[Bioc-devel] The story of tracing a derfinder bug on OSX that sometimes popped up, sometimes it didn't. Related to IRanges/S4Vectors '$<-'
Michael Lawrence
lawrence.michael at gene.com
Mon Mar 27 17:41:45 CEST 2017
My bad guys, I'll fix when I get to work.
On Mon, Mar 27, 2017 at 3:59 AM, Martin Morgan
<martin.morgan at roswellpark.org> wrote:
> On 03/22/2017 01:12 PM, Hervé Pagès wrote:
>>
>> Hi Martin,
>>
>> On 03/22/2017 03:17 AM, Martin Maechler wrote:
>>>>>>>>
>>>>>>>> Andrzej Oleś <andrzej.oles at gmail.com>
>>>>>>>> on Wed, 22 Mar 2017 10:29:57 +0100 writes:
>>>
>>>
>>> > Just for the record, on R-3.3.2 Herve's code fails with the
>>> following error:
>>> > Error in x[TRUE] <- new("A") :
>>> > incompatible types (from S4 to logical) in subassignment type fix
>>>
>>> yes, (of course).... and I would be interested in a small
>>> reproducible example which uses _valid_ code.
>>
>>
>> Looks like before performing the subassignment itself, [<- first tries
>> to coerce the RHS to the "mode" of the LHS by calling as.vector() on the
>> former. So if we define an as.vector S3 method for A objects:
>>
>> setClass("A", representation(stuff="numeric"))
>> as.vector.A <- function (x, mode="any") x at stuff
>> a <- new("A", stuff=c(3.5, 0.1))
>> x <- numeric(10)
>> x[3:4] <- a
>
>
> The relevant stack trace is
>
> * frame #0: 0x000000010dded77a libR.dylib`R_has_methods(op=<unavailable>)
> + 74 at objects.c:1415
> frame #1: 0x000000010ddaabf4
> libR.dylib`Rf_DispatchOrEval(call=0x00007fcea36f68a8, op=0x00007fcea201a178,
> generic=0x000000010df0a185, args=<unavailable>, rho=0x00007fcea2053318,
> ans=0x00007fff51f60c48, dropmissing=<unavailable>, argsevald=1) + 404 at
> eval.c:3150
> frame #2: 0x000000010de4e658 libR.dylib`SubassignTypeFix [inlined]
> dispatch_asvector(x=<unavailable>, call=0x00007fcea36f68a8,
> rho=0x00007fcea2053318) + 295 at subassign.c:283
>
>
> The segfault is at objects.c:1415
>
> offset = PRIMOFFSET(op);
> if(offset > curMaxOffset || prim_methods[offset] == NO_METHODS
> || prim_methods[offset] == SUPPRESSED)
>
> where offset is negative and prim_methods[offset] fails.
>
> (lldb) p *op
> (SEXPREC) $8 = {
> sxpinfo = (type = 0, obj = 0, named = 2, gp = 0, mark = 1, debug = 0,
> trace = 0, spare = 0, gcgen = 1, gccls = 0)
> attrib = 0x00007fcea201a178
> gengc_next_node = 0x00007fcea21874e8
> gengc_prev_node = 0x00007fcea2019ff0
> u = {
> primsxp = (offset = -1576951432)
> symsxp = {
>
>
> 'op' is assigned from subassign.c:287, op = R_Primitive("as.vector")
>
> static Rboolean dispatch_asvector(SEXP *x, SEXP call, SEXP rho) {
> static SEXP op = NULL;
> SEXP args;
> Rboolean ans;
> if (op == NULL)
> op = R_Primitive("as.vector");
> PROTECT(args = list2(*x, mkString("any")));
> ans = DispatchOrEval(call, op, "as.vector", args, rho, x, 0, 1);
> UNPROTECT(1);
> return ans;
> }
>
> But as.vector is not a primitive, so gets R_NilValue. This is passed to
> DispatchOrEval, and then to R_has_methods.
>
> It seems like dispatch_asvector() was introduced by
>
> $ svn log -c69747
> ------------------------------------------------------------------------
> r69747 | lawrence | 2015-12-09 09:04:56 -0500 (Wed, 09 Dec 2015) | 3 lines
>
> subassignment of an S4 value into an atomic vector coerces the value
> with as.vector
>
> ------------------------------------------------------------------------
>
> So maybe Michael can tell us about his thinking here.
>
> Also, should R_has_methods be robust to R_NilValue? And R_NilValue
> explicitly zero it's data?
>
> Martin
>
>
>
>>
>> then the code is now valid and we still get the segfault on Mac.
>>
>> I didn't define as.vector.A in my original minimalist reproducible
>> code in order to keep it as simple as possible.
>>
>> H.
>>
>>
>>> 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.morgan at 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 at 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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