From: Benjamin Auder Date: Fri, 10 Mar 2017 07:10:49 +0000 (+0100) Subject: TODO: args, et finir tests; relancer X-Git-Url: https://git.auder.net/variants/Apocalypse/scripts/current/pieces/img/cross.svg?a=commitdiff_plain;h=eef6f6c97277ea3ce760981e5244cbde7fc904a0;p=epclust.git TODO: args, et finir tests; relancer --- diff --git a/TODO b/TODO index 53b4c97..53a82b3 100644 --- a/TODO +++ b/TODO @@ -67,3 +67,6 @@ cwt : trim R part // : clever by rows retenir cwt... Stockage matrices : en colonnes systématiquement ? + +TODO: revoir les arguments, simplifier (dans les clustering...), + permettre algos de clustering quelconques, args: medoids (curves puis dists), K diff --git a/epclust/R/clustering.R b/epclust/R/clustering.R index a4c273a..14915ab 100644 --- a/epclust/R/clustering.R +++ b/epclust/R/clustering.R @@ -1,6 +1,6 @@ #' @name clustering #' @rdname clustering -#' @aliases clusteringTask1 computeClusters1 computeClusters2 +#' @aliases clusteringTask1 clusteringTask2 computeClusters1 computeClusters2 #' #' @title Two-stage clustering, withing one task (see \code{claws()}) #' @@ -31,7 +31,7 @@ NULL #' @rdname clustering #' @export clusteringTask1 = function( - indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE) + indices, getContribs, K1, nb_items_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE) { if (verbose) cat(paste("*** Clustering task 1 on ",length(indices)," lines\n", sep="")) @@ -87,7 +87,7 @@ computeClusters1 = function(contribs, K1, verbose=FALSE) { if (verbose) cat(paste(" computeClusters1() on ",nrow(contribs)," lines\n", sep="")) - cluster::pam(contribs, K1, diss=FALSE)$id.med + cluster::pam( t(contribs) , K1, diss=FALSE)$id.med } #' @rdname clustering @@ -96,7 +96,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE) { if (verbose) cat(paste(" computeClusters2() on ",nrow(distances)," lines\n", sep="")) - cluster::pam(distances, K2, diss=TRUE)$id.med + cluster::pam( distances , K2, diss=TRUE)$id.med } #' computeSynchrones @@ -110,7 +110,7 @@ computeClusters2 = function(distances, K2, verbose=FALSE) #' @param nb_ref_curves How many reference series? (This number is known at this stage) #' @inheritParams claws #' -#' @return A big.matrix of size K1 x L where L = data_length +#' @return A big.matrix of size L x K1 where L = length of a serie #' #' @export computeSynchrones = function(medoids, getRefSeries, @@ -142,8 +142,8 @@ computeSynchrones = function(medoids, getRefSeries, { if (parll) synchronicity::lock(m) - synchrones[ mi[i], ] = synchrones[ mi[i], ] + ref_series[i,] - counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts? + synchrones[, mi[i] ] = synchrones[, mi[i] ] + ref_series[,i] + counts[ mi[i] ] = counts[ mi[i] ] + 1 #TODO: remove counts? ...or as arg?! if (parll) synchronicity::unlock(m) } @@ -152,7 +152,7 @@ computeSynchrones = function(medoids, getRefSeries, K = nrow(medoids) ; L = ncol(medoids) # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in // # TODO: if size > RAM (not our case), use file-backed big.matrix - synchrones = bigmemory::big.matrix(nrow=K, ncol=L, type="double", init=0.) + synchrones = bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.) counts = bigmemory::big.matrix(nrow=K, ncol=1, type="double", init=0) # synchronicity is only for Linux & MacOS; on Windows: run sequentially parll = (requireNamespace("synchronicity",quietly=TRUE) @@ -181,14 +181,14 @@ computeSynchrones = function(medoids, getRefSeries, #TODO: can we avoid this loop? ( synchrones = sweep(synchrones, 1, counts, '/') ) for (i in seq_len(K)) - synchrones[i,] = synchrones[i,] / counts[i,1] + synchrones[,i] = synchrones[,i] / counts[i] #NOTE: odds for some clusters to be empty? (when series already come from stage 2) # ...maybe; but let's hope resulting K1' be still quite bigger than K2 - noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[i,]))) + noNA_rows = sapply(seq_len(K), function(i) all(!is.nan(synchrones[,i]))) if (all(noNA_rows)) return (synchrones) # Else: some clusters are empty, need to slice synchrones - synchrones[noNA_rows,] + bigmemory::as.big.matrix(synchrones[,noNA_rows]) } #' computeWerDists @@ -272,7 +272,7 @@ computeWerDists = function(synchrones, nbytes,endian,ncores_clust=1,verbose=FALS { #from cwt_file ... res <- getDataInFile(c(2*index-1,2*index), cwt_file, nbytes, endian) - ###############TODO: + ###############TODO: } # Distance between rows i and j