Welcome to drizzlepac’s API documentation!¶
This package supports the use of
AstroDrizzle as an integrated set of
modules that can be run in an automated manner to combine images, along
with other tasks to support image alignment and coordinate transformations
with distortion included. The version of
DrizzlePac described here
implements a single task to run the entire
pipeline, while also providing the framework for users to create their own
custom pipeline based on the modules in this package merged with their own
custom code if desired. These pages document what functions and classes are
available for use under Python while providing the syntax for calling those
functions from Python tasks.
Full documentation of how to run the primary
tasks, along with fully worked examples, can be found in the
This package relies on the STWCS package in order to provide the support for the WCS-based distortion models and alignment of the input images.
- Primary User Interface: AstroDrizzle()
- Process Input
- Static Mask Step
- Sky-Subtraction Step
- Image Drizzling Step
- Median Image Computation Step
- Median Image Blotting Step
- Cosmic-ray Identification Step
DrizzlePac Release Notes¶
The code for this package gets released through a number of methods: namely, the use of the package for pipeline and archive processing of ACS and WFC3 data, SSB’s semi-annual public release of the stsci_python package, and a weekly beta release of the development version. The following notes provide some details on what has been revised for each version.
Image Registration Tasks¶
Documentation for the replacement task for IRAF’s
TweakReg, has been added to this package.
These new modules describe how to run the new
as well as use the classes in the task to generate catalogs interactively
for any chip and work with that catalog. The current implementation of this
code relies on a very basic source finding algorithm loosely patterned
after the DAOFIND algorithm and does not provide all the same features
or outputs found in DAOFIND. The fitting algorithm also reproduces the
fitting performed by IRAF’s
geomap in a limited fashion; primarily,
it only performs fits equivalent to
geomap’s ‘shift’ and ‘rscale’
solutions. These algorithms will be upgraded as soon as replacements
- TWEAKREG: Image Alignment
- Refmagefindpars: Source finding parameters for the reference image
- Imagefindpars: Source finding parameters
- Image Class
- Classes to manage Catalogs and WCS’s
- Catalog Generation
- Functions to Manage WCS Table Extension
- Functions to Manage Legacy OPUS WCS Keywords in the WCS Table
- TWEAKUTILS: Utility Functions for
- UPDATEHDR: Functions for Updating WCS with New Solutions
- Region mapping for TweakReg
- Photometric equalization for AstroDrizzle
- pixreplace: Replace pixels which have one value with another value
Coordinate Transformation Tasks¶
These tasks support transformations of source positions to and from distorted and drizzled images.
ACS Header Update Task¶
A task, ‘updatenpol’, has been written to automate the updating of ACS image headers with the filename of the appropriate NPOLFILE based on the DGEOFILE specified in the image header. This task should be used to update all ACS images prior to processing them with ‘astrodrizzle’.
Reproducing Pipeline Processing¶
The task ‘runastrodriz’ can be used to reproduce the same Drizzle processing that gets performed on HST data when retrieving data from the HST archive.
- Running Astrodriz
- Hubble Advanced Products API
- Single-visit Mosaic Processing
- API for runsinglehap
Astrometry and Advanced Pipeline Products¶
drizzlepac package can be used for many purposes, all related to aligning and combining images to create products which can provide the deepest available views of the data. Combining the data with
drizzlepac relies on the WCS solution specified in the input image headers. These WCS solutions are expected to align the images to each other (relative astrometry) as well as align the image to the correct position on the sky (absolute astrometry). The telemetry from HST allows the relative astrometry to be known extremely accurately (sub-milli-arcsecond level) when all images use the same guide stars and when the images were taken in the same visit. However, data taken at different times using different guide stars have historically had errors in the alignment with a sigma of 1 arc-second (or more). As a result, corrections to the alignment need to be made in order to successfully combine the images.
The code being used in the automated HST calibration pipeline to generate the products served by the HST Archive through the MAST Portal relies on multiple methods to do the best job possible in aligning and combining data regardless of whether they came from the same visit (or instrument) or not.