# warp

0th

Percentile

##### Apply a warping to a given timeseries

Returns the indexing required to apply the optimal warping curve to a given timeseries (warps either into a query or into a reference).

Keywords
ts
##### Usage
warp(d,index.reference=FALSE)
##### Arguments
d

dtw object specifying the warping curve to apply

index.reference

TRUE to warp a reference, FALSE to warp a query

##### Details

The warping is returned as a set of indices, which can be used to subscript the timeseries to be warped (or rows in a matrix, if one wants to warp a multivariate time series). In other words, warp converts the warping curve, or its inverse, into a function in the explicit form.

Multiple indices that would be mapped to a single point are averaged, with a warning. Gaps in the index sequence are filled by linear interpolation.

##### Value

A list of indices to subscript the timeseries.

Examples in dtw show how to graphically apply the warping via parametric plots.
library(dtw) # NOT RUN { idx<-seq(0,6.28,len=100); query<-sin(idx)+runif(100)/10; reference<-cos(idx) alignment<-dtw(query,reference); wq<-warp(alignment,index.reference=FALSE); wt<-warp(alignment,index.reference=TRUE); old.par <- par(no.readonly = TRUE); par(mfrow=c(2,1)); plot(reference,main="Warping query"); lines(query[wq],col="blue"); plot(query,type="l",col="blue", main="Warping reference"); points(reference[wt]); par(old.par); ############## ## ## Asymmetric step makes it "natural" to warp ## the reference, because every query index has ## exactly one image (q->t is a function) ## alignment<-dtw(query,reference,step=asymmetric) wt<-warp(alignment,index.reference=TRUE); plot(query,type="l",col="blue", main="Warping reference, asymmetric step"); points(reference[wt]); # }