Photometric Data¶

Photometric data stored in AstroPy Table¶

In sncosmo, photometric data for a supernova is stored in an astropy Table: each row in the table is a photometric observation. The table must contain certain columns. To see what such a table looks like, you can load an example with the following function:

>>> data = sncosmo.load_example_data()
>>> print data
time      band        flux          fluxerr      zp  zpsys
------------- ----- ----------------- -------------- ---- -----
55070.0 sdssg    0.813499900062 0.651728140824 25.0    ab
55072.0512821 sdssr  -0.0852238865812 0.651728140824 25.0    ab
55074.1025641 sdssi -0.00681659003089 0.651728140824 25.0    ab
55076.1538462 sdssz     2.23929135407 0.651728140824 25.0    ab
55078.2051282 sdssg  -0.0308977349373 0.651728140824 25.0    ab
55080.2564103 sdssr     2.35450321853 0.651728140824 25.0    ab
... etc ...


This example data table above has the minimum six columns necessary for sncosmo’s light curve fitting and plotting functions to interpret the data. (There’s no harm in having more columns for other supplementary data.)

Additionally, metadata about the photometric data can be stored with the table: data.meta is an OrderedDict of the metadata.

Including Covariance¶

If your table contains a column 'fluxcov' (or any similar name; see below) it will be interpreted as covariance between the data points and will be used instead of the 'fluxerr' column when calculating a $$\chi^2$$ value in fitting functions. For each row, the 'fluxcov' column should be a length N array, where N is the number of rows in the table. In other, words, table['fluxcov'] should have shape (N, N), where other columns like table['time'] have shape (N,).

As an example, let’s add a 'fluxcov' column to the example data table above.

>>> data['fluxcov'] = np.diag(data['fluxerr']**2)
>>> len(data)
40
>>> data['fluxcov'].shape
(40, 40)

# diagonal elements are error squared:
>>> data['fluxcov'][0, 0]
0.45271884317377648

>>> data['fluxerr'][0]
0.67284384754100002

# off diagonal elements are zero:
>>> data['fluxcov'][0, 1]
0.0


As is, this would be completely equivalent to just having the 'fluxerr' column. But now we have the flexibility to represent non-zero off-diagonal covariance.

Note

When sub-selecting data from a table with covariance, be sure to use sncosmo.select_data. For example, rather than table[mask], use sncosmo.select_data(table, mask). This ensures that the covariance column is sliced appropriately! See the documentation for select_data for details.

Flexible column names¶

What if you’d rather call the time column 'date', or perhaps 'mjd'? Good news! SNCosmo is flexible about the column names. For each column, it accepts a variety of alias names:

Column Acceptable aliases (case-independent) Description Type
time ‘mjd’, ‘mjdobs’, ‘date’, ‘time’, ‘jd’, ‘mjd_obs’ Time of observation in days float
band ‘filter’, ‘band’, ‘flt’, ‘bandpass’ Bandpass of observation str
flux ‘flux’, ‘f’ Flux of observation float
fluxerr ‘flux_error’, ‘fluxerr’, ‘fluxerror’, ‘flux_err’, ‘fe’ Gaussian uncertainty on flux float
zp ‘zp’, ‘zeropoint’, ‘zpt’, ‘zero_point’ Zeropoint corresponding to flux float
zpsys ‘zpsys’, ‘magsys’, ‘zpmagsys’ Magnitude system for zeropoint str
fluxcov ‘covar’, ‘covmat’, ‘covariance’, ‘cov’ Covariance between observations (array; optional) ndarray

Note that each column must be present in some form or another, with no repeats. For example, you can have either a 'flux' column or a 'f' column, but not both.

The units of the flux and flux uncertainty are effectively given by the zeropoint system, with the zeropoint itself serving as a scaling factor: For example, if the zeropoint is 25.0 and the zeropoint system is 'vega', a flux of 1.0 corresponds to 10**(-25/2.5) times the integrated flux of Vega in the given bandpass.

Reading and Writing photometric data from files¶

SNCosmo strives to be agnostic with respect to file format. In practice there are a plethora of different file formats, both standard and non-standard, used to represent tables. Rather than picking a single supported file format, or worse, creating yet another new “standard”, we choose to leave the file format mostly up to the user: A user can use any file format as long as they can read their data into an astropy Table.

That said, SNCosmo does include a couple convenience functions for reading and writing tables of photometric data: sncosmo.read_lc and sncosmo.write_lc:

>>> data = sncosmo.load_example_data()
>>> sncosmo.write_lc(data, 'test.txt')


This creates an output file test.txt that looks like:

@x1 0.5
@c 0.2
@z 0.5
@x0 1.20482820761e-05
@t0 55100.0
time band flux fluxerr zp zpsys
55070.0 sdssg 0.36351153597 0.672843847541 25.0 ab
55072.0512821 sdssr -0.200801295864 0.672843847541 25.0 ab
55074.1025641 sdssi 0.307494232981 0.672843847541 25.0 ab
55076.1538462 sdssz 1.08776103656 0.672843847541 25.0 ab
55078.2051282 sdssg -0.43667895645 0.672843847541 25.0 ab
55080.2564103 sdssr 1.09780966779 0.672843847541 25.0 ab
... etc ...


>>> data2 = sncosmo.read_lc('test.txt')


There are a few other available formats, which can be specified using the format keyword:

>>> data = sncosmo.read_lc('test.json', format='json')


The supported formats are listed below. If your preferred format is not included, use a standard reader/writer from astropy or the Python universe.

Format name Description Notes
ascii (default) ASCII with metadata lines marked by ‘@’ Not readable by standard ASCII table parsers due to metadata lines.
json JavaScript Object Notation Good performance, but not as human-readable as ascii
salt2 SALT2 new-style data files
salt2-old SALT2 old-style data files

Manipulating data tables¶

Because photometric data tables are astropy Tables, they can be manipulated any way that Tables can. Here’s a few things you might want to do.

Rename a column:

>>> data.rename_column('oldname', 'newname')


>>> data['zp'] = 26.

>>> data['zp'] += 0.03