**UPDATE JANUARY 2019**
New python drizzling tutorials are now available as Jupyter Notebooks.
Starting in July 2012, all drizzled data products obtained from MAST are produced with AstroDrizzle. An abbreviation for Astrometric Drizzle, AstroDrizzle was designed from the ground-up to substantially improve the handling of distortion in the image header World Coordinate System.
AstroDrizzle removes geometric distortion, corrects for sky background variations, flags cosmic-rays, and combines images with optional subsampling. Drizzled data products from MAST are generated for single visit associations only.
To combine data from additional visits, TweakReg may be used to update the image WCS using matched source lists. Once the full set of images of a given target are properly aligned, they may be combined with AstroDrizzle.
DrizzlePac may be obtained by installing AstroConda .
The most recent software versions for both linux and osx are listed here
and the latest release notes may be found here.
Instructions for setting up your computing environment to use the DrizzlePac software are described in the following Jupyter Initialization notebook.
|AUTOMATED MOSAIC BUILDING||Align sets of images into a large mosaic in one step Read more|
|IMPROVED SKY MATCHING ALGORITHMS||Produce seamless mosaics using various sky matching techniques. More details can be found in an example where these techniques are compared.|
|MITIGATION OF FALSE DETECTIONS||Select point-source targets for alignment based on sharpness/roundness and use of DQ masks to limit inclusion of artifacts/cosmic-rays. Read more|
|ALIGNING DIFFERENT HST CAMERAS||Specify separate source finding parameters for input and reference images to optimize source detection from images taken with different HST cameras. Read more|
|ALIGNING HST IMAGES TO NON-HST IMAGES||Allows undistorted non-HST images to be used as references for aligning and drizzling HST data. Read more|
|USING EXCLUSION REGIONS WHEN ALIGNING IMAGES||Select specific areas of interest to use for alignment. We provide an example where these techniques are discussed.|
- Initializing DrizzlePac
- Aligning observations obtained in multiple HST visits
- Aligning HST images to an absolute reference catalog (e.g. GAIA, SDSS)
- Aligning sparse fields
- Improving alignment with DS9 exclusion regions
- Masking satellite trails in DQ arrays prior to drizzling
- Optimizing the image sampling for dithered datasets
- Drizzling WFPC2 data to use a single zeropoint
- Sky matching features for HST mosaics
- Aligning HST mosaics observed with multiple detectors
- WFC3/UVIS: Optimizing Image Alignment for Multiple Visits
- WFC3/IR: Optimizing Image Sampling for a Single Visit
- ACS/WFC: Optimizing Image Alignment for Multiple Visits
- ACS/WFC: Optimizing Image Sampling for a Single Visit
- WFC3/UVIS: Aligning Images in Different Filters
- WFC3/UVIS: Alignment of Sparse Fields Using Headerlets
- WFPC2: Aligning and Drizzling WFPC2 Images
- ACS/WFC: Using SExtractor Catalogs to Align Images
- DrizzlePac Quick Start Guide
- DrizzlePac Handbook
- Frequently Asked Questions
- Instrument Characteristics
- Instrument & Data Handbook Links