diff --git a/epclust/R/de_serialize.R b/epclust/R/de_serialize.R index b6684d2..f04c13a 100644 --- a/epclust/R/de_serialize.R +++ b/epclust/R/de_serialize.R @@ -45,7 +45,7 @@ binarize = function(data_ascii, data_bin_file, nb_per_chunk, #number of items always on 8 bytes writeBin(0L, data_bin, size=8, endian=endian) if ( is_matrix ) - data_length = ncol(data_ascii) + data_length = nrow(data_ascii) else #connection { data_line = scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE) @@ -61,8 +61,8 @@ binarize = function(data_ascii, data_bin_file, nb_per_chunk, if ( is_matrix ) { data_chunk = - if (index <= nrow(data_ascii)) - as.double(t(data_ascii[index:min(nrow(data_ascii),index+nb_per_chunk-1),])) + if (index <= ncol(data_ascii)) + as.double(data_ascii[,index:min(nrow(data_ascii),index+nb_per_chunk-1)]) else double(0) index = index + nb_per_chunk @@ -113,14 +113,13 @@ getDataInFile = function(indices, data_bin_file, nbytes=4, endian=.Platform$endi data_bin = file(data_bin_file, "rb") data_size = file.info(data_bin_file)$size data_length = readBin(data_bin, "integer", n=1, size=8, endian=endian) - #Ou t(sapply(...)) (+ rapide ?) - data_ascii = do.call( rbind, lapply( indices, function(i) { + data_ascii = sapply( indices, function(i) { offset = 8+(i-1)*data_length*nbytes if (offset > data_size) return (vector("double",0)) ignored = seek(data_bin, offset) readBin(data_bin, "double", n=data_length, size=nbytes, endian=endian) - } ) ) + } ) close(data_bin) if (ncol(data_ascii)>0) data_ascii else NULL } diff --git a/epclust/R/main.R b/epclust/R/main.R index 28217c3..86dac64 100644 --- a/epclust/R/main.R +++ b/epclust/R/main.R @@ -1,67 +1,97 @@ #' CLAWS: CLustering with wAvelets and Wer distanceS #' -#' Groups electricity power curves (or any series of similar nature) by applying PAM -#' algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series -#' must be sampled on the same time grid, no missing values. +#' Cluster electricity power curves (or any series of similar nature) by applying a +#' two stage procedure in parallel (see details). +#' Input series must be sampled on the same time grid, no missing values. +#' +#' @details Summary of the function execution flow: +#' \enumerate{ +#' \item Compute and serialize all contributions, obtained through discrete wavelet +#' decomposition (see Antoniadis & al. [2013]) +#' \item Divide series into \code{ntasks} groups to process in parallel. In each task: +#' \enumerate{ +#' \item iterate the first clustering algorithm on its aggregated outputs, +#' on inputs of size \code{nb_items_clust} +#' \item optionally, if WER=="mix": +#' a) compute the K1 synchrones curves, +#' b) compute WER distances (K1xK1 matrix) between synchrones and +#' c) apply the second clustering algorithm +#' } +#' \item Launch a final task on the aggregated outputs of all previous tasks: +#' in the case WER=="end" this task takes indices in input, otherwise +#' (medoid) curves +#' } +#' The main argument -- \code{getSeries} -- has a quite misleading name, since it can be +#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve +#' series; the name was chosen because all types of arguments are converted to a function. +#' When \code{getSeries} is given as a function, it must take a single argument, +#' 'indices', integer vector equal to the indices of the curves to retrieve; +#' see SQLite example. The nature and role of other arguments should be clear #' #' @param getSeries Access to the (time-)series, which can be of one of the three #' following types: #' \itemize{ -#' \item [big.]matrix: each line contains all the values for one time-serie, ordered by time +#' \item [big.]matrix: each column contains the (time-ordered) values of one time-serie #' \item connection: any R connection object providing lines as described above #' \item character: name of a CSV file containing series in rows (no header) #' \item function: a custom way to retrieve the curves; it has only one argument: -#' the indices of the series to be retrieved. See examples +#' the indices of the series to be retrieved. See SQLite example #' } -#' @inheritParams clustering -#' @param K1 Number of super-consumers to be found after stage 1 (K1 << N) +#' @param K1 Number of clusters to be found after stage 1 (K1 << N [number of series]) #' @param K2 Number of clusters to be found after stage 2 (K2 << K1) -#' @param wf Wavelet transform