1.1 Introduction and Definitions

An HDF5 dataset is an array of data elements, arranged according to the specifications of the dataspace. In general, a data element is the smallest addressable unit of storage in the HDF5 file. (Compound datatypes are the exception to this rule.) The HDF5 datatype defines the storage format for a single data element (Figure 1).

The model for HDF5 attributes is extremely similar to datasets: an attribute has a dataspace and a datatype, as shown in Figure 1. The information in this chapter applies to both datasets and attributes.

Figure 1

Abstractly, each data element within the dataset is a sequence of bits, interpreted as a single value from a set of values (e.g., a number or a character). For a given data type, there is a standard or convention for representing the values as bits, and when the bits are represented in a particular storage the bits are laid out in a specific storage scheme, e.g., as 8-bit bytes, with a specific ordering and alignment of bytes within the storage array.

HDF5 datatypes implement a flexible, extensible, and portable mechanism for specifying and discovering the storage layout of the data elements, determining how to interpret the elements (e.g., as floating point numbers), and for transferring data from different compatible layouts.

An HDF5 datatype describes one specific layout of bits, a dataset has a single datatype which applies to every data element. When a dataset is created, the storage datatype is defined, the datatype cannot be changed.

• The datatype describes the storage layout of a single data element.
• All elements of the dataset must have the same type.
• The datatype of a dataset is immutable.

When data is transferred (e.g., a read or write), each end point of the transfer has a datatype, which describes the correct storage for the elements. The source and destination may have different (but compatible) layouts, in which case the data elements are automatically transformed during the transfer.

HDF5 datatypes describe commonly used binary formats for numbers (integers and floating point) and characters (ASCII). A given computing architecture and programming language supports certain number and character representations. For example, a computer may support 8-, 16-, 32-, and 64-bit signed integers, stored in memory in little-endian byte order. These would presumably correspond to the C programming language types 'char', 'short', 'int', and 'long'.

When reading and writing from memory, the HDF5 library must know the appropriate datatype that describes the architecture specific layout. The HDF5 library provides the platform independent 'NATIVE' types, which are mapped to an appropriate datatype for each platform. So the type 'H5T_NATIVE_INT' is an alias for the appropriate descriptor for each platform.

Data in memory has a datatype

• The storage layout in memory is architecture-specific.
• The HDF5 'NATIVE' types are predefined aliases for the architecture-specific memory layout.
• The memory datatype need not be the same as the stored datatype of the dataset.

In addition to numbers and characters, an HDF5 datatype can describe more abstract classes of types, including enumerations, strings, bit strings, and references (pointers to objects in the HDF5 file). HDF5 supports several classes of composite datatypes, which are compose one or more other datatypes. In addition to the standard predefined datatypes, users can define new datatypes within the datatype classes.

The HDF5 datatype model is very general and flexible

• For common simple purposes, only predefined types will be needed
• Datatypes can be composed to create complex structured datatypes.
• If needed, users can define custom atomic datatypes.

1.2 HDF5 Datatype Model

The HDF5 Library implements an object-oriented model of datatypes. HDF5 datatypes are organized as a logical set of base types, or datatype classes. Each datatype class defines a format for representing logical values as a sequence of bits. For example the H5T_INTEGER class is a format for representing twos complement integers of various sizes.

A datatype class is defined as a set of one or more datatype properties. A datatype property is a property of the bit string. The datatype properties are defined by the logical model of the datatype class. For example, the integer class (twos complement integers) has properties such as "signed or unsigned", "length", and "byte-order". The float class (IEEE floating point numbers) has these properties, plus "exponent bits", "exponent sign", etc.

A datatype is derived from one datatype class: a given datatype has a specific value for the datatype properties defined by the class. For example, for 32-bit signed integers, stored big-endian, the HDF5 datatype is a sub-type of integer, with the properties set to: signed=1, size=4 (bytes), byte-order=BE.

The HDF5 datatype API provides methods to create datatypes of different datatype classes, to set the datatype properties of a new datatype, and to discover the datatype properties of an existing datatype.

The datatype for a dataset is stored in the HDF5 file as part of the metadata for the dataset. A datatype can be shared by more than one dataset in the file. A datatype can optionally be stored as a named object in the file.

