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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:`table_io` for more details.
    """

    from __future__ import (absolute_import, division, print_function,
                            unicode_literals)

    import os
    import warnings

    import numpy as np
    from ...utils.exceptions import AstropyUserWarning
    from ...extern import six
    from ...table import meta

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

    __all__ = ['read_table_hdf5', 'write_table_hdf5']

    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') or filepath.endswith('.h5')

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

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

        This requires `h5py <http://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.
        """

        try:
            import h5py
        except ImportError:
            raise Exception("h5py is required to read and write HDF5 files")

        if isinstance(input, (h5py.highlevel.File, h5py.highlevel.Group)):

            # If a path was specified, follow the path

            if path:
                try:
                    input = input[path]
                except (KeyError, ValueError):
                    raise IOError("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.highlevel.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]
                    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.highlevel.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)
            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 ...table import Table
        table = Table(np.array(input))

        # Read the meta-data from the file
        if META_KEY in input.attrs:
            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[col.name]:
                        setattr(col, attr, header_cols[col.name][attr])
        else:
            # Read the meta-data from the file
            table.meta.update(input.attrs)

        return table

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

        This requires `h5py <http://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.
        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.
        """

        try:
            import h5py
        except ImportError:
            raise Exception("h5py is required to read and write HDF5 files")

        # Tables with mixin columns are not supported
        if table.has_mixin_columns:
            mixin_names = [name for name, col in table.columns.items()
                           if not isinstance(col, table.ColumnClass)]
            raise ValueError('cannot write table with mixin column(s) {0} to HDF5'
                             .format(mixin_names))

        if path is None:
            raise ValueError("table path should be set via the path= argument")
        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.highlevel.File, h5py.highlevel.Group)):

            if group:
                try:
                    output_group = output[group]
                except (KeyError, ValueError):
                    output_group = output.create_group(group)
            else:
                output_group = output

        elif isinstance(output, six.string_types):

            if os.path.exists(output) and not append:
                if overwrite and not append:
                    os.remove(output)
                else:
                    raise IOError("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)
            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 IOError("Table {0} already exists".format(path))

        # 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)

            try:
                dset.attrs[META_KEY] = [h.encode('utf8') for h in header_yaml]
            except Exception as e:
                warnings.warn("Attributes could not be written to the output HDF5 "
                              "file: {0}".format(e))

        else:
            # Write the meta-data to the file
            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)