Atomizer/atomizer-field/neural_field_parser.py

"""
neural_field_parser.py
Parses NX Nastran files into Neural Field training data

AtomizerField Data Parser v1.0.0
Converts NX Nastran BDF/OP2 files into standardized neural field training format.
This format is designed to be future-proof for years of neural network training.

Usage:
    python neural_field_parser.py <case_directory>

Example:
    python neural_field_parser.py training_case_001
"""

import json
import numpy as np
import h5py
from pathlib import Path
from datetime import datetime
import hashlib
import warnings

# pyNastran imports
try:
    from pyNastran.bdf.bdf import BDF
    from pyNastran.op2.op2 import OP2
except ImportError:
    print("ERROR: pyNastran is required. Install with: pip install pyNastran")
    raise


class NastranToNeuralFieldParser:
    """
    Parses Nastran BDF/OP2 files into Neural Field data structure v1.0

    This parser extracts complete field data (stress, displacement, strain at every node/element)
    rather than just scalar maximum values. This enables neural networks to learn complete
    physics fields for 1000x faster structural optimization.

    Data Structure v1.0:
    -------------------
    - metadata: Version, timestamps, analysis info, units
    - mesh: Complete node coordinates, element connectivity
    - materials: Full material properties (E, nu, rho, etc.)
    - boundary_conditions: All constraints (SPC, MPC)
    - loads: All loading conditions (forces, pressures, gravity, thermal)
    - results: Complete field results (displacement, stress, strain at ALL points)

    Attributes:
        case_dir (Path): Directory containing input/output subdirectories
        bdf_file (Path): Path to Nastran input deck (.bdf or .dat)
        op2_file (Path): Path to Nastran binary results (.op2)
        bdf (BDF): pyNastran BDF reader object
        op2 (OP2): pyNastran OP2 reader object
        neural_field_data (dict): Complete parsed data structure
    """

    def __init__(self, case_directory):
        """
        Initialize parser with case directory

        Args:
            case_directory (str or Path): Path to case directory containing:
                - input/model.bdf (or model.dat)
                - output/model.op2
        """
        self.case_dir = Path(case_directory)

        # Find BDF file (try both .bdf and .dat extensions)
        bdf_candidates = list((self.case_dir / "input").glob("model.bdf")) + \
                        list((self.case_dir / "input").glob("model.dat"))
        if not bdf_candidates:
            raise FileNotFoundError(
                f"No model.bdf or model.dat found in {self.case_dir / 'input'}/"
            )
        self.bdf_file = bdf_candidates[0]

        # Find OP2 file
        op2_candidates = list((self.case_dir / "output").glob("model.op2"))
        if not op2_candidates:
            raise FileNotFoundError(
                f"No model.op2 found in {self.case_dir / 'output'}/"
            )
        self.op2_file = op2_candidates[0]

        print(f"Found BDF: {self.bdf_file.name}")
        print(f"Found OP2: {self.op2_file.name}")

        # Initialize readers with minimal debug output
        self.bdf = BDF(debug=False)
        self.op2 = OP2(debug=False)

        # Initialize data structure v1.0
        self.neural_field_data = {
            "metadata": {},
            "geometry": {},
            "mesh": {},
            "materials": {},
            "boundary_conditions": {},
            "loads": {},
            "results": {}
        }

    def parse_all(self):
        """
        Main parsing function - extracts all data from BDF/OP2 files

        Returns:
            dict: Complete neural field data structure
        """
        print("\n" + "="*60)
        print("Starting AtomizerField Neural Field Parser v1.0")
        print("="*60)

        # Parse input deck
        print("\n[1/6] Reading BDF file...")
        self.bdf.read_bdf(str(self.bdf_file))
        print(f"      Loaded {len(self.bdf.nodes)} nodes, {len(self.bdf.elements)} elements")

        # Parse results
        print("\n[2/6] Reading OP2 file...")
        self.op2.read_op2(str(self.op2_file))
        # Check for sol attribute (may not exist in all pyNastran versions)
        sol_num = getattr(self.op2, 'sol', 'Unknown')
        print(f"      Loaded solution: SOL {sol_num}")

