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| Photutils | |
|---|---|
| Name | Photutils |
| Title | Photutils |
| Developer | Astropy Project |
| Released | 2014 |
| Programming language | Python |
| Operating system | Cross-platform |
| License | BSD |
Photutils
Photutils is an open-source Python library for astronomical photometry and image analysis, developed as part of the Astropy ecosystem. It provides tools for source detection, aperture photometry, point-spread function modeling, background estimation, and segmentation that are widely used in observational programs and survey pipelines. Photutils integrates with scientific projects and institutions and is used alongside tools from major observatories and data archives.
Photutils originated within the community that created Astropy Project and draws contributors from institutions such as Space Telescope Science Institute, European Southern Observatory, National Radio Astronomy Observatory, Harvard–Smithsonian Center for Astrophysics, and Max Planck Society. It serves as a bridge between data products from missions like Hubble Space Telescope, James Webb Space Telescope, Kepler (spacecraft), Transiting Exoplanet Survey Satellite, and ground-based surveys such as Sloan Digital Sky Survey and Pan-STARRS. The library complements software from projects like IRAF, DAOPHOT, SExtractor, CASA (software), and Source Extractor while interoperating with arrays and numerical tools from NumPy, SciPy, matplotlib, and scikit-image. Photutils development follows practices promoted by organizations like Python Software Foundation and uses platforms such as GitHub for code hosting and Continuous integration systems maintained by services like Travis CI, GitHub Actions, and AppVeyor.
Photutils offers modular features including aperture photometry, point-spread function (PSF) photometry, source detection, segmentation, and background estimation. Aperture routines support circular, elliptical, and arbitrary polygon apertures compatible with outputs from DS9, Aladin (software), and TOPCAT. PSF modeling integrates with fitting backends using solvers from SciPy and modeling frameworks used by Sherpa (software) and Astropy Modeling. Source detection algorithms are inspired by techniques used in SExtractor and adapted for modern Python stacks such as scikit-image and OpenCV. Background estimation supports box, mesh, and model fitting approaches similar to algorithms employed by teams at European Space Agency and NASA. Photutils also provides tools to handle image WCS information compliant with World Coordinate System, linking to metadata conventions defined by FITS and used by missions like Gaia and Wide-field Infrared Survey Explorer.
Photutils is written in Python (programming language) and requires complementary packages including NumPy, SciPy, Astropy Project, matplotlib, and optionally scikit-image. Binary distribution and package management can be done via pip (package installer) or Conda (package manager), commonly used by researchers at CERN and universities like University of Cambridge and Massachusetts Institute of Technology. Compatible operating systems include Linux, macOS, and Microsoft Windows. Development environments often integrate with editors and platforms such as Jupyter Notebook, JupyterLab, Visual Studio Code, and PyCharm. Contributors adhere to coding standards inspired by organizations like NumPy Project and testing frameworks used by pytest and coverage.py.
Typical workflows involve loading images from archives such as Mikulski Archive for Space Telescopes or Canadian Astronomy Data Centre and performing source detection and photometry for science cases tied to institutions like European Southern Observatory or missions like Hubble Space Telescope. Example tasks include aperture photometry for transient sources reported by Zwicky Transient Facility, PSF fitting for crowded fields studied by OGLE project, and segmentation for extended objects in surveys like Dark Energy Survey. Integration examples pair Photutils with visualization tools such as DS9, with catalog cross-matching using utilities similar to TOPCAT, and with machine-learning pipelines employing frameworks like TensorFlow or PyTorch. Community tutorials and workshops occur at conferences and meetings organized by American Astronomical Society, European Astronomical Society, and nodes of International Astronomical Union.
Photutils development is coordinated via repositories on GitHub where issues and pull requests are reviewed by contributors affiliated with centers such as Space Telescope Science Institute and research groups at University of Chicago. Contributions follow governance models advocated by Astropy Project and use continuous integration from services like GitHub Actions. Code reviews reference style guides from the Python Software Foundation and testing practices using pytest. Documentation is generated with tools such as Sphinx (documentation generator) and published alongside examples used in workshops at institutions like Carnegie Institution for Science and California Institute of Technology. Funding and support have come from agencies including National Science Foundation and European Research Council through projects that fund astronomical software infrastructure.
Photutils is optimized for Python scientific stacks and leverages accelerations available via NumPy vectorization and compiled routines from SciPy. Performance scales with image size and algorithm choice; aperture photometry scales linearly while PSF fitting and segmentation can be more computationally intensive, often parallellized in pipelines at facilities like European Southern Observatory or NOIRLab. Limitations include sensitivity to crowded-field confusion similar to challenges faced by DAOPHOT and SExtractor and tolerance to varying PSF across wide-field cameras like those on Subaru Telescope or Vera C. Rubin Observatory. Users mitigate limitations by combining Photutils with instrument-specific calibration pipelines developed at observatories such as Keck Observatory and Gemini Observatory.
Photutils is used extensively in research for exoplanet photometry in programs tied to Kepler (spacecraft) and Transiting Exoplanet Survey Satellite, transient detection for projects like Zwicky Transient Facility and Pan-STARRS, and crowding-limited photometry in stellar clusters observed by Hubble Space Telescope. It supports survey data reduction workflows for Sloan Digital Sky Survey, Dark Energy Survey, and preparatory analyses for Vera C. Rubin Observatory and Euclid (spacecraft). Scientific collaborations at institutions including Harvard–Smithsonian Center for Astrophysics and Max Planck Institute for Astronomy apply Photutils in studies of galaxy photometry, supernova light curves, and gravitational lensing analyses performed by teams affiliated with European Southern Observatory and Space Telescope Science Institute.
Category:Astronomy software