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 AstroDrizzle processing 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 AstroDrizzle and TweakReg tasks, along with fully worked examples, can be found in the DrizzlePac Handbook.

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.

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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.

Documentation for the replacement task for IRAF’s tweakshifts, currently named TweakReg, has been added to this package. These new modules describe how to run the new TEAL-enabled task, 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 are available.

These tasks support transformations of source positions to and from distorted and drizzled images.

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.

The 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.