When transferring data (e.g., a read or write), the data elements of the source and destination storage must have compatible types. As a general rule, data elements with the same datatype class are compatible, while elements from different datatype classes are not compatible. When transferring data of one datatype to another compatible datatype, the HDF5 Library uses the datatype properties of the source and destination to automatically transform each data element. For example, when reading from data stored as 32-bit, signed integers, big-endian, into 32-bit signed integers, little-endian, the HDF5 Library will automatically swap the bytes.

Thus, data transfer operations (H5Dread, H5Dwrite, H5Aread, H5Awrite) require a datatype for both the source and the destination.

Figure 2

The HDF5 Library defines a set of predefined datatypes, corresponding to commonly used storage formats, such as twos complement integers, IEEE Floating point numbers, etc., 4- and 8-byte sizes, big endian and little endian byte orders. In addition, a user can derive types with custom values for the properties. For example, a user program may create a datatype to describe a 6-bit integer, or a 600-bit floating point number.

In addition to atomic datatypes, the HDF5 Library supports composite datatypes. A composite datatype is an aggregation of one or more datatypes. Each class of composite datatypes has properties that describe the organization of the composite datatype (Figure 3). Composite datatypes include:

• Compound datatypes: structured records
• Array: a multidimensional array of a datatype
• Variable length: a one-dimensional array of a datatype

Figure 3

1.2.1 Datatype Classes and Properties

Figure 4 shows the HDF5 datatype classes. Each class is defined to have a set of properties which describe layout of the data element and the interpretation of the bits. Table 1 lists the properties for the datatype classes.

Figure 4

1.2.2 Predefined Datatypes

The HDF5 library predefines a modest number of commonly used datatypes. These types have standard symbolic names of the form H5T_arch_base where arch is an architecture name and base is a programming type name (Table 2). New types can be derived from the predefined types by copying the predefined type (see H5Tcopy()) and then modifying the result.

The base name of most types consists of a letter to indicate the class (Table 3), a precision in bits, and an indication of the byte order (Table 4).

Table 5 shows examples of predefined datatypes. The full list can be found in the "HDF5 Predefined Datatypes" section of the HDF5 Reference Manual.

Table 2
Architecture Name	Description
IEEE	IEEE-754 standard floating point types in various byte orders.
STD	This is an architecture that contains semi-standard datatypes like signed two's complement integers, unsigned integers, and bitfields in various byte orders.
C FORTRAN	Types which are specific to the C or Fortran programming languages are defined in these architectures. For instance, H5T_C_S1 defines a base string type with null termination which can be used to derive string types of other lengths.
NATIVE	This architecture contains C-like datatypes for the machine on which the library was compiled. The types were actually defined by running the H5detect program when the library was compiled. In order to be portable, applications should almost always use this architecture to describe things in memory.
CRAY	Cray architectures. These are word-addressable, big-endian systems with non-IEEE floating point.
INTEL	All Intel and compatible CPU's including 80286, 80386, 80486, Pentium, Pentium-Pro, and Pentium-II. These are little-endian systems with IEEE floating-point.
MIPS	All MIPS CPU's commonly used in SGI systems. These are big-endian systems with IEEE floating-point.
ALPHA	All DEC Alpha CPU's, little-endian systems with IEEE floating-point.

Table 3
	Bitfield
F	Floating point
I	Signed integer
R	References
S	Character string
U	Unsigned integer

Table 4
BE	Big endian
LE	Little endian

Table 5
Example	Description
H5T_IEEE_F64LE	Eight-byte, little-endian, IEEE floating-point
H5T_IEEE_F32BE	Four-byte, big-endian, IEEE floating point
H5T_STD_I32LE	Four-byte, little-endian, signed two's complement integer
H5T_STD_U16BE	Two-byte, big-endian, unsigned integer
H5T_C_S1	One-byte, null-terminated string of eight-bit characters
H5T_INTEL_B64	Eight-byte bit field on an Intel CPU
H5T_CRAY_F64	Eight-byte Cray floating point
H5T_STD_ROBJ	Reference to an entire object in a file

The HDF5 Library predefines a set of NATIVE datatypes which are similar to C type names. The native types are set to be an alias for the appropriate HDF5 datatype for each platform. For example, H5T_NATIVE_INT corresponds to a C int type. On an Intel based PC, this type is the same as H5T_STD_I32LE, while on a MIPS system this would be equivalent to H5T_STD_I32BE. Table 6 shows examples of NATIVE types and corresponding C types for a common 32-bit workstation.

