intervals/0000755000176200001440000000000013642055242012264 5ustar liggesusersintervals/NAMESPACE0000644000176200001440000000227413642043061013504 0ustar liggesusers######## Compiled code useDynLib( "intervals" ) ######## Imports # Class, method, and regular imports from the methods package are not listed # here, since we depend on the package. import("methods") importFrom( "utils", "head", "tail" ) importFrom( "graphics", "par", "points", "text", "axis", "segments" ) ######## Exports export( "Intervals", "Intervals_full" ) S3method( "split", "Intervals_virtual" ) S3method( "as.matrix", "Intervals_virtual" ) S3method( "c", "Intervals" ) S3method( "c", "Intervals_full" ) S3method( "plot", "Intervals" ) S3method( "plot", "Intervals_full" ) exportClasses( "Intervals", "Intervals_full", "Intervals_virtual", "Intervals_virtual_or_numeric" ) exportMethods( "[", "[<-", "as.matrix", "adjust_closure", "close_intervals", "closed", "closed<-", "clusters", "coerce", "contract", "distance_to_nearest", "empty", "expand", "head", "initialize", "interval_complement", "interval_difference", "interval_included", "interval_intersection", "interval_overlap", "interval_union", "is.na", "open_intervals", "plot", "reduce", "show", "size", "split", "t", "tail", "type", "type<-", "which_nearest" ) intervals/data/0000755000176200001440000000000013642043061013171 5ustar liggesusersintervals/data/sgd.rdata0000755000176200001440000050530313642043061014774 0ustar liggesuserslM4[*$Z%1#: 0 'yS'-<[~q/?__^:wl{~xx_͵~Ws~~}/;> 6ugx~ks<]쟛:.&}y]?\w]~]s}rz?y_swwm99w7k칾t}vXVq}/qf;y>?5]]ϗ}ڽ|>=w|<'iuz?wѽ\<ϰ>}~9l ξͽ'pv]^?{y_ g<}{}Mc}^5o{c/o>$-b͏wwz61]o,qV׹{Wf,uFhרr5ZO茶~VڰiLhpa:G{.I^:OJ1KTpl1g=yb &Kzmݯf/?=/>?OG?S#=7W~{]y_룯}}Q׷G 긺Ʀ{>Omr?S75iq5/xsZy8z}os|vOsoĢwe߮F,]XsˎiS4j~z6zwrų>ýgӛ/w={ݓzUo߀V!>-_OW{MϯՌu>EԦ׹5>oxfvcz§xqa/~} Z{jV[k׭m Nkya+|lj'LU0uoXgZXX'nk~~Pl$zI vn/j ^kN޵q=즽kj|lZC~ CdoEK<޽?s+gGczG_gGUiSoliCGخSD|5~V[1+9H`STA)ϧZgL~|5Af̧^6řvKlRb&'0I]H.j/^?f =SϿ_ׇS/uzR= f{uw7N˼kChFxу@j9ZX'ouE}>G36hm u9{?LAȜG~fjƆt_#j#G+o=׋^⦧ ]N1""⨿3s[8*ěAuB ס q;U{>+'kSCY>ZMLJ)y?TjmsMf<kϝn3su5ޓP&l:Zya"ݳfdMG`Z;32li+'l} +'t*'"CkDg_:%hu岻}42- SL=̅csh:ty as@y8|<:zrw.rlSluMMO⯬vdž7bAL~BiQ_ջt|ykq_ V3Ʀk+o9[xSsCG?jƲzMYcTI)Gô8Fo gӺj<7:Y3ve>EL|P9/Z93[Ʀx5}mg~@t~lf&jOkDLt_:%S \n1KcSZp6-ڰqxu+o־nz,#}2!"Qs7":ۮkc,_3Gښ ;?sf}jgk oߵzHvNy5 WÇ^#݊Zxc~FV ق8_96T\o&%hv'Jٿv%ܡu'l]sv=Ԭ.8/-9eNMfKj)o5d{~{qqc}ڹ9S0ء+{9.ctlOw8[SƦy72zQec9~>}t EKy kl]b+zlZoKluiӳu%x.q~@>O~oN}9p2 vOLnP^gž-avSlqv!>[X]/f3A;uV_Mϔyg2V ǧyvq6lMQg?r.VrƦVdž55v7]jie_\=WϸGz{v{v,ϠzGMo՘,؀y5F::o5$L//}ƫC?jܻu𾈱S>cys5/ KٮFhcC7)c9݊~Fy5g~='iW9:=ߋ32MתjFw']  ]!fr{3kZ' ,0Ӂ)lg0B.*N͊<)2dl|GAzǸ<@A8`aJvRn_p¨Nؐt߲axZq2}{gVL82u!e{6pe:8v֎g屾=z_`O2a1#f&鱖|qg>1{78Ο S%=dzj!~~u NX~/8lp2|Vt3w<\2`ɴk6mL$^-KEtL.!3olLmy!&w1gLB<c , vcӵY40FЊks+#ӳroGNkOS#з˽D'9)^DM`9/~][*ܭcG[KY):}YιN_oz[z[?8Ymo3 S]Xt*̧ GK⟻\^-V~M[/:6}ϏQ?F|8s\\g\< q3swzcsV`\/;x <vQj|~0Eb?]i?hbrƙ>g]9];$;Lx)&];*<1T?37q}Qaz`9pփ>Io&56]rdzsR;;'^;s@;{ pz.{;,K5WajTP*~ugױG_^ZVWƦgAcÏoWzlp_\ix~gNojgn g|a=͝yxlFEw8;l <6ͅA=wS9XIwfdSTn1Laû26mulr[{򠝷wJ}x\)O5ƦQ\ƬQŢ ю;vb ,vn8wn*wN[M=Ɖ q}0rt'w=5 aa#٫<6 #/eabYp0 ܆TwA3Ԫ]󰈠&|vobvXǏj`M lmFF|0/00;ϗzϣe8P僗h ƹ.T`){/P; 3twt^?cU TQ\|)kX(vq^AT)~vlOD]0Ǻ`aflKt2hlK V>8/{ؙƆYCE_Xsw ``;ˀx'\vOj;6-0ش"}REG/鄵cGw@eGh=6L3O[+8KEclZl/rlZl06d- =Anjv;#MQ ]z9[Žc~q~%v7o׵a^*(/Ld궗Uil;t[R:Vn) ܻzM4Qw9Zr5ϪbԪ6;0\Ui\p^P_ENmcGDbG~*򡒎=zts\^D:hl|m oPu7a_86c_<̊緫Y(Ȍqc=7?e˵)w5Z&^&I0"Vʼ h}x-5kQէ`[gSq/̊rZì5&`Vj46%S P'֚ƆN>vdžΈ̊50+^cēU]fEr0">"aD|aD\?PQ lul}_)0Lu%̧tgl"ד562߫D^(Y<ìxìxìx<<єaVYvB8x裏0:b;<_;s?:Pgw4@wMnt]wt=^v]͎van1QQOe2u#|iLڥjl<6 wesˤ.& D] ~U~쉍YuͩN,{s~^ǭin滣WyTiEaQE_xp0ɾãxuIxZwEݝ=ptsD}P_}p8XOT]dL7%A,5bjlD?!XNeM])\ʗ8Iw5Ͻ/Es4{^Ub.}ڀw߻9_g4]x }f(UMqN=M?Ў٫3)faܬqR <#niV{6כ۩ve6]pqyntw6wynV;6 ;bpug'y<<;76=OyuwlɤUK686cC~ ͻӔcSQV}t񹛟4g.{GE<ϭLGyavtl5b26a#@k z6"vOʨʎ2(ut;ܡW'V^ q$h;ܡ)wOnj5%icӪ ?E]ܡr㞙M~@H۹;tF ^ixA:]ŝ 5F*^pxA/ޝ~86Q{061V|lZcW#; \/:78Y=kul÷I06 yi.شColZׂ}o͛Ջ}OyQq&G2*kqlebQS Çi89;m:χ9Ʌ̷ P㻳xcSRI'0% ز0Z:\[" 9,i:\4%. ;:q/kbǦynq;cÔ=sOyi+xO|'?m6qׁQ<Ax)>g$x`DD)rlC(}l枍Ʀټe;elZ xL3DFL4xL486R:6ԄcT? dw)t兀P`nYcߟvƦ%{ziMkŴ")<8X~luI:~LM׼6e0h. 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Used by, e.g., \code{\link{distance_to_nearest}} and \code{\link{which_nearest}}. } \section{Methods}{ \describe{ \item{distance_to_nearest}{\code{signature(from = "Intervals_virtual_or_numeric", to = "Intervals_virtual_or_numeric")}} \item{interval_overlap}{\code{signature(from = "Intervals_virtual_or_numeric", to = "Intervals_virtual_or_numeric")}} } } \keyword{classes}intervals/man/expand-methods.Rd0000644000176200001440000000554013642043061016246 0ustar liggesusers\name{expand} \alias{expand} \alias{expand,Intervals_virtual-method} \alias{contract} \alias{contract,Intervals_virtual-method} \title{Expand or contract intervals} \description{ It is often useful to shrink or grow each interval in a set of intervals: to smooth over small, uninteresting gaps, or to address possible imprecision resulting from floating point arithmetic. The \code{expand} and \code{contract} methods implement this, using either absolute or relative difference. } \usage{ \S4method{expand}{Intervals_virtual}(x, delta = 0, type = c("absolute", "relative")) \S4method{contract}{Intervals_virtual}(x, delta = 0, type = c("absolute", "relative")) } \arguments{ \item{x}{ An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{delta}{ A non-negative adjustement value. A vector is permitted, and its entries will be recycled if necessary. } \item{type}{ Should adjustment be based on relative or absolute difference. When \code{type == "relative"} intervals are expanded/contracted to include/exclude points for which a relative difference with respect to the nominal value is less than or equal to \code{delta}. (See the note below.) When \code{type == "absolute"}, absolute rather than relative difference is used, i.e., all intervals are expanded or contracted by the same amount. } } \value{ A single object of appropriate class, with endpoint positions adjusted as requested. Expansion returns an object with the same dimension as \code{x}; contraction may lead to the elimination of now-empty rows. } \note{ Here, the relative difference between \emph{x} and \emph{y} is |\emph{x} - \emph{y}|/max(|\emph{x}|, |\emph{y}|). } \examples{ # Using adjustment to remove small gaps x <- Intervals( c(1,10,100,8,50,200), type = "Z" ) close_intervals( contract( reduce( expand(x, 1) ), 1 ) ) # Finding points for which, as a result of possible floating point # error, intersection may be ambiguous. Whether y1 intersects y2[2,] # depends on precision. delta <- .Machine$double.eps^0.5 y1 <- Intervals( c( .5, 1 - delta / 2 ) ) y2 <- Intervals( c( .25, 1, .75, 2 ) ) # Nominal interval_intersection( y1, y2 ) # Inner limit inner <- interval_intersection( contract( y1, delta, "relative" ), contract( y2, delta, "relative" ) ) # Outer limit outer <- interval_intersection( expand( y1, delta, "relative" ), expand( y2, delta, "relative" ) ) # The ambiguous set, corresponding to points which may or may not be in # the intersection -- depending on numerical values for endpoints # which are, with respect to relative difference, indistinguishable from # the nominal values. interval_difference( outer, inner ) }intervals/man/close_intervals-methods.Rd0000644000176200001440000000456313642043061020167 0ustar liggesusers\name{close_intervals} \alias{close_intervals} \alias{close_intervals,Intervals_virtual-method} \alias{open_intervals} \alias{open_intervals,Intervals_virtual-method} \alias{adjust_closure} \alias{adjust_closure,Intervals-method} \alias{adjust_closure,Intervals_full-method} \title{Re-represent integer intervals with open or closed endpoints} \description{ Given an integer interval matrix, adjust endpoints so that all intervals have the requested closure status. } \usage{ \S4method{close_intervals}{Intervals_virtual}(x) \S4method{open_intervals}{Intervals_virtual}(x) \S4method{adjust_closure}{Intervals}(x, close_left = TRUE, close_right = TRUE) \S4method{adjust_closure}{Intervals_full}(x, close_left = TRUE, close_right = TRUE) } \arguments{ \item{x}{ An object of appropriate class, and for which \code{x@type == "Z"}. If \code{x@type == "R"}, an error is generated. } \item{close_left}{ Should the left endpoints be closed or open? } \item{close_right}{ Should the right endpoints be closed or open? } } \value{ An object of the same class as \code{x}, with endpoints adjusted as necessary and all \code{closed(x)} set to either \code{TRUE} or \code{FALSE}, as appropriate. } \note{ The \code{close_intervals} and \code{open_intervals} are for convenience, and just call \code{adjust_closure} with the approriate arguments. The \code{x} object may contain empty intervals, with at least one open endpoint, and still be valid. (Intervals are invalid if their second endpoint is less than their first.) The \code{close_intervals} method would, in such cases, create an invalid result; to prevent this, empty intervals are detected and removed, with a warning. This package does not make a distinction between closed and open infinite endpoints: an interval with an infinite endpoint extends to (plus or minus) infinity regardless of the closure state. For example, \code{\link{distance_to_nearest}} will return a \code{0} when \code{Inf} is compared to both \code{"[0, Inf)"} and \code{"[0, Inf]"}. } \examples{ x <- Intervals( c( 1, 5, 10, 1, 6, 20 ), closed = c( TRUE, FALSE ), type = "Z" ) # Empties are dropped close_intervals(x) adjust_closure(x, FALSE, TRUE) # Intervals_full y <- as( x, "Intervals_full" ) closed(y)[1,2] <- TRUE open_intervals(y) }intervals/man/empty-methods.Rd0000644000176200001440000000324613642043061016126 0ustar liggesusers\name{empty} \alias{empty} \alias{empty,Intervals-method} \alias{empty,Intervals_full-method} \title{Identify empty interval rows} \description{ A valid interval matrix may contain empty intervals: those with common endpoints, at least one of which is open. The \code{empty} method identifies these rows. } \usage{ \S4method{empty}{Intervals}(x) \S4method{empty}{Intervals_full}(x) } \arguments{ \item{x}{An \code{"Intervals"} or \code{"Intervals_full"} object.} } \details{ Intervals are deemed to be empty when their endpoints are equal and not both closed, or for \code{type == "Z"}, when their endpoints differ by 1 and both are open. The matrices \code{x} and \code{x[!empty(x),]} represent the same subset of the integers or the real line. } \value{ A boolean vector with length equal to \code{nrow(x)}. } \section{Warning}{ Exact equality (\code{==}) comparisons are used by \code{empty}. See the package vignette for a discussion of equality and floating point numbers. } \note{ Note that intervals of size 0 may not be empty over the reals, and intervals whose second endpoint is strictly greater than the first \emph{may} be empty over the integers, if both endpoints are open. } \seealso{ See \code{\link{size}} to compute the size of each interval in an object. } \examples{ z1 <- Intervals( cbind( 1, 1:3 ), type = "Z" ) z2 <- z1; closed(z2)[1] <- FALSE z3 <- z1; closed(z3) <- FALSE empty(z1) empty(z2) empty(z3) r1 <- z1; type(r1) <- "R" r2 <- z2; type(r2) <- "R" r3 <- z3; type(r3) <- "R" empty(r1) empty(r2) empty(r3) s1 <- Intervals_full( matrix( 1, 3, 2 ), type = "Z" ) closed(s1)[2,2] <- FALSE closed(s1)[3,] <- FALSE empty(s1) } intervals/man/reduce-methods.Rd0000644000176200001440000000234513642043061016236 0ustar liggesusers\name{reduce} \alias{reduce} \alias{reduce,Intervals_virtual-method} \title{Compactly re-represent the points in a set of intervals} \description{ In general, \code{"\linkS4class{Intervals}"} and \code{"\linkS4class{Intervals_full}"} objects may be redundant, the intervals they contain may be in arbitrary order, and they may contain non-informative intervals for which one or both endpoints are \code{NA}. The \code{reduce} function re-represents the underlying subsets of the integers or the real line in the unique, minimal form, removing intervals with \code{NA} endpoints (with warning). } \usage{ \S4method{reduce}{Intervals_virtual}( x, check_valid = TRUE ) } \arguments{ \item{x}{ An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? Also see \code{\link{interval_overlap}}. } } \value{ A single object of appropriate class, compactly representing the union of all intervals in \code{x}. All intervals in \code{reduce(x)} have numeric (i.e., not \code{NA}) endpoints. } \seealso{ See \code{\link{interval_union}}, which is really just concatenates its arguments and then calls \code{reduce}. }intervals/man/clusters-methods.Rd0000644000176200001440000000574513642043061016642 0ustar liggesusers\name{clusters} \alias{clusters} \alias{clusters,numeric-method} \alias{clusters,Intervals_virtual-method} \title{Identify clusters in a collection of positions or intervals} \description{ This function uses tools in the \pkg{intervals} package to quickly identify clusters -- contiguous collections of positions or intervals which are separated by no more than a given distance from their neighbors to either side. } \usage{ \S4method{clusters}{numeric}(x, w, which = FALSE, check_valid = TRUE) \S4method{clusters}{Intervals_virtual}(x, w, which = FALSE, check_valid = TRUE) } \arguments{ \item{x}{An appropriate object.} \item{w}{ Maximum permitted distance between a cluster member and its neighbors to either side. } \item{which}{ Should indices into the \code{x} object be returned instead of actual subsets? } \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? Also see \code{\link{interval_overlap}} and \code{\link{reduce}}. } } \details{ A cluster is defined to be a maximal collection, with at least two members, of components of \code{x} which are separated by no more than \code{w}. Note that when \code{x} represents intervals, an interval must actually \emph{contain a point} at distance \code{w} or less from a neighboring interval to be assigned to the same cluster. If the ends of both intervals in question are open and exactly at distance \code{w}, they will not be deemed to be cluster co-members. See the example below. } \note{ Implementation is by a call to \code{\link{reduce}} followed by a call to \code{\link{interval_overlap}}. The \code{clusters} methods are included to illustrate the utility of the core functions in the \pkg{intervals} package, although they are also useful in their own right. } \value{ A list whose components are the clusters. Each component is thus a subset of \code{x}, or, if \code{which == TRUE}, a vector of indices into the \code{x} object. (The indices correspond to row numbers when \code{x} is of class \code{"Intervals_virtual"}.) } \examples{ # Numeric method w <- 20 x <- sample( 1000, 100 ) c1 <- clusters( x, w ) # Check results sapply( c1, function( x ) all( diff(x) <= w ) ) d1 <- diff( sort(x) ) all.equal( as.numeric( d1[ d1 <= w ] ), unlist( sapply( c1, diff ) ) ) # Intervals method, starting with a reduced object so we know that all # intervals are disjoint and sorted. B <- 100 left <- runif( B, 0, 1e4 ) right <- left + rexp( B, rate = 1/10 ) y <- reduce( Intervals( cbind( left, right ) ) ) gaps <- function(x) x[-1,1] - x[-nrow(x),2] hist( gaps(y), breaks = 30 ) w <- 200 c2 <- clusters( y, w ) head( c2 ) sapply( c2, function(x) all( gaps(x) <= w ) ) # Clusters and open end points. See "Details". z <- Intervals( matrix( 1:4, 2, 2, byrow = TRUE ), closed = c( TRUE, FALSE ) ) z clusters( z, 1 ) closed(z)[1] <- FALSE z clusters( z, 1 ) }intervals/man/interval_complement-methods.Rd0000644000176200001440000000203413642043061021031 0ustar liggesusers\name{interval_complement} \alias{interval_complement} \alias{interval_complement,Intervals_virtual-method} \title{Compute the complement of a set of intervals} \description{ Compute the complement of a set of intervals. } \usage{ \S4method{interval_complement}{Intervals_virtual}(x, check_valid = TRUE) } \arguments{ \item{x}{An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? Also see \code{\link{interval_overlap}}. } } \value{ An object of the same class as \code{x}, compactly representing the complement of the intervals described in \code{x}. } \note{ For objects of class \code{"Intervals"}, closure on \code{-Inf} or \code{Inf} endpoints is set to match that of all the intervals with finite endpoints. For objects of class \code{"Intervals_full"}, non-finite endpoints are left open (although in general, this package does not make a distinction between closed and open infinite endpoints). } intervals/man/c.Intervals.Rd0000644000176200001440000000333213642043061015513 0ustar liggesusers\name{c} \alias{c} \alias{c.Intervals} \alias{c.Intervals_full} \title{Combine different interval matrix objects} \description{ S3 methods for concatenating sets of intervals into a single set. } \usage{ \S3method{c}{Intervals}(...) \S3method{c}{Intervals_full}(...) } \arguments{ \item{...