diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index 2c7c284cfb0f2b3da38e81b700961ae77530759b..46e42223a8315c3d9ef3ecd06bc79c9b117f33ec 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -30,7 +30,7 @@
source('processing/analyse.R', encoding='UTF-8')
-## 1. DATASHEET PANEL
+## 1. DATASHEET PANEL ________________________________________________
# Manages datasheets creations for all stations. Makes the call to
# the different headers, trend analysis graphs and realises arranging
# every plots.
@@ -538,8 +538,8 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
}
-## 2. OTHER PANEL FOR THE DATASHEET
-### 2.1. Time panel
+## 2. OTHER PANEL FOR THE DATASHEET __________________________________
+### 2.1. Time panel __________________________________________________
time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, 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) {
# If 'type' is square root apply it to data
@@ -1410,7 +1410,7 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, missRe
}
-### 2.2. Info panel
+### 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) {
@@ -1530,7 +1530,7 @@ info_panel = function(list_df2plot, df_meta, period, df_shapefile, codeLight, df
return(plot)
}
-### 2.3. Hydrograph panel
+### 2.3. Hydrograph panel ____________________________________________
# Creates a hydrograph for a station with the data serie of flow
hydrograph_panel = function (df_data_code, period, margin=NULL) {
diff --git a/plotting/layout.R b/plotting/layout.R
index 0ebbee1ab4fcd06819e0ba5029512d4e0a3089b7..2cc723dd44ea99ab1089a50db3cf308b08976789 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -51,8 +51,8 @@ source('plotting/matrix.R', encoding='UTF-8')
source('plotting/break.R', encoding='UTF-8')
-## 1. PERSONALISATION
-### 1.1. Personal theme
+## 1. PERSONALISATION ________________________________________________
+### 1.1. Personal theme ______________________________________________
theme_ash =
theme(
# White background
@@ -87,7 +87,7 @@ theme_ash =
axis.line.y=element_blank(),
)
-### 1.2. Color palette
+### 1.2. Color palette _______________________________________________
palette_perso = c('#0f3b57', # cold
'#1d7881',
'#80c4a9',
@@ -97,8 +97,8 @@ palette_perso = c('#0f3b57', # cold
'#7e392f') # hot
-## 2. USEFUL GENERICAL PLOT
-### 2.1. Void plot
+## 2. USEFUL GENERICAL PLOT __________________________________________
+### 2.1. Void plot ___________________________________________________
# A plot completly blank
void = ggplot() + geom_blank(aes(1,1)) +
theme(
@@ -120,7 +120,7 @@ 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
+### 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))
@@ -131,7 +131,7 @@ gg_circle = function(r, xc, yc, color="black", fill=NA, ...) {
}
-## 3. LAYOUT
+## 3. LAYOUT _________________________________________________________
# Generates a PDF that gather datasheets, map and summarize matrix about the trend analyses realised on selected stations
datasheet_layout = function (df_data, df_meta, layout_matrix,
toplot=c('datasheet', 'matrix', 'map'),
@@ -336,170 +336,8 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
}
-## 4. COLOR MANAGEMENT
-### 4.1. 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)
-}
-
-### 4.2. 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)
-}
-
-### 4.3. 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)
-}
-
-
-### Summary panel
+## 4. PDF ORGANISATION PANEL _________________________________________
+### 4.1. Summary _____________________________________________________
summary_panel = function (df_page, foot_note, foot_height, resources_path, logo_dir, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp) {
text_title = paste(
@@ -690,8 +528,7 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, logo_
width=width, height=height, units='cm', dpi=100)
}
-
-### Foot note panel
+### 4.2. Foot note panel______________________________________________
foot_panel = function (name, n_page, resources_path, logo_dir, AEAGlogo_file, INRAElogo_file, FRlogo_file, foot_height) {
text_page = paste(
@@ -762,8 +599,170 @@ foot_panel = function (name, n_page, resources_path, logo_dir, AEAGlogo_file, IN
}
-## 5. OTHER TOOLS
-### 5.1. Number formatting
+## 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) {
@@ -793,7 +792,7 @@ get_power = function (value) {
return(power)
}
-### 5.2. Pourcentage of variable
+### 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) {
@@ -822,7 +821,7 @@ gpct = function (pct, L, min_lim=NULL, shift=FALSE) {
return (xL)
}
-### 5.3. Add months
+### 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 ae7628f3fce02df57ff3b97973788f888f4a90d0..ed432ace52c2381daa556150d57095cf83d8f6f4 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -26,7 +26,7 @@
# Deals with the creation of a map for presenting the trend analysis of hydrological variables
-## 1. MAP PANEL
+## 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,
diff --git a/plotting/matrix.R b/plotting/matrix.R
index e14b1054438bdc02e57f2e80d1c8dfcbe1bcb3f5..4816c12d9c86891fe8c6babb1ed7df6bad37da20 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -26,7 +26,7 @@
# Allows the creation of a summarizing matrix of trend and break analyses
-## 1. MATRIX PANEL
+## 1. MATRIX PANEL ___________________________________________________
# Generates a summarizing matrix of the trend analyses of all station for different hydrological variables and periods. Also shows difference of means between specific periods.
matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice=NULL, outdirTmp='', outnameTmp='matrix', title=NULL, A3=FALSE,
foot_note=FALSE,
diff --git a/processing/analyse.R b/processing/analyse.R
index 267b891d953ac72eb36d511ca33d7028a95623ca..b39e8b5e7eb8a8852897eca41f68aa76a87ed0ab 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -31,7 +31,7 @@
# Usefull library
library(dplyr)
-library(zoo)
+library(zoo) # rollmean
library(StatsAnalysisTrend)
library(lubridate)
library(trend)
@@ -40,8 +40,8 @@ library(trend)
source('processing/format.R', encoding='latin1')
-## 1. TREND ANALYSIS
-### 1.0. Intercept of trend
+## 1. TREND ANALYSIS _________________________________________________
+### 1.0. Intercept of trend __________________________________________
# Compute intercept values of linear trends with first order values
# of trends and the data on which analysis is performed.
get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
@@ -94,15 +94,17 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
return (df_Xtrend)
}
-### 1.1. QA
+### 1.1. QA __________________________________________________________
# Realise the trend analysis of the average annual flow (QA)
# hydrological variable
-get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day) {
+get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day, df_mod=tibble()) {
# Removes incomplete data from time series
- df_data = missing_data(df_data, df_meta,
- yearLac_day=yearLac_day,
- yearStart='01-01')
+ res = missing_data(df_data, df_meta,
+ yearLac_day=yearLac_day,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
# Make sure to convert the period to a list
period = as.list(period)
@@ -145,20 +147,29 @@ get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day) {
}
# Clean results of trend analyse
res_QAtrend = clean(df_QAtrendB, df_QAExB, df_QAlistB)
- return (res_QAtrend)
+
+ res = list(data=df_data, mod=df_mod, analyse=res_QAtrend)
+ return (res)
}
-### 1.2. QMNA
+### 1.2. QMNA ________________________________________________________
# Realise the trend analysis of the monthly minimum flow in the
# year (QMNA) hydrological variable
-get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
+get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
# Removes incomplete data from time series
- df_data = missing_data(df_data, df_meta,
- yearLac_day=yearLac_day, yearStart='01-01')
+ res = missing_data(df_data, df_meta,
+ yearLac_day=yearLac_day,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
+
# Samples the data
- df_data = sampling_data(df_data, df_meta,
- sampleSpan=sampleSpan)
+ res = sampling_data(df_data, df_meta,
+ sampleSpan=sampleSpan,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
# Make sure to convert the period to a list
period = as.list(period)
@@ -214,27 +225,15 @@ get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
}
# Clean results of trend analyse
res_QMNAtrend = clean(df_QMNAtrendB, df_QMNAExB, df_QMNAlistB)
- return (res_QMNAtrend)
+
+ res = list(data=df_data, mod=df_mod, analyse=res_QMNAtrend)
+ return (res)
}
-### 1.3. VCN10
-# Realises the trend analysis of the minimum 10 day average flow
-# over the year (VCN10) hydrological variable
-get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
-
- # Removes incomplete data from time series
- df_data = missing_data(df_data, df_meta,
- yearLac_day=yearLac_day, yearStart='01-01')
-
- # Samples the data
- df_data = sampling_data(df_data, df_meta,
- sampleSpan=sampleSpan)
-
- # Get all different stations code
- Code = levels(factor(df_meta$code))
+### 1.3. VCN10 _______________________________________________________
+rollmean_code = function (df_data, Code, nroll=10, df_mod=NULL) {
# Blank tibble to store the data averaged
df_data_roll = tibble()
-
# For all the code
for (code in Code) {
# Get the data associated to the code
@@ -247,7 +246,47 @@ get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_
