diff --git a/plotting/datasheet.R b/plotting/datasheet.R
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+# \\\
+# Copyright 2021-2022 Louis Héraut*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/datasheet.R
+#
+# Regroups all the graphical tools to generates the datasheets. More precisely, the 'datasheet_panel' function manages all the call for each station of the different graphical functions that generates info header, time serie visualisation and trend analysis graphs for every variable. It also deals with the arranging of all the plots in a single PDF page.
+
+
+# Sourcing R file
+source('processing/analyse.R', encoding='UTF-8')
+
+
+## 1. DATASHEET PANEL
+# 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, resource_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp) {
+
+ # The percentage of augmentation and diminution of the min
+ # and max limits for y axis
+ lim_pct = 10
+
+ # 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)
+
+ nPeriod_max = 0
+ 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$period_end
+ # Get the name of the different period
+ UStart = levels(factor(Start))
+ UEnd = levels(factor(End))
+
+ # Compute the max of different start and end
+ # so the number of different period
+ nPeriod = max(length(UStart), length(UEnd))
+
+ # If the number of period for the trend is greater
+ # than the current max period, stocks it
+ if (nPeriod > nPeriod_max) {
+ nPeriod_max = nPeriod
+ }
+ }
+
+ # Blank array to store time info
+ tab_Start = array(rep('', nCode*nbp*nPeriod_max),
+ dim=c(nCode, nbp, nPeriod_max))
+ tab_End = array(rep('', nCode*nbp*nPeriod_max),
+ dim=c(nCode, nbp, nPeriod_max))
+ tab_Code = array(rep('', nCode*nbp*nPeriod_max),
+ dim=c(nCode, nbp, nPeriod_max))
+ tab_Periods = array(rep('', nCode*nbp*nPeriod_max),
+ dim=c(nCode, nbp, nPeriod_max))
+
+ # For all code
+ for (k in 1:nCode) {
+ # Gets the code
+ code = Code[k]
+ # For all the variable
+ for (i in 1:nbp) {
+ df_trend = list_df2plot[[i]]$trend
+ # Extracts the trend corresponding to the code
+ 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$period_end
+ # Get the name of the different period
+ UStart = levels(factor(Start))
+ UEnd = levels(factor(End))
+
+ # Compute the max of different start and end
+ # so the number of different period
+ nPeriod = max(length(UStart), length(UEnd))
+
+ # For all the period
+ for (j in 1:nPeriod_max) {
+ # Stocks period
+ Periods = paste(Start[j],
+ End[j],
+ sep=' / ')
+
+ # Saves the time info
+ tab_Start[k, i, j] = as.character(Start[j])
+ tab_End[k, i, j] = as.character(End[j])
+ tab_Code[k, i, j] = code
+ tab_Periods[k, i, j] = Periods
+
+ }
+ }
+ }
+
+ # Blank array to store mean of the trend for each
+ # station, perdiod and variable
+ TrendValue_code = array(rep(1, nPeriod_trend*nbp*nCode),
+ dim=c(nPeriod_trend, nbp, nCode))
+
+ # For all the period
+ for (j in 1:nPeriod_max) {
+ # For all the code
+ for (k in 1:nCode) {
+ # Gets the code
+ code = Code[k]
+
+ for (i in 1:nbp) {
+ # Extracts the data corresponding to the
+ # current variable
+ df_data = list_df2plot[[i]]$data
+ # Extracts the trend corresponding to the
+ # current variable
+ df_trend = list_df2plot[[i]]$trend
+ # Extracts the type of the variable
+ type = list_df2plot[[i]]$type
+ p_threshold = list_df2plot[[i]]$p_threshold
+ # Extracts the data corresponding to the code
+ df_data_code = df_data[df_data$code == code,]
+ df_trend_code = df_trend[df_trend$code == code,]
+
+ # Gets the associated time info
+ Start = tab_Start[k, i, j]
+ End = tab_End[k, i, j]
+ Periods = tab_Periods[k, i, j]
+
+ # Extracts the corresponding data for the period
+ df_data_code_per =
+ df_data_code[df_data_code$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
+ }
+
+ # If the p value is under the threshold
+ if (df_trend_code_per$p <= p_threshold) {
+ # Stores the mean trend
+ TrendValue_code[j, i, k] = trendValue
+ # Otherwise
+ } else {
+ # Do not stocks it
+ TrendValue_code[j, i, k] = NA
+ }
+ }
+ }
+ }
+
+ # Compute the min and the max of the mean trend for all the station
+ minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
+ maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
+
+ # Blank vector to store the max number of digit of label for
+ # each station
+ NspaceMax = c()
+ # For all the station
+ for (code in Code) {
+
+ # Default max digit
+ NspaceMax_code = 0
+
+ # If the time header is given
+ if (!is.null(time_header)) {
+ nbpMod = nbp + 1
+ }
+
+ # For all type of graph
+ for (i in 1:nbpMod) {
+
+ if (i > nbp) {
+ # Extracts the data serie corresponding to the code
+ df_data_code = time_header[time_header$code == code,]
+ type = 'sévérité'
+ } else {
+ # Extracts the data corresponding to the current variable
+ df_data = list_df2plot[[i]]$data
+ # Extracts the type corresponding to the current variable
+ type = list_df2plot[[i]]$type
+ # Extracts the data corresponding to the code
+ df_data_code = df_data[df_data$code == code,]
+ }
+
+ # If variable type is date
+ if (type == 'saisonnalité') {
+ # The number of digit is 6 because months are display
+ # with 3 characters
+ Nspace = 6
+ # If it is a flow variable
+ } else if (type == 'sévérité') {
+ # Gets the max number of digit on the label
+ maxtmp = max(df_data_code$Value, na.rm=TRUE)
+ # Taking into account of the augmentation of
+ # max for the window
+ maxtmp = maxtmp * (1 + lim_pct/100)
+
+ # If the max is greater than 10
+ if (maxtmp >= 10) {
+ # The number of digit is the magnitude plus
+ # the first number times 2
+ Nspace = (get_power(maxtmp) + 1)*2
+ # Plus spaces between thousands hence every 8 digits
+ Nspace = Nspace + as.integer(Nspace/8)
+ # If the max is less than 10 and greater than 1
+ } else if (maxtmp < 10 & maxtmp >= 1) {
+ # The number of digit is the magnitude plus
+ # the first number times 2 plus 1 for the dot
+ # and 2 for the first decimal
