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datasheet.R 59.17 KiB
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# \\\
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# Copyright 2021-2022 Louis Héraut*1,
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#                     Éric Sauquet*2,
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#                     Valentin Mansanarez
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#
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# *1   INRAE, France
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#      louis.heraut@inrae.fr
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# *2   INRAE, France
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#      eric.sauquet@inrae.fr
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#
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# This file is part of ash R toolbox.
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#
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# Ash R toolbox is free software: you can redistribute it and/or
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# modify it under the terms of the GNU General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at your option) any later version.
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#
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# Ash R toolbox is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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# General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with ash R toolbox.
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# If not, see <https://www.gnu.org/licenses/>.
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# ///
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#
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#
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# Rcode/plotting/datasheet.R
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#
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# 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.
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# Sourcing R file
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source(file.path('Rcode', 'processing', 'analyse.R'), encoding='UTF-8') # hydrograph
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source(file.path('Rcode', 'plotting', 'shortcut.R'), encoding='UTF-8')
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## 1. DATASHEET PANEL MANAGER ________________________________________
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# Manages datasheets creations for all stations. Makes the call to
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# the different headers, trend analysis graphs and realises arranging
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# every plots.
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datasheet_panel = function (list_df2plot, df_meta, trend_period,
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                            mean_period, linetype_per, axis_xlim,
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                            colorForce, info_header, time_header,
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                            foot_note, layout_matrix, info_height,
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                            time_ratio, var_ratio, foot_height,
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                            paper_size, resources_path, df_shapefile,
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                            logo_dir, PRlogo_file, AEAGlogo_file,
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                            INRAElogo_file, FRlogo_file,
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                            outdirTmp_pdf, outdirTmp_png,
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                            df_page=NULL) {
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    # The percentage of augmentation and diminution of the min
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    # and max limits for y axis
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    lim_pct = 10
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    # Number of variable/plot
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    nbVar = length(list_df2plot)
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    # Get all different stations code
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    Code = levels(factor(df_meta$code))
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    nCode = length(Code)
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    if (!is.null(trend_period)) {
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        # Convert 'trend_period' to list
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        trend_period = as.list(trend_period)
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        # Number of trend period
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        nPeriod_trend = length(trend_period)
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# Extracts the min and the max of the mean trend for all the station res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbVar, nCode, colorForce) minTrendValue = res$min maxTrendValue = res$max } # Blank vector to store the max number of digit of label for # each station NspaceMax = c() # For all the station for (code in Code) { # Default max digit NspaceMax_code = 0 # If the time header is given it adds one to the number of plot nbVarMod = nbVar + as.numeric(!is.null(time_header)) # For all type of graph for (i in 1:nbVarMod) { if (i > nbVar) { # Extracts the data serie corresponding to the code df_data_code = time_header[time_header$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$code == code,] } # If variable type is date if (type == 'saisonnalité') { # The number of digit is 6 because months are display # with 3 characters Nspace = 6 # If it is a flow variable } else if (type == 'sévérité' | type == 'data' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { # Gets the max number of digit on the label maxtmp = max(df_data_code$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 } }
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# 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(!is.null(info_header)) + as.numeric(!is.null(time_header)) nbp = max(layout_matrix, na.rm=TRUE) # 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) for (id in 1:nbg) { P[[id]] = void } # If the info header is needed if (!is.null(info_header)) { if ("data.frame" %in% class(info_header)) { # Extracts the data serie corresponding to the code info_header_code = info_header[info_header$code == code,] to_do = 'all' } else { info_header_code = NULL to_do = info_header } # Gets the info plot Hinfo = info_panel(list_df2plot, df_meta, trend_period=trend_period, mean_period=mean_period, periodHyd=mean_period[[1]], df_shapefile=df_shapefile, codeLight=code, df_data_code=info_header_code, to_do=to_do) # Stores it P[[1]] = Hinfo } # If the time header is given if (!is.null(time_header)) { # Extracts the data serie corresponding to the code time_header_code = time_header[time_header$code == code,] if (is.null(axis_xlim)) { # Gets the limits of the time serie axis_xlim_code = c(min(time_header_code$Date), max(time_header_code$Date)) } else { axis_xlim_code = axis_xlim
