The user has to derive a concrete class from this base class
and implement the (pure) virtual functions.
The main function is process, which needs to do the
calculation.
Other functions make it possible to perform an initial check.
The class is Doubly templated. Ths first template type is for the data type you are processing. The second type is for what type you want the results of the processing assigned to. For example, if you are computing sums of squares for statistical purposes, you might use higher precision (FLoat->Double) for this. No check is made that the template types are self-consistent.
The init function for a derived class. It can be used to check if nOutPixelsPerCollapse corresponds with the number of pixels produced per collapsed line.
Can the process function in the derived class handle a null mask?
If not, LatticeApply ensures that it'll always pass a filled mask vector,
even if the lattice does not have a mask (in that case that mask
contains all True values).
The default implementation returns False.
The function is there to make optimization possible when no masks
are involved. On the other side, it allows the casual user to ignore
optimization.
Collapse the given line and return one value from that operation.
The position in the Lattice at the start of the line is input
as well.
When function canHandleNullMask returned True,
it is possible that mask is an empty vector indicating
that the input has no mask, thus all values are valid.
If not empty, the mask has the same length as the line.
Collapse the given line and return a line of values from that operation.
The position in the Lattice at the start of the line is input
as well.
When function canHandleNullMask returned True,
it is possible that mask is an empty vector indicating
that the input has no mask, thus all values are valid.
If not empty, the mask has the same length as the line.