Package 'resourcecode' reference manual

Title:	Access to the 'RESOURCECODE' Hindcast Database
Description:	Utility functions to download data from the 'RESOURCECODE' hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.
Authors:	Nicolas Raillard [aut, cre] (ORCID: <https://orcid.org/0000-0003-3385-5104>)
Maintainer:	Nicolas Raillard <[email protected]>
License:	GPL (>= 3)
Version:	0.5.4.9000
Built:	2026-06-06 06:18:16 UTC
Source:	https://github.com/Resourcecode-project/r-resourcecode

Value Matching

Description

Value Matching

Usage

x %nin% table
x %nin% table

Arguments

x

value to search

table

table of values

Value

the opposite of x %in% table

Examples

1:10 %in% c(1, 3, 5, 9)
1:10 %nin% c(1, 3, 5, 9)
1:10 %in% c(1, 3, 5, 9)
1:10 %nin% c(1, 3, 5, 9)

Find the closest point of the FIELD grid to the specified position

Description

Find the closest point of the FIELD grid to the specified position

Usage

closest_point_field(x, lat = NULL, closest = 1L, ...)
closest_point_field(x, lat = NULL, closest = 1L, ...)

Arguments

x

vector of coordinates in the form longitude/latitude data frame

lat

alternatively, x and lat can be vector of the same length

closest

an integer to specify the number of point to output.

...

currently unused

Value

a list with two components: the closest point(s) of the grid and the distance (s).

Examples

semrev_west <- closest_point_field(c(-2.786, 47.239))
semrev_west
semrev_west <- closest_point_field(c(-2.786, 47.239))
semrev_west

Find the closest point of the SPEC grid to the specified position

Description

Find the closest point of the SPEC grid to the specified position

Usage

closest_point_spec(x, lat = NULL, closest = 1L, ...)
closest_point_spec(x, lat = NULL, closest = 1L, ...)

Arguments

x

vector of coordinates in the form longitude/latitude data frame

lat

alternatively, x and lat can be vector of the same length

closest

an integer to specify the number of point to output.

...

currently unused

Value

a list with two components: the closest point(s) of the grid and the distance (s).

Examples

semrev_west <- closest_point_spec(c(-2.786, 47.239))
semrev_west
semrev_west <- closest_point_spec(c(-2.786, 47.239))
semrev_west

Compute the orbital speed at a given depth from the wave elevation 1D spectra

Description

Compute the orbital speed at a given depth from the wave elevation 1D spectra

Usage

compute_orbital_speeds(spec, freq, z = 0, depth = Inf, output_speeds = FALSE)
compute_orbital_speeds(spec, freq, z = 0, depth = Inf, output_speeds = FALSE)

Arguments

spec

1D spectral data: TxM matrix

freq

the M frequencies

z

distance above the floor at which we want the orbital speed (single numeric)

depth

depth time series (vector length T. Recycled if a single value is given)

output_speeds

TRUE if the spectral speed are needed. Otherwise, returns the RMS (default)

Value

depending on spec, a list with the spectral velocities for each component (if output_speeds==FALSE) or a data.frame with a time series of horizontal and vertical components of (spectral) orbital speed.

Examples

# Compute orbital speed for varying Hs
S <- t(sapply(1:10, function(h) {
  jonswap(h)$spec
}))
orb_speeds <- compute_orbital_speeds(S, resourcecodedata::rscd_freq, depth = 100, z = 10)
plot(1:10, orb_speeds[, 1],
  type = "l",
  ylim = range(orb_speeds),
  xlab = "Hs (m)",
  ylab = "Orbital speed RMS (m/s)"
)
lines(1:10, orb_speeds[, 2], type = "l", col = "red")
# Compute orbital speed for varying Hs
S <- t(sapply(1:10, function(h) {
  jonswap(h)$spec
}))
orb_speeds <- compute_orbital_speeds(S, resourcecodedata::rscd_freq, depth = 100, z = 10)
plot(1:10, orb_speeds[, 1],
  type = "l",
  ylim = range(orb_speeds),
  xlab = "Hs (m)",
  ylab = "Orbital speed RMS (m/s)"
)
lines(1:10, orb_speeds[, 2], type = "l", col = "red")

Compute sea_state parameter from wave spectrum

Description

Compute sea_state parameter from wave spectrum

Usage

compute_sea_state_1d_spectrum(spec, ...)
compute_sea_state_1d_spectrum(spec, ...)

