kzfs (version 1.0.1.5)

kzpdr.eval: Evaluate Directional Spectrum Data for Wave Frequencies and Directions

Description

Functions in this group are designed to estimate wave parameters based on directional periodogram records.

kzpdr samples the data of wave field, and outputs the average pattern of periodogram for series in a given direction. A collection of these pattern records will be sent to kzpdr.eval or kzpdr.estimate to estimate the wave frequecies and directions.

Usage

kzpdr.eval(rec = ls(1), t.D = 2, t.F = 0.01, ...)
kzpdr.estimate(rec = ls(1), ...)

Arguments

rec

Data list from the outputs of function kzpdr. It includes the data frame for the marked frequency values and corresponding directions. Defaults is searching for available records in the environment.

t.D

Tolerance of direction in degree. Default is 2.

t.F

Tolerance of frequency. Default value is 0.01.

...

Other arguments.

• D3 Logic. Deafult is FALSE. If TRUE, output 3D perspective plot; otherwise, 2D plot on frequency-direction surface.

• scale A two element vector for grid on frequency-direction plant. The first element is for frequency. The second is for degree of direction. Default is c(0.005,1).

• ... ...

Value

Both kzpdr.eval and kzpdr.estimate will return suggested wave frequency and direction values. The data frame of detailed estimation for each direction are also include in their returned data list. Beside these, kzpdr.estimate can generate 3D or 2D plots for the supports of each suggested wave on direction-frequency parameter plane.

Details

The average periodograms for a few pairs of orthogonal spatial directions can be used to identify frequencies and directions of waves.

First, function kzpdr samples the spatial data and generates periodograms in orthogonal direction pairs, and the frequencies of spikes for each directional periodogram are identified and recorded as the output.

Then, kzpdr.spikes can be used to summarized the outputs of kzpdr. Function kzpdr.eval or kzpdr.estimate all can be used to estimate the wave parameters (frequencies and directions). kzpdr.estimate is based on clustering-closure and the tolerances could be decided automatically. It also provides visualization of the results, thus this function is more convenient to use.

Usually, if noise level is low, periodograms of a few direction pairs may provide satisfied results. But when the noise is high, you may need to intensively sample on different directions over the spatial data array with kzpdr. Generally speaking, when the number of samples increases, the estimation will become more stable and reliable.

See Also

kzpdr, kzpdr.valid, kzp2 kzpdr.tol, kzpdr.spikes

Examples

# load pre-saved data to save running-time
data(kzpdr.demo);  

# estimate the wave parameters
kzpdr.eval(kzpdr.demo, t.D = 3, t.F = 0.01)

# estimation & visualization
kzpdr.estimate(kzpdr.demo)

# For validation of the estimation, see \code{kzpdr.valid}
# For reconstruction of the signals, see \code{kzrc}