diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index 46e42223a8315c3d9ef3ecd06bc79c9b117f33ec..106a21dfd6e8a0382f28bf981aabe51aa982a5fc 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -27,7 +27,8 @@
# Sourcing R file
-source('processing/analyse.R', encoding='UTF-8')
+source('processing/analyse.R', encoding='UTF-8') # hydrograph
+source('plotting/shortcut.R', encoding='UTF-8')
## 1. DATASHEET PANEL ________________________________________________
@@ -47,161 +48,20 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
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
- alpha = list_df2plot[[i]]$alpha
- # Extracts the data corresponding to the code
- df_data_code = df_data[df_data$code == code,]
- df_trend_code = df_trend[df_trend$code == code,]
+ # Extracts number of period of trend
+ res = short_nPeriodMax(list_df2plot, Code)
+ nPeriod_trend = res$npt
+ nPeriodMax = res$npM
- # Gets the associated time info
- Start = tab_Start[k, i, j]
- End = tab_End[k, i, j]
- Periods = tab_Periods[k, i, j]
-
- # Extracts the corresponding data for the period
- df_data_code_per =
- df_data_code[df_data_code$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$period_end == End,]
-
- # Computes the number of trend analysis selected
- Ntrend = nrow(df_trend_code_per)
- # If there is more than one trend on the same period
- if (Ntrend > 1) {
- # Takes only the first because they are similar
- df_trend_code_per = df_trend_code_per[1,]
- }
+ # Extracts time info for each period of every station
+ res = short_tab(list_df2plot, Code, nbp, nCode, nPeriodMax)
+ tab_Start = res$start
+ tab_End = res$end
- # If it is a flow variable
- if (type == 'sévérité') {
- # Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$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$p <= alpha) {
- # Stores the mean trend
- TrendValue_code[j, i, k] = trendValue
- # Otherwise
- } else {
- # Do not stocks it
- TrendValue_code[j, i, k] = NA
- }
- }
- }
- }
-
- # Compute the min and the max of the mean trend for all the station
- minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
- maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
+ # Extracts the min and the max of the mean trend for all the station
+ res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ minTrendValue = res$min
+ maxTrendValue = res$max
# Blank vector to store the max number of digit of label for
# each station
@@ -303,7 +163,9 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# Gets the info plot
Hinfo = info_panel(list_df2plot,
df_meta,
- period=mean_period[[1]],
+ trend_period=trend_period,
+ mean_period=mean_period,
+ periodHyd=mean_period[[1]],
df_shapefile=df_shapefile,
codeLight=code,
df_data_code=time_header_code)
@@ -356,14 +218,14 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# grey = 85
# For all the period
- for (j in 1:nPeriod_max) {
+ for (j in 1:nPeriodMax) {
# If the trend is significant
if (df_trend_code$p[j] <= alpha){
# Gets the associated time info
Start = tab_Start[k, i, j]
End = tab_End[k, i, j]
- Periods = tab_Periods[k, i, j]
+ # Periods = tab_Periods[k, i, j]
# Extracts the corresponding data for the period
df_data_code_per =
@@ -405,12 +267,6 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
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='')
@@ -1409,15 +1265,14 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
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) {
+info_panel = function(list_df2plot, df_meta, trend_period, mean_period, periodHyd, 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,
+ hyd = hydrograph_panel(df_data_code, period=periodHyd,
margin=margin(t=0, r=0, b=0, l=5,
unit="mm"))
# Otherwise
@@ -1429,6 +1284,8 @@ info_panel = function(list_df2plot, df_meta, period, df_shapefile, codeLight, df
# Computes the map associated to the station
map = map_panel(list_df2plot,
df_meta,
+ trend_period=trend_period,
+ mean_period=mean_period,
df_shapefile=df_shapefile,
codeLight=codeLight,
margin=margin(t=0, r=-12, b=0, l=0, unit="mm"),
diff --git a/plotting/layout.R b/plotting/layout.R
index 2cc723dd44ea99ab1089a50db3cf308b08976789..1f4c725e7560755f4b387ca386582316819f13b3 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -49,6 +49,8 @@ source('plotting/datasheet.R', encoding='UTF-8')
source('plotting/map.R', encoding='UTF-8')
source('plotting/matrix.R', encoding='UTF-8')
source('plotting/break.R', encoding='UTF-8')
+source('plotting/color_manager.R', encoding='UTF-8')
+source('plotting/tools.R', encoding='UTF-8')
## 1. PERSONALISATION ________________________________________________
@@ -115,21 +117,12 @@ void = ggplot() + geom_blank(aes(1,1)) +
axis.line = element_blank()
)
+### 2.2. Contour void plot ___________________________________________
# 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, ...) {
- x = xc + r*cos(seq(0, pi, length.out=100))
- ymax = yc + r*sin(seq(0, pi, length.out=100))
- ymin = yc + r*sin(seq(0, -pi, length.out=100))
- annotate("ribbon", x=x, ymin=ymin, ymax=ymax, color=color,
- fill=fill, ...)
