_0 = \\mu(z) \\times A \\times slip$$\n",
    "\n",
    "**Workflow:**\n",
    "1. Load configuration and forward model\n",
    "2. Generate controlled synthetic observed waveforms from a 7-parameter ellipse model\n",
    "3. Run NA search\n",
    "4. Analyze best model and compare $M_0$/$M_w$\n",
    "5. Visualize convergence and export results\n",
    "\n",
    "**Parameters inverted:**\n",
    "- a1, a2: Ellipse semi-axes (km)\n",
    "- theta: Rotation angle (x \u03c0)\n",
    "- np, tp: Center position\n",
    "- dmax: Maximum slip (m)\n",
    "- vr: Rupture velocity (km/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "017e60b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 All imports successful\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def find_project_root(start: Path) -> Path:\n",
    "    for p in [start, *start.parents]:\n",
    "        if (p / 'kdellipspy').exists():\n",
    "            return p\n",
    "    raise FileNotFoundError('No se encontro PROJECT_ROOT con carpeta kdellipspy.')\n",
    "\n",
    "PROJECT_ROOT = find_project_root(Path.cwd().resolve())\n",
    "KIN_ROOT = PROJECT_ROOT / 'Kinematic_inversion'\n",
    "INPUT_CTL = KIN_ROOT / 'input.ctl'\n",
    "\n",
    "if not INPUT_CTL.exists():\n",
    "    raise FileNotFoundError(f'No se encontro input.ctl en {INPUT_CTL}')\n",
    "\n",
    "if str(PROJECT_ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(PROJECT_ROOT))\n",
    "\n",
    "from kdellipspy import ConfigParser\n",
    "from kdellipspy import AxitraForwardModel\n",
    "from kdellipspy import NAInversionModel, NAConfig, MisfitCalculator\n"
,
    "from kdellipspy import load_and_filter_observed_data, bandpass_filter_waveforms\n",
    "\n",
    "# Compatibilidad con celdas existentes\n",
    "root = KIN_ROOT\n",
    "input_ctl = INPUT_CTL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb0f47e",
   "metadata": {},
   "source": [
    "## Step 1: Load configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ac3a560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Loaded configuration from [PROJECT_ROOT]/Kinematic_inversion/input.ctl\n",
      "\n",
      "Inversion parameters (from input.ctl):\n",
      "  1. Length of axis 1                 (km) [   5.000,   10.000] INVERT\n",
      "  2. Length of axis 2                 (km) [   5.000,   10.000] INVERT\n",
      "  3. Rotation angle                 (x pi) [   0.000,    2.000] INVERT\n",
      "  4. Position of the center np      [   0.000,    1.000] INVERT\n",
      "  5. Position of the center tp     (x 2pi) [   0.000,    1.000] INVERT\n",
      "  6. Maximum slip (Dmax)               (m) [   1.000,    3.000] INVERT\n",
      "  7. Rupture velocity (Vr)          (km/s) [   0.500,    3.500] INVERT\n",
      "\n",
      "Inversion process parameters:\n",
      "  Algorithm: NA\n",
      "  Iterations: 10\n",
      "  Initial samples: 100\n",
      "  Iteration samples: 30\n",
      "  Resample cells: 7\n"
     ]
    }
   ],
   "source": [
    "input_ctl = root / 'input.ctl'\n",
    "\n",
    "cfg = ConfigParser(str(input_ctl))\n",
    "print(f\"\u2713 Loaded configuration from {input_ctl}\")\n",
    "print(f\"\\nInversion parameters (from input.ctl):\")\n",
    "for i, param in enumerate(cfg.inversion_params.parameters, 1):\n",
    "    status = \"INVERT\" if param.flag else \"FIXED\"\n",
    "    print(f\"  {i}. {param.name:30s} [{param.min_val:8.3f}, {param.max_val:8.3f}] {status}\")\n",
    "\n",
    "print(f\"\\nInversion process parameters:\")\n",
    "print(f\"  Algorithm: {'NA' if cfg.inversion_process.algorithm_type == 0 else 'MC'}\")\n",
    "print(f\"  Iterations: {cfg.inversion_process.num_iterations}\")\n",
    "print(f\"  Initial samples: {cfg.inversion_process.ss1}\")\n",
    "print(f\"  Iteration samples: {cfg.inversion_process.ss_other}\")\n",
    "print(f\"  Resample cells: {cfg.inversion_process.cells_resample}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6525dff",
   "metadata": {},
   "source": [
    "## Step 2: Load REAL DATA and check it.\n",
    "\n",
    "This apply for root / 'DATA' / 'real_disp_x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1f4c504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Observed data loaded\n",
