#' Value Matching
#'
#' @param x value to search
#' @param table table of values
#'
#' @return the opposite of x %in% table
#' @export
#'
#' @examples
#' 1:10 %in% c(1, 3, 5, 9)
#' 1:10 %nin% c(1, 3, 5, 9)
"%nin%" <- function(x, table) {
  !(x %in% table)
}


#' Find the closest point of the FIELD grid to the specified position
#'
#' @param x vector of coordinates in the form longitude/latitude data frame
#' @param lat alternatively, x and lat can be vector of the same length
#' @param closest an integer to specify the number of point to output.
#' @param ... currently unused
#'
#' @return a list with two components: the closest point(s) of the grid and the distance (s).
#' @export
#'
#' @examples
#' semrev_west <- closest_point_field(c(-2.786, 47.239))
#' semrev_west
closest_point_field <- function(x, lat = NULL, closest = 1L, ...) {
  if (!is.null(lat)) {
    if (!(length(x) == length(lat))) {
      stop("If 'lat' is provided, it sould be the same length as 'x'.")
    }
    return(closest_point_field(
      cbind(x, lat),
      lat = NULL,
      closest = closest,
      ...
    ))
  }

  # Concert the input to a matrix with 2 columns, one point per line
  x <- matrix(x, ncol = 2)

  if (!is.numeric(closest) || closest < 1L) {
    stop("'closest' must be an integer greater than 1.")
  }

  # Compute the distance matrix between the points and Resourcecode's grid
  dist <- geosphere::distm(x, resourcecodedata::rscd_field[, c(2, 3)])

  if (closest == 1L) {
    ind_min <- apply(dist, 1, which.min)
  } else {
    ind_min <- apply(dist, 1, function(distances) {
      order(distances)[1:closest]
    })
  }

  distance <- matrix(NA, NROW(x), closest)
  for (p in seq_len(NROW(x))) {
    distance[p, ] <- if (closest == 1) {
      dist[p, ind_min[p]]
    } else {
      dist[p, ind_min[, p]]
    }
  }

  list(
    points = t(ind_min),
    distances = distance
  )
}

#' Find the closest point of the SPEC grid to the specified position
#'
#' @param x vector of coordinates in the form longitude/latitude data frame
#' @param lat alternatively, x and lat can be vector of the same length
#' @param closest an integer to specify the number of point to output.
#' @param ... currently unused
#'
#' @return a list with two components: the closest point(s) of the grid and the distance (s).
#' @export
#'
#' @examples
#' semrev_west <- closest_point_spec(c(-2.786, 47.239))
#' semrev_west
closest_point_spec <- function(x, lat = NULL, closest = 1L, ...) {
  if (!is.null(lat)) {
    if (!(length(x) == length(lat))) {
      stop("If 'lat' is provided, it sould be the same length as 'x'.")
    }
    return(closest_point_spec(
      cbind(x, lat),
      lat = NULL,
      closest = closest,
      ...
    ))
  }

  # Concert the input to a matrix with 2 columns, one point per line
  x <- matrix(x, ncol = 2)

  if (!is.numeric(closest) || closest < 1L) {
    stop("'closest' must be an integer greater than 1.")
  }

  # Compute the distance matrix between the points and Resourcecode's grid
  dist <- geosphere::distm(x, resourcecodedata::rscd_spectral[, c(1, 2)])

  if (closest == 1L) {
    ind_min <- apply(dist, 1, which.min)
  } else {
    ind_min <- apply(dist, 1, function(distances) {
      order(distances)[1:closest]
    })
  }

  distance <- matrix(NA, NROW(x), closest)
  for (p in seq_len(NROW(x))) {
    distance[p, ] <- if (closest == 1) {
      dist[p, ind_min[p]]
    } else {
      dist[p, ind_min[, p]]
    }
  }

  list(
    points = t(ind_min),
    distances = distance
  )
}

#' Convert u/v to meteorological wind speed and direction
#'
#' Converts wind or current zonal and meridional velocity components to
#' magnitude and direction according to meteorological convention.
#'
#' @param u zonal velocity (1D vector) or matrix with zonal and meridional velocity (Nx2 matrix)
#' @param v meridional velocity (1D vector)
#' @param names names to construct the resulting data.frame
#'
#' @return a Nx2 data.frame with the norm and direction (meteorological convention)
#' @export
#'
#' @examples
#' u <- matrix(rnorm(200), nrow = 100, ncol = 2)
#' vdir <- zmcomp2metconv(u)
zmcomp2metconv <- function(u, v = NULL, names = c("wspd", "wdir")) {
  if (is.vector(u)) {
    stopifnot(length(v) == length(u))
    u <- cbind(u, v)
  }

  stopifnot(is.matrix(u) & dim(u)[2] == 2)

  speed <- sqrt(u[, 1]^2 + u[, 2]^2)
  direction <- (270 - atan2(u[, 2], u[, 1]) * 180 / pi) %% 360
  out <- data.frame(speed, direction)
  names(out) <- names
  out
}

