--- /dev/null
+#datasets
+/data/*
+!/data/README
+!/data/preprocessing/
+!/data/curves_to_db/
+
+#files generated by initialize.sh
+/.gitfat
+/.gitattributes
+
+#ignore temporary files
+*~
+*.swp
+
+#ignore binary files generated by claws()
+*.epclust.bin
+
+#ignore R session files
+.Rhistory
+.RData
+
+#ignore roxygen2 generated files
+/epclust/NAMESPACE
+/epclust/man/*.Rd
+!/epclust/man/*-package.Rd
+
+#ignore R CMD build/check genrated files
+/*.Rcheck/
+/*.tar.gz
+
+#ignore jupyter generated file (HTML vignette, and reports)
+*.ipynb.html
+
+#ignore object files and executables
+*.o
+*.so
+*.exe
+
+#ignore RcppExports, generated by Rcpp::compileAttributes
+/epclust/R/RcppExports.R
+/epclust/src/RcppExports.cpp
+
+#misc
+Rprof.out
+*.zip
+.Rproj.user
+*.Rproj
+
+# temp
+/temp/*.xls
--- /dev/null
+# Clustering of EDF power curves from Linky data
+
+Joint work with [Jairo Cugliari](http://eric.univ-lyon2.fr/~jcugliari/),
+[Yannig Goude](https://scholar.google.com/citations?user=D-OLEG4AAAAJ&hl=fr) and
+[Jean-Michel Poggi](http://www.math.u-psud.fr/~poggi/)
+
+---
+
+This program takes N time-series in input and is divided in two stages:
+ 1. (Heuristic) k-medoids algorithm in parallel to obtain K1 centers, K1 ≪ N
+ 2. Clustering from WER distances to obtain the final K2 < K1 group representations
+
+See ?epclust once the package is loaded.
--- /dev/null
+simulateur : complètement aléatoire ? from Irish dataset ?
+wmtsa : wavBootstrap
+wavelets methods in statistics with R - p180
+
+Essayer distance wdist du package biwavelet ?
+
+geometric structure of high dim data and dim reduction 2011
+
+Piste à explorer pour les comparaisons: H20
+
+renvoyer nombre d'individues par classe ?
+hypothèse : données déjà ordonnées 48 1/2H sur 365j
+utiliser mixmod avec modèles allongés
+doit tourner sur machine plutôt standard, utilisateur "lambda"
+
+#https://stat.ethz.ch/pipermail/r-help/2011-June/280133.html
+#randCov = function(d)
+#{
+# x <- matrix(rnorm(d*d), nrow=d)
+# x <- x / sqrt(rowSums(x^2))
+# x %*% t(x)
+#}
+
+#TODO: map-reduce more appropriate R/clustering.R ligne 88
+
+#TODO: use dbs(),
+ #https://www.r-bloggers.com/debugging-parallel-code-with-dbs/
+ #http://gforge.se/2015/02/how-to-go-parallel-in-r-basics-tips/
+
+PLOT:
+plot manifold 2D distances WER /
+fenetre tempo forme des courbes /
+medoids /
+gain en prevision: clust puis full --> enercast
+
+réduire taille 17519 trop long ?
+
+TODO: revoir les arguments, simplifier (dans les clustering...),
+
+#si pas lissage --> on doit trouver 1 : TOCHECK
+#regarder biwavelets smooth.wavelet : s'en inspirer
--- /dev/null
+Available dataset: data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households
+
+Power-Networks-LCL-June2015(withAcornGps) meter reading
+
+########################################################################
+
+Australian smart-city dataset:
+data.gouv.au/dataset/smart-grid-smart-city-customer-trial-data
+Now unavailable, but maybe some hope https://data.gov.au/datarequest/98266a0b-6e0c-4092-9e3b-9e3b4cab20ad
+
+CDINTERVALREADINGALLNOQUOTES.CSV.7z meter reading
+sgsc-cthanplug-readings.7z Home Aread Network Plug readings
+
+########################################################################
+
+EDF data (undisclosed)
+2009.rar 25K data for year 2009
+2010.rar 25K data for year 2010
+2011.zip 25K data for year 2011
--- /dev/null
+0] Precondition: current folder, containing init_db.sql and curves2db.R
+ must be readable by the postgres user (some other weird restrictions ...)
+
+1] Initialization:
+ a) log in as postgres user:
+ sudo -i -u postgres
+ b) launch PostgreSQL shell:
+ psql
+ c) execute init script:
+ \i /path/to/init_db.sql
+
+2] Launch DB-filling script:
+ a) check/tune parameters at the beginning of curves2db.R
+ b) run the script
+
+=====
+
+WARNING: very slow for large datasets
+
+read_db.R: example usage to read curves from DB
--- /dev/null
+require(RPostgreSQL)
+require(data.table)
+
+nb_curves_per_request <- 100 #curves per (insert) request
+tot_nb_curves <- 25e3 #total number of curves
+dimension <- 15000 #number of sample points
+nb_clust <- 15 #number of clusters
+temp_file <- "tmp_curves_batch" #(accessible) temporary file to store curves
+
+# Init connection with DB
+driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request)
+con <- dbConnect(driver, user="user", password="pwd",
+ host="localhost", port="5432", dbname="db")
+
+# Replace next call + func with any custom initialization
+ref_centroids <- sapply(1:nb_clust, function(k) cumsum(rnorm(dimension)))
+genRandCurves <- function(indices) {
+ mat <- sapply(indices, function(i) {
+ if (i > tot_nb_curves)
+ return (NULL)
+ j <- sample(ncol(ref_centroids), 1)
+ ref_centroids[,j] + rnorm(dimension)
+ })
+ # fwrite() write per columns => need to "transpose" mat; but it's quite inefficient...
+ lapply(1:dimension, function(i) mat[i,])
+}
+
+# Loop: generate nb_curves_per_request curves, store them on a temp file,
+# and insert into DB using COPY command (should be faster than insert)
+nb_curves <- 0
+while (nb_curves < tot_nb_curves)
+{
+ curves <- genRandCurves((nb_curves+1):(nb_curves+nb_curves_per_request))
+ fwrite(curves, temp_file, append=FALSE, sep=",")
+ # Required hack: add brackets (PostgreSQL syntax ...)
+ system(paste("sed -i 's/\\(.*\\)/{\\1}/g' ",temp_file,sep=''))
+ query <- paste("COPY series (curve) FROM '", normalizePath(temp_file), "';", sep='')
+ dbSendQuery(con, query)
+ nb_curves <- nb_curves + nb_curves_per_request
+}
+
+dbDisconnect(con)
+unlink(temp_file)
--- /dev/null
+library(RPostgreSQL)
+library(data.table)
+
+nb_curves_per_request <- 5 # curves per (insert) request
+#tot_nb_curves <- 25e3 # total number of curves
+#dimension <- 17519 # number of sample points
+#nb_clust <- 15 # number of clusters
+temp_file <- "tmp_curves_batch" # (accessible) temporary file to store curves
+
+# Init connection with DB
+driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request)
+con <- dbConnect(driver, user = "irsdi", password = "irsdi2017",
+ host = "localhost", port = "5432", dbname = "edf25m")
+
+setwd("~/tmp/")
+ref_centroids <- fread("2009_matrix-dt.csv")
+#ref_centroids <- sapply(1:nb_clust, function(k) cumsum(rnorm(dimension)))
+
+genRandCurves <- function(line, times) {
+ #ids <- as.integer(sprintf("%04i", seq_len(times) - 1)) * 1e6 + line[1]
+ ids <- as.integer(sprintf("%010i", (seq_len(times) - 1) * 1e6 + line[1]))
+ curve <- as.matrix(line[-1])
+ # simus <- lapply(1:times, function(i) line[-1] * runif(length(curve), .95, 1.05))
+ perturbances <- matrix(runif(length(curve) * times, .95, 1.05), nrow = times)
+ #curves_sim <- cbind(ids, t(apply(perturbances, 1, FUN = '*', curve)))
+ curves_sim <- data.frame(ids, t(apply(perturbances, 1, FUN = '*', curve)))
+ # series in columns, data as data.frame (as fwrite requests)
+ return(curves_sim)
+}
+
+# Loop: generate nb_curves_per_request curves, store them on a temp file,
+# and insert into DB using COPY command (should be faster than insert)
+system.time(
+ for (i in seq_len(nrow(ref_centroids))) {
+ curves <- genRandCurves(line = as.matrix(ref_centroids[i, ]), times = nb_curves_per_request)
+ fwrite(curves, temp_file, append = FALSE, sep = ",", col.names = FALSE)
+ # Required hack: add brackets (PostgreSQL syntax ...)
+ system(paste("sed -i 's/\\(.*\\)/{\\1}/g' ", temp_file, sep = ''))
+ query <- paste("COPY series (curve) FROM '", normalizePath(temp_file), "';", sep = '')
+ dbSendQuery(con, query)
+ }
+)
+
+dbDisconnect(con)
+unlink(temp_file)
--- /dev/null
+require(RPostgreSQL)
+
+##############################
+# Follow steps in README first
+##############################
+
+nb_curves_per_request <- 100 #curves per (select) request
+
+# Init connection with DB
+driver <- PostgreSQL(fetch.default.rec = nb_curves_per_request)
+con <- dbConnect(driver, user="irsdi", password="irsdi2017",
+ host="localhost", port="5432", dbname="edf25m")
+
+# Fill associative array, map index to identifier
+indexToID_inDB <- as.character(
+ dbGetQuery(con, 'SELECT DISTINCT id FROM series')[,"id"] )
+
+# Function to retrieve curves within some indices range
+getCurves <- function(indices)
+{
+ indices = indices[ indices <= length(indexToID_inDB) ]
+ if (length(indices) == 0)
+ return (NULL)
+ request <- "SELECT curve FROM series WHERE id in ("
+ for (i in seq_along(indices))
+ {
+ request <- paste(request, indexToID_inDB[ indices[i] ], sep="")
+ if (i < length(indices))
+ request <- paste(request, ",", sep="")
+ }
+ request <- paste(request, ")", sep="")
+ df_series <- dbGetQuery(con, request)
+
+#weird result: an integer, and then string "{val1,val2,val3,...,valD}" :/
+#print(summary(df_series))
+df_series
+# matrix(df_series[,"value"], ncol=length(indices))
+}
+
+# Test
+#curves <- getCurves(c(1:3,7,11))
+library(epclust)
+res <- claws(getCurves, 50, 15, 500, 500, random=FALSE, ncores_clust=3, verbose=TRUE)
+
+dbDisconnect(con)
+unlink(temp_file)
--- /dev/null
+-- Se loguer avec droits admin sur un serveur PostgreSQL
+-- (en local ou non). Puis :
+
+-- superuser status required to use command 'copy' ...
+create role irsdi with superuser login password 'irsdi2017';
+create database edf25m with owner irsdi;
+\c edf25m irsdi -- connect to DB as irsdi
+create table series ( id serial primary key, curve real[] );
--- /dev/null
+TARGET = transform.exe
+
+$(TARGET): convert_to_CSV.c
+ gcc convert_to_CSV.c -lm -lcgds -o $(TARGET)
--- /dev/null
+Converter from raw by-columns CSV format to by-rows CSV file
+(much smaller), as epclust::claws() expect as CSV input.
+
+Dependency: cgds, https://git.auder.net/?p=cgds.git
--- /dev/null
+#define __STDC_FORMAT_MACROS //to print 64bits unsigned integers
+#include <inttypes.h>
+#include <cgds/Vector.h>
+#include <string.h>
+#include <math.h>
+#include <float.h>
+#include <stdio.h>
+
+// Read an integer char by char, and position the cursor to next character
+char readInt(FILE* stream, int* integer)
+{
+ *integer = 0;
+ char curChar = fgetc(stream);
+ int sign = (curChar == '-' ? -1 : 1);
+ while (curChar < '0' || curChar > '9')
+ curChar = fgetc(stream);
+ while (curChar >= '0' && curChar <= '9')
+ {
+ *integer = 10 * (*integer) + (int) (curChar - '0');
+ curChar = fgetc(stream);
+ }
+ (*integer) *= sign;
+ return curChar; //separator, endline or .,e,E (if inside readReal)
+}
+
+// Read a real number char by char, and position the cursor to next character
+char readReal(FILE* stream, float* real)
+{
+ int integerPart, exponent = 0, fractionalPart = 0, countZeros = 0;
+ char curChar = readInt(stream, &integerPart);
+ if (curChar == '.')
+ {
+ //need to count zeros
+ while ((curChar = fgetc(stream)) == '0')
+ countZeros++;
+ if (curChar >= '1' && curChar <= '9')
+ {
+ ungetc(curChar, stream);
+ curChar = readInt(stream, &fractionalPart);
+ }
+ }
+ if (curChar == 'e' || curChar == 'E')
+ curChar = readInt(stream, &exponent);
+ *real = ( integerPart + (integerPart>=0 ? 1. : -1.) * (float)fractionalPart
+ / pow(10,countZeros+floor(log10(fractionalPart>0 ? fractionalPart : 1)+1)) )
+ * pow(10,exponent);
+
+ return curChar; //separator or endline
+}
+
+// Parse a line into integer+float (ID, value)
+static void scan_line(FILE* ifile, char sep,
+ int posID, int* ID, int posValue, float* value)
+{
+ char curChar;
+ int position = 1;
+ while (1)
+ {
+ if (position == posID)
+ curChar = readInt(ifile, ID);
+ else if (position == posValue)
+ curChar = readReal(ifile, value);
+ else
+ curChar = fgetc(ifile); //erase the comma (and skip field then)
+
+ // Continue until next separator (or line end or file end)
+ while (!feof(ifile) && curChar != '\n' && curChar != sep)
+ curChar = fgetc(ifile);
+ position++;
+
+ if (curChar == '\n' || feof(ifile))
+ {
+ // Reached end of line
+ break;
+ }
+ }
+}
+
+// Main job: parse a data file into a conventional CSV file in rows, without header
+// Current limitations:
+// - remove partial series (we could fill missing values instead)
+// - consider missing fields == 0 (if ,,)
+// - IDs should be st. pos. integers
+// - UNIX linebreaks only (\n)
+int transform(const char* ifileName, int posID, int posValue,
+ const char* ofileName, int nbItems, char sep)
+{
+ uint64_t processedLines = 0; //execution trace
+ uint32_t seriesCount=0, skippedSeriesCount=0, mismatchLengthCount=0;
+ int tsLength, lastID=0, ID, firstID, eof;
+ float value, tmpVal;
+ Vector* tsBuffer = vector_new(float);
+ FILE* ifile = fopen(ifileName, "r");
+ // Output file to write time-series sequentially, CSV format.
+ FILE* ofile = fopen(ofileName, "w");
+
+ // Skip header
+ char curChar;
+ do
+ curChar = fgetc(ifile);
+ while (curChar != '\n');
+
+ // Process one client (ID in first column) at a time
+ while (1)
+ {
+
+ eof = feof(ifile);
+ if (!eof)
+ {
+ // Is there anything left to read? (file may end with '\n')
+ curChar = fgetc(ifile);
+ if (!feof(ifile) && curChar != '\n')
+ {
+ // Yes: read current line
+ ungetc(curChar, ifile);
+ scan_line(ifile, sep, posID, &ID, posValue, &value);
+ }
+ else
+ eof = 1;
+ }
+
+ if (ID != lastID || eof)
+ {
+ if (lastID > 0)
+ {
+ // Just starting a new time-series (or EOF): process the last one
+ if (tsLength == vector_size(tsBuffer))
+ {
+ for (int i=0; i<tsLength-1; i++)
+ {
+ vector_get(tsBuffer, i, tmpVal);
+ fprintf(ofile, "%g%c", tmpVal, sep);
+ }
+ vector_get(tsBuffer, tsLength-1, tmpVal);
+ fprintf(ofile, "%g\n", tmpVal);
+ seriesCount++;
+ if (nbItems > 0 && ++seriesCount >= nbItems)
+ break;
+ }
+ else
+ {
+ // Mismatch lengths: skip series
+ mismatchLengthCount++;
+ }
+ }
+ else
+ firstID = ID;
+ if (eof)
+ {
+ // Last serie is processed
+ break;
+ }
+ // Reinitialize current index of new serie
+ tsLength = 0;
+ lastID = ID;
+ }
+
+ // Fill values buffer
+ if (ID != firstID)
+ {
+ if (tsLength < vector_size(tsBuffer))
+ vector_set(tsBuffer, tsLength, value);
+ }
+ else
+ {
+ // First serie is reference: push all values
+ vector_push(tsBuffer, value);
+ }
+ tsLength++;
+
+ if ((++processedLines) % 1000000 == 0)
+ fprintf(stdout,"Processed %"PRIu64" lines\n", processedLines);
+ }
+
+ // finally print some statistics
+ fprintf(stdout,"NOTE: %u series retrieved.\n",seriesCount);
+ if (mismatchLengthCount > 0)
+ fprintf(stdout,"WARNING: %u mismatch series lengths.\n",mismatchLengthCount);
+
+ fclose(ifile);
+ fclose(ofile);
+ return 0;
+}
+
+int main(int argc, char** argv)
+{
+ if (argc < 4) //program name + 3 arguments
+ {
+ printf("Usage: transform ifileName posID posValue [ofileName [nbItems [sep]]]\n \
+ - ifileName: name of by-columns CSV input file\n \
+ - posID: position of the identifier in a line (start at 1)\n \
+ - posValue: position of the value of interest in a line\n \
+ - ofileName: name of the output file; default: out.csv\n \
+ - nbItems: number of series to retrieve; default: 0 (all)\n \
+ - sep: fields separator; default: ','\n");
+ return 0;
+ }
+ else
+ {
+ return transform(argv[1], atoi(argv[2]), atoi(argv[3]),
+ argc > 4 ? argv[4] : "out.csv",
+ argc > 5 ? atoi(argv[5]) : 0,
+ argc > 6 ? argv[6][0] : ',');
+ }
+}
--- /dev/null
+1.05,2,3
+10000,1,0
+325,-2000,15.55
--- /dev/null
+ID,field1,field2,field3
+1,Z28,1.05,NA
+1,A32,2.,NA
+1,B21,3.,0
+2,F12,1e4,0
+2,H234,1.,1
+2,J785,0.,1
+3,S213,4.,0
+3,NaN,50.,-15,NA
+4,R234,2.1e2,1
+4,P0,2,NA
+4,D2,-23e3,0
+4,V23,-3.1e1,0
+5,N52,3.25e2,NaN
+5,NaN,-2.0e3,NaN
+5,I3,15.55,NA
--- /dev/null
+#$# git-fat 5f2ff090cd11aeff66809f2757b3f6ea6bfb035b 905139
--- /dev/null
+#$# git-fat 96fc0307b651a06a05f81c1f5d0f79a1fbd2bd45 2760474
--- /dev/null
+Package: epclust
+Title: Clustering Individual Electricity Power Curves
+Description: Electric Power curves CLUSTering, through their wavelets
+ decomposition. The main function 'claws' takes (usually long) time-series
+ in input, and return as many clusters centers as requested, along with their
+ ranks and synchrones (sum of all curves in one group).
+ For the method, see ?claws and https://arxiv.org/abs/1101.4744.
+ For usage examples, see ?claws and package vignette.
