astropy-fundamentals

Astropy Fundamentals

Master Astropy, the foundational Python library for astronomy and astrophysics. Learn to work with astronomical data formats, coordinate systems, physical units, precise time calculations, and scientific tables - the essential toolkit for modern astronomical computing.

Official Documentation: https://docs.astropy.org/en/stable/

GitHub: https://github.com/astropy/astropy

Quick Reference Card

Installation & Setup

# Using pixi (recommended for scientific projects)
pixi add astropy photutils specutils

# Using pip
pip install astropy[all]

# Optional affiliated packages
pixi add photutils specutils astroquery reproject

Essential Operations

import astropy.units as u
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.time import Time
from astropy.table import Table, QTable
from astropy.wcs import WCS

# Units and Quantities
distance = 10 * u.parsec
wavelength = 5000 * u.angstrom
freq = wavelength.to(u.Hz, equivalencies=u.spectral())

# FITS I/O
with fits.open('image.fits') as hdul:
    data = hdul[0].data
    header = hdul[0].header

# Coordinates
coord = SkyCoord(ra=10.625*u.degree, dec=41.2*u.degree, frame='icrs')
galactic = coord.galactic
separation = coord.separation(other_coord)

# Time
t = Time('2024-01-01T00:00:00', format='isot', scale='utc')
jd = t.jd
future = t + 1*u.day

# Tables
tbl = Table([ra_col, dec_col, flux_col], names=['ra', 'dec', 'flux'])
filtered = tbl[tbl['flux'] > 100]

# WCS
wcs = WCS(header)
ra, dec = wcs.pixel_to_world(x_pix, y_pix)

Quick Decision Tree

Working with astronomical data?
├─ FITS files → astropy.io.fits
├─ Celestial coordinates → astropy.coordinates (SkyCoord)
├─ Physical quantities → astropy.units
├─ Astronomical time → astropy.time
├─ Catalogs/tables → astropy.table
├─ Image coordinates → astropy.wcs
├─ Photometry → photutils
└─ Spectroscopy → specutils

Need coordinate transformation?
├─ Simple conversions → SkyCoord.transform_to()
├─ Sky separations → SkyCoord.separation()
├─ Matching catalogs → match_to_catalog_sky()
└─ Custom frames → Define new frame class

Working with units?
├─ Standard units → multiply by u.unit
├─ Unit conversions → quantity.to()
├─ Wavelength/frequency → Use u.spectral() equivalency
└─ Dimensionless → quantity.decompose()

Need precise time?
├─ Single instant → Time()
├─ Arrays of times → Time(array)
├─ Time scales → specify scale='utc', 'tai', etc.
└─ Time formats → specify format='jd', 'isot', etc.

When to Use This Skill

Use Astropy fundamentals when working with:

• FITS files from telescopes, surveys, or simulations
• Celestial coordinates requiring precise transformations between reference frames
• Physical quantities with units (distances, velocities, energies, magnitudes)
• Astronomical time needing sub-nanosecond precision or specific time scales
• Catalogs and tables from surveys, observations, or simulations
• Image astrometry relating pixel positions to sky coordinates
• Photometric analysis of point sources or aperture measurements
• Spectroscopic data requiring wavelength calibration or line analysis
• Multi-wavelength astronomy combining data across electromagnetic spectrum
• Observational planning calculating rise/set times, airmass, visibility

Core Concepts

1. Units and Quantities

Physical quantities with dimensional correctness. Attach units to values and convert automatically.

Key operations:

• Create: distance = 10 * u.parsec
• Convert: distance.to(u.lightyear)
• Equivalencies: wavelength.to(u.Hz, equivalencies=u.spectral())
• Arithmetic: Units propagate automatically

See references/patterns.md for custom units, logarithmic units, and advanced equivalencies.

2. FITS I/O

Standard format for astronomical data. Read/write images and tables with headers.

Key operations:

• Open: fits.open('file.fits') with context manager
• Access: hdul[0].data, hdul[0].header
• Headers: Dictionary-like access, add comments
• Memory mapping: fits.open('file.fits', memmap=True) for large files

See references/patterns.md for multi-extension FITS, header inheritance, and large file handling.