#' Convert meteorological wind speed and direction to u/v components
#'
#' @description
#' Converts wind speed (magnitude) and direction (in degrees, meteorological
#' convention: direction from which the wind blows, measured clockwise from north)
#' into zonal (u) and meridional (v) components.
#'
#' @param speed Numeric vector of wind speeds.
#' @param direction Numeric vector of wind directions in degrees (0° = from north,
#' 90° = from east, 180° = from south, 270° = from west).
#' @param names (optional) ames to construct the resulting data.frame.
#'
#' @return A data.frame with two columns:
#' \describe{
#'   \item{u}{Zonal wind component (m/s), positive eastward.}
#'   \item{v}{Meridional wind component (m/s), positive northward.}
#' }
#'
#' @examples
#' # Example 1: North wind of 10 m/s (blowing southward)
#' metconv2zmcomp(10, 0)
#'
#' # Example 2: East wind of 5 m/s (blowing westward)
#' metconv2zmcomp(5, 90)
#'
#' # Example 3: South wind of 8 m/s (blowing northward)
#' metconv2zmcomp(8, 180)
#'
#' @export
metconv2zmcomp <- function(speed, direction, names = c("uwnd", "vwnd")) {
  # Convert to radians
  dir_rad <- direction * pi / 180

  # Components
  u <- -speed * sin(dir_rad)
  v <- -speed * cos(dir_rad)

  out <- data.frame(u = u, v = v)
  names(out) <- names
  out
}


#' JONWSAP spectrum
#'
#' Creates a JONWSAP density spectrum (one-sided), defined by its integral parameters.
#'
#' Reference :
#'   - O.G.Houmb and T.Overvik, "Parametrization of Wave Spectra and Long Term
#'        Joint Distribution of Wave Height and Period,"
#'        in Proceedings, First International Conference
#'        on Behaviour of Offshore Structures (BOSS), Trondheim 1976.
#'        23rd International Towing Tank Conference, vol. II, pp. 544-551
#'   - ITTC Committee, 2002, "The Specialist Committee on Waves -
#'        Final Report and Recommendations to the 23rd ITTC",
#'        Proc. ITTC, vol. II, pp. 505-736.
#'
#' @param hs Hs (default: 5m)
#' @param tp Period (default: 10s)
#' @param fmax higher frequency of the spectrum or
#'             vector of frequencies (default to resourcecode frequency vector)
#' @param df frequency step (unused if fmax=vector of frequencies)
#' @param gam peak enhancement factor (default: 3.3)
#'
#' @return Density spectrum with corresponding parameters
#' @export
#'
#' @examples
#' S1 <- jonswap(tp = 15)
#' S2 <- jonswap(tp = 15, fmax = 0.95, df = 0.003)
#' plot(S1, type = "l", ylim = c(0, 72))
#' lines(S2, col = "red")
#' abline(v = 1 / 15)
jonswap <- function(
  hs = 5,
  tp = 15,
  fmax = resourcecodedata::rscd_freq,
  df = NULL,
  gam = 3.3
) {
  if (length(fmax) > 1) {
    # Case when the frequency vector if given
    freq <- fmax
    fmin <- min(freq)
    fmax <- max(freq)
    df <- min(diff(freq))
    # generate a uniform-sampling to ease computations:
    frq <- seq(from = fmin, to = fmax + df, by = df)
  } else {
    if (is.null(df)) {
      stop("df must be provided when fmax is a single value")
    }
    fmin <- df
    frq <- freq <- seq(from = df, to = fmax + df, by = df)
  }
  nptsp <- length(frq)

  # Stops if the Gamma parameter is lower than 1
  if (gam < 1) {
    stop("Gamma parameter `gam` should be greater than 1")
  }

