DAP Design: shared dimensions, groups and types: Difference between revisions

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# The enclosed type can include attributes.
# The enclosed type can include attributes.
# The type definition itself can include (optional) attributes.
# The type definition itself can include (optional) attributes.
Use of a type definition, assuming the typedef above
<pre>
<type name="point">
    <dim size="100>
</type>
</pre>
Notes:
# The XML element ''<type ...>'' is used because the typedef name can be any legal XML attribute value (a requirement because names in HDF5 are essentially not restricted to a particular character set) and thus cannot be an XML element name.
# This example uses the new (proposed) syntax for array declarations.


=== Proposal two ===
=== Proposal two ===

Revision as of 05:11, 15 April 2009

Back: DAP3/4#NC-DAP

Definitions

Type definition
In the DDX all items are declarations which describe things actually defined (assigned or otherwise associated with values) elsewhere. The term Type Definitions refers to the representation of something in a data set which defines a data type; the types are defined in the data set and the DDX merely holds a representation of that definition - a declaration.
Dimension
A name bound to a size, e.g., "lon" has a size of 1024
Coordinate variable
A name bound to both a size and a datatype, e.g.,"height" is a vector of ten 32-bit floating point numbers, or "latitude" is an array of 1024 by 1024 32-bit floating point numbers.
Grid
One or more N-dimensional array of values bound to 1 to N coordinate variables.

DDX Document Organization

DAP and the DDX will be extended to include Groups, Shared dimensions and user-defined types. Groups will be added as a kind of constructor-type with properties similar to Structure and to Java or C++ namespaces. Unlike Structure, Groups cannot be dimensioned.

A rough syntax which describes how these additions will fit into the DAP and the existing DDX Notation is (Replace with XML schema):

Dataset :== Groups
Groups :== null | Group Groups
Group :== Types Dimensions Attributes Variables
Types :== null | Type Types
Dimensions :== null | Dimension Dimensions
Attributes :== null | Attribute Attributes
Variables :== null | Variable Variables

This pseudo-grammar does not capture what can be produced for a Group, et cetera. Instead it shows how these sections of the DDX must be organized. It also does not show that a valid Dataset can have only Types (user-define types) and does not need to have variables, but it must have one or the other or both.

Group

The DDX will be modified so that it contains one or more Groups. If only one Group is present (which describes the case for DAP 3.2 and earlier) then the declaration can be left out, but if there are two or more groups, the declarations must be present.

Group characteristics:

  • Any configuration of Groups other than one (anonymous) Group which holds all the variables in a data set must be declared.
  • If declared, Groups must be named.
  • A Group can contain any object, including a Group
  • Variables and Attributes are named using / <group name> / ... / <variable name> to reflect their hierarchy.
  • Each Group declares a new lexical scope for values.
  • A Group cannot be an Array or a Grid (although the distinction between those two might become blurred or non-existent; Group is fundamentally a scalar container-type).
  • This definition does not completely subsume the HDF5 Group type but is equivalent to the netCDF 4 version of it.

Examples:

This data set contains one Group - the root group - which has by convention the name '/'

<Dataset ... >
    ...
</Dataset>

This data set contains two Groups, one after the other.

<Dataset ... >
<group name="primary">
    ...
</group>
<group name="secondary">
    ...
</group>
</Dataset>

This data set contains more Groups, and shows they can be nested.

<Dataset ... >
<group name="primary">
    ...
    <group name="in_situ">
        ...
    </group>

</group>
<group name="secondary">
    ...
</group>
</Dataset>

Discussion

In the past we have often talked about Dataset as a kind of Structure but implicitly it's not exactly the same since there cannot be an Array of datasets; The Group type captures this semantic distinction.

In HDF5, the Group object is modeled after a general graph but here it's uses a strict hierarchy, which simplifies both servers and clients while retaining most of the utility of the HDF5 data type.

Shared Dimensions

Background on shared dimensions and coordinate variables

From an email exchange, John Caron wrote:

James:

Is it that an dimension is a formal declaration of an independent parameter?

John:

I know that some people prefer that interpretation. My own opinion is that's it more complicated.

Abstractly, I think its reasonable to say that the number of dimensions of a variable indicates its dimensionality in the topological sense. I think its necessary to allow "independent variables" to have topological dimensionality > 1. eg lat(x,y), lon(x,y). lat and lon can still be considered independent variables, but they are not orthogonal. Neither is associated exclusively with one

dimension.

Concretely, dimensions are used for all sorts of reasons, and are not just about topological dimensionality. For instance, they control the grouping of data and the layout of files. So in real files, you see this mixture of uses.

That's why the explicit assignment of coord variables is needed, which makes your Grid attractive, because that's a way of explicitly saying what the independent variables are. One needs shared dimensions between data and coordinate variables, so that one can unambiguously assign coordinate values to a data value.

