# What are TargetPixelFile objects?¶

Target Pixel Files (TPFs) are a file common to Kepler/K2 and the TESS mission. They contain data that is usually centered around a single star.

TPFs can be thought of as stacks of images, with one image for every timestamp the telescope took data. Each timestamp is referred to as a cadence. These images are cut out ‘postage stamps’ of the full observation to make them easier to work with.

TPFs are given in FITS files, which you can read more about here. lightkurve includes tools for you to work directly with these files easily and intuitively.

In this tutorial we’ll cover the basics of working with TPFs. In lightcurve there are classes to work with each mission. For example KeplerTargetPixelFile is used to work with data from the Kepler (and K2) mission. TessTargetPixelFile is used to work with data from the TESS mission.

We’ll use a Kepler TPF as an example. First, let’s open a file. We can do this easily with the KeplerTargetPixelFile.from_archive function. This will retrieve the data from the MAST data archive, which holds all of the Kepler and K2 data. In this case we are downloading the Target Pixel File with the Kepler ID 6922244 for Quarter 4 (Kepler’s observations were split into quarters of a year). You can also download a file with from_archive using the name of the target, or using the astronomical coordinates (Right Ascension and Declination, often referred to as “RA” and “Dec”).

In [1]:

from lightkurve import KeplerTargetPixelFile
tpf = KeplerTargetPixelFile.from_archive(6922244, quarter=4)


We can access lots of meta data using this object very simply with properties of the KeplerTargetPixelFile object. For example, we can find the mission name, and the quarter that the data was taken in by typing the following:

In [2]:

tpf.mission

Out[2]:

'Kepler'

In [3]:

tpf.quarter

Out[3]:

4


You can find the full list of properties in the API documentation.

The most interesting data in a KeplerTargetPixelFile object are the flux and time values which give access to the brightness of the observed target over time. You can access the timestamps of the observations using the time property:

In [4]:

tpf.time

Out[4]:

array([ 352.37632485,  352.39675805,  352.43762445, ...,  442.16263546,
442.18306983,  442.2035041 ])


By default, time is in the Kepler-specific Barycentric Kepler Julian Day format (BKJD). You can easily convert this into AstroPy Time objects using the astropy_time property:

In [5]:

tpf.astropy_time

Out[5]:

<Time object: scale='tdb' format='jd' value=[ 2455185.37632485  2455185.39675805  2455185.43762445 ...,
2455275.16263546  2455275.18306983  2455275.2035041 ]>


In turn, this gives you access to human-readable ISO timestamps using the astropy_time.iso property:

In [6]:

tpf.astropy_time.iso

Out[6]:

array(['2009-12-19 21:01:54.467', '2009-12-19 21:31:19.895',
'2009-12-19 22:30:10.752', ..., '2010-03-19 15:54:11.704',
'2010-03-19 16:23:37.233', '2010-03-19 16:53:02.754'],
dtype='<U23')


Beware: these ISO timestamps are in the Barycentric frame, and do not include corrections for light travel time or leap seconds.

Next, let’s look at the actual image data, which is available via the flux property:

In [7]:

tpf.flux.shape

Out[7]:

(4116, 5, 5)


The flux data is a 4116x5x5 array in units electrons/second. The first axis is the time axis, and the images themselves are 5 pixels by 5 pixels. You can use the plot method on the KeplerTargetPixelFile object to view the data. (By default, this will show just one cadence of the data. But you can pass the cadence you want to look at to the frame keyword if you would like to check a particular flux point for thruster firings, cosmic rays or asteroids.)

In [8]:

%matplotlib inline
tpf.plot(frame=0);


The values shown in this image are also directly accessible as an array:

In [9]:

tpf.flux[0]

Out[9]:

array([[             nan,   5.60793352e+00,   5.14911423e+01,
8.42417450e+01,   3.02213345e+01],
[  4.40456200e+01,   7.68612289e+01,   1.12277588e+03,
3.22620288e+03,   4.54867767e+02],
[  2.59111652e+01,   2.29075928e+02,   9.36265430e+03,
2.36062734e+04,   1.20877502e+03],
[  4.01008301e+01,   8.85439270e+02,   1.71021179e+03,
2.62548706e+03,   7.07966064e+02],
[  1.57194168e+02,   8.37134399e+02,   5.10215393e+02,
1.15010413e+03,   1.83133698e+02]], dtype=float32)


You can use normal numpy methods on these to find the shape, mean etc!

We can now turn this Target Pixel File into a light curve, with a single flux value for every time value. Each of the pixels are 4 arcseconds across. The point spread function (PSF) of the telescope causes the light from the star fall onto several different pixels, which can be seen in the image above. Because of this spreading, we have to sum up many pixels to collect all the light from the source. To do this we sum up all the pixels in an aperture. An aperture is a pixel mask, where we take only the pixels related to the target.

The Kepler pipeline adds an aperture mask to each target pixel file. This aperture determines which pixels are summed to create a 1D light curve of the target. There are some science cases where you might want to create a different aperture. For example, there may be a nearby contaminant or you may want to measure the background.

The standard pipeline aperture is easily accessed in a KeplerTargetPixelFile object using tpf.pipeline_mask, which is a boolean array:

In [10]:

tpf.pipeline_mask

Out[10]:

array([[False, False, False, False, False],
[False, False,  True,  True, False],
[False, False,  True,  True, False],
[False,  True,  True,  True, False],
[False, False, False,  True, False]], dtype=bool)


We can also plot this aperture over the target pixel file above to see if the flux of the star is all contained within the aperture.

In [11]:

tpf.plot(aperture_mask=tpf.pipeline_mask)

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f59f8f17d30>


Now that we have the aperture we can create a Simple Aperture Photometry light curve in the next tutorial.

Finally, note that you can inspect all the raw metadata of the target by taking a look at the ‘header’ of the FITS file, which contains information about the data set. Let’s just print the first 10 lines:

In [12]:

tpf.header()[:10]

Out[12]:

SIMPLE  =                    T / conforms to FITS standards
BITPIX  =                    8 / array data type
NAXIS   =                    0 / number of array dimensions
EXTEND  =                    T / file contains extensions
NEXTEND =                    2 / number of standard extensions
EXTNAME = 'PRIMARY '           / name of extension
EXTVER  =                    1 / extension version number (not format version)
ORIGIN  = 'NASA/Ames'          / institution responsible for creating this file
DATE    = '2015-09-23'         / file creation date.
CREATOR = '917482 TargetPixelExporterPipelineModule' / pipeline job and program


We can look at the values in the second extention of the fits file by passing an extention number to the header function. For example, to look at all the column titles:

In [13]:

tpf.header(1)['TTYPE*']

Out[13]:

TTYPE1  = 'TIME    '           / column title: data time stamps
TTYPE2  = 'TIMECORR'           / column title: barycenter - timeslice correction