SCULIB_CONVOLVE - convolve array A with array B to give result in R

Description:

This routine convolves array A with array B using multiplication only (i.e. no FFTs are used). The routine assumes that the A arrays are N_A long and that the convolution array B is N_B long, centred at N_MIDDLE. The final result is normalised by dividing by NORM. Elements of the A array that have bad quality are ignored. The input variances are propagated as:-

$$R_{Variance} = \frac{ \sum B^2A_{Variance}}{norm^2}$$ (26)

After this process the errors can no longer be considered independent.

Invocation:

CALL SCULIB_CONVOLVE (A_DATA, A_VARIANCE, A_QUALITY, B, N_A, N_B, N_MIDDLE, NORM, R_DATA, R_VARIANCE, R_QUALITY, STATUS)

Arguments:

A_DATA (N_A) = REAL (Given)

input data array

A_VARIANCE (N_A) = REAL (Given)

variance on A_DATA

A_QUALITY (N_A) = BYTE (Given)

quality on A_DATA

B (N_B) = REAL (Given)

convolution function

N_A = INTEGER (Given)

the size of A_DATA

N_B = INTEGER (Given)

length of convolution function

N_MIDDLE = INTEGER (Given)

index of zero point of convolution function

NORM = REAL (Given)

normalisation factor for convolution function

R_DATA (N_A) = REAL (Returned)

result of the convolution

R_VARIANCE (N_A) = REAL (Returned)

variance on R_DATA

R_QUALITY (N_A) = QUALITY (Returned)

quality on R_DATA

BADBIT = BYTE (Given)

badbit mask

STATUS = INTEGER (Given and returned)

global status

Copyright

Copyright ©1995,1996,1997,1998,1999 Particle Physics and Astronomy Research Council. All Rights Reserved.