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https://github.com/astroaure/astrocal

Astronomy images treatment and global time series creation and analysis

https://github.com/astroaure/astrocal

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Academic email domains
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary

Keywords

astronomy calibration photometry reduction time-series
Last synced: 6 months ago · JSON representation

Repository

Astronomy images treatment and global time series creation and analysis

Basic Info
  • Host: GitHub
  • Owner: AstroAure
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 5.07 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
astronomy calibration photometry reduction time-series
Created over 1 year ago · Last pushed 9 months ago
Metadata Files
Readme License

README.md

Pipeline for light-curve

PyPI - Version

Calibration

Level 1

  1. Create master files
    1. $m_d$ : Dark (darkflat)
    2. $m_d^f$ : Dark for flats
    3. $m_f$ : Flat
  2. Create dead pixels maps:
    1. Hot pixels map from $m_d$ (mask, or set them to median)
    2. Dead pixels map from $m_f$ (mask, or set them to local gaussian)
  3. Calibrate images : $s=\frac{s-md}{mf-m_d^f}$

Level 2

  1. Clean $s$ with dead pixels maps (local gaussian or mask)
  2. Find sources in $s$ (coarse) (photutils or SExtractor)
  3. Plate-solve and update WCS (astroquery or SCAMP)
  4. Calibrate photometry
    1. Search stars in SDSS (once for all frames)
    2. RANSAC fit to find ZP and slope
    3. Calibrate pixel values with RANSAC (to Jy)

Level 3

  1. Clean cosmic rays (local gaussian or mask)
  2. Remove sky

PSF

  1. Co-add images (reproject or SWarp)
  2. Build PSF (photutils or PSFEx)

Light-curve

  1. For all $s$:
    1. Find sources in $s$ (fine) (photutils or SExtractor)
    2. Aperture and PSF photometry (photutils or SExtractor)
  2. Match sources in different catalogs by DBSCAN clustering
  3. Find moving sources
    1. Calculate dispersion of RA/DEC (MAD + threshold)
    2. Linear regression for RA/DEC
    3. Plot RA/DEC for moving sources
  4. Find variable and stable sources
    1. Calculate dispersion of flux/mag (MAD + threshold)
    2. Plot flux/mag
  5. Identify target: Match target RA/DEC to global catalog

Owner

  • Login: AstroAure
  • Kind: user

GitHub Events

Total
  • Watch event: 1
  • Fork event: 1
Last Year
  • Watch event: 1
  • Fork event: 1

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 19 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
pypi.org: astrocal

Package to calibrate astro images and analyse them

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 19 Last month
Rankings
Dependent packages count: 9.1%
Average: 30.3%
Dependent repos count: 51.4%
Maintainers (1)
AstroAure
Last synced: 6 months ago

Dependencies

pyproject.toml pypi
  • Pillow >=10.4.0
  • astropy *
  • astroquery *
  • numpy *
  • photutils *
  • reproject *
  • scikit-learn *
  • scipy >=1.12.0