sampling_training_testing<-function(list_param_sampling){

  #This function works for montly time scale if dates are provided as mid-months or other forms of for monthly records.

  # 1) seed_number: allow comparison across runs, if seed zero then no seed number is used

  # 2) nb_sample: number of time random sampling must be repeated for every hold out proportion 

  # 3) step : step for proportion range 

  # 4) constant: if value 1 then use the same samples as date one for the all set of dates

  # 5) prop_minmax: if prop_min=prop_max and step=0 then predicitons are done for the number of dates...

  # 6) dates: list of dates for prediction and subsetting                              

  # 7) ghcn: station data as data.frame -daily input                                                                                

  # 11) out_prefix: output suffix added to output names--it is the same in the interpolation script

  #

  #The output is a list of four shapefile names produced by the function:

  # 1) sampling_dat: sampling information for every run by date and sampling combintation

  # 2) sampling_index: list of indexes for training and testing for every dates

  # 3) sampling_stat_id: list of station ID for training and testing for every dates

  #AUTHOR: Benoit Parmentier                                                                       

  #Comments and TODO

  #

  ##################################################################################################

  #Parsing input arguments

  seed_number <-list_param_sampling$seed_number

  nb_sample <- list_param_sampling$nb_sample

  step<-list_param_sampling$step #if seed zero then no seed?     

  constant <- list_param_sampling$constant

  prop_minmax<-list_param_sampling$prop_minmax

  dates<-list_param_sampling$dates

  #ghcn_name<-list_param_sampling$ghcn_name

  ### BEGIN FUNCTION ####

  if (seed_number>0) {

    set.seed(seed_number)                        #Using a seed number allow results based on random number to be compared...

  }

  nel<-length(dates)

  dates_list<-vector("list",nel) #list of one row data.frame

  prop_min<-prop_minmax[1]

  prop_max<-prop_minmax[2]

  sn<-length(dates)*nb_sample*length(prop_range)               #Number of samples to run

  for(i in 1:length(dates)){

    d_tmp<-rep(dates[i],nb_sample*length(prop_range)) #repeating same date

    s_nb<-rep(1:nb_sample,length(prop_range))         #number of random sample per proportion

    prop_tmp<-sort(rep(prop_range, nb_sample))

    tab_run_tmp<-cbind(d_tmp,s_nb,prop_tmp)

    dates_list[[i]]<-tab_run_tmp

  }

  sampling_dat<-as.data.frame(do.call(rbind,dates_list))

  names(sampling_dat)<-c("date","run_samp","prop")

  for(i in 2:3){            # start of the for loop #1

    sampling_dat[,i]<-as.numeric(as.character(sampling_dat[,i]))  

  }

  #ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates

  ghcn.subsets <-lapply(as.character(sampling_dat$date), function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates

  #Make this a function??

  ## adding choice of constant sample 

  if (seed_number>0) {

    set.seed(seed_number)                        #Using a seed number allow results based on random number to be compared...

  }

  sampling<-vector("list",length(ghcn.subsets))

  sampling_station_id<-vector("list",length(ghcn.subsets))

  for(i in 1:length(ghcn.subsets)){

    n<-nrow(ghcn.subsets[[i]])

    prop<-(sampling_dat$prop[i])/100

    ns<-n-round(n*prop)   #Create a sample from the data frame with 70% of the rows

    nv<-n-ns              #create a sample for validation with prop of the rows

    ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly

    ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)

    #Find the corresponding 

    data_sampled<-ghcn.subsets[[i]][ind.training,] #selected the randomly sampled stations

    station_id.training<-data_sampled$station     #selected id for the randomly sampled stations (115)

    #Save the information

    sampling[[i]]<-ind.training #index of training sample from data.frame

    sampling_station_id[[i]]<- station_id.training #station ID for traning samples

  }

  ## Use same samples across the year...

  if (constant==1){

    sampled<-sampling[[1]]

    data_sampled<-ghcn.subsets[[1]][sampled,] #selected the randomly sampled stations

    station_sampled<-data_sampled$station     #selected id for the randomly sampled stations (115)

    list_const_sampling<-vector("list",sn)

    list_const_sampling_station_id<-vector("list",sn)

    for(i in 1:sn){

      station_id.training<-intersect(station_sampled,ghcn.subsets[[i]]$station)

      ind.training<-match(station_id.training,ghcn.subsets[[i]]$station)

      list_const_sampling[[i]]<-ind.training

      list_const_sampling_station_id[[i]]<-station_id.training

    }

    sampling<-list_const_sampling 

    sampling_station_id<-list_const_sampling_station_id

  }

  sampling_obj<-list(sampling_dat,sampling,sampling_station_id,ghcn.subsets)

  return(sampling_obj)

}