The implementation of datasets as directories does complicate
some manipulations of your datasets, since your favourite image, etc, is not
just a file anymore.
On the other hand, as the host system sees a Miriad dataset as a normal
directory, all the
usual host commands to manipulate directories can be used. On UNIX a
switch often has to be used with the command, to indicate that the operation
is to be applied `recursively' (i.e. to all files in the directory). For
example, to delete a dataset, use
% rm -r datasetIf you have aliased rm to prompt you before deleting a file (as is common in a number of the standard login scripts at Epping), you will be prompted before deleting each individual file within a dataset. This can become somewhat tedious, so you might want to make another alias to delete without prompting. For example, insert
alias rrm 'rm'in your .cshrc file. Similarly to copy a dataset, you would use
% cp -r dataset1 dataset2
Generally the user is insulated from this internal organisation of a dataset and can always think of them as a whole. However there are a few Miriad utilities to manipulate at the item level. These tasks do not contain any astronomical knowledge. Consequently they may seem somewhat crude. These tasks include:
As an example, consider the itemize task, which lists the items in a dataset. For the test image dataset, gauss (created with imgen in Chapter 2) itemize will tell us of the following items:
% itemize in=gauss Itemize: version 1.1 4-mar-91 naxis = 2 naxis1 = 256 naxis2 = 256 crpix1 = 129 crpix2 = 129 cdelt1 = -4.848137e-06 cdelt2 = 4.848137e-06 crval1 = 0 crval2 = 0 history (text data, 38 elements) image (real data, 65536 elements)Here the item naxis consists of a single integer, having the value of 2. The item image is a larger item (being the pixel data) consisting of 65536 (256 by 256) real numbers.
Note for images that many items have FITS-like names (although they are lower case, and the units can be different from the FITS standard). A list of the items in an image and visibility dataset are given in Appendices B and C.