Source code for

# Licensed under a 3-clause BSD style license - see LICENSE.rst
This package contains functions for reading and writing HDF5 tables that are
not meant to be used directly, but instead are available as readers/writers in
`astropy.table`. See :ref:`table_io` for more details.

import os
import warnings

import numpy as np

# NOTE: Do not import anything from astropy.table here.
from astropy.utils.exceptions import AstropyUserWarning, AstropyDeprecationWarning

HDF5_SIGNATURE = b'\x89HDF\r\n\x1a\n'
META_KEY = '__table_column_meta__'

__all__ = ['read_table_hdf5', 'write_table_hdf5']

def meta_path(path):
    return path + '.' + META_KEY

def _find_all_structured_arrays(handle):
    Find all structured arrays in an HDF5 file
    import h5py
    structured_arrays = []

    def append_structured_arrays(name, obj):
        if isinstance(obj, h5py.Dataset) and obj.dtype.kind == 'V':
    return structured_arrays

def is_hdf5(origin, filepath, fileobj, *args, **kwargs):

    if fileobj is not None:
        loc = fileobj.tell()
            signature =
        return signature == HDF5_SIGNATURE
    elif filepath is not None:
        return filepath.endswith(('.hdf5', '.h5'))

        import h5py
    except ImportError:
        return False
        return isinstance(args[0], (h5py.File, h5py.Group, h5py.Dataset))

