# \\\
# 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, colorForce=FALSE) {
# 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]]$type
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$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$period_end == End,]
# Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
# If there is more than one trend on the same period
if (Ntrend > 1) {
# Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
# If it is a flow variable
if (type == 'sévérité') {
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendValue = df_trend_code_per$trend / dataMean
# If it is a date variable
} else if (type == 'saisonnalité') {
trendValue = df_trend_code_per$trend
}
# If the p value is under the threshold
if (df_trend_code_per$p <= alpha | colorForce) {
# Stores the mean trend
TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
TrendValue_code[j, i, k] = NA
}
}
}
}
# Compute the min and the max of the mean trend for all the station
minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
res = list(min=minTrendValue, max=maxTrendValue)
return (res)
}
### 1.2. Mean ________________________________________________________
short_meanExtremes = function (list_df2plot, Code, nPeriod_mean, nbp, nCode) {
# Blank array to store difference of mean between two periods
breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
dim=c(nPeriod_mean, nbp, nCode))
# Blank array to store mean for a temporary period in order
# to compute the difference of mean with a second period
dataMeantmp = array(rep(NA, nbp*nCode),
dim=c(nbp, nCode))
# For all period of breaking analysis
for (j in 1:nPeriod_mean) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to
# the current variable
df_data = list_df2plot[[i]]$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$code == code,]
# Get the current start and end of the sub period
Start_mean = mean_period[[j]][1]
End_mean = mean_period[[j]][2]
# Extract the data corresponding to this sub period
df_data_code_per =
df_data_code[df_data_code$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)
}