-}
-
## 3. LAYOUT _________________________________________________________
# Generates a PDF that gather datasheets, map and summarize matrix about the trend analyses realised on selected stations
@@ -598,231 +591,3 @@ foot_panel = function (name, n_page, resources_path, logo_dir, AEAGlogo_file, IN
return (plot)
}
-
-## 5. COLOR MANAGEMENT _______________________________________________
-### 5.1. Color on colorbar ___________________________________________
-# Returns a color of a palette corresponding to a value included
-# between the min and the max of the variable
-get_color = function (value, min, max, ncolor=256, palette_name='perso', reverse=FALSE) {
-
- # If the value is a NA return NA color
- if (is.na(value)) {
- return (NA)
- }
-
- # If the palette chosen is the personal ones
- if (palette_name == 'perso') {
- colorList = palette_perso
- # Else takes the palette corresponding to the name given
- } else {
- colorList = brewer.pal(11, palette_name)
- }
-
- # Gets the number of discrete colors in the palette
- nSample = length(colorList)
- # Recreates a continuous color palette
- palette = colorRampPalette(colorList)(ncolor)
- # Separates it in the middle to have a cold and a hot palette
- Sample_hot = 1:(as.integer(nSample/2)+1)
- Sample_cold = (as.integer(nSample/2)+1):nSample
- palette_hot = colorRampPalette(colorList[Sample_hot])(ncolor)
- palette_cold = colorRampPalette(colorList[Sample_cold])(ncolor)
-
- # Reverses the palette if it needs to be
- if (reverse) {
- palette = rev(palette)
- palette_hot = rev(palette_hot)
- palette_cold = rev(palette_cold)
- }
-
- # Computes the absolute max
- maxAbs = max(abs(max), abs(min))
-
- # If the value is negative
- if (value < 0) {
- # Gets the relative position of the value in respect
- # to its span
- idNorm = (value + maxAbs) / maxAbs
- # The index corresponding
- id = round(idNorm*(ncolor - 1) + 1, 0)
- # The associated color
- color = palette_cold[id]
- # Same if it is a positive value
- } else {
- idNorm = value / maxAbs
- id = round(idNorm*(ncolor - 1) + 1, 0)
- color = palette_hot[id]
- }
- return(color)
-}
-
-### 5.2. Colorbar ____________________________________________________
-# Returns the colorbar but also positions, labels and colors of some
-# ticks along it
-get_palette = function (min, max, ncolor=256, palette_name='perso', reverse=FALSE, nbTick=10) {
-
- # If the palette chosen is the personal ones
- if (palette_name == 'perso') {
- colorList = palette_perso
- # Else takes the palette corresponding to the name given
- } else {
- colorList = brewer.pal(11, palette_name)
- }
-
- # Gets the number of discrete colors in the palette
- nSample = length(colorList)
- # Recreates a continuous color palette
- palette = colorRampPalette(colorList)(ncolor)
- # Separates it in the middle to have a cold and a hot palette
- Sample_hot = 1:(as.integer(nSample/2)+1)
- Sample_cold = (as.integer(nSample/2)+1):nSample
- palette_hot = colorRampPalette(colorList[Sample_hot])(ncolor)
- palette_cold = colorRampPalette(colorList[Sample_cold])(ncolor)
-
- # Reverses the palette if it needs to be
- if (reverse) {
- palette = rev(palette)
- palette_hot = rev(palette_hot)
- palette_cold = rev(palette_cold)
- }
-
- # If the min and the max are below zero
- if (min < 0 & max < 0) {
- # The palette show is only the cold one
- paletteShow = palette_cold
- # If the min and the max are above zero
- } else if (min > 0 & max > 0) {
- # The palette show is only the hot one
- paletteShow = palette_hot
- # Else it is the entire palette that is shown
- } else {
- paletteShow = palette
- }
-
- # The position of ticks is between 0 and 1
