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 ( Group and Dataset ) objects.
Group objects also contain most of the machinery which makes HDF5 useful. The File object 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.
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'
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"]
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.
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 HDF5 datasets:
>>> grp["name"] = 42
>>> out = grp["name"]
<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"]
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)'
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.
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.
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 Filenames on different systems for
more details.
class h5py.Group(identifier)
Generally Group objects are created by opening objects in the file, or
by the method Group.create_group().  Call the constructor with
a GroupID instance to create a new Group
bound to an existing low-level identifier.
__iter__()
Iterate over the names of objects directly attached to the group.
Use Group.visit() or Group.visititems() for recursive
access to group members.
__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
Get the names of directly attached group members.
Use Group.visit() or Group.visititems() for recursive
access to group members.
Returns:
set-like object.
values()
Get the objects contained in the group (Group and Dataset instances).
Broken soft or external links show up as None.
Returns:
a collection or bag-like object.
items()
Get (name, value) pairs for object directly attached to this group.
Values for broken soft or external links show up as None.
Returns:
a set-like object.
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.
Parameters:




    

name – Name of the object to retrieve.  May be a relative or
absolute path.
default – If the object isn’t found, return this instead.
getclass – If True, return the class of object instead;
Group or Dataset.
getlink – If true, return the type of link via a HardLink,
SoftLink or ExternalLink instance.
If getclass is also True, returns the corresponding
Link class without instantiating it.
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'
visititems(callable)
Recursively visit all objects in this group and subgroups.  Like
Group.visit(), except your callable should have the signature:
callable(name, object) -> None or return value
In this case object will be a Group or Dataset
instance.
visititems_links(callable)
These methods are like visit() and 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 visit() and
visititems() ignore.
The second argument to the callback for visititems_links is an
instance of one of the link classes.
New in version 3.11.
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.
Parameters:
source (String) – Name of object or link to move.
dest (String) – New location for object or link.
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.
Parameters:
source – What to copy.  May be a path in the file or a Group/Dataset object.
dest – Where to copy it.  May be a path or Group object.
name – If the destination is a Group object, use this for the
name of the copied object (default is basename).
shallow – Only copy immediate members of a group.
expand_soft – Expand soft links into new objects.
expand_external – Expand external links into new objects.
expand_refs – Copy objects which are pointed to by references.
without_attrs – Copy object(s) without copying HDF5 attributes.
name (String or None) – 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.
track_order – Track dataset/group/attribute creation order under
this group if True.  Default is
h5.get_config().track_order.
Returns:
The new Group object.
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 Group.create_group().
create_dataset(name, shape=None, dtype=None, data=None, **kwds)
Create a new dataset.  Options are explained in Creating datasets.
Parameters:
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.
shape – Shape of new dataset (Tuple).
dtype – Data type for new dataset
data – Initialize dataset to this (NumPy array).
chunks – Chunk shape, or True to enable auto-chunking.
maxshape – Dataset will be resizable up to this shape (Tuple).
Automatically enables chunking.  Use None for the
axes you want to be unlimited.
compression – Compression strategy.  See Filter pipeline.
compression_opts – Parameters for compression filter.
scaleoffset – See Scale-Offset filter.
shuffle – Enable shuffle filter (T/F).  See Shuffle filter.
fletcher32 – Enable Fletcher32 checksum (T/F).  See Fletcher32 filter.
fillvalue – This value will be used when reading
uninitialized parts of the dataset.
track_times – Enable dataset creation timestamps (T/F).
track_order – Track attribute creation order if
True.  Default is
h5.get_config().track_order.
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)].
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.
rdcc_nbytes – Total size of the dataset’s chunk cache in bytes.
The default size is 1024**2 (1 MiB).
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.
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.
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.
Parameters:
exact – Require shape and type to match exactly (T/F)
create_dataset_like(name, other, **kwds)
Create a dataset similar to other, much like numpy’s _like functions.
Parameters:
name – Name of the dataset (absolute or relative).  Provide None to make
an anonymous dataset.
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.
create_virtual_dataset(name, layout, fillvalue=None)
Create a new virtual dataset in this group. See Virtual Datasets (VDS) for more
details.
Parameters:
name (str) – Name of the dataset (absolute or relative).
layout (VirtualLayout) – Defines what source data fills which parts of the virtual dataset.
fillvalue – The value to use where there is no data.
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 VirtualLayout object.
The file is only modified when you leave the context, and if there’s
no error.
Parameters:
name (str) – Name of the dataset (absolute or relative)
shape (tuple) – Shape of the dataset
dtype – A numpy dtype for data read from the virtual dataset
maxshape (tuple) – Maximum dimensions if the dataset can grow
(optional). Use None for unlimited dimensions.
fillvalue – The value used where no data is available.
class h5py.HardLink
Exists only to support Group.get().  Has no state and provides no
properties or methods.
class h5py.SoftLink(path)
Exists to allow creation of soft links in the file.
See Soft links.  These only serve as containers for a path;
they are not related in any way to a particular file.
Parameters:
path (String) – Value of the soft link.
class h5py.ExternalLink(filename, path)
Like SoftLink, only they specify a filename in addition to a
path.  See External links.
Parameters:
filename (String) – Name of the file to which the link points
path (String) – Path to the object in the external file.