diff --git a/plotting/layout.R b/plotting/layout.R
index a7ab458f0d985218d58ea3fd740d15be94ef43c1..b2b8d361fdb671f6d1c10878a871e09983f77b2d 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -14,7 +14,7 @@ library(RColorBrewer)
source('plotting/panel.R', encoding='latin1')
-panels_layout = function (df_data, df_meta, layout_matrix, figdir='', filedir_opt='', filename_opt='', variable='', df_trend=NULL, p_threshold=0.1, unit2day=365.25, type='', period=NULL, missRect=FALSE, time_header=NULL, info_header=TRUE, header_ratio=2) {
+panels_layout = function (df_data, df_meta, layout_matrix, figdir='', filedir_opt='', filename_opt='', variable='', df_trend=NULL, p_threshold=0.1, unit2day=365.25, type='', period=NULL, missRect=FALSE, time_header=NULL, info_header=TRUE, time_ratio=2, var_ratio=3) {
if (all(class(df_data) != 'list')) {
df_data = list(df_data)
@@ -175,12 +175,20 @@ panels_layout = function (df_data, df_meta, layout_matrix, figdir='', filedir_op
LMrow = nrow(layout_matrix_H)
for (i in 1:(LMrow+nbh)) {
- if (i <= nbh) {
+ if (info_header & i == 1) {
LM = rbind(LM, rep(i, times=LMcol))
+ } else if (!is.null(time_header) & i == 2) {
+ LM = rbind(LM,
+ matrix(rep(rep(i, times=LMcol),
+ times=time_ratio),
+ ncol=LMcol, byrow=TRUE))
+ # if (i <= nbh) {
+ # LM = rbind(LM, rep(i, times=LMcol))
+
} else {
LM = rbind(LM,
matrix(rep(layout_matrix_H[i-nbh,],
- times=header_ratio),
+ times=var_ratio),
ncol=LMcol, byrow=TRUE))
}}
diff --git a/plotting/panel.R b/plotting/panel.R
index 835d9a7769f6852a04961cc79c852309c6537f72..9a9ce89f0a07f9c31069a815c3d56a2b5a42e5b1 100644
--- a/plotting/panel.R
+++ b/plotting/panel.R
@@ -10,7 +10,7 @@ library(ggh4x)
library(RColorBrewer)
-time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missRect=FALSE, unit2day=365.25, period=NULL, last=FALSE, color=NULL) {
+time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missRect=FALSE, unit2day=365.25, period=NULL, last=FALSE, color=NULL, norm=FALSE) {
if (type == 'sqrt(Q)') {
@@ -18,42 +18,48 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
}
maxQ = max(df_data_code$Qm3s, na.rm=TRUE)
-
- power = get_power(maxQ)
-
- dbrk = 10^power
+ power = get_power(maxQ)
+
+ if (norm) {
+ dbrk = 10^power
+ } else {
+ dbrk = 1
+ }
df_data_code$Qm3sN = df_data_code$Qm3s / dbrk
if (!is.null(df_trend_code)) {
-
df_trend_code$trendN = df_trend_code$trend / dbrk
df_trend_code$interceptN = df_trend_code$intercept / dbrk
}
-
- maxQN = max(df_data_code$Qm3sN, na.rm=TRUE)
- if (maxQN >= 5) {
+ maxQN = max(df_data_code$Qm3sN, na.rm=TRUE)
+
+ maxQtmp = maxQ/10^power
+ if (maxQtmp >= 5) {
dbrk = 1.0
accuracy = 0.1
- } else if (maxQN < 5 & maxQN >= 3) {
+ } else if (maxQtmp < 5 & maxQtmp >= 3) {
dbrk = 0.5
accuracy = 0.1
- } else if (maxQN < 3 & maxQN >= 2) {
+ } else if (maxQtmp < 3 & maxQtmp >= 2) {
dbrk = 0.4
accuracy = 0.1
- } else if (maxQN < 2 & maxQN >= 1) {
