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using sparse matrix #70

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using sparse matrix #70

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0b59f34
using sparse matrix
paoloinglese Oct 16, 2021
d2aef0c
Added Matrix dep
paoloinglese Oct 20, 2021
aa59b28
support to sparse matrices
paoloinglese Oct 20, 2021
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9 changes: 8 additions & 1 deletion NAMESPACE
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@@ -1,5 +1,12 @@
import("methods")

importFrom("Matrix",
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Please add Matrix to Depends in the DESCRIPTION file to make R CMD check working.

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Ok I'll do! Thanks!

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I've committed the fixed DESCRIPTION file.

"t",
"Matrix",
"sparseMatrix",
"colSums",
"colMeans")

importFrom("parallel",
"mclapply",
"mcmapply")
Expand All @@ -9,7 +16,7 @@ importFrom("graphics",
"arrows",
"lines",
"par",
"plot.default",
"plot.default",
"points",
"rasterImage",
"rect",
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15 changes: 7 additions & 8 deletions R/as.matrix-functions.R
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Expand Up @@ -18,9 +18,9 @@

i <- findInterval(mass, uniqueMass)

m <- matrix(NA_real_, nrow=length(l), ncol=length(uniqueMass),
dimnames=list(NULL, uniqueMass))
m[cbind(r, i)] <- intensity
m <- sparseMatrix(i = r, j = i, x = intensity,
dims = c(length(l), length(uniqueMass)),
dimnames = list(NULL, uniqueMass))
attr(m, "mass") <- uniqueMass
m
}
Expand All @@ -34,10 +34,9 @@
## returns:
## a binary matrix
.as.binary.matrix <- function(m) {
stopifnot(is.matrix(m))
isNA <- which(is.na(m))
m[] <- 1L
m[isNA] <- 0L
mode(m) <- "integer"
stopifnot(is.matrix(m) | is(m, 'sparseMatrix'))
mass <- attr(m, 'mass')
m[m != 0] <- 1
attr(m, 'mass') <- mass
m
}
4 changes: 2 additions & 2 deletions R/filterPeaks-functions.R
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Expand Up @@ -55,7 +55,7 @@ filterPeaks <- function(l, minFrequency, minNumber, labels,
m <- .as.binary.matrix(.as.matrix.MassObjectList(l))

## whitelist
w <- matrix(0L, nrow=nrow(m), ncol=ncol(m))
w <- Matrix(nrow = nrow(m), ncol = ncol(m), data = 0, sparse = TRUE)

## group indices by labels
idx <- lapply(ll, function(x)which(labels == x))
Expand Down Expand Up @@ -131,5 +131,5 @@ filterPeaks <- function(l, minFrequency, minNumber, labels,
## calculate minimal number of peaks
minPeakNumber <- max(minFrequency * length(rows), minNumber, na.rm=TRUE)

colSums(m[rows, , drop=FALSE]) >= minPeakNumber
colSums(m[rows, , drop = FALSE]) >= minPeakNumber
}