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tessl/pypi-drizzlepac

HST image combination using the drizzle algorithm to combine astronomical images, to model image distortion, to remove cosmic rays, and generally to improve the fidelity of data in the final image.

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pypipkg:pypi/drizzlepac@3.10.x

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npx @tessl/cli install tessl/pypi-drizzlepac@3.10.0

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DrizzlePac

DrizzlePac is a comprehensive Python library for astronomical image processing and combination, specifically designed for Hubble Space Telescope (HST) data. It implements sophisticated algorithms for aligning and combining astronomical images using the drizzle algorithm, which preserves intrinsic resolution while reducing noise, correcting geometric distortion, removing cosmic rays, and improving overall data fidelity.

Package Information

  • Package Name: drizzlepac
  • Package Type: pypi
  • Language: Python
  • Installation: pip install drizzlepac
  • Documentation: https://drizzlepac.readthedocs.io/en/latest/
  • Source: https://github.com/spacetelescope/drizzlepac

Core Imports

import drizzlepac

Common imports for specific tasks:

from drizzlepac import astrodrizzle, tweakreg, tweakback
from drizzlepac import pixtosky, skytopix, pixtopix
from drizzlepac import resetbits, updatenpol

For HAP (Hubble Advanced Products) processing:

from drizzlepac import haputils
from drizzlepac.haputils import catalog_utils, align_utils

Basic Usage

import drizzlepac
from drizzlepac import astrodrizzle

# Display available help information
drizzlepac.help()

# Basic AstroDrizzle processing - align and combine HST images
astrodrizzle.AstroDrizzle('input_files.txt', output='combined',
                         clean=True, driz_separate=True,
                         driz_sep_wcs=True, median=True,
                         blot=True, driz_cr=True,
                         driz_combine=True)

# Alternative: using Python interface directly
from drizzlepac.astrodrizzle import run
from drizzlepac import util
configobj = util.build_configobj('config.cfg')
run(configobj)

Architecture

DrizzlePac is built around a modular architecture supporting multiple workflow patterns:

Task-Based Processing

  • Primary Tasks: Core image processing functions (astrodrizzle, tweakreg, etc.)
  • Coordinate Tasks: Bidirectional coordinate transformations
  • Utility Tasks: Data quality management, calibration, and region mapping

HAP (Hubble Advanced Products) Framework

  • Product Classes: Hierarchical product management (Total, Filter, Exposure products)
  • Catalog System: Advanced source detection and catalog management
  • Quality Assessment: Comprehensive flagging and diagnostic systems

Configuration System

  • Parameter Files: Standardized .cfg files for reproducible processing
  • TEAL Integration: Interactive parameter editing through TEAL interface
  • Pipeline Mode: Automated processing with minimal user intervention

Capabilities

Primary Image Processing

Core functionality for astronomical image alignment, combination, and calibration using the drizzle algorithm for optimal preservation of image resolution and removal of artifacts.

def AstroDrizzle(input=None, mdriztab=False, editpars=False,
                configobj=None, wcsmap=None, **input_dict): ...

Image Processing

Image Registration and Alignment

Advanced algorithms for computing offsets between images and reference frames, with support for multiple alignment methods and quality assessment.

def TweakReg(files=None, editpars=False, configobj=None,
            imagefindcfg=None, refimagefindcfg=None, **input_dict): ...

Registration and Alignment

Coordinate Transformations

Bidirectional coordinate transformation capabilities between pixel coordinates, sky coordinates, and different WCS reference frames with full distortion correction.

def xy2rd(input, x=None, y=None, coords=None, coordfile=None, **kwargs): ...
def rd2xy(input, ra=None, dec=None, coordfile=None, **kwargs): ...
def tran(inimage, outimage, direction='forward', x=None, y=None, **kwargs): ...

Coordinate Transformations

Data Quality and Calibration

Tools for managing data quality flags, photometric equalization, and applying calibration corrections to HST observations.

def reset_dq_bits(input, bits, extver=None, extname='dq'): ...
def photeq(files='*_flt.fits', sciext='SCI', errext='ERR', **kwargs): ...
def update(input, refdir="jref$", local=None, interactive=False): ...

Data Quality and Calibration

HAP Processing Framework

Advanced processing capabilities for Hubble Advanced Products including sophisticated catalog management, source detection, and automated quality assessment.

class HAPProduct: ...
class HAPCatalogs: ...
def perform_align(input_list, catalog_list, num_sources, **pars): ...

HAP Processing

WCS and Region Management

World Coordinate System utilities, region file mapping, and WCS solution management for complex astronomical workflows.

def apply_tweak(drz_file, orig_wcs_name, output_wcs_name=None, input_files=None, **kwargs): ...
def MapReg(input_reg, images, img_wcs_ext='sci', refimg='', ref_wcs_ext='sci', **kwargs): ...
def buildwcs(outwcs, configObj=None, editpars=False, **input_dict): ...

WCS and Region Management

Command-Line Tools

DrizzlePac provides several command-line scripts for common operations:

  • mdriz - MultiDrizzle interface
  • resetbits - Reset DQ bits in FLT files
  • updatenpol - Update NPOL distortion corrections
  • runastrodriz - Pipeline AstroDrizzle processing
  • runsinglehap - Process single HAP observations
  • runmultihap - Process multiple HAP observations

Error Handling

DrizzlePac functions typically raise standard Python exceptions:

  • ValueError - Invalid parameter values or incompatible inputs
  • IOError - File access or format issues
  • RuntimeError - Processing failures or convergence issues
  • Custom exceptions defined in individual modules for specific error conditions

Common error patterns include validation of input file formats, WCS compatibility checks, and memory management for large image processing operations.