Nothing
inst/scripts/torture.R
In MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics
library(MSnbase)
library(msdata)
f <- msdata::proteomics(full.names = TRUE)
f <- dir(system.file("sciex/", package = "msdata"), full.names = TRUE)
## readMSData
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
res <- readMSData(f[1], verbose = FALSE, msLevel. = 1)
res <- readMSData(f[1], verbose = FALSE, msLevel. = 2)
sp <- res[[12]]
sp <- spectra(res)
}
## readMSData onDisk
for (i in 1:7500) {
if (i %% 200 == 0)
cat(i, "\n")
res <- readMSData(f[1], verbose = FALSE, mode = "onDisk")
sp <- res[[6]]
res <- readMSData(f[1], verbose = FALSE, mode = "onDisk")
sp <- spectra(res)
chr <- chromatogram(res)
}
## OK without gc()
## spectrapply2
res <- readMSData(f, verbose = FALSE, mode = "onDisk")
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
sp <- res[[6]]
sp <- spectrapply(res)
sp <- spectrapply(res, FUN = mz)
tmp <- filterRt(res, rt = c(440, 470))
sp <- spectrapply(tmp)
}
## OK without gc()
## With finding chrom peaks
f <- dir("/Users/jo/data/2017/2017_10/centroid/", full.names = TRUE)
f <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)[1:6]
library(testthat)
library(xcms)
rawd <- readMSData(f, mode = "onDisk")
cwp <- CentWaveParam(peakwidth = c(1.5, 10), ppm = 40)
res <- findChromPeaks(rawd, param = cwp)
for (i in 1:1000) {
if (i %% 20 == 0)
cat(i, "\n")
tmp <- findChromPeaks(rawd, param = cwp)
expect_equal(chromPeaks(tmp), chromPeaks(res))
}
## spectrapply2
res <- readMSData(f[4], verbose = FALSE, mode = "onDisk")
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
## sp <- res[[6]]
res <- readMSData(f[4], verbose = FALSE, mode = "onDisk")
sp <- MSnbase:::spectrapply2(res)
sp <- MSnbase:::spectrapply2(res, FUN = mz)
tmp <- filterRt(res, rt = c(1108, 1109))
sp <- MSnbase:::spectrapply2(tmp)
}
## OK without gc()
## Issue #170, i.e. random "[MSData::Spectrum::getMZIntensityPairs()] Sizes do
## not match." with the pwiz backend (segfault with ramp). The solution was to
## read call mzR::header() before the mzR::peaks, but this significantly slows
## things down.
library(MSnbase)
torturing <- function(x) {
tmp <- readMSData(x, msLevel. = 1, mode = "onDisk")
register(SerialParam())
for (i in 1:10) {
cat("--- ", i, " ---", "\n")
cat("first spectrapply\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
## sp has a size of 21.9GB!!!
rm(sp)
gc()
cat("second spectrapply\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
rm(sp)
gc()
tmp <- filterRt(tmp, rt = c(5, 500))
cat("third spectrapply after filter rt\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
cat("\n\n")
}
}
torturing2 <- function(x) {
tmp <- readMSData(x, msLevel. = 1, mode = "onDisk")
register(SerialParam())
for (i in 1:10) {
cat("--- ", i, " ---", "\n")
cat("first spectrapply\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
## sp has a size of 21.9GB!!!
rm(sp)
gc()
cat("second spectrapply\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
rm(sp)
gc()
tmp <- filterRt(tmp, rt = c(5, 500))
cat("third spectrapply after filter rt\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
cat("\n\n")
}
}
## All tests are now WITHOUT gc().
SN <- "/Users/jo/data/2016/2016-11/NoSN/"
fl <- dir(SN, full.names = TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS without gc(): 1x OK
## spectrapply2
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS without gc(): 1x OK
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 2x OK.
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## maxOS: 2x OK.
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 8 ---
## first spectrapply
## second spectrapply
## third spectrapply after filter rt
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## --- 6 ---
## first spectrapply
## second spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 1x FAIL: that's problematic; it's using the header(last spectrum).
