They are small named pieces of data attached directly to Group and Dataset objects. This is the official way to store metadata in HDF5. Attributes have the following properties:. This class supports a dictionary-style interface. By default, attributes are iterated in alphanumeric order.
The latter is consistent with Python 3. AttributeManager objects are created directly by h5py. You should access instances by group. Create an attribute, overwriting any existing attribute. The type and shape of the attribute are determined automatically by h5py.
Get the names of all attributes attached to this object. On Py2, this is a list. Get the values of all attributes attached to this object. Get name, value tuples for all attributes attached to this object. On Py2, this is a list of tuples. Py2 only Get an iterator over name, value pairs. Get the low-level AttrID for the named attribute.
Create a new attribute, with control over the shape and type. Any existing attribute will be overwritten. Change the value of an attribute while preserving its type and shape. Unlike AttributeManager. This can be useful for interacting with externally generated files, where the type and shape must not be altered.
Parameters: name String — Name of the new attribute data — Value of the attribute; will be put through numpy. Overrides data. Parameters: name String — Name of attribute to modify. Will be put through numpy. Read the Docs v: stable Versions latest stable 2.A primary data object may be a dataset, group, or committed datatype. Attributes are assumed to be very small as data objects go, so storing them as standard HDF5 datasets would be quite inefficient.
HDF5 attributes are therefore managed through a special attributes interface, H5A, which is designed to easily attach attributes to primary data objects as small datasets containing metadata information and to minimize storage requirements.
Consider, as examples of the simplest case, a set of laboratory readings taken under known temperature and pressure conditions of In the following discussions, attributes are generally attached to datasets. Attributes attached to other primary data objects, i. Creating an attribute is similar to creating a dataset.
h5py 将 mat 数据集中的 转成 string
To create an attribute, the application must specify the object to which the attribute is attached, the datatype and dataspace of the attribute data, and the attribute creation property list. Functions that can be used with attributes H5A functions and functions that can be used with property lists H5P functions are listed below. Each attribute attached to an object has a unique name.
In other words, attributes can be written and read only in full with no sub-setting. H5Acreate and H5Awrite are used together to place the attribute in the file.
H5Acreate creates the attribute in memory. The attribute does not exist in the file until H5Awrite writes it there. H5Awrite writes the attribute data from the buffer buf to the file. H5Aread reads attribute data from the file into buf. Attributes can be accessed by name or index value. The use of an index value makes it possible to iterate through all of the attributes associated with a given object.
Use H5Aiterate if the intent is to perform the same operation on every attribute attached to an object.
H5R: Reference Interface
In the course of working with HDF5 attributes, one may need to obtain any of several pieces of information:. This is generally the preferred first step in determining attribute index values. It is sometimes useful to be able to perform the identical operation across all of the attributes attached to an object. At the simplest level, you might just want to open each attribute.
At a higher level, you might wish to perform a rather complex operation on each attribute as you iterate across the set. When H5Aiterate is called, index contains the index of the attribute to be accessed in this call. When H5Aiterate returns, index will contain the index of the next attribute.
If the returned index is the null pointer, then all attributes have been processed, and the iterative process is complete. Beyond providing a parameter with which to pass this data, HDF5 provides no tools for its management and imposes no restrictions. Once an attribute has outlived its usefulness or is no longer appropriate, it may become necessary to delete it. Such a call can cause the internal attribute indexes to change; future writes to an open attribute would then produce unintended results.
As is the case with all HDF5 objects, once access to an attribute it is no longer needed, that attribute must be closed. It is best practice to close it as soon as practicable; it is mandatory that it be closed prior to the H5close call closing the HDF5 Library.
The dense attribute storage scheme was added in version 1.
Attributes start out being stored in an object's header. This is known as compact storage. As the number of attributes grows, attribute-related performance slows.
When dense attribute storage is enabled, a threshold is defined for the number of attributes kept in compact storage.Groups are the container mechanism by which HDF5 files are organized. From a Python perspective, they operate somewhat like dictionaries. Group objects also contain most of the machinery which makes HDF5 useful.
The File object does double duty as the HDF5 root groupand serves as your entry point into the file:. Names of all objects in the file are all text strings unicode on Py2, str on Py3. Objects may also be retrieved using byte strings, which will be passed on to HDF5 as-is. Groups implement a subset of the Python dictionary convention.
They have methods like keysvalues and support iteration. Most importantly, they support the indexing syntax, and standard exceptions:. When using h5py from Python 3, the keysvalues and items methods will return view-like objects instead of lists. By default, objects inside group are iterated in alphanumeric order.
The latter is consistent with Python 3. What happens when assigning an object to a name in the group? It depends on the type of object being assigned. Note that this is not a copy of the dataset!
You can easily create these in h5py by using h5py. SoftLink :. New in HDF5 1. You can refer to objects in any file you wish. Use similar syntax as for soft links:. This is related to how HDF5 manages file permissions internally. How the filename is processed is operating system dependent, it is recommended to read Filenames on different systems to understand potential limitations on filenames on your operating system.
Note especially that Windows is particularly susceptible to problems with external links, due to possible encoding errors and how filenames are structured. Generally Group objects are created by opening objects in the file, or by the method Group. Call the constructor with a GroupID instance to create a new Group bound to an existing low-level identifier. Iterate over the names of objects directly attached to the group.
