# \\\
# Copyright 2021-2022 Louis H�raut*1
#
# *1 INRAE, France
# louis.heraut@inrae.fr
#
# This file is part of ash R toolbox.
#
# ash R toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or (at
# your option) any later version.
#
# ash R toolbox is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ash R toolbox. If not, see <https://www.gnu.org/licenses/>.
# ///
#
#
# plotting/matrix.R
#
#
## 1. MATRIX PANEL
matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice=NULL, outdirTmp='', outnameTmp='matrix', title=NULL, A3=FALSE) {
# Number of variable/plot
nbp = length(list_df2plot)
# Get all different stations code
Code = levels(factor(df_meta$code))
nCode = length(Code)
# Gets a trend example
df_trend = list_df2plot[[1]]$trend
# Convert 'trend_period' to list
trend_period = as.list(trend_period)
# Number of trend period
nPeriod_trend = length(trend_period)
# Fix the maximal number of period to the minimal possible
nPeriod_max = 0
# For all code
for (code in 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
End = df_trend_code$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# If the number of period for the trend is greater
# than the current max period, stocks it
if (nPeriod > nPeriod_max) {
nPeriod_max = nPeriod
}
}
# Blank list to store time info by station code
Start_code = vector(mode='list', length=nCode)
End_code = vector(mode='list', length=nCode)
Code_code = vector(mode='list', length=nCode)
Periods_code = vector(mode='list', length=nCode)
# For all the code
for (j in 1:nCode) {
# Gets the code
code = Code[j]
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Extract start and end of trend periods
Start = df_trend_code$period_start
End = df_trend_code$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# Vector to store trend period
Periods = c()
# For all the trend period
for (i in 1:nPeriod_trend) {
# Stocks period
Periods = append(Periods,
paste(Start[i],
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
for (j in 1:nPeriod_trend) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to the
# current variable
df_data = list_df2plot[[i]]$data
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
p_threshold = list_df2plot[[i]]$p_threshold
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Gets the associated time info
Start = Start_code[Code_code == code][[1]][j]
End = End_code[Code_code == code][[1]][j]
Periods = Periods_code[Code_code == code][[1]][j]
# Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$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)
Periods_trend = c()
NPeriod_trend = c()
Type_trend = list()
Code_trend = c()
Pthresold_trend = c()
TrendMean_trend = c()
DataMean_trend = c()
Fill_trend = c()
Color_trend = c()
# For all the trend period
for (j in 1:nPeriod_trend) {
# For all code
for (code in Code) {
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to the current variable
df_data = list_df2plot[[i]]$data
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
p_threshold = list_df2plot[[i]]$p_threshold
# Extract the variable of the plot
type = list_df2plot[[i]]$type
# 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,]
Start = Start_code[Code_code == code][[1]][j]
