diff --git a/plotting/layout.R b/plotting/layout.R
index 03def19190ad08b4855b4c60079b2ff5955547df..d22be3c8f8d3807efd2704c8541ff3f9706ca1b8 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -1,3 +1,31 @@
+# \\\
+# 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/>.
+# ///
+#
+#
+# plotting/layout.R
+#
+#
+
+
# Usefull library
library(ggplot2)
library(scales)
@@ -9,7 +37,6 @@ library(grid)
library(ggh4x)
library(RColorBrewer)
-
# Sourcing R file
source('plotting/panel.R', encoding='latin1')
diff --git a/plotting/panel.R b/plotting/panel.R
index 8555629563abcba9c8f7ee33cba82083654d02b7..5f50ab808598d40cdaeddf89887cc81ba070e350 100644
--- a/plotting/panel.R
+++ b/plotting/panel.R
@@ -1,3 +1,31 @@
+# \\\
+# 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/>.
+# ///
+#
+#
+# plotting/panel.R
+#
+#
+
+
# Usefull library
library(ggplot2)
library(scales)
diff --git a/processing/analyse.R b/processing/analyse.R
index 2d5e0f603237236276c182730e2d959805daf224..51a782973d866ff3734ab9f35b84e8cdd53f5343 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -1,3 +1,34 @@
+# \\\
+# 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/>.
+# ///
+#
+#
+# processing/analyse.R
+#
+# File that realise all the possible analysis of data.
+# This file regroup mainly the functions use to compute the trend
+# analysis of hydrologic variables thanks to the Mann-Kendall Test.
+# Functions needed for break or gap analysis are also present.
+
+
# Usefull library
library(dplyr)
library(zoo)
@@ -5,7 +36,6 @@ library(StatsAnalysisTrend)
library(lubridate)
library(trend)
-
# Sourcing R file
source('processing/format.R', encoding='latin1')
@@ -16,9 +46,9 @@ get_lacune = function (df_data, df_meta) {
# Get all different stations code
Code = levels(factor(df_meta$code))
- # Create new vector to stock results for cumulative time gap by station
+ # Create new vector to stock results for cumulative and mean
+ # time gap by station
tLac = c()
- # Create new vector to stock results for mean time gap by station
meanLac = c()
# Get rows where there is no NA
@@ -27,8 +57,7 @@ get_lacune = function (df_data, df_meta) {
df_data_NoNA = df_data[NoNA,]
# For every station
- for (code in Code) {
-
+ for (code in Code) {
# Get only the data rows for the selected station
df_data_code = df_data[df_data$code==code,]
# Get date for the selected station
@@ -52,62 +81,65 @@ get_lacune = function (df_data, df_meta) {
# Compute the mean gap
lac_mean = mean(lac[lac != 0])
# Store the mean gap
- meanLac = c(meanLac, lac_mean)
-
+ meanLac = c(meanLac, lac_mean)
}
# Compute the cumulative gap rate in pourcent
tLac100 = tLac * 100
-
# Create tibble for lacune
df_lac = tibble(code=Code, tLac100=tLac100, meanLac=meanLac)
-
# Join a tibble
df_meta = full_join(df_meta, df_lac)
-
return (df_meta)
}
-
+# 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) {
-
+
+ # Create a column in trend full of NA
df_Xtrend$intercept = NA
+ # For all different group
for (g in df_Xlist$info$group) {
+ # Get the data and trend value linked to this group
df_data_code = df_Xlist$data[df_Xlist$data$group == g,]
-
df_Xtrend_code = df_Xtrend[df_Xtrend$group == g,]
+ # Get the time start and end of the different periods
Start = df_Xtrend_code$period_start
- UStart = levels(factor(Start))
End = df_Xtrend_code$period_end
+ # Extract only the unrepeated dates
+ UStart = levels(factor(Start))
UEnd = levels(factor(End))
-
+ # Get the number of different periods of trend analysis
nPeriod = max(length(UStart), length(UEnd))
+ # For each of these perdiods
for (i in 1:nPeriod) {
-
+ # Get data and trend associated to the period
df_data_code_per =
df_data_code[df_data_code$Date >= Start[i]
& df_data_code$Date <= End[i],]
-
df_Xtrend_code_per =
df_Xtrend_code[df_Xtrend_code$period_start == Start[i]