+ } else if (maxQtmp < 2 & maxQtmp >= 1) {
dbrk = 0.2
accuracy = 0.1
- } else if (maxQN < 1) {
+ } else if (maxQtmp < 1) {
dbrk = 0.1
accuracy = 0.1
}
+ if (!norm) {
+ dbrk = dbrk * 10^power
+ accuracy = NULL
+ }
+
dDate = as.numeric(df_data_code$Date[length(df_data_code$Date)] -
df_data_code$Date[1]) / unit2day
-
- # datebreak = round(as.numeric(dDate) / unit2day / 11 , 0)
if (dDate >= 100) {
datebreak = 25
@@ -72,12 +78,18 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
theme(panel.background=element_rect(fill='white'),
text=element_text(family='sans'),
- panel.border=element_blank(),
- panel.grid.major.y=element_line(color='grey85', size=0.3),
+ # panel.border=element_blank(),
+ panel.border = element_rect(color="grey85",
+ fill=NA,
+ size=0.7),
+
+ # panel.grid.major.y=element_line(color='grey85', size=0.3),
+ panel.grid.major.y=element_line(color='grey85', size=0.15),
panel.grid.major.x=element_blank(),
- axis.ticks.y=element_blank(),
+ # axis.ticks.y=element_blank(),
+ axis.ticks.y=element_line(color='grey75', size=0.3),
axis.ticks.x=element_line(color='grey75', size=0.3),
axis.text.x=element_text(color='grey40'),
@@ -86,7 +98,7 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
ggh4x.axis.ticks.length.minor=rel(0.5),
axis.ticks.length=unit(1.5, 'mm'),
- plot.title=element_text(size=9, vjust=-3,
+ plot.title=element_text(size=9, vjust=-2,
hjust=-1E-3, color='grey20'),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
@@ -111,8 +123,6 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
size=0.3)
} else {
p = p +
- # geom_line(aes(x=df_data_code$Date, y=df_data_code$Qm3sN),
- # color='grey70') +
geom_point(aes(x=df_data_code$Date, y=df_data_code$Qm3sN),
shape=1, color='grey20', size=1)
}
@@ -169,27 +179,42 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
color='cornflowerblue')
}
- p = p +
- ggtitle(bquote(.(type)~~'['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}~~~'tendance :'~.(format(df_trend_code$trend, scientific=TRUE, digits=3))~m^{3}*'.'*s^{-1}*'.'*an^{-1}))
+ if (norm) {
+ p = p +
+ ggtitle(bquote(bold(.(type))~~'['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}~~~bold('tendance')~.(format(df_trend_code$trend, scientific=TRUE, digits=3))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'))
+ } else {
+ p = p +
+ ggtitle(bquote(bold(.(type))~~'['*m^{3}*'.'*s^{-1}*']'~~~bold('tendance')~.(format(df_trend_code$trend, scientific=TRUE, digits=3))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'))
+ }
} else {
- p = p +
- ggtitle(bquote(.(type)~' ['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}))
+ if (norm) {
+ p = p +
+ ggtitle(bquote(bold(.(type))~~'['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}~~~bold('tendance')~.(format(df_trend_code$trend, scientific=TRUE, digits=3))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'))
+ } else {