      "  Shape: (10, 3, 512)  (n_stations=10, 3_components, npts=512)\n",
      "  Time range: 0.000 - 127.750 s\n",
      "  Sampling rate: 4.0 Hz\n",
      "  Frequency band: 0.020 - 0.100 Hz\n"
     ]
    }
   ],
   "source": [
    "from src.signal_utils import load_and_filter_observed_data\n",
    "\n",
    "observed, time = load_and_filter_observed_data(\n",
    "    input_ctl_path=root / 'input.ctl',\n",
    "    data_dir=root / 'DATA'\n",
    ")\n",
    "\n",
    "print(f\"\u2713 Observed data loaded\")\n",
    "print(f\"  Shape: {observed.shape}  (n_stations={observed.shape[0]}, 3_components, npts={observed.shape[2]})\")\n",
    "print(f\"  Time range: {time[0]:.3f} - {time[-1]:.3f} s\")\n",
    "print(f\"  Sampling rate: {1/(time[1]-time[0]):.1f} Hz\")\n",
    "print(f\"  Frequency band: {float(cfg.ellipse.freq1):.3f} - {float(cfg.ellipse.freq2):.3f} Hz\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a77ccc",
   "metadata": {},
   "source": [
    "Plot the data to visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8a49bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Observed data visualized (3 stations)\n"
     ]
    }
   ],
   "source": [
    "# Visualize observed data\n",
    "fig, axes = plt.subplots(3, 3, figsize=(14, 8))\n",
    "\n",
    "n_stations_show = min(3, observed.shape[0])\n",
    "for i in range(n_stations_show):\n",
    "    axes[i, 0].plot(time, observed[i, 0], 'g-', linewidth=1.5)\n",
    "    axes[i, 0].set_title(f'Station {i+1} - X Component')\n",
    "    axes[i, 0].grid(True, alpha=0.3)\n",
    "    \n",
    "    axes[i, 1].plot(time, observed[i, 1], 'g-', linewidth=1.5)\n",
    "    axes[i, 1].set_title(f'Station {i+1} - Y Component')\n",
    "    axes[i, 1].grid(True, alpha=0.3)\n",
    "    \n",
    "    axes[i, 2].plot(time, observed[i, 2], 'g-', linewidth=1.5)\n",
    "    axes[i, 2].set_title(f'Station {i+1} - Z Component')\n",
    "    axes[i, 2].grid(True, alpha=0.3)\n",
    "\n",
    "plt.suptitle('Observed Waveforms (Filtered)', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\u2713 Observed data visualized ({n_stations_show} stations)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d514fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Forward model initialized\n",
      "  axitra dir: [PROJECT_ROOT]/Kinematic_inversion/axitra\n",
      "\n",
      "\u2713 Using midpoint model parameters:\n",
      "  a1     = 7.5000\n",
      "  a2     = 7.5000\n",
      "  theta  = 1.0000\n",
      "  np     = 0.5000\n",
      "  tp     = 0.5000\n",
      "  dmax   = 2.0000\n",
      "  vr     = 2.0000\n",
      "\n",
      "\u2713 Geometry built:\n",
      "  Total moment M0 = 1.838e+19 N.m\n",
      "  Moment magnitude Mw = 6.78\n"
     ]
    }
   ],
   "source": [
    "# Initialize forward model and use midpoint model parameters\n",
    "fwd = AxitraForwardModel(str(root / 'input.ctl'))\n",
    "\n",
    "# Use midpoint of parameter ranges from input.ctl\n",
    "midpoint_model = np.array([\n",
    "    0.5 * (float(p.min_val) + float(p.max_val))\n",
    "    for p in cfg.inversion_params.parameters\n",
    "], dtype=float)\n",
    "\n",
    "print(f\"\u2713 Forward model initialized\")\n",
    "print(f\"\\n\u2713 Using midpoint model parameters:\")\n",
    "param_names = ['a1', 'a2', 'theta', 'np', 'tp', 'dmax', 'vr']\n",
    "for name, val in zip(param_names, midpoint_model):\n",
    "    print(f\"  {name:6s} = {val:.4f}\")\n",
    "\n",
    "# Build geometry\n",
    "geometry = fwd.build_geometry_with_ellipse_slip(midpoint_model)\n",
    "m0, mw = fwd.estimate_total_moment_and_mw(midpoint_model, geometry)\n",
    "print(f\"\\n\u2713 Geometry built:\")\n",
    "print(f\"  Total moment M0 = {m0:.3e} N.m\")\n",
    "print(f\"  Moment magnitude Mw = {mw:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cf698a",
   "metadata": {},
   "source": [
    "## Step 3: Build forward model and generate synthetic waveforms\n",
    "\n",
