* using log directory ‘/data/gannet/ripley/R/packages/tests-devel/seqtrie.Rcheck’
* using R Under development (unstable) (2025-02-27 r87831)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
    GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
* running under: Fedora Linux 40 (Workstation Edition)
* using session charset: UTF-8
* using option ‘--no-stop-on-test-error’
* checking for file ‘seqtrie/DESCRIPTION’ ... OK
* this is package ‘seqtrie’ version ‘0.2.8’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘seqtrie’ can be installed ... [99s/69s] OK
See 'https://www.r-project.org/nosvn/R.check/r-devel-linux-x86_64-fedora-gcc/seqtrie-00install.html' for details.
* used C++ compiler: ‘g++ (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)’
* checking C++ specification ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [8s/11s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... INFO
GNU make is a SystemRequirements.
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... OK
* checking compiled code ... NOTE
File ‘seqtrie/libs/seqtrie.so’:
  Found non-API call to R: ‘STRING_PTR’

Compiled code should not call non-API entry points in R.

See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual,
and section ‘Moving into C API compliance’ for issues with the use of
non-API entry points.
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ... [896s/479s] ERROR
  Running ‘test_RadixForest.R’ [24s/19s]
  Running ‘test_RadixTree.R’ [694s/361s]
  Running ‘test_pairwise.R’ [177s/99s]
Running the tests in ‘tests/test_pairwise.R’ failed.
Complete output:
  > # This test file tests the `dist_matrix` and `dist_pairwise` functions
  > # These two functions are simple dynamic programming algorithms for computing pairwise distances and are themselves used to validate
  > # the RadixTree imeplementation (see test_radix_tree.R)
  > 
  > if(requireNamespace("seqtrie", quietly=TRUE) &&
  +    requireNamespace("stringi", quietly=TRUE) &&
  +    requireNamespace("stringdist", quietly=TRUE) &&
  +    requireNamespace("Biostrings", quietly=TRUE) &&
  +    requireNamespace("dplyr", quietly=TRUE) && 
  +    # pwalign is only required for Biostrings >= 2.72.0 in R 4.4+
  +    ( (packageVersion("Biostrings") < "2.72.0") || requireNamespace("pwalign", quietly=TRUE) )
  + ) {
  + 
  + library(seqtrie)
  + library(stringi)
  + library(stringdist)
  + library(Biostrings)
  + library(dplyr)
  + 
  + # Use 2 threads on github actions and CRAN, 4 threads locally
  + IS_LOCAL  <- Sys.getenv("IS_LOCAL") != ""
  + NTHREADS  <- ifelse(IS_LOCAL, 4, 2)
  + NITER     <- ifelse(IS_LOCAL, 4, 1)
  + NSEQS     <- 10000
  + MAXSEQLEN <- 200
  + CHARSET   <- "ACGT"
  + 
  + random_strings <- function(N, charset = "abcdefghijklmnopqrstuvwxyz") {
  +   charset_stri <- paste0("[", charset, "]")
  +   len <- sample(0:MAXSEQLEN, N, replace=TRUE)
  +   result <- lapply(0:MAXSEQLEN, function(x) {
  +     nx <- sum(len == x)
  +     if(nx == 0) return(character())
  +     stringi::stri_rand_strings(nx, x, pattern = charset_stri)
  +   })
  +   sample(unlist(result))
  + }
  + 
  + mutate_strings <- function(x, prob = 0.025, indel_prob = 0.025, charset = "abcdefghijklmnopqrstuvwxyz") {
  +   charset <- unlist(strsplit(charset, ""))
  +   xsplit <- strsplit(x, "")
  +   sapply(xsplit, function(a) {
  +     r <- runif(length(a)) < prob
  +     a[r] <- sample(charset, sum(r), replace=TRUE)
  +     ins <- runif(length(a)) < indel_prob
  +     a[ins] <- paste0(sample(charset, sum(ins), replace=TRUE), sample(charset, sum(ins), replace=TRUE))
  +     del <- runif(length(a)) < indel_prob
  +     a[del] <- ""
  +     paste0(a, collapse = "")
  +   })
  + }
  + 
  + # Biostrings notes:
  + # subject (target) must be of length 1 or equal to pattern (query)
  + # To get a distance matrix, iterate over target and perform a column bind
  + # special_zero_case -- if both query and target are empty, Biostrings fails with an error
  + pairwiseAlignmentFix <- function(pattern, subject, ...) {
  +     results <- rep(0, length(subject))
  +     special_zero_case <- nchar(pattern) == 0 & nchar(subject) == 0
  +     if(all(special_zero_case)) {
  +       results
  +     } else {
  +       results[!special_zero_case] <- Biostrings::pairwiseAlignment(pattern=pattern[!special_zero_case], subject=subject[!special_zero_case], ...)
  +       results
  +     }
  + }
  + 
  + biostrings_matrix_global <- function(query, target, cost_matrix, gap_cost, gap_open_cost = 0) {
  +   substitutionMatrix <- -cost_matrix
  +   lapply(query, function(x) {
  +     query2 <- rep(x, length(target))
