diff --git a/plotting/color_manager.R b/plotting/color_manager.R
deleted file mode 100644
index bb3c728d99bef6caea8889cdc6c5d16235419987..0000000000000000000000000000000000000000
--- a/plotting/color_manager.R
+++ /dev/null
@@ -1,194 +0,0 @@
-# \\\
-# 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/>.
-# ///
-#
-#
-# color_manager.R
-
-
-### 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)
-}
-
-## 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)
-}
-
-## 3. PALETTE TESTER _________________________________________________
-# Allows to display the current personal palette
-palette_tester = function (palette_name='perso', n=256) {
-
- # 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)
- }
-
- # 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)
-}
diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index 106a21dfd6e8a0382f28bf981aabe51aa982a5fc..b781b3ee68d04dff09888802ba7f012d27181098 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -48,18 +48,13 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Extracts number of period of trend
- res = short_nPeriodMax(list_df2plot, Code)
- nPeriod_trend = res$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$end
+ # Convert 'trend_period' to list
+ trend_period = as.list(trend_period)
+ # Number of trend period
+ nPeriod_trend = length(trend_period)
# Extracts the min and the max of the mean trend for all the station
- res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
minTrendValue = res$min
maxTrendValue = res$max
@@ -218,14 +213,13 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
# grey = 85
# For all the period
- for (j in 1:nPeriodMax) {
+ for (j in 1:nPeriod_trend) {
# If the trend is significant
if (df_trend_code$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]
+ # 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 =
diff --git a/plotting/map.R b/plotting/map.R
index 48b311cca422221ad89311b99a769f5b2719db7d..8571ce6d659548aca378ab7271559d3d801a2c36 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -52,115 +52,28 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Extracts number of period of trend
- res = short_nPeriodMax(list_df2plot, Code)
- nPeriod_trend = res$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$end
+ # Convert 'trend_period' to list
+ trend_period = as.list(trend_period)
+ # Number of trend period
+ nPeriod_trend = length(trend_period)
# Extracts the min and the max of the mean trend for all the station
- res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
minTrendValue = res$min
maxTrendValue = res$max
# If there is a 'mean_period'
if (!is.null(mean_period)) {
- # Blank vectors to store info about breaking analysis
- Var_mean = c()
- Type_mean = c()
- Code_mean = c()
- DataMean_mean = c()
- breakValue_mean = c()
-
# Convert 'mean_period' to list
mean_period = as.list(mean_period)
# Number of mean period
nPeriod_mean = length(mean_period)
- # Blank array to store difference of mean between two periods
- breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
- dim=c(nPeriod_mean, nbp, nCode))
- # Blank array to store mean for a temporary period in order
- # to compute the difference of mean with a second period
- dataMeantmp = array(rep(NA, nbp*nCode),
- dim=c(nbp, nCode))
-
- # For all period of breaking analysis
- for (j in 1:nPeriod_mean) {
- # For all the code
- for (k in 1:nCode) {
- # Gets the code
- code = Code[k]
- # For all variable
- for (i in 1:nbp) {
- # Extracts the data corresponding to
- # the current variable
- df_data = list_df2plot[[i]]$data
- # Extract the variable of the plot
- var = list_df2plot[[i]]$var
- # Extract the type of the variable to plot
- type = list_df2plot[[i]]$type
- # Extracts the data corresponding to the code
- df_data_code = df_data[df_data$code == code,]
-
- # Get the current start and end of the sub period
- Start_mean = mean_period[[j]][1]
- End_mean = mean_period[[j]][2]
-
- # Extract the data corresponding to this sub period
- df_data_code_per =
- df_data_code[df_data_code$Date >= Start_mean
- & df_data_code$Date <= End_mean,]
-
- # Min max for the sub period
- Datemin = min(df_data_code_per$Date)
- Datemax = max(df_data_code_per$Date)
-
- # Mean of the flow over the sub period
- dataMean = mean(df_data_code_per$Value,
- na.rm=TRUE)
-
- # If this in not the first period
- if (j > 1) {
- # Compute the difference of mean
- Break = dataMean - dataMeantmp[i, k]
- # Otherwise for the first period
- } else {
- # Stocks NA
- Break = NA
- }
-
- # If it is a flow variable
- if (type == 'sévérité') {
- # Normalises the break by the mean of the
- # initial period
- breakValue = Break / dataMeantmp[i, k]
- # If it is a date variable
- } else if (type == 'saisonnalité') {
- # Just stocks the break value
- breakValue = Break
- }
-
- # Stores the result