+Version: 0.1-0
+Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
+ Jairo Cugliari <Jairo.Cugliari@univ-lyon2.fr> [aut],
+ Yannig Goude <Yannig.Goude@edf.fr> [ctb],
+ Jean-Michel Poggi <Jean-Michel.Poggi@math.u-psud.fr> [ctb]
+Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
+Depends:
+ R (>= 3.0.0)
+Imports:
+ methods,
+ parallel,
+ cluster,
+ bigmemory,
+ wavethresh,
+ Rwave
+Suggests:
+ synchronicity,
+ devtools,
+ testthat,
+ roxygen2,
+ MASS,
+ wmtsa,
+ DBI,
+ digest
+License: MIT + file LICENSE
+RoxygenNote: 6.0.1
+Collate:
+ 'main.R'
+ 'clustering.R'
+ 'de_serialize.R'
+ 'A_NAMESPACE.R'
+ 'computeSynchrones.R'
+ 'computeWerDists.R'
+ 'plot.R'
+ 'simulateCurve.R'
+ 'utils.R'
--- /dev/null
+Copyright (c) 2016-2017, Benjamin Auder
+ 2016-2017, Jairo Cugliari
+ 2016-2017, Yannig Goude
+ 2016-2017, Jean-Michel Poggi
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
--- /dev/null
+# Generated by roxygen2: do not edit by hand
+
+export(binarize)
+export(binarizeTransform)
+export(claws)
+export(clusteringTask1)
+export(clusteringTask2)
+export(computeClusters1)
+export(computeClusters2)
+export(computeSynchrones)
+export(computeWerDists)
+export(curvesToContribs)
+export(getDataInFile)
+importFrom(Rcpp,evalCpp)
+importFrom(Rcpp,sourceCpp)
+importFrom(Rwave,cwt)
+importFrom(bigmemory,as.big.matrix)
+importFrom(bigmemory,big.matrix)
+importFrom(bigmemory,is.big.matrix)
+importFrom(cluster,pam)
+importFrom(methods,is)
+importFrom(parallel,clusterExport)
+importFrom(parallel,makeCluster)
+importFrom(parallel,parLapply)
+importFrom(parallel,stopCluster)
+importFrom(stats,spline)
+importFrom(wavelets,dwt)
+importFrom(wavelets,wt.filter)
+useDynLib(epclust)
--- /dev/null
+#' @include de_serialize.R
+#' @include clustering.R
+#' @include main.R
+#'
+#' @useDynLib epclust
+#'
+#' @importFrom Rwave cwt
+#' @importFrom cluster pam
+#' @importFrom parallel makeCluster clusterExport parLapply stopCluster
+#' @importFrom wavethresh wd filter.select
+#' @importFrom stats spline
+#' @importFrom methods is
+#' @importFrom bigmemory big.matrix as.big.matrix is.big.matrix
+#' @importFrom utils head tail
+NULL
--- /dev/null
+#' Two-stage clustering, within one task (see \code{claws()})
+#'
+#' \code{clusteringTask1()} runs one full stage-1 task, which consists in iterated
+#' clustering on nb_curves / ntasks energy contributions, computed through
+#' discrete wavelets coefficients.
+#' \code{clusteringTask2()} runs a full stage-2 task, which consists in WER distances
+#' computations between medoids (indices) output from stage 1, before applying
+#' the second clustering algorithm on the distances matrix.
+#'
+#' @param getContribs Function to retrieve contributions from initial series indices:
+#' \code{getContribs(indices)} outputs a contributions matrix, in columns
+#' @inheritParams claws
+#' @inheritParams computeSynchrones
+#' @inheritParams computeWerDists
+#'
+#' @return The indices of the computed (resp. K1 and K2) medoids.
+#'
+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 clusteringTask2
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask1 <- function(indices, getContribs, K1, algoClust1, nb_items_clust,
+ ncores_clust=3, verbose=FALSE)
+{
+ if (verbose)
+ cat(paste("*** Clustering task 1 on ",length(indices)," series [start]\n", sep=""))
+
+ if (length(indices) <= K1)
+ return (indices)
+
+ parll <- (ncores_clust > 1)
+ if (parll)
+ {
+ # outfile=="" to see stderr/stdout on terminal
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_clust, outfile = "")
+ else
+ parallel::makeCluster(ncores_clust)
+ parallel::clusterExport(cl, c("getContribs","K1","verbose"), envir=environment())
+ }
+ # Iterate clustering algorithm 1 until K1 medoids are found
+ while (length(indices) > K1)
+ {
+ # Balance tasks by splitting the indices set - as evenly as possible
+ indices_workers <- .splitIndices(indices, nb_items_clust, min_size=K1+1)
+ indices <-
+ if (parll)
+ {
+ unlist( parallel::parLapply(cl, indices_workers, function(inds) {
+ require("epclust", quietly=TRUE)
+ inds[ algoClust1(getContribs(inds), K1) ]
+ }) )
+ }
+ else
+ {
+ unlist( lapply(indices_workers, function(inds)
+ inds[ algoClust1(getContribs(inds), K1) ]
+ ) )
+ }
+ if (verbose)
+ {
+ cat(paste("*** Clustering task 1 on ",length(indices)," medoids [iter]\n", sep=""))
+ }
+ }
+ if (parll)
+ parallel::stopCluster(cl)
+
+ indices #medoids
+}
+
+#' @rdname clustering
+#' @export
+clusteringTask2 <- function(indices, getSeries, K2, algoClust2, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust=3, verbose=FALSE)
+{
+ if (verbose)
+ cat(paste("*** Clustering task 2 on ",length(indices)," medoids\n", sep=""))
+
+ if (length(indices) <= K2)
+ return (indices)
+
+ # A) Compute the WER distances (Wavelets Extended coefficient of deteRmination)
+ distances <- computeWerDists(indices, getSeries, nb_series_per_chunk,
+ smooth_lvl, nvoice, nbytes, endian, ncores_clust, verbose)
+
+ # B) Apply clustering algorithm 2 on the WER distances matrix
+ if (verbose)
+ cat(paste("*** algoClust2() on ",nrow(distances)," items\n", sep=""))
+ indices[ algoClust2(distances,K2) ]
+}
--- /dev/null
+#' computeSynchrones
+#'
+#' Compute the synchrones curves (sums of clusters elements) from a matrix of medoids,
+#' using euclidian distance.
+#'
+#' @param medoids matrix of K medoids curves in columns
+#' @param nb_curves How many series? (this is known, at this stage)
+#' @inheritParams claws
+#' @inheritParams computeWerDists
+#'
+#' @return A matrix of K synchrones in columns (same length as the series)
+#'
+#' @export
+computeSynchrones <- function(medoids, getSeries, nb_curves,
+ nb_series_per_chunk, ncores=3, verbose=FALSE)
+{
+ # Synchrones computation is embarassingly parallel: compute it by chunks of series
+ computeSynchronesChunk <- function(indices)
+ {
+ # Obtain a chunk of reference series
+ series_chunk <- getSeries(indices)
+ nb_series_chunk <- ncol(series_chunk)
+
+ # Get medoids indices for this chunk of series
+ mi <- assignMedoids(series_chunk, medoids[,])
+
+ # Update synchrones using mi above, grouping it by values of mi (in 1...K)
+ # to avoid too many lock/unlock
+ for (i in seq_len(K))
+ {
+ # lock / unlock required because several writes at the same time
+ if (parll)
+ synchronicity::lock(m)
+ synchrones[,i] <- synchrones[,i] + rowSums(as.matrix(series_chunk[,mi==i]))
+ if (parll)
+ synchronicity::unlock(m)
+ }
+ NULL
+ }
+
+ K <- ncol(medoids)
+ L <- nrow(medoids)
+ # Use bigmemory (shared==TRUE by default) + synchronicity to fill synchrones in //
+ synchrones <- bigmemory::big.matrix(nrow=L, ncol=K, type="double", init=0.)
+ # NOTE: synchronicity is only for Linux & MacOS; on Windows: run sequentially
+ parll <- (ncores > 1 && requireNamespace("synchronicity",quietly=TRUE)
+ && Sys.info()['sysname'] != "Windows")
+
+ if (parll)
+ m <- synchronicity::boost.mutex() #for lock/unlock, see computeSynchronesChunk
+
+ if (verbose)
+ cat(paste("--- Compute ",K," synchrones with ",nb_curves," series\n", sep=""))
+
+ # Balance tasks by splitting 1:nb_curves into groups of size <= nb_series_per_chunk
+ indices_workers <- .splitIndices(seq_len(nb_curves), nb_series_per_chunk)
+ ignored <-
+ if (parll)
+ {
+ parallel::mclapply(indices_workers,
+ function(inds) computeSynchronesChunk(inds), mc.cores=ncores)
+ }
+ else
+ lapply(indices_workers, computeSynchronesChunk)
+
+ return (synchrones[,])
+}
--- /dev/null
+#' computeWerDists
+#'
+#' Compute the WER distances between the series at specified indices, which are
+#' obtaind by \code{getSeries(indices)}
+#'
+#' @param indices Indices of the series to consider
+#' @param getSeries Function to retrieve series (argument: 'inds', integer vector),
+#' as columns of a matrix
+#' @param ncores Number of cores for parallel runs
+#' @inheritParams claws
+#'
+#' @return A distances matrix of size K x K where K == length(indices)
+#'
+#' @export
+computeWerDists <- function(indices, getSeries, nb_series_per_chunk, smooth_lvl=3,
+ nvoice=4, nbytes=4, endian=.Platform$endian, ncores=3, verbose=FALSE)
+{
+ n <- length(indices)
+ L <- length(getSeries(1)) #TODO: not very neat way to get L
+ noctave <- ceiling(log2(L)) #min power of 2 to cover serie range
+ # Since a CWT contains noctave*nvoice complex series, we deduce the number of CWT to
+ # retrieve/put in one chunk.
+ nb_cwt_per_chunk <- max(1, floor(nb_series_per_chunk / (nvoice*noctave*2)))
+
+ # Initialize result as a square big.matrix of size 'number of medoids'
+ Xwer_dist <- bigmemory::big.matrix(nrow=n, ncol=n, type="double")
+
+ shift <- 1 #roughly equivalent to s0 in biwavelets & cie. TODO: as arg?
+
+ cwt_file <- tempfile(pattern="epclust_cwt.bin_")
+ # Compute the getSeries(indices) CWT, and store the results in the binary file
+ computeSaveCWT <- function(inds)
+ {
+ if (verbose)
+ cat(" Compute save CWT on ",length(inds)," indices\n", sep="")
+
+ # Obtain CWT as big vectors of real part + imaginary part (concatenate)
+ ts_cwt <- sapply(inds, function(i) {
+ ts <- scale(ts(getSeries(i)), center=TRUE, scale=FALSE)
+ ts_cwt <- Rwave::cwt(ts, noctave+ceiling(shift/nvoice), nvoice,
+ w0=2*pi, twoD=TRUE, plot=FALSE)
+ ts_cwt <- ts_cwt[,(1+shift):(noctave*nvoice+shift)]
+ c( as.double(Re(ts_cwt)),as.double(Im(ts_cwt)) )
+ })
+
+ # Serialization
+ binarize(ts_cwt, cwt_file, nb_cwt_per_chunk, ",", nbytes, endian)
+ }
+
+ # Function to retrieve a synchrone CWT from (binary) file
+ getCWT <- function(index, L)
+ {
+ flat_cwt <- getDataInFile(index, cwt_file, nbytes, endian)
+ cwt_length <- length(flat_cwt) / 2
+ re_part <- as.matrix(flat_cwt[1:cwt_length], nrow=L)
+ im_part <- as.matrix(flat_cwt[(cwt_length+1):(2*cwt_length)], nrow=L)
+ re_part + 1i * im_part
+ }
+
+ # Compute distances between columns i and j for j>i
+ computeDistances <- function(i)
+ {
+ if (parll)
+ {
+ # parallel workers start with an empty environment
+ require("epclust", quietly=TRUE)
+ Xwer_dist <- bigmemory::attach.big.matrix(Xwer_dist_desc)
+ }
+
+ if (verbose)
+ cat(paste(" Distances from ",i," to ",i+1,"...",n,"\n", sep=""))
+
+ # Get CWT of column i, and run computations for columns j>i
+ cwt_i <- getCWT(i, L)
+ WX <- filterMA(Mod(cwt_i * Conj(cwt_i)), smooth_lvl)
+
+ for (j in (i+1):n)
+ {
+ cwt_j <- getCWT(j, L)
+
+ # Compute the ratio of integrals formula 5.6 for WER^2
+ # in https://arxiv.org/abs/1101.4744v2 paragraph 5.3
+ num <- filterMA(Mod(cwt_i * Conj(cwt_j)), smooth_lvl)
+ WY <- filterMA(Mod(cwt_j * Conj(cwt_j)), smooth_lvl)
+ wer2 <- sum(colSums(num)^2) / sum(colSums(WX) * colSums(WY))
+
+ Xwer_dist[i,j] <- sqrt(L * ncol(cwt_i) * (1 - wer2))
+ Xwer_dist[j,i] <- Xwer_dist[i,j]
+ }
+ Xwer_dist[i,i] <- 0.
+ }
+
+ if (verbose)
+ cat(paste("--- Precompute and serialize synchrones CWT\n", sep=""))
+
+ # Split indices by packets of length at most nb_cwt_per_chunk
+ indices_cwt <- .splitIndices(indices, nb_cwt_per_chunk)
+ # NOTE: next loop could potentially be run in //. Indices would be permuted (by
+ # serialization order), and synchronicity would be required because of concurrent
+ # writes. Probably not worth the effort - but possible.
+ for (inds in indices_cwt)
+ computeSaveCWT(inds)
+
+ parll <- (ncores > 1)
+ if (parll)
+ {
+ # outfile=="" to see stderr/stdout on terminal
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores, outfile="")
+ else
+ parallel::makeCluster(ncores)
+ Xwer_dist_desc <- bigmemory::describe(Xwer_dist)
+ parallel::clusterExport(cl, envir=environment(),
+ varlist=c("parll","n","L","Xwer_dist_desc","getCWT","verbose"))
+ }
+
+ if (verbose)
+ cat(paste("--- Compute WER distances\n", sep=""))
+
+ ignored <-
+ if (parll)
+ parallel::clusterApplyLB(cl, seq_len(n-1), computeDistances)
+ else
+ lapply(seq_len(n-1), computeDistances)
+ Xwer_dist[n,n] <- 0.
+
+ if (parll)
+ parallel::stopCluster(cl)
+
+ unlink(cwt_file) #remove binary file
+
+ Xwer_dist[,] #~small matrix K1 x K1
+}
--- /dev/null
+#' (De)Serialization of a [big]matrix or data stream
+#'
+#' \code{binarize()} serializes a matrix or CSV file with minimal overhead, into a
+#' binary file. \code{getDataInFile()} achieves the inverse task: she retrieves (ASCII)
+#' data rows from indices in the binary file. Finally, \code{binarizeTransform()}
+#' serialize transformations of all data chunks. To use it a data-retrieval function
+#' must be provided -- thus \code{binarize} will most likely be used first
+#' (and then a function defined to seek in generated binary file)
+#'
+#' @param data_ascii Matrix (by columns) or CSV file or connection (by rows)
+#' @param data_bin_file Name of binary file on output of \code{binarize()}
+#' or input of \code{getDataInFile()}
+#' @param nb_per_chunk Number of lines to process in one batch
+#' @param getData Function to retrieve data chunks
+#' @param transform Transformation function to apply on data chunks
+#' @param indices Indices of the lines to retrieve
+#' @inheritParams claws
+#'
+#' @return For \code{getDataInFile()}, a matrix with columns corresponding to the
+#' requested indices. \code{binarizeTransform()} returns the number of processed lines.
+#' \code{binarize()} is designed to serialize in several calls, thus returns nothing.
+#'
+#' @name de_serialize
+#' @rdname de_serialize
+#' @aliases binarize binarizeTransform getDataInFile
+NULL
+
+#' @rdname de_serialize
+#' @export
+binarize <- function(data_ascii, data_bin_file, nb_per_chunk,
+ sep=",", nbytes=4, endian=.Platform$endian)
+{
+ # data_ascii can be of two types: [big.]matrix, or connection
+ if (is.character(data_ascii))
+ data_ascii <- file(data_ascii, open="r")
+ else if (methods::is(data_ascii,"connection") && !isOpen(data_ascii))
+ open(data_ascii)
+ is_matrix <- !methods::is(data_ascii,"connection")
+
+ # At first call, the length of a stored row is written. So it's important to determine
+ # if the serialization process already started.
+ first_write <- (!file.exists(data_bin_file) || file.info(data_bin_file)$size == 0)
+
+ # Open the binary file for writing (or 'append' if already exists)
+ data_bin <- file(data_bin_file, open=ifelse(first_write,"wb","ab"))
+
+ if (first_write)
+ {
+ # Write data length on first call: number of items always on 8 bytes
+ writeBin(0L, data_bin, size=8, endian=endian)
+ if (is_matrix)
+ data_length <- nrow(data_ascii)
+ else #connection
+ {
+ # Read the first line to know data length, and write it then
+ data_line <- scan(data_ascii, double(), sep=sep, nlines=1, quiet=TRUE)
+ writeBin(data_line, data_bin, size=nbytes, endian=endian)
+ data_length <- length(data_line)
+ }
+ }
+
+ if (is_matrix)
+ {
+ # Data is processed by chunks; although this may not be so useful for (normal) matrix
+ # input, it could for a file-backed big.matrix. It's easier to follow a unified pattern.
+ index <- 1
+ }
+ repeat
+ {
+ if (is_matrix)
+ {
+ data_chunk <-
+ if (index <= ncol(data_ascii))
+ as.double(data_ascii[,index:min(ncol(data_ascii),index+nb_per_chunk-1)])
+ else
+ double(0)
+ index <- index + nb_per_chunk
+ }
+ else #connection
+ data_chunk <- scan(data_ascii, double(), sep=sep, nlines=nb_per_chunk, quiet=TRUE)
+
+ # Data size is unknown in the case of a connection
+ if (length(data_chunk)==0)
+ break
+
+ # Write this chunk of data to the binary file
+ writeBin(data_chunk, data_bin, size=nbytes, endian=endian)
+ }
+
+ if (first_write)
+ {
+ # Write data_length, == (file_size-1) / (nbytes*nbWritten) at offset 0 in data_bin
+ ignored <- seek(data_bin, 0)
+ writeBin(data_length, data_bin, size=8, endian=endian)
+ }
+ close(data_bin)
+
+ if ( ! is_matrix )
+ close(data_ascii)
+}
+
+#' @rdname de_serialize
+#' @export
+binarizeTransform <- function(getData, transform, data_bin_file, nb_per_chunk,
+ nbytes=4, endian=.Platform$endian)
+{
+ nb_items <- 0 #side-effect: store the number of transformed items
+ index <- 1
+ repeat
+ {
+ # Retrieve a chunk of data in a binary file (generally obtained by binarize())
+ data_chunk <- getData((index-1)+seq_len(nb_per_chunk))
+ if (is.null(data_chunk))
+ break
+
+ # Apply transformation on the current chunk (by columns)
+ transformed_chunk <- transform(data_chunk)
+
+ # Save the result in binary format
+ binarize(transformed_chunk, data_bin_file, nb_per_chunk, ",", nbytes, endian)
+
+ index <- index + nb_per_chunk
+ nb_items <- nb_items + ncol(data_chunk)
+ }
+ nb_items #number of transformed items
+}
+
+#' @rdname de_serialize
+#' @export
+getDataInFile <- function(indices, data_bin_file, nbytes=4, endian=.Platform$endian)
+{
+ data_bin <- file(data_bin_file, "rb") #source binary file
+
+ data_size <- file.info(data_bin_file)$size #number of bytes in the file
+ # data_length: length of a vector in the binary file (first element, 8 bytes)
+ data_length <- readBin(data_bin, "integer", n=1, size=8, endian=endian)
+
+ # Seek all 'indices' columns in the binary file, using data_length and nbytes
+ # to compute the offset ( index i at 8 + i*data_length*nbytes )
+ data_ascii <- do.call( cbind, lapply( indices, function(i) {
+ offset <- 8+(i-1)*data_length*nbytes
+ if (offset >= data_size)
+ return (NULL)
+ ignored <- seek(data_bin, offset) #position cursor at computed offset
+ readBin(data_bin, "double", n=data_length, size=nbytes, endian=endian)
+ } ) )
+ close(data_bin)
+
+ data_ascii #retrieved data, in columns
+}
--- /dev/null
+#' CLAWS: CLustering with wAvelets and Wer distanceS
+#'
+#' Cluster electricity power curves (or any series of similar nature) by applying a
+#' two stage procedure in parallel (see details).
