User's Manual - version: 0.2
KEYVALUE's parameters, from second onwards,
define the data set. Although there is some flexibility on how they are
organized, they must follow certain patterns. It allows the library to break down the
parameters in key-value pairs. Recalculate cell
B2 of sheet The KEYVALUE
function and take a look at the console logger to see the
key-value pairs of data set Triangle.
Figure 5. Console logger shows key-value pairs in data set Triangle.
[Debug ] DataSet: Triangle
Size : 4
Key #1: Base
Value #1: [Single] 2
Key #2: Height
Value #2: [Single] 3
Key #3: IsRegular
Value #3: [Single] 0
Key #4: Processor
Value #4: [Single] PolygonFor a text to define a key, it is necessary but not sufficient that:
excluding trailing spaces it ends with " ="
(space + equal sign); and
excluding the ending "=", it contains a non
space character.
KeyValue replaces the last "=" (equal sign) by
" " (space) and, from the result, removes leading and
trailing spaces. What remains is the key. For instance, all data sets in
sheet The KEYVALUE function contain a key called
Processor defined by the text "Processor
=".
The conditions above are not sufficient to define a key since the
patterns mentioned earlier also play a role in this matter. For
instance, in data set Trap of sheet
Key-value patterns , "Foo =" does not define a
key Foo.
Actually, it assigns the value "Foo =" to key
B as you can verify in the console after recalculating
B2.
Figure 7. Console shows that "Foo =" is the value assigned
to key B.
[Debug ] DataSet: Trap
Size : 4
Key #1: A
Value #1: [Single] 1
Key #2: B
Value #2: [Single] Foo =
Key #3: C
Value #3: [Single] 3
Key #4: D
Value #4: [Single] 4The following sections explain the patterns and clarify this point.
This pattern is composed by two parts: A single containing a text defining a key (i.e. verifying the necessary conditions) followed by either a single, vector or matrix, which will be the associated value. Those three possibilities are shown on the sheet Key-value patterns.
There are two cases of this pattern. The first (the transpose of the second) is composed by a column vector and a matrix such that
they have the same number of rows; and
the vector contains only keys (i.e. all cells contain text verifying the necessary conditions).
Furthermore, for each key in the vector, the corresponding row in the matrix defines a vector which is the value associated to the key.
There are two cases of this pattern. The first (the transpose of the second) is composed by a matrix such that
it has at least two columns;
the first column contains only keys (i.e. all cells contain text verifying the necessary conditions); and
the first cell of second column is not a key (i.e. it does not contain text verifying the necessary conditions ).
Furthermore, for each key in the first column, the remaining cells on the same row define a vector which is the value associated to the key.
Useful for tables, this pattern is made by one matrix M = M(i, j) , for i = 0, ..., m-1 and j = 0, ..., n-1 (with m>2 and n>2). In M we find three key-value pairs: row, column and table. There are two variants of this pattern:
The row key is in M(1, 0) and its value is the column vector M(i, 0) for i = 2, ..., m-1. The column key is in M(0, 1) and its value is the row vector M(0, j) for j = 2, ..., n-1. Finally, the table key is in M(0,0) and its value is the sub-matrix M(i, j) for i = 2, ..., m-1 and j = 2, ..., n-1.
The row key is in M(2, 0) and its value is the column vector M(i, 1) for i = 2, ..., m-1. The column key is in M(0, 2) and its value is the row vector M(1, j) for j = 2, ..., n-1. Table key and value are as in Format 1. This variant is more aestheticly pleasant when some cells are merged together as show in data set Table #2 (merged) in Figure 11.