- breakValue_code[j, i, k] = breakValue
- # Stores temporarily the mean of the current period
- dataMeantmp[i, k] = dataMean
- }
- }
- }
- # Computes the min and the max of the averaged trend for
- # all the station
- minBreakValue = apply(breakValue_code, c(1, 2),
- min, na.rm=TRUE)
- maxBreakValue = apply(breakValue_code, c(1, 2),
- max, na.rm=TRUE)
- }
-
- if (is.null(mean_period)) {
+ res = short_meanExtremes(list_df2plot, Code, nPeriod_mean, nbp, nCode)
+ minBreakValue = res$min
+ maxBreakValue = res$max
+ breakValue_code = res$value
+ } else {
nPeriod_mean = 1
}
@@ -399,10 +312,9 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
- # Gets the associated time info
- Start = tab_Start[k, i, idPer_trend]
- End = tab_End[k, i, idPer_trend]
- # Periods = tab_Periods[k, i, idPer_trend]
+ # Extract start and end of trend periods
+ Start = df_trend_code$period_start[idPer_trend]
+ End = df_trend_code$period_end[idPer_trend]
# Extracts the corresponding data for the period
df_data_code_per =
diff --git a/plotting/matrix.R b/plotting/matrix.R
index 5edca9303e944af76f07ad647a07f2fe1865c8fc..663c553dd7f132cbd134c0ea8025d410ecdc02a5 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -42,18 +42,13 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Code = levels(factor(df_meta$code))
nCode = length(Code)
- # Extracts number of period of trend
- res = short_nPeriodMax(list_df2plot, Code)
- nPeriod_trend = res$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$end
+ # Convert 'trend_period' to list
+ trend_period = as.list(trend_period)
+ # Number of trend period
+ nPeriod_trend = length(trend_period)
# Extracts the min and the max of the mean trend for all the station
- res = short_trendExtremes(list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriodMax)
+ res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode)
minTrendValue = res$min
maxTrendValue = res$max
@@ -91,9 +86,9 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
- # Gets the associated time info
- Start = tab_Start[k, i, j]
- End = tab_End[k, i, j]
+ # Extract start and end of trend periods
+ Start = df_trend_code$period_start[j]
+ End = df_trend_code$period_end[j]
# Creates a period name
Periods = paste(Start, End,
@@ -164,6 +159,20 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
+ # 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)
+
+ res = short_meanExtremes(list_df2plot, Code, nPeriod_mean, nbp, nCode)
+ minBreakValue = res$min
+ maxBreakValue = res$max
+ } else {
+ nPeriod_mean = 1
+ }
+
# If there is a 'mean_period'
if (!is.null(mean_period)) {
# Blank vectors to store info about breaking analysis
@@ -175,14 +184,6 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
DataMean_mean = c()
breakValue_mean = c()
- # Convert 'mean_period' to list
- mean_period = as.list(mean_period)
- # Number of mean period
- nPeriod_mean = length(mean_period)
-
- # Blank array to store difference of mean between two periods
- breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
- dim=c(nPeriod_mean, nbp, nCode))
# Blank array to store mean for a temporary period in order
# to compute the difference of mean with a second period
dataMeantmp = array(rep(NA, nbp*nCode),
@@ -247,8 +248,6 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
breakValue = Break
}
- # Stores the result
- breakValue_code[j, i, k] = breakValue
# Stores temporarily the mean of the current period
dataMeantmp[i, k] = dataMean
@@ -264,13 +263,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
}
- # Computes the min and the max of the averaged trend for
- # all the station
- minBreakValue = apply(breakValue_code, c(1, 2),
- min, na.rm=TRUE)
- maxBreakValue = apply(breakValue_code, c(1, 2),
- max, na.rm=TRUE)
-
+
# Blanks vector to store color info
Fill_mean = c()
Color_mean = c()
diff --git a/plotting/shortcut.R b/plotting/shortcut.R
index 2b18663ca013e8602827de96f9db68ec8a6eeded..76bebdfaaf684f5fb29035294ca7eda3b4a5cf8f 100644
--- a/plotting/shortcut.R
+++ b/plotting/shortcut.R
@@ -23,86 +23,9 @@
#
# shortcut.R
-
-short_nPeriodMax = function (list_df2plot, Code) {
- # Gets a trend example
- df_trend = list_df2plot[[1]]$trend
-
- # Convert 'trend_period' to list
- trend_period = as.list(trend_period)
- # Number of trend period
- nPeriod_trend = length(trend_period)
-
- # Fix the maximal number of period to the minimal possible
- nPeriodMax = 0
- # For all code
- for (code in Code) {
- # Extracts the trend corresponding to the code
- df_trend_code = df_trend[df_trend$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 > nPeriodMax) {
- nPeriodMax = nPeriod
- }
- }
-
- res = list(npt=nPeriod_trend, npM=nPeriodMax)
- return (res)
-}
-
-short_tab = function (list_df2plot, Code, nbp, nCode, nPeriod_max) {
- # 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))
