/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Copyright by The HDF Group. * * All rights reserved. * * * * This file is part of HDF5. The full HDF5 copyright notice, including * * terms governing use, modification, and redistribution, is contained in * * the COPYING file, which can be found at the root of the source code * * distribution tree, or in https://www.hdfgroup.org/licenses. * * If you do not have access to either file, you may request a copy from * * help@hdfgroup.org. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /* * Purpose: This file contains declarations which define macros for the * H5 package. Including this header means that the source file * is part of the H5 package. */ #ifndef H5module_H #define H5module_H /* Define the proper control macros for the generic FUNC_ENTER/LEAVE and error * reporting macros. */ #define H5_MODULE #define H5_MY_PKG H5 #define H5_MY_PKG_ERR H5E_LIB #define H5_MY_PKG_INIT YES /** \page H5DM_UG The HDF5 Data Model and File Structure * * \section sec_data_model The HDF5 Data Model and File Structure * \subsection subsec_data_model_intro Introduction * The Hierarchical Data Format (HDF) implements a model for managing and storing data. The * model includes an abstract data model and an abstract storage model (the data format), and * 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, an efficient * movement of data from one stored representation to another stored representation. The figure * below illustrates the relationships between the models and implementations. This chapter * explains these models in detail. * * * * * *
* \image html Dmodel_fig1.gif "HDF5 models and implementations" *
* * The Abstract Data Model is a conceptual model of data, data types, and data organization. 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 * HDF5 File Format Specification * defines the storage 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. The library exports the * HDF5 APIs as its interface. 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 files), * and memory representations. * * The HDF5 library is a C module that implements the programming model and abstract data * model. The HDF5 library calls the operating system or other storage management software (for * example, the MPI/IO Library) to store and retrieve persistent data. The HDF5 library may also * link to other software such as filters for compression. The HDF5 library is linked to an * application program which may be written in C, C++, Fortran, or Java. 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. * * * * * *
* \image html Dmodel_fig2.gif "The library, the application program, and other 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 retrieval), it 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. * * The HDF5 Library implements the objects of the HDF5 abstract data model. 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. Each group or dataset is stored as one or more header and data * blocks. * @see HDF5 File Format Specification * for more information on how these objects are organized. The HDF5 library can also use other * libraries and modules such as compression. * * * * * * * * *
Data structures in different layers
* \image html Dmodel_fig3_a.gif * * \image html Dmodel_fig2.gif * * \image html Dmodel_fig3_c.gif *
* * 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. Most applications need not consider * any details of the * HDF5 File Format Specification * or the details of how objects of abstract data model are translated to and from storage. * * \subsection subsec_data_model_abstract The Abstract Data Model * The abstract data model (ADM) defines concepts for defining and describing complex data * stored in files. The ADM is a very general model which is designed to conceptually cover many * specific models. Many different kinds of data can be mapped to objects of the ADM, and * therefore stored and retrieved using HDF5. The ADM is not, however, a model of any particular * problem or application domain. Users need to map their data to the concepts of the ADM. * * The key concepts include: * * * These key concepts are described in more detail below. * * \subsubsection subsubsec_data_model_abstract_file File * Abstractly, an HDF5 file is a container for an organized collection of objects. The objects are * groups, datasets, and other objects as defined below. The objects are organized as a rooted, * directed graph. Every HDF5 file has at least one object, the root group. See the figure below. All * objects are members of the root group or descendants of the root group. * * * * * * * * * *
The HDF5 file
* \image html Dmodel_fig4_b.gif *
* \image html Dmodel_fig4_a.gif *
* * HDF5 objects have a unique identity within a single HDF5 file and can be accessed only by their * names within the hierarchy of the file. HDF5 objects in different files do not necessarily have * unique identities, and it is not possible to access a permanent HDF5 object except through a file. * For more information, see \ref subsec_data_model_structure. * * When the file is created, the file creation properties specify settings for the file. The file creation * properties include version information and parameters of global data structures. When the file is * opened, the file access properties specify settings for the current access to the file. File access * properties include parameters for storage drivers and parameters for caching and garbage * collection. The file creation properties are set permanently for the life of the file, and the file * access properties can be changed by closing and reopening the file. * * An HDF5 file can be “mounted” as part of another HDF5 file. This is analogous to Unix file * system mounts. The root of the mounted file is attached to a group in the mounting file, and all * the contents can be accessed as if the mounted file were part of the mounting file. * * @see @ref sec_file. * * \subsubsection subsubsec_data_model_abstract_group Group * An HDF5 group is analogous to a file system directory. Abstractly, a group contains zero or * more objects, and every object must be a member of at least one group. The root group is a * special case; it may not be a member of any group. * * Group membership is actually implemented via link objects. See the figure below. A link object * is owned by a group and points to a named object. Each link has a name, and each link points to * exactly one object. Each named object has at least one and possibly many links to it. * * * * * *
* \image html Dmodel_fig5.gif "Group membership via link objects" *
* * There are three classes of named objects: group, dataset, and committed (named) datatype. See * the figure below. Each of these objects is the member of at least one group, and this means there * is at least one link to it. * * * * * *
* \image html Dmodel_fig6.gif "Classes of named objects" *