& df_Xtrend_code$period_end == End[i],]
-
+
+ # Get the group associated to this period
id = which(df_Xtrend$group == g
& df_Xtrend$period_start == Start[i]
& df_Xtrend$period_end == End[i])
+ # Compute mean of flow and time period
mu_X = mean(df_data_code_per$Qm3s, na.rm=TRUE)
-
mu_t = as.numeric(mean(c(Start[i],
End[i]),
na.rm=TRUE)) / unit2day
-
+
+ # Get the intercept of the trend
b = mu_X - mu_t * df_Xtrend_code_per$trend
-
+ # And store it
df_Xtrend$intercept[id] = b
}
}
@@ -115,26 +147,35 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
}
+# Compute the start and the end of the period for a trend analysis
+# according to the accessible data
get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
+ # Convert results of trend to tibble
df_Xtrend = tibble(df_Xtrend)
+ # Fix the period start and end of the accessible period to a
+ # default date
df_Xtrend$period_start = as.Date("1970-01-01")
df_Xtrend$period_end = as.Date("1970-01-01")
+ # Change the format of the date variable to date
df_Xlisttmp = reprepare(df_XEx, df_Xlist, colnamegroup=c('code'))
df_XExtmp = df_Xlisttmp$data
+ # For all the different group
for (g in df_Xlisttmp$info$group) {
-
+ # Get the analyse data associated to the group
df_XExtmp_code = df_XExtmp[df_XExtmp$group == g,]
-
+ # Get the id in the trend result associated to the group
+ id = which(df_Xtrend$group1 == g)
+
+ # Compute index of the nearest accessible start and end date
iStart = which.min(abs(df_XExtmp_code$Date
- as.Date(per[1])))
iEnd = which.min(abs(df_XExtmp_code$Date
- as.Date(per[2])))
- id = which(df_Xtrend$group1 == g)
-
+ # Store the start and end of the trend analysis
df_Xtrend$period_start[id] =
as.Date(df_XExtmp_code$Date[iStart])
df_Xtrend$period_end[id] =
@@ -144,90 +185,105 @@ get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
}
-
+# Compute the break date of the flow data by station
get_break = function (df_data, df_meta, p_thresold=0.05) {
# Get all different stations code
Code = levels(factor(df_meta$code))
+ # Number of stations
nCode = length(Code)
+ # Blank date break list and associated station code vector
date_break = list()
Code_break = c()
+
+ # For all accessible code
for (code in Code) {
-
+ # Get the associated data
df_data_code = df_data[df_data$code == code,]
+ # Remove NA data
df_data_codeNoNA = df_data_code[!is.na(df_data_code$Qm3s),]
+ # Perform the break analysis thanks to the Pettitt test
res_break = pettitt.test(df_data_codeNoNA$Qm3s)
+ # Extract p value
p_value = res_break$p
+ # The length of the data analysed
nbreak = res_break$nobs
+ # Index of the break date
ibreak = res_break$estimate
- if (length(ibreak) > 1) {
- ibreak = ibreak[1]
- }
- # step1 = mean(df_data_codeNoNA$Qm3s[1:ibreak])
- # step2 = mean(df_data_codeNoNA$Qm3s[(ibreak+1):nbreak])
+ # If the p value results is under the thresold
if (p_value <= p_thresold) {
+ # Get the mean of the index break if there is several
+ ibreak = round(mean(ibreak), 0)
+ # Store the date break with its associated code
date_break = append(date_break,
df_data_codeNoNA$Date[ibreak])
Code_break = append(Code_break, code)
}
+ # step1 = mean(df_data_codeNoNA$Qm3s[1:ibreak])
+ # step2 = mean(df_data_codeNoNA$Qm3s[(ibreak+1):nbreak])
}
+ # Create a tibble with the break analysis results
df_break = tibble(code=Code_break, Date=as.Date(date_break))
-
return (df_break)
}
-
+# Realise the trend analysis of the average annual flow (QA)
+# hydrological variable
get_QAtrend = function (df_data, period, p_thresold) {
- # AVERAGE ANNUAL FLOW : QA #
-
+
+ # Make sure to convert the period to a list
period = as.list(period)
-
+
+ # Set the max interval period as the minimal possible
Imax = 0
+ # Blank tibble for data to return
df_QAtrendB = tibble()
+ # For all periods
for (per in period) {
