This repository contains the codes used to create the high level science products (HLSPs) for the targets in the Hubble Space Telescope’s (HST) Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) program. In particular, the spectral coaddition algorithm used by the HASP (Hubble Advanced Spectral Products) is included in the ULLYSES package.
See more info about ULLYSES and its targets at ullyses.stsci.edu. A full description of the data products produced by the ULLYSES team can be found at ULLYSES Data Products.
The ullyses package can be installed into a virtualenv or conda environment via pip. We recommend that for each installation you start by creating a fresh environment that only has Python installed and then install the ullyses package and its dependencies into that bare environment. If using conda environments, first make sure you have a recent version of Anaconda or Miniconda installed.
All package dependencies will be installed with ullyses, including
ullyses-utils, which contains the utility scripts and
data files used to create HLSPs.
The only exceptions are the stisblazefix and CalSTIS packages, which must be installed
manually, but only if you wish to create blaze-corrected products or custom-calibrated STIS products.
Instructions for installing stisblazefix are available in the
stisblazefix documentation. To install CalSTIS, you must
use stenv.
If you do not already have conda installed, you can do so by following these instructions.
If you do not need to run the custom STIS calibration pipeline, you can create and activate your conda environment like so:
conda create -n <env_name> python=3.9
conda activate <env_name>
Python version 3.9 or greater is required for some dependencies, including calcos, the COS data calibration pipeline used in these scripts.
If you want to run the custom STIS calibration pipeline, you must create the environment like so:
conda env create -n <env_name> --file <filename>
conda activate <env_name>
Where <filename> is determined by following the instructions for
choosing a release in the stenv documentation.
After creating your conda environemnt, simply install the latest release of ullyses from pip:
pip install ullyses
To install your own copy of the development version from github, you first need to fork and clone the ullyses repo:
cd <where you want to put the repo>
git clone https://github.com/spacetelescope/ullyses
cd ullyses
Then install from your local checked-out copy:
pip install -e .
There are four main types of ULLYSES HLSPs:
- coadded and abutted spectra
- timeseries spectra
- re-packaged drizzled images
- custom-calibrated individual spectra, or level0 HLSPs
Below are instructions for creating each type of HLSP.
Coadded
and abutted
spectra are created for each target.
Currently supported instruments are HST/COS, HST/STIS, and FUSE.
The input files are _x1d.fits or _sx1.fits files for COS and STIS,
and _vo.fits for FUSE. These input files may also be level0 (custom-calibrated spectra, see
below)
themselves. Coadded and abutted spectra can then be created programmatically,
or using the command-line script, coadd.
From the command line:
coadd -i <input_directory> -o <output_directory>
Where <input_directory> contains the data to be coadded, and the products will
be written to <output_directory>.
A directory, or a specific set of files, can be provided programmatically:
from ullyses.ullyses_coadd_abut_wrapper import main, coadd_and_abut_files
coadd_and_abut_files(file_list, output_directory)
main(input_directory, output_directory)
Regardless of what files are specified in the input list or directory, only files of the same instrument and grating combination will be coadded. Data from all input gratings will be abutted according to the strategy adopted by ULLYSES.
There are two main flavors of timeseries spectra: exposure level and sub-exposure level. Exposure level timeseries spectra are essentially stacked individual 1D spectra. Sub-exposure level timeseries spectra are made from split 1D spectra. That is, for time-tag data (currently only COS/UV), exposures are broken down into even smaller time chunks, then stacked.
For HST, both exposure and sub-exposure timeseries spectra are created the same way. To create an HST timeseries spectrum, you must supply a configuration YAML file. The ULLYSES team has already created such files for the monitoring stars (V-TW-HYA, V-BP-TAU, V-RU-LUP, V-GM-AUR) and recorded the optimal parameters in YAML files stored in the ullyses-utils repository. You may use the ULLYSES YAML files as input, or supply your own, but they must conform to the required format. See the TW Hydra YAML file as an example.
WARNING: To create the HST timeseries spectra, individual split exposures must be created and calibrated. This process can be very time-consuming, taking up to several hours on some systems.
Once you have a YAML file, you create the timeseries spectrum like so:
python ctts_cal.py --orig <origdir> --copydir <copydir> --hlspdir <hlspdir> --targ <targ> --yaml <yaml>
where <origdir> is the directory which houses all the input data, <copydir> is the directory
to copy input data to (data will be modified), <hlspdir> is the directory to write the final
timeseries spectra, <targ> is the ULLYSES name of the target being calibrated, and <yaml> is the
YAML confirmation file. If no YAML file is supplied, the target name will be used to fetch the
appropriate file from the ullyses-utils repository.
The ctts_cal.py also corrects for vignetting in the COS/NUV data.
It is possible to create a timeseries "spectrum" using photometric measurements over time. The ULLYSES team has performed photometry on ULLYSES low-mass stars using the LCOGT network of telescopes.
To create LCOGT photometric timeseries spectra:
python lcogt_hlsps_wrapper.py -i <indir> -o <outdir> -t <targ>
where <indir> is the directory which contain the original LCOGT FITS images (used to
extract observational metadata), <outdir> is the directory to write the HLSPs, and
<targ> is the ULLYSES target name. The target name will be used to fetch the
appropriate photometric measurements in the ullyses-utils repository.