- posTick = seq(0, 1, length.out=nbTick)
- # Blank vector to store corresponding labels and colors
- labTick = c()
- colTick = c()
- # For each tick
- for (i in 1:nbTick) {
- # Computes the graduation between the min and max
- lab = (i-1)/(nbTick-1) * (max - min) + min
- # Gets the associated color
- col = get_color(lab, min=min, max=max,
- ncolor=ncolor,
- palette_name=palette_name,
- reverse=reverse)
- # Stores them
- labTick = c(labTick, lab)
- colTick = c(colTick, col)
- }
- # List of results
- res = list(palette=paletteShow, posTick=posTick,
- labTick=labTick, colTick=colTick)
- return(res)
-}
-
-### 5.3. Palette tester ______________________________________________
-# Allows to display the current personal palette
-palette_tester = function (n=256) {
-
- # An arbitrary x vector
- X = 1:n
- # All the same arbitrary y position to create a colorbar
- Y = rep(0, times=n)
-
- # Recreates a continuous color palette
- palette = colorRampPalette(palette_perso)(n)
-
- # Open a plot
- p = ggplot() +
- # Make the theme blank
- theme(
- plot.background = element_blank(),
- panel.grid.major = element_blank(),
- panel.grid.minor = element_blank(),
- panel.border = element_blank(),
- panel.background = element_blank(),
- axis.title.x = element_blank(),
- axis.title.y = element_blank(),
- axis.text.x = element_blank(),
- axis.text.y = element_blank(),
- axis.ticks = element_blank(),
- axis.line = element_blank()
- ) +
- # Plot the palette
- geom_line(aes(x=X, y=Y), color=palette[X], size=60) +
- scale_y_continuous(expand=c(0, 0))
-
- # Saves the plot
- ggsave(plot=p,
- filename=paste('palette_test', '.pdf', sep=''),
- width=10, height=10, units='cm', dpi=100)
-}
-
-## 6. OTHER TOOLS ____________________________________________________
-### 6.1. Number formatting ___________________________________________
-# Returns the power of ten of the scientific expression of a value
-get_power = function (value) {
-
- # Do not care about the sign
- value = abs(value)
-
- # If the value is greater than one
- if (value >= 1) {
- # The magnitude is the number of character of integer part
- # of the value minus one
- power = nchar(as.character(as.integer(value))) - 1
- # If value is zero
- } else if (value == 0) {
- # The power is zero
- power = 0
- # If the value is less than one
- } else {
- # Extract the decimal part
- dec = gsub('0.', '', as.character(value), fixed=TRUE)
- # Number of decimal with zero
- ndec = nchar(dec)
- # Number of decimal without zero
- nnum = nchar(as.character(as.numeric(dec)))
- # Compute the power of ten associated
- power = -(ndec - nnum + 1)
- }
- return(power)
-}
-
-### 6.2. Pourcentage of variable _____________________________________
-# Returns the value corresponding of a certain percentage of a
-# data serie
-gpct = function (pct, L, min_lim=NULL, shift=FALSE) {
-
- # If no reference for the serie is given
- if (is.null(min_lim)) {
- # The minimum of the serie is computed
- minL = min(L, na.rm=TRUE)
- # If a reference is specified
- } else {
- # The reference is the minimum
- minL = min_lim
- }
-
- # Gets the max
- maxL = max(L, na.rm=TRUE)
- # And the span
- spanL = maxL - minL
- # Computes the value corresponding to the percentage
- xL = pct/100 * as.numeric(spanL)
-
- # If the value needs to be shift by its reference
- if (shift) {
- xL = xL + minL
- }
- return (xL)
-}
-
-### 6.3. Add months __________________________________________________
-add_months = function (date, n) {
- new_date = seq(date, by = paste (n, "months"), length = 2)[2]
- return (new_date)
-}
diff --git a/plotting/map.R b/plotting/map.R