+#' Input series must be sampled on the same time grid, no missing values.
+#'
+#' Summary of the function execution flow:
+#' \enumerate{
+#' \item Compute and serialize all contributions, obtained through discrete wavelet
+#' decomposition (see Antoniadis & al. [2013])
+#' \item Divide series into \code{ntasks} groups to process in parallel. In each task:
+#' \enumerate{
+#' \item iterate the first clustering algorithm on its aggregated outputs,
+#' on inputs of size \code{nb_items_clust}\cr
+#' -> K1 medoids indices
+#' \item optionally, if WER=="mix":\cr
+#' a. compute WER distances (K1xK1) between medoids\cr
+#' b. apply the 2nd clustering algorithm\cr
+#' -> K2 medoids indices
+#' }
+#' \item Launch a final task on the aggregated outputs of all previous tasks:
+#' ntasks*K1 if WER=="end", ntasks*K2 otherwise
+#' \item Compute synchrones (sum of series within each final group)
+#' }
+#'
+#' The main argument -- \code{series} -- has a quite misleading name, since it can be
+#' either a [big.]matrix, a CSV file, a connection or a user function to retrieve series.
+#' When \code{series} is given as a function it must take a single argument,
+#' 'indices': integer vector equal to the indices of the curves to retrieve;
+#' see SQLite example.
+#' WARNING: the return value must be a matrix (in columns), or NULL if no matches.
+#'
+#' Note: Since we don't make assumptions on initial data, there is a possibility that
+#' even when serialized, contributions do not fit in RAM. For example,
+#' 30e6 series of length 100,000 would lead to a +4Go contribution matrix. Therefore,
+#' it's safer to place these in (binary) files; that's what we do.
+#'
+#' @param series Access to the N (time-)series, which can be of one of the four
+#' following types:
+#' \itemize{
+#' \item [big.]matrix: each column contains the (time-ordered) values of one
+#' time-serie
+#' \item connection: any R connection object providing lines as described above
+#' \item character: name of a CSV file containing series in rows (no header)
+#' \item function: a custom way to retrieve the curves; it has only one argument:
+#' the indices of the series to be retrieved. See SQLite example
+#' }
+#' @param K1 Number of clusters to be found after stage 1 (K1 << N)
+#' @param K2 Number of clusters to be found after stage 2 (K2 << K1)
+#' @param nb_series_per_chunk Number of series to retrieve in one batch
+#' @param nb_items_clust Number of items in 1st clustering algorithm input
+#' @param algoClust1 Clustering algorithm for stage 1. A function which takes (data, K)
+#' as argument where data is a matrix in columns and K the desired number of clusters,
+#' and outputs K medoids ranks. Default: PAM.
+#' @param algoClust2 Clustering algorithm for stage 2. A function which takes (dists, K)
+#' as argument where dists is a matrix of distances and K the desired number of
+#' clusters, and outputs K medoids ranks. Default: PAM.
+#' @param wav_filt Wavelet transform filter, as a string "Family:FilterNumber"; see
+#' ?wavethresh::wd
+#' @param contrib_type Type of contribution: "relative", "logit" or "absolute" (any
+#' prefix)
+#' @param WER "end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply
+#' stage 2 at the end of each task
+#' @param smooth_lvl Smoothing level: odd integer, 1 == no smoothing.
+#' @param nvoice Number of voices within each octave for CWT computations
+#' @param random TRUE (default) for random chunks repartition
+#' @param ntasks Number of tasks (parallel iterations to obtain K1 [if WER=="end"]
+#' or K2 [if WER=="mix"] medoids); default: 1.\cr
+#' Note: ntasks << N (number of series), so that N is "roughly divisible" by ntasks
+#' @param ncores_tasks Number of parallel tasks ('1' == sequential tasks)
+#' @param ncores_clust Number of parallel clusterings in one task
+#' @param sep Separator in CSV input file (if any provided)
+#' @param nbytes 4 or 8 bytes to (de)serialize a floating-point number
+#' @param endian Endianness for (de)serialization: "little" or "big"
+#' @param verbose FALSE: nothing printed; TRUE: some execution traces
+#'
+#' @return A list:
+#' \itemize{
+#' \item medoids: matrix of the final K2 medoids curves
+#' \item ranks: corresponding indices in the dataset
+#' \item synchrones: sum of series within each final group
+#' }
+#'
+#' @references Clustering functional data using Wavelets [2013];
+#' A. Antoniadis, X. Brossat, J. Cugliari & J.-M. Poggi.
+#' Inter. J. of Wavelets, Multiresolution and Information Procesing,
+#' vol. 11, No 1, pp.1-30. doi:10.1142/S0219691313500033
+#'
+#' @examples
+#' \dontrun{
+#' # WER distances computations are too long for CRAN (for now)
+#' # Note: on this small example, sequential run is faster
+#'
+#' # Random series around cos(x,2x,3x)/sin(x,2x,3x)
+#' x <- seq(0,50,0.05)
+#' L <- length(x) #1001
+#' ref_series <- matrix( c(cos(x),cos(2*x),cos(3*x),sin(x),sin(2*x),sin(3*x)), ncol=6 )
+#' library(wmtsa)
+#' series <- do.call( cbind, lapply( 1:6, function(i)
+#' do.call(cbind, wmtsa::wavBootstrap(ref_series[,i], n.realization=40)) ) )
+#' # Mix series so that all groups are evenly spread
+#' permut <- (0:239)%%6 * 40 + (0:239)%/%6 + 1
+#' series = series[,permut]
+#' #dim(series) #c(240,1001)
+#' res_ascii <- claws(series, K1=30, K2=6, nb_series_per_chunk=500,
+#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=1)
+#'
+#' # Same example, from CSV file
+#' csv_file <- tempfile(pattern="epclust_series.csv_")
+#' write.table(t(series), csv_file, sep=",", row.names=FALSE, col.names=FALSE)
+#' res_csv <- claws(csv_file, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
+#'
+#' # Same example, from binary file
+#' bin_file <- tempfile(pattern="epclust_series.bin_")
+#' nbytes <- 8
+#' endian <- "little"
+#' binarize(csv_file, bin_file, 500, ",", nbytes, endian)
+#' getSeries <- function(indices) getDataInFile(indices, bin_file, nbytes, endian)
+#' res_bin <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
+#' unlink(csv_file)
+#' unlink(bin_file)
+#'
+#' # Same example, from SQLite database
+#' library(DBI)
+#' series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
+#' # Prepare data.frame in DB-format
+#' n <- ncol(series)
+#' times_values <- data.frame(
+#' id = rep(1:n,each=L),
+#' time = rep( as.POSIXct(1800*(1:L),"GMT",origin="2001-01-01"), n ),
+#' value = as.double(series) )
+#' dbWriteTable(series_db, "times_values", times_values)
+#' # Fill associative array, map index to identifier
+#' indexToID_inDB <- as.character(
+#' dbGetQuery(series_db, 'SELECT DISTINCT id FROM times_values')[,"id"] )
+#' serie_length <- as.integer( dbGetQuery(series_db,
+#' paste("SELECT COUNT(*) FROM times_values WHERE id == ",indexToID_inDB[1],sep="")) )
+#' getSeries <- function(indices) {
+#' indices = indices[ indices <= length(indexToID_inDB) ]
+#' if (length(indices) == 0)
+#' return (NULL)
+#' request <- "SELECT id,value FROM times_values WHERE id in ("
+#' for (i in seq_along(indices)) {
+#' request <- paste(request, indexToID_inDB[ indices[i] ], sep="")
+#' if (i < length(indices))
+#' request <- paste(request, ",", sep="")
+#' }
+#' request <- paste(request, ")", sep="")
+#' df_series <- dbGetQuery(series_db, request)
+#' matrix(df_series[,"value"], nrow=serie_length)
+#' }
+#' res_db <- claws(getSeries, 30, 6, 500, 100, random=FALSE, ncores_clust=1)
+#' dbDisconnect(series_db)
+#'
+#' # All results should be equal:
+#' all(res_ascii$ranks == res_csv$ranks
+#' & res_ascii$ranks == res_bin$ranks
+#' & res_ascii$ranks == res_db$ranks)
+#' }
+#' @export
+claws <- function(series, K1, K2, nb_series_per_chunk, nb_items_clust=5*K1,
+ algoClust1=function(data,K) cluster::pam(t(data),K,diss=FALSE,pamonce=1)$id.med,
+ algoClust2=function(dists,K) cluster::pam(dists,K,diss=TRUE,pamonce=1)$id.med,
+ wav_filt="Coiflets:1", contrib_type="absolute", WER="end", smooth_lvl=3, nvoice=4,
+ random=TRUE, ntasks=1, ncores_tasks=1, ncores_clust=3, sep=",", nbytes=4,
+ endian=.Platform$endian, verbose=FALSE)
+{
+ # Check/transform arguments
+ if (!is.matrix(series) && !bigmemory::is.big.matrix(series)
+ && !is.function(series)
+ && !methods::is(series,"connection") && !is.character(series))
+ {
+ stop("'series': [big]matrix, function, file or valid connection (no NA)")
+ }
+ K1 <- .toInteger(K1, function(x) x>=2)
+ K2 <- .toInteger(K2, function(x) x>=2)
+ nb_series_per_chunk <- .toInteger(nb_series_per_chunk, function(x) x>=1)
+ nb_items_clust <- .toInteger(nb_items_clust, function(x) x>K1)
+ random <- .toLogical(random)
+ wav_filt <- strsplit(wav_filt, ':')[[1]]
+ wav_family <- wav_filt[1]
+ wav_number <- wav_filt[2]
+ tryCatch({ignored <- wavethresh::filter.select(wav_number, wav_family)},
+ error=function(e) stop("Invalid wavelet filter; see ?wavethresh::filter.select") )
+ ctypes <- c("relative","absolute","logit")
+ contrib_type <- ctypes[ pmatch(contrib_type,ctypes) ]
+ if (is.na(contrib_type))
+ stop("'contrib_type' in {'relative','absolute','logit'}")
+ if (WER!="end" && WER!="mix")
+ stop("'WER': in {'end','mix'}")
+ random <- .toLogical(random)
+ ntasks <- .toInteger(ntasks, function(x) x>=1)
+ ncores_tasks <- .toInteger(ncores_tasks, function(x) x>=1)
+ ncores_clust <- .toInteger(ncores_clust, function(x) x>=1)
+ if (!is.character(sep))
+ stop("'sep': character")
+ nbytes <- .toInteger(nbytes, function(x) x==4 || x==8)
+ verbose <- .toLogical(verbose)
+
+ # Binarize series if it is not a function; the aim is to always use a function,
+ # to uniformize treatments. An equally good alternative would be to use a file-backed
+ # bigmemory::big.matrix, but it would break the "all-is-function" pattern.
+ if (!is.function(series))
+ {
+ if (verbose)
+ cat("...Serialize time-series (or retrieve past binary file)\n")
+ series_file <- ".series.epclust.bin"
+ if (!file.exists(series_file))
+ binarize(series, series_file, nb_series_per_chunk, sep, nbytes, endian)
+ getSeries <- function(inds) getDataInFile(inds, series_file, nbytes, endian)
+ }
+ else
+ getSeries <- series
+
+ # Serialize all computed wavelets contributions into a file
+ contribs_file <- ".contribs.epclust.bin"
+ if (verbose)
+ cat("...Compute contributions and serialize them (or retrieve past binary file)\n")
+ if (!file.exists(contribs_file))
+ {
+ nb_curves <- binarizeTransform(getSeries,
+ function(curves) curvesToContribs(curves, wav_filt, contrib_type),
+ contribs_file, nb_series_per_chunk, nbytes, endian)
+ }
+ else
+ {
+ # TODO: duplicate from getDataInFile() in de_serialize.R
+ contribs_size <- file.info(contribs_file)$size #number of bytes in the file
+ contrib_length <- readBin(contribs_file, "integer", n=1, size=8, endian=endian)
+ nb_curves <- (contribs_size-8) / (nbytes*contrib_length)
+ }
+ getContribs <- function(indices) getDataInFile(indices, contribs_file, nbytes, endian)
+
+ # A few sanity checks: do not continue if too few data available.
+ if (nb_curves < K2)
+ stop("Not enough data: less series than final number of clusters")
+ nb_series_per_task <- round(nb_curves / ntasks)
+ if (nb_series_per_task < K2)
+ stop("Too many tasks: less series in one task than final number of clusters")
+
+ # Generate a random permutation of 1:N (if random==TRUE);
+ # otherwise just use arrival (storage) order.
+ indices_all <- if (random) sample(nb_curves) else seq_len(nb_curves)
+ # Split (all) indices into ntasks groups of ~same size
+ indices_tasks <- lapply(seq_len(ntasks), function(i) {
+ upper_bound <- ifelse( i<ntasks, min(nb_series_per_task*i,nb_curves), nb_curves )
+ indices_all[((i-1)*nb_series_per_task+1):upper_bound]
+ })
+
+ parll <- (ncores_tasks > 1)
+ if (parll && ntasks>1)
+ {
+ # Initialize parallel runs: outfile="" allow to output verbose traces in the console
+ # under Linux. All necessary variables are passed to the workers.
+ cl <-
+ if (verbose)
+ parallel::makeCluster(ncores_tasks, outfile="")
+ else
+ parallel::makeCluster(ncores_tasks)
+ varlist <- c("ncores_clust","verbose", #task 1 & 2
+ "K1","getContribs","algoClust1","nb_items_clust") #task 1
+ if (WER=="mix")
+ {
+ # Add variables for task 2
+ varlist <- c(varlist, "K2","getSeries","algoClust2","nb_series_per_chunk",
+ "smooth_lvl","nvoice","nbytes","endian")
+ }
+ parallel::clusterExport(cl, varlist, envir <- environment())
+ }
+
+ # This function achieves one complete clustering task, divided in stage 1 + stage 2.
+ # stage 1: n indices --> clusteringTask1(...) --> K1 medoids (indices)
+ # stage 2: K1 indices --> K1xK1 WER distances --> clusteringTask2(...) --> K2 medoids,
+ # where n == N / ntasks, N being the total number of curves.
+ runTwoStepClustering <- function(inds)
+ {
+ # When running in parallel, the environment is blank: we need to load the required
+ # packages, and pass useful variables.
+ if (parll && ntasks>1)
+ require("epclust", quietly=TRUE)
+ indices_medoids <- clusteringTask1(inds, getContribs, K1, algoClust1,
+ nb_items_clust, ncores_clust, verbose)
+ if (WER=="mix")
+ {
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_clust,verbose)
+ }
+ indices_medoids
+ }
+
+ if (verbose)
+ {
+ message <- paste("...Run ",ntasks," x stage 1", sep="")
+ if (WER=="mix")
+ message <- paste(message," + stage 2", sep="")
+ cat(paste(message,"\n", sep=""))
+ }
+
+ # As explained above, we obtain after all runs ntasks*[K1 or K2] medoids indices,
+ # depending whether WER=="end" or "mix", respectively.
+ indices_medoids_all <-
+ if (parll && ntasks>1)
+ unlist( parallel::parLapply(cl, indices_tasks, runTwoStepClustering) )
+ else
+ unlist( lapply(indices_tasks, runTwoStepClustering) )
+
+ if (parll && ntasks>1)
+ parallel::stopCluster(cl)
+
+ # For the last stage, ncores_tasks*(ncores_clusts+1) cores should be available:
+ # - ntasks for level 1 parallelism
+ # - ntasks*ncores_clust for level 2 parallelism,
+ # but since an extension MPI <--> tasks / OpenMP <--> sub-tasks is on the way,
+ # it's better to just re-use ncores_clust
+ ncores_last_stage <- ncores_clust
+
+ # Run last clustering tasks to obtain only K2 medoids indices
+ if (verbose)
+ cat("...Run final // stage 1 + stage 2\n")
+ indices_medoids <- clusteringTask1(indices_medoids_all, getContribs, K1, algoClust1,
+ nb_items_clust, ncores_tasks*ncores_clust, verbose)
+
+ indices_medoids <- clusteringTask2(indices_medoids, getSeries, K2, algoClust2,
+ nb_series_per_chunk,smooth_lvl,nvoice,nbytes,endian,ncores_last_stage,verbose)
+
+ # Compute synchrones, that is to say the cumulated power consumptions for each of the
+ # K2 final groups.
+ medoids <- getSeries(indices_medoids)
+ synchrones <- computeSynchrones(medoids, getSeries, nb_curves, nb_series_per_chunk,
+ ncores_last_stage, verbose)
+
+ # NOTE: no need to use big.matrix here, because only K2 << K1 << N remaining curves
+ list("medoids"=medoids, "ranks"=indices_medoids, "synchrones"=synchrones)
+}
--- /dev/null
+#TODO: some visualization
+#for (i in 1:6) {plot(medoids_ascii[i,1:200],type="l",ylim=c(-5,5),col=i);par(new=TRUE)}
+#...
+#PLOT:
+#plot manifold 2D distances WER / --> récupérer les distances ? quand ?
+#fenetre tempo forme des courbes / --> OK (jour[type] / semaine, indices en arg)
+#medoids / --> OK (moyennés sur 1 jour / type de jour / semaine)
+#gain en prevision: clust puis full --> enercast (comment l'utiliser ?)
+#
+#> plot(cr$medoids[1:100,1],type="l")
+#> #for (i in 1:15)plot(cr$medoids[1:100,1],type="l")
+#> r = range(cr$medoids[1:96,])
+#> for (i in 1:15) {plot(cr$medoids[1:96,i],type="l",ylim=r,col=i); par(new=TRUE) }
+#>
+#
--- /dev/null
+Ref_Centroids <<- NULL
+Tot_Nb_Curves <<- NA
+
+#' initCentroids
+#'
+#' Must be called before getDetRandCurve()
+#'
+#' @param N Number of curves
+#' @param D Number of sample points
+#' @param K Number of clusters
+#'
+#' @export
+initClustersParams <- function(N, D, K)
+{
+ # Generate K centroids
+ Ref_Centroids <<- sapply(1:K, function(k) cumsum(rnorm(D)))
+ Tot_Nb_Curves <<- N
+}
+
+#' getDetRandCurve
+#'
+#' TODO: improve this (using known clusters centers...)
