ewoksfluo.io.hdf5.ReadHdf5File#

class ewoksfluo.io.hdf5.ReadHdf5File(*args, mode='r', **kwargs)[source]#

Bases: File

Use in cases where you want to read something from an HDF5 which might be already open for writing.

Parameters:: mode (str)

property attrs#: Attributes attached to this object

build_virtual_dataset(name, shape, dtype, maxshape=None, fillvalue=None)#

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,))

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

clear() → None. Remove all items from D.#

close()#: Close the file. All open objects become invalid

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 destinations need not be in the same file.

If the source is a Group object, all objects contained in 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.

There are various options which all default to “False”:

shallow: copy only 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 that are pointed to by references.

without_attrs: copy object without copying attributes.

Example:

>>> f = File('myfile.hdf5', 'w')
>>> f.create_group("MyGroup")
>>> list(f.keys())
['MyGroup']
>>> f.copy('MyGroup', 'MyCopy')
>>> list(f.keys())
['MyGroup', 'MyCopy']

create_dataset(name, shape=None, dtype=None, data=None, **kwds)#

Create a new HDF5 dataset

name: Name of the dataset (absolute or relative). Provide None to make an anonymous dataset.
shape: Dataset shape. Use “()” for scalar datasets. Required if “data” isn’t provided.
dtype: Numpy dtype or string. If omitted, dtype(‘f’) will be used. Required if “data” isn’t provided; otherwise, overrides data array’s dtype.
data: Provide data to initialize the dataset. If used, you can omit shape and dtype arguments.

Keyword-only arguments:

chunks: (Tuple or int) Chunk shape, or True to enable auto-chunking. Integers can be used for 1D shape.
maxshape: (Tuple or int) Make the dataset resizable up to this shape. Use None for axes you want to be unlimited. Integers can be used for 1D shape.
compression: (String or int) Compression strategy. Legal values are ‘gzip’, ‘szip’, ‘lzf’. If an integer in range(10), this indicates gzip compression level. Otherwise, an integer indicates the number of a dynamically loaded compression filter.
compression_opts: Compression settings. This is an integer for gzip, 2-tuple for szip, etc. If specifying a dynamically loaded compression filter number, this must be a tuple of values.
scaleoffset: (Integer) Enable scale/offset filter for (usually) lossy compression of integer or floating-point data. For integer data, the value of scaleoffset is the number of bits to retain (pass 0 to let HDF5 determine the minimum number of bits necessary for lossless compression). For floating point data, scaleoffset is the number of digits after the decimal place to retain; stored values thus have absolute error less than 0.5*10**(-scaleoffset).
shuffle: (T/F) Enable shuffle filter.
fletcher32: (T/F) Enable fletcher32 error detection. Not permitted in conjunction with the scale/offset filter.
fillvalue: (Scalar) Use this value for uninitialized parts of the dataset.
track_times: (T/F) Enable dataset creation timestamps.
track_order: (T/F) Track attribute creation order if True. If omitted use global default h5.get_config().track_order.
external: (Iterable of tuples) Sets the external storage property, thus designating that the dataset will be stored in one or more non-HDF5 files external to the HDF5 file. Adds each tuple of (name, offset, size) to the dataset’s list of external files. Each name must be a str, bytes, or os.PathLike; each offset and size, an integer. If only a name is given instead of an iterable of tuples, it is equivalent to [(name, 0, h5py.h5f.UNLIMITED)].
efile_prefix: (String) External dataset file prefix for dataset access property list. Does not persist in the file.
virtual_prefix: (String) Virtual dataset file prefix for dataset access property list. Does not persist in the file.
allow_unknown_filter: (T/F) Do not check that the requested filter is available for use. This should only be used with write_direct_chunk, where the caller compresses the data before handing 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.

create_dataset_like(name, other, **kwupdate)#

Create a dataset similar to other.

name: Name of the dataset (absolute or relative). Provide None to make an anonymous dataset.
other: The dataset which 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_group(name, track_order=None)#

Create and return a new subgroup.