3. Coordinates

Celestial positions with automatic frame transformations.

Key operations:

• Create: SkyCoord(ra=10*u.deg, dec=20*u.deg, frame='icrs')
• Transform: coord.galactic, coord.transform_to('fk5')
• Separations: coord1.separation(coord2)
• Matching: coord.match_to_catalog_sky(catalog)

See references/patterns.md for custom frames, catalog cross-matching, and observer-dependent coordinates.

4. Time

Sub-nanosecond precision with multiple time scales and formats.

Key operations:

• Create: Time('2024-01-01', scale='utc')
• Formats: .jd, .mjd, .iso, .datetime
• Scales: .utc, .tai, .tt, .tdb
• Arithmetic: t + 1*u.day

See references/patterns.md for high-precision calculations, time series, and barycentric corrections.

5. Tables

Flexible tabular data with units and metadata.

Key operations:

• Create: Table([col1, col2], names=['a', 'b'])
• QTable: Preserves Quantity units
• Filter: tbl[tbl['mag'] < 15]
• Join: join(tbl1, tbl2, keys='id')
• I/O: .read(), .write() for FITS, CSV, HDF5

See references/patterns.md for masked tables, joins, metadata, and indexing.

6. WCS (World Coordinate System)

Maps pixel coordinates to sky coordinates.

Key operations:

• Load: WCS(header)
• Pixel→Sky: wcs.pixel_to_world(x, y) returns SkyCoord
• Sky→Pixel: wcs.world_to_pixel(coord)
• Legacy: wcs.all_pix2world(x, y, 0) for arrays

See references/patterns.md for creating WCS, SIP distortions, and cutouts.

7. Photometry (Photutils)

Source detection and aperture photometry.

Key operations:

• Detect: DAOStarFinder(fwhm=3.0, threshold=5*std)
• Aperture: CircularAperture(positions, r=5.0)
• Measure: aperture_photometry(image, apertures)
• Background: Use CircularAnnulus for local background

See references/patterns.md for PSF photometry and grouped sources.

8. Spectroscopy (Specutils)

1D spectroscopy with wavelength calibration and line analysis.

Key operations:

• Create: Spectrum1D(spectral_axis=wavelength, flux=flux)
• Read: Spectrum1D.read('spec.fits')
• Convert: .with_spectral_axis_unit(u.Hz)
• Lines: line_flux(spectrum, region)

See references/patterns.md for line fitting, continuum normalization, and redshift measurement.

Patterns

See references/patterns.md for detailed patterns including:

FITS Manipulation:

• Multi-extension FITS handling
• Header inheritance and propagation
• Large file processing with memory mapping
• Updating headers without loading data

Units and Quantities:

• Custom unit definitions
• Logarithmic units (magnitudes, decibels)
• Spectral equivalencies (wavelength/frequency/energy)
• Structured arrays with units

Coordinates:

• Custom coordinate frames
• Efficient catalog cross-matching
• Observer-dependent coordinates (AltAz)
• Proper motion and radial velocity

Time:

• High-precision time arithmetic
• Time series analysis and folding
• Time zone handling
• Barycentric corrections

Tables:

• Masked tables for missing data
• Multi-key joins
• Table metadata and descriptions
• Fast indexing for large tables

WCS:

• Creating WCS from astrometry solutions
• SIP distortion polynomials
• Cutouts with WCS preservation

Photometry:

• PSF photometry
• Grouped source photometry
• Background subtraction strategies

Spectroscopy:

• Spectral line fitting
• Continuum normalization
• Redshift measurement via cross-correlation

Real-World Examples

See references/examples.md for complete workflows:

1. Telescope Image Processing Pipeline: FITS loading → background subtraction → source detection → aperture photometry → catalog creation with WCS

2. Catalog Cross-Matching: Multi-catalog matching with coordinate transformations, quality filtering, and color calculations