+#'
+#' @param i Some index
+#'
+#' @examples
+#' \dontrun{
+#' initCentroids(N=250000, D=15000, K=15)
+#' res_ascii <- claws(getDetRandCurve, K1=50, K2=15, nb_series_per_chunk=500,
+#' nb_items_clust=100, random=FALSE, verbose=TRUE, ncores_clust=3)
+#' }
+#' @export
+getDetRandCurve <- function(indices)
+{
+ sapply(indices, function(i) {
+ if (i > Tot_Nb_Curves)
+ return (NULL)
+ set.seed(i)
+ j <- sample(ncol(Ref_Centroids), 1)
+ Ref_Centroids[,j] + rnorm(nrow(Ref_Centroids))
+ })
+}
--- /dev/null
+# Check integer arguments with functional conditions
+.toInteger <- function(x, condition)
+{
+ errWarn <- function(ignored)
+ paste("Cannot convert argument' ",substitute(x),"' to integer", sep="")
+ if (!is.integer(x))
+ tryCatch({x <- as.integer(x)[1]; if (is.na(x)) stop()},
+ warning=errWarn, error=errWarn)
+ if (!condition(x))
+ {
+ stop(paste("Argument '",substitute(x),
+ "' does not verify condition ",body(condition), sep=""))
+ }
+ x
+}
+
+# Check logical arguments
+.toLogical <- function(x)
+{
+ errWarn <- function(ignored)
+ paste("Cannot convert argument' ",substitute(x),"' to logical", sep="")
+ if (!is.logical(x))
+ tryCatch({x <- as.logical(x)[1]; if (is.na(x)) stop()},
+ warning=errWarn, error=errWarn)
+ x
+}
+
+#' curvesToContribs
+#'
+#' Compute the discrete wavelet coefficients for each series, and aggregate them in
+#' energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2
+#'
+#' @param curves [big.]matrix of series (in columns), of size L x n
+#' @param wav_filt Wavelet transform filter, as a vector c(Family,FilterNumber)
+#' @inheritParams claws
+#'
+#' @return A matrix of size log(L) x n containing contributions in columns
+#'
+#' @export
+curvesToContribs <- function(curves, wav_filt, contrib_type)
+{
+ series <- as.matrix(curves)
+ L <- nrow(series)
+ D <- ceiling( log2(L) )
+ # Series are interpolated to all have length 2^D
+ nb_sample_points <- 2^D
+ apply(series, 2, function(x) {
+ interpolated_curve <- spline(1:L, x, n=nb_sample_points)$y
+ W <- wavethresh::wd(interpolated_curve, wav_filt[2], wav_filt[1])$D
+ # Compute the sum of squared discrete wavelet coefficients, for each scale
+ nrj <- sapply( 1:D, function(i) ( sqrt( sum(W[(2^D-(2^i-1)):(2^D-2^(i-1))]^2) ) ) )
+ if (contrib_type!="absolute")
+ nrj <- nrj / sum(nrj)
+ if (contrib_type=="logit")
+ nrj <- - log(1 - nrj)
+ unname( nrj )
+ })
+}
+
+# Helper function to divide indices into balanced sets.
+# Ensure that all indices sets have at least min_size elements.
+.splitIndices <- function(indices, nb_per_set, min_size=1)
+{
+ L <- length(indices)
+ nb_workers <- floor( L / nb_per_set )
+ rem <- L %% nb_per_set
+ if (nb_workers == 0 || (nb_workers==1 && rem==0))
+ {
+ # L <= nb_per_set, simple case
+ return (list(indices))
+ }
+
+ indices_workers <- lapply( seq_len(nb_workers), function(i)
+ indices[(nb_per_set*(i-1)+1):(nb_per_set*i)] )
+
+ rem <- L %% nb_per_set #number of remaining unassigned items
+ if (rem == 0)
+ return (indices_workers)
+
+ rem <- (L-rem+1):L
+ # If remainder is smaller than min_size, feed it with indices from other sets
+ # until either its size exceed min_size (success) or other sets' size
+ # get lower min_size (failure).
+ while (length(rem) < min_size)
+ {
+ index <- length(rem) %% nb_workers + 1
+ if (length(indices_workers[[index]]) <= min_size)
+ {
+ stop("Impossible to split indices properly for clustering.
+ Try increasing nb_items_clust or decreasing K1")
+ }
+ rem <- c(rem, tail(indices_workers[[index]],1))
+ indices_workers[[index]] <- head( indices_workers[[index]], -1)
+ }
+ return ( c(indices_workers, list(rem) ) )
+}
+
+#' assignMedoids
+#'
+#' Find the closest medoid for each curve in input
+#'
+#' @param curves (Chunk) of series whose medoids indices must be found
+#' @param medoids Matrix of medoids (in columns)
+#'
+#' @return The vector of integer assignments
+#' @export
+assignMedoids <- function(curves, medoids)
+{
+ nb_series <- ncol(curves)
+ mi <- rep(NA,nb_series)
+ for (i in seq_len(nb_series))
+ mi[i] <- which.min( colSums( sweep(medoids, 1, curves[,i], '-')^2 ) )
+ mi
+}
+
+#' filterMA
+#'
+#' Filter [time-]series by replacing all values by the moving average of values
+#' centered around current one. Border values are averaged with available data.
+#'
+#' @param M_ A real matrix of size LxD
+#' @param w_ The (odd) number of values to average
+#'
+#' @return The filtered matrix (in columns), of same size as the input
+#' @export
+filterMA <- function(M_, w_)
+ .Call("filterMA", M_, w_, PACKAGE="epclust")
+
+#' cleanBin
+#'
+#' Remove binary files to re-generate them at next run of \code{claws()}.
+#' To be run in the folder where computations occurred (or no effect).
+#'
+#' @export
+cleanBin <- function()
+{
+ bin_files <- list.files(pattern="*.epclust.bin", all.files=TRUE)
+ for (file in bin_files)
+ unlink(file)
+}
--- /dev/null
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--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/main.R
+\name{claws}
+\alias{claws}
+\title{CLAWS: CLustering with wAvelets and Wer distanceS}
+\usage{
+claws(getSeries, K1, K2, wf, ctype, WER = "end", random = TRUE,
+ ntasks = 1, ncores_tasks = 1, ncores_clust = 4,
+ nb_series_per_chunk = 50 * K1, min_series_per_chunk = 5 * K1, sep = ",",
+ nbytes = 4, endian = .Platform$endian, verbose = FALSE, parll = TRUE)
+}
+\arguments{
+\item{getSeries}{Access to the (time-)series, which can be of one of the three
+following types:
+\itemize{
+ \item [big.]matrix: each line contains all the values for one time-serie, ordered by time
+ \item connection: any R connection object providing lines as described above
+ \item character: name of a CSV file containing series in rows (no header)
+ \item function: a custom way to retrieve the curves; it has only one argument:
+ the indices of the series to be retrieved. See examples
+}}
+
+\item{K1}{Number of super-consumers to be found after stage 1 (K1 << N)}
+
+\item{K2}{Number of clusters to be found after stage 2 (K2 << K1)}
+
+\item{wf}{Wavelet transform filter; see ?wavelets::wt.filter}
+
+\item{ctype}{Type of contribution: "relative" or "absolute" (or any prefix)}
+
+\item{WER}{"end" to apply stage 2 after stage 1 has fully iterated, or "mix" to apply stage 2
+at the end of each task}
+
+\item{random}{TRUE (default) for random chunks repartition}
+
+\item{ntasks}{Number of tasks (parallel iterations to obtain K1 medoids); default: 1.
+Note: ntasks << N, so that N is "roughly divisible" by N (number of series)}
+
+\item{ncores_tasks}{"MPI" number of parallel tasks (1 to disable: sequential tasks)}
+
+\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task}
+
+\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task}
+
+\item{min_series_per_chunk}{Minimum number of series in each group}
+
+\item{sep}{Separator in CSV input file (if any provided)}
+
+\item{nbytes}{Number of bytes to serialize a floating-point number; 4 or 8}
+
+\item{endian}{Endianness to use for (de)serialization. Use "little" or "big" for portability}
+
+\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)}
+
+\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)}
+}
+\value{
+A big.matrix of the final medoids curves (K2) in rows
+}
+\description{
+Groups electricity power curves (or any series of similar nature) by applying PAM
+algorithm in parallel to chunks of size \code{nb_series_per_chunk}. Input series
+must be sampled on the same time grid, no missing values.
+}
+\examples{
+\dontrun{
+# WER distances computations are a bit too long for CRAN (for now)
+
+# Random series around cos(x,2x,3x)/sin(x,2x,3x)
+x = seq(0,500,0.05)
+L = length(x) #10001
+ref_series = matrix( c(cos(x), cos(2*x), cos(3*x), sin(x), sin(2*x), sin(3*x)),
+ byrow=TRUE, ncol=L )
+library(wmtsa)
+series = do.call( rbind, lapply( 1:6, function(i)
+ do.call(rbind, wmtsa::wavBootstrap(ref_series[i,], n.realization=400)) ) )
+#dim(series) #c(2400,10001)
+medoids_ascii = claws(series, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+
+# Same example, from CSV file
+csv_file = "/tmp/epclust_series.csv"
+write.table(series, csv_file, sep=",", row.names=FALSE, col.names=FALSE)
+medoids_csv = claws(csv_file, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+
+# Same example, from binary file
+bin_file = "/tmp/epclust_series.bin"
+nbytes = 8
+endian = "little"
+epclust::binarize(csv_file, bin_file, 500, nbytes, endian)
+getSeries = function(indices) getDataInFile(indices, bin_file, nbytes, endian)
+medoids_bin = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+unlink(csv_file)
+unlink(bin_file)
+
+# Same example, from SQLite database
+library(DBI)
+series_db <- dbConnect(RSQLite::SQLite(), "file::memory:")
+# Prepare data.frame in DB-format
+n = nrow(series)
+time_values = data.frame(
+ id = rep(1:n,each=L),
+ time = rep( as.POSIXct(1800*(0:n),"GMT",origin="2001-01-01"), L ),
+ value = as.double(t(series)) )
+dbWriteTable(series_db, "times_values", times_values)
+# Fill associative array, map index to identifier
+indexToID_inDB <- as.character(
+ dbGetQuery(series_db, 'SELECT DISTINCT id FROM time_values')[,"id"] )
+getSeries = function(indices) {
+ request = "SELECT id,value FROM times_values WHERE id in ("
+ for (i in indices)
+ request = paste(request, i, ",", sep="")
+ request = paste(request, ")", sep="")
+ df_series = dbGetQuery(series_db, request)
+ # Assume that all series share same length at this stage
+ ts_length = sum(df_series[,"id"] == df_series[1,"id"])
+ t( as.matrix(df_series[,"value"], nrow=ts_length) )
+}
+medoids_db = claws(getSeries, K1=60, K2=6, "d8", "rel", nb_series_per_chunk=500)
+dbDisconnect(series_db)
+
+# All computed medoids should be the same:
+digest::sha1(medoids_ascii)
+digest::sha1(medoids_csv)
+digest::sha1(medoids_bin)
+digest::sha1(medoids_db)
+}
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/clustering.R
+\name{clustering}
+\alias{clustering}
+\alias{clusteringTask1}
+\alias{computeClusters1}
+\alias{computeClusters2}
+\alias{clusteringTask1}
+\alias{clusteringTask2}
+\alias{computeClusters1}
+\alias{computeClusters2}
+\title{Two-stage clustering, withing one task (see \code{claws()})}
+\usage{
+clusteringTask1(indices, getContribs, K1, nb_series_per_chunk,
+ ncores_clust = 1, verbose = FALSE, parll = TRUE)
+
+clusteringTask2(medoids, K2, getRefSeries, nb_ref_curves, nb_series_per_chunk,
+ ncores_clust = 1, verbose = FALSE, parll = TRUE)
+
+computeClusters1(contribs, K1, verbose = FALSE)
+
+computeClusters2(distances, K2, verbose = FALSE)
+}
+\arguments{
+\item{indices}{Range of series indices to cluster in parallel (initial data)}
+
+\item{getContribs}{Function to retrieve contributions from initial series indices:
+\code{getContribs(indices)} outpus a contributions matrix}
+
+\item{K1}{Number of super-consumers to be found after stage 1 (K1 << N)}
+
+\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task}
+
+\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task}
+
+\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)}
+
+\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)}
+
+\item{medoids}{big.matrix of medoids (curves of same length as initial series)}
+
+\item{K2}{Number of clusters to be found after stage 2 (K2 << K1)}
+
+\item{getRefSeries}{Function to retrieve initial series (e.g. in stage 2 after series
+have been replaced by stage-1 medoids)}
+
+\item{nb_ref_curves}{How many reference series? (This number is known at this stage)}
+
+\item{contribs}{matrix of contributions (e.g. output of \code{curvesToContribs()})}
+
+\item{distances}{matrix of K1 x K1 (WER) distances between synchrones}
+}
+\value{
+For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the
+ computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
+ \code{computeClusters2()} outputs a big.matrix of medoids
+ (of size limited by nb_series_per_chunk)
+}
+\description{
+\code{clusteringTask1()} runs one full stage-1 task, which consists in
+ iterated stage 1 clustering (on nb_curves / ntasks energy contributions, computed
+ through discrete wavelets coefficients).
+ \code{clusteringTask2()} runs a full stage-2 task, which consists in synchrones
+ and then WER distances computations, before applying the clustering algorithm.
+ \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
+ clustering procedures respectively for stage 1 and 2. The former applies the
+ clustering algorithm (PAM) on a contributions matrix, while the latter clusters
+ a chunk of series inside one task (~max nb_series_per_chunk)
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/RcppExports.R
+\name{computeMedoidsIndices}
+\alias{computeMedoidsIndices}
+\title{computeMedoidsIndices}
+\usage{
+computeMedoidsIndices(pMedoids, ref_series)
+}
+\arguments{
+\item{pMedoids}{External pointer}
+
+\item{ref_series}{reference series}
+}
+\value{
+A map serie number -> medoid index
+}
+\description{
+Compute medoids indices
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/clustering.R
+\name{computeSynchrones}
+\alias{computeSynchrones}
+\title{computeSynchrones}
+\usage{
+computeSynchrones(medoids, getRefSeries, nb_ref_curves, nb_series_per_chunk,
+ ncores_clust = 1, verbose = FALSE, parll = TRUE)
+}
+\arguments{
+\item{medoids}{big.matrix of medoids (curves of same length as initial series)}
+
+\item{getRefSeries}{Function to retrieve initial series (e.g. in stage 2 after series
+have been replaced by stage-1 medoids)}
+
+\item{nb_ref_curves}{How many reference series? (This number is known at this stage)}
+
+\item{nb_series_per_chunk}{(~Maximum) number of series in each group, inside a task}
+
+\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task}
+
+\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)}
+
+\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)}
+}
+\value{
+A big.matrix of size K1 x L where L = data_length
+}
+\description{
+Compute the synchrones curves (sum of clusters elements) from a matrix of medoids,
+using L2 distances.
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/clustering.R
+\name{computeWerDists}
+\alias{computeWerDists}
+\title{computeWerDists}
+\usage{
+computeWerDists(synchrones, ncores_clust = 1, verbose = FALSE,
+ parll = TRUE)
+}
+\arguments{
+\item{synchrones}{A big.matrix of synchrones, in rows. The series have same length
+as the series in the initial dataset}
+
+\item{ncores_clust}{"OpenMP" number of parallel clusterings in one task}
+
+\item{verbose}{Level of verbosity (0/FALSE for nothing or 1/TRUE for all; devel stage)}
+
+\item{parll}{TRUE to fully parallelize; otherwise run sequentially (debug, comparison)}
+}
+\value{
+A matrix of size K1 x K1
+}
+\description{
+Compute the WER distances between the synchrones curves (in rows), which are
+returned (e.g.) by \code{computeSynchrones()}
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/main.R
+\name{curvesToContribs}
+\alias{curvesToContribs}
+\title{curvesToContribs}
+\usage{
+curvesToContribs(series, wf, ctype)
+}
+\arguments{
+\item{series}{Matrix of series (in rows), of size n x L}
+
+\item{wf}{Wavelet transform filter; see ?wavelets::wt.filter}
+
+\item{ctype}{Type of contribution: "relative" or "absolute" (or any prefix)}
+}
+\value{
+A matrix of size n x log(L) containing contributions in rows
+}
+\description{
+Compute the discrete wavelet coefficients for each series, and aggregate them in
+energy contribution across scales as described in https://arxiv.org/abs/1101.4744v2
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/de_serialize.R
+\name{de_serialize}
+\alias{de_serialize}
+\alias{binarize}
+\alias{binarizeTransform}
+\alias{getDataInFile}
+\alias{binarize}
+\alias{binarizeTransform}
+\alias{getDataInFile}
+\title{(De)Serialization of a [big]matrix or data stream}
+\usage{
+binarize(data_ascii, data_bin_file, nb_per_chunk, sep = ",", nbytes = 4,
+ endian = .Platform$endian)
+
+binarizeTransform(getData, transform, data_bin_file, nb_per_chunk, nbytes = 4,
+ endian = .Platform$endian)
+
+getDataInFile(indices, data_bin_file, nbytes = 4, endian = .Platform$endian)
+}
+\arguments{
+\item{data_ascii}{Either a matrix or CSV file, with items in rows}
+
+\item{data_bin_file}{Name of binary file on output (\code{binarize})
+or intput (\code{getDataInFile})}
+
+\item{nb_per_chunk}{Number of lines to process in one batch}
+
+\item{sep}{Separator in CSV input file (if any provided)}
+
+\item{nbytes}{Number of bytes to serialize a floating-point number; 4 or 8}
+
+\item{endian}{Endianness to use for (de)serialization. Use "little" or "big" for portability}
+
+\item{getData}{Function to retrieve data chunks}
+
+\item{transform}{Transformation function to apply on data chunks}
+
+\item{indices}{Indices of the lines to retrieve}
+}
+\value{
+For \code{getDataInFile()}, the matrix with rows corresponding to the
+ requested indices. \code{binarizeTransform} returns the number of processed lines.
+ \code{binarize} is designed to serialize in several calls, thus returns nothing.
+}
+\description{
+\code{binarize()} serializes a matrix or CSV file with minimal overhead,
+ into a binary file. \code{getDataInFile()} achieves the inverse task: she retrieves
+ (ASCII) data rows from indices in the binary file. Finally,
+ \code{binarizeTransform()} serialize transformations of all data chunks; to use it,
+ a data-retrieval function must be provided, thus \code{binarize} will most likely be
+ used first (and then a function defined to seek in generated binary file)
+}
--- /dev/null
+\name{epclust-package}
+\alias{epclust-package}
+\alias{epclust}
+\docType{package}
+
+\title{
+ \packageTitle{epclust}
+}
+
+\description{
+ \packageDescription{epclust}
+}
+
+\details{
+ Non-R-base dependencies:
+ \itemize{
+ \item cluster: for PAM algorithm
+ \item bigmemory: to share (big) matrices between processes
+ \item wavelets: to compute curves contributions using DWT
+ \item Rwave: to compute the CWT
+ }
+
+ Suggested packages:
+ \itemize{
+ \item synchronicity: to compute synchrones in // (concurrent writes)
+ \item devtools,testthat,roxygen2: development environment (doc, reload, test...)
+ \item MASS: generate multivariate gaussian samples in tests
+ \item wmtsa: generate sample curves for \code{claws} examples
+ \item DBI: for the example with series in an SQLite DB
+ \item digest: to compare \code{claws} examples results
+ }
+
+ The package vignette was generated with Jupyter, outside R packaging flow.
+}
+
+\author{
+ \packageAuthor{epclust}
+
+ Maintainer: \packageMaintainer{epclust}
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/RcppExports.R
+\name{epclustFilter}
+\alias{epclustFilter}
+\title{filter}
+\usage{
+epclustFilter(cwt)
+}
+\arguments{
+\item{cwt}{Continuous wavelets transform}
+}
+\value{
+The filtered CWT
+}
+\description{
+Filter time-series
+}
--- /dev/null
+#include <R.h>
+#include <Rdefines.h>
+
+// filterMA
+//
+// Filter [time-]series by replacing all values by the moving average of values
+// centered around current one. Border values are averaged with available data.
