createFBTS: Creation of time series of typical days for Antares studies
In rte-antares-rpackage/antaresFlowbased: Run FlowBased Simulations on Antares

This function defines for each studied day in an Antares study the most representative typical flow-based day, and finally creates daily time series. To establish this correlation, the inputs of the function must include a probability matrix (calculated for each set of typical days with the package flowBasedClustering) and the path to the Antares study to provide flow-based domains with. The probability matrix will be used to compute a weighted draw among the possible typical days.

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createFBTS(opts, probabilityMatrix, multiplier, interSeasonBegin,
  interSeasonEnd, firstDay, seed = 4052017, silent = FALSE,
  outputPath = getwd())

`opts`	`list` of simulation parameters returned by the function setSimulationPath. Link to the Antares study. By default, the value is `antaresRead::simOptions()`
`probabilityMatrix`	`list`, correlation between climatic factors and flow-based typical days, such as returned by `flowBasedClustering::getProbability`. The columns names must be renamed to match Antares' inputs, use the function setNamesProbabilityMatrix. Initial format is : area_variable (Ex: fr_load, de_solar ...)
`multiplier`	`data.frame` enabling to convert load factors or normalised values into production/consumption in MW. Two columns: variable : Name of variable (ex: `"fr@wind"`) coef : mutiplier coefficient, for example the installed capacity.
`interSeasonBegin`	`character or date`, date or vector of dates, YYYY-MM-DD, begin of interseason
`interSeasonEnd`	`character or date`, date or vector of dates, YYYY-MM-DD, end of interseason
`firstDay`	`numeric` Type of the first day of the study (between 1 and 7). For example, if the first day is a Wednesday, you must specify firstDay = 3. The first day can be directly calculated by the function identifyFirstDay.
`seed`	`numeric` fixed random seed, used for the weighted draw of the typical days. By default, the value is 04052017.
`silent`	`boolean`, non display of a progress bar. By default, the value is FALSE.
`outputPath`	`character`, path of the folder where the time series of typical flow-based output file (ts.txt) will be written. The current directory is chosen by default.

## Not run: 
library(antaresRead)
library(flowBasedClustering)
library(data.table)

# load climate daily time series
climate <- fread(system.file("dataset/climate_example.txt",package = "flowBasedClustering"))

# load clustering results (or build them with clusteringTypicalDays function())
clusterTD <- readRDS(system.file("dataset/cluster_example.RDS",package = "flowBasedClustering"))

levelsProba <- list(summerWd = list(FR_load = c(0.5), DE_wind = c(1/3, 2/3), DE_solar = .5),
                    summerWe = list(FR_load = c(0.5, 0.7), DE_wind = c(.5)),
                    interSeasonWd = list(FR_load = c(0.5, 0.6),
                                         DE_wind = c(1/3, 2/3),
                                         DE_solar = 0.3), 
                    
                    interSeasonWe = list(FR_load = c(0.5, 0.6),
                                         DE_wind = c(1/3, 2/3),
                                         DE_solar = 0.3))
matProb <- getProbability(climate, clusterTD,
 levelsProba = levelsProba, extrapolationNA = TRUE)


# Set the probabilityMatrix names and coefficients
matProb <- setNamesProbabilityMatrix(matProb, c("FR_load", "DE_wind", "DE_solar"),
                                   c("fr@load", "de@wind", "de@solar"))

multiplier <- data.frame(variable = c("fr@load", "de@wind", "de@solar"),
                         coef = c(1, 352250, 246403))

# Set the path to Antares study inputs 
opts <- antaresRead::setSimulationPath("D:/Users/titorobe/Desktop/antaresStudy", 1)

# calendar
# first day identified based on the input data of the 
# Antares study designated by opts
firstDay <- identifyFirstDay(opts) 
interSeasonBegin <- as.Date(c("2017-09-03", "2018-02-02"))
interSeasonEnd <- as.Date(c("2020-10-04", "2018-05-02"))

# Generate flow-based time series
ts <- createFBTS(opts = opts, probabilityMatrix = matProb, multiplier = multiplier,
                 interSeasonBegin = interSeasonBegin,
                 interSeasonEnd = interSeasonEnd, firstDay = firstDay, outputPath = getwd())

## End(Not run)