+ Nspace = (get_power(maxtmp) + 1)*2 + 3
+ # If the max is less than 1 (and obviously more than 0)
+ } else if (maxtmp < 1) {
+ # Fixes the number of significant decimals to 3
+ maxtmp = signif(maxtmp, 3)
+ # The number of digit is the number of character
+ # of the max times 2 minus 1 for the dots that
+ # count just 1 space
+ Nspace = nchar(as.character(maxtmp))*2 - 1
+ }
+ }
+
+ # If it is the temporary max number
+ if (Nspace > NspaceMax_code) {
+ # Stores it
+ NspaceMax_code = Nspace
+ }
+ }
+ # 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
+ code = Code[k]
+ # Print code of the station for the current plotting
+ print(paste("Datasheet for station : ", code,
+ " (", round(k/nCode*100, 0), " %)",
+ sep=''))
+
+ # Number of header (is info and time serie are needed)
+ nbh = as.numeric(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) {
+ # Extracts the data serie corresponding to the code
+ time_header_code = time_header[time_header$code == code,]
+ # Gets the info plot
+ Hinfo = info_panel(list_df2plot,
+ df_meta,
+ period=mean_period[[1]],
+ df_shapefile=df_shapefile,
+ codeLight=code,
+ df_data_code=time_header_code)
+ # Stores it
+ P[[1]] = Hinfo
+ }
+
+ # If the time header is given
+ if (!is.null(time_header)) {
+ # Extracts the data serie corresponding to the code
+ time_header_code = time_header[time_header$code == code,]
+ # Gets the limits of the time serie
+ axis_xlim = c(min(time_header_code$Date),
+ max(time_header_code$Date))
+ # Gets the time serie plot
+ Htime = time_panel(time_header_code, df_trend_code=NULL,
+ trend_period=trend_period, missRect=TRUE,
+ unit2day=365.25, var='Q', type='sévérité',
+ grid=TRUE, ymin_lim=0,
+ NspaceMax=NspaceMax[k],
+ first=TRUE, lim_pct=lim_pct)
+ # Stores it
+ P[[2]] = Htime
+ }
+
+ # Computes the number of column of plot asked on the datasheet
+ nbcol = ncol(as.matrix(layout_matrix))
+ # 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]]$p_threshold
+ unit2day = list_df2plot[[i]]$unit2day
+ missRect = list_df2plot[[i]]$missRect
+ # Extract the variable of the plot
+ var = list_df2plot[[i]]$var
+ 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$code == code,]
+
+ # Blank vector to store color
+ color = c()
+
+ # # Default grey color for not significant trend
+ # grey = 85
+
+ # For all the period
+ for (j in 1:nPeriod_max) {
+
+ # If the trend is significant
+ if (df_trend_code$p[j] <= p_threshold){
+ # Gets the associated time info
+ Start = tab_Start[k, i, j]
+ End = tab_End[k, i, j]
+ Periods = tab_Periods[k, i, 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('grey', grey, sep='')
+ # # And gets a new shade of grey if there is
+ # # an other not significant trend
+ # grey = grey - 10
+
+ # Stores the default grey color
+ colortmp = paste('grey85', sep='')
+
+ }
+ # Stores the color
+ color = append(color, colortmp)
+ }
+
+ # Computes the time panel associated to the current variable
+ p = time_panel(df_data_code, df_trend_code, var=var,
+ type=type, p_threshold=p_threshold,
+ 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),
+ lim_pct=lim_pct)
+
+ # Stores the plot
+ P[[i+nbh]] = p
+ }
+
+ Foot = foot_panel(k, nCode, resource_path,
+ AEAGlogo_file, INRAElogo_file,
+ FRlogo_file, foot_height)
+
+ P[[nbg]] = Foot
+
+
+ # Convert the 'layout_matrix' to a matrix if it is not already
+ layout_matrix = as.matrix(layout_matrix)
+
+ # Number of element of the matrix
+ nel = nrow(layout_matrix)*ncol(layout_matrix)
+ # Gets the place where there is NA value
+ idNA = which(is.na(layout_matrix), arr.ind=TRUE)
+
+ LM = layout_matrix
+ # Adds non existing plot is where the is NA
+ LM[idNA] = seq(max(layout_matrix, na.rm=TRUE) + 1,
+ max(layout_matrix, na.rm=TRUE) + 1 +
+ nel)
+ # Shifts all plots to be coherent with the adding of header
+ LM = LM + nbh
+
+ if (!is.null(time_header)) {
+ LM = rbind(2, LM)
+ } else {
+ time_ratio = 0
+ }
+ if (info_header) {
+ LM = rbind(1, LM)
+ } else {
+ info_ratio = 0
+ }
+
+ if (foot_note) {
+ id_foot = length(LM) + 1
+ LM = rbind(LM, id_foot)
+ } else {
+ foot_height = 0
+ }
+
+ LMcol = ncol(LM)
+ LMrow = nrow(LM)
+
+ LM = rbind(rep(99, times=LMcol), LM, rep(99, times=LMcol))
+ LMrow = nrow(LM)
+ LM = cbind(rep(99, times=LMrow), LM, rep(99, times=LMrow))
+ LMcol = ncol(LM)
+
+ margin = 0.5
+ height = 29.7
+ width = 21
+
+ Norm_ratio = height * (info_ratio + time_ratio + var_ratio*nbp) / (height - 2*margin - foot_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[Hcut == 1] = info_height
+ heightLM[Hcut == 2] = time_height
+ heightLM[Hcut > 2 & Hcut < id_foot] = var_height
+ heightLM[Hcut == id_foot] = foot_height
+ heightLM[Hcut == 99] = margin
+
+ col_width = (width - 2*margin) / (LMcol - 2)
+
+ Wcut = LM[(nrow(LM)-1),]
+ widthLM = rep(col_width, times=LMcol)
+ widthLM[Wcut == 99] = margin
+
+ # Plot the graph as the layout
+ plot = grid.arrange(grobs=P, layout_matrix=LM,
+ heights=heightLM, widths=widthLM)
+
+ # Saving
+ ggsave(plot=plot,
+ path=outdirTmp,
+ filename=paste(as.character(code), '.pdf', sep=''),
+ width=width, height=height, units='cm', dpi=100)
+
+ }
+}
+
+
+## 2. OTHER PANEL FOR THE DATASHEET
+### 2.1. Time panel
+time_panel = function (df_data_code, df_trend_code, var, type, p_threshold=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)
+ minQ = min(df_data_code$Value, na.rm=TRUE)
+
+ spread = maxQ - minQ
+
+ nTick = 6
+ maxQ_win = maxQ + spread*lim_pct/100
+ minQ_win = minQ - spread*lim_pct/100
+
+ if (minQ_win < 0) {
+ minQtmp_lim = 0
+ } else {
+ minQtmp_lim = minQ_win
+ }
+
+ if (!is.null(ymin_lim)) {
+ minQ_win = ymin_lim
+ }
+
+ spreadtmp = maxQ_win - minQtmp_lim
+
+ breakQtmp = spreadtmp / (nTick - 1)
+
+ GradQ_10 = c(0, 1, 1.5, 2, 2.5, 3, 4, 5, 10)
+
+ Grad = GradQ_10 * 10^get_power(breakQtmp)
+ dist = abs(Grad - breakQtmp)
+ idGrad = which.min(dist)
+ breakQ = Grad[idGrad]
+
+ if (is.null(ymin_lim)) {
+ Grad = GradQ_10 * 10^get_power(minQtmp_lim)
