add code Jairo

diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2009.r b/codes_Jairo_stage2/00_convertir-donnnes_2009.r
new file mode 100644 (file)
index 0000000..bf2578b
--- /dev/null
+++ b/codes_Jairo_stage2/00_convertir-donnnes_2009.r
@@ -0,0 +1,172 @@
+## File : 00_convertir-donnnes_2009.r
+## Description : Converts flat EDF's 32K data into a full data matrix 
+##               layout [individuals, variables]. Rownames are EDF's ids.
+##               We process the original flat file sequentially by lines
+##               to avoid exceding the available RAM memory (and so avoiding
+##               swaping which is a computational burden).
+
+
+rm(list = ls())
+
+# setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes")
+
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6000, 3), 7011)  # We'll process 1000 x p lines at each
+                                  # iteration of the reading loop
+
+## 2. Process the large flat file ####
+## We want to check that every time step recorded for each id.
+
+con <- file("~/tmp/data/2009.csv")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line
+
+for(b in seq_along(blocks)){      # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb * length(dates))
+  auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE)
+  rm(actual)
+  auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
+                       date = auxmat[, 2],
+                       val  = as.numeric(
+                         ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])))
+  rm(auxmat) # free up some space
+  
+  tab <- table(auxdf$id)
+  idtt <- NULL
+  for(tt in as.integer(names(which(tab < p)))) {  # id with less than p records!
+    print(tt)
+    idtt <- c(idtt, which(auxdf$id == tt))
+  }
+  
+  idmat         <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE)
+  alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0
+  valmat        <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE)
+  
+  # store separatelly full records from partial records
+  write.table(file = paste0("~/tmp/2009_full_", b, ".txt"), valmat,
+              row.names = idmat[, 1], col.names = FALSE) 
+  write.table(file = paste0("~/tmp/2009_partial_", b, ".txt"), auxdf[idtt, ])
+}
+
+close(con)                      # close connection to the file
+
+rm(auxdf, idmat, valmat, alldatesperid, b,  # clean up some memory
+   idtt, blocks, tab, tt, con)
+
+
+## 3. Complete partial records ####
+## After analysis, partial records are only 119 clients from which one only
+## time step (01JAN2009:00:00:00) is lacking.
+
+df_partial <- NULL
+for(f in list.files("~/tmp/", "2009_partial_*")) 
+  df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f)))
+
+tab <- table(df_partial$id)
+id_incomp <- as.integer(names(which(tab < p))) # Incomplete records
+
+df_partial_full <- rbind(df_partial, 
+                         data.frame(id   = id_incomp,
+                                    date = "01JAN2009:00:00:00",  
+                                    val  = NA))
+
+rm(df_partial)
+
+# tab2 <- table(df_partial_full$id)  # Check that df_partial_full is full
+# head(sort(tab2))
+
+
+## 4. Reorder the lines to get the data matrix ####
+## As we paste chunks of partial records and impute some time steps,
+## the original order of the data is broken. We fix it by reordering
+## the ids and then the data.
+
+idx_ordered <- order(df_partial_full$id)             # order ids
+df_partial_full2 <- df_partial_full[idx_ordered, ] 
+rm(df_partial_full)
+
+# Order data values following the correct dates (as the date is a factor
+# we need to seek for each value: this is computationnaly innefficient).
+
+valmat  <- matrix(df_partial_full2$val,  ncol = p, byrow = TRUE)
+datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE)
+idmat   <- matrix(df_partial_full2$id,   ncol = p, byrow = TRUE)
+
+# Use this for as a check by running it twice. On the second run no
+# printing should be done (because records should be ordered).
+for(line in 1:nrow(datemat)) {
+  if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered
+    cat(sprintf("\nline %i is not ordered", line))
+    
+    neworder         <- match(dates, datemat[line, ])
+    valmat[line , ]  <- valmat[ line, neworder]
+    datemat[line , ] <- datemat[line, neworder]
+  } 
+}
+
+
+## 5. Write on disk the full data matrix of partial records ####
+
+write.table(file = "~/tmp/2009_full_Z.txt", valmat, 
+            row.names = idmat[, 1], col.names = FALSE) 
+rm(list = ls())
+gc()
+
+## 6. Compile data files in BASH ####
+
+# cat 2009_full*.txt > 2009_full.txt
+# rm 2009_full_*.txt 2009_partial_*.txt
+
+
+## A. data.table & reshape2 #### 
+## When large RAM memory is available, one could use this code to process
+## everything in memory.
+
+#library(data.table)
+#library(reshape2)
+
+#dt <- fread(input  = "~/tmp/data/2009_chunk.csv")
+
+#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), 
+#                      CPP_PUISSANCE_BRUTE,
+#                      CPP_PUISSANCE_CORRIGEE), ]
+#dt[, CPP_PUISSANCE_CORRIGEE := NULL]
+#dt[, CPP_PUISSANCE_BRUTE := NULL]
+
+#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge)
+
+
+## Z. Probably stuff to be deleted 
+
+# searchpos <- function(row) {
+#   str  <- strsplit(row, ",")
+#   
+#   auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str)
+#   
+#   auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
+#                        date = auxmat[, 2],
+#                        val  = as.numeric(
+#                          ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))
+#   )
+#   rm(auxmat)
+#   
+#   idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE)
+#   alldatesperid <- apply(idmat, 1, sd) == 0
+#   
+#   
+#   #  lines <- match(auxdf$id, identifiants)
+#   #  cols  <- match(auxdf$date, dates)
+#   
+#   return(cbind(lines, cols, auxdf$val))
+# }

diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2010.r b/codes_Jairo_stage2/00_convertir-donnnes_2010.r
new file mode 100644 (file)
index 0000000..1f2326b
--- /dev/null
+++ b/codes_Jairo_stage2/00_convertir-donnnes_2010.r
@@ -0,0 +1,174 @@
+## File : 00_convertir-donnnes_2010.r
+## Description : Converts flat EDF's 32K data into a full data matrix 
+##               layout [individuals, variables]. Rownames are EDF's ids.
+##               We process the original flat file sequentially by lines
+##               to avoid exceding the available RAM memory (and so avoiding
+##               swaping which is a computational burden).
+
+
+rm(list = ls())
+
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2010", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(1000, 24), 1011)  # We'll process 1000 x p lines at each
+                                  # iteration of the reading loop
+
+## 2. Process the large flat file ####
+## We want to check that every time step recorded for each id.
+
+con <- file("~/tmp/data/2010.csv")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line
+
+for(b in seq_along(blocks)){      # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb * length(dates))
+  auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE)
+  rm(actual)
+  auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
+                       date = auxmat[, 2],
+                       val  = as.numeric(
+                         ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])))
+  rm(auxmat) # free up some space
+  
+  tab <- table(auxdf$id)
+  idtt <- NULL
+  for(tt in as.integer(names(which(tab < p)))) {  # id with less than p records!
+    print(tt)
+    idtt <- c(idtt, which(auxdf$id == tt))
+  }
+  
+  if(is.null(idtt)) { # no incomplete records
+    idmat         <- matrix(auxdf$id, ncol = p, byrow = TRUE)
+    alldatesperid <- apply(idmat, 1, sd) == 0
+    valmat        <- matrix(auxdf$val, ncol = p, byrow = TRUE)
+  } else {
+    idmat         <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE)
+    alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0
+    valmat        <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE)
+    
+    # store separatelly partial records
+    write.table(file = paste0("~/tmp/2010_partial_", b, ".txt"), auxdf[idtt, ])
+  }
+  
+  # store full records
+  write.table(file = paste0("~/tmp/2010_full_", b, ".txt"), valmat,
+              row.names = idmat[, 1], col.names = FALSE) 
+}
+
+close(con)                      # close connection to the file
+
+rm(auxdf, idmat, valmat, alldatesperid, b,  # clean up some memory
+   idtt, blocks, tab, tt, con)
+
+
+## 3. Complete partial records ####  NOT NECESSARY FOR 2010
+## After analysis, partial records are only 119 clients from which one only
+## time step (01JAN2009:00:00:00) is lacking.
+
+#df_partial <- NULL
+#for(f in list.files("~/tmp/", "2009_partial_*")) 
+#  df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f)))
+
+#tab <- table(df_partial$id)
+#id_incomp <- as.integer(names(which(tab < p))) # Incomplete records
+
+#df_partial_full <- rbind(df_partial, 
+#                         data.frame(id   = id_incomp,
+#                                    date = "01JAN2009:00:00:00",  
+#                                    val  = NA))
+
+#rm(df_partial)
+
+# tab2 <- table(df_partial_full$id)  # Check that df_partial_full is full
+# head(sort(tab2))
+
+
+## 4. Reorder the lines to get the data matrix #### NOT NECESSARY FOR 2010
+## As we paste chunks of partial records and impute some time steps,
+## the original order of the data is broken. We fix it by reordering
+## the ids and then the data.
+
+#idx_ordered <- order(df_partial_full$id)             # order ids
+#df_partial_full2 <- df_partial_full[idx_ordered, ] 
+#rm(df_partial_full)
+
+# Order data values following the correct dates (as the date is a factor
+# we need to seek for each value: this is computationnaly innefficient).
+
+#valmat  <- matrix(df_partial_full2$val,  ncol = p, byrow = TRUE)
+#datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE)
+#idmat   <- matrix(df_partial_full2$id,   ncol = p, byrow = TRUE)
+
+# Use this for as a check by running it twice. On the second run no
+# printing should be done (because records should be ordered).
+#for(line in 1:nrow(datemat)) {
+#  if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered
+#    cat(sprintf("\nline %i is not ordered", line))
+#    
+#    neworder         <- match(dates, datemat[line, ])
+#    valmat[line , ]  <- valmat[ line, neworder]
+#    datemat[line , ] <- datemat[line, neworder]
+#  } 
+#}
+
+
+## 5. Write on disk the full data matrix of partial records ####
+
+#write.table(file = "~/tmp/2009_full_Z.txt", valmat, 
+#            row.names = idmat[, 1], col.names = FALSE) 
+rm(list = ls())
+gc()
+
+
+## A. data.table & reshape2 #### 
+## When large RAM memory is available, one could use this code to process
+## everything in memory.
+
+#library(data.table)
+#library(reshape2)
+
+#dt <- fread(input  = "~/tmp/data/2009_chunk.csv")
+
+#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), 
+#                      CPP_PUISSANCE_BRUTE,
+#                      CPP_PUISSANCE_CORRIGEE), ]
+#dt[, CPP_PUISSANCE_CORRIGEE := NULL]
+#dt[, CPP_PUISSANCE_BRUTE := NULL]
+
+#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge)
+
+
+## Z. Probably stuff to be deleted 
+
+# searchpos <- function(row) {
+#   str  <- strsplit(row, ",")
+#   
+#   auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str)
+#   
+#   auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
+#                        date = auxmat[, 2],
+#                        val  = as.numeric(
+#                          ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))
+#   )
+#   rm(auxmat)
+#   
+#   idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE)
+#   alldatesperid <- apply(idmat, 1, sd) == 0
+#   
+#   
+#   #  lines <- match(auxdf$id, identifiants)
+#   #  cols  <- match(auxdf$date, dates)
+#   
+#   return(cbind(lines, cols, auxdf$val))
+# }

