layout.R

# Usefull library
library(ggplot2)
library(scales)
library(qpdf)
library(gridExtra)
library(gridtext)
library(dplyr)
library(grid)
library(ggh4x)
library(RColorBrewer)


# Sourcing R file
source('plotting/panel.R', encoding='latin1')


panels_layout = function (df_data, df_meta, layout_matrix, 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, time_ratio=2, var_ratio=3, fr_shpdir=NULL, fr_shpname=NULL,  bs_shpdir=NULL, bs_shpname=NULL, rv_shpdir=NULL, rv_shpname=NULL, computer_data_path=NULL) {
    
    outfile = "Panels"
    if (filename_opt != '') {
        outfile = paste(outfile, '_', filename_opt, sep='')
    }
    outfile = paste(outfile, '.pdf', sep='')

    # If there is not a dedicated figure directory it creats one
    outdir = file.path(figdir, filedir_opt, sep='')
    if (!(file.exists(outdir))) {
        dir.create(outdir)
    }

    outdirTmp = file.path(outdir, 'tmp')
    if (!(file.exists(outdirTmp))) {
        dir.create(outdirTmp)
    } else {
        unlink(outdirTmp, recursive=TRUE)
        dir.create(outdirTmp)
    }

    nbp = length(df_data)

    if (all(class(df_data) != 'list')) {
        df_data = list(df_data)
    }

    if (all(class(df_trend) != 'list')) {
        df_trend = list(df_trend)
        if (length(df_trend) == 1) {
            df_trend = replicate(nbp, df_trend)
        }}

    if (all(class(p_threshold) != 'list')) {
        p_threshold = list(p_threshold)
        if (length(p_threshold) == 1) {
            p_threshold = replicate(nbp, p_threshold)
        }}
  
    if (all(class(unit2day) != 'list')) {
        unit2day = list(unit2day)
        if (length(unit2day) == 1) {
            unit2day = replicate(nbp, unit2day)
        }}

    if (all(class(type) != 'list')) {
        type = list(type)
        if (length(type) == 1) {
            type = replicate(nbp, type)
        }}

    if (all(class(missRect) != 'list')) {
        missRect = list(missRect)
        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
    }

    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))
        
        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

                TrendMean_code[j, i, k] = trendMean
            }
        }
    }

    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) {
        
        # 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) {
            Htext = text_panel(code, df_meta)
            P[[1]] = Htext
        }

        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
        }


        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,]
                    
                    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$color 
                } 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


        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))
            } else if (!is.null(time_header) & i == 2) {
                LM = rbind(LM,
                           matrix(rep(rep(i, times=LMcol),
                                      times=time_ratio),
                                  ncol=LMcol, byrow=TRUE))

            } else {
                LM = rbind(LM, 
                           matrix(rep(layout_matrix_H[i-nbh,],
                                      times=var_ratio),
                                  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)

    }

    matrice_panel(list_df2plot, df_meta, trend_period,
                  slice=12, outdirTmp=outdirTmp)

    
    map_panel(list_df2plot, 
              df_meta,
              idPer=length(trend_period),
              computer_data_path=computer_data_path,
              fr_shpdir=fr_shpdir,
              fr_shpname=fr_shpname,
              bs_shpdir=bs_shpdir,
              bs_shpname=bs_shpname,
              rv_shpdir=rv_shpdir,
              rv_shpname=rv_shpname,
              outdirTmp=outdirTmp)

    
    # PDF combine
    pdf_combine(input=file.path(outdirTmp, list.files(outdirTmp)),
                output=file.path(outdir, outfile))
    # unlink(outdirTmp, recursive=TRUE)

}