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layout.R 14.32 KiB
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# Usefull library
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library(ggplot2)
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library(scales)
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library(qpdf)
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library(gridExtra)
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library(gridtext)
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library(dplyr)
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library(grid)
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library(ggh4x)
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library(RColorBrewer)
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# Sourcing R file
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source('plotting/panel.R', encoding='latin1')
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panels_layout = function (df_data, df_meta, layout_matrix, isplot=c('datasheet', 'matrix', 'map'), figdir='', filedir_opt='', filename_opt='', variable='', df_trend=NULL, p_threshold=0.1, unit2day=365.25, type='', trend_period=NULL, mean_period=NULL, axis_xlim=NULL, missRect=FALSE, time_header=NULL, info_header=TRUE, info_ratio=1, time_ratio=2, var_ratio=3, df_shapefile=NULL) {
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    outfile = "Panels"
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    if (filename_opt != '') {
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        outfile = paste(outfile, '_', filename_opt, sep='')
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    }
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    outfile = paste(outfile, '.pdf', sep='')
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    # If there is not a dedicated figure directory it creats one
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    outdir = file.path(figdir, filedir_opt, sep='')
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    if (!(file.exists(outdir))) {
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        dir.create(outdir)
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    }
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    outdirTmp = file.path(outdir, 'tmp')
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    if (!(file.exists(outdirTmp))) {
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        dir.create(outdirTmp)
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    } else {
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        unlink(outdirTmp, recursive=TRUE)
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        dir.create(outdirTmp)
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    }
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    nbp = length(df_data)
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    if (all(class(df_data) != 'list')) {
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        df_data = list(df_data)
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    }
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    if (all(class(df_trend) != 'list')) {
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        df_trend = list(df_trend)
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        if (length(df_trend) == 1) {
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            df_trend = replicate(nbp, df_trend)
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        }}
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    if (all(class(p_threshold) != 'list')) {
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        p_threshold = list(p_threshold)
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        if (length(p_threshold) == 1) {
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            p_threshold = replicate(nbp, p_threshold)
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        }}
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    if (all(class(unit2day) != 'list')) {
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        unit2day = list(unit2day)
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        if (length(unit2day) == 1) {
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            unit2day = replicate(nbp, unit2day)
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        }}
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    if (all(class(type) != 'list')) {
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        type = list(type)
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        if (length(type) == 1) {
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            type = replicate(nbp, type)
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        }}
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    if (all(class(missRect) != 'list')) {
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        missRect = list(missRect)
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if (length(missRect) == 1) { missRect = replicate(nbp, missRect) }} # Get all different stations code Code = levels(factor(df_meta$code)) nCode = length(Code) # print(df_trend) df_trendtmp = df_trend[[1]] # print(df_trendtmp) nPeriod_max = 0 for (code in Code) { df_trend_code = df_trendtmp[df_trendtmp$code == code,] Start = df_trend_code$period_start UStart = levels(factor(Start)) End = df_trend_code$period_end UEnd = levels(factor(End)) nPeriod = max(length(UStart), length(UEnd)) if (nPeriod > nPeriod_max) { nPeriod_max = nPeriod } } list_df2plot = vector(mode='list', length=nbp) # minTrend = c() # maxTrend = c() for (i in 1:nbp) { df2plot = list(data=df_data[[i]], trend=df_trend[[i]], p_threshold=p_threshold[[i]], unit2day=unit2day[[i]], type=type[[i]], missRect=missRect[[i]]) # okTrend = df_trend[[i]]$trend[df_trend[[i]]$p <= p_threshold[[i]]] # minTrend[i] = min(okTrend, na.rm=TRUE) # maxTrend[i] = max(okTrend, na.rm=TRUE) list_df2plot[[i]] = df2plot } if ('datasheet' %in% isplot) { 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 (j in 1:nCode) { code = Code[j] df_trend_code = df_trendtmp[df_trendtmp$code == code,] Start = df_trend_code$period_start UStart = levels(factor(Start))