Arguments

spec

1D spectrum data, e.g. from get_1Dspectrum

...

currently unused

Value

a tibble with the sea-state parameters computed from the time series of 1D spectrum

Examples


rscd_params <- get_parameters(
  node = "134865",
  start = "1994-01-01",
  end = "1994-01-31 23:00:00",
  parameters = c("hs", "tp")
)
spec <- resourcecodedata::rscd_1d_spectra
param_calc <- compute_sea_state_1d_spectrum(spec)
oldpar <- par(mfcol = c(2, 1))
plot(param_calc$time, param_calc$hs, type = "l", xlab = "Time", ylab = "Hs (m)")
lines(rscd_params$time, rscd_params$hs, col = "red")
plot(param_calc$time, param_calc$tp, type = "l", xlab = "Time", ylab = "Tp (s)")
lines(rscd_params$time, rscd_params$tp, col = "red")
par(oldpar)

rscd_params <- get_parameters(
  node = "134865",
  start = "1994-01-01",
  end = "1994-01-31 23:00:00",
  parameters = c("hs", "tp")
)
spec <- resourcecodedata::rscd_1d_spectra
param_calc <- compute_sea_state_1d_spectrum(spec)
oldpar <- par(mfcol = c(2, 1))
plot(param_calc$time, param_calc$hs, type = "l", xlab = "Time", ylab = "Hs (m)")
lines(rscd_params$time, rscd_params$hs, col = "red")
plot(param_calc$time, param_calc$tp, type = "l", xlab = "Time", ylab = "Tp (s)")
lines(rscd_params$time, rscd_params$tp, col = "red")
par(oldpar)

Compute sea_state parameter from wave directional spectrum

Description

Compute sea_state parameter from wave directional spectrum

Usage

compute_sea_state_2d_spectrum(spec, ...)
compute_sea_state_2d_spectrum(spec, ...)

Arguments

spec

2D spectrum data, e.g. from get_2Dspectrum

...

currently unused

Value

a tibble with the sea-state parameters computed from the time series of 2D spectrum

Examples


rscd_params <- get_parameters(
  node = "134865",
  start = "1994-01-01",
  end = "1994-01-31 23:00:00",
  parameters = c("hs", "tp")
)
spec <- resourcecodedata::rscd_2d_spectra
param_calc <- compute_sea_state_2d_spectrum(spec)
oldpar <- par(mfcol = c(2, 1))
plot(param_calc$time, param_calc$hs, type = "l", xlab = "Time", ylab = "Hs (m)")
lines(rscd_params$time, rscd_params$hs, col = "red")
plot(param_calc$time, param_calc$tp, type = "l", xlab = "Time", ylab = "Tp (s)")
lines(rscd_params$time, rscd_params$tp, col = "red")
par(oldpar)

rscd_params <- get_parameters(
  node = "134865",
  start = "1994-01-01",
  end = "1994-01-31 23:00:00",
  parameters = c("hs", "tp")
)
spec <- resourcecodedata::rscd_2d_spectra
param_calc <- compute_sea_state_2d_spectrum(spec)
oldpar <- par(mfcol = c(2, 1))
plot(param_calc$time, param_calc$hs, type = "l", xlab = "Time", ylab = "Hs (m)")
lines(rscd_params$time, rscd_params$hs, col = "red")
plot(param_calc$time, param_calc$tp, type = "l", xlab = "Time", ylab = "Tp (s)")
lines(rscd_params$time, rscd_params$tp, col = "red")
par(oldpar)

Converts a 2D spectrum time series to a 1D spectrum

Description

Converts a 2D spectrum time series to a 1D spectrum

Usage

convert_spectrum_2d1d(spec, ...)
convert_spectrum_2d1d(spec, ...)

Arguments

spec

a structure with the needed fields, as an output from 'get_2Dspectrum' for example

...

unused yet

Value

a structure comparable to 'get_1Dspectrum'.