+ p = p +
+ ggtitle(bquote(bold(.(type))~~'['*m^{3}*'.'*s^{-1}*']'~~~bold('tendance')~.(format(df_trend_code$trend, scientific=TRUE, digits=3))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'))
+ }
}
} else {
- p = p +
- ggtitle(bquote(.(type)~' ['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}))
+ if (norm) {
+ p = p +
+ ggtitle(bquote(bold(.(type))~' ['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}))
+ } else {
+ p = p +
+ ggtitle(bquote(bold(.(type))~' ['*m^{3}*'.'*s^{-1}*']'))
+ }
}
- # if (norm) {
- # p = p +
- # ylab(bquote('débit ['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}))
- # } else {
- # p = p +
- # ylab(expression(paste('débit [', m^{3}, '.',
- # s^{-1}, ']', sep='')))
- # }
+ if (norm) {
+ p = p +
+ ylab(bquote('débit ['*m^{3}*'.'*s^{-1}*'] x'~10^{.(as.character(power))}))
+ } else {
+ p = p +
+ ylab(expression(paste('débit [', m^{3}, '.',
+ s^{-1}, ']', sep='')))
+ }
p = p +
# xlab('date') +
@@ -201,8 +226,9 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
date_labels="%Y",
limits=c(min(df_data_code$Date),
max(df_data_code$Date)),
- expand=c(0, 0)) +
-
+ expand=c(0, 0))
+
+ p = p +
scale_y_continuous(breaks=seq(0, maxQN*10, dbrk),
limits=c(0, maxQN*1.1),
expand=c(0, 0),
@@ -215,26 +241,82 @@ time_panel = function (df_data_code, df_trend_code, type, p_threshold=0.1, missR
text_panel = function(code, df_meta) {
df_meta_code = df_meta[df_meta$code == code,]
- text = paste(
- "<span style='font-size:18pt'> station <b>", code, "</b></span><br>",
- "nom : ", df_meta_code$nom, "<br>",
- "région hydrographique : ", df_meta_code$region_hydro, "<br>",
- "position : (", df_meta_code$L93X, "; ", df_meta_code$L93Y, ")", "<br>",
- "surface : ", df_meta_code$surface_km2, " km<sup>2</sup>",
+ text1 = paste(
+ "<b>", code, '</b> - ', df_meta_code$nom, "<br>",
+ sep='')
+
+ text2 = paste(
+ "<b>",
+ "Région hydro : ", df_meta_code$region_hydro, "<br>",
+ "</b>",
+ sep='')
+
+ text3 = paste(
+ "<b>",
+ "Superficie : ", df_meta_code$surface_km2, " [km<sup>2</sup>] <br>",
+ "X = ", df_meta_code$L93X, " [m ; Lambert 93]",
+ "</b>",
+ sep='')
+
+ text4 = paste(
+ "<b>",
+ "Altitude : ", df_meta_code$altitude_m, " [m]<br>",
+ "Y = ", df_meta_code$L93Y, " [m ; Lambert 93]",
+ "</b>",
+ sep='')
+
+ text5 = paste(
+ "<b>",
+ "(Banque Hydro)<br>",
+ "(Banque Hydro)",
+ "</b>",
sep='')
- gtext = richtext_grob(text,
- x=0, y=1,
- margin=unit(c(5, 5, 5, 5), "mm"),
- hjust=0, vjust=1,
- gp=gpar(col="grey20", fontsize=12))
- return(gtext)
+ gtext1 = richtext_grob(text1,
+ x=0, y=1,
+ margin=unit(c(t=5, r=5, b=0, l=5), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="#00A3A8", fontsize=14))
+
+ gtext2 = richtext_grob(text2,
+ x=0, y=0.6,
+ margin=unit(c(t=0, r=5, b=0, l=5), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ gtext3 = richtext_grob(text3,
+ x=0, y=1,