        # Extract all data
        print("\n[3/6] Extracting metadata...")
        self.extract_metadata()

        print("\n[4/6] Extracting mesh data...")
        self.extract_mesh()

        print("\n[5/6] Extracting materials, BCs, and loads...")
        self.extract_materials()
        self.extract_boundary_conditions()
        self.extract_loads()

        print("\n[6/6] Extracting field results...")
        self.extract_results()

        # Save to file
        print("\nSaving data to disk...")
        self.save_data()

        print("\n" + "="*60)
        print("Parsing complete! [OK]")
        print("="*60)
        return self.neural_field_data

    def extract_metadata(self):
        """
        Extract metadata and analysis information

        This includes:
        - Data format version (v1.0.0)
        - Timestamps
        - Analysis type (SOL 101, 103, etc.)
        - Units system
        - File hashes for provenance
        """
        # Generate file hash for data provenance
        with open(self.bdf_file, 'rb') as f:
            bdf_hash = hashlib.sha256(f.read()).hexdigest()
        with open(self.op2_file, 'rb') as f:
            op2_hash = hashlib.sha256(f.read()).hexdigest()

        # Extract title if available
        title = ""
        if hasattr(self.bdf, 'case_control_deck') and self.bdf.case_control_deck:
            if hasattr(self.bdf.case_control_deck, 'title'):
                title = str(self.bdf.case_control_deck.title)

        # Get solution type if available
        sol_num = getattr(self.op2, 'sol', 'Unknown')

        self.neural_field_data["metadata"] = {
            "version": "1.0.0",
            "created_at": datetime.now().isoformat(),
            "source": "NX_Nastran",
            "case_directory": str(self.case_dir),
            "case_name": self.case_dir.name,
            "analysis_type": f"SOL_{sol_num}",
            "title": title,
            "file_hashes": {
                "bdf": bdf_hash,
                "op2": op2_hash
            },
            "units": {
                "length": "mm",  # Standard NX units
                "force": "N",
                "stress": "MPa",
                "mass": "kg",
                "temperature": "C"
            },
            "parser_version": "1.0.0",
            "notes": "Complete field data for neural network training"
        }
        print(f"      Analysis: {self.neural_field_data['metadata']['analysis_type']}")

    def extract_mesh(self):
        """
        Extract complete mesh data from BDF

        This preserves:
        - All node coordinates (global coordinate system)
        - All element connectivity
        - Element types (solid, shell, beam, rigid)
        - Material and property IDs for each element
        """
        print("      Extracting nodes...")

        # Nodes - store in sorted order for consistent indexing
        nodes = []
        node_ids = []
        for nid, node in sorted(self.bdf.nodes.items()):
            node_ids.append(nid)
            # Get position in global coordinate system
            pos = node.get_position()
            nodes.append([pos[0], pos[1], pos[2]])

        nodes_array = np.array(nodes, dtype=np.float64)

        print(f"      Extracted {len(nodes)} nodes")
        print(f"      Extracting elements...")

        # Elements - organize by type for efficient neural network processing
        element_data = {
            "solid": [],
            "shell": [],
            "beam": [],
            "rigid": []
        }

        element_type_counts = {}

        for eid, elem in sorted(self.bdf.elements.items()):
            elem_type = elem.type
            element_type_counts[elem_type] = element_type_counts.get(elem_type, 0) + 1

            # Solid elements (3D stress states)
            if elem_type in ['CTETRA', 'CHEXA', 'CPENTA', 'CTETRA10', 'CHEXA20', 'CPENTA15']:
                element_data["solid"].append({
                    "id": eid,
                    "type": elem_type,
                    "nodes": list(elem.node_ids),
                    "material_id": elem.pid,  # Property ID which links to material
                    "property_id": elem.pid if hasattr(elem, 'pid') else None
                })

            # Shell elements (2D plane stress)
            elif elem_type in ['CQUAD4', 'CTRIA3', 'CQUAD8', 'CTRIA6', 'CQUAD', 'CTRIA']:
                thickness = None
                try:
                    # Get thickness from property
                    if hasattr(elem, 'pid') and elem.pid in self.bdf.properties:
                        prop = self.bdf.properties[elem.pid]
                        if hasattr(prop, 't'):
                            thickness = prop.t
                except:
                    pass

                element_data["shell"].append({
                    "id": eid,
                    "type": elem_type,
                    "nodes": list(elem.node_ids),
                    "material_id": elem.pid,
                    "property_id": elem.pid,
                    "thickness": thickness
                })

