We have seen such examples with something (more complicated
than, but basically like)
df <- data.frame(x=1:5, y=5:1, m=matrix(-pi*1:30, 5,6))
M <- Matrix::Matrix(exp(0:3),2)
df[1:2,1:2] <- M
which actually calls `[<-`, and then `[<-.data.frame` and
always works for me but does seg.fault (in the CRAN checks of
package FastImputation (on 3 of the dozen platforms,
https://urldefense.proofpoint.com/v2/url?u=https-3A__cran.r-2Dproject.org_web_checks_check-5Fresults-5FFastImputation.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=zUahQYlBHRwNf6lPnSA1515Rm-iL5ffQI7hUcDW-JkE&e=
one of them is
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.r-2Dproject.org_nosvn_R.check_r-2Ddevel-2Dmacos-2Dx86-5F64-2Dclang_FastImputation-2D00check.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=ILfV0tHrE_BxAkWYlvUUwWcBdBdtVD7BlEljGiO3WbY&s=Z7LkVlUzmdmhqxGNFl4LuMVxYwQQGHSV7KdpKCJu12k&e=
I strongly suspect this is the same bug as yours, but for a case
where the correct behavior is *not* giving an error.
I have also written and shown Herve's example to the R-core team.
Unfortunately, I have no platform where I can trigger the bug.
Martin
> Cheers,
> Andrzej
> On Wed, Mar 22, 2017 at 1:28 AM, Martin Morgan <
> martin.mor...@roswellpark.org> wrote:
>> On 03/21/2017 08:21 PM, Hervé Pagès wrote:
>>
>>> Hi Leonardo,
>>>
>>> Thanks for hunting down and isolating that bug! I tried to simplify
>>> your code even more and was able to get a segfault with just:
>>>
>>> setClass("A", representation(stuff="numeric"))
>>> x <- logical(10)
>>> x[TRUE] <- new("A")
>>>
>>> I get the segfault about 50% of the time on a fresh R session on Mac.
>>> I tried this with R 3.3.3 on Mavericks, and with R devel (r72372)
>>> on El Capitan. I get the segfault on both.
>>>
>>> So it looks like a bug in the `[<-` primitive to me (subassignment).
>>>
>>
>> Any insight from
>>
>> R -d valgrind -f herve.R
>>
>> where herve.R contains the code above?
>>
>> Martin
>>
>>
>>
>>> Cheers,
>>> H.
>>>
>>> On 03/21/2017 03:06 PM, Leonardo Collado Torres wrote:
>>>
>>>> Hi bioc-devel,
>>>>
>>>> This is a story about a bug that took me a long time to trace. The
>>>> behaviour was really weird, so I'm sharing the story in case this
>>>> helps others in the future. I was originally writing it to request
>>>> help, but then I was able to find the issue ^^. The story ends right
>>>> now with code that will reproduce the problem with '$<-' from
>>>> IRanges/S4Vectors.
>>>>
>>>>
>>>>
>>>>
>>>> During this Bioc cycle, frequently my package derfinder has failed to
>>>> pass R CMD check in OSX. The error is always the same when it appears
>>>> and sometimes it shows up in release, but not devel and viceversa.
>>>> Right now (3/21/2017) it's visible in both
>>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>> ctor.org_checkResults_release_bioc-2DLATEST_derfinder_
>>>> morelia-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfh
>>>> Q&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=RS-lsygPtDdgWKAhjA2BcSLk
>>>> Vy9RxxshXWAJaBZa_Yc&e=
>>>>
>>>> and
>>>> https://urldefense.proofpoint.com/v2/url?u=http-3A__biocondu
>>>> ctor.org_checkResults_devel_bioc-2DLATEST_derfinder_toluca
>>>> 2-2Dchecksrc.html&d=DwIGaQ&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3X
>>>> eAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=Bw-1Kqy-M_
>>>> t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=a_K-yK7w2LEV72lpHrpp0UoK
>>>> Rru_7Aad74T5Uk0R-Fo&e=
>>>> .
