ATpy

ATpy is a high-level Python package providing a way to manipulate tables of astronomical data in a uniform way. The two main features of ATpy are:

• It provides an abstract Table object that contains tabular data along with meta-data to describe the columns, and methods to manipulate the table (e.g. adding/removing/renaming columns, selecting rows, changing values, sorting, ...).
• It provides built-in support for reading and writing to several common file/database formats, including FITS, VO, HDF5, and ASCII tables, as well as SQLite, MySQL and PostgreSQL databases, with a very simple interface.

Reading in an existing table

First, download this FITS table, then launch IPython in the directory where you have the file:

$ ipython -pylab

If you have trouble downloading the file, then start up IPython (ipython -pylab) and enter:

import urllib2
url = 'http://python4astronomers.github.com/_downloads/catalog.fits.gz'
open('catalog.fits.gz', 'wb').write(urllib2.urlopen(url).read())
ls

Then import atpy:

import atpy

and read in the table:

t = atpy.Table('catalog.fits.gz')

We can now see what columns are available in the table:

>>> t.describe()
Table : MY CATALOG
--------------------------------------
|     Name |    Unit | Type | Format |
--------------------------------------
| SOURCEID |         | |S20 |    68s |
|       RA | RADIANS |  >f8 | 25.17e |
|      DEC | RADIANS |  >f8 | 25.17e |
|    EPOCH |         |  >f8 | 25.17e |
|   LAMBDA |         |  >f8 | 25.17e |
|      ETA |         |  >f8 | 25.17e |
| DISTANCE |         |  >f8 | 25.17e |
--------------------------------------

and how many lines there are:

>>> len(t)
1002

To access a particular column, use:

>>> t['RA']
array([ 4.64681193,  4.64686867,  4.64682644, ...,  4.64694334,
        4.64690389,  4.64698136])

As you can see, a single column is returned as a Numpy array, so any Numpy operation will work on this:

>>> t['RA'].min()
4.6465723005143351

>>> t['RA'].max()
4.6472357622326737

and you can then specify a particular row to access a single value:

>>> t['RA'][4]
4.6467757499903897

Let’s select a subset of the table:

>>> tsub = t.where(t['RA'] < 4.647)
>>> len(tsub)
742

And we can also remove the 'DISTANCE' column which we don’t care about:

tsub.remove_columns('DISTANCE')

As you can see, the column is no longer there:

>>> tsub.describe()
Table : MY CATALOG
--------------------------------------
|     Name |    Unit | Type | Format |
--------------------------------------
| SOURCEID |         | |S20 |    68s |
|       RA | RADIANS |  >f8 | 25.17e |
|      DEC | RADIANS |  >f8 | 25.17e |
|    EPOCH |         |  >f8 | 25.17e |
|   LAMBDA |         |  >f8 | 25.17e |
|      ETA |         |  >f8 | 25.17e |
--------------------------------------

We can sort the table by Declination:

tsub.sort('DEC')

Finally, let’s write out this sub-table as an IPAC table:

tsub.write('subset.tbl')

Creating a table from scratch

Making a table from scratch is also very easy:

t = atpy.Table()
t.add_column('time', [1., 2., 3])
t.add_column('flux', [7.2, 1.1, 5.9])

and you can then write this out to e.g. a FITS file:

t.write('simple.fits')

And much more!

For more information on how to use ATpy, see the Documentation.