code=code)
# Store the results
df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
+
+ if (!is.null(df_mod)) {
+ df_mod = add_mod(df_mod, code,
+ type='Rolling average',
+ fun_name='rollmean',
+ comment='Rolling average of 10 day over all the data')
+ }
}
+ if (!is.null(df_mod)) {
+ res = list(data=df_data, mod=df_mod)
+ return (res)
+ } else {
+ return (df_data_roll)
+ }
+}
+
+# Realises the trend analysis of the minimum 10 day average flow
+# over the year (VCN10) hydrological variable
+get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
+
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+
+ # Computes the rolling average by 10 days over the data
+ res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
+
+ # Removes incomplete data from time series
+ res = missing_data(df_data_roll, df_meta,
+ yearLac_day=yearLac_day,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
+
+ # Samples the data
+ res = sampling_data(df_data_roll, df_meta,
+ sampleSpan=sampleSpan,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
# Make sure to convert the period to a list
period = as.list(period)
@@ -289,10 +328,13 @@ get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_
}
# Clean results of trend analyse
res_VCN10trend = clean(df_VCN10trendB, df_VCN10ExB, df_VCN10listB)
- return (res_VCN10trend)
+
+ res = list(data=df_data_roll, mod=df_mod,
+ analyse=res_VCN10trend)
+ return (res)
}
-### 1.4. tDEB date
+### 1.4. tDEB date ___________________________________________________
which_underfirst = function (L, UpLim, select_longest=TRUE) {
ID = which(L <= UpLim)
@@ -328,47 +370,43 @@ which_underfirst = function (L, UpLim, select_longest=TRUE) {
return (id)
}
-get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, thresold_type='VCN10', select_longest=TRUE) {
+get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, thresold_type='VCN10', select_longest=TRUE, df_mod=tibble()) {
# Get all different stations code
Code = levels(factor(df_meta$code))
# Gets the number of station
nCode = length(Code)
- # Blank tibble to store the data averaged
- df_data_roll = tibble()
-
- # For all the code
- for (code in Code) {
- # Get the data associated to the code
- df_data_code = df_data[df_data$code == code,]
- # Perform the roll mean of the flow over 10 days
- df_data_roll_code = tibble(Date=df_data_code$Date,
- Value=rollmean(df_data_code$Value,
- 10,
- fill=NA),
- code=code)
- # Store the results
- df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
- }
+ # Computes the rolling average by 10 days over the data
+ res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
# Removes incomplete data from time series
df_data = missing_data(df_data,
df_meta=df_meta,
yearLac_day=yearLac_day)
+
# Samples the data
df_data = sampling_data(df_data,
df_meta=df_meta,
sampleSpan=sampleSpan)
# Removes incomplete data from the averaged time series
- df_data_roll = missing_data(df_data_roll,
- df_meta=df_meta,
- yearLac_day=yearLac_day)
+ res = missing_data(df_data_roll,
+ df_meta=df_meta,
+ yearLac_day=yearLac_day,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
+
# Samples the data
- df_data_roll = sampling_data(df_data_roll,
- df_meta=df_meta,
- sampleSpan=sampleSpan)
+ res = sampling_data(df_data_roll,
+ df_meta=df_meta,
+ sampleSpan=sampleSpan,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
# Make sure to convert the period to a list
period = as.list(period)
@@ -475,41 +513,41 @@ get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
df_tDEBtrendB = bind_rows(df_tDEBtrendB, df_tDEBtrend)
}
# Clean results of trend analyse
- res_tDEBtrend = clean(df_tDEBtrendB, df_tDEBExB, df_tDEBlistB)
- return (res_tDEBtrend)
+ res_tDEBtrend = clean(df_tDEBtrendB, df_tDEBExB, df_tDEBlistB)
+
+ res = list(data=df_data_roll, mod=df_mod,
+ analyse=res_tDEBtrend)
+ return (res)
}
-### 1.5. tCEN date
+### 1.5. tCEN date ___________________________________________________