Examples

spec <- resourcecodedata::rscd_2d_spectra
spec1D_RSCD <- resourcecodedata::rscd_1d_spectra
spec1D <- convert_spectrum_2d1d(spec)
# Check the differences, should be low
max(abs(spec1D_RSCD$ef - spec1D$ef))

# Plot the different spectrum
plot(spec1D$freq, spec1D$ef[, 1], type = "l", log = "xy")
lines(spec1D_RSCD$freq, spec1D_RSCD$ef[, 1], col = "red")

# Images
lims <- c(0, 360)
r <- as.POSIXct(round(range(spec1D$forcings$time), "hours"))
oldpar <- par(mfcol = c(2, 1))
image(spec1D$forcings$time, spec1D$freq, t(spec1D$th1m),
  zlim = lims,
  xlab = "Time",
  ylab = "Freq (Hz)",
  xaxt = "n",
  main = "Directionnal spreading"
)
axis.POSIXct(1, spec1D$forcings$time,
  at = seq(r[1], r[2], by = "week"),
  format = "%Y-%m-%d",
  las = 2
)
image(spec1D_RSCD$forcings$time, spec1D_RSCD$freq, t(spec1D_RSCD$th1m),
  zlim = lims,
  xlab = "Time",
  ylab = "Freq (Hz)",
  xaxt = "n"
)
axis.POSIXct(1, spec1D$forcings$time,
  at = seq(r[1], r[2], by = "week"),
  format = "%Y-%m-%d",
  las = 2
)
par(oldpar)
spec <- resourcecodedata::rscd_2d_spectra
spec1D_RSCD <- resourcecodedata::rscd_1d_spectra
spec1D <- convert_spectrum_2d1d(spec)
# Check the differences, should be low
max(abs(spec1D_RSCD$ef - spec1D$ef))

# Plot the different spectrum
plot(spec1D$freq, spec1D$ef[, 1], type = "l", log = "xy")
lines(spec1D_RSCD$freq, spec1D_RSCD$ef[, 1], col = "red")

# Images
lims <- c(0, 360)
r <- as.POSIXct(round(range(spec1D$forcings$time), "hours"))
oldpar <- par(mfcol = c(2, 1))
image(spec1D$forcings$time, spec1D$freq, t(spec1D$th1m),
  zlim = lims,
  xlab = "Time",
  ylab = "Freq (Hz)",
  xaxt = "n",
  main = "Directionnal spreading"
)
axis.POSIXct(1, spec1D$forcings$time,
  at = seq(r[1], r[2], by = "week"),
  format = "%Y-%m-%d",
  las = 2
)
image(spec1D_RSCD$forcings$time, spec1D_RSCD$freq, t(spec1D_RSCD$th1m),
  zlim = lims,
  xlab = "Time",
  ylab = "Freq (Hz)",
  xaxt = "n"
)
axis.POSIXct(1, spec1D$forcings$time,
  at = seq(r[1], r[2], by = "week"),
  format = "%Y-%m-%d",
  las = 2
)
par(oldpar)

Directional binning

Description

Cuts direction vector into directional bins

Usage

cut_directions(directions, n_bins = 8, labels = NULL)
cut_directions(directions, n_bins = 8, labels = NULL)

Arguments

directions

vector of directions to be binned, in degree, 0° being the North.

n_bins

number of bins, default: 8 sectors.

labels

optional character vector giving the sectors names.

Value

a factor vector the same size as directions with the values binned into sectors.

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)
# 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)

Get season from date time object

Description

Get season from date time object

Usage

cut_seasons(
  datetime,
  definition = "meteorological",
  hemisphere = "northern",
  labels = NULL
)
cut_seasons(
  datetime,
  definition = "meteorological",
  hemisphere = "northern",
  labels = NULL
)

Arguments

datetime

a POSIXct vector from with the season is constructed

definition

the definition used to compute the season. See details section.

hemisphere

in the Southern hemisphere, seasons are reversed compared to the Northern one.

labels

optional, a character vector of length fours with the seasons' names.

