Reading/writing time series

Build-in readers

Since TimeSeries and BinnedTimeSeries are sub-classes of Table, they have read() and write() methods that can be used to read time series from files. We include a few readers for well-defined formats in astropy.timeseries - for example we have readers for light curves in FITS format from the Kepler and TESS missions.

Here is an example of using Kepler FITS time series - we start off by fetching an example file:

from astropy.utils.data import get_pkg_data_filename
example_data = get_pkg_data_filename('timeseries/kplr010666592-2009131110544_slc.fits')

Note

The light curve provided here is hand-picked for example purposes. To get other Kepler light curves for science purposes using Python, see the astroquery affiliated package.

This will set example_data to the filename of the downloaded file (so you can replace this by the filename for the file you want to read in). We can then read in the time series using:

from astropy.timeseries import TimeSeries
kepler = TimeSeries.read(example_data, format='kepler.fits')

Let’s check that the time series has been read in correctly:

import matplotlib.pyplot as plt

plt.plot(kepler.time.jd, kepler['sap_flux'], 'k.', markersize=1)
plt.xlabel('Julian Date')
plt.ylabel('SAP Flux (e-/s)')

()

../_images/io-3.png

Reading other formats

At the moment only a few formats are defined in astropy itself, in part because there are not many well documented formats for storing time series. So in many cases, you will likely have to first read in your files using the more generic Table class (see Reading and writing Table objects). In fact, the TimeSeries.read and BinnedTimeSeries.read methods can do this behind the scenes - if the table cannot be read by any of the time series readers, these methods will try and use some of the default Table readers and then require users to specify the name of the important columns.

For example, if you are reading in a file called sampled.csv where the time column is called Date and is an ISO string, you can do:

>>> from astropy.timeseries import TimeSeries
>>> ts = TimeSeries.read('docs/timeseries/sampled.csv', format='ascii.csv',
...                      time_column='Date')
>>> ts[:3]
<TimeSeries length=3>
          time             A       B       C       D       E       F       G
         object         float64 float64 float64 float64 float64 float64 float64
----------------------- ------- ------- ------- ------- ------- ------- -------
2008-03-18 00:00:00.000   24.68  164.93  114.73   26.27   19.21   28.87   63.44
2008-03-19 00:00:00.000   24.18  164.89  114.75   26.22   19.07   27.76   59.98
2008-03-20 00:00:00.000   23.99  164.63  115.04   25.78   19.01   27.04   59.61

If you are reading in a binned time series from a file called binned.csv and with a column time_start giving the start time and bin_size giving the size of each bin, you can do:

>>> from astropy import units as u
>>> from astropy.timeseries import BinnedTimeSeries
>>> ts = BinnedTimeSeries.read('docs/timeseries/binned.csv', format='ascii.csv',
...                            time_bin_start_column='time_start',
...                            time_bin_size_column='bin_size',
...                            time_bin_size_unit=u.s)
>>> ts[:3]
<BinnedTimeSeries length=3>
     time_bin_start     time_bin_size ...    E       F
                              s       ...
         object            float64    ... float64 float64
----------------------- ------------- ... ------- -------
2016-03-22T12:30:31.000           3.0 ...   28.87   63.44
2016-03-22T12:30:34.000           3.0 ...   27.76   59.98
2016-03-22T12:30:37.000           3.0 ...   27.04   59.61

See the documentation for TimeSeries.read and BinnedTimeSeries.read for more details.

Alternatively, you can read in the table using your own code then construct the time series object as described in Creating time series, although then you cannot write out another time series in the same format.

If you have written a reader/writer for a commonly used format, please feel free to contribute it to astropy!