The downsides of using Grid for this purpose:

  • the name "Grid" connotes gridded data, eg model data, and this shared dimension thing is needed for other types of data, eg point data.
  • If Grid scopes the dimension, then all variables sharing a dimension have to be contained in the grid. So its impossible to have some dimensions globally shared, and others locally shared.

So my preference would be to use Groups to scope shared dimensions, rather than Grids. But still use Grids (or some evolution of Grids) to assign coordinate variables to data variables.

Dimensions

Shared dimensions will be added to DAP in the dimensions section of the Dataset or Group objects. Each dimension will consist of a name and a size.

 
<dimension name="lat" size="1024"/>
<dimension name="lon" size="1024"/>

Characteristics of dimensions:

  • Dimensions are not associated with a data type.
  • Dimensions do not have attributes.
  • Dimensions bound to a type define coordinate variables.

Coordinate variables and Grids

While dimensions are scoped at the Dataset or Group level, coordinate variables are defined at the level of a Grid object. Grid objects in DAP4 are different from those in DAP2 in two ways beyond using (shared) dimensions:

  1. Each Grid object may hold more than one Array (what is often a dependent variable); and
  2. Each Array within a Grid is not constrained to use all of the Grid's coordinate variables.

N.B: Coordinate variables in a Grid object are called Maps to conform to the old nomenclature.

Features of the DAP4 and DAP2 Grid object:

  1. Each Grid object defines a lexical scope.
  2. There is an explicit relation between the Grid object's maps (coordinate variables) and the indicial extents of the array.
A very simple Grid object
<grid>
    <map name="lon" dim="lon" type="Float32"/>
    <map name="lat" dim="lat" type="Float32"/>

    <array name="SST">
        <Byte/>
        <map name="lon">
        <map name="lat">
    </array>
</grid>

Notes:

  1. The map object may have the same name as a dimension object.
  2. Map objects may have attributes.
  3. In an array object, <map...> elements are used to specify the array's dimensions; the word dimension is avoided to cut down on confusion.
A more complex Grid object
<dataset>
    <dimension name="pt" size="4096">

    <grid>
        <map name="longitude" dim="pt" type="Float32"/>
        <map name="latitude" dim="pt" type="Float32"/>
        <map name="altitude" dim="pt" type="Float32"/>
        <map name="time" dim="pt" type="Float32">
            << attributes >> <!-- The syntax for attributes is in flux -->
        </map>

        <array name="Radioactivity">
            << attributes >> <!-- for example, scale_factor and add_offset -->
            <Byte/>
            <map name="longitude"/>
            <map name="latitude"/>
            <map name="altitude"/>
            <map name="time"/>
        </array>

        <array name="surface_temp">
             << attributes >>
             <float64/>
             <map name="longitude"/>
             <map name="latitude"/>
             <map name="time"/>
        </array>
    </grid>
</dataset>

Types

There are two proposals for type definitions.

Proposal one

Add a section for type definitions (technically, these are type equivalents in the sense of C's typedef) and allow those to be used interchangeably with the existing DDX notation. This would provide a way for a dataset stored using HDF5 to be presented without loosing any type definitions it uses. At the same time this would allow the existing notation which does not require types be defined in cases where none was (e.g., a NetCDF 3 data file cannot contain type definitions).

The syntax for a type definition

<typedef name="point">
    << attributes >>
    <structure name="point">
        <int32 name="x"/>
        <int32 name="y"/>
    </structure>
</typedef>

Notes:

  1. The contents of the <typedef> element are a DDX type
  2. The enclosed type and the name become synonymous.
  3. The enclosed type can include attributes.
  4. The type definition itself can include (optional) attributes.

Use of a type definition, assuming the typedef above

<type name="point">
    <dim size="100>
</type>

Notes:

  1. The XML element <type ...> is used because the typedef name can be any legal XML attribute value (a requirement because names in HDF5 are essentially not restricted to a particular character set) and thus cannot be an XML element name.
  2. This example uses the new (proposed) syntax for array declarations.

Proposal two

Adopt type definitions as required for all non-cardinal types (or arrays of cardinal types). This would mean that all structure, sequence and grid objects would be wrapped in a <typedef> element and those names would appear in the variables section of the dataset/group object(s). This would streamline the notation considerably and enforce a high level of uniformity on the DDX.

To consider: How would authors of data handlers build type definitions for data sets which lack them originally. Consider some HDF4 files. The main issues are that as variable in the HDF4 files are discovered, their types would have to be defined and then instances created.

Suppose the type definitions were limited to structures and sequences became sequences of a typedef. How would nested sequences be represented? What about grid objects?