2.1 The Datatype object and the HDF5 Datatype API

The HDF5 Library manages datatypes as objects. The HDF5 datatype API manipulates the datatype objects through C function calls. New datatypes can be created from scratch or copied from existing datatypes. When a datatype is no longer needed its resources should be released by calling H5Tclose().

The datatype object is used in several roles in the HDF5 data model and library. Essentially, a datatype is used whenever the format of data elements is needed. There are four major uses of datatypes in the HDF5 Library: at dataset creation, during data transfers, when discovering the contents of a file, and for specifying user defined data types (Table 7).

2.2 Dataset creation

All the data elements of a dataset have the same datatype. When a dataset is created (H5Tcreate), the datatype for the data elements must be specified. The datatype of a dataset can never be changed. Figure 5 shows the use of a datatype to create a dataset called "/dset". In this example, the dataset will be stored as 32-bit signed integers, in big endian order.

  hid_t dt;
  dt = H5Tcopy(H5T_STD_I32BE);
  dataset_id = H5Dcreate(file_id, "/dset", dt, dataspace_id,   
      H5P_DEFAULT);

2.3 Data transfer (Read and Write)

Probably the most common use of datatypes is to write or read data from a dataset or attribute. In these operations, each data element is transferred from the source to the destination (possibly rearranging the order of the elements). Since the source and destination do not need to be identical (i.e., one is disk and the other is memory) the transfer requires both the format of the source element and the destination element. Therefore, data transfers use two datatype objects, for the source and destination.

When data is written, the source is memory and the destination is disk (file). The memory datatype describes the format of the data element in the machine memory, and the file datatype describes the desired format of the data element on disk. Similarly, when reading, the source datatype describes the format of the data element on disk, and the destination datatype describes the format in memory.

In the most common cases, the file datatype is the datatype specified when the dataset was created, and the memory datatype should be the appropriate NATIVE type.

Figures 5 and 6, respectively, show examples of writing data to and reading data from a dataset. The data in memory is declared C type 'int', the datatype H5T_NATIVE_INT corresponds to this type. The datatype of the dataset should be of datatype class H5T_INTEGER.

   int  dset_data[DATA_SIZE];

   status = H5Dwrite(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, 
         H5P_DEFAULT, dset_data);

 int dset_data[DATA_SIZE];

  status = H5Dread(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, 
      H5P_DEFAULT,  dset_data);

2.4 Discovery of data format

The HDF5 Library enables a program to determine the datatype class and properties for any data type. In order to discover the storage format of data in a dataset, the datatype is obtained, and the properties determined by queries to the datatype object. Figure 8 shows an example of code that analyzes the datatype for an integer, and prints out a description of its storage properties (byte Order, signed, size.)

    switch (H5Tget_class(type)) {
    case H5T_INTEGER:
	ord = H5Tget_order(type);
	sgn = H5Tget_sign(type);
	printf("Integer ByteOrder= ");
	switch (ord) {
	case H5T_ORDER_LE:
	    printf("LE");
	    break;
	case H5T_ORDER_BE:
	    printf("BE");
	    break;
	}
	printf(" Sign= ");
	switch (sgn) {
	case H5T_SGN_NONE:
	    printf("false");
	    break;
	case H5T_SGN_2:
	    printf("true");
	    break;
	}
	printf(" Size= ");
	sz = H5Tget_size(type);
	printf("%d", sz);
	printf("\n");
	break;

2.5 Creating and using user defined datatypes

Most programs will primarily use the predefined datatypes described above, possibly in composite datatypes such as compound or array datatypes. However, the HDF5 datatype model is extremely general; a user program can define a great variety of atomic datatypes (storage layouts). In particular, the datatype properties can define signed and unsigned integers of any size and byte order, and floating point numbers with different formats, size, and byte order. The HDF5 datatype API provides methods to set these properties.

