get_seasonality_parameters

@param eirs A numeric vector representing EIR values.  
             If \code{timeframe} is "monthly", this should contain one value per month.  
             If \code{timeframe} is "daily", this should contain one value per day.  
@param timeframe A string indicating the timeframe of the \code{eirs} data. Accepted values are:  
        \itemize{  
          \item \code{"daily"}: EIR data is provided for each day of the year.  
          \item \code{"monthly"}: EIR data is provided for each month of the year.  
        }  
@param midpoint_setting A string specifying how to calculate midpoints for the monthly timeframe.  
        Accepted values are:  
        \itemize{  
         \item \code{"default"}: Midpoints are set to the 15th day of each month.  
         \item \code{"calendar"}: Midpoints are calculated based on the exact day distribution of each month.  
        }  
@return A list of Fourier coefficients that model the seasonality based on the provided EIR data.  
@export  

get_seasonality_parameters <- function(eirs, timeframe, midpoint_setting = "default") {
  if(!timeframe %in% c("daily", "monthly")) {
    stop("EIR timeframe not from accepted values (daily or monthly")
  }
  if(!midpoint_setting %in% c("default", "calendar")) {
    stop("EIR timeframe not from accepted values (default or calendar")
  }
  # If the EIRs provided are on a monthly timeframe:
  if(timeframe == "monthly") {
    # If the default midpoint setting has been selected:
    if (midpoint_setting == "default") {
      # Generate the monthly midpoints:
      monthly_midpoints <- (0:11 * 30) + 15
      # Generate the Fourier coefficients:
      seasonality_parameters <- umbrella::fit_fourier(rainfall = eirs, t = monthly_midpoints)
      # Return the Fourier coefficients:
      return(seasonality_parameters)
    } else if (midpoint_setting == "calendar") {
      # Generate a vector of the days per calendar month in a non-leap year:
      days_per_month <- as.numeric(days_in_month(seq(from = dmy("01-01-2023"), length.out = 12, by = "months")))
      # Get the midpoint of each month by subtracting half the number of days from the cumulative sum of days:
      monthly_midpoints <- cumsum(days_per_month) - ceiling(days_per_month/2)
      # Generate the Fourier coefficients:
      seasonality_parameters <- umbrella::fit_fourier(rainfall = eirs, t = monthly_midpoints)
      # Return the Fourier coefficients:
      return(seasonality_parameters)
    }
  }
  # If the EIRs are provided on the daily timeframe:
  if(timeframe == "daily") {
    # Generate the Fourier coefficients:
    seasonality_parameters <- umbrella::fit_fourier(rainfall = eirs, t = 1:365)
    # Return the Fourier coefficients
    return(seasonality_parameters)
  }
}