End = End_code[Code_code == code][[1]][j]
Periods = Periods_code[Code_code == code][[1]][j]
df_data_code_per =
df_data_code[df_data_code$Date >= Start
& df_data_code$Date <= End,]
df_trend_code_per =
df_trend_code[df_trend_code$period_start == Start
& df_trend_code$period_end == End,]
Ntrend = nrow(df_trend_code_per)
if (Ntrend > 1) {
df_trend_code_per = df_trend_code_per[1,]
}
dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
trendMean = df_trend_code_per$trend / dataMean
if (df_trend_code_per$p <= p_threshold){
color_res = get_color(trendMean,
minTrendMean[j, i],
maxTrendMean[j, i],
palette_name='perso',
reverse=TRUE)
fill = color_res
color = 'white'
Pthresold = p_thresold
} else {
fill = 'white'
color = 'grey85'
Pthresold = NA
}
Periods_trend = append(Periods_trend, Periods)
NPeriod_trend = append(NPeriod_trend, j)
Type_trend = append(Type_trend, type)
Code_trend = append(Code_trend, code)
Pthresold_trend = append(Pthresold_trend, Pthresold)
TrendMean_trend = append(TrendMean_trend, trendMean)
DataMean_trend = append(DataMean_trend, dataMean)
Fill_trend = append(Fill_trend, fill)
Color_trend = append(Color_trend, color)
}
}
}
# If there is a 'mean_period'
if (!is.null(mean_period)) {
Periods_mean = c()
NPeriod_mean = c()
Type_mean = list()
Code_mean = c()
DataMean_mean = c()
BreakMean_mean = c()
# Convert 'mean_period' to list
mean_period = as.list(mean_period)
# Number of mean period
nPeriod_mean = length(mean_period)
BreakMean_code = array(rep(1, nPeriod_mean*nbp*nCode),
dim=c(nPeriod_mean, nbp, nCode))
dataMeantmp = array(rep(NA, nbp*nCode),
dim=c(nbp, nCode))
# For all mean period
for (j in 1:nPeriod_mean) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to
# the current variable
df_data = list_df2plot[[i]]$data
# Extract the variable of the plot
type = list_df2plot[[i]]$type
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
# Get the current start and end of the sub period
Start_mean = mean_period[[j]][1]
End_mean = mean_period[[j]][2]
# Extract the data corresponding to this sub period
df_data_code_per =
df_data_code[df_data_code$Date >= Start_mean
& df_data_code$Date <= End_mean,]
# Min max for the sub period
Datemin = min(df_data_code_per$Date)
Datemax = max(df_data_code_per$Date)
# Creates a period name
Periods = paste(Datemin, Datemax,
sep=' / ')
# Mean of the flow over the sub period
dataMean = mean(df_data_code_per$Qm3s,
na.rm=TRUE)
if (j > 1) {
Break = dataMean - dataMeantmp[i, k]
} else {
Break = NA
}
BreakMean = Break / dataMeantmp[i, k]
BreakMean_code[j, i, k] = BreakMean
dataMeantmp[i, k] = dataMean
Periods_mean = append(Periods_mean, Periods)
NPeriod_mean = append(NPeriod_mean, j)
Type_mean = append(Type_mean, type)
Code_mean = append(Code_mean, code)
DataMean_mean = append(DataMean_mean, dataMean)
BreakMean_mean = append(BreakMean_mean,
BreakMean)
}
}
}
minBreakMean = apply(BreakMean_code, c(1, 2),
min, na.rm=TRUE)
maxBreakMean = apply(BreakMean_code, c(1, 2),
max, na.rm=TRUE)
Fill_mean = c()
Color_mean = c()
ii = 1
for (j in 1:nPeriod_mean) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
BreakMean = BreakMean_mean[ii]
color_res = get_color(BreakMean,
