The box size, in pixels, over which to calculate the correlation coefficient between the input and reference images. This should be set to an estimate of the maximum expected shift between the two images, but should not be less than typical size of features within the two images. See also Parameter CORLIMIT. [5]

If set TRUE, then the output pixel values will be scaled in such a way as to preserve the total data value in a feature on the sky. The scaling factor is the ratio of the output pixel size to the input pixel size. This option can only be used if the Mapping is successfully approximated by one or more linear transformations. Thus an error will be reported if it used when the TOL parameter is set to zero (which stops the use of linear approximations), or if the Mapping is too non-linear to be approximated by a piece-wise linear transformation. The ratio of output to input pixel size is evaluated once for each panel of the piece-wise linear approximation to the Mapping, and is assumed to be constant for all output pixels in the panel. This parameter is ignored if the NORM parameter is set FALSE. [TRUE]

If CORLIMIT is not null (!), each pixel in the input image is checked to see if the pixel values in its locality are well correlated with the corresponding locality in the reference image. The input pixel is excluded from the fitting process if the local correlation is below CORLIMIT. The supplied value should be between zero and 1.0. The size of the locality used around each input pixel is given by Parameter BOX. [!]

In addition, if a value is supplied for CORLIMIT, the input and reference pixel values that pass the above check are scaled so that they have a mean value of zero and a standard deviation of unity before being used in the fitting process. [!]

If TRUE, the fitting process will adjust the scale and offset of the input data values, in addition to the geometric position of the the input values, in order to minimise the sum of the squared residuals. [FALSE]

The form of the affine transformation to use:

• 0 — full unrestricted six-coefficient fit;

• 1 — shift, rotation and a common X/Y scale but no shear;

• 2 — shift and rotation but no scale or shear; or

• 3 — shift but not rotation, scale or shear.

[0]

NDF to be transformed.

The method to use when sampling the input pixel values (if resampling), or dividing an input pixel value between a group of neighbouring output pixels (if rebinning). For details of these schemes, see the descriptions of routines AST_RESAMPLEx and AST_REBINSEQx in SUN/210. METHOD can take the following values.

• "Linear" — When resampling, the output pixel values are calculated by bi-linear interpolation among the four nearest pixels values in the input NDF. When rebinning, the input pixel value is divided bi-linearly between the four nearest output pixels. Produces smoother output NDFs than the nearest-neighbour scheme, but is marginally slower.

• "Nearest" — When resampling, the output pixel values are assigned the value of the single nearest input pixel. When rebinning, the input pixel value is assigned completely to the single nearest output pixel.

• "Sinc" — Uses the $\mathrm{\text{sinc}}\left(\pi x\right)$ kernel, where $x$ is the pixel offset from the interpolation point (resampling) or transformed input pixel centre (rebinning), and $\mathrm{\text{sinc}}\left(z\right)=sin\left(z\right)/z$. Use of this scheme is not recommended.

• "SincSinc" — Uses the $\mathrm{\text{sinc}}\left(\pi x\right)\mathrm{\text{sinc}}\left(k\pi x\right)$ A valuable general-purpose scheme, intermediate in its visual effect on NDFs between the bi-linear and nearest-neighbour schemes.

• "SincCos" — Uses the $\mathrm{\text{sinc}}\left(\pi x\right)cos\left(k\pi x\right)$ kernel. Gives similar results to the "SincSinc" scheme.

• "SincGauss" — Uses the $\mathrm{\text{sinc}}\left(\pi x\right)e^{-k{x}^2}$ kernel. Good results can be obtained by matching the FWHM of the envelope function to the point-spread function of the input data (see Parameter PARAMS).

• "Somb" — Uses the $\mathrm{\text{somb}}\left(\pi x\right)$ kernel, where $x$ is the pixel offset from the interpolation point (resampling), or transformed input pixel centre (rebinning), and $\mathrm{\text{somb}}\left(z\right)=2\ast J_1\left(z\right)/z$. $J_1$ is the first-order Bessel function of the first kind. This scheme is similar to the "Sinc" scheme.

