naginterfaces.library.wav.dim1_​mxolap_​inv

naginterfaces.library.wav.dim1_mxolap_inv(ca, cd, n, comm)[source]

dim1_mxolap_inv computes the inverse one-dimensional maximal overlap discrete wavelet transform (MODWT) at a single level. The initialization function dim1_init() must be called first to set up the MODWT options.

For full information please refer to the NAG Library document for c09db

https://www.nag.com/numeric/nl/nagdoc_30/flhtml/c09/c09dbf.html

Parameters
cafloat, array-like, shape

The approximation coefficients, . These will normally be the result of some transformation on the coefficients computed by dim1_mxolap_fwd().

cdfloat, array-like, shape

The detail coefficients, . These will normally be the result of some transformation on the coefficients computed by dim1_mxolap_fwd().

nint

, the length of the original data array from which the wavelet coefficients were computed by dim1_mxolap_fwd() and the length of the data array that is to be reconstructed by this function.

commdict, communication object

Communication structure.

This argument must have been initialized by a prior call to dim1_init().

Returns
yfloat, ndarray, shape

The reconstructed data based on approximation and detail coefficients and and the transform options supplied to the initialization function dim1_init().

Raises
NagValueError
(errno )

On entry, array dimension not large enough: but must be at least .

(errno )

On entry, is inconsistent with the value passed to the initialization function: , should be .

(errno )

On entry, the initialization function dim1_init() has not been called first or it has not been called with , or the communication array [‘icomm’] has become corrupted.

Notes

dim1_mxolap_inv performs the inverse operation of dim1_mxolap_fwd(). That is, given sets of approximation coefficients and detail coefficients, computed by dim1_mxolap_fwd() using a MODWT as set up by the initialization function dim1_init(), on a real data array of length , dim1_mxolap_inv will reconstruct the data array , for , from which the coefficients were derived.

References

Percival, D B and Walden, A T, 2000, Wavelet Methods for Time Series Analysis, Cambridge University Press