}{\code{"Intervals"} or \code{"Intervals_full"} objects.} } \details{ All objects are expected to have the same value in the \code{type} slot. If the \code{closed} slots differ for \code{"\linkS4class{Intervals}"} objects and \code{type == "Z"}, the objects will be adjusted to have \code{closed} values matching that of \code{x}; if \code{type == "R"}, however, then all objects must first be coerced to class \code{"\linkS4class{Intervals_full}"}, with a warning. This coercion also occurs when a mixture of object types is passed in. A \code{NULL} in any argument is ignored. } \value{ A single \code{"\linkS4class{Intervals}"} or \code{"\linkS4class{Intervals_full}"} object. Input objects are concatenated in their order of appearance in the the argument list. If any input argument is not a set of intervals, \code{list(...)} is returned instead. } \note{ These methods will be converted to S4 once the necessary dispatch on \code{...} is supported. } \examples{ f1 <- Intervals( 1:2, type = "Z" ) g1 <- open_intervals( f1 + 5 ) # Combining Intervals objects over Z may require closure adjustment c( f1, g1 ) f2 <- f1; g2 <- g1 type( f2 ) <- type( g2 ) <- "R" # Combine Intervals objects over R which have different closure requires # coercion h <- c( f2, g2 ) # Coercion for mixed combinations as well c( h, g2 + 10 ) \dontrun{ # Combining different types is not permitted c( h, g1 + 10 ) } }intervals/man/plot.Intervals.Rd0000644000176200001440000000711413642043061016251 0ustar liggesusers\name{plot.Intervals} \alias{plot} \alias{plot.Intervals} \alias{plot.Intervals_full} \alias{plot,Intervals,missing-method} \alias{plot,Intervals_full,missing-method} \alias{plot,Intervals,ANY-method} \alias{plot,Intervals_full,ANY-method} \title{Plotting methods for interval objects} \description{ S3 methods for plotting \code{"Intervals"} and \code{"Intervals_full"} objects. } \usage{ \S3method{plot}{Intervals}(x, y, ...) \S3method{plot}{Intervals_full}( x, y = NULL, axes = TRUE, xlab = "", ylab = "", xlim = NULL, ylim = NULL, col = "black", lwd = 1, cex = 1, use_points = TRUE, use_names = TRUE, names_cex = 1, ... ) \S4method{plot}{Intervals,missing}(x, y, ...) \S4method{plot}{Intervals_full,missing}(x, y, ...) \S4method{plot}{Intervals,ANY}(x, y, ...) \S4method{plot}{Intervals_full,ANY}(x, y, ...) } \arguments{ \item{x}{An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{y}{ Optional vector of heights at which to plot intervals. If omitted, \code{y} will be automatically computed to generate a compact plot but with no overlap. } \item{axes}{As for \code{\link{plot.default}}.} \item{xlab}{As for \code{\link{plot.default}}.} \item{ylab}{As for \code{\link{plot.default}}.} \item{xlim}{As for \code{\link{plot.default}}.} \item{ylim}{ If not explicitly supplied, \code{ylim} is set to the maximum value required for intervals which are visible for the given \code{xlim}. } \item{col}{ Color used for segments and endpoint points and interiors. Recycled if necessary. } \item{lwd}{Line width for segments. See \code{\link{par}}.} \item{cex}{ Endpoint magnification. Only relevant if \code{use_points = TRUE}. See \code{\link{par}}. } \item{use_points}{Should points be plotted at interval endpoints?} \item{use_names}{ Should \code{rownames(x)} by used for segment labels in the plot? } \item{names_cex}{ Segment label magnification. Only relevant if \code{use_names = TRUE}. } \item{...}{Other arguments for \code{\link{plot.default}}.} } \details{ Intervals with \code{NA} for either endpoint are not plotted. Vertical placement is on the integers, beginning with 0. } \value{None.} \examples{ # Note plot symbol for empty interval in 'from'. from <- Intervals( matrix( c( 2, 8, 8, 9, 6, 9, 11, 12, 3, 3 ), ncol = 2, byrow = TRUE ), closed = c( FALSE, TRUE ), type = "Z" ) rownames(from) <- c("a","b","c","d","e") to <- Intervals( matrix( c( 2, 8, 3, 4, 5, 10 ), ncol = 2, byrow = TRUE ), closed = c( FALSE, TRUE ), type = "Z" ) rownames(to) <- c("x","y","z") empty(from) plot( c(from,to), col = rep(1:2, c(nrow(from), nrow(to))) ) legend("topright", c("from","to"), col=1:2, lwd=1) # More intervals. The maximal height shown is adapted to the plotting # window. B <- 10000 left <- runif( B, 0, 1e5 ) right <- left + rexp( B, rate = 1/10 ) x <- Intervals( cbind( left, right ) ) plot(x, use_points=FALSE) plot(x, use_points=FALSE, xlim = c(0, 500)) }intervals/man/interval_overlap-methods.Rd0000644000176200001440000000721613642043110020340 0ustar liggesusers\name{interval_overlap} \alias{interval_overlap} \alias{interval_overlap,Intervals_virtual_or_numeric,Intervals_virtual_or_numeric-method} \alias{interval_overlap,missing,ANY-method} \alias{interval_overlap,ANY,missing-method} \title{Assess overlap from one set of intervals to another} \description{ Assess overlap from intervals in one set to intervals in another set, and return the relevant indices. } \usage{ \S4method{interval_overlap}{Intervals_virtual_or_numeric,Intervals_virtual_or_numeric}(from, to, check_valid = TRUE) } \arguments{ \item{from}{ An \code{"Intervals"} or \code{"Intervals_full"} object, or a vector of class \code{"numeric"}. \emph{Note!} Prior to v. 0.11.1, this argument was called \code{target}. } \item{to}{ An \code{"Intervals"} or \code{"Intervals_full"} object, or a vector of class \code{"numeric"}. \emph{Note!} Prior to v. 0.11.1, this argument was called \code{query}. } \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? This, among other things, verifies that endpoints are of data type \code{"numeric"} and the \code{closed} vector/matrix is appropriately sized and of the correct data type. (Compiled code does no further checking.) } } \details{ Intervals which meet at endpoints overlap only if both endpoints are closed. Intervals in \code{to} with \code{NA} endpoints are ignored, with a warning; in \code{from}, such intervals produce no matches. Intervals in either \code{to} or \code{from} which are actually empty have their endpoints set to \code{NA} before proceeding, with warning, and so do not generate matches. If eith \code{to} or \code{from} is a vector of class \code{"numeric"}, overlap will be assess for the corresponding set of points. } \value{ A list, with one element for each row/component of \code{from}. The elements are vectors of indices, indicating which \code{to} rows (or components, for the \code{"numeric"} method) overlap each interval in \code{from}. A list element of length 0 indicates no overlapping elements. } \note{ If you want real (\code{type == "R"}) intervals that overlap in a set of positive measure --- not just at endpoints --- set all endpoints to open (i.e., \code{close(from) <- FALSE; closed(to) <- FALSE}) first. This function is now just a wrapper for \code{\link{which_nearest}}. } \seealso{ See \code{\link{which_nearest}} for details on nearby as well as overlapping intervals in \code{to}. } \examples{ # Note that 'from' contains a valid but empty interval. to <- Intervals( matrix( c( 2, 8, 3, 4, 5, 10 ), ncol = 2, byrow = TRUE ), closed = c( TRUE, FALSE ), type = "Z" ) from <- Intervals( matrix( c( 2, 8, 8, 9, 6, 9, 11, 12, 3, 3 ), ncol = 2, byrow = TRUE ), closed = c( TRUE, FALSE ), type = "Z" ) rownames(from) <- letters[1:nrow(from)] empty(to) empty(from) interval_overlap(from, to) # Non-empty real intevals of size 0 can overlap other intervals. u <- to type(u) <- "R" v <- Intervals_full( rep(3,4) ) closed(v)[2,] <- FALSE v empty(v) size(v) interval_overlap(v, u) # Working with points interval_overlap( from, c( 2, 3, 6, NA ) ) } intervals/man/intervals-package.Rd0000644000176200001440000000573113642043061016730 0ustar liggesusers\name{intervals-package} \alias{intervals-package} \alias{intervals} \docType{package} \title{ Tools for working with points and intervals } \description{ Tools for working with and comparing sets of points and intervals. } \details{ Index: \describe{ \item{\code{\link{Intervals-class}}}{Classes \code{"Intervals"} and \code{"Intervals_full"}.} \item{\code{\link{Intervals_virtual-class}}}{Class \code{"Intervals_virtual"}.} \item{\code{\link{Intervals_virtual_or_numeric-class}}}{Class union \code{"Intervals_virtual_or_numeric"}.} \item{\code{\link[=as.matrix.Intervals_virtual]{as.matrix}}}{Coerce endpoints to a matrix.} \item{\code{\link[=c.Intervals]{c}}}{Concatenate different sets of intervals.} \item{\code{\link{close_intervals}}}{Re-represent integer intervals with open or closed endpoints.} \item{\code{\link{closed}}}{Accessor for \code{closed} slot: closure vector/matrix.} \item{\code{\link{clusters}}}{Identify clusters in a collection of positions or intervals.} \item{\code{\link{contract}}}{Contract sets.} \item{\code{\link{distance_to_nearest}}}{Compute distance to nearest position in a set of intervals.} \item{\code{\link{empty}}}{Identify empty interval rows.} \item{\code{\link{expand}}}{Expand sets.} \item{\code{\link{interval_complement}}}{Compute the complement of a set of intervals.} \item{\code{\link{interval_difference}}}{Compute set difference.} \item{\code{\link{interval_included}}}{Assess inclusion of one set of intervals with respect to another.} \item{\code{\link{interval_intersection}}}{Compute the intersection of one or more sets of intervals.} \item{\code{\link{interval_overlap}}}{Assess which query intervals overlap which targets.} \item{\code{\link{interval_union}}}{Compute the union of intervals in one or more interval matrices.} \item{\code{\link{is.na}}}{Identify interval rows with \code{NA} endpoints.} \item{\code{\link[=plot.Intervals]{plot}}}{S3 plotting methods for intervals objects.} \item{\code{\link{reduce}}}{Compactly re-represent the points in a set of intervals.} \item{\code{\link{sgd}}}{Yeast gene model sample data.} \item{\code{\link{size}}}{Compute interval sizes.} \item{\code{\link[=split.Intervals_virtual]{split}}}{Split an intervals object according to a factor.} \item{\code{\link{type}}}{Accessor for \code{type} slot: Z or R.} \item{\code{\link{which_nearest}}}{Identify nearest member(s) in a set of intervals.} } Further information is available in the following vignettes: \describe{ \item{\code{intervals_overview}}{Overview of the intervals package.} } } \author{ Richard Bourgon } \keyword{ package } \section{Acknowledgments}{ Thanks to Julien Gagneur, Simon Anders, and Wolfgang Huber for numerous helpful suggestions about the package content and code. } \seealso{ See the \pkg{genomeIntervals} package in Bioconductor, which extends the functionality of this package. } intervals/man/sgd.Rd0000644000176200001440000000530013642043061014075 0ustar liggesusers\name{sgd} \alias{sgd} \docType{data} \title{Yeast gene model sample data} \description{ This data set contains a data frame describing a subset of the chromosome feature data represented in Fall 2007 version of \file{saccharomyces\_cerevisiae.gff}, available for download from the \emph{Saccharomyces} Genome Database (\url{http://www.yeastgenome.org}). } \usage{data(sgd)} \format{ A data frame with 14080 observations on the following 8 variables. \describe{ \item{\code{SGDID}}{SGD feature ID.} \item{\code{type}}{ Only four feature types have been retatined: \code{"CDS"}, \code{"five_prime_UTR_intron"}, \code{"intron"}, and \code{"ORF"}. Note that \code{"ORF"} correspond to a whole gene while \code{"CDS"}, to an exon. \emph{S. cerevisae} does not, however, have many multi-exonic genes. } \item{\code{feature_name}}{A character vector} \item{\code{parent_feature_name}}{ The \code{feature_name} of the a larger element to which the current feature belongs. All retained \code{"CDS"} entries, for example, belong to an \code{"ORF"} entry. } \item{\code{chr}}{ The chromosome on which the feature occurs. } \item{\code{start}}{Feature start base.} \item{\code{stop}}{Feature stop base.} \item{\code{strand}}{Is the feature on the Watson or Crick strand?} } } \examples{ # An example to compute "promoters", defined to be the 500 bases # upstream from an ORF annotation, provided these bases don't intersect # another orf. See documentation for the sgd data set for more details # on the annotation set. use_chr <- "chr01" data( sgd ) sgd <- subset( sgd, chr == use_chr ) orf <- Intervals( subset( sgd, type == "ORF", c( "start", "stop" ) ), type = "Z" ) rownames( orf ) <- subset( sgd, type == "ORF" )$feature_name W <- subset( sgd, type == "ORF", "strand" ) == "W" promoters_W <- Intervals( cbind( orf[W,1] - 500, orf[W,1] - 1 ), type = "Z" ) promoters_W <- interval_intersection( promoters_W, interval_complement( orf ) ) # Many Watson-strand genes have another ORF upstream at a distance of # less than 500 bp hist( size( promoters_W ) ) # All CDS entries are completely within their corresponding ORF entry. cds_W <- Intervals( subset( sgd, type == "CDS" & strand == "W", c( "start", "stop" ) ), type = "Z" ) rownames( cds_W ) <- NULL interval_intersection( cds_W, interval_complement( orf[W,] ) ) } \keyword{datasets} intervals/man/as.matrix.Intervals.Rd0000644000176200001440000000111213642043061017171 0ustar liggesusers\name{as.matrix} \alias{as.matrix} \alias{as.matrix.Intervals_virtual} \alias{as.matrix,Intervals_virtual-method} \title{Extract matrix of endpoints} \description{ S3 and S4 methods for extracting the matrix of endpoints from S4 objects. } \usage{ \S3method{as.matrix}{Intervals_virtual}(x, ...) \S4method{as.matrix}{Intervals_virtual}(x, ...) } \arguments{ \item{x}{\code{"Intervals"} or \code{"Intervals_full"} objects.} \item{...}{Unused, but required by the S3 generic.