User defined types can be used to define the layout of data in memory, e.g., to match some platform specific number format or application defined bit-field. The user defined type can also describe data in the file, e.g., some application-defined format. The user defined types can be translated to and from standard types of the same class, as described above.

3. Datatype (H5T) Function Summaries

3.1 General Datatype Operations

3.2 Conversion Functions

3.3 Atomic Datatype Properties

3.4 Enumeration Datatypes

3.5 Compound Datatype Properties

3.6 Array Datatypes

3.7 Variable-length Datatypes

3.8 Opaque Datatypes

3.9 Datatype-to-text and Text-to-datatype Conversions

4.1 Introduction

The HDF5 Library implements an object-oriented model of datatypes. HDF5 datatypes are organized as a logical set of base types, or datatype classes. The HDF5 Library manages datatypes as objects. The HDF5 datatype API manipulates the datatype objects through C function calls. Figure 9 shows the abstract view of the datatype object. Table 8 shows the methods (C functions) that operate on datatype object as a whole. New datatypes can be created from scratch or copied from existing datatypes.

In order to use a datatype, the object must be created (H5Tcreate), or a reference obtained by cloning from an existing type (H5Tcopy), or opened (H5Topen). In addition, a reference to the datatype of a dataset or attribute can be obtained with H5Dget_type or H5Aget_type. For composite datatypes a reference to the datatype for members or base types can be obtained (H5Tget_member_type, H5Tget_super). When the datatype object is no longer needed, the reference is discarded with H5Tclose.

Two datatype objects can be tested to see if they are the same with H5Tequal. This function returns true if the two datatype references refer to the same datatype object. However, if two datatype objects define equivalent datatypes (the same datatype class and datatype properties), they will not be considered 'equal'.

A datatype can be written to the file as a first class object (H5Tcommit). Named datatypes can be used in the same way as any other dataype. Named datatypes are explained below.

4.2 Discovery of Datatype Properties

Any HDF5 datatype object can be queried to discover all of its datatype properties. For each datatype class, there are a set of API functions to retrieve the datatype properties for this class.

4.2.1 Properties of Atomic Datatypes

Table 9 lists the functions to discover the properties of atomic datatypes. Table 10 lists the queries relevant to specific numeric types. Table 11 gives the properties for atomic string datatype, and Table 12 gives the property of the opaque datatype.

4.2.2 Properties of Composite Datatypes

The composite datatype classes can also be analyzed to discover their datatype properties and the datatypes that are members or base types of the composite datatype. The member or base type can, in turn, be analyzed. Table 13 lists the functions that can access the datatype properties of the different composite datatypes.

4.3 Definition of Datatypes

The HDF5 Library enables user programs to create and modify datatypes. The essential steps are:

1. a) Create a new datatype object of a specific composite datatype class, or
   b) Copy an existing atomic datatype object.
2. Set properties of the datatype object.
3. Use the datatype object.
4. Close the datatype object.

To create a user defined atomic datatype, the procedure is to clone a predefined datatype of the appropriate datatype class (H5Tcopy). Then set the datatype properties appropriate to the datatype class. For example, Table 14 shows how to create a datatype to describe a 1024-bit unsigned integer.

    hid_t new_type = H5Tcopy (H5T_NATIVE_INT);
    H5Tset_precision(new_type, 1024);
    H5Tset_sign(new_type, H5T_SGN_NONE);

Composite datatypes are created with a specific API call for each datatype class. Table 15 shows the creation method for each datatype class. A newly created datatype cannot be used until the datatype properties are set. For example, a newly created compound datatype has no members and cannot be used.

Once the datatype is created and the datatype properties set, the datatype object can be used.

Predefined datatypes are defined by the library during initialization using the same mechanisms as described here. Each predefined datatype is locked (H5Tlock), so that it cannot be changed or destroyed. User defined datatypes may also be locked using H5Tlock.

4.3.1 User Defined Atomic Datatypes

Table 16 summarizes the API methods that set properties of atomic types. Table 17 shows properties specific to numeric types, Table 18 shows properties specific to the string datatype class. Note that offset, pad, etc. don't apply to strings. Table 19 shows the specific property of the OPAQUE datatype class.

