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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.