+//
+// @param M_ A real matrix of size LxD
+// @param w_ The (odd) number of values to average
+//
+// @return The filtered matrix, of same size as the input
+SEXP filterMA(SEXP M_, SEXP w_)
+{
+ int L = INTEGER(getAttrib(M_, R_DimSymbol))[0],
+ D = INTEGER(getAttrib(M_, R_DimSymbol))[1],
+ w = INTEGER_VALUE(w_),
+ half_w = (w-1)/2,
+ i,
+ nt; //number of terms in the current sum (max: w)
+ double *M = REAL(M_),
+ cs, //current sum (max: w terms)
+ left; //leftward term in the current moving sum
+
+ SEXP fM_; //the filtered result
+ PROTECT(fM_ = allocMatrix(REALSXP, L, D));
+ double* fM = REAL(fM_); //pointer to the encapsulated vector
+
+ // NOTE: unused loop parameter: shifting at the end of the loop is more efficient
+ for (int col=D-1; col>=0; col--)
+ {
+ // Initialization
+ nt = half_w + 1;
+ left = M[0];
+ cs = 0.;
+ for (i=half_w; i>=0; i--)
+ cs += M[i];
+
+ // Left border
+ for (i=0; i<half_w; i++)
+ {
+ fM[i] = cs / nt; //(partial) moving average at current index i
+ cs += M[i+half_w+1];
+ nt++;
+ }
+
+ // Middle: now nt == w, i == half_w
+ for (; i<L-half_w-1; i++)
+ {
+ fM[i] = cs / w; //moving average at current index i
+ cs = cs - left + M[i+half_w+1]; //remove oldest items, add next
+ left = M[i-half_w+1]; //update first value for next iteration
+ }
+
+ // (Last "fully averaged" index +) Right border
+ for (; i<L; i++)
+ {
+ fM[i] = cs / nt; //(partial) moving average at current index i
+ cs -= M[i-half_w];
+ nt--;
+ }
+
+ // Shift by L == increment column index by 1 (storage per column)
+ M = M + L;
+ fM = fM + L;
+ }
+
+ UNPROTECT(1);
+ return fM_;
+}
--- /dev/null
+library(testthat)
+library(epclust)
+
+test_check("epclust")
--- /dev/null
+# Compute the sum of (normalized) sum of squares of closest distances to a medoid.
+computeDistortion <- function(series, medoids)
+{
+ n <- ncol(series)
+ L <- nrow(series)
+ distortion <- 0.
+ for (i in seq_len(n))
+ distortion <- distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
+
+ sqrt( distortion / n )
+}
--- /dev/null
+# Shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (if base==3)
+I = function(i, base)
+ (i-1) %% base + 1
--- /dev/null
+context("assignMedoids")
+
+test_that("assignMedoids behave as expected",
+{
+ # Generate a gaussian mixture
+ n <- 999
+ L <- 7
+ medoids <- cbind( rep(0,L), rep(-5,L), rep(5,L) )
+ # short series...
+ require("MASS", quietly=TRUE)
+ series <- t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)),
+ MASS::mvrnorm(n/3, medoids[,2], diag(L)),
+ MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) )
+
+ # With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,...
+ mi <- assignMedoids(series, medoids)
+ mi_ref <- rep(1:3, each=n/3)
+ expect_lt( mean(mi != mi_ref), 0.01 )
+})
--- /dev/null
+context("clustering")
+
+test_that("clusteringTask1 behave as expected",
+{
+ # Generate 60 reference sinusoïdal series (medoids to be found),
+ # and sample 900 series around them (add a small noise)
+ n <- 900
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K1 <- 60
+ s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ wf <- "haar"
+ ctype <- "absolute"
+ getContribs <- function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype)
+
+ require("cluster", quietly=TRUE)
+ algoClust1 <- function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
+ indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE)
+ medoids_K1 <- getSeries(indices1)
+
+ expect_equal(dim(medoids_K1), c(L,K1))
+ # Not easy to evaluate result: at least we expect it to be better than random selection of
+ # medoids within initial series
+ distor_good <- computeDistortion(series, medoids_K1)
+ for (i in 1:3)
+ expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) )
+})
+
+test_that("clusteringTask2 behave as expected",
+{
+ # Same 60 reference sinusoïdal series than in clusteringTask1 test,
+ # but this time we consider them as medoids - skipping stage 1
+ # Here also we sample 900 series around the 60 "medoids"
+ n <- 900
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K1 <- 60
+ K2 <- 3
+ #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
+ s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ # Perfect situation: all medoids "after stage 1" are ~good
+ algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+ indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little",
+ verbose=TRUE)
+ medoids_K2 <- getSeries(indices2)
+
+ expect_equal(dim(medoids_K2), c(L,K2))
+ # Not easy to evaluate result: at least we expect it to be better than random selection of
+ # synchrones within 1...K1 (from where distances computations + clustering was run)
+ distor_good <- computeDistortion(series, medoids_K2)
+#TODO: This fails; why?
+# for (i in 1:3)
+# expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) )
+})
--- /dev/null
+context("computeSynchrones")
+
+test_that("computeSynchrones behave as expected",
+{
+ # Generate 300 sinusoïdal series of 3 kinds: all series of indices == 0 mod 3 are the same
+ # (plus noise), all series of indices == 1 mod 3 are the same (plus noise) ...
+ n <- 300
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K <- 3
+ s1 <- cos(x)
+ s2 <- sin(x)
+ s3 <- c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+ #sum((s1-s2)^2) == 96
+ #sum((s1-s3)^2) == 58
+ #sum((s2-s3)^2) == 38
+ s <- list(s1, s2, s3)
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ synchrones <- computeSynchrones(cbind(s1,s2,s3),getSeries,n,100,verbose=TRUE)
+
+ expect_equal(dim(synchrones), c(L,K))
+ for (i in 1:K)
+ {
+ # Synchrones are (for each medoid) sums of closest curves.
+ # Here, we expect exactly 100 curves of each kind to be assigned respectively to
+ # synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve
+ expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
+ }
+})
--- /dev/null
+context("computeWerDists")
+
+test_that("computeWerDists output correct results",
+{
+ nbytes <- 8
+ endian <- "little"
+
+ # On two identical series
+ serie <- rnorm(212, sd=5)
+ series <- cbind(serie, serie)
+ getSeries <- function(indices) as.matrix(series[,indices])
+ dists <- computeWerDists(1:2, getSeries, 50, 3, 4, nbytes, endian,
+ verbose=TRUE)
+ expect_equal(dists, matrix(0.,nrow=2,ncol=2))
+
+ # On two constant series
+ # TODO: ref results. Ask Jairo to check function.
+})
--- /dev/null
+context("de_serialize")
+
+test_that("serialization + getDataInFile retrieve original data / from matrix",
+{
+ data_bin_file <- ".epclust_test_m.bin"
+ unlink(data_bin_file)
+
+ # Dataset 200 cols / 30 rows
+ data_ascii <- matrix(runif(200*30,-10,10),nrow=30)
+ nbytes <- 4 #lead to a precision of 1e-7 / 1e-8
+ endian <- "little"
+
+ # Simulate serialization in one single call
+ binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_lines <- getDataInFile(indices, data_bin_file, nbytes, endian)
+ expect_equal(data_lines, data_ascii[,indices], tolerance=1e-6)
+ }
+ unlink(data_bin_file)
+
+ # Serialization in several calls (last call complete, next call NULL)
+ for (i in 1:20)
+ binarize(data_ascii[,((i-1)*10+1):(i*10)], data_bin_file, 20, ",", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_lines <- getDataInFile(indices, data_bin_file, nbytes, endian)
+ expect_equal(data_lines, data_ascii[,indices], tolerance=1e-6)
+ }
+ unlink(data_bin_file)
+})
+
+test_that("serialization + transform + getDataInFile retrieve original transforms",
+{
+ data_bin_file <- ".epclust_test_t.bin"
+ unlink(data_bin_file)
+
+ # Dataset 200 cols / 30 rows
+ data_ascii <- matrix(runif(200*30,-10,10),nrow=30)
+ nbytes <- 8
+ endian <- "little"
+
+ binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian)
+ # Serialize transformation (just compute range) into a new binary file
+ trans_bin_file <- ".epclust_test_t_trans.bin"
+ unlink(trans_bin_file)
+ getSeries <- function(inds) getDataInFile(inds, data_bin_file, nbytes, endian)
+ binarizeTransform(getSeries, function(series) apply(series, 2, range),
+ trans_bin_file, 250, nbytes, endian)
+ unlink(data_bin_file)
+ expect_equal(file.info(trans_bin_file)$size, 2*ncol(data_ascii)*nbytes+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ trans_cols <- getDataInFile(indices, trans_bin_file, nbytes, endian)
+ expect_equal(trans_cols, apply(data_ascii[,indices],2,range), tolerance=1e-6)
+ }
+ unlink(trans_bin_file)
+})
+
+test_that("serialization + getDataInFile retrieve original data / from connection",
+{
+ data_bin_file <- ".epclust_test_c.bin"
+ unlink(data_bin_file)
+
+ # Dataset 300 cols / 50 rows
+ data_csv <- system.file("testdata","de_serialize.csv",package="epclust")
+ nbytes <- 8
+ endian <- "big"
+ data_ascii <- unname( t( as.matrix(read.table(data_csv,sep=";",header=FALSE)) ) ) #ref
+
+ # Simulate serialization in one single call
+ binarize(data_csv, data_bin_file, 350, ";", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, 300*50*8+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_cols <- getDataInFile(indices,data_bin_file,nbytes,endian)
+ expect_equal(data_cols, data_ascii[,indices])
+ }
+ unlink(data_bin_file)
+
+ # Serialization in several calls / chunks of 29 --> 29*10 + 10, incomplete last
+ data_con <- file(data_csv, "r")
+ binarize(data_con, data_bin_file, 29, ";", nbytes, endian)
+ expect_equal(file.info(data_bin_file)$size, 300*50*8+8)
+ for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
+ {
+ data_cols <- getDataInFile(indices,data_bin_file,nbytes,endian)
+ expect_equal(data_cols, data_ascii[,indices])
+ }
+ unlink(data_bin_file)
+ #close(data_con) --> done in binarize()
+})
--- /dev/null
+context("filterMA")
+
+test_that("[time-]serie filtering behave as expected",
+{
+ # Mean of 3 values
+ M <- matrix(runif(1000,min=-7,max=7), ncol=10)
+ ref_fM <- stats::filter(M, rep(1/3,3), circular=FALSE)
+ fM <- epclust:::filterMA(M, 3)
+
+ # Expect an agreement on all inner values
+ expect_equal(dim(fM), c(100,10))
+ expect_equal(fM[2:99,], ref_fM[2:99,])
+
+ # Border values should be averages of 2 values
+ expect_equal(fM[1,], colMeans(M[1:2,]))
+ expect_equal(fM[100,], colMeans(M[99:100,]))
+
+ # Mean of 5 values
+ ref_fM <- stats::filter(M, rep(1/5,5), circular=FALSE)
+ fM <- epclust:::filterMA(M, 5)
+
+ # Expect an agreement on all inner values
+ expect_equal(dim(fM), c(100,10))
+ expect_equal(fM[3:98,], ref_fM[3:98,])
+
+ # Border values should be averages of 3 or 4 values
+ expect_equal(fM[1,], colMeans(M[1:3,]))
+ expect_equal(fM[2,], colMeans(M[1:4,]))
+ expect_equal(fM[99,], colMeans(M[97:100,]))
+ expect_equal(fM[100,], colMeans(M[98:100,]))
+})
--- /dev/null
+context("utils functions")
+
+test_that("Helper function to split indices work properly",
+{
+ indices <- 1:400
+
+ # bigger nb_per_set than length(indices)
+ expect_equal(epclust:::.splitIndices(indices,500), list(indices))
+
+ # nb_per_set == length(indices)
+ expect_equal(epclust:::.splitIndices(indices,400), list(indices))
+
+ # length(indices) %% nb_per_set == 0
+ expect_equal(epclust:::.splitIndices(indices,200),
+ c( list(indices[1:200]), list(indices[201:400]) ))
+ expect_equal(epclust:::.splitIndices(indices,100),
+ c( list(indices[1:100]), list(indices[101:200]),
+ list(indices[201:300]), list(indices[301:400]) ))
+
+ # length(indices) / nb_per_set == 1, length(indices) %% nb_per_set == 100
+ expect_equal(epclust:::.splitIndices(indices,300,min_size=1),
+ list(1:300, 301:400))
+ split_inds <- epclust:::.splitIndices(indices,300,min_size=200)
+ expect_equal(length(unique(unlist(split_inds))), 400)
+ expect_equal(length(split_inds), 2)
+ expect_equal(length(split_inds[[1]]), 200)
+ expect_equal(length(split_inds[[2]]), 200)
+ expect_error(epclust:::.splitIndices(indices,300,min_size=300), "Impossible to split*")
+
+ # length(indices) / nb_per_set == 2, length(indices) %% nb_per_set == 42
+ expect_equal(epclust:::.splitIndices(indices,179,min_size=1),
+ list(1:179, 180:358, 359:400))
+ split_inds <-epclust:::.splitIndices(indices,179,min_size=60)
+ expect_equal(length(unique(unlist(split_inds))), 400)
+ expect_equal(length(split_inds), 3)
+ expect_equal(length(split_inds[[1]]), 170)
+ expect_equal(length(split_inds[[2]]), 170)
+ expect_equal(length(split_inds[[3]]), 60)
+ expect_error(epclust:::.splitIndices(indices,179,min_size=150), "Impossible to split*")
+})
+
+test_that("curvesToContribs output correct results",
+{
+ L <- 220 #extended to 256, log2(256) == 8
+
+ # Zero serie
+ expect_equal(curvesToContribs(rep(0,L), "d8", "absolute"), as.matrix(rep(0,8)))
+ expect_equal(curvesToContribs(rep(0,L), "haar", "absolute"), as.matrix(rep(0,8)))
+
+ # Constant serie
+ expect_equal(curvesToContribs(rep(5,L), "haar", "absolute"), as.matrix(rep(0,8)))
+ expect_equal(curvesToContribs(rep(10,L), "haar", "absolute"), as.matrix(rep(0,8)))
+# expect_equal(curvesToContribs(rep(5,L), "d8", ctype), rep(0,8))
+#TODO:
+})
--- /dev/null
+TODO from jupyter; generate HTML, in final package
--- /dev/null
+#Ligne 83 dans epclust/R/computeWerDists.R :
+# TODO:
+# [X,period,scale,coix] =
+#wavelet(x(:,2),dt,Args.Pad,Args.Dj,Args.S0,Args.J1,Args.Mother);%#ok
+#[Y,period,scale,coiy] = wavelet(y(:,2),dt,Args.Pad,Args.Dj,Args.S0,Args.J1,Args.Mother);
+#
+#%Smooth X and Y before truncating! (minimize coi)
+#%sinv=1./(scale');
+#%
+#%
+#%sX=smoothwavelet(sinv(:,ones(1,nx)).*(abs(X).^2),dt,period,Args.Dj,scale);
+#%sY=smoothwavelet(sinv(:,ones(1,ny)).*(abs(Y).^2),dt,period,Args.Dj,scale);
+#%
+#%Wxy=X.*conj(Y);
+#%
+#%% ----------------------- Wavelet coherence ---------------------------------
+#%sWxy=smoothwavelet(sinv(:,ones(1,n)).*Wxy,dt,period,Args.Dj,scale);
+#%Rsq=abs(sWxy).^2./(sX.*sY);
+#
+
+
+#https://github.com/grinsted/wavelet-coherence/blob/master/wtc.m
+
+
+library(Rwave) # CWT
+library(cluster) # pam
+#library(flexclust) # kcca
+
+
+ toCWT <- function(X, sw= 0, tw= 0, swabs= 0,
+ nvoice= 12, noctave= 5,
+ s0= 2, w0= 2*pi, lt= 24, dt= 0.5,
+ spectra = FALSE, smooth = TRUE,
+ scaled = FALSE,
+ scalevector)
+ { noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
+ if(missing(scalevector))
+ scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
+ res <- lapply(1:nrow(X), function(n)
+ { tsX <- ts( X[n,] )
+ tsCent <- tsX - mean(tsX)
+ if(scaled) tsCent <- ts(scale(tsCent))
+ tsCent.cwt <- cwt.ts(tsCent, s0, noctave, nvoice, w0)
+ tsCent.cwt
+ } )
+ if( spectra ) res <- lapply(res, function(l) Mod(l)^2 )
+ if( smooth ) res <- lapply(res, smCWT, swabs = swabs,
+ tw = tw, dt = dt,
+ scalevector = scalevector)
+ resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]),
+ length(res)))
+ for( l in 1:length(res) ) resArray[ , , l] <- res[[l]]
+ resArray
+ }
+
+
+ # ===============================================================
+
+ smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0,
+ nvoice= 12, noctave= 2, s0= 2, w0= 2*pi,
+ lt= 24, dt= 0.5, scalevector )
+ {
+# noctave <- adjust.noctave(lt, dt, s0, tw, noctave)
+# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0
+ wsp <- Mod(CWT)
+ smwsp <- smooth.matrix(wsp, swabs)
+ smsmwsp <- smooth.time(smwsp, tw, dt, scalevector)
+ smsmwsp
+ }
+
+
+ # ===============================================================
+
+ toDWT <- function(x, filter.number = 6, family = "DaubLeAsymm")
+{ x2 <- spline(x, n = 2^ceiling( log(length(x), 2) ),
+ method = 'natural')$y
+ Dx2 <- wd(x2, family = family, filter.number = filter.number)$D
+ Dx2
+}
+
+ # ===============================================================
+
+ contrib <- function(x)
+ { J <- log( length(x)+1, 2)
+ nrj <- numeric(J)
+ t0 <- 1
+ t1 <- 0
+ for( j in 1:J ) {
+ t1 <- t1 + 2^(J-j)
+ nrj[j] <- sqrt( sum( x[t0:t1]^2 ) )
+ t0 <- t1 + 1
+ }
+ return(nrj)
+ }
+
+
+ # ========================================= distance for coh ===
+
+ coherence <- function( x, y)
+ { J <- log(length(x) + 1, 2)
+ t0 <- 1
+ sg2_x <- 0
+ sg2_y <- 0
+ sg_xy <- 0
+ for(j in 0:(J - 1))
+ { t1 <- t0 + 2^(J - j)/2 - 1
+ tt <- t0:t1
+ sg2_x <- sg2_x + mean(x[t0:t1]^2)
+ sg2_y <- sg2_y + mean(y[t0:t1]^2)
+ sg_xy <- sg_xy + mean(x[t0:t1] * y[t0:t1])
+ t0 <- t1 + 1
+ }
+ res <- sg_xy^2 / sg2_x / sg2_y
+ res
+ }
+
+
+ vect2mat <- function(vect){
+ vect <- as.vector(vect)
+ matrix(vect[-(1:2)], delta, lscvect)
+ }
+
+
+ # ========================================= # myimg for graphics
+ jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF",
+ "cyan", "#7FFF7F", "yellow",
+ "#FF7F00", "red", "#7F0000"))
+
+ myimg <- function(MAT, x = 1:nrow(MAT), y = 1:col(MAT), ... )
+ filled.contour( x = x, y = y, z = MAT,
+ xlab= 'Time', ylab= 'scale',
+ color.palette = jet.colors,
+ ... )
+
+
+#TODO: [plus tard] alternative à sowa (package disparu) : cwt..