    "This section demonstrates the complete workflow for computing synthetic waveforms and filtering them to match the observed data's frequency band.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb4678f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create output directory\n",
    "from datetime import datetime\n",
    "\n",
    "output_dir = root / 'output' / f'na_inversion_{datetime.now().strftime(\"%Y%m%d_%H%M%S\")}'\n",
    "output_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "print(f\"\u2713 Output directory created: {output_dir}\")\n",
    "\n",
    "# Export results using the built-in method\n",
    "result.export_results(output_dir / 'inversion_results.json')\n",
    "print(f\"\u2713 Results exported to: {output_dir / 'inversion_results.json'}\")\n",
    "\n",
    "# Save additional analysis\n",
    "import json\n",
    "\n",
    "summary = {\n",
    "    'timestamp': datetime.now().isoformat(),\n",
    "    'total_models': len(result.all_models),\n",
    "    'iterations': int(na_config.n_iterations),\n",
    "    'best_misfit': float(result.best_model.misfit),\n",
    "    'best_iteration': int(result.best_model.iteration),\n",
    "    'best_model': result.best_model.model.tolist(),\n",
    "    'best_model_m0': float(m0_best),\n",
    "    'best_model_mw': float(mw_best),\n",
    "    'initial_model': midpoint_model.tolist(),\n",
    "    'initial_misfit': float(misfit_test),\n",
    "    'search_time_seconds': float(t_elapsed),\n",
    "    'misfit_improvement_percent': float(improvement),\n",
    "    'config': {\n",
    "        'freq1': float(freq1),\n",
    "        'freq2': float(freq2),\n",
    "        't0': float(cfg.ellipse.t0),\n",
    "        'time_window_s': 20.0,\n",
    "    }\n",
    "}\n",
    "\n",
    "summary_path = output_dir / 'inversion_summary.json'\n",
    "with open(summary_path, 'w') as f:\n",
    "    json.dump(summary, f, indent=2)\n",
    "\n",
    "print(f\"\u2713 Summary saved to: {summary_path}\")\n",
    "\n",
    "# Save waveform comparison\n",
    "waveform_data = {\n",
    "    'time': time.tolist(),\n",
    "    'observed': observed.tolist(),\n",
    "    'synthetic_best': synthetic_best_filtered.tolist(),\n",
    "    'residual': (observed - synthetic_best_filtered).tolist(),\n",
    "}\n",
    "\n",
    "waveform_path = output_dir / 'waveform_comparison.json'\n",
    "with open(waveform_path, 'w') as f:\n",
    "    json.dump(waveform_data, f)\n",
    "\n",
    "print(f\"\u2713 Waveform data saved to: {waveform_path}\")\n",
    "\n",
    "# Print summary\n",
    "print(\"\\n\" + \"=\" * 70)\n",
    "print(\"INVERSION SUMMARY\")\n",
    "print(\"=\" * 70)\n",
    "print(f\"Total models evaluated: {len(result.all_models)}\")\n",
    "print(f\"Search iterations: {na_config.n_iterations}\")\n",
    "print(f\"Search time: {t_elapsed:.1f} seconds\")\n",
    "print(f\"\\nInitial model misfit: {misfit_test:.6f}\")\n",
    "print(f\"Best model misfit:    {result.best_model.misfit:.6f}\")\n",
    "print(f\"Improvement:          {improvement:.1f}%\")\n",
    "print(f\"\\nBest model:\")\n",
    "for name, val in zip(param_names, result.best_model.model):\n",
    "    print(f\"  {name:6s} = {val:.4f}\")\n",
    "print(f\"\\nBest model moment:\")\n",
    "print(f\"  M0 = {m0_best:.3e} N.m\")\n",
    "print(f\"  Mw = {mw_best:.2f}\")\n",
    "print(f\"\\nResults saved to: {output_dir}\")\n",
    "print(\"=\" * 70)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcfa6c7",
   "metadata": {},
   "source": [
    "## Step 7: Export Results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa4ad6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate and visualize synthetic waveforms from best model\n",
    "print(\"Generating synthetic waveforms from best model...\")\n",
    "\n",
    "# Build geometry with best model\n",
    "geometry_best = fwd.build_geometry_with_ellipse_slip(result.best_model.model)\n",
    "\n",
    "# Compute Green functions and convolve\n",
    "axitra_best = fwd.build_axitra(geometry_best, latlon=False, freesurface=True)\n",
    "ap_best = fwd.green(axitra_best, quiet=True)\n",
    "\n",
    "result_best = fwd.conv(ap_best, geometry_best, source_type=1, t0=float(cfg.ellipse.t0), quiet=True)\n",
    "\n",
    "if isinstance(result_best, tuple):\n",