* * @see @ref sec_group. * * \subsubsection subsubsec_data_model_abstract_dataset Dataset * An HDF5 dataset is a multidimensional (rectangular) array of data elements. See the figure * below. The shape of the array (number of dimensions, size of each dimension) is described by * the dataspace object (described in the next section below). * * A data element is a single unit of data which may be a number, a character, an array of numbers * or characters, or a record of heterogeneous data elements. A data element is a set of bits. The * layout of the bits is described by the datatype (see below). * * The dataspace and datatype are set when the dataset is created, and they cannot be changed for * the life of the dataset. The dataset creation properties are set when the dataset is created. The * dataset creation properties include the fill value and storage properties such as chunking and * compression. These properties cannot be changed after the dataset is created. * * The dataset object manages the storage and access to the data. While the data is conceptually a * contiguous rectangular array, it is physically stored and transferred in different ways depending * on the storage properties and the storage mechanism used. The actual storage may be a set of * compressed chunks, and the access may be through different storage mechanisms and caches. * The dataset maps between the conceptual array of elements and the actual stored data. * * * * * *
* \image html Dmodel_fig7_b.gif "The dataset" *
* * @see @ref sec_dataset. * * \subsubsection subsubsec_data_model_abstract_space Dataspace * The HDF5 dataspace describes the layout of the elements of a multidimensional array. * Conceptually, the array is a hyper-rectangle with one to 32 dimensions. HDF5 dataspaces can be * extendable. Therefore, each dimension has a current size and a maximum size, and the maximum * may be unlimited. The dataspace describes this hyper-rectangle: it is a list of dimensions with * the current and maximum (or unlimited) sizes. See the figure below. * * * * * *
* \image html Dmodel_fig8.gif "The dataspace" *
* * Dataspace objects are also used to describe hyperslab selections from a dataset. Any subset of the * elements of a dataset can be selected for read or write by specifying a set of hyperslabs. A * non-rectangular region can be selected by the union of several (rectangular) dataspaces. * * @see @ref sec_dataspace. * * \subsubsection subsubsec_data_model_abstract_type Datatype * The HDF5 datatype object describes the layout of a single data element. A data element is a * single element of the array; it may be a single number, a character, an array of numbers or * carriers, or other data. The datatype object describes the storage layout of this data. * * Data types are categorized into 11 classes of datatype. Each class is interpreted according to a set * of rules and has a specific set of properties to describe its storage. For instance, floating point * numbers have exponent position and sizes which are interpreted according to appropriate * standards for number representation. Thus, the datatype class tells what the element means, and * the datatype describes how it is stored. * * The figure below shows the classification of datatypes. Atomic datatypes are indivisible. Each * may be a single object such as a number or a string. Composite datatypes are composed of * multiple elements of atomic datatypes. In addition to the standard types, users can define * additional datatypes such as a 24-bit integer or a 16-bit float. * A dataset or attribute has a single datatype object associated with it. See Figure 7 above. The * datatype object may be used in the definition of several objects, but by default, a copy of the * datatype object will be private to the dataset. * * Optionally, a datatype object can be stored in the HDF5 file. The datatype is linked into a group, * and therefore given a name. A committed datatype (formerly called a named datatype) can be * opened and used in any way that a datatype object can be used. * * * * * *
* \image html Dmodel_fig9.gif "Datatype classifications" *
* * @see @ref sec_datatype. * * \subsubsection subsubsec_data_model_abstract_attr Attribute * Any HDF5 named data object (group, dataset, or named datatype) may have zero or more user * defined attributes. Attributes are used to document the object. The attributes of an object are * stored with the object. * * An HDF5 attribute has a name and data. The data portion is similar in structure to a dataset: a * dataspace defines the layout of an array of data elements, and a datatype defines the storage * layout and interpretation of the elements See the figure below. * * * * * *
* \image html Dmodel_fig10.gif "Attribute data elements" *
* * In fact, an attribute is very similar to a dataset with the following limitations: * * * Note that the value of an attribute can be an object reference. A shared attribute or an attribute * that is a large array can be implemented as a reference to a dataset. * * The name, dataspace, and datatype of an attribute are specified when it is created and cannot be * changed over the life of the attribute. An attribute can be opened by name, by index, or by * iterating through all the attributes of the object. * * @see @ref sec_attribute. * * \subsubsection subsubsec_data_model_abstract_plist Property List * HDF5 has a generic property list object. Each list is a collection of name-value pairs. Each class * of property list has a specific set of properties. Each property has an implicit name, a datatype, * and a value. See the figure below. A property list object is created and used in ways similar to * the other objects of the HDF5 library. * * Property Lists are attached to the object in the library, and they can be used by any part of the * library. Some properties are permanent (for example, the chunking strategy for a dataset), others * are transient (for example, buffer sizes for data transfer). A common use of a Property List is to * pass parameters from the calling program to a VFL driver or a module of the pipeline. * * Property lists are conceptually similar to attributes. Property lists are information relevant to the * behavior of the library while attributes are relevant to the user’s data and application. * * * * * *
* \image html Dmodel_fig11_b.gif "The property list" *
* * Property lists are used to control optional behavior for file creation, file access, dataset creation, * dataset transfer (read, write), and file mounting. Some property list classes are shown in the table * below. Details of the different property lists are explained in the relevant sections of this * document. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Property list classes and their usage
Property List ClassUsedExamples
#H5P_FILE_CREATEProperties for file creation.Set size of user block.
#H5P_FILE_ACCESSProperties for file access.Set parameters for VFL driver. An example is MPI I/O.
#H5P_DATASET_CREATEProperties for dataset creation.Set chunking, compression, or fill value.
#H5P_DATASET_XFERProperties for raw data transfer (read and write).Tune buffer sizes or memory management.
#H5P_FILE_MOUNTProperties for file mounting.
