sw.plot: Plot of Observation, Network Design, Field, SW Coefficient,...
In SpherWave: Spherical Wavelets and SW-based Spatially Adaptive Methods

Description Usage Arguments Details Examples

This function performs plotting of observation, network design, field, SW coefficients, decomposition or reconstruction result.

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sw.plot(sw = NULL, z = NULL, latlon = NULL, latlim = NULL, 
    lonlim = NULL, type = "field", nlevel = 256, pch = NULL, 
    cex = NULL, ...)

`sw`	`sbf` or `swd` object
`z`	observations, network design labels or reconstruction
`latlon`	grid points of observation sites in degree
`latlim`	range of latitudes in degree
`lonlim`	range of longitudes in degree
`type`	specifies the type "obs", "network", "field", "swcoeff", "decom" or "recon"
`nlevel`	number of color levels used in legend strip
`pch`	either an integer specifying a symbol or a single character to be used as the default in plotting points
`cex`	a numerical value giving the amount by which plotting text and symbols should be scaled relative to the default
`...`	the usual arguments to the image function or plot function

This function plots spherical wavelet results. Possible types are

‘"obs"’ for observations

‘"network"’ for network design

‘"field"’ for field

‘"swcoeff"’ for spherical wavelet coefficients

‘"decom"’ for decomposition results

‘"recon"’ for reconstruction result.

For ‘sw’, sbf or swd object must be provided. For sbf object, type ‘"obs"’, ‘"network"’, ‘"field"’ are possible whereas all types are possible for swd object. Or specify ‘z’ and ‘latlon’ without ‘sw’.

### Observations of year 1967
data(temperature)
names(temperature)

# Temperatures on 939 weather stations of year 1967    
temp67 <- temperature$obs[temperature$year == 1967] 
# Locations of 939 weather stations    
latlon <- temperature$latlon[temperature$year == 1967, ]

### Draw the temperature data
sw.plot(z=temp67, latlon=latlon, type="obs")

### Network design by BUD
data(netlab)
sw.plot(z=netlab, latlon=latlon, type="network")

### SBF representation of the observations
#eta <- c(0.961,0.923,0.852,0.723,0.506)
#out.pls <- sbf(obs=temp67, latlon=latlon, netlab=netlab, eta=eta,
#    method="pls", grid.size=c(50, 100), lambda=0.89)
# observation
#sw.plot(out.pls, type="obs")
# network design
#sw.plot(out.pls, type="network")
# field 
#sw.plot(out.pls, type="field")  

### Decomposition
#out.dpls <- swd(out.pls)
# observation
#sw.plot(out.dpls, type="obs")
# network design
#sw.plot(out.dpls, type="network")   
# SBF representation of the observations
#sw.plot(out.dpls, type="field")
# sw coefficient
#sw.plot(out.dpls, type="swcoeff")
# decomposition result
#sw.plot(out.dpls, type="decom")

# Thresholding  
#out.univ <- swthresh(out.dpls, policy="universal", by.level=TRUE, 
#    type="hard", nthresh=4)  
#par(oma=c(0,0,3.5,0))
#sw.plot(out.univ, type="decom") 
#mtext("Decomposition & Threshold", side = 3, outer = TRUE, 
#    cex = 1.2, line = 1)

# Reconstruction
#out.rec <- swr(out.univ)
#sw.plot(z=out.rec, type="recon", xlab="", ylab="")