HDF5 Dataset Reading

class Hdf5MemoryMapped(DatasetMemoryMapped):
    """
    Implements the vaex hdf5 file format with memory mapping support.
    
    Provides zero-copy access to HDF5 datasets through memory mapping,
    supporting both read and write operations with automatic format detection.
    """
    def __init__(self, path, write=False, fs_options={}, fs=None, nommap=None, group=None, _fingerprint=None):
        """
        Initialize HDF5 memory-mapped dataset.
        
        Parameters:
        - path: Path to HDF5 file
        - write: Enable write mode (default: False)
        - fs_options: Filesystem options for remote storage
        - fs: Filesystem implementation (for remote storage)
        - nommap: Force disable memory mapping
        - group: HDF5 group path to read from
        - _fingerprint: Cached fingerprint for testing
        """

@classmethod
def create(cls, path, N, column_names, dtypes=None, write=True):
    """
    Create a new empty HDF5 file with specified columns.
    
    Parameters:
    - path: Output file path
    - N: Number of rows to allocate
    - column_names: List of column names
    - dtypes: List of numpy dtypes (default: float64 for all)
    - write: Enable write mode
    
    Returns:
    Hdf5MemoryMapped instance of the created file
    
    Raises:
    ValueError: If N is 0 (cannot export empty table)
    """

@classmethod
def quick_test(cls, path, fs_options={}, fs=None):
    """
    Quick test if file has HDF5 extension.
    
    Parameters:
    - path: File path to test
    - fs_options: Filesystem options
    - fs: Filesystem implementation
    
    Returns:
    bool: True if path ends with .hdf5 or .h5
    """

@classmethod
def can_open(cls, path, fs_options={}, fs=None, group=None, **kwargs):
    """
    Check if file can be opened as vaex HDF5 format.
    
    Parameters:
    - path: File path to check
    - fs_options: Filesystem options
    - fs: Filesystem implementation  
    - group: Specific HDF5 group to check
    
    Returns:
    bool: True if file can be opened
    """

@classmethod
def get_options(cls, path):
    """Get available options for opening file."""
    
@classmethod  
def option_to_args(cls, option):
    """Convert option to constructor arguments."""

def write_meta(self):
    """
    Write metadata (units, descriptions, UCDs) as HDF5 attributes.
    
    UCDs, descriptions and units are written as attributes in the HDF5 file,
    instead of a separate file as the default Dataset.write_meta().
    """

def close(self):
    """Close the HDF5 file and clean up resources."""

class AmuseHdf5MemoryMapped(Hdf5MemoryMapped):
    """
    Implements reading Amuse HDF5 files from amusecode.org.
    
    AMUSE (Astrophysical Multipurpose Software Environment) creates HDF5 files
    with specific structure containing particle data and metadata.
    """
    def __init__(self, path, write=False, fs_options={}, fs=None):
        """
        Initialize AMUSE HDF5 dataset reader.
        
        Parameters:
        - path: Path to AMUSE HDF5 file  
        - write: Enable write mode (default: False)
        - fs_options: Filesystem options
        - fs: Filesystem implementation
        """

    @classmethod
    def can_open(cls, path, *args, **kwargs):
        """
        Check if file is AMUSE HDF5 format.
        
        Parameters:
        - path: File path to check
        
        Returns:
        bool: True if file contains 'particles' group
        """

class Hdf5MemoryMappedGadget(DatasetMemoryMapped):
    """
    Implements reading Gadget2 HDF5 files.
    
    Gadget2 is a cosmological N-body/SPH simulation code that outputs
    HDF5 files with particle data organized by particle type.
    """
    def __init__(self, path, particle_name=None, particle_type=None, fs_options={}, fs=None):
        """
        Initialize Gadget2 HDF5 dataset reader.
        
        Parameters:
        - path: Path to Gadget2 HDF5 file (can include #<particle_type>)
        - particle_name: Name of particle type ("gas", "halo", "disk", "bulge", "stars", "dm")
        - particle_type: Numeric particle type (0-5)
        - fs_options: Filesystem options
        - fs: Filesystem implementation
        
        Note: Either particle_name, particle_type, or #<type> in path must be specified
        """

    @classmethod
    def can_open(cls, path, fs_options={}, fs=None, *args, **kwargs):
        """
        Check if file is Gadget2 HDF5 format with specified particle type.
        
        Parameters:
        - path: File path (may include #<particle_type>)
        - fs_options: Filesystem options
        - fs: Filesystem implementation
        
        Returns:
        bool: True if file contains the specified particle type data
        """

    @classmethod
    def get_options(cls, path):
        """Get available options for Gadget2 file."""
        
    @classmethod
    def option_to_args(cls, option):
        """Convert option to constructor arguments."""

import vaex
from vaex.hdf5.dataset import Hdf5MemoryMapped

# Automatic detection via vaex.open
df = vaex.open('data.hdf5')

# Direct instantiation
dataset = Hdf5MemoryMapped('data.hdf5')
df = vaex.from_dataset(dataset)

# Reading specific group
dataset = Hdf5MemoryMapped('data.hdf5', group='/table')

# Reading from remote storage
dataset = Hdf5MemoryMapped('s3://bucket/data.hdf5', 
                          fs_options={'anon': True})

from vaex.hdf5.dataset import Hdf5MemoryMapped
import numpy as np

# Create empty file with specified structure
dataset = Hdf5MemoryMapped.create(
    'new_data.hdf5', 
    N=1000,
    column_names=['x', 'y', 'z', 'velocity'],
    dtypes=[np.float64, np.float64, np.float64, np.float32],
    write=True
)

# Populate with data
df = vaex.from_dataset(dataset)
df.x[:] = np.random.random(1000)
df.y[:] = np.random.random(1000)
# ... continue with data population

# AMUSE files auto-detected by vaex.open
df = vaex.open('amuse_simulation.hdf5')

# Direct instantiation
from vaex.hdf5.dataset import AmuseHdf5MemoryMapped
dataset = AmuseHdf5MemoryMapped('amuse_simulation.hdf5')
df = vaex.from_dataset(dataset)

# Using path with particle type
df_gas = vaex.open('snapshot_001.hdf5#0')  # Gas particles
df_dm = vaex.open('snapshot_001.hdf5#5')   # Dark matter particles

# Using particle name
from vaex.hdf5.dataset import Hdf5MemoryMappedGadget
dataset = Hdf5MemoryMappedGadget('snapshot_001.hdf5', particle_name='gas')
df = vaex.from_dataset(dataset)

# Using particle type number
dataset = Hdf5MemoryMappedGadget('snapshot_001.hdf5', particle_type=0)

gadget_particle_names = ["gas", "halo", "disk", "bulge", "stars", "dm"]