## --- 3 ---
## first spectrapply
## second spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## macOS without gc(): 1x FAIL, like above!
## Instead of reading the last header we read ALL header for spectrapply
## macOS with header(all): 1x OK, 1x FAILED (like above).
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS without gc(): 3x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 1x FAIL:
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 2x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing2(fl)
## macOS: 1x FAIL (typical error).
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS: 2x OK
###################
## Testing eventual less memory demanding versions of spectrapply.
## Variangs are:
## o spectrapply: reading all mz/intensity values. Creating the list of
## Spectrum objects for them and calling lapply on the function.
## o spectrapply3: processing each spectrum separately, i.e. load the data,
## create the spectrum object and apply the function.
## o spectrapply4: same as _3, but using a for loop instead of split, lapply
## What I should test here:
## 1) Can I use fastLoad = TRUE for spectrapply4? NO!
## 2) Do I need the gc() for the spectrapply4? TEST LATER.
library(MSnbase)
register(SerialParam())
setMSnbaseFastLoad(FALSE)
fl <- dir("/Users/jo/data/2016/2016-11/NoSN", full.names = TRUE)
tmp <- readMSData(fl, mode = "onDisk")
for (i in 1:10) {
cat("\nIteration", i, "\n\n")
maxi <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
}
## 3) Compare the performance.LLLLLLL
library(MSnbase)
library(microbenchmark)
library(testthat)
## mzML
fl <- system.file("microtofq/MM14.mzML", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
sp_1 <- spectrapply(od, FUN = mz)
sp_2 <- MSnbase:::spectrapply2(od, FUN = mz)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 85.15372 88.22235 109.80866 88.24776 89.24869
## MSnbase:::spectrapply2(od) 95.21179 96.86848 99.96095 98.65392 103.79570
## max neval cld
## 198.1708 5 a
## 105.2749 5 a
## With a method
microbenchmark(spectrapply(od, FUN = mz), MSnbase:::spectrapply2(od, FUN = mz),
times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od, FUN = mz) 82.87224 95.75993 96.40574 97.41207
## MSnbase:::spectrapply2(od, FUN = mz) 96.75382 101.77152 104.28466 101.86804
## uq max neval cld
## 99.92526 106.0592 5 a
## 109.38800 111.6419 5 a
## Large file:
fl <- "/Users/jo/data/2017/2017_02/090217_111m_RT_-80_24h_a.mzML"
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
setMSnbaseFastLoad(FALSE)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 969.9947 992.4444 1161.431 1166.618 1187.340
## MSnbase:::spectrapply2(od) 1177.2195 1190.1962 1345.919 1380.541 1458.512
## max neval cld
## 1490.758 5 a
## 1523.124 5 a
microbenchmark(spectrapply(od, FUN = mz), MSnbase:::spectrapply2(od, FUN = mz),
times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od, FUN = mz) 970.0606 986.7927 1033.393 1015.398
## MSnbase:::spectrapply2(od, FUN = mz) 1163.2397 1181.7938 1291.004 1197.522
## uq max neval cld
## 1017.076 1177.636 5 a
## 1384.279 1528.184 5 b
## gzipped mzML
fl <- system.file("proteomics/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: seconds
## expr min lq mean median uq
## spectrapply(od) 23.77440 24.01636 25.46504 24.95036 27.19821
## MSnbase:::spectrapply2(od) 20.35279 20.77263 21.26155 21.26453 21.90014
## max neval cld
## 27.38587 5 b
## 22.01765 5 a
## mzXML
fl <- system.file("lockmass/LockMass_test.mzXML", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 177.9468 181.9812 195.6146 186.8247 205.5001
