-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathsourceHOBO.R
More file actions
175 lines (146 loc) · 9.12 KB
/
Copy pathsourceHOBO.R
File metadata and controls
175 lines (146 loc) · 9.12 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
#'########################################################################################
#' #'
#' #'
#' Codes para processamento de dados meteorologicos a partir da HOBOWARE #'
#' Autor: Germano #'
#' Versao: 2.0 (Agosto de 2019, versao anterior de Abril de 2019) #'
#' #'
#' Dados oriundos do processamento dos files binarios: #'
#' Anhumas_end.dtf e usp_end.dtf #'
#' #'
#' #'
#'########################################################################################'
#---------------------- Source Codes ----------------------------------------------------
#' Funcao para leitura e processamento inicial de arquivos csv exportados do HOBOWARE
read.HOBO = function(file, #' nome do arquivo (ex: dados.csv)
sep =",", #' dados com colunas separadas por ,
skip = 1, #' pula a primeira linha que tem informacoes
row.names = 1, #' ignora a 1 coluna como rowname
pattern = "H", #' padrao para cortar a coluna de hora (pode ser h tb)
id.vars = NULL, #' nome das colunas de variaveis climaticas e de solo
path = NULL, #' diretorio onde estao os arquivos .csv
daynight = TRUE, #' TRUE ou FALSE se ira criar coluna separando dia da noite
save = FALSE, #' opcao que define se ira ou nao salvar o output
start = NULL, #' inicio da janela (ex: 02-03-2019, para 2 de marco de 2019)
end = NULL, #' fim da janela (idem ao start)
formatDay = "%d/%m/%y" # formato de entrada do dia
){
#' -------------------------------------------------------------------------------------
#' Pacotes necessarios
require(reshape2);
require(plyr);
#' -------------------------------------------------------------------------------------
#' Organizando diretorios de saida
if(is.null(path)){path=getwd()};
DF = read.csv(as.character(paste0(path,"/",file)),
sep=sep,skip=skip,row.names = row.names);
#' -------------------------------------------------------------------------------------
#' Organizando colunas de data e hora
DF = data.frame(colsplit(as.character(DF[,2]),pattern,c("Hora","Minutos")),DF[,-2]);
DF$Data = as.Date(as.POSIXlt(as.character(DF$Data), format=formatDay))
print("Converting date to year-month-day as R default")
#' -------------------------------------------------------------------------------------
#' Renomendo variaveis (caso seja necessario)
if(!is.null(id.vars)){
colnames(DF)[-c(1:3)] = var.id; # obs: estou ignorando as 3 colunas iniciais (data etc)
}
#' -------------------------------------------------------------------------------------
#' Criando coluna que separa dia de noite
if(isTRUE(daynight)){
print("daynight assigned as TRUE: separating day from night");
DF$DayTime = "day"
DF$DayTime[DF$Hora %in% c(seq(from=19, to=23,by=1),seq(from=0, to=6,by=1))] = "night"
}
#' -------------------------------------------------------------------------------------
#' Ajustando a janela de coleta de dados (vai ignorar caso nao tenha especificado)
if(!is.null(start)){
start = as.Date(as.POSIXlt(as.character(start), format="%d/%m/%y"))
DF = DF[DF$Data >=start,]
}
if(!is.null(end)){
end = as.Date(as.POSIXlt(as.character(end), format="%d/%m/%y"))
DF = DF[DF$Data <= end,]
}
if(isTRUE(save)){
output = as.character(paste0(gsub(x = file,pattern = ".csv",replacement = ""),"_processed.csv"))
write.csv(x = DF,file = output,row.names = F)