diff --git a/codes_Jairo_stage2/00_convertir-donnnes_2011.r b/codes_Jairo_stage2/00_convertir-donnnes_2011.r
new file mode 100644 (file)
index 0000000..53e8cdf
--- /dev/null
+++ b/codes_Jairo_stage2/00_convertir-donnnes_2011.r
@@ -0,0 +1,176 @@
+## File : 00_convertir-donnnes_2011.r
+## Description : Converts flat EDF's 32K data into a full data matrix 
+##               layout [individuals, variables]. Rownames are EDF's ids.
+##               We process the original flat file sequentially by lines
+##               to avoid exceding the available RAM memory (and so avoiding
+##               swaping which is a computational burden).
+
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2011", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(1000, 8), 685)  # We'll process 1000 x p lines at each
+                                # iteration of the reading loop
+
+## 2. Process the large flat file ####
+## We want to check that every time step recorded for each id.
+
+con <- file("~/tmp/data/2011.csv")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line
+
+for(b in seq_along(blocks)){      # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb * length(dates))
+  auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 3, byrow = TRUE)
+  rm(actual)
+  auxdf  <- data.frame(id   = as.integer(auxmat[, 3]),
+                       date = auxmat[, 1],
+                       val  = as.numeric(auxmat[, 2]))
+  rm(auxmat) # free up some space
+  
+  tab <- table(auxdf$id)
+  idtt <- NULL
+  for(tt in as.integer(names(which(tab < p)))) {  # id with less than p records!
+    print(tt)
+    idtt <- c(idtt, which(auxdf$id == tt))
+  }
+  
+  if(is.null(idtt)) { # no incomplete records
+    idmat         <- matrix(auxdf$id, ncol = p, byrow = TRUE)
+    alldatesperid <- apply(idmat, 1, sd) == 0
+    valmat        <- matrix(auxdf$val, ncol = p, byrow = TRUE)
+  } else {
+    idmat         <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE)
+    alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0
+    valmat        <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE)
+    
+    # store separatelly partial records
+    write.table(file = paste0("~/tmp/2011_partial_", b, ".txt"), auxdf[idtt, ])
+  }
+  
+  # store full records
+  write.table(file = paste0("~/tmp/2011_full_", b, ".txt"), valmat,
+              row.names = idmat[, 1], col.names = FALSE) 
+}
+
+close(con)                      # close connection to the file
+
+rm(auxdf, idmat, valmat, alldatesperid, b,  # clean up some memory
+   idtt, blocks, tab, tt, con)
+
+
+## 3. Complete partial records ####
+
+# Missing data in 2011 is quite messy. The number of missing records
+# vary from one client to another (see tab)
+
+df_partial <- NULL
+for(f in list.files("~/tmp/", "2011_partial_*")) 
+  df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f)))
+
+tab <- table(df_partial$id)
+id_incomp <- as.integer(names(which(tab < p))) # Incomplete records
+
+# The equivalent of 2009's df_partial_full is not easy to construct
+
+#df_partial_full <- rbind(df_partial, 
+#                         data.frame(id   = id_incomp,
+#                                    date = "01JAN2009:00:00:00",  
+#                                    val  = NA))
+
+rm(df_partial)
+
+# tab2 <- table(df_partial_full$id)  # Check that df_partial_full is full
+# head(sort(tab2))
+
+
+## 4. Reorder the lines to get the data matrix ####
+## As we paste chunks of partial records and impute some time steps,
+## the original order of the data is broken. We fix it by reordering
+## the ids and then the data.
+
+idx_ordered <- order(df_partial_full$id)             # order ids
+df_partial_full2 <- df_partial_full[idx_ordered, ] 
+rm(df_partial_full)
+
+# Order data values following the correct dates (as the date is a factor
+# we need to seek for each value: this is computationnaly innefficient).
+
+valmat  <- matrix(df_partial_full2$val,  ncol = p, byrow = TRUE)
+datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE)
+idmat   <- matrix(df_partial_full2$id,   ncol = p, byrow = TRUE)
+
+# Use this for as a check by running it twice. On the second run no
+# printing should be done (because records should be ordered).
+for(line in 1:nrow(datemat)) {
+  if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered
+    cat(sprintf("\nline %i is not ordered", line))
+    
+    neworder         <- match(dates, datemat[line, ])
+    valmat[line , ]  <- valmat[ line, neworder]
+    datemat[line , ] <- datemat[line, neworder]
+  } 
+}
+
+
+## 5. Write on disk the full data matrix of partial records ####
+
+write.table(file = "~/tmp/2009_full_Z.txt", valmat, 
+            row.names = idmat[, 1], col.names = FALSE) 
+rm(list = ls())
+gc()
+
+
+## A. data.table & reshape2 #### 
+## When large RAM memory is available, one could use this code to process
+## everything in memory.
+
+#library(data.table)
+#library(reshape2)
+
+#dt <- fread(input  = "~/tmp/data/2009_chunk.csv")
+
+#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), 
+#                      CPP_PUISSANCE_BRUTE,
+#                      CPP_PUISSANCE_CORRIGEE), ]
+#dt[, CPP_PUISSANCE_CORRIGEE := NULL]
+#dt[, CPP_PUISSANCE_BRUTE := NULL]
+
+#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge)
+
+
+## Z. Probably stuff to be deleted 
+
+# searchpos <- function(row) {
+#   str  <- strsplit(row, ",")
+#   
+#   auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str)
+#   
+#   auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
+#                        date = auxmat[, 2],
+#                        val  = as.numeric(
+#                          ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))
+#   )
+#   rm(auxmat)
+#   
+#   idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE)
+#   alldatesperid <- apply(idmat, 1, sd) == 0
+#   
+#   
+#   #  lines <- match(auxdf$id, identifiants)
+#   #  cols  <- match(auxdf$date, dates)
+#   
+#   return(cbind(lines, cols, auxdf$val))
+# }

diff --git a/codes_Jairo_stage2/00_createCalendar.r b/codes_Jairo_stage2/00_createCalendar.r
new file mode 100644 (file)
index 0000000..caf48ce
--- /dev/null
+++ b/codes_Jairo_stage2/00_createCalendar.r
@@ -0,0 +1,58 @@
+## File: createCalendar.r
+## Description: Create the calendar transitions to be used on KWF predictor
+## Author: jc @ September 2015
+
+## Function: irishCalendar
+## Description: Create the calendar with day of the week and daytype.
+##              (It is the first step, then we need to obtain the transitions)
+irishCalendar <- function(from, to){
+   require(timeDate)
+   y0 <- as.character(substr(from, 1, 4))
+   y1 <- as.character(substr(to,   1, 4))
+   years <- y0:y1
+   IRholidays <- c("NewYearsDay", 
+                     #saint patrick
+                   "EasterMonday",
+                   "GBMayDay",            # may day
+                   #june holiday
+                   "GBSummerBankHoliday", # august holiday  
+                   #october holiday
+                   "ChristmasDay"
+                   #,                    st stephen day
+                   )
+                   
+   
+   jf    <- holiday(year = years, Holiday = IRholidays)
+   jf    <- sort(unique(jf))
+   dts   <- timeSequence(from = paste(y0, '01', '01', sep = '-'),
+                         to   = paste(y1, '12', '31', sep = '-')) 
+   wday  <- dayOfWeek(dts)
+   wdayf <- unname(wday)   
+   for(d in 1:length(wdayf)){
+     if(d == 1) fpos <- 1
+     if(dts[d] == jf[fpos]){
+         wdayf[d] <- "Hol"
+         if(fpos < length(jf)) fpos <- fpos + 1
+     } 
+   }
+
+   res <- data.frame(dow = wday, daytype = wdayf)
+   i0  <- which(rownames(res) == from)
+   i1  <- which(rownames(res) == to)
+   
+   return(res[i0:i1, ])
+}
+
+
+# Call irishCalendar to create the calendar.
+myCal <- irishCalendar(from="2011-01-01", to= "2013-12-31")
+n     <- nrow(myCal)
+
+# Obtention of the transitions
+gr    <- paste(myCal$daytype[1:(n - 1)], myCal$daytype[2:n], sep = '_')
+gr[gr == "Hol_Hol"] <- "Sun_Hol"
+myCal$gr <- c(gr, "Sat_Sun")
+
+# Write output to a file
+# write.table(file = "calendar_ir.txt", myCal)
+

diff --git a/codes_Jairo_stage2/00_plots-energycon.r b/codes_Jairo_stage2/00_plots-energycon.r
new file mode 100644 (file)
index 0000000..9f70085
--- /dev/null
+++ b/codes_Jairo_stage2/00_plots-energycon.r
@@ -0,0 +1,190 @@
+## File : 00_plots-energycon.r
+## Description : Using the full data matrix, we extract handy features to
+##               cluster.
+
+rm(list = ls())
+
+library(Rwave)       # CWT
+#library(cluster)     # pam
+#library(flexclust)   # kcca
+source("aux.r")               # auxiliary clustering functions 
+source("sowas-superseded.r")  # auxiliary CWT functions
+source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/")
+
+
+## 1. Read auxiliar data files ####
+
+#identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+#rm(dates0)
+
+#n <- length(identifiants)
+p <- length(dates)
+
+#blocks <- c(rep(6500, 3), 5511)  
+
+# table( substr(dates, 11, 15) ) # Sunlight time saving produces an 
+# unbalanced number of time points
+# per time stepa across the year
+
+
+## 2. Process the large file ####
+
+# if(exists("con")) close(con)
+# con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+# open(con, "r")                      # Open the connection
+# 
+# nb <- 4
+# actual <- readLines(con = con, n = nb )[-3]
+# auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+# rm(actual)
+#   
+# datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+# rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+# rm(auxmat)
+# 
+# datamat <- datamat[, 1:(48 * 14)]
+# p <- ncol(datamat)
+# 
+# auxDWT <- t(apply(datamat, 1, toDWT))
+# matcontrib <- t(apply(auxDWT,  1, contrib))
+# rm(auxDWT)
+# 
+# close(con); rm(con)                    # close connection to the file
+
+
+load("~/data/Irlande/Data_CER_clean/SME.RData")
+
+smp <- c(4, 79, 126)
+
+datamat <- t(SME[1:(48 * 7), smp])
+p <- ncol(datamat)
+
+auxDWT <- t(apply(datamat, 1, toDWT))
+matcontrib <- t(apply(auxDWT,  1, contrib))
+rm(auxDWT)
+
+matplot(t(datamat), type = "l", lty = 1, 
+        col = 1:8, lwd = 2)
+
+pdf("~/courbes.pdf")
+#par(mai = c(1, 1, 0.8, 0.6), mfcol = c(4, 2), cex  = 2)
+for(courbe in 1:ncol(SME)) {
+  plot(SME[1:(48 * 7), courbe], main = paste(courbe),
+       xlab = "", ylab = "", type = "l")
+}
+dev.off()
+
+
+
+
+## Plots for ENERGYCON full article ####
+op <- par()
+
+col <- c("grey", "black", "black")
+## Curves
+pdf('~/curves.pdf', width = 12)
+par(mai = c(1.3, 1.3, 0.1, 0.1), cex  = 1.2)
+matplot(t(datamat), type = "l", lty = c(1, 1, 2), 
+        lwd = 2, col = col, # c(1, 2, 4),
+        ylab = "Load", xlab = "Time (1/2 hours)")
+legend("top", c("Cust. A", "Cust. B", "Cust. C"),
+       col = col, lty = c(1, 1, 2), ncol = 3, lwd = 2)
+# matplot(scale(t(datamat), scale = F), 
+#         type = "l", lty = 1, col = c(1, 2, 4), lwd = 2, 
+#         ylab = "Load", xlab = "Time (1/2 hours)")
+dev.off()
+
+normi <- function(x) x / max(x)
+normi2 <- function(x) (x - min(x)) / (max(x) - min(x))
+
+matplot(apply(datamat, 1, normi2),
+        type = "l", lty = 1, col = c(1, 2, 4), lwd = 2, 
+        ylab = "Load", xlab = "Time (1/2 hours)")
+
+
+cont_dist <- dist(scale(matcontrib)) 
+cont_dist <- cont_dist / max(cont_dist)
+
+
+delta <- p
+n     <- nrow(datamat)
+
+## _.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 = 10)
+# 10 here represent 2^10 = 1024 half-hours ~ 2 weeks day
+scalevector4  <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
+lscvect4      <- length(scalevector4)
+lscvect <- lscvect4  # i should clean my code: werFam demands a lscvect
+Xcwt4   <- toCWT(datamat, noctave = noctave4, dt = 1,
+                 scalevector = scalevector4,
+                 lt = delta, smooth = FALSE, 
+                 nvoice = nvoice)      # observations node with CWT
+
+Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length(c(Xcwt4[, ,1])))
+
+
+for(i in 1:n) 
+  Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) 
+
+rm(conso, Xcwt4); gc()
+
+lscvect <- 41 ## very very nasty: toCWT changes scalevector to 41 (instead of
+              ## the original length--37--)
+## _.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)
+
+
+Xwer_dist <- Xwer_dist / max(Xwer_dist)
+
+
+pdf("~/cmdscale.pdf", width = 14)
+#layout(matrix(1:2, 1, 2))
+par(mai = c(1, 1, 0.8, 0.6), mfcol = c(1, 2), cex  = 2)
+plot(cmdscale(cont_dist), pch = c("A", "B", "C"),#c(15, 17, 19), 
+     main = "RC based distance",
+     #col = c(1, 2, 4), 
+     xlim = c(-1, 1), ylim = c(-1, 1), 
+     xlab = "", ylab = "", asp = 1)
+abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1)
+
+plot(cmdscale(Xwer_dist), pch = c("A", "B", "C"),#c(15, 17, 19),
+     main = "WER distance",
+     #col = c(1, 2, 4), 
+     xlim = c(-1, 1), ylim = c(-1, 1), 
+     xlab = "", ylab = "", asp = 1)
+abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1)
+dev.off()
+
+
+
+## Effectives distribution
+#load('../res/clfit500.Rdata')
+pdf("~/distro500.pdf", width = 12)
+plot(sort(table(clfit$clustering), decreasing = TRUE), 
+     type ="s", ylab = "Effectives", xlab = "Class", lwd = 2)
+abline(v = 200)
+#dev.off()
+

