diff --git a/plotting/layout.R b/plotting/layout.R
index e46e4d5d423bc7bd782f975dde835e86a771df99..b118aecb8bb2cc2fa0fabf16cd680b436e3792f7 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -436,6 +436,10 @@ get_power = function (value) {
# The magnitude is the number of character of integer part
# of the value minus one
power = nchar(as.character(as.integer(value))) - 1
+ # If value is zero
+ } else if (value == 0) {
+ # The power is zero
+ power = 0
# If the value is less than one
} else {
# Extract the decimal part
diff --git a/plotting/map.R b/plotting/map.R
index cdba53ce6c316e2f16a97824249bc411e525b4c1..31acce4a9940e2122aa78af52969163be23793c1 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -69,6 +69,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
}
+ # Blank array to store time info
tab_Start = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_End = array(rep('', nCode*nbp*nPeriod_max),
@@ -82,7 +83,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
for (k in 1:nCode) {
# Gets the code
code = Code[k]
-
+ # For all the variable
for (i in 1:nbp) {
df_trend = list_df2plot[[i]]$trend
# Extracts the trend corresponding to the code
@@ -105,7 +106,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
Periods = paste(Start[j],
End[j],
sep=' / ')
-
+
+ # Saves the time info
tab_Start[k, i, j] = as.character(Start[j])
tab_End[k, i, j] = as.character(End[j])
tab_Code[k, i, j] = code
@@ -118,7 +120,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# Blank array to store mean of the trend for each
# station, perdiod and variable
- TrendMean_code = array(rep(1, nPeriod_max*nbp*nCode),
+ TrendValue_code = array(rep(1, nPeriod_max*nbp*nCode),
dim=c(nPeriod_max, nbp, nCode))
# For all the period
for (j in 1:nPeriod_max) {
@@ -134,6 +136,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
+ # Extracts the type of the variable
+ type = list_df2plot[[i]]$type
p_threshold = list_df2plot[[i]]$p_threshold
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
@@ -162,26 +166,32 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
df_trend_code_per = df_trend_code_per[1,]
}
- # Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
- # Normalises the trend value by the mean of the data
- trendMean = df_trend_code_per$trend / dataMean
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Computes the mean of the data on the period
+ dataMean = mean(df_data_code_per$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
+ }
# If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
# Stores the mean trend
- TrendMean_code[j, i, k] = trendMean
+ TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
- TrendMean_code[j, i, k] = NA
+ TrendValue_code[j, i, k] = NA
}
}
}
}
# Compute the min and the max of the mean trend for all the station
- minTrendMean = apply(TrendMean_code, c(1, 2), min, na.rm=TRUE)
- maxTrendMean = apply(TrendMean_code, c(1, 2), max, na.rm=TRUE)
+ minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
+ maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
# Number of ticks for the colorbar
nbTick = 10
@@ -195,6 +205,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
# Extracts the variable of the plot
var = list_df2plot[[i]]$var
+ # Extracts the type of variable of the plot
+ type = list_df2plot[[i]]$type
# Createsa name for the map
outname = paste('map_', var, sep='')
# If there is the verbose option
@@ -381,21 +393,27 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
df_trend_code_per = df_trend_code_per[1,]
}
- # Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
- # Normalises the trend value by the mean of the data
- trendMean = df_trend_code_per$trend / dataMean
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Computes the mean of the data on the period
+ dataMean = mean(df_data_code_per$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
+ }
# Computes the color associated to the mean trend
- color_res = get_color(trendMean,
- minTrendMean[idPer, i],
- maxTrendMean[idPer, i],
+ color_res = get_color(trendValue,
+ minTrendValue[idPer, i],
+ maxTrendValue[idPer, i],
palette_name='perso',
reverse=TRUE,
ncolor=256)
# Computes the colorbar info
- palette_res = get_palette(minTrendMean[idPer, i],
- maxTrendMean[idPer, i],
+ palette_res = get_palette(minTrendValue[idPer, i],
+ maxTrendValue[idPer, i],
palette_name='perso',
reverse=TRUE,
ncolor=256,
@@ -406,7 +424,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# The computed color is stored
filltmp = color_res
# If the mean tend is positive
- if (trendMean >= 0) {
+ if (trendValue >= 0) {
# Uses a triangle up for the shape of the marker
shapetmp = 24
# If negative
@@ -433,7 +451,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
lat = c(lat, lattmp)
fill = c(fill, filltmp)
shape = c(shape, shapetmp)
- trend = c(trend, trendMean)
+ trend = c(trend, trendValue)
# If the trend analysis is significative a TRUE is stored
p_threshold_Ok = c(p_threshold_Ok,
df_trend_code_per$p <= p_threshold)
@@ -468,7 +486,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
geom_point(data=plot_map_code,
aes(x=lon, y=lat),
shape=21, size=1.5, stroke=0.5,
- color='grey40', fill='grey40')