+ margin=unit(c(t=0, r=5, b=5, l=5), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ gtext4 = richtext_grob(text4,
+ x=0, y=1,
+ margin=unit(c(t=0, r=5, b=5, l=5), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ gtext5 = richtext_grob(text5,
+ x=0, y=1,
+ margin=unit(c(t=0, r=5, b=5, l=5), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ gtext_merge = grid.arrange(grobs=list(gtext1, gtext2, gtext3,
+ gtext4, gtext5),
+ layout_matrix=matrix(c(1, 1, 1,
+ 2, 2, 2,
+ 3, 4, 5),
+ nrow=3,
+ byrow=TRUE))
+
+ return(gtext_merge)
}
matrice_panel = function (list_df2plot, df_meta) {
-
+
nbp = length(list_df2plot)
minTrend = c()
@@ -304,6 +386,8 @@ matrice_panel = function (list_df2plot, df_meta) {
X = as.integer(factor(as.character(Type_mat)))
Y = as.integer(factor(Code_mat))
+
+ options(repr.plot.width=X, repr.plot.height=Y)
mat = ggplot() +
@@ -335,11 +419,6 @@ matrice_panel = function (list_df2plot, df_meta) {
plot.margin=margin(5, 5, 5, 5, unit="mm"),
)
-
-
- # geom_point(aes(x=X, y=Y),
- # shape=21, fill=Fill_mat, color=Color_mat,
- # size=15, stroke=1) +
for (i in 1:length(X)) {
mat = mat +
@@ -350,20 +429,14 @@ matrice_panel = function (list_df2plot, df_meta) {
mat = mat +
coord_fixed() +
-
- scale_x_continuous(limits=c(min(c(X, Y)) - rel(1.5),
- max(c(X, Y)) + rel(0.5)),
+
+ scale_x_continuous(limits=c(min(X) - rel(1.5),
+ max(X) + rel(0.5)),
expand=c(0, 0)) +
- scale_y_continuous(limits=c(min(c(X, Y)) - rel(0.5),
- max(c(X, Y)) + rel(1)),
+ scale_y_continuous(limits=c(min(Y) - rel(0.5),
+ max(Y) + rel(1)),
expand=c(0, 0))
-
- # scale_x_continuous(limits=c(min(X)-0.4, max(X)+0.2),
- # expand=c(0, 0)) +
-
- # scale_y_continuous(limits=c(min(Y)-0.2, max(Y)+0.4),
- # expand=c(0, 0))
for (i in 1:length(Code)) {
mat = mat +
@@ -382,7 +455,6 @@ matrice_panel = function (list_df2plot, df_meta) {
size=3.5, color='grey40')
}
-
for (i in 1:length(Trend_mat)) {
trend = Trend_mat[i]
if (!is.na(trend)) {
diff --git a/processing/analyse.R b/processing/analyse.R
index e83f5bdb5e1336dbd7148d768d143ba0c8fac946..83d7c1aec14eab7f63b0df660d5f579fdaecbf43 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -87,71 +87,89 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
get_QAtrend = function (df_data, period) {
# AVERAGE ANNUAL FLOW : QA #
- ### /!\ verify order conservation ###
- df_QAlist = prepare(df_data, colnamegroup=c('code'))
-
- df_QAEx = extract.Var(data.station=df_QAlist,
- funct=mean,
- timestep='year',
- period=period,
- pos.datetime=1,
- na.rm=TRUE)
+
+ period = as.list(period)
+
+ Imax = 0
+ df_QAtrendB = tibble()
+
+ for (per in period){
+
+ df_QAlist = prepare(df_data, colnamegroup=c('code'))
+
+ df_QAEx = extract.Var(data.station=df_QAlist,
+ funct=mean,
+ timestep='year',
+ period=per,
+ pos.datetime=1,
+ na.rm=TRUE)
+
+ df_QAtrend = Estimate.stats(data.extract=df_QAEx)
+
+ I = interval(per[1], per[2])
+ if (I > Imax) {
+ Imax = I