+ Grad[Grad > minQtmp_lim] = NA
+ dist = abs(Grad - minQtmp_lim)
+ idGrad = which.min(dist)
+ minQ_lim = Grad[idGrad]
+ } else {
+ minQ_lim = ymin_lim
+ }
+ maxQ_list = c()
+ i = 1
+ maxQtmp = minQ_lim
+
+ while (maxQtmp <= maxQ_win) {
+ maxQtmp = minQ_lim + i*breakQ
+ i = i + 1
+ }
+ maxQ_lim = maxQtmp
+
+
+ # If x axis limits are specified
+ if (!is.null(axis_xlim)) {
+ minor_minDatetmp_lim = as.Date(axis_xlim[1])
+ minor_maxDatetmp_lim = as.Date(axis_xlim[2])
+ # Otherwise
+ } else {
+ minor_minDatetmp_lim = as.Date(df_data_code$Date[1])
+ minor_maxDatetmp_lim =
+ as.Date(df_data_code$Date[length(df_data_code$Date)])
+ }
+
+ minor_minDatetmp_lim = as.numeric(format(minor_minDatetmp_lim, "%Y"))
+ minor_maxDatetmp_lim = as.numeric(format(minor_maxDatetmp_lim, "%Y"))
+
+ minDatetmp_lim = minor_minDatetmp_lim
+ maxDatetmp_lim = minor_maxDatetmp_lim
+
+ nTick = 8
+
+ spreadtmp = minor_maxDatetmp_lim - minor_minDatetmp_lim
+ breakDatetmp = spreadtmp / (nTick - 1)
+
+ GradDate_10 = c(1, 2.5, 5, 10)
+
+ Grad = GradDate_10 * 10^get_power(breakDatetmp)
+ dist = abs(Grad - breakDatetmp)
+ idGrad = which.min(dist)
+ breakDate = Grad[idGrad]
+
+ listDate = seq(round(minDatetmp_lim, -1)-10^(get_power(breakDate)+1),
+ round(maxDatetmp_lim, -1)+10^(get_power(breakDate)+1),
+ by=breakDate)
+
+ minDate_lim = listDate[which.min(abs(listDate - minDatetmp_lim))]
+ maxDate_lim = listDate[which.min(abs(listDate - maxDatetmp_lim))]
+ minDate_lim = as.Date(paste(minDate_lim, '-01-01', sep=''))
+ maxDate_lim = as.Date(paste(maxDate_lim, '-01-01', sep=''))
+
+
+ minor_breakDatetmp = breakDate / 5
+
+ GradMinorDate_10 = c(1, 2, 5, 10)
+
+ Grad = GradMinorDate_10 * 10^get_power(minor_breakDatetmp)
+ dist = abs(Grad - minor_breakDatetmp)
+ idGrad = which.min(dist)
+ minor_breakDate = Grad[idGrad]
+
+ listDate = seq(round(minor_minDatetmp_lim,
+ -1) - 10^(get_power(minor_breakDate)+1),
+ round(minor_maxDatetmp_lim,
+ -1) + 10^(get_power(minor_breakDate)+1),
+ by=minor_breakDate)
+
+ minor_minDate_lim =
+ listDate[which.min(abs(listDate - minor_minDatetmp_lim))]
+ minor_maxDate_lim =
+ listDate[which.min(abs(listDate - minor_maxDatetmp_lim))]
+ minor_minDate_lim = as.Date(paste(minor_minDate_lim,
+ '-01-01', sep=''))
+ minor_maxDate_lim = as.Date(paste(minor_maxDate_lim,
+ '-01-01', sep=''))
+
+ # Open new plot
+ p = ggplot() + theme_ash
+
+ # Margins
+ if (first) {
+ p = p +
+ theme(plot.margin=margin(t=2.5, r=0, b=3, l=0, unit="mm"))
+ } else if (last) {
+ p = p +
+ theme(plot.margin=margin(t=2, r=0, b=0, l=0, unit="mm"))
+ } else if (first & last) {
+ p = p +
+ theme(plot.margin=margin(t=2.5, r=0, b=0, l=0, unit="mm"))
+ } else {
+ p = p +
+ theme(plot.margin=margin(t=2, r=0, b=2, l=0, unit="mm"))
+ }
+
+
+ ## Sub period background ##
+ if (!is.null(trend_period)) {
+
+ # trend_period = as.list(trend_period)
+ # Imin = 10^99
+ # for (per in trend_period) {
+ # I = interval(per[1], per[2])
+ # if (I < Imin) {
+ # Imin = I
+ # trend_period_min = as.Date(per)
+ # }
+ # }
+ # p = p +
+ # geom_rect(aes(xmin=min(df_data_code$Date),
+ # ymin=0,
+ # xmax=trend_period_min[1],
+ # ymax= maxQ*1.1),
+ # linetype=0, fill='grey97') +
+
+ # geom_rect(aes(xmin=trend_period_min[2],
+ # ymin=0,
+ # xmax=max(df_data_code$Date),
+ # ymax= maxQ*1.1),
+ # linetype=0, fill='grey97')
+
+ # Convert trend period to list if it is not
+ trend_period = as.list(trend_period)
+ # Fix a disproportionate minimum for period
+ Imin = 10^99
+ # For all the sub period of analysis in 'trend_period'
+ for (per in trend_period) {
+ # Compute time interval of period
+ I = interval(per[1], per[2])
+ # If it is the smallest interval
+ if (I < Imin) {
+ # Store it
+ Imin = I
+ # Fix min period of analysis
+ trend_period_min = as.Date(per)
+ }
+ }
+
+ minPer = trend_period_min[1]
+ maxPer = trend_period_min[2]
+
+ # If it is not a flow or sqrt of flow time serie
+ if (var != 'sqrt(Q)' & var != 'Q') {
+ # If there is an 'axis_lim'
+ if (!is.null(axis_xlim)) {
+ # If the temporary start of period is smaller
+ # than the fix start of x axis limit
+ if (minPer < axis_xlim[1]) {
+ # Set the start of the period to the start of
+ # the x axis limit
+ minPer = axis_xlim[1]
+ }
+ }
+ }
+
+ # If it is not a flow or sqrt of flow time serie
+ if (var != 'sqrt(Q)' & var != 'Q') {
+ # If there is an 'axis_lim'
+ if (!is.null(axis_xlim)) {
+ # If the temporary end of period plus one year
+ # is smaller than the fix end of x axis limit
+ if (maxPer + years(1) < axis_xlim[2]) {
+ # Add one year the the temporary end of period
+ maxPer = maxPer + years(1)
+ } else {
+ # Set the start of the period to the start of
+ # the x axis limit
+ maxPer = axis_xlim[2]
+ }
+ # Add one year the the temporary end of period
+ # if there is no 'axis_lim'
+ } else {
+ maxPer = maxPer + years(1)
+ }
+ }
+
+ # Draw rectangle to delimiting the sub period
+ p = p +
+ geom_rect(aes(xmin=minPer,
+ ymin=minQ_win,
+ xmax=maxPer,
+ ymax= maxQ_win),
+ linetype=0, fill='grey97')
+ }
+
+ ## Mean step ##
+ # If there is a 'mean_period'
+ if (!is.null(mean_period)) {
+ # Convert 'mean_period' to list
+ mean_period = as.list(mean_period)
+ # Number of mean period
+ nPeriod_mean = length(mean_period)
+
+ # Blank tibble to store variable in order to plot
+ # rectangle for mean period
+ plot_mean = tibble()
+ # Blank tibble to store variable in order to plot
+ # upper limit of rectangle for mean period
+ plot_line = tibble()
+ # For all mean period
+ for (j in 1:nPeriod_mean) {
+ # Get the current start and end of the sub period
+ xmin = as.Date(mean_period[[j]][1])
+ xmax = as.Date(mean_period[[j]][2])
+
+ # Extract the data corresponding to this sub period
+ df_data_code_per =
+ df_data_code[df_data_code$Date >= xmin
+ & df_data_code$Date <= xmax,]
+
+ # If the min over the sub period is greater
+ # than the min of the entier period and
+ # it is not the first sub period
