diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index b781b3ee68d04dff09888802ba7f012d27181098..37c776404c4364a1d31cc328cb0336e3d5aee6a9 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -35,7 +35,7 @@ source('plotting/shortcut.R', encoding='UTF-8')
# Manages datasheets creations for all stations. Makes the call to
# the different headers, trend analysis graphs and realises arranging
# every plots.
-datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, time_header, foot_note, layout_matrix, info_ratio, time_ratio, var_ratio, foot_height, resources_path, logo_dir, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp, df_page=NULL) {
+datasheet_panel = function (list_df2plot, df_meta, trend_period, mean_period, info_header, time_header, foot_note, layout_matrix, info_height, time_ratio, var_ratio, foot_height, resources_path, logo_dir, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp, df_page=NULL) {
# The percentage of augmentation and diminution of the min
# and max limits for y axis
@@ -48,15 +48,17 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Convert 'trend_period' to list
- trend_period = as.list(trend_period)
- # Number of trend period
- nPeriod_trend = length(trend_period)
+ if (!is.null(trend_period)) {
+ # Convert 'trend_period' to list
+ trend_period = as.list(trend_period)
+ # Number of trend period
+ nPeriod_trend = length(trend_period)
- # Extracts the min and the max of the mean trend for all the station
- res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
- minTrendValue = res$min
- maxTrendValue = res$max
+ # Extracts the min and the max of the mean trend for all the station
+ res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
+ minTrendValue = res$min
+ maxTrendValue = res$max
+ }
# Blank vector to store the max number of digit of label for
# each station
@@ -67,10 +69,8 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# Default max digit
NspaceMax_code = 0
- # If the time header is given
- if (!is.null(time_header)) {
- nbpMod = nbp + 1
- }
+ # If the time header is given it adds one to the number of plot
+ nbpMod = nbp + as.numeric(!is.null(time_header))
# For all type of graph
for (i in 1:nbpMod) {
@@ -94,7 +94,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# with 3 characters
Nspace = 6
# If it is a flow variable
- } else if (type == 'sévérité') {
+ } else if (type == 'sévérité' | type == 'data') {
# Gets the max number of digit on the label
maxtmp = max(df_data_code$Value, na.rm=TRUE)
# Taking into account of the augmentation of
@@ -134,7 +134,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# Stores the max digit number for labels of a station
NspaceMax = c(NspaceMax, NspaceMax_code)
}
-
+
# For all the station
for (k in 1:nCode) {
# Gets the code
@@ -145,16 +145,18 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
sep=''))
# Number of header (is info and time serie are needed)
- nbh = as.numeric(info_header) + as.numeric(!is.null(time_header))
+ nbh = as.numeric(!is.null(info_header)) + as.numeric(!is.null(time_header))
# Actualises the number of plot
nbg = nbp + nbh + as.numeric(foot_note)
# Opens a blank list to store plot
P = vector(mode='list', length=nbg)
# If the info header is needed
- if (info_header) {
+ if (!is.null(info_header)) {
+
# Extracts the data serie corresponding to the code
- time_header_code = time_header[time_header$code == code,]
+ info_header_code = info_header[info_header$code == code,]
+
# Gets the info plot
Hinfo = info_panel(list_df2plot,
df_meta,
@@ -163,11 +165,11 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
periodHyd=mean_period[[1]],
df_shapefile=df_shapefile,
codeLight=code,
- df_data_code=time_header_code)
+ df_data_code=info_header_code)
# Stores it
P[[1]] = Hinfo
}
-
+
# If the time header is given
if (!is.null(time_header)) {
# Extracts the data serie corresponding to the code
@@ -208,76 +210,94 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# Blank vector to store color
color = c()
-
- # # Default grey color for not significant trend
- # grey = 85
-
- # For all the period
- for (j in 1:nPeriod_trend) {
-
- # If the trend is significant
- if (df_trend_code$p[j] <= alpha){
- # Extract start and end of trend periods
- Start = df_trend_code$period_start[j]
- End = df_trend_code$period_end[j]
-
- # Extracts the corresponding data for the period
- df_data_code_per =
- df_data_code[df_data_code$Date >= Start
- & df_data_code$Date <= End,]
- # Same for trend
- df_trend_code_per =
- df_trend_code[df_trend_code$period_start == Start
- & df_trend_code$period_end == End,]
-
- # Computes the number of trend analysis selected
- Ntrend = nrow(df_trend_code_per)
- # If there is more than one trend on the same period
- if (Ntrend > 1) {
- # Takes only the first because they are similar
- df_trend_code_per = df_trend_code_per[1,]
- }
- # If it is a flow variable
- if (type == 'sévérité') {
- # Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Value,
- na.rm=TRUE)
- # Normalises the trend value by the mean
- # of the data
- trendValue = df_trend_code_per$trend / dataMean
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- trendValue = df_trend_code_per$trend
- }
-
- # Gets the color corresponding to the mean trend
- color_res = get_color(trendValue,
