.. currentmodule:: h5py
.. _group:


Groups
======


Groups are the container mechanism by which HDF5 files are organized.  From
a Python perspective, they operate somewhat like dictionaries.  In this case
the "keys" are the names of group members, and the "values" are the members
themselves (:class:`Group` and :class:`Dataset`) objects.

Group objects also contain most of the machinery which makes HDF5 useful.
The :ref:`File object <file>` does double duty as the HDF5 *root group*, and
serves as your entry point into the file:

    >>> f = h5py.File('foo.hdf5','w')
    >>> f.name
    '/'
    >>> list(f.keys())
    []

Names of all objects in the file are all text strings (``str``).  These will be encoded with the HDF5-approved UTF-8
encoding before being passed to the HDF5 C library.  Objects may also be
retrieved using byte strings, which will be passed on to HDF5 as-is.


.. _group_create:

Creating groups
---------------

New groups are easy to create::

    >>> grp = f.create_group("bar")
    >>> grp.name
    '/bar'
    >>> subgrp = grp.create_group("baz")
    >>> subgrp.name
    '/bar/baz'

Multiple intermediate groups can also be created implicitly::

    >>> grp2 = f.create_group("/some/long/path")
    >>> grp2.name
    '/some/long/path'
    >>> grp3 = f['/some/long']
    >>> grp3.name
    '/some/long'


.. _group_links:

Dict interface and links
------------------------

Groups implement a subset of the Python dictionary convention.  They have
methods like ``keys()``, ``values()`` and support iteration.  Most importantly,
they support the indexing syntax, and standard exceptions:

    >>> myds = subgrp["MyDS"]
    >>> missing = subgrp["missing"]
    KeyError: "Name doesn't exist (Symbol table: Object not found)"

Objects can be deleted from the file using the standard syntax::

    >>> del subgroup["MyDataset"]

.. note::
    When using h5py from Python 3, the keys(), values() and items() methods
    will return view-like objects instead of lists.  These objects support
    membership testing and iteration, but can't be sliced like lists.

By default, objects inside group are iterated in alphanumeric order.
However, if group is created with ``track_order=True``, the insertion
order for the group is remembered (tracked) in HDF5 file, and group
contents are iterated in that order.  The latter is consistent with
Python 3.7+ dictionaries.

The default ``track_order`` for all new groups can be specified
globally with ``h5.get_config().track_order``.


.. _group_hardlinks:

Hard links
~~~~~~~~~~

What happens when assigning an object to a name in the group?  It depends on
the type of object being assigned.  For NumPy arrays or other data, the default
is to create an :ref:`HDF5 datasets <dataset>`::

    >>> grp["name"] = 42
    >>> out = grp["name"]
    >>> out
    <HDF5 dataset "name": shape (), type "<i8">

When the object being stored is an existing Group or Dataset, a new link is
made to the object::

    >>> grp["other name"] = out
    >>> grp["other name"]
    <HDF5 dataset "other name": shape (), type "<i8">

Note that this is `not` a copy of the dataset!  Like hard links in a UNIX file
system, objects in an HDF5 file can be stored in multiple groups::

    >>> grp["other name"] == grp["name"]
    True


.. _group_softlinks:

Soft links
~~~~~~~~~~

Also like a UNIX filesystem, HDF5 groups can contain "soft" or symbolic links,
which contain a text path instead of a pointer to the object itself.  You
can easily create these in h5py by using ``h5py.SoftLink``::

    >>> myfile = h5py.File('foo.hdf5','w')
    >>> group = myfile.create_group("somegroup")
    >>> myfile["alias"] = h5py.SoftLink('/somegroup')

If the target is removed, they will "dangle":

    >>> del myfile['somegroup']
    >>> print(myfile['alias'])
    KeyError: 'Component not found (Symbol table: Object not found)'


.. _group_extlinks:

