/** \file types.dox Documentation related to NetCDF Types
Documentation of types.

\page data_type Data Types

\tableofcontents

Data in a netCDF file may be one of the \ref external_types, or may be
a user-defined data type (see \ref user_defined_types).

\section external_types External Data Types

The atomic external types supported by the netCDF interface are:
- ::NC_BYTE 	8-bit signed integer
- ::NC_UBYTE 	8-bit unsigned integer *
- ::NC_CHAR 	8-bit character
- ::NC_SHORT 	16-bit signed integer
- ::NC_USHORT 	16-bit unsigned integer *
- ::NC_INT (or ::NC_LONG) 	32-bit signed integer
- ::NC_UINT 	32-bit unsigned integer *
- ::NC_INT64 	64-bit signed integer *
- ::NC_UINT64 	64-bit unsigned integer *
- ::NC_FLOAT 	32-bit floating point
- ::NC_DOUBLE 	64-bit floating point
- ::NC_STRING 	variable length character string +

\remark * These types are available only for CDF5 (NC_CDF5) and netCDF-4 format (NC_NETCDF4) files. All the unsigned ints and the 64-bit ints are for CDF5 or netCDF-4 files only.
\remark + These types are available only for netCDF-4 (NC_NETCDF4) files.

These types were chosen to provide a reasonably wide range of
trade-offs between data precision and number of bits required for each
value. These external data types are independent from whatever
internal data types are supported by a particular machine and language
combination.

These types are called "external", because they correspond to the
portable external representation for netCDF data. When a program reads
external netCDF data into an internal variable, the data is converted,
if necessary, into the specified internal type. Similarly, if you
write internal data into a netCDF variable, this may cause it to be
converted to a different external type, if the external type for the
netCDF variable differs from the internal type.

The separation of external and internal types and automatic type
conversion have several advantages. You need not be aware of the
external type of numeric variables, since automatic conversion to or
from any desired numeric type is available. You can use this feature
to simplify code, by making it independent of external types, using a
sufficiently wide internal type, e.g., double precision, for numeric
netCDF data of several different external types. Programs need not be
changed to accommodate a change to the external type of a variable.

If conversion to or from an external numeric type is necessary, it is
handled by the library.

Converting from one numeric type to another may result in an error if
the target type is not capable of representing the converted
value. For example, an internal short integer type may not be able to
hold data stored externally as an integer. When accessing an array of
values, a range error is returned if one or more values are out of the
range of representable values, but other values are converted
properly.

Note that mere loss of precision in type conversion does not return an
error. Thus, if you read double precision values into a
single-precision floating-point variable, for example, no error
results unless the magnitude of the double precision value exceeds the
representable range of single-precision floating point numbers on your
platform. Similarly, if you read a large integer into a float
incapable of representing all the bits of the integer in its mantissa,
this loss of precision will not result in an error. If you want to
avoid such precision loss, check the external types of the variables
you access to make sure you use an internal type that has adequate
precision.

The names for the primitive external data types (char, byte, ubyte, short,
ushort, int, uint, int64, uint64, float or real, double, string) are
reserved words in CDL, so the names of variables, dimensions, and
attributes must not be type names.

It is possible to interpret byte data as either signed (-128 to 127)
or unsigned (0 to 255). However, when reading byte data to be
converted into other numeric types, it is interpreted as signed.

For the correspondence between netCDF external data types and the data
types of a language see \ref variables.

\section classic_structures Data Structures in Classic Files

The only kind of data structure directly supported by the netCDF
classic abstraction, i.e. CDF-1, 2, and 5 formats, is a collection of named
arrays with attached vector attributes. NetCDF is not particularly
well-suited for storing linked lists, trees, sparse matrices, ragged
arrays or other kinds of data structures requiring pointers.

It is possible to build other kinds of data structures in netCDF
classic formats, from sets of arrays by adopting
various conventions regarding the use of data in one array as pointers
into another array. The netCDF library won't provide much help or
hindrance with constructing such data structures, but netCDF provides
the mechanisms with which such conventions can be designed.

The following netCDF classic example stores a ragged array ragged_mat
using an attribute row_index to name an associated index variable
giving the index of the start of each row. In this example, the first
row contains 12 elements, the second row contains 7 elements (19 -
12), and so on. (NetCDF-4 includes native support for variable length
arrays. See below.)

\code
             float   ragged_mat(max_elements);
                     ragged_mat:row_index = "row_start";
             int     row_start(max_rows);
     data:
             row_start   = 0, 12, 19, ...
\endcode

As another example, netCDF variables may be grouped within a netCDF
classic dataset by defining attributes that list the
names of the variables in each group, separated by a conventional
delimiter such as a space or comma. Using a naming convention for
attribute names for such groupings permits any number of named groups
of variables. A particular conventional attribute for each variable
might list the names of the groups of which it is a member. Use of
attributes, or variables that refer to other attributes or variables,
provides a flexible mechanism for representing some kinds of complex
structures in netCDF datasets.

\section nc4_user_defined_types NetCDF-4 User Defined Data Types

NetCDF supported six data types through version 3.6.0 (char, byte,
short, int, float, and double). Starting with version 4.0, many new
data types are supported (unsigned int types, strings, compound types,
variable length arrays, enums, opaque).

In addition to the new atomic types the user may define types.

Types are defined in define mode, and must be fully defined before
they are used. New types may be added to a file by re-entering define
mode.

Once defined the type may be used to create a variable or attribute.

Types may be nested in complex ways. For example, a compound type
containing an array of VLEN types, each containing variable length
arrays of some other compound type, etc. Users are cautioned to keep
types simple. Reading data of complex types can be challenging for
Fortran users.