+ color='#00A3A8', fill='#00A3A8')
}
# Extracts the position of the tick of the colorbar
@@ -482,9 +500,17 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
valNorm = nbTick * 10
# Normalisation of the position of ticks
ytick = posTick / max(posTick) * valNorm
- # Formatting of label in pourcent
- labTick = as.character(round(labTick*100, 2))
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Formatting of label in pourcent
+ labTick = as.character(round(labTick*100, 2))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Formatting of label
+ labTick = as.character(round(labTick, 2))
+ }
+
# X position of ticks all similar
xtick = rep(0, times=nbTick)
@@ -522,6 +548,14 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
shape=21, size=5, stroke=1,
color='white', fill=colTick)
+ # 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',
@@ -532,7 +566,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# Unit legend of the colorbar
annotate('text',
x=-0.2, y= valNorm + 13,
- label=bquote(bold("% par an")),
+ label=unit,
hjust=0, vjust=0.5,
size=4, color='grey40')
# For all the ticks
@@ -584,8 +618,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# Normalises all the trend values for each station
# according to the colorbar
- yTrend = (trend - minTrendMean[idPer, i]) /
- (maxTrendMean[idPer, i] - minTrendMean[idPer, i]) * valNorm
+ yTrend = (trend - minTrendValue[idPer, i]) /
+ (maxTrendValue[idPer, i] - minTrendValue[idPer, i]) * valNorm
# Takes only the significative ones
yTrend = yTrend[p_threshold_Ok]
@@ -656,21 +690,22 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# color="grey20", fill="grey20")
alpha=0.4)
-
- pal = pal +
- # Arrow to show a worsening of the situation
- geom_segment(aes(x=2.7, y=valNorm*0.75,
- xend=2.7, yend=valNorm*0.25),
- color='grey50', size=0.3,
- arrow=arrow(length=unit(2, "mm"))) +
- # Text associated to the arrow
- annotate('text',
- x=2.8, y=valNorm*0.5,
- label= "Plus sévère",
- angle=90,
- hjust=0.5, vjust=1,
- size=3, color='grey50')
-
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ pal = pal +
+ # Arrow to show a worsening of the situation
+ geom_segment(aes(x=2.7, y=valNorm*0.75,
+ xend=2.7, yend=valNorm*0.25),
+ color='grey50', size=0.3,
+ arrow=arrow(length=unit(2, "mm"))) +
+ # Text associated to the arrow
+ annotate('text',
+ x=2.8, y=valNorm*0.5,
+ label= "Plus sévère",
+ angle=90,
+ hjust=0.5, vjust=1,
+ size=3, color='grey50')
+ }
pal = pal +
# X axis of the colorbar
diff --git a/plotting/matrix.R b/plotting/matrix.R
index e376c8ee0ca0900cbf53b62282e8ea210787280b..aeeb47b34bd4cdf66ff150bf94d194f517b16a04 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -70,6 +70,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
+ # Blank array to store time info
tab_Start = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_End = array(rep('', nCode*nbp*nPeriod_max),
@@ -83,7 +84,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
for (k in 1:nCode) {
# Gets the code
code = Code[k]
-
+ # For all the variable
for (i in 1:nbp) {
df_trend = list_df2plot[[i]]$trend
# Extracts the trend corresponding to the code
@@ -106,7 +107,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Periods = paste(Start[j],
End[j],
sep=' / ')
-
+
+ # Saves the time info
tab_Start[k, i, j] = as.character(Start[j])
tab_End[k, i, j] = as.character(End[j])
tab_Code[k, i, j] = code
@@ -120,7 +122,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Blank array to store mean of the trend for each
# station, perdiod and variable
- TrendMean_code = array(rep(1, nPeriod_trend*nbp*nCode),
+ TrendValue_code = array(rep(1, nPeriod_trend*nbp*nCode),
dim=c(nPeriod_trend, nbp, nCode))
# For all the trend period
for (j in 1:nPeriod_trend) {
@@ -136,6 +138,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
+ # Extracts the type of the variable
+ type = list_df2plot[[i]]$type
p_threshold = list_df2plot[[i]]$p_threshold
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
@@ -163,28 +167,35 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
-
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
- # Normalises the trend value by the mean of the data
- trendMean = df_trend_code_per$trend / dataMean
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Normalises the trend value by the mean of the data
+ trendValue = df_trend_code_per$trend / dataMean
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Just stocks the trend value
+ trendValue = df_trend_code_per$trend
+ }
# If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
# Stores the averaged trend
- TrendMean_code[j, i, k] = trendMean
+ TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
- TrendMean_code[j, i, k] = NA
+ TrendValue_code[j, i, k] = NA
}
}
}
}
# Computes the min and the max of the mean trend for
# all the station
- minTrendMean = apply(TrendMean_code, c(1, 2), min, na.rm=TRUE)
- maxTrendMean = apply(TrendMean_code, c(1, 2), max, na.rm=TRUE)
+ minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
+ maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
# Blank vectors to store info about trend analyses
Periods_trend = c()