>>>> The end of "test-all.Rout.fail" looks like this:
>>>>
>>>> Loading required package: foreach
>>>> Loading required package: iterators
>>>> Loading required package: locfit
>>>> locfit 1.5-9.1 2013-03-22
>>>> getSegments: segmenting
>>>> getSegments: splitting
>>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>> 2017-03-20 02:36:52 findRegions: identifying potential segments
>>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with cutoff(s)
>>>> 16.3681899295041
>>>> 2017-03-20 02:36:52 findRegions: identifying candidate regions
>>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>> 2017-03-20 02:36:52 findRegions: identifying potential segments
>>>> 2017-03-20 02:36:52 findRegions: segmenting information
>>>> 2017-03-20 02:36:52 .getSegmentsRle: segmenting with cutoff(s)
>>>> 19.7936614060235
>>>> 2017-03-20 02:36:52 findRegions: identifying candidate regions
>>>> 2017-03-20 02:36:52 findRegions: identifying region clusters
>>>> 2017-03-20 02:36:52 findRegions: smoothing
>>>>
>>>> *** caught segfault ***
>>>> address 0x7f87d2f917e0, cause 'memory not mapped'
>>>>
>>>> Traceback:
>>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>> hostPackage <- environmentName(environment(smoothFun))
>>>> requireNamespace(hostPackage) smoothed <- .runFunFormal(smoothFun,
>>>> y = y, x = x, cluster = cluster, weights = weights, ...) if
>>>> (any(!smoothed$smoothed)) { smoothed$fitted[!smoothed$smoothed]
>>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]], dots[[3L]][[1L]],
>>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL, cluster,
>>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan = 0,
>>>> verbose = TRUE) { if (is.null(dim(y))) y <-
>>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>> is.null(dim(weights))) weights <- matrix(weights, ncol =
>>>> 1) if (is.null(x)) x <- seq(along = y) if
>>>> (is.null(weights)) weights <- matrix(1, nrow = nrow(y),
>>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster), cluster)
>>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>> if (verbose) if (i%%10000 == 0)
>>>> cat(".") Index <- Indexes[[i]] if (clusterL[i]
>>>>
>>>>> = minNum & sum(rowSums(is.na(y[Index, , drop =
>>>>>
>>>> FALSE])) == 0) >= minNum) { nn <-
>>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>> j], weights = weights[Index, j]) fit <-
>>>> locfit(y ˜ lp(pos, nn = nn, h = bpSpan), data =
>>>> sdata, weights = weights, family = "gaussian",
>>>> maxk = 10000) pp <- preplot(fit, where = "data", band
>>>> = "local", newdata = data.frame(pos = x[Index]))
>>>> y[Index, j] <- pp$trans(pp$fit) }
>>>> } else { y[Index, ] <- NA
>>>> smoothed[Index] <- FALSE } }
>>>> return(list(fitted = y, smoothed = smoothed, smoother = "locfit"))
>>>> }, verbose = TRUE, minNum = 1435)
>>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>> 3: FUN(...)
>>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>> 8: withCallingHandlers({ tryCatch({ FUN(...) }, error =
>>>> handle_error)}, warning = handle_warning)
>>>> 9: FUN(X[[i]], ...)
>>>> 10: lapply(X, FUN, ...)
>>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd = ddd,
>>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd = ddd,
>>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks, clusterChunks,
>>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>> ...), BPPARAM = BPPARAM)
>>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks, clusterChunks,
>>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>> ...), BPPARAM = BPPARAM)
>>>> 15: .smootherFstats(fstats = fstats, position = position, weights =
>>>> weights, smoothFunction = smoothFunction, ...)
>>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose = TRUE,
>>>> smooth = TRUE, minNum = 1435)
>>>> 17: eval(exprs, env)
>>>> 18: eval(exprs, env)
>>>> 19: source_file(path, new.env(parent = env), chdir = TRUE)
>>>> 20: force(code)
>>>> 21: with_reporter(reporter = reporter, start_end_reporter =
>>>> start_end_reporter, { lister$start_file(basename(path))
>>>> source_file(path, new.env(parent = env), chdir = TRUE)
>>>> end_context() })
>>>> 22: FUN(X[[i]], ...)