## MSnbase:::spectrapply2(od) 224.5103 259.7205 279.5995 271.8644 313.5613
## max neval cld
## 225.8203 5 a
## 328.3411 5 b
## At last with the same file but gzipped...
fl <- "/Users/jo/data/2017/mzXML/1405_blk1.mzXML.gz"
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: seconds
## expr min lq mean median uq
## spectrapply(od) 41.49147 41.94676 43.41425 42.21200 42.23053
## MSnbase:::spectrapply2(od) 39.08837 43.56239 44.41015 45.10258 47.06231
## max neval cld
## 49.19049 5 a
## 47.23510 5 a
fl <- system.file("cdf/ko15.CDF", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od) 266.9588 268.5773 338.2803 269.8898
## MSnbase:::spectrapply2(od) 13483.9753 13582.1238 13843.0532 13800.6454
## uq max neval cld
## 402.648 483.3276 5 a
## 14100.982 14247.5391 5 b
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library(MSnbase)
library(msdata)
f <- msdata::proteomics(full.names = TRUE)
f <- dir(system.file("sciex/", package = "msdata"), full.names = TRUE)
## readMSData
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
res <- readMSData(f[1], verbose = FALSE, msLevel. = 1)
res <- readMSData(f[1], verbose = FALSE, msLevel. = 2)
sp <- res[[12]]
sp <- spectra(res)
}
## readMSData onDisk
for (i in 1:7500) {
if (i %% 200 == 0)
cat(i, "\n")
res <- readMSData(f[1], verbose = FALSE, mode = "onDisk")
sp <- res[[6]]
res <- readMSData(f[1], verbose = FALSE, mode = "onDisk")
sp <- spectra(res)
chr <- chromatogram(res)
}
## OK without gc()
## spectrapply2
res <- readMSData(f, verbose = FALSE, mode = "onDisk")
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
sp <- res[[6]]
sp <- spectrapply(res)
sp <- spectrapply(res, FUN = mz)
tmp <- filterRt(res, rt = c(440, 470))
sp <- spectrapply(tmp)
}
## OK without gc()
## With finding chrom peaks
f <- dir("/Users/jo/data/2017/2017_10/centroid/", full.names = TRUE)
f <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)[1:6]
library(testthat)
library(xcms)
rawd <- readMSData(f, mode = "onDisk")
cwp <- CentWaveParam(peakwidth = c(1.5, 10), ppm = 40)
res <- findChromPeaks(rawd, param = cwp)
for (i in 1:1000) {
if (i %% 20 == 0)
cat(i, "\n")
tmp <- findChromPeaks(rawd, param = cwp)
expect_equal(chromPeaks(tmp), chromPeaks(res))
}
## spectrapply2
res <- readMSData(f[4], verbose = FALSE, mode = "onDisk")
for (i in 1:10000) {
if (i %% 200 == 0)
cat(i, "\n")
## sp <- res[[6]]
res <- readMSData(f[4], verbose = FALSE, mode = "onDisk")
sp <- MSnbase:::spectrapply2(res)
sp <- MSnbase:::spectrapply2(res, FUN = mz)
tmp <- filterRt(res, rt = c(1108, 1109))
sp <- MSnbase:::spectrapply2(tmp)
}
## OK without gc()
## Issue #170, i.e. random "[MSData::Spectrum::getMZIntensityPairs()] Sizes do
## not match." with the pwiz backend (segfault with ramp). The solution was to
## read call mzR::header() before the mzR::peaks, but this significantly slows
## things down.
library(MSnbase)
torturing <- function(x) {
tmp <- readMSData(x, msLevel. = 1, mode = "onDisk")
register(SerialParam())
for (i in 1:10) {
cat("--- ", i, " ---", "\n")
cat("first spectrapply\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
## sp has a size of 21.9GB!!!
rm(sp)
gc()
cat("second spectrapply\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
rm(sp)
gc()
tmp <- filterRt(tmp, rt = c(5, 500))
cat("third spectrapply after filter rt\n")
sp <- MSnbase::spectrapply(tmp, FUN = function(z) {max(mz(z))})
cat("\n\n")
}
}
torturing2 <- function(x) {
tmp <- readMSData(x, msLevel. = 1, mode = "onDisk")
register(SerialParam())
for (i in 1:10) {
cat("--- ", i, " ---", "\n")
cat("first spectrapply\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
## sp has a size of 21.9GB!!!
rm(sp)
gc()
cat("second spectrapply\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
rm(sp)
gc()
tmp <- filterRt(tmp, rt = c(5, 500))
cat("third spectrapply after filter rt\n")
sp <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
cat("\n\n")
}
}
## All tests are now WITHOUT gc().
SN <- "/Users/jo/data/2016/2016-11/NoSN/"
fl <- dir(SN, full.names = TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS without gc(): 1x OK
## spectrapply2
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS without gc(): 1x OK
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 2x OK.
fl <- dir("/Users/jo/data/2017/2017_02/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## maxOS: 2x OK.
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 2x FAIL
## --- 8 ---
## first spectrapply
## second spectrapply
## third spectrapply after filter rt
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## --- 6 ---
## first spectrapply
## second spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 1x FAIL: that's problematic; it's using the header(last spectrum).
## --- 3 ---
## first spectrapply
## second spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## macOS without gc(): 1x FAIL, like above!
## Instead of reading the last header we read ALL header for spectrapply
## macOS with header(all): 1x OK, 1x FAILED (like above).
fl <- dir("/Users/jo/data/2017/nalden01/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS without gc(): 3x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing(fl)
## macOS: 1x FAIL:
## --- 1 ---
## first spectrapply
## Error in object@backend$getPeakList(x) :
## [MSData::Spectrum::getMZIntensityPairs()] Sizes do not match.
## Linux: 2x OK.
## Windows: 2x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing(fl)
## macOS: 2x OK.
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(TRUE)
torturing2(fl)
## macOS: 1x FAIL (typical error).
fl <- dir("/Users/jo/data/2016/2016_06/", full.names = TRUE)
setMSnbaseFastLoad(FALSE)
torturing2(fl)