-
- # 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) {
- # Saves the time info
- tab_Start[k, i, j] = as.character(Start[j])
- tab_End[k, i, j] = as.character(End[j])
- }
- }
- }
- res = list(start=tab_Start, end=tab_End)
- return (res)
-}
-
-short_trendExtremes = function (list_df2plot, tab_Start, tab_End, Code, nPeriod_trend, nbp, nCode, nPeriod_max) {
+## 1. EXTREMES OF VALUE FOR ALL STATION ______________________________
+### 1.1. Trend _______________________________________________________
+short_trendExtremes = function (list_df2plot, Code, nPeriod_trend, nbp, nCode) {
# Blank array to store mean of the trend for each
# station, perdiod and variable
@@ -110,7 +33,7 @@ short_trendExtremes = function (list_df2plot, tab_Start, tab_End, Code, nPeriod_
dim=c(nPeriod_trend, nbp, nCode))
# For all the period
- for (j in 1:nPeriod_max) {
+ for (j in 1:nPeriod_trend) {
# For all the code
for (k in 1:nCode) {
# Gets the code
@@ -130,9 +53,9 @@ short_trendExtremes = function (list_df2plot, tab_Start, tab_End, Code, nPeriod_
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]
+ # 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 =
@@ -183,3 +106,87 @@ short_trendExtremes = function (list_df2plot, tab_Start, tab_End, Code, nPeriod_
res = list(min=minTrendValue, max=maxTrendValue)
return (res)
}
+
+### 1.2. Mean ________________________________________________________
+short_meanExtremes = function (list_df2plot, Code, nPeriod_mean, nbp, nCode) {
+ # Blank array to store difference of mean between two periods
+ breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
+ dim=c(nPeriod_mean, nbp, nCode))
+ # Blank array to store mean for a temporary period in order
+ # to compute the difference of mean with a second period
+ dataMeantmp = array(rep(NA, nbp*nCode),
+ dim=c(nbp, nCode))
+
+ # For all period of breaking analysis
+ for (j in 1:nPeriod_mean) {
+ # 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]]$data
+ # Extract the variable of the plot
+ var = list_df2plot[[i]]$var
+ # Extract the type of the variable to plot
+ type = list_df2plot[[i]]$type
+ # Extracts the data corresponding to the code
+ df_data_code = df_data[df_data$code == code,]
+
+ # Get the current start and end of the sub period
+ Start_mean = mean_period[[j]][1]
+ End_mean = mean_period[[j]][2]
+
+ # Extract the data corresponding to this sub period
+ df_data_code_per =
+ df_data_code[df_data_code$Date >= Start_mean
+ & df_data_code$Date <= End_mean,]
+
+ # Min max for the sub period
+ Datemin = min(df_data_code_per$Date)
+ Datemax = max(df_data_code_per$Date)
+
+ # Mean of the flow over the sub period
+ dataMean = mean(df_data_code_per$Value,
+ na.rm=TRUE)
+
+ # If this in not the first period
+ if (j > 1) {
+ # Compute the difference of mean
+ Break = dataMean - dataMeantmp[i, k]
+ # Otherwise for the first period
+ } else {
+ # Stocks NA
+ Break = NA
+ }
+
+ # If it is a flow variable
+ if (type == 'sévérité') {
+ # Normalises the break by the mean of the
+ # initial period
+ breakValue = Break / dataMeantmp[i, k]
+ # If it is a date variable
+ } else if (type == 'saisonnalité') {
+ # Just stocks the break value
+ breakValue = Break
+ }
+
+ # Stores the result
+ breakValue_code[j, i, k] = breakValue
+ # Stores temporarily the mean of the current period
+ dataMeantmp[i, k] = dataMean
+ }
+ }
+ }
+ # Computes the min and the max of the averaged trend for
+ # all the station
+ minBreakValue = apply(breakValue_code, c(1, 2),
+ min, na.rm=TRUE)
+ maxBreakValue = apply(breakValue_code, c(1, 2),
+ max, na.rm=TRUE)
+
+ res = list(min=minBreakValue, max=maxBreakValue, value=breakValue_code)
+ return (res)
+}
diff --git a/plotting/tools.R b/plotting/tools.R
index 81f881ac28a8b1f08aeb82d16906896575f1f82b..c297b27f8495708941d08a8152b71652b5666115 100644
--- a/plotting/tools.R
+++ b/plotting/tools.R
@@ -24,9 +24,179 @@
# tools.R
+## 1. COLOR MANAGEMENT
+### 1.1. 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)
+}
+
+### 1.2. 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)
+}
+
+### 1.3. Palette tester ______________________________________________
+# Allows to display the current personal palette
+palette_tester = function (palette_name='perso', n=256) {
+
+ # 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)
+ }
+
+ # 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))
-### 2.3. Circle ______________________________________________________
+ # Saves the plot
+ ggsave(plot=p,
+ filename=paste('palette_test', '.pdf', sep=''),
+ width=10, height=10, units='cm', dpi=100)
+}
+
+
+## 2. PERSONAL PLOT __________________________________________________
+### 2.1. 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))
@@ -36,8 +206,9 @@ gg_circle = function(r, xc, yc, color="black", fill=NA, ...) {
fill=fill, ...)
