Description Usage Arguments Details Value See Also Examples
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 frequencies and directions.
1 2 3  kzpdr.eval(rec = ls(1), t.D = 2, t.F = 0.01, ...)
kzpdr.estimate(rec = ls(1), ...)

rec 
Data list from the outputs of function 
t.D 
Tolerance of direction in degree. Default is 2. 
t.F 
Tolerance of frequency. Default value is 0.01. 
... 
Other arguments.

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 summarize 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 clusteringclosure 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.
Both kzpdr.eval
and kzpdr.estimate
will return suggested
wave frequency and direction values. The data frame of detailed estimation
for each direction is also included in their returned data list. Beside these,
kzpdr.estimate
can generate 3D or 2D plots for the supports of each
suggested wave on directionfrequency parameter plane.
kzpdr
, kzpdr.valid
, kzp2
kzpdr.tol
, kzpdr.spikes
1 2 3 4 5 6 7 8 9 10 11  # load presaved data to save runningtime
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}

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