diff --git a/codes_Jairo_stage2/01_StBr.r b/codes_Jairo_stage2/01_StBr.r
new file mode 100644 (file)
index 0000000..980a855
--- /dev/null
+++ b/codes_Jairo_stage2/01_StBr.r
@@ -0,0 +1,57 @@
+## File: StBr.r
+## Description: screens meaningful variables and performns
+##              data transformation on clustering 
+
+# rm(list = ls())
+
+
+## Description: Steinley & Brusco (2006) data transform to cluster 
+StBrtransform <- function(X){
+  apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 )  
+}
+
+
+## Description: Clustering index (Steinley & Brusco (2006))
+CI <- function(X, B = 1000) { # B : number of boostrap replications
+
+  n <- nrow(X)
+  p <- ncol(X)
+  
+  #ci <- apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 )
+  ci <- StBrtransform(X)
+  
+  rc    <- ci / min(ci)
+  minV  <- which.min(rc)
+  
+  Xstar    <- scale(X)
+  newRange <- apply(Xstar, 2, function(x) max(x) - min(x))
+  
+  rmin <- newRange[minV]
+
+  datat <- array(0.0, dim = dim(X))
+  
+  # Reweighting X into datat
+  for(i in 1:p){
+    v          <- Xstar[, i]
+    temp       <- rc[i] * (rmin / newRange[i])^2    
+    datat[, i] <- sqrt(temp) * v
+  }
+  
+  xboot  <- matrix(rnorm(n * B), nrow = n)
+  #cinorm <- apply(xboot, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 )
+  cinorm <- StBrtransform(xboot)
+  ci95   <- median(cinorm)
+  
+  #ciStar <- apply(datat, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 )
+  ciStar  <- StBrtransform(datat)
+  selectv <- which(ciStar > ci95)
+  
+  return(list(selectv = selectv, 
+              tdata  = datat) )  
+}
+
+
+
+
+#test <- matrix(rnorm(200), 40, 5)
+#CI(test)$selectv

diff --git a/codes_Jairo_stage2/01_extract-features2_2009.r b/codes_Jairo_stage2/01_extract-features2_2009.r
new file mode 100644 (file)
index 0000000..85a2b5b
--- /dev/null
+++ b/codes_Jairo_stage2/01_extract-features2_2009.r
@@ -0,0 +1,67 @@
+## File : 01_extract-features_2009.r
+## Description : Using the full data matrix, we extract handy features to
+##               cluster.
+
+rm(list = ls())
+
+#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+source('aux2.r')
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# table( substr(dates, 11, 15) ) # Sunlight time saving produces an 
+                                 # unbalanced number of time points
+                                 # per time stepa across the year
+
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  nas <- which(is.na(datamat)[, 1]) # some 1/1/2009 are missing
+  if(length(nas)>0) datamat[nas, 1] <- rowMeans(datamat[nas, 2:4])
+  
+  imput   <- datamat[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+  datamat <- cbind(datamat[, 1:4180], imput, datamat[, 4181:17518])
+  
+  dayimp   <- t(apply(datamat, 1, dayimpact))
+  nightimp <- t(apply(datamat, 1, nightimpact))
+  lunchimp <- t(apply(datamat, 1, lunchimpact))
+  
+  auxfeat  <- cbind(dayimp, nightimp, lunchimp)
+  
+  if(b == 1) {
+    matfeat <- auxfeat 
+  } else {
+    matfeat <- rbind(matfeat, auxfeat)
+  }
+  
+}
+
+close(con)                      # close connection to the file
+
+write.table(matfeat, file = "~/tmp/2009_impacts.txt")
+

diff --git a/codes_Jairo_stage2/01_extract-features_2009.r b/codes_Jairo_stage2/01_extract-features_2009.r
new file mode 100644 (file)
index 0000000..99ab8c5
--- /dev/null
+++ b/codes_Jairo_stage2/01_extract-features_2009.r
@@ -0,0 +1,58 @@
+## File : 01_extract-features_2009.r
+## Description : Using the full data matrix, we extract handy features to
+##               cluster.
+
+rm(list = ls())
+
+source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# table( substr(dates, 11, 15) ) # Sunlight time saving produces an 
+                                 # unbalanced number of time points
+                                 # per time stepa across the year
+
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  auxDWT <- t(apply(datamat, 1, toDWT))
+  auxcontrib <- t(apply(auxDWT,  1, contrib))
+  rm(auxDWT)
+  
+  if(b == 1) {
+    matcontrib <- auxcontrib
+  } else {
+    matcontrib <- rbind(matcontrib, auxcontrib)
+  }
+  
+}
+
+close(con)                      # close connection to the file
+
+write.table(matcontrib, file = "~/tmp/2009_contrib.txt")
+

diff --git a/codes_Jairo_stage2/01_extract-features_2010.r b/codes_Jairo_stage2/01_extract-features_2010.r
new file mode 100644 (file)
index 0000000..a1d7d49
--- /dev/null
+++ b/codes_Jairo_stage2/01_extract-features_2010.r
@@ -0,0 +1,54 @@
+## File : 01_extract-features_2010.r
+## Description : Using the full data matrix, we extract handy features to
+##               cluster.
+
+rm(list = ls())
+
+source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2010", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2010_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  auxDWT <- t(apply(datamat, 1, toDWT))
+  auxcontrib <- t(apply(auxDWT,  1, contrib))
+  rm(auxDWT)
+  
+  if(b == 1) {
+    matcontrib <- auxcontrib
+  } else {
+    matcontrib <- rbind(matcontrib, auxcontrib)
+  }
+  
+}
+
+close(con)                      # close connection to the file
+
+write.table(matcontrib, file = "~/tmp/2010_contrib.txt")
+

diff --git a/codes_Jairo_stage2/02_cluster-par_2009.r b/codes_Jairo_stage2/02_cluster-par_2009.r
new file mode 100644 (file)
index 0000000..0f138c9
--- /dev/null
+++ b/codes_Jairo_stage2/02_cluster-par_2009.r
@@ -0,0 +1,106 @@
+## File: extract-features.r
+
+rm(list = ls())
+
+## a. Load data & libraries ####
+
+#library(cluster)
+#library(snow)
+library(foreach)
+library(doParallel)
+
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+source('01_StBr.r')
+
+matcontrib0 <- read.table(file = "~/tmp/2009_contrib.txt")
+n     <- nrow(matcontrib0)
+
+sdcontrib <- apply(matcontrib0, 1, sd)
+lims      <- quantile(sdcontrib, probs = c(.005, .995)) # obtain 1%-extreme data
+is_normal <- which((sdcontrib > lims[1]) & (sdcontrib < lims[2]))
+
+matcontri_ext <- matcontrib0[-is_normal, ]
+matcontrib    <- matcontrib0[is_normal, ]     # wipe out aberrant data
+
+matcontrib <- t(apply(matcontrib, 1, function(x) x / sum(x)))
+matcontrib <- t(apply(matcontrib, 1, function(p) log(p / (1 - p)) ))
+
+
+## b. Transform data & compute CI ####
+ci     <- CI(matcontrib)
+tdata  <- ci$tdata; rownames(tdata) <- rownames(matcontrib)
+selvar <- ci$selectv
+  
+## c. Clustering  ##########
+
+#number of iterations
+iters <- 20
+
+#setup parallel backend to use 8 processors
+cl <- makeCluster(20)
+registerDoParallel(cl)
+
+clfitlist <- foreach(icount(iters)) %dopar% {
+  library(cluster)
+  K <- 200
+  clara(x        = tdata[, selvar], 
+        k        = K, 
+        sampsize = 4000, 
+        samples  = 4, 
+        rngR     = TRUE)
+}
+
+stopCluster(cl)
+
+#save(clfit, file = 'clfit500.Rdata')
+# save(clfit, file = 'clfit200RC.Rdata')
+#save(clfitlist, file = 'clfitlist200.Rdata')
+#rm(ci, matcontrib0, is_normal, lims, selvar)
+#gc()
+
+
+res <- lapply(clfitlist, function(x) x$clustering)
+names(res) <- 1:iters
+
+save(data.frame(res), file = 'res/clfitdf200.Rdata')
+
+
+## d. Analyze results  ##########
+
+#1. Répartition du nombre d'observation par cluster
+#plot(sort(table(clfit$clustering), decreasing = TRUE), 
+#     type = 'l', ylab = 'Fréquence', xlab = 'Classe')
+
+
+#clust <- res$clustering
+#  centres <- aggregate(conso, clust)
+#  table(clust)
+  
+  #sel_veille    <- as.Date(rownames(conso)[sel - 1])
+  #sel_lendemain <- as.Date(rownames(conso)[sel + 1])
+  
+  #res_clust <- data.frame(date     = rownames(conso),
+                          #veille    = weekdays(sel_veille),
+                          #lendemain = weekdays(sel_lendemain),
+  #                        clust     = clust)
+  
+  #for(k in 1:K) {
+  #  assign(paste0("dates_clust", K),
+  #         substr(subset(res_clust, clust == k)$date, 1, 7) )
+  #}
+  
+  #dev.off()
+  
+  #save(file = paste0(dtitle, "_clust.Rdata"), 
+       #res_clust, selvar, K, gap)
+#}
+
+#dates_clust1 <- substr(subset(dates, clust == 1)$date, 1, 7)