@@ -193,7 +204,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Type_trend = c()
Code_trend = c()
Pthresold_trend = c()
- TrendMean_trend = c()
+ TrendValue_trend = c()
DataMean_trend = c()
Fill_trend = c()
Color_trend = c()
@@ -244,15 +255,23 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
- # Normalises the trend value by the mean of the data
- trendMean = df_trend_code_per$trend / dataMean
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Normalises the trend value by the mean of the data
+ trendValue = df_trend_code_per$trend / dataMean
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Just stocks the trend value
+ trendValue = df_trend_code_per$trend
+ }
# If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
# Gets the color associated to the averaged trend
- color_res = get_color(trendMean,
- minTrendMean[j, i],
- maxTrendMean[j, i],
+ color_res = get_color(trendValue,
+ minTrendValue[j, i],
+ maxTrendValue[j, i],
palette_name='perso',
reverse=TRUE)
# Specifies the color fill and contour of
@@ -274,7 +293,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Type_trend = append(Type_trend, type)
Code_trend = append(Code_trend, code)
Pthresold_trend = append(Pthresold_trend, Pthresold)
- TrendMean_trend = append(TrendMean_trend, trendMean)
+ TrendValue_trend = append(TrendValue_trend, trendValue)
DataMean_trend = append(DataMean_trend, dataMean)
Fill_trend = append(Fill_trend, fill)
Color_trend = append(Color_trend, color)
@@ -291,7 +310,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Type_mean = c()
Code_mean = c()
DataMean_mean = c()
- BreakMean_mean = c()
+ breakValue_mean = c()
# Convert 'mean_period' to list
mean_period = as.list(mean_period)
@@ -299,7 +318,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
nPeriod_mean = length(mean_period)
# Blank array to store difference of mean between two periods
- BreakMean_code = array(rep(1, nPeriod_mean*nbp*nCode),
+ breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
dim=c(nPeriod_mean, nbp, nCode))
# Blank array to store mean for a temporary period in order
# to compute the difference of mean with a second period
@@ -353,11 +372,20 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Stocks NA
Break = NA
}
- # Normalises the break by the mean of the
- # initial period
- BreakMean = Break / dataMeantmp[i, k]
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Normalises the break by the mean of the
+ # initial period
+ breakValue = Break / dataMeantmp[i, k]
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Just stocks the break value
+ breakValue = Break
+ }
+
# Stores the result
- BreakMean_code[j, i, k] = BreakMean
+ breakValue_code[j, i, k] = breakValue
# Stores temporarily the mean of the current period
dataMeantmp[i, k] = dataMean
@@ -368,16 +396,16 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Type_mean = append(Type_mean, type)
Code_mean = append(Code_mean, code)
DataMean_mean = append(DataMean_mean, dataMean)
- BreakMean_mean = append(BreakMean_mean,
- BreakMean)
+ breakValue_mean = append(breakValue_mean,
+ breakValue)
}
}
}
# Computes the min and the max of the averaged trend for
# all the station
- minBreakMean = apply(BreakMean_code, c(1, 2),
+ minBreakValue = apply(breakValue_code, c(1, 2),
min, na.rm=TRUE)
- maxBreakMean = apply(BreakMean_code, c(1, 2),
+ maxBreakValue = apply(breakValue_code, c(1, 2),
max, na.rm=TRUE)
# Blanks vector to store color info
Fill_mean = c()
@@ -392,11 +420,11 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# For all variable
for (i in 1:nbp) {
# Extracts averaged breaking
- BreakMean = BreakMean_mean[ii]
+ breakValue = breakValue_mean[ii]
# Gets the color associated
- color_res = get_color(BreakMean,
- minBreakMean[j, i],
- maxBreakMean[j, i],
+ color_res = get_color(breakValue,
+ minBreakValue[j, i],
+ maxBreakValue[j, i],
palette_name='perso',
reverse=TRUE)
# Gets the fill and contour color
@@ -473,7 +501,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subType_trend = Type_trend[CodefL_trend]
subCode_trend = Code_trend[CodefL_trend]
subPthresold_trend = Pthresold_trend[CodefL_trend]
- subTrendMean_trend = TrendMean_trend[CodefL_trend]
+ subTrendValue_trend = TrendValue_trend[CodefL_trend]
subDataMean_trend = DataMean_trend[CodefL_trend]
subFill_trend = Fill_trend[CodefL_trend]
subColor_trend = Color_trend[CodefL_trend]
@@ -488,7 +516,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subType_mean = Type_mean[CodefL_mean]
subCode_mean = Code_mean[CodefL_mean]
subDataMean_mean = DataMean_mean[CodefL_mean]
- subBreakMean_mean = BreakMean_mean[CodefL_mean]
+ subbreakValue_mean = breakValue_mean[CodefL_mean]
subFill_mean = Fill_mean[CodefL_mean]
subColor_mean = Color_mean[CodefL_mean]
@@ -553,8 +581,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subCode_trend[subNPeriod_trend == j]
Pthresold_trend_per =
subPthresold_trend[subNPeriod_trend == j]
- TrendMean_trend_per =
- subTrendMean_trend[subNPeriod_trend == j]
+ TrendValue_trend_per =
+ subTrendValue_trend[subNPeriod_trend == j]
DataMean_trend_per =
subDataMean_trend[subNPeriod_trend == j]
Fill_trend_per =
@@ -625,14 +653,28 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
