Continuous wavelet transform with filter bank
returns the continuous wavelet transform (CWT) coefficients of the signal
cfs
= wt(fb
,x
)x
, using fb
, a CWT filter bank.
x
is a real- or complex-valued vector.
x
must have at least 4 samples. If x
is real-valued, cfs
is a 2-D matrix, where each row corresponds
to one scale. The column size of cfs
is equal to the length of
x
. If x
is complex-valued,
cfs
is a 3-D array, where the first page is the CWT for the
positive scales (analytic part or counterclockwise component), and the second page
is the cwt for the negative scales (anti-analytic part or clockwise
component).
The first time you use a filter bank to take the CWT of a signal, the wavelet filters are constructed to have the same datatype as the signal. A warning message is generated when you apply the same filter bank to a signal with a different datatype. Changing datatypes comes with the cost of redesigning or changing the precision of the filter bank. For optimal performance, use a consistent datatype.
When performing multiple CWTs, for example inside a for-loop, the recommended
workflow is to first create a cwtfilterbank
object and then use
the wt
object function. This workflow minimizes overhead
and maximizes performance. See Using CWT Filter Bank on Multiple Time Series.