# Realises the trend analysis of the date of the minimum 10 day
# average flow over the year (VCN10) hydrological variable
-get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
+get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
# Get all different stations code
Code = levels(factor(df_meta$code))
# Blank tibble to store the data averaged
df_data_roll = tibble()
- # For all the code
- for (code in Code) {
- # Get the data associated to the code
- df_data_code = df_data[df_data$code == code,]
- # Perform the roll mean of the flow over 10 days
- df_data_roll_code = tibble(Date=df_data_code$Date,
- Value=rollmean(df_data_code$Value,
- 10,
- fill=NA),
- code=code)
- # Store the results
- df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
- }
+ # Computes the rolling average by 10 days over the data
+ res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
# Removes incomplete data from time series
- df_data_roll = missing_data(df_data_roll, df_meta,
- yearLac_day=yearLac_day,
- yearStart='01-01')
+ res = missing_data(df_data_roll, df_meta,
+ yearLac_day=yearLac_day,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
+
# Samples the data
- df_data_roll = sampling_data(df_data_roll, df_meta,
- sampleSpan=sampleSpan)
+ res = sampling_data(df_data_roll, df_meta,
+ sampleSpan=sampleSpan,
+ df_mod=df_mod)
+ df_data_roll = res$data
+ df_mod = res$mod
# Make sure to convert the period to a list
period = as.list(period)
@@ -554,12 +592,15 @@ get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
}
# Clean results of trend analyse
res_tCENtrend = clean(df_tCENtrendB, df_tCENExB, df_tCENlistB)
- return (res_tCENtrend)
+
+ res = list(data=df_data_roll, mod=df_mod,
+ analyse=res_tCENtrend)
+ return (res)
}
-## 2. OTHER ANALYSES
-### 2.1. Hydrograph
+## 2. OTHER ANALYSES _________________________________________________
+### 2.1. Hydrograph __________________________________________________
xref = matrix(
c(0.099, 0.100, 0.101, 0.099, 0.088, 0.078, 0.072,
0.064, 0.064, 0.069, 0.076, 0.089,
@@ -689,7 +730,7 @@ get_hydrograph = function (df_data, period=NULL, df_meta=NULL) {
return (list(QM=df_QM, meta=df_meta))
}
-### 2.2. Break date
+### 2.2. Break date __________________________________________________
# Compute the break date of the flow data by station
get_break = function (df_data, df_meta, alpha=0.05) {
@@ -736,7 +777,7 @@ get_break = function (df_data, df_meta, alpha=0.05) {
return (df_break)
}
-### 2.3. Time gap
+### 2.3. Time gap ____________________________________________________
# Compute the time gap by station
get_lacune = function (df_data, df_meta) {
@@ -789,4 +830,3 @@ get_lacune = function (df_data, df_meta) {
df_meta = full_join(df_meta, df_lac)
return (df_meta)
}
-
diff --git a/processing/extract.R b/processing/extract.R
index fc99c92dec05b426a412085e019baa553aef222b..ba0ac8e51d8a666976b7b3e53da7bb8c3067834f 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -37,7 +37,7 @@ library(dplyr)
library(officer)
-### 1. GENERAL METADATA ON STATION
+### 1. GENERAL METADATA ON STATION ___________________________________
# Status of the station
iStatut = c('0'='inconnu',
'1'='station avec signification hydrologique',
@@ -135,8 +135,8 @@ iRegHydro = c('D'='Affluents du Rhin',
'4'='Réunion')
-## 2. SELECTION
-### 2.1. Creation of selection
+## 2. SELECTION ______________________________________________________
+### 2.1. Creation of selection _______________________________________
# Create a txt file that resume all the station data files present
# in a filedir
create_selection = function (computer_data_path, filedir, outname, optname='_HYDRO_QJM') {
@@ -180,7 +180,7 @@ create_selection = function (computer_data_path, filedir, outname, optname='_HYD
# "France207",
# "INRAE_selection.txt")
-### 2.2. Agence de l'eau Adour-Garonne selection
+### 2.2. Agence de l'eau Adour-Garonne selection _____________________