-
+ # Prepare the data to fit the entry of extract.Var
df_QAlist = prepare(df_data, colnamegroup=c('code'))
+ # Compute the QA over the data
df_QAEx = extract.Var(data.station=df_QAlist,
funct=mean,
timestep='year',
period=per,
pos.datetime=1,
na.rm=TRUE)
-
+ # Compute the trend analysis
df_QAtrend = Estimate.stats(data.extract=df_QAEx,
level=p_thresold)
+ # Get the associated time interval
I = interval(per[1], per[2])
+ # If it is the largest interval
if (I > Imax) {
+ # Store it and the associated data and info
Imax = I
df_QAlistB = df_QAlist
df_QAExB = df_QAEx
}
+ # Specify the period of analyse
df_QAtrend = get_period(per, df_QAtrend, df_QAEx, df_QAlist)
-
-
-
- df_QAtrendB = bind_rows(df_QAtrendB, df_QAtrend)
-
+ # Store the trend
+ df_QAtrendB = bind_rows(df_QAtrendB, df_QAtrend)
}
-
+ # Clean results of trend analyse
res_QAtrend = clean(df_QAtrendB, df_QAExB, df_QAlistB)
-
return (res_QAtrend)
}
-
+# Realise the trend analysis of the monthly minimum flow in the
+# year (QMNA) hydrological variable
get_QMNAtrend = function (df_data, period, p_thresold) {
- # MONTHLY MINIMUM FLOW IN THE YEAR : QMNA #
period = as.list(period)
@@ -237,13 +293,6 @@ get_QMNAtrend = function (df_data, period, p_thresold) {
for (per in period) {
df_QMNAlist = prepare(df_data, colnamegroup=c('code'))
-
- # df_QMNAEx = extract.Var(data.station=df_QMNAlist,
- # funct=mean,
- # period=per,
- # timestep='month',
- # pos.datetime=1,
- # na.rm=TRUE)
df_QMNAEx = extract.Var(data.station=df_QMNAlist,
funct=mean,
@@ -253,7 +302,9 @@ get_QMNAtrend = function (df_data, period, p_thresold) {
pos.datetime=1,
na.rm=TRUE)
- df_QMNAlist = reprepare(df_QMNAEx, df_QMNAlist, colnamegroup=c('code'))
+ df_QMNAlist = reprepare(df_QMNAEx,
+ df_QMNAlist,
+ colnamegroup=c('code'))
df_QMNAEx = extract.Var(data.station=df_QMNAlist,
funct=min,
@@ -272,7 +323,8 @@ get_QMNAtrend = function (df_data, period, p_thresold) {
df_QMNAExB = df_QMNAEx
}
- df_QMNAtrend = get_period(per, df_QMNAtrend, df_QMNAEx,
+ df_QMNAtrend = get_period(per, df_QMNAtrend,
+ df_QMNAEx,
df_QMNAlist)
df_QMNAtrendB = bind_rows(df_QMNAtrendB, df_QMNAtrend)
@@ -284,8 +336,8 @@ get_QMNAtrend = function (df_data, period, p_thresold) {
}
+# Realise the trend analysis of the minimum 10 day average flow over the year (VCN10) hydrological variable
get_VCN10trend = function (df_data, df_meta, period, p_thresold) {
- # MINIMUM 10 DAY AVERAGE FLOW OVER THE YEAR : VCN10 #
# Get all different stations code
Code = levels(factor(df_meta$code))
diff --git a/processing/extract.R b/processing/extract.R
index 3a004d7a76da5d21ebc6ec409a8d70b2c83bd616..408398012569b37fa58400abfb82c88e9cf33545 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -1,3 +1,31 @@
+# \\\
+# 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/>.
+# ///
+#
+#
+# processing/extract.R
+#
+#
+
+
# Usefull library
library(tools)
library(dplyr)
@@ -175,7 +203,7 @@ get_selection_AG = function (computer_data_path, listdir, listname,
# 'longueur_serie'))
-get_selection_NV = function (computer_data_path, listdir, listname) {
+get_selection_IN = function (computer_data_path, listdir, listname) {
# Get the file path to the data
list_path = file.path(computer_data_path, listdir, listname)
@@ -193,7 +221,7 @@ get_selection_NV = function (computer_data_path, listdir, listname) {
return (df_selec)
}
# Example
-# df_selec_NV = get_selection_NV(
+# df_selec_IN = get_selection_IN(
# "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
# "",
# "nival_selection.txt")
diff --git a/processing/format.R b/processing/format.R
index 21d6c928c6480bf9d607995025d1d5b17e2f7329..4c4cbeacf94c711dda18088506a01485aadd0500 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -1,3 +1,31 @@
+# \\\
+# 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/>.