>>>> 23: lapply(paths, test_file, env = env, reporter = current_reporter,
>>>> start_end_reporter = FALSE, load_helpers = FALSE)
>>>> 24: force(code)
>>>> 25: with_reporter(reporter = current_reporter, results <-
>>>> lapply(paths, test_file, env = env, reporter = current_reporter,
>>>> start_end_reporter = FALSE, load_helpers = FALSE))
>>>> 26: test_files(paths, reporter = reporter, env = env, ...)
>>>> 27: test_dir(test_path, reporter = reporter, env = env, filter =
>>>> filter, ...)
>>>> 28: with_top_env(env, { test_dir(test_path, reporter = reporter,
>>>> env = env, filter = filter, ...)})
>>>> 29: run_tests(package, test_path, filter, reporter, ...)
>>>> 30: test_check("derfinder")
>>>> An irrecoverable exception occurred. R is aborting now ...
>>>>
>>>> I was finally able to reproduce this error on my Mac OSX laptop after
>>>> running R CMD build and R CMD check (same options as in Bioc) several
>>>> times. It took me a while, but I figured out what's the exact code
>>>> that's failing. It can be reproduced (noting that it won't always
>>>> fail...) in OSX by running:
>>>>
>>>> library('derfinder')
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>
>>>>
>>>> Here is the output from my laptop one time it actually failed:
>>>>
>>>> library('derfinder')
>>>>>
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>>
>>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>>> chunksize=1e3,
>>>>>
>>>> + colsubset=NULL)
>>>>
>>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>>
>>>> 2017-03-21 16:37:39 findRegions: smoothing
>>>>
>>>> *** caught segfault ***
>>>> address 0x7f958dbf2be0, cause 'memory not mapped'
>>>>
>>>> Traceback:
>>>> 1: (function (y, x, cluster, weights, smoothFun, ...) {
>>>> hostPackage <- environmentName(environment(smoothFun))
>>>> requireNamespace(hostPackage) smoothed <- .runFunFormal(smoothFun,
>>>> y = y, x = x, cluster = cluster, weights = weights, ...) if
>>>> (any(!smoothed$smoothed)) { smoothed$fitted[!smoothed$smoothed]
>>>> <- y[!smoothed$smoothed] } res <- Rle(smoothed$fitted)
>>>> return(res)})(dots[[1L]][[1L]], dots[[2L]][[1L]], dots[[3L]][[1L]],
>>>> dots[[4L]][[1L]], smoothFun = function (y, x = NULL, cluster,
>>>> weights = NULL, minNum = 7, bpSpan = 1000, minInSpan = 0,
>>>> verbose = TRUE) { if (is.null(dim(y))) y <-
>>>> matrix(y, ncol = 1) if (!is.null(weights) &&
>>>> is.null(dim(weights))) weights <- matrix(weights, ncol =
>>>> 1) if (is.null(x)) x <- seq(along = y) if
>>>> (is.null(weights)) weights <- matrix(1, nrow = nrow(y),
>>>> ncol = ncol(y)) Indexes <- split(seq(along = cluster), cluster)
>>>> clusterL <- sapply(Indexes, length) smoothed <-
>>>> rep(TRUE, nrow(y)) for (i in seq(along = Indexes)) {
>>>> if (verbose) if (i%%10000 == 0)
>>>> cat(".") Index <- Indexes[[i]] if (clusterL[i]
>>>>
>>>>> = minNum & sum(rowSums(is.na(y[Index, , drop =
>>>>>
>>>> FALSE])) == 0) >= minNum) { nn <-
>>>> minInSpan/length(Index) for (j in 1:ncol(y)) {
>>>> sdata <- data.frame(pos = x[Index], y = y[Index,
>>>> j], weights = weights[Index, j]) fit <-
>>>> locfit(y ~ lp(pos, nn = nn, h = bpSpan), data =
>>>> sdata, weights = weights, family = "gaussian",
>>>> maxk = 10000) pp <- preplot(fit, where = "data", band
>>>> = "local", newdata = data.frame(pos = x[Index]))
>>>> y[Index, j] <- pp$trans(pp$fit) }
>>>> } else { y[Index, ] <- NA
>>>> smoothed[Index] <- FALSE } }
>>>> return(list(fitted = y, smoothed = smoothed, smoother = "locfit"))
>>>> }, verbose = TRUE, minNum = 1435)
>>>> 2: .mapply(.FUN, dots, .MoreArgs)
>>>> 3: FUN(...)