* * @see @ref sec_plist. * * \subsubsection subsubsec_data_model_abstract_link Link * This section is under construction. * * \subsection subsec_data_model_storage The HDF5 Storage Model * \subsubsection subsubsec_data_model_storage_spec The Abstract Storage Model: the HDF5 Format Specification * The HDF5 File Format Specification * defines how HDF5 objects and data are mapped to a linear * address space. The address space is assumed to be a contiguous array of bytes stored on some * random access medium. The format defines the standard for how the objects of the abstract data * model are mapped to linear addresses. The stored representation is self-describing in the sense * that the format defines all the information necessary to read and reconstruct the original objects * of the abstract data model. * * The HDF5 File Format Specification is organized in three parts: * * * The Level 0 specification defines the header block for the file. Header block elements include a * signature, version information, key parameters of the file layout (such as which VFL file drivers * are needed), and pointers to the rest of the file. Level 1 defines the data structures used * throughout the file: the B-trees, heaps, and groups. Level 2 defines the data structure for storing * the data objects and data. In all cases, the data structures are completely specified so that every * bit in the file can be faithfully interpreted. * * It is important to realize that the structures defined in the HDF5 file format are not the same as * the abstract data model: the object headers, heaps, and B-trees of the file specification are not * represented in the abstract data model. The format defines a number of objects for managing the * storage including header blocks, B-trees, and heaps. The HDF5 File Format Specification defines * how the abstract objects (for example, groups and datasets) are represented as headers, B-tree * blocks, and other elements. * * The HDF5 library implements operations to write HDF5 objects to the linear format and to read * from the linear format to create HDF5 objects. It is important to realize that a single HDF5 * abstract object is usually stored as several objects. A dataset, for example, might be stored in a * header and in one or more data blocks, and these objects might not be contiguous on the hard * disk. * * \subsubsection subsubsec_data_model_storage_imple Concrete Storage Model * The HDF5 file format defines an abstract linear address space. This can be implemented in * different storage media such as a single file or multiple files on disk or in memory. The HDF5 * Library defines an open interface called the Virtual File Layer (VFL). The VFL allows different * concrete storage models to be selected. * * The VFL defines an abstract model, an API for random access storage, and an API to plug in * alternative VFL driver modules. The model defines the operations that the VFL driver must and * may support, and the plug-in API enables the HDF5 library to recognize the driver and pass it * control and data. * * A number of VFL drivers have been defined in the HDF5 library. Some work with a single file, * and some work with multiple files split in various ways. Some work in serial computing * environments, and some work in parallel computing environments. Most work with disk copies * of HDF5 files, but one works with a memory copy. These drivers are listed in the * \ref table_file_drivers "Supported file drivers" table. * * @see @ref subsec_file_alternate_drivers. * * Each driver isolates the details of reading and writing storage so that the rest of the HDF5 library * and user program can be almost the same for different storage methods. The exception to this * rule is that some VFL drivers need information from the calling application. This information is * passed using property lists. For example, the Parallel driver requires certain control information * that must be provided by the application. * * \subsection subsec_data_model_structure The Structure of an HDF5 File * \subsubsection subsubsec_data_model_structure_file Overall File Structure * An HDF5 file is organized as a rooted, directed graph. Named data objects are the nodes of the * graph, and links are the directed arcs. Each arc of the graph has a name, and the root group has * the name “/”. Objects are created and then inserted into the graph with the link operation which * creates a named link from a group to the object. For example, the figure below illustrates the * structure of an HDF5 file when one dataset is created. An object can be the target of more than * one link. The names on the links must be unique within each group, but there may be many links * with the same name in different groups. Link names are unambiguous: some ancestor will have a * different name, or they are the same object. The graph is navigated with path names similar to * Unix file systems. An object can be opened with a full path starting at the root group or with a * relative path and a starting node (group). Note that all paths are relative to a single HDF5 file. In * this sense, an HDF5 file is analogous to a single Unix file system. * * * * * * * *
An HDF5 file with one dataset
* \image html Dmodel_fig12_a.gif * * \image html Dmodel_fig12_b.gif *
* * Note: In the figure above are two figures. The top figure represents a newly created file with one * group, /. In the bottom figure, a dataset called /dset1 has been created. * * It is important to note that, just like the Unix file system, HDF5 objects do not have names. The * names are associated with paths. An object has a unique (within the file) object identifier, but a * single object may have many names because there may be many paths to the same object. An * object can be renamed (moved to another group) by adding and deleting links. In this case, the * object itself never moves. For that matter, membership in a group has no implication for the * physical location of the stored object. * * Deleting a link to an object does not necessarily delete the object. The object remains available * as long as there is at least one link to it. After all the links to an object are deleted, it can no * longer be opened although the storage may or may not be reclaimed. * * It is important to realize that the linking mechanism can be used to construct very complex * graphs of objects. For example, it is possible for an object to be shared between several groups * and even to have more than one name in the same group. It is also possible for a group to be a * member of itself or to be in a “cycle” in the graph. An example of a cycle is where a child is the * parent of one of its own ancestors. * * \subsubsection subsubsec_data_model_structure_path HDF5 Path Names and Navigation * The structure of the file constitutes the name space for the objects in the file. A path name is a * string of components separated by ‘/’. Each component is the name of a link or the special * character “.” for the current group. Link names (components) can be any string of ASCII * characters not containing ‘/’ (except the string “.” which is reserved). However, users are advised * to avoid the use of punctuation and non-printing characters because they may create problems for * other software. The figure below gives a BNF grammar for HDF5 path names. * * A BNF grammar for path names * \code * PathName ::= AbsolutePathName | RelativePathName * Separator ::= "/" ["/"]* * AbsolutePathName ::= Separator [ RelativePathName ] * RelativePathName ::= Component [ Separator RelativePathName ]* * Component ::= "." | Name * Name ::= Character+ - {"."} * Character ::= {c: c in {{ legal ASCII characters } - {'/'}} * \endcode * * An object can always be addressed by a full or absolute path which would start at the root group. * As already noted, a given object can have more than one full path name. An object can also be * addressed by a relative path which would start at a group and include the path to the object. * * The structure of an HDF5 file is “self-describing.” This means that it is possible to navigate the * file to discover all the objects in the file. Basically, the structure is traversed as a graph starting at * one node and recursively visiting the nodes of the graph. * * \subsubsection subsubsec_data_model_structure_example Examples of HDF5 File Structures * The figures below show some possible HDF5 file structures with groups and datasets. The first * figure shows the structure of a file with three groups. The second shows a dataset created in * “/group1”. The third figure shows the structure after a dataset called dset2 has been added to the * root group. The fourth figure shows the structure after another group and dataset have been * added. * * * * * *
* \image html Dmodel_fig14_a.gif "An HDF5 file structure with groups" *
* * Note: The figure above shows three groups; /group1 and /group2 are members of the root group. * * * * * *
* \image html Dmodel_fig14_b.gif "An HDF5 file structure with groups and a dataset" *
* * Note: The figure above shows that a dataset has been created in /group1: /group1/dset1. * * * * * *
* \image html Dmodel_fig14_c.gif " An HDF5 file structure with groups and datasets" *
* * Note: In the figure above, another dataset has been added as a member of the root group: /dset2. * * * * * *
* \image html Dmodel_fig14_c.gif " Another HDF5 file structure with groups and datasets" *
* * Note: In the figure above, another group and dataset have been added reusing object names: * /group2/group2/dset2. *
  1. HDF5 requires random access to the linear address space. For this reason it is not * well suited for some data media such as streams.
  2. *
  3. It could be said that HDF5 extends the organizing concepts of a file system to the internal * structure of a single file.