- minTrendValue[j, i],
- maxTrendValue[j, i],
- palette_name='perso',
- reverse=TRUE)
- # Stores it temporarily
- colortmp = color_res
- # Otherwise
- } else {
- # Stores the default grey color
- colortmp = paste('grey85', sep='')
+ if (!is.null(trend_period)) {
+ # For all the period
+ for (j in 1:nPeriod_trend) {
+ # If the trend is significant
+ if (df_trend_code$p[j] <= alpha){
+ # Extract start and end of trend periods
+ Start = df_trend_code$period_start[j]
+ End = df_trend_code$period_end[j]
+
+ # Extracts the corresponding data for the period
+ df_data_code_per =
+ df_data_code[df_data_code$Date >= Start
+ & df_data_code$Date <= End,]
+ # Same for trend
+ df_trend_code_per =
+ df_trend_code[df_trend_code$period_start == Start
+ & df_trend_code$period_end == End,]
+
+ # Computes the number of trend analysis selected
+ Ntrend = nrow(df_trend_code_per)
+ # If there is more than one trend on the same period
+ if (Ntrend > 1) {
+ # Takes only the first because they are similar
+ df_trend_code_per = df_trend_code_per[1,]
+ }
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Computes the mean of the data on the period
+ dataMean = mean(df_data_code_per$Value,
+ na.rm=TRUE)
+ # Normalises the trend value by the mean
+ # of the data
+ trendValue = df_trend_code_per$trend / dataMean
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ trendValue = df_trend_code_per$trend
+ }
+
+ # Gets the color corresponding to the mean trend
+ color_res = get_color(trendValue,
+ minTrendValue[j, i],
+ maxTrendValue[j, i],
+ palette_name='perso',
+ reverse=TRUE)
+ # Stores it temporarily
+ colortmp = color_res
+ # Otherwise
+ } else {
+ # Stores the default grey color
+ colortmp = paste('grey85', sep='')
+
+ }
+ # Stores the color
+ color = append(color, colortmp)
+ grid = FALSE
}
- # Stores the color
- color = append(color, colortmp)
+ } else {
+ axis_xlim = NULL
+ }
+
+ if (var != 'sqrt(Q)' & var != 'Q') {
+ grid = FALSE
+ ymin_lim = NULL
+ } else {
+ grid = TRUE
+ ymin_lim = 0
}
+ if (is.null(time_header) & i == 1) {
+ first = TRUE
+ } else {
+ first = FALSE
+ }
+
# Computes the time panel associated to the current variable
p = time_panel(df_data_code, df_trend_code, var=var,
type=type, alpha=alpha,
missRect=missRect, trend_period=trend_period,
mean_period=mean_period, axis_xlim=axis_xlim,
- unit2day=unit2day, grid=FALSE, color=color,
- NspaceMax=NspaceMax[k], last=(i == nbp),
+ unit2day=unit2day, grid=grid,
+ ymin_lim=ymin_lim, color=color,
+ NspaceMax=NspaceMax[k], first=first,
+ last=(i == nbp),
lim_pct=lim_pct)
-
+
# Stores the plot
P[[i+nbh]] = p
}
@@ -321,15 +341,19 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
nel)
# Shifts all plots to be coherent with the adding of header
LM = LM + nbh
-
+
if (!is.null(time_header)) {
- LM = rbind(2, LM)
+ id_time = nbh
+ LM = rbind(nbh, LM)
} else {
+ id_time = NA
time_ratio = 0
}
- if (info_header) {
- LM = rbind(1, LM)
+ if (!is.null(info_header)) {
+ id_info = nbh - 1
+ LM = rbind(nbh - 1, LM)
} else {
+ id_info = NA
info_ratio = 0
}
@@ -352,18 +376,17 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
height = 29.7
width = 21
- Norm_ratio = height * (info_ratio + time_ratio + var_ratio*nbp) / (height - 2*margin_size - foot_height)
+ Norm_ratio = height * (time_ratio + var_ratio*nbp) / (height - 2*margin_size - foot_height - info_height)
- info_height = height * info_ratio / Norm_ratio
time_height = height * time_ratio / Norm_ratio
var_height = height * var_ratio / Norm_ratio
Hcut = LM[, 2]
- heightLM = rep(0, times=LMrow)
+ heightLM = rep(0, times=LMrow)
- heightLM[Hcut == 1] = info_height
- heightLM[Hcut == 2] = time_height
- heightLM[Hcut > 2 & Hcut < id_foot] = var_height
+ heightLM[Hcut == id_info] = info_height
+ heightLM[Hcut == id_time] = time_height
+ heightLM[Hcut > nbh & Hcut < id_foot] = var_height
heightLM[Hcut == id_foot] = foot_height
heightLM[Hcut == 99] = margin_size
@@ -391,11 +414,6 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
## 2. OTHER PANEL FOR THE DATASHEET __________________________________
### 2.1. Time panel __________________________________________________
time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRect=FALSE, unit2day=365.25, trend_period=NULL, mean_period=NULL, axis_xlim=NULL, grid=TRUE, ymin_lim=NULL, color=NULL, NspaceMax=NULL, first=FALSE, last=FALSE, lim_pct=10) {
-
- # If 'type' is square root apply it to data
- if (var == 'sqrt(Q)') {
- df_data_code$Value = sqrt(df_data_code$Value)
- }
# Compute max and min of flow
maxQ = max(df_data_code$Value, na.rm=TRUE)
@@ -1115,9 +1133,12 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
# Y axis title
# If it is a flow variable
- if (type == 'sévérité') {
+ if (type == 'sévérité' | var == 'Q') {
p = p +
ylab(bquote(bold(.(var))~~'['*m^{3}*'.'*s^{-1}*']'))
+ } else if (var == 'sqrt(Q)') {
+ p = p +
+ ylab(bquote(bold(.(var))~~'['*m^{3/2}*'.'*s^{-1/2}*']'))
# If it is a date variable
} else if (type == 'saisonnalité') {
p = p +
@@ -1167,7 +1188,7 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
accuracy = NULL
# If it is a flow variable
- } else if (type == 'sévérité') {
+ } else if (type == 'sévérité' | type == 'data') {
# Gets the max number of digit on the label
maxtmp = max(df_data_code$Value, na.rm=TRUE)