+ if (xmin > min(df_data_code$Date) & j != 1) {
+ # Substract 6 months to be in the middle of
+ # the previous year
+ xmin = add_months(xmin, -6)
+ }
+ # If it is not a flow or sqrt of flow time serie and
+ # it is the first period
+ if (var != 'sqrt(Q)' & var != 'Q' & j == 1) {
+ # If there is an x axis limit
+ if (!is.null(axis_xlim)) {
+ # If the min of the period is before the x axis min
+ if (xmin < axis_xlim[1]) {
+ # The min for the sub period is the x axis
+ xmin = axis_xlim[1]
+ }
+ }
+ }
+
+ # If the max over the sub period is smaller
+ # than the max of the entier period and
+ # it is not the last sub period
+ if (xmax < max(df_data_code$Date) & j != nPeriod_mean) {
+ # Add 6 months to be in the middle of
+ # the following year
+ xmax = add_months(xmax, 6)
+ }
+ # If it is not a flow or sqrt of flow time serie and
+ # it is the last period
+ if (var != 'sqrt(Q)' & var != 'Q' & j == nPeriod_mean) {
+ # If there is an x axis limit
+ if (!is.null(axis_xlim)) {
+ # If the max of the period plus 1 year
+ # is smaller thant the max of the x axis limit
+ if (xmax + years(1) < axis_xlim[2]) {
+ # Add one year to the max to include
+ # the entire last year graphically
+ xmax = xmax + years(1)
+ } else {
+ # The max of this sub period is the max
+ # of the x axis limit
+ xmax = axis_xlim[2]
+ }
+ # If there is no axis limit
+ } else {
+ # Add one year to the max to include
+ # the entire last year graphically
+ xmax = xmax + years(1)
+ }
+ }
+
+ # Mean of the flow over the sub period
+ ymax = mean(df_data_code_per$Value, na.rm=TRUE)
+
+ # Create temporary tibble with variable
+ # to create rectangle for mean step
+ plot_meantmp = tibble(xmin=xmin, xmax=xmax,
+ ymin=minQ_win, ymax=ymax, period=j)
+ # Bind it to the main tibble to store it with other period
+ plot_mean = bind_rows(plot_mean, plot_meantmp)
+
+ # Create vector for the upper limit of the rectangle
+ abs = c(xmin, xmax)
+ ord = c(ymax, ymax)
+
+ # Create temporary tibble with variable
+ # to create upper limit for rectangle
+ plot_linetmp = tibble(abs=abs, ord=ord, period=j)
+ # Bind it to the main tibble to store it with other period
+ plot_line = bind_rows(plot_line, plot_linetmp)
+ }
+ # Plot rectangles
+ p = p +
+ geom_rect(data=plot_mean,
+ aes(xmin=xmin, ymin=ymin,
+ xmax=xmax, ymax=ymax),
+ linetype=0, fill='grey93')
+ # Plot upper line for rectangle
+ p = p +
+ geom_line(data=plot_line,
+ aes(x=abs, y=ord, group=period),
+ color='grey85',
+ size=0.15)
+
+
+ # for all the sub periods except the last one
+ for (i in 1:(nPeriod_mean - 1)) {
+ # Computes the time difference in days between periods
+ dPeriod = abs(as.Date(mean_period[[i+1]][1]) - as.Date(mean_period[[i]][2]))
+
+ if (dPeriod < 10) {
+ # The x limit is the x max of the ith rectangle
+ xLim = plot_mean$xmax[i]
+ # The y limit of rectangle is the max of
+ # the two neighboring mean step rectangle
+ yLim = max(c(plot_mean$ymax[i], plot_mean$ymax[i+1]))
+ # Make a tibble to store data
+ plot_lim = tibble(x=c(xLim, xLim), y=c(minQ_win, yLim))
+ # Plot the limit of rectangles
+ p = p +
+ geom_line(data=plot_lim, aes(x=x, y=y),
+ linetype='dashed', size=0.15,
+ color='grey85')
+
+ } else {
+ # Takes the x and y limits for the ith rectangle
+ xLim_i = plot_mean$xmax[i]
+ yLim_i = plot_mean$ymax[i]
+ # Takes the x and y limits for the i+1th rectangle
+ xLim_i1 = plot_mean$xmin[i+1]
+ yLim_i1 = plot_mean$ymax[i+1]
+
+ # Make a tibble to store data
+ plot_lim = tibble(x_i=c(xLim_i, xLim_i),
+ y_i=c(minQ_win, yLim_i),
+ x_i1=c(xLim_i1, xLim_i1),
+ y_i1=c(minQ_win, yLim_i1))
+ # Plot the limit of rectangles
+ p = p +
+ geom_line(data=plot_lim, aes(x=x_i, y=y_i),
+ linetype='dashed', size=0.15,
+ color='grey85') +
+ geom_line(data=plot_lim, aes(x=x_i1, y=y_i1),
+ linetype='dashed', size=0.15,
+ color='grey85')
+ }
+ }
+ }
+
+ ### Grid ###
+ if (grid) {
+ # If there is no axis limit
+ if (is.null(axis_xlim)) {
+ # The min and the max is set by
+ # the min and the max of the date data
+ xmin = min(df_data_code$Date)
+ xmax = max(df_data_code$Date)
+ } else {
+ # Min and max is set with the limit axis parameter
+ xmin = axis_xlim[1]
+ xmax = axis_xlim[2]
+ }
+ # Create a vector for all the y grid position
+ ygrid = seq(minQ_win, maxQ_win, breakQ)
+ # Blank vector to store position
+ ord = c()
+ abs = c()
+ # For all the grid element
+ for (i in 1:length(ygrid)) {
+ # Store grid position
+ ord = c(ord, rep(ygrid[i], times=2))
+ abs = c(abs, xmin, xmax)
+ }
+ # Create a tibble to store all the position
+ plot_grid = tibble(abs=as.Date(abs), ord=ord)
+ # Plot the y grid
+ p = p +
+ geom_line(data=plot_grid,
+ aes(x=abs, y=ord, group=ord),
+ color='grey85',
+ size=0.15)
+ }
+
+ ### Data ###
+ # If it is a square root flow or flow
+ if (var == 'sqrt(Q)' | var == 'Q') {
+ # Plot the data as line
+ p = p +
+ geom_line(aes(x=df_data_code$Date, y=df_data_code$Value),
+ color='grey20',
+ size=0.3,
+ lineend="round")
+ } else {
+ # Plot the data as point
+ p = p +
+ geom_point(aes(x=df_data_code$Date, y=df_data_code$Value),
+ shape=19, color='grey50', alpha=1,
+ stroke=0, size=1)
+ }
+
+ ### Missing data ###
+ # If the option is TRUE
+ if (missRect) {
+ # Remove NA data
+ NAdate = df_data_code$Date[is.na(df_data_code$Value)]
+ # Get the difference between each point of date data without NA
+ dNAdate = diff(NAdate)
+ # If difference of day is not 1 then
+ # it is TRUE for the beginning of each missing data period
+ NAdate_Down = NAdate[append(Inf, dNAdate) != 1]
+ # If difference of day is not 1 then
+ # it is TRUE for the ending of each missing data period
+ NAdate_Up = NAdate[append(dNAdate, Inf) != 1]
+
+ # Plot the missing data period
+ p = p +
+ geom_rect(aes(xmin=NAdate_Down,
+ ymin=minQ_win,
+ xmax=NAdate_Up,
+ ymax=maxQ_win),
+ linetype=0, fill='Wheat', alpha=0.4)
+ }
+
+ ### Trend ###
+ # If there is trends
