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:-

$$R_{Variance}=\frac{\sum B^2{A}_{Variance}}{nor{m}^2}$$

(26)

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

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

A_DATA (N_A) = REAL (Given)

A_VARIANCE (N_A) = REAL (Given)

A_QUALITY (N_A) = BYTE (Given)

B (N_B) = REAL (Given)

N_A = INTEGER (Given)

N_B = INTEGER (Given)

N_MIDDLE = INTEGER (Given)

NORM = REAL (Given)

R_DATA (N_A) = REAL (Returned)

R_VARIANCE (N_A) = REAL (Returned)

R_QUALITY (N_A) = QUALITY (Returned)

BADBIT = BYTE (Given)

STATUS = INTEGER (Given and returned)

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