# Taking into account of the augmentation of
@@ -1214,7 +1235,7 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
# Parameters of the y axis
# If it is a flow variable
- if (type == 'sévérité') {
+ if (type == 'sévérité' | type == 'data') {
p = p +
scale_y_continuous(breaks=seq(minQ_lim, maxQ_lim, breakQ),
limits=c(minQ_win, maxQ_win),
@@ -1262,7 +1283,7 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
### 2.2. Info panel __________________________________________________
# Plots the header that regroups all the info on the station
info_panel = function(list_df2plot, df_meta, trend_period, mean_period, periodHyd, df_shapefile, codeLight, df_data_code=NULL) {
-
+
# If there is a data serie for the given code
if (!is.null(df_data_code)) {
# Computes the hydrograph
diff --git a/plotting/layout.R b/plotting/layout.R
index 6211d69af631889c72bb17287b274ae5aa337831..0bbd98c0441fe5b516c3ba94ae8962b45357d7fa 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -130,12 +130,12 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
figdir='', filedir_opt='', filename_opt='',
variable='', df_trend=NULL,
alpha=0.1, unit2day=365.25, var='',
- type='', glose='', trend_period=NULL,
+ type='', glose=NULL, trend_period=NULL,
mean_period=NULL, axis_xlim=NULL,
- missRect=FALSE, time_header=NULL,
- info_header=TRUE, foot_note=FALSE,
- info_ratio=1, time_ratio=2,
- var_ratio=3, foot_height=1,
+ missRect=TRUE, time_header=NULL,
+ info_header=NULL, foot_note=TRUE,
+ info_height=2.8, time_ratio=2,
+ var_ratio=3, foot_height=1.25,
df_shapefile=NULL,
resources_path=NULL,
logo_dir=NULL,
@@ -286,12 +286,13 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
if ('datasheet' %in% toplot) {
df_page = datasheet_panel(list_df2plot,
df_meta,
- trend_period,
+ trend_period=trend_period,
+ mean_period=mean_period,
info_header=info_header,
time_header=time_header,
foot_note=foot_note,
layout_matrix=layout_matrix,
- info_ratio=info_ratio,
+ info_height=info_height,
time_ratio=time_ratio,
var_ratio=var_ratio,
foot_height=foot_height,
diff --git a/plotting/map.R b/plotting/map.R
index 8571ce6d659548aca378ab7271559d3d801a2c36..644092b63455548e35db8f3e9557a87c94f55cf2 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -52,15 +52,17 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Convert 'trend_period' to list
- trend_period = as.list(trend_period)
- # Number of trend period
- nPeriod_trend = length(trend_period)
-
- # Extracts the min and the max of the mean trend for all the station
- res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
- minTrendValue = res$min
- maxTrendValue = res$max
+ if (!is.null(trend_period)) {
+ # Convert 'trend_period' to list
+ trend_period = as.list(trend_period)
+ # Number of trend period
+ nPeriod_trend = length(trend_period)
+
+ # Extracts the min and the max of the mean trend for all the station
+ res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
+ minTrendValue = res$min
+ maxTrendValue = res$max
+ }
# If there is a 'mean_period'
if (!is.null(mean_period)) {
@@ -118,7 +120,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
round(n_loop/N_loop*100, 0),
" %)",
sep=''))
- }
+ }
# If there is no specified station code to highlight
# (mini map)
@@ -297,8 +299,15 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
minValue = minBreakValue[j, i]
maxValue = maxBreakValue[j, i]
pvalue = 0
+
+ } else if (is.null(trend_period)) {
+ value = NA
+ minValue = NULL
+ maxValue = NULL
+ pvalue = 0
} else {
+
# Extracts the data corresponding to the
# current variable
df_data = list_df2plot[[i]]$data
@@ -309,9 +318,10 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
alpha = list_df2plot[[i]]$alpha
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
+
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
-
+
# Extract start and end of trend periods
Start = df_trend_code$period_start[idPer_trend]
End = df_trend_code$period_end[idPer_trend]
@@ -367,7 +377,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
ncolor=256,
nbTick=nbTick)
- if (j > 1) {
+ if (j > 1 | is.null(trend_period)) {
# The computed color is stored
filltmp = color_res
# The marker is a circle
@@ -442,7 +452,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
shape=shape[OkVal], size=5, stroke=1,
color='grey50', fill=fill[OkVal])
}
-
+
# If there is a specified station code
} else {
# Extract data of all stations not to highlight
@@ -463,118 +473,116 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
shape=21, size=2, stroke=0.5,
color='grey97', fill='#00A3A8')
}
+
+ if (!is.null(trend_period)) {
- # Extracts the position of the tick of the colorbar
- posTick = palette_res$posTick
- # Extracts the label of the tick of the colorbar
- labTick = palette_res$labTick
- # Extracts the color corresponding to the tick of the colorbar
- colTick = palette_res$colTick
-
- # Spreading of the colorbar
- valNorm = nbTick * 10
- # Normalisation of the position of ticks
- ytick = posTick / max(posTick) * valNorm
-
- # If it is a flow variable
- if (type == 'sévérité') {
- # Formatting of label in pourcent
- labTick = as.character(signif(labTick*100, 2))
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- # Formatting of label
- labTick = as.character(signif(labTick, 2))
- }
-
- # X position of ticks all similar
