#
# Generate some test data
#
test.data <- example.1()$y
#
# Make it noisy
#
ynoise <- test.data + rnorm(512, sd=0.1)
#
# Do packet ordered non-decimated wavelet transform
#
ynwst <- wst(ynoise)
#
# Now threshold the coefficients
#
ynwstT <- threshold(ynwst)
#
# Select basis number 9 (why not?)
#
NodeVector9 <- numtonv(9, nlevelsWT(ynwstT))
#
# Let's print it out to see what it looks like
# (nb, if you're repeating this examples, the basis might be different
# as you may have generated different pseudo random noise to me)
#
NodeVector9
# Level : 8 Action is R (getpacket Index: 1 )
# Level : 7 Action is L (getpacket Index: 2 )
# Level : 6 Action is L (getpacket Index: 4 )
# Level : 5 Action is R (getpacket Index: 9 )
# Level : 4 Action is L (getpacket Index: 18 )
# Level : 3 Action is L (getpacket Index: 36 )
# Level : 2 Action is L (getpacket Index: 72 )
# Level : 1 Action is L (getpacket Index: 144 )
# Level : 0 Action is L (getpacket Index: 288 )
# There are 9 reconstruction steps
#
# The print-out describes the tree through ynwstT that corresponds to
# basis 9.
#
# The NodeVector9 and ynwstT objects could now be supplied to
# InvBasis.wst for inverting ynwstT according
# to the NodeVector9 or basis number 9.
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