# Gets the selection of station from the 'Liste-station_RRSE.docx' file
get_selection_AEAG = function (computer_data_path, listdir, listname,
cnames=c('code','station', 'BV_km2',
@@ -240,7 +240,7 @@ get_selection_AEAG = function (computer_data_path, listdir, listname,
# c_num=c('BV_km2',
# 'longueur_serie'))
-### 2.3. INRAE selection
+### 2.3. INRAE selection _____________________________________________
# Gets the selection of station from the selection txt file generated
# by the 'create_selection' function
get_selection_INRAE = function (computer_data_path, listdir, listname) {
@@ -265,7 +265,7 @@ get_selection_INRAE = function (computer_data_path, listdir, listname) {
# "INRAE_selection.txt")
-## 3. EXTRACTION
+## 3. EXTRACTION _____________________________________________________
### 3.1. Extraction of metadata
# Extraction of metadata of stations
extract_meta = function (computer_data_path, filedir, filename,
@@ -410,7 +410,7 @@ extract_meta = function (computer_data_path, filedir, filename,
# "BanqueHydro_Export2021",
# c('H5920011_HYDRO_QJM.txt', 'K4470010_HYDRO_QJM.txt'))
-### 3.2. Extraction of data
+### 3.2. Extraction of data __________________________________________
# Extraction of data
extract_data = function (computer_data_path, filedir, filename,
verbose=TRUE) {
@@ -505,7 +505,7 @@ extract_data = function (computer_data_path, filedir, filename,
# c('H5920011_HYDRO_QJM.txt', 'K4470010_HYDRO_QJM.txt'))
-## 4. SHAPEFILE MANAGEMENT
+## 4. SHAPEFILE MANAGEMENT ___________________________________________
# Generates a list of shapefiles to draw a hydrological map of
# the France
ini_shapefile = function (resources_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, sbs_shpdir, sbs_shpname, rv_shpdir, rv_shpname, is_river=TRUE) {
diff --git a/processing/format.R b/processing/format.R
index 175109e3532b00ff25ab5eb3c046d535c1cca660..b994dce4e14866b72afb14f1c4b23f905cc1c03b 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -34,16 +34,16 @@ library(dplyr)
library(Hmisc)
-## 1. BEFORE TREND ANALYSE
-### 1.1. Joining selection
+## 1. BEFORE TREND ANALYSE ___________________________________________
+### 1.1. Joining selection ___________________________________________
# Joins tibbles of different selection of station as a unique one
-join = function (df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE) {
+join_selection = function (df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE) {
# If there is an INRAE and an Agence de l'eau Adour-Garonne selection
if (!is.null(df_data_INRAE) & !is.null(df_data_AEAG)) {
# Gets the station in common
- common = levels(factor(df_meta_INRAE[df_meta_INRAE$code %in% df_meta_AEAG$code,]$code))
+ common = levels(factor(df_meta_INRAE[df_meta_INRAE$code %in% df_meta_AEAG$code,]$code))
# Gets the Nv station to add
INRAEadd = levels(factor(df_meta_INRAE[!(df_meta_INRAE$code %in% df_meta_AEAG$code),]$code))
@@ -81,8 +81,8 @@ join = function (df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE) {
return (list(data=df_data, meta=df_meta))
}
-### 1.2. Manages missing data
-missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Code=NULL) {
+### 1.2. Manages missing data ________________________________________
+missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Code=NULL, df_mod=NULL) {
if (is.null(Code)) {
# Get all different stations code
@@ -133,6 +133,14 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
if (nbNA > yearLac_day) {
df_data_code_year$Value = NA
df_data_code[OkYear,] = df_data_code_year
+
+ if (!is.null(df_mod)) {
+ df_mod = add_mod(df_mod, code,
+ type='Missing data management',
+ fun_name='NA assignment',
+ comment=paste('From ', Start,
+ ' to ', End, sep=''))
+ }
} else if (nbNA <= yearLac_day & nbNA > 1) {
DateJ = as.numeric(df_data_code_year$Date)
@@ -144,16 +152,29 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
method="linear",
na.rm=TRUE)$y
df_data_code$Value[OkYear] = Value
+
+ if (!is.null(df_mod)) {