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

Value

a Factor vector with 4 levels depending on the definitions (and labels if provided)

Examples

dates <- seq(
  from = as.POSIXct("2023-01-15"),
  to = as.POSIXct("2023-12-15"),
  by = "month"
)
cut_seasons(dates)
dates <- seq(
  from = as.POSIXct("2023-01-15"),
  to = as.POSIXct("2023-12-15"),
  by = "month"
)
cut_seasons(dates)

Compute the dispersion relation of waves Find k s.t. (2.pi.f)^2 = g.k.tanh(k.d)

Description

Compute the dispersion relation of waves Find k s.t. (2.pi.f)^2 = g.k.tanh(k.d)

Usage

dispersion(frequencies, depth, iter_max = 200, tol = 1e-06)
dispersion(frequencies, depth, iter_max = 200, tol = 1e-06)

Arguments

frequencies

frequency vector

depth

depth (m)

iter_max

maximum number of iterations in the solver

tol

tolerance for termination.

Value

the wave numbers (same size as frequencies)

Examples

freq <- seq(from = 0, to = 1, length.out = 100)
k1 <- dispersion(freq, depth = 1)
k10 <- dispersion(freq, depth = 10)
kInf <- dispersion(freq, depth = Inf)
plot(freq, k1, type = "l")
lines(freq, k10, col = "red")
lines(freq, kInf, col = "green")
freq <- seq(from = 0, to = 1, length.out = 100)
k1 <- dispersion(freq, depth = 1)
k10 <- dispersion(freq, depth = 10)
kInf <- dispersion(freq, depth = Inf)
plot(freq, k1, type = "l")
lines(freq, k10, col = "red")
lines(freq, kInf, col = "green")

Fast implementation of pracma::trapz from the Armadillo C++ library

Description

Compute the area of a multivariate function with (matrix) values Y at the points x.

Usage

fastTrapz(x, Y, dim = 1L)
fastTrapz(x, Y, dim = 1L)

Arguments

x

x-coordinates of points on the x-axis (vector)

Y

y-coordinates of function values (matrix)

dim

an integer giving the subscripts which the function will be applied over. 1 indicates rows, 2 indicates columns

Value

a vector with one dimension less than Y

Examples

x = 1:10
Y = sin(pi/10*matrix(1:10,ncol=10,nrow=10))
fastTrapz(x*pi/10,Y,2)
x = 1:10
Y = sin(pi/10*matrix(1:10,ncol=10,nrow=10))
fastTrapz(x*pi/10,Y,2)

Compute Fractional Day of Year from POSIXct

Description

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).

Usage

fractional_day_of_year(datetime)
fractional_day_of_year(datetime)

Arguments

datetime

A POSIXct object or vector of POSIXct objects representing date-time values. Must have a timezone attribute.

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.

Value

A numeric vector of the same length as 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.

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

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

Download the 1D spectrum data from IFREMER ftp

Description

Download the 1D spectrum data from IFREMER ftp

Usage

get_1d_spectrum(point, start = "1994-01-01", end = "1994-02-28")
get_1d_spectrum(point, start = "1994-01-01", end = "1994-02-28")

Arguments

point

the location name (string) requested. Alternatively, the node number. The consistency is checked internally.

start

the starting date (as a string). The consistency is checked internally.

end

the ending date as a string

Value

A list with 12 elements:

longitude

Longitude

latitude

Latitude

frequency1

Lower frequency

frequency2

Upper frequency

ef

Surface elevation variance spectral density

th1m

Mean direction from first spectral moment

th2m

Mean direction from second spectral moment

sth1m

Mean directional spreading from first spectral moment

sth2m

Mean directional spreading from second spectral moment

freq

Central frequency

forcings

A data.frame with 14 variables:

time: Time
dpt: Depth, positive downward
wnd: Wind intensity, at 10m above sea level
wnddir: Wind direction, comes from
cur: Current intensity, at the surface
curdir: Current direction, going to
hs: Significant wave height
fp: Peak wave frequency
f02: Mean wave frequency
f0m1: Mean wave frequency at spectral moment minus one
th1p: Mean wave direction at spectral peak
sth1p: Directional spreading at spectral peak
dir: Mean wave direction
spr: Mean directional spreading