Types may be defined in any group in the data file, but they are
always available globally in the file.

Types cannot have attributes (but variables of the type may have
attributes).

Only files created with the netCDF-4/HDF5 mode flag (::NC_NETCDF4) but
without the classic model flag (::NC_CLASSIC_MODEL) may use
user-defined types or the new atomic data types.

Once types are defined, use their ID like any other type ID when
defining variables or attributes. Use functions

- nc_put_att() / nc_get_att()
- nc_put_var() / nc_get_var()
- nc_put_var1() / nc_get_var1()
- nc_put_vara() / nc_get_vara()
- nc_put_vars() / nc_get_vars()

functions to access attribute and variable data of user defined type.

\subsection types_compound_types Compound Types

Compound types allow the user to combine atomic and user-defined types
into C-like structs. Since users defined types may be used within a
compound type, they can contain nested compound types.

Users define a compound type, and (in their C code) a corresponding C
struct. They can then use nc_put_vara() and related functions to write
multi-dimensional arrays of these structs, and nc_get_vara() calls
to read them.

While structs, in general, are not portable from platform to platform,
the HDF5 layer (when installed) performs the magic required to figure
out your platform's idiosyncrasies, and adjust to them. The end result
is that HDF5 compound types (and therefore, netCDF-4 compound types),
are portable.

For more information on creating and using compound types, see
Compound Types in The NetCDF C Interface Guide.

\subsection vlen_types VLEN Types

Variable length arrays can be used to create a ragged array of data,
in which one of the dimensions varies in size from point to point.

An example of VLEN use would the to store a 1-D array of dropsonde
data, in which the data at each drop point is of variable length.

There is no special restriction on the dimensionality of VLEN
variables. It's possible to have 2D, 3D, 4D, etc. data, in which each
point contains a VLEN.

A VLEN has a base type (that is, the type that it is a VLEN of). This
may be one of the atomic types (forming, for example, a variable
length array of ::NC_INT), or it can be another user defined type,
like a compound type.

With VLEN data, special memory allocation and deallocation procedures
must be followed, or memory leaks may occur.

Compression is permitted but may not be effective for VLEN data,
because the compression is applied to structures containing lengths
and pointers to the data, rather than the actual data.

For more information on creating and using variable length arrays, see
Variable Length Arrays in The NetCDF C Interface Guide.

\subsection types_opaque_types Opaque Types

Opaque types allow the user to store arrays of data blobs of a fixed size.

For more information on creating and using opaque types, see Opaque
Type in The NetCDF C Interface Guide.

\subsection enum_types Enum Types

Enum types allow the user to specify an enumeration.

For more information on creating and using enum types, see Enum Type
in The NetCDF C Interface Guide.

\section type_conversion Type Conversion

Each netCDF variable has an external type, specified when the variable
is first defined. This external type determines whether the data is
intended for text or numeric values, and if numeric, the range and
precision of numeric values.

If the netCDF external type for a variable is char, only character
data representing text strings can be written to or read from the
variable. No automatic conversion of text data to a different
representation is supported.

If the type is numeric, however, the netCDF library allows you to
access the variable data as a different type and provides automatic
conversion between the numeric data in memory and the data in the
netCDF variable. For example, if you write a program that deals with
all numeric data as double-precision floating point values, you can
read netCDF data into double-precision arrays without knowing or
caring what the external type of the netCDF variables are. On reading
netCDF data, integers of various sizes and single-precision
floating-point values will all be converted to double-precision, if
you use the data access interface for double-precision values. Of
course, you can avoid automatic numeric conversion by using the netCDF
interface for a value type that corresponds to the external data type
of each netCDF variable, where such value types exist.

The automatic numeric conversions performed by netCDF are easy to
understand, because they behave just like assignment of data of one
type to a variable of a different type. For example, if you read
floating-point netCDF data as integers, the result is truncated
towards zero, just as it would be if you assigned a floating-point
value to an integer variable. Such truncation is an example of the
loss of precision that can occur in numeric conversions.

Converting from one numeric type to another may result in an error if
the target type is not capable of representing the converted
value. For example, an integer may not be able to hold data stored
externally as an IEEE floating-point number. When accessing an array
of values, a range error is returned if one or more values are out of
the range of representable values, but other values are converted
properly.

Note that mere loss of precision in type conversion does not result in
an error. For example, if you read double precision values into an
integer, no error results unless the magnitude of the double precision
value exceeds the representable range of integers on your
platform. Similarly, if you read a large integer into a float
incapable of representing all the bits of the integer in its mantissa,
this loss of precision will not result in an error. If you want to
avoid such precision loss, check the external types of the variables
you access to make sure you use an internal type that has a compatible
precision.

Whether a range error occurs in writing a large floating-point value
near the boundary of representable values may be depend on the
platform. The largest floating-point value you can write to a netCDF
float variable is the largest floating-point number representable on
your system that is less than 2 to the 128th power. The largest double
precision value you can write to a double variable is the largest
double-precision number representable on your system that is less than
2 to the 1024th power.

The _uchar and _schar functions were introduced in netCDF-3 to
eliminate an ambiguity, and support both signed and unsigned byte data.
In netCDF-2, whether the external NC_BYTE type represented signed or
unsigned values was left up to the user. In netcdf-3, we treat NC_BYTE
as signed for the purposes of conversion to short, int, long, float, or
double. (Of course, no conversion takes place when the internal type is
signed char.) In the _uchar functions, we treat NC_BYTE as if it were
unsigned. Thus, no NC_ERANGE error can occur converting between NC_BYTE
and unsigned char.

*/