            # Beam elements (1D elements)
            elif elem_type in ['CBAR', 'CBEAM', 'CROD', 'CONROD']:
                element_data["beam"].append({
                    "id": eid,
                    "type": elem_type,
                    "nodes": list(elem.node_ids),
                    "material_id": elem.pid if hasattr(elem, 'pid') else None,
                    "property_id": elem.pid if hasattr(elem, 'pid') else None
                })

            # Rigid elements (kinematic constraints)
            elif elem_type in ['RBE2', 'RBE3', 'RBAR', 'RROD']:
                element_data["rigid"].append({
                    "id": eid,
                    "type": elem_type,
                    "nodes": list(elem.node_ids)
                })

        print(f"      Extracted {len(self.bdf.elements)} elements:")
        for etype, count in element_type_counts.items():
            print(f"        {etype}: {count}")

        # Calculate mesh bounding box for reference
        bbox_min = nodes_array.min(axis=0)
        bbox_max = nodes_array.max(axis=0)
        bbox_size = bbox_max - bbox_min

        # Store mesh data
        self.neural_field_data["mesh"] = {
            "statistics": {
                "n_nodes": len(nodes),
                "n_elements": len(self.bdf.elements),
                "element_types": {
                    "solid": len(element_data["solid"]),
                    "shell": len(element_data["shell"]),
                    "beam": len(element_data["beam"]),
                    "rigid": len(element_data["rigid"])
                },
                "element_type_breakdown": element_type_counts
            },
            "bounding_box": {
                "min": bbox_min.tolist(),
                "max": bbox_max.tolist(),
                "size": bbox_size.tolist()
            },
            "nodes": {
                "ids": node_ids,
                "coordinates": nodes_array.tolist(),  # Will be stored in HDF5
                "shape": list(nodes_array.shape),
                "dtype": str(nodes_array.dtype)
            },
            "elements": element_data
        }

    def extract_materials(self):
        """
        Extract all material properties

        Captures complete material definitions including:
        - Isotropic (MAT1): E, nu, rho, G, alpha
        - Orthotropic (MAT8, MAT9): directional properties
        - Stress limits for validation
        """
        print("      Extracting materials...")

        materials = []
        for mid, mat in sorted(self.bdf.materials.items()):
            mat_data = {
                "id": mid,
                "type": mat.type
            }

            if mat.type == 'MAT1':  # Isotropic material
                mat_data.update({
                    "E": float(mat.e) if mat.e is not None else None,      # Young's modulus
                    "nu": float(mat.nu) if mat.nu is not None else None,   # Poisson's ratio
                    "rho": float(mat.rho) if mat.rho is not None else None,# Density
                    "G": float(mat.g) if mat.g is not None else None,      # Shear modulus
                    "alpha": float(mat.a) if hasattr(mat, 'a') and mat.a is not None else None,  # Thermal expansion
                    "tref": float(mat.tref) if hasattr(mat, 'tref') and mat.tref is not None else None,
                })

                # Stress limits (if defined)
                try:
                    if hasattr(mat, 'St') and callable(mat.St):
                        mat_data["ST"] = float(mat.St()) if mat.St() is not None else None
                    if hasattr(mat, 'Sc') and callable(mat.Sc):
                        mat_data["SC"] = float(mat.Sc()) if mat.Sc() is not None else None
                    if hasattr(mat, 'Ss') and callable(mat.Ss):
                        mat_data["SS"] = float(mat.Ss()) if mat.Ss() is not None else None
                except:
                    pass