gg_circle(r=0.45, xc=Xc, yc=Y[i],
fill='white', color='grey40')
}
+
# For all averaged trends on this periods
- for (i in 1:length(TrendMean_trend_per)) {
+ for (i in 1:length(TrendValue_trend_per)) {
# Extracts the value of the averaged trend
- trendMean = TrendMean_trend_per[i]
- # Converts it to the right format with two
- # significant figures
- trendMeanC = signif(trendMean*100, 2)
+ trendValue = TrendValue_trend_per[i]
+ type = Type_trend_per[i]
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ Nsign_mean = 2
+ # Converts it to the right format with
+ # two significant figures
+ trendValueC = signif(trendValue*100, 2)
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Fixes the significants number for mean to 3
+ Nsign_mean = 3
+ # Converts the trend value with two
+ # significant figures
+ trendValueC = signif(trendValue, 2)
+ }
+
# If it is significative
if (!is.na(Pthresold_trend_per[i])) {
# The text color is white
@@ -646,12 +688,12 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Same for averaged variables over
# the current period
dataMean = DataMean_trend_per[i]
- dataMeanC = signif(dataMean, 2)
+ dataMeanC = signif(dataMean, Nsign_mean)
mat = mat +
# Writes the mean trend
annotate('text', x=X[i], y=Y[i],
- label=trendMeanC,
+ label=trendValueC,
hjust=0.5, vjust=0.5,
size=3, color=Tcolor) +
# Writes the mean of the associated variable
@@ -676,10 +718,26 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
for (i in 1:nbpMod) {
# Extract the variable of the plot
var = subVar_trend[i]
+ type = subType_trend[i]
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Fixes the unit of the mean and the trend
+ # for the flow
+ unit_mean = bquote('['*m^3*'.'*s^{-1}*']')
+ unit_trend = bquote('[%.'*an^{-1}*']')
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Fixes the unit of the mean and the trend
+ # for the date
+ unit_mean = bquote('[jour]')
+ unit_trend = bquote('[jour.'*an^{-1}*']')
+ }
+
mat = mat +
# Writes the unit of the variable
annotate('text', x=X[i], y=max(Y) + 0.63,
- label=bquote('[%.'*ans^{-1}*']'),
+ label=unit_trend,
hjust=0.5, vjust=0.5,
size=2, color='grey40') +
# Writes the type of the variable
@@ -689,7 +747,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
size=3.25, color='grey20') +
# Writes the unit of the averaged variable
annotate('text', x=Xm[i], y=max(Y) + 0.63,
- label=bquote('['*m^3*'.'*s^{-1}*']'),
+ label=unit_mean,
hjust=0.5, vjust=0.5,
size=2, color='grey40') +
# Writes the type of the averaged variable
@@ -742,8 +800,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subCode_mean[subNPeriod_mean == j]
DataMean_mean_per =
subDataMean_mean[subNPeriod_mean == j]
- BreakMean_mean_per =
- subBreakMean_mean[subNPeriod_mean == j]
+ breakValue_mean_per =
+ subbreakValue_mean[subNPeriod_mean == j]
Fill_mean_per =
subFill_mean[subNPeriod_mean == j]
Color_mean_per =
@@ -839,14 +897,27 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
color=Color_mean_per[i])
}
}
-
+
# For all averaged variables on this period
for (i in 1:length(DataMean_mean_per)) {
+ type = Type_mean_per[i]
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # The number of significant figures for
+ # flow mean is 2
+ Nsign_mean = 2
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # The number of significant figures for
+ # date mean is 3
+ Nsign_mean = 3
+ }
# Extracts values of averaged variables
dataMean = DataMean_mean_per[i]
+
# Converts it to the right format with two
# significant figures
- dataMeanC = signif(dataMean, 2)
+ dataMeanC = signif(dataMean, Nsign_mean)
# Writes averaged variables values
mat = mat +
annotate('text', x=Xm_mean[i], y=Y[i],
@@ -856,14 +927,23 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# If this is not the first period
if (j > 1) {
# Extracts values of breaking between periods
- BreakMean = BreakMean_mean_per[i]
- # Converts it to the right format with two
- # significant figures
- BreakMeanC = signif(BreakMean*100, 2)
+ breakValue = breakValue_mean_per[i]
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Converts it to the right format with two
+ # significant figures
+ breakValueC = signif(breakValue*100, 2)
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Converts the break value with two
+ # significant figures
+ breakValueC = signif(breakValue, 2)
+ }
+
# Writes breaking values
mat = mat +
annotate('text', x=Xr_mean[i], y=Y[i],
- label=BreakMeanC,
+ label=breakValueC,
hjust=0.5, vjust=0.5,
size=3, color='white')
}
@@ -884,11 +964,27 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
for (i in 1:nbpMod) {
# Extract the variable of the plot
var = subVar_mean[i]
+ type = subType_mean[i]
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Fixes the unit of the mean and the break
+ # for the flow
+ unit_mean = bquote('['*m^3*'.'*s^{-1}*']')
+ unit_break = bquote('[%]')
+ # If it is a date variable
+ # Fixes the unit of the mean and the break
+ # for the date
+ } else if (type == 'saisonnalité') {
+ unit_mean = bquote('[jour]')
+ unit_break = bquote('[jour]')
+ }
+
mat = mat +
# Writes the unit of the averaged variable
annotate('text',
x=Xm_mean[i], y=max(Y) + 0.63,
- label=bquote('['*m^3*'.'*s^{-1}*']'),
