Author: Jayshil A Patel (jayshil.patel@astro.su.se)
pycdata is a module to import datasets from various telescopes/instruments in pycheops. pycheops is a tool that is specifically designed to model CHEOPS observations of transits, eclipses and phase curves. While being a genius tool, what it lacks is a facility to model transits (and eclipses and phase curves) from other telescopes/instruments, even the PSF photometry produced by PIPE. pycdata is here to fill up this very need! A good thing about pycdata is that it automatically put resultant data products to the pycheops cache directory so that it can be directly readable from pycheops command line.
Below, you will find a general instruction on its installation, usage and resultant data products.
Installation for pycdata can be done using setup.py file in the repository, by following commands below:
git clone https://github.com/Jayshil/pycdata.git
cd pycdata
python setup.py install
There you are! You are now ready to use this package!
Note that the installation of pycdata requires astropy and astroquery to be installed on your machine, besides pycheops.
As already been mentioned, PIPE produces PSF photometry for the CHEOPS targets. To make the data products from PIPE accessible to pycheops, follow,
from pycdata import dpr
dpr.pipe_data(name, fileid)
name is the name of the fits file (data products from PIPE), and fileid is a unique file key for each of the CHEOPS observations. The columns for which the data was not available, e.g., contamination, dark, and smearing was set to zero.
It is even simpler to use pycdata with TESS (the Transiting Exoplanets Survey Satellite) data products:
from pycdata import dpr
dpr.tess_data(name, pdc=True, verbose=True)
By providing the name of the target to the name keyword, and choosing whether to use PDC-SAP flux with pdc boolean, the TESS data products, which are readable to pycheops, can be downloaded. pycdata uses astroquery package to download TESS data products directly from the Mikulski Archive for Space Telescopes (MAST). The resultant data products contain time, pdc-sap (or sap) flux, uncertainty in flux, and centroids of the aperture. The rest of the columns (which are imporant for CHEOPS but not available for TESS, e.g., roll angle, smear etc.) are set to zero.
The procedure to download Kepler/K2 lightcurves are mostly similar to that in case of TESS data:
from pycdata import dpr
dpr.kepler_data(name, pdc=True, long_cadence=True, verbose=True)
The only difference is the long_cadence keyword since Kepler mission offers its data products in two cadences.
How to use data products with pycheops?
It is really simple -- upon using any commands from above, pycdata will create data products directly into pycheops cache directory. It will also show the name of the newly created .tgz file in the pycheops cache directory. What remains to do is, while creating a dataset object with pycheops, the source of file to be mentioned, e.g.,
dataset = pycheops.Dataset('name_of_the_tgz_file', source='PIPE')
Where the source keyword can take values PIPE, TESS or Kepler according to the source file. Note that the default is set to CHEOPS, so that while downloading data products from pycheops no additional arguments to be provided.
We would love to hear your comments and/or suggestions to make pycdata more accessible. Furthermore, if you want to make any contributions to the project, you are more than welcome --- feel free to open a pull request.