            elif mat.type == 'MAT8':  # Orthotropic shell material
                mat_data.update({
                    "E1": float(mat.e11) if hasattr(mat, 'e11') and mat.e11 is not None else None,
                    "E2": float(mat.e22) if hasattr(mat, 'e22') and mat.e22 is not None else None,
                    "nu12": float(mat.nu12) if hasattr(mat, 'nu12') and mat.nu12 is not None else None,
                    "G12": float(mat.g12) if hasattr(mat, 'g12') and mat.g12 is not None else None,
                    "G1z": float(mat.g1z) if hasattr(mat, 'g1z') and mat.g1z is not None else None,
                    "G2z": float(mat.g2z) if hasattr(mat, 'g2z') and mat.g2z is not None else None,
                    "rho": float(mat.rho) if hasattr(mat, 'rho') and mat.rho is not None else None,
                })

            materials.append(mat_data)

        self.neural_field_data["materials"] = materials
        print(f"      Extracted {len(materials)} materials")

    def extract_boundary_conditions(self):
        """
        Extract all boundary conditions

        Includes:
        - SPC: Single point constraints (fixed DOFs)
        - MPC: Multi-point constraints (equations)
        - SUPORT: Free body supports
        """
        print("      Extracting boundary conditions...")

        bcs = {
            "spc": [],      # Single point constraints
            "mpc": [],      # Multi-point constraints
            "suport": []    # Free body supports
        }

        # SPC (fixed DOFs) - critical for neural network to understand support conditions
        spc_count = 0
        for spc_id, spc_list in self.bdf.spcs.items():
            for spc in spc_list:
                try:
                    # Handle different SPC types
                    if hasattr(spc, 'node_ids'):
                        nodes = spc.node_ids
                    elif hasattr(spc, 'node'):
                        nodes = [spc.node]
                    else:
                        continue

                    for node in nodes:
                        bcs["spc"].append({
                            "id": spc_id,
                            "node": int(node),
                            "dofs": str(spc.components) if hasattr(spc, 'components') else "123456",
                            "enforced_motion": float(spc.enforced) if hasattr(spc, 'enforced') and spc.enforced is not None else 0.0
                        })
                        spc_count += 1
                except Exception as e:
                    warnings.warn(f"Could not parse SPC {spc_id}: {e}")

        # MPC equations
        mpc_count = 0
        for mpc_id, mpc_list in self.bdf.mpcs.items():
            for mpc in mpc_list:
                try:
                    bcs["mpc"].append({
                        "id": mpc_id,
                        "nodes": list(mpc.node_ids) if hasattr(mpc, 'node_ids') else [],
                        "coefficients": list(mpc.coefficients) if hasattr(mpc, 'coefficients') else [],
                        "components": list(mpc.components) if hasattr(mpc, 'components') else []
                    })
                    mpc_count += 1
                except Exception as e:
                    warnings.warn(f"Could not parse MPC {mpc_id}: {e}")

        self.neural_field_data["boundary_conditions"] = bcs
        print(f"      Extracted {spc_count} SPCs, {mpc_count} MPCs")

    def extract_loads(self):
        """
        Extract all loading conditions

        Includes:
        - Point forces and moments
        - Distributed pressures
        - Gravity loads
        - Thermal loads
        """
        print("      Extracting loads...")

        loads = {
            "point_forces": [],
            "pressure": [],
            "gravity": [],
            "thermal": []
        }

        force_count = 0
        pressure_count = 0
        gravity_count = 0

        # Point forces, moments, and pressures
        for load_id, load_list in self.bdf.loads.items():
            for load in load_list:
                try:
                    if load.type == 'FORCE':
                        loads["point_forces"].append({
                            "id": load_id,
                            "type": "force",
                            "node": int(load.node),
                            "magnitude": float(load.mag),
                            "direction": [float(load.xyz[0]), float(load.xyz[1]), float(load.xyz[2])],
                            "coord_system": int(load.cid) if hasattr(load, 'cid') else 0
                        })
                        force_count += 1

                    elif load.type == 'MOMENT':
                        loads["point_forces"].append({
                            "id": load_id,
                            "type": "moment",
                            "node": int(load.node),
                            "magnitude": float(load.mag),
                            "direction": [float(load.xyz[0]), float(load.xyz[1]), float(load.xyz[2])],
                            "coord_system": int(load.cid) if hasattr(load, 'cid') else 0
                        })
                        force_count += 1

                    elif load.type in ['PLOAD', 'PLOAD2', 'PLOAD4']:
                        pressure_data = {
                            "id": load_id,
                            "type": load.type
                        }