  # Compute the parameters of the spectrum
  fm <- 1 / tp
  lgam <- log(gam)
  fr <- frq * tp
  ifm <- trunc(fm / df)

  # sigma = c(.07*ones(1,ifm),.09*ones(1,nptsp-ifm))
  # sigma=rep(c(0.07,0.09),c(ifm,nptsp-ifm))
  sigma <- c(0.07 * rep(1, ifm), 0.09 * rep(1, nptsp - ifm))
  frm4 <- fr^-4
  sp <- (1 - fr) / sigma
  sp <- exp(-0.5 * (sp^2))
  sp <- (frm4 / fr) * exp(-1.25 * frm4) * exp(lgam * sp)

  sp <- sp * (hs / 4)^2 / sum(sp * df)
  sp <- Re(c(0, sp))
  sp <- sp[1:nptsp]
  fr <- c(0, frq)
  fr <- fr[1:nptsp]

  sp <- stats::approx(frq, sp, freq)
  names(sp) <- c("freq", "spec")
  attr(sp, "Note") <- paste0(
    "JONSWAP Spectrum, Hs=",
    hs,
    ", Tp=",
    tp,
    ", gamma=",
    gam
  )
  sp$freq <- as.vector(sp$freq)
  tibble::as_tibble(sp)
}

#' Mean Direction
#'
#' Function for computing the (weighted) arithmetic mean of
#' directional data in \strong{meteorological convention}.
#'
#' @param directions numeric vector of directions, in degree, 0° being the North
#' @param weights numeric vector, usually wind speed of wave height.
#'
#' @returns
#' The (weighted) mean of the values in \code{directions} is computed.
#'
#' @export
#'
#' @examples
#' # Test with some wind directions (unweighted)
#' wind_directions <- c(10, 20, 350, 5, 15) # Directions mostly around North
#' mean_dir <- mean_direction(wind_directions)
#' cat("Mean wind direction (unweighted):", round(mean_dir, 1), "degrees\n")
# Test with wind speeds as weights
#' wind_directions <- c(350, 10, 20, 340, 30) # Directions around North
#' wind_speeds <- c(15, 5, 2, 12, 3) # Higher speeds for directions closer to North
#' mean_dir_weighted <- mean_direction(wind_directions, wind_speeds)
#' cat("Mean wind direction (weighted):", round(mean_dir_weighted, 1), "degrees\n")
#'
#' # Compare weighted vs unweighted for the same data
#' mean_dir_unweighted <- mean_direction(wind_directions)
#' cat("Same data unweighted:", round(mean_dir_unweighted, 1), "degrees\n")
mean_direction <- function(directions, weights = NULL) {
  # Verify that directions are numeric
  if (!is.numeric(directions)) {
    stop("'directions' must be numeric")
  }
  # If weights provided, check they have the same length as directions
  if (!is.null(weights)) {
    if (length(directions) != length(weights)) {
      stop("Length of 'directions' and 'speeds' must be equal")
    }
    # Verify that weights are numeric
    if (!is.numeric(weights)) {
      stop("'weights' must be numeric")
    }
    valid_indices <- !is.na(directions) & !is.na(weights)
    directions <- directions[valid_indices]
    weights <- weights[valid_indices]
  } else {
    # Remove NA values from directions only
    directions <- directions[!is.na(directions)]
  }

  # Check for negative weights (which would cause issues with weighting)
  if (any(weights < 0)) {
    warning("Negative weights detected. Using absolute values.")
    weights <- abs(weights)
  }

  # Ensure directions are in [0, 360) range
  directions <- directions %% 360

  # Check if we have any valid directions
  if (length(directions) == 0) {
    return(numeric(0))
  }

  # Set default weights (equal weighting) if weights not provided
  if (is.null(weights)) {
    weights <- rep(1, length(directions))
  }

  # Convert degrees to radians
  radians <- directions * pi / 180

  # Convert to unit vectors (x = sin, y = cos for meteorological convention)
  # In meteorological convention: North = 0°, East = 90°
  x_components <- sin(radians)
  y_components <- cos(radians)

  # Calculate weighted mean components
  mean_x <- stats::weighted.mean(x_components, w = weights)
  mean_y <- stats::weighted.mean(y_components, w = weights)