} } \value{ A two-column matrix, equivalent to \code{x@.Data} or \code{as(x, "matrix")}. } intervals/man/size-methods.Rd0000644000176200001440000000302713642043061015737 0ustar liggesusers\name{size} \alias{size} \alias{size,Intervals-method} \alias{size,Intervals_full-method} \title{Compute interval sizes} \description{ Compute the size, in either Z or R as appropriate, for each interval in an interval matrix. } \usage{ \S4method{size}{Intervals}(x, as = type(x)) \S4method{size}{Intervals_full}(x, as = type(x)) } \arguments{ \item{x}{An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{as}{ Should the intervals be thought of as in Z or R? This is usually determined automatically from the \code{type} slot, but because changing type may cause object copying, it is sometimes convenient to temporarily override this slot without actually resetting it. } } \details{ For type \code{"Z"} objects, counting measure; for type \code{"R"} objects, Lebesgue measure. For type \code{"Z"} objects, intervals of form (\emph{a},\emph{a}] and (\emph{a},\emph{a}) are both of length 0. } \value{ A numeric vector with length equal to \code{nrow(x)}. } \seealso{ See \code{\link{empty}} to identify empty intervals. Note that when \code{type(x) == "R"}, a size of 0 does not imply that an interval is empty. } \examples{ z1 <- Intervals( cbind( 1, 1:3 ), type = "Z" ) z2 <- z1; closed(z2)[1] <- FALSE z3 <- z1; closed(z3) <- FALSE size(z1) size(z2) size(z3) r1 <- z1; type(r1) <- "R" r2 <- z2; type(r2) <- "R" r3 <- z3; type(r3) <- "R" size(r1) size(r2) size(r3) s1 <- Intervals_full( matrix( 1, 3, 2 ), type = "Z" ) closed(s1)[2,2] <- FALSE closed(s1)[3,] <- FALSE size(s1) } intervals/man/interval_intersection-methods.Rd0000644000176200001440000000216613642043061021402 0ustar liggesusers\name{interval_intersection} \alias{interval_intersection} \alias{interval_intersection,Intervals_virtual-method} \alias{interval_intersection,missing-method} \title{Compute the intersection of one or more sets of intervals} \description{ Given one or more sets of intervals, produce a new set compactly representing points contained in at least one interval of each input object. } \usage{ \S4method{interval_intersection}{Intervals_virtual}(x, ..., check_valid = TRUE) \S4method{interval_intersection}{missing}(x, ..., check_valid = TRUE) } \arguments{ \item{x}{ An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{\dots}{Additional objects of the same classes permitted for \code{x}.} \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? Also see \code{\link{interval_overlap}}. } } \value{ A single object representing points contained in each of the objects supplied in the \code{x} and \code{...} arguments. } \seealso{ See \code{\link{interval_union}} and \code{\link{interval_complement}}, which are used to produce the results. }intervals/man/interval_difference-methods.Rd0000644000176200001440000000176713642043061020774 0ustar liggesusers\name{interval_difference} \alias{interval_difference} \alias{interval_difference,Intervals_virtual,Intervals_virtual-method} \title{Compute set difference} \description{ Compute the set difference between two objects. } \usage{ \S4method{interval_difference}{Intervals_virtual,Intervals_virtual}(x, y, check_valid = TRUE) } \arguments{ \item{x}{An \code{"Intervals"} or \code{"Intervals_full"} object.} \item{y}{ An \code{"Intervals"} or \code{"Intervals_full"} object, with a \code{type} slot matching that of \code{x}. } \item{check_valid}{ Should \code{\link{validObject}} be called on \code{x} and \code{y} before passing to compiled code? Also see \code{\link{interval_overlap}}. } } \value{ An object representing the subset of the integers or real line, as determined by \code{type(x)}, found in \code{x} but not in \code{y}. } \seealso{ These methods are just wrappers for \code{\link{interval_intersection}} and \code{\link{interval_complement}}. }intervals/man/split.Intervals_virtual.Rd0000644000176200001440000000175013642043061020174 0ustar liggesusers\name{split} \alias{split} \alias{split.Intervals_virtual} \alias{split,Intervals_virtual-method} \title{Split an intervals object according to a factor} \description{ S3 and S4 methods for splitting \code{"Intervals"} or \code{"Intervals_full"} objects. } \usage{ \S3method{split}{Intervals_virtual}(x, f, drop = FALSE, ...) \S4method{split}{Intervals_virtual}(x, f, drop = FALSE, ...) } \arguments{ \item{x}{ \code{"Intervals"} or \code{"Intervals_full"} objects. } \item{f}{ Passed to \code{\link{split.data.frame}}. } \item{drop}{ Passed to \code{\link{split.data.frame}}. } \item{...}{ Passed to \code{\link{split.data.frame}}. } } \value{ A list of objects of the same class as \code{x}, split by the levels of \code{f}. Until R 2.15, special handling was not required. Subsequent changes to the \pkg{base} package \code{\link{split}} function required an explicit method here, but code already provided by \code{\link{split.data.frame}} was sufficient. } intervals/man/distance_to_nearest-methods.Rd0000644000176200001440000000277413642043061021012 0ustar liggesusers\name{distance_to_nearest} \alias{distance_to_nearest} \alias{distance_to_nearest,Intervals_virtual_or_numeric,Intervals_virtual_or_numeric-method} \title{Compute distance to nearest position in a set of intervals} \description{ For each point or interval in the \code{from} argument, compute the distance to the nearest position in the \code{to} argument. } \usage{ \S4method{distance_to_nearest}{Intervals_virtual_or_numeric,Intervals_virtual_or_numeric}(from, to, check_valid = TRUE) } \arguments{ \item{from}{An object of appropriate type.} \item{to}{An object of appropriate type.} \item{check_valid}{ Should \code{\link{validObject}} be called before passing to compiled code? Also see \code{\link{interval_overlap}}. } } \value{ A vector of distances, with one entry per point or interval in \code{from}. Any intervals in \code{from} which are either empty (see \code{\link{empty}}) or have \code{NA} endpoints produce a \code{NA} result. } \note{ This function is now just a wrapper for \code{\link{which_nearest}}. } \seealso{ See \code{\link{which_nearest}}, which also returns indices for the interval or intervals (in case of ties) at the distance reported. } \examples{ # Point to interval to <- Intervals( c(0,5,3,Inf) ) from <- -5:10 plot( from, distance_to_nearest( from, to ), type = "l" ) segments( to[,1], 1, pmin(to[,2], par("usr")[2]), 1, col = "red" ) # Interval to interval from <- Intervals( c(-Inf,-Inf,3.5,-1,1,4) ) distance_to_nearest( from, to ) } intervals/DESCRIPTION0000755000176200001440000000103613642055242013775 0ustar liggesusersPackage: intervals Version: 0.15.2 Type: Package Title: Tools for Working with Points and Intervals Author: Richard Bourgon Maintainer: Edzer Pebesma Depends: R (>= 2.9.0) Imports: utils, graphics, methods Description: Tools for working with and comparing sets of points and intervals. License: Artistic-2.0 LazyLoad: yes URL: http://github.com/edzer/intervals NeedsCompilation: yes Packaged: 2020-04-04 09:41:39 UTC; edzer Repository: CRAN Date/Publication: 2020-04-04 10:00:02 UTC intervals/build/0000755000176200001440000000000013642053123013357 5ustar liggesusersintervals/build/vignette.rds0000644000176200001440000000033713642053123015721 0ustar liggesusersb```b`fcf`b2 1# '+I-*K)/22ST)CJ2R S0 % M6ہ&$=yh楀aM wjey~L6̜T!%ps QY_/( @hrNb1GRKҊAaintervals/tests/0000755000176200001440000000000013642043061013422 5ustar liggesusersintervals/tests/intervals_test_code.R0000644000176200001440000000404413642043061017607 0ustar liggesuserslibrary( "intervals" ) ######## NAs and empty intervals u <- Intervals_full( as.numeric(NA), type = "Z" ) u <- c( u, u ) v <- Intervals_full( c(1,3,1,Inf), type = "Z" ) x <- Intervals( 1, closed = FALSE, type = "Z" ) # empty w <- c( x, u, v ) rownames(w) <- letters[ 1:nrow(w) ] x u v w is.na(w) empty(w) distance_to_nearest( u, v ) distance_to_nearest( w, v ) pts <- c( -Inf, Inf, NA, NaN, 0:2 ) distance_to_nearest( pts, v ) identical( distance_to_nearest( pts, v ), distance_to_nearest( pts, open_intervals( v ) ) ) interval_overlap( w, v ) reduce( w ) open_intervals(w) ######## Subset assignment u <- Intervals( 1:8, type = "Z" ) rownames( u ) <- letters[ 1:nrow(u) ] v <- Intervals( -4:-1, type = "Z" ) rownames( v ) <- letters[ nrow(u) + 1:nrow(v) ] w <- open_intervals(u) u v w # Basic z <- u; z[2:3,] <- v z # With names z <- u; z[c("a","d"),] <- v z # Missing row name result <- try( { z <- u; z[c("a","e"),] <- v }, TRUE ) result # Closure adjustment z <- w; z[2:3,] <- v z # Size mismatch error result <- try( { z <- w; z[2:3,] <- v[1,] }, TRUE ) result # Intervals_full method x <- Intervals_full( 1:6, c(TRUE,FALSE) ) rownames( x ) <- letters[ 1:nrow(x) ] y <- Intervals_full( -4:-1 ) x y # Missing value names z <- x; z[2,] <- y[1,] z # Missing x names z <- y; z[1,] <- x[1,] z # Type mismatch error result <- try( { z <- x; z[2:3,] <- v }, TRUE ) result # Coercion up type(v) <- "R" closed(v) <- c( FALSE, TRUE ) x v z <- x; z[2:3,] <- v z # With warning due to assignment z <- v; z[1,] <- x[3,] z # No row name copying with matrices A <- matrix( 0, 2, 2 ) rownames(A) <- c("1","2") z <- x; z[1:2,] <- A z ######## distance_to_nearest() behavior a <- Intervals_full( c(2,5), FALSE ) b <- Intervals_full( c(1,5,2,10), matrix(c(TRUE,FALSE,FALSE,TRUE),2,2) ) a b distance_to_nearest(a,b) type(a) <- "Z" type(b) <- "Z" distance_to_nearest(a,b) a <- as( a, "Intervals" ) b <- as( open_intervals( b ), "Intervals" ) a b distance_to_nearest(a,b) type(a) <- "R" type(b) <- "R" distance_to_nearest(a,b) intervals/src/0000755000176200001440000000000013642053123013047 5ustar liggesusersintervals/src/Endpoint.cpp0000644000176200001440000000422113642043061015332 0ustar liggesusers#include "Endpoint.h" //////// Ordering for tied endpoints /* We use two different tied endpoint orderings, one for reduce, and a different one for interval_overlap. These should be set by any code that uses sorting, so we initialize to -1, and have the sort method check this and throw an error if it's still found. Let Q/T be query/target, L/R be left/right, and O/C be open/closed. Our ordering, when pos is effectively tied, is then: QRO < TRO ... < TLC < QLC < QRC < TRC ... < TLO < QLO 0 1 2 3 4 5 6 7 The basic principals are, for similar closure, start targets before queries but finish them after queries. For abutting intervals, start new intervals before finishing old ones, unless one or both endpoints are open, in which case we should finish old intervals first. */ int Endpoint::state_array[2][2][2] = {{{0,0},{0,0}},{{0,0},{0,0}}}; //////// Endpoint methods Endpoint::Endpoint(int i, double p, bool q, bool l, bool c) { index = i; pos = p; query = q; left = l; closed = c; } void Endpoint::R_print() const { Rprintf( "index = %i, pos = %f (%s, %s, %s)\n", index, pos, query ? "query" : "target", left ? "left" : "right", closed ? "closed" : "open" ); } //////// Endpoints methods Endpoints::Endpoints( const double * pos, const int * closed, int n, bool query, bool is_full ) { /* The pos pointer should point to an n x 2 array of endpoints, and the closed pointer, to either an array of booleans of the same size (if full = true) or an array of two booleans (if full = false). Note that R uses int, not bool, for logicals. Intervals with R numeric NA in either slot are dropped. */ int i; this->reserve( 2 * n ); for ( i = 0; i < n; i++ ) { if ( ISNA( pos[i] ) || ISNA( pos[i+n] ) ) continue; this->push_back( Endpoint( i, pos[i], query, true, (bool) closed[ is_full ? i : 0 ] ) ); this->push_back( Endpoint( i, pos[i+n], query, false, (bool) closed[ is_full ? i+n : 1 ] ) ); } } void Endpoints::R_print() const { Endpoints::const_iterator it; for ( it = this->begin(); it < this->end(); it++ ) it->R_print(); } intervals/src/Endpoint.h0000644000176200001440000000266513642043061015011 0ustar liggesusers#ifndef ENDPOINT_H #define ENDPOINT_H #include #include #include //////// Endpoint class class Endpoint{ private: static int state_array[2][2][2]; inline int state() const { return( state_array[query][left][closed] ); } public: int index; double pos; bool query, left, closed; Endpoint(int i, double p, bool q, bool l, bool c); inline bool operator< (const Endpoint& other) const { /* We assume that the calling code is aware of the difficulty in assessing equality for floating point numbers and that values are passed in as exactly equal (in their floating point representation) if and only if exact equality is intended by the calling code. Given this assumption, there is no need for a relative difference approach here. */ if ( this->pos == other.pos ) return( this->state() < other.state() ); else return( this->pos < other.pos ); } static void set_state_array( const int new_array[2][2][2] ) { int i, j, k; for( i = 0; i < 2; i++ ) for( j = 0; j < 2; j++ ) for( k = 0; k < 2; k++ ) Endpoint::state_array[i][j][k] = new_array[i][j][k]; } void R_print() const; }; //////// Endpoints class class Endpoints : public std::vector< Endpoint > { public: Endpoints( const double * pos, const int * closed, int n, bool query, bool is_full ); void R_print() const; }; #endif // #ifndef ENDPOINT_H intervals/src/init.c0000644000176200001440000000122613642045602014162 0ustar liggesusers#include #include #include extern SEXP _which_nearest(SEXP qe, SEXP te, SEXP qc, SEXP tc, SEXP q_full, SEXP t_full); extern SEXP _plot_overlap(SEXP e, SEXP c, SEXP full); extern SEXP _reduce(SEXP e, SEXP c, SEXP full); #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} const static R_CallMethodDef R_CallDef[] = { CALLDEF(_which_nearest, 6), CALLDEF(_plot_overlap, 3), CALLDEF(_reduce, 3), {NULL, NULL, 0} }; void // attribute_visible // optional R_init_intervals(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); R_useDynamicSymbols(dll, FALSE); R_forceSymbols(dll, TRUE); } intervals/src/plot_overlap.cpp0000644000176200001440000000403013642043061016256 0ustar liggesusers#include #include #include "Endpoint.h" #include #include #include /* What we require to prevent segfaults is the same as for interval_overlap.cpp. See details there. Everything should be checked by the calling code in R. For plotting without visual overlap, we do not care about endpoint closure, and we always open new intervals before closing old ones. When an interval opens, we assign it the minimal free interior option, if there is one; otherwise, we assign it the current count of open intervals. When an interval closes, if it does not have the current maximum y, its y gets added to the free_interior set. */ const int reduce_order[2][2][2] = { {{2,2},{1,1}}, // Target: {{ ), ] }, { (, [ }} {{0,0},{0,0}} // Query: {{ ), ] }, { (, [ }} }; extern "C" { SEXP _plot_overlap(SEXP e, SEXP c, SEXP full) { // Load data and sort int n = nrows(e); bool full_bool = *LOGICAL(full); Endpoints ep ( REAL(e), LOGICAL(c), n, false, full_bool ); // Set sorting order, then sort Endpoint::set_state_array( reduce_order ); sort( ep.begin(), ep.end() ); // Process int i; int active_count = 0; std::set free_interior; std::vector y (n); Endpoints::const_iterator it; // Initialize to NA for ( i = 0; i < n; i++ ) y[i] = R_NaInt; for ( it = ep.begin(); it < ep.end(); it++ ) { if ( it->left ) { // Opening an interval if ( free_interior.size() > 0 ) { y[ it->index ] = *free_interior.begin(); free_interior.erase( free_interior.begin() ); } else y[ it->index ] = active_count; active_count++; } else{ // Closing an interval active_count--; if ( y[ it->index ] < active_count + free_interior.size() ) free_interior.insert( y[ it->index ] ); } } // Prepare and return result. SEXP result; PROTECT( result = allocVector( INTSXP, n ) ); copy( y.begin(), y.end(), INTEGER( result ) ); UNPROTECT(1); return( result ); } } intervals/src/reduce.cpp0000644000176200001440000000461613642043061015031 