I committed a fix into R trunk with a regression test. On Mon, Mar 27, 2017 at 8:41 AM, Michael Lawrence <micha...@gene.com> wrote: > My bad guys, I'll fix when I get to work. > > On Mon, Mar 27, 2017 at 3:59 AM, Martin Morgan > <martin.mor...@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.o...@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@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.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 >>>> >>>> >>>> >>>> _______________________________________________ >>>> >>>> Bioc-devel@r-project.org mailing list >>>> >>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.et >>>> >>>> hz.ch_mailman_listinfo_bioc-2Ddevel&d=DwIGaQ&c=eRAMFD45gAfqt >>>> >>>> 84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m= >>>> >>>> Bw-1Kqy-M_t4kmpYWTpYkt5bvj_eTpxriUM3UvtOIzQ&s=hEBTd8bPfLVp6H >>>> >>>> oN3XSBk6ppmeRZhdLoB8VseYM_Byk&e= >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>> >>>> >> >>>> >> This email message may contain legally privileged >>>> and/or...{{dropped:2}} >>>> >> >>>> >> >>>> >> _______________________________________________ >>>> >> Bioc-devel@r-project.org mailing list >>>> >> 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