+#source("sowas-superseded.r") # auxiliary CWT functions
+
+
+cwt.ts <- function(ts,s0,noctave=5,nvoice=10,w0=2*pi){
+
+ if (class(ts)!="ts"){
+
+ cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n")
+
+ }
+ else{
+
+ t=time(ts)
+ dt=t[2]-t[1]
+
+ s0unit=s0/dt*w0/(2*pi)
+ s0log=as.integer((log2(s0unit)-1)*nvoice+1.5)
+
+ if (s0log<1){
+ cat(paste("# s0unit = ",s0unit,"\n",sep=""))
+ cat(paste("# s0log = ",s0log,"\n",sep=""))
+ cat("# s0 too small for w0! \n")
+ }
+ totnoct=noctave+as.integer(s0log/nvoice)+1
+
+ totts.cwt=cwt(ts,totnoct,nvoice,w0,plot=0)
+
+ ts.cwt=totts.cwt[,s0log:(s0log+noctave*nvoice)]
+
+ #Normalization
+ sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0)
+ smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE)
+
+ ts.cwt*smat
+
+ }
+
+}
+
+#####################################
+# WSP
+#####################################
+
+wsp <- function(ts,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,siglevel=0.95,critval=NULL,nreal=1000,arealsiglevel=0.9,kernel=0,markt=-999,marks=-999,logscale=FALSE,plot=TRUE,units="",device="screen",file="wsp",color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=3){
+
+ if (class(ts)!="ts"){
+
+ cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n")
+
+ }
+ else{
+
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+ if (swabs!=0 & sw==0)
+ sw <- swabs/nvoice
+
+ sllist <- checkarealsiglevel(sw,tw,w0,arealsiglevel,siglevel,0)
+ arealsiglevel <- sllist$arealsiglevel
+ siglevel <- sllist$siglevel
+
+ at <- NULL
+
+ t <- time(ts)
+ dt <- deltat(ts)
+ s0rem <- s0
+ s0 <- adjust.s0(s0,dt)
+ dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1
+
+ noctave <- adjust.noctave(length(ts),dt,s0,tw,noctave)
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+ tsanom <- ts-mean(ts)
+
+ #WAVELET TRANSFORMATION
+ ts.cwt <- cwt.ts(tsanom,s0,noctave,nvoice,w0)
+
+ #SMOOTHING
+ wsp <- smooth.matrix(Mod(ts.cwt)^2,swabs)
+ smwsp <- smooth.time(wsp,tw,dt,scalevector)
+
+ #POINTWISE SIGNIFICANCE TEST
+ if (is.null(critval)==FALSE){ # is critval empty?
+ if (dim(critval)[2]!=dim(smwsp)[2]){ # is critval of the wrong dimension?
+ if (siglevel[1]!=0 & nreal!=0) critval <-
+ criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal)
+ #critval is of wrong dimension and siglevel and nreal are given
+ else {
+ critval <- NULL # no test possible
+ arealsiglevel <- 0
+ cat("# dimension of critval is wrong \n")
+ cat("# areawise test only possible with pointwise test \n")
+ }
+ }
+ }
+ else{ # critval is empty, but nreal or siglevel is given
+ if (siglevel[1]!=0 & nreal!=0) critval <-
+ criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal)
+ else {
+ critval <- NULL
+ arealsiglevel <- 0
+ cat("# areawise test only possible with pointwise test \n")
+ }
+ }
+
+ #AREAL SIGNIFICANCE TEST
+ if (arealsiglevel!=0){
+ v <- critval[1,]
+ v[v==0] <- 9999
+ cvmat <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE)
+ atest <- arealtest(smwsp/cvmat,dt,s0,noctave,nvoice,w0,swabs,tw,siglevel,arealsiglevel,kernel,0)
+ at <- atest$at
+ kernel <- atest$kernel
+ }
+
+ if (s0rem<s0){
+ smwsp <- addvalues(nvoice,dnoctave,smwsp,NA)
+ critval <- addvalues(nvoice,dnoctave,critval,1)
+ #at <- addvalues(nvoice,dnoctave,at,NA)
+ noctave <- noctave+dnoctave
+ s0 <- s0/2^dnoctave
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+ }
+
+ #PARAMETERS
+ wclist <-
+ list(modulus=smwsp,phase=NULL,time=t,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,scales=scalevector,critval=critval,at=at,kernel=kernel)
+
+ class(wclist) <- "wt"
+
+ #PLOTTING
+ if (plot)
+ plot(wclist,markt,marks,NULL,NULL,logscale,FALSE,units,"Wavelet Power Spectrum",device,file,FALSE,color,pwidth,pheight,labsc,labtext,sigplot)
+
+ wclist
+
+ }
+
+}
+
+#####################################
+# WCO
+#####################################
+
+wco <-
+ function(ts1,ts2,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,siglevel=0.95,arealsiglevel=0.9,kernel=0,markt=-999,marks=-999,sqr=FALSE,phase=TRUE,plot=TRUE,units="",device="screen",file="wcoh",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=3){
+
+ if (class(ts1)!="ts"){
+
+ cat("# This function needs two time series objects as input. You may construct them by using the function ts(data,start,deltat). Try '?ts' for help.\n")
+
+ }
+ else{
+
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+ if (swabs!=0 & sw==0)
+ sw <- swabs/nvoice
+
+ sllist <- checkarealsiglevel(sw,tw,w0,arealsiglevel,siglevel,1)
+ arealsiglevel <- sllist$arealsiglevel
+ siglevel <- sllist$siglevel
+
+ if (sw==0 & tw==0 & swabs==0) {
+ cat("# coherence without averaging makes no sense! \n")
+ siglevel <- 0
+ arealsiglevel <- 0
+ }
+
+ if (phase==FALSE) phs <- NULL
+
+ at <- NULL
+
+ tsadjust <- adjust.timeseries(ts1,ts2)
+ ts1 <- tsadjust$ts1
+ ts2 <- tsadjust$ts2
+
+ t <- time(ts1)
+ dt <- deltat(ts1)
+
+ s0rem <- s0
+ s0 <- adjust.s0(s0,dt)
+ dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1
+
+ noctave <- adjust.noctave(length(ts1),dt,s0,tw,noctave)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ ts1anom <- ts1-mean(ts1)
+ ts2anom <- ts2-mean(ts2)
+
+ ts1.cwt <- cwt.ts(ts1anom,s0,noctave,nvoice,w0)
+ ts2.cwt <- cwt.ts(ts2anom,s0,noctave,nvoice,w0)
+
+ cosp <- Re(ts1.cwt)*Re(ts2.cwt) + Im(ts1.cwt)*Im(ts2.cwt)
+ quad <- Im(ts1.cwt)*Re(ts2.cwt) - Re(ts1.cwt)*Im(ts2.cwt)
+ wsp1 <- Mod(ts1.cwt)^2
+ wsp2 <- Mod(ts2.cwt)^2
+
+ smcosp <- smooth.matrix(cosp,swabs)
+ smquad <- smooth.matrix(quad,swabs)
+ smwsp1 <- smooth.matrix(wsp1,swabs)
+ smwsp2 <- smooth.matrix(wsp2,swabs)
+
+ smsmcosp <- smooth.time(smcosp,tw,dt,scalevector)
+ smsmquad <- smooth.time(smquad,tw,dt,scalevector)
+ smsmwsp1 <- smooth.time(smwsp1,tw,dt,scalevector)
+ smsmwsp2 <- smooth.time(smwsp2,tw,dt,scalevector)
+
+ if (sqr==FALSE)
+ wcoh <- sqrt((smsmcosp^2+smsmquad^2)/(smsmwsp1*smsmwsp2))
+ else
+ wcoh <- (smsmcosp^2+smsmquad^2)/(smsmwsp1*smsmwsp2)
+
+ if (phase)
+ phs <- atan2(smsmquad,smsmcosp)
+ else phs <- NULL
+
+ #POINTWISE SIGNIFICANCE TEST
+ if (siglevel[1]!=0) critval <- criticalvaluesWCO(s0,noctave,nvoice,w0,swabs,tw,siglevel)
+ else critval <- NULL
+ if (sqr==TRUE & is.null(critval)==FALSE)
+ critval <- critval^2
+
+ #AREAWISE SIGNIFICANCE TEST
+ if (arealsiglevel!=0){
+ atest <- arealtest(wcoh/critval,dt,s0,noctave,nvoice,w0,swabs,tw,siglevel,arealsiglevel,kernel,1)
+ at <- atest$at
+ kernel <- atest$kernel
+ }
+
+ wcoh[1,1] <- 0
+ wcoh[1,2] <- 1
+
+ if (phase){
+ phs[1,1] <- -pi
+ phs[1,2] <- pi
+ }
+
+ if (s0rem<s0){
+ wcoh <- addvalues(nvoice,dnoctave,wcoh,NA)
+ phs <- addvalues(nvoice,dnoctave,phs,NA)
+ noctave <- noctave+dnoctave
+ s0 <- s0/2^dnoctave
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+ }
+
+ wclist <- list(modulus=wcoh,phase=phs,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=t,scales=scalevector,critval=critval,at=at,kernel=kernel)
+
+ class(wclist) <- "wt"
+
+ if (plot) plot(wclist,markt,marks,NULL,NULL,FALSE,phase,units,"Wavelet Coherence",device,file,split,color,pwidth,pheight,labsc,labtext,sigplot)
+
+ wclist
+
+ }
+
+ }
+
+#####################################
+# WCS
+#####################################
+
+wcs <- function(ts1,ts2,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,markt=-999,marks=-999,logscale=FALSE,phase=TRUE,plot=TRUE,units="",device="screen",file="wcsp",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext=""){
+
+ if (class(ts1)!="ts"){
+
+ cat("# This function needs two time series objects as input. You may construct them by using the function ts(data,start,deltat). Try '?ts' for help. \n")
+
+ }
+ else{
+
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+ if (swabs!=0 & sw==0)
+ sw <- swabs/nvoice
+
+ tsadjust <- adjust.timeseries(ts1,ts2)
+ ts1 <- tsadjust$ts1
+ ts2 <- tsadjust$ts2
+
+ t <- time(ts1)
+ dt <- deltat(ts1)
+
+ s0rem <- s0
+ s0 <- adjust.s0(s0,dt)
+ dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1
+
+ noctave <- adjust.noctave(length(ts1),dt,s0,tw,noctave)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ ts1anom <- ts1-mean(ts1)
+ ts2anom <- ts2-mean(ts2)
+
+ ts1.cwt <- cwt.ts(ts1anom,s0,noctave,nvoice,w0)
+ ts2.cwt <- cwt.ts(ts2anom,s0,noctave,nvoice,w0)
+
+ cosp <- Re(ts1.cwt)*Re(ts2.cwt) + Im(ts1.cwt)*Im(ts2.cwt)
+ quad <- Im(ts1.cwt)*Re(ts2.cwt) - Re(ts1.cwt)*Im(ts2.cwt)
+
+ smcosp <- smooth.matrix(cosp,swabs)
+ smquad <- smooth.matrix(quad,swabs)
+
+ smsmcosp <- smooth.time(smcosp,tw,dt,scalevector)
+ smsmquad <- smooth.time(smquad,tw,dt,scalevector)
+
+ wcsp <- smsmcosp^2+smsmquad^2
+
+ if (phase)
+ phs <- atan2(smsmquad,smsmcosp)
+ else phs <- NULL
+
+ if (phase){
+ phs[1,1] <- -pi
+ phs[1,2] <- pi
+ }
+
+ if (s0rem<s0){
+ wcsp <- addvalues(nvoice,dnoctave,wcoh,NA)
+ phs <- addvalues(nvoice,dnoctave,phs,NA)
+ noctave <- noctave+dnoctave
+ s0 <- s0/2^dnoctave
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+ }
+
+ wclist <- list(modulus=wcsp,phase=phs,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=t,scales=scalevector,critval=NULL,at=NULL,kernel=NULL)
+
+ class(wclist) <- "wt"
+
+ if (plot) plot(wclist,markt,marks,NULL,NULL,logscale,phase,units,"Wavelet Cross Spectrum",device,file,split,color,pwidth,pheight,labsc,labtext)
+
+ wclist
+
+ }
+
+}
+
+##########################################
+# POINTWISE SIGNIFICANCE TEST
+##########################################
+
+rawWSP <- function(ts,s0=1,noctave=5,nvoice=20,w0=2*pi,swabs=0,tw=0,scalevector){
+
+ tsanom <- ts-mean(ts)
+
+ ts.cwt <- cwt.ts(tsanom,s0,noctave,nvoice,w0)
+
+ wsp <- Mod(ts.cwt)^2
+
+ smwsp <- smooth.matrix(wsp,swabs)
+ smsmwsp <- smooth.time(smwsp,tw,deltat(ts),scalevector)
+
+ smsmwsp
+
+}
+
+criticalvaluesWSP <- function(ts,s0=1,noctave=5,nvoice=10,w0=2*pi,swabs=0,tw=0,siglevel=0.95,nreal=1000){
+
+ t=time(ts)
+ dt=deltat(ts)
+
+ s0 <- adjust.s0(s0,dt)
+ noctave <- adjust.noctave(length(ts),dt,s0,tw,noctave)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ cat("# calculating critical values by means of MC-simulations \n")
+
+ s0unit=s0/dt*w0/(2*pi)
+ s0log=as.integer((log2(s0unit)-1)*nvoice+1.5)
+
+ wsp0 <- rawWSP(ts,s0,noctave,nvoice,w0,swabs,tw,scalevector)
+
+ S <- dim(wsp0)[2]
+
+ n1 <- 1 + 2*as.integer( sqrt(2) * 2^((S-swabs+s0log-1)/nvoice+1) )
+ n2 <- max(scalevector)*tw*2/dt*1.1
+ n3 <- 2*tw*s0*2^noctave/dt+100
+ n <- max(n1,n2,n3)
+
+ center <- (n-1)/2+1
+ cv <- matrix(rep(0,S*length(siglevel)),ncol=S)
+ rmatrix <- matrix(0,nrow=nreal,ncol=S)
+
+ # Fitting AR1-process (red noise) to data
+ arts0 <- ar(ts,order.max=1)
+ sdts0 <- sd(ts[1:length(ts)])
+
+ if (arts0$order==0){
+ se <- sqrt(arts0$var)
+ arts0$ar <- 0.000001
+ }
+ else
+ se <- sqrt(sdts0*sdts0*(1-arts0$ar*arts0$ar))
+
+ tsMC <- ts(data=rep(0,n),start=t[1],frequency=1/dt)
+
+ # MC Simulations
+ for (i in 1:nreal){
+
+ tsMC[1:n] <- arima.sim(list(ar = arts0$ar), n+1, sd = se)[2:(n+1)]
+
+ rmatrix[i,] <- rawWSP(tsMC,s0,noctave,nvoice,w0,swabs,tw,scalevector)[center,]
+
+ }
+
+ for (s in (1+swabs):(S-swabs)) rmatrix[,s] <- sort(rmatrix[,s])
+
+ for (i in 1:length(siglevel)){
+ sigindex <- as.integer(nreal*siglevel[i])
+ cvv <- rmatrix[sigindex,]
+ cvv[is.na(cvv)] <- 0
+ cv[i,] <- cvv
+ }
+
+ cv
+
+}
+
+###########
+
+criticalvaluesWCO <- function(s0,noctave,nvoice,w0,swabs,tw,siglevel=0.95){
+
+ cv=rep(0,length(siglevel))
+
+ for (i in 1:length(siglevel)){
+
+ if (siglevel[i]!=0.9 && siglevel[i]!=0.95 && siglevel[i]!=0.99) siglevel[i] <- 0.95
+
+ if (siglevel[i]==0.9){
+ cat("# significance level set to 0.90 \n")
+ sw <- 1.0*swabs/nvoice
+ cv[i] <- 0.00246872*w0^2*sw + 0.0302419*w0*sw^2 + 0.342056*sw^3 -
+ 0.000425853*w0^2 - 0.101749*w0*sw - 0.703537*sw^2 +
+ 0.00816219*w0 + 0.442342*sw + 0.970315
+ }
+
+ if (siglevel[i]==0.95){
+ cat("# significance level set to 0.95 \n")
+ sw <- swabs*100.0/3.0/nvoice
+ cv[i] <- 0.0000823*w0^3 + 0.0000424*w0^2*sw + 0.0000113*w0*sw^2 +
+ 0.0000154*sw^3 - 0.0023*w0^2 - 0.00219*w0*sw - 0.000751*sw^2 +
+ 0.0205*w0 + 0.0127*sw + 0.95
+ }
+
+ if (siglevel[i]==0.99){
+ cat("# significance level set to 0.99 \n")
+ sw <- 1.0*swabs/nvoice
+ cv[i] <- 0.00102304*w0^2*sw + 0.00745772*w0*sw^2 + 0.230035*sw^3 -
+ 0.000361565*w0^2 - 0.0502861*w0*sw - 0.440777*sw^2 +
+ 0.00694998*w0 + 0.29643*sw + 0.972964
+ }
+
+ if (cv[i]>1) cv[i] <- 1
+
+ cat(paste("# significance testing, cv=",cv[i]," \n",sep=""))
+
+ }
+
+ cv
+
+}
+
+#############################
+# AREAWISE SIGNIFICANCE TEST
+#############################
+
+slide <- function(data,kernellist,s0,noctave,nvoice,cv){
+
+ # slides kernel over data set
+ #----------------------------
+ # data: matrix containing data
+ # kernellist: matrix containing kernel
+ # s0: lowest scale
+ # noctave: number of octaves
+ # nvoice: number of voices per octave
+ # cv: critical value, all values higher are set to one
+
+ #Time: (rows) n,i the kernel is to be scaled in this direction
+ #Scale: (cols) m,j
+
+ data[data<cv] <- 0
+
+ kernel <- kernellist$bitmap
+
+ js <- kernellist$is
+
+ sm <- s0*2^noctave
+
+ dbin <- tobin(data)
+ kbin <- tobin(kernel)
+
+ dn <- nrow(dbin)
+ dm <- ncol(dbin)
+
+ kn <- nrow(kbin)
+ km <- ncol(kbin)
+
+ mark <- matrix(rep(0,dn*dm),nrow=dn)
+
+ for (j in 1:(dm-km+1)){
+
+ s <- s0*2^((j+js-1)/nvoice)
+ kscn <- as.integer(kn*s/sm);
+ if (kscn==0) kscn <- 1
+
+ ksc <- scaleKernel(kbin,kscn)
+ kscm <- km
+
+ for (i in 1:(dn-kscn+1)){
+
+ subbin <- dbin[i:(i+kscn-1),j:(j+kscm-1)]
+
+ if (sum(ksc*subbin)==sum(ksc))
+ mark[i:(i+kscn-1),j:(j+kscm-1)] <- mark[i:(i+kscn-1),j:(j+kscm-1)]+ksc
+
+ }
+
+ }
+
+ mark <- tobin(mark)
+
+ mark
+
+}
+
+arealtest <- function(wt,dt=1,s0=1,noctave=5,nvoice=20,w0=2*pi,swabs=0,tw=0,siglevel,arealsiglevel=0.9,kernel=0,type=0){
+
+ slp <- slope(w0,swabs,tw,nvoice,siglevel,arealsiglevel,type)
+
+ if (length(kernel)<2){
+ maxarea <- s0*2^noctave*slp/10*nvoice/dt
+ cat(paste("# calculating kernel (maxarea=",maxarea,")\n",sep=""))
+ cvkernel <-
+ kernelRoot(s0,w0,a=maxarea,noctave,nvoice,swabs,tw,dt)
+
+ cat("# building kernel bitmap \n")
+ kernel <-
+ kernelBitmap(cvkernel,s0,w0,noctave,nvoice,swabs,tw,dt)
+
+ }
+
+ cat("# performing arealtest \n")
+ sl <- slide(wt,kernel,s0,noctave,nvoice,1)
+
+ list(at=sl,kernel=kernel)
+
+}
+
+#############################
+# PLOTTING
+#############################
+
+plotat <- function(t,wt,at,sigplot){
+
+ if (length(at)>1){
+ linewidth <- 1
+ if (sigplot==3)
+ linewidth <- 5
+
+ contour(x=t,z=at,levels=0.5,add=TRUE,drawlabels=FALSE,axes=FALSE,lwd=linewidth,col="black")
+ }
+
+}
+
+
+plotcv <- function(t,wt,cv){
+
+ if (length(dim(cv))==0)
+
+ contour(x=t,z=wt,levels=c(cv),drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1)
+
+ else{
+
+ for(i in 1:nrow(cv)){
+
+ v <- cv[i,]
+ v[v==0] <- 9999
+ m <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE)
+
+ contour(x=t,z=wt/m,levels=1,drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1)
+
+ }
+
+ }
+
+}
+
+plotcoi <- function(t,s0,noctave,w0){
+
+ tv <- as.vector(t)
+ tvl <- tv[tv-tv[1]<(tv[length(tv)]-tv[1])/2]
+ tvr <- tv[tv-tv[1]>=(tv[length(tv)]-tv[1])/2]
+
+ lines(tvl,log2(((tvl-tv[1])*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black")
+ lines(tvr,log2(((tv[length(tv)]-tvr)*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black")
+
+}
+
+plotmarks <- function(t,s0,noctave,markt,marks){
+
+ if (markt[1]!=-999){
+
+ for (i in 1:length(markt)){
+ lines(c(markt[i],markt[i]),c(0,1),lty="dotted")
+ }
+
+ }
+
+ if (marks[1]!=-999){
+
+ for (i in 1:length(marks)){
+ lines(c(t[1],t[length(t)]),c(log2(marks[i]/s0)/noctave,log2(marks[i]/s0)/noctave),lty="dotted")
+ }
+
+ }
+
+}
+
+
+#####################
+# PLOT.WT
+#####################
+
+plot.wt <- function(wt,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="",device="screen",file="wt",split=FALSE,color=TRUE,pwidth=10,pheight=5,labsc=1.5,labtext="",sigplot=3,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){
+
+ plotwt(wt$modulus,wt$phase,wt$time,wt$s0,wt$noctave,wt$w0,wt$critval,wt$at,markt,marks,t1,t2,logscale,phase,units,plottitle,device,file,split,color,pwidth,pheight,labsc,labtext,sigplot,xax,xlab,yax,ylab)
+
+}
+
+plotwt <-