  # Calculate mean direction in radians
  mean_radians <- atan2(mean_x, mean_y)

  # Convert back to degrees
  mean_degrees <- mean_radians * 180 / pi

  # Ensure result is in [0, 360) range using modulo
  mean_degrees <- mean_degrees %% 360

  mean_degrees
}

#' Directional binning
#'
#' Cuts direction vector into directional bins
# with North bin always centred on 0 degrees.
#'
#' @param directions vector of directions to be binned, in degree, 0° being the North.
#' @param n_bins number of bins, default: 8 sectors.
#' @param labels optional character vector giving the sectors names.
#'
#' @returns
#' a factor vector the same size as \code{directions} with the values binned into sectors.
#'
#' @export
#'
#' @examples
#' # Example usage and demonstration
#' set.seed(123)
#' directions <- runif(20, 0, 360)
#'
#' # Test with different numbers of bins
#' cat("Original directions:\n")
#' print(round(directions, 1))
#'
#' cat("\n8 bins (default):\n")
#' bins_8 <- cut_directions(directions, n_bins = 8)
#' print(bins_8)
#'
#' cat("\n4 bins:\n")
#' bins_4 <- cut_directions(directions, n_bins = 4)
#' print(bins_4)
cut_directions <- function(directions, n_bins = 8, labels = NULL) {
  # Validate inputs
  if (!is.numeric(directions)) {
    stop("'directions' must be numeric")
  }

  if (length(directions) == 0) {
    return(numeric(0))
  }

  if (n_bins < 2) {
    stop("'n_bins' must be at least 2")
  }

  # Normalize directions to 0-360 range
  directions <- directions %% 360

  # Calculate bin width
  bin_width <- 360 / n_bins
  half_bin <- bin_width / 2

  # Create breaks - North bin is centered on 0
  # So breaks go from -half_bin to 360-half_bin
  breaks <- seq(-half_bin, 360 - half_bin, by = bin_width)

  # Adjust directions for the split North bin
  # Values in the upper part of North bin (360-half_bin to 360)
  # need to be mapped to negative values (-half_bin to 0)
  adjusted_directions <- ifelse(
    directions > (360 - half_bin),
    directions - 360,
    directions
  )

  # Create labels if not provided
  if (is.null(labels)) {
    # Calculate center angles for each bin
    centers <- seq(0, 360 - bin_width, by = bin_width)

    # Create descriptive labels based on number of bins
    if (n_bins == 4) {
      labels <- c("N", "E", "S", "W")
    } else if (n_bins == 8) {
      labels <- c("N", "NE", "E", "SE", "S", "SW", "W", "NW")
    } else if (n_bins == 16) {
      labels <- c(
        "N",
        "NNE",
        "NE",
        "ENE",
        "E",
        "ESE",
        "SE",
        "SSE",
        "S",
        "SSW",
        "SW",
        "WSW",
        "W",
        "WNW",
        "NW",
        "NNW"
      )
    } else {
      # Generic labels with center angles
      labels <- paste0("Bin_", sprintf("%.0f", centers))
    }
  }
  # Cut the adjusted directions
  result <- cut(
    adjusted_directions,
    breaks = breaks,
    labels = labels,
    include.lowest = TRUE,
    right = FALSE
  )

  result
}


#' Get season from date time object
#'
#' @param datetime a POSIXct vector from with the season is constructed
#' @param definition the definition used to compute the season. See details section.
#' @param hemisphere in the Southern hemisphere, seasons are reversed compared to the Northern one.
#' @param labels optional, a character vector of length fours with the seasons' names.
#'
#' @returns a Factor vector with 4 levels depending on the definitions (and labels if provided)
#' @export
#'
#' @details
#' Available Definitions:
#'   - meteorological: Standard seasons (Dec-Feb = Winter, etc.)
#'   - astronomical: Based on equinoxes/solstices
#'   - djf: Dec-Jan-Feb, Mar-Apr-May, Jun-Jul-Aug, Sep-Oct-Nov
#'   - jfm: Jan-Feb-Mar, Apr-May-Jun, Jul-Aug-Sep, Oct-Nov-Dec
#'   - fma: Feb-Mar-Apr, May-Jun-Jul, Aug-Sep-Oct, Nov-Dec-Jan
#'   - amj, jas, ond: Alternative starting points for quarterly seasons
#'
#' @examples
#' dates <- seq(
#'   from = as.POSIXct("2023-01-15"),
#'   to = as.POSIXct("2023-12-15"),
#'   by = "month"
#' )
#' cut_seasons(dates)
cut_seasons <- function(
  datetime,
  definition = "meteorological",
  hemisphere = "northern",
  labels = NULL
) {
  # Validate inputs
  if (!inherits(datetime, "POSIXct")) {
    stop("datetime must be a POSIXct object")
  }

  if (
    !definition %in%
      c(
        "meteorological",
        "astronomical",
        "djf",
        "jfm",
        "amj",
        "jas",
        "ond",
        "fma"
      )
  ) {
    stop(
      "definition must be one of: 'meteorological', 'astronomical',
         'djf', 'jfm', 'amj', 'jas', 'ond', 'fma'"
    )
  }

  if (!hemisphere %in% c("northern", "southern")) {
    stop("hemisphere must be 'northern' or 'southern'")
  }