[docs]def read_table_hdf5(input, path=None, character_as_bytes=True): """ Read a Table object from an HDF5 file This requires `h5py <>`_ to be installed. If more than one table is present in the HDF5 file or group, the first table is read in and a warning is displayed. Parameters ---------- input : str or :class:`h5py:File` or :class:`h5py:Group` or :class:`h5py:Dataset` If a string, the filename to read the table from. If an h5py object, either the file or the group object to read the table from. path : str The path from which to read the table inside the HDF5 file. This should be relative to the input file or group. character_as_bytes: boolean If `True` then Table columns are left as bytes. If `False` then Table columns are converted to unicode. """ try: import h5py except ImportError: raise Exception("h5py is required to read and write HDF5 files") # This function is iterative, and only gets to writing the file when # the input is an hdf5 Group. Moreover, the input variable is changed in # place. # Here, we save its value to be used at the end when the conditions are # right. input_save = input if isinstance(input, (h5py.File, h5py.Group)): # If a path was specified, follow the path if path is not None: try: input = input[path] except (KeyError, ValueError): raise OSError("Path {0} does not exist".format(path)) # `input` is now either a group or a dataset. If it is a group, we # will search for all structured arrays inside the group, and if there # is one we can proceed otherwise an error is raised. If it is a # dataset, we just proceed with the reading. if isinstance(input, h5py.Group): # Find all structured arrays in group arrays = _find_all_structured_arrays(input) if len(arrays) == 0: raise ValueError("no table found in HDF5 group {0}". format(path)) elif len(arrays) > 0: path = arrays[0] if path is None else path + '/' + arrays[0] if len(arrays) > 1: warnings.warn("path= was not specified but multiple tables" " are present, reading in first available" " table (path={0})".format(path), AstropyUserWarning) return read_table_hdf5(input, path=path) elif not isinstance(input, h5py.Dataset): # If a file object was passed, then we need to extract the filename # because h5py cannot properly read in file objects. if hasattr(input, 'read'): try: input = except AttributeError: raise TypeError("h5py can only open regular files") # Open the file for reading, and recursively call read_table_hdf5 with # the file object and the path. f = h5py.File(input, 'r') try: return read_table_hdf5(f, path=path, character_as_bytes=character_as_bytes) finally: f.close() # If we are here, `input` should be a Dataset object, which we can now # convert to a Table. # Create a Table object from astropy.table import Table, meta, serialize table = Table(np.array(input)) # Read the meta-data from the file. For back-compatibility, we can read # the old file format where the serialized metadata were saved in the # attributes of the HDF5 dataset. # In the new format, instead, metadata are stored in a new dataset in the # same file. This is introduced in Astropy 3.0 old_version_meta = META_KEY in input.attrs new_version_meta = path is not None and meta_path(path) in input_save if old_version_meta or new_version_meta: if new_version_meta: header = meta.get_header_from_yaml( h.decode('utf-8') for h in input_save[meta_path(path)]) elif old_version_meta: header = meta.get_header_from_yaml( h.decode('utf-8') for h in input.attrs[META_KEY]) if 'meta' in list(header.keys()): table.meta = header['meta'] header_cols = dict((x['name'], x) for x in header['datatype']) for col in table.columns.values(): for attr in ('description', 'format', 'unit', 'meta'): if attr in header_cols[]: setattr(col, attr, header_cols[][attr]) # Construct new table with mixins, using tbl.meta['__serialized_columns__'] # as guidance. table = serialize._construct_mixins_from_columns(table) else: # Read the meta-data from the file table.meta.update(input.attrs) if not character_as_bytes: table.convert_bytestring_to_unicode() return table
def _encode_mixins(tbl): """Encode a Table ``tbl`` that may have mixin columns to a Table with only astropy Columns + appropriate meta-data to allow subsequent decoding. """ from astropy.table import serialize from astropy.table.table import has_info_class from astropy import units as u from astropy.utils.data_info import MixinInfo, serialize_context_as # If PyYAML is not available then check to see if there are any mixin cols # that *require* YAML serialization. HDF5 already has support for # Quantity, so if those are the only mixins the proceed without doing the # YAML bit, for backward compatibility (i.e. not requiring YAML to write # Quantity). try: import yaml except ImportError: for col in tbl.itercols(): if (has_info_class(col, MixinInfo) and col.__class__ is not u.Quantity): raise TypeError("cannot write type {} column '{}' " "to HDF5 without PyYAML installed." .format(col.__class__.__name__, # Convert the table to one with no mixins, only Column objects. This adds # meta data which is extracted with meta.get_yaml_from_table. with serialize_context_as('hdf5'): encode_tbl = serialize.represent_mixins_as_columns(tbl) return encode_tbl
[docs]def write_table_hdf5(table, output, path=None, compression=False, append=False, overwrite=False, serialize_meta=False, compatibility_mode=False): """ Write a Table object to an HDF5 file This requires `h5py <>`_ to be installed. Parameters ---------- table : `~astropy.table.Table` Data table that is to be written to file. output : str or :class:`h5py:File` or :class:`h5py:Group` If a string, the filename to write the table to. If an h5py object, either the file or the group object to write the table to. path : str The path to which to write the table inside the HDF5 file. This should be relative to the input file or group. If not specified, defaults to ``__astropy_table__``. compression : bool or str or int Whether to compress the table inside the HDF5 file. If set to `True`, ``'gzip'`` compression is used. If a string is specified, it should be one of ``'gzip'``, ``'szip'``, or ``'lzf'``. If an integer is specified (in the range 0-9), ``'gzip'`` compression is used, and the integer denotes the compression level. append : bool Whether to append the table to an existing HDF5 file. overwrite : bool Whether to overwrite any existing file without warning. If ``append=True`` and ``overwrite=True`` then only the dataset will be replaced; the file/group will not be overwritten. """ from astropy.table import meta try: import h5py except ImportError: raise Exception("h5py is required to read and write HDF5 files") if path is None: # table is just an arbitrary, hardcoded string here. path = '__astropy_table__' elif path.endswith('/'): raise ValueError("table path should end with table name, not /") if '/' in path: group, name = path.rsplit('/', 1) else: group, name = None, path if isinstance(output, (h5py.File, h5py.Group)): if len(list(output.keys())) > 0 and name == '__astropy_table__': raise ValueError("table path should always be set via the " "path= argument when writing to existing " "files") elif name == '__astropy_table__': warnings.warn("table path was not set via the path= argument; " "using default path {}".format(path)) if group: try: output_group = output[group] except (KeyError, ValueError): output_group = output.create_group(group) else: output_group = output elif isinstance(output, str): if os.path.exists(output) and not append: if overwrite and not append: os.remove(output) else: raise OSError("File exists: {0}".format(output)) # Open the file for appending or writing f = h5py.File(output, 'a' if append else 'w') # Recursively call the write function try: return write_table_hdf5(table, f, path=path, compression=compression, append=append, overwrite=overwrite, serialize_meta=serialize_meta, compatibility_mode=compatibility_mode) finally: f.close() else: raise TypeError('output should be a string or an h5py File or ' 'Group object') # Check whether table already exists if name in output_group: if append and overwrite: # Delete only the dataset itself del output_group[name] else: raise OSError("Table {0} already exists".format(path)) # Encode any mixin columns as plain columns + appropriate metadata table = _encode_mixins(table) # Table with numpy unicode strings can't be written in HDF5 so # to write such a table a copy of table is made containing columns as # bytestrings. Now this copy of the table can be written in HDF5. if any( == 'U' for col in table.itercols()): table = table.copy(copy_data=False) table.convert_unicode_to_bytestring() # Warn if information will be lost when serialize_meta=False. This is # hardcoded to the set difference between column info attributes and what # HDF5 can store natively (name, dtype) with no meta. if serialize_meta is False: for col in table.itercols(): for attr in ('unit', 'format', 'description', 'meta'): if getattr(, attr, None) not in (None, {}): warnings.warn("table contains column(s) with defined 'unit', 'format'," " 'description', or 'meta' info attributes. These will" " be dropped since serialize_meta=False.", AstropyUserWarning) # Write the table to the file if compression: if compression is True: compression = 'gzip' dset = output_group.create_dataset(name, data=table.as_array(), compression=compression) else: dset = output_group.create_dataset(name, data=table.as_array()) if serialize_meta: header_yaml = meta.get_yaml_from_table(table) header_encoded = [h.encode('utf-8') for h in header_yaml] if compatibility_mode: warnings.warn("compatibility mode for writing is deprecated", AstropyDeprecationWarning) try: dset.attrs[META_KEY] = header_encoded except Exception as e: warnings.warn( "Attributes could not be written to the output HDF5 " "file: {0}".format(e)) else: output_group.create_dataset(meta_path(name), data=header_encoded) else: # Write the Table meta dict key:value pairs to the file as HDF5 # attributes. This works only for a limited set of scalar data types # like numbers, strings, etc., but not any complex types. This path # also ignores column meta like unit or format. for key in table.meta: val = table.meta[key] try: dset.attrs[key] = val except TypeError: warnings.warn("Attribute `{0}` of type {1} cannot be written to " "HDF5 files - skipping. (Consider specifying " "serialize_meta=True to write all meta data)".format(key, type(val)), AstropyUserWarning)
def register_hdf5(): """ Register HDF5 with Unified I/O. """ from import registry as io_registry from astropy.table import Table io_registry.register_reader('hdf5', Table, read_table_hdf5) io_registry.register_writer('hdf5', Table, write_table_hdf5) io_registry.register_identifier('hdf5', Table, is_hdf5)