diff --git a/plotting/map.R b/plotting/map.R
index a7ccdbd36169662bf3e11c1814433c172d33ccd3..c918ea0e5123385faf2574399077a36fae28edc5 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -27,6 +27,7 @@
## 1. MAP PANEL
+# Generates a map plot of the tendancy of a hydrological variable
map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp='', codeLight=NULL, margin=NULL, showSea=TRUE, verbose=TRUE) {
# Extract shapefiles
@@ -100,14 +101,15 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
substr(End[i], 1, 4),
sep=' / '))
}
-
+ # Stores time info by station
Start_code[[j]] = Start
End_code[[j]] = End
Code_code[[j]] = code
Periods_code[[j]] = Periods
-
}
-
+
+ # Blank array to store mean of the trend for each
+ # station, perdiod and variable
TrendMean_code = array(rep(1, nPeriod_max*nbp*nCode),
dim=c(nPeriod_max, nbp, nCode))
# For all the period
@@ -130,54 +132,72 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
+ # Gets the associated time info
Start = Start_code[Code_code == code][[1]][j]
End = End_code[Code_code == code][[1]][j]
Periods = Periods_code[Code_code == code][[1]][j]
+ # Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$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$period_end == End,]
+ # Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
+ # If there is more than one trend on the same period
if (Ntrend > 1) {
+ # Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
-
+
+ # Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ # Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
+ # If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
+ # Stores the mean trend
TrendMean_code[j, i, k] = trendMean
+ # Otherwise
} else {
+ # Do not stocks it
TrendMean_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)
- ncolor = 256
+ # Number of ticks for the colorbar
nbTick = 10
# For all variable
for (i in 1:nbp) {
-
+ # If there is a specified station code to highlight (mini map)
+ # and there has already been one loop
if (i > 1 & !is.null(codeLight)) {
+ # Stop the for loop over the variable
break
}
- # Extract the variable of the plot
+ # Extracts the variable of the plot
type = list_df2plot[[i]]$type
+ # Createsa name for the map
outname = paste('map_', type, sep='')
+ # If there is the verbose option
if (verbose) {
+ # Prints the name of the map
print(paste('Map for variable :', type))
}
+ # If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
+ # Sets the size of the countour
sizefr = 0.45
sizebs = 0.4
sizerv = 0.3
@@ -186,19 +206,20 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
sizebs = 0.3
sizerv = 0.2
}
-
+
+ # Open a new plot with the personalise theme
map = ggplot() + theme_void() +
-
# theme(plot.background=element_rect(fill=NA,
# color="#EC4899")) +
# Fixed coordinate system
coord_fixed() +
-
+ # Plot the background of France
geom_polygon(data=df_france,
aes(x=long, y=lat, group=group),
color=NA, fill="grey97")
-
+ # If the river shapefile exists
if (!is.null(df_river)) {
+ # Plot the river
map = map +
geom_path(data=df_river,
aes(x=long, y=lat, group=group),
@@ -206,30 +227,44 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
map = map +
+ # Plot the hydrological basin
geom_polygon(data=df_bassin,
aes(x=long, y=lat, group=group),
color="grey70", fill=NA, size=sizebs) +
-
+ # Plot the countour of France
geom_polygon(data=df_france,
aes(x=long, y=lat, group=group),
color="grey40", fill=NA, size=sizefr)
-
+
+ # If the sea needs to be shown
if (showSea) {
+ # Leaves space around the France
xlim = c(280000, 790000)
ylim = c(6110000, 6600000)
+ # Otherwise
} else {
+ # Leaves minimal space around France
xlim = c(305000, 790000)
ylim = c(6135000, 6600000)
}
+ # If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
+ # Sets a legend scale start
xmin = gpct(7, xlim, shift=TRUE)
+ # Sets graduations
xint = c(0, 10*1E3, 50*1E3, 100*1E3)
+ # Sets the y postion
ymin = gpct(5, ylim, shift=TRUE)
+ # Sets the height of graduations
ymax = ymin + gpct(1, ylim)
+ # Size of the value
size = 3
+ # Size of the 'km' unit
sizekm = 2.5
+ # If there is a specified station code
} else {
+ # Same but with less graduation and smaller size
xmin = gpct(5, xlim, shift=TRUE)
xint = c(0, 100*1E3)
ymin = gpct(1, ylim, shift=TRUE)
@@ -239,38 +274,47 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
map = map +
-
+ # Adds the base line of the scale
geom_line(aes(x=c(xmin, max(xint)+xmin),
y=c(ymin, ymin)),
color="grey40", size=0.2) +
+ # Adds the 'km' unit
annotate("text",
x=max(xint)+xmin+gpct(1, xlim), y=ymin,
vjust=0, hjust=0, label="km",
color="grey40", size=sizekm)
-
+ # For all graduations