    "    _, sx_best, sy_best, sz_best = result_best\n",
    "    synthetic_best = np.array([sx_best, sy_best, sz_best], dtype=float)\n",
    "    synthetic_best = np.transpose(synthetic_best, (1, 0, 2))  # (nsta, 3, npts)\n",
    "else:\n",
    "    synthetic_best = result_best\n",
    "\n",
    "# Apply filtering\n",
    "synthetic_best_filtered = bandpass_filter_waveforms(\n",
    "    synthetic_best, time,\n",
    "    freq1=freq1, freq2=freq2,\n",
    "    corners=4, zerophase=True\n",
    ")\n",
    "\n",
    "print(\"\u2713 Synthetic waveforms from best model generated\")\n",
    "\n",
    "# Calculate misfit for best model\n",
    "misfit_best = misfit_calc.l2_misfit(synthetic_best_filtered)\n",
    "print(f\"  Misfit (best model): {misfit_best:.6f}\")\n",
    "\n",
    "# Visualize comparison\n",
    "station_idx = 0\n",
    "fig, axes = plt.subplots(2, 3, figsize=(14, 7))\n",
    "\n",
    "components = ['X', 'Y', 'Z']\n",
    "\n",
    "# Top row: Time series\n",
    "for icomp in range(3):\n",
    "    ax = axes[0, icomp]\n",
    "    ax.plot(time, observed[station_idx, icomp], 'g-', linewidth=2, label='Observed', alpha=0.8)\n",
    "    ax.plot(time, synthetic_best_filtered[station_idx, icomp], 'r--', linewidth=1.5, label='Best synthetic', alpha=0.8)\n",
    "    ax.set_title(f'Station {station_idx+1} - {components[icomp]} Component')\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.set_ylabel('Amplitude')\n",
    "    ax.legend()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Bottom row: Residuals\n",
    "for icomp in range(3):\n",
    "    ax = axes[1, icomp]\n",
    "    residual = observed[station_idx, icomp] - synthetic_best_filtered[station_idx, icomp]\n",
    "    ax.plot(time, residual, 'orange', linewidth=1.5)\n",
    "    ax.axhline(0, color='k', linestyle=':', alpha=0.5)\n",
    "    ax.set_title(f'Residual - {components[icomp]}')\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.set_ylabel('Amplitude')\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.suptitle(f'Best Model Fit (Misfit: {misfit_best:.6f})', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\u2713 Best model visualization complete\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27bcde18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter distribution: show range explored\n",
    "fig, axes = plt.subplots(2, 4, figsize=(16, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for iparam in range(len(param_names)):\n",
    "    param_name = param_names[iparam]\n",
    "    param_vals = all_models[:, iparam]\n",
    "    param_min = float(cfg.inversion_params.parameters[iparam].min_val)\n",
    "    param_max = float(cfg.inversion_params.parameters[iparam].max_val)\n",
    "    \n",
    "    ax = axes[iparam]\n",
    "    ax.hist(param_vals, bins=20, alpha=0.7, color='steelblue', edgecolor='black')\n",
    "    ax.axvline(result.best_model.model[iparam], color='red', linestyle='--', linewidth=2, label='Best')\n",
    "    ax.axvline(midpoint_model[iparam], color='orange', linestyle=':', linewidth=2, label='Initial')\n",
    "    ax.set_xlim([param_min, param_max])\n",
    "    ax.set_xlabel(param_name)\n",
    "    ax.set_ylabel('Frequency')\n",
    "    ax.set_title(f'{param_name} distribution (n={len(param_vals)})')\n",
    "    ax.legend()\n",
    "    ax.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Hide extra subplot\n",
    "axes[7].axis('off')\n",
    "\n",
    "plt.suptitle('Parameter Space Exploration', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\u2713 Parameter distribution plot complete\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8157715",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract all models and misfits for analysis\n",
    "all_models = np.array([m.model for m in result.all_models])\n",
    "all_misfits = np.array([m.misfit for m in result.all_models])\n",
    "all_iterations = np.array([m.iteration for m in result.all_models])\n",
    "\n",
    "print(f\"\u2713 Extracted {len(all_models)} models from search\")\n",
    "print(f\"  Misfit range: [{all_misfits.min():.6f}, {all_misfits.max():.6f}]\")\n",