- xtick = rep(0, times=nbTick)
+ # Extracts the position of the tick of the colorbar
+ posTick = palette_res$posTick
+ # Extracts the label of the tick of the colorbar
+ labTick = palette_res$labTick
+ # Extracts the color corresponding to the tick of the colorbar
+ colTick = palette_res$colTick
+
+ # Spreading of the colorbar
+ valNorm = nbTick * 10
+ # Normalisation of the position of ticks
+ ytick = posTick / max(posTick) * valNorm
- # Creates a tibble to store all parameters of colorbar
- plot_palette = tibble(xtick=xtick, ytick=ytick,
- colTick=colTick, labTick=labTick)
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Formatting of label in pourcent
+ labTick = as.character(signif(labTick*100, 2))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Formatting of label
+ labTick = as.character(signif(labTick, 2))
+ }
+
+ # X position of ticks all similar
+ xtick = rep(0, times=nbTick)
-
- nbLine = as.integer(nchar(glose)/40) + 1
-
-
- nbNewline = 0
-
- nbLim = 43
- gloseName = glose
- nbChar = nchar(gloseName)
- while (nbChar > nbLim) {
- nbNewline = nbNewline + 1
- posSpace = which(strsplit(gloseName, "")[[1]] == " ")
- idNewline = which.min(abs(posSpace - nbLim * nbNewline))
- posNewline = posSpace[idNewline]
- gloseName = paste(substring(gloseName,
- c(1, posNewline + 1),
- c(posNewline,
- nchar(gloseName))),
- collapse="\n")
- Newline = substr(gloseName,
- posNewline + 2,
- nchar(gloseName))
- nbChar = nchar(Newline)
- }
+ # Creates a tibble to store all parameters of colorbar
+ plot_palette = tibble(xtick=xtick, ytick=ytick,
+ colTick=colTick, labTick=labTick)
- Yline = 0.6 + 0.47*nbNewline
- Ytitle = Yline + 0.15
-
- # New plot with void theme
- title = ggplot() + theme_void() +
- # Plots separation lines
- geom_line(aes(x=c(-0.3, 3.9), y=c(0.05, 0.05)),
- size=0.6, color="#00A3A8") +
- geom_line(aes(x=c(-0.3, 3.9), y=c(Yline, Yline)),
- size=0.6, color="#00A3A8") +
- # Writes title
- geom_shadowtext(data=tibble(x=-0.3, y=Ytitle,
- label=var),
- aes(x=x, y=y, label=label),
- fontface="bold",
- color="#00A3A8",
- bg.colour="white",
- hjust=0, vjust=0, size=10) +
- # Writes title
- geom_shadowtext(data=tibble(x=-0.3, y=0.2,
- label=gloseName),
- aes(x=x, y=y, label=label),
- fontface="bold",
- color="#00A3A8",
- bg.colour="white",
- hjust=0, vjust=0, size=3) +
+ nbLine = as.integer(nchar(glose)/40) + 1
+
+
+ nbNewline = 0
+
+ nbLim = 43
+ gloseName = glose
+ nbChar = nchar(gloseName)
+ while (nbChar > nbLim) {
+ nbNewline = nbNewline + 1
+ posSpace = which(strsplit(gloseName, "")[[1]] == " ")
+ idNewline = which.min(abs(posSpace - nbLim * nbNewline))
+ posNewline = posSpace[idNewline]
+ gloseName = paste(substring(gloseName,
+ c(1, posNewline + 1),
+ c(posNewline,
+ nchar(gloseName))),
+ collapse="\n")
+ Newline = substr(gloseName,
+ posNewline + 2,
+ nchar(gloseName))
+ nbChar = nchar(Newline)
+ }
+
+ Yline = 0.6 + 0.47*nbNewline
+ Ytitle = Yline + 0.15
- # X axis
- scale_x_continuous(limits=c(-0.3, 1 + 3),
- expand=c(0, 0)) +
- # Y axis
- scale_y_continuous(limits=c(0, 10),
- expand=c(0, 0)) +
- # Margin
- theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
-
- # New plot with void theme
- pal = ggplot() + theme_void() +
- # Plots the point of the colorbar
- geom_point(data=plot_palette,
- aes(x=xtick, y=ytick),
- shape=21, size=5, stroke=1,
- color='white', fill=colTick)
+ # New plot with void theme
+ title = ggplot() + theme_void() +
+ # Plots separation lines
+ geom_line(aes(x=c(-0.3, 3.9), y=c(0.05, 0.05)),
+ size=0.6, color="#00A3A8") +
+ geom_line(aes(x=c(-0.3, 3.9), y=c(Yline, Yline)),
+ size=0.6, color="#00A3A8") +
+ # Writes title
+ geom_shadowtext(data=tibble(x=-0.3, y=Ytitle,
+ label=var),
+ aes(x=x, y=y, label=label),
+ fontface="bold",
+ color="#00A3A8",
+ bg.colour="white",
+ hjust=0, vjust=0, size=10) +
+
+ # Writes title
+ geom_shadowtext(data=tibble(x=-0.3, y=0.2,
+ label=gloseName),
+ aes(x=x, y=y, label=label),
+ fontface="bold",
+ color="#00A3A8",
+ bg.colour="white",
+ hjust=0, vjust=0, size=3) +
+
+ # X axis
+ scale_x_continuous(limits=c(-0.3, 1 + 3),
+ expand=c(0, 0)) +
+ # Y axis
+ scale_y_continuous(limits=c(0, 10),
+ expand=c(0, 0)) +
+ # Margin
+ theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
+
+ # New plot with void theme
+ pal = ggplot() + theme_void() +
+ # Plots the point of the colorbar
+ geom_point(data=plot_palette,
+ aes(x=xtick, y=ytick),
+ shape=21, size=5, stroke=1,
+ color='white', fill=colTick)
+
- if (!is.null(trend_period)) {
periodName_trend = paste(
format(as.Date(trend_period[[idPer_trend]][1]),
'%Y'),
format(as.Date(trend_period[[idPer_trend]][2]),
'%Y'),
sep='-')
- } else {
- periodName_trend = NA
- }
-
- if (!is.null(mean_period)) {
+
periodName1_mean = paste(
format(as.Date(mean_period[[1]][1]),
'%Y'),
@@ -587,215 +595,216 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
format(as.Date(mean_period[[2]][2]),
'%Y'),
sep='-')
- } else {
- periodName1_mean = NA
- periodName2_mean = NA
- }
-
- if (j > 1) {
- ValueName1 = "Écarts observés entre"
- ValueName2 = paste(periodName1_mean,
- " et ",
- periodName2_mean,
- sep='')
- # If it is a flow variable
- if (type == 'sévérité') {
- unit = bquote(bold("(%)"))
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- unit = bquote(bold("(jour)"))
- }
- } else {