+ if (!is.null(df_trend_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_trend = max(length(UStart), length(UEnd))
+
+ # Blank tibble to store trend data and legend data
+ plot_trend = tibble()
+ leg_trend = tibble()
+ # For all the different period
+ for (i in 1:nPeriod_trend) {
+
+ # Extracts the corresponding data for the period
+ df_data_code_per =
+ df_data_code[df_data_code$Date >= Start[i]
+ & df_data_code$Date <= End[i],]
+
+ # Computes the mean of the data on the period
+ dataMean = mean(df_data_code_per$Value,
+ na.rm=TRUE)
+
+ # Get the trend associated to the first period
+ df_trend_code_per =
+ df_trend_code[df_trend_code$period_start == Start[i]
+ & df_trend_code$period_end == End[i],]
+
+ # Number of trend selected
+ Ntrend = nrow(df_trend_code_per)
+ # If the number of trend is greater than a unique one
+ if (Ntrend > 1) {
+ # Extract only the first hence it is the same period
+ df_trend_code_per = df_trend_code_per[1,]
+ }
+
+ # Search for the index of the closest existing date
+ # to the start of the trend period of analysis
+ iStart = which.min(abs(df_data_code$Date - Start[i]))
+ # Same for the end
+ iEnd = which.min(abs(df_data_code$Date - End[i]))
+
+ # Get the start and end date associated
+ xmin = df_data_code$Date[iStart]
+ xmax = df_data_code$Date[iEnd]
+
+ # If there is a x axis limit
+ if (!is.null(axis_xlim)) {
+ # If the min of the current period
+ # is smaller than the min of the x axis limit
+ if (xmin < axis_xlim[1]) {
+ # The min of the period is the min
+ # of the x axis limit
+ xmin = axis_xlim[1]
+ }
+ # Same for end
+ if (xmax > axis_xlim[2]) {
+ xmax = axis_xlim[2]
+ }
+ }
+
+ # Create vector to store x data
+ abs = c(xmin, xmax)
+ # Convert the number of day to the unit of the period
+ abs_num = as.numeric(abs) / unit2day
+ # Compute the y of the trend
+ ord = abs_num * df_trend_code_per$trend +
+ df_trend_code_per$intercept
+
+ # Create temporary tibble with variable to plot trend
+ # for each period
+ plot_trendtmp = tibble(abs=abs, ord=ord, period=i)
+ # Bind it to the main tibble to store it with other period
+ plot_trend = bind_rows(plot_trend, plot_trendtmp)
+
+ # If there is a x axis limit
+ if (!is.null(axis_xlim)) {
+ # The x axis limit is selected
+ codeDate = axis_xlim
+ } else {
+ # The entire date data is selected
+ codeDate = df_data_code$Date
+ }
+ # The y limit is stored in a vector
+ codeValue = c(minQ_win, maxQ_win)
+
+ # Position of the x beginning and end of the legend symbol
+ x = gpct(1.5, codeDate, shift=TRUE)
+ xend = x + gpct(3, codeDate)
+
+ # Spacing between legend symbols
+ dy = gpct(9, codeValue, min_lim=ymin_lim)
+ # Position of the y beginning and end of the legend symbol
+ y = gpct(92, codeValue,
+ min_lim=ymin_lim, shift=TRUE) - (i-1)*dy
+ yend = y
+
+ # Position of x for the beginning of the associated text
+ xt = xend + gpct(1, codeDate)
+
+ # Position of the background rectangle of the legend
+ xminR = x - gpct(1, codeDate)
+ yminR = y - gpct(5, codeValue, min_lim=ymin_lim)
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ xmaxR = x + gpct(32.5, codeDate)
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ xmaxR = x + gpct(20.5, codeDate)
+ }
+ ymaxR = y + gpct(5, codeValue, min_lim=ymin_lim)
+
+ # Gets the trend
+ trend = df_trend_code_per$trend
+ # Gets the p value
+ pVal = df_trend_code_per$p
+ # Converts it to character
+ pValC = as.character(format(round(pVal, 2),
+ nsmall=2))
+ # Computes the mean trend
+ trendMean = trend/dataMean
+ # Computes the magnitude of the trend
+ power = get_power(trend)
+ # Converts it to character
+ powerC = as.character(power)
+ # If the power is positive
+ if (powerC >= 0) {
+ # Adds a space in order to compensate for the minus
+ # sign that sometimes is present for the other periods
+ spaceC = ' '
+ # Otherwise
+ } else {
+ # No space is added
+ spaceC = ''
+ }
+
+ # Gets the power of ten of magnitude
+ brk = 10^power
+ # Converts trend to character for sientific expression
+ trendC = as.character(format(round(trend / brk, 2),
+ nsmall=2))
+ # If the trend is positive
+ if (trendC >= 0) {
+ # Adds two space in order to compensate for the minus
+ # sign that sometimes is present for the other periods
+ trendC = paste(' ', trendC, sep='')
+ }
+ # Converts mean trend to character
+ trendMeanC = as.character(format(round(trendMean*100, 2),
+ nsmall=2))
+ if (trendMeanC >= 0) {
+ # Adds two space in order to compensate for the minus
+ # sign that sometimes is present for the other periods
+ trendMeanC = paste(' ', trendMeanC, sep='')
+ }
+
+ # Create temporary tibble with variable to plot legend
+ leg_trendtmp = tibble(x=x, xend=xend,
+ y=y, yend=yend,
+ xt=xt,
+ trendC=trendC,
+ powerC=powerC,
+ spaceC=spaceC,
+ trendMeanC=trendMeanC,
+ pValC=pValC,
+ xminR=xminR, yminR=yminR,
+ xmaxR=xmaxR, ymaxR=ymaxR,
+ period=i)
+ # Bind it to the main tibble to store it with other period
+ leg_trend = bind_rows(leg_trend, leg_trendtmp)
+ }
+
+ # For all periods
+ for (i in 1:nPeriod_trend) {
+ # Extract the trend of the current sub period
+ leg_trend_per = leg_trend[leg_trend$period == i,]
+
+ # Plot the background for legend
+ p = p +
+ geom_rect(data=leg_trend_per,
+ aes(xmin=xminR,
+ ymin=yminR,
+ xmax=xmaxR,
+ ymax=ymaxR),
+ linetype=0, fill='white', alpha=0.5)
+ }
+
+ # For all periods
+ for (i in 1:nPeriod_trend) {
+ # Extract the trend of the current sub period
+ leg_trend_per = leg_trend[leg_trend$period == i,]
+
+ # Get the character variable for naming the trend
+ trendC = leg_trend_per$trendC
+ powerC = leg_trend_per$powerC
+ spaceC = leg_trend_per$spaceC
+ trendMeanC = leg_trend_per$trendMeanC
+ pValC = leg_trend_per$pValC
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Create the name of the trend
+ label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'~~bold(.(trendMeanC))~'[%.'*an^{-1}*']')
+
+ # If it is a date variable
+ } else if ( type == 'saisonnalité') {
+ # Create the name of the trend