>>>> 4: doTryCatch(return(expr), name, parentenv, handler)
>>>> 5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
>>>> 6: tryCatchList(expr, classes, parentenv, handlers)
>>>> 7: tryCatch({ FUN(...)}, error = handle_error)
>>>> 8: withCallingHandlers({ tryCatch({ FUN(...) }, error =
>>>> handle_error)}, warning = handle_warning)
>>>> 9: FUN(X[[i]], ...)
>>>> 10: lapply(X, FUN, ...)
>>>> 11: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd = ddd,
>>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>> 12: bplapply(X = seq_along(ddd[[1L]]), wrap, .FUN = FUN, .ddd = ddd,
>>>> .MoreArgs = MoreArgs, BPREDO = BPREDO, BPPARAM = BPPARAM)
>>>> 13: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks, clusterChunks,
>>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>> ...), BPPARAM = BPPARAM)
>>>> 14: bpmapply(.smoothFstatsFun, fstatsChunks, posChunks, clusterChunks,
>>>> weightChunks, MoreArgs = list(smoothFun = smoothFunction,
>>>> ...), BPPARAM = BPPARAM)
>>>> 15: .smootherFstats(fstats = fstats, position = position, weights =
>>>> weights, smoothFunction = smoothFunction, ...)
>>>> 16: findRegions(prep$position, genomeFstats, "chr21", verbose = TRUE,
>>>> smooth = TRUE, minNum = 1435)
>>>>
>>>> Possible actions:
>>>> 1: abort (with core dump, if enabled)
>>>> 2: normal R exit
>>>> 3: exit R without saving workspace
>>>> 4: exit R saving workspace
>>>>
>>>> The traceback information ends at's bumphunter::loessByCluster().
>>>>
>>>>
>>>> I have successfully used the following code other times (see below)
>>>> where I test the culprit line 100 times. By successfully, I mean that
>>>> the code ran without problems... so it was unsuccessful at reproducing
>>>> the problem.
>>>>
>>>> library('derfinder')
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>>
>>>> for(i in 1:100) {
>>>> print(i)
>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>> }
>>>> options(width = 120)
>>>> devtools::session_info()
>>>>
>>>>
>>>> I had several R processes open the one time it did fail, but well,
>>>> I've had multiple of them open the times that the code didn't fail. So
>>>> having multiple R processes doesn't seem to be an issue.
>>>>
>>>> The line that triggers the segfault is used simply to test that
>>>> passing the argument 'minNum' to bumphunter::loessByCluster() via
>>>> '...' works. It's not a relevant test for derfinder and I was tempted
>>>> to remove it, although before tracing the bug I talked with Valerie
>>>> about not removing it. With the upcoming Bioconductor release I
>>>> decided to finally trace the line that triggers the segfault. At this
>>>> point I was feeling lost...
>>>>
>>>>
>>>> Running the following code seems to trigger the segfault more often (I
>>>> got it like 4 times in a row):
>>>>
>>>> library('derfinder')
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE)
>>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>> bumphunter::runmedByCluster)
>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>>
>>>> But then I can still run the same code without problems on a for loop
>>>> for 100 times:
>>>>
>>>> library('derfinder')
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>>
>>>> for(i in 1:100) {
>>>> print(i)
>>>> regs_s1 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE)