diff --git a/codes_Jairo_stage2/02_cluster_2009.r b/codes_Jairo_stage2/02_cluster_2009.r
new file mode 100644 (file)
index 0000000..f08d82b
--- /dev/null
+++ b/codes_Jairo_stage2/02_cluster_2009.r
@@ -0,0 +1,86 @@
+## File: extract-features.r
+
+rm(list = ls())
+
+## a. Load data & libraries ####
+
+library(cluster)
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+source('01_StBr.r')
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+
+matcontrib0 <- read.table(file = "~/tmp/2009_contrib.txt")
+n     <- nrow(matcontrib0)
+
+sdcontrib <- apply(matcontrib0, 1, sd)
+lims      <- quantile(sdcontrib, probs = c(.005, .995)) # obtain 1%-extreme data
+is_normal <- which((sdcontrib > lims[1]) & (sdcontrib < lims[2]))
+
+matcontri_ext <- matcontrib0[-is_normal, ]""
+matcontrib    <- matcontrib0[is_normal, ]     # wipe out aberrant data
+
+matcontrib <- t(apply(matcontrib, 1, function(x) x / sum(x)))
+matcontrib <- t(apply(matcontrib, 1, function(p) log(p / (1 - p)) ))
+
+
+## b. Transform data & compute CI ####
+ci     <- CI(matcontrib)
+tdata  <- ci$tdata; rownames(tdata) <- rownames(matcontrib)
+selvar <- ci$selectv
+  
+## c. Clustering  ##########
+K <- 200
+system.time(
+  clfit <- clara(x        = tdata[, selvar], 
+                 k        = K, 
+                 sampsize = 4000, 
+                 samples  = 4, 
+                 rngR     = TRUE)
+)
+
+#save(clfit, file = 'clfit500.Rdata')
+# save(clfit, file = 'clfit200RC.Rdata')
+save(clfit, file = 'clfit200.Rdata')
+
+rm(ci, matcontrib0, is_normal, lims, selvar)
+gc()
+
+## d. Analyze results  ##########
+
+#1. Répartition du nombre d'observation par cluster
+#plot(sort(table(clfit$clustering), decreasing = TRUE), 
+#     type = 'l', ylab = 'Fréquence', xlab = 'Classe')
+
+
+#clust <- res$clustering
+#  centres <- aggregate(conso, clust)
+#  table(clust)
+  
+  #sel_veille    <- as.Date(rownames(conso)[sel - 1])
+  #sel_lendemain <- as.Date(rownames(conso)[sel + 1])
+  
+  #res_clust <- data.frame(date     = rownames(conso),
+                          #veille    = weekdays(sel_veille),
+                          #lendemain = weekdays(sel_lendemain),
+  #                        clust     = clust)
+  
+  #for(k in 1:K) {
+  #  assign(paste0("dates_clust", K),
+  #         substr(subset(res_clust, clust == k)$date, 1, 7) )
+  #}
+  
+  #dev.off()
+  
+  #save(file = paste0(dtitle, "_clust.Rdata"), 
+       #res_clust, selvar, K, gap)
+#}
+
+#dates_clust1 <- substr(subset(dates, clust == 1)$date, 1, 7)

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r
new file mode 100644 (file)
index 0000000..8e5e756
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classes-par_2009.r
@@ -0,0 +1,76 @@
+## File : 03_compute-sum-of-classes_2009.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+if(MOJARRITA) { 
+  blocks <- c(rep(6500, 3), 5511) 
+  } else {
+  blocks <- 25011  
+  } 
+
+# Fit of the clustering : clfit 
+load('../res/clfitdf200.Rdata') # Loads res that containts  
+                                  # clusterings memberships
+res <- as.data.frame(res)
+
+lres <- length(res)
+K       <- 200 #nrow(clfit$clusinfo)
+
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by as.numeric
+                                                   # are NA strings
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  synchros <- lapply(res, 
+                     function(ll) { 
+                        aux <- matrix(0, ncol = p, nrow = K)
+                        for(k in 1:K) {
+                          clustk <- which(ll == k)
+                          if(length(clustk) > 1) {
+                            aux[k, ] <- colSums(datamat[ll ==  k, ])
+                          } else {
+                            aux[k, ] <- datamat[ll ==  k, ]
+                          }
+                        }
+                        aux
+                      })
+}
+
+close(con)                # close connection to the file
+
+# save(synchros, file = "~/tmp/2009synchrosdf200WER")
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r
new file mode 100644 (file)
index 0000000..cbd373b
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM-par_2009.r
@@ -0,0 +1,89 @@
+## File : 03_compute-sum-of-classes_2009.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+if(MOJARRITA) { 
+  blocks <- c(rep(6500, 3), 5511) 
+  } else {
+  blocks <- 25011  
+  } 
+
+# Fit of the clustering : clfit 
+load('../res/clfitdf200.Rdata') # Loads res that containts  
+                                  # clusterings memberships
+res <- as.data.frame(res)
+
+resRDN <- as.data.frame(lapply(res, sample))
+#save(file = "../res/clfitdf200RDN.Rdata", resRDN)
+load("../res/clfitdf200RDN.Rdata")
+
+lres <- length(res)
+K       <- 200 #nrow(clfit$clusinfo)
+rm(res)
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  actual_split <- strsplit(actual, " ")
+  rm(actual)
+  
+  #auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  #datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by 
+                                                    # as.numeric
+  auxlist <- lapply(actual_split, function(x) as.numeric(x[-1]))
+  datamat <- matrix(unlist(auxlist), ncol = p, byrow = TRUE)
+  rm(auxlist)
+
+                                                   # are NA strings
+  #rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  auxnames          <- unlist(lapply(strsplit(actual, " "), "[", 1))
+  rownames(datamat) <- substr(auxnames, 2, 7)
+
+  rm(auxmat, actual_split)
+
+  synchros <- lapply(resRDN, 
+                     function(ll) { 
+                        aux <- matrix(0, ncol = p, nrow = K)
+                        for(k in 1:K) {
+                          clustk <- which(ll == k)
+                          if(length(clustk) > 1) {
+                            aux[k, ] <- colSums(datamat[clustk, ])
+                          } else {
+                            aux[k, ] <- datamat[clustk, ]
+                          }
+                        }
+                        aux
+                      })
+}
+
+close(con)                # close connection to the file
+
+# save(synchros, file = "~/tmp/2009synchrosdf200RND")
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r
new file mode 100644 (file)
index 0000000..016b6de
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2009.r
@@ -0,0 +1,82 @@
+## File : 03_compute-sum-of-classes_2009.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)-
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# Fit of the clustering : clfit 
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata')
+load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata')
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata')
+#  table(clfit$clustering)
+
+dfclust <- data.frame(cluster = clfit$clustering) # real clustering 
+K       <- nrow(clfit$clusinfo)
+
+#dfclust2 <- dfclust
+dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust
+
+
+
+#dfclust <- head(dfclust, 50)           # just for testing purpouses
+synchros <- matrix(0, ncol = p, nrow = K)
+rm(clfit)
+                   
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  # obtain for each line of datamat the cluster membership (if any)
+  clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] 
+  
+#  if(length(which(rownames(datamat) == "218095"))>0) print(b);
+  
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+    
+    if(length(clustk) > 1) {
+      synchrosk <- colSums(datamat[which(clustfactor == k), ])
+    } else {
+      synchrosk <- datamat[which(clustfactor == k), ]
+    }
+    synchros[k, ] <- synchros[k, ] + synchrosk
+    rm(synchrosk)
+    }
+  }
+}
+
+close(con)                # close connection to the file
+
+synchros <- data.frame(t(synchros), total = colSums(synchros))
+
+write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt")
+write.table(dfclust,  file = "../res/dfclust200RANDOM.txt")
+#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt")
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r
new file mode 100644 (file)
index 0000000..0c75bd0
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classesRANDOM_2010.r
@@ -0,0 +1,82 @@
+## File : 03_compute-sum-of-classes_2010.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# Fit of the clustering : clfit 
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata')
+load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata')
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata')
+#  table(clfit$clustering)
+
+dfclust <- data.frame(cluster = clfit$clustering) # real clustering 
+K       <- nrow(clfit$clusinfo)
+
+#dfclust2 <- dfclust
+dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust
+
+
+
+#dfclust <- head(dfclust, 50)           # just for testing purpouses
+synchros <- matrix(0, ncol = p, nrow = K)
+rm(clfit)
+                   
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2010_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  # obtain for each line of datamat the cluster membership (if any)
+  clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] 
+  
+#  if(length(which(rownames(datamat) == "218095"))>0) print(b);
+  
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+    
+    if(length(clustk) > 1) {
+      synchrosk <- colSums(datamat[which(clustfactor == k), ])
+    } else {
+      synchrosk <- datamat[which(clustfactor == k), ]
+    }
+    synchros[k, ] <- synchros[k, ] + synchrosk
+    rm(synchrosk)
+    }
+  }
+}
+
+close(con)                # close connection to the file
+
+synchros <- data.frame(t(synchros), total = colSums(synchros))
+
+write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt")
+write.table(dfclust,  file = "../res/dfclust200RANDOM.txt")
+#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt")
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r
new file mode 100644 (file)
index 0000000..0623ba1
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2009.r
@@ -0,0 +1,78 @@
+## File : 03_compute-sum-of-classes_2009.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2009", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# Fit of the clustering : clfit 
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata')
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata')
+load('../res/clfit200RC.Rdata')
+#  table(clfit$clustering)
+
+names(clfit$clustering) <- sample(names(clfit$clustering))# for random classes
+dfclust <- data.frame(cluster = clfit$clustering)
+# write.table(dfclust, file = "../res/clfit200-random.txt")
+
+K       <- nrow(clfit$clusinfo)
+#dfclust <- head(dfclust, 50)           # just for testing purpouses
+synchros <- matrix(0, ncol = p, nrow = K)
+rm(clfit)
+                   
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  # obtain for each line of datamat the cluster membership (if any)
+  clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] 
+  
+#  if(length(which(rownames(datamat) == "218095"))>0) print(b);
+  
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+    
+    if(length(clustk) > 1) {
+      synchrosk <- colSums(datamat[which(clustfactor == k), ])
+    } else {
+      synchrosk <- datamat[which(clustfactor == k), ]
+    }
+    synchros[k, ] <- synchros[k, ] + synchrosk
+    rm(synchrosk)
+    }
+  }
+}
+
+close(con)                # close connection to the file
+
+synchros <- data.frame(t(synchros), total = colSums(synchros))
+
+# write.table(synchros, file = "~/tmp/2009_synchros200RC.txt")
+# write.table(synchros, file = "~/tmp/2009_synchros200-random.txt")
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r
new file mode 100644 (file)
index 0000000..1a4c9a1
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2010-par.r
@@ -0,0 +1,79 @@
+## File : 03_compute-sum-of-classes_2010-par.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+MOJARRITA <- Sys.info()[4] ==  "mojarrita"
+
+if(MOJARRITA){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2010", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+
+if(MOJARRITA) { 
+  blocks <- c(rep(6500, 3), 5511) 
+} else {
+  blocks <- 25011  
+} 
+
+# Fit of the clustering : clfit 
+load('../res/clfitdf200.Rdata') # Loads res that containts  
+# clusterings memberships
+res <- as.data.frame(res)
+
+
+lres <- length(res)
+K       <- 200 #nrow(clfit$clusinfo)
+
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2010_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+  
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by as.numeric
+  # are NA strings
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+    
+  synchros <- lapply(res, 
+                     function(ll) { 
+                       aux <- matrix(0, ncol = p, nrow = K)
+                       for(k in 1:K) {
+                         clustk <- which(ll == k)
+                         if(length(clustk) > 1) {
+                           aux[k, ] <- colSums(datamat[ll ==  k, ])
+                         } else {
+                           aux[k, ] <- datamat[ll ==  k, ]
+                         }
+                       }
+                       aux
+                     })
+}
+  
+  close(con)                # close connection to the file
+  
+# save(synchros, file = "~/tmp/2010synchrosdf200WER")
+
+