- ValueName1 = "Tendances observées"
- ValueName2 = paste("sur la période ",
- periodName_trend, sep='')
- # If it is a flow variable
- if (type == 'sévérité') {
- unit = bquote(bold("(% par an)"))
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- unit = bquote(bold("(jour par an)"))
+
+ if (j > 1) {
+ ValueName1 = "Écarts observés entre"
+ ValueName2 = paste(periodName1_mean,
+ " et ",
+ periodName2_mean,
+ sep='')
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ unit = bquote(bold("(%)"))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ unit = bquote(bold("(jour)"))
+ }
+ } else {
+ ValueName1 = "Tendances observées"
+ ValueName2 = paste("sur la période ",
+ periodName_trend, sep='')
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ unit = bquote(bold("(% par an)"))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ unit = bquote(bold("(jour par an)"))
+ }
}
- }
-
- pal = pal +
- # Name of the colorbar
- annotate('text',
- x=-0.3, y= valNorm + 37,
- label=ValueName1,
- hjust=0, vjust=0.5,
- size=6, color='grey40') +
- # Second line
- annotate('text',
- x=-0.3, y= valNorm + 26,
- label=ValueName2,
- hjust=0, vjust=0.5,
- size=6, color='grey40') +
- # Unit legend of the colorbar
- annotate('text',
- x=-0.3, y= valNorm + 14,
- label=unit,
- hjust=0, vjust=0.5,
- size=4, color='grey40')
-
- # For all the ticks
- for (id in 1:nbTick) {
- pal = pal +
- # Adds the value
- annotate('text', x=xtick[id]+0.3,
- y=ytick[id],
- label=bquote(bold(.(labTick[id]))),
- hjust=0, vjust=0.7,
- size=3, color='grey40')
- }
-
- if (j == 1) {
- upLabel = bquote(bold("Hausse significative à 10%"))
- noneLabel = bquote(bold("Non significatif à 10%"))
- downLabel = bquote(bold("Baisse significative à 10%"))
- yUp = -20
- yNone = -29
- yDown = -40
-
pal = pal +
- # Up triangle in the marker legend
- geom_point(aes(x=0, y=yUp),
- shape=24, size=4, stroke=1,
- color='grey50', fill='grey97') +
- # Up triangle text legend
+ # Name of the colorbar
annotate('text',
- x=0.3, y=yUp,
- label=upLabel,
+ x=-0.3, y= valNorm + 37,
+ label=ValueName1,
hjust=0, vjust=0.5,
- size=3, color='grey40')
+ size=6, color='grey40') +
+ # Second line
+ annotate('text',
+ x=-0.3, y= valNorm + 26,
+ label=ValueName2,
+ hjust=0, vjust=0.5,
+ size=6, color='grey40') +
+ # Unit legend of the colorbar
+ annotate('text',
+ x=-0.3, y= valNorm + 14,
+ label=unit,
+ hjust=0, vjust=0.5,
+ size=4, color='grey40')
+
+ # For all the ticks
+ for (id in 1:nbTick) {
+ pal = pal +
+ # Adds the value
+ annotate('text', x=xtick[id]+0.3,
+ y=ytick[id],
+ label=bquote(bold(.(labTick[id]))),
+ hjust=0, vjust=0.7,
+ size=3, color='grey40')
+ }
+
+ if (j == 1) {
+ upLabel = bquote(bold("Hausse significative à 10%"))
+ noneLabel = bquote(bold("Non significatif à 10%"))
+ downLabel = bquote(bold("Baisse significative à 10%"))
+
+ yUp = -20
+ yNone = -29
+ yDown = -40
+
+ pal = pal +
+ # Up triangle in the marker legend
+ geom_point(aes(x=0, y=yUp),
+ shape=24, size=4, stroke=1,
+ color='grey50', fill='grey97') +
+ # Up triangle text legend
+ annotate('text',
+ x=0.3, y=yUp,
+ label=upLabel,
+ hjust=0, vjust=0.5,
+ size=3, color='grey40')
+
+ pal = pal +
+ # Circle in the marker legend
+ geom_point(aes(x=0, y=yNone),
+ shape=21, size=4, stroke=1,
+ color='grey50', fill='grey97') +
+ # Circle text legend
+ annotate('text',
+ x=0.3, y=yNone,
+ label=noneLabel,
+ hjust=0, vjust=0.7,
+ size=3, color='grey40')
+
+ pal = pal +
+ # Down triangle in the marker legend
+ geom_point(aes(x=0, y=yDown),
+ shape=25, size=4, stroke=1,
+ color='grey50', fill='grey97') +
+ # Down triangle text legend
+ annotate('text',
+ x=0.3, y=yDown,
+ label=downLabel,
+ hjust=0, vjust=0.5,
+ size=3, color='grey40')
+
+ }
+
+ # Normalises all the trend values for each station
+ # according to the colorbar
+ if (j > 1) {
+ yValue = (Value - minBreakValue[j, i]) / (maxBreakValue[j, i] - minBreakValue[j, i]) * valNorm
+ } else {
+ yValue = (Value - minTrendValue[idPer_trend, i]) / (maxTrendValue[idPer_trend, i] - minTrendValue[idPer_trend, i]) * valNorm
+ }
+ # Takes only the significative ones
+ yValue = yValue[OkVal]
+
+ # Histogram distribution
+ # Computes the histogram of values
+ res_hist = hist(yValue, breaks=ytick, plot=FALSE)
+ # Extracts the number of counts per cells
+ counts = res_hist$counts
+ # Extracts limits of cells
+ breaks = res_hist$breaks
+ # Extracts middle of cells
+ mids = res_hist$mids
+
+ # Blank vectors to store position of points of
+ # the distribution to plot
+ xValue = c()
+ yValue = c()
+ # Start X position of the distribution
+ start_hist = 1
+
+ # X separation bewteen point
+ hist_sep = 0.15
+
+ # Gets the maximun number of point of the distribution
+ maxCount = max(counts, na.rm=TRUE)
+ # Limit of the histogram
+ lim_hist = 2
+ # If the number of point will exceed the limit
+ if (maxCount * hist_sep > lim_hist) {
+ # Computes the right amount of space between points
+ hist_sep = lim_hist / maxCount
+ }
+
+ # For all cells of the histogram
+ for (ii in 1:length(mids)) {
+ # If the count in the current cell is not zero
+ if (counts[ii] != 0) {
+ # Stores the X positions of points of the
+ # distribution for the current cell
+ xValue = c(xValue,
+ seq(start_hist,
+ start_hist+(counts[ii]-1)*hist_sep,
+ by=hist_sep))
+ }