  4. *
  5. As of HDF5-1.4, the storage used for an object is reclaimed, even if all links are * deleted.
* * Next Chapter \ref sec_program * */ /** \page H5_UG The HDF5 Library and Programming Model * * \section sec_program The HDF5 Library and Programming Model * \subsection subsec_program_intro Introduction * The HDF5 library implements the HDF5 abstract data model and storage model. These models * were described in the preceding chapter. * * Two major objectives of the HDF5 products are to provide tools that can be used on as many * computational platforms as possible (portability), and to provide a reasonably object-oriented * data model and programming interface. * * To be as portable as possible, the HDF5 library is implemented in portable C. C is not an * object-oriented language, but the library uses several mechanisms and conventions to implement an * object model. * * One mechanism the HDF5 library uses is to implement the objects as data structures. To refer to * an object, the HDF5 library implements its own pointers. These pointers are called identifiers. * An identifier is then used to invoke operations on a specific instance of an object. For example, * when a group is opened, the API returns a group identifier. This identifier is a reference to that * specific group and will be used to invoke future operations on that group. The identifier is valid * only within the context it is created and remains valid until it is closed or the file is closed. This * mechanism is essentially the same as the mechanism that C++ or other object-oriented languages * use to refer to objects except that the syntax is C. * * Similarly, object-oriented languages collect all the methods for an object in a single name space. * An example is the methods of a C++ class. The C language does not have any such mechanism, * but the HDF5 library simulates this through its API naming convention. API function names * begin with a common prefix that is related to the class of objects that the function operates on. * The table below lists the HDF5 objects and the standard prefixes used by the corresponding * HDF5 APIs. For example, functions that operate on datatype objects all have names beginning * with H5T. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Access flags and modes
PrefixOperates on
@ref H5AAttributes
@ref H5DDatasets
@ref H5EError reports
@ref H5FFiles
@ref H5GGroups
@ref H5IIdentifiers
@ref H5LLinks
@ref H5OObjects
@ref H5PProperty lists
@ref H5RReferences
@ref H5SDataspaces
@ref H5TDatatypes
@ref H5ZFilters
* * \subsection subsec_program_model The HDF5 Programming Model * In this section we introduce the HDF5 programming model by means of a series of short code * samples. These samples illustrate a broad selection of common HDF5 tasks. More details are * provided in the following chapters and in the HDF5 Reference Manual. * * \subsubsection subsubsec_program_model_create Creating an HDF5 File * Before an HDF5 file can be used or referred to in any manner, it must be explicitly created or * opened. When the need for access to a file ends, the file must be closed. The example below * provides a C code fragment illustrating these steps. In this example, the values for the file * creation property list and the file access property list are set to the defaults #H5P_DEFAULT. * * Creating and closing an HDF5 file * \code * hid_t file; // declare file identifier * * // Create a new file using H5F_ACC_TRUNC to truncate and overwrite * // any file of the same name, default file creation properties, and * // default file access properties. Then close the file. * file = H5Fcreate(FILE, H5F_ACC_TRUNC, H5P_DEFAULT, H5P_DEFAULT); * status = H5Fclose(file); * \endcode * * Note: If there is a possibility that a file of the declared name already exists and you wish to open * a new file regardless of that possibility, the flag #H5F_ACC_TRUNC will cause the operation to * overwrite the previous file. If the operation should fail in such a circumstance, use the flag * #H5F_ACC_EXCL instead. * * \subsubsection subsubsec_program_model_dset Creating and Initializing a Dataset * The essential objects within a dataset are datatype and dataspace. These are independent objects * and are created separately from any dataset to which they may be attached. Hence, creating a * dataset requires, at a minimum, the following steps: *
  1. Create and initialize a dataspace for the dataset
  2. *
  3. Define a datatype for the dataset
  4. *
  5. Create and initialize the dataset
* * The code in the example below illustrates the execution of these steps. * * Create a dataset * \code * hid_t dataset, datatype, dataspace; // declare identifiers * * // Create a dataspace: Describe the size of the array and * // create the dataspace for a fixed-size dataset. * dimsf[0] = NX; * dimsf[1] = NY; * dataspace = H5Screate_simple(RANK, dimsf, NULL); * * // Define a datatype for the data in the dataset. * // We will store little endian integers. * datatype = H5Tcopy(H5T_NATIVE_INT); * status = H5Tset_order(datatype, H5T_ORDER_LE); * * // Create a new dataset within the file using the defined * // dataspace and datatype and default dataset creation * // properties. * // NOTE: H5T_NATIVE_INT can be used as the datatype if * // conversion to little endian is not needed. * dataset = H5Dcreate(file, DATASETNAME, datatype, dataspace, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); * \endcode * * \subsubsection subsubsec_program_model_close Closing an Object * An application should close an object such as a datatype, dataspace, or dataset once the object is * no longer needed. Since each is an independent object, each must be released (or closed) * separately. This action is frequently referred to as releasing the object’s identifier. The code in * the example below closes the datatype, dataspace, and dataset that were created in the preceding * section. * * Close an object * \code * H5Tclose(datatype); * H5Dclose(dataset); * H5Sclose(dataspace); * \endcode * * There is a long list of HDF5 library items that return a unique identifier when the item is created * or opened. Each time that one of these items is opened, a unique identifier is returned. Closing a * file does not mean that the groups, datasets, or other open items are also closed. Each opened * item must be closed separately. * * For more information, * @see Using Identifiers * in the HDF5 Application Developer’s Guide under General Topics in HDF5. * *

How Closing a File Effects Other Open Structural Elements