station

Station name

Examples

spec1D <- get_1d_spectrum("SEMREVO", start = "1994-01-01", end = "1994-02-28")
if(!is.null(spec1D)){
  r <- as.POSIXct(round(range(spec1D$forcings$time), "month"))
  image(spec1D$forcings$time, spec1D$freq, t(spec1D$ef),
    xaxt = "n", xlab = "Time",
    ylab = "Frequency (Hz)"
  )
  axis.POSIXct(1, spec1D$forcings$time,
    at = seq(r[1], r[2], by = "week"),
    format = "%Y-%m-%d", las = 2
  )
}
spec1D <- get_1d_spectrum("SEMREVO", start = "1994-01-01", end = "1994-02-28")
if(!is.null(spec1D)){
  r <- as.POSIXct(round(range(spec1D$forcings$time), "month"))
  image(spec1D$forcings$time, spec1D$freq, t(spec1D$ef),
    xaxt = "n", xlab = "Time",
    ylab = "Frequency (Hz)"
  )
  axis.POSIXct(1, spec1D$forcings$time,
    at = seq(r[1], r[2], by = "week"),
    format = "%Y-%m-%d", las = 2
  )
}

Download the 2D spectrum data from IFREMER ftp

Description

Download the 2D spectrum data from IFREMER ftp

Usage

get_2d_spectrum(point, start = "1994-01-01", end = "1994-02-28")
get_2d_spectrum(point, start = "1994-01-01", end = "1994-02-28")

Arguments

point

the location name (string) requested. Alternatively, the node number. The consistency is checked internally.

start

the starting date (as a string). The consistency is checked internally.

end

the ending date as a string

Value

A list with 9 elements:

longitude

Longitude

latitude

Latitude

frequency1

Lower frequency

frequency2

Upper frequency

ef

Surface elevation variance spectral density

th1m

Mean direction from first spectral moment

th2m

Mean direction from second spectral moment

sth1m

Mean directional spreading from first spectral moment

sth2m

Mean directional spreading from second spectral moment

freq

Central frequency

dir

Directionnal bins

forcings

A data.frame with 6 variables:

time: Time
dpt: Depth, positive downward
wnd: Wind intensity, at 10m above sea level
wnddir: Wind direction, comes from
cur: Current intensity, at the surface
curdir: Current direction, going to

station

Station name

Examples

spec2D <- get_2d_spectrum("SEMREVO", start = "1994-01-01", end = "1994-02-28")
if(!is.null(spec2D)){
  image(spec2D$dir, spec2D$freq, spec2D$efth[, , 1],
    xlab = "Direction (°)",
    ylab = "Frequency (Hz"
  )
}
spec2D <- get_2d_spectrum("SEMREVO", start = "1994-01-01", end = "1994-02-28")
if(!is.null(spec2D)){
  image(spec2D$dir, spec2D$freq, spec2D$efth[, , 1],
    xlab = "Direction (°)",
    ylab = "Frequency (Hz"
  )
}

Download time series of sea-state parameters from RESOURCECODE database

Description

If the remote resource is unavailable or returns an error, the function returns NULL and emits an informative message.

Usage

get_parameters(
  parameters = "hs",
  node = 42,
  start = as.POSIXct("1994-01-01 00:00:00", tz = "UTC"),
  end = as.POSIXct("1994-12-31 23:00:00", tz = "UTC")
)
get_parameters(
  parameters = "hs",
  node = 42,
  start = as.POSIXct("1994-01-01 00:00:00", tz = "UTC"),
  end = as.POSIXct("1994-12-31 23:00:00", tz = "UTC")
)

Arguments

parameters

character vector of sea-state parameters

node

single integer with the node to get

start

starting date (as integer, character or posixct)

end

ending date (as integer, character or posixct)

Value

a tibble with N-rows and length(parameters) columns.

Examples

rscd_data <- get_parameters(parameters = c("hs", "tp"), node = 42)
if(!is.null(rscd_data)) plot(rscd_data$time, rscd_data$hs, type = "l")
rscd_data <- get_parameters(parameters = c("hs", "tp"), node = 42)
if(!is.null(rscd_data)) plot(rscd_data$time, rscd_data$hs, type = "l")

JONWSAP spectrum

Description

Creates a JONWSAP density spectrum (one-sided), defined by its integral parameters.