index ed432ace52c2381daa556150d57095cf83d8f6f4..48b311cca422221ad89311b99a769f5b2719db7d 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -29,8 +29,8 @@
## 1. MAP PANEL ______________________________________________________
# Generates a map plot of the tendancy of a hydrological variable
map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
- trend_period=NULL,
- mean_period=NULL, outdirTmp='', codeLight=NULL,
+ trend_period,
+ mean_period, outdirTmp='', codeLight=NULL,
margin=NULL, showSea=TRUE,
foot_note=FALSE,
foot_height=0, resources_path=NULL,
@@ -52,155 +52,20 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Gets a trend example
- df_trend = list_df2plot[[1]]$trend
-
- 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$period_end
- # Get the name of the different period
- UStart = levels(factor(Start))
- UEnd = levels(factor(End))
-
- # Compute the max of different start and end
- # so the number of different period
- nPeriod = max(length(UStart), length(UEnd))
+ # Extracts number of period of trend
+ res = short_nPeriodMax(list_df2plot, Code)
+ nPeriod_trend = res$npt
+ nPeriodMax = res$npM
- # If the number of period for the trend is greater
- # than the current max period, stocks it
- if (nPeriod > nPeriod_max) {
- nPeriod_max = nPeriod
- }
- }
-
- # Blank array to store time info
- tab_Start = array(rep('', nCode*nbp*nPeriod_max),
- dim=c(nCode, nbp, nPeriod_max))
- tab_End = array(rep('', nCode*nbp*nPeriod_max),
- dim=c(nCode, nbp, nPeriod_max))
- tab_Code = array(rep('', nCode*nbp*nPeriod_max),
- dim=c(nCode, nbp, nPeriod_max))
- tab_Periods = array(rep('', nCode*nbp*nPeriod_max),
- dim=c(nCode, nbp, nPeriod_max))
-
- # For all code
- for (k in 1:nCode) {
- # Gets the code
- code = Code[k]
- # For all the variable
- for (i in 1:nbp) {
- df_trend = list_df2plot[[i]]$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$period_end
- # Get the name of the different period
- UStart = levels(factor(Start))
- UEnd = levels(factor(End))
-
- # Compute the max of different start and end
- # so the number of different period
- nPeriod = max(length(UStart), length(UEnd))
-
- # For all the period
- for (j in 1:nPeriod_max) {
- # Stocks period
- Periods = paste(Start[j],
- End[j],
- sep=' / ')
-
- # Saves the time info
- tab_Start[k, i, j] = as.character(Start[j])
- tab_End[k, i, j] = as.character(End[j])
- tab_Code[k, i, j] = code
- tab_Periods[k, i, j] = Periods
-
- }
- }
- }
-
+ # Extracts time info for each period of every station
+ res = short_tab(list_df2plot, Code, nbp, nCode, nPeriodMax)
+ tab_Start = res$start
+ tab_End = res$end
- # Blank array to store mean of the trend for each
- # station, perdiod and variable
- TrendValue_code = array(rep(1, nPeriod_max*nbp*nCode),
- dim=c(nPeriod_max, nbp, nCode))
- # For all the period
- for (j in 1:nPeriod_max) {
- # For all the code
- for (k in 1:nCode) {
- # Gets the code
- code = Code[k]
- # For all variable
- for (i in 1:nbp) {
- # Extracts the data corresponding to the
- # current variable
- df_data = list_df2plot[[i]]$data
- # Extracts the trend corresponding to the
- # current variable
- df_trend = list_df2plot[[i]]$trend
- # Extracts the type of the variable
- type = list_df2plot[[i]]$type
- alpha = list_df2plot[[i]]$alpha
- # Extracts the data corresponding to the code
- df_data_code = df_data[df_data$code == code,]
- # Extracts the trend corresponding to the code
- df_trend_code = df_trend[df_trend$code == code,]
-
- # Gets the associated time info
- Start = tab_Start[k, i, j]
- End = tab_End[k, i, j]
- Periods = tab_Periods[k, i, j]
-
- # Extracts the corresponding data for the period
- df_data_code_per =
