Astropy Data Tables

In our notebooks we are keeping the SDSS data in an astropy data table called SDSS. A data table has rows and columns.

Rows are the objects (stars, galaxies), while columns are the properties of the objects (RA, Dec, g-mag, etc).

SDSS['g'] is the column of g-magnitudes for all the objects. Something in quotes means a column!

You can get a column listing by saying SDSS.colnames

Each row is all the data for a single object, which you can access by adding a bracketed number

SDSS['g'][12] # the g-magnitude for the 13th object in the table (remember python numbering starts at 0)

SDSS[99] # show all the information for the 100th object in the table

You can make new columns simply by defining them:

SDSS['abs_gmag'] = SDSS['g'] - (5*np.log10(lumdist_pc) - 5)

SDSS['g-r'] = SDSS['g'] - SDSS['r']

We can access subsets of full data table using selection flags like this:

has_redshift = SDSS['redshift'] != -999

SDSS[has_redshift] # subset of just the things that have a redshift.

SDSS[~has_redshift] # subset of just the things that DO NOT have a redshift

We can display a sortable table in your web browser of things that have a redshift:

SDSS[has_redshift].show_in_browser(jsviewer=True) # but don't do this for the full table!

Plotting and Analysis Tips

When plotting mags and colors, don't autoscale, or you'll get unreadable plots. For CMDs, for example, reasonable limits on the magnitude range would be r = 13-24, and limits on the color range would be g-r = -1 to +2.

When plotting lots of data points, make the marker sizes small. Try something like scatter(x,y,s=1)

Also, to select subsamples, an easy way to do this is to set a selection flag like this:

want = SDSS['g']<20 # for selecting objects with a g mag brighter than 20
or

want = np.abs(SDSS['redshift']-0.1)<0.05 # for selecting objects in the redshift range 0.05 to 0.15
or

want = SDSS['type']==3 # for selecting spatially extended source (i.e., not point sources)

followed by (for example):

# plot the whole sample

scatter(SDSS['r'], SDSS['g']-SDSS['r'], s=1)

# overplot the subsample
scatter(SDSS['r'][want], SDSS['g'][want]-SDSS['r'][want],s=20,color='red')

You can also "stack" selections like this:

bright = SDSS['g']<18 # has a g-mag brighter than 18
red = SDSS['g']-SDSS['r']>0.7 # is redder than g-r=0.7
has_redshift = SDSS['redshift'] != -99) # has a measured redshift

want = np.logical_and(bright,red)

want = np.logical_and(want,has_redshift)

which would give you a "want" selection that is bright red galaxies with measured redshifts