                        if hasattr(load, 'eids'):
                            pressure_data["elements"] = list(load.eids)
                        elif hasattr(load, 'eid'):
                            pressure_data["elements"] = [int(load.eid)]

                        if hasattr(load, 'pressures'):
                            pressure_data["pressure"] = [float(p) for p in load.pressures]
                        elif hasattr(load, 'pressure'):
                            pressure_data["pressure"] = float(load.pressure)

                        loads["pressure"].append(pressure_data)
                        pressure_count += 1

                    elif load.type == 'GRAV':
                        loads["gravity"].append({
                            "id": load_id,
                            "acceleration": float(load.scale),
                            "direction": [float(load.N[0]), float(load.N[1]), float(load.N[2])],
                            "coord_system": int(load.cid) if hasattr(load, 'cid') else 0
                        })
                        gravity_count += 1

                except Exception as e:
                    warnings.warn(f"Could not parse load {load_id} type {load.type}: {e}")

        # Temperature loads (if available)
        thermal_count = 0
        if hasattr(self.bdf, 'temps'):
            for temp_id, temp_list in self.bdf.temps.items():
                for temp in temp_list:
                    try:
                        loads["thermal"].append({
                            "id": temp_id,
                            "node": int(temp.node),
                            "temperature": float(temp.temperature)
                        })
                        thermal_count += 1
                    except Exception as e:
                        warnings.warn(f"Could not parse thermal load {temp_id}: {e}")

        self.neural_field_data["loads"] = loads
        print(f"      Extracted {force_count} forces, {pressure_count} pressures, {gravity_count} gravity, {thermal_count} thermal")

    def extract_results(self):
        """
        Extract complete field results from OP2

        This is the CRITICAL function for neural field learning.
        We extract COMPLETE fields, not just maximum values:
        - Displacement at every node (6 DOF)
        - Stress at every element (full tensor)
        - Strain at every element (full tensor)
        - Reaction forces at constrained nodes

        This complete field data enables the neural network to learn
        the physics of how structures respond to loads.
        """
        print("      Extracting field results...")

        results = {}

        # Determine subcase ID (usually 1 for linear static)
        subcase_id = 1
        if hasattr(self.op2, 'isubcase_name_map'):
            available_subcases = list(self.op2.isubcase_name_map.keys())
            if available_subcases:
                subcase_id = available_subcases[0]

        print(f"      Using subcase ID: {subcase_id}")

        # Displacement - complete field at all nodes
        if hasattr(self.op2, 'displacements') and subcase_id in self.op2.displacements:
            print("      Processing displacement field...")
            disp = self.op2.displacements[subcase_id]
            disp_data = disp.data[0, :, :]  # [itime=0, all_nodes, 6_dofs]

            # Extract node IDs
            node_ids = disp.node_gridtype[:, 0].tolist()

            # Calculate magnitudes for quick reference
            translation_mag = np.linalg.norm(disp_data[:, :3], axis=1)
            rotation_mag = np.linalg.norm(disp_data[:, 3:], axis=1)

            results["displacement"] = {
                "node_ids": node_ids,
                "data": disp_data.tolist(),  # Will be stored in HDF5
                "shape": list(disp_data.shape),
                "dtype": str(disp_data.dtype),
                "max_translation": float(np.max(translation_mag)),
                "max_rotation": float(np.max(rotation_mag)),
                "units": "mm and radians"
            }
            print(f"        Displacement: {len(node_ids)} nodes, max={results['displacement']['max_translation']:.6f} mm")

        # Stress - handle different element types
        stress_results = {}

        # Solid element stress (CTETRA, CHEXA, etc.)
        stress_attrs = ['ctetra_stress', 'chexa_stress', 'cpenta_stress']
        for attr in stress_attrs:
            if hasattr(self.op2, attr):
                stress_obj = getattr(self.op2, attr)
                if subcase_id in stress_obj:
                    elem_type = attr.replace('_stress', '')
                    print(f"      Processing {elem_type} stress...")
                    stress = stress_obj[subcase_id]
                    stress_data = stress.data[0, :, :]