  # Extract month and day-of-year
  month <- as.numeric(format(datetime, "%m"))
  yday <- as.numeric(format(datetime, "%j"))
  year <- as.numeric(format(datetime, "%Y"))

  # Define season boundaries based on definition
  if (definition == "meteorological") {
    # Dec-Jan-Feb = Winter, Mar-Apr-May = Spring, etc.
    season_month <- c(12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
    season_labels <- c(
      "Winter",
      "Winter",
      "Winter",
      "Spring",
      "Spring",
      "Spring",
      "Summer",
      "Summer",
      "Summer",
      "Autumn",
      "Autumn",
      "Autumn"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "astronomical") {
    # Based on equinoxes and solstices (approximate dates)
    # Northern hemisphere: Dec 21 - Mar 20 = Winter, etc.
    seasons <- rep(NA, length(datetime))

    for (i in seq_along(datetime)) {
      y <- year[i]
      d <- yday[i]

      # Approximate astronomical dates (can vary by 1-2 days)
      spring_equinox <- as.numeric(format(
        as.Date(paste(y, "03", "20", sep = "-")),
        "%j"
      ))
      summer_solstice <- as.numeric(format(
        as.Date(paste(y, "06", "21", sep = "-")),
        "%j"
      ))
      autumn_equinox <- as.numeric(format(
        as.Date(paste(y, "09", "22", sep = "-")),
        "%j"
      ))
      winter_solstice <- as.numeric(format(
        as.Date(paste(y, "12", "21", sep = "-")),
        "%j"
      ))

      if (d >= winter_solstice || d < spring_equinox) {
        seasons[i] <- "Winter"
      } else if (d >= spring_equinox && d < summer_solstice) {
        seasons[i] <- "Spring"
      } else if (d >= summer_solstice && d < autumn_equinox) {
        seasons[i] <- "Summer"
      } else {
        seasons[i] <- "Autumn"
      }
    }
  } else if (definition == "djf") {
    # Dec-Jan-Feb, Mar-Apr-May, Jun-Jul-Aug, Sep-Oct-Nov
    season_month <- c(12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
    season_labels <- c(
      "DJF",
      "DJF",
      "DJF",
      "MAM",
      "MAM",
      "MAM",
      "JJA",
      "JJA",
      "JJA",
      "SON",
      "SON",
      "SON"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "jfm") {
    # Jan-Feb-Mar, Apr-May-Jun, Jul-Aug-Sep, Oct-Nov-Dec
    season_month <- 1:12
    season_labels <- c(
      "JFM",
      "JFM",
      "JFM",
      "AMJ",
      "AMJ",
      "AMJ",
      "JAS",
      "JAS",
      "JAS",
      "OND",
      "OND",
      "OND"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "amj") {
    # Apr-May-Jun, Jul-Aug-Sep, Oct-Nov-Dec, Jan-Feb-Mar
    season_month <- 1:12
    season_labels <- c(
      "JFM",
      "JFM",
      "JFM",
      "AMJ",
      "AMJ",
      "AMJ",
      "JAS",
      "JAS",
      "JAS",
      "OND",
      "OND",
      "OND"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "jas") {
    # Jul-Aug-Sep, Oct-Nov-Dec, Jan-Feb-Mar, Apr-May-Jun
    season_month <- 1:12
    season_labels <- c(
      "JFM",
      "JFM",
      "JFM",
      "AMJ",
      "AMJ",
      "AMJ",
      "JAS",
      "JAS",
      "JAS",
      "OND",
      "OND",
      "OND"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "ond") {
    # Oct-Nov-Dec, Jan-Feb-Mar, Apr-May-Jun, Jul-Aug-Sep
    season_month <- 1:12
    season_labels <- c(
      "JFM",
      "JFM",
      "JFM",
      "AMJ",
      "AMJ",
      "AMJ",
      "JAS",
      "JAS",
      "JAS",
      "OND",
      "OND",
      "OND"
    )
    seasons <- season_labels[match(month, season_month)]
  } else if (definition == "fma") {
    # Feb-Mar-Apr, May-Jun-Jul, Aug-Sep-Oct, Nov-Dec-Jan
    season_month <- 1:12
    season_labels <- c(
      "NDJ",
      "FMA",
      "FMA",
      "FMA",
      "MJJ",
      "MJJ",
      "MJJ",
      "ASO",
      "ASO",
      "ASO",
      "NDJ",
      "NDJ"
    )
    seasons <- season_labels[match(month, season_month)]
  }