filter; see ?wavelets::wt.filter -#' @param ctype Type of contribution: "relative" or "absolute" (or any prefix) -#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2 -#' at the end of each task +#' @param nb_per_chunk (Maximum) number of items to retrieve in one batch, for both types of +#' retrieval: resp. series and contribution; in a vector of size 2 +#' @param nb_items_clust1 (Maximum) number of items in input of the clustering algorithm +#' for stage 1 +#' @param wav_filt Wavelet transform filter; see ?wavelets::wt.filter +#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any prefix) +#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply +#' stage 2 at the end of each task #' @param random TRUE (default) for random chunks repartition -#' @param ntasks Number of tasks (parallel iterations to obtain K1 medoids); default: 1. -#' Note: ntasks << N, so that N is "roughly divisible" by N (number of series) -#' @param ncores_tasks "MPI" number of parallel tasks (1 to disable: sequential tasks) -#' @param ncores_clust "OpenMP" number of parallel clusterings in one task -#' @param nb_series_per_chunk (~Maximum) number of series in each group, inside a task -#' @param min_series_per_chunk Minimum number of series in each group +#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"] +#' or K2 [if WER=="mix"] medoids); default: 1. +#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks +#' @param ncores_tasks Number of parallel tasks (1 to disable: sequential tasks) +#' @param ncores_clust Number of parallel clusterings in one task (4 should be a minimum) #' @param sep Separator in CSV input file (if any provided) #' @param nbytes Number of bytes to serialize a floating-point number; 4 or 8 -#' @param endian Endianness to use for (de)serialization. Use "little" or "big" for portability +#' @param endian Endianness for (de)serialization ("little" or "big") #' @param verbose Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage) #' @param parll TRUE to fully parallelize; otherwise run sequentially (debug, comparison) #' -#' @return A big.matrix of the final medoids curves (K2) in rows +#' @return A matrix of the final K2 medoids curves, in columns +#' +#' @references Clustering functional data using Wavelets [2013]; +#' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi. +#' Inter. J. of Wavelets, Multiresolution and Information Procesing, +#' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033 #' #' @examples #' \dontrun{ -#' # WER distances computations are a bit too long for CRAN (for now) +#' # WER distances computations are too long for CRAN (for now) #' #' # Random series around cos(x,2x,3x)/sin(x,2x,3x) #' x = seq(0,500,0.05) #' L = length(x) #10001 -#' ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)), -#' byrow=TRUE, ncol=L ) +#' ref_series = matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 ) #' library(wmtsa) -#' series = do.call( rbind, lapply( 1:6, function(i) -#' do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) +#' series = do.call( cbind, lapply( 1:6, function(i) +#' do.call(cbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) ) #' #dim(series) #c(2400,10001) -#' medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' medoids_ascii = claws(series, K1=60, K2=6, nb_per_chunk=c(200,500), verbose=TRUE) #' #' # Same example, from CSV file #' csv_file = "/tmp/epclust_series.csv" #' write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE) -#' medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' medoids_csv = claws(csv_file, K1=60, K2=6, nb_per_chunk=c(200,500)) #' #' # Same example, from binary file -#' bin_file = "/tmp/epclust_series.bin" -#' nbytes = 8 -#' endian = "little" -#' epclust::binarize(csv_file, bin_file, 500, nbytes, endian) -#' getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian) -#' medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' bin_file <- "/tmp/epclust_series.bin" +#' nbytes <- 8 +#' endian <- "little" +#' binarize(csv_file, bin_file, 500, nbytes, endian) +#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian) +#' medoids_bin <- claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500)) #' unlink(csv_file) #' unlink(bin_file) #' @@ -69,8 +99,8 @@ #' library(DBI) #' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:") #' # Prepare data.frame in DB-format -#' n = nrow(series) -#' time_values = data.frame( +#' n <- nrow(series) +#' time_values <- data.frame( #' id = rep(1:n,each=L), #' time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ), #' value = as.double(t(series)) ) @@ -78,17 +108,17 @@ #' # Fill associative array, map index to identifier #' indexToID_inDB <- as.character( #' dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] ) -#' getSeries = function(indices) { -#' request = "SELECT id,value FROM times_values WHERE id in (" +#' serie_length <- as.integer( dbGetQuery(series_db, +#' paste("SELECT COUNT * FROM time_values WHERE id == ",indexToID_inDB[1],sep="")) ) +#' getSeries <- function(indices) { +#' request <- "SELECT id,value FROM times_values WHERE id in (" #' for (i in indices) -#' request = paste(request, i, ",", sep="") -#' request = paste(request, ")", sep="") -#' df_series = dbGetQuery(series_db, request) -#' # Assume that all series share same length at this stage -#' ts_length = sum(df_series[,"id"] == df_series[1,"id"]) -#' t( as.matrix(df_series[,"value"], nrow=ts_length) ) +#' request <- paste(request, indexToID_inDB[i], ",", sep="") +#' request <- paste(request, ")", sep="") +#' df_series <- dbGetQuery(series_db, request) +#' as.matrix(df_series[,"value"], nrow=serie_length) #' } -#' medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500) +#' medoids_db = claws(getSeries, K1=60, K2=6, nb_per_chunk=c(200,500)) #' dbDisconnect(series_db) #' #' # All computed medoids should be the same: @@ -98,12 +128,12 @@ #' digest::sha1(medoids_db) #' } #' @export -claws = function(getSeries, K1, K2, - wf,ctype, #stage 1 +claws <- function(getSeries, K1, K2, + nb_per_chunk,nb_items_clust1=7*K1 #volumes of data + wav_filt="d8",contrib_type="absolute", #stage 1 WER="end", #stage 2 random=TRUE, #randomize series order? - ntasks=1, ncores_tasks=1, ncores_clust=4, #control parallelism - nb_series_per_chunk=50*K1, min_series_per_chunk=5*K1, #chunk size + ntasks=1, ncores_tasks=1, ncores_clust=4, #parallelism sep=",", #ASCII input separator nbytes=4, endian=.Platform$endian, #serialization (write,read) verbose=FALSE, parll=TRUE) @@ -115,26 +145,39 @@ claws = function(getSeries, K1, K2, { stop("'getSeries': [big]matrix, function, file or valid connection (no NA)") } - K1 = .toInteger(K1, function(x) x>=2) - K2 = .toInteger(K2, function(x) x>=2) - if (!is.logical(random)) - stop("'random': logical") - tryCatch( - {ignored <- wavelets::wt.filter(wf)}, - error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter")) + K1 <- .toInteger(K1, function(x) x>=2) + K2 <- .toInteger(K2, function(x) x>=2) + if (!is.numeric(nb_per_chunk) || length(nb_per_chunk)!=2) + stop("'nb_per_chunk': numeric, size 2") + nb_per_chunk[1] <- .toInteger(nb_per_chunk[1], function(x) x>=1) + # A batch of contributions should have at least as many elements as a batch of series, + # because it always contains much less values + nb_per_chunk[2] <- max(.toInteger(nb_per_chunk[2],function(x) x>=1), nb_per_chunk[1]) + nb_items_clust1 <- .toInteger(nb_items_clust1, function(x) x>K1) + random <- .toLogical(random) + tryCatch + ( + {ignored <- wavelets::wt.filter(wav_filt)}, + error = function(e) stop("Invalid wavelet filter; see ?wavelets::wt.filter") + ) + ctypes = c("relative","absolute","logit") + contrib_type = ctypes[ pmatch(contrib_type,ctypes) ] + if (is.na(contrib_type)) + stop("'contrib_type' in {'relative','absolute','logit'}") if (WER!="end" && WER!="mix") - stop("WER takes values in {'end','mix'}") - ntasks = .toInteger(ntasks, function(x) x>=1) - ncores_tasks = .toInteger(ncores_tasks, function(x) x>=1) - ncores_clust = .toInteger(ncores_clust, function(x) x>=1) - nb_series_per_chunk = .toInteger(nb_series_per_chunk, function(x) x>=K1) - min_series_per_chunk = .toInteger(K1, function(x) x>=K1 && x<=nb_series_per_chunk) + stop("'WER': in {'end','mix'}") + random <- .toLogical(random) + ntasks <- .toInteger(ntasks, function(x) x>=1) + ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1) + ncores_clust <- .toInteger(ncores_clust, function(x) x>=1) if (!is.character(sep)) stop("'sep': character") - nbytes = .toInteger(nbytes, function(x) x==4 || x==8) + nbytes <- .toInteger(nbytes, function(x) x==4 || x==8) + verbose <- .toLogical(verbose) + parll <- .toLogical(parll) # Serialize series if required, to always use a function - bin_dir = ".epclust_bin/" + bin_dir <- ".epclust_bin/" dir.create(bin_dir, showWarnings=FALSE, mode="0755") if (!is.function(getSeries)) { @@ -156,11 +199,11 @@ claws = function(getSeries, K1, K2, contribs_file, nb_series_per_chunk, nbytes, endian) getContribs = function(indices) getDataInFile(indices, contribs_file, nbytes, endian) - if (nb_curves < min_series_per_chunk) - stop("Not enough data: less rows than min_series_per_chunk!") + if (nb_curves < K2) + stop("Not enough data: less series than final number of clusters") nb_series_per_task = round(nb_curves / ntasks) - if (nb_series_per_task < min_series_per_chunk) - stop("Too many tasks: less series in one task than min_series_per_chunk!") + if (nb_series_per_task < K2) + stop("Too many tasks: less series in one task than final number of clusters") runTwoStepClustering = function(inds) { @@ -170,7 +213,8 @@ claws = function(getSeries, K1, K2, inds, getContribs, K1, nb_series_per_chunk, ncores_clust, verbose, parll) if (WER=="mix") { - require("bigmemory", quietly=TRUE) + if (parll && ntasks>1) + require("bigmemory", quietly=TRUE) medoids1 = bigmemory::as.big.matrix( getSeries(indices_medoids) ) medoids2 = clusteringTask2(medoids1, K2, getSeries, nb_curves, nb_series_per_chunk, nbytes, endian, ncores_clust, verbose, parll) @@ -197,7 +241,7 @@ claws = function(getSeries, K1, K2, {synchrones_file = paste(bin_dir,"synchrones",sep="") ; unlink(synchrones_file)} if (parll && ntasks>1) { - cl = parallel::makeCluster(ncores_tasks) + cl = parallel::makeCluster(ncores_tasks, outfile="") varlist = c("getSeries","getContribs","K1","K2","verbose","parll", "nb_series_per_chunk","ntasks","ncores_clust","sep","nbytes","endian") if (WER=="mix") @@ -206,10 +250,11 @@ claws = function(getSeries, K1, K2, } # 1000*K1 indices [if WER=="end"], or empty vector [if WER=="mix"] --> series on file - if (parll && ntasks>1) - indices = unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) - else - indices = unlist( lapply(indices_tasks, runTwoStepClustering) ) + indices <- + if (parll && ntasks>1) + unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) ) + else + unlist( lapply(indices_tasks, runTwoStepClustering) ) if (parll && ntasks>1) parallel::stopCluster(cl) @@ -241,7 +286,7 @@ claws = function(getSeries, K1, K2, # Cleanup unlink(bin_dir, recursive=TRUE) - medoids2 + medoids2[,] } #' curvesToContribs @@ -249,36 +294,52 @@ claws = function(getSeries, K1, K2, #' Compute the discrete wavelet coefficients for each series, and aggregate them in #' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2 #' -#' @param series Matrix of series (in rows), of size n x L +#' @param series [big.]matrix of series (in columns), of size L x n #' @inheritParams claws #' -#' @return A matrix of size n x log(L) containing contributions in rows +#' @return A [big.]matrix of size log(L) x n containing contributions in columns #' #' @export -curvesToContribs = function(series, wf, ctype) +curvesToContribs = function(series, wav_filt, contrib_type) { - L = length(series[1,]) + L = nrow(series) D = ceiling( log2(L) ) nb_sample_points = 2^D - cont_types = c("relative","absolute") - ctype = cont_types[ pmatch(ctype,cont_types) ] - t( apply(series, 1, function(x) { + apply(series, 2, function(x) { interpolated_curve = spline(1:L, x, n=nb_sample_points)$y W = wavelets::dwt(interpolated_curve, filter=wf, D)@W nrj = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) ) - if (ctype=="relative") nrj / sum(nrj) else nrj - }) ) + if (contrib_type!="absolute") + nrj = nrj / sum(nrj) + if (contrib_type=="logit") + nrj = - log(1 - nrj) + nrj + }) } # Check integer arguments with functional conditions .toInteger <- function(x, condition) { + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to integer", sep="") if (!is.integer(x)) - tryCatch( - {x = as.integer(x)[1]}, - error = function(e) paste("Cannot convert argument",substitute(x),"to integer") - ) + tryCatch({x = as.integer(x)[1]; if (is.na(x)) stop()}, + warning = errWarn, error = errWarn) if (!condition(x)) - stop(paste("Argument",substitute(x),"does not verify condition",body(condition))) + { + stop(paste("Argument '",substitute(x), + "' does not verify condition ",body(condition), sep="")) + } + x +} + +# Check logical arguments +.toLogical <- function(x) +{ + errWarn <- function(ignored) + paste("Cannot convert argument' ",substitute(x),"' to logical", sep="") + if (!is.logical(x)) + tryCatch({x = as.logical(x)[1]; if (is.na(x)) stop()}, + warning = errWarn, error = errWarn) x }