                    # Extract element IDs
                    element_ids = stress.element_node[:, 0].tolist()

                    # Von Mises stress is usually the last column
                    von_mises = None
                    if stress_data.shape[1] >= 7:  # Has von Mises
                        von_mises = stress_data[:, -1]
                        max_vm = float(np.max(von_mises))
                        von_mises = von_mises.tolist()
                    else:
                        max_vm = None

                    stress_results[f"{elem_type}_stress"] = {
                        "element_ids": element_ids,
                        "data": stress_data.tolist(),  # Full stress tensor
                        "shape": list(stress_data.shape),
                        "dtype": str(stress_data.dtype),
                        "von_mises": von_mises,
                        "max_von_mises": max_vm,
                        "units": "MPa"
                    }
                    print(f"        {elem_type}: {len(element_ids)} elements, max VM={max_vm:.2f} MPa" if max_vm else f"        {elem_type}: {len(element_ids)} elements")

        # Shell element stress
        shell_stress_attrs = ['cquad4_stress', 'ctria3_stress', 'cquad8_stress', 'ctria6_stress']
        for attr in shell_stress_attrs:
            if hasattr(self.op2, attr):
                stress_obj = getattr(self.op2, attr)
                if subcase_id in stress_obj:
                    elem_type = attr.replace('_stress', '')
                    print(f"      Processing {elem_type} stress...")
                    stress = stress_obj[subcase_id]
                    stress_data = stress.data[0, :, :]

                    element_ids = stress.element_node[:, 0].tolist()

                    stress_results[f"{elem_type}_stress"] = {
                        "element_ids": element_ids,
                        "data": stress_data.tolist(),
                        "shape": list(stress_data.shape),
                        "dtype": str(stress_data.dtype),
                        "units": "MPa"
                    }
                    print(f"        {elem_type}: {len(element_ids)} elements")

        results["stress"] = stress_results

        # Strain - similar to stress
        strain_results = {}
        strain_attrs = ['ctetra_strain', 'chexa_strain', 'cpenta_strain',
                       'cquad4_strain', 'ctria3_strain']
        for attr in strain_attrs:
            if hasattr(self.op2, attr):
                strain_obj = getattr(self.op2, attr)
                if subcase_id in strain_obj:
                    elem_type = attr.replace('_strain', '')
                    strain = strain_obj[subcase_id]
                    strain_data = strain.data[0, :, :]
                    element_ids = strain.element_node[:, 0].tolist()

                    strain_results[f"{elem_type}_strain"] = {
                        "element_ids": element_ids,
                        "data": strain_data.tolist(),
                        "shape": list(strain_data.shape),
                        "dtype": str(strain_data.dtype),
                        "units": "mm/mm"
                    }

        if strain_results:
            results["strain"] = strain_results
            print(f"      Extracted strain for {len(strain_results)} element types")

        # SPC Forces (reaction forces at constraints)
        if hasattr(self.op2, 'spc_forces') and subcase_id in self.op2.spc_forces:
            print("      Processing reaction forces...")
            spc = self.op2.spc_forces[subcase_id]
            spc_data = spc.data[0, :, :]
            node_ids = spc.node_gridtype[:, 0].tolist()

            # Calculate total reaction force magnitude
            force_mag = np.linalg.norm(spc_data[:, :3], axis=1)
            moment_mag = np.linalg.norm(spc_data[:, 3:], axis=1)

            results["reactions"] = {
                "node_ids": node_ids,
                "forces": spc_data.tolist(),
                "shape": list(spc_data.shape),
                "dtype": str(spc_data.dtype),
                "max_force": float(np.max(force_mag)),
                "max_moment": float(np.max(moment_mag)),
                "units": "N and N-mm"
            }
            print(f"        Reactions: {len(node_ids)} nodes, max force={results['reactions']['max_force']:.2f} N")

        self.neural_field_data["results"] = results

    def save_data(self):
        """
        Save parsed data to JSON and HDF5 files

        Data structure:
        - neural_field_data.json: Metadata, structure, small arrays
        - neural_field_data.h5: Large arrays (node coordinates, field results)