Name may be absolute or relative. Fails if the target name already exists.

track_order: Track dataset/group/attribute creation order under this group if True. If None use global default h5.get_config().track_order.

create_virtual_dataset(name, layout, fillvalue=None)#

Create a new virtual dataset in this group.

See virtual datasets in the docs for more information.

name: (str) Name of the new dataset
layout: (VirtualLayout) Defines the sources for the virtual dataset
fillvalue: The value to use where there is no data.

property driver#: Low-level HDF5 file driver used to open file

property file#: Return a File instance associated with this object

property filename#: File name on disk

flush()#: Tell the HDF5 library to flush its buffers.

get(name, default=None, getclass=False, getlink=False)#

Retrieve an item or other information.

“name” given only:: Return the item, or “default” if it doesn’t exist
“getclass” is True:: Return the class of object (Group, Dataset, etc.), or “default” if nothing with that name exists
“getlink” is True:: Return HardLink, SoftLink or ExternalLink instances. Return “default” if nothing with that name exists.
“getlink” and “getclass” are True:: Return HardLink, SoftLink and ExternalLink classes. Return “default” if nothing with that name exists.

Example:

>>> cls = group.get('foo', getclass=True)
>>> if cls == SoftLink:

property id#: Low-level identifier appropriate for this object

items()#: Get a view object on member items

keys()#: Get a view object on member names

property libver#: File format version bounds (2-tuple: low, high)

property meta_block_size#: Meta block size (in bytes)

property mode#: Python mode used to open file

move(source, dest)#

Move a link to a new location in the file.

If “source” is a hard link, this effectively renames the object. If “source” is a soft or external link, the link itself is moved, with its value unmodified.

property name#: Return the full name of this object. None if anonymous.

property parent#

Return the parent group of this object.

This is always equivalent to obj.file[posixpath.dirname(obj.name)]. ValueError if this object is anonymous.

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair#: as a 2-tuple; but raise KeyError if D is empty.

property ref#: An (opaque) HDF5 reference to this object

property regionref#

Create a region reference (Datasets only).

The syntax is regionref[<slices>]. For example, dset.regionref[…] creates a region reference in which the whole dataset is selected.

Can also be used to determine the shape of the referenced dataset (via .shape property), or the shape of the selection (via the .selection property).

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.

require_group(name)#

Return a group, creating it if it doesn’t exist.

TypeError is raised if something with that name already exists that isn’t a group.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D#

property swmr_mode#: Controls single-writer multiple-reader mode

update([E, ]**F) → None. Update D from mapping/iterable E and F.#: If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

property userblock_size#: User block size (in bytes)

values()#: Get a view object on member objects

visit(func)#

Recursively visit all names in this group and subgroups.

Note: visit ignores soft and external links. To visit those, use visit_links.

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

>>> # List the entire contents of the file
>>> f = File("foo.hdf5")
>>> list_of_names = []
>>> f.visit(list_of_names.append)

visit_links(func)#

Recursively visit all names in this group and subgroups. Each link will be visited exactly once, regardless of its target.

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

>>> # List the entire contents of the file
>>> f = File("foo.hdf5")
>>> list_of_names = []
>>> f.visit_links(list_of_names.append)

visititems(func)#

Recursively visit names and objects in this group.

Note: visititems ignores soft and external links. To visit those, use visititems_links.

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>, <object>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

# Get a list of all datasets in the file >>> mylist = [] >>> def func(name, obj): … if isinstance(obj, Dataset): … mylist.append(name) … >>> f = File(‘foo.hdf5’) >>> f.visititems(func)

visititems_links(func)#

Recursively visit links in this group. Each link will be visited exactly once, regardless of its target.

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>, <link>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

# Get a list of all softlinks in the file >>> mylist = [] >>> def func(name, link): … if isinstance(link, SoftLink): … mylist.append(name) … >>> f = File(‘foo.hdf5’) >>> f.visititems_links(func)