+ # Stores the Y position which is the middle of the
+ # current cell the number of times it has been counted
+ yValue = c(yValue, rep(mids[ii], times=counts[ii]))
+ }
+
+ # Makes a tibble to plot the distribution
+ plot_value = tibble(xValue=xValue, yValue=yValue)
+
pal = pal +
- # Circle in the marker legend
- geom_point(aes(x=0, y=yNone),
- shape=21, size=4, stroke=1,
- color='grey50', fill='grey97') +
- # Circle text legend
- annotate('text',
- x=0.3, y=yNone,
- label=noneLabel,
- hjust=0, vjust=0.7,
- size=3, color='grey40')
+ # Plots the point of the distribution
+ geom_point(data=plot_value,
+ aes(x=xValue, y=yValue),
+ shape=21, color='white',
+ fill='grey50', stroke=0.4,
+ alpha=1)
+
+ if (type == 'sévérité') {
+ labelArrow = 'Plus sévère'
+ } else if (type == 'saisonnalité') {
+ labelArrow = 'Plus tôt'
+ }
+
+ # Position of the arrow
+ xArrow = 3.3
pal = pal +
- # Down triangle in the marker legend
- geom_point(aes(x=0, y=yDown),
- shape=25, size=4, stroke=1,
- color='grey50', fill='grey97') +
- # Down triangle text legend
+ # Arrow to show a worsening of the situation
+ geom_segment(aes(x=xArrow, y=valNorm*0.75,
+ xend=xArrow, yend=valNorm*0.25),
+ color='grey50', size=0.3,
+ arrow=arrow(length=unit(2, "mm"))) +
+ # Text associated to the arrow
annotate('text',
- x=0.3, y=yDown,
- label=downLabel,
- hjust=0, vjust=0.5,
- size=3, color='grey40')
+ x=xArrow+0.1, y=valNorm*0.5,
+ label=labelArrow,
+ angle=90,
+ hjust=0.5, vjust=1,
+ size=3, color='grey50')
+
+ pal = pal +
+ # X axis of the colorbar
+ scale_x_continuous(limits=c(-0.3, 4),
+ expand=c(0, 0)) +
+ # Y axis of the colorbar
+ scale_y_continuous(limits=c(-47, valNorm + 48),
+ expand=c(0, 0)) +
+ # Margin of the colorbar
+ theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
- }
-
- # Normalises all the trend values for each station
- # according to the colorbar
- if (j > 1) {
- yValue = (Value - minBreakValue[j, i]) / (maxBreakValue[j, i] - minBreakValue[j, i]) * valNorm
} else {
- yValue = (Value - minTrendValue[idPer_trend, i]) / (maxTrendValue[idPer_trend, i] - minTrendValue[idPer_trend, i]) * valNorm
+ pal = void
+ title = void
}
-
- # Takes only the significative ones
- yValue = yValue[OkVal]
-
- # Histogram distribution
- # Computes the histogram of values
- res_hist = hist(yValue, breaks=ytick, plot=FALSE)
- # Extracts the number of counts per cells
- counts = res_hist$counts
- # Extracts limits of cells
- breaks = res_hist$breaks
- # Extracts middle of cells
- mids = res_hist$mids
-
- # Blank vectors to store position of points of
- # the distribution to plot
- xValue = c()
- yValue = c()
- # Start X position of the distribution
- start_hist = 1
-
- # X separation bewteen point
- hist_sep = 0.15
-
- # Gets the maximun number of point of the distribution
- maxCount = max(counts, na.rm=TRUE)
- # Limit of the histogram
- lim_hist = 2
- # If the number of point will exceed the limit
- if (maxCount * hist_sep > lim_hist) {
- # Computes the right amount of space between points
- hist_sep = lim_hist / maxCount
- }
-
- # For all cells of the histogram
- for (ii in 1:length(mids)) {
- # If the count in the current cell is not zero
- if (counts[ii] != 0) {
- # Stores the X positions of points of the
- # distribution for the current cell
- xValue = c(xValue,
- seq(start_hist,
- start_hist+(counts[ii]-1)*hist_sep,
- by=hist_sep))
- }
- # Stores the Y position which is the middle of the
- # current cell the number of times it has been counted
- yValue = c(yValue, rep(mids[ii], times=counts[ii]))
- }
-
- # Makes a tibble to plot the distribution
- plot_value = tibble(xValue=xValue, yValue=yValue)
-
- pal = pal +
- # Plots the point of the distribution
- geom_point(data=plot_value,
- aes(x=xValue, y=yValue),
- shape=21, color='white',
- fill='grey50', stroke=0.4,
- alpha=1)
-
- if (type == 'sévérité') {
- labelArrow = 'Plus sévère'
- } else if (type == 'saisonnalité') {
- labelArrow = 'Plus tôt'
- }
-
- # Position of the arrow
- xArrow = 3.3
-
- pal = pal +
- # Arrow to show a worsening of the situation
- geom_segment(aes(x=xArrow, y=valNorm*0.75,
- xend=xArrow, yend=valNorm*0.25),
- color='grey50', size=0.3,
- arrow=arrow(length=unit(2, "mm"))) +
- # Text associated to the arrow
- annotate('text',
- x=xArrow+0.1, y=valNorm*0.5,
- label=labelArrow,
- angle=90,
- hjust=0.5, vjust=1,
- size=3, color='grey50')
-
- pal = pal +
- # X axis of the colorbar
- scale_x_continuous(limits=c(-0.3, 4),
- expand=c(0, 0)) +
- # Y axis of the colorbar
- scale_y_continuous(limits=c(-47, valNorm + 48),
- expand=c(0, 0)) +
- # Margin of the colorbar
- theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
if (!is.null(df_page)) {
if (j > 1) {
diff --git a/plotting/tools.R b/plotting/tools.R
index c297b27f8495708941d08a8152b71652b5666115..94624efcbeada58bde26fed61fa7aab3361603e6 100644
--- a/plotting/tools.R
+++ b/plotting/tools.R
@@ -85,6 +85,11 @@ get_color = function (value, min, max, ncolor=256, palette_name='perso', reverse
# Returns the colorbar but also positions, labels and colors of some
# ticks along it
get_palette = function (min, max, ncolor=256, palette_name='perso', reverse=FALSE, nbTick=10) {
+
+ # If the value is a NA return NA color
+ if (is.null(min) | is.null(max)) {
+ return (NA)
+ }
# If the palette chosen is the personal ones
if (palette_name == 'perso') {
