Description of the CCDPACK routines

In this Appendix a more exhaustive catalogue of the capabilities and parameters of the CCDPACK routines are given. Do not read it if the previous descriptions have met your present needs. Read it only if they don’t. Remember that help is available at any time in the XREDUCE Help menus, from the programs by returning a ‘?’ in response to a prompt, or by entering the on-line or hypertext help systems after starting CCDPACK.

B.1 General considerations

Throughout the following descriptions various methodologies exist which are worthy of discussion as topics. They cover such aspects of data processing as the control of, saturation values, data types and data combination.

B.1.1 Data saturation

CCDPACK allows you to flag data values above a given limit as saturated. It performs this task using one of two methods, either setting the pixels BAD (often referred to as invalidating or setting to a magic value), in which case the future processing is transparent if applications which can accommodate BAD values are used, or alternatively by setting all such pixels to a defined value (this option may be necessary if the destination analysis programs cannot handle BAD values). In this latter case care is required because future operations to the data can easily modify the values, so that unintentional differentiation of the saturated data may occur. This will only happen in such situations as flatfielding where the pixels are modified singly, global operations such as subtraction, multiplication etc. by a constant will preserve the saturated value dataset, although modifying the actual saturation value.

If you process saturated data using a specified value within CCDPACK then a CCDPACK extension item is created and the saturation value is written to it. Future work within CCDPACK will then stop modification of these saturated values (the routines CALCOR and FLATCOR do this). In general if you can safely use BAD values this is by far the better option. If you are determined to mark saturated data using a specific value then it is recommended that calibration (dark, flash and flat) frames are processed using BAD values as the combination processes do not support saturated value preservation. If the resultant master calibration frames contain BAD values then replacement (by the value 1 or by the mean) of these can be performed in KAPPA (SUN/95: SETMAGIC).

B.1.2 Data types and sizes

The CCD data frames given to CCDPACK can be of any non-complex HDS (SUN/92) numeric type (e.g. they could be of type _WORD or _UWORD - Fortran INTEGER*2, _REAL, _INTEGER or even _DOUBLE). CCDPACK usually processes the data using this type. On occasion, however, frames, such as the master flatfield, will not be returned in their original type. This is because normalising to a mean of one precludes data storage of a precision less than _REAL. However, the flatfield correction routine FLATCOR will return the data in your input type regardless of the flatfield type so types are preserved in the longer term.

If your input frames are of a mixed data type CCDPACK will preserve the data type of each individual frame. However, if you are combining mixed data types into a calibration master of some kind, CCDPACK will choose the least precise type which represents best all the input data types.

In the routines MAKEBIAS, MAKECAL and MAKEFLAT input images which have different physical sizes (because they have been previously sectioned, for some reason) will be padded to a common size before processing. This is so that no calibration data is lost.

The corrective routines (CALCOR, DEBIAS and FLATCOR) trim the data down to the size which contains the smallest dataset. The trimming processes occur separately for each input image. The most efficient method of processing is to keep the input data files of the same type and size, as this avoids costly trimming, padding, and mapping/unmapping of the data (CCDPACK always attempts to minimize the amount of re-mapping of calibration frames when processing lists of images).

The MAKEMOS application is specially designed to deal with datasets which may have very small regions in common and which produce large output mosaics.

B.1.3 Image combination techniques

The aim is to provide you with a fairly exhaustive list of ways in which you can combine your data. The methods include the most efficient (mean) and the most robust (median) estimators and a range of options in between these ideals. A description of the basis of the methods follows:

MEAN: a weighted mean.
WEIGHTED MEDIAN: a weighted median. The weighted average of the values nearest to the half weight value. A more even handed estimator than the ordinary median which takes no account of the errors in the individual measurements.
TRIMMED MEAN: Alpha trimmed mean. The final estimate is the mean of the values excluding the alpha (a fraction between 0 and 0.5) upper and lower values.
MODE: a maximum likelihood mean. This is essentially an iteratively sigma (the standard deviation) clipped mean, where values outside of a given number of sigmas of the mean value are rejected on each pass until convergence is achieved. The standard deviation is always based on the variation of the data contributing to each output value.
SIGMA CLIPPED MEAN: the mean of the values left after rejecting those outside of a given number of standard deviation of the initial mean. The standard deviation is derived from data variances if available, otherwise a standard deviation based on the variation of the data is used.
THRESHOLD CLIPPED MEAN: the mean of the values after rejecting values above and below defined thresholds. Note this usually applies to the output data range if some internal normalisation is performed (MAKEBIAS and MAKEFLAT).
MINIMUM AND MAXIMUM EXCLUSION MEAN: the mean after the
minimum and maximum values are rejected.
BROADENED MEDIAN: the median if the number of input data values is less than five. The mean of the central few values if the number of inputs is larger.
SIGMA CLIPPED MEDIAN: the weighted median of the values left after rejecting those outside of a given number of standard deviations of the initial mean. The standard deviation is derived from data variances if available, otherwise a standard deviation based on the variation of the data is used.
UNWEIGHTED MEDIAN: an unweighted median. A simple median of the data values. No weighting is taken into account. This is significantly faster than the weighted median, but takes no account of the known errors in the measurements.
DRIZZLE: or variable-pixel linear reconstruction, maps weighted input data into pixels in a subsampled output image. In order to avoid convoluting the output image with the large input pixel size, the input pixels are shrunk before it is averaged into the output image.

All of these methods, support variance propagation, provided that the input data errors have an approximately normal distribution.

In general if the input data comprise less than 5 datasets and spurious values are expected to be present, it is very difficult to perform better than the median, and this is the normal default.

B.2 Alphabetic list of CCDPACK routines.

CCDFORK Creates a script for executing CCDPACK commands in a background process. 156

DEBIAS Debiasses lists of images either by bias image subtraction or by interpolation – applies bad data masks – extracts a subset of the data area – produces variances – applies saturation values. 166

FINDOFF Performs pattern-matching between position lists related by simple offsets. 194

B Description of the CCDPACK routines

B.1 General considerations

B.1.1 Data saturation

B.1.2 Data types and sizes

B.1.3 Image combination techniques

B.2 Alphabetic list of CCDPACK routines.

B.3 Complete routine descriptions