0ustar liggesusers#include #include #include "Endpoint.h" #include #include /* What we require to prevent segfaults is the same as for interval_overlap.cpp. See details there. Everything should be checked by the calling code in R. */ const int reduce_order[2][2][2] = { {{2,4},{3,1}}, // Target: {{ ), ] }, { (, [ }} {{0,0},{0,0}} // Query: {{ ), ] }, { (, [ }} }; extern "C" { SEXP _reduce(SEXP e, SEXP c, SEXP full) { // Load data and sort int n = nrows(e); bool full_bool = *LOGICAL(full); Endpoints ep ( REAL(e), LOGICAL(c), n, false, full_bool ); // Set sorting order, then sort Endpoint::set_state_array( reduce_order ); sort( ep.begin(), ep.end() ); // Process int score = 0; std::vector start, end; std::vector start_c, end_c; Endpoints::const_iterator it; for ( it = ep.begin(); it < ep.end(); it++ ) { if ( score == 0 ) { if ( !it->left ) error("Internal error: unexpected endpoint type when score = 0."); start.push_back( it->pos ); if ( full_bool ) start_c.push_back( (int) it->closed ); } score += ( it->left ? +1 : -1 ); if ( score == 0 ) { if ( it->left ) error("Internal error: unexpected endpoint type when score = 0."); end.push_back( it->pos ); if ( full_bool ) end_c.push_back( (int) it->closed ); } } if ( start.size() != end.size() ) error("Internal error: mismatched start and end endpoint sets."); // Prepare and return result. SEXP result; PROTECT( result = allocVector( VECSXP, 2 ) ); SET_VECTOR_ELT( result, 0, allocMatrix( REALSXP, start.size(), 2 ) ); copy( start.begin(), start.end(), REAL( VECTOR_ELT( result, 0 ) ) ); copy( end.begin(), end.end(), REAL( VECTOR_ELT( result, 0 ) ) + start.size() ); if ( full_bool ) { SET_VECTOR_ELT( result, 1, allocMatrix( LGLSXP, start.size(), 2 ) ); copy( start_c.begin(), start_c.end(), LOGICAL( VECTOR_ELT( result, 1 ) ) ); copy( end_c.begin(), end_c.end(), LOGICAL( VECTOR_ELT( result, 1 ) ) + start.size() ); } else { SET_VECTOR_ELT( result, 1, allocVector( LGLSXP, 2 ) ); LOGICAL( VECTOR_ELT( result, 1 ) )[0] = LOGICAL(c)[0]; LOGICAL( VECTOR_ELT( result, 1 ) )[1] = LOGICAL(c)[1]; } UNPROTECT(1); return( result ); } } intervals/src/which_nearest.cpp0000644000176200001440000001620013642043061016375 0ustar liggesusers#include #include #include "Endpoint.h" #include #include #include #include /* #### What we require to prevent segfaults: 1. For Intervals_full objects, the endpoint and closure matrices must be of the same dimension, and have two columns each. For Intervals objects, we expect a closure vector of length 2. 2. Endpoints must be type double, and closure must be R logical, which ends up as int in C++. The validity function for the classes should verify both of these properties, so we do no additional checks here, i.e., we assume that we've been passed valid objects. #### To-do - Try hash_set instead of set, if it compiles OK. #### The "active" and "pending" sets - The q_active and t_active sets record indices of intervals which are currently open as we proceed through the list of points. - The q_pending set records the query interval (or intervals, in the case of ties) which has closed most recently to the left. If multiple target intervals come by before the next query interval is opened, each will need to be compare to the right endpoint of the intervals in q_pending. Once we open a new query interval, q_pending is cleared. - The t_pending set records indices of target intervals which have closed since the last time a query interval opened -- and distances to the left were therefor checked. Each of these target intervals needs to be checked against the next query interval to become active, as well as any additional query intervals that start at the same location. The t_pending set will be cleared when we get ready to add a new index to it but see that (i) a query interval has been opened since the last time we tried to add an index, and (ii) that pos has changed. the last closure of a query interval. When we open a new query interval, each of these target intervals needs to be compared to the new query interval's left endpoint. Once we do this -- and provided that the next query interval does not start in the same place -- then t_pending is cleared. */ const int overlap_order[2][2][2] = { {{1,5},{6,2}}, // Target: {{ ), ] }, { (, [ }} {{0,4},{7,3}} // Query: {{ ), ] }, { (, [ }} }; extern "C" { SEXP _which_nearest(SEXP qe, SEXP te, SEXP qc, SEXP tc, SEXP q_full, SEXP t_full) { // Load data, combine int qn = nrows(qe); int tn = nrows(te); Endpoints q ( REAL(qe), LOGICAL(qc), qn, true, *LOGICAL(q_full) ); Endpoints t ( REAL(te), LOGICAL(tc), tn, false, *LOGICAL(t_full) ); double* q_right = REAL(qe) + qn; double* t_right = REAL(te) + tn; q.insert( q.end(), t.begin(), t.end() ); // Set sorting order, then sort Endpoint::set_state_array( overlap_order ); sort(q.begin(), q.end()); // Process overlaps std::set q_active, t_active; std::vector< std::set > indices ( tn ); // For distance_to_nearest and which_nearest double q_check_pos = std::numeric_limits::infinity(); std::set q_pending, t_pending; std::vector delta ( tn ); std::vector< std::set > which ( tn ); double diff; // For looping Endpoints::const_iterator it; std::set::iterator set_it; int i; // Initialize delta for ( i = 0; i < tn; i++ ) delta[i] = std::numeric_limits::infinity(); for ( it = q.begin(); it < q.end(); it++ ) { if ( it->query ) { if ( it->left ) { // Query left for( set_it = t_active.begin(); set_it != t_active.end(); set_it++ ) { indices[ *set_it ].insert( it->index + 1 ); if ( delta[ *set_it ] > 0 ) which[ *set_it ].clear(); delta[ *set_it ] = 0; which[ *set_it ].insert( it->index + 1 ); } for( set_it = t_pending.begin(); set_it != t_pending.end(); set_it++ ) { diff = it->pos - t_right[ *set_it ]; if ( delta[ *set_it ] == diff ) which[ *set_it ].insert( it->index + 1 ); if ( delta[ *set_it ] > diff ) { which[ *set_it ].clear(); delta[ *set_it ] = diff; which[ *set_it ].insert( it->index + 1 ); } } q_active.insert( it->index ); q_check_pos = it->pos; q_pending.clear(); } else { // Query right q_active.erase( it->index ); // if ( q_right[ it->index ] > q_right[ *q_pending.begin() ] ) // q_pending.clear(); // EJP: trying to fix the clang-ASAN error, // EJP: replace these to lines with if (!q_pending.empty()) { if (it->index < length(qe) && *q_pending.begin() < length(qe) ) { if ( q_right[ it->index ] > q_right[ *q_pending.begin() ] ) q_pending.clear(); } } q_pending.insert( it->index ); } } else { if ( it->left ) { // Target left /* Note that some care is required here. It is possible that there is another interval at distance 0 to the left, but which, due to endpoint closure, does not actually overlap the target interval we are activating. In this case, q_active could be empty but we should still set delta to 0 for this target interval and add the index of the query interval immediately to the left to the which set. Similarly, if there are both overlapping and non-overlapping distance-zero query intervals, we want to return delta = 0 and the indices for both types. */ if ( q_active.size() > 0 ) { delta[ it->index ] = 0; for( set_it = q_active.begin(); set_it != q_active.end(); set_it++ ) { indices[ it->index ].insert( *set_it + 1 ); which[ it->index ].insert( *set_it + 1 ); } } for ( set_it = q_pending.begin(); set_it != q_pending.end(); set_it++ ) { diff = it->pos - q_right[ *set_it ]; if ( delta[ it->index ] == diff ) which[ it->index ].insert( *set_it + 1 ); if ( delta[ it->index ] > diff ) { which[ it->index ].clear(); delta[ it->index ] = diff; which[ it->index ].insert( *set_it + 1 ); } } t_active.insert( it->index ); } else { // Target right t_active.erase( it->index ); if ( it->pos > q_check_pos ) { t_pending.clear(); q_check_pos = std::numeric_limits::infinity(); } t_pending.insert( it->index ); } } } // Prepare and return result. SEXP result; PROTECT( result = allocVector( VECSXP, 3 ) ); SET_VECTOR_ELT( result, 0, allocVector( REALSXP, tn ) ); // delta SET_VECTOR_ELT( result, 1, allocVector( VECSXP, tn ) ); // which SET_VECTOR_ELT( result, 2, allocVector( VECSXP, tn ) ); // which_overlap copy( delta.begin(), delta.end(), REAL( VECTOR_ELT( result, 0 ) ) ); for( i = 0; i < tn; i++ ) { SET_VECTOR_ELT( VECTOR_ELT( result, 1 ), i, allocVector( INTSXP, which[i].size() ) ); copy( which[i].begin(), which[i].end(), INTEGER( VECTOR_ELT( VECTOR_ELT( result, 1 ), i ) ) ); SET_VECTOR_ELT( VECTOR_ELT( result, 2 ), i, allocVector( INTSXP, indices[i].size() ) ); copy( indices[i].begin(), indices[i].end(), INTEGER( VECTOR_ELT( VECTOR_ELT( result, 2 ), i ) ) ); } UNPROTECT(1); return( result ); } } intervals/vignettes/0000755000176200001440000000000013642053123014270 5ustar liggesusersintervals/vignettes/intervals_overview.Rnw0000644000176200001440000003421313642043061020720 0ustar liggesusers@ \documentclass[a4paper]{article} \usepackage{Sweave, amssymb, amsmath} \usepackage[ pdftex, pdfpagelabels, plainpages=false, pdfborder={0 0 0}, pdfstartview=FitH, bookmarks=true, bookmarksnumbered=true, bookmarksopen=true ] {hyperref} \title{Overview of the \emph{intervals} package} \author{Richard Bourgon} \date{06 June 2009} % The is for R CMD check, which finds it in spite of the "%", and also for % automatic creation of links in the HTML documentation for the package: % \VignetteIndexEntry{Overview of the intervals package.} \begin{document} %%%%%%%% Setup % Don't reform code \SweaveOpts{keep.source=TRUE, eps=FALSE} % Size for figures \setkeys{Gin}{width=.7\textwidth} % Fonts \DefineVerbatimEnvironment{Sinput}{Verbatim}{xleftmargin=1cm,fontshape=sl,fontsize=\small} \DefineVerbatimEnvironment{Soutput}{Verbatim}{xleftmargin=1cm,fontsize=\small} % Reduce characters per line in R output @ <>= options( width = 80 ) @ % Make title \maketitle % Typesetting commands \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} %%%%%%%% TOC \tableofcontents \vspace{.25in} %%%%%%%% Main text \section{Introduction} The \emph{intervals} packages defines two S4 classes which represent collections of intervals over either the integers ($\Z$) or the real number line ($\R$). An instance of either class consists of a two-column matrix of endpoints, plus additional slots describing endpoint closure and whether the intervals are to be thought of as being over $\Z$ or $\R$. @ <>= library( intervals ) x <- Intervals( matrix( 1:6, ncol = 2 ) ) x x[2,2] <- NA x[3,1] <- 6 x @ Objects of class \texttt{Intervals} represent collections of intervals with common endpoint closure, e.g., all left-closed, right-open. More control over endpoints is permitted with the \texttt{Intervals\_full} class. (Both classes are derived from \texttt{Intervals\_virtual}, which is not intended for use by package users.) @ <>= y <- as( x, "Intervals_full" ) y <- c( y, Intervals_full( c(2,3,5,7) ) ) closed(y)[2:3,1] <- FALSE closed(y)[4,2] <- FALSE rownames(y) <- letters[1:5] y @ The \texttt{size} method gives measure --- counting measure over $\Z$ or Lebesgue measure over $\R$ --- for each interval represented in an object. The \texttt{empty} method identifies intervals that are in fact empty sets, which over $\R$ is not the same thing as having size 0. (Valid objects must have each right endpoint no less than the corresponding left endpoint. When one or both endpoints are open, however, valid intervals may be empty.) @ <>= size(x) empty(x) empty(y) @ \begin{figure}[!tb] \centering @ <>= plot( y ) @ \caption{The \texttt{Intervals\_full} object \texttt{y}, plotted with \texttt{plot(y)}. The second row contains an \texttt{NA} endpoint, and is not shown in the plot. The empty interval is plotted as a hollow point. By default, vertical placement avoids overlaps but is compact. } \label{fig:plotting} \end{figure} \section{Interpretation of objects} An \texttt{Intervals} or \texttt{Intervals\_full} object can be thought of in two different modes, each of which is useful in certain contexts: \begin{enumerate} \item As a (non-unique) representation of a subset of $\Z$ or $\R$. \item As a collection of (possibly overlapping) intervals, each of which has a meaningful identity. \end{enumerate} \subsection{As a subset of $\Z$ or $\R$} The \emph{intervals} package provides a number of basic tools for working in the first mode, where an object represents a subset of $\Z$ or $\R$ but the rows of the endpoint matrix do not have any external identity. Basic tools include \texttt{reduce}, which returns a sorted minimal representation equivalent to the original (dropping any intervals with \texttt{NA} endpoints), as well as \texttt{interval\_union}, \texttt{interval\_complement}, and \texttt{interval\_intersection}. @ <>= reduce( y ) interval_intersection( x, x + 2 ) interval_complement( x ) @ Note that combining \texttt{x} and its complement in order to compute a union requires mixing endpoint closure types; coercion to \texttt{Intervals\_full} is automatic. @ <>= interval_union( x, interval_complement( x ) ) @ The \texttt{distance\_to\_nearest} function treats its \texttt{to} argument in the first mode, as just a subset of $\Z$ or $\R$; it treats its \texttt{from} argument, however, in the second mode, returning one distance for every row of the \texttt{from} object. In the example below, we also look at performance for large data sets (less than one second on a 2 GHz Intel Core 2 Duo Macintosh, although the time shown below will likely differ). A histogram of \texttt{d} is given in Figure~\ref{fig:distance}. @ <>= B <- 100000 left <- runif( B, 0, 1e8 ) right <- left + rexp( B, rate = 1/10 ) v <- Intervals( cbind( left, right ) ) head( v ) mean( size( v ) ) dim( reduce( v ) ) system.time( d <- distance_to_nearest( sample( 1e8, B ), v ) ) @ \begin{figure}[!tb] \centering @ <>= hist( d, main = "Distance to nearest interval" ) @ \caption{Histogram of distances from a random set of points to the nearest point in \texttt{v}. There is also a \texttt{distance\_to\_nearest} method for comparing two sets of intervals.