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#########################################################################
# (c) Copyright 1997
# by
# Author: Rene Carmona, Bruno Torresani, Wen L. Hwang, Andrea Wang
# Princeton University
# All right reserved
########################################################################
mrecons <- function(extrema, filtername="Gaussian1", readflag=FALSE)
#****************************************************************
# mrecons
# --------
# Reconstruct from dyadic extrema. The reconstructed signal
# preserves locations and values at extrema. This is Carmona's
# method of extrema reconstruction.
#
# input
# -----
# extrema : the extrema representation
# filtername : filters (Gaussain1 stands filters corresponding to
# Mallat and Zhong's wavelet. And, the Haar stands for the
# filters for Haar basis.
# readflag: if set to TRUE, read kernel from a precomputed file.
#
# output
# ------
# f: signal reconstructed from the wavelet transform extrema
# g: f plus mean of f
# h: f plus coarse scale of f
#****************************************************************
{
pure <- extrema$original
x <- adjust.length(pure)
pure.data <- x$signal
data <- extrema$original
maxresoln <- extrema$maxresoln
np <- extrema$np
if(2^maxresoln > np)
stop("The support of the coarsest scale filter is bigger than signal size")
if((readflag && (maxresoln > 9)) || (np > 4096))
stop("Can not use readflag")
dim(extrema$extrema) <- c(length(extrema$extrema), 1)
f <- 1:np
z <- .C("extrema_reconst",
as.character(filtername),
a = as.double(f),
as.double(extrema$extrema),
as.integer(maxresoln),
as.integer(np),
as.integer(readflag),
PACKAGE="Rwave")
f <- z$a
g <- f + mean(pure.data)
h <- f + extrema$Sf
lim <- range(f, g, h, pure.data)
par(mfrow=c(3,1))
plot.ts(f, ylim=lim, xlab="", ylab="", main="Reconstruction")
plot.ts(g, ylim=lim, xlab="", ylab="", main="Reconstruction + Mean")
plot.ts(h, ylim=lim, xlab="", ylab="", main="Reconstruction + Sf")
par(mfrow=c(1,1))
list(f=f, g=g, h=h)
}
dwinverse <- function(wt, filtername="Gaussian1")
#****************************************************************
# dwinverse
# ---------
# Invert the dyadic wavelet transform.
#
# input
# -----
# wt: the wavelet transform
# filtername : filters used. ("Gaussian1" stands for the filters
# corresponds to those of Mallat and Zhong's wavlet. And
# "Haar" stands for the filters of Haar basis.
#
# output
# ------
# f: the reconstructed signal
#****************************************************************
{
Sf <- wt$Sf
Sf <- c(Sf)
dim(Sf) <- c(length(Sf), 1)
Wf <- wt$Wf
Wf <- c(Wf)
dim(Wf) <- c(length(Wf), 1)
np <- wt$np
maxresoln <- wt$maxresoln
fback <- 1:np
dim(fback) <- c(length(fback), 1)
z <- .C("Sinverse_wavelet_transform",
a = as.double(fback),
as.double(Sf),
as.double(Wf),
as.integer(maxresoln),
as.integer(np),
as.character(filtername),
PACKAGE="Rwave")
f <- z$a
f
}
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