diff --git a/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r b/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r
new file mode 100644 (file)
index 0000000..18d23b4
--- /dev/null
+++ b/codes_Jairo_stage2/03_compute-sums-of-classes_2010.r
@@ -0,0 +1,101 @@
+## File : 03_compute-sum-of-classes_2010.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates        <- dates0[grep("2010", dates0)]
+rm(dates0)
+
+n <- length(identifiants)
+p <- length(dates)
+
+blocks <- c(rep(6500, 3), 5511)  
+
+# Fit of the clustering : clfit 
+#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata')
+load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata')
+load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata')
+#  table(clfit$clustering)
+
+dfclust <- data.frame(cluster = clfit$clustering)
+# read write.table(dfclust, file = "../res/clfit200RC-random.txt") for random
+
+K       <- nrow(clfit$clusinfo)
+#dfclust <- head(dfclust, 50)           # just for testing purpouses
+synchros <- matrix(0, ncol = p, nrow = K)
+rm(clfit)
+                   
+## 2. Process the large file ####
+
+close(con)
+con <- file("~/tmp/2010_full.txt")  # Establish a connection to the file
+open(con, "r")                      # Open the connection
+
+for(b in seq_along(blocks)){        # Reading loop
+  nb <- blocks[b]
+  actual <- readLines(con = con, n = nb )
+  auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
+  rm(actual)
+
+  datamat <- t(apply(auxmat[, -1], 1, as.numeric))
+  rownames(datamat) <- substr(auxmat[, 1], 2, 7)
+  rm(auxmat)
+
+  # obtain for each line of datamat the cluster membership (if any)
+  clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] 
+  
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      
+      if(length(clustk) > 1) {
+        synchrosk <- colSums(datamat[which(clustfactor == k), ])
+      } else {
+        synchrosk <- datamat[which(clustfactor == k), ]
+      }
+      synchros[k, ] <- synchros[k, ] + synchrosk
+      rm(synchrosk)
+    }
+    
+  }
+}
+
+close(con)                      # close connection to the file
+
+synchros <- data.frame(t(synchros), total = colSums(synchros))
+
+# write.table(synchros, file = "~/tmp/2010_synchros200RC.txt")
+# write.table(synchros, file = "~/tmp/2010_synchros200-random.txt")
+# 
+# 
+
+
+dfclust <- read.table("clfit200muchos.txt")
+
+for(pepe in 1:10) {
+  synchros <- matrix(0, ncol = p, nrow = K)
+  clustfactor <- dfclust[match(rownames(datamat), rownames(tdata)), pepe]
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      if(length(clustk) > 1) {
+        synchrosk <- colSums(datamat[which(clustfactor == k), ])
+        } else {
+        synchrosk <- datamat[which(clustfactor == k), ]
+        }
+      synchros[k, ] <- synchros[k, ] + synchrosk
+      rm(synchrosk)
+    }
+  }
+  
+  synchros <- data.frame(t(synchros), total = colSums(synchros))
+  write.table(synchros, file = paste0(colnames(dfclust)[pepe], "2010.txt"))
+}
+

diff --git a/codes_Jairo_stage2/04_predictions.r b/codes_Jairo_stage2/04_predictions.r
new file mode 100644 (file)
index 0000000..010880e
--- /dev/null
+++ b/codes_Jairo_stage2/04_predictions.r
@@ -0,0 +1,159 @@
+## File : 04_predictions.r
+## Description : Calculer les synchrones pour chaque groupe obtenu par le
+##               clustering. 
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+## 1. Read auxiliar data files ####
+
+#identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates09      <- as.character(dates0[grep("2009", dates0)])
+dates10      <- as.character(dates0[grep("2010", dates0)])
+dates        <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen   <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+                 to   = as.Date("2010/12/31"),
+                 by   = 'day')
+
+synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt"))
+synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt"))
+
+## 2. Imputation of missing values #### 
+#     (done by linear interpolation)
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518])
+
+
+rm(imput09, imput10, nas)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00      <- which(days == "2009-01-01")
+n_00      <- which(days == "2009-12-31") 
+n_01      <- which(days == "2010-12-31")
+
+
+## 3. Construct predictors ####
+library(kerwavfun)
+synchros <- (cbind( synchros09, synchros10 ))
+
+clustfactors <- dir("~/tmp/", pattern = 'clustfactor')
+maxK <- length(clustfactors) - 1
+
+performK <- data.frame(K    = rep(NA_real_, maxK),
+                       mape = rep(NA_real_, maxK),
+                       rmse = rep(NA_real_, maxK))
+
+setwd("~/tmp/")
+for(cf in seq_along(clustfactors)) { 
+  #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt")
+  clustfactor <- read.table(file = clustfactors[cf])
+
+  K <- nrow(unique(clustfactor))
+
+  performK[cf, 1] <- K
+
+  SC <- matrix(0, ncol = ncol(synchros), nrow = K)
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      if(length(clustk) > 1) {
+        SCk <- colSums(synchros[which(clustfactor == k), ])
+      } else {
+        SCk <- synchros[which(clustfactor == k), ]
+      }
+      SC[k, ] <- SC[k, ] + SCk
+      rm(SCk)
+    }
+  }
+
+  mat_Load <- matrix(colSums(SC),   ncol = 48, byrow = TRUE)
+  mat_ks   <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE))
+
+
+  ## 2. Transform to wkdata (performs DWT) ##
+ 
+  wk_Load <- wkdata(X  = c(t(mat_Load)), 
+                    gr = gr[1:nrow(mat_Load)], 
+                    p  = 48)
+
+  wks <- lapply(mat_ks, function(mat_k) 
+          wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+
+  ## 3. Begin of big loop over ##
+  grid <- n_00:(n_01 - 1) # these are the reference days
+  #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+
+  # output matrix of predicted days 
+  # names of the predicted days (reference days + 1)
+  pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+  preds     <- lapply(1:K, function(k) pred_Load)
+
+  go <- Sys.time()
+  for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+    if((q %/% 10) == (q / 10)) cat(paste("  iteration: ", q, "\n"))
+    Q  <- grid[q]
+  
+    select_Load <- select.wkdata(wk_Load, i_00:Q)
+    aux_Load  <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+                           EPS = .Machine$double.eps) 
+    pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+  
+    selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+    auxs    <- lapply(selects, 
+                      function(k) wavkerfun(obj = k, 
+                                          kerneltype = "gaussian",
+                                          EPS = .Machine$double.eps))  
+    for(k in 1:K) 
+      preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+
+  }
+  
+  top <- Sys.time()
+  #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+
+  # Compute the prediction of synchrone by the sum of individuals
+  pred_Load_sum <- Reduce('+', preds) 
+
+  ## 4. Analisis of the predictions  ####
+  resids_Load     <- pred_Load     - mat_Load[grid + 1, ] # i.e. reference days + 1
+  resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+
+  mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+  rmse_Load <- sqrt(rowMeans(resids_Load^2))
+
+  mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+  rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+  performK[cf, 2] <- mean(mape_Load, na.rm = TRUE)
+  performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+
+}
+
+performK <- performK[order(performK$K), ]
+plot(performK$K, performK$rmse, type = 'l', ylim = c(2,3))
+abline(h = mean(performK$mape), col = 2)

diff --git a/codes_Jairo_stage2/05_cluster2step.r b/codes_Jairo_stage2/05_cluster2step.r
new file mode 100644 (file)
index 0000000..70dd0df
--- /dev/null
+++ b/codes_Jairo_stage2/05_cluster2step.r
@@ -0,0 +1,74 @@
+## File : 05_cluster2step.r
+## Description : 
+
+rm(list = ls())
+
+setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+source("~/Documents/projects/2014_EDF-Lyon2/R/01_StBr.r") # aux functs
+source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
+library(cluster)
+
+## 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 <- length(dates)
+
+synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt"))
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))
+nas <- which(is.na(synchros09)[1, ]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- colMeans(synchros09[2:4, nas])
+
+#synchros09 <- t(synchros09) # series on rows, use if necessary
+
+## 2. Compute contributions ####
+auxDWT     <- t(apply(synchros09, 1, toDWT))
+matcontrib <- t(apply(auxDWT,  1, contrib, rel = TRUE, logit = TRUE))
+rm(auxDWT)
+
+## 3. Transform data & compute CI ####
+ci     <- CI(matcontrib[-201, ]) # last row has the total 
+tdata  <- ci$tdata; rownames(tdata) <- rownames(matcontrib)[-201]
+
+hc    <- hclust(dist(tdata[,  ci$selectv]), method = "ward.D")
+plot(hc)
+
+#clust <- cutree(hc, 2)
+
+for(K in 2:30){ 
+  #K <- 3
+  pamfit <- pam(tdata[-201, ci$selectv], k = K, stand = FALSE)
+
+  #table(pamfit$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")
+}
+
+# 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)
+
+
+

diff --git a/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r b/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r
new file mode 100644 (file)
index 0000000..5ba44a1
--- /dev/null
+++ b/codes_Jairo_stage2/05_cluster2stepWER-RANDOM.r
@@ -0,0 +1,135 @@
+## File : 05_cluster2stepWER.r
+## Description : 
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+  
+library(Rwave)       # CWT
+library(cluster)     # pam
+#library(flexclust)   # kcca
+source("aux.r")               # auxiliary clustering functions 
+source("sowas-superseded.r")  # auxiliary CWT functions
+
+## 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)
+
+load("~/tmp/2009_synchros200RND")
+synchros09 <- synchros[[1]]
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt"))
+#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt")))
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+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
+Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
+                 scalevector = scalevector4,
+                 lt = delta, smooth = FALSE, 
+                 nvoice = nvoice)      # observations node with CWT
+
+Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+
+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)){
+  cat(sprintf('\nIter: , %i', i))
+  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_synchros200RANDOM-WER.Rdata")
+
+#load("../res/2009_synchros200WER.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/clustfactorRANDOM", 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)
+# 
+# 
+# 

diff --git a/codes_Jairo_stage2/05_cluster2stepWER-par.r b/codes_Jairo_stage2/05_cluster2stepWER-par.r
new file mode 100644 (file)
index 0000000..90988dc
--- /dev/null
+++ b/codes_Jairo_stage2/05_cluster2stepWER-par.r
@@ -0,0 +1,153 @@
+## File : 05_cluster2stepWER.r
+## Description : 
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+}
+  
+library(Rwave)       # CWT
+library(cluster)     # pam
+#library(flexclust)   # kcca
+source("aux.r")               # auxiliary clustering functions 
+source("sowas-superseded.r")  # auxiliary CWT functions
+
+## 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 <- length(dates)
+
+load('~/tmp/2009synchrosdf200WER')
+#load('../res/2009_synchros200WER.Rdata')
+synchros09 <- synchros
+load('~/tmp/2010synchrosdf200WER')
+synchros10 <- synchros; rm(synchros)
+
+conso <- lapply(synchros09, function(ll) { 
+                nas <- which(is.na(ll)[, 1]) # some 1/1/2009 are missing
+                ll[nas, 1] <- rowMeans(ll[nas, 2:4])
+                imput <- ll[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+                cbind(ll[, 1:4180], imput, ll[, 4181:17518]) } )
+
+n     <- nrow(conso[[1]])
+delta <- ncol(conso[[1]])
+rm(synchros09)
+
+
+## 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
+
+Xcwt4 <- lapply(conso, function(ll)
+  toCWT(ll, noctave = noctave4, dt = 1,
+        scalevector = scalevector4,
+        lt = delta, smooth = FALSE, 
+        nvoice = nvoice)      # observations node with CWT
+  )
+
+rm(conso)
+
+Xcwt4 <- lapply(conso, function(ll) {
+  aux <- toCWT(ll, noctave = noctave4, dt = 1,
+               scalevector = scalevector4,
+               lt = delta, smooth = FALSE, 
+               nvoice = nvoice)      # observations node with CWT
+  res <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(aux[,,1]))))
+  for(i in 1:n) 
+    res[i, ] <- c(delta, lscvect, res[,,i] / max(Mod(res[,,i])) ) 
+  res })
+
+rm(conso)
+
+
+#Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+#Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+
+#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")
+
+load("../res/2009_synchros200WER.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"))
+}
+# 
+# # 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)
+# 
+# 
+# 