        HDF5 is used for efficient storage and loading of large numerical arrays.
        JSON provides human-readable metadata and structure.
        """
        # Save JSON metadata
        json_file = self.case_dir / "neural_field_data.json"

        # Create a copy for JSON (will remove large arrays)
        json_data = self._prepare_json_data()

        with open(json_file, 'w') as f:
            json.dump(json_data, f, indent=2, default=str)

        print(f"  [OK] Saved metadata to: {json_file.name}")

        # Save HDF5 for large arrays
        h5_file = self.case_dir / "neural_field_data.h5"
        with h5py.File(h5_file, 'w') as f:
            # Metadata attributes
            f.attrs['version'] = '1.0.0'
            f.attrs['created_at'] = self.neural_field_data['metadata']['created_at']
            f.attrs['case_name'] = self.neural_field_data['metadata']['case_name']

            # Save mesh data
            mesh_grp = f.create_group('mesh')
            node_coords = np.array(self.neural_field_data["mesh"]["nodes"]["coordinates"])
            mesh_grp.create_dataset('node_coordinates',
                                   data=node_coords,
                                   compression='gzip',
                                   compression_opts=4)
            mesh_grp.create_dataset('node_ids',
                                   data=np.array(self.neural_field_data["mesh"]["nodes"]["ids"]))

            # Save results
            if "results" in self.neural_field_data:
                results_grp = f.create_group('results')

                # Displacement
                if "displacement" in self.neural_field_data["results"]:
                    disp_data = np.array(self.neural_field_data["results"]["displacement"]["data"])
                    results_grp.create_dataset('displacement',
                                             data=disp_data,
                                             compression='gzip',
                                             compression_opts=4)
                    results_grp.create_dataset('displacement_node_ids',
                                             data=np.array(self.neural_field_data["results"]["displacement"]["node_ids"]))

                # Stress fields
                if "stress" in self.neural_field_data["results"]:
                    stress_grp = results_grp.create_group('stress')
                    for stress_type, stress_data in self.neural_field_data["results"]["stress"].items():
                        type_grp = stress_grp.create_group(stress_type)
                        type_grp.create_dataset('data',
                                              data=np.array(stress_data["data"]),
                                              compression='gzip',
                                              compression_opts=4)
                        type_grp.create_dataset('element_ids',
                                              data=np.array(stress_data["element_ids"]))

                # Strain fields
                if "strain" in self.neural_field_data["results"]:
                    strain_grp = results_grp.create_group('strain')
                    for strain_type, strain_data in self.neural_field_data["results"]["strain"].items():
                        type_grp = strain_grp.create_group(strain_type)
                        type_grp.create_dataset('data',
                                              data=np.array(strain_data["data"]),
                                              compression='gzip',
                                              compression_opts=4)
                        type_grp.create_dataset('element_ids',
                                              data=np.array(strain_data["element_ids"]))

                # Reactions
                if "reactions" in self.neural_field_data["results"]:
                    reactions_data = np.array(self.neural_field_data["results"]["reactions"]["forces"])
                    results_grp.create_dataset('reactions',
                                             data=reactions_data,
                                             compression='gzip',
                                             compression_opts=4)
                    results_grp.create_dataset('reaction_node_ids',
                                             data=np.array(self.neural_field_data["results"]["reactions"]["node_ids"]))

        print(f"  [OK] Saved field data to: {h5_file.name}")

        # Calculate and display file sizes
        json_size = json_file.stat().st_size / 1024  # KB
        h5_size = h5_file.stat().st_size / 1024  # KB
        print(f"\n  File sizes:")
        print(f"    JSON: {json_size:.1f} KB")
        print(f"    HDF5: {h5_size:.1f} KB")
        print(f"    Total: {json_size + h5_size:.1f} KB")

    def _prepare_json_data(self):
        """
        Prepare data for JSON export by removing large arrays
        (they go to HDF5 instead)
        """
        import copy
        json_data = copy.deepcopy(self.neural_field_data)

        # Remove large arrays from nodes (keep metadata)
        if "mesh" in json_data and "nodes" in json_data["mesh"]:
            json_data["mesh"]["nodes"]["coordinates"] = f"<stored in HDF5: shape {json_data['mesh']['nodes']['shape']}>"