Use Group. Retrieve an object. See Dict interface and links.
Create a new link, or automatically create a dataset. Get the names of directly attached group members. On Py2, this is a list. Get the objects contained in the group Group and Dataset instances. Broken soft or external links show up as None. Get name, value pairs for object directly attached to this group. Values for broken soft or external links show up as None. Py2 only Get an iterator over key names. Exactly equivalent to iter group.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. HDF5 files work generally like standard Python file objects. 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:. 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. Store the file on disk as a series of fixed-length chunks. Note: the filename you provide must contain a printf-style integer format code e.
The first argument to File may be a Python file-like object, such as an io. BytesIO or tempfile. TemporaryFile instance. This is a convenient way to create temporary HDF5 files, e.
Accessing the File instance after the underlying file object has been closed will result in undefined behaviour. When using an in-memory object such as io.
BytesIOthe 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 tempfile. 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. Specifying version bounds has changed from HDF5 version 1. There are two new compatibility levels: v for HDF5 1.
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Have you checked the data layout? Use h5dump. Active Oldest Votes. To get an idea of the data layout you could execute h5dump. I get for example: I am sorry the question was not well rendered due to the [ characters. I have fixed it. Please check it out, thanks a lot. If I tried this, it is really really slow about 3 minutes to get 50 names on a good i7do you have an idea? Sign up or log in Sign up using Google. Sign up using Facebook.
Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog.In addition to soft and external links, HDF5 supplies one more mechanism to refer to objects and data in a file. HDF5 references are low-level pointers to other objects. The great advantage of references is that they can be stored and retrieved as data; you can create an attribute or an entire dataset of reference type.
References come in two flavors, object references and region references. As the name suggests, object references point to a particular object in a file, either a dataset, group or named datatype.
Region references always point to a dataset, and additionally contain information about a certain selection dataset region on that dataset. For example, if you have a dataset representing an image, you could specify a region of interest, and store it as an attribute on the dataset. Region references always contain a selection. Note that for h5py release before 2. In addition to storing a selection, region references inherit from object references, and can be used anywhere an object reference is accepted.
In this case the object they point to is the dataset used to create them. HDF5 treats object and region references as data. Consequently, there is a special HDF5 type to represent them. However, NumPy has no equivalent type. A small amount of metadata attached to the dtype tells h5py to interpret the data as containing reference objects. Reference for object references and h5py.
RegionReference for region references. Simply assign the reference to a name; h5py will figure it out and store it with the correct type:. H5py uses the truth value of a reference object to indicate whether or not it is null:. File 'myfile.
Read the Docs v: stable Versions latest stable 2.The model includes an abstract data model and an abstract storage model the data formatand libraries to implement the abstract model and to map the storage model to different storage mechanisms. The HDF5 library provides a programming interface to a concrete implementation of the abstract models. The library also implements a model of data transfer, i.
The figure below illustrates the relationships between the models and implementations. This chapter explains these models in detail. The Abstract Data Model is a conceptual model of data, data types, and data organization.
h5py 将 mat 数据集中的 转成 string
The abstract data model is independent of storage medium or programming environment. The Storage Model is a standard representation for the objects of the abstract data model.
The Programming Model is a model of the computing environment and includes platforms from small single systems to large multiprocessors and clusters. The programming model manipulates instantiates, populates, and retrieves objects from the abstract data model.
The Library is the concrete implementation of the programming model. In addition to implementing the objects of the abstract data model, the Library manages data transfers from one stored form to another.
Data transfer examples include reading from disk to memory and writing from memory to disk. Stored Data is the concrete implementation of the storage model. The storage model is mapped to several storage mechanisms including single disk files, multiple files family of filesand memory representations. The HDF5 Library calls the operating system or other storage management software e. The HDF5 Library may also link to other software such as filters for compression. The application program implements problem specific algorithms and data structures and calls the HDF5 Library to store and retrieve data.
The figure below shows the dependencies of these modules. It is important to realize that each of the software components manages data using models and data structures that are appropriate to the component. When data is passed between layers during storage or retrievalit is transformed from one representation to another. The figure below suggests some of the kinds of data structures used in the different layers. The Application Program uses data structures that represent the problem and algorithms including variables, tables, arrays, and meshes among other data structures.
Depending on its design and function, an application may have quite a few different kinds of data structures and different numbers and sizes of objects. Some of these objects include groups, datasets, and attributes. The application program maps the application data structures to a hierarchy of HDF5 objects. Each application will create a mapping best suited to its purposes. The objects of the HDF5 abstract data model are mapped to the objects of the HDF5 storage model, and stored in a storage medium.
The stored objects include header blocks, free lists, data blocks, B-trees, and other objects.How to open an HDF-EOS5 file and dataset with HDFView
Each group or dataset is stored as one or more header and data blocks. The HDF5 Library can also use other libraries and modules such as compression. The important point to note is that there is not necessarily any simple correspondence between the objects of the application program, the abstract data model, and those of the Format Specification. The organization of the data of application program, and how it is mapped to the HDF5 abstract data model is up to the application developer.
The application program only needs to deal with the library and the abstract data model.