diff --git a/processing/analyse.R b/processing/analyse.R
index b39e8b5e7eb8a8852897eca41f68aa76a87ed0ab..8bcffaf3d112cb932334a09c798aa040492b8289 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -830,3 +830,45 @@ get_lacune = function (df_data, df_meta) {
df_meta = full_join(df_meta, df_lac)
return (df_meta)
}
+
+### 2.4. Compute square root of data _________________________________
+compute_sqrt = function (df_data) {
+
+ df_sqrt = tibble(Date=df_data$Date,
+ Value=sqrt(df_data$Value),
+ code=df_data$code)
+
+ return (df_sqrt)
+}
+
+### 2.5. Criticism of data ___________________________________________
+add_critique = function (df_critique, Code, author, level, start_date, variable, type, comment='', end_date=NULL, df_meta=NULL, resdir=NULL) {
+ if (Code == 'all' & is.null(df_meta)) {
+ Code = NA # erreur
+ } else if (Code == 'all' & !is.null(df_meta)) {
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+ }
+
+ if (is.null(end_date)) {
+ end_date = start_date
+ }
+
+ df_tmp = tibble(code=Code, author=author, level=level,
+ start_date=start_date, end_date=end_date,
+ variable=variable, type=type,
+ comment=comment)
+ df_critique = bind_rows(df_critique, df_tmp)
+
+ nc = nrow(df_critique)
+ print('Criticism registered')
+ print(df_critique[(nc-2):nc,])
+
+ if (!is.null(resdir)) {
+ write_critique(df_critique, resdir)
+ }
+
+ return (df_critique)
+}
+
+# df_critique = add_critique(df_critique, Code='', author='', level=, start_date='', end_date='', variable='', type='', comment='')
diff --git a/processing/read_write.R b/processing/read_write.R
index d564cd58e4c5cfd28f74a95fd44e2efbe16e849f..ac975438c5c5692695c23b2974975d23521c42b1 100644
--- a/processing/read_write.R
+++ b/processing/read_write.R
@@ -27,7 +27,7 @@
## 1. WRITING ________________________________________________________
### 1.1. List of dataframe ___________________________________________
-write_listofdf = function (Ldf, resdir, filedir, optdir='') {
+write_analyse = function (Ldf, resdir, filedir, optdir='') {
outdir = file.path(resdir, optdir, filedir)
if (!(file.exists(outdir))) {
@@ -50,7 +50,7 @@ write_listofdf = function (Ldf, resdir, filedir, optdir='') {
}
### 2.2. Dataframe of modified data __________________________________
-write_dfdata = function (df_data, df_mod, resdir, filedir, optdir='') {
+write_data = function (df_data, df_mod, resdir, filedir, optdir='') {
Code = rle(sort(df_mod$code))$values
print(Code)
@@ -81,7 +81,26 @@ write_dfdata = function (df_data, df_mod, resdir, filedir, optdir='') {
row.names=FALSE)
}
}
-
+
+### 2.3. Dataframe of criticism ______________________________________
+write_critique = function (df_critique, resdir, filename='critique', optdir='') {
+
+ outdir = file.path(resdir, optdir)
+ if (!(file.exists(outdir))) {
+ dir.create(outdir, recursive=TRUE)
+ }
+
+ print(paste('Writing criticism in : ', outdir, sep=''))
+
+ outfile = paste(filename, '.txt', sep='')
+ write.table(df_critique,
+ file=file.path(outdir, outfile),
+ sep=";",
+ quote=FALSE,
+ row.names=FALSE)
+}
+# write_critique(df_critique, resdir)
+
## 2. READING ________________________________________________________
### 2.1. List of dataframe ___________________________________________
@@ -121,6 +140,35 @@ read_listofdf = function (resdir, filedir) {
return (Ldf)
}
+### 2.3. Dataframe of criticism ______________________________________
+read_critique = function (resdir, filename='critique', optdir='') {
+
+ outdir = file.path(resdir, optdir)
+ outfile = paste(filename, '.txt', sep='')
+ file_path = file.path(outdir, outfile)
+
+ print(paste('Reading criticism in : ', file_path, sep=''))
+
+ df = as_tibble(read.table(file=file_path,
+ header=TRUE,
+ sep=";"))
+
+ for (j in 1:ncol(df)) {
+ if (is.factor(df[[j]])) {
+ d = try(as.Date(df[[1, j]], format="%Y-%m-%d"))
+ if("try-error" %in% class(d) || is.na(d)) {
+ df[j] = as.character(df[[j]])
+ } else {
+ df[j] = as.Date(df[[j]])
+ }
+ }
+ }
+
+ return (df)
+}
+# df_critique = read_critique(resdir)
+
+
## 3. OTHER __________________________________________________________
splitext = function(file) {
ex = strsplit(basename(file), split="\\.")[[1]]
diff --git a/script.R b/script.R
index b59d74f1bc67206eafd084b9e7fda650a079f903..ab347504bd58212fdf07e2825d0be3de6de4d82c 100644
--- a/script.R
+++ b/script.R
@@ -64,7 +64,7 @@ filename =
# "O5055010_HYDRO_QJM.txt",
# "O0384010_HYDRO_QJM.txt",
# "S4214010_HYDRO_QJM.txt",
- # "Q7002910_HYDRO_QJM.txt",
+ # "Q7002910_HYDRO_QJM.txt"
# "Q0214010_HYDRO_QJM.txt"
# "O3035210_HYDRO_QJM.txt",
# "O0554010_HYDRO_QJM.txt",
@@ -79,8 +79,8 @@ AEAGlistdir =
""
AEAGlistname =
- # ""
- "Liste-station_RRSE.docx"
+ ""
+ # "Liste-station_RRSE.docx"
## NIVALE SELECTION
@@ -91,8 +91,8 @@ INRAElistdir =
""
INRAElistname =
- # ""
- "INRAE_selection.txt"
+ ""
+ # "INRAE_selection.txt"
## TREND ANALYSIS
@@ -269,58 +269,60 @@ glose = list(
df_meta = get_lacune(df_data, df_meta)
# Hydrograph
df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
+# Square root
+df_sqrt = compute_sqrt(df_data)
### 3.2. Trend analysis ______________________________________________
-# QA trend
-res = get_QAtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- yearLac_day=yearLac_day)
-df_QAdata = res$data
-df_QAmod = res$mod
