File objects serve as your entry point into the world of HDF5. In addition to the File-specific capabilities listed here, every File instance is also an HDF5 group representing the root group of the file.
Opening & creating files¶
HDF5 files work generally like standard Python file objects. They support standard modes like r/w/a, and should be closed when they are no longer in use. However, there is obviously no concept of “text” vs “binary” mode.
>>> f = h5py.File('myfile.hdf5','r')
The file name may be a byte string or unicode string. Valid modes are:
r Readonly, file must exist (default) r+ Read/write, file must exist w Create file, truncate if exists w- or x Create file, fail if exists a Read/write if exists, create otherwise
Changed in version 3.0: Files are now opened read-only by default. Earlier versions of h5py would pick different modes depending on the presence and permissions of the file.
HDF5 ships with a variety of different low-level drivers, which map the logical HDF5 address space to different storage mechanisms. You can specify which driver you want to use when the file is opened:
>>> f = h5py.File('myfile.hdf5', driver=<driver name>, <driver_kwds>)
For example, the HDF5 “core” driver can be used to create a purely in-memory HDF5 file, optionally written out to disk when it is closed. Here’s a list of supported drivers and their options:
- Strongly recommended. Use the standard HDF5 driver appropriate for the current platform. On UNIX, this is the H5FD_SEC2 driver; on Windows, it is H5FD_WINDOWS.
- Unbuffered, optimized I/O using standard POSIX functions.
- Buffered I/O using functions from stdio.h.
Store and manipulate the data in memory, and optionally write it back out when the file is closed. Using this with an existing file and a reading mode will read the entire file into memory. Keywords:
Store the file on disk as a series of fixed-length chunks. Useful if the file system doesn’t allow large files. Note: the filename you provide must contain a printf-style integer format code (e.g. %d”), which will be replaced by the file sequence number. Keywords:
memb_size: Maximum file size (default is 2**31-1).
- Store the data in a Python file-like object; see below. This is the default if a file-like object is passed to
Splits the meta data and raw data into separate files. Keywords:
- Metadata filename extension. Default is ‘-m.h5’.
- Raw data filename extension. Default is ‘-r.h5’.
Python file-like objects¶
New in version 2.9.
The first argument to
File may be a Python file-like object, such as
This is a convenient way to create temporary HDF5 files, e.g. for testing or to
send over the network.
The file-like object must be open for binary I/O, and must have these methods:
>>> tf = tempfile.TemporaryFile() >>> f = h5py.File(tf, 'w')
File instance after the underlying file object has been
closed will result in undefined behaviour.
When using an in-memory object such as
io.BytesIO, the data written
will take up space in memory. If you want to write large amounts of data,
a better option may be to store temporary data on disk using the functions in
"""Create an HDF5 file in memory and retrieve the raw bytes This could be used, for instance, in a server producing small HDF5 files on demand. """ import io import h5py bio = io.BytesIO() with h5py.File(bio) as f: f['dataset'] = range(10) data = bio.getvalue() # data is a regular Python bytes object. print("Total size:", len(data)) print("First bytes:", data[:10])
HDF5 has been evolving for many years now. By default, the library will write
objects in the most compatible fashion possible, so that older versions will
still be able to read files generated by modern programs. However, there can be
feature or performance advantages if you are willing to forgo a certain level of
backwards compatibility. By using the “libver” option to
File, you can
specify the minimum and maximum sophistication of these structures:
>>> f = h5py.File('name.hdf5', libver='earliest') # most compatible >>> f = h5py.File('name.hdf5', libver='latest') # most modern
Here “latest” means that HDF5 will always use the newest version of these structures without particular concern for backwards compatibility. The “earliest” option means that HDF5 will make a best effort to be backwards compatible.
The default is “earliest”.
Specifying version bounds has changed from HDF5 version 1.10.2. There are two new compatibility levels: v108 (for HDF5 1.8) and v110 (for HDF5 1.10). This change enables, for example, something like this:
>>> f = h5py.File('name.hdf5', libver=('earliest', 'v108'))
which enforces full backward compatibility up to HDF5 1.8. Using any HDF5 feature that requires a newer format will raise an error.
latest is now an alias to another bound label that represents the latest version. Because of this, the File.libver property will not use latest in its output for HDF5 1.10.2 or later.
If you call
File.close(), or leave a
with h5py.File(...) block,
the file will be closed and any objects (such as groups or datasets) you have
from that file will become unusable. This is equivalent to what HDF5 calls
If a file object goes out of scope in your Python code, the file will only be closed when there are no remaining objects belonging to it. This is what HDF5 calls ‘weak’ closing.
HDF5 allows the user to insert arbitrary data at the beginning of the file,
in a reserved space called the user block. The length of the user block
must be specified when the file is created. It can be either zero
(the default) or a power of two greater than or equal to 512. You
can specify the size of the user block when creating a new file, via the
userblock_size keyword to File; the userblock size of an open file can
likewise be queried through the
Modifying the user block on an open file is not supported; this is a limitation of the HDF5 library. However, once the file is closed you are free to read and write data at the start of the file, provided your modifications don’t leave the user block region.
Filenames on different systems¶
Different operating systems (and different file systems) store filenames with different encodings. Additionally, in Python there are at least two different representations of filenames, as encoded bytes (via str on Python 2, bytes on Python 3) or as a unicode string (via unicode on Python 2 and str on Python 3). The safest bet when creating a new file is to use unicode strings on all systems.
macOS is the simplest system to deal with, it only accepts UTF-8, so using unicode paths will just work (and should be preferred).