minBreakMean[j, i],
maxBreakMean[j, i],
palette_name='perso',
reverse=TRUE)
fill = color_res
color = 'white'
Fill_mean = append(Fill_mean, fill)
Color_mean = append(Color_mean, color)
ii = ii + 1
}
}
}
}
if (is.null(slice)) {
slice = nCode
}
firstLetter = levels(factor(substr(Code, 1, 1)))
for (fL in firstLetter) {
print(paste('Matrix for region :', fL))
# Get only station code with the same first letter
subCodefL = Code[substr(Code, 1, 1) == fL]
nsubCodefL = length(subCodefL)
nMat = as.integer(nsubCodefL/slice) + 1
for (imat in 1:nMat) {
subCode = subCodefL[(slice*(imat-1)+1):(slice*imat)]
subCode = subCode[!is.na(subCode)]
nsubCode = length(subCode)
CodefL_trend = Code_trend %in% subCode
subPeriods_trend = Periods_trend[CodefL_trend]
subNPeriod_trend = NPeriod_trend[CodefL_trend]
subType_trend = Type_trend[CodefL_trend]
subCode_trend = Code_trend[CodefL_trend]
subPthresold_trend = Pthresold_trend[CodefL_trend]
subTrendMean_trend = TrendMean_trend[CodefL_trend]
subDataMean_trend = DataMean_trend[CodefL_trend]
subFill_trend = Fill_trend[CodefL_trend]
subColor_trend = Color_trend[CodefL_trend]
CodefL_mean = Code_mean %in% subCode
subPeriods_mean = Periods_mean[CodefL_mean]
subNPeriod_mean = NPeriod_mean[CodefL_mean]
subType_mean = Type_mean[CodefL_mean]
subCode_mean = Code_mean[CodefL_mean]
subDataMean_mean = DataMean_mean[CodefL_mean]
subBreakMean_mean = BreakMean_mean[CodefL_mean]
subFill_mean = Fill_mean[CodefL_mean]
subColor_mean = Color_mean[CodefL_mean]
title = df_meta[df_meta$code == subCode[1],]$region_hydro
### Plot ###
height = nsubCode
width = nbp * 2 * nPeriod_trend + nPeriod_trend + nPeriod_mean * nbp + nPeriod_mean + nbp
options(repr.plot.width=width, repr.plot.height=height)
mat = ggplot() + theme_ash +
theme(
panel.border=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
plot.margin=margin(t=5, r=5, b=5, l=5, unit="mm")
)
xt = 1 - 6
yt = height + 2
Title = bquote(bold(.(title)))
mat = mat +
annotate("text", x=xt, y=yt,
label=Title,
hjust=0, vjust=1,
size=6, color="#00A3A8")
### Trend ###
# For all the trend period
for (j in 1:nPeriod_trend) {
Type_trend_per =
subType_trend[subNPeriod_trend == j]
Code_trend_per =
subCode_trend[subNPeriod_trend == j]
Pthresold_trend_per =
subPthresold_trend[subNPeriod_trend == j]
TrendMean_trend_per =
subTrendMean_trend[subNPeriod_trend == j]
DataMean_trend_per =
subDataMean_trend[subNPeriod_trend == j]
Fill_trend_per =
subFill_trend[subNPeriod_trend == j]
Color_trend_per =
subColor_trend[subNPeriod_trend == j]
Xtmp = as.integer(factor(as.character(Type_trend_per)))
Xc = j + (j - 1)*nbp*2
Xm = Xtmp + (j - 1)*nbp*2 + j
X = Xtmp + (j - 1)*nbp*2 + nbp + j
Y = as.integer(factor(Code_trend_per))
x = Xc - 0.4
xend = X[length(X)] + 0.25
y = height + 1
yend = height + 1
mat = mat +
annotate("segment",
x=x, xend=xend,
y=y, yend=yend,
color="grey40", size=0.35)
yt = y + 0.15
Start = trend_period[[j]][1]
End = trend_period[[j]][2]
periodName = bquote(bold('P�riode')~bold(.(as.character(j))))
mat = mat +
annotate("text", x=x, y=yt,
label=periodName,
hjust=0, vjust=0.5,
size=3, color='grey40')
for (i in 1:length(X)) {
mat = mat +
gg_circle(r=0.45, xc=X[i], yc=Y[i],