} \label{fig:distance} \end{figure} \subsection{As a set of meaningful, possibly overlapping intervals} In some applications, each row of an object's endpoint matrix has a meaningful identity, and particular points from $\Z$ or $\R$ may be found in more than one row. To support this mode, objects may be given row names, which are propagated through calculations when appropriate. The \texttt{c} methods simply stack objects (like \texttt{rbind}), preserving row names and retaining redundancy, if any. The \texttt{interval\_overlap} method works in this mode. In the next example we use it to identify rows of \texttt{v} which are at least partially redundant, i.e., which intersect at least one other row of \texttt{v}. All rows overlap themselves, so we look for rows that overlap at least two rows: @ <>= rownames(v) <- sprintf( "%06i", 1:nrow(v) ) io <- interval_overlap( v, v ) head( io, n = 3 ) n <- sapply( io, length ) sum( n > 1 ) k <- which.max( n ) io[ k ] v[ k, ] v[ io[[ k ]], ] @ The \texttt{which\_nearest} method also respects row identity, for both its \texttt{to} and \texttt{from} arguments. In addition to computing the distance from each \texttt{from} interval to the nearest point in \texttt{to}, it also returns the row index of the \texttt{to} interval (or intervals, in case of ties) located at the indicated distance. Another function which operates in this mode is \texttt{clusters}, which takes a set of points or intervals and identifies maximal groups which cluster together --- which are separated from one another by no more than a user-specified threshold. The following code is taken from the \texttt{clusters} documentation: @ <>= B <- 100 left <- runif( B, 0, 1e4 ) right <- left + rexp( B, rate = 1/10 ) y <- reduce( Intervals( cbind( left, right ) ) ) w <- 100 c2 <- clusters( y, w ) c2[1:3] @ \section{Floating point and intervals over $\R$} When \texttt{type == "R"}, interval endpoints are not truly in $\R$, but rather, in the subset which can be represented by floating point arithmetic. (For the moment, this is also true when \texttt{type == "Z"}. See Section~\ref{sec:representation}.) This limits the endpoint values which can be represented; more importantly, if computations are performed on interval endpoints, it means that floating point error can affect whether or not endpoints coincide, whether intervals which meet at or near endpoints overlap one another, etc. In spite of this potentially serious limitation, it is still often convenient to work with intervals with non-integer endpoints, including data where adjacent intervals exactly meet at a non-integer endpoint. To address this, the \emph{intervals} package takes the following approach: \begin{itemize} \item Floating point representations of interval endpoints are assumed to be \emph{exactly equal} (in the sense of \texttt{==} in R or C++) if and only if the user intends the real values corresponding to these representations to be exactly equal. \item For cases where floating point error and approximate equality are a concern, tools are provided to permit distinguishing between ambiguous and unambiguous intersection, union, etc. \end{itemize} In the next example, \texttt{y1} does not literally overlap \texttt{y2[2,]}, although R's \texttt{all.equal} function asserts that the gap between them is smaller than the default tolerance for equivalence up to floating point precision. @ <>= delta <- .Machine[[ "double.eps" ]]^0.5 y1 <- Intervals( c( .5, 1 - delta / 2 ) ) y2 <- Intervals( c( .25, 1, .75, 2 ) ) y1 y2 all.equal( y1[1,2], y2[2,1] ) interval_intersection( y1, y2 ) @ The \texttt{expand} and \texttt{contract} methods, used with \texttt{type = "relative"}, permit consideration of the maximal and minimal interval sets which are consistent with the nominal endpoints --- from the point of view of endpoint relative difference. The \texttt{contract} method, for example, contracts each interval in a collection so that the relative difference between original and contracted endpoints is equal to tolerance \texttt{delta}. Thus, if a relative difference less than or equal to \texttt{delta} is our criterion for approximate floating point equality, the contracted object has endpoints which are approximately equal to those of the original --- even though the contracted object is a proper subset of the original. The \texttt{expand} method is similar, but generates a proper superset. @ <>= contract( y1, delta, "relative" ) @ We compute two separate intersections which bound the nominal intersection: @ <>= inner <- interval_intersection( contract( y1, delta, "relative" ), contract( y2, delta, "relative" ) ) inner outer <- interval_intersection( expand( y1, delta, "relative" ), expand( y2, delta, "relative" ) ) outer @ Finally, we identify points which may or may not be in the intersection, depending on whether we make a conservative, literal, or anti-conservative interpretation of the nominal endpoints. @ <>= interval_difference( outer, inner ) @ The \texttt{expand} and \texttt{contract} methods have other uses as well. Here, we eliminate gaps of size 2 or smaller: @ <>= x <- Intervals( c(1,10,100,8,50,200), type = "Z" ) x w <- 2 close_intervals( contract( reduce( expand(x, w/2) ), w/2 ) ) @ \section{Notes on implementation} \subsection{Endpoint representation} \label{sec:representation} For the moment, interval endpoints are always stored using R's \emph{numeric} data type. Although this is wasteful from an memory and speed point of view, we do it for two reasons. First, use of R's \texttt{Inf} and \texttt{-Inf} --- not possible with the \emph{integer} type --- is very convenient when computing complements. Second, the range of integers which can be represented using the \emph{numeric} data type is considerably greater: @ <>= .Machine$integer.max numeric_max <- with( .Machine, double.base^double.digits ) options( digits = ceiling( log10( numeric_max ) ) ) numeric_max @ \subsection{Efficiency} All computations are accomplished by treating intervals as pairs of tagged endpoints, sorting these endpoints (along with their tags), and then making a single pass through the results. Computational complexity for set operations is therefore $O(n \log n)$, where input object $i$ contains $n_i$ rows and $n = \sum_i n_i$. The same sorting approach is also used for \texttt{interval\_overlap}, although if every interval in a query object of $m$ rows overlaps every intervals in a target object of $n$ rows, generating output alone must of necessity be $O(mn)$. Sorted endpoint vectors are not retained in memory. If one wishes to query a particular object over and over, repeated sorting would be inefficient; in practice so far, however, such repeated querying has not been needed. \subsection{Checking validity} The code behind \texttt{which\_nearest} and \texttt{reduce} (key methods in the \emph{intervals} package, which may be directly called by the user and are also used internally in numerous locations) is written in C++ for efficiency. The compiled code makes a number of assumptions about the \texttt{SEXP} objects passed in as arguments, but does not explicitly check these assumptions. Nonetheless, when the R wrappers for the compiled code are applied to \emph{valid} objects from the \texttt{Intervals} or \texttt{Intervals\_full} classes, all assumptions will always be met. This design decision was taken so that the requirements for individual objects and their contents could be gathered together in a single, natural location: the classes' \texttt{validity} functions. The \emph{intervals} package provides replacement methods --- e.g., \texttt{type} and \texttt{closed} --- which implement error checking and preserve object validity. R's implementation of S4 classes, however, leaves object data slots exposed to the user. As a consequence, a user can directly manipulate the data slots of a valid \texttt{Intervals} or \texttt{Intervals\_full} object in a way that invalidates the object, but does not generate any warning or error. To prevent invalid objects from being passed to compiled code --- and potentially generating segmentation faults or other problems --- all wrapper code in this package includes a \texttt{check\_valid} argument. This argument is set to \texttt{TRUE} by default, so that \texttt{validObject} is called on relevant objects before handing them off to the compiled code. For efficiency, users may choose to override this extra check if they are certain they have not manually assigned inappropriate content to objects' data slots. \section{Session information} @ <>= si <- as.character( toLatex( sessionInfo() ) ) cat( si[ -grep( "Locale", si ) ], sep = "\n" ) @ \end{document} intervals/NEWS0000644000176200001440000000505513642043061012764 0ustar liggesusers ******** VERSION 0.13.3 ******** BUG FIXES - The S3 plot() method for class Intervals was ignoring the y argument. (The method for class Intervals_full did, however, use the argument properly.) This has been fixed. ******** VERSION 0.13.2 ******** BUG FIXES - The initialization method for the Intervals class threw errors when empty objects were created. This has been fixed. ******** VERSION 0.13.0 ******** NEW FEATURES - New interval_included methods, similar to interval_overlap, but requiring full inclusion. ******** VERSION 0.12.3 ******** NEW FEATURES - S3 as.matrix methods for Intervals and Intervals_full objects. ******** VERSION 0.12.0 ******** NEW FEATURES - S3 plot methods for Intervals and Intervals_full objects. ******** VERSION 0.11.1 ******** SIGNIFICANT USER-VISIBLE CHANGES - The interval_overlap and distance_to_nearest methods are now just wrappers for which_nearest. Because of this, the argument names for interval_overlap, have been changed from 'target' and 'query' to 'from' and 'to', respectively, in order to match the other two functions. Argument order is the same as before, but functions which call arguments by name and use the old names will now generate an (informative) error message. BUG FIXES - The C++ code for interval_overlap correctly handles all varieties of endpoint closure, but does not handle cases like (0,2) vs. (1,3) over Z. (These intervals do not overlap, even though the right endpoint of the first interval is nominally after the left endpoint of the second interval.) This should have been handled by the R wrapper -- by applying close_intervals before passing on to compiled code -- but this step had previously been omitted. This is now fixed. NEW FEATURES - The interval_overlap, which_nearest, and distance_to_nearest methods now all accept a numeric vector in both the 'from' and 'to' arguments. IMPROVEMENTS - The distance_to_nearest methods were previously based on old code using approxfun(). They now work of the same algorithm used for interval_overlap and which_nearest. This algorithm simultaneously generates all three types of information: identity of overlaps and nearest neighbors, as well as distance to nearest neighbors. ******** VERSION 0.11.0 ******** NEW FEATURES - Adding a NEWS file! - Added which_nearest methods, as a complement to distance_to_nearest. The C++ code previously used for interval_overlap calculations was augmented to provide this functionality.intervals/R/0000755000176200001440000000000013642045000012454 5ustar liggesusersintervals/R/head-methods.R0000644000176200001440000000103413642043061015144 0ustar liggesuserssetGeneric( "head", function( x, ... ) standardGeneric( "head" ) ) setMethod( "head", signature( "Intervals_virtual" ), function( x, n = 6 ) { if ( nrow(x) > 0 ) x[ 1:min( n, nrow(x) ), ] else x } ) setGeneric( "tail", function( x, ... ) standardGeneric( "tail" ) ) setMethod( "tail", signature( "Intervals_virtual" ), function( x, n = 6 ) { if ( nrow(x) > 0 ) x[ max( 1, nrow(x) - n + 1 ):nrow(x), ] else x } ) intervals/R/interval_included-methods.R0000644000176200001440000000541313642043061017743 0ustar liggesuserssetGeneric( "interval_included", def = function( from, to, ... ) standardGeneric( "interval_included" ) ) setMethod( "interval_included", signature( "Intervals", "Intervals" ), function( from, to, check_valid = TRUE ) { # For inclusion, both endpoints from a "to" interval should be # inside an particular "from" interval. Because interval_overlap # treats numerics as double-closed single-point intervals, some care # is required with open endpoints for "to": these should be # considered to overlap closed OR open "from" endpoints with which # they coincide. We handle this, over R, by adjusting the "from" # closure. if ( any( empty(to) ) ) { warning( "Some empty 'to' intervals encountered. Setting to NA...", call. = FALSE ) to[ empty(to), ] <- NA } if ( type(to) == "Z" ) to <- close_intervals(to) else closed( from ) <- closed( from ) | !closed( to ) mapply( intersect, interval_overlap( from, to[,1], check_valid ), interval_overlap( from, to[,2], check_valid ) ) } ) setMethod( "interval_included", signature( "Intervals_full", "Intervals_full" ), function( from, to, check_valid = TRUE ) { # For the same reasons given above, open endpoints in "to" need to # be re-checked against open endpoints in "from", since equality of # the endpoint value is consistent with inclusion. if ( any( empty(to) ) ) { warning( "Some empty 'to' intervals encountered. Setting to NA...", call. = FALSE ) to[ empty(to), ] <- NA } if ( type(to) == "Z" ) to <- close_intervals(to) # Left side left <- interval_overlap( from, to[,1], check_valid ) to[ closed(to)[,1], 1 ] <- NA left_open <- interval_overlap( from[,1], to[,1], check_valid ) # Right side right <- interval_overlap( from, to[,2], check_valid ) to[ closed(to)[,2], 2 ] <- NA right_open <- interval_overlap( from[,2], to[,2], check_valid ) mapply( function(l,lo,r,ro) intersect( c(l,lo), c(r,ro) ), left, left_open, right, right_open ) } ) # Note: there isn't much sense in asking what's included in a set of # points. Further, overlap and inclusion are the same when "from" contains # actual intervals while "to" contains point. For these reasons, there are no # methods for class "numeric." intervals/R/interval_intersection-methods.R0000644000176200001440000000274613642043061020670 0ustar liggesuserssetGeneric( "interval_intersection", def = function( x, ... ) standardGeneric( "interval_intersection" ) ) setMethod( "interval_intersection", signature( "Intervals_virtual" ), function( x, ..., check_valid = TRUE ) { args <- c( list(x), list(...) ) if ( check_valid ) for ( y in args ) validObject( y ) complements <- lapply( args, interval_complement, check_valid = FALSE ) interval_complement( do.call( interval_union, c( complements, list( check_valid = FALSE ) ) ) ) } ) setMethod( "interval_intersection", signature( "missing" ), function( x, ..., check_valid = TRUE ) { # Permitting do.call use with named lists, since do.call will put # elements whose names are not "x" into the ... argument. Stripping # names, however, puts arguments in place positionally. args <- list(...) names( args ) <- NULL if ( length( args ) == 0 ) return ( NULL ) else return( do.call( interval_intersection, c( args, list( check_valid = check_valid ) ) ) ) } ) intervals/R/type-methods.R0000644000176200001440000000141113642043061015223 0ustar liggesuserssetGeneric( "type", function(x) standardGeneric( "type" ) ) setMethod( "type", signature( "Intervals_virtual" ), function( x ) x@type ) setGeneric( "type<-", function( x, value ) standardGeneric( "type<-" ) ) setReplaceMethod( "type", "Intervals_virtual", function( x, value ) { if ( length( value ) != 1 || !