+ df_QAlistB = df_QAlist
+ df_QAExB = df_QAEx
+ }
+
+ df_QAtrend = bind_cols(df_QAtrend,
+ tibble(period_start=as.Date(per[1])),
+ tibble(period_end=as.Date(per[2])))
+ df_QAtrendB = bind_rows(df_QAtrendB, df_QAtrend)
- df_QAtrend = Estimate.stats(data.extract=df_QAEx)
+ }
- res_QAtrend = clean(df_QAtrend, df_QAEx, df_QAlist)
+ res_QAtrend = clean(df_QAtrendB, df_QAExB, df_QAlistB)
return (res_QAtrend)
}
get_QMNAtrend = function (df_data, period) {
# MONTHLY MINIMUM FLOW IN THE YEAR : QMNA #
- df_QMNAlist = prepare(df_data, colnamegroup=c('code'))
-
- ### /!\ PLUS RAPIDE ###
- # fMNA = function (X) {
- # # prendre un paquet de 1 ans et faire la moyenne par mois et retourner le minimum des debit
- # dpm = length(X)/12
- # # print(dpm)
- # # print(length(X))
- # monthmean = c()
- # for (i in 1:12) {
- # id = round(dpm*(i-1)+1, 0)
- # iu = round(i*dpm, 0)
- # monthmean = append(monthmean, mean(X[id:iu], na.rm=TRUE))
- # # print(paste('start', id))
- # # print(paste('end', iu))
- # # print('')
- # }
- # # print(monthmean)
- # return (min(monthmean, na.rm=TRUE))
- # }
- # df_QMNAEx = extract.Var(data.station=df_QMNAlist,
- # funct=fMNA,
- # period=period,
- # pos.datetime=1)#,
- # na.rm=TRUE) ### /!\ PAS COMPRIS ###
-
- df_QMNAEx = extract.Var(data.station=df_QMNAlist,
- funct=mean,
- period=period,
- timestep='month',
- pos.datetime=1,
- na.rm=TRUE)
-
- ### /!\ NOM DE COLONNE PAS CONSERVER ###
- df_QMNAlist = reprepare(df_QMNAEx, df_QMNAlist, colnamegroup=c('code'))
-
- df_QMNAEx = extract.Var(data.station=df_QMNAlist,
- funct=min,
- period=period,
- timestep='year',
- pos.datetime=1,
- na.rm=TRUE)
-
- df_QMNAtrend = Estimate.stats(data.extract=df_QMNAEx)
-
- res_QMNAtrend = clean(df_QMNAtrend, df_QMNAEx, df_QMNAlist)
+
+ period = as.list(period)
+
+ Imax = 0
+ df_QMNAtrendB = tibble()
+
+ for (per in period) {
+
+ df_QMNAlist = prepare(df_data, colnamegroup=c('code'))
+
+ df_QMNAEx = extract.Var(data.station=df_QMNAlist,
+ funct=mean,
+ period=per,
+ timestep='month',
+ pos.datetime=1,
+ na.rm=TRUE)
+
+ df_QMNAlist = reprepare(df_QMNAEx, df_QMNAlist, colnamegroup=c('code'))
+
+ df_QMNAEx = extract.Var(data.station=df_QMNAlist,
+ funct=min,
+ period=per,
+ timestep='year',
+ pos.datetime=1,
+ na.rm=TRUE)
+
+ df_QMNAtrend = Estimate.stats(data.extract=df_QMNAEx)
+
+ I = interval(per[1], per[2])
+ if (I > Imax) {
+ Imax = I
+ df_QMNAlistB = df_QMNAlist
+ df_QMNAExB = df_QMNAEx
+ }
+
+ df_QMNAtrend = bind_cols(df_QMNAtrend,
+ tibble(period_start=as.Date(per[1])),
+ tibble(period_end=as.Date(per[2])))
+ df_QMNAtrendB = bind_rows(df_QMNAtrendB, df_QMNAtrend)
+ }
+
+
+ res_QMNAtrend = clean(df_QMNAtrendB, df_QMNAExB, df_QMNAlistB)
return (res_QMNAtrend)
}
@@ -162,7 +180,7 @@ get_VCN10trend = function (df_data, df_meta, period) {
# Get all different stations code
Code = levels(factor(df_meta$code))
-
+
df_data_roll = tibble()