## macOS: 2x OK
###################
## Testing eventual less memory demanding versions of spectrapply.
## Variangs are:
## o spectrapply: reading all mz/intensity values. Creating the list of
## Spectrum objects for them and calling lapply on the function.
## o spectrapply3: processing each spectrum separately, i.e. load the data,
## create the spectrum object and apply the function.
## o spectrapply4: same as _3, but using a for loop instead of split, lapply
## What I should test here:
## 1) Can I use fastLoad = TRUE for spectrapply4? NO!
## 2) Do I need the gc() for the spectrapply4? TEST LATER.
library(MSnbase)
register(SerialParam())
setMSnbaseFastLoad(FALSE)
fl <- dir("/Users/jo/data/2016/2016-11/NoSN", full.names = TRUE)
tmp <- readMSData(fl, mode = "onDisk")
for (i in 1:10) {
cat("\nIteration", i, "\n\n")
maxi <- MSnbase:::spectrapply2(tmp, FUN = function(z) {max(mz(z))})
}
## 3) Compare the performance.LLLLLLL
library(MSnbase)
library(microbenchmark)
library(testthat)
## mzML
fl <- system.file("microtofq/MM14.mzML", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
sp_1 <- spectrapply(od, FUN = mz)
sp_2 <- MSnbase:::spectrapply2(od, FUN = mz)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 85.15372 88.22235 109.80866 88.24776 89.24869
## MSnbase:::spectrapply2(od) 95.21179 96.86848 99.96095 98.65392 103.79570
## max neval cld
## 198.1708 5 a
## 105.2749 5 a
## With a method
microbenchmark(spectrapply(od, FUN = mz), MSnbase:::spectrapply2(od, FUN = mz),
times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od, FUN = mz) 82.87224 95.75993 96.40574 97.41207
## MSnbase:::spectrapply2(od, FUN = mz) 96.75382 101.77152 104.28466 101.86804
## uq max neval cld
## 99.92526 106.0592 5 a
## 109.38800 111.6419 5 a
## Large file:
fl <- "/Users/jo/data/2017/2017_02/090217_111m_RT_-80_24h_a.mzML"
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
setMSnbaseFastLoad(FALSE)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 969.9947 992.4444 1161.431 1166.618 1187.340
## MSnbase:::spectrapply2(od) 1177.2195 1190.1962 1345.919 1380.541 1458.512
## max neval cld
## 1490.758 5 a
## 1523.124 5 a
microbenchmark(spectrapply(od, FUN = mz), MSnbase:::spectrapply2(od, FUN = mz),
times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od, FUN = mz) 970.0606 986.7927 1033.393 1015.398
## MSnbase:::spectrapply2(od, FUN = mz) 1163.2397 1181.7938 1291.004 1197.522
## uq max neval cld
## 1017.076 1177.636 5 a
## 1384.279 1528.184 5 b
## gzipped mzML
fl <- system.file("proteomics/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: seconds
## expr min lq mean median uq
## spectrapply(od) 23.77440 24.01636 25.46504 24.95036 27.19821
## MSnbase:::spectrapply2(od) 20.35279 20.77263 21.26155 21.26453 21.90014
## max neval cld
## 27.38587 5 b
## 22.01765 5 a
## mzXML
fl <- system.file("lockmass/LockMass_test.mzXML", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median uq
## spectrapply(od) 177.9468 181.9812 195.6146 186.8247 205.5001
## MSnbase:::spectrapply2(od) 224.5103 259.7205 279.5995 271.8644 313.5613
## max neval cld
## 225.8203 5 a
## 328.3411 5 b
## At last with the same file but gzipped...
fl <- "/Users/jo/data/2017/mzXML/1405_blk1.mzXML.gz"
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: seconds
## expr min lq mean median uq
## spectrapply(od) 41.49147 41.94676 43.41425 42.21200 42.23053
## MSnbase:::spectrapply2(od) 39.08837 43.56239 44.41015 45.10258 47.06231
## max neval cld
## 49.19049 5 a
## 47.23510 5 a
fl <- system.file("cdf/ko15.CDF", package = "msdata")
od <- readMSData(fl, mode = "onDisk")
sp_1 <- spectrapply(od)
sp_2 <- MSnbase:::spectrapply2(od)
expect_equal(sp_1, sp_2)
microbenchmark(spectrapply(od), MSnbase:::spectrapply2(od), times = 5)
## Unit: milliseconds
## expr min lq mean median
## spectrapply(od) 266.9588 268.5773 338.2803 269.8898
## MSnbase:::spectrapply2(od) 13483.9753 13582.1238 13843.0532 13800.6454
## uq max neval cld
## 402.648 483.3276 5 a
## 14100.982 14247.5391 5 b
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