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End = df_trend_code$period_end UEnd = levels(factor(End)) nPeriod = max(length(UStart), length(UEnd)) Periods = c() for (i in 1:nPeriod_max) { Periods = append(Periods, paste(substr(Start[i], 1, 4), substr(End[i], 1, 4), sep=' / ')) } Start_code[[j]] = Start End_code[[j]] = End Code_code[[j]] = code Periods_code[[j]] = Periods } TrendMean_code = array(rep(1, nPeriod_max*nbp*nCode), dim=c(nPeriod_max, nbp, nCode)) for (j in 1:nPeriod_max) { for (k in 1:nCode) { code = Code[k] for (i in 1:nbp) { df_data = list_df2plot[[i]]$data df_trend = list_df2plot[[i]]$trend p_threshold = list_df2plot[[i]]$p_threshold df_data_code = df_data[df_data$code == code,] df_trend_code = df_trend[df_trend$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$p <= p_threshold){ TrendMean_code[j, i, k] = trendMean } else { TrendMean_code[j, i, k] = NA } } } } minTrendMean = apply(TrendMean_code, c(1, 2), min, na.rm=TRUE) maxTrendMean = apply(TrendMean_code, c(1, 2), max, na.rm=TRUE) for (code in Code) {
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# Print code of the station for the current plotting print(paste("Plotting for station :", code)) nbh = as.numeric(info_header) + as.numeric(!is.null(time_header)) nbg = nbp + nbh P = vector(mode='list', length=nbg) if (info_header) { time_header_code = time_header[time_header$code == code,] Hinfo = info_panel(list_df2plot, df_meta, df_shapefile=df_shapefile, codeLight=code, df_data_code=time_header_code) P[[1]] = Hinfo # P[[1]] = void } if (!is.null(time_header)) { time_header_code = time_header[time_header$code == code,] axis_xlim = c(min(time_header_code$Date), max(time_header_code$Date)) Htime = time_panel(time_header_code, df_trend_code=NULL, trend_period=trend_period, missRect=TRUE, unit2day=365.25, type='Q', first=FALSE) P[[2]] = Htime } # map = map_panel() nbcol = ncol(as.matrix(layout_matrix)) for (i in 1:nbp) { df_data = list_df2plot[[i]]$data df_trend = list_df2plot[[i]]$trend p_threshold = list_df2plot[[i]]$p_threshold unit2day = list_df2plot[[i]]$unit2day missRect = list_df2plot[[i]]$missRect type = list_df2plot[[i]]$type df_data_code = df_data[df_data$code == code,] df_trend_code = df_trend[df_trend$code == code,] color = c() # for (j in 1:nrow(df_trend_code)) { grey = 85 for (j in 1:nPeriod_max) { if (df_trend_code$p[j] <= p_threshold){ # color_res = get_color(df_trend_code$trend[j], # minTrend[i], # maxTrend[i], # palette_name='perso', # reverse=TRUE) Start = Start_code[Code_code == code][[1]][j] End = End_code[Code_code == code][[1]][j] Periods = Periods_code[Code_code == code][[1]][j] df_data_code_per = df_data_code[df_data_code$Date >= Start & df_data_code$Date <= End,] df_trend_code_per = df_trend_code[df_trend_code$period_start == Start & df_trend_code$period_end == End,]
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Ntrend = nrow(df_trend_code_per) if (Ntrend > 1) { df_trend_code_per = df_trend_code_per[1,] } dataMean = mean(df_data_code$Qm3s, na.rm=TRUE) trendMean = df_trend_code_per$trend / dataMean color_res = get_color(trendMean, minTrendMean[j, i], maxTrendMean[j, i], palette_name='perso', reverse=TRUE) colortmp = color_res } else { colortmp = paste('grey', grey, sep='') grey = grey - 10 } color = append(color, colortmp) } p = time_panel(df_data_code, df_trend_code, type=type, p_threshold=p_threshold, missRect=missRect, trend_period=trend_period, mean_period=mean_period, axis_xlim=axis_xlim, unit2day=unit2day, last=(i > nbp-nbcol), color=color) P[[i+nbh]] = p } layout_matrix = as.matrix(layout_matrix) nel = nrow(layout_matrix)*ncol(layout_matrix) idNA = which(is.na(layout_matrix), arr.ind=TRUE) layout_matrix[idNA] = seq(max(layout_matrix, na.rm=TRUE) + 1, max(layout_matrix, na.rm=TRUE) + 1 + nel) layout_matrix_H = layout_matrix + nbh info_ratio_scale = info_ratio time_ratio_scale = time_ratio var_ratio_scale = var_ratio ndec_info = 0 ndec_time = 0 ndec_var = 0 if (info_ratio_scale != round(info_ratio_scale)) { ndec_info = nchar(gsub('^[0-9]+.', '', as.character(info_ratio_scale))) } if (time_ratio_scale != round(time_ratio_scale)) { ndec_time = nchar(gsub('^[0-9]+.', '', as.character(time_ratio_scale))) } if (var_ratio_scale != round(var_ratio_scale)) { ndec_var = nchar(gsub('^[0-9]+.', '', as.character(var_ratio_scale))) } ndec = max(c(ndec_info, ndec_time, ndec_var))
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info_ratio_scale = info_ratio_scale * 10^ndec time_ratio_scale = time_ratio_scale * 10^ndec var_ratio_scale = var_ratio_scale * 10^ndec LM = c() LMcol = ncol(layout_matrix_H) LMrow = nrow(layout_matrix_H) for (i in 1:(LMrow+nbh)) { if (info_header & i == 1) { # LM = rbind(LM, rep(i, times=LMcol)) LM = rbind(LM, matrix(rep(rep(i, times=LMcol), times=info_ratio_scale), ncol=LMcol, byrow=TRUE)) } else if (!is.null(time_header) & i == 2) { LM = rbind(LM, matrix(rep(rep(i, times=LMcol), times=time_ratio_scale), ncol=LMcol, byrow=TRUE)) } else { LM = rbind(LM, matrix(rep(layout_matrix_H[i-nbh,], times=var_ratio_scale), ncol=LMcol, byrow=TRUE)) }} plot = grid.arrange(grobs=P, layout_matrix=LM) # plot = grid.arrange(rbind(cbind(ggplotGrob(P[[2]]), ggplotGrob(P[[2]])), cbind(ggplotGrob(P[[3]]), ggplotGrob(P[[3]]))), heights=c(1/3, 2/3)) # Saving ggsave(plot=plot, path=outdirTmp, filename=paste(as.character(code), '.pdf', sep=''), width=21, height=29.7, units='cm', dpi=100) } } if ('matrix' %in% isplot) { matrice_panel(list_df2plot, df_meta, trend_period, mean_period, slice=12, outdirTmp=outdirTmp, A3=TRUE) } if ('map' %in% isplot) { map_panel(list_df2plot, df_meta, idPer=length(trend_period), df_shapefile=df_shapefile, outdirTmp=outdirTmp, margin=margin(t=5, r=0, b=5, l=5, unit="mm")) } # PDF combine pdf_combine(input=file.path(outdirTmp, list.files(outdirTmp)), output=file.path(outdir, outfile)) # unlink(outdirTmp, recursive=TRUE) }