External links
~~~~~~~~~~~~~~

External links are "soft links plus", which allow you to
specify the name of the file as well as the path to the desired object.  You
can refer to objects in any file you wish.  Use similar syntax as for soft
links:

    >>> myfile = h5py.File('foo.hdf5','w')
    >>> myfile['ext link'] = h5py.ExternalLink("otherfile.hdf5", "/path/to/resource")

When the link is accessed, the file "otherfile.hdf5" is opened, and object at
"/path/to/resource" is returned.

Since the object retrieved is in a different file, its ".file" and ".parent"
properties will refer to objects in that file, *not* the file in which the
link resides.

.. note::

    Currently, you can't access an external link if the file it points to is
    already open.  This is related to how HDF5 manages file permissions
    internally.

.. note::

    The filename is stored in the file as bytes, normally UTF-8 encoded.
    In most cases, this should work reliably, but problems are possible if a
    file created on one platform is accessed on another. Older versions of HDF5
    may have problems on Windows in particular. See :ref:`file_filenames` for
    more details.

Reference
---------

.. class:: Group(identifier)

    Generally Group objects are created by opening objects in the file, or
    by the method :meth:`Group.create_group`.  Call the constructor with
    a :class:`GroupID <low:h5py.h5g.GroupID>` instance to create a new Group
    bound to an existing low-level identifier.

    .. method:: __iter__()

        Iterate over the names of objects directly attached to the group.
        Use :meth:`Group.visit` or :meth:`Group.visititems` for recursive
        access to group members.

    .. method:: __contains__(name)

        Dict-like membership testing.  `name` may be a relative or absolute
        path.

    .. method:: __getitem__(name)

        Retrieve an object.  `name` may be a relative or absolute path, or
        an :ref:`object or region reference <refs>`. See :ref:`group_links`.

    .. method:: __setitem__(name, value)

        Create a new link, or automatically create a dataset.
        See :ref:`group_links`.

    .. method:: __bool__()

        Check that the group is accessible.
        A group could be inaccessible for several reasons. For instance, the
        group, or the file it belongs to, may have been closed elsewhere.

        >>> f = h5py.open(filename)
        >>> group = f["MyGroup"]
        >>> f.close()
        >>> if group:
        ...     print("group is accessible")
        ... else:
        ...     print("group is inaccessible")
        group is inaccessible

    .. method:: keys()

        Get the names of directly attached group members.
        Use :meth:`Group.visit` or :meth:`Group.visititems` for recursive
        access to group members.

       :return: set-like object.

    .. method:: values()

        Get the objects contained in the group (Group and Dataset instances).
        Broken soft or external links show up as None.

        :return: a collection or bag-like object.

    .. method:: items()

        Get ``(name, value)`` pairs for object directly attached to this group.
        Values for broken soft or external links show up as None.

        :return: a set-like object.

    .. method:: get(name, default=None, getclass=False, getlink=False)

        Retrieve an item, or information about an item.  `name` and `default`
        work like the standard Python ``dict.get``.

        :param name:    Name of the object to retrieve.  May be a relative or
                        absolute path.
        :param default: If the object isn't found, return this instead.
        :param getclass:    If True, return the class of object instead;
                            :class:`Group` or :class:`Dataset`.
        :param getlink: If true, return the type of link via a :class:`HardLink`,
                        :class:`SoftLink` or :class:`ExternalLink` instance.
                        If ``getclass`` is also True, returns the corresponding
                        Link class without instantiating it.

    .. method:: visit(callable)

        Recursively visit all objects in this group and subgroups.  You supply
        a callable with the signature::

            callable(name) -> None or return value

        `name` will be the name of the object relative to the current group.
        Return None to continue visiting until all objects are exhausted.
        Returning anything else will immediately stop visiting and return
        that value from ``visit``::

            >>> def find_foo(name):
            ...     """ Find first object with 'foo' anywhere in the name """
            ...     if 'foo' in name:
            ...         return name
            >>> group.visit(find_foo)
            'some/subgroup/foo'


    .. method:: visititems(callable)

        Recursively visit all objects in this group and subgroups.  Like
        :meth:`Group.visit`, except your callable should have the signature::

            callable(name, object) -> None or return value

        In this case `object` will be a :class:`Group` or :class:`Dataset`
        instance.

    .. method:: visit_links(callable)
                visititems_links(callable)

       These methods are like :meth:`visit` and :meth:`visititems`, but work on
       the links in groups, rather than the objects those links point to. So if
       you have two links pointing to the same object, these will 'see' both.
       They also see soft & external links, which :meth:`visit` and
       :meth:`visititems` ignore.

       The second argument to the callback for ``visititems_links`` is an
       instance of one of the :ref:`link classes <group_link_classes>`.

       .. versionadded:: 3.11

    .. method:: move(source, dest)

        Move an object or link in the file.  If `source` is a hard link, this