+ label=unit_mean,
hjust=0.5, vjust=0.5,
size=2, color='grey40') +
# Writes the type of the averaged variable
@@ -904,7 +1000,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Writes the unit of the breaking variable
annotate('text', x=Xr_mean[i],
y=max(Y) + 0.63,
- label=bquote('[%]'),
+ label=unit_break,
hjust=0.5, vjust=0.5,
size=2, color='grey40') +
# Writes the type of the breaking variable
diff --git a/processing/analyse.R b/processing/analyse.R
index 5bcb229c29015c2c72af23cc44a7af30f645912d..b58519d2e35c8dc339bd67e843139e231602b70d 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -97,8 +97,12 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
### 1.1. QA
# Realise the trend analysis of the average annual flow (QA)
# hydrological variable
-get_QAtrend = function (df_data, period, p_thresold) {
+get_QAtrend = function (df_data, df_meta, period, p_thresold) {
+ # Removes incomplete data from time series
+ df_data = remove_incomplete_data(df_data, df_meta,
+ yearLac_pct=1, yearStart='01-01')
+
# Make sure to convert the period to a list
period = as.list(period)
@@ -146,8 +150,15 @@ get_QAtrend = function (df_data, period, p_thresold) {
### 1.2. QMNA
# Realise the trend analysis of the monthly minimum flow in the
# year (QMNA) hydrological variable
-get_QMNAtrend = function (df_data, period, p_thresold) {
-
+get_QMNAtrend = function (df_data, df_meta, period, p_thresold) {
+
+ # Removes incomplete data from time series
+ df_data = remove_incomplete_data(df_data, df_meta,
+ yearLac_pct=1, yearStart='01-01')
+ # Samples the data
+ df_data = sampling_data(df_data, df_meta,
+ sampleSpan=c('05-01', '11-30'))
+
# Make sure to convert the period to a list
period = as.list(period)
@@ -210,6 +221,14 @@ get_QMNAtrend = function (df_data, period, p_thresold) {
# over the year (VCN10) hydrological variable
get_VCN10trend = function (df_data, df_meta, period, p_thresold) {
+ # Removes incomplete data from time series
+ df_data = remove_incomplete_data(df_data, df_meta,
+ yearLac_pct=1, yearStart='01-01')
+
+ # Samples the data
+ df_data = sampling_data(df_data, df_meta,
+ sampleSpan=c('05-01', '11-30'))
+
# Get all different stations code
Code = levels(factor(df_meta$code))
# Blank tibble to store the data averaged
@@ -286,22 +305,22 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
# Gets the number of station
nCode = length(Code)
- # Blank tibble to store the data averaged
- df_data_roll = tibble()
-
- # For all the code
- for (code in Code) {
- # Get the data associated to the code
- df_data_code = df_data[df_data$code == code,]
- # Perform the roll mean of the flow over 10 days
- df_data_roll_code = tibble(Date=df_data_code$Date,
- Value=rollmean(df_data_code$Value,
- 10,
- fill=NA),
- code=code)
- # Store the results
- df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
- }
+ # # Blank tibble to store the data averaged
+ # df_data_roll = tibble()
+
+ # # For all the code
+ # for (code in Code) {
+ # # Get the data associated to the code
+ # df_data_code = df_data[df_data$code == code,]
+ # # Perform the roll mean of the flow over 10 days
+ # df_data_roll_code = tibble(Date=df_data_code$Date,
+ # Value=rollmean(df_data_code$Value,
+ # 10,
+ # fill=NA),
+ # code=code)
+ # # Store the results
+ # df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
+ # }
# Make sure to convert the period to a list
period = as.list(period)
@@ -322,16 +341,46 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
code = Code[k]
# print(code)
+ # Get the data associated to the code
+ df_data_code = df_data[df_data$code == code,]
+ # Perform the roll mean of the flow over 10 days
+ df_data_roll_code = tibble(Date=df_data_code$Date,
+ Value=rollmean(df_data_code$Value,
+ 10,
+ fill=NA),
+ code=code)
+
per.start = df_meta$start_year[df_meta$code == code]
per.start = paste(sprintf("%02d", per.start), '-01', sep='')
-
- # Get the data associated to the code
- df_data_roll_code = df_data_roll[df_data_roll$code == code,]
# print('aa')
- # Get the data associated to the code
- df_data_code = df_data[df_data$code == code,]
+ # Removes incomplete data from time series
+ df_data_code = remove_incomplete_data(df_data_code,
+ df_meta=NULL,
+ yearLac_pct=1,
+ yearStart=per.start,
+ Code=code)
+ # Samples the data
+ df_data_code = sampling_data(df_data_code,
+ df_meta=NULL,
+ sampleSpan=c('05-01',
+ '11-30'),
+ Code=code)
+
+ # Removes incomplete data from the averaged time series
+ df_data_roll_code =
+ remove_incomplete_data(df_data_roll_code,
+ df_meta=NULL,
+ yearLac_pct=1,
+ yearStart=per.start,
+ Code=code)
+ # Samples the data
+ df_data_roll_code = sampling_data(df_data_roll_code,
+ df_meta=NULL,
+ sampleSpan=c('05-01',
+ '11-30'),
+ Code=code)
# print('bb')
@@ -436,7 +485,7 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
# Realises the trend analysis of the date of the minimum 10 day
# average flow over the year (VCN10) hydrological variable
get_tMIDtrend = function (df_data, df_meta, period, p_thresold) {
-
+
# Get all different stations code
Code = levels(factor(df_meta$code))
# Blank tibble to store the data averaged
@@ -456,6 +505,14 @@ get_tMIDtrend = function (df_data, df_meta, period, p_thresold) {