+# ///
+#
+#
+# processing/format.R
+#
+#
+
+
# Usefull library
library(dplyr)
@@ -43,6 +71,7 @@ join = function (df_data_AG, df_data_NV, df_meta_AG, df_meta_NV) {
}
+# Prepare 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 'StatsAnalysisTrend' package
prepare = function(df_data, colnamegroup=NULL) {
colnamegroup = c(colnamegroup)
diff --git a/script.R b/script.R
index 1e75cde9f82610fe9fbadf4301f4582c4ef96c9f..fa56fd167f1d441f5547c7b514c28fe6d78535a0 100644
--- a/script.R
+++ b/script.R
@@ -1,6 +1,37 @@
-############ A MODIFIER ############
-
-
+# \\\
+# 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/>.
+# ///
+#
+#
+# script.R
+#
+# Script file to manage the trend analysis of the Adour-Garonne basin.
+# Performs the necessary calls to processing and plotting functions in
+# order to realise the hydrologic trend analysis of stations according
+# to the input parameters. The nearest area belove is where you need to
+# write your prefer parameters for the analysis. See the 'README.txt'
+# file for more information.
+
+
+############## START OF REGION TO MODIFY (without risk) ##############
# Path to the data
computer_data_path =
"/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data"
@@ -12,15 +43,17 @@ computer_work_path =
# "C:\\Users\\louis.heraut\\Documents\\CDD_stationnarite\\ASH"
-### BANQUE HYDRO ###
-# Path to the directory where BH data is stored from the work path
+## BANQUE HYDRO
+# Path to the directory where Banque Hydro (BH) data is stored
+# from the work path
filedir =
# ""
"BanqueHydro_Export2021"
-### MANUAL SELECTION ###
-# Name of the file that will be analysed from the AG directory
+## MANUAL SELECTION
+# Name of the file that will be analysed from the BH directory
+# (if 'all', all the file of the directory will be chosen)
filename =
# ""
@@ -38,8 +71,9 @@ filename =
-### AGENCE EAU ADOUR GARONNE SELECTION ###
-# Path to the list file of AG data that will be analysed
+## AGENCE EAU ADOUR GARONNE SELECTION
+# Path to the 'docx' list file of station from the Agence de l'eau
+# Adour-Garonne that will be analysed
AGlistdir =
""
@@ -48,17 +82,17 @@ AGlistname =
# "Liste-station_RRSE.docx"
-### NIVALE SELECTION ###
-# Path to the list file of metadata about station that will be analysed
-NVlistdir =
+## NIVALE SELECTION
+# Path to the 'txt' list file of station from INRAE that will be analysed
+INlistdir =
""
-NVlistname =
+INlistname =
""
# "nival_selection.txt"
-### TREND ANALYSIS ###
+## TREND ANALYSIS
# Time period to analyse
periodAll = c("1800-01-01", "2019-12-31")
periodSub = c("1968-01-01", "2019-12-31")
@@ -69,25 +103,26 @@ period1 = c("1968-01-01", "1994-12-31")
period2 = c("1995-01-01", "2019-12-31")
mean_period = list(period1, period2)
-# p value
+# p value thresold
p_thresold = 0.1 #c(0.01, 0.05, 0.1)
-### MAP ###
+## MAP
+# Path to the shapefile for france contour from 'computer_data_path'
fr_shpdir = 'map/france'
fr_shpname = 'gadm36_FRA_0.shp'
+# Path to the shapefile for basin shape from 'computer_data_path'
bs_shpdir = 'map/bassin'
bs_shpname = 'BassinHydrographique.shp'
+# Path to the shapefile for river shape from 'computer_data_path'
rv_shpdir = 'map/river'
rv_shpname = 'CoursEau_FXX.shp'
+############### END OF REGION TO MODIFY (without risk) ###############
-####################################
-
-
-# FILE STRUCTURE #
+## 1. FILE STRUCTURE
# Set working directory
setwd(computer_work_path)
@@ -98,7 +133,6 @@ source('processing/analyse.R', encoding='latin1')
source('plotting/panel.R', encoding='latin1')
source('plotting/layout.R', encoding='latin1')
-
# Result directory
resdir = file.path(computer_work_path, 'results')
if (!(file.exists(resdir))) {
@@ -114,14 +148,14 @@ if (!(file.exists(figdir))) {