3. Light Curve Analysis: Time series handling, period search with Lomb-Scargle, phase folding, and binning

4. Multi-Wavelength SED Construction: Combining multi-band photometry with proper unit handling and flux conversions

5. Spectroscopic Redshift Measurement: Emission line identification, template cross-correlation, and redshift refinement

6. Observability Calculation: Target visibility from specific location, airmass calculation, sun/moon constraints

Common Issues and Solutions

See references/common-issues.md for troubleshooting:

FITS I/O Issues:

• VerifyError with non-standard FITS
• Memory errors with large files
• Compressed FITS handling

Unit Problems:

• UnitConversionError between incompatible units
• Losing units after NumPy operations
• Logarithmic unit confusion

Coordinate Issues:

• Frame attribute mismatches
• AltAz requiring location and time
• Slow coordinate transformations

Time Issues:

• Time scale confusion (UTC vs TAI vs TDB)
• ISO format parsing ambiguity
• Precision loss in calculations

Table Issues:

• Column type mismatches
• Units lost when writing to CSV
• Joining tables with different column names

WCS Problems:

• WCS transformations giving NaN
• Pixel origin convention confusion (origin=0 vs origin=1)
• SIP distortion handling

Performance:

• Out of memory with large FITS files
• Slow operations on large catalogs
• Vectorization strategies

Best Practices Checklist

Units and Quantities

• Always attach units to physical quantities
• Use .to() for explicit conversions
• Leverage equivalencies for wavelength/frequency/energy
• Decompose complex units to verify correctness

FITS Files

• Use context managers (with fits.open()) for safe file handling
• Include descriptive header keywords with comments
• Use memmap=True for files larger than RAM
• Validate headers before writing

Coordinates

• Use SkyCoord for all coordinate operations
• Specify frames explicitly
• Use catalog matching instead of manual loops
• Cache transformations for repeated use

Time

• Specify both format and scale when creating Time objects
• Use appropriate time scale for your science (UTC for observations, TDB for dynamics)
• Work in arrays for efficiency
• Document time system in metadata

Tables

• Use QTable to preserve units
• Add metadata (descriptions, units) to columns
• Index tables for fast lookups
• Use masked columns for missing data

Performance

• Work with arrays instead of loops
• Use lazy loading for large FITS files
• Cache expensive computations
• Profile before optimizing

Resources and References

Official Documentation

• Astropy Documentation: https://docs.astropy.org/en/stable/
• Astropy Tutorials: https://learn.astropy.org/
• Photutils: https://photutils.readthedocs.io/
• Specutils: https://specutils.readthedocs.io/

Core Modules

• astropy.io.fits: https://docs.astropy.org/en/stable/io/fits/
• astropy.units: https://docs.astropy.org/en/stable/units/
• astropy.coordinates: https://docs.astropy.org/en/stable/coordinates/
• astropy.time: https://docs.astropy.org/en/stable/time/
• astropy.table: https://docs.astropy.org/en/stable/table/
• astropy.wcs: https://docs.astropy.org/en/stable/wcs/

Community

• Astropy Discourse: https://community.openastronomy.org/c/astropy
• GitHub Issues: https://github.com/astropy/astropy/issues
• Stack Overflow: Tag astropy

Summary

Astropy is the foundational library for astronomical computing in Python, providing essential tools for FITS files, coordinates, units, time, tables, and more.

Key takeaways:

• Units: Always attach physical units to prevent dimensional errors
• FITS I/O: Standard format for astronomical images and tables
• Coordinates: SkyCoord simplifies transformations between reference frames
• Time: Sub-nanosecond precision with multiple scales and formats
• Tables: Flexible tabular data with unit and metadata support
• WCS: Maps pixel coordinates to sky positions
• Photutils: Source detection and aperture photometry
• Specutils: 1D spectroscopy with wavelength calibration

Progressive Learning Path:

1. Start with FITS I/O and tables for basic data handling
2. Learn units and quantities for physical calculations
3. Master coordinates for astrometry and sky matching
4. Explore time handling for time-series analysis
5. Use WCS for image astrometry
6. Apply photutils for source extraction
7. Add specutils for spectroscopic analysis

For detailed patterns, complete examples, and troubleshooting, see the reference files in the references/ directory.