df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
}
+ # Removes incomplete data from time series
+ df_data_roll = remove_incomplete_data(df_data_roll, df_meta,
+ yearLac_pct=1,
+ yearStart='01-01')
+ # Samples the data
+ df_data_roll = sampling_data(df_data_roll, df_meta,
+ sampleSpan=c('05-01', '11-30'))
+
# Make sure to convert the period to a list
period = as.list(period)
# Set the max interval period as the minimal possible
@@ -507,27 +564,27 @@ get_tMIDtrend = function (df_data, df_meta, period, p_thresold) {
### 2.1. Hydrograph
xref = matrix(
c(0.099, 0.100, 0.101, 0.099, 0.088, 0.078, 0.072,
- 0.064, 0.064, 0.069, 0.076, 0.089
+ 0.064, 0.064, 0.069, 0.076, 0.089,
0.133, 0.126, 0.111, 0.110, 0.081, 0.056, 0.038,
- 0.027, 0.042, 0.063, 0.098, 0.117
+ 0.027, 0.042, 0.063, 0.098, 0.117,
0.128, 0.142, 0.122, 0.128, 0.105, 0.065, 0.035,
- 0.024, 0.031, 0.044, 0.074, 0.101
+ 0.024, 0.031, 0.044, 0.074, 0.101,
0.157, 0.130, 0.119, 0.094, 0.062, 0.042, 0.028,
- 0.021, 0.035, 0.062, 0.099, 0.150
+ 0.021, 0.035, 0.062, 0.099, 0.150,
0.204, 0.163, 0.118, 0.102, 0.060, 0.030, 0.018,
- 0.012, 0.023, 0.041, 0.087, 0.143
+ 0.012, 0.023, 0.041, 0.087, 0.143,
0.156, 0.154, 0.117, 0.119, 0.086, 0.044, 0.025,
- 0.015, 0.025, 0.044, 0.089, 0.127
+ 0.015, 0.025, 0.044, 0.089, 0.127,
0.139, 0.092, 0.082, 0.099, 0.087, 0.039, 0.015,
- 0.012, 0.036, 0.108, 0.159, 0.131
+ 0.012, 0.036, 0.108, 0.159, 0.131,
0.112, 0.098, 0.101, 0.125, 0.122, 0.072, 0.036,
- 0.024, 0.039, 0.067, 0.102, 0.102
+ 0.024, 0.039, 0.067, 0.102, 0.102,
0.058, 0.050, 0.100, 0.142, 0.158, 0.092, 0.067,
- 0.050, 0.042, 0.058, 0.083, 0.100
+ 0.050, 0.042, 0.058, 0.083, 0.100,
0.050, 0.050, 0.058, 0.083, 0.150, 0.167, 0.117,
- 0.083, 0.058, 0.058, 0.067, 0.058
+ 0.083, 0.058, 0.058, 0.067, 0.058,
0.033, 0.025, 0.033, 0.075, 0.167, 0.217, 0.142,
- 0.092, 0.067, 0.058, 0.050, 0.042
+ 0.092, 0.067, 0.058, 0.050, 0.042,
0.017, 0.008, 0.017, 0.042, 0.108, 0.183, 0.200,
0.175, 0.117, 0.067, 0.042, 0.025),
ncol=12, byrow=TRUE)
diff --git a/processing/format.R b/processing/format.R
index a46934fef19d3fd7853a0602238fb6738f40bb69..911ff7762bb36ea63da5af96e9568c13a54d97df 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -33,8 +33,145 @@
library(dplyr)
-## 1. INPUT
-### 1.1. Preparation
+## 1. BEFORE TREND ANALYSE
+### 1.1. Joining selection
+# Joins tibbles of different selection of station as a unique one
+join = function (df_data_AG, df_data_IN, df_meta_AG, df_meta_IN) {
+
+ # If there is an INRAE and an Agence de l'eau Adour-Garonne selection
+ if (!is.null(df_data_IN) & !is.null(df_data_AG)) {
+
+ # Gets the station in common
+ common = levels(factor(df_meta_IN[df_meta_IN$code %in% df_meta_AG$code,]$code))
+ # Gets the Nv station to add
+ INadd = levels(factor(df_meta_IN[!(df_meta_IN$code %in% df_meta_AG$code),]$code))
+
+ # Selects only the IN meta to add
+ df_meta_INadd = df_meta_IN[df_meta_IN$code %in% INadd,]
+
+ # Names the source of the selection
+ df_meta_AG$source = 'AG'
+ df_meta_INadd$source = 'IN'
+
+ # Joins IN data to AG data
+ df_meta = full_join(df_meta_AG, df_meta_INadd)
+
+ # Selects only the IN data to add
+ df_data_INadd = df_data_IN[df_data_IN$code %in% INadd,]
+ # Joins IN meta to AG meta
+ df_data = full_join(df_data_AG, df_data_INadd)
+
+ # If there is just an Agence de l'eau Adour-Garonne selection
+ } else if (is.null(df_data_IN) & !is.null(df_data_AG)) {
+ df_meta_AG$source = 'AG'
+ df_meta = df_meta_AG
+ df_data = df_data_AG
+
+ # If there is just an INRAE selection
+ } else if (!is.null(df_data_IN) & is.null(df_data_AG)) {
+ df_meta_IN$source = 'IN'
+ df_meta = df_meta_IN
+ df_data = df_data_IN
+
+ # If there is no selection
+ } else {
+ stop('No data')
+ }
+ return (list(data=df_data, meta=df_meta))
+}
+
+### 1.2. Remove incomplete data
+remove_incomplete_data = function (df_data, df_meta, yearLac_pct=1, yearStart='01-01', Code=NULL) {
+
+ if (is.null(Code)) {
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+ nCode = length(Code)
+ } else {
+ nCode = length(Code)
+ }
+
+ for (code in Code) {
+ # Extracts the data corresponding to the code
+ df_data_code = df_data[df_data$code == code,]
+ DateMD = substr(df_data_code$Date, 6, 10)
+
+ idyearStart = which(DateMD == yearStart)
+ if (DateMD[1] != yearStart) {
+ idyearStart = c(1, idyearStart)
+ }
+ NidyearStart = length(idyearStart)
+
+ for (i in 1:NidyearStart) {
+ Start = df_data_code$Date[idyearStart[i]]
+ if (i < NidyearStart) {
+ End = df_data_code$Date[idyearStart[i+1] - 1]
+ } else {
+ End = df_data_code$Date[length(df_data_code$Date)]
+ }
+
+ OkYear = df_data_code$Date >= Start & df_data_code$Date <= End
+ df_data_code_year = df_data_code[OkYear,]
+
+ StartReal = as.Date(paste(substr(Start, 1, 4),
+ yearStart, sep='-'))
+ EndReal = as.Date(paste(as.numeric(substr(Start, 1, 4)) + 1,
+ yearStart, sep='-'))
+
+ nbDate = as.numeric(difftime(EndReal, StartReal,
+ units="days"))
+
+ nbNA = sum(as.numeric(is.na(df_data_code_year$Value)))
+ nbNA = nbNA + abs(as.numeric(difftime(StartReal, Start,
+ units="days")))
+ nbNA = nbNA + abs(as.numeric(difftime(EndReal, End+1,
+ units="days")))