    "print(f\"  Iterations: {all_iterations.min()} to {all_iterations.max()}\")\n",
    "\n",
    "# Plot convergence\n",
    "fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n",
    "\n",
    "# Misfit convergence (all models)\n",
    "axes[0, 0].scatter(range(len(all_misfits)), all_misfits, alpha=0.6, s=30)\n",
    "axes[0, 0].axhline(result.best_model.misfit, color='r', linestyle='--', linewidth=2, label='Best')\n",
    "axes[0, 0].set_xlabel('Model Index')\n",
    "axes[0, 0].set_ylabel('Misfit (L2)')\n",
    "axes[0, 0].set_title('Convergence: All Models')\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Misfit vs iteration\n",
    "axes[0, 1].scatter(all_iterations, all_misfits, alpha=0.6, s=30, c=all_iterations, cmap='viridis')\n",
    "axes[0, 1].set_xlabel('Iteration')\n",
    "axes[0, 1].set_ylabel('Misfit (L2)')\n",
    "axes[0, 1].set_title('Misfit by Iteration')\n",
    "axes[0, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Best misfit evolution by iteration\n",
    "best_per_iteration = []\n",
    "for it in range(int(all_iterations.max()) + 1):\n",
    "    mask = all_iterations == it\n",
    "    if mask.any():\n",
    "        best_per_iteration.append(np.min(all_misfits[mask]))\n",
    "\n",
    "axes[1, 0].plot(range(len(best_per_iteration)), best_per_iteration, 'o-', linewidth=2, markersize=8)\n",
    "axes[1, 0].set_xlabel('Iteration')\n",
    "axes[1, 0].set_ylabel('Best Misfit in Iteration')\n",
    "axes[1, 0].set_title('Best Misfit Evolution')\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Parameter space: best models\n",
    "n_best = min(20, len(result.all_models))\n",
    "best_indices = np.argsort(all_misfits)[:n_best]\n",
    "best_models_subset = all_models[best_indices]\n",
    "best_misfits_subset = all_misfits[best_indices]\n",
    "\n",
    "axes[1, 1].scatter(best_models_subset[:, 0], best_models_subset[:, 1], \n",
    "                   c=best_misfits_subset, cmap='RdYlGn_r', s=50, alpha=0.8)\n",
    "axes[1, 1].scatter(result.best_model.model[0], result.best_model.model[1], \n",
    "                   color='red', s=200, marker='*', label='Best model', edgecolor='black', linewidth=2)\n",
    "axes[1, 1].set_xlabel('a1 (km)')\n",
    "axes[1, 1].set_ylabel('a2 (km)')\n",
    "axes[1, 1].set_title('Parameter Space (a1 vs a2)')\n",
    "axes[1, 1].legend()\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.suptitle('NA Inversion Convergence Analysis', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\u2713 Convergence analysis plot complete\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d1eb60",
   "metadata": {},
   "source": [
    "## Step 6: Analyze Results and Visualize\n",
    "\n",
    "Examine the inversion results and visualize convergence and parameter distribution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47400be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "STARTING NEIGHBOURHOOD ALGORITHM SEARCH\n",
      "======================================================================\n",
      "[NA] Adjusting n_samples_iteration from 30 to 35 to satisfy neighpy constraint (multiple of n_cells_resample=7).\n",
      "[NA] Starting search: ni=100, ns=35, n=10, nr=7 -> expected evaluations=450\n",
      "NAI - Initial Random Search\n",
      "=========================\n",
      "[NA] iter=000 eval=00001 misfit=2.769579e+02 best=2.769579e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00002 misfit=7.618012e+02 best=2.769579e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00003 misfit=2.285451e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00004 misfit=4.513069e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00005 misfit=6.275357e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00006 misfit=4.133923e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00007 misfit=4.572672e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00008 misfit=6.211029e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00009 misfit=6.818219e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00010 misfit=5.649620e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00011 misfit=3.688018e+02 best=2.285451e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00012 misfit=1.902730e+02 best=1.902730e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00013 misfit=1.746109e+02 best=1.746109e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00014 misfit=5.637256e+02 best=1.746109e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00015 misfit=1.008369e+03 best=1.746109e+02\n",
