Commit | Line | Data |
---|---|---|
ad642dc6 BA |
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)') |