>>>> regs_s2 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, smoothFunction =
>>>> bumphunter::runmedByCluster)
>>>> regs_s3 <- findRegions(prep$position, genomeFstats, 'chr21',
>>>> verbose=TRUE, smooth = TRUE, minNum = 1435)
>>>> }
>>>> options(width = 120)
>>>> devtools::session_info()
>>>>
>>>>
>>>>
>>>>
>>>> I next thought of going through findRegions() to produce simple
>>>> objects that could reproduce the error. I had in mine sharing these
>>>> objects so it would be easier for others to help me figure out what
>>>> was failing. It turns out that this code segfaulted reliably (all the
>>>> times I tested it at least):
>>>>
>>>>
>>>> library('derfinder')
>>>> library('BiocParallel')
>>>> library('IRanges')
>>>> prep <- preprocessCoverage(genomeData, cutoff=0, scalefac=32,
>>>> chunksize=1e3,
>>>> colsubset=NULL)
>>>> fstats <- genomeFstats
>>>> position <- prep$position
>>>> weights <- NULL
>>>> cluster <- derfinder:::.clusterMakerRle(position, 300L)
>>>> cluster
>>>> BPPARAM <- SerialParam()
>>>> iChunks <- rep(1, length(cluster))
>>>>
>>>> fstatsChunks <- split(fstats, iChunks)
>>>> posChunks <- split(which(position), iChunks)
>>>> clusterChunks <- split(cluster, iChunks)
>>>> weightChunks <- vector('list', length = length(unique(iChunks)))
>>>>
>>>> res <- bpmapply(bumphunter::loessByCluster, fstatsChunks, posChunks,
>>>> clusterChunks, weightChunks, MoreArgs = list(minNum = 1435),
>>>> BPPARAM = BPPARAM, SIMPLIFY = FALSE)
>>>>
>>>> y <- fstatsChunks[[1]]
>>>> smoothed <- res[[1]]
>>>>
>>>> ## This segfaults:
>>>> if(any(!smoothed$smoothed)) {
>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>> }
>>>>
>>>>
>>>> The objects on the line that fail are a list and an Rle:
>>>>
>>>> y
>>>>>
>>>> numeric-Rle of length 1434 with 358 runs
>>>> Lengths: 1 5
>>>> ... 1
>>>> Values : 5.109484425367 3.85228949953674 ...
>>>> 3.99765511645983
>>>>
>>>>> lapply(smoothed, head)
>>>>>
>>>> $fitted
>>>> [,1]
>>>> [1,] NA
>>>> [2,] NA
>>>> [3,] NA
>>>> [4,] NA
>>>> [5,] NA
>>>> [6,] NA
>>>>
>>>> $smoothed
>>>> [1] FALSE FALSE FALSE FALSE FALSE FALSE
>>>>
>>>> $smoother
>>>> [1] "loess"
>>>>
>>>>> table(!smoothed$smoothed)
>>>>>
>>>>
>>>> TRUE
>>>> 1434
>>>>
>>>>> y[!smoothed$smoothed]
>>>>>
>>>> numeric-Rle of length 1434 with 358 runs
>>>> Lengths: 1 5
>>>> ... 1
>>>> Values : 5.109484425367 3.85228949953674 ...
>>>> 3.99765511645983
>>>>
>>>> So in my derfinder code I was assigning an Rle to a matrix, and that
>>>> was the segfault. I have no idea why this doesn't always fail on OSX
>>>> and why it never failed on Linux or Windows.
>>>>
>>>>
>>>> This is the super simplified IRanges code that fails:
>>>>
>>>> library('IRanges')
>>>> y <- Rle(runif(10, 1, 1))
>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>> 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>> sessionInfo()
>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>
>>>> ## Segfault on OSX
>>>>
>>>> library('IRanges')
>>>>> y <- Rle(runif(10, 1, 1))
>>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>>
>>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>
>>>>>
>>>>> sessionInfo()
>>>>>
>>>> R Under development (unstable) (2016-10-26 r71594)
>>>> Platform: x86_64-apple-darwin13.4.0 (64-bit)
>>>> Running under: macOS Sierra 10.12.3
>>>>
>>>> locale:
>>>> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>>>>