fill=Fill_trend_per[i],
color=Color_trend_per[i]) +
gg_circle(r=0.45, xc=Xm[i], yc=Y[i],
fill='white', color='grey40') +
gg_circle(r=0.45, xc=Xc, yc=Y[i],
fill='white', color='grey40')
}
for (i in 1:length(TrendMean_trend_per)) {
trendMean = TrendMean_trend_per[i]
trendC = signif(trendMean*100, 2)
if (!is.na(Pthresold_trend_per[i])) {
Tcolor = 'white'
} else {
Tcolor = 'grey85'
}
dataMean = signif(DataMean_trend_per[i], 2)
mat = mat +
annotate('text', x=X[i], y=Y[i],
label=trendC,
hjust=0.5, vjust=0.5,
size=3, color=Tcolor) +
annotate('text', x=Xm[i], y=Y[i],
label=dataMean,
hjust=0.5, vjust=0.5,
size=3, color='grey40')
}
mat = mat +
annotate('text', x=Xc, y=max(Y) + 0.85,
label=bquote(bold('D�but')),
hjust=0.5, vjust=0.5,
size=3, color='grey20') +
annotate('text', x=Xc, y=max(Y) + 0.6,
label=bquote(bold('Fin')),
hjust=0.5, vjust=0.5,
size=3, color='grey20')
# For all variable
for (i in 1:nbp) {
# Extract the variable of the plot
type = list_df2plot[[i]]$type
mat = mat +
annotate('text', x=X[i], y=max(Y) + 0.82,
label=bquote(.(type)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
annotate('text', x=X[i], y=max(Y) + 0.6,
label=bquote('[%.'*ans^{-1}*']'),
hjust=0.5, vjust=0.5,
size=2, color='grey40') +
annotate('text', x=Xm[i], y=max(Y) + 0.82,
label=bquote('�'*.(type)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
annotate('text', x=Xm[i], y=max(Y) + 0.6,
label=bquote('['*m^3*'.'*s^{-1}*']'),
hjust=0.5, vjust=0.5,
size=2, color='grey40')
}
for (k in 1:nsubCode) {
# Gets the code
code = subCode[k]
label = Periods_trend[subNPeriod_trend == j
& subCode_trend == code][1]
periodStart = substr(label, 1, 4)
periodEnd = substr(label, 14, 17)
mat = mat +
annotate('text', x=Xc, y=k + 0.13,
label=bquote(bold(.(periodStart))),
hjust=0.5, vjust=0.5,
size=3, color='grey40') +
annotate('text', x=Xc, y=k - 0.13,
label=bquote(bold(.(periodEnd))),
hjust=0.5, vjust=0.5,
size=3, color='grey40')
}
}
### Mean ###
for (j in 1:nPeriod_mean) {
Type_mean_per =
subType_mean[subNPeriod_mean == j]
Code_mean_per =
subCode_mean[subNPeriod_mean == j]
DataMean_mean_per =
subDataMean_mean[subNPeriod_mean == j]
BreakMean_mean_per =
subBreakMean_mean[subNPeriod_mean == j]
Fill_mean_per =
subFill_mean[subNPeriod_mean == j]
Color_mean_per =
subColor_mean[subNPeriod_mean == j]
Xtmp_mean = as.integer(factor(as.character(Type_mean_per)))
Xc_mean = j + (j - 1)*nbp + X[length(X)]
Xm_mean = Xtmp_mean + (j - 1)*nbp + j + X[length(X)]
Xr_mean = Xtmp_mean + (j - 1)*nbp*2 + j + X[length(X)]
Y_mean = as.integer(factor(Code_mean_per))
x = Xc_mean - 0.4
xend = Xm_mean[length(Xm_mean)] + 0.25
y = height + 1
yend = height + 1
mat = mat +
annotate("segment",
x=x, xend=xend,
y=y, yend=yend,
color="grey40", size=0.35)
yt = y + 0.15
Start = mean_period[[j]][1]
End = mean_period[[j]][2]
periodName = bquote(bold('P�riode')~bold(.(as.character(j+nPeriod_trend))))
mat = mat +
annotate("text", x=x, y=yt,
label=periodName,
hjust=0, vjust=0.5,
size=3, color='grey40')
if (j > 1) {
x = Xr_mean[1] - 0.4
xend = Xr_mean[length(Xr_mean)] + 0.25
mat = mat +
annotate("segment",
x=x, xend=xend,
y=y, yend=yend,
color="grey40", size=0.35)