diff --git a/codes_Jairo_stage2/05_cluster2stepWER.r b/codes_Jairo_stage2/05_cluster2stepWER.r
new file mode 100644 (file)
index 0000000..dcc7fa3
--- /dev/null
+++ b/codes_Jairo_stage2/05_cluster2stepWER.r
@@ -0,0 +1,131 @@
+## File : 05_cluster2stepWER.r
+## Description : 
+
+rm(list = ls())
+
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+  
+library(Rwave)       # CWT
+library(cluster)     # pam
+#library(flexclust)   # kcca
+source("aux.r")               # auxiliary clustering functions 
+source("sowas-superseded.r")  # auxiliary CWT functions
+
+## 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])
+
+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
+# Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
+#                 scalevector = scalevector4,
+#                 lt = delta, smooth = FALSE, 
+#                 nvoice = nvoice)      # observations node with CWT
+# 
+# #Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+# 
+# 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)
+# 
+# 
+# 

diff --git a/codes_Jairo_stage2/06_predictions-ICAME.r b/codes_Jairo_stage2/06_predictions-ICAME.r
new file mode 100644 (file)
index 0000000..b7696a2
--- /dev/null
+++ b/codes_Jairo_stage2/06_predictions-ICAME.r
@@ -0,0 +1,160 @@
+## File : 06_predictions-ICAME.r
+## Description : Prévoir les synchrones des classes ICAME. 
+
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+## 1. Read auxiliar data files ####
+#identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates09      <- as.character(dates0[grep("2009", dates0)])
+dates10      <- as.character(dates0[grep("2010", dates0)])
+dates        <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+#n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen   <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+                 to   = as.Date("2010/12/31"),
+                 by   = 'day')
+
+
+setwd("../classes ICAME/synchrones_classif_ICAME/")
+files_ICAME <- dir(pattern = "*TR-32V*")
+K_ICAME  <- length(files_ICAME)
+SC       <- matrix(NA_real_, nrow = K_ICAME, ncol = p + 4) # NA missing in dates 
+                                                           # (summer time saving)
+
+for(k in 1:K_ICAME){ 
+  df <- read.table(file = files_ICAME[k], header = TRUE)
+  SC[k, ] <- subset(x      = df,                    # series must be on rows
+                    subset = Year == "2009" | Year == "2010", 
+                    select = "Load")[[1]]
+}
+
+
+rm(df, files_ICAME, k)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00      <- which(days == "2009-01-01")
+n_00      <- which(days == "2009-12-31") 
+n_01      <- which(days == "2010-12-31")
+
+
+## 2. Transform data into wkdata ####
+library(kerwavfun)
+
+mat_Load <- matrix(colSums(SC),   ncol = 48, byrow = TRUE)
+mat_ks   <- lapply(1:K_ICAME, function(k) matrix(data  = SC[k, ], 
+                                                 ncol  = 48, 
+                                                 byrow = TRUE))
+  
+wk_Load <- wkdata(X  = c(t(mat_Load)), 
+                  gr = gr[1:nrow(mat_Load)], 
+                  p  = 48)
+  
+wks <- lapply(mat_ks, function(mat_k) 
+       wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+  
+## 3. Begin of big loop over grid ##
+grid <- n_00:(n_01 - 1) # these are the reference days
+#h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+  
+# output matrix of predicted days 
+# names of the predicted days (reference days + 1)
+pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+preds     <- lapply(1:K_ICAME, function(k) pred_Load)
+  
+go <- Sys.time()
+for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+  if((q %/% 10) == (q / 10)) cat(paste("  iteration: ", q, "\n"))
+  Q  <- grid[q]
+    
+  select_Load <- select.wkdata(wk_Load, i_00:Q)
+  aux_Load  <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+                         EPS = .Machine$double.eps) 
+  pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+    
+  selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+  auxs    <- lapply(selects, 
+                    function(k) wavkerfun(obj = k, 
+                                          kerneltype = "gaussian",
+                                          EPS = .Machine$double.eps))  
+  for(k in 1:K_ICAME) 
+    preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+    
+}
+top <- Sys.time()
+#rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+  
+
+preds <- lapply(preds, function(dd) {dd[dd<0] <- 0; dd} )
+# Compute the prediction of synchrone by the sum of individuals
+pred_Load_sum <- Reduce('+', preds) 
+  
+
+vect_preds <- data.frame(lapply(preds, function(dd) c(t(dd))))
+names(vect_preds) <- paste0("icame", 1:K_ICAME)
+
+vect_Load <- c(t(pred_Load))
+vect_gtop <- matrix(NA_real_, ncol = K_ICAME, nrow = length(vect_Load))
+
+
+for(tt in seq_along(vect_gtop)) {
+  VALID <- !any(is.na(vect_preds[tt, ])) & # compute gtop only when no NAs
+           tt < 17511  # these rows present a problem ==> why? (matbe too much 0s in preds_indiv)
+    if(VALID) { 
+    res <- gtop(preds_indiv   = as.numeric(vect_preds[tt, ]),
+                pred_total    = vect_Load[tt],
+                weights_indiv = rep(1, K_ICAME),
+                weight_total = 1,
+                bounds_indiv = 0.05 * as.numeric(vect_preds[tt, ]))
+    vect_gtop[tt, ] <- res$preds_indiv[1:K_ICAME] # last value is the sum
+    }
+  }
+
+vect_gtop_sum <- rowSums(vect_gtop)
+pred_gtop_sum <- matrix(vect_gtop_sum, ncol = 48, byrow = TRUE)
+
+## 4. Analisis of the predictions  ####
+resids_Load     <- pred_Load     - mat_Load[grid + 1, ] # i.e. reference days + 1
+resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+resids_gtop_sum <- pred_gtop_sum - mat_Load[grid + 1, ]
+
+mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+rmse_Load <- sqrt(rowMeans(resids_Load^2))
+  
+mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+mape_gtop_sum <- 100 * rowMeans(abs(resids_gtop_sum / mat_Load[grid + 1, ]))
+rmse_gtop_sum <- sqrt(rowMeans(resids_gtop_sum^2))
+
+
+mean(mape_Load, na.rm = TRUE)
+mean(mape_Load_sum, na.rm = TRUE)
+mean(mape_gtop_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+  
+
+mean(rmse_Load, na.rm = TRUE)
+mean(rmse_Load_sum, na.rm = TRUE)
+mean(rmse_gtop_sum, na.rm = TRUE)

diff --git a/codes_Jairo_stage2/06_predictions-RANDOM.r b/codes_Jairo_stage2/06_predictions-RANDOM.r
new file mode 100644 (file)
index 0000000..efc315c
--- /dev/null
+++ b/codes_Jairo_stage2/06_predictions-RANDOM.r
@@ -0,0 +1,218 @@
+## File : 06_predictions.r
+## Description : Calculer les synchrones pour chaque groupe 
+##               obtenu par le clustering. 
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates09      <- as.character(dates0[grep("2009", dates0)])
+dates10      <- as.character(dates0[grep("2010", dates0)])
+dates        <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen   <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+                 to   = as.Date("2010/12/31"),
+                 by   = 'day')
+
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt"))
+#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt"))
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))
+#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt"))
+synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))
+synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt"))
+
+
+synchros09 <- t(synchros09) # series must be on rows
+synchros10 <- t(synchros10) # series must be on rows
+
+## 2. Imputation of missing values #### 
+#     (done by linear interpolation)
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518])
+
+
+rm(imput09, imput10, nas)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00      <- which(days == "2009-01-01")
+n_00      <- which(days == "2009-12-31") 
+n_01      <- which(days == "2010-12-31")
+
+
+## 3. Construct predictors ####
+library(kerwavfun)
+synchros <- (cbind( synchros09, synchros10 ))[-201, ]
+
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER')
+clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM')
+
+maxK <- length(clustfactors) - 1
+
+performK <- data.frame(K    = rep(NA_real_, maxK),
+                       mape = rep(NA_real_, maxK),
+                       rmse = rep(NA_real_, maxK))
+
+setwd("~/tmp/")
+for(cf in seq_along(clustfactors)) { 
+  #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt")
+  clustfactor <- read.table(file = clustfactors[cf])
+
+  K <- nrow(unique(clustfactor))
+
+  performK[cf, 1] <- K
+
+  SC <- matrix(0, ncol = ncol(synchros), nrow = K)
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      if(length(clustk) > 1) {
+        SCk <- colSums(synchros[which(clustfactor == k), ])
+      } else {
+        SCk <- synchros[which(clustfactor == k), ]
+      }
+      SC[k, ] <- SC[k, ] + SCk
+      rm(SCk)
+    }
+  }
+
+  mat_Load <- matrix(colSums(SC),   ncol = 48, byrow = TRUE)
+  mat_ks   <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE))
+
+
+  ## 2. Transform to wkdata (performs DWT) ##
+ 
+  wk_Load <- wkdata(X  = c(t(mat_Load)), 
+                    gr = gr[1:nrow(mat_Load)], 
+                    p  = 48)
+
+  wks <- lapply(mat_ks, function(mat_k) 
+          wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+
+  ## 3. Begin of big loop over ##
+  grid <- n_00:(n_01 - 1) # these are the reference days
+  #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+
+  # output matrix of predicted days 
+  # names of the predicted days (reference days + 1)
+  pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+  preds     <- lapply(1:K, function(k) pred_Load)
+
+  go <- Sys.time()
+  for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+    if((q %/% 10) == (q / 10)) cat(paste("  iteration: ", q, "\n"))
+    Q  <- grid[q]
+  
+    select_Load <- select.wkdata(wk_Load, i_00:Q)
+    aux_Load  <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+                           EPS = .Machine$double.eps) 
+    pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+  
+    selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+    auxs    <- lapply(selects, 
+                      function(k) wavkerfun(obj = k, 
+                                          kerneltype = "gaussian",
+                                          EPS = .Machine$double.eps))  
+    for(k in 1:K) 
+      preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+
+  }
+  
+  top <- Sys.time()
+  #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+
+  # Compute the prediction of synchrone by the sum of individuals
+  pred_Load_sum <- Reduce('+', preds) 
+
+  ## 4. Analisis of the predictions  ####
+  resids_Load     <- pred_Load     - mat_Load[grid + 1, ] # i.e. reference days + 1
+  resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+
+  mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+  rmse_Load <- sqrt(rowMeans(resids_Load^2))
+
+  mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+  rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+  performK[cf, 2] <- mean(mape_Load, na.rm = TRUE)
+  performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+
+}
+
+
+performKwer <- performK
+
+#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt")
+#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt',
+#            performK)
+#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200RANDOM.txt',
+#            performK)
+
+#performK <- data.frame(K   = performKrc$K,
+#                       rc  = performKrc$rmse,
+#                       wer = performKwer$rmse)
+
+
+setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/res/")
+performKWER <- read.table("perform200WER.txt")
+performKRND <- read.table("perform200RANDOM.txt")
+performKrnd <- read.table("perform200-random.txt")
+
+performKWER <- performKWER[order(performKWER$K), ]
+performKRND <- performKRND[order(performKRND$K), ]
+performKrnd <- performKrnd[order(performKrnd$K), ]
+
+perform <- data.frame(k       = performKRND$K,
+                      k_check = performKWER$K,
+                      random1 = performKrnd$rmse,
+                      random2 = performKRND$rmse,
+                      wer     = performKWER$rmse,
+                      agg     = performKWER$mape)
+
+pdf("~/perf.pdf", width = 10)
+plot(perform$k, perform$wer, type = 'l', ylim = c(1.7,2.2),
+     lwd = 2, col = "darkgrey", xlab = "Nb Cluster", 
+     ylab = "MAPE (in  %)")
+lines(perform$k, perform$random2, lty = 2, lwd = 2)
+lines(perform$k, perform$random1, lty = 3, lwd = 2)
+abline(h = mean(perform$agg), lwd = 2)
+legend("topright", c('AGG', "Random > WER", "RC > Random", "RC > WER"), 
+       lwd = 2, lty = c(1, 3, 2, 1),
+       col = c(1, 1, 1, "darkgrey"), bg = "white")
+dev.off()
+
+
+
+
+