Examples

Figure 10 shows an example of how to create a 128-bit, little-endian signed integer type one could use the following (increasing the precision of a type automatically increases the total size). Note that the proper procedure is to begin from a type of the intended datatype class, in this case, a NATIVE INT.

    hid_t new_type = H5Tcopy (H5T_NATIVE_INT);
    H5Tset_precision (new_type, 128);
    H5Tset_order (new_type, H5T_ORDER_LE);
    

Figure 11 shows the storage layout as the type is defined. The H5Tcopy creates a datatype that is the same as H5T_NATIVE_INT. In this example, suppose this is a 32-bit big endian number (Figure 11a). The precision is set to 128 bits, which automatically extends the size to 8 bytes (Figure 11b). Finally, the byte order is set to little-endian (Figure 11c).

The significant bits of a data element can be offset from the beginning of the memory for that element by an amount of padding. The offset property specifies the number of bits of padding that appear to the "right of" the value. Table 20 and Figure 12 shows how a 32-bit unsigned integer with 16-bits of precision having the value 0x1122 will be laid out in memory.

If the offset is incremented then the total size is incremented also if necessary to prevent significant bits of the value from hanging over the edge of the datatype.

The bits of the entire data are numbered beginning at zero at the least significant bit of the least significant byte (the byte at the lowest memory address for a little-endian type or the byte at the highest address for a big-endian type). The offset property defines the bit location of the least signficant bit of a bit field whose length is precision. If the offset is increased so the significant bits "hang over" the edge of the datum, then the size property is automatically incremented.

To illustrate the properties of the integer datatype class, Figure 13 shows how to create a user defined datatype that describes a 24-bit signed integer that starts on the third bit of a 32-bit word. The datatype is specialized from a 32-bit integer, the precision is set to 24 bits, and the offset is set to 3.

    hid_t dt;

    dt = H5Tcopy(H5T_SDT_I32LE);

    H5Tset_precision(dt, 24);
    H5Tset_offset(dt,3);
    H5Tset_pad(dt, H5T_PAD_ZERO,H5T_PAD_ONE);
    

Figure 14 shows the storage layout for a data element. Note that the unused bits in the offset will be set to zero and the unused bits at the end will be set to one, as specified in the H5Tset_pad call.

Byte 0 Byte 1 Byte 2 Byte 3 01234567 89012345 67890123 45678967 ooo00000 00000000 00000000 00sppppp

Figure 14. A User defined integer Datatype: range -1,048,583 to 1,048,584

To illustrate a user defined floating point number, Figure 13 shows how to create a 24-bit floating point number, that starts 5 bits into a 4 byte word. The floating point number is defined to have a mantissa of 19 bits (bits 5-23), and exponent of 3 bits (25-27) and the sign bit is bit 28. (Note that this is an illustration of what can be done, not necessarily a floating point format that a user would require.)

    hid_t dt;

    dt = H5Tcopy(H5T_IEEE_F32LE);

    H5Tset_precision(dt, 24);
    H5Tset_fields (dt, 28, 25, 3, 5, 19);
    H5Tset_pad(dt, H5T_PAD_ZERO,H5T_PAD_ONE);
    H5Tset_inpad(dt, H5T_PAD_ZERO);
    

Byte 0 Byte 1 Byte 2 Byte 3 01234567 89012345 67890123 45678967 ooooommm mmmmmmmm mmmmmmmm ieeesppp

Figure 16. A User defined Floating Point Datatype.

Figure 16 shows the storage layout of a data element for this datatype. Note that there is an unused bit (24) between the mantissa and the exponent. This bit is filled with the inpad value, in this case 0.

The sign bit is always of length one and none of the fields are allowed to overlap. When expanding a floating-point type one should set the precision first; when decreasing the size one should set the field positions and sizes first.

4.3.2 Composite Datatypes

All composite datatypes must be user defined; there are no predefined composite datatypes.

4.3.2.1 Compound Datatypes

The subsections below describe how to create a compound datatype and how to write and read data of compound datatype.