+ label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'[jour.'*an^{-1}*']')
+ }
+
+ # Plot the trend symbole and value of the legend
+ p = p +
+ annotate("segment",
+ x=leg_trend_per$x, xend=leg_trend_per$xend,
+ y=leg_trend_per$y, yend=leg_trend_per$yend,
+ color=color[i],
+ linetype='solid',
+ lwd=0.8) +
+
+ annotate("text",
+ label=label, size=2.8,
+ x=leg_trend_per$xt, y=leg_trend_per$y,
+ hjust=0, vjust=0.5,
+ color=color[i])
+ }
+
+ # For all periods
+ for (i in 1:nPeriod_trend) {
+ # Extract the trend of the current sub period
+ plot_trend_per = plot_trend[plot_trend$period == i,]
+
+ # Plot the line of white background of each trend
+ p = p +
+ geom_line(data=plot_trend_per,
+ aes(x=abs, y=ord),
+ color='white',
+ linetype='solid',
+ size=1.5,
+ lineend="round")
+ }
+
+ # For all periods
+ for (i in 1:nPeriod_trend) {
+ # Extract the trend of the current sub period
+ plot_trend_per = plot_trend[plot_trend$period == i,]
+
+ # Plot the line of trend
+ p = p +
+ geom_line(data=plot_trend_per,
+ aes(x=abs, y=ord),
+ color=color[i],
+ linetype='solid',
+ size=0.75,
+ lineend="round")
+ }
+ }
+
+ # Y axis title
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ p = p +
+ ylab(bquote(bold(.(var))~~'['*m^{3}*'.'*s^{-1}*']'))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ p = p +
+ ylab(bquote(bold(.(var))~~"[jour de l'année]"))
+ }
+
+ if (!last & !first) {
+ p = p +
+ theme(axis.text.x=element_blank())
+ }
+
+ if (first) {
+ position = 'top'
+ } else {
+ position = 'bottom'
+ }
+
+ if (is.null(axis_xlim)) {
+ limits = c(min(df_data_code$Date), max(df_data_code$Date))
+ } else {
+ limits = axis_xlim
+ }
+
+ # Parameters of the x axis contain the limit of the date data
+ p = p +
+ scale_x_date(breaks=seq(minDate_lim, maxDate_lim,
+ by=paste(breakDate, 'years')),
+ minor_breaks=seq(minor_minDate_lim,
+ minor_maxDate_lim,
+ by=paste(minor_breakDate,
+ 'years')),
+ guide='axis_minor',
+ date_labels="%Y",
+ limits=limits,
+ position=position,
+ expand=c(0, 0))
+
+
+
+ # If it is a date variable
+ if (type == 'saisonnalité') {
+ # The number of digit is 6 because months are display
+ # with 3 characters
+ Nspace = 6
+
+ prefix = strrep(' ', times=NspaceMax-Nspace)
+ accuracy = NULL
+
+ # If it is a flow variable
+ } else if (type == 'sévérité') {
+ # Gets the max number of digit on the label
+ maxtmp = max(df_data_code$Value, na.rm=TRUE)
+ # Taking into account of the augmentation of
+ # max for the window
+ maxtmp = maxtmp * (1 + lim_pct/100)
+
+ # If the max is greater than 10
+ if (maxtmp >= 10) {
+ # The number of digit is the magnitude plus
+ # the first number times 2
+ Nspace = (get_power(maxtmp) + 1)*2
+ # Plus spaces between thousands hence every 8 digits
+ Nspace = Nspace + as.integer(Nspace/8)
+ # Gets the associated number of white space
+ prefix = strrep(' ', times=NspaceMax-Nspace)
+ # The accuracy is 1
+ accuracy = 1
+
+ # If the max is less than 10 and greater than 1
+ } else if (maxtmp < 10 & maxtmp >= 1) {
+ # The number of digit is the magnitude plus
+ # the first number times 2 plus 1 for the dot
+ # and 2 for the first decimal
+ Nspace = (get_power(maxtmp) + 1)*2 + 3
+ # Gets the associated number of white space
+ prefix = strrep(' ', times=NspaceMax-Nspace)
+ # The accuracy is 0.1
+ accuracy = 0.1
+
+ # If the max is less than 1 (and obviously more than 0)
+ } else if (maxtmp < 1) {
+ # Fixes the number of significant decimals to 3
+ maxtmp = signif(maxtmp, 3)
+ # The number of digit is the number of character
+ # of the max times 2 minus 1 for the dots that
+ # count just 1 space
+ Nspace = nchar(as.character(maxtmp))*2 - 1
+ # Gets the associated number of white space
+ prefix = strrep(' ', times=NspaceMax-Nspace)
+ # Computes the accuracy
+ accuracy = 10^(-nchar(as.character(maxtmp))+2)
+ }
+ }
+
+ # Parameters of the y axis
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ p = p +
+ scale_y_continuous(breaks=seq(minQ_lim, maxQ_lim, breakQ),
+ limits=c(minQ_win, maxQ_win),
+ expand=c(0, 0),
+ labels=number_format(accuracy=accuracy,
+ prefix=prefix))
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # monthNum = as.numeric(format(seq(as.Date(minQ_lim),
+ # as.Date(maxQ_lim),
+ # by=paste(breakQ, 'days')),
+ # "%m"))
+
+ monthStart = as.Date(paste(substr(as.Date(minQ_lim), 1, 7),
+ '-01', sep=''))
+ monthEnd = as.Date(paste(substr(as.Date(maxQ_lim), 1, 7),
+ '-01', sep=''))
+
+ byMonth = round(breakQ/30.4, 0)
+ if (byMonth == 0) {
+ byMonth = 1
+ }
+
+ breaksDate = seq(monthStart, monthEnd,
+ by=paste(byMonth, 'months'))
+ breaksNum = as.numeric(breaksDate)
+ breaksMonth = as.numeric(format(breaksDate, "%m"))
+
+ monthName = c('Jan', 'Fév', 'Mar', 'Avr', 'Mai', 'Jui',
+ 'Jui', 'Aou', 'Sep', 'Oct', 'Nov', 'Déc')
+ monthName = paste(prefix, monthName, sep='')
+
+ labels = monthName[breaksMonth]
+
+ p = p +
+ scale_y_continuous(breaks=breaksNum,
+ limits=c(minQ_win, maxQ_win),
+ labels=labels,
+ expand=c(0, 0))
+
+ }
+ return(p)
+}
+
+
+### 2.2. Info panel
+# Plots the header that regroups all the info on the station
+info_panel = function(list_df2plot, df_meta, period, 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
+ hyd = hydrograph_panel(df_data_code, period=period,
+ margin=margin(t=0, r=0, b=0, l=5,
+ unit="mm"))
+ # Otherwise
+ } else {
+ # Puts it blank
+ hyd = void
+ }
+
+ # Computes the map associated to the station
+ map = map_panel(list_df2plot,
+ df_meta,
+ df_shapefile=df_shapefile,
+ codeLight=codeLight,
+ margin=margin(t=0, r=-12, b=0, l=0, unit="mm"),
+ showSea=FALSE,
+ verbose=FALSE)
+
+ # Gets the metadata about the station
+ df_meta_code = df_meta[df_meta$code == codeLight,]
+ # Extracts the name
+ nom = df_meta_code$nom
+ # Corrects some errors about the formatting of title with dash
+ nom = gsub("-", "- ", nom)
+
+ # Computes the time span of data, the start and the end
