Description Usage Arguments Details Value References See Also Examples

Handles the transformation itself, boundary conditions and bias correction and returns the unbiased local wavelet spectrum at each grid-point.

1 2 3 | ```
fld2dt(fld, Nx = NULL, Ny = NULL, J = NULL, correct = TRUE,
rsm = 0, verbose = FALSE, boundaries = "pad",
fb1 = near_sym_b_bp, fb2 = qshift_b_bp)
``` |

`fld` |
a real matrix |

`Nx` |
size to which the field is padded in x-direction |

`Ny` |
size to which the field is padded in y-direction |

`J` |
number of levels for the decomposition |

`correct` |
logical, whether or not to apply the bias correction |

`rsm` |
number of pixels to be linearly smoothed along each edge before applying the boundary conditions (see |

`verbose` |
whether or not you want the transform to talk to you |

`boundaries` |
how to handle the boundary conditions, either "pad", "mirror" or "periodic" |

`fb1` |
filter bank for level 1 |

`fb2` |
filter bank for all further levels |

The input is blown up to `Nx x Ny`

and transformed by `dtcwt(..., dec=FALSE)`

. Then the original domain is cut out, the coefficients are squared and the bias is corrected (for details on the bias, see `A`

).

an array of size `J x nx x ny x 6`

where `dim(fld)=c(nx,ny)`

Nelson, J. D. B., A. J. Gibberd, C. Nafornita, and N. Kingsbury (2018) <doi:10.1007/s11222-017-9784-0>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
oldpar <- par( no.readonly=TRUE )
dt <- fld2dt( blossom )
par( mfrow=c(2,2), mar=rep(2,4) )
for( j in 1:4 ){
image( blossom, col=gray.colors(128, 0,1), xaxt="n", yaxt="n" )
for(d in 1:6) contour( dt[j,,,d], levels=quantile(dt[,,,], .995),
col=d+1, add=TRUE, lwd=2, drawlabels=FALSE )
title( main=paste0("j=",j) )
}
x0 <- seq( .1,.5,,6 )
y0 <- rep( 0.01,6 )
a <- .075
phi <- seq( 15,,30,6 )*pi/180
x1 <- x0 + a*cos( phi )
y1 <- y0 + a*sin( phi )
rect( min(x0,x1)-.05, min(y0,y1)-.05,
max(x0,x1)+.05, max(y0,y1), col="black", border=NA )
arrows( x0, y0, x1, y1, length=.05, col=2:7, lwd=2, code=3 )
par( oldpar )
``` |

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