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des_stacks is a simple Python module based on SWarp, SExtractor, and PSFEx available for free at https://www.astromatic.net/software des_stacks is intended to be used on data from the Dark Energy Survey Supernova Programme (DES-SN). A description of the process is available in Wiseman et al. 2020 https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4040W.
For details about using the software, please contact Phil Wiseman (pacelweb@gmail.com)
- Clone the repository
- Set up a root directory (COADDING_ROOT/), ideally on a disk with a lot of space.
- Copy the config/ directory from this repo into COADDING_ROOT/
- Download the snobsinfo table from DESDB into the config/ directory using easyaccess
- Hope!
For a simple stack, use stack_all.py with command line arguments:
-f: field (e.g. -f X2,X3) -b: band (e.g. -b g,r,i,z, default = all) -my: minusyear, i.e. the year to leave out of the stack (e.g. -my 1 or default = none) -ch: chips - (e,g [1,3,4,5], default = all -wd: working directory - This should be the highest level of directory for the stacks, in which sub-directories will be created automatically -pc: psf cut - if you want to stack excluding objects above a certain seeing value, define tit here. If nothing is given, default is 2.5. -tc: teff cut - same as above but for t_effective. Default is 0.15 OR -o: optimized - uses the optimized values for each field/band as defined in Wiseman et al. 2020. -t: tidy - tidies up temporary files (1/0 for y/n)
For an optimized stack, some or all of the following parameters are needed in addition:
-l: looptype - If you want to do an optimized stack, this determines the parameter you want to optimize. Current options: zeropoint (zp), seeing (psf), or both. -ps: psf_step - If optimizing the psf, this is the step size of the iterations (in arcsec). default = 0.25 -zs: zp_step - If optimizing the zeropoint, this is the step size of the iterations (in mag). default = 0.025 -ic: init_cuts - Initial cuts in (residual) zeropoint and seeing. Default is [-0.150,2.5].
- numpy
- matplotlib
- scipy
- astropy
- pandas
- configparser
- subprocess
- multiprocessing
- pathos
- SWarp
- Source Extractor
- PSFex