+ duration = as.numeric(format(as.Date(df_meta_code$fin), "%Y")) -
+ as.numeric(format(as.Date(df_meta_code$debut), "%Y"))
+ debut = format(as.Date(df_meta_code$debut), "%d/%m/%Y")
+ fin = format(as.Date(df_meta_code$fin), "%d/%m/%Y")
+
+ # Name of the datasheet
+ text1 = paste(
+ "<b>", codeLight, '</b> - ', nom,
+ sep='')
+
+ # Subitle info
+ text2 = paste(
+ "<b>",
+ "Gestionnaire : ", df_meta_code$gestionnaire, "<br>",
+ "Région hydro : ", df_meta_code$region_hydro,
+ "</b>",
+ sep='')
+
+ # Spatial info about station
+ text3 = paste(
+ "<b>",
+ "Superficie : ", df_meta_code$surface_km2_BH, " [km<sup>2</sup>] <br>",
+ "Altitude : ", df_meta_code$altitude_m_BH, " [m]<br>",
+ "X = ", df_meta_code$L93X_m_BH, " [m ; Lambert 93]<br>",
+ "Y = ", df_meta_code$L93Y_m_BH, " [m ; Lambert 93]",
+ "</b>",
+ sep='')
+
+ # Time info about station
+ text4 = paste(
+ "<b>",
+ "Date de début : ", debut, "<br>",
+ "Date de fin : ", fin, "<br>",
+ "Nombre d'années : ", duration, " [ans]", "<br>",
+ "Taux de lacunes : ", signif(df_meta_code$tLac100, 2), " [%]",
+ "</b>",
+ sep='')
+
+ # Converts all texts to graphical object in the right position
+ gtext1 = richtext_grob(text1,
+ x=0, y=1,
+ margin=unit(c(t=0, r=5, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="#00A3A8", fontsize=14))
+
+ gtext2 = richtext_grob(text2,
+ x=0, y=1.25,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=8))
+
+ gtext3 = richtext_grob(text3,
+ x=0, y=1,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ gtext4 = richtext_grob(text4,
+ x=0, y=1,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="grey20", fontsize=9))
+
+ # Makes a list of all plots
+ P = list(gtext1, gtext2, gtext3, gtext4, hyd, map)
+ # P = list(void, void, void, void, void, void, void)
+
+ # Creates the matrix layout
+ LM = matrix(c(1, 1, 1, 6,
+ 2, 2, 5, 6,
+ 3, 4, 5, 6,
+ 3, 4, 5, 6),
+ nrow=4,
+ byrow=TRUE)
+ # And sets the relative height of each plot
+ heights = rep(1, times=nrow(LM))
+ # heights[2] = 0.1
+ heights[2] = 0.8
+
+ # Arranges all the graphical objetcs
+ plot = grid.arrange(grobs=P,
+ layout_matrix=LM,
+ heights=heights)
+ # Return the plot object
+ return(plot)
+}
+
+### 2.3. Hydrograph panel
+# Creates a hydrograph for a station with the data serie of flow
+hydrograph_panel = function (df_data_code, period, margin=NULL) {
+
+ # Computes the hydrograph
+ res_hydrograph = get_hydrograph(df_data_code, period=period)
+ # Extracts the results
+ monthMean = res_hydrograph$QM
+ regime_hydro = res_hydrograph$meta
+
+ # Vector of month index
+ monthNum = 1:12
+ # Vector of month name abbreviation
+ monthName = c("J", "F", "M", "A", "M", "J",
+ "J", "A", "S", "O", "N", "D")
+
+ # Open a new plot with the personalise theme
+ hyd = ggplot() + theme_ash +
+ # Theme modification
+ theme(
+ # plot.background=element_rect(fill=NA, color="#EC4899"),
+ panel.border=element_blank(),
+ axis.text.x=element_text(margin=unit(c(0, 0, 0, 0), "mm"),
+ vjust=1, hjust=0.5),
+ axis.ticks.x=element_blank(),
+ axis.line.y=element_line(color='grey85', size=0.3),
+ plot.title=element_text(size=8, vjust=-0.5,
+ hjust=-1E-3, color='grey40'),
+ axis.title.y=element_text(size=8, vjust=0,
+ hjust=0.5,
+ color='grey40')) +
+
+ # Adds a title to the y axis
+ ggtitle(regime_hydro) +
+ # Y axis title
+ ylab(bquote(bold('QM')~~'['*m^{3}*'.'*s^{-1}*']'))
+
+ # If there is no margins specified
+ if (is.null(margin)) {
+ # Sets all margins to 0
+ hyd = hyd +
+ theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
+ # Otherwise
+ } else {
+ # Sets margins to the given ones
+ hyd = hyd +
+ theme(plot.margin=margin)
+ }
+
+ hyd = hyd +
+ # Plots the bar
+ geom_bar(aes(x=monthNum, y=monthMean),
+ stat='identity',
+ fill="grey70",
+ width=0.75, size=0.2) +
+ # X axis
+ scale_x_continuous(breaks=monthNum,
+ labels=monthName,
+ limits=c(0, max(monthNum)+0.5),
+ expand=c(0, 0)) +
+ # Y axis
+ scale_y_continuous(limits=c(0, max(monthMean)),
+ expand=c(0, 0))
+ # Returns the plot
+ return (hyd)
+}
+
+
+foot_panel = function (n_page, N_page, resource_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, foot_height) {
+
+ text_page = paste(
+ "fiche station <b>p. ", n_page, "/", N_page, "</b>",
+ sep='')
+
+ text_date = paste (
+ format(Sys.Date(), "%B %Y"),
+ sep='')
+
+ # Converts all texts to graphical object in the right position
+ gtext_page = richtext_grob(text_page,
+ x=1, y=0,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=1, vjust=0.5,
+ gp=gpar(col="#00A3A8", fontsize=8))
+
+ gtext_date = richtext_grob(text_date,
+ x=1, y=0.4,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=1, vjust=0.5,
+ gp=gpar(col="#00A3A8", fontsize=6))
+
+ AEAGlogo_path = file.path(resource_path, AEAGlogo_file)
+ INRAElogo_path = file.path(resource_path, INRAElogo_file)
+ FRlogo_path = file.path(resource_path, FRlogo_file)
+
+ AEAGlogo_img = readPNG(AEAGlogo_path)
+ AEAGlogo_grob = rasterGrob(AEAGlogo_img,
+ width=unit(0.7*foot_height, "cm"))
+
+ INRAElogo_img = readPNG(INRAElogo_path)
+ INRAElogo_grob = rasterGrob(INRAElogo_img,
+ y=0.57,
+ vjust=0.5,
+ width=unit(1.1*foot_height, "cm"))
+
+ FRlogo_img = readPNG(FRlogo_path)
+ FRlogo_grob = rasterGrob(FRlogo_img,
+ x=0, hjust=0,
+ width=unit(1*foot_height, "cm"))
+
+ P = list(void,
+ FRlogo_grob, INRAElogo_grob, AEAGlogo_grob,
+ gtext_page, gtext_date)
+
+ # Creates the matrix layout
+ LM = matrix(c(1, 2, 3, 4, 5,
+ 1, 2, 3, 4, 6),
+ nrow=2,
+ byrow=TRUE)
+
+ # And sets the relative width of each plot
+ widths = rep(1, times=ncol(LM))
+ widths[2] = 0.18
+ widths[3] = 0.25
+ widths[4] = 0.2
+
+ # Arranges all the graphical objetcs
+ plot = grid.arrange(grobs=P,
+ layout_matrix=LM,
+ widths=widths)
+
+ # Return the plot object
+ return (plot)
+}
diff --git a/plotting/layout.R b/plotting/layout.R
index c96ccb288a9f7c7ceafa1aae38bfe3be47ff7c9a..91fd5cfd58a362f74c2c3393cf22d14dcfc038b7 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -41,6 +41,7 @@ library(ggh4x)