  +     -pairwiseAlignmentFix(pattern=query2, subject=target, substitutionMatrix = substitutionMatrix, gapOpening=gap_open_cost, gapExtension=gap_cost, scoreOnly=TRUE, type="global")
  +   }) %>% do.call(rbind, .)
  + }
  + 
  + biostrings_pairwise_global <- function(query, target, cost_matrix, gap_cost, gap_open_cost = 0) {
  +   substitutionMatrix <- -cost_matrix
  +   -pairwiseAlignment(pattern=query, subject=target, substitutionMatrix = substitutionMatrix,gapOpening=gap_open_cost, gapExtension=gap_cost, scoreOnly=TRUE, type="global")
  + }
  + 
  + biostrings_matrix_anchored <- function(query, target, query_size, target_size, cost_matrix, gap_cost, gap_open_cost = 0) {
  +   substitutionMatrix <- -cost_matrix
  +   lapply(seq_along(query), function(i) {
  +     query2 <- substring(query[i], 1, query_size[i,,drop=TRUE])
  +     target2 <- substring(target, 1, target_size[i,,drop=TRUE])
  +     -pairwiseAlignmentFix(pattern=query2, subject=target2, substitutionMatrix = substitutionMatrix, gapOpening=gap_open_cost, gapExtension=gap_cost, scoreOnly=TRUE, type="global")
  +   }) %>% do.call(rbind, .)
  + }
  + 
  + biostrings_pairwise_anchored <- function(query, target, query_size, target_size, cost_matrix, gap_cost, gap_open_cost = 0) {
  +   substitutionMatrix <- -cost_matrix
  +   query2 <- substring(query, 1, query_size)
  +   target2 <- substring(target, 1, target_size)
  +   -pairwiseAlignmentFix(pattern=query2, subject=target2, substitutionMatrix = substitutionMatrix, gapOpening=gap_open_cost, gapExtension=gap_cost, scoreOnly=TRUE, type="global")
  + }
  + 
  + for(. in 1:NITER) {
  + 
  +     print("Checking hamming search correctness")
  +     local({
  +       # Note: seqtrie returns `NA_integer_` for hamming distance when the lengths are different
  +       # whereas stringdist returns `Inf`
  +       # This is why we need to replace `NA_integer_` with `Inf` when comparing results
  + 
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       results_seqtrie <- dist_matrix(query, target, mode = "hamming", nthreads=NTHREADS)
  +       results_seqtrie[is.na(results_seqtrie)] <- Inf
  +       results_stringdist <- stringdist::stringdistmatrix(query, target, method = "hamming", nthread=NTHREADS)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "hamming", nthreads=NTHREADS)
  +       results_seqtrie[is.na(results_seqtrie)] <- Inf
  +       results_stringdist <- stringdist::stringdist(query_pairwise, target, method = "hamming", nthread=NTHREADS)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  +     })
  + 
  +     print("Checking levenshtein search correctness")
  +     local({
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       results_seqtrie <- dist_matrix(query, target, mode = "levenshtein", nthreads=NTHREADS)
  +       results_stringdist <- stringdist::stringdistmatrix(query, target, method = "lv", nthread=NTHREADS)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "levenshtein", nthreads=NTHREADS)
  +       results_stringdist <- stringdist::stringdist(query_pairwise, target, method = "lv", nthread=NTHREADS)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  +     })
  + 
  +     print("Checking anchored search correctness")
  +     local({
  +       # There is no anchored search in stringdist (or elsewhere). To get the same results, we substring the query and target sequences
  +       # By the results of the seqtrie anchored search and then compare the results
  + 
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       results_seqtrie <- dist_matrix(query, target, mode = "anchored", nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       results_stringdist <- lapply(seq_along(query), function(i) {
  +         query_size2 <- query_size[i,,drop=TRUE]
  +         target_size2 <- target_size[i,,drop=TRUE]
  +         query2 <- substring(query[i], 1, query_size2) # query[i] is recycled
  +         target2 <- substring(target, 1, target_size2)
  +         stringdist::stringdist(query2, target2, method = "lv", nthread=NTHREADS)
  +       }) %>% do.call(rbind, .)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "anchored", nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       query2 <- substring(query_pairwise, 1, query_size)
  +       target2 <- substring(target, 1, target_size)
  +       results_stringdist <- stringdist::stringdist(query2, target2, method = "lv", nthread=NTHREADS)
  +       stopifnot(all(results_seqtrie == results_stringdist))
  +     })
  + 
  +     print("Checking global search with linear gap for correctness")
  +     local({
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       cost_matrix <- matrix(sample(1:3, size = nchar(CHARSET)^2, replace=TRUE), nrow=nchar(CHARSET))
