SWIMS Imaging Data Reduction Pipeline, written in Python, processes SWSB*/SWSR* FITS data to produce a final stacked image in a standard manner.
File | Contents |
swsred_20210423a.tgz | Python core scripts (swsred/*py) and configuration/utility files |
File | Contents |
swsred_calib_bpm_s18a_20210131.tgz | Bad pixel mask files for S18A data |
swsred_calib_bpm_s18b_20210131.tgz | Bad pixel mask files for S18B data |
swsred_calib_bpm_s20b_20210131.tgz | Bad pixel mask files for S20B data |
swsred_calib_bpm_s21a_20210418.tgz | Bad pixel mask files for S21A data |
File | Contents |
swsred_calib_mosaic_s18a_20210131.tgz | Mosaic parameter files for S18A data |
swsred_calib_mosaic_s18b_20210131.tgz | Mosaic parameter files for S18B data |
swsred_calib_mosaic_s21a_20210413.tgz | Tentative use only!! Mosaic parameter files for S21A data |
BBF: Broad-band filters, MBF: Medium-band filters, NBF: Narrow-band filters
File | Contents |
swsred_calib_flat_bbf_s18a_20210131.tgz | Flat files for S18A BBF data |
swsred_calib_flat_bbf_s18b_20210131.tgz | Flat files for S18B BBF data |
swsred_calib_flat_mbf_s18b_20210131.tgz | Flat files for S18B MBF data |
swsred_calib_flat_nbf_blue_s18b_20210131.tgz | Flat files for S18B Blue NBF data |
swsred_calib_flat_nbf_red_s18b_20210131.tgz | Flat files for S18B Red NBF data |
swsred_calib_flat_bbf_s20b_20210131.tgz | Flat files for S20B BBF data |
swsred_calib_flat_bbf_s21a_20210418.tgz | Flat files for S21A BBF data |
swsred_calib_flat_mbf_s21a_20210418.tgz | Flat files for S21A MBF data |
Use these test data (taken in S18B) to check if your swsred works.
File | Contents |
swsred_sample_b.tgz | J1-band data (4 frames x 2 arrays) |
swsred_sample_r.tgz | H1-band data (4 frames x 2 arrays) |
The reduced images produced with two iterations (n_iter=2) should be like these.
File | Contents |
fs12_j1_b1_stack1.fits | Stacked image (after two iterations) on the blue array #1 |
fs12_j1_b2_stack1.fits | Stacked image (after two iterations) on the blue array #2 |
fs12_h1_r1_stack1.fits | Stacked image (after two iterations) on the red array #1 |
fs12_h1_r2_stack1.fits | Stacked image (after two iterations) on the redarray #2 |
fs12_j1.fits | Mosaicked image (after two iterations) on the blue arrays Mosaicking parameters still under verification. Use this image (and mosaicking methods) only to check if swsred works correcctly. |
fs12_h1.fits | Mosaicked image (after two iterations) on the red arrays Mosaicking parameters still under verification. Use this image (and mosaicking methods) only to check if swsred works correctly. |
SWSRED requires the following python packages and astrOmatic softwares.
Package | Tested version | |
Python | 2.7.17 | 3.7.7 |
astropy | 2.0.9 | 4.0 |
numpy | 1.16.6 | 1.18.1 |
scipy | 1.2.1 | 1.4.1 |
matplotlib | 2.2.3 | 3.1.3 |
sextractor | 2.19.5 | 2.19.5 |
scamp | 2.7.8 | 2.7.8 |
swarp | 2.38.0 | 2.38.0 |
Also, Internet connection is required during reducing data to access Pan-STARRS/2MASS catalogs.
% cd SWSRED_DIRECTORY % tar xvf swsred_20210414c.tgz % tar xvf swsred_calib_bpm_s21a_20210418.tgz % tar xvf swsred_calib_mosaic_s21a_20210413.tgz % tar xvf swsred_calib_flat_bbf_s21a_20210418.tgz % tar xvf swsred_calib_flat_mbf_s21a_20210418.tgz
% export PYTHONPATH="SWSRED_DIRECTORY:${PYTHONPATH}
SWSRED has no documentations yet, but you can use a dirty script 'swsred/reduce_all.py' as a quick tutorial to learn how to use the software.
Be sure to use SWSRED within python interpreter (e.g., in ipython or jupyter, etc.), not via the command line as no command-line interfaces prepared.
Here is a quick introduction to use SWSRED.
> import glob > in_arr1 = glob.glob("DATA/PATH/SWSB*1.fits") # blue chip1 data > in_arr1.sort() # must be sorted before using.
import glob import astropy.io.fits dct1 = {} dct2 = {} infiles = glob.glob("SWS*fits") infiles.sort() for infile in infiles: hdr = astropy.io.fits.getheader(infile) ch = hdr["2_DETCH"] if ch == 1: dct = dct1 else: dct = dct2 if hdr["2_DSETID"] not in dct.keys(): dct[hdr["2_DSETID"]] = [] dct[hdr["2_DSETID"]].append(infile)
> import swsred.make_file_lst > file_lst1 = swsred.make_file_lst.make_file_lst(in_arr1)
Key | Value | Description |
in | data/path/SWSB00000001.fits | input filename |
ff | ffSWSB00000001.fits | filename of flat-fielded data |
ms | msSWSB00000001.fits | filename of self-sky data |
ss | ssSWSB00000001.fits | filename of sky-subtracted data |
wc | wcSWSB00000001.fits | filename of WCS-corrected data |
bpm | /SWSRED_DIRECTORY/calib/bpm_sXXY_b1.fits | filename of Bad-pixel mask (XXY denotes a semester) |
wht | SWSB00000001_wht.fits | filename of weight map (created by make_weight_map module) |
omsk | SWSB00000001_omsk.fits | filename of object mask |
ldac | ssSWSB00000001.ldac | filename of SExtractor LDAC catalog |
ascii | ssSWSB00000001.ascii | filename of SExtractor ASCII catalog |
head | ssSWSB00000001.head | filename of SExtractor HEAD file |
> import swsred.flat_field > swsred.flat_field.flat_field(file_lst1["in"], file_lst1["ff"])
Refer to 'swsred/reduce_all.py' for all the procedures.
Please send your bug reports or questions to konishi__at__ioa.s.u-tokyo.ac.jp.