for (c in Code) {
@@ -177,21 +195,40 @@ get_VCN10trend = function (df_data, df_meta, period) {
code=c)
df_data_roll = bind_rows(df_data_roll, df_data_code)
-
}
- df_VCN10list = prepare(df_data_roll, colnamegroup=c('code'))
-
- df_VCN10Ex = extract.Var(data.station=df_VCN10list,
- funct=min,
- period=period,
- timestep='year',
- pos.datetime=1,
- na.rm=TRUE)
-
- df_VCN10trend = Estimate.stats(data.extract=df_VCN10Ex)
+ period = as.list(period)
+
+ Imax = 0
+ df_VCN10trendB = tibble()
+
+ for (per in period) {
+
+ df_VCN10list = prepare(df_data_roll, colnamegroup=c('code'))
+
+ df_VCN10Ex = extract.Var(data.station=df_VCN10list,
+ funct=min,
+ period=per,
+ timestep='year',
+ pos.datetime=1,
+ na.rm=TRUE)
+
+ df_VCN10trend = Estimate.stats(data.extract=df_VCN10Ex)
+
+ I = interval(per[1], per[2])
+ if (I > Imax) {
+ Imax = I
+ df_VCN10listB = df_VCN10list
+ df_VCN10ExB = df_VCN10Ex
+ }
+
+ df_VCN10trend = bind_cols(df_VCN10trend,
+ tibble(period_start=as.Date(per[1])),
+ tibble(period_end=as.Date(per[2])))
+ df_VCN10trendB = bind_rows(df_VCN10trendB, df_VCN10trend)
+ }
- res_VCN10trend = clean(df_VCN10trend, df_VCN10Ex, df_VCN10list)
+ res_VCN10trend = clean(df_VCN10trendB, df_VCN10ExB, df_VCN10listB)
return (res_VCN10trend)
}
diff --git a/processing/format.R b/processing/format.R
index b5b79b02511ba1af06652c0768782b86d1bcc448..7ac9101f8a02e2a06568e414e20a3252d0bc2f04 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -83,10 +83,10 @@ reprepare = function(df_XEx, df_Xlist, colnamegroup=NULL) {
clean = function (df_Xtrend, df_XEx, df_Xlist) {
- # print(str(df_XEx))
-
df_Xlist = reprepare(df_XEx, df_Xlist, colnamegroup=c('code'))
+ # print(df_Xlist)
+
df_Xlist$data$code = NA
for (g in df_Xlist$info$group) {
df_Xlist$data$code[which(df_Xlist$data$group == g)] = df_Xlist$info$code[df_Xlist$info$group == g]
diff --git a/script.R b/script.R
index 56575dc1ccedfb7c5953ea64dee6dc4b402b1dc1..c34e2d92ddfcc3828ace843cc8d4984b68d14672 100644
--- a/script.R
+++ b/script.R
@@ -22,13 +22,13 @@ BHfiledir =
## Manual selection ##
# Name of the file that will be analysed from the BH directory
BHfilename =
- ""
- # c("S2235610_HYDRO_QJM.txt",
- # "P1712910_HYDRO_QJM.txt",
- # "P0885010_HYDRO_QJM.txt",
- # "A1000030_HYDRO_QJM.txt",
- # "A2250310_HYDRO_QJM.txt"
- # )
+ # ""
+ c("S2235610_HYDRO_QJM.txt",
+ "P1712910_HYDRO_QJM.txt",
+ "P0885010_HYDRO_QJM.txt",
+ "A1000030_HYDRO_QJM.txt",
+ "A2250310_HYDRO_QJM.txt"
+ )
## Or list selection ##
# Path to the list file of BH data that will be analysed
@@ -36,20 +36,20 @@ BHlistdir =
""
BHlistname =
- # ""
- "Liste-station_RRSE.docx"
+ ""
+ # "Liste-station_RRSE.docx"
### NIVALE ###
# Path to the directory where NV data is stored
NVfiledir =
- # ""
- "France207"
+ ""
+ # "France207"
# Name of the file that will be analysed from the NV directory
NVfilename =
- # ""
- "all"
+ ""
+ # "all"
