throw away old code, prepare tests

[epclust.git] / old_C_code / stage2_UNFINISHED / src / unused / descriptive-32K.r

diff --git a/old_C_code/stage2_UNFINISHED/src/unused/descriptive-32K.r b/old_C_code/stage2_UNFINISHED/src/unused/descriptive-32K.r
deleted file mode 100644 (file)
index 5e1b29b..0000000
--- a/old_C_code/stage2_UNFINISHED/src/unused/descriptive-32K.r
+++ /dev/null
@@ -1,81 +0,0 @@
-# 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)')