This is a current version of PISCO data reduction pipeline, developed by Taweewat Somboonpanyakul at MIT in order to reduce PISCO raw images to calibrated images with calibrated photometry.
Required softwares before using the pipeline:
- Astrometry.net (including necessary index files for your field of interest)
- Sextractor
- SCAMP
- SWARP
- DS9 [required for pisco_combine.py in order to make an image, can comment out the last line in pisco_combine.py (save_rgb_image(fieldname)) to remove the requirement.]
Included packages:
- Cosmics ray removal python packages: comics.py python package (developed from LA-Cosmic. The package is also included here, but required to be installed before.
- Stellar Locus Regression python package: Big-macs-calibrate by Kelly et al. 2014 (http://arxiv.org/abs/1208.0602)
Other Required Python Packages:
- astropy
- photutils (an affiliated package of Astropy) (required only for the Photometry pipeline)
- numpy
- matplotlib
- subprocess, shlex, os, sys
python pisco_pipeline/pisco_combine.py data/ Field026where data/ is the directory wherer the PISCO raw data Field024_A_82.fits and Field024_A_83.fits are located, and Field024 is the prefix for all the files that you want to combine together. Run python script outside pisco_pipeline/ directory next to data/ directory is located.
The main outputs (4 fits images based on 4 different bands) will be located in final/ directory.
python pisco_pipeline/pisco_photometry.py Field026Need to run Astrometry pipeline first as the input for the photometry pipeline. The main output for the pipeline (csv file with corrected magnitude in different bands with their uncertainty) is located in slr_output/ directory.
MIT