4.3.2.1.1 Defining Compound Datatypes

Compound datatypes are conceptually similar to a C struct or Fortran 95 derived types. The compound datatype defines a contiguous sequence of bytes, which are formatted using one up to 2^16 datatypes (members). A compound datatype may have any number of members, in any order, and the members may have any datatype, including compound. Thus, complex nested compound datatypes can be created. The total size of the compound datatype is greater than or equal to the sum of the size of its members, up to a maximum of 2^32 bytes. HDF5 does not support datatypes with distinguished records or the equivalent of C unions or Fortran 95 EQUIVALENCE statement.

Usually a C struct or Fortran derived type 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 HDF5 C library provides a macro HOFFSET (s,m) to calculate the member's offset. The HDF5 Fortran applications have to calculate offsets by using sizes of members datatypes and by taking in consideration the order of members in the Fortran derived type.

HOFFSET(s,m)

This macro computes the offset of member m within a struct s

offsetof(s,m)

This macro defined in stddef.h does exactly the same thing as the HOFFSET() macro.

Note for Fortran users: Offsets of Fortran structure members correspond to the offsets within a packed datatype (see explanation below) stored in an HDF5 file.

Each member of a compound datatype must have a descriptive name which 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 member in the C struct of Fortran derived type, although this is often the case. Nor does one need to define all members of the C struct of Fortran derived type in the HDF5 compound datatype (or vice versa).

Unlike atomic datatypes which are derived from other atomic datatypes, compound datatypes are created from scratch. First, one creates an empty compound datatype and specifies its total size. Then members are added to the compound datatype in any order. Each member type is inserted at a designated offset. Each member has a name which is the key used to uniquely identify the member within the compound datatype.

Figure 17a shows an example of creating an HDF5 C compound datatype to describe a complex number, which is a structure with two components, "real" and "imaginary", each double. An equivalent C struct is whose type is defined by the complex_t struct, is shown.

typedef struct { double re; /*real part*/ double im; /*imaginary part*/ } complex_t; hid_t complex_id = H5Tcreate (H5T_COMPOUND, sizeof (complex_t)); H5Tinsert (complex_id, "real", HOFFSET(complex_t,re), H5T_NATIVE_DOUBLE); H5Tinsert (complex_id, "imaginary", HOFFSET(complex_t,im), H5T_NATIVE_DOUBLE);

Figure 17a

Figure 17b shows an example of creating an HDF5 Fortran compound datatype to describe a complex number, which is a Fortran derived type with two components, "real" and "imaginary", each DOUBLE PRECISION. An equivalent Fortran TYPE is whose type is defined by the TYPE complex_t, is shown.

TYPE complex_t DOUBLE PRECISION re ! real part DOUBLE PRECISION im; ! imaginary part END TYPE complex_t CALL h5tget_size_f(H5T_NATIVE_DOUBLE, re_size, error) CALL h5tget_size_f(H5T_NATIVE_DOUBLE, im_size, error) complex_t_size = re_size + im_size CALL h5tcreaet_f(H5T_COMPOUND_F, complex_t_size, type_id) offset = 0 CALL h5tinsert_f(type_id, "real", offset, H5T_NATIVE_DOUBLE, error) offset = offset + re_size CALL h5tinsert_f(type_id, "imaginary", offset, H5T_NATIVE_DOUBLE, error)

Figure 17b

Important Note: The compound datatype is created with a size sufficient to hold all its members. In the C example above, the size of the C struct and the HOFFSET macro are used as a convenient mechanism to determine the appropriate size and offset. Alternatively, the size and offset could be manually determined, e.g., the size can be set to 16 with "real" at offset 0 and "imaginary" at offset 8. However, different platforms and compilers have different sizes for "double", and may have alignment restrictions which require additional padding within the structure. It is much more portable to use the HOFFSET macro, which assures that the values will be correct for any platform.

Figure 18 shows how the compound datatype would be laid out, assuming that NATIVE_DOUBLE are 64-bit numbers, and there are no alignment requirements. The total size of the compound datatype will be 16 bytes, the "real" component will start at byte 0, and "imaginary" will start at byte 8.