for (x in xint) {
map = map +
+ # Draws the tick
annotate("segment",
x=x+xmin, xend=x+xmin, y=ymin, yend=ymax,
color="grey40", size=0.2) +
+ # Adds the value
annotate("text",
x=x+xmin, y=ymax+gpct(0.5, ylim),
vjust=0, hjust=0.5, label=x/1E3,
color="grey40", size=size)
}
- map = map +
+ map = map +
+ # Allows to crop shapefile without graphical problem
coord_sf(xlim=xlim, ylim=ylim,
expand=FALSE)
-
+
+ # If there is no margins specified
if (is.null(margin)) {
- map = map +
+ # Sets all margins to 0
+ map = map +
theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
+ # Otherwise
} else {
- map = map +
+ # Sets margins to the given ones
+ map = map +
theme(plot.margin=margin)
}
-
+
+ # Blank vector to store data about station
lon = c()
lat = c()
fill = c()
@@ -314,13 +358,13 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
maxTrendMean[idPer, i],
palette_name='perso',
reverse=TRUE,
- ncolor=ncolor)
+ ncolor=256)
palette_res = get_palette(minTrendMean[idPer, i],
maxTrendMean[idPer, i],
palette_name='perso',
reverse=TRUE,
- ncolor=ncolor,
+ ncolor=256,
nbTick=nbTick)
if (df_trend_code_per$p <= p_threshold){
@@ -355,12 +399,14 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
plot_map = tibble(lon=lon, lat=lat, fill=fill,
shape=shape, code=Code)
+ # If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
map = map +
geom_point(data=plot_map,
aes(x=lon, y=lat),
shape=shape, size=5, stroke=1,
color='grey50', fill=fill)
+ # If there is a specified station code
} else {
plot_map_codeNo = plot_map[plot_map$code != codeLight,]
plot_map_code = plot_map[plot_map$code == codeLight,]
@@ -513,7 +559,6 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
yTrend = c(yTrend, rep(mids[ii], times=counts[ii]))
}
-
## No touch distribution ##
# start_hist = 1.25
# min_xsep = 0.15
@@ -533,10 +578,11 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# }
# }
-
+ # Makes a tibble to plot the trend distribution
plot_trend = tibble(xTrend=xTrend, yTrend=yTrend)
pal = pal +
+ # Plots the point of the trend distribution
geom_point(data=plot_trend,
aes(x=xTrend, y=yTrend),
# shape=21, size=1,
@@ -545,11 +591,12 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
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",
@@ -559,23 +606,24 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
pal = pal +
-
+ # X axis of the colorbar
scale_x_continuous(limits=c(-1, 1 + 3),
expand=c(0, 0)) +
-
+ # Y axis of the colorbar
scale_y_continuous(limits=c(-60, valNorm + 35),
expand=c(0, 0)) +
-
+ # Margin of the colorbar
theme(plot.margin=margin(t=0, r=5, b=5, l=0, unit="mm"))
-
+ # Stores the map, the title and the colorbarin a list
Map = list(map, title, pal)
-
+ # Arranges the graphical object
plot = grid.arrange(grobs=Map, layout_matrix=
matrix(c(1, 1, 1, 2,
1, 1, 1, 3),
nrow=2, byrow=TRUE))
+ # If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
# Saving matrix plot
ggsave(plot=plot,
@@ -584,6 +632,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
width=29.7, height=21, units='cm', dpi=100)
}
}
+ # Returns the map object
return (map)
}
diff --git a/plotting/matrix.R b/plotting/matrix.R
index 16750b96509ab76efcca743382ffdc29a0a5ff7c..ba442bb566d9df2558a1fea7b4d50ade401d8c94 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -101,14 +101,15 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
End[i],
sep=' / '))
}
-
+ # Stores time info by station
Start_code[[j]] = Start
End_code[[j]] = End
Code_code[[j]] = code
Periods_code[[j]] = Periods
-
}
-
+
+ # Blank array to store mean of the trend for each
+ # station, perdiod and variable
TrendMean_code = array(rep(1, nPeriod_trend*nbp*nCode),
dim=c(nPeriod_trend, nbp, nCode))
# For all the trend period
@@ -131,35 +132,46 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
+ # Gets the associated time info
Start = Start_code[Code_code == code][[1]][j]
End = End_code[Code_code == code][[1]][j]
Periods = Periods_code[Code_code == code][[1]][j]
+ # Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$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$period_end == End,]
+ # Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
+ # If there is more than one trend on the same period
if (Ntrend > 1) {
+ # Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
-
+
+ # Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ # Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
+ # If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
+ # Stores the mean trend
TrendMean_code[j, i, k] = trendMean
+ # Otherwise
} else {
+ # Do not stocks it
TrendMean_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)