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} # Gets the time serie plot Htime = time_panel(time_header_code, df_trend_code=NULL, trend_period=trend_period, axis_xlim=axis_xlim_code, 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 } else { axis_xlim_code = axis_xlim } # Computes the number of column of plot asked on the datasheet nbcol = ncol(as.matrix(layout_matrix)) # For all variable for (i in 1:nbVar) { # 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 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() if (!is.null(trend_period)) { # For all the period for (j in 1:nPeriod_trend) { # If the trend is significant # if (df_trend_code$p[j] <= alpha | colorForce){ if (df_trend_code$p[j] <= alpha){ # Extract start and end of trend periods Start = df_trend_code$period_start[j] End = df_trend_code$period_end[j] # Extracts the corresponding data for the period df_data_code_per = df_data_code[df_data_code$Date >= Start & df_data_code$Date <= End,] # Same for trend df_trend_code_per = df_trend_code[df_trend_code$period_start == Start & df_trend_code$period_end == End,] # Computes the number of trend analysis selected Ntrend = nrow(df_trend_code_per) # If there is more than one trend on the same period if (Ntrend > 1) { # Takes only the first because they are similar df_trend_code_per = df_trend_code_per[1,] } # If it is a flow variable if (type == 'sévérité') { # Computes the mean of the data on the period
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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' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { trendValue = df_trend_code_per$trend } # gets the color corresponding to the mean trend color_res = get_color(trendValue, minTrendValue[j, i], maxTrendValue[j, i], palette_name='perso', reverse=TRUE) # Stores it temporarily colortmp = color_res # Otherwise } else { # Stores the default grey color colortmp = 'grey85' } # Stores the color color = append(color, colortmp) grid = FALSE } } if (var != 'sqrt(Q)' & var != 'Q') { grid = FALSE ymin_lim = NULL } else { grid = TRUE ymin_lim = 0 } if (is.null(time_header) & i == 1) { first = TRUE } else { first = FALSE } # Computes the time panel associated to the current variable p = time_panel(df_data_code, df_trend_code, var=var, type=type, linetype_per=linetype_per, alpha=alpha, colorForce=colorForce, missRect=missRect, trend_period=trend_period, mean_period=mean_period, axis_xlim=axis_xlim_code, unit2day=unit2day, grid=grid, ymin_lim=ymin_lim, color=color, NspaceMax=NspaceMax[k], first=first, last=(i == nbVar), lim_pct=lim_pct) # Stores the plot P[[i+nbh]] = p } if (!is.null(df_page)) { section = 'Fiche station' subsection = code n_page = df_page$n[nrow(df_page)] + 1 df_page = bind_rows( df_page, tibble(section=section, subsection=subsection,
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n=n_page)) } if (foot_note) { footName = 'fiche station' if (is.null(df_page)) { n_page = k } foot = foot_panel(footName, n_page, resources_path, logo_dir, PRlogo_file, 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 there 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)) { id_time = nbh LM = rbind(nbh, LM) } else { id_time = NA time_ratio = 0 } if (!is.null(info_header)) { id_info = nbh - as.numeric(!is.null(time_header)) LM = rbind(nbh - as.numeric(!is.null(time_header)), LM) } else { id_info = NA info_ratio = 0 } if (foot_note) { id_foot = length(LM) + 1 LM = rbind(LM, id_foot) } else { id_foot = max(LM) + 1 foot_height = 0 } # If paper format is A4 if (paper_size == 'A4') { width = 21 height = 29.7 } else if (is.vector(paper_size) & length(paper_size) > 1) { width = paper_size[1] height = paper_size[2] } 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))
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LMcol = ncol(LM) margin_size = 0.5 Norm_ratio = height * (time_ratio + var_ratio*nbp) / (height - 2*margin_size - foot_height - info_height) time_height = height * time_ratio / Norm_ratio var_height = height * var_ratio / Norm_ratio Hcut = LM[, 2] heightLM = rep(0, times=LMrow) heightLM[Hcut == id_info] = info_height heightLM[Hcut == id_time] = time_height heightLM[Hcut > nbh & Hcut < id_foot] = var_height heightLM[Hcut == id_foot] = foot_height heightLM[Hcut == 99] = margin_size col_width = (width - 2*margin_size) / (LMcol - 2) Wcut = LM[(nrow(LM)-1),] widthLM = rep(col_width, times=LMcol) widthLM[Wcut == 99] = margin_size # Plot the graph as the layout plot = grid.arrange(grobs=P, layout_matrix=LM, heights=heightLM, widths=widthLM) # Saving ggsave(plot=plot, path=outdirTmp_pdf, filename=paste(as.character(code), '.pdf', sep=''), width=width, height=height, units='cm', dpi=100) # Saving ggsave(plot=plot, path=outdirTmp_png, filename=paste(as.character(code), '.png', sep=''), width=width, height=height, units='cm', dpi=400) } return (df_page) } ## 2. PANEL FOR THE DATASHEET __________________________________ ### 2.1. Time panel __________________________________________________ time_panel = function (df_data_code, df_trend_code, var, type, linetype_per='solid', alpha=0.1, colorForce=FALSE, 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) { # 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 }
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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)) { axis_xlim = as.Date(axis_xlim) minor_minDatetmp_lim = axis_xlim[1] minor_maxDatetmp_lim = 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))]
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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(round(minor_minDate_lim), '-01-01', sep='')) minor_maxDate_lim = as.Date(paste(round(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)) { # 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]
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# 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 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] } # If there is no 'axis_lim' } else { if (minPer < min(df_data_code$Date)) { minPer = min(df_data_code$Date) } if (maxPer > max(df_data_code$Date)) { maxPer = max(df_data_code$Date) } } # Draw rectangle to delimiting the sub period p = p + geom_rect(aes(xmin=minPer, ymin=minQ_win, xmax=maxPer, ymax= maxQ_win), linetype=0, fill='grey97') } ## Mean step ## # If there is a 'mean_period' if (!is.null(mean_period)) { # Convert 'mean_period' to list mean_period = as.list(mean_period) # Number of mean period nPeriod_mean = length(mean_period) # Blank tibble to store variable in order to plot # rectangle for mean period plot_mean = tibble() # Blank tibble to store variable in order to plot # upper limit of rectangle for mean period plot_line = tibble() # For all mean period for (j in 1:nPeriod_mean) { # Get the current start and end of the sub period xmin = as.Date(mean_period[[j]][1]) xmax = as.Date(mean_period[[j]][2]) # Extract the data corresponding to this sub period df_data_code_per = df_data_code[df_data_code$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
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# 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) }
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# 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]