Byte 0 Byte 1 Byte 2 Byte 3 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 4 Byte 5 Byte 6 Byte 7 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 8 Byte 9 Byte 10 Byte 11 iiiiiiii iiiiiiii iiiiiiii iiiiiiii Byte 12 Byte 13 Byte 14 Byte 15 iiiiiiii iiiiiiii iiiiiiii iiiiiiii   Total size of Compound Datatype is 16 bytes

Figure 18

The members of a compound datatype may be any HDF5 datatype, including compound, array, and VL. Figures 19 and 20 show an example which creates a compound datatype composed of two complex values, each of which is a compound datatype as in Figure 18 above.

Byte 0 Byte 1 Byte 2 Byte 3 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 4 Byte 5 Byte 6 Byte 7 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 8 Byte 9 Byte 10 Byte 11 iiiiiiii iiiiiiii iiiiiiii iiiiiiii Byte 12 Byte 13 Byte 14 Byte 15 iiiiiiii iiiiiiii iiiiiiii iiiiiiii Byte 16 Byte 17 Byte 18 Byte 19 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 20 Byte 21 Byte 22 Byte 23 rrrrrrrr rrrrrrrr rrrrrrrr rrrrrrrr Byte 24 Byte 25 Byte 26 Byte 27 iiiiiiii iiiiiiii iiiiiiii iiiiiiii Byte 28 Byte 29 Byte 30 Byte 31 iiiiiiii iiiiiiii iiiiiiii iiiiiiii   Total size of Compound Datatype is 32 bytes.

Figure 19

typedef struct { complex_t x; complex_t y; } surf_t; hid_t complex_id, surf_id; /*hdf5 datatypes*/ complex_id = H5Tcreate (H5T_COMPOUND, sizeof(complex_t)); H5Tinsert (complex_id, "re", HOFFSET(complex_t,re), H5T_NATIVE_DOUBLE); H5Tinsert (complex_id, "im", HOFFSET(complex_t,im), H5T_NATIVE_DOUBLE); surf_id = H5Tcreate (H5T_COMPOUND, sizeof(surf_t)); H5Tinsert (surf_id, "x", HOFFSET(surf_t,x), complex_id); H5Tinsert (surf_id, "y", HOFFSET(surf_t,y), complex_id);

Figure 20

Note that a similar result could be accomplished by creating a compound datatype and inserting four fields (Figure 21). This results in the same layout as above (Figure 19). The difference would be how the fields are addressed. In the first case, the real part of 'y' is called 'y.re'; in the second case it is 'y-re'.

typedef struct { complex_t x; complex_t y; } surf_t; hid_t surf_id = H5Tcreate (H5T_COMPOUND, sizeof(surf_t)); H5Tinsert (surf_id, "x-re", HOFFSET(surf_t,x.re), H5T_NATIVE_DOUBLE); H5Tinsert (surf_id, "x-im", HOFFSET(surf_t,x.im), H5T_NATIVE_DOUBLE); H5Tinsert (surf_id, "y-re", HOFFSET(surf_t,y.re), H5T_NATIVE_DOUBLE); H5Tinsert (surf_id, "y-im", HOFFSET(surf_t,y.im), H5T_NATIVE_DOUBLE);

Figure 21

The members of a compound datatype do not always fill all the bytes. The HOFFSET macro assures that the members will be laid out according to the requirements of the platform and language. Figure 22 shows an example of a C struct which requires extra bytes of padding on many platforms. The second element, 'b', is a 1-byte character, followed by an 8 byte double, 'c'. On many systems, the 8-byte value must be stored on a 4- or 8-byte boundary, requiring the struct to be larger than the sum of the size of its elements.

In Figure 22 , the sizeof and HOFFSET macro is used to assure that the members are inserted at the correct offset to match the memory conventions of the platform. Figure 23 shows how this data element would be stored in memory, assuming the double must start on a 4-byte boundary. Notice the extra bytes between 'b' and 'c'.

typedef struct s1_t { int a; char b; double c; } s1_t; s1_tid = H5Tcreate (H5T_COMPOUND, sizeof(s1_t)); H5Tinsert(s1_tid, "a_name", HOFFSET(s1_t, a), H5T_NATIVE_INT); H5Tinsert(s1_tid, "b_name", HOFFSET(s1_t, b), H5T_NATIVE_CHAR); H5Tinsert(s1_tid, "c_name", HOFFSET(s1_t, c), H5T_NATIVE_DOUBLE);

Figure 22

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