Usage

jonswap(
  hs = 5,
  tp = 15,
  fmax = resourcecodedata::rscd_freq,
  df = NULL,
  gam = 3.3
)
jonswap(
  hs = 5,
  tp = 15,
  fmax = resourcecodedata::rscd_freq,
  df = NULL,
  gam = 3.3
)

Arguments

hs

Hs (default: 5m)

tp

Period (default: 10s)

fmax

higher frequency of the spectrum or vector of frequencies (default to resourcecode frequency vector)

df

frequency step (unused if fmax=vector of frequencies)

gam

peak enhancement factor (default: 3.3)

Details

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.

Value

Density spectrum with corresponding parameters

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)
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)

Mean Direction

Description

Function for computing the (weighted) arithmetic mean of directional data in meteorological convention.

Usage

mean_direction(directions, weights = NULL)
mean_direction(directions, weights = NULL)

Arguments

directions

numeric vector of directions, in degree, 0° being the North

weights

numeric vector, usually wind speed of wave height.

Value

The (weighted) mean of the values in directions is computed.

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")
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")
# 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")
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")

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.

Usage

metconv2zmcomp(speed, direction, names = c("uwnd", "vwnd"))
metconv2zmcomp(speed, direction, names = c("uwnd", "vwnd"))

Arguments

speed

Numeric vector of wind speeds.

direction

Numeric vector of wind directions in degrees (0° = from north, 90° = from east, 180° = from south, 270° = from west).

names

(optional) ames to construct the resulting data.frame.

Value

A data.frame with two columns:

u: Zonal wind component (m/s), positive eastward.
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)

# 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)

Plot a wave density 1D spectrum at a given time

Description

Plot a wave density 1D spectrum at a given time

Usage

plot_1d_specta(spec, time = 1L, print_sea_state = TRUE, ...)
plot_1d_specta(spec, time = 1L, print_sea_state = TRUE, ...)

Arguments

spec

the spectral data, as an output from get_2Dspectrum

time

the time to plot. Either an integer or the date.

print_sea_state

should the sea_states parameters being plot ? Default to TRUE.

...

currently unused

Value

a ggplot object

Examples

plot_1d_specta(resourcecodedata::rscd_1d_spectra, 1)
plot_1d_specta(resourcecodedata::rscd_1d_spectra, 1)

Plot a wave density 2D spectrum

Description

Plot a wave density 2D spectrum

Usage

plot_2d_specta(
  spec,
  time = 1L,
  normalize = TRUE,
  trim = 0.01,
  cut_off = 0.4,
  ...
)
plot_2d_specta(
  spec,
  time = 1L,
  normalize = TRUE,
  trim = 0.01,
  cut_off = 0.4,
  ...
)

Arguments

spec

the spectral data, as an output from get_2Dspectrum

time

the time to plot. Either an integer or the date.

normalize

Should the spectrum be normalized to have maximum 1 before plotting

trim

removes the values of the spectral density lower than this value

cut_off

cut-off frequency above which the spectrum is not plotted

...

currently unused

Value

a ggplot object

Examples

plot_2d_specta(resourcecodedata::rscd_2d_spectra, 1)
plot_2d_specta(resourcecodedata::rscd_2d_spectra, 1)

Resourcecode data extract

Description

An extract of the Resourcecode Hindcast database, at node 123456, spanning from 1994-01-01 00:00:00 to 1999-12-31 23:00:00.

Usage

rscd_data_example
rscd_data_example

Format

A data frame with 87,648 rows and 7 variables:

time: POSIXct. Timestamp in UTC (hourly resolution).
hs: numeric. Significant wave height (m).
tp: numeric. Peak wave period (s).
dp: numeric. Mean wave direction (degrees, coming from true North, clockwise).
uwnd: numeric. Zonal (east–west) component of the 10-m wind (m/s). Positive eastward.
vwnd: numeric. Meridional (north–south) component of the 10-m wind (m/s). Positive northward.
dpt: numeric.Depth (m).

