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Kernel: Python 2

Astropy: Tables

Documentation

For more information about the features presented below, you can read the astropy.table docs.

Creating tables

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import numpy as np
from astropy.table import Table

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t1 = Table()
t1['name'] = ['source 1', 'source 2', 'source 3']
t1['flux'] = [1.2, 2.2, 3.1]

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t1

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print(t1)

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t1['size'] = [1,5,4]
t1

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t1['size']

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np.array(t1['size'])

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t1['size'][0]

Iterating over tables

It is possible to iterate over rows or over columns. To iterate over rows, simply iterate over the table itself:

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for row in t1:
    print(row)

Rows can act like dictionaries, so you can access specific columns from a row:

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for row in t1:
    print(row['name'])

You can also access rows by accessing a numerical item in the table:

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row = t1[0]

Iterating over columns is also easy:

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for colname in t1.columns:
    column = t1[colname]
    print(column)

Accessing specific rows from a column object can also be done with the item notation:

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for colname in t1.columns:
    column = t1[colname]
    print(column[0])

Joining tables

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from astropy.table import join

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t2 = Table()
t2['name'] = ['source 1', 'source 3']
t2['flux2'] = [1,9]

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t3 = join(t1, t2, join_type='outer')
t3

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np.mean(t3['flux2'])

Grouping and Aggregation

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from astropy.table import Table
obs = Table.read("""name    obs_date    mag_b  mag_v
                    M31     2012-01-02  17.0   17.5
                    M31     2012-01-02  17.1   17.4
                    M101    2012-01-02  15.1   13.5
                    M82     2012-02-14  16.2   14.5
                    M31     2012-02-14  16.9   17.3
                    M82     2012-02-14  15.2   15.5
                    M101    2012-02-14  15.0   13.6
                    M82     2012-03-26  15.7   16.5
                    M101    2012-03-26  15.1   13.5
                    M101    2012-03-26  14.8   14.3
                    """, format='ascii')

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obs_by_name = obs.group_by('name')

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obs_by_name

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for group in obs_by_name.groups:
    print(group)
    print("")

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obs_by_name.groups.aggregate(np.mean)

Masked tables

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t4 = Table(masked=True)
t4['name'] = ['source 1', 'source 2', 'source 3']
t4['flux'] = [1.2, 2.2, 3.1]

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t4['flux'].mask = [1,0,1]
t4

Writing data

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t3.write('test.fits', overwrite=True)

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t3.write('test.vot', format='votable', overwrite=True)

Reading data

You can download the data used below here.

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t4 = Table.read('data/2mass.tbl', format='ascii.ipac')

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t4

Practical Exercises

These exercises use the all-sky ROSAT catalog which you can download from here.

Level 1

Try and find a way to make a table of the ROSAT point source catalog that contains only the RA, Dec, and count rate. Hint: you can see what methods are available on an object by typing e.g. t. and then pressing <TAB>. You can also find help on a method by typing e.g. t.remove_column?.

Level 2

Make an all-sky equatorial plot of the ROSAT sources, with all sources shown in black, and only the sources with a count rate larger than 2. shown in red (bonus points if you use an Aitoff projection!)

Level 3

Try and write out the ROSAT catalog into a format that you can read into another software package. For example, try and write out the catalog into CSV format, then read it into a spreadsheet software package (e.g. Excel, Google Docs, Numbers, OpenOffice).