Preparing your Data

Before you calibrate, you must prepare your data. This consists of loading, flagging, perhaps converting to ``channel-0'' datasets, and then splitting.

  1. ATCA data will be initially in RPFITS format. Miriad's task to read RPFITS files is described in Chapter 8. Alternatively, the data may be in FITS format (e.g. VLA data). Again see Chapter 8 for information on loading data in this format. Generally you will want to load all of an observation into a single Miriad file (i.e. use atlod to read multiple RPFITS files into a single output if necessary).

  2. You should now flag your data, and possibly convert to channel-0 (using uvaver). Tasks for these operations are described in Chapter 10.

  3. As Miriad datasets can contain only a single set of calibration tables, it is rather poor at handling the calibration of datasets containing multiple sources and multiple frequency bands. For calibration purposes, it is best to work on datasets containing a single source and single frequency band. So, it is best to break the multi-source, multi-band dataset into a collection of single-source, single-band datasets. The best task to do this is uvsplit. Task uvsplit generates the names of the output datasets itself, forming these from the source name and the central frequency (in MHz) of the data. It is rather unforgiving if you already have a files with one of the names that it wants to use. Make sure your directory is free of files that may usurp uvsplit's name choice. Task uvsplit allows you to perform extra selection if you wish, which may be convenient if you only want to deal with part of your observation at a time.

    vis=multi.uvxy The input dataset.
    select Extra selection.

    For large spectral line data-sets, if disk space is low, it may be useful not to split off the program source. Rather you can split off the calibrators, determine the calibration tables from them, copy the calibration back to the multi-source file, and then image directly from the multi-source file. This way you avoid making a second copy of your program source data. For example, to avoid the source ``vela'' from being split off, use

    vis=multi.uv The input dataset.
    select=-source(vela) Do not select vela data.

Miriad manager