The ULLYSES team creates drizzled WFC3 images for the low-metallicity galaxies NGC3109 and SextansA. These images are repackaged to conform to the ULLYSES HLSP requirements, but the data array values are left untouched.
To create drizzled image HLSPs:
python imaging_hlsps_wrapper.py <drcfile> -o <outdir> -t <targ>
where <drcfile> is the name of the original drizzled DRC file, <outdir> is the
directory to write the HLSP to, and <targ> is the ULLYSES target name.
Optional arguments are:
--hdr_targ HDR_TARG If specified, alternative target name to use in HLSP file name
--hlspname HLSPNAME Name of output HLSP file. By default, follows ULLYSES standard
Prior to turning spectra into ULLYSES HLSPs, some targets require extra processing to fix various calibration issues. For example, STIS/G750L data must be defringed, or wavelength offsets must be corrected. Once these custom calibration steps have been applied, a keyword is added to the output FITS file signifying that the file should be considered a level0 HLSP- that is, a custom-calibrated individual 1D spectrum. The various level0 products, and how to create them, are described below.
All T Tauri star STIS CCD observations, and a subset of STIS NUV- and FUV-MAMA observations, require tailored calibrations. Special calibration steps can include: custom hot pixel identification and flagging, defringing for G750L observations, and customized spectral extraction parameters for T Tauri stars and their companions.
To create a custom-calibrated STIS spectra, you must supply a configuration YAML file which lists the specific calibration parameters. The ULLYSES team has already examined each T Tauri star and recorded the optimal custom calibration parameters in YAML files stored in the ullyses-utils repository. You may use the ULLYSES YAML files as input, or supply your own, but it must conform to the format outlined here.
Once you have a YAML file, you create the custom-calibrated STIS spectrum like so:
python calibrate_stis_data.py -i <indir> -y <yaml> -o <outdir>
where <indir> is the input directory that houses the data you wish to calibrate,
<yaml> is the name of the yaml file, and <outdir> is the output directory
where products, logs, and diagnostic plots will be written. A log file of the format
YYYYMMDD_HHmm_cal.log will be written, unless otherwise specified using the optional
arguments below.
Optional arguments are:
-c, --clobber If True, overwrite existing products
--nolog If True, do not produce log file
-l LOGFILE, --logfile LOGFILE
Alternative name of output log file
If a target is not perfectly centered in the COS aperture, the wavelength array can be offset from its true values. Wavelength offsets can be easily corrected by recalibrating the data and supplying a shift file to CalCOS, as described in the COS Data Handbook.
The ULLYSES team has identified stars which require such wavelength offset corrections and documented the necessary shifts in text files stored in the ullyses-utils repository.
Once you have a shift file, you can create wavelength-shifted COS spectra by supplying a directory with multiple exposures, or by supplying a single file. You can create wavelength-shifted COS spectra using the pre-defined ULLYSES shift files like so:
python apply_cos_shifts.py <infiledir> <outdir>
where <infiledir> is the input filename or directory of files that should be shifted,
and <outdir> is the directory to write shifted 1D spectra.
In this case, the target name in the input file(s) header(s) must match the target name
in the shift file. If for some reason it does not, you must also supply the target name
as it appears in the shift file using an additional -t <targ> argument.
You may also create wavelength-shifted COS spectra using your own custom shift file, which is done like so:
python apply_cos_shifts.py <infiledir> <outdir> -s <shift_file>
where <shift_file> is the name of your custom shift file.
Other optional arguments are:
--copydir COPYDIR Name of directory to copy shifted products to
-c, --overwrite If True, overwrite existing products
The ULLYSES team typically uses FUSE VO (Virtual Observatory) files with minimal modification. A DQ (Data Quality) array is added to each VO file, as is required by the ULLYSES pipeline. For the majority of FUSE targets, this DQ array is uniformly zero, meaning there are no data quality issues. However, for a handful of targets, custom flagging is imposed in order to screen out bad spectral regions. Possible DQ flags include:
- DQ=1 (region was affected by the worm)
- DQ=2 (poor photometric quality)
To create these custom-flagged FUSE spectra:
python make_flagged_fuse.py -i <infile> -o <outdir>
where <infile> is the file to flag and <outdir> is the output directory.
Other optional arguments are:
-t TARG or --targ TARG ULLYSES name of target
-c or --overwrite If True, overwrite existing products
We welcome contributions and feedback on this project. If you want to suggest changes to this content, please do the following:
- Fork it.
- Create your feature branch (git checkout -b my-new-feature).
- Add your changes to staging area (git add myfile); This can be repeated multiple times.
- If you are adding a new style guide, do not forget to update guides listing at README.md.
- Commit your changes in staging area (git commit -m 'Added some feature').
- Push to the branch (git push origin my-new-feature).
- Create new Pull Request (PR).
- Ask for a PR review.
We strive to provide a welcoming community to all of our users by abiding with the Code of Conduct.
If you have questions or concerns regarding the software, please open an issue or contact the HST Help Desk. If you have questions regarding the ULLYSES program design or data, please contact the HST Help Desk.