      "=========================\n",
      "[NA] iter=000 eval=00016 misfit=8.488220e+01 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00017 misfit=2.297137e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00018 misfit=2.990200e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00019 misfit=3.661088e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00020 misfit=1.361535e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00021 misfit=8.510534e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00022 misfit=1.214458e+02 best=8.488220e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00023 misfit=6.576112e+01 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00024 misfit=4.581872e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00025 misfit=7.365747e+01 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00026 misfit=1.512020e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00027 misfit=4.657471e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00028 misfit=9.151568e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00029 misfit=1.917657e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00030 misfit=4.426384e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00031 misfit=1.443503e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00032 misfit=4.627750e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00033 misfit=7.229074e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00034 misfit=4.095387e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00035 misfit=2.421097e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00036 misfit=3.756428e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00037 misfit=1.435802e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00038 misfit=1.147086e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00039 misfit=6.602224e+01 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00040 misfit=8.644089e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00041 misfit=4.220766e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00042 misfit=8.174270e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00043 misfit=4.995017e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00044 misfit=9.882750e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00045 misfit=6.887415e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00046 misfit=4.356548e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00047 misfit=4.201063e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00048 misfit=3.499546e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00049 misfit=9.236685e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00050 misfit=4.875200e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00051 misfit=7.304803e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00052 misfit=2.215242e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00053 misfit=5.268643e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00054 misfit=1.043245e+03 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00055 misfit=1.671066e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00056 misfit=3.826328e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00057 misfit=6.115258e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00058 misfit=8.224414e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00059 misfit=1.786400e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00060 misfit=9.329408e+01 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00061 misfit=9.415829e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00062 misfit=7.946245e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00063 misfit=3.471845e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00064 misfit=1.377649e+03 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00065 