breakName = bquote(bold('�cart')~bold(.(as.character(j-1+nPeriod_trend)))*bold('-')*bold(.(as.character(j+nPeriod_trend))))
mat = mat +
annotate("text", x=x, y=yt,
label=breakName,
hjust=0, vjust=0.5,
size=3, color='grey40')
}
for (i in 1:length(Xm_mean)) {
mat = mat +
gg_circle(r=0.45, xc=Xm_mean[i], yc=Y[i],
fill='white', color='grey40') +
gg_circle(r=0.45, xc=Xc_mean, yc=Y[i],
fill='white', color='grey40')
if (j > 1) {
mat = mat +
gg_circle(r=0.45, xc=Xr_mean[i], yc=Y[i],
fill=Fill_mean_per[i],
color=Color_mean_per[i])
}
}
for (i in 1:length(DataMean_mean_per)) {
dataMean = signif(DataMean_mean_per[i], 2)
mat = mat +
annotate('text', x=Xm_mean[i], y=Y[i],
label=dataMean,
hjust=0.5, vjust=0.5,
size=3, color='grey40')
if (j > 1) {
BreakMean = BreakMean_mean_per[i]
BreakC = signif(BreakMean*100, 2)
mat = mat +
annotate('text', x=Xr_mean[i], y=Y[i],
label=BreakC,
hjust=0.5, vjust=0.5,
size=3, color='white')
}
}
mat = mat +
annotate('text', x=Xc_mean, y=max(Y) + 0.85,
label=bquote(bold('D�but')),
hjust=0.5, vjust=0.5,
size=3, color='grey20') +
annotate('text', x=Xc_mean, y=max(Y) + 0.6,
label=bquote(bold('Fin')),
hjust=0.5, vjust=0.5,
size=3, color='grey20')
# For all variable
for (i in 1:nbp) {
# Extract the variable of the plot
type = list_df2plot[[i]]$type
mat = mat +
annotate('text', x=Xm_mean[i], y=max(Y) + 0.82,
label=bquote('�'*.(type)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
annotate('text', x=Xm_mean[i], y=max(Y) + 0.6,
label=bquote('['*m^3*'.'*s^{-1}*']'),
hjust=0.5, vjust=0.5,
size=2, color='grey40')
if (j > 1) {
mat = mat +
annotate('text', x=Xr_mean[i], y=max(Y) + 0.82,
label=bquote('d'*.(type)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
annotate('text', x=Xr_mean[i], y=max(Y) + 0.6,
label=bquote('[%]'),
hjust=0.5, vjust=0.5,
size=2, color='grey40')
}
}
for (k in 1:nsubCode) {
code = subCode[k]
label = Periods_mean[subNPeriod_mean == j
& subCode_mean == code][1]
periodStart = substr(label, 1, 4)
periodEnd = substr(label, 14, 17)
mat = mat +
annotate('text', x=Xc_mean, y=k + 0.13,
label=bquote(bold(.(periodStart))),
hjust=0.5, vjust=0.5,
size=3, color='grey40') +
annotate('text', x=Xc_mean, y=k - 0.13,
label=bquote(bold(.(periodEnd))),
hjust=0.5, vjust=0.5,
size=3, color='grey40')
}
}
### Code ###
for (k in 1:nsubCode) {
# Gets the code
code = subCode[k]
name = df_meta[df_meta$code == code,]$nom
ncharMax = 38
if (nchar(name) > ncharMax) {
name = paste(substr(name, 1, ncharMax), '...', sep='')
}
mat = mat +
annotate('text', x=0.3, y=k + 0.14,
label=bquote(bold(.(code))),
hjust=1, vjust=0.5,
size=3.5, color="#00A3A8") +
annotate('text', x=0.3, y=k - 0.14,
label=name,
hjust=1, vjust=0.5,
size=3.5, color="#00A3A8")
}
### Environment ###
mat = mat +
# Fixed coordinate system
coord_fixed() +
scale_x_continuous(limits=c(1 - rel(6),
width + rel(0.5)),
expand=c(0, 0)) +
scale_y_continuous(limits=c(1 - rel(0.5),
height + rel(2)),
expand=c(0, 0))
# Saving matrix plot
if (A3) {
width = 42
height = 29.7
dpi = 300
} else {
width = 29.7
height = 21
dpi = 100
}
ggsave(plot=mat,
path=outdirTmp,
filename=paste(outnameTmp, '_', fL, imat, '.pdf',
sep=''),
width=width, height=height, units='cm', dpi=dpi)
}
}
}