+ function(wt,phs,t,s0,noctave,w0,cv=NULL,at=NULL,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="Wavelet Plot",device="screen",file="wavelet",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=1,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){
+
+ if (is.null(phs)) phase <- FALSE
+
+ mgpv <- c(3,1,0)
+ if (labsc>1) mgpv[1] <- 3-(labsc-1.5)
+
+ ncolors <- 64
+ colors <- wtcolors(ncolors)
+ if (color==FALSE) colors <- gray((0:ncolors)/ncolors*0.7+0.3)
+
+ rangev <- (0:(ncolors-1))/(ncolors-1)
+ rangebar <- matrix(rangev,nrow=2,ncol=64,byrow=TRUE)
+
+ s <- 2^(0:(noctave))*s0
+ sn <- (0:(noctave))/noctave
+
+ deltat <- (t[length(t)]-t[1])/(length(t)-1)
+ cut <- FALSE
+ if ((!is.null(t1)) | (!is.null(t2))){
+ if (t1<t2 & t1>=t[1] & t2<=t[length(t)]){
+
+ cut <- TRUE
+
+ i1 <- (t1-t[1])/deltat+1
+ i2 <- (t2-t[1])/deltat+1
+
+ t <- t[t>=t1 & t<=t2]
+
+ wt <- wt[i1:i2,]
+ if (phase) phs <- phs[i1:i2,]
+ if (length(at)>1) at <- at[i1:i2,]
+
+ }
+ }
+
+ #----------------
+ # Setting layout
+ #----------------
+
+ if (device=="ps" && split==FALSE){
+
+ file <- paste(file,".eps",sep="")
+
+ postscript(file,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight)
+ cat(paste("# writing",file," \n"))
+
+ }
+
+ if (device=="ps" && split==TRUE){
+
+ file1 <- paste(file,".mod.eps",sep="")
+
+ postscript(file1,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight)
+ cat(paste("# writing",file1," \n"))
+
+ }
+
+ if (phase==TRUE && split==FALSE) nlo <- layout(matrix(c(1,2,3,4),2,2,byrow=TRUE),widths=c(4,1))
+ else nlo <- layout(matrix(c(1,2),ncol=2,byrow=TRUE),widths=c(4,1))
+
+
+ #-----------------
+ # Plotting modulus
+ #-----------------
+
+ if (logscale){
+ if (units=="")
+ image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ else
+ image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ }
+ else{
+ if (units=="")
+ image(x=t,z=wt,col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ else
+ image(x=t,z=wt,col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ }
+
+ text(t[1+as.integer(length(t)*0.1)],0.8,labtext,cex=2)
+
+ if (sigplot==1 | sigplot==3){
+ if (is.null(cv)==FALSE){ #Critical values
+ if (cv[1]!=0 & cv[1]!=-1) plotcv(t,wt,cv)
+ }
+ }
+
+ if (sigplot>1) plotat(t,wt,at,sigplot)
+
+ if (!cut) plotcoi(t,s0,noctave,w0) #cone of influence
+ plotmarks(t,s0,noctave,markt,marks) #additional guiding lines
+
+ if (is.null(xax))
+ axis(side=1,at=axTicks(1),cex.axis=labsc)
+ else
+ if (is.null(xlab))
+ axis(side=1,xax,labels=as.character(xax),cex.axis=labsc)
+ else
+ axis(side=1,xax,labels=xlab,cex.axis=labsc)
+
+ if (is.null(yax))
+ axis(side=2,sn,labels=as.character(s),cex.axis=labsc)
+ else
+ if (is.null(ylab))
+ axis(side=2,yax,labels=as.character(yax),cex.axis=labsc)
+ else
+ axis(side=2,yax,labels=ylab,cex.axis=labsc)
+
+ title(main=plottitle,cex=labsc)
+
+ image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+
+ if (is.null(cv)==FALSE){
+ if (length(dim(cv))==0){
+ for (i in 1:length(cv))
+ if (cv[i]!=0) lines(c(-1,2),c(cv[i],cv[i]))
+ }
+ }
+
+ if (!logscale)
+ axis(side=2,(0:5)/5,labels=c("0","","","","","1"),cex.axis=labsc)
+ else{
+ labelv <- substr(as.character(c(0:5)*(max(log10(wt),na.rm=TRUE)-min(log10(wt),na.rm=TRUE))/5),1,4)
+ axis(side=2,(0:5)/5,labels=labelv,cex.axis=labsc)
+ }
+
+
+ #-----------------
+ # Plotting phase
+ #-----------------
+ if (phase==TRUE){
+
+ if (device=="ps" && split==TRUE){
+
+ dev.off()
+
+ file2 <- paste(file,".phs.eps",sep="")
+
+ postscript(file2,onefile=TRUE,horizontal=TRUE,paper="special",width=10,height=5)
+ cat(paste("# writing",file2," \n"))
+
+ }
+
+ colors <- rainbow(ncolors)
+ if (color==FALSE){
+ dummy <- gray((0:ncolors)/ncolors)
+ colors[1:(ncolors/2)] <- dummy[(ncolors/2+1):ncolors]
+ colors[(ncolors/2+1):ncolors] <- dummy[1:(ncolors/2)]
+ }
+
+ if (units=="")
+ image(x=t,z=phs,col=colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ else
+ image(x=t,z=phs,col=colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+
+ if (is.null(cv)==FALSE) plotcv(t,wt,cv)
+ if (sigplot>1) plotat(t,wt,at,sigplot)
+ if (!cut) plotcoi(t,s0,noctave,w0)
+ plotmarks(t,s0,noctave,markt,marks)
+
+ if (is.null(xax))
+ axis(side=1,at=axTicks(1),cex.axis=labsc)
+ else
+ if (is.null(xlab))
+ axis(side=1,xax,labels=as.character(xax),cex.axis=labsc)
+ else
+ axis(side=1,xax,labels=xlab,cex.axis=labsc)
+
+ if (is.null(yax))
+ axis(side=2,sn,labels=as.character(s),cex.axis=labsc)
+ else
+ if (is.null(ylab))
+ axis(side=2,yax,labels=as.character(yax),cex.axis=labsc)
+ else
+ axis(side=2,yax,labels=ylab,cex.axis=labsc)
+
+
+ title(main="Phase")
+
+ image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv)
+ axis(side=2,(0:4)/4,labels=c("-PI","","0","","PI"),cex.axis=labsc)
+
+ }
+
+ if (device=="ps") dev.off()
+
+ }
+
+##############################
+# Surrogates
+##############################
+
+createwavesurrogates <- function(nsurr=1,wt=1,n,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){
+
+ surrmat <- matrix(rep(0,n*nsurr),ncol=nsurr)
+
+ for (i in 1:nsurr){
+
+ cat(paste("# Creating realization ",i,"\n",sep=""))
+
+ x <- rnorm(n)
+ xts <- ts(x,start=0,deltat=dt)
+
+ xwt <- cwt.ts(xts,s0,noctave,nvoice,w0)
+ wtsurr <- wt*xwt
+
+ surri <- as.vector(invmorlet(wtsurr,0,dt,s0,noctave,nvoice,w0))
+
+ surrmat[,i] <- Re(surri)
+
+ }
+
+ surrmat
+
+}
+
+surrwave <- function(x,...)
+ UseMethod("surrwave")
+
+surrwave.wt <- function(wt,nsurr=1,spec=TRUE){
+
+ n <- length(wt$time)
+ t0 <- wt$time[1]
+ dt <- (wt$time[13]-t0)/12
+ s0 <- wt$s0
+ noctave <- wt$noctave
+ nvoice <- wt$nvoice
+ w0 <- wt$w0
+
+ wt <- wt$modulus
+ if (spec==TRUE)
+ wt <- sqrt(wt)
+
+ surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0)
+
+ surrts <- ts(surrmat,start=t0,deltat=dt)
+
+ surrts
+
+}
+
+surrwave.matrix <- function(mat,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,spec=FALSE){
+
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ if ((noctave*nvoice+1)!=dim(mat)[2])
+ cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n")
+
+ n <- dim(mat)[1]
+
+ if (spec==FALSE)
+ mat <- Mod(mat)
+ else
+ mat <- sqrt(Mod(mat))
+
+ wtsm <- smooth.matrix(mat,swabs)
+ wt <- smooth.time(wtsm,tw,dt,scalevector)
+
+ surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0)
+
+ surrts <- ts(surrmat,start=t0,deltat=dt)
+
+ surrts
+
+}
+
+surrwave.character <-
+ function(file,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,transpose=TRUE,spec=FALSE){
+
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ if (transpose==FALSE)
+ mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=TRUE)
+ else
+ mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=FALSE)
+
+ if ((noctave*nvoice+1)!=dim(mat)[2])
+ cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n")
+
+ n <- dim(mat)[1]
+
+ if (spec==FALSE)
+ mat <- Mod(mat)
+ else
+ mat <- sqrt(Mod(mat))
+
+ wtsm <- smooth.matrix(mat,swabs)
+ wt <- smooth.time(wtsm,tw,dt,scalevector)
+
+ surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0)
+
+ surrts <- ts(surrmat,start=t0,deltat=dt)
+
+ surrts
+
+ }
+
+surrwave.ts <- function(ts,nsurr=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0){
+
+ n <- length(ts)
+ t0 <- time(ts)[1]
+ dt <- deltat(ts)
+ if (sw!=0 & swabs==0)
+ swabs <- as.integer(sw*nvoice)
+
+ scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0
+
+ wt <- Mod(cwt.ts(ts,s0,noctave,nvoice,w0))
+
+ wtsm <- smooth.matrix(wt,swabs)
+ wt <- smooth.time(wtsm,tw,dt,scalevector)
+
+ surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0)
+
+ surrts <- ts(surrmat,start=t0,deltat=dt)
+
+ surrts
+
+}
+
+invmorlet <- function(wt,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){
+
+ if ((noctave*nvoice+1)!=dim(wt)[2])
+ cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n")
+
+ n <- dim(wt)[1]
+
+ wt[is.na(wt)] <- 0
+
+ tsRe <- rep(0,n)
+ tsIm <- rep(0,n)
+
+ wtRe <- t(Re(wt))
+ wtIm <- t(Im(wt))
+
+ z <- .C("invmorlet",
+ as.double(as.vector(wtRe)),
+ as.double(as.vector(wtIm)),
+ as.integer(n),
+ as.double(dt),
+ as.double(s0),
+ as.integer(noctave),
+ as.integer(nvoice),
+ as.double(w0),
+ tsRetmp = as.double(tsRe),
+ tsImtmp = as.double(tsIm),
+ PACKAGE="sowas")
+
+ invvec=complex(real=z$tsRetmp,imaginary=z$tsImtmp)
+ invts <- ts(data=invvec,start=t0,deltat=dt)
+
+ invts
+
+}
+
+#################################
+# INPUT / OUTPUT
+#################################
+
+readmatrix <- function(file,M){
+
+ A <- matrix(scan(file,comment.char="#"),ncol=M,byrow=TRUE)
+
+ A
+
+}
+
+
+readts <- function(file){
+
+ A <- matrix(scan(file,comment.char="#"),ncol=2,byrow=TRUE)
+
+ Adum <- A
+
+ Adum[is.na(Adum)] <- 0
+
+ t <- Adum %*% c(1,0)
+ x <- A %*% c(0,1)
+
+ N=length(t)
+ f=1/(t[13]-t[1])*12
+
+ if ((f>11) && (f<13)) f <- 12
+
+ timeseries<-ts(data=x,start=t[1],frequency=f)
+
+ timeseries
+
+}
+
+writematrix <- function(file,data,header="# R Matrix"){
+
+ write(header,file)
+ write(t(data),file,ncol(data),append=TRUE)
+
+}
+
+############################
+# HELP FUNCTIONS
+############################
+
+smooth.matrix <- function(wt,swabs){
+
+ if (swabs != 0)
+ smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1)))
+ else
+ smwt <- wt
+
+ smwt
+
+}
+
+smooth.time <- function(wt,tw,dt,scalevector){
+
+ smwt <- wt
+
+ if (tw != 0){
+ for (i in 1:length(scalevector)){
+
+ twi <- as.integer(scalevector[i]*tw/dt)
+ smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1))
+
+ }
+ }
+
+ smwt
+
+}
+
+
+adjust.noctave <- function(N,dt,s0,tw,noctave){
+
+ if (tw>0){
+ dumno <- as.integer((log(N*dt)-log(2*tw*s0))/log(2))
+ if (dumno<noctave){
+ cat("# noctave adjusted to time smoothing window \n")
+ noctave <- dumno
+ }
+ }
+
+ noctave
+
+}
+
+adjust.s0 <- function(s0,dt){
+
+ if (s0<2*dt){
+ s0 <- 2*dt
+ cat(paste("# s0 set to ",s0," \n"))
+ }
+
+ s0
+
+}
+
+adjust.timeseries <- function(ts1,ts2){
+
+ if (length(ts1)!=length(ts2)){
+ tsint <- ts.intersect(ts1,ts2)
+ dt <- deltat(ts1)
+ ts1 <- ts(data=tsint[,1],start=time(tsint)[1],frequency=1/dt)
+ ts2 <- ts(data=tsint[,2],start=time(tsint)[1],frequency=1/dt)
+ t <- time(ts1)
+ }
+
+ list(ts1=ts1,ts2=ts2)
+
+}
+
+checkarealsiglevel <- function(sw,tw,w0,arealsiglevel,siglevel,type){
+
+ if (type==0){
+
+ swv <- c(0,0.5,1)
+ twv <- c(0,1.5,3)
+ w0v <- c(pi,2*pi)
+
+ if (length(swv[swv==sw])==0 || length(twv[twv==tw])==0 ||
+ length(w0v[w0v==w0])==0){
+ arealsiglevel <- 0
+ cat("# areawise test for spectrum currently \n")
+ cat("# only possible for \n")
+ cat("# sw = 0 \n")
+ cat("# tw = 0 \n")
+ cat("# w0 = 2pi \n")
+ cat("# No areawise test performed \n")
+ }
+ }
+
+ if (type==1){
+
+ swv <- c(0.5)
+ twv <- c(1.5)
+ w0v <- c(2*pi)
+
+ if (length(swv[swv==sw])==0 || length(twv[twv==tw])==0 ||
+ length(w0v[w0v==w0])==0){
+ arealsiglevel <- 0
+ cat("# areawise test for coherence currently \n")
+ cat("# only possible for \n")
+ cat("# sw = 0.5 \n")
+ cat("# tw = 1.5 \n")
+ cat("# w0 = 2pi \n")
+ cat("# No areawise test performed \n")
+ }
+ }
+
+
+ if (arealsiglevel!=0){
+ arealsiglevel <- 0.9
+ siglevel <- 0.95
+ cat("# currently only siglevel=0.95 and arealsiglevel=0.9 possible for areawise test \n")
+ }
+
+ list(siglevel=siglevel,arealsiglevel=arealsiglevel)
+
+}
+
+########################
+
+as.wt <- function(modulus,phase=NULL,s0=NULL,noctave=NULL,nvoice=NULL,w0=NULL,dt=1,time=NULL,scales=NULL,critval=NULL,at=NULL,kernel=NULL,N=NULL,t0=NULL){
+
+ if (is.null(scales))
+ gotscales <- FALSE
+ else
+ gotscales <- TRUE
+
+ if ((!gotscales) & (!is.null(s0)) & (!is.null(noctave)) &(!is.null(nvoice))){
+ gotscales <- TRUE
+ scales=2^(0:(noctave*nvoice)/nvoice)*s0
+ }
+
+ if (gotscales & (is.null(s0) | is.null(noctave) |
+ is.null(nvoice))){
+ s0 <- scales[1]
+ noctave <- log(scales[length(scales)]/s0)/log(2)
+ nvoice <- (length(scales)-1)/noctave
+ }
+
+
+ if (!gotscales)
+ cat("# ERROR! No scales given! \n")
+
+ if (is.null(time)) gottimes <- FALSE
+ else gottimes <- TRUE
+
+ if ((!gottimes) & (!is.null(dt)) & (!is.null(t0)) &(!is.null(N))){
+ gottimes <- TRUE
+ time=(0:(N-1))*dt+t0
+ }
+
+ if (!gottimes)
+ cat("# ERROR! No time vector given! \n")
+
+ wcolist <- list(modulus=modulus,phase=phase,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=time,scales=scales,critval=critval,at=at,kernel=kernel)
+
+ class(wcolist) <- "wt"
+
+ wcolist
+
+}
+
+########################
+
+wtcolors <- function(ncolors){
+
+ upside <- rainbow(ncolors,start=0,end=.7)
+ #upside <- heat.colors(ncolors+5)
+ #upside <- upside[1:ncolors]
+
+
+ down <- upside
+
+ for (i in 1:ncolors){
+ down[i] <- upside[ncolors+1-i]
+ }#
+
+ down
+
+}
+
+####################
+
+createwgn <- function(N,sig,dt){
+
+ timeseries<-ts(data=rnorm(N,0,sig),start=0,deltat=dt)
+
+ timeseries
+
+}
+
+
+createar <- function(N,a,sig,dt){
+
+ if (a==0) a <- 0.000000001
+
+ se <- sqrt(sig*sig*(1-a*a))
+ tsMC <- ts(data=rep(0,N),start=0,deltat=dt)
+
+ tsMC[1:N] <- arima.sim(list(ar = a), N, sd = se)
+
+}
+
+######################
+
+rk <- function(N=1000,s=8,noctave=5,nvoice=10,w0=2*pi,plot=TRUE){
+
+ t <- 1:N
+
+ sunit <- s*(w0+sqrt(2+w0*w0))/(4*pi)
+
+ s0 <- 4
+ #s0unit <- s0*(w0+sqrt(2+w0*w0))/(4*pi)
+ s0unit=s0/dt*w0/(2*pi) #(CORRECT)
+ s0log <- as.integer((log2(s0unit)-1)*nvoice+1.5)
+
+ totnoct <- noctave+as.integer(s0log/nvoice)+1
+
+ x <- morlet(N,N/2,sunit,w0)
+
+ totts.cwt <- Mod(cwt(x,totnoct,nvoice,w0,plot=0))
+ wt=totts.cwt[,s0log:(s0log+noctave*nvoice)]
+
+ wt <- wt/max(wt)
+
+ if (plot==TRUE) plotwt(wt,0,t,s0,noctave,w0,units="",plottitle="Reproducing Kernel")
+
+ wt
+
+}
+
+###################
+
+addvalues <- function(nvoice,dnoctave,x,value){
+
+ nr <- dim(x)[1]
+ nc <- dim(x)[2]
+ dnc <- nvoice*dnoctave
+
+ y <- matrix(rep(value,nr*(nc+dnc)),nrow=nr)
+
+ y[,(dnc+1):(dnc+nc)] <- x
+
+ y
+
+}
+
+####################
+
+scalematrix <- function(wt){
+
+ # scales matrix, such that the maximal value is one
+ # wt: matrix to be scaled
+
+ mval <- max(wt,na.rm=TRUE)
+
+ wt <- wt/mval
+
+ wt
+
+}
+
+
+foldKernel <- function(F,swabs,tw,s,dt){
+
+ # folds a matrix (e.g. kernel with smoothing window
+ # F: matrix input
+ # swabs: smooting window width
+
+ smsF <- smooth.matrix(F,swabs)
+ smsF[is.na(smsF)] <- 0
+
+ smtsF <- smooth.time(smsF,tw,dt,s)
+
+ smtsF[is.na(smtsF)] <- 0
+
+ scF <- scalematrix(smtsF)
+
+ scF
+
+}
+
+kernelBitmap <- function(c,s0=1,w0=6,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){
+ # produces kernel bitmap
+ # c: height of contour, that defines area
+ # s0: lowest scale
+ # noctave: number of octaves
+ # nvoice: number of voices per octave
+ # swabs: smoothing window length in scale direction
+ # dt: sampling time
+
+ s <- s0*2^noctave
+ is <- noctave*nvoice
+
+ x <- s0*2^(((1:(nvoice*(noctave+2)))-1)/nvoice)
+
+ t <- max(2000,max(x)*tw*2/dt*1.1)
+
+ y <- ((0:(2*t))-t)/2*dt
+
+ X <- matrix(x,ncol=nvoice*(noctave+2),nrow=2*t+1,byrow=T)
+ Y <- matrix(y,ncol=nvoice*(noctave+2),nrow=2*t+1)
+
+ F <- sqrt(2*s*X/(s*s+X*X))*exp(-0.5*(Y*Y+w0*w0*(X-s)*(X-s))/(s*s+X*X))
+
+ F <- foldKernel(F,swabs,tw,x,dt)
+
+ F[F<c] <- 0
+ F[F>=c] <- 1
+
+ is1 <- 1
+ is2 <- nvoice*(noctave+1)
+ it1 <- 1
+ it2 <- 2*t+1
+
+ L <- F[1:(2*t+1),is1]
+
+ while (length(L[L!=0])==0) {
+ is1 <- is1+1
+ L <- F[1:(2*t+1),is1]
+ }
+
+ L <- F[1:(2*t+1),is2]
+
+ while (length(L[L!=0])==0) {
+ is2 <- is2-1
+ L <- F[1:(2*t+1),is2]
+ }
+
+
+ L <- F[it1,1:(nvoice*(noctave+2))]
+
+ while (length(L[L!=0])==0) {
+ it1 <- it1+1
+ L <- F[it1,1:(nvoice*(noctave+2))]
+ }
+
+ L <- F[it2,1:(nvoice*(noctave+2))]
+
+ while (length(L[L!=0])==0) {
+ it2 <- it2-1
+ L <- F[it2,1:(nvoice*(noctave+2))]
+ }
+
+ kernel <- list(bitmap=F[(it1-1):(it2+1),(is1-1):(is2+1)],is=is-is1)
+
+ kernel
+
+}
+
+kernelRoot <- function(s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){
+
+ tol <- 0.005
+ cmin <- 0
+ cmax <- 1
+ cntr <- 0.5
+ da <- a
+
+ while (abs(da/a)>tol){
+
+ da <- kernelArea(cntr,s0,w0,a,noctave,nvoice,swabs,tw,dt)
+ if (da>0){
+ cmin <- cntr
+ cntr <- mean(c(cntr,cmax))
+ }
+ if (da<0){
+ cmax <- cntr
+ cntr <- mean(c(cntr,cmin))
+ }
+ }
+
+ cntr
+
+}
+
+kernelArea <- function(cntr,s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){
+
+ # calulates area of reproducing kernel for smoothed data at scale s0*2^noctave
+ # cntr: height of contour line to define kernel area. This
+ # parameter is to be estimated!