( value %in% c( "Z", "R" ) ) ) stop( "The 'type' slot should be 'Z' or 'R'." ) if ( value == "Z" && !all( x@.Data %% 1 == 0, na.rm = TRUE ) ) stop( "Non-integer-valued endpoints not permitted for type 'Z'." ) x@type <- value return(x) } ) intervals/R/split.R0000644000176200001440000000036113642043061013737 0ustar liggesusers# Cause split.data.frame to be used. See "Methods for S3 Generic Functions" in # help for "Methods", for guidance and examples. split.Intervals_virtual <- split.data.frame setMethod( "split", "Intervals_virtual", split.Intervals_virtual ) intervals/R/reduce-methods.R0000644000176200001440000000174013642043061015516 0ustar liggesuserssetGeneric( "reduce", def = function( x, ... ) standardGeneric( "reduce" ) ) setMethod( "reduce", signature( "Intervals_virtual" ), function( x, check_valid = TRUE ) { if ( check_valid ) validObject( x ) has_na <- is.na( x[,1] ) | is.na( x[,2] ) if ( any( has_na ) ) { warning( "Intervals with NA endpoints removed.", call. = FALSE ) x <- x[ !has_na, ] } if ( any( empty( x ) ) ) x <- x[ !empty(x), ] # In order to collapse over abutting intervals over Z if ( type(x) == "Z" ) x <- open_intervals( x ) result <- .Call( "_reduce", x@.Data, closed( x ), is( x, "Intervals_full" ) ) new( class(x), result[[1]], closed = result[[2]], type = type(x) ) } ) intervals/R/size-methods.R0000644000176200001440000000245113642043061015221 0ustar liggesusers# For R, size is Lebesgue measure, so closure is irrelevant. Note that we don't # use the close_intervals method here, which is important since this method # requires empty, and empty currently uses size. We need to avoid circular # dependency. setGeneric( "size", def = function( x, ... ) standardGeneric( "size" ) ) setMethod( "size", signature( "Intervals" ), function( x, as = type(x) ) { result <- x[,2] - x[,1] if ( as == "Z" ) { ties <- x[,2] == x[,1] ties[ is.na( ties ) ] <- FALSE result[ ties ] <- ifelse( all( closed(x) ), 1, 0 ) result[ !ties ] <- result[ !ties ] + sum( closed(x) ) - 1 # NAs just stay NA } return( result ) } ) setMethod( "size", signature( "Intervals_full" ), function( x, as = type(x) ) { result <- x[,2] - x[,1] if ( as == "Z" ) { ties <- x[,2] == x[,1] ties[ is.na( ties ) ] <- FALSE rs <- rowSums( closed(x) ) result[ ties ] <- ifelse( rs[ ties ] == 2, 1, 0 ) result[ !ties ] <- result[ !ties ] + rs[ !ties ] - 1 } return( result ) } ) intervals/R/expand-methods.R0000644000176200001440000000422213642043061015524 0ustar liggesusers# Not exported adjust <- function( x, delta, type, direction = 1 ) { signs <- rep( c( direction, -direction ), c( nrow(x), nrow(x) ) ) if ( nrow(x) %% length(delta) != 0 ) warning( "Length of 'delta' does not evenly divide number of intervals.", call. = FALSE ) delta <- rep( delta, length = nrow( x ) ) switch( type, relative = x * ( 1 - delta ) ^ ( sign(x) * signs ), absolute = x - delta * signs ) } setGeneric( "expand", def = function( x, ... ) standardGeneric( "expand" ) ) setMethod( "expand", signature( "Intervals_virtual" ), function( x, delta = 0, type = c( "absolute", "relative" ) ) { if ( any( delta < 0, na.rm = TRUE ) ) stop( "The 'delta' argument should not contain negative values." ) type = match.arg( type ) if ( type(x) == "Z" && ( type == "relative" || any( delta %% 1 != 0, na.rm = TRUE ) ) ) stop( "Only absolute, integer-valued expansion permitted for type 'Z'." ) x@.Data <- adjust( x, delta, type, 1 ) return( x ) } ) setGeneric( "contract", def = function( x, ... ) standardGeneric( "contract" ) ) setMethod( "contract", signature( "Intervals_virtual" ), function( x, delta = 0, type = c( "absolute", "relative" ) ) { if ( any( delta < 0, na.rm = TRUE ) ) stop( "The 'delta' argument should not contain negative values." ) type = match.arg( type ) if ( type(x) == "Z" && ( type == "relative" || any( delta %% 1 != 0, na.rm = TRUE ) ) ) stop( "Only absolute, integer-valued contraction permitted for type 'Z'." ) x@.Data <- adjust( x, delta, type, -1 ) drop <- x[,1] > x[,2] | empty(x) if ( any( drop ) ) { warning( "Some empty intervals eliminated.", call. = FALSE ) x <- x[ !drop, ] } return( x ) } ) intervals/R/closed-methods.R0000644000176200001440000000303513642043061015517 0ustar liggesuserssetGeneric( "closed", function(x) standardGeneric( "closed" ) ) setMethod( "closed", signature( "Intervals_virtual" ), function( x ) x@closed ) setGeneric( "closed<-", function( x, value ) standardGeneric( "closed<-" ) ) setReplaceMethod( "closed", "Intervals", function( x, value ) { if ( !is.vector( value ) || !( length( value ) %in% 1:2 ) ) stop( "The 'closed' argument should be a vector of length 1 or 2." ) x@closed[ 1:2 ] <- value return(x) } ) setReplaceMethod( "closed", "Intervals_full", function( x, value ) { error_msg <- "The 'value' argument should be a matrix, or a vector of length 1 or 2." if ( is.vector( value ) ) { if ( length( value ) > 2 ) stop( error_msg ) value <- matrix( if ( nrow( x ) == 0 ) logical() else value, nrow( x ), 2, byrow = TRUE ) } if ( !is.matrix( value ) || nrow( value ) != nrow( x ) || ncol( value ) != 2 ) stop( error_msg ) x@closed <- value return(x) } ) intervals/R/clusters.R0000644000176200001440000000271113642043061014451 0ustar liggesuserssetGeneric( "clusters", function( x, ... ) standardGeneric( "clusters" ) ) setMethod( "clusters", signature( "numeric" ), function( x, w, which = FALSE, check_valid = TRUE ) { if ( is.integer(x) ) x <- as.numeric(x) regions <- reduce( Intervals( cbind( x, x + w ), type = "R" ), check_valid ) clusters <- interval_overlap( regions, x, check_valid ) clusters <- clusters[ sapply( clusters, length ) > 1 ] if ( which ) return( clusters ) else return( lapply( clusters, function(i) x[i] ) ) } ) setMethod( "clusters", signature( "Intervals_virtual" ), function( x, w, which = FALSE, check_valid = TRUE ) { if ( type(x) == "Z" && ( w %% 1 != 0 ) ) stop( "Non-integer 'w' supplied for intervals over Z.", call. = FALSE ) regions <- reduce( new( class(x), cbind( x[,1], x[,2] + w ), closed = closed(x), type = type(x) ), check_valid ) clusters <- interval_overlap( regions, x, check_valid ) clusters <- clusters[ sapply( clusters, length ) > 1 ] if ( which ) return( clusters ) else return( lapply( clusters, function(i) x[i,] ) ) } ) intervals/R/plot.Intervals_virtual.R0000644000176200001440000000457213642043061017306 0ustar liggesusersplot.Intervals_full <- function( x, y = NULL, axes = TRUE, xlab = "", ylab = "", xlim = NULL, ylim = NULL, col = "black", lwd = 1, cex = 1, use_points = TRUE, use_names = TRUE, names_cex = 1, ... ) { # Subset first, for efficiency, and so that the maximal y plotted is # appropriate for the region shown. if ( any( is.na( x ) ) ) x <- x[ !is.na(x), ] if ( is.null(xlim) ) xlim <- range( x@.Data ) else x <- x[ x[,2] >= xlim[1] & x[,1] <= xlim[2], ] if ( is.null(y) ) y <- .Call( "_plot_overlap", x@.Data, closed(x), is( x, "Intervals_full" ) ) if ( is.null(ylim) ) ylim <- c( 0, max( y ) ) plot( 0, 0, type = "n", xlim = xlim, ylim = ylim, axes = FALSE, xlab = xlab, ylab = ylab, ... ) # Careful with non-finite endpoints, which segments() ignores. segments( pmax( x[,1], par("usr")[1] ), y, pmin( x[,2], par("usr")[2] ), y, col = col, lwd = lwd ) if ( use_points ) { # Careful with points... adjust <- ( x[,1] == x[,2] ) & !closed(x)[,1] closed(x)[ adjust, 2 ] <- FALSE points( x@.Data, rep( y, 2 ), pch = 21, cex = cex, col = col, bg = ifelse( closed(x), col, "white" ) ) } if ( use_names && !is.null( rownames(x) ) ) { mids <- ( x[,1] + x[,2] ) / 2 text( mids, y, rownames( x ), pos = 3, offset = .5, cex = names_cex, xpd = NA ) } if ( axes ) axis( 1 ) } plot.Intervals <- function( x, y = NULL, ... ) { plot( as( x, "Intervals_full" ), y, ... ) } setMethod( "plot", c( "Intervals", "missing" ), function( x, y, ... ) plot.Intervals( x, ... ) ) setMethod( "plot", c( "Intervals", "ANY" ), function( x, y, ... ) plot.Intervals( x, y, ... ) ) setMethod( "plot", c( "Intervals_full", "missing" ), function( x, y, ... ) plot.Intervals_full( x, ... ) ) setMethod( "plot", c( "Intervals_full", "ANY" ), function( x, y, ... ) plot.Intervals_full( x, y, ... ) ) intervals/R/interval_difference-methods.R0000644000176200001440000000117613642043061020250 0ustar liggesuserssetGeneric( "interval_difference", def = function(x, y, ...) standardGeneric( "interval_difference" ) ) setMethod( "interval_difference", signature( "Intervals_virtual", "Intervals_virtual" ), function(x, y, check_valid = TRUE) { if ( check_valid ) { validObject( x ) validObject( y ) } interval_intersection( x, interval_complement( y, check_valid = FALSE ), check_valid = FALSE ) } ) intervals/R/Intervals-class.R0000644000176200001440000003015713642045000015657 0ustar liggesusers# We define two classes for two-column interval endpoint matrices. The basic class # has a two-element boolean vector indicating whether endpoints are closed or # not. The full class caries a matrix with one boolean per endpoint, permitting # full control. ######## Class definitions #### Virtual # (R 2.6.2) If I add the "VIRTUAL" tag to the representation, I am not able to # extend this class! I believe this arises because I am already extending a base # class, but I am not sure. This tag would be more appropriate, but I leave it # off... setClass( "Intervals_virtual", representation( type = "character" ), prototype( matrix( 0, 0, 2 ), type = "R" ), contains = "matrix", validity = function( object ) { # Check main matrix if ( !is.double( object@.Data ) || ncol( object@.Data ) != 2 ) return( "The 'Intervals' classes are based on two-column, numeric matrices." ) # Check 'type' string if ( length( object@type ) != 1 || !( object@type %in% c( "Z", "R" ) ) ) return( "The 'type' slot should be 'Z' or 'R'." ) # For type 'Z', check for integral endpoints if ( object@type == "Z" && !all( object@.Data[ is.finite( object@.Data ) ] %% 1 == 0 ) ) return( "Non-integer-valued endpoints not permitted for type 'Z'." ) # Check for valid intervals if ( any( object@.Data[,2] < object@.Data[,1], na.rm = TRUE ) ) return( "One or more intervals with second endpoint before first." ) return( TRUE ) } ) setMethod( "initialize", signature( "Intervals_virtual" ), function( .Object, .Data, ... ) { if ( missing( .Data ) ) callNextMethod( .Object, ... ) else { if ( is.data.frame( .Data ) ) .Data <- as.matrix( .Data ) if ( !is.matrix( .Data ) ) .Data <- matrix( .Data, ncol = 2 ) if ( is.integer( .Data ) ) { # warning( "Converting endpoints from 'integer' to 'numeric' data type. See class documentation.", call. = FALSE ) .Data <- matrix( as.numeric( .Data ), nrow( .Data ), ncol( .Data ) ) } callNextMethod( .Object, .Data, ... ) } } ) #### Intervals # Common endpoint closure state for all intervals setClass( "Intervals", representation( closed = "logical" ), prototype( closed = c( TRUE, TRUE ) ), contains = "Intervals_virtual", validity = function( object ) { # Check 'closed' slot if ( length( object@closed ) != 2 || any( is.na( object@closed ) ) ) return( "The 'closed' slot should be a logical of length 2. NA values are not permitted." ) return( TRUE ) } ) setMethod( "initialize", signature( "Intervals" ), function( .Object, .Data, closed, ... ) { if ( missing( .Data ) ) callNextMethod( .Object, ... ) else { if ( missing( closed ) ) callNextMethod( .Object, .Data, ... ) else { if ( length( closed ) == 1 ) closed <- c( closed, closed ) callNextMethod( .Object, .Data, closed = closed, ... ) } } } ) #### Intervals_full # Full control of endpoint closure. Note that if the 'closed' slot is omitted, # we use an 'initialize' method to create an appropriately sized matrix of TRUE # values. We also permit vector input, with recycling, for the 'closed' slot. setClass( "Intervals_full", representation( closed = "matrix" ), prototype( closed = matrix( TRUE, 0, 2 ) ), contains = "Intervals_virtual", validity = function( object ) { # Check 'closed' slot if ( !is.logical( object@closed ) || any( dim( object@.Data ) != dim( object@closed )) || any( is.na( object@closed ) ) ) return( "The 'closed' slot should be a logical matrix with the same dimensions as the main endpoints matrix. NA values are not permitted." ) return( TRUE ) } ) setMethod( "initialize", signature( "Intervals_full" ), function( .Object, .Data, closed, ... ) { if ( missing( .Data ) ) callNextMethod( .Object, ... ) else { if ( !is.matrix( .Data ) ) .Data <- matrix( .Data, ncol = 2 ) if ( missing( closed ) ) closed <- matrix( TRUE, nrow( .Data ), 2 ) if ( is.vector( closed ) ) { if ( length( closed ) > 2 ) stop( "The 'closed' argument should be a matrix, or a vector of length 1 or 2." ) closed <- matrix( if ( nrow( .Data ) == 0 ) logical() else closed, nrow( .Data ), 2, byrow = TRUE ) } callNextMethod( .Object, .Data, closed = closed, ... ) } } ) ######## Constructor functions Intervals <- function( ... ) new( "Intervals", ... ) Intervals_full <- function( ... ) new( "Intervals_full", ... ) ######## Subsetting setMethod( "[", signature( "Intervals" ), function( x, i, j, ..., drop ) { if ( missing(i) ) i <- rep( TRUE, nrow(x) ) if ( missing(j) ) { # Preserve class. Note that both [i,] and [i] syntax subset rows. if ( any( is.na( i ) ) ) warning( "NA indices encountered.", call. = FALSE ) x@.Data <- x@.Data[i,,drop=FALSE] return( x ) } else return( x@.Data[i,j] ) } ) # Note: row name handling for matices is completely inadequate for our purposes # here. Lots of obviously desirable behavior (like setting rownames(x)[i] when x # doesn't yet have rownames) tends to produce errors. setMethod( "[<-", signature( x = "Intervals", i = "ANY", j = "missing", value = "Intervals_virtual" ), function( x, i, j, value ) { #### Error checking if ( type(x) != type(value) ) stop( "Types do not match (Z vs. R)." ) if ( is.character(i) ) i <- match( i, rownames( x ) ) if ( any( is.na( i ) ) ) stop( "Cannot assign to NA indices or row names which do not exist." ) n <- length( (1:nrow(x))[i] ) if ( n != nrow( value ) ) stop( "Replacement object is of the wrong size." ) #### Coerce up? coerce_x <- FALSE if ( is( value, "Intervals_full" ) ) coerce_x <- TRUE else { matches <- all( closed(x) == closed(value) ) if ( !matches ) { if ( type(x) == "Z" ) value <- adjust_closure( value, closed(x)[1], closed(x)[2] ) else coerce_x <- TRUE } } if ( coerce_x ) { warning( "Coercion to 'Intervals_full' required.", call. = FALSE ) x <- as( x, "Intervals_full" ) x[ i, ] <- value return(x) } #### Data x@.Data[i,] <- value@.Data #### Rownames has_names_x <- !is.null( rownames(x) ) has_names_value <- !is.null( rownames(value) ) if ( has_names_x ) { if ( has_names_value ) rownames(x)[i] <- rownames(value) else rownames(x)[i] <- "" } else { if ( has_names_value ) { rownames(x) <- rep( "", nrow(x) ) rownames(x)[i] <- rownames(value) } } return(x) } ) setMethod( "[", signature( "Intervals_full" ), function( x, i, j, ..., drop ) { if ( missing(i) ) i <- rep( TRUE, nrow(x) ) if ( missing(j) ) { # Preserve class. Note that both [i,] and [i] syntax subset rows. if ( is.character(i) ) i <- match( i, rownames( x ) ) if ( any( is.na( i ) ) ) warning( "NA indices encountered.", call. = FALSE ) x@.Data <- x@.Data[i,,drop=FALSE] x@closed <- x@closed[i,,drop=FALSE] # We may have NAs in closed if present in i, if any(is.na(i)) == TRUE x@closed[ is.na(x@closed) ] <- TRUE return( x ) } else return( x@.Data[i,j] ) } ) setMethod( "[<-", signature( x = "Intervals_full", i = "ANY", j = "missing", value = "Intervals_virtual" ), function( x, i, j, value ) { #### Error checking if ( type(x) != type(value) ) stop( "Types do not match (Z vs. R)." ) if ( is.character(i) ) i <- match( i, rownames( x ) ) if ( any( is.na( i ) ) ) stop( "Cannot assign to NA indices or row names which do not exist." ) n <- length( (1:nrow(x))[i] ) if ( n != nrow( value ) ) stop( "Replacement object is of the wrong size." ) #### Data x@.Data[i,] <- value@.Data if ( is( value, "Intervals" ) ) x@closed[i,] <- matrix( value@closed, n, 2, byrow = TRUE ) else x@closed[i,] <- value@closed #### Rownames has_names_x <- !is.null( rownames(x) ) has_names_value <- !is.null( rownames(value) ) if ( has_names_x ) { if ( has_names_value ) rownames(x)[i] <- rownames(value) else rownames(x)[i] <- "" } else { if ( has_names_value ) { rownames(x) <- rep( "", nrow(x) ) rownames(x)[i] <- rownames(value) } } return(x) } ) ######## Coercion setMethod( "coerce", signature( from = "Intervals", to = "Intervals_full" ), function( from, to, strict ) { new( "Intervals_full", from@.Data, type = type( from ), closed = cbind( rep( closed(from)[1], nrow( from ) ), rep( closed(from)[2], nrow( from ) ) ) ) } ) setMethod( "coerce", signature( from = "Intervals_full", to = "Intervals" ), function( from, to, strict ) { if ( nrow( from ) == 0 ) new_closed <- rep( TRUE, 2 ) else new_closed <- closed( from )[1,] if ( !all( t( closed( from ) ) == new_closed ) ) stop( "Intervals do not all have the same endpoint closure." ) new( "Intervals", from@.Data, type = type( from ), closed = new_closed ) } ) setMethod( "coerce", signature( from = "Intervals_virtual", to = "character" ), function( from, to, strict ) { if ( nrow( from ) == 0 ) return( character() ) else { cl <- closed( from ) # So we only write main code once if ( is( from, "Intervals" ) ) cl <- matrix( cl, nrow(from), 2, byrow = TRUE ) result <- paste( ifelse( cl[,1], "[", "(" ), from[,1], ", ", from[,2], ifelse( cl[,2], "]", ")" ), sep = "" ) names( result ) <- rownames( from ) return( result ) } } ) ######## Union setClassUnion( "Intervals_virtual_or_numeric", c( "Intervals_virtual", "numeric" ) ) intervals/R/interval_union-methods.R0000644000176200001440000000201413642043061017276 0ustar liggesuserssetGeneric( "interval_union", def = function( x, ... ) standardGeneric( "interval_union" ) ) setMethod( "interval_union", signature( "Intervals_virtual" ), function( x, ..., check_valid = TRUE ) { reduce( c( x, ... ), check_valid ) } ) setMethod( "interval_union", signature( "missing" ), function( x, ..., check_valid = TRUE ) { # Permitting do.call use with named lists, since do.call will put # elements whose names are not "x" into the ... argument. Stripping # names, however, puts arguments in place positionally. args <- list(...) names( args ) <- NULL if ( length( args ) == 0 ) return ( NULL ) else return( do.call( interval_union, c( args, list( check_valid = check_valid ) ) ) ) } ) intervals/R/interval_complement-methods.R0000644000176200001440000000365413642043061020324 0ustar liggesuserssetGeneric( "interval_complement", def = function(x, ...) standardGeneric( "interval_complement" ) ) setMethod( "interval_complement", signature( "Intervals_virtual" ), function(x, check_valid = TRUE) { # Sort and clean up x <- reduce( x, check_valid ) # When the data type of the endpoints matrix is integer, # complications arise from maximum/minimum representable integer # values. For Intervals objects, the complement will often # need to include the minimal/maximal represenable integer, but one # or both endpoints may be open. In such cases, we adjust close with # a warning. For the moment, we force numeric endpoints throughout # the package. endpoints <- if ( nrow(x) == 0 ) matrix( c( -Inf, Inf ), 1 ) else rbind( if ( is.finite( x[1,1] ) ) c( -Inf, x[1,1] ) else NULL, cbind( x[-nrow(x),2], x[-1,1] ), if ( is.finite( x[nrow(x),2] ) ) c( x[nrow(x),2], Inf ) else NULL ) closed <- if ( class(x) == "Intervals" ) # Note that we ignore closure for non-finite endpoints. !closed(x)[2:1] else if ( nrow(x) == 0 ) TRUE else !rbind( if ( is.finite( x[1,1] ) ) c( TRUE, closed(x)[1,1] ) else NULL, cbind( closed(x)[-nrow(x),2], closed(x)[-1,1] ), if ( is.finite( x[nrow(x),2] ) ) c( closed(x)[nrow(x),2], TRUE ) else NULL ) new( class(x), endpoints, type = type(x), closed = closed ) } ) intervals/R/which_nearest-methods.R0000644000176200001440000000671313642043061017077 0ustar liggesuserssetGeneric( "which_nearest", def = function( from, to, ... ) standardGeneric( "which_nearest" ) ) setMethod( "which_nearest", signature( "Intervals_virtual", "Intervals_virtual" ), function( from, to, check_valid = TRUE ) { if ( check_valid && !( validObject(to) && validObject(from) ) ) stop( "The 'to' and/or 'from' objects are invalid." ) if ( type(to) != type(from) ) stop( "Both 'to' and 'from' should have the same type." ) if ( any( empty( to ), na.rm = TRUE ) ) { warning( "Some empty 'to' intervals encountered. Setting to NA...", call. = FALSE ) to[ empty(to), ] <- NA } if ( any( empty( from ), na.rm = TRUE ) ) { warning( "Some empty 'from' intervals encountered. Setting to NA...", call. = FALSE ) from[ empty(from), ] <- NA } if( type(to) == "Z" ) { to <- close_intervals( to ) from <- close_intervals( from ) } result <- .Call( "_which_nearest", to@.Data, from@.Data, closed(to), closed(from), class(to) == "Intervals_full", class(from) == "Intervals_full" ) result[[1]][ !is.finite( result[[1]] ) ] <- as.numeric( NA ) data.frame( distance_to_nearest = result[[1]], which_nearest = I( result[[2]] ), which_overlap = I( result[[3]] ), row.names = rownames( from ) ) } ) setMethod( "which_nearest", signature( "numeric", "Intervals_virtual" ), function( from, to, check_valid = TRUE ) { if ( type( to ) == "Z" ) { non_int <- ( from %% 1 != 0 ) if ( any( non_int, na.rm = TRUE ) ) stop( "The 'to' object is of type 'Z'. Non-integral values are not permitted in 'from'.", call. = FALSE ) } which_nearest( new( class( to ), cbind( from, from ), closed = TRUE, type = type( to ) ), to, check_valid = check_valid ) } ) setMethod( "which_nearest", signature( "Intervals_virtual", "numeric" ), function( from, to, check_valid = TRUE ) { if ( type( from ) == "Z" ) { non_int <- ( to %% 1 != 0 ) if ( any( non_int, na.rm = TRUE ) ) stop( "The 'from' object is of type 'Z'. Non-integral values are not permitted in 'to'.", call. = FALSE ) } which_nearest( from, new( class( from ), cbind( to, to ), closed = TRUE, type = type( from ) ), check_valid = check_valid ) } ) setMethod( "which_nearest", signature( "numeric", "numeric" ), function( from, to, check_valid = TRUE ) { which_nearest( Intervals( cbind( from, from ) ), Intervals( cbind( to, to ) ), check_valid = FALSE ) } ) intervals/R/show-methods.R0000644000176200001440000000140213642043061015222 0ustar liggesuserssetMethod( "show", signature( "Intervals_virtual" ), function( object ) { cat( "Object of class ", class( object ), "\n", nrow( object ), " interval", ifelse( nrow( object ) == 1, "", "s" ), " over ", type(object), ":\n", sep = "" ) ints <- as( object, "character") if ( !is.null( rownames( object ) ) ) { fmt <- sprintf( "%%%is", max( nchar( rownames( object ) ) ) ) ints <- paste( sprintf( fmt, rownames( object ) ), ints ) } cat( ints, sep = "\n" ) } ) intervals/R/close_intervals-methods.R0000644000176200001440000000436713642043061017453 0ustar liggesuserssetGeneric( "close_intervals", def = function(x) standardGeneric( "close_intervals" ) ) setMethod( "close_intervals", signature( "Intervals_virtual" ), function( x ) adjust_closure( x, close_left = TRUE, close_right = TRUE ) ) setGeneric( "open_intervals", def = function(x) standardGeneric( "open_intervals" ) ) setMethod( "open_intervals", signature( "Intervals_virtual" ), function( x ) adjust_closure( x, close_left = FALSE, close_right = FALSE ) ) setGeneric( "adjust_closure", def = function(x, ...) standardGeneric( "adjust_closure" ) ) setMethod( "adjust_closure", signature( "Intervals" ), function(x, close_left = TRUE, close_right = TRUE) { if ( type(x) == "R" ) stop( "Only applicable to type 'Z'." ) if ( any( empty(x), na.rm = TRUE ) ) { warning( "Empty intervals encountered and removed.", call. = FALSE ) x <- x[ is.na(x) | !empty(x), ] } if ( !closed(x)[1] && close_left ) x[,1] <- x[,1] + 1 if ( closed(x)[1] && !close_left ) x[,1] <- x[,1] - 1 if ( !closed(x)[2] && close_right ) x[,2] <- x[,2] - 1 if ( closed(x)[2] && !close_right ) x[,2] <- x[,2] + 1 closed(x) <- c( close_left, close_right ) return( x ) } ) setMethod( "adjust_closure", signature( "Intervals_full" ), function(x, close_left = TRUE, close_right = TRUE) { if ( type(x) == "R" ) stop( "Only applicable to type 'Z'." ) if ( any( empty(x), na.rm = TRUE ) ) { warning( "Empty intervals encountered and removed.", call. = FALSE ) x <- x[ is.na(x) | !empty(x), ] } # Left side if ( close_left ) x[ !closed(x)[,1], 1 ] <- x[ !closed(x)[,1], 1 ] + 1 else x[ closed(x)[,1], 1 ] <- x[ closed(x)[,1], 1 ] - 1 # Right side if ( close_right ) x[ !closed(x)[,2], 2 ] <- x[ !closed(x)[,2], 2 ] - 1 else x[ closed(x)[,2], 2 ] <- x[ closed(x)[,2], 2 ] + 1 closed(x) <- c( close_left, close_right ) return( x ) } ) intervals/R/is.na-methods.R0000644000176200001440000000022013642043061015247 0ustar liggesuserssetMethod( "is.na", signature( x = "Intervals_virtual" ), function(x) is.na( x[,1] ) | is.na( x[,2] ) ) intervals/R/t-methods.R0000644000176200001440000000025413642043061014511 0ustar liggesuserssetGeneric( "t", function(x) standardGeneric( "t" ) ) setMethod( "t", signature( "Intervals_virtual" ), function(x) t( x@.Data ) ) intervals/R/distance_to_nearest-methods.R0000644000176200001440000000072713642043061020270 0ustar liggesuserssetGeneric( "distance_to_nearest", function( from, to, ... ) standardGeneric( "distance_to_nearest" ) ) setMethod( "distance_to_nearest", signature( "Intervals_virtual_or_numeric", "Intervals_virtual_or_numeric" ), function( from, to, check_valid = TRUE ) { result <- which_nearest( from, to, check_valid )$distance_to_nearest names( result ) <- rownames( from ) return( result ) } ) intervals/R/as.matrix.R0000644000176200001440000000023613642043061014513 0ustar liggesusers# S3 methods as.matrix.Intervals_virtual <- function( x, ... ) as( x, "matrix" ) setMethod( "as.matrix", "Intervals_virtual", as.matrix.Intervals_virtual ) intervals/R/empty-methods.R0000644000176200001440000000216413642043061015406 0ustar liggesuserssetGeneric( "empty", def = function(x) standardGeneric( "empty" ) ) setMethod( "empty", signature( "Intervals" ), function(x) { result <- rep( FALSE, nrow(x) ) result[ is.na( x[,1] ) | is.na( x[,2] ) ] <- NA if ( !all( closed(x) ) ) { # Valid objects have x[,1] <= x[,2], so we only check this case. result[ x[,1] == x[,2] ] <- TRUE if ( type(x) == "Z" && !any( closed( x ) ) ) result[ x[,1] + 1 == x[,2] ] <- TRUE } return( result ) } ) setMethod( "empty", signature( "Intervals_full" ), function(x) { result <- rep( FALSE, nrow(x) ) result[ is.na( x[,1] ) | is.na( x[,2] ) ] <- NA any_open <- !( closed(x)[,1] & closed(x)[,2] ) both_open <- !closed(x)[,1] & !closed(x)[,2] result[ any_open & ( x[,1] == x[,2] ) ] <- TRUE if ( type(x) == "Z" ) result[ both_open & ( x[,1] + 1 == x[,2] ) ] <- TRUE return( result ) } ) intervals/R/interval_overlap-methods.R0000644000176200001440000000217213642043061017623 0ustar liggesuserssetGeneric( "interval_overlap", def = function( from, to, ... ) standardGeneric( "interval_overlap" ) ) setMethod( "interval_overlap", signature( "Intervals_virtual_or_numeric", "Intervals_virtual_or_numeric" ), function( from, to, check_valid = TRUE ) { result <- which_nearest( from, to, check_valid )$which_overlap names( result ) <- rownames( from ) return( result ) } ) argument_error <- paste( "The 'from' and 'to' arguments are required. Note that the", " interval_overlap argument names changed at v. 0.11.1.", " See documentation.", sep = "\n" ) setMethod( "interval_overlap", signature( from = "missing", to = "ANY" ), function( from, to, check_valid, ... ) stop( argument_error ) ) setMethod( "interval_overlap", signature( from = "ANY", to = "missing" ), function( from, to, check_valid, ... ) stop( argument_error ) ) intervals/R/c.R0000644000176200001440000000537713642043061013042 0ustar liggesusers# As of 2.8.1, we are still not able to write S4 methods for rbind(). Our # original plan was to use combine() as in biobase, but this seems to be causing # clashes for the generic function when both packages are loaded. Improved S4 # support for functions whose first argument is ... seems to be on the way, so # we'll just do S3 for now, and move up to S4 once it's implemented. Note that # as of 2.8.1, rbind.data.frame() still exists and is used for data frames. # Update! S3 method dispatch for rbind() is non-standard (see its documentation) # and it produced unexpected dispatch to the matrix method when presented with a # mix of Intervals and Intervals_full objects. As a consequence, we switched to # c(), which uses standard S3 dispatch. c.Intervals <- function( ... ) { args <- list(...) # Drop NULL arguments if ( any( sapply( args, is.null ) ) ) args <- args[ !sapply( args, is.null ) ] # Check if we should just return a list classes <- sapply( args, class ) if ( !all( classes %in% c( "Intervals", "Intervals_full" ) ) ) return( list( ... ) ) same_class <- all( classes == "Intervals" ) # We are in fact dealing with intervals only... if ( !all( sapply( args, type ) == type( args[[1]] ) ) ) stop( "All arguments should have the same 'type' slot." ) # Check for common closure same_closed <- all( sapply( args[-1], function(x) identical( closed( args[[1]] ), closed( x ) ) ) ) # Coerce up if necessary if ( !same_class || ( type( args[[1]] ) == "R" & !same_closed ) ) { warning( "Coercion to 'Intervals_full' required.", call. = FALSE ) return( do.call( c, lapply( args, as, "Intervals_full" ) ) ) } # Convert to common closure for Z if ( type( args[[1]] ) == "Z" & !same_closed ) args <- lapply( args, adjust_closure, close_left = closed( args[[1]] )[1], close_right = closed( args[[1]] )[2] ) result <- args[[1]] result@.Data <- do.call( rbind, lapply( args, function(x) x@.Data ) ) return( result ) } c.Intervals_full <- function( ... ) { args <- list(...) if ( any( sapply( args, is.null ) ) ) args <- args[ !sapply( args, is.null ) ] classes <- sapply( args, class ) if ( !all( classes %in% c( "Intervals", "Intervals_full" ) ) ) return( list( ... ) ) if ( !all( sapply( args, type ) == type( args[[1]] ) ) ) stop( "All arguments should have the same 'type' slot." ) if ( !all( classes == "Intervals_full" ) ) { warning( "Coercion to 'Intervals_full' required.", call. = FALSE ) args <- lapply( args, as, "Intervals_full" ) } result <- args[[1]] result@.Data <- do.call( rbind, lapply( args, function(y) y@.Data ) ) closed(result) <- do.call( rbind, lapply( args, closed ) ) return(result) } intervals/MD50000644000176200001440000000712113642055242012575 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An instance of either class consists of a two-column matrix of endpoints, plus additional slots describing endpoint closure and whether the intervals are to be thought of as being over $\Z$ or $\R$. @ <>= library( intervals ) x <- Intervals( matrix( 1:6, ncol = 2 ) ) x x[2,2] <- NA x[3,1] <- 6 x @ Objects of class \texttt{Intervals} represent collections of intervals with common endpoint closure, e.g., all left-closed, right-open. More control over endpoints is permitted with the \texttt{Intervals\_full} class. (Both classes are derived from \texttt{Intervals\_virtual}, which is not intended for use by package users.) @ <>= y <- as( x, "Intervals_full" ) y <- c( y, Intervals_full( c(2,3,5,7) ) ) closed(y)[2:3,1] <- FALSE closed(y)[4,2] <- FALSE rownames(y) <- letters[1:5] y @ The \texttt{size} method gives measure --- counting measure over $\Z$ or Lebesgue measure over $\R$ --- for each interval represented in an object. The \texttt{empty} method identifies intervals that are in fact empty sets, which over $\R$ is not the same thing as having size 0. (Valid objects must have each right endpoint no less than the corresponding left endpoint. When one or both endpoints are open, however, valid intervals may be empty.) @ <>= size(x) empty(x) empty(y) @ \begin{figure}[!tb] \centering @ <>= plot( y ) @ \caption{The \texttt{Intervals\_full} object \texttt{y}, plotted with \texttt{plot(y)}. The second row contains an \texttt{NA} endpoint, and is not shown in the plot. The empty interval is plotted as a hollow point. By default, vertical placement avoids overlaps but is compact. } \label{fig:plotting} \end{figure} \section{Interpretation of objects} An \texttt{Intervals} or \texttt{Intervals\_full} object can be thought of in two different modes, each of which is useful in certain contexts: \begin{enumerate} \item As a (non-unique) representation of a subset of $\Z$ or $\R$. \item As a collection of (possibly overlapping) intervals, each of which has a meaningful identity. \end{enumerate} \subsection{As a subset of $\Z$ or $\R$} The \emph{intervals} package provides a number of basic tools for working in the first mode, where an object represents a subset of $\Z$ or $\R$ but the rows of the endpoint matrix do not have any external identity. Basic tools include \texttt{reduce}, which returns a sorted minimal representation equivalent to the original (dropping any intervals with \texttt{NA} endpoints), as well as \texttt{interval\_union}, \texttt{interval\_complement}, and \texttt{interval\_intersection}. @ <>= reduce( y ) interval_intersection( x, x + 2 ) interval_complement( x ) @ Note that combining \texttt{x} and its complement in order to compute a union requires mixing endpoint closure types; coercion to \texttt{Intervals\_full} is automatic. @ <>= interval_union( x, interval_complement( x ) ) @ The \texttt{distance\_to\_nearest} function treats its \texttt{to} argument in the first mode, as just a subset of $\Z$ or $\R$; it treats its \texttt{from} argument, however, in the second mode, returning one distance for every row of the \texttt{from} object. In the example below, we also look at performance for large data sets (less than one second on a 2 GHz Intel Core 2 Duo Macintosh, although the time shown below will likely differ). A histogram of \texttt{d} is given in Figure~\ref{fig:distance}. @ <>= B <- 100000 left <- runif( B, 0, 1e8 ) right <- left + rexp( B, rate = 1/10 ) v <- Intervals( cbind( left, right ) ) head( v ) mean( size( v ) ) dim( reduce( v ) ) system.time( d <- distance_to_nearest( sample( 1e8, B ), v ) ) @ \begin{figure}[!tb] \centering @ <>= hist( d, main = "Distance to nearest interval" ) @ \caption{Histogram of distances from a random set of points to the nearest point in \texttt{v}. There is also a \texttt{distance\_to\_nearest} method for comparing two sets of intervals.