  +       diag(cost_matrix) <- 0
  +       colnames(cost_matrix) <- rownames(cost_matrix) <- strsplit(CHARSET, "")[[1]]
  +       gap_cost <- sample(1:3, size = 1)
  +       results_seqtrie <- dist_matrix(query, target, mode = "levenshtein", cost_matrix = cost_matrix, gap_cost = gap_cost, nthreads=NTHREADS)
  +       results_biostrings <- biostrings_matrix_global(query, target, cost_matrix = cost_matrix, gap_cost = gap_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "levenshtein", cost_matrix = cost_matrix, gap_cost = gap_cost, nthreads=NTHREADS)
  +       results_biostrings <- biostrings_pairwise_global(query_pairwise, target, cost_matrix = cost_matrix, gap_cost = gap_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  +     })
  + 
  +     print("Checking anchored search with linear gap for correctness")
  +     local({
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       cost_matrix <- matrix(sample(1:3, size = nchar(CHARSET)^2, replace=TRUE), nrow=nchar(CHARSET))
  +       diag(cost_matrix) <- 0
  +       colnames(cost_matrix) <- rownames(cost_matrix) <- strsplit(CHARSET, "")[[1]]
  +       gap_cost <- sample(1:3, size = 1)
  +       results_seqtrie <- dist_matrix(query, target, mode = "anchored", cost_matrix = cost_matrix, gap_cost = gap_cost, nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       results_biostrings <- biostrings_matrix_anchored(query, target, query_size, target_size, cost_matrix = cost_matrix, gap_cost = gap_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "anchored", cost_matrix = cost_matrix, gap_cost = gap_cost, nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       results_biostrings <- biostrings_pairwise_anchored(query_pairwise, target, query_size, target_size, cost_matrix = cost_matrix, gap_cost = gap_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  +     })
  + 
  + 
  +     print("Checking global search with affine gap for correctness")
  +     local({
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       cost_matrix <- matrix(sample(1:3, size = nchar(CHARSET)^2, replace=TRUE), nrow=nchar(CHARSET))
  +       diag(cost_matrix) <- 0
  +       colnames(cost_matrix) <- rownames(cost_matrix) <- strsplit(CHARSET, "")[[1]]
  +       gap_cost <- sample(1:3, size = 1)
  +       gap_open_cost <- sample(1:3, size = 1)
  +       results_seqtrie <- dist_matrix(query, target, mode = "levenshtein", cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cos=gap_open_cost, nthreads=NTHREADS)
  +       results_biostrings <- biostrings_matrix_global(query, target, cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "levenshtein", cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost, nthreads=NTHREADS)
  +       results_biostrings <- biostrings_pairwise_global(query_pairwise, target, cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  +     })
  + 
  +     print("Checking anchored search with affine gap for correctness")
  +     local({
  +       target <- c(random_strings(NSEQS, CHARSET),"") %>% unique
  +       query <- sample(c(sample(target, NSEQS/1000), random_strings(NSEQS/1000, CHARSET)))
  +       query <- c(mutate_strings(query, indel_prob=0, charset = CHARSET), "") %>% unique
  + 
  +       # Check matrix results
  +       cost_matrix <- matrix(sample(1:3, size = nchar(CHARSET)^2, replace=TRUE), nrow=nchar(CHARSET))
  +       diag(cost_matrix) <- 0
  +       colnames(cost_matrix) <- rownames(cost_matrix) <- strsplit(CHARSET, "")[[1]]
  +       gap_cost <- sample(1:3, size = 1)
  +       gap_open_cost <- sample(1:3, size = 1)
  +       results_seqtrie <- dist_matrix(query, target, mode = "anchored", cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost, nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       results_biostrings <- biostrings_matrix_anchored(query, target, query_size, target_size, cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  + 
  +       # Check pairwise results
  +       query_pairwise <- mutate_strings(target, prob=0.025, indel_prob=0.05, charset = CHARSET)
  +       results_seqtrie <- dist_pairwise(query_pairwise, target, mode = "anchored", cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost, nthreads=NTHREADS)
  +       query_size <- attr(results_seqtrie, "query_size")
  +       target_size <- attr(results_seqtrie, "target_size")
  +       results_biostrings <- biostrings_pairwise_anchored(query_pairwise, target, query_size, target_size, cost_matrix = cost_matrix, gap_cost = gap_cost, gap_open_cost=gap_open_cost)
  +       stopifnot(all(results_seqtrie == results_biostrings))
  +     })
  + }
  + 
  + }
  Loading required package: BiocGenerics
  Loading required package: generics
  