* Every structural element in an HDF5 file can be opened, and these elements can be opened more * than once. Elements range in size from the entire file down to attributes. When an element is * opened, the HDF5 library returns a unique identifier to the application. Every element that is * opened must be closed. If an element was opened more than once, each identifier that was * returned to the application must be closed. For example, if a dataset was opened twice, both * dataset identifiers must be released (closed) before the dataset can be considered closed. Suppose * an application has opened a file, a group in the file, and two datasets in the group. In order for * the file to be totally closed, the file, group, and datasets must each be closed. Closing the file * before the group or the datasets will not affect the state of the group or datasets: the group and * datasets will still be open. * * There are several exceptions to the above general rule. One is when the #H5close function is used. * #H5close causes a general shutdown of the library: all data is written to disk, all identifiers are * closed, and all memory used by the library is cleaned up. Another exception occurs on parallel * processing systems. Suppose on a parallel system an application has opened a file, a group in the * file, and two datasets in the group. If the application uses the #H5Fclose function to close the file, * the call will fail with an error. The open group and datasets must be closed before the file can be * closed. A third exception is when the file access property list includes the property * #H5F_CLOSE_STRONG. This property closes any open elements when the file is closed with * #H5Fclose. For more information, see the #H5Pset_fclose_degree function in the HDF5 Reference * Manual. * * \subsubsection subsubsec_program_model_data Writing or Reading a Dataset to or from a File * Having created the dataset, the actual data can be written with a call to #H5Dwrite. See the * example below. * * Writing a dataset * \code * // Write the data to the dataset using default transfer properties. * status = H5Dwrite(dataset, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT, data); * \endcode * * Note that the third and fourth #H5Dwrite parameters in the above example describe the * dataspaces in memory and in the file, respectively. For now, these are both set to * #H5S_ALL which indicates that the entire dataset is to be written. The selection of partial datasets * and the use of differing dataspaces in memory and in storage will be discussed later in this * chapter and in more detail elsewhere in this guide. * * Reading the dataset from storage is similar to writing the dataset to storage. To read an entire * dataset, substitute #H5Dread for #H5Dwrite in the above example. * * \subsubsection subsubsec_program_model_partial Reading and Writing a Portion of a Dataset * The previous section described writing or reading an entire dataset. HDF5 also supports access to * portions of a dataset. These parts of datasets are known as selections. * * The simplest type of selection is a simple hyperslab. This is an n-dimensional rectangular sub-set * of a dataset where n is equal to the dataset’s rank. Other available selections include a more * complex hyperslab with user-defined stride and block size, a list of independent points, or the * union of any of these. * * The figure below shows several sample selections. * * * * * * * * * * * * * * * *
Dataset selections
* \image html Pmodel_fig5_a.gif *
* \image html Pmodel_fig5_b.gif *
* \image html Pmodel_fig5_c.gif *
* \image html Pmodel_fig5_d.gif
* \image html Pmodel_fig5_e.gif *
* * Note: In the figure above, selections can take the form of a simple hyperslab, a hyperslab with * user-defined stride and block, a selection of points, or a union of any of these forms. * * Selections and hyperslabs are portions of a dataset. As described above, a simple hyperslab is a * rectangular array of data elements with the same rank as the dataset’s dataspace. Thus, a simple * hyperslab is a logically contiguous collection of points within the dataset. * * The more general case of a hyperslab can also be a regular pattern of points or blocks within the * dataspace. Four parameters are required to describe a general hyperslab: the starting coordinates, * the block size, the stride or space between blocks, and the number of blocks. These parameters * are each expressed as a one-dimensional array with length equal to the rank of the dataspace and * are described in the table below. * * * * * * * * * * * * * * * * * * * * * * * *
ParameterDefinition
startThe coordinates of the starting location of the hyperslab in the dataset’s dataspace.
blockThe size of each block to be selected from the dataspace. If the block parameter * is set to NULL, the block size defaults to a single element in each dimension, as * if the block array was set to all 1s (all ones). This will result in the selection of a * uniformly spaced set of count points starting at start and on the interval defined * by stride.
strideThe number of elements separating the starting point of each element or block to * be selected. If the stride parameter is set to NULL, the stride size defaults to 1 * (one) in each dimension and no elements are skipped.
countThe number of elements or blocks to select along each dimension.
* *

Reading Data into a Differently Shaped Memory Block

* For maximum flexibility in user applications, a selection in storage can be mapped into a * differently-shaped selection in memory. All that is required is that the two selections contain the * same number of data elements. In this example, we will first define the selection to be read from * the dataset in storage, and then we will define the selection as it will appear in application * memory. * * Suppose we want to read a 3 x 4 hyperslab from a two-dimensional dataset in a file beginning at * the dataset element <1,2>. The first task is to create the dataspace that describes the overall rank * and dimensions of the dataset in the file and to specify the position and size of the in-file * hyperslab that we are extracting from that dataset. See the code below. * * Define the selection to be read from storage * \code * // Define dataset dataspace in file. * dataspace = H5Dget_space(dataset); // dataspace identifier * rank = H5Sget_simple_extent_ndims(dataspace); * * status_n = H5Sget_simple_extent_dims(dataspace, dims_out, NULL); * * // Define hyperslab in the dataset. * offset[0] = 1; * offset[1] = 2; * count[0] = 3; * count[1] = 4; * status = H5Sselect_hyperslab(dataspace, H5S_SELECT_SET, offset, NULL, count, NULL); * \endcode * * The next task is to define a dataspace in memory. Suppose that we have in memory a * three-dimensional 7 x 7 x 3 array into which we wish to read the two-dimensional 3 x 4 hyperslab * described above and that we want the memory selection to begin at the element <3,0,0> and * reside in the plane of the first two dimensions of the array. Since the in-memory dataspace is * three-dimensional, we have to describe the in-memory selection as three-dimensional. Since we * are keeping the selection in the plane of the first two dimensions of the in-memory dataset, the * in-memory selection will be a 3 x 4 x 1 array defined as <3,4,1>. * * Notice that we must describe two things: the dimensions of the in-memory array, and the size * and position of the hyperslab that we wish to read in. The code below illustrates how this would * be done. * * Define the memory dataspace and selection * \code * // Define memory dataspace. * dimsm[0] = 7; * dimsm[1] = 7; * dimsm[2] = 3; * memspace = H5Screate_simple(RANK_OUT,dimsm,NULL); * * // Define memory hyperslab. * offset_out[0] = 3; * offset_out[1] = 0; * offset_out[2] = 0; * count_out[0] = 3; * count_out[1] = 4; * count_out[2] = 1; * status = H5Sselect_hyperslab(memspace, H5S_SELECT_SET, offset_out, NULL, count_out, NULL); * \endcode * * The hyperslab defined in the code above has the following parameters: start=(3,0,0), * count=(3,4,1), stride and block size are NULL. * *

