SONG stands for Stellar Observations Network Group.
This package, songcn, is designed for the SONG-China project.
The affliated song package is the SONG-China project data processing pipeline. The affliated twodspec is to provide basic operations for raw 2D spectral data.
Bo Zhang, [email protected]
- for the latest stable version:
pip install -U songcn - for the latest github version:
pip install -U git+git://github.com/hypergravity/songcn
song
- song.py
song image collection management - thar.py
ThAr wavelength calibration module for SONG. Loads templates.
twodspec
- aperture.py
the aperture class - background.py
background modelling (scattered light substraction) - calibration.py
wavelength calibration module - ccd.py
basic CCD operations - extract.py
spectral extraction module - trace
trace aperture
SmoothingSpline is from https://github.com/wafo-project/pywafo
# step1: import Song
from song import Song
# step2: initialize Song object by scanning files
s = Song.init(rootdir="/Users/cham/projects/song/star_spec", date="20191030", jdnight=True, n_jobs=-1, verbose=True, subdir="night", node=2)
# usually you can just modify rootdir and datestr
# rootdir: the root directory
# datestr: datestr of the observation
# jdnight: if True, split data at 12:00 next day; if False, only use the data in this directory
# n_jobs: number of jobs for scanning files, -1 for all
# verbose: if True, print information
# subdir: "night"
# node: Danish-->1, Delingha-->2.
# step3: run the daily pipeline
s.daily(proc_slits='all', sharebias=True, star=True, stari2=False, flati2=False, ipcprofile='default')
# proc_slits: a list of slit names, i.e., [5, 8]. or just "all"
# sharebias: if True, share bias images when processing different slit data
# star, stari2, flati2: if set to True, process corresponding images
# ipcprofile: the ipcluster profile name. if not None, you should type
# `ipcluster start --n=8 --profile=default` to start ipcluster The verbose information:
@SONG: scanning files ...
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 0.7s
[Parallel(n_jobs=-1)]: Done 260 out of 260 | elapsed: 1.1s finished
@SONG: 1 unique config found! [5]
SLIT BIAS FLAT STAR THAR
---- ---- ---- ---- ----
5 120 120 11 9
===========================================
@Song: unique slits are [5]
===========================================
@SONG: no images matched!
@SONG: no images matched!
@Slit[5]: processing bias ...Done!
@Slit[5]: processing flat ...
@Slit[5]: tracing orders ...
@Aperture: tracing apertures using [naive] method >>> 51 apertures found!
@Slit[5]: modeling background ...
@Slit[5]: extracting blaze & sensitivity ...Done!
@Slit[5]: cleared tws ...
@Slit[5]: dispatching 9 thar to ipcluster (profile=default, nproc=8) ...
@Slit[5]: dispatching 11 star to ipcluster (profile=default, nproc=8) ...
@Slit[5]: Done!)
saved to files:
========
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T20-37-34.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T20-39-11.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T20-44-31.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T20-46-38.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T23-36-39.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T23-46-46.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-31T00-09-56.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-31T00-12-02.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-31T00-15-42.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-31T00-27-30.fits
/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-31T00-37-37.fits
========
===========================================
DONE!~
===========================================
The generated files are:
tstar_s2_2019-10-31T00-37-37.fits # reduced spectrum
tstar_s2_2019-10-31T00-27-30.fits
tstar_s2_2019-10-31T00-15-42.fits
tstar_s2_2019-10-31T00-12-02.fits
tstar_s2_2019-10-31T00-09-56.fits
tstar_s2_2019-10-30T23-46-46.fits
tstar_s2_2019-10-30T23-36-39.fits
tstar_s2_2019-10-30T20-46-38.fits
tstar_s2_2019-10-30T20-44-31.fits
tstar_s2_2019-10-30T20-39-11.fits
tstar_s2_2019-10-30T20-37-34.fits
summary.svg # summary figure
20191030_song.dump # Song object
20191030_slit5.dump # Slit object
To access the dump files, use joblib.load() to load data. To access the reduced spectra,
from astropy.table import Table
spec = Table.read("/Users/cham/projects/song/star_spec/20191030/night/ext/tstar_s2_2019-10-30T20-37-34.fits")
print(spec.colnames)
['blaze', 'bvc', 'err', 'err1', 'err2', 'err_sum', 'exptime', 'jdmid', 'mask', 'flux', 'flux1', 'flux2', 'flux_sum', 'wave', 'wave_rms']
# 'blaze' [51,2048] blaze function
# 'bvc' float barycenter velocity correction
# 'err' [51,2048] flux error
# 'err1' [51,2048] optimal extracted 1 flux error
# 'err2' [51,2048] optimal extracted 2 flux error (clipped)
# 'err_sum' [51,2048] simple sum flux error
# 'exptime' float exptime
# 'jdmid' float mid-jd
# 'mask' [51,2048] True for bad pixels
# 'flux' [51,2048] combined flux
# 'flux1' [51,2048] optimal extracted 1 flux
# 'flux2' [51,2048] optimal extracted 2 flux
# 'flux_sum' [51,2048] simple sum flux
# 'wave' [51,2048] wavelength
# 'wave_rms' float rms of the ThAr calibrationThe Slit object contains bg, tws etc... slit.tws has colnames:
["fp", "jdmid", "exptime", "wave_init", "wave_solu", "tlines", "nlines", "rms", "pf1", "pf2", "mpflux", "thar_obs"]