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Numerical data storage (MathVision - Seven Seas)

MTRX FORM, for matrix data storage 19-July-1990

Submitted by: Doug Houck
Seven Seas Software
(address, etc)

INTRODUCTION:

Numerical data, as it comes from the real world, is an ill-mannered beast.
Often much is assumed about the data, such as the number of dimensions,
formatting, compression, limits, and sizes. As such, data is not portable.
The MTRX FORM will both store the data, and completely describe its
format, such that programs no longer need to guess the parameters of
a data file. There needs to be but one program to read ascii files and
output MTRX IFF files.

A matrix, by our definition, is composed of three types of things.
Firstly, the atomic data, such as an integer, or floating point number.
Secondly, arrays, which are simply lists of things which are all the same.
Thirdly, structures, which are lists of things which are different.
Both arrays and structures may be composed of things besides atomic data -
they may contain other structures and arrays as well. This concept
of nesting structures may be repeated to any desired depth.

For example, a list of data pairs could be encoded as an array of structures,
where each structure contains two numbers. A two-dimensional array is
simply an array of arrays.

Since space conservation is often desirable, there is provision for
representing each number with fewer bits, and compressing the bits together.

CHUNKS

The MTRX FORM is composed of the definition of the structure, followed
by the BODY which contains the data which is defined. Usually, there
is only one set of data, but a smarter IFF read could use the definition
as a PROPerty, with identically formatted data sets (BODYs) in a LIST.

FORM MTRX
definition (ARRY | STRU | DTYP)
BODY

ARRY: The array chunk defines a counted list of similar items.
The first (required) chunk in an ARRY is ELEM, which gives the number
of elements in the array. Optionally, there may be limits given, (LOWR
and UPPR), which could be used in scaling during sampling of the data.
Lastly is the definition of an element of the array, which may be a
nested definition like everything else.

ARRY ::= "ARRY" #{ ELEM [LOWR] [UPPR] [PACK] ARRY|STRU|DTYP }

STRU: The structure chunk defines a counted list of dissimilar things.
The first (required) chunk in a STRU is FLDS, which gives the number
of fields in the structure. Lastly are definitions of each field
in the structure. Again, each field may have a nested definition like
everything else.

STRU ::= "STRU" #{ FLDS ([PACK] ARRY|STRU|DTYP)* }

VALU: The value contains a datatype, and then a constant of that type.
The datatype contains the size of the constant, so this chunk has variable
size. VALU is used in the ARRY chunk to give the scaling limits of the array.

BODY: This is the actual data we went to so much effort to describe.
It is stored in "row-first" format, that is, items at the bottom of the
nested description are stored next to each other. In most cases, it
should be sufficient to simply block-read the whole chunk from disk,
unless the reader needs to adjust byte-ordering or store in a more
time-efficient format in memory. Data is assumed to be byte-aligned.

PACK: The PACK chunk is necessary when the bit length of the data is
not a multiple of 8, that is, not byte-aligned, and the user wishes
to conserve space by packing data items together. PACK is simply a
number - the number of items to bit-pack before aligning on a byte.
A PACK is in effect for the remainder of its nested scope, or until
overridden by a new specification. A STRU or ARRY is assumed to have
a PACK of 1 by default - it is not affected by PACKs in definitions above.
A PACK of 0 means to byte-align before processing the next definition.
The PACK specifier should be normalized. For example, when packing a large
array of 3-bit numbers, PACK should be 8 since 3*8 = 24. In this case 8 is
the smallest PACK number which aligns on a byte naturally.

DTYP: The DataType is the most interesting chunk, as it attempts to define
every conceivable type of numeric data with 32 bits. The 32 bits are broken
down into three fields, 1) the size in bits, 2) the Class, and 3) SubClass.
The Class makes the most major distinction, separating integers from floating
point numbers from Binary Coded Decimal and etc. Within each class is a
SubClass, which gives the specific encoding used. Finally, the Size tells
what how much room the data occupies. The basic division of datatypes is
given in the tree structure below.

Class SubClass Size Final Specific Type
===== ======== ==== ===================
|
Binary Unsigned - 0 ------------ 8 UByte
| 16 UWord
| 32 ULong
|
Binary Signed --- 0 ------------ 8 Byte
| 16 Word
| 32 ULong
|
Real ------------Ieee38 -------- 32 Ieee Single Precision
| |
| Ieee308 ------- 64 Double Precision
| | 32 Truncated Double Precision
| |
| FFP ----------- 32 Motorola Fast Floating Point
|
Text ----------- Text0 --------- ?? Null-terminated text
| |
| CText --------- ?? Number of characters in first byte
| |
| FText --------- ?? Fixed length, space padded
|
BCD ------------ Nibble -------- ??
|
Character ----- ??

A design goal was to create a classification system which other people
can easily plug into. Many data types are simply size variations on
existing data types. For example, a 4-bit integer can be specified by
giving the size as four bits in the Signed Binary class. Be aware that
not all MTRX readers may support your new type, but there will not be
any type clashes or ambiguities by following these rules. If you have
a truly unique Class or SubClass, you will need to register it with
Commodore to prevent clashes.

A second design goal was to create a format which is easily decoded
by software. By aligning fields on bytes, you have the option of redefining
the datatype as a structure, so as to avoid shifting when accessing the
fields. Since the numbers are sequentially assigned, they are suitable
as array indicies, and may be optimized in a C switch statement.

A third design goal was allowing for naive and sophisticated readers.
In checking for a certain datatype, a naive reader can simply compare
the whole datatype with a small set of known types, which assumes that
each different Size defines a unique datatype. Sophisticated readers
will consider the Class, SubClass and Size separately, so as to support
arbitrary size integers, and truncated Floating Point numbers, for example.

*
* MTRX ::= "FORM" #{ "MTRX" ARRY|STRU|DTYP BODY } Matrix
* ARRY ::= "ARRY" #{ ELEM [LOWR] [UPPR] [PACK] ARRY|STRU|DTYP } Array
* STRU ::= "STRU" #{ FLDS ([PACK] ARRY|STRU|DTYP)* } Structure
* ELEM ::= "ELEM" #{ elements } Array elements
* LOWR ::= "LOWR" { VALU } Minimum limit
* UPPR ::= "UPPR" { VALU } Maximum limit
* VALU ::= #{ dtyp value } Value (in union)
* dtyp ::= { size, subclass, class } Data Type (scalar)
* DTYP ::= "DTYP" #{ dtyp }
* FLDS ::= "FLDS" #{ number of fields } Number of Fields
* PACK ::= "PACK" #{ units packed b4 byte alignment } Packing
* BODY ::= "BODY" #{ inner-first binary dump } Data
*
* [] means optional
* # means the size of the unit following
* * means one or more of
*
</pre>

MTRX IFF Matrix Data Storage - Revision history

Steven Solie: Created page with "= MTRX =
Numerical data storage (MathVision - Seven Seas) MTRX FORM, for matrix data storage 19-July-1990 Submitted by: Doug Houck Se..."