+ df_mod = add_mod(df_mod, code,
+ type='Missing data management',
+ fun_name='approxExtrap',
+ comment=paste(
+ 'Linear extrapolation of NA from ',
+ Start, ' to ', End, sep=''))
+ }
}
}
df_data[df_data$code == code,] = df_data_code
}
-
- return (df_data)
+ if (!is.null(df_mod)) {
+ res = list(data=df_data, mod=df_mod)
+ return (res)
+ } else {
+ return (df_data)
+ }
}
-### 1.3. Sampling of the data
-sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code=NULL) {
+### 1.3. Sampling of the data ________________________________________
+sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code=NULL, df_mod=NULL) {
if (is.null(Code)) {
# Get all different stations code
@@ -176,18 +197,33 @@ sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code
df_data_code$Value[Date < sampleStart | Date > sampleEnd] = NA
+
+ if (!is.null(df_mod)) {
+ df_mod = add_mod(df_mod, code,
+ type='Seasonal sampling ',
+ fun_name='NA assignment',
+ comment=paste('Before ', sampleStart,
+ ' and after ', sampleEnd,
+ sep=''))
+ }
+
# Leap year verification
# print(df_data_code[df_data_code$Date > as.Date("1992-02-25"),])
df_data[df_data$code == code,] = df_data_code
}
-
- return (df_data)
+
+ if (!is.null(df_mod)) {
+ res = list(data=df_data, mod=df_mod)
+ return (res)
+ } else {
+ return (df_data)
+ }
}
-## 2. DURING TREND ANALYSE
-### 2.1. Preparation
+## 2. DURING TREND ANALYSE ___________________________________________
+### 2.1. Preparation _________________________________________________
# Prepares the data in order to have a list of a data tibble with
# date, group and flow column and a info tibble with the station code
# and group column to fit the entry of the 'extract.Var' function in
@@ -218,7 +254,7 @@ prepare = function(df_data, colnamegroup=NULL) {
return (res)
}
-### 2.2. Re-preparation
+### 2.2. Re-preparation ______________________________________________
# Re-prepares the data in outing of the 'extract.Var' function in
# the 'StatsAnalysisTrend' package in order to fit again to the
# entry of the same function
@@ -259,7 +295,7 @@ reprepare = function(df_XEx, df_Xlist, colnamegroup=NULL) {
return (df_XlistEx)
}
-### 2.3. Prepare date
+### 2.3. Prepare date ________________________________________________
prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
df_dateStart = summarise(group_by(df_Xlist$data, group),
@@ -325,8 +361,8 @@ prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
}
-## 3. AFTER TREND ANALYSE
-### 3.1. Period of trend
+## 3. AFTER TREND ANALYSE ____________________________________________
+### 3.1. Period of trend _____________________________________________
# Compute the start and the end of the period for a trend analysis
# according to the accessible data
get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
@@ -374,7 +410,7 @@ get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
return (df_Xtrend)
}
-### 3.2. Cleaning
+### 3.2. Cleaning ____________________________________________________
# Cleans the trend results of the function 'Estimate.stats' in the
# 'StatsAnalysisTrend' package. It adds the station code and the
# intercept of the trend to the trend results. Also makes the data
@@ -408,3 +444,20 @@ clean = function (df_Xtrend, df_XEx, df_Xlist) {
return (res)
}
+## 4. OTHER __________________________________________________________
+add_mod = function (df_mod, Code, type, fun_name, comment, df_meta=NULL) {
+ if (Code == 'all' & is.null(df_meta)) {
+ Code = NA # erreur
+ } else if (Code == 'all' & !is.null(df_meta)) {
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+ }
+
+ for (code in Code) {
+ df_modtmp = tibble(code=code, type=type,
+ fun_name=fun_name,
+ comment=comment)
+ df_mod = bind_rows(df_mod, df_modtmp)
+ }
+ return (df_mod)
+}
diff --git a/processing/results_manager.R b/processing/results_manager.R
index fdfe9977ea76fa14e0426a0bdc64524cdd4a2710..d564cd58e4c5cfd28f74a95fd44e2efbe16e849f 100644