diff --git a/codes_Jairo_stage2/06_predictions-par.r b/codes_Jairo_stage2/06_predictions-par.r
new file mode 100644 (file)
index 0000000..4df8d27
--- /dev/null
+++ b/codes_Jairo_stage2/06_predictions-par.r
@@ -0,0 +1,190 @@
+## File : 06_predictions.r
+## Description : Calculer les synchrones pour chaque groupe 
+##               obtenu par le clustering. 
+
+rm(list = ls())
+
+if(Sys.info()[4] ==  "mojarrita"){ 
+  setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+  setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates09      <- as.character(dates0[grep("2009", dates0)])
+dates10      <- as.character(dates0[grep("2010", dates0)])
+dates        <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen   <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+                 to   = as.Date("2010/12/31"),
+                 by   = 'day')
+
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt"))
+#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt"))
+synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))
+synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt"))
+
+synchros09 <- t(synchros09) # series must be on rows
+synchros10 <- t(synchros10) # series must be on rows
+
+## 2. Imputation of missing values #### 
+#     (done by linear interpolation)
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518])
+
+
+rm(imput09, imput10, nas)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00      <- which(days == "2009-01-01")
+n_00      <- which(days == "2009-12-31") 
+n_01      <- which(days == "2010-12-31")
+
+
+## 3. Construct predictors ####
+library(kerwavfun)
+synchros <- (cbind( synchros09, synchros10 ))[-201, ]
+
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC')
+clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER')
+
+maxK <- length(clustfactors) - 1
+
+performK <- data.frame(K    = rep(NA_real_, maxK),
+                       mape = rep(NA_real_, maxK),
+                       rmse = rep(NA_real_, maxK))
+
+setwd("~/tmp/")
+for(cf in seq_along(clustfactors)) { 
+  #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt")
+  clustfactor <- read.table(file = clustfactors[cf])
+
+  K <- nrow(unique(clustfactor))
+
+  performK[cf, 1] <- K
+
+  SC <- matrix(0, ncol = ncol(synchros), nrow = K)
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      if(length(clustk) > 1) {
+        SCk <- colSums(synchros[which(clustfactor == k), ])
+      } else {
+        SCk <- synchros[which(clustfactor == k), ]
+      }
+      SC[k, ] <- SC[k, ] + SCk
+      rm(SCk)
+    }
+  }
+
+  mat_Load <- matrix(colSums(SC),   ncol = 48, byrow = TRUE)
+  mat_ks   <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE))
+
+
+  ## 2. Transform to wkdata (performs DWT) ##
+ 
+  wk_Load <- wkdata(X  = c(t(mat_Load)), 
+                    gr = gr[1:nrow(mat_Load)], 
+                    p  = 48)
+
+  wks <- lapply(mat_ks, function(mat_k) 
+          wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+
+  ## 3. Begin of big loop over ##
+  grid <- n_00:(n_01 - 1) # these are the reference days
+  #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+
+  # output matrix of predicted days 
+  # names of the predicted days (reference days + 1)
+  pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+  preds     <- lapply(1:K, function(k) pred_Load)
+
+  go <- Sys.time()
+  for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+    if((q %/% 10) == (q / 10)) cat(paste("  iteration: ", q, "\n"))
+    Q  <- grid[q]
+  
+    select_Load <- select.wkdata(wk_Load, i_00:Q)
+    aux_Load  <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+                           EPS = .Machine$double.eps) 
+    pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+  
+    selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+    auxs    <- lapply(selects, 
+                      function(k) wavkerfun(obj = k, 
+                                          kerneltype = "gaussian",
+                                          EPS = .Machine$double.eps))  
+    for(k in 1:K) 
+      preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+
+  }
+  
+  top <- Sys.time()
+  #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+
+  # Compute the prediction of synchrone by the sum of individuals
+  pred_Load_sum <- Reduce('+', preds) 
+
+  ## 4. Analisis of the predictions  ####
+  resids_Load     <- pred_Load     - mat_Load[grid + 1, ] # i.e. reference days + 1
+  resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+
+  mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+  rmse_Load <- sqrt(rowMeans(resids_Load^2))
+
+  mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+  rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+  performK[cf, 2] <- mean(mape_Load, na.rm = TRUE)
+  performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+
+}
+
+
+performKwer <- performK
+
+#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt")
+#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt',
+#            performK)
+write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt',
+            performK)
+
+#performK <- data.frame(K   = performKrc$K,
+#                       rc  = performKrc$rmse,
+#                       wer = performKwer$rmse)
+
+
+performK <- performK[order(performK$K), ]
+
+
+#plot(performK$K, performK$rc, type = 'l', ylim = c(1.7,2.2),
+#     lwd = 2, col = 2)
+#lines(performK$K, performK$wer, col = 3, lwd = 2)
+#abline(h = mean(performKrc$mape), lwd = 2)
+#legend("topright", c('Sync 32K', 'Cl. RC', "Cl. WER"), lwd = 2, col = 1:3)

diff --git a/codes_Jairo_stage2/06_predictions.r b/codes_Jairo_stage2/06_predictions.r
new file mode 100644 (file)
index 0000000..6697161
--- /dev/null
+++ b/codes_Jairo_stage2/06_predictions.r
@@ -0,0 +1,204 @@
+## File : 06_predictions.r
+## Description : Calculer les synchrones pour chaque groupe 
+##               obtenu par le clustering. 
+
+rm(list = ls())
+
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+
+
+## 1. Read auxiliar data files ####
+
+identifiants <- read.table("identifs.txt")[ ,1]
+dates0       <- read.table("datesall.txt")[, 1]
+dates09      <- as.character(dates0[grep("2009", dates0)])
+dates10      <- as.character(dates0[grep("2010", dates0)])
+dates        <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen   <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+                 to   = as.Date("2010/12/31"),
+                 by   = 'day')
+
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt"))
+#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt"))
+#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))
+#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt"))
+synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200-random.txt"))
+synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200-random.txt"))
+
+
+synchros09 <- t(synchros09) # series must be on rows
+synchros10 <- t(synchros10) # series must be on rows
+
+## 2. Imputation of missing values #### 
+#     (done by linear interpolation)
+nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing
+synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4])
+
+imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2)
+
+synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518])
+synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518])
+
+
+rm(imput09, imput10, nas)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00      <- which(days == "2009-01-01")
+n_00      <- which(days == "2009-12-31") 
+n_01      <- which(days == "2010-12-31")
+
+
+## 3. Construct predictors ####
+library(kerwavfun)
+synchros <- (cbind( synchros09, synchros10 ))[-201, ]
+
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER')
+#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM')
+clustfactors <- dir("~/tmp/", pattern = 'clustfactor-randomWER')
+
+maxK <- length(clustfactors) - 1
+
+performK <- data.frame(K    = rep(NA_real_, maxK),
+                       mape = rep(NA_real_, maxK),
+                       rmse = rep(NA_real_, maxK))
+
+setwd("~/tmp/")
+for(cf in seq_along(clustfactors)) { 
+#  clustfactor <- read.table(file = "~/tmp/clustfactor3.txt")
+  clustfactor <- read.table(file = clustfactors[cf])
+
+  K <- nrow(unique(clustfactor))
+
+  performK[cf, 1] <- K
+
+  SC <- matrix(0, ncol = ncol(synchros), nrow = K)
+  for(k in 1:K){ 
+    clustk <- which(clustfactor == k)
+    if(length(clustk) > 0) {
+      if(length(clustk) > 1) {
+        SCk <- colSums(synchros[which(clustfactor == k), ])
+      } else {
+        SCk <- synchros[which(clustfactor == k), ]
+      }
+      SC[k, ] <- SC[k, ] + SCk
+      rm(SCk)
+    }
+  }
+
+  mat_Load <- matrix(colSums(SC),   ncol = 48, byrow = TRUE)
+  mat_ks   <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE))
+
+
+  ## 2. Transform to wkdata (performs DWT) ##
+ 
+  wk_Load <- wkdata(X  = c(t(mat_Load)), 
+                    gr = gr[1:nrow(mat_Load)], 
+                    p  = 48)
+
+  wks <- lapply(mat_ks, function(mat_k) 
+          wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+
+  ## 3. Begin of big loop over ##
+  grid <- n_00:(n_01 - 1) # these are the reference days
+  #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+
+  # output matrix of predicted days 
+  # names of the predicted days (reference days + 1)
+  pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+  preds     <- lapply(1:K, function(k) pred_Load)
+
+  go <- Sys.time()
+  for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+    if((q %/% 50) == (q / 50)) cat(paste("  iteration: ", q, "\n"))
+    Q  <- grid[q]
+  
+    select_Load <- select.wkdata(wk_Load, i_00:Q)
+    aux_Load  <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+                           EPS = .Machine$double.eps) 
+    pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+  
+    selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+    auxs    <- lapply(selects, 
+                      function(k) wavkerfun(obj = k, 
+                                          kerneltype = "gaussian",
+                                          EPS = .Machine$double.eps))  
+    for(k in 1:K) 
+      preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+
+  }
+  
+  top <- Sys.time()
+  #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+
+  # Compute the prediction of synchrone by the sum of individuals
+  pred_Load_sum <- Reduce('+', preds) 
+
+  ## 4. Analisis of the predictions  ####
+  resids_Load     <- pred_Load     - mat_Load[grid + 1, ] # i.e. reference days + 1
+  resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+
+  mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+  rmse_Load <- sqrt(rowMeans(resids_Load^2))
+
+  mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+  rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+  performK[cf, 2] <- mean(mape_Load, na.rm = TRUE)
+  performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+
+}
+
+
+#performKrc <- read.table("../res/perform200RC.txt")
+#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200-random.txt',
+#            performK)
+#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt',
+#            performK)
+#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt',
+#            performK)
+
+#performK <- data.frame(K   = performKrc$K,
+#                       rc  = performKrc$rmse,
+#                       wer = performKwer$rmse)
+
+#perform <- rbind(performK, performKwer)
+#perform <- perform[order(perform$K), ]
+
+plot(perform$K, perform$rmse, type = 'l', ylim = c(1.7,2.2),
+     lwd = 2, col = 2, xlab = "Nb Cluster", ylab = "MAPE (en  %)")
+lines(performK$K, performKrc$rmse, col = 3, lwd = 2)
+abline(h = mean(performK$mape), lwd = 2)
+legend("topright", c('AGG', 'DIS euclid', "DIS WER"), lwd = 2, col = 1:3)
+
+
+## Plot article ENERGYCON
+performKwer <- read.table("../res/perform200WER.txt")
+performKwer <- performKwer[order(performKwer$K), ]
+
+pdf("~/perf_wer.pdf")
+plot(performKwer$K, performKwer$rmse, type = 'l', 
+     ylim = c(1.7,2.2), lwd = 2, col = 2, 
+     xlab = "Nb Cluster", ylab = "MAPE (en  %)")
+abline(h = mean(performKwer$mape), lwd = 2)
+legend("topright", c('AGG', "DIS WER"), lwd = 2, col = 1:2)
+dev.off()