        # Remove large result arrays
        if "results" in json_data:
            if "displacement" in json_data["results"]:
                shape = json_data["results"]["displacement"]["shape"]
                json_data["results"]["displacement"]["data"] = f"<stored in HDF5: shape {shape}>"

            if "stress" in json_data["results"]:
                for stress_type in json_data["results"]["stress"]:
                    shape = json_data["results"]["stress"][stress_type]["shape"]
                    json_data["results"]["stress"][stress_type]["data"] = f"<stored in HDF5: shape {shape}>"

            if "strain" in json_data["results"]:
                for strain_type in json_data["results"]["strain"]:
                    shape = json_data["results"]["strain"][strain_type]["shape"]
                    json_data["results"]["strain"][strain_type]["data"] = f"<stored in HDF5: shape {shape}>"

            if "reactions" in json_data["results"]:
                shape = json_data["results"]["reactions"]["shape"]
                json_data["results"]["reactions"]["forces"] = f"<stored in HDF5: shape {shape}>"

        return json_data


# ============================================================================
# MAIN ENTRY POINT
# ============================================================================

def main():
    """
    Main function to run the parser from command line

    Usage:
        python neural_field_parser.py <case_directory>
    """
    import sys

    if len(sys.argv) < 2:
        print("\nAtomizerField Neural Field Parser v1.0")
        print("="*60)
        print("\nUsage:")
        print("  python neural_field_parser.py <case_directory>")
        print("\nExample:")
        print("  python neural_field_parser.py training_case_001")
        print("\nCase directory should contain:")
        print("  input/model.bdf  (or model.dat)")
        print("  output/model.op2")
        print("\n")
        sys.exit(1)

    case_dir = sys.argv[1]

    # Verify directory exists
    if not Path(case_dir).exists():
        print(f"ERROR: Directory not found: {case_dir}")
        sys.exit(1)

    # Create parser
    try:
        parser = NastranToNeuralFieldParser(case_dir)
    except FileNotFoundError as e:
        print(f"\nERROR: {e}")
        print("\nPlease ensure your case directory contains:")
        print("  input/model.bdf (or model.dat)")
        print("  output/model.op2")
        sys.exit(1)

    # Parse all data
    try:
        data = parser.parse_all()

        # Print summary
        print("\n" + "="*60)
        print("PARSING SUMMARY")
        print("="*60)
        print(f"Case: {data['metadata']['case_name']}")
        print(f"Analysis: {data['metadata']['analysis_type']}")
        print(f"\nMesh:")
        print(f"  Nodes: {data['mesh']['statistics']['n_nodes']:,}")
        print(f"  Elements: {data['mesh']['statistics']['n_elements']:,}")
        for elem_type, count in data['mesh']['statistics']['element_types'].items():
            if count > 0:
                print(f"    {elem_type}: {count:,}")
        print(f"\nMaterials: {len(data['materials'])}")
        print(f"Boundary Conditions: {len(data['boundary_conditions']['spc'])} SPCs")
        print(f"Loads: {len(data['loads']['point_forces'])} forces, {len(data['loads']['pressure'])} pressures")

        if "displacement" in data['results']:
            print(f"\nResults:")
            print(f"  Displacement: {len(data['results']['displacement']['node_ids'])} nodes")
            print(f"    Max: {data['results']['displacement']['max_translation']:.6f} mm")
        if "stress" in data['results']:
            for stress_type in data['results']['stress']:
                if 'max_von_mises' in data['results']['stress'][stress_type]:
                    max_vm = data['results']['stress'][stress_type]['max_von_mises']
                    if max_vm is not None:
                        print(f"  {stress_type}: Max VM = {max_vm:.2f} MPa")

        print("\n[OK] Data ready for neural network training!")
        print("="*60 + "\n")

    except Exception as e:
        print(f"\n" + "="*60)
        print("ERROR DURING PARSING")
        print("="*60)
        print(f"{e}\n")
        import traceback
        traceback.print_exc()
        sys.exit(1)


if __name__ == "__main__":
    main()