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Pierre-Antoine Rouby authored48c98cdd
# \\\# 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/>.# ///### shortcut.R## 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 TrendValue_code = array(rep(1, nPeriod_trend*nbp*nCode), dim=c(nPeriod_trend, nbp, nCode)) # For all the period for (j in 1:nPeriod_trend) { # For all the code for (k in 1:nCode) { # Gets the code code = Code[k] for (i in 1:nbp) { # Extracts the data corresponding to the # current variable df_data = list_df2plot[[i]]$data # Extracts the trend corresponding to the # current variable df_trend = list_df2plot[[i]]$trend # Extracts the type of the variable type = list_df2plot[[i]]$type alpha = list_df2plot[[i]]$alpha # Extracts the data corresponding to the code df_data_code = df_data[df_data$code == code,] df_trend_code = df_trend[df_trend$code == code,] # 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 = df_data_code[df_data_code$Date >= Start & df_data_code$Date <= End,] # Same for trend df_trend_code_per = df_trend_code[df_trend_code$period_start == Start & df_trend_code$period_end == End,] # Computes the number of trend analysis selected7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
Ntrend = nrow(df_trend_code_per)
# If there is more than one trend on the same period
if (Ntrend > 1) {
# Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
# If it is a flow variable
if (type == 'sévérité') {
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendValue = df_trend_code_per$trend / dataMean
# If it is a date variable
} else if (type == 'saisonnalité') {
trendValue = df_trend_code_per$trend
}
# If the p value is under the threshold
if (df_trend_code_per$p <= alpha) {
# Stores the mean trend
TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
TrendValue_code[j, i, k] = NA
}
}
}
}
# Compute the min and the max of the mean trend for all the station
minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
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]
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# 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)
}