} \label{fig:distance} \end{figure} \subsection{As a set of meaningful, possibly overlapping intervals} In some applications, each row of an object's endpoint matrix has a meaningful identity, and particular points from $\Z$ or $\R$ may be found in more than one row. To support this mode, objects may be given row names, which are propagated through calculations when appropriate. The \texttt{c} methods simply stack objects (like \texttt{rbind}), preserving row names and retaining redundancy, if any. The \texttt{interval\_overlap} method works in this mode. In the next example we use it to identify rows of \texttt{v} which are at least partially redundant, i.e., which intersect at least one other row of \texttt{v}. All rows overlap themselves, so we look for rows that overlap at least two rows: @ <>= rownames(v) <- sprintf( "%06i", 1:nrow(v) ) io <- interval_overlap( v, v ) head( io, n = 3 ) n <- sapply( io, length ) sum( n > 1 ) k <- which.max( n ) io[ k ] v[ k, ] v[ io[[ k ]], ] @ The \texttt{which\_nearest} method also respects row identity, for both its \texttt{to} and \texttt{from} arguments. In addition to computing the distance from each \texttt{from} interval to the nearest point in \texttt{to}, it also returns the row index of the \texttt{to} interval (or intervals, in case of ties) located at the indicated distance. Another function which operates in this mode is \texttt{clusters}, which takes a set of points or intervals and identifies maximal groups which cluster together --- which are separated from one another by no more than a user-specified threshold. The following code is taken from the \texttt{clusters} documentation: @ <>= B <- 100 left <- runif( B, 0, 1e4 ) right <- left + rexp( B, rate = 1/10 ) y <- reduce( Intervals( cbind( left, right ) ) ) w <- 100 c2 <- clusters( y, w ) c2[1:3] @ \section{Floating point and intervals over $\R$} When \texttt{type == "R"}, interval endpoints are not truly in $\R$, but rather, in the subset which can be represented by floating point arithmetic. (For the moment, this is also true when \texttt{type == "Z"}. See Section~\ref{sec:representation}.) This limits the endpoint values which can be represented; more importantly, if computations are performed on interval endpoints, it means that floating point error can affect whether or not endpoints coincide, whether intervals which meet at or near endpoints overlap one another, etc. In spite of this potentially serious limitation, it is still often convenient to work with intervals with non-integer endpoints, including data where adjacent intervals exactly meet at a non-integer endpoint. To address this, the \emph{intervals} package takes the following approach: \begin{itemize} \item Floating point representations of interval endpoints are assumed to be \emph{exactly equal} (in the sense of \texttt{==} in R or C++) if and only if the user intends the real values corresponding to these representations to be exactly equal. \item For cases where floating point error and approximate equality are a concern, tools are provided to permit distinguishing between ambiguous and unambiguous intersection, union, etc. \end{itemize} In the next example, \texttt{y1} does not literally overlap \texttt{y2[2,]}, although R's \texttt{all.equal} function asserts that the gap between them is smaller than the default tolerance for equivalence up to floating point precision. @ <>= delta <- .Machine[[ "double.eps" ]]^0.5 y1 <- Intervals( c( .5, 1 - delta / 2 ) ) y2 <- Intervals( c( .25, 1, .75, 2 ) ) y1 y2 all.equal( y1[1,2], y2[2,1] ) interval_intersection( y1, y2 ) @ The \texttt{expand} and \texttt{contract} methods, used with \texttt{type = "relative"}, permit consideration of the maximal and minimal interval sets which are consistent with the nominal endpoints --- from the point of view of endpoint relative difference. The \texttt{contract} method, for example, contracts each interval in a collection so that the relative difference between original and contracted endpoints is equal to tolerance \texttt{delta}. Thus, if a relative difference less than or equal to \texttt{delta} is our criterion for approximate floating point equality, the contracted object has endpoints which are approximately equal to those of the original --- even though the contracted object is a proper subset of the original. The \texttt{expand} method is similar, but generates a proper superset. @ <>= contract( y1, delta, "relative" ) @ We compute two separate intersections which bound the nominal intersection: @ <>= inner <- interval_intersection( contract( y1, delta, "relative" ), contract( y2, delta, "relative" ) ) inner outer <- interval_intersection( expand( y1, delta, "relative" ), expand( y2, delta, "relative" ) ) outer @ Finally, we identify points which may or may not be in the intersection, depending on whether we make a conservative, literal, or anti-conservative interpretation of the nominal endpoints. @ <>= interval_difference( outer, inner ) @ The \texttt{expand} and \texttt{contract} methods have other uses as well. Here, we eliminate gaps of size 2 or smaller: @ <>= x <- Intervals( c(1,10,100,8,50,200), type = "Z" ) x w <- 2 close_intervals( contract( reduce( expand(x, w/2) ), w/2 ) ) @ \section{Notes on implementation} \subsection{Endpoint representation} \label{sec:representation} For the moment, interval endpoints are always stored using R's \emph{numeric} data type. Although this is wasteful from an memory and speed point of view, we do it for two reasons. First, use of R's \texttt{Inf} and \texttt{-Inf} --- not possible with the \emph{integer} type --- is very convenient when computing complements. Second, the range of integers which can be represented using the \emph{numeric} data type is considerably greater: @ <>= .Machine$integer.max numeric_max <- with( .Machine, double.base^double.digits ) options( digits = ceiling( log10( numeric_max ) ) ) numeric_max @ \subsection{Efficiency} All computations are accomplished by treating intervals as pairs of tagged endpoints, sorting these endpoints (along with their tags), and then making a single pass through the results. Computational complexity for set operations is therefore $O(n \log n)$, where input object $i$ contains $n_i$ rows and $n = \sum_i n_i$. The same sorting approach is also used for \texttt{interval\_overlap}, although if every interval in a query object of $m$ rows overlaps every intervals in a target object of $n$ rows, generating output alone must of necessity be $O(mn)$. Sorted endpoint vectors are not retained in memory. If one wishes to query a particular object over and over, repeated sorting would be inefficient; in practice so far, however, such repeated querying has not been needed. \subsection{Checking validity} The code behind \texttt{which\_nearest} and \texttt{reduce} (key methods in the \emph{intervals} package, which may be directly called by the user and are also used internally in numerous locations) is written in C++ for efficiency. The compiled code makes a number of assumptions about the \texttt{SEXP} objects passed in as arguments, but does not explicitly check these assumptions. Nonetheless, when the R wrappers for the compiled code are applied to \emph{valid} objects from the \texttt{Intervals} or \texttt{Intervals\_full} classes, all assumptions will always be met. This design decision was taken so that the requirements for individual objects and their contents could be gathered together in a single, natural location: the classes' \texttt{validity} functions. The \emph{intervals} package provides replacement methods --- e.g., \texttt{type} and \texttt{closed} --- which implement error checking and preserve object validity. R's implementation of S4 classes, however, leaves object data slots exposed to the user. As a consequence, a user can directly manipulate the data slots of a valid \texttt{Intervals} or \texttt{Intervals\_full} object in a way that invalidates the object, but does not generate any warning or error. To prevent invalid objects from being passed to compiled code --- and potentially generating segmentation faults or other problems --- all wrapper code in this package includes a \texttt{check\_valid} argument. This argument is set to \texttt{TRUE} by default, so that \texttt{validObject} is called on relevant objects before handing them off to the compiled code. For efficiency, users may choose to override this extra check if they are certain they have not manually assigned inappropriate content to objects' data slots. \section{Session information} @ <>= si <- as.character( toLatex( sessionInfo() ) ) cat( si[ -grep( "Locale", si ) ], sep = "\n" ) @ \end{document} intervals/inst/doc/intervals_overview.R0000644000176200001440000001106413642053123020064 0ustar liggesusers### R code from vignette source 'intervals_overview.Rnw' ################################################### ### code chunk number 1: width ################################################### options( width = 80 ) ################################################### ### code chunk number 2: Intervals ################################################### library( intervals ) x <- Intervals( matrix( 1:6, ncol = 2 ) ) x x[2,2] <- NA x[3,1] <- 6 x ################################################### ### code chunk number 3: Intervals_full ################################################### y <- as( x, "Intervals_full" ) y <- c( y, Intervals_full( c(2,3,5,7) ) ) closed(y)[2:3,1] <- FALSE closed(y)[4,2] <- FALSE rownames(y) <- letters[1:5] y ################################################### ### code chunk number 4: size ################################################### size(x) empty(x) empty(y) ################################################### ### code chunk number 5: plotting ################################################### plot( y ) ################################################### ### code chunk number 6: set_operations ################################################### reduce( y ) interval_intersection( x, x + 2 ) interval_complement( x ) ################################################### ### code chunk number 7: set_operations ################################################### interval_union( x, interval_complement( x ) ) ################################################### ### code chunk number 8: distance ################################################### B <- 100000 left <- runif( B, 0, 1e8 ) right <- left + rexp( B, rate = 1/10 ) v <- Intervals( cbind( left, right ) ) head( v ) mean( size( v ) ) dim( reduce( v ) ) system.time( d <- distance_to_nearest( sample( 1e8, B ), v ) ) ################################################### ### code chunk number 9: distanceplot ################################################### hist( d, main = "Distance to nearest interval" ) ################################################### ### code chunk number 10: overlap ################################################### rownames(v) <- sprintf( "%06i", 1:nrow(v) ) io <- interval_overlap( v, v ) head( io, n = 3 ) n <- sapply( io, length ) sum( n > 1 ) k <- which.max( n ) io[ k ] v[ k, ] v[ io[[ k ]], ] ################################################### ### code chunk number 11: clusters ################################################### B <- 100 left <- runif( B, 0, 1e4 ) right <- left + rexp( B, rate = 1/10 ) y <- reduce( Intervals( cbind( left, right ) ) ) w <- 100 c2 <- clusters( y, w ) c2[1:3] ################################################### ### code chunk number 12: expand ################################################### delta <- .Machine[[ "double.eps" ]]^0.5 y1 <- Intervals( c( .5, 1 - delta / 2 ) ) y2 <- Intervals( c( .25, 1, .75, 2 ) ) y1 y2 all.equal( y1[1,2], y2[2,1] ) interval_intersection( y1, y2 ) ################################################### ### code chunk number 13: expand ################################################### contract( y1, delta, "relative" ) ################################################### ### code chunk number 14: expand ################################################### inner <- interval_intersection( contract( y1, delta, "relative" ), contract( y2, delta, "relative" ) ) inner outer <- interval_intersection( expand( y1, delta, "relative" ), expand( y2, delta, "relative" ) ) outer ################################################### ### code chunk number 15: expand ################################################### interval_difference( outer, inner ) ################################################### ### code chunk number 16: gaps ################################################### x <- Intervals( c(1,10,100,8,50,200), type = "Z" ) x w <- 2 close_intervals( contract( reduce( expand(x, w/2) ), w/2 ) ) ################################################### ### code chunk number 17: integer_range ################################################### .Machine$integer.max numeric_max <- with( .Machine, double.base^double.digits ) options( digits = ceiling( log10( numeric_max ) ) ) numeric_max ################################################### ### code chunk number 18: sessionInfo ################################################### si <- as.character( toLatex( sessionInfo() ) ) cat( si[ -grep( "Locale", si ) ], sep = "\n" )