misfit=2.057245e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00066 misfit=9.334342e+01 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00067 misfit=3.017572e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00068 misfit=3.656392e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00069 misfit=2.566395e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00070 misfit=1.590060e+03 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00071 misfit=4.428784e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00072 misfit=2.531554e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00073 misfit=5.598516e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00074 misfit=6.005383e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00075 misfit=7.093158e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00076 misfit=6.846966e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00077 misfit=7.099600e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00078 misfit=1.378297e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00079 misfit=3.805219e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00080 misfit=1.052804e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00081 misfit=1.283322e+03 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00082 misfit=2.716042e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00083 misfit=1.764444e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00084 misfit=2.277101e+03 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00085 misfit=3.485750e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00086 misfit=5.811730e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00087 misfit=1.424227e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00088 misfit=2.894929e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00089 misfit=3.624547e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00090 misfit=2.402042e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00091 misfit=6.369413e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00092 misfit=1.045446e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00093 misfit=3.678291e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00094 misfit=4.154266e+02 best=6.576112e+01\n",
      "=========================\n",
      "[NA] iter=000 eval=00095 misfit=6.264214e+02 best=6.576112e+01\n"
     ]
    }
   ],
   "source": [
    "# Run NA search\n",
    "print(\"=\" * 70)\n",
    "print(\"STARTING NEIGHBOURHOOD ALGORITHM SEARCH\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "import time as time_module\n",
    "\n",
    "t0 = time_module.time()\n",
    "\n",
    "result = na_inversion.run_na_search(na_config=na_config)\n",
    "\n",
    "t_elapsed = time_module.time() - t0\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"NA SEARCH COMPLETE\")\n",
    "print(\"=\" * 70)\n",
    "print(f\"\\nSearch completed in {t_elapsed:.1f} seconds\")\n",
    "print(f\"\\n\u2713 Best model found:\")\n",
    "print(f\"  Misfit: {result.best_model.misfit:.6f}\")\n",
    "print(f\"  Iteration: {result.best_model.iteration}\")\n",
    "\n",
    "# Show best model parameters\n",
    "print(f\"\\n\u2713 Best model parameters:\")\n",
    "best_params = result.best_model.model\n",
    "for name, val in zip(param_names, best_params):\n",
    "    print(f\"  {name:6s} = {val:.4f}\")\n",
    "\n",
    "# Estimate moment magnitude for best model\n",
    "m0_best, mw_best = fwd.estimate_total_moment_and_mw(best_params)\n",
    "print(f\"\\n\u2713 Best model moment:\")\n",
    "print(f\"  M0 = {m0_best:.3e} N.m\")\n",
    "print(f\"  Mw = {mw_best:.2f}\")\n",
    "\n",
    "# Compare with initial midpoint model\n",
    "print(f\"\\n\u2713 Comparison: Initial vs Best\")\n",
    "print(f\"  Initial misfit: {misfit_test:.6f}\")\n",
    "print(f\"  Best misfit:    {result.best_model.misfit:.6f}\")\n",
    "improvement = (misfit_test - result.best_model.misfit) / misfit_test * 100\n",
    "print(f\"  Improvement:    {improvement:.1f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d9140acd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 NA Configuration:\n",
      "  Initial samples: 100\n",
      "  Samples per iteration: 30\n",