Writing Data into a Differently Shaped Disk Storage Block

* Now let’s consider the opposite process of writing a selection from memory to a selection in a * dataset in a file. Suppose that the source dataspace in memory is a 50-element, one-dimensional * array called vector and that the source selection is a 48-element simple hyperslab that starts at the * second element of vector. See the figure below. * * * * * *
* \image html Pmodel_fig2.gif "A one-dimensional array" *
* * Further suppose that we wish to write this data to the file as a series of 3 x 2-element blocks in a * two-dimensional dataset, skipping one row and one column between blocks. Since the source * selection contains 48 data elements and each block in the destination selection contains 6 data * elements, we must define the destination selection with 8 blocks. We will write 2 blocks in the * first dimension and 4 in the second. The code below shows how to achieve this objective. * * The destination selection * \code * // Select the hyperslab for the dataset in the file, using * // 3 x 2 blocks, a (4,3) stride, a (2,4) count, and starting * // at the position (0,1). * start[0] = 0; start[1] = 1; * stride[0] = 4; stride[1] = 3; * count[0] = 2; count[1] = 4; * block[0] = 3; block[1] = 2; * ret = H5Sselect_hyperslab(fid, H5S_SELECT_SET, start, stride, count, block); * * // Create dataspace for the first dataset. * mid1 = H5Screate_simple(MSPACE1_RANK, dim1, NULL); * * // Select hyperslab. * // We will use 48 elements of the vector buffer starting at the * // second element. Selected elements are 1 2 3 . . . 48 * start[0] = 1; * stride[0] = 1; * count[0] = 48; * block[0] = 1; * ret = H5Sselect_hyperslab(mid1, H5S_SELECT_SET, start, stride, count, block); * * // Write selection from the vector buffer to the dataset in the file. * ret = H5Dwrite(dataset, H5T_NATIVE_INT, mid1, fid, H5P_DEFAULT, vector); * \endcode * * \subsubsection subsubsec_program_model_info Getting Information about a Dataset * Although reading is analogous to writing, it is often first necessary to query a file to obtain * information about the dataset to be read. For instance, we often need to determine the datatype * associated with a dataset, or its dataspace (in other words, rank and dimensions). As illustrated in * the code example below, there are several get routines for obtaining this information. * * Routines to get dataset parameters * \code * // Get datatype and dataspace identifiers, * // then query datatype class, order, and size, and * // then query dataspace rank and dimensions. * datatype = H5Dget_type (dataset); // datatype identifier * class = H5Tget_class (datatype); * if (class == H5T_INTEGER) * printf("Dataset has INTEGER type \n"); * * order = H5Tget_order (datatype); * if (order == H5T_ORDER_LE) * printf("Little endian order \n"); * * size = H5Tget_size (datatype); * printf ("Size is %d \n", size); * * dataspace = H5Dget_space (dataset); // dataspace identifier * * // Find rank and retrieve current and maximum dimension sizes. * rank = H5Sget_simple_extent_dims (dataspace, dims, max_dims); * \endcode * * \subsubsection subsubsec_program_model_compound Creating and Defining Compound Datatypes * A compound datatype is a collection of one or more data elements. Each element might be an * atomic type, a small array, or another compound datatype. * * The provision for nested compound datatypes allows these structures to become quite complex. * An HDF5 compound datatype has some similarities to a C struct or a Fortran common block. * Though not originally designed with databases in mind, HDF5 compound datatypes are * sometimes used in a way that is similar to a database record. Compound datatypes can become * either a powerful tool or a complex and difficult-to-debug construct. Reasonable caution is * advised. * * To create and use a compound datatype, you need to create a datatype with class compound * (#H5T_COMPOUND) and specify the total size of the data element in bytes. A compound * datatype consists of zero or more uniquely named members. Members can be defined in any * order but must occupy non-overlapping regions within the datum. The table below lists the * properties of compound datatype members. * * * * * * * * * * * * * * * * * * * * * * * *
ParameterDefinition
IndexAn index number between zero and N-1, where N is the number of * members in the compound. The elements are indexed in the order of their * location in the array of bytes.
NameA string that must be unique within the members of the same datatype.
DatatypeAn HDF5 datatype.
OffsetA fixed byte offset which defines the location of the first byte of that * member in the compound datatype.
* * Properties of the members of a compound datatype are defined when the member is added to the * compound type. These properties cannot be modified later. * *

Defining Compound Datatypes

* Compound datatypes must be built out of other datatypes. To do this, you first create an empty * compound datatype and specify its total size. Members are then added to the compound datatype * in any order. * * Each member must have a descriptive name. This is the key used to uniquely identify the * member within the compound datatype. A member name in an HDF5 datatype does not * necessarily have to be the same as the name of the corresponding member in the C struct in * memory although this is often the case. You also do not need to define all the members of the C * struct in the HDF5 compound datatype (or vice versa). * * Usually a C struct will be defined to hold a data point in memory, and the offsets of the members * in memory will be the offsets of the struct members from the beginning of an instance of the * struct. The library defines the macro that computes the offset of member m within a struct * variable s: * \code * HOFFSET(s,m) * \endcode * * The code below shows an example in which a compound datatype is created to describe complex * numbers whose type is defined by the complex_t struct. * * A compound datatype for complex numbers * \code * Typedef struct { * double re; //real part * double im; //imaginary part * } complex_t; * * complex_t tmp; //used only to compute offsets * hid_t complex_id = H5Tcreate (H5T_COMPOUND, sizeof tmp); * H5Tinsert (complex_id, "real", HOFFSET(tmp,re), H5T_NATIVE_DOUBLE); * H5Tinsert (complex_id, "imaginary", HOFFSET(tmp,im), H5T_NATIVE_DOUBLE); * \endcode * * \subsubsection subsubsec_program_model_extend Creating and Writing Extendable Datasets * An extendable dataset is one whose dimensions can grow. One can define an HDF5 dataset to * have certain initial dimensions with the capacity to later increase the size of any of the initial * dimensions. For example, the figure below shows a 3 x 3 dataset (a) which is later extended to * be a 10 x 3 dataset by adding 7 rows (b), and further extended to be a 10 x 5 dataset by adding * two columns (c). * * * * * *