>>>> attached base packages:
>>>> [1] stats4 parallel stats graphics grDevices utils
>>>> datasets methods base
>>>>
>>>> other attached packages:
>>>> [1] IRanges_2.9.19 S4Vectors_0.13.15 BiocGenerics_0.21.3
>>>>
>>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>
>>>>
>>>> *** caught segfault ***
>>>> address 0x7fcdc31dffe0, cause 'memory not mapped'
>>>>
>>>> Possible actions:
>>>> 1: abort (with core dump, if enabled)
>>>> 2: normal R exit
>>>> 3: exit R without saving workspace
>>>> 4: exit R saving workspace
>>>>
>>>>
>>>> ## No problems on Linux
>>>>
>>>> library('IRanges')
>>>>> y <- Rle(runif(10, 1, 1))
>>>>> smoothed <- list('fitted' = matrix(NA, ncol = 1, nrow = 10),
>>>>>
>>>> + 'smoothed' = rep(FALSE, 10), smoother = 'loess')
>>>>
>>>>>
>>>>> sessionInfo()
>>>>>
>>>> R version 3.3.1 Patched (2016-09-30 r71426)
>>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>> Running under: Red Hat Enterprise Linux Server release 6.6 (Santiago)
>>>>
>>>> locale:
>>>> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
>>>> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
>>>> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
>>>> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
>>>> [9] LC_ADDRESS=C LC_TELEPHONE=C
>>>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>>>
>>>> attached base packages:
>>>> [1] stats4 parallel stats graphics grDevices datasets utils
>>>> [8] methods base
>>>>
>>>> other attached packages:
>>>> [1] IRanges_2.8.2 S4Vectors_0.12.2 BiocGenerics_0.20.0
>>>> [4] colorout_1.1-2
>>>>
>>>> loaded via a namespace (and not attached):
>>>> [1] tools_3.3.1
>>>>
>>>>> smoothed$fitted[!smoothed$smoothed] <- y[!smoothed$smoothed]
>>>>>
>>>>
>>>>
>>>> Best,
>>>> Leo
>>>>
>>>>
>>>>
>>>> The session information for my first tests is below:
>>>>
>>>> devtools::session_info()
>>>>>
>>>> Session info
>>>> ------------------------------------------------------------
>>>> -----------------------------------------------
>>>>
>>>> setting value
>>>> version R Under development (unstable) (2016-10-26 r71594)
>>>> system x86_64, darwin13.4.0
>>>> ui X11
>>>> language (EN)
>>>> collate en_US.UTF-8
>>>> tz America/New_York
>>>> date 2017-03-21
>>>>
>>>> Packages
>>>> ------------------------------------------------------------
>>>> ---------------------------------------------------
>>>>
>>>> package * version date source
>>>> acepack 1.4.1 2016-10-29 CRAN (R 3.4.0)
>>>> AnnotationDbi 1.37.4 2017-03-10 Bioconductor
>>>> assertthat 0.1 2013-12-06 CRAN (R 3.4.0)
>>>> backports 1.0.5 2017-01-18 CRAN (R 3.4.0)
>>>> base64enc 0.1-3 2015-07-28 CRAN (R 3.4.0)
>>>> Biobase 2.35.1 2017-02-23 Bioconductor
>>>> BiocGenerics * 0.21.3 2017-01-12 Bioconductor
>>>> BiocParallel 1.9.5 2017-01-24 Bioconductor
>>>> biomaRt 2.31.4 2017-01-13 Bioconductor
>>>> Biostrings 2.43.5 2017-03-19 cran (@2.43.5)
>>>> bitops 1.0-6 2013-08-17 CRAN (R 3.4.0)
>>>> BSgenome 1.43.7 2017-02-24 Bioconductor
>>>> bumphunter * 1.15.0 2016-10-23 Bioconductor
>>>> checkmate 1.8.2 2016-11-02 CRAN (R 3.4.0)
>>>> cluster 2.0.6 2017-03-16 CRAN (R 3.4.0)
>>>> codetools 0.2-15 2016-10-05 CRAN (R 3.4.0)
>>>> colorout * 1.1-2 2016-11-15 Github
>>>> (jalvesaq/colorout@6d84420)
>>>> colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