Linux (and non-macOS Unix)¶
Unix-like systems use locale settings to determine the correct encoding to use.
These are set via a number of different environment variables, of which
LC_ALL are the ones of most interest. Of special interest is the
locale, which Python will interpret as only allowing ASCII, meaning unicode
paths should be pre-encoded. This will likely change in Python 3.7 with
https://www.python.org/dev/peps/pep-0538/, but this will likely be backported by
distributions to earlier versions.
To summarise, use unicode strings where possible, but be aware that sometimes using encoded bytes may be necessary to read incorrectly encoded filenames.
Windows systems have two different APIs to perform file-related operations, a ANSI (char, legacy) interface and a unicode (wchar) interface. HDF5 currently only supports the ANSI interface, which is limited in what it can encode. This means that it may not be possible to open certain files, and because External links do not specify their encoding, it is possible that opening an external link may not work. There is work being done to fix this (see https://github.com/h5py/h5py/issues/839), but it is likely there will need to be breaking changes make to allow Windows to have the same level of support for unicode filenames as other operating systems.
The best suggestion is to use unicode strings, but to keep to ASCII for filenames to avoid possible breakage.
Chunked storage allows datasets to be stored on disk in separate pieces. When a part of any one of these pieces is needed, the entire chunk is read into memory before the requested part is copied to the user’s buffer. To the extent possible those chunks are cached in memory, so that if the user requests a different part of a chunk that has already been read, the data can be copied directly from memory rather than reading the file again. The details of a given dataset’s chunks are controlled when creating the dataset, but it is possible to adjust the behavior of the chunk cache when opening the file.
The parameters controlling this behavior are prefixed by
rdcc, for raw data
rdcc_nbytessets the total size (measured in bytes) of the raw data chunk cache for each dataset. The default size is 1 MB. This should be set to the size of each chunk times the number of chunks that are likely to be needed in cache.
rdcc_w0sets the policy for chunks to be removed from the cache when more space is needed. If the value is set to 0, then the library will always evict the least recently used chunk in cache. If the value is set to 1, the library will always evict the least recently used chunk which has been fully read or written, and if none have been fully read or written, it will evict the least recently used chunk. If the value is between 0 and 1, the behavior will be a blend of the two. Therefore, if the application will access the same data more than once, the value should be set closer to 0, and if the application does not, the value should be set closer to 1.
rdcc_nslotsis the number of chunk slots in the cache for this entire file. In order to allow the chunks to be looked up quickly in cache, each chunk is assigned a unique hash value that is used to look up the chunk. The cache contains a simple array of pointers to chunks, which is called a hash table. A chunk’s hash value is simply the index into the hash table of the pointer to that chunk. While the pointer at this location might instead point to a different chunk or to nothing at all, no other locations in the hash table can contain a pointer to the chunk in question. Therefore, the library only has to check this one location in the hash table to tell if a chunk is in cache or not. This also means that if two or more chunks share the same hash value, then only one of those chunks can be in the cache at the same time. When a chunk is brought into cache and another chunk with the same hash value is already in cache, the second chunk must be evicted first. Therefore it is very important to make sure that the size of the hash table (which is determined by the
rdcc_nslotsparameter) is large enough to minimize the number of hash value collisions. 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_nbytesbytes. For maximum performance, this value should be set approximately 100 times that number of chunks. The default value is 521.
Chunks and caching are described in greater detail in the HDF5 documentation.
Unlike Python file objects, the attribute
File.name gives the
HDF5 name of the root group, “
/”. To access the on-disk name, use
File(name, mode=None, driver=None, libver=None, userblock_size=None, swmr=False, rdcc_nslots=None, rdcc_nbytes=None, rdcc_w0=None, track_order=None, **kwds)¶
Open or create a new file.
Note that in addition to the File-specific methods and properties listed below, File objects inherit the full interface of
- name – Name of file (bytes or str), or an instance of
h5f.FileIDto bind to an existing file identifier, or a file-like object (see Python file-like objects).
- mode – Mode in which to open file; one of (“w”, “r”, “r+”, “a”, “w-“). See Opening & creating files.
- driver – File driver to use; see File drivers.
- libver – Compatibility bounds; see Version bounding.
- userblock_size – Size (in bytes) of the user block. If nonzero, must be a power of 2 and at least 512. See User block.
- swmr – If
Trueopen the file in single-writer-multiple-reader mode. Only used when mode=”r”.
- rdcc_nbytes – Total size of the raw data chunk cache in bytes. The default size is \(1024^2\) (1 MB) per dataset.
- rdcc_w0 – Chunk preemption policy for all datasets. Default value is 0.75.
- rdcc_nslots – Number of chunk slots in the raw data chunk cache for this file. Default value is 521.
- track_order – Track dataset/group/attribute creation order under
root group if
True. Default is
- kwds – Driver-specific keywords; see File drivers.
Check that the file descriptor is valid and the file open:
>>> f = h5py.File(filename) >>> f.close() >>> if f: ... print("file is open") ... else: ... print("file is closed") file is closed
Close this file. All open objects will become invalid.
Request that the HDF5 library flush its buffers to disk.
Name of this file on disk. Generally a Unicode string; a byte string will be used if HDF5 returns a non-UTF-8 encoded string.
String indicating if the file is open readonly (“r”) or read-write (“r+”). Will always be one of these two values, regardless of the mode used to open the file.
String giving the driver used to open the file. Refer to File drivers for a list of drivers.
- name – Name of file (bytes or str), or an instance of