}
-## 6. OTHER TOOLS ____________________________________________________
-### 6.1. Number formatting ___________________________________________
+
+## 3. NUMBER MANAGEMENT ______________________________________________
+### 3.1. Number formatting ___________________________________________
# Returns the power of ten of the scientific expression of a value
get_power = function (value) {
@@ -67,7 +238,7 @@ get_power = function (value) {
return(power)
}
-### 6.2. Pourcentage of variable _____________________________________
+### 3.2. Pourcentage of variable _____________________________________
# Returns the value corresponding of a certain percentage of a
# data serie
gpct = function (pct, L, min_lim=NULL, shift=FALSE) {
@@ -96,7 +267,7 @@ gpct = function (pct, L, min_lim=NULL, shift=FALSE) {
return (xL)
}
-### 6.3. Add months __________________________________________________
+### 3.3. Add months __________________________________________________
add_months = function (date, n) {
new_date = seq(date, by = paste (n, "months"), length = 2)[2]
return (new_date)
diff --git a/processing/results_manager.R b/processing/read_write.R
similarity index 100%
rename from processing/results_manager.R
rename to processing/read_write.R
diff --git a/script.R b/script.R
index 020a1763ae8a7065b4375a0f39104349d154511b..a79824a90e9a88368938a0e9075e0d34b7c7ff89 100644
--- a/script.R
+++ b/script.R
@@ -250,6 +250,21 @@ var = list(
'tDEB',
'tCEN'
)
+type = list(
+ 'sévérité',
+ 'sévérité',
+ 'sévérité',
+ 'saisonnalité',
+ 'saisonnalité'
+)
+glose = list(
+ "Moyenne annuelle du débit journalier",
+ "Minimum annuel de la moyenne mensuelle du débit journalier",
+ "Minimum annuel de la moyenne sur 10 jours du débit journalier",
+ "Début d'étiage (jour de l'année de la première moyenne sur 10 jours sous le maximum des VCN10)",
+ "Centre d'étiage (jour de l'année du VCN10)"
+)
+
### 3.1. Compute other parameters for stations _______________________
# Time gap
df_meta = get_lacune(df_data, df_meta)
@@ -358,9 +373,9 @@ df_shapefile = ini_shapefile(resources_path,
### 5.2. Analysis layout _____________________________________________
datasheet_layout(toplot=c(
- 'datasheet'
- # 'matrix',
- # 'map'
+ 'datasheet',
+ 'matrix',
+ 'map'
),
df_meta=df_meta,
@@ -381,22 +396,8 @@ datasheet_layout(toplot=c(
),
var=var,
-
- type=list(
- 'sévérité',
- 'sévérité',
- 'sévérité',
- 'saisonnalité',
- 'saisonnalité'
- ),
-
- glose=list(
- "Moyenne annuelle du débit journalier",
- "Minimum annuel de la moyenne mensuelle du débit journalier",
- "Minimum annuel de la moyenne sur 10 jours du débit journalier",
- "Début d'étiage (jour de l'année de la première moyenne sur 10 jours sous le maximum des VCN10)",
- "Centre d'étiage (jour de l'année du VCN10)"
- ),
+ type=type,
+ glose=glose,
layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),