- df_data_code[df_data_code$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$p <= alpha){
- # Stores the mean trend
- TrendValue_code[j, i, k] = trendValue
- # Otherwise
- } else {
- # Do not stocks it
- TrendValue_code[j, i, k] = NA
- }
- }
- }
- }
- # Compute the min and the max of the mean trend for all the station
- minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
- maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
+ # Extracts the min and the max of the mean trend for all the station
+ res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ minTrendValue = res$min
+ maxTrendValue = res$max
# If there is a 'mean_period'
if (!is.null(mean_period)) {
@@ -537,7 +402,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# Gets the associated time info
Start = tab_Start[k, i, idPer_trend]
End = tab_End[k, i, idPer_trend]
- Periods = tab_Periods[k, i, idPer_trend]
+ # Periods = tab_Periods[k, i, idPer_trend]
# Extracts the corresponding data for the period
df_data_code_per =
diff --git a/plotting/matrix.R b/plotting/matrix.R
index 4816c12d9c86891fe8c6babb1ed7df6bad37da20..5edca9303e944af76f07ad647a07f2fe1865c8fc 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -42,165 +42,20 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Gets a trend example
- df_trend = list_df2plot[[1]]$trend
-
- # Convert 'trend_period' to list
- trend_period = as.list(trend_period)
- # Number of trend period
- nPeriod_trend = length(trend_period)
-
- # Fix the maximal number of period to the minimal possible
- nPeriod_max = 0
- # For all code
- for (code in Code) {
- # Extracts the trend corresponding to the code
- df_trend_code = df_trend[df_trend$code == code,]
-
- # Extract start and end of trend periods
- Start = df_trend_code$period_start
- End = df_trend_code$period_end
- # Get the name of the different period
- UStart = levels(factor(Start))
- UEnd = levels(factor(End))
-
- # Compute the max of different start and end
- # so the number of different period
- nPeriod = max(length(UStart), length(UEnd))
-
- # If the number of period for the trend is greater
- # than the current max period, stocks it
- if (nPeriod > nPeriod_max) {
- nPeriod_max = nPeriod
- }
- }
-
- # Blank 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
-
- }
- }
- }
-
-
+ # Extracts number of period of trend
+ res = short_nPeriodMax(list_df2plot, Code)
+ nPeriod_trend = res$npt
+ nPeriodMax = res$npM
+
+ # Extracts time info for each period of every station
+ res = short_tab(list_df2plot, Code, nbp, nCode, nPeriodMax)
+ tab_Start = res$start
+ tab_End = res$end
- # 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 trend period
- for (j in 1:nPeriod_trend) {
- # For all the code
- for (k in 1:nCode) {
- # Gets the code
- code = Code[k]
- # For all variable
- for (i in 1:nbp) {
- # Extracts the data corresponding to the
- # current variable
- df_data = list_df2plot[[i]]$data
- # Extracts the trend corresponding to the
- # current variable
- df_trend = list_df2plot[[i]]$trend
- # Extracts the type of the variable
- type = list_df2plot[[i]]$type
- alpha = list_df2plot[[i]]$alpha
- # Extracts the data corresponding to the code
- df_data_code = df_data[df_data$code == code,]
- # Extracts the trend corresponding to the code
- df_trend_code = df_trend[df_trend$code == code,]
-
- # Gets the associated time info
- Start = 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,]
- }
- # Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
-
- # If it is a flow variable
- if (type == 'sévérité') {
- # Normalises the trend value by the mean of the data
- trendValue = df_trend_code_per$trend / dataMean
