DelayedNaryIsoOp-class: DelayedNaryIsoOp objects

In Bioconductor/DelayedArray: A unified framework for working transparently with on-disk and in-memory array-like datasets

DelayedNaryIsoOp objects

Description

NOTE: This man page is about DelayedArray internals and is provided for developers and advanced users only.

The DelayedNaryIsoOp class provides a formal representation of a delayed N-ary isometric operation. It is a concrete subclass of the DelayedNaryOp virtual class, which itself is a subclass of the DelayedOp virtual class:

                          DelayedOp
                              ^
                              |
                        DelayedNaryOp
                              ^
                              |
                       DelayedNaryIsoOp
  

DelayedNaryIsoOp objects are used inside a DelayedArray object to represent the delayed N-ary isometric operation carried by the object. They're never exposed to the end user and are not intended to be manipulated directly.

Usage

## S4 method for signature 'DelayedNaryIsoOp'
summary(object, ...)

## ~ ~ ~ Seed contract ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

## S4 method for signature 'DelayedNaryIsoOp'
dim(x)

## S4 method for signature 'DelayedNaryIsoOp'
dimnames(x)

## S4 method for signature 'DelayedNaryIsoOp'
extract_array(x, index)

## ~ ~ ~ Propagation of sparsity ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

## S4 method for signature 'DelayedNaryIsoOp'
is_sparse(x)

## S4 method for signature 'DelayedNaryIsoOp'
extract_sparse_array(x, index)

Arguments

x, object	A DelayedNaryIsoOp object.
index	See ?extract_array in the S4Arrays package for a description of the index argument.
...	Not used.

See Also

• DelayedOp objects.

• showtree to visualize the nodes and access the leaves in the tree of delayed operations carried by a DelayedArray object.

• extract_array in the S4Arrays package.

• extract_sparse_array in the SparseArray package.

Examples

## DelayedNaryIsoOp extends DelayedNaryOp which extends DelayedOp:
extends("DelayedNaryIsoOp")

## ---------------------------------------------------------------------
## BASIC EXAMPLE
## ---------------------------------------------------------------------
m1 <- matrix(101:130, ncol=5)
m2 <- matrix(runif(30), ncol=5)
M1 <- DelayedArray(m1)
M2 <- DelayedArray(m2)
showtree(M1)
showtree(M2)

M <- M1 / M2
showtree(M)
class(M@seed)        # a DelayedNaryIsoOp object

## ---------------------------------------------------------------------
## PROPAGATION OF SPARSITY
## ---------------------------------------------------------------------
sm1 <- sparseMatrix(i=c(1, 6), j=c(1, 4), x=c(11, 64), dims=6:5)
SM1 <- DelayedArray(sm1)
sm2 <- sparseMatrix(i=c(2, 6), j=c(1, 5), x=c(21, 65), dims=6:5)
SM2 <- DelayedArray(sm2)
showtree(SM1)
showtree(SM2)
is_sparse(SM1)       # TRUE
is_sparse(SM2)       # TRUE

SM3 <- SM1 - SM2
showtree(SM3)
class(SM3@seed)      # a DelayedNaryIsoOp object
is_sparse(SM3@seed)  # TRUE

M4 <- SM1 / SM2
showtree(M4)
class(M4@seed)       # a DelayedNaryIsoOp object
is_sparse(M4@seed)   # FALSE

## ---------------------------------------------------------------------
## SANITY CHECKS
## ---------------------------------------------------------------------
stopifnot(class(M@seed) == "DelayedNaryIsoOp")
stopifnot(class(SM3@seed) == "DelayedNaryIsoOp")
stopifnot(is_sparse(SM3@seed))
stopifnot(class(M4@seed) == "DelayedNaryIsoOp")
stopifnot(!is_sparse(M4@seed))

Bioconductor/DelayedArray documentation built on Dec. 7, 2024, 10:27 p.m.