#
# Generate a sequence of 32 random normals (say) and take their
# \code{packed-ordered non-decimated wavelet transform}
#
myrand <- wst(rnorm(32))
#
# Print out the result (to verify the class and type of the object)
#
#myrand
#Class 'wst' : Stationary Wavelet Transform Object:
# ~~~ : List with 8 components with names
# wp Carray nlevelsWT filter date
#
#$WP and $Carray are the coefficient matrices
#
#Created on : Tue Sep 29 12:29:45 1998
#
#summary(.):
#----------
#Levels: 5
#Length of original: 32
#Filter was: Daub cmpct on least asymm N=10
#Boundary handling: periodic
#Date: Tue Sep 29 12:29:45 1998
#
# Yep, the myrand object is of class: \code{\link{wst.object}}.
#
# Now let's convert it to class \code{\link{wd}}. The object
# gets returned and, as usual in S, is printed.
#
convert(myrand)
#Class 'wd' : Discrete Wavelet Transform Object:
# ~~ : List with 8 components with names
# C D nlevelsWT fl.dbase filter type bc date
#
#$ C and $ D are LONG coefficient vectors !
#
#Created on : Tue Sep 29 12:29:45 1998
#Type of decomposition: station
#
#summary(.):
#----------
#Levels: 5
#Length of original: 32
#Filter was: Daub cmpct on least asymm N=10
#Boundary handling: periodic
#Transform type: station
#Date: Tue Sep 29 12:29:45 1998
#
# The returned object is of class \code{\link{wd}} with a
# type of "station".
# I.e. it has been converted successfully.
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