        effectively renames the object.  If a soft or external link, the
        link itself is moved.

        :param source:  Name of object or link to move.
        :type source:   String
        :param dest:    New location for object or link.
        :type dest:   String


    .. method:: copy(source, dest, name=None, shallow=False, expand_soft=False, expand_external=False, expand_refs=False, without_attrs=False)

        Copy an object or group.  The source can be a path, Group, Dataset, or
        Datatype object.  The destination can be either a path or a Group
        object.  The source and destination need not be in the same file.

        If the source is a Group object, by default all objects within that
        group will be copied recursively.

        When the destination is a Group object, by default the target will be
        created in that group with its current name (basename of obj.name). You
        can override that by setting "name" to a string.

        :param source:  What to copy.  May be a path in the file or a Group/Dataset object.
        :param dest:    Where to copy it.  May be a path or Group object.
        :param name:    If the destination is a Group object, use this for the
                        name of the copied object (default is basename).
        :param shallow: Only copy immediate members of a group.
        :param expand_soft: Expand soft links into new objects.
        :param expand_external: Expand external links into new objects.
        :param expand_refs: Copy objects which are pointed to by references.
        :param without_attrs:   Copy object(s) without copying HDF5 attributes.


    .. method:: create_group(name, track_order=None)

        Create and return a new group in the file.

        :param name:    Name of group to create.  May be an absolute
                        or relative path.  Provide None to create an anonymous
                        group, to be linked into the file later.
        :type name:     String or None
        :param track_order:  Track dataset/group/attribute creation order under
                        this group if ``True``.  Default is
                        ``h5.get_config().track_order``.

        :return:        The new :class:`Group` object.


    .. method:: require_group(name)

        Open a group in the file, creating it if it doesn't exist.
        TypeError is raised if a conflicting object already exists.
        Parameters as in :meth:`Group.create_group`.


    .. method:: create_dataset(name, shape=None, dtype=None, data=None, **kwds)

        Create a new dataset.  Options are explained in :ref:`dataset_create`.

        :param name:    Name of dataset to create.  May be an absolute
                        or relative path.  Provide None to create an anonymous
                        dataset, to be linked into the file later.

        :param shape:   Shape of new dataset (Tuple).

        :param dtype:   Data type for new dataset

        :param data:    Initialize dataset to this (NumPy array).

        :keyword chunks:    Chunk shape, or True to enable auto-chunking.

        :keyword maxshape:  Dataset will be resizable up to this shape (Tuple).
                            Automatically enables chunking.  Use None for the
                            axes you want to be unlimited.

        :keyword compression:   Compression strategy.  See :ref:`dataset_compression`.

        :keyword compression_opts:  Parameters for compression filter.

        :keyword scaleoffset:   See :ref:`dataset_scaleoffset`.

        :keyword shuffle:   Enable shuffle filter (T/**F**).  See :ref:`dataset_shuffle`.

        :keyword fletcher32: Enable Fletcher32 checksum (T/**F**).  See :ref:`dataset_fletcher32`.

        :keyword fillvalue: This value will be used when reading
                            uninitialized parts of the dataset.

        :keyword fill_time: Control when to write the fill value. One of the
            following choices: `alloc`, write fill value before writing
            application data values or when the dataset is created; `never`,
            never write fill value; `ifset`, write fill value if it is defined.
            Default to `ifset`, which is the default of HDF5 library. If the
            whole dataset is going to be written by the application, setting
            this to `never` can avoid unnecessary writing of fill value and
            potentially improve performance.

        :keyword track_times: Enable dataset creation timestamps (**T**/F).

        :keyword track_order: Track attribute creation order if
                        ``True``.  Default is
                        ``h5.get_config().track_order``.

        :keyword external: Store the dataset in one or more external, non-HDF5
            files. This should be an iterable (such as a list) of tuples of
            ``(name, offset, size)`` to store data from ``offset`` to
            ``offset + size`` in the named file. Each name must be a str,
            bytes, or os.PathLike; each offset and size, an integer. The last
            file in the sequence may have size ``h5py.h5f.UNLIMITED`` to let
            it grow as needed. If only a name is given instead of an iterable
            of tuples, it is equivalent to
            ``[(name, 0, h5py.h5f.UNLIMITED)]``.

        :keyword allow_unknown_filter: Do not check that the requested filter is
            available for use (T/F). This should only be set if you will
            write any data with ``write_direct_chunk``, compressing the
            data before passing it to h5py.

        :keyword rdcc_nbytes: Total size of the dataset's chunk cache in bytes.
            The default size is 1024**2 (1 MiB).

        :keyword rdcc_w0: The chunk preemption policy for this dataset. This
            must be between 0 and 1 inclusive and indicates the weighting