>>>> crayon 1.3.2 2016-06-28 CRAN (R 3.4.0)
>>>> data.table 1.10.4 2017-02-01 CRAN (R 3.4.0)
>>>> DBI 0.6 2017-03-09 CRAN (R 3.4.0)
>>>> DelayedArray 0.1.7 2017-02-17 Bioconductor
>>>> derfinder * 1.9.10 2017-03-17 cran (@1.9.10)
>>>> derfinderHelper 1.9.4 2017-03-07 Bioconductor
>>>> devtools 1.12.0 2016-12-05 CRAN (R 3.4.0)
>>>> digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
>>>> doRNG 1.6 2014-03-07 CRAN (R 3.4.0)
>>>> foreach * 1.4.3 2015-10-13 CRAN (R 3.4.0)
>>>> foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
>>>> Formula 1.2-1 2015-04-07 CRAN (R 3.4.0)
>>>> GenomeInfoDb * 1.11.9 2017-02-08 Bioconductor
>>>> GenomeInfoDbData 0.99.0 2017-02-14 Bioconductor
>>>> GenomicAlignments 1.11.12 2017-03-16 cran (@1.11.12)
>>>> GenomicFeatures 1.27.10 2017-03-16 cran (@1.27.10)
>>>> GenomicFiles 1.11.4 2017-03-10 Bioconductor
>>>> GenomicRanges * 1.27.23 2017-02-25 Bioconductor
>>>> ggplot2 2.2.1 2016-12-30 CRAN (R 3.4.0)
>>>> gridExtra 2.2.1 2016-02-29 CRAN (R 3.4.0)
>>>> gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
>>>> Hmisc 4.0-2 2016-12-31 CRAN (R 3.4.0)
>>>> htmlTable 1.9 2017-01-26 CRAN (R 3.4.0)
>>>> htmltools 0.3.5 2016-03-21 CRAN (R 3.4.0)
>>>> htmlwidgets 0.8 2016-11-09 CRAN (R 3.4.0)
>>>> IRanges * 2.9.19 2017-03-15 cran (@2.9.19)
>>>> iterators * 1.0.8 2015-10-13 CRAN (R 3.4.0)
>>>> knitr 1.15.1 2016-11-22 CRAN (R 3.4.0)
>>>> lattice 0.20-34 2016-09-06 CRAN (R 3.4.0)
>>>> latticeExtra 0.6-28 2016-02-09 CRAN (R 3.4.0)
>>>> lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
>>>> locfit * 1.5-9.1 2013-04-20 CRAN (R 3.4.0)
>>>> magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
>>>> Matrix 1.2-8 2017-01-20 CRAN (R 3.4.0)
>>>> matrixStats 0.51.0 2016-10-09 CRAN (R 3.4.0)
>>>> memoise 1.0.0 2016-01-29 CRAN (R 3.4.0)
>>>> munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
>>>> nnet 7.3-12 2016-02-02 CRAN (R 3.4.0)
>>>> pkgmaker 0.22 2014-05-14 CRAN (R 3.4.0)
>>>> plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
>>>> qvalue 2.7.0 2016-10-23 Bioconductor
>>>> R6 2.2.0 2016-10-05 CRAN (R 3.4.0)
>>>> RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.4.0)
>>>> Rcpp 0.12.10 2017-03-19 CRAN (R 3.4.0)
>>>> RCurl 1.95-4.8 2016-03-01 CRAN (R 3.4.0)
>>>> registry 0.3 2015-07-08 CRAN (R 3.4.0)
>>>> reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
>>>> rngtools 1.2.4 2014-03-06 CRAN (R 3.4.0)
>>>> rpart 4.1-10 2015-06-29 CRAN (R 3.4.0)
>>>> Rsamtools 1.27.13 2017-03-14 cran (@1.27.13)
>>>> RSQLite 1.1-2 2017-01-08 CRAN (R 3.4.0)
>>>> rtracklayer 1.35.9 2017-03-19 cran (@1.35.9)
>>>> S4Vectors * 0.13.15 2017-02-14 cran (@0.13.15)
>>>> scales 0.4.1 2016-11-09 CRAN (R 3.4.0)
>>>> stringi 1.1.2 2016-10-01 CRAN (R 3.4.0)
>>>> stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
>>>> SummarizedExperiment 1.5.7 2017-02-23 Bioconductor
>>>> survival 2.41-2 2017-03-16 CRAN (R 3.4.0)
>>>> testthat * 1.0.2 2016-04-23 CRAN (R 3.4.0)
>>>> tibble 1.2 2016-08-26 CRAN (R 3.4.0)
>>>> VariantAnnotation 1.21.17 2017-02-12 Bioconductor
>>>> withr 1.0.2 2016-06-20 CRAN (R 3.4.0)
>>>> XML 3.98-1.5 2016-11-10 CRAN (R 3.4.0)
>>>> xtable 1.8-2 2016-02-05 CRAN (R 3.4.0)
>>>> XVector 0.15.2 2017-02-02 Bioconductor
>>>> zlibbioc 1.21.0 2016-10-23 Bioconductor
>>>>
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