Source

User Manual of the RESOURCECODE database https://archimer.ifremer.fr/doc/00751/86306/

Create a map of the provided variable on the RESOURCECODE field grid

Description

Create a map of the provided variable on the RESOURCECODE field grid

Usage

rscd_mapplot(
  z,
  name = "Depth (m)",
  zlim = NULL,
  palette = "YlOrRd",
  direction = 1,
  transform = "identity"
)
rscd_mapplot(
  z,
  name = "Depth (m)",
  zlim = NULL,
  palette = "YlOrRd",
  direction = 1,
  transform = "identity"
)

Arguments

z

the data ro plot: a vector of the same size as the grid (328,030 rows).

name

name of the variable plored, to be included in the legend.

zlim

limits of the scale. See continuous_scale for details.

palette

If a string, will use that named palette. See scale_colour_brewer for other options.

direction

Sets the order of colours in the scale. See scale_colour_brewer for details.

transform

Transformation to apply to the color scale. See continuous_scale for details.

Value

a ggplot2 object

Examples


rscd_mapplot(resourcecodedata::rscd_field$depth)

rscd_mapplot(resourcecodedata::rscd_field$depth)

Compute Weather Windows

Description

Computes and returns start date of each weather window, implemented in C++ for speed.

Usage

weather_windows(
  valid_periods,
  window_length,
  allow_overlap = TRUE,
  time_step = 3600
)
weather_windows(
  valid_periods,
  window_length,
  allow_overlap = TRUE,
  time_step = 3600
)

Arguments

valid_periods

A data frame with a 'time' column (POSIXct).

window_length

Minimum window duration (hours).

allow_overlap

Logical; If TRUE, the algorithm searches for window, if a window is found, search of next window will start from the end of the previous window. If FALSE, it uses continuous window search: The algorithm searches for window starting from every time step that meets the criteria.

time_step

Expected time step between consecutive timestamps (seconds).

Value

POSIXct vector of detected window start times.

Convert u/v to meteorological wind speed and direction

Description

Converts wind or current zonal and meridional velocity components to magnitude and direction according to meteorological convention.

Usage

zmcomp2metconv(u, v = NULL, names = c("wspd", "wdir"))
zmcomp2metconv(u, v = NULL, names = c("wspd", "wdir"))

Arguments

u

zonal velocity (1D vector) or matrix with zonal and meridional velocity (Nx2 matrix)

v

meridional velocity (1D vector)

names

names to construct the resulting data.frame

Value

a Nx2 data.frame with the norm and direction (meteorological convention)

Examples

u <- matrix(rnorm(200), nrow = 100, ncol = 2)
vdir <- zmcomp2metconv(u)
u <- matrix(rnorm(200), nrow = 100, ncol = 2)
vdir <- zmcomp2metconv(u)

Package 'resourcecode'

Help Index

Value Matching

Description

Usage

Arguments

Value

Examples

Find the closest point of the FIELD grid to the specified position

Description

Usage

Arguments

Value

Examples

Find the closest point of the SPEC grid to the specified position

Description

Usage

Arguments

Value

Examples

Compute the orbital speed at a given depth from the wave elevation 1D spectra

Description

Usage

Arguments

Value

Examples

Compute sea_state parameter from wave spectrum

Description

Usage

Arguments

Value

Examples

Compute sea_state parameter from wave directional spectrum

Description

Usage

Arguments

Value

Examples

Converts a 2D spectrum time series to a 1D spectrum

Description

Usage

Arguments

Value

Examples

Directional binning

Description

Usage

Arguments

Value

Examples

Get season from date time object

Description

Usage

Arguments

Details

Value

Examples

Compute the dispersion relation of waves Find k s.t. (2.pi.f)^2 = g.k.tanh(k.d)

Description

Usage

Arguments

Value

Examples

Fast implementation of pracma::trapz from the Armadillo C++ library

Description

Usage

Arguments

Value

Examples

Compute Fractional Day of Year from POSIXct

Description

Usage

Arguments

Details

Value

Examples

Download the 1D spectrum data from IFREMER ftp

Description

Usage

Arguments