            according to which chunks which have been fully read or written are
            penalized when determining which chunks to flush from cache. A value
            of 0 means fully read or written chunks are treated no differently
            than other chunks (the preemption is strictly LRU) while a value of
            1 means fully read or written chunks are always preempted before
            other chunks. If your application only reads or writes data once,
            this can be safely set to 1. Otherwise, this should be set lower
            depending on how often you re-read or re-write the same data. The
            default value is 0.75.

        :keyword rdcc_nslots: The number of chunk slots in the dataset's chunk
            cache. Increasing this value reduces the number of cache collisions,
            but slightly increases the memory used. Due to the hashing strategy,
            this value should ideally be a prime number. As a rule of thumb,
            this value should be at least 10 times the number of chunks that can
            fit in rdcc_nbytes bytes. For maximum performance, this value should
            be set approximately 100 times that number of chunks. The default
            value is 521.

    .. method:: require_dataset(name, shape, dtype, exact=False, **kwds)

        Open a dataset, creating it if it doesn't exist.

        If keyword "exact" is False (default), an existing dataset must have
        the same shape and a conversion-compatible dtype to be returned.  If
        True, the shape and dtype must match exactly.

        If keyword "maxshape" is given, the maxshape and dtype must match
        instead.

        If any of the keywords "rdcc_nslots", "rdcc_nbytes", or "rdcc_w0" are
        given, they will be used to configure the dataset's chunk cache.

        Other dataset keywords (see create_dataset) may be provided, but are
        only used if a new dataset is to be created.

        Raises TypeError if an incompatible object already exists, or if the
        shape, maxshape or dtype don't match according to the above rules.

        :keyword exact:     Require shape and type to match exactly (T/**F**)


    .. method:: create_dataset_like(name, other, **kwds)

        Create a dataset similar to `other`, much like numpy's `_like` functions.

        :param name:
            Name of the dataset (absolute or relative).  Provide None to make
            an anonymous dataset.
        :param other:
            The dataset whom the new dataset should mimic. All properties, such
            as shape, dtype, chunking, ... will be taken from it, but no data
            or attributes are being copied.

        Any dataset keywords (see create_dataset) may be provided, including
        shape and dtype, in which case the provided values take precedence over
        those from `other`.

    .. method:: create_virtual_dataset(name, layout, fillvalue=None)

       Create a new virtual dataset in this group. See :doc:`/vds` for more
       details.

       :param str name:
           Name of the dataset (absolute or relative).
       :param VirtualLayout layout:
           Defines what source data fills which parts of the virtual dataset.
       :param fillvalue:
           The value to use where there is no data.

    .. method:: build_virtual_dataset()

       Assemble a virtual dataset in this group.

       This is used as a context manager::

           with f.build_virtual_dataset('virt', (10, 1000), np.uint32) as layout:
               layout[0] = h5py.VirtualSource('foo.h5', 'data', (1000,))

       Inside the context, you populate a :class:`VirtualLayout` object.
       The file is only modified when you leave the context, and if there's
       no error.

       :param str name: Name of the dataset (absolute or relative)
       :param tuple shape: Shape of the dataset
       :param dtype: A numpy dtype for data read from the virtual dataset
       :param tuple maxshape: Maximum dimensions if the dataset can grow
           (optional). Use None for unlimited dimensions.
       :param fillvalue: The value used where no data is available.

    .. attribute:: attrs

        :ref:`attributes` for this group.

    .. attribute:: id

        The groups's low-level identifier; an instance of
        :class:`GroupID <low:h5py.h5g.GroupID>`.

    .. attribute:: ref

        An HDF5 object reference pointing to this group.  See
        :ref:`refs_object`.

    .. attribute:: regionref

        A proxy object allowing you to interrogate region references.
        See :ref:`refs_region`.

    .. attribute:: name

        String giving the full path to this group.

    .. attribute:: file

        :class:`File` instance in which this group resides.

    .. attribute:: parent

        :class:`Group` instance containing this group.

.. _group_link_classes:

Link classes
------------

.. class:: HardLink()

    Exists only to support :meth:`Group.get`.  Has no state and provides no
    properties or methods.

.. class:: SoftLink(path)

    Exists to allow creation of soft links in the file.
    See :ref:`group_softlinks`.  These only serve as containers for a path;
    they are not related in any way to a particular file.

    :param path:    Value of the soft link.
    :type path:     String

    .. attribute:: path

        Value of the soft link

.. class:: ExternalLink(filename, path)

    Like :class:`SoftLink`, only they specify a filename in addition to a
    path.  See :ref:`group_extlinks`.

    :param filename:    Name of the file to which the link points
    :type filename:     String

    :param path:        Path to the object in the external file.
    :type path:         String

    .. attribute:: filename

        Name of the external file as a Unicode string

    .. attribute::  path

        Path to the object in the external file