--- a/processing/results_manager.R
+++ b/processing/results_manager.R
@@ -25,7 +25,8 @@
#
# Manages the writing and reading of results data of the trend analysis.
-
+## 1. WRITING ________________________________________________________
+### 1.1. List of dataframe ___________________________________________
write_listofdf = function (Ldf, resdir, filedir, optdir='') {
outdir = file.path(resdir, optdir, filedir)
@@ -33,8 +34,8 @@ write_listofdf = function (Ldf, resdir, filedir, optdir='') {
dir.create(outdir, recursive=TRUE)
}
- print(outdir)
-
+ print(paste('Writing of list of dataframe in : ', outdir, sep=''))
+
Lname = names(Ldf)
Nname = length(Lname)
for (i in 1:Nname) {
@@ -48,12 +49,50 @@ write_listofdf = function (Ldf, resdir, filedir, optdir='') {
}
}
+### 2.2. Dataframe of modified data __________________________________
+write_dfdata = function (df_data, df_mod, resdir, filedir, optdir='') {
+
+ Code = rle(sort(df_mod$code))$values
+ print(Code)
+
+ outdir = file.path(resdir, optdir, filedir)
+ if (!(file.exists(outdir))) {
+ dir.create(outdir, recursive=TRUE)
+ }
+
+ print(paste('Writing of modified data in : ', outdir, sep=''))
+
+ for (code in Code) {
+ df_data_code = df_data[df_data$code == code,]
+ df_mod_code = df_mod[df_mod$code == code,]
+
+ outfile1 = paste(code, '.txt', sep='')
+ write.table(df_data_code,
+ file=file.path(outdir, outfile1),
+ sep=";",
+ quote=TRUE,
+ row.names=FALSE)
+
+ outfile2 = paste(code, '_modification', '.txt', sep='')
+ write.table(df_mod_code,
+ file=file.path(outdir, outfile2),
+ sep=";",
+ quote=TRUE,
+ row.names=FALSE)
+ }
+}
+
+
+## 2. READING ________________________________________________________
+### 2.1. List of dataframe ___________________________________________
read_listofdf = function (resdir, filedir) {
outdir = file.path(resdir, filedir)
files = list.files(outdir)
Nfile = length(files)
+ print(paste('Reading of list of dataframe in : ', outdir, sep=''))
+
Ldf = list()
for (i in 1:Nfile) {
name = splitext(files[i])$name
@@ -82,6 +121,7 @@ read_listofdf = function (resdir, filedir) {
return (Ldf)
}
+## 3. OTHER __________________________________________________________
splitext = function(file) {
ex = strsplit(basename(file), split="\\.")[[1]]
res = list(name=ex[1], extension=ex[2])
diff --git a/script.R b/script.R
index a4a4e1f28653fe1416e65c68be7f61b45b51cf2f..3fe43893c024c8b17a2e4e4e6bfed46f7fd87050 100644
--- a/script.R
+++ b/script.R
@@ -124,7 +124,7 @@ is_river = FALSE
############### END OF REGION TO MODIFY (without risk) ###############
-## 1. FILE STRUCTURE
+## 1. FILE STRUCTURE _________________________________________________
# Set working directory
setwd(computer_work_path)
@@ -179,15 +179,15 @@ rv_shpdir = 'map/river'
rv_shpname = 'CoursEau_FXX.shp'
-## 2. SELECTION OF STATION
+## 2. SELECTION OF STATION ___________________________________________
# Initialization of null data frame if there is no data selected
df_data_AEAG = NULL
df_data_INRAE = NULL
df_meta_AEAG = NULL
df_meta_INRAE = NULL
-### 2.1. Selection of the Agence de l'eau Adour-Garonne
-if (AEAGlistname != ""){
+### 2.1. Selection of the Agence de l'eau Adour-Garonne ______________
+if (AEAGlistname != "") {
# Get only the selected station from a list station file
df_selec_AEAG = get_selection_AEAG(computer_data_path,
@@ -211,7 +211,7 @@ if (AEAGlistname != ""){
df_data_AEAG = extract_data(computer_data_path, filedir, filename)
}
-### 2.2. INRAE selection
+### 2.2. INRAE selection _____________________________________________
if (INRAElistname != ""){
# Get only the selected station from a list station file
@@ -227,7 +227,7 @@ if (INRAElistname != ""){
df_data_INRAE = extract_data(computer_data_path, filedir, filename)
}
-### 2.3. Manual selection
+### 2.3. Manual selection ____________________________________________
if (AEAGlistname == "" & INRAElistname == "") {
# Extract metadata about selected stations
df_meta_AEAG = extract_meta(computer_data_path, filedir, filename)
@@ -235,57 +235,80 @@ if (AEAGlistname == "" & INRAElistname == "") {
df_data_AEAG = extract_data(computer_data_path, filedir, filename)
}
-### 2.4. Data join
-df_join = join(df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE)
+### 2.4. Data join ___________________________________________________
+df_join = join_selection(df_data_AEAG, df_data_INRAE,
+ df_meta_AEAG, df_meta_INRAE)