+
+ yearLacMiss_pct = nbNA/nbDate * 100
+
+ if (yearLacMiss_pct > yearLac_pct) {
+ df_data_code_year$Value = NA
+ df_data_code[OkYear,] = df_data_code_year
+ }
+ }
+ df_data[df_data$code == code,] = df_data_code
+ }
+
+ return (df_data)
+}
+
+### 1.3. Sampling of the data
+sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code=NULL) {
+
+ if (is.null(Code)) {
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+ nCode = length(Code)
+ } else {
+ nCode = length(Code)
+ }
+
+ sampleStart = as.Date(paste('1970', sampleSpan[1], sep='-'))
+ sampleEnd = as.Date(paste('1970', sampleSpan[2], sep='-'))
+
+ for (code in Code) {
+ # Extracts the data corresponding to the code
+ df_data_code = df_data[df_data$code == code,]
+
+ DateMD = substr(df_data_code$Date, 6, 10)
+ Date = paste('1970', DateMD, sep='-')
+
+ df_data_code$Value[Date < sampleStart | Date > sampleEnd] = NA
+
+ df_data[df_data$code == code,] = df_data_code
+ }
+
+ return (df_data)
+}
+
+
+## 2. DURING TREND ANALYSE
+### 2.1. Preparation
# Prepares the data in order to have a list of a data tibble with
# date, group and flow column and a info tibble with the station code
# and group column to fit the entry of the 'extract.Var' function in
@@ -65,7 +202,7 @@ prepare = function(df_data, colnamegroup=NULL) {
return (res)
}
-### 1.2. Re-preparation
+### 2.2. Re-preparation
# Re-prepares the data in outing of the 'extract.Var' function in
# the 'StatsAnalysisTrend' package in order to fit again to the
# entry of the same function
@@ -106,10 +243,7 @@ reprepare = function(df_XEx, df_Xlist, colnamegroup=NULL) {
return (df_XlistEx)
}
-
-
-
-
+### 2.3. Prepare date
prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
df_dateStart = summarise(group_by(df_Xlist$data, group),
@@ -175,93 +309,8 @@ prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
}
-
-
-
-
-## 2. OUTPUT
-# Cleans the trend results of the function 'Estimate.stats' in the
-# 'StatsAnalysisTrend' package. It adds the station code and the
-# intercept of the trend to the trend results. Also makes the data
-# more presentable.
-clean = function (df_Xtrend, df_XEx, df_Xlist) {
-
- # Reprepares the list of data and info in order to be presentable
- df_Xlist = reprepare(df_XEx, df_Xlist, colnamegroup=c('code'))
-
- # Adds a column of station code
- df_Xlist$data$code = NA
- # For all the group
- for (g in df_Xlist$info$group) {
- # Adds the station code corresponding to each group info
- df_Xlist$data$code[which(df_Xlist$data$group == g)] = df_Xlist$info$code[df_Xlist$info$group == g]
- }
-
- # Adds the info to trend tibble
- df_Xtrend = bind_cols(df_Xtrend,
- df_Xlist$info[df_Xtrend$group1,
- 2:ncol(df_Xlist$info)])
- # Renames the column of group of trend results
- colnames(df_Xtrend)[1] = 'group'
- # Adds the intercept value of trend
- df_Xtrend = get_intercept(df_Xtrend, df_Xlist, unit2day=365.25)
- # Changes the position of the intercept column
- df_Xtrend = relocate(df_Xtrend, intercept, .after=trend)
-
- # Creates a list of results to return
- res = list(trend=df_Xtrend, data=df_Xlist$data, info=df_Xlist$info)
- return (res)
-}
-
-
-## 3. OTHER
-### 3.1. Joining selection
-# Joins tibbles of different selection of station as a unique one
-join = function (df_data_AG, df_data_IN, df_meta_AG, df_meta_IN) {
-
- # If there is an INRAE and an Agence de l'eau Adour-Garonne selection
- if (!is.null(df_data_IN) & !is.null(df_data_AG)) {
-
- # Gets the station in common
- common = levels(factor(df_meta_IN[df_meta_IN$code %in% df_meta_AG$code,]$code))
- # Gets the Nv station to add
- INadd = levels(factor(df_meta_IN[!(df_meta_IN$code %in% df_meta_AG$code),]$code))
-
- # Selects only the IN meta to add
- df_meta_INadd = df_meta_IN[df_meta_IN$code %in% INadd,]
-
- # Names the source of the selection
- df_meta_AG$source = 'AG'
- df_meta_INadd$source = 'IN'
-
- # Joins IN data to AG data
- df_meta = full_join(df_meta_AG, df_meta_INadd)
-
- # Selects only the IN data to add
- df_data_INadd = df_data_IN[df_data_IN$code %in% INadd,]
- # Joins IN meta to AG meta
- df_data = full_join(df_data_AG, df_data_INadd)
-
- # If there is just an Agence de l'eau Adour-Garonne selection
- } else if (is.null(df_data_IN) & !is.null(df_data_AG)) {
- df_meta_AG$source = 'AG'
- df_meta = df_meta_AG
- df_data = df_data_AG
-
- # If there is just an INRAE selection
- } else if (!is.null(df_data_IN) & is.null(df_data_AG)) {
- df_meta_IN$source = 'IN'
- df_meta = df_meta_IN
- df_data = df_data_IN
-
- # If there is no selection
- } else {
- stop('No data')
- }
- return (list(data=df_data, meta=df_meta))
-}
-
-### 3.2. Period of trend
+## 3. AFTER TREND ANALYSE
+### 3.1. Period of trend
# Compute the start and the end of the period for a trend analysis
# according to the accessible data
get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
@@ -308,3 +357,38 @@ get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
}
return (df_Xtrend)
}
+
+### 3.2. Cleaning
+# Cleans the trend results of the function 'Estimate.stats' in the
+# 'StatsAnalysisTrend' package. It adds the station code and the
+# intercept of the trend to the trend results. Also makes the data
+# more presentable.