* \image html Pmodel_fig3.gif "Extending a dataset" *
* * HDF5 requires the use of chunking when defining extendable datasets. Chunking makes it * possible to extend datasets efficiently without having to reorganize contiguous storage * excessively. * * To summarize, an extendable dataset requires two conditions: *
  1. Define the dataspace of the dataset as unlimited in all dimensions that might eventually be * extended
  2. *
  3. Enable chunking in the dataset creation properties
* * For example, suppose we wish to create a dataset similar to the one shown in the figure above. * We want to start with a 3 x 3 dataset, and then later we will extend it. To do this, go through the * steps below. * * First, declare the dataspace to have unlimited dimensions. See the code shown below. Note the * use of the predefined constant #H5S_UNLIMITED to specify that a dimension is unlimited. * * Declaring a dataspace with unlimited dimensions * \code * // dataset dimensions at creation time * hsize_t dims[2] = {3, 3}; * hsize_t maxdims[2] = {H5S_UNLIMITED, H5S_UNLIMITED}; * * // Create the data space with unlimited dimensions. * dataspace = H5Screate_simple(RANK, dims, maxdims); * \endcode * * Next, set the dataset creation property list to enable chunking. See the code below. * * Enable chunking * \code * hid_t cparms; * hsize_t chunk_dims[2] ={2, 5}; * * // Modify dataset creation properties to enable chunking. * cparms = H5Pcreate (H5P_DATASET_CREATE); * status = H5Pset_chunk(cparms, RANK, chunk_dims); * \endcode * * The next step is to create the dataset. See the code below. * * Create a dataset * \code * // Create a new dataset within the file using cparms creation properties. * dataset = H5Dcreate(file, DATASETNAME, H5T_NATIVE_INT, dataspace, H5P_DEFAULT, cparms, H5P_DEFAULT); * \endcode * * Finally, when the time comes to extend the size of the dataset, invoke #H5Dextend. Extending the * dataset along the first dimension by seven rows leaves the dataset with new dimensions of * <10,3>. See the code below. * * Extend the dataset by seven rows * \code * // Extend the dataset. Dataset becomes 10 x 3. * dims[0] = dims[0] + 7; * size[0] = dims[0]; * size[1] = dims[1]; * * status = H5Dextend (dataset, size); * \endcode * * \subsubsection subsubsec_program_model_group Creating and Working with Groups * Groups provide a mechanism for organizing meaningful and extendable sets of datasets within * an HDF5 file. The @ref H5G API provides several routines for working with groups. * *

Creating a Group

* With no datatype, dataspace, or storage layout to define, creating a group is considerably simpler * than creating a dataset. For example, the following code creates a group called Data in the root * group of file. * * Create a group * \code * // Create a group in the file. * grp = H5Gcreate(file, "/Data", H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); * \endcode * * A group may be created within another group by providing the absolute name of the group to the * #H5Gcreate function or by specifying its location. For example, to create the group Data_new in * the group Data, you might use the sequence of calls shown below. * * Create a group within a group * \code * // Create group "Data_new" in the group "Data" by specifying * // absolute name of the group. * grp_new = H5Gcreate(file, "/Data/Data_new", H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); * * // or * * // Create group "Data_new" in the "Data" group. * grp_new = H5Gcreate(grp, "Data_new", H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); * \endcode * * This first parameter of #H5Gcreate is a location identifier. file in the first example specifies only * the file. \em grp in the second example specifies a particular group in a particular file. Note that in * this instance, the group identifier \em grp is used as the first parameter in the #H5Gcreate call so that * the relative name of Data_new can be used. * * The third parameter of #H5Gcreate optionally specifies how much file space to reserve to store * the names of objects that will be created in this group. If a non-positive value is supplied, the * library provides a default size. * * Use #H5Gclose to close the group and release the group identifier. * *

Creating a Dataset within a Group

* As with groups, a dataset can be created in a particular group by specifying either its absolute * name in the file or its relative name with respect to that group. The next code excerpt uses the * absolute name. * * Create a dataset within a group using a relative name * \code * // Create the dataset "Compressed_Data" in the group Data using * // the absolute name. The dataset creation property list is * // modified to use GZIP compression with the compression * // effort set to 6. Note that compression can be used only when * // the dataset is chunked. * dims[0] = 1000; * dims[1] = 20; * cdims[0] = 20; * cdims[1] = 20; * dataspace = H5Screate_simple(RANK, dims, NULL); * * plist = H5Pcreate(H5P_DATASET_CREATE); * H5Pset_chunk(plist, 2, cdims); * H5Pset_deflate(plist, 6); * * dataset = H5Dcreate(file, "/Data/Compressed_Data", H5T_NATIVE_INT, dataspace, H5P_DEFAULT, * plist, H5P_DEFAULT); * \endcode * * Alternatively, you can first obtain an identifier for the group in which the dataset is to be * created, and then create the dataset with a relative name. * * Create a dataset within a group using a relative name * \code * // Open the group. * grp = H5Gopen(file, "Data", H5P_DEFAULT); * * // Create the dataset "Compressed_Data" in the "Data" group * // by providing a group identifier and a relative dataset * // name as parameters to the H5Dcreate function. * dataset = H5Dcreate(grp, "Compressed_Data", H5T_NATIVE_INT, dataspace, H5P_DEFAULT, plist, H5P_DEFAULT); * \endcode * *

Accessing an Object in a Group

* Any object in a group can be accessed by its absolute or relative name. The first code snippet * below illustrates the use of the absolute name to access the dataset Compressed_Data in the * group Data created in the examples above. The second code snippet illustrates the use of the * relative name. * * Accessing a group using its relative name * \code * // Open the dataset "Compressed_Data" in the "Data" group. * dataset = H5Dopen(file, "/Data/Compressed_Data", H5P_DEFAULT); * * // Open the group "data" in the file. * grp = H5Gopen(file, "Data", H5P_DEFAULT); * * // Access the "Compressed_Data" dataset in the group. * dataset = H5Dopen(grp, "Compressed_Data", H5P_DEFAULT); * \endcode * * \subsubsection subsubsec_program_model_attr Working with Attributes * An attribute is a small dataset that is attached to a normal dataset or group. Attributes share many * of the characteristics of datasets, so the programming model for working with attributes is * similar in many ways to the model for working with datasets. The primary differences are that an * attribute must be attached to a dataset or a group