-res_QAtrend = res$analyse
-
-# QMNA tend
-res = get_QMNAtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
-df_QMNAdata = res$data
-df_QMNAmod = res$mod
-res_QMNAtrend = res$analyse
-
-# VCN10 trend
-res = get_VCN10trend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
-df_VCN10data = res$data
-df_VCN10mod = res$mod
-res_VCN10trend = res$analyse
-
-# Start date for low water trend
-res = get_tDEBtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- thresold_type='VCN10',
- select_longest=TRUE,
- yearLac_day=yearLac_day)
-df_tDEBdata = res$data
-df_tDEBmod = res$mod
-res_tDEBtrend = res$analyse
-
-# Center date for low water trend
-res = get_tCENtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
-df_tCENdata = res$data
-df_tCENmod = res$mod
-res_tCENtrend = res$analyse
+# # QA trend
+# res = get_QAtrend(df_data, df_meta,
+# period=trend_period,
+# alpha=alpha,
+# yearLac_day=yearLac_day)
+# df_QAdata = res$data
+# df_QAmod = res$mod
+# res_QAtrend = res$analyse
+
+# # QMNA tend
+# res = get_QMNAtrend(df_data, df_meta,
+# period=trend_period,
+# alpha=alpha,
+# sampleSpan=sampleSpan,
+# yearLac_day=yearLac_day)
+# df_QMNAdata = res$data
+# df_QMNAmod = res$mod
+# res_QMNAtrend = res$analyse
+
+# # VCN10 trend
+# res = get_VCN10trend(df_data, df_meta,
+# period=trend_period,
+# alpha=alpha,
+# sampleSpan=sampleSpan,
+# yearLac_day=yearLac_day)
+# df_VCN10data = res$data
+# df_VCN10mod = res$mod
+# res_VCN10trend = res$analyse
+
+# # Start date for low water trend
+# res = get_tDEBtrend(df_data, df_meta,
+# period=trend_period,
+# alpha=alpha,
+# sampleSpan=sampleSpan,
+# thresold_type='VCN10',
+# select_longest=TRUE,
+# yearLac_day=yearLac_day)
+# df_tDEBdata = res$data
+# df_tDEBmod = res$mod
+# res_tDEBtrend = res$analyse
+
+# # Center date for low water trend
+# res = get_tCENtrend(df_data, df_meta,
+# period=trend_period,
+# alpha=alpha,
+# sampleSpan=sampleSpan,
+# yearLac_day=yearLac_day)
+# df_tCENdata = res$data
+# df_tCENmod = res$mod
+# res_tCENtrend = res$analyse
### 3.3. Break analysis ______________________________________________
# df_break = get_break(res_QAtrend$data, df_meta)
@@ -340,10 +342,10 @@ res_tCENtrend = res$analyse
# df_modtmp = get(paste('df_', v, 'mod', sep=''))
# res_trendtmp = get(paste('res_', v, 'trend', sep=''))
# # Modified data saving
-# write_dfdata(df_datatmp, df_modtmp, resdir, optdir='modified_data',
+# write_data(df_datatmp, df_modtmp, resdir, optdir='modified_data',
# filedir=v)
# # Trend analysis saving
-# write_listofdf(res_trendtmp, resdir, optdir='trend_analyse',
+# write_analyse(res_trendtmp, resdir, optdir='trend_analyse',
# filedir=v)
# }
# res_tDEBtrend = read_listofdf(resdir, 'res_tDEBtrend')
@@ -359,62 +361,68 @@ df_shapefile = ini_shapefile(resources_path,
### 5.1. Simple time panel to criticize station data _________________
# Plot time panel of debit by stations
-# datasheet_layout(list(df_data, df_data),
- # layout_matrix=c(1, 2),
- # df_meta=df_meta,
- # missRect=list(TRUE, TRUE),
- # var=list('Q', 'sqrt(Q)'),
- # info_header=TRUE,
- # time_header=NULL,
- # var_ratio=3,
- # figdir=figdir,
- # filename_opt='time')
-
-### 5.2. Analysis layout _____________________________________________
-datasheet_layout(toplot=c(
- 'datasheet'
- # 'matrix',
- # 'map'
- ),
+datasheet_layout(toplot=c('datasheet'),
df_meta=df_meta,
-
- df_data=list(
- res_QAtrend$data,
- res_QMNAtrend$data,
- res_VCN10trend$data,
- res_tDEBtrend$data,
- res_tCENtrend$data
- ),
-
- df_trend=list(
- res_QAtrend$trend,
- res_QMNAtrend$trend,
- res_VCN10trend$trend,
- res_tDEBtrend$trend,
- res_tCENtrend$trend
- ),
-
- var=var,
- type=type,
- glose=glose,
-
- layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),
-
- missRect=TRUE,
- trend_period=trend_period,
- mean_period=mean_period,
- info_header=TRUE,
- time_header=df_data,
- foot_note=TRUE,
- info_ratio=2,
- time_ratio=2,
- var_ratio=3,
- foot_height=1.25,
+ df_data=list(df_data,
+ df_sqrt),
+ var=list('Q', 'sqrt(Q)'),
+ type=list('data', 'data'),
+ layout_matrix=matrix(c(1, 2), ncol=1),
+ info_header=df_data,
df_shapefile=df_shapefile,
figdir=figdir,
- filename_opt='',
resources_path=resources_path,
logo_dir=logo_dir,
AEAGlogo_file=AEAGlogo_file,
INRAElogo_file=INRAElogo_file,
FRlogo_file=FRlogo_file)
+
+
+### 5.2. Analysis layout _____________________________________________
+# datasheet_layout(toplot=c(
+# 'datasheet'
+# # 'matrix',
+# # 'map'
+# ),
+# df_meta=df_meta,
+
+# df_data=list(
+# res_QAtrend$data,
+# res_QMNAtrend$data,
+# res_VCN10trend$data,
+# res_tDEBtrend$data,
+# res_tCENtrend$data
+# ),
+
+# df_trend=list(
+# res_QAtrend$trend,
+# res_QMNAtrend$trend,
+# res_VCN10trend$trend,
+# res_tDEBtrend$trend,
+# res_tCENtrend$trend
+# ),
+
+# var=var,
+# type=type,
+# glose=glose,
+
+# layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),
+
+# missRect=TRUE,
+# trend_period=trend_period,
+# mean_period=mean_period,
+# info_header=df_data,
+# time_header=df_data,
+# foot_note=TRUE,
+# info_height=2.8,
+# time_ratio=2,
+# var_ratio=3,
+# foot_height=1.25,
+# df_shapefile=df_shapefile,
+# figdir=figdir,
+# filename_opt='',
+# resources_path=resources_path,
+# logo_dir=logo_dir,
+# AEAGlogo_file=AEAGlogo_file,
+# INRAElogo_file=INRAElogo_file,
+# FRlogo_file=FRlogo_file)