      "  Iterations: 10\n",
      "  Cells to resample: 7\n",
      "  Total models: 400\n",
      "\u2713 NA inversion model initialized\n"
     ]
    }
   ],
   "source": [
    "# Configure NA search\n",
    "na_config = NAConfig(\n",
    "    n_samples_initial=int(cfg.inversion_process.ss1),      # Initial samples\n",
    "    n_samples_iteration=int(cfg.inversion_process.ss_other), # Samples per iteration\n",
    "    n_iterations=int(cfg.inversion_process.num_iterations),  # Number of iterations\n",
    "    n_cells_resample=int(cfg.inversion_process.cells_resample),\n",
    "    n_jobs=-1,\n",
    "    random_seed=42,\n",
    "    keep_axitra_files=False,\n",
    ")\n",
    "\n",
    "print(\"\u2713 NA Configuration:\")\n",
    "print(f\"  Initial samples: {na_config.n_samples_initial}\")\n",
    "print(f\"  Samples per iteration: {na_config.n_samples_iteration}\")\n",
    "print(f\"  Iterations: {na_config.n_iterations}\")\n",
    "print(f\"  Cells to resample: {na_config.n_cells_resample}\")\n",
    "print(f\"  Total models: {na_config.n_samples_initial + na_config.n_samples_iteration * na_config.n_iterations}\")\n",
    "\n",
    "# Initialize NA inversion model\n",
    "na_inversion = NAInversionModel(\n",
    "    input_ctl_path=str(root / 'input.ctl'),\n",
    "    ,\n",
    "    observed_waveforms=observed,\n",
    "    time_array=time,\n",
    "    azi_times_array=azi_times,\n",
    ")\n",
    "print(\"\u2713 NA inversion model initialized\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2616a9ec",
   "metadata": {},
   "source": [
    "## Step 5: Run Neighbourhood Algorithm (NA) Inversion\n",
    "\n",
    "Configure and execute the NA search to find the best model parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d26f346b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building azi_times array...\n",
      "\u2713 azi_times array built: shape (10, 3)\n",
      "\u2713 MisfitCalculator initialized\n",
      "\n",
      "\u2713 Misfit calculation (synthetic vs observed):\n",
      "  L2 misfit: 1.026894\n",
      "\n",
      "--- Misfit Diagnostics ---\n",
      "[MISFIT DIAG] rms_global(obs)=1.971e-04 rms_global(syn)=4.901e-06 syn/obs=2.487e-02\n",
      "[MISFIT DIAG] sta=01 P(R): obs=1.152e-04 syn=3.150e-05 | P(Z): obs=1.359e-04 syn=2.343e-05 | S(T): obs=5.550e-04 syn=4.990e-06\n",
      "[MISFIT DIAG] sta=02 P(R): obs=2.337e-04 syn=2.002e-05 | P(Z): obs=8.872e-05 syn=9.913e-06 | S(T): obs=1.207e-04 syn=1.922e-05\n",
      "[MISFIT DIAG] sta=03 P(R): obs=1.309e-04 syn=9.204e-06 | P(Z): obs=1.160e-04 syn=5.515e-06 | S(T): obs=1.258e-04 syn=8.568e-06\n",
      "[MISFIT DIAG] window_energy(obs)=4.150e-05 window_energy(syn)=2.329e-07 syn/obs=5.611e-03\n"
     ]
    }
   ],
   "source": [
    "# Build azi_times array and initialize MisfitCalculator\n",
    "from src.signal_utils import build_azi_times_array\n",
    "\n",
    "print(\"Building azi_times array...\")\n",
    "azi_times = build_azi_times_array(input_ctl_path=root / 'input.ctl')\n",
    "print(f\"\u2713 azi_times array built: shape {azi_times.shape}\")\n",
    "\n",
    "# Initialize misfit calculator\n",
    "misfit_calc = MisfitCalculator(\n",
    "    observed_waveforms=observed,\n",
    "    time_array=time,\n",
    "    azi_times_array=azi_times,\n",
    "    time_window_s=20.0,\n",
    ")\n",
    "print(\"\u2713 MisfitCalculator initialized\")\n",
    "\n",
    "# Calculate misfit for the synthetic waveforms\n",
    "misfit_test = misfit_calc.l2_misfit(synthetic_filtered)\n",
    "print(f\"\\n\u2713 Misfit calculation (synthetic vs observed):\")\n",
    "print(f\"  L2 misfit: {misfit_test:.6f}\")\n",
    "\n",
    "# Show diagnostics\n",
    "print(\"\\n--- Misfit Diagnostics ---\")\n",
    "diag_text = misfit_calc.diagnostics_summary(synthetic_filtered, max_stations=3)\n",
    "print(diag_text)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb68920",
   "metadata": {},
   "source": [
    "## Step 4: Compute Misfit and Build Misfit Calculator\n",
    "\n",
    "Now calculate the misfit between observed and filtered synthetic waveforms.\n"
   ]
  }
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