NASA's James Webb Space Telescope

AstroConda: New Release of Science Software

News Feature May 2, 2016

AstroConda release graphic

The Science Software Branch of the Space Telescope Science Institute wishes to announce the availability of AstroConda, a replacement for the previously released Ureka environment along with updates to many of the packages installed as part of the STScI_Python releases.  Full details on the new software environment and how to install it can be found at:


The transition to AstroConda had one unexpected consequence that only appeared the week of April 25; specifically, the Ureka-based SSBX environment can no longer be installed.  We have two options available for those who have relied on the SSBX environment:

We regret the problems this unexpectedly forced transition has caused for many in the STScI community, but all the lessons learned from providing and maintaining Ureka over the last few years have been applied to our new distribution.  AstroConda is more stable, and will not disappear like Ureka's SSBX environment did; as problems with any package will simply result in a lack of updates for that package until the problems are resolved by the developer.


We continue to provide IRAF as a component of this new AstroConda environment.  However, more modern computer environments have placed increasing pressure on our ability to build IRAF for new operating systems.  The current installation includes IRAF v2.16.1 built under OS X 10.6 (for the Mac install) and under Red Hat Enterprise Linux 5 (for the Linux install).  Instructions are provided to allow you to install IRAF under the Python 2.7 environment:

These binaries, however, are known to be incompatible with some newer systems based on reports from users; specific configurations that produce these problems are not clearly identified, but (as one example only) a Linux system with an XFS disk partition larger than 2Tb has been seen to be incompatible with IRAF. Should you find this version of IRAF (should you choose to install it) does not work, we have developed a Virtual Machine install for all the IRAF-related software (contact us for details should you need this solution).

We plan to enhance the IRAF environment so it may be installed in a more modular fashion using Conda (under AstroConda). This enables Gemini and other contributors relying on IRAF to provide updates without requiring re-installation of the entire IRAF environment.  Unfortunately, the non-VM/native installation of IRAF can only be supported for as long as we are able to build and run IRAF, which we anticipate will be possible for at least the next year or two barring any unforeseen OS/compiler changes.

Python Support

The AstroConda environment provides installations for Python 2.7, Python 3.4, and Python 3.5.  Unfortunately, at this time, the IRAF installation can only be supported under Python 2.7.  


Full installation instructions, and explanations, can be found on the AstroConda web page.  However, should you wish to jump head-long into the new Conda-based environment, you can follow these simple steps:

  1. Install Anaconda locally on your system 
  2. Start a BASH shell - the entire conda environment inextricably relies on BASH
  3. Point to our AstroConda channel using:  conda config --add channels
  4. ​Install the software from the AstroConda channel using: conda create -n astroconda stsci
  5. Start it up (under the BASH command shell) with: source activate astroconda

Please go the web page for full details on how to work best with this environment.


Questions about this installation and the STScI-based and Gemini-based software included in this environment can be directed to:

Updated Software

The full set of release notes for all the STScI software included in this installation can be found at: These notes provide expanded details for updates and bug fixes implemented for the following major packages. Please see the web page for the full set of release notes.

Dependencies and SciPy

An effort has been made to start replacing internal dependencies on stsci.* packages where equivalents are available through SciPy.  One example would be stsci.convolve, which was an early fork of scipy's convolve package, with the stsci_python code being modified to use scipy instead.  As a result, we will start deprecating some of these packages in future releases for eventual removal from our distribution.  After all, who needs our version when a better scipy version exists?  The names of those deprecated packages will be clearly noted when we start this transition, and we will not remove these packages for at least 6 months after we announce their deprecation.


CALWF3 Version 3.3 applies instrument calibrations to HST/WFC3 data.  This version includes the following updates and bug fixes (and more...):

  • Addition of a new processing step, FLUXCORR, to the UVIS pipeline
  • Addition of CTE correction for full-frame UVIS data
  • sink pixel detection is now performed in the UVIS pipeline for full-frame UVIS images
  • CRCORR processing turned off by default for IR SCAN data
  • assorted bug fixes (including memory leaks)


CALACS Version v8.3.3 applies instrument calibrations to HST/ACS data. This version includes the following updates and bug fixes (and more):

  • Added support for 2K subarrays in PCTECORR
  • Improved temporary file handling.
  • Added support for very long input list for ACSREJ.


CALCOS Version 3.1.3 applies instrument calibrations to HST/COS data.  This version includes the following updates:

  • Revisions needed to allow calcos to run under Python 3
  • Allow flux extraction from 0 to 100%
  • Ignore DQ flag DQ_GAIN_SAG_HOLE in background regions in profile alignment step
  • Shift DQ data array the same as data array in TRCECORR and ALGNCORR steps of calcos
  • Fix up x1d output for boxcar option
  • CalCOS should print warnings for certain conditions of HOTSPOT calibration
  • Events in COS data should now be flagged using the SPOTTAB


The DrizzlePac package, a component of the STScI_Python environment, implements the distortion correction and image combination algorithm for HST data.  This release of DrizzlePac Version 2 updates the software to provide a number of new capabilities and bug fixes; including:

  • support for automated mosaic building
  • improved sky matching
  • support for the simplified/improved ACS time-dependent distortion correction
  • allowing undistorted non-HST images to be used as references for aligning HST images
  • better capability for aligning images from different HST cameras
  • ability to run under Python 2.7 and Python 3.5

A full set of release notes can be found online at the DrizzlePac home page at:


This release includes pysynphot v0.9.8.2. The following changes (and a little more) have been made since the last release of v0.9.7 (Oct 2015):

  • Updated spectra data including ACS wavecat and Vega reference spectrum.
  • Replaced PyFITS dependency with
  • Added a lot of documentation and tutorials
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