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# Source code for astropy.io.misc.hdf5

    # 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:`astropy:table_io` for more details.
    """

    import os
    import warnings

    import numpy as np

    # NOTE: Do not import anything from astropy.table here.
    # https://github.com/astropy/astropy/issues/6604
    from astropy.utils.exceptions import AstropyUserWarning
    from astropy.utils.misc import NOT_OVERWRITING_MSG

    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":
                structured_arrays.append(name)

        handle.visititems(append_structured_arrays)
        return structured_arrays

    def is_hdf5(origin, filepath, fileobj, *args, **kwargs):
        if fileobj is not None:
            loc = fileobj.tell()
            try:
                signature = fileobj.read(8)
            finally:
                fileobj.seek(loc)
            return signature == HDF5_SIGNATURE
        elif filepath is not None:
            return filepath.endswith((".hdf5", ".h5"))

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

    def read_table_hdf5(input, path=None, character_as_bytes=True):
        """
        Read a Table object from an HDF5 file

        This requires `h5py <http://www.h5py.org/>`_ 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 : bool
            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(f"Path {path} does not exist")

            # `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(f"no table found in HDF5 group {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"
                            f" table (path={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 = input.name
                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)]
                )
            else:
                # Must be old_version_meta is True. if (A or B) and not A then B is True
                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 = {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[col.name]:
                        setattr(col, attr, header_cols[col.name][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.utils.data_info import serialize_context_as

        # 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

    def write_table_hdf5(
        table,
        output,
        path=None,
        compression=False,
        append=False,
        overwrite=False,
        serialize_meta=False,
        **create_dataset_kwargs,
    ):
        """
        Write a Table object to an HDF5 file

        This requires `h5py <http://www.h5py.org/>`_ 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.
        serialize_meta : bool
            Whether to serialize rich table meta-data when writing the HDF5 file, in
            particular such data required to write and read back mixin columns like
            ``Time``, ``SkyCoord``, or ``Quantity`` to the file.
        **create_dataset_kwargs
            Additional keyword arguments are passed to
            ``h5py.File.create_dataset()`` or ``h5py.Group.create_dataset()``.
        """

        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; "
                    f"using default path {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(NOT_OVERWRITING_MSG.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,
                )
            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]
                if serialize_meta and name + ".__table_column_meta__" in output_group:
                    del output_group[name + ".__table_column_meta__"]
            else:
                raise OSError(f"Table {path} already exists")

        # 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(col.info.dtype.kind == "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(col.info, 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,
                **create_dataset_kwargs,
            )
        else:
            dset = output_group.create_dataset(
                name, data=table.as_array(), **create_dataset_kwargs
            )

        if serialize_meta:
            header_yaml = meta.get_yaml_from_table(table)
            header_encoded = np.array([h.encode("utf-8") for h in header_yaml])
            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(
                        f"Attribute `{key}` of type {type(val)} cannot be written to "
                        "HDF5 files - skipping. (Consider specifying "
                        "serialize_meta=True to write all meta data)",
                        AstropyUserWarning,
                    )

    def register_hdf5():
        """
        Register HDF5 with Unified I/O.
        """
        from astropy.io 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)