library(RColorBrewer)
library(rgdal)
library(shadowtext)
+library(png)
# Sourcing R file
@@ -114,6 +115,11 @@ void = ggplot() + geom_blank(aes(1,1)) +
axis.line = element_blank()
)
+# A plot completly blank with a contour
+contour = void +
+ theme(plot.background=element_rect(fill=NA, color="#EC4899"),
+ plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
+
### 2.2. Circle
# Allow to draw circle in ggplot2 with a radius and a center position
gg_circle = function(r, xc, yc, color="black", fill=NA, ...) {
@@ -137,8 +143,12 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
missRect=FALSE, time_header=NULL,
info_header=TRUE, foot_note=FALSE,
info_ratio=1, time_ratio=2,
- var_ratio=3, foot_ratio=0.5,
- df_shapefile=NULL) {
+ var_ratio=3, foot_height=0.5,
+ df_shapefile=NULL,
+ resource_path=NULL,
+ AEAGlogo_file=NULL,
+ INRAElogo_file=NULL,
+ FRlogo_file=NULL) {
# Name of the document
outfile = "Panels"
@@ -235,7 +245,7 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
# If datasheets needs to be plot
if ('datasheet' %in% toplot) {
- datasheet_panel(list_df2plot, df_meta, trend_period, info_header=info_header, time_header=time_header, foot_note=foot_note, layout_matrix=layout_matrix, info_ratio=info_ratio, time_ratio=time_ratio, var_ratio=var_ratio, foot_ratio=foot_ratio, outdirTmp=outdirTmp)
+ datasheet_panel(list_df2plot, df_meta, trend_period, info_header=info_header, time_header=time_header, foot_note=foot_note, layout_matrix=layout_matrix, info_ratio=info_ratio, time_ratio=time_ratio, var_ratio=var_ratio, foot_height=foot_height, resource_path=resource_path, AEAGlogo_file=AEAGlogo_file, INRAElogo_file=INRAElogo_file, FRlogo_file=FRlogo_file, outdirTmp=outdirTmp)
}
diff --git a/processing/results_manager.R b/processing/results_manager.R
new file mode 100644
index 0000000000000000000000000000000000000000..3f8f1685a30b19b49607f07b7f1e71a9aa38c1b9
--- /dev/null
+++ b/processing/results_manager.R
@@ -0,0 +1,89 @@
+# \\\
+# Copyright 2021-2022 Louis Héraut*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/>.
+# ///
+#
+#
+# results_manager.R
+#
+# Manages the writing and reading of results data of the trend analysis.
+
+
+write_listofdf = function (Ldf, resdir, filedir) {
+
+ outdir = file.path(resdir, filedir)
+ if (!(file.exists(outdir))) {
+ dir.create(outdir)
+ }
+
+ Lname = names(Ldf)
+ Nname = length(Lname)
+ for (i in 1:Nname) {
+ outfile = paste(Lname[i], '.txt', sep='')
+
+ write.table(Ldf[[i]],
+ file=file.path(outdir, outfile),
+ sep=";",
+ quote=TRUE,
+ row.names=FALSE)
+ }
+}
+# write_listofdf(res_tINItrend, resdir, 'res_tINItrend')
+
+read_listofdf = function (resdir, filedir) {
+
+ outdir = file.path(resdir, filedir)
+ files = list.files(outdir)
+ Nfile = length(files)
+
+ Ldf = list()
+ for (i in 1:Nfile) {
+ name = splitext(files[i])$name
+
+ df = as_tibble(read.table(file=file.path(outdir, files[i]),
+ header=TRUE,
+ sep=";",
+ quote='"'))
+
+ 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]])
+ }
+ }
+ }
+ # OkFact = sapply(df, is.factor)
+ # df[OkFact] = lapply(df[OkFact], as.character)
+
+ Ldf = append(Ldf, list(df))
+ names(Ldf)[length(Ldf)] = name
+ }
+ return (Ldf)
+}
+# res_tINItrend = read_listofdf(resdir, 'res_tINItrend')
+
+splitext = function(file) {
+ ex = strsplit(basename(file), split="\\.")[[1]]
+ res = list(name=ex[1], extension=ex[2])
+ return (res)
+}
diff --git a/resources/Logo-INRAE_Transparent.png b/resources/Logo-INRAE_Transparent.png
new file mode 100644
index 0000000000000000000000000000000000000000..0cd93142e9ad2127cf167eac4acf97afed6be74b
Binary files /dev/null and b/resources/Logo-INRAE_Transparent.png differ
diff --git a/resources/Republique_Francaise_RVB.png b/resources/Republique_Francaise_RVB.png
new file mode 100644
index 0000000000000000000000000000000000000000..668b03d3b93ff589a7dc375e59c8d084a896e3d0
Binary files /dev/null and b/resources/Republique_Francaise_RVB.png differ
diff --git a/resources/agence-de-leau-adour-garonne_logo.png b/resources/agence-de-leau-adour-garonne_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..f6df308202f2a77c0ca6b66264a893687a90a54c
Binary files /dev/null and b/resources/agence-de-leau-adour-garonne_logo.png differ
diff --git a/script.R b/script.R
index 6a9125b7ceb0fbe2e7e04768dfd367daaac56d58..2292f482ca28887a9ff2d1c3cfadc3b1837f2757 100644
--- a/script.R
+++ b/script.R
@@ -63,7 +63,7 @@ filename =
# "P0885010_HYDRO_QJM.txt",
# "O5055010_HYDRO_QJM.txt",
# "O0384010_HYDRO_QJM.txt",
- # "S4214010_HYDRO_QJM.txt"
+ # "S4214010_HYDRO_QJM.txt",
"Q7002910_HYDRO_QJM.txt"
# "O3035210_HYDRO_QJM.txt",
# "O0554010_HYDRO_QJM.txt",
@@ -154,6 +154,18 @@ if (!(file.exists(figdir))) {
}
print(paste('figdir :', figdir))
+# Resource directory
+resource_path = file.path(computer_work_path, 'resource')
+if (!(file.exists(resource_path))) {
+ dir.create(resource_path)
+}
+print(paste('resource_path :', resource_path))
+
+# Logo filename
+AEAGlogo_file = 'agence-de-leau-adour-garonne_logo.png'
+INRAElogo_file = 'Logo-INRAE_Transparent.png'
+FRlogo_file = 'Republique_Francaise_RVB.png'
+
## 2. SELECTION OF STATION
# Initialization of null data frame if there is no data selected
@@ -335,8 +347,11 @@ datasheet_layout(toplot=c(
info_ratio=2,
time_ratio=2,
var_ratio=3,
- foot_ratio=0.5,
+ foot_height=1,
df_shapefile=df_shapefile,
figdir=figdir,
- filename_opt='')
-
+ filename_opt='',
+ resource_path=resource_path,
+ AEAGlogo_file=AEAGlogo_file,
+ INRAElogo_file=INRAElogo_file,
+ FRlogo_file=FRlogo_file)
diff --git a/script_install.R b/script_install.R
index 3597700cf0a48c3aef8bd86f8575d0f05e642e9b..762033e8f281c249e65199797f045d7bb31b418d 100644
--- a/script_install.R
+++ b/script_install.R
@@ -15,6 +15,7 @@ install.packages('extrafont')
install.packages("RColorBrewer")
install.packages('trend')
install.packages("shadowtext")
+install.packages("png")
# linux