  Attaching package: 'generics'
  
  The following objects are masked from 'package:base':
  
      as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
      setequal, union
  
  
  Attaching package: 'BiocGenerics'
  
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      IQR, mad, sd, var, xtabs
  
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      Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
      as.data.frame, basename, cbind, colnames, dirname, do.call,
      duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
      mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
      rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
      unsplit, which.max, which.min
  
  Loading required package: S4Vectors
  Loading required package: stats4
  
  Attaching package: 'S4Vectors'
  
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      findMatches
  
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  Loading required package: IRanges
  Loading required package: XVector
  Loading required package: GenomeInfoDb
  
  Attaching package: 'Biostrings'
  
  The following object is masked from 'package:base':
  
      strsplit
  
  
  Attaching package: 'dplyr'
  
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  [1] "Checking hamming search correctness"
  [1] "Checking levenshtein search correctness"
  [1] "Checking anchored search correctness"
  [1] "Checking global search with linear gap for correctness"
  Error in h(simpleError(msg, call)) : 
    error in evaluating the argument 'args' in selecting a method for function 'do.call': 'recursive' must be a length-1 vector
  Calls: <Anonymous> ... mpi.XStringSet.pairwiseAlignment -> XStringSet.pairwiseAlignment -> array -> unlist
  In addition: Warning message:
  In .call_fun_in_pwalign("pairwiseAlignment", ...) :
    pairwiseAlignment() has moved from Biostrings to the pwalign package, and is
    formally deprecated in Biostrings >= 2.75.1. Please call
    pwalign::pairwiseAlignment() to get rid of this warning.
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [29s/25s] OK
* checking PDF version of manual ... OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE
Status: 1 ERROR, 1 NOTE