• "SombCos" — Uses the $\mathrm{\text{somb}}\left(\pi x\right)cos\left(k\pi x\right)$ kernel. This scheme is similar to the "SincCos" scheme.

• "Gauss" — Uses the $e^{-k{x}^2}$ kernel. The FWHM of the Gaussian is given by Parameter PARAMS(2), and the point at which to truncate the Gaussian to zero is given by Parameter PARAMS(1).

• "BlockAve" — Block averaging over all pixels in the surrounding $N$-dimensional cube. This option is only available when resampling (i.e. if REBIN is set to FALSE).

All methods propagate variances from input to output, but the variance estimates produced by interpolation schemes other than nearest neighbour need to be treated with care since the spatial smoothing produced by these methods introduces correlations in the variance estimates. Also, the degree of smoothing produced varies across the NDF. This is because a sample taken at a pixel centre will have no contributions from the neighbouring pixels, whereas a sample taken at the corner of a pixel will have equal contributions from all four neighbouring pixels, resulting in greater smoothing and lower noise. This effect can produce complex Moiré patterns in the output variance estimates, resulting from the interference of the spatial frequencies in the sample positions and in the pixel-centre positions. For these reasons, if you want to use the output variances, you are generally safer using nearest-neighbour interpolation. The initial default is "Nearest". [current value]

In general, each output pixel contains contributions from multiple input pixel values, and the number of input pixels contributing to each output pixel will vary from pixel to pixel. If NORM is set TRUE (the default), then each output value is normalised by dividing it by the number of contributing input pixels, resulting in each output value being the weighted mean of the contributing input values. However, if NORM is set FALSE, this normalisation is not applied. See also Parameter CONSERVE. [TRUE]

An optional output NDF to contain a copy of IN aligned with OUT. No output is created if null (!) is supplied. If FITVALS is TRUE, the output data values will be scaled so that they have the same normalisation as the reference values.

An optional array which consists of additional parameters required by the Sinc, SincSinc, SincCos, SincGauss, Somb, SombCos, and Gauss methods.

PARAMS( 1 ) is required by all the above schemes. It is used to specify how many pixels are to contribute to the interpolated result on either side of the interpolation or binning point in each dimension. Typically, a value of 2 is appropriate and the minimum allowed value is 1 (i.e. one pixel on each side). A value of zero or fewer indicates that a suitable number of pixels should be calculated automatically. [0]

PARAMS( 2 ) is required only by the SombCos, Gauss, SincSinc, SincCos, and SincGauss schemes. For the SombCos, SincSinc, and SincCos schemes, it specifies the number of pixels at which the envelope of the function goes to zero. The minimum value is 1.0, and the run-time default value is 2.0. For the Gauss and SincGauss scheme, it specifies the full-width at half-maximum (FWHM) of the Gaussian envelope measured in output pixels. The minimum value is 0.1, and the run-time default is 1.0. On astronomical images and spectra, good results are often obtained by approximately matching the FWHM of the envelope function, given by PARAMS( 2 ), to the point-spread function of the input data. []

Determines the algorithm used to calculate the output pixel values. If a TRUE value is given, a rebinning algorithm is used. Otherwise, a resampling algorithm is used. See the “Choice of Algorithm” topic below. [current value]

NDF to be used as a refernece.

The maximum tolerable geometrical distortion that may be introduced as a result of approximating non-linear Mappings by a set of piece-wise linear transforms. Both algorithms approximate non-linear co-ordinate transformations in order to improve performance, and this parameter controls how inaccurate the resulting approximation is allowed to be, as a displacement in pixels of the input NDF. A value of zero will ensure that no such approximation is done, at the expense of increasing execution time. [0.05]

This parameter is only used if REBIN is set TRUE. It specifies the minimum number of good pixels which must contribute to an output pixel for the output pixel to be valid. Note, fractional values are allowed. A null (!) value causes a very small positive value to be used resulting in output pixels being set bad only if they receive no significant contribution from any input pixel. [!]

An output parameter to which is written the RMS residual between the aligned data and the reference data.

An output parameter to which are written the coefficients of the fit. If FITVALS is TRUE, then this will include the scale and offset (written to the seventh and eighth entries).