old_C_code/stage2_UNFINISHED/src/unused/descriptive-32K.r

   1 # File: descriptive-32K.r
   2
   3 rm(list = ls())
   4 #library(dplyr)
   5
   6 ## Following lines allow to construct the data file
   7 ## totconso-par-indiv.txt.
   8 ## They are very time&memory-consumming
   9 #library(data.table)
  10 #d10 <- fread("~/tmp/data/2010.csv")
  11 #tot <- d10[, list(tot= sum(CPP_PUISSANCE_BRUTE),
  12 #                  min= min(CPP_PUISSANCE_BRUTE),
  13 #                  max= max(CPP_PUISSANCE_BRUTE),
  14 #                  sd = sd( CPP_PUISSANCE_BRUTE)),
  15 #                  by = FK_CCU_ID]
  16 # write.table(file = "~/tmp/data/totconso-par-indiv.txt", tot)
  17
  18 tot     <- read.table("~/tmp/data/totconso-par-indiv.txt")
  19 synchro <- read.table("~/tmp/data/syncrone2010.txt")
  20 mat_synchro <- matrix(c(synchro$V1[1:2], synchro$V1) / 1e6,
  21                       ncol = 48,
  22                       byrow = TRUE)
  23
  24 xHour <- seq(1/2, 24, length.out = 48)
  25 xDate <- seq.Date(from = as.Date("2010/1/1"),
  26                   to   = as.Date("2011/1/1"),
  27                   length.out = 365 * 48 - 2)
  28
  29 # Filter infra daily
  30 plot(xDate, filter(synchro$V1 / 1e6, rep(1 / 48, 48)),
  31      type = 'l',
  32      main = 'Synchrone 2010',
  33      ylab = 'Charge (en Mwh)',
  34      xlab = 'Time')
  35
  36 # Daily
  37 matplot(xHour, scale(t(mat_synchro)), type = 'l', lty = 1,
  38         col  = rgb(0, 0, 0, .25),
  39         main = 'Synchrone jounalière (norm.)',
  40         xlab = 'Heures', ylab = 'Charge (en Mwh)')
  41
  42
  43 tot_order <- tot[order(tot$tot), ]
  44 max_order <- tot[order(tot$max), ]
  45
  46 ## Mesures aberrantes ?
  47 tail(cbind(max_order[,1], max_order[,-1] / 1e3))
  48
  49
  50 ## I want to recuperate ids: 186733, 191819, 193174
  51 ## which corresponds to places 1, 2481, 3478
  52 # identiants <- read.table('~/tmp/data/identifs.txt')
  53 # match(c(186733, 191819, 193174), identiants$V1)
  54
  55 #c186733 <- d10[, FK_CCU_ID == 186733]
  56 #c191819 <- d10[, FK_CCU_ID == 191819]
  57 #c193174 <- d10[, FK_CCU_ID == 193174]
  58
  59 c186733 <- read.csv("~/tmp/data/c186733.csv")[, 4] / 1e3
  60
  61 mat_186733 <- matrix(c(c186733[1:2], c186733),
  62                       ncol = 48,
  63                       byrow = TRUE)
  64
  65 # Filter infra daily
  66 plot(xDate, filter(c186733, rep(1 / 48, 48)),
  67      type = 'l',
  68      main = "Consommation individuelle (2010)",
  69      ylab = 'Charge (en Mwh)',
  70      xlab = 'Temps')
  71
  72 # Daily (raw)
  73 matplot(xHour, t(mat_186733), type = 'l', lty = 1,
  74         col  = rgb(0, 0, 0, .25),
  75         #main = "Consommation individuelle journalière "
  76         xlab = 'Heures', ylab = 'Charge (en Mwh)')
  77
  78 # Daily (standarized)
  79 matplot(xHour, scale(t(mat_186733)), type = 'l', lty = 1,
  80         col  = rgb(0, 0, 0, .25),
  81         xlab = 'Heures', ylab = 'Charge (en Mwh)')