print(paste0("save assigned as TRUE: saving processed output file at ",dir))
}
#' -------------------------------------------------------------------------------------
#' Informacao sobre output
print(paste0("Returning a data.frame with ",nrow(DF)," rows and ",ncol(DF)," columns"))
return(DF)
#' -------------------------------------------------------------------------------------
}
# funcao para processamento inicial dos dados em escala diaria
process.HOBO = function(dataset,
target=FALSE, #' TRUE usa so os targets definidos na collect
var.id,
collect = c(6,9,12,15),#' horario das coletas para media diaria
nightConsider = TRUE #' considera a noite toda para coleta de dados (coloque FALSE)
){
dataset$Collect = NA
dataset$Collect[dataset$Hora %in% collect] = "TARGET"
if(isTRUE(nightConsider)){
dataset$Collect[dataset$DayTime %in% "night"] = "TARGET"}
t = melt(dataset,id.vars=colnames(dataset)[!names(dataset) %in% var.id]);
if(isTRUE(target)){t=t[t$Collect %in% "TARGET",]}
t = dlply(ddply(t,.(DayTime,Data,variable),summarise,
min.value = quantile(value,na.rm=T)[1],
quant25 = quantile(value,na.rm=T)[2],
median.value = median(value,na.rm=T),
quant75 = quantile(value,na.rm=T)[4],
max.value = quantile(value,na.rm=T)[5],
mean.value = mean(value,na.rm=T),
sd.value = sd(value,na.rm=T),
cv.value = round(sd.value/mean.value,4),
soma.value=sum(value,na.rm=T)),.(DayTime))
return(t)
}
# funcao para gerar arquivos para exportacao
weatherReady = function(dataset,stationID="station"){
p2=dcast(dataset, DayTime+Data~variable, value.var = "soma.value")
head(p2)
p2 = p2[,c(1:3,6)]
p2=rbind(p2,data.frame(DayTime="all",ddply(p2,.(Data),summarise, PAR = sum(PAR),RAIN=sum(RAIN))))
names(p2)[-c(1:2)] = c("PAR_cum","RAIN_cum")
p3 = dcast(dataset, DayTime+Data~variable, value.var = "mean.value")
p3 = p3[,c(1:5,7:9)]
colnames(p3)[-c(1:2)] = c("PAR_mean","ST_mean","SWC_mean","T_mean","RH_mean","DEW_mean");
p3=rbind(p3,data.frame(DayTime="all",ddply(p3,.(Data),summarise,
PAR_mean = mean(PAR_mean,na.rm=T),
ST_mean = mean(ST_mean,na.rm=T),
SWC_mean = mean(SWC_mean,na.rm=T),
T_mean = mean(T_mean,na.rm=T),
RH_mean = mean(RH_mean,na.rm=T),
DEW_mean = mean(DEW_mean,na.rm=T))));
p3$SRad = p3$PAR_mean/(2.1*4.6*10) # convering to MJm2
p4 = dcast(dataset, DayTime+Data~variable, value.var = "min.value")
p4 = p4[,c(1,2,4:5,7:9)]
colnames(p4)[-c(1:2)] = c("ST_min","SWC_min","T_min","RH_min","DEW_min")
p4=rbind(p4,data.frame(DayTime="all",ddply(p4,.(Data),summarise,
ST_min = min(ST_min ,na.rm=T),
SWC_min = min(SWC_min ,na.rm=T),
T_min = min(T_min ,na.rm=T),
RH_min = min(RH_min ,na.rm=T),
DEW_min = min(DEW_min,na.rm=T))))
p5 = dcast(dataset, DayTime+Data~variable, value.var = "max.value")
p5 = p5[,c(1,2,4:5,7:9)]
colnames(p5)[-c(1:2)] = c("ST_max","SWC_max","T_max","RH_max","DEW_max")
p5=rbind(p5,data.frame(DayTime="all",ddply(p5,.(Data),summarise,
ST_max = max(ST_max ,na.rm=T),
SWC_max = max(SWC_max ,na.rm=T),
T_max = max(T_max ,na.rm=T),
RH_max = max(RH_max ,na.rm=T),
DEW_max = max(DEW_max,na.rm=T))))
final = merge(merge(merge(p2,p3,by=c("DayTime","Data")),
p4,by=c("DayTime","Data")),p5,by=c("DayTime","Data"))
rm(p2,p3,p4,p5)
final$stationID = stationID
return(dlply(final,.(DayTime),mutate,final))
}
#'---------------------------------------------------------------------------------------#'
#' Abaixo segue um passo-a-passo para processar os dados #'
#' Duvidas procure o autor ou atual responsavel #'
#'---------------------------------------------------------------------------------------#'
#' STEP BY STEP