+computeCoeffs = function(data, index, nb_series_per_chunk, wf)
+{
+ coeffs_chunk = NULL
+ if (is.data.frame(data) && index < nrow(data))
+ {
+ #full data matrix
+ coeffs_chunk = curvesToCoeffs(
+ data[index:(min(index+nb_series_per_chunk-1,nrow(data))),], wf)
+ }
+ else if (is.function(data))
+ {
+ #custom user function to retrieve next n curves, probably to read from DB
+ coeffs_chunk = curvesToCoeffs( data(rank=(index-1)+seq_len(nb_series_per_chunk)), wf )
+ }
+ else if (exists(data_con))
+ {
+ #incremental connection ; TODO: more efficient way to parse than using a temp file
+ ascii_lines = readLines(data_con, nb_series_per_chunk)
+ if (length(ascii_lines > 0))
+ {
+ series_chunk_file = ".series_chunk"
+ writeLines(ascii_lines, series_chunk_file)
+ coeffs_chunk = curvesToCoeffs( read.csv(series_chunk_file), wf )
+ unlink(series_chunk_file)
+ }
+ }
+ coeffs_chunk
+}
+
+#NOTE: always keep ID in first column
+curvesToCoeffs = function(series, wf)
+{
+ if (!require(wavelets, quietly=TRUE))
+ stop("Couldn't load wavelets library")
+ L = length(series[1,])
+ D = ceiling( log2(L-1) )
+ nb_sample_points = 2^D
+ #TODO: parallel::parApply() ?!
+ res = apply(series, 1, function(x) {
+ interpolated_curve = spline(1:(L-1), x[2:L], n=nb_sample_points)$y
+ W = wavelets::dwt(interpolated_curve, filter=wf, D)@W
+ nrj_coeffs = rev( sapply( W, function(v) ( sqrt( sum(v^2) ) ) ) )
+ return ( c(x[1], nrj_coeffs) )
+ })
+ return (as.data.frame(res))
+}