df_data = df_join$data
df_meta = df_join$meta
-## 3. ANALYSE
-### 3.1. Compute other parameters for stations
+## 3. ANALYSE ________________________________________________________
+var = list(
+ 'QA',
+ 'QMNA',
+ 'VCN10',
+ 'tDEB',
+ 'tCEN'
+)
+### 3.1. Compute other parameters for stations _______________________
# Time gap
df_meta = get_lacune(df_data, df_meta)
# Hydrograph
df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
-### 3.2. Trend analysis
+### 3.2. Trend analysis ______________________________________________
# QA trend
-res_QAtrend = get_QAtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- yearLac_day=yearLac_day)
+res = get_QAtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ yearLac_day=yearLac_day)
+df_QAdata = res$data
+df_QAmod = res$mod
+res_QAtrend = res$analyse
# QMNA tend
-res_QMNAtrend = get_QMNAtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
+res = get_QMNAtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
+df_QMNAdata = res$data
+df_QMNAmod = res$mod
+res_QMNAtrend = res$analyse
# VCN10 trend
-res_VCN10trend = get_VCN10trend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
+res = get_VCN10trend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
+df_VCN10data = res$data
+df_VCN10mod = res$mod
+res_VCN10trend = res$analyse
# Start date for low water trend
-res_tDEBtrend = get_tDEBtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- thresold_type='VCN10',
- select_longest=TRUE,
- yearLac_day=yearLac_day)
+res = get_tDEBtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan,
+ thresold_type='VCN10',
+ select_longest=TRUE,
+ yearLac_day=yearLac_day)
+df_tDEBdata = res$data
+df_tDEBmod = res$mod
+res_tDEBtrend = res$analyse
# Center date for low water trend
-res_tCENtrend = get_tCENtrend(df_data, df_meta,
- period=trend_period,
- alpha=alpha,
- sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
-
-### 3.3. Break analysis
+res = get_tCENtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
+df_tCENdata = res$data
+df_tCENmod = res$mod
+res_tCENtrend = res$analyse
+
+### 3.3. Break analysis ______________________________________________
# df_break = get_break(res_QAtrend$data, df_meta)
# df_break = get_break(res_QMNAtrend$data, df_meta)
# df_break = get_break(res_VCN10trend$data, df_meta)
@@ -297,23 +320,22 @@ res_tCENtrend = get_tCENtrend(df_data, df_meta,
# figdir=figdir)
-
-## 4. SAVING
-### 4.1. Modified data saving
-
-
-### 4.2. Trend analysis saving
-trenddir = 'trend_analyse'
-write_listofdf(res_QAtrend, resdir, optdir=trenddir, filedir='QA')
-write_listofdf(res_QMNAtrend, resdir, optdir=trenddir, filedir='QMNA')
-write_listofdf(res_VCN10trend, resdir, optdir=trenddir, filedir='VCN10')
-write_listofdf(res_tDEBtrend, resdir, optdir=trenddir, filedir='tDEB')
-write_listofdf(res_tCENtrend, resdir, optdir=trenddir, filedir='tCEN')
-
+## 4. SAVING _________________________________________________________
+for (v in var) {
+ df_datatmp = get(paste('df_', v, 'data', sep=''))
+ df_modtmp = get(paste('df_', v, 'mod', sep=''))
+ res_trendtmp = get(paste('res_', v, 'trend', sep=''))
+ # Modified data saving
+ write_dfdata(df_datatmp, df_modtmp, resdir, optdir='modified_data',
+ filedir=v)
+ # Trend analysis saving
+ write_listofdf(res_trendtmp, resdir, optdir='trend_analyse',
+ filedir=v)
+}
# res_tDEBtrend = read_listofdf(resdir, 'res_tDEBtrend')
-## 5. PLOTTING
+## 5. PLOTTING _______________________________________________________
# Shapefile importation in order to it only once time
df_shapefile = ini_shapefile(resources_path,
fr_shpdir, fr_shpname,
@@ -321,7 +343,7 @@ df_shapefile = ini_shapefile(resources_path,
sbs_shpdir, sbs_shpname,
rv_shpdir, rv_shpname, is_river=is_river)
-### 5.1. Simple time panel to criticize station data
+### 5.1. Simple time panel to criticize station data _________________
# Plot time panel of debit by stations
# datasheet_layout(list(df_data, df_data),
# layout_matrix=c(1, 2),
@@ -334,7 +356,7 @@ df_shapefile = ini_shapefile(resources_path,
# figdir=figdir,
# filename_opt='time')
-### 5.2. Analysis layout
+### 5.2. Analysis layout _____________________________________________
datasheet_layout(toplot=c(
'datasheet'
# 'matrix'
@@ -358,13 +380,7 @@ datasheet_layout(toplot=c(
res_tCENtrend$trend
),
- var=list(
- 'QA',
- 'QMNA',
- 'VCN10',
- 'tDEB',
- 'tCEN'
- ),
+ var=var,
type=list(
'sévérité',