diff --git a/codes_Jairo_stage2/analysis-SME.r b/codes_Jairo_stage2/analysis-SME.r
new file mode 100644 (file)
index 0000000..89668fa
--- /dev/null
+++ b/codes_Jairo_stage2/analysis-SME.r
@@ -0,0 +1,104 @@
+## File : ireland-data.r
+## Description : 
+
+rm(list = ls())
+setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
+library(Rwave)       # CWT
+library(cluster)     # pam
+
+## 1. Read auxiliar data files ####
+source("aux.r")               # auxiliary clustering functions 
+source("sowas-superseded.r")  # auxiliary CWT functions
+load("~/data/Irlande/Data_CER_clean/SME.RData")
+SME <- as.matrix(SME)
+
+nbdays   <- nrow(SME) / 48
+nb_clust <- nbdays - 365 # last year to forecast
+
+id_clust <- 1:(48 * nb_clust)
+
+## 2. Compute WER distance matrix ####
+conso  <- t(SME[id_clust, ]) # ts are in lines
+N      <- delta <- ncol(conso) # length of one ts
+n      <- nrow(conso) # number of ts
+
+# # _.a CWT -- Filtering the lowest freqs (>6m) ####
+# nvoice   <- 4
+# # noctave4 = 2^12 = 4096 half hours ~ 90 days
+# noctave4 <- adjust.noctave(N = N, 
+#                            dt = 1, s0 = 2,
+#                            tw = 0, noctave = 12)
+# # 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
+# Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
+#                 scalevector = scalevector4,
+#                 lt = N, smooth = FALSE, 
+#                 nvoice = nvoice)      # observations node with CWT
+# 
+# 
+# Xcwt2 <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(Xcwt4[,,1]))))
+# 
+# for(i in 1:n) 
+#  Xcwt2[i,] <- c(N, 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(N * lscvect * (1 - wer2))
+#     Xwer_dist[j, i] <- Xwer_dist[i, j]
+#   }
+# }
+# diag(Xwer_dist) <- numeric(n)
+# 
+# save(Xwer_dist, file = "~/werdist-irlanda.Rdata")
+
+load("~/werdist-irlanda.Rdata")
+hc <- hclust(as.dist(Xwer_dist))
+
+plot(hc)
+
+
+Ks <- c(2:10, 15, 20, 25, 30)
+for(k in seq_along(Ks)){ 
+  K      <- Ks[k]
+  pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE)
+  
+  fname <- paste0("clustfactor", K)
+  write.table(pamfit$clustering, 
+              file = paste0("~/tmp/clustfactorSME-", K, ".txt")) 
+}
+
+
+#dir(path = '~/tmp/', pattern = 'clustfac-
+K <- 2
+fname <- paste0("~/tmp/clustfactorSME-", K, ".txt")
+clustfactor <- read.table(fname)$x # pamfit$clustering
+ for(k in 1:K){ 
+   clustk <- which(clustfactor == k)
+ if(length(clustk) > 0) {
+   if(length(clustk) > 1) {
+     SCk <- colSums(SME[, which(clustfactor == k)])
+     } else {
+       SCk <- synchros09[which(clustfactor == k), ]
+     }
+     SC[k, ] <- SC[k, ] + SCk
+     rm(SCk)
+ }
+ }
+
+

diff --git a/codes_Jairo_stage2/aux.r b/codes_Jairo_stage2/aux.r
new file mode 100644 (file)
index 0000000..bb9077b
--- /dev/null
+++ b/codes_Jairo_stage2/aux.r
@@ -0,0 +1,126 @@
+####################################################################
+##
+## File:        aux.r
+##
+## Description: Miscelaneous functions for clustering with kcca
+##
+## Modified:    june 2010
+##
+####################################################################
+
+
+  #######################################################
+
+  # Transforms a matrix of data (one observation by row)
+  #     into an array where position[ , , i] gives
+  #     the smoothed modulus of the i-th cwt observation 
+
+  ########################################################
+
+
+  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,
+                              ... )
+
+

diff --git a/codes_Jairo_stage2/aux2.r b/codes_Jairo_stage2/aux2.r
new file mode 100644 (file)
index 0000000..d7bdb79
--- /dev/null
+++ b/codes_Jairo_stage2/aux2.r
@@ -0,0 +1,25 @@
+# File: aux2.r
+
+dayimpact <- function(vec) {
+  num <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) 
+  den <- tapply(vec, rep(1:365, each = 48), max, na.rm = TRUE) 
+  return(num / den)
+}
+
+nightimpact <- function(vec) { 
+  idnuits <- logical(48)
+  idnuits[c(1:14, 47:48)] <- TRUE
+  nuits <-  vec[rep(idnuits, 365)] 
+  num <- tapply(nuits, rep(1:365, each = sum(idnuits)), mean, na.rm = TRUE)
+  den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) 
+  return(num / den / 3)
+}
+
+lunchimpact <- function(vec) { 
+  idlunchs <- logical(48)
+  idlunchs[25:30] <- TRUE
+  lunchs <- vec[rep(idlunchs, 365)]
+  num <- tapply(lunchs, rep(1:365, each = sum(idlunchs)), mean, na.rm = TRUE)
+  den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) 
+  return(num / den / 8)
+}

diff --git a/codes_Jairo_stage2/calendar_ir.txt b/codes_Jairo_stage2/calendar_ir.txt
new file mode 100644 (file)
index 0000000..ed312aa
--- /dev/null
+++ b/codes_Jairo_stage2/calendar_ir.txt
@@ -0,0 +1,533 @@
+       dow     daytype gr
+2011-01-01     Sat     Hol     Hol_Sun
+2011-01-02     Sun     Sun     Sun_Mon
+2011-01-03     Mon     Mon     Mon_Tue
+2011-01-04     Tue     Tue     Tue_Wed
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diff --git a/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt b/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt
new file mode 100644 (file)
index 0000000..5d118ba
--- /dev/null
+++ b/codes_Jairo_stage2/calendar_transition_groups-1996-2011.txt
@@ -0,0 +1,5845 @@
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diff --git a/codes_Jairo_stage2/datesall.txt b/codes_Jairo_stage2/datesall.txt
new file mode 100644 (file)
index 0000000..e4622a9
--- /dev/null
+++ b/codes_Jairo_stage2/datesall.txt
@@ -0,0 +1,43721 @@
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diff --git a/codes_Jairo_stage2/descriptive-32K.r b/codes_Jairo_stage2/descriptive-32K.r
new file mode 100644 (file)
index 0000000..5e1b29b
--- /dev/null
+++ b/codes_Jairo_stage2/descriptive-32K.r
@@ -0,0 +1,81 @@
+# File: descriptive-32K.r
+
+rm(list = ls())
+#library(dplyr)
+
+## Following lines allow to construct the data file
+## totconso-par-indiv.txt. 
+## They are very time&memory-consumming 
+#library(data.table)  
+#d10 <- fread("~/tmp/data/2010.csv")
+#tot <- d10[, list(tot= sum(CPP_PUISSANCE_BRUTE), 
+#                  min= min(CPP_PUISSANCE_BRUTE), 
+#                  max= max(CPP_PUISSANCE_BRUTE), 
+#                  sd = sd( CPP_PUISSANCE_BRUTE)), 
+#                  by = FK_CCU_ID]
+# write.table(file = "~/tmp/data/totconso-par-indiv.txt", tot)
+
+tot     <- read.table("~/tmp/data/totconso-par-indiv.txt")
+synchro <- read.table("~/tmp/data/syncrone2010.txt")
+mat_synchro <- matrix(c(synchro$V1[1:2], synchro$V1) / 1e6, 
+                      ncol = 48,
+                      byrow = TRUE)
+
+xHour <- seq(1/2, 24, length.out = 48)
+xDate <- seq.Date(from = as.Date("2010/1/1"),
+                  to   = as.Date("2011/1/1"),
+                  length.out = 365 * 48 - 2)  
+
+# Filter infra daily
+plot(xDate, filter(synchro$V1 / 1e6, rep(1 / 48, 48)), 
+     type = 'l', 
+     main = 'Synchrone 2010',
+     ylab = 'Charge (en Mwh)',
+     xlab = 'Time')
+
+# Daily
+matplot(xHour, scale(t(mat_synchro)), type = 'l', lty = 1, 
+        col  = rgb(0, 0, 0, .25),
+        main = 'Synchrone jounalière (norm.)',
+        xlab = 'Heures', ylab = 'Charge (en Mwh)')
+
+
+tot_order <- tot[order(tot$tot), ]
+max_order <- tot[order(tot$max), ]
+
+## Mesures aberrantes ?
+tail(cbind(max_order[,1], max_order[,-1] / 1e3))
+
+
+## I want to recuperate ids: 186733, 191819, 193174
+## which corresponds to places 1, 2481, 3478
+# identiants <- read.table('~/tmp/data/identifs.txt')
+# match(c(186733, 191819, 193174), identiants$V1)
+
+#c186733 <- d10[, FK_CCU_ID == 186733]
+#c191819 <- d10[, FK_CCU_ID == 191819]
+#c193174 <- d10[, FK_CCU_ID == 193174]
+
+c186733 <- read.csv("~/tmp/data/c186733.csv")[, 4] / 1e3
+
+mat_186733 <- matrix(c(c186733[1:2], c186733), 
+                      ncol = 48,
+                      byrow = TRUE)
+
+# Filter infra daily
+plot(xDate, filter(c186733, rep(1 / 48, 48)), 
+     type = 'l', 
+     main = "Consommation individuelle (2010)",
+     ylab = 'Charge (en Mwh)',
+     xlab = 'Temps')
+
+# Daily (raw)
+matplot(xHour, t(mat_186733), type = 'l', lty = 1, 
+        col  = rgb(0, 0, 0, .25),
+        #main = "Consommation individuelle journalière "
+        xlab = 'Heures', ylab = 'Charge (en Mwh)')
+
+# Daily (standarized)
+matplot(xHour, scale(t(mat_186733)), type = 'l', lty = 1, 
+        col  = rgb(0, 0, 0, .25),
+        xlab = 'Heures', ylab = 'Charge (en Mwh)')

diff --git a/codes_Jairo_stage2/effectives-per-classe.r b/codes_Jairo_stage2/effectives-per-classe.r
new file mode 100644 (file)
index 0000000..a63a8fb
--- /dev/null
+++ b/codes_Jairo_stage2/effectives-per-classe.r
@@ -0,0 +1,6 @@
+# compute the effectives per class
+
+load("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata")
+clustfactor <- read.table("~/tmp/clustfactorWER15.txt")
+
+aggregate(c(table(res[,1])), by = list(clustfactor$x), FUN = sum)

diff --git a/codes_Jairo_stage2/identifs.txt b/codes_Jairo_stage2/identifs.txt
new file mode 100644 (file)
index 0000000..6526f54
--- /dev/null
+++ b/codes_Jairo_stage2/identifs.txt
@@ -0,0 +1,25011 @@
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diff --git a/codes_Jairo_stage2/old/export2csv.sh b/codes_Jairo_stage2/old/export2csv.sh
new file mode 100644 (file)
index 0000000..39ef399
--- /dev/null
+++ b/codes_Jairo_stage2/old/export2csv.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+sqlite3 indiv32000.db <<!
+.headers on
+.mode csv
+.output out.csv
+SELECT * FROM Conso2 LIMIT 15;
+!
+

diff --git a/codes_Jairo_stage2/sowas-superseded.r b/codes_Jairo_stage2/sowas-superseded.r
new file mode 100644 (file)
index 0000000..dca3353
--- /dev/null
+++ b/codes_Jairo_stage2/sowas-superseded.r
@@ -0,0 +1,1563 @@
+###########################################################
+# CONTINUOUS WAVELET TRANSFORMATION OF A TIME SERIES OBJECT
+###########################################################
+
+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
+  
+}