+ # s0: lowest scale
+ # w0: parameter of Morlet Wavelet
+ # a: area offset (only needed, when finding root. Is set to
+ # desired area
+ # noctave: number of octaves
+ # nvoice: number of voices per octave
+ # swabs: smoothing window width in scale direction
+ # dt: sampling time
+
+ s <- s0*2^noctave
+
+ x <- s0*2^(((1:(nvoice*(noctave+2)))-1)/nvoice)
+
+ t <- max(2000,max(x)*tw*2/dt*1.1)
+
+ y <- ((0:(2*t))-t)/2*dt
+
+ X <- matrix(x,ncol=nvoice*(noctave+2),nrow=2*t+1,byrow=T)
+ Y <- matrix(y,ncol=nvoice*(noctave+2),nrow=2*t+1)
+
+ F <- sqrt(2*s*X/(s*s+X*X))*exp(-0.5*(Y*Y+w0*w0*(X-s)*(X-s))/(s*s+X*X))
+
+ F <- foldKernel(F,swabs,tw,x,dt)
+
+ F[F>=cntr] <- 1
+ F[F<cntr] <- 0
+
+ area <- length(F[F==1])-a
+
+ area
+
+}
+
+
+tobin <- function(x){
+
+ # sets nonzero values to one
+
+ y <- x/x
+ y[is.na(y)] <- 0
+
+ y
+
+}
+
+scaleKernel <- function(kernel,l){
+
+ # scales kernel length in time direction proportional to scale
+ # kernel: data bitmap of width n
+ # l: new width of kernel
+
+ n <- nrow(kernel)
+ m <- ncol(kernel)
+
+ newKernel <- matrix(rep(0,m*l),nrow=l)
+
+ d <- as.double(n)/as.double(l)
+
+ for (i in 1:l){
+ j <- as.integer((i-0.5)*d)
+ if (j==0) j <- 1
+ newKernel[i,1:m] <- kernel[j,1:m]
+ }
+
+ newKernel
+
+}
+
+slope <- function(w0,swabs,tw,nvoice,siglevel,arealsiglevel,type){
+
+ sw <- swabs/nvoice
+
+ if (type==0){ # wavelet spectrum
+
+ if (tw == 0 & sw == 0 & w0 == 1 *pi) slp <- 5.82518 # w = 18.35831
+ if (tw == 1.5 & sw == 0 & w0 == 1 *pi) slp <- 24.69852 # w = 14.30709
+ if (tw == 3 & sw == 0 & w0 == 1 *pi) slp <- 35.48368 # w = 14.72354
+ if (tw == 0 & sw == 5 & w0 == 1 *pi) slp <- 7.347707 # w = 17.96942
+ if (tw == 1.5 & sw == 5 & w0 == 1 *pi) slp <- 28.24291 # w = 12.65993
+ if (tw == 3 & sw == 5 & w0 == 1 *pi) slp <- 51.13723 # w = 10.96359
+ if (tw == 0 & sw == 10 & w0 == 1 *pi) slp <- 10.47856 # w = 15.5941
+ if (tw == 1.5 & sw == 10 & w0 == 1 *pi) slp <- 45.07387 # w = 15.29793
+ if (tw == 3 & sw == 10 & w0 == 1 *pi) slp <- 52.82886 # w = 12.72361
+
+ if (tw == 0 & sw == 0 & w0 == 2 *pi) slp <- 8.718912 # w = 17.75933
+ if (tw == 1.5 & sw == 0 & w0 == 2 *pi) slp <- 11.88006 # w = 15.39648
+ if (tw == 3 & sw == 0 & w0 == 2 *pi) slp <- 26.55977 # w = 1.064384
+ if (tw == 0 & sw == 5 & w0 == 2 *pi) slp <- 14.64761 # w = 16.27518
+ if (tw == 1.5 & sw == 5 & w0 == 2 *pi) slp <- 28.27798 # w = 14.57059
+ if (tw == 3 & sw == 5 & w0 == 2 *pi) slp <- 63.54121 # w = 12.83778
+ if (tw == 0 & sw == 10 & w0 == 2 *pi) slp <- 27.78735 # w = 11.95813
+ if (tw == 1.5 & sw == 10 & w0 == 2 *pi) slp <- 41.27260 # w = 12.03379
+ if (tw == 3 & sw == 10 & w0 == 2 *pi) slp <- 67.37015 # w = 10.63935
+
+ }
+
+ if (type==1){ # wavelet coherence
+
+ if (tw==0 & sw==0 & w0==pi) slp <- 999 #not valid
+ if (tw==1.5 & sw==0 & w0==pi) slp <- 1
+ if (tw==3 & sw==0 & w0==pi) slp <- 1
+ if (tw==0 & sw==0.5 & w0==pi) slp <- 1
+ if (tw==1.5 & sw==0.5 & w0==pi) slp <- 1
+ if (tw==3 & sw==0.5 & w0==pi) slp <- 1
+ if (tw==0 & sw==1 & w0==pi) slp <- 1
+ if (tw==1.5 & sw==1 & w0==pi) slp <- 1
+ if (tw==3 & sw==1 & w0==pi) slp <- 1
+
+ if (tw==0 & sw==0 & w0==2*pi) slp <- 999 #not valid
+ if (tw==1.5 & sw==0 & w0==2*pi) slp <- 1
+ if (tw==3 & sw==0 & w0==2*pi) slp <- 1
+ if (tw==0 & sw==0.5 & w0==2*pi) slp <- 1
+ if (tw==1.5 & sw==0.5 & w0==2*pi) slp <- 8.3
+ if (tw==3 & sw==0.5 & w0==2*pi) slp <- 1
+ if (tw==0 & sw==1 & w0==2*pi) slp <- 1
+ if (tw==1.5 & sw==1 & w0==2*pi) slp <- 1
+ if (tw==3 & sw==1 & w0==2*pi) slp <- 1
+
+ if (tw==0 & sw==0 & w0==3*pi) slp <- 999 #not valid
+ if (tw==1.5 & sw==0 & w0==3*pi) slp <- 1
+ if (tw==3 & sw==0 & w0==3*pi) slp <- 1
+ if (tw==0 & sw==0.5 & w0==3*pi) slp <- 1
+ if (tw==1.5 & sw==0.5 & w0==3*pi) slp <- 1
+ if (tw==3 & sw==0.5 & w0==3*pi) slp <- 1
+ if (tw==0 & sw==1 & w0==3*pi) slp <- 1
+ if (tw==1.5 & sw==1 & w0==3*pi) slp <- 1
+ if (tw==3 & sw==1 & w0==3*pi) slp <- 1
+
+ if (tw==0 & sw==0 & w0==4*pi) slp <- 999 #not valid
+ if (tw==1.5 & sw==0 & w0==4*pi) slp <- 1
+ if (tw==3 & sw==0 & w0==4*pi) slp <- 1
+ if (tw==0 & sw==0.5 & w0==4*pi) slp <- 1
+ if (tw==1.5 & sw==0.5 & w0==4*pi) slp <- 1
+ if (tw==3 & sw==0.5 & w0==4*pi) slp <- 1
+ if (tw==0 & sw==1 & w0==4*pi) slp <- 1
+ if (tw==1.5 & sw==1 & w0==4*pi) slp <- 1
+ if (tw==3 & sw==1 & w0==4*pi) slp <- 1
+
+ }
+
+ cat(paste("# slope ",slp,"\n",sep=""))
+
+ slp
+
+}
+
+#============================
+#============================
+#============================
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0 <- read.table("datesall.txt")[, 1]
+dates <- as.character(dates0[grep("2009", dates0)])
+rm(dates0)
+
+n <- length(identifiants)
+p <- delta <- length(dates)
+
+synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt")))
+#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt")))
+
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) #valeurs après 1er janvier
+
+#moyenne pondérée pour compléter deux demi-heures manquantes
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+
+conso <- synchros09[-201, ]; # series must be on rows
+n <- nrow(conso)
+delta <- ncol(conso)
+
+rm(synchros09, nas)
+
+## 2. Compute WER distance matrix ####
+
+## _.a CWT -- Filtering the lowest freqs (>6m) ####
+# nvoice <- 4
+# # noctave4 = 2^13 = 8192 half hours ~ 180 days
+# noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2,
+# tw = 0, noctave = 13)
+# # 4 here represent 2^5 = 32 half-hours ~ 1 day
+# scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
+# lscvect4 <- length(scalevector4)
+# lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect
+
+
+#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
+
+#TODO: une fonction qui fait lignes 59 à 91
+
+#cube:
+# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1,
+# scalevector = scalevector4,
+# lt = delta, smooth = FALSE,
+# nvoice = nvoice) # observations node with CWT
+#
+# #matrix:
+# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+#
+# #NOTE: delta et lscvect pourraient etre gardés à part (communs)
+# for(i in 1:n)
+# Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) )
+#
+# #rm(conso, Xcwt4); gc()
+#
+# ## _.b WER^2 distances ########
+# Xwer_dist <- matrix(0.0, n, n)
+# for(i in 1:(n - 1)){
+# mat1 <- vect2mat(Xcwt2[i,])
+# for(j in (i + 1):n){
+# mat2 <- vect2mat(Xcwt2[j,])
+# num <- Mod(mat1 * Conj(mat2))
+# WX <- Mod(mat1 * Conj(mat1))
+# WY <- Mod(mat2 * Conj(mat2))
+# smsmnum <- smCWT(num, scalevector = scalevector4)
+# smsmWX <- smCWT(WX, scalevector = scalevector4)
+# smsmWY <- smCWT(WY, scalevector = scalevector4)
+# wer2 <- sum(colSums(smsmnum)^2) /
+# sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
+# Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
+# Xwer_dist[j, i] <- Xwer_dist[i, j]
+# }
+# }
+# diag(Xwer_dist) <- numeric(n)
+#
+# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata")
+# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
+
+load("../res/2009_synchros200WER.Rdata")
+#load("../res/2009_synchros200-randomWER.Rdata")
+
+## 3. Cluster using WER distance matrix ####
+
+#hc <- hclust(as.dist(Xwer_dist), method = "ward.D")
+#plot(hc)
+#
+# #clust <- cutree(hc, 2)
+#
+for(K in 2:30){
+ #K <- 3
+ #pamfit <- pam(tdata[-201, ci$selectv], k = K)
+ pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
+
+ #table(pamfit$clustering)
+
+ SC <- matrix(0, ncol = p, nrow = K)
+
+ clustfactor <- pamfit$clustering
+# for(k in 1:K){
+# clustk <- which(clustfactor == k)
+# if(length(clustk) > 0) {
+# if(length(clustk) > 1) {
+# SCk <- colSums(synchros09[which(clustfactor == k), ])
+# } else {
+# SCk <- synchros09[which(clustfactor == k), ]
+# }
+# SC[k, ] <- SC[k, ] + SCk
+# rm(SCk)
+# }
+#}
+
+#write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt"))
+#write.table(clustfactor, file = "~/tmp/clustfactor3.txt")
+#write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt"))
+write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt"))
+}
+#
+# # Plots
+# layout(1)
+# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1)
+# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1)
+#
+#
+#
--- /dev/null
+t = 1:128
+
+n=length(t);
+
+%----------default arguments for the wavelet transform-----------
+Args=struct('Pad',1,... % pad the time series with zeroes (recommended)
+ 'Dj',1/12, ... % this will do 12 sub-octaves per octave
+ 'S0',2*dt,... % this says start at a scale of 2 years
+ 'J1',[],...
+ 'Mother','Morlet', ...
+ 'MaxScale',[],... %a more simple way to specify J1
+
+J1 = noctave * nvoice - 1
+
+nx=size(x,1);
+ny=size(y,1);
+
+[X,period,scale] = wavelet(x,Args.Dj,Args.S0,Args.J1);
+[Y,period,scale] = wavelet(y,Args.Dj,Args.S0,Args.J1);
+
+
+
+
+
+## pad with 0 ?
+## compare Rwave and biwavelets
+x = runif(128) #runif(100)
+w1 = wt(cbind(1:128,x), pad = TRUE, dt = NULL, do.sig=FALSE, dj=1/12, J1=51)
+w2 = Rwave::cwt(x, 13, 4, w0=2*pi, twoD=TRUE, plot=FALSE)
+
+
+
+
+
+
+
+
+%Smooth X and Y before truncating! (minimize coi)
+sinv=1./(scale');
+sX=smoothwavelet(sinv(:,ones(1,nx)).*(abs(X).^2),dt,period,Args.Dj,scale);
+sY=smoothwavelet(sinv(:,ones(1,ny)).*(abs(Y).^2),dt,period,Args.Dj,scale);
+
+% -------- Cross wavelet -------
+Wxy=X.*conj(Y);
+
+% ----------------------- Wavelet coherence ---------------------------------
+sWxy=smoothwavelet(sinv(:,ones(1,n)).*Wxy,dt,period,Args.Dj,scale);
+
+%%%%%% SMOOTHWAVELET
+n=size(wave,2);
+
+%swave=zeros(size(wave));
+twave=zeros(size(wave));
+
+%zero-pad to power of 2... Speeds up fft calcs if n is large
+npad=2.^ceil(log2(n));
+
+k = 1:fix(npad/2);
+k = k.*((2.*pi)/npad);
+k = [0., k, -k(fix((npad-1)/2):-1:1)];
+
+k2=k.^2;
+snorm=scale./dt;
+for ii=1:size(wave,1)
+ F=exp(-.5*(snorm(ii)^2)*k2); %Thanks to Bing Si for finding a bug here.
+ smooth=ifft(F.*fft(wave(ii,:),npad));
+ twave(ii,:)=smooth(1:n);
+end
+
+if isreal(wave)
+ twave=real(twave); %-------hack-----------
+end
+
+%scale smoothing (boxcar with width of .6)
+
+%
+% TODO: optimize. Because this is done many times in the monte carlo run.
+%
+
+
+dj0=0.6;
+dj0steps=dj0/(dj*2);
+% for ii=1:size(twave,1)
+% number=0;
+% for l=1:size(twave,1);
+% if ((abs(ii-l)+.5)<=dj0steps)
+% number=number+1;
+% swave(ii,:)=swave(ii,:)+twave(l,:);
+% elseif ((abs(ii-l)+.5)<=(dj0steps+1))
+% fraction=mod(dj0steps,1);
+% number=number+fraction;
+% swave(ii,:)=swave(ii,:)+twave(l,:)*fraction;
+% end
+% end
+% swave(ii,:)=swave(ii,:)/number;
+% end
+
+kernel=[mod(dj0steps,1); ones(2 * round(dj0steps)-1,1); ...
+ mod(dj0steps,1)]./(2*round(dj0steps)-1+2*mod(dj0steps,1));
+swave=conv2(twave,kernel,'same'); %thanks for optimization by Uwe Graichen
+
+
+
+
+
+
+
+
+Rsq=abs(sWxy).^2./(sX.*sY);
+return Rsq
+
+%varargout={Rsq,period,scale,coi,wtcsig,t};
+%varargout=varargout(1:nargout);
projects / epclust.git / commitdiff