+clean = function (df_Xtrend, df_XEx, df_Xlist) {
+
+ # Reprepares the list of data and info in order to be presentable
+ df_Xlist = reprepare(df_XEx, df_Xlist, colnamegroup=c('code'))
+
+ # Adds a column of station code
+ df_Xlist$data$code = NA
+ # For all the group
+ for (g in df_Xlist$info$group) {
+ # Adds the station code corresponding to each group info
+ df_Xlist$data$code[which(df_Xlist$data$group == g)] = df_Xlist$info$code[df_Xlist$info$group == g]
+ }
+
+ # Adds the info to trend tibble
+ df_Xtrend = bind_cols(df_Xtrend,
+ df_Xlist$info[df_Xtrend$group1,
+ 2:ncol(df_Xlist$info)])
+ # Renames the column of group of trend results
+ colnames(df_Xtrend)[1] = 'group'
+ # Adds the intercept value of trend
+ df_Xtrend = get_intercept(df_Xtrend, df_Xlist, unit2day=365.25)
+ # Changes the position of the intercept column
+ df_Xtrend = relocate(df_Xtrend, intercept, .after=trend)
+
+ # Creates a list of results to return
+ res = list(trend=df_Xtrend, data=df_Xlist$data, info=df_Xlist$info)
+ return (res)
+}
+
diff --git a/script.R b/script.R
index d48b522f4cc6fd8e37c99a6a8ca2b57e8a41f016..ce5eb3868deb231729f75a44187ea614e248fd3e 100644
--- a/script.R
+++ b/script.R
@@ -55,20 +55,20 @@ filedir =
# Name of the file that will be analysed from the BH directory
# (if 'all', all the file of the directory will be chosen)
filename =
- ""
+ # ""
- # c(
- # "S2235610_HYDRO_QJM.txt",
+ c(
+ # "S2235610_HYDRO_QJM.txt"
# "P1712910_HYDRO_QJM.txt",
# "P0885010_HYDRO_QJM.txt",
# "O5055010_HYDRO_QJM.txt",
- # "O0384010_HYDRO_QJM.txt",
+ "O0384010_HYDRO_QJM.txt"
# "S4214010_HYDRO_QJM.txt",
- # "Q7002910_HYDRO_QJM.txt",
+ # "Q7002910_HYDRO_QJM.txt"
# "O3035210_HYDRO_QJM.txt",
- # "O3121010_HYDRO_QJM.txt"
- # "O7635010_HYDRO_QJM.txt"
- # )
+ # "O3121010_HYDRO_QJM.txt",
+ # "O0362510_HYDRO_QJM.txt"
+ )
## AGENCE EAU ADOUR GARONNE SELECTION
@@ -78,8 +78,8 @@ AGlistdir =
""
AGlistname =
- # ""
- "Liste-station_RRSE.docx"
+ ""
+ # "Liste-station_RRSE.docx"
## NIVALE SELECTION
@@ -179,10 +179,6 @@ if (AGlistname != ""){
# Get filenames of the selection
filename = df_selec_AG[df_selec_AG$ok,]$filename
- #####
- # filename = filename[(1):(10)]
- #####
-
# Extract metadata about selected stations
df_meta_AG = extract_meta(computer_data_path, filedir, filename)
# Extract data about selected stations
@@ -200,10 +196,6 @@ if (INlistname != ""){
# Get filenames of the selection
filename = df_selec_IN[df_selec_IN$ok,]$filename
- #####
- # filename = filename[(1+20):(16+20)]
- #####
-
# Extract metadata about selected stations
df_meta_IN = extract_meta(computer_data_path, filedir, filename)
# Extract data about selected stations
@@ -233,11 +225,13 @@ df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
### 3.2. Trend analysis
# QA trend
-res_QAtrend = get_QAtrend(df_data, period=trend_period,
+res_QAtrend = get_QAtrend(df_data, df_meta,
+ period=trend_period,
p_thresold=p_thresold)
# QMNA tend
-res_QMNAtrend = get_QMNAtrend(df_data, period=trend_period,
+res_QMNAtrend = get_QMNAtrend(df_data, df_meta,
+ period=trend_period,
p_thresold=p_thresold)
# VCN10 trend
@@ -246,10 +240,10 @@ res_VCN10trend = get_VCN10trend(df_data, df_meta,
p_thresold=p_thresold)
# Start date for low water trend
-# res_tINItrend = get_tINItrend(df_data, df_meta,
- # period=trend_period,
- # p_thresold=p_thresold)
-res_tINItrend = read_listofdf(resdir, 'res_tINItrend')
+res_tINItrend = get_tINItrend(df_data, df_meta,
+ period=trend_period,
+ p_thresold=p_thresold)
+# res_tINItrend = read_listofdf(resdir, 'res_tINItrend')
# Center date for low water trend
res_tMIDtrend = get_tMIDtrend(df_data, df_meta,
@@ -274,7 +268,7 @@ df_shapefile = ini_shapefile(computer_data_path,
fr_shpdir, fr_shpname,
bs_shpdir, bs_shpname,
sbs_shpdir, sbs_shpname,
- rv_shpdir, rv_shpname, riv=TRUE)
+ rv_shpdir, rv_shpname, riv=FALSE)
### 4.1. Simple time panel to criticize station data
# Plot time panel of debit by stations
@@ -291,9 +285,9 @@ df_shapefile = ini_shapefile(computer_data_path,
### 4.2. Analysis layout
datasheet_layout(toplot=c(
- 'datasheet',
- 'matrix',
- 'map'
+ 'datasheet'
+ # 'matrix',
+ # 'map'
),
df_meta=df_meta,
@@ -315,11 +309,11 @@ datasheet_layout(toplot=c(
'tINI',
'tMID'),
- type=list('flow',
- 'flow',
- 'flow',
- 'date',
- 'date'),
+ type=list('sévérité',
+ 'sévérité',
+ 'sévérité',
+ 'saisonnalité',
+ 'saisonnalité'),
layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),