lksearch 1.0.0

Quickstart

The easiest way to install lksearch and all of its dependencies is to use the pip command, which is a standard part of all Python distributions. (upon release)

To install lksearch, run the following command in a terminal window:

$ python -m pip install lksearch --upgrade

The --upgrade flag is optional, but recommended if you already have lksearch installed and want to upgrade to the latest version.

Depending on the specific Python environment, you may need to replace python with the correct Python interpreter, e.g., python3.

from lksearch import MASTSearch, TESSSearch, KeplerSearch, K2Search
### Get long-cadence target pixel files for Kepler
res = search.KeplerSearch("KIC 11904151", exptime="long").cubedata
### Get TESScut cutouts for a particular target and sector
res = TESSSearch("TOI 2257").tesscut
res.download()

Contributing

lksearch is an open-source, community driven package. We welcome users to contribute and develop new features for lksearch.

For further information, please see the Lightkurve Community guidelines.

Citing

If you find lksearch useful in your research, please cite it and give us a GitHub star!

If you use Lightkurve for work or research presented in a publication, we request the following acknowledgment or citation:

This research made use of Lightkurve, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration, 2018).

See full citation instuctions, including dependencies, in the Lightkurve documentation.

Contact

lksearch is an open source community project created by the TESS Science Support Center. The best way to contact us is to open an issue or to e-mail tesshelp@bigbang.gsfc.nasa.gov.

Please include a self-contained example that fully demonstrates your problem or question.

Changelog:

• The class structure has been modified. The base class is MASTSearch. Users are intended to use mission-specific classes (KeplerSearch/K2Search/TESSSearch) to obtain mission-specific results.

• Result tables are saved as pandas dataframs

• The TESScut search functionality now uses tesswcs to identify observed sectors

• Data products are now generalized (timeseries contains lightcurve products, cubedata contains target pixel files and TESSCut, and dvreports contains pdfs contining data validation reports)

• ‘download’ now defaults to the AWS cloud storage.

• ‘download’ only downloads files to disk. It no longer returns a lightkurve object.