print(paste('figdir :', figdir))
+## 2. SELECTION OF STATION
# Initialization of null data frame if there is no data selected
df_data_AG = NULL
-df_data_NV = NULL
+df_data_IN = NULL
df_meta_AG = NULL
-df_meta_NV = NULL
+df_meta_IN = NULL
-
-# AGENCE EAU ADOUR GARONNE SELECTION #
+### 2.1. Selection of the Agence de l'eau Adour-Garonne
if (AGlistname != ""){
# Get only the selected station from a list station file
@@ -147,81 +181,80 @@ if (AGlistname != ""){
# Extract metadata about selected stations
df_meta_AG = extract_meta(computer_data_path, filedir, filename)
-
# Extract data about selected stations
df_data_AG = extract_data(computer_data_path, filedir, filename)
}
-# NIVALE SELECTION #
-if (NVlistname != ""){
+### 2.2. INRAE selection
+if (INlistname != ""){
# Get only the selected station from a list station file
- df_selec_NV = get_selection_NV(computer_data_path,
- NVlistdir,
- NVlistname)
+ df_selec_IN = get_selection_IN(computer_data_path,
+ INlistdir,
+ INlistname)
# Get filenames of the selection
- filename = df_selec_NV[df_selec_NV$ok,]$filename
+ filename = df_selec_IN[df_selec_IN$ok,]$filename
#####
# filename = filename[(1+20):(16+20)]
#####
# Extract metadata about selected stations
- df_meta_NV = extract_meta(computer_data_path, filedir, filename)
-
+ df_meta_IN = extract_meta(computer_data_path, filedir, filename)
# Extract data about selected stations
- df_data_NV = extract_data(computer_data_path, filedir, filename)
+ df_data_IN = extract_data(computer_data_path, filedir, filename)
}
-# MANUAL SELECTION #
-if (AGlistname == "" & NVlistname == "") {
-
+### 2.3. Manual selection
+if (AGlistname == "" & INlistname == "") {
# Extract metadata about selected stations
df_meta_AG = extract_meta(computer_data_path, filedir, filename)
-
# Extract data about selected stations
df_data_AG = extract_data(computer_data_path, filedir, filename)
}
-
-# JOIN #
-# Make the join
-df_join = join(df_data_AG, df_data_NV, df_meta_AG, df_meta_NV)
+### 2.4. Data join
+df_join = join(df_data_AG, df_data_IN, df_meta_AG, df_meta_IN)
df_data = df_join$data
df_meta = df_join$meta
-# ANALYSE #
-# Compute gap parameters for stations
+## 3. ANALYSE
+### 3.1. Compute gap parameters for stations
df_meta = get_lacune(df_data, df_meta)
-
-# QA TREND #
+### 3.2. Trend analysis
+# QA trend
res_QAtrend = get_QAtrend(df_data, period=trend_period,
p_thresold=p_thresold)
-# QMNA TREND #
+# QMNA tend
res_QMNAtrend = get_QMNAtrend(df_data, period=trend_period,
p_thresold=p_thresold)
-# VCN10 TREND #
+# VCN10 trend
res_VCN10trend = get_VCN10trend(df_data, df_meta,
period=trend_period,
p_thresold=p_thresold)
-
+### 3.3. Break analysis
# df_break = get_break(res_QAtrend$data, df_meta)
# df_break = get_break(res_QMNAtrend$data, df_meta)
# df_break = get_break(res_VCN10trend$data, df_meta)
# histogram(df_break$Date, df_meta,
- # figdir=figdir)
+# figdir=figdir)
# cumulative(df_break$Date, df_meta, dyear=8,
- # figdir=figdir)
+# figdir=figdir)
+
+
+## 4. PLOTTING
+# Shapefile importation in order to it only once time
+df_shapefile = ini_shapefile(computer_data_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, rv_shpdir, rv_shpname, riv=FALSE)
-# TIME PANEL #
+### 4.1. Simple time panel to criticize station data
# Plot time panel of debit by stations
# panels_layout(list(df_data, df_data),
# layout_matrix=c(1, 2),
@@ -234,10 +267,7 @@ res_VCN10trend = get_VCN10trend(df_data, df_meta,
# figdir=figdir,
# filename_opt='time')
-
-df_shapefile = ini_shapefile(computer_data_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, rv_shpdir, rv_shpname, riv=FALSE)
-
-
+### 4.2. Analysis layout
panels_layout(isplot=c(
'datasheet',
'matrix',