and sub-setting operations cannot be performed * on attributes. * * To create an attribute belonging to a particular dataset or group, first create a dataspace for the * attribute with the call to #H5Screate, and then create the attribute using #H5Acreate. For example, * the code shown below creates an attribute called “Integer attribute” that is a member of a dataset * whose identifier is dataset. The attribute identifier is attr2. #H5Awrite then sets the value of the * attribute of that of the integer variable point. #H5Aclose then releases the attribute identifier. * * Create an attribute * \code * int point = 1; // Value of the scalar attribute * * // Create scalar attribute. * aid2 = H5Screate(H5S_SCALAR); * attr2 = H5Acreate(dataset, "Integer attribute", H5T_NATIVE_INT, aid2, H5P_DEFAULT, H5P_DEFAULT); * * // Write scalar attribute. * ret = H5Awrite(attr2, H5T_NATIVE_INT, &point); * * // Close attribute dataspace. * ret = H5Sclose(aid2); * * // Close attribute. * ret = H5Aclose(attr2); * \endcode * * Read a known attribute * \code * // Attach to the scalar attribute using attribute name, then * // read and display its value. * attr = H5Aopen_by_name(file_id, dataset_name, "Integer attribute", H5P_DEFAULT, H5P_DEFAULT); * ret = H5Aread(attr, H5T_NATIVE_INT, &point_out); * printf("The value of the attribute \"Integer attribute\" is %d \n", point_out); * ret = H5Aclose(attr); * \endcode * * To read a scalar attribute whose name and datatype are known, first open the attribute using * #H5Aopen_by_name, and then use #H5Aread to get its value. For example, the code shown below * reads a scalar attribute called “Integer attribute” whose datatype is a native integer and whose * parent dataset has the identifier dataset. * * To read an attribute whose characteristics are not known, go through these steps. First, query the * file to obtain information about the attribute such as its name, datatype, rank, and dimensions, * and then read the attribute. The following code opens an attribute by its index value using * #H5Aopen_by_idx, and then it reads in information about the datatype with #H5Aread. * * Read an unknown attribute * \code * // Attach to the string attribute using its index, then read and * // display the value. * attr = H5Aopen_by_idx(file_id, dataset_name, index_type, iter_order, 2, H5P_DEFAULT, H5P_DEFAULT); * * atype = H5Tcopy(H5T_C_S1); * H5Tset_size(atype, 4); * * ret = H5Aread(attr, atype, string_out); * printf("The value of the attribute with the index 2 is %s \n", string_out); * \endcode * * In practice, if the characteristics of attributes are not known, the code involved in accessing and * processing the attribute can be quite complex. For this reason, HDF5 includes a function called * #H5Aiterate. This function applies a user-supplied function to each of a set of attributes. The * user-supplied function can contain the code that interprets, accesses, and processes each attribute. * * \subsection subsec_program_transfer_pipeline The Data Transfer Pipeline * The HDF5 library implements data transfers between different storage locations. At the lowest * levels, the HDF5 Library reads and writes blocks of bytes to and from storage using calls to the * virtual file layer (VFL) drivers. In addition to this, the HDF5 library manages caches of metadata * and a data I/O pipeline. The data I/O pipeline applies compression to data blocks, transforms * data elements, and implements selections. * * A substantial portion of the HDF5 library’s work is in transferring data from one environment or * media to another. This most often involves a transfer between system memory and a storage * medium. Data transfers are affected by compression, encryption, machine-dependent differences * in numerical representation, and other features. So, the bit-by-bit arrangement of a given dataset * is often substantially different in the two environments. * * Consider the representation on disk of a compressed and encrypted little-endian array as * compared to the same array after it has been read from disk, decrypted, decompressed, and * loaded into memory on a big-endian system. HDF5 performs all of the operations necessary to * make that transition during the I/O process with many of the operations being handled by the * VFL and the data transfer pipeline. * * The figure below provides a simplified view of a sample data transfer with four stages. Note that * the modules are used only when needed. For example, if the data is not compressed, the * compression stage is omitted. * * * * * *
* \image html Pmodel_fig6.gif "A data transfer from storage to memory" *
* * For a given I/O request, different combinations of actions may be performed by the pipeline. The * library automatically sets up the pipeline and passes data through the processing steps. For * example, for a read request (from disk to memory), the library must determine which logical * blocks contain the requested data elements and fetch each block into the library’s cache. If the * data needs to be decompressed, then the compression algorithm is applied to the block after it is * read from disk. If the data is a selection, the selected elements are extracted from the data block * after it is decompressed. If the data needs to be transformed (for example, byte swapped), then * the data elements are transformed after decompression and selection. * * While an application must sometimes set up some elements of the pipeline, use of the pipeline is * normally transparent to the user program. The library determines what must be done based on the * metadata for the file, the object, and the specific request. An example of when an application * might be required to set up some elements in the pipeline is if the application used a custom * error-checking algorithm. * * In some cases, it is necessary to pass parameters to and from modules in the pipeline or among * other parts of the library that are not directly called through the programming API. This is * accomplished through the use of dataset transfer and data access property lists. * * The VFL provides an interface whereby user applications can add custom modules to the data * transfer pipeline. For example, a custom compression algorithm can be used with the HDF5 * Library by linking an appropriate module into the pipeline through the VFL. This requires * creating an appropriate wrapper for the compression module and registering it with the library * with #H5Zregister. The algorithm can then be applied to a dataset with an #H5Pset_filter call which * will add the algorithm to the selected dataset’s transfer property list. * * Previous Chapter \ref sec_data_model - Next Chapter \ref sec_file * */ /** * \defgroup H5 Library General (H5) * * Use the functions in this module to manage the life cycle of HDF5 library * instances. * * * * * * * * * * * *
CreateRead
* \snippet{lineno} H5_examples.c create * * \snippet{lineno} H5_examples.c read *
UpdateDelete
* \snippet{lineno} H5_examples.c update * * \snippet{lineno} H5_examples.c closing_shop * \snippet{lineno} H5_examples.c delete *
* */ #endif /* H5module_H */