C09BAF computes the real, continuous wavelet transform in one dimension.
C09BAF computes the real part of the one-dimensional, continuous wavelet transform
of a signal
at scale
and position
, where the signal is sampled discretely at
equidistant points
, for
.
is the wavelet function, which can be chosen to be the Morlet wavelet, the derivatives of a Gaussian or the Mexican hat wavelet (
denotes the complex conjugate). The integrals of the scaled, shifted wavelet function are approximated and the convolution is then computed.
The mother wavelets supplied for use with this routine are defined as follows.
- The Morlet wavelet (real part) with nondimensional wave number is
where the correction term, (required to satisfy the admissibility condition) is included.
- The derivatives of a Gaussian are obtained from
taking . These are the Hermite polynomials multiplied by the Gaussian. The sign is then adjusted to give when is even while the sign of the succeeding odd derivative, , is made consistent with the preceding even numbered derivative. They are normalized by the -norm,
The resulting normalized derivatives can be written in terms of the Hermite polynomials,
, as
where
Thus, the derivatives of a Gaussian provided here are,
- The second derivative of a Gaussian is known as the Mexican hat wavelet and is supplied as an additional function in the form
The remaining normalized derivatives of a Gaussian can be expressed as multiples of the exponential by applying the substitution followed by multiplication with the scaling factor, .
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
The accuracy of C09BAF is determined by the fact that the convolution must be computed as a discrete approximation to the continuous form. The input signal, , is taken to be piecewise constant using the supplied discrete values.
Workspace is internally allocated by C09BAF. The total size of these arrays is real elements and integer elements, where and when or and
when .
This example computes the continuous wavelet transform of a dataset containing a single nonzero value representing an impulse. The Morlet wavelet is used with wave number and scales , , , .