  # Flip seasons for southern hemisphere
  if (
    hemisphere == "southern" &&
      definition %in% c("meteorological", "astronomical")
  ) {
    season_mapping <- c(
      "Spring" = "Autumn",
      "Summer" = "Winter",
      "Autumn" = "Spring",
      "Winter" = "Summer"
    )
    seasons <- season_mapping[seasons]
  }

  # Define level order BEFORE applying custom labels
  if (definition == "meteorological" || definition == "astronomical") {
    if (hemisphere == "northern") {
      level_order <- c("Spring", "Summer", "Autumn", "Winter")
    } else {
      level_order <- c("Autumn", "Winter", "Spring", "Summer")
    }
  } else if (definition == "djf") {
    level_order <- c("DJF", "MAM", "JJA", "SON")
  } else if (definition == "jfm") {
    level_order <- c("JFM", "AMJ", "JAS", "OND")
  } else if (definition == "fma") {
    level_order <- c("FMA", "MJJ", "ASO", "NDJ")
  } else {
    level_order <- c("JFM", "AMJ", "JAS", "OND") # Default for amj, jas, ond
  }

  # Apply custom labels if provided
  if (!is.null(labels)) {
    unique_seasons <- unique(seasons[!is.na(seasons)])
    if (length(labels) != length(unique_seasons)) {
      stop(paste(
        "Number of labels (",
        length(labels),
        ") must match number of unique seasons (",
        length(unique_seasons),
        ")",
        sep = ""
      ))
    }
    # Create mapping from old to new labels (based on level_order)
    label_mapping <- stats::setNames(labels, level_order)
    seasons <- label_mapping[seasons]
    # Update level order to use custom labels
    level_order <- labels
  }

  result <- factor(seasons, levels = level_order)
  result
}

#' Compute Fractional Day of Year from POSIXct
#'
#' Calculates the fractional day of year from a POSIXct datetime object.
#' The fractional day is zero-indexed, starting at 0 for January 1st at
#' midnight and ending at approximately 365.958 for December 31st at 23:00
#' in a non-leap year (or 366.958 in a leap year).
#'
#' @param datetime A POSIXct object or vector of POSIXct objects representing
#'   date-time values. Must have a timezone attribute.
#'
#' @return A numeric vector of the same length as \code{datetime}, containing
#'   fractional day of year values. January 1st at midnight corresponds to 0,
#'   and each hour adds approximately 0.04167 (1/24) to the value.
#'
#' @details
#' The function computes the time difference in hours between the input
#' datetime and midnight on January 1st of the same year, then divides by 24
#' to obtain fractional days. The calculation accounts for leap years
#' automatically.
#'
#' @examples
#' dates <- seq(
#'   from = as.POSIXct("2024-01-01 00:00:00", tz = "UTC"),
#'   to = as.POSIXct("2024-01-02 00:00:00", tz = "UTC"),
#'   by = "6 hours"
#' )
#' fractional_day_of_year(dates) # Returns 0.00, 0.25, 0.50, 0.75, 1.00
#'
#' # End of year
#' dt_end <- as.POSIXct("2024-12-31 23:00:00", tz = "UTC")
#' fractional_day_of_year(dt_end) # Returns ~365.958
#'
#' @export
fractional_day_of_year <- function(datetime) {
  # Input validation
  if (!inherits(datetime, "POSIXct")) {
    stop("datetime must be a POSIXct object")
  }

  # Extract timezone (defaults to UTC if missing)
  tz <- attr(datetime, "tzone")
  if (is.null(tz) || tz == "") {
    tz <- "UTC"
  }

  # Get the year for each datetime
  year <- as.POSIXlt(datetime, tz = tz)$year + 1900

  # Initialize output
  fractional_day <- rep(NA_real_, length(datetime))

  # Process only non-NA entries
  valid <- !is.na(datetime)
  if (any(valid)) {
    # Start of the year in same timezone
    start_of_year <- as.POSIXct(
      paste0(year[valid], "-01-01 00:00:00"),
      tz = tz
    )

    # Compute time difference in hours
    hours_diff <- as.numeric(difftime(
      datetime[valid],
      start_of_year,
      units = "hours"
    ))

    # Convert to fractional days
    fractional_day[valid] <- hours_diff / 24
  }

  fractional_day
}