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- # Just stocks the trend value
- trendValue = df_trend_code_per$trend
- }
-
- # If the p value is under the threshold
- if (df_trend_code_per$p <= alpha){
- # Stores the averaged trend
- TrendValue_code[j, i, k] = trendValue
- # Otherwise
- } else {
- # Do not stocks it
- TrendValue_code[j, i, k] = NA
- }
- }
- }
- }
- # Computes 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)
+ # Extracts the min and the max of the mean trend for all the station
+ res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ minTrendValue = res$min
+ maxTrendValue = res$max
# Blank vectors to store info about trend analyses
Periods_trend = c()
@@ -239,7 +94,10 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Gets the associated time info
Start = tab_Start[k, i, j]
End = tab_End[k, i, j]
- Periods = tab_Periods[k, i, j]
+
+ # Creates a period name
+ Periods = paste(Start, End,
+ sep=' / ')
# Extracts the corresponding data for the period
df_data_code_per =
diff --git a/processing/format.R b/processing/format.R
index b994dce4e14866b72afb14f1c4b23f905cc1c03b..11ca6fe6a1cfecd436fa954ce4d49350c8567194 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -340,19 +340,20 @@ prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
ShiftHydro = df_dateStart$DateHydro_julian[Ok_dateStart]
df_XEx$values[OkXEx_code] = df_XEx$values[OkXEx_code] + ShiftHydro
-
- XEx_code = df_XEx$values[OkXEx_code]
- meanXEx_code = mean(XEx_code, na.rm=TRUE)
- dXEx_code = meanXEx_code - XEx_code
- stdXEx_code = sd(XEx_code, na.rm=TRUE)
- OkOverStd = dXEx_code >= stdXEx_code*3
- OkOverStd[is.na(OkOverStd)] = FALSE
- XEx_code[OkOverStd] = XEx_code[OkOverStd] + 365
+
+ ## Add 365 when the point is too remote
+ # XEx_code = df_XEx$values[OkXEx_code]
+ # meanXEx_code = mean(XEx_code, na.rm=TRUE)
+ # dXEx_code = meanXEx_code - XEx_code
+ # stdXEx_code = sd(XEx_code, na.rm=TRUE)
+ # OkOverStd = dXEx_code >= stdXEx_code*3
+ # OkOverStd[is.na(OkOverStd)] = FALSE
+ # XEx_code[OkOverStd] = XEx_code[OkOverStd] + 365
+ # df_XEx$values[OkXEx_code] = XEx_code
# print(group)
# print(df_XEx$datetime[df_XEx$group1 == group][dXEx_code >= stdXEx_code*3])
- df_XEx$values[OkXEx_code] = XEx_code
}
df_XEx$datetime = as.double(df_XEx$datetime)
diff --git a/script.R b/script.R
index 3fe43893c024c8b17a2e4e4e6bfed46f7fd87050..020a1763ae8a7065b4375a0f39104349d154511b 100644
--- a/script.R
+++ b/script.R
@@ -321,17 +321,17 @@ res_tCENtrend = res$analyse
## 4. SAVING _________________________________________________________
-for (v in var) {
- df_datatmp = get(paste('df_', v, 'data', sep=''))
- df_modtmp = get(paste('df_', v, 'mod', sep=''))
- res_trendtmp = get(paste('res_', v, 'trend', sep=''))
- # Modified data saving
- write_dfdata(df_datatmp, df_modtmp, resdir, optdir='modified_data',
- filedir=v)
- # Trend analysis saving
- write_listofdf(res_trendtmp, resdir, optdir='trend_analyse',
- filedir=v)
-}
+# for (v in var) {
+# df_datatmp = get(paste('df_', v, 'data', sep=''))
+# df_modtmp = get(paste('df_', v, 'mod', sep=''))
+# res_trendtmp = get(paste('res_', v, 'trend', sep=''))
+# # Modified data saving
+# write_dfdata(df_datatmp, df_modtmp, resdir, optdir='modified_data',
+# filedir=v)
+# # Trend analysis saving
+# write_listofdf(res_trendtmp, resdir, optdir='trend_analyse',
+# filedir=v)
+# }
# res_tDEBtrend = read_listofdf(resdir, 'res_tDEBtrend')
@@ -359,7 +359,7 @@ df_shapefile = ini_shapefile(resources_path,
### 5.2. Analysis layout _____________________________________________
datasheet_layout(toplot=c(
'datasheet'
- # 'matrix'
+ # 'matrix',
# 'map'
),
df_meta=df_meta,