old_C_code/stage2_UNFINISHED/src/01_extract-features_2009.r

   1 ## File : 01_extract-features_2009.r
   2 ## Description : Using the full data matrix, we extract handy features to
   3 ##               cluster.
   4
   5 rm(list = ls())
   6
   7 source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r')
   8 setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/")
   9
  10 ## 1. Read auxiliar data files ####
  11
  12 identifiants <- read.table("identifs.txt")[ ,1]
  13 dates0       <- read.table("datesall.txt")[, 1]
  14 dates        <- dates0[grep("2009", dates0)]
  15 rm(dates0)
  16
  17 n <- length(identifiants)
  18 p <- length(dates)
  19
  20 blocks <- c(rep(6500, 3), 5511)
  21
  22 # table( substr(dates, 11, 15) ) # Sunlight time saving produces an
  23                                  # unbalanced number of time points
  24                                  # per time stepa across the year
  25
  26
  27 ## 2. Process the large file ####
  28
  29 close(con)
  30 con <- file("~/tmp/2009_full.txt")  # Establish a connection to the file
  31 open(con, "r")                      # Open the connection
  32
  33 for(b in seq_along(blocks)){        # Reading loop
  34   nb <- blocks[b]
  35   actual <- readLines(con = con, n = nb )
  36   auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE)
  37   rm(actual)
  38
  39   datamat <- t(apply(auxmat[, -1], 1, as.numeric))
  40   rownames(datamat) <- substr(auxmat[, 1], 2, 7)
  41   rm(auxmat)
  42
  43   auxDWT <- t(apply(datamat, 1, toDWT))
  44   auxcontrib <- t(apply(auxDWT,  1, contrib))
  45   rm(auxDWT)
  46
  47   if(b == 1) {
  48     matcontrib <- auxcontrib
  49   } else {
  50     matcontrib <- rbind(matcontrib, auxcontrib)
  51   }
  52
  53 }
  54
  55 close(con)                      # close connection to the file
  56
  57 write.table(matcontrib, file = "~/tmp/2009_contrib.txt")
  58