# Path to the list file of metadata about station that will be analysed
@@ -57,14 +57,14 @@ NVlistdir =
""
NVlistname =
- # ""
- "liste_bv_principaux_global.txt"
+ ""
+ # "liste_bv_principaux_global.txt"
### TREND ANALYSIS ###
# Time period to analyse
-period = c("1980-01-01","2019-12-31")
-
+period_all = c("1700-01-01", "2020-12-31")
+period2 = c("1968-01-01", "2020-12-31")
########################
@@ -120,11 +120,6 @@ if (BHlistname != ""){
}
-######
-BHfilename = BHfilename[1:10]
-######
-
-
# Extract metadata about selected stations
df_meta_BH = extractBH_meta(computer_data_path, BHfiledir, BHfilename)
@@ -153,13 +148,13 @@ df_meta = df_join$meta
# QA TREND #
-res_QAtrend = get_QAtrend(df_data, period)
+# res_QAtrend = get_QAtrend(df_data, period=list(period_all, period2))
# QMNA TREND #
-res_QMNAtrend = get_QMNAtrend(df_data, period)
+res_QMNAtrend = get_QMNAtrend(df_data, period=list(period_all, period2))
# VCN10 TREND #
-res_VCN10trend = get_VCN10trend(df_data, df_meta, period)
+# res_VCN10trend = get_VCN10trend(df_data, period=list(period_all, period2))
# TIME PANEL #
@@ -171,41 +166,43 @@ res_VCN10trend = get_VCN10trend(df_data, df_meta, period)
# type=list('Q', 'sqrt(Q)'),
# info_header=TRUE,
# time_header=NULL,
-# header_ratio=3,
+# var_ratio=3,
# figdir=figdir,
# filename_opt='time')
-panels_layout(list(res_QAtrend$data, res_QMNAtrend$data,
- res_VCN10trend$data),
- layout_matrix=c(1, 2, 3),
- df_meta=df_meta,
- df_trend=list(res_QAtrend$trend, res_QMNAtrend$trend,
- res_VCN10trend$trend),
- type=list(bquote(Q[A]), bquote(Q[MNA]), bquote(V[CN10])),
- missRect=list(TRUE, TRUE, TRUE),
- period=period,
- info_header=TRUE,
- time_header=df_data,
- header_ratio=2,
- figdir=figdir,
- filename_opt='')
-
-
-# panels_layout(list(res_QAtrend$data, res_VCN10trend$data),
-# layout_matrix=c(1, 2),
+# panels_layout(list(res_QAtrend$data, res_QMNAtrend$data,
+# res_VCN10trend$data),
+# layout_matrix=c(1, 2, 3),
# df_meta=df_meta,
-# df_trend=list(res_QAtrend$trend,
+# df_trend=list(res_QAtrend$trend, res_QMNAtrend$trend,
# res_VCN10trend$trend),
-# type=list(bquote(Q[A]), bquote(V[CN10])),
-# missRect=list(TRUE, TRUE),
-# period=period,
+# type=list(bquote(Q[A]), bquote(Q[MNA]), bquote(V[CN10])),
+# missRect=list(TRUE, TRUE, TRUE),
+# period=period_all,
# info_header=TRUE,
# time_header=df_data,
-# header_ratio=2,
+# time_ratio=2,
+# var_ratio=3,
# figdir=figdir,
# filename_opt='')
+panels_layout(list(res_QAtrend$data, res_VCN10trend$data),
+ layout_matrix=c(1, 2),
+ df_meta=df_meta,
+ df_trend=list(res_QAtrend$trend,
+ res_VCN10trend$trend),
+ type=list(bquote(Q[A]), bquote(V[CN10])),
+ missRect=list(TRUE, TRUE),
+ period=period_all,
+ info_header=TRUE,
+ time_header=df_data,
+ time_ratio=2,
+ var_ratio=5,
+ figdir=figdir,
+ filename_opt='')
+
+
### /!\ Removed 185 row(s) containing missing values (geom_path) -> remove NA ###