Astropy

Astropy

Overview

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis. Use astropy for coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing.

When to Use This Skill

Use astropy when tasks involve:

• Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)
• Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)
• Reading, writing, or manipulating FITS files (images or tables)
• Cosmological calculations (luminosity distance, lookback time, Hubble parameter)
• Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)
• Table operations (reading catalogs, cross-matching, filtering, joining)
• WCS transformations between pixel and world coordinates
• Astronomical constants and calculations

Quick Start

Targets Astropy 8.0.1. Numerical and small synthetic file examples were executed; blocks using observation/catalog filenames, online services or a GUI are illustrative and require those inputs. See review evidence and sources.

import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.time import Time
from astropy.io import fits
from astropy.table import Table
from astropy.cosmology import Planck18

# Units and quantities
distance = 100 * u.pc
distance_km = distance.to(u.km)

# Coordinates
coord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')
coord_galactic = coord.galactic

# Time
t = Time('2023-01-15 12:30:00', scale='utc')
jd = t.jd  # Julian Date

# FITS files
data, header = fits.getdata('image.fits', ext=0, header=True)  # Select actual image HDU

# Tables
table = Table.read('catalog.fits')

# Cosmology
d_L = Planck18.luminosity_distance(1.0)

Core Capabilities

1. Units and Quantities (astropy.units)

Handle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.

Key operations:

• Create quantities by multiplying values with units
• Convert between units using .to() method
• Perform arithmetic with automatic unit handling
• Use equivalencies for domain-specific conversions (spectral, doppler, parallax)
• Work with logarithmic units (magnitudes, decibels)

See: references/units.md for comprehensive documentation, unit systems, equivalencies, performance optimization, and unit arithmetic.

2. Coordinate Systems (astropy.coordinates)

Represent celestial positions and transform between different coordinate frames.

Key operations:

• Create coordinates with SkyCoord in any frame (ICRS, Galactic, FK5, AltAz, etc.)
• Transform between coordinate systems
• Calculate angular separations and position angles
• Match coordinates to catalogs
• Include distance for 3D coordinate operations
• Handle proper motions and radial velocities
• Query named objects from online databases

See: references/coordinates.md for detailed coordinate frame descriptions, transformations, observer-dependent frames (AltAz), catalog matching, and performance tips.

3. Cosmological Calculations (astropy.cosmology)

Perform cosmological calculations using standard cosmological models.

Key operations:

• Use built-in cosmologies (Planck18, WMAP9, etc.)
• Create custom cosmological models
• Calculate distances (luminosity, comoving, angular diameter)
• Compute ages and lookback times
• Determine Hubble parameter at any redshift
• Calculate density parameters and volumes
• Perform inverse calculations (find z for given distance)

See: references/cosmology.md for available models, distance calculations, time calculations, density parameters, and neutrino effects.

4. FITS File Handling (astropy.io.fits)

Read, write, and manipulate FITS (Flexible Image Transport System) files.

Key operations:

• Open FITS files with context managers
• Access HDUs (Header Data Units) by index or name
• Read and modify headers (keywords, comments, history)
• Work with image data (NumPy arrays)
• Handle table data (binary and ASCII tables)
• Create new FITS files (single or multi-extension)
• Use memory mapping for large files
• Access remote FITS files (S3, HTTP)

See: references/fits.md for comprehensive file operations, header manipulation, image and table handling, multi-extension files, and performance considerations.

5. Table Operations (astropy.table)

Work with tabular data with support for units, metadata, and various file formats.

Key operations:

• Create tables from arrays, lists, or dictionaries
• Read/write tables in multiple formats (FITS, CSV, HDF5, VOTable)
• Access and modify columns and rows
• Sort, filter, and index tables
• Perform database-style operations (join, group, aggregate)
• Stack and concatenate tables
• Work with unit-aware columns (QTable)
• Handle missing data with masking

See: references/tables.md for table creation, I/O operations, data manipulation, sorting, filtering, joins, grouping, and performance tips.

6. Time Handling (astropy.time)

Precise time representation and conversion between time scales and formats.

Key operations:

• Create Time objects in various formats (ISO, JD, MJD, Unix, etc.)
• Convert between time scales (UTC, TAI, TT, TDB, etc.)
• Perform time arithmetic with TimeDelta
• Calculate sidereal time for observers
• Compute light travel time corrections (barycentric, heliocentric)
• Work with time arrays efficiently
• Handle masked (missing) times

See: references/time.md for time formats, time scales, conversions, arithmetic, observing features, and precision handling.

7. World Coordinate System (astropy.wcs)

Transform between pixel coordinates in images and world coordinates.

Key operations:

• Read WCS from FITS headers
• Convert pixel coordinates to world coordinates (and vice versa)
• Calculate image footprints
• Access WCS parameters (reference pixel, projection, scale)
• Create custom WCS objects

See: references/wcs_and_other_modules.md for WCS operations and transformations. High-level WCS pixel methods use zero-based (x, y) coordinates, while NumPy images index [row, column], or [y, x]. Use world_to_array_index for array indexing, check bounds, and verify a pixel → world → pixel round trip before extracting sources. FITS header CRPIX values retain the FITS one-based convention. See the WCS interface guide.

Additional Capabilities

The references/wcs_and_other_modules.md file also covers:

NDData and CCDData

Containers for n-dimensional datasets with metadata, uncertainty, masking, and WCS information.

Modeling

Framework for creating and fitting mathematical models to astronomical data.

Visualization

Tools for astronomical image display with appropriate stretching and scaling.

Constants

Physical and astronomical constants with proper units (speed of light, solar mass, Planck constant, etc.).

Convolution

Image processing kernels for smoothing and filtering.

Statistics

Robust statistical functions including sigma clipping and outlier rejection.

Installation

# Reproducible install against the current stable release
uv pip install "astropy==8.0.1"

# Recommended optional dependencies for plotting and common workflows
uv pip install "astropy[recommended]==8.0.1"

# Illustrative broad optional install; the full extra set was not tested
uv pip install "astropy[all]==8.0.1"

Astropy 8.0.1 requires Python 3.11+ and NumPy 2+, and depends on PyERFA, astropy-iers-data, PyYAML, and packaging. SciPy is needed for the cosmology, catalog matching and fitting examples; use the recommended extra for these workflows. Use an isolated virtual environment; do not install Astropy with elevated privileges.

Note that the [recommended] and [all] extras pull in transitive dependencies (matplotlib, scipy, etc.) at unpinned versions. For reproducible production environments, pin the full dependency tree with a lockfile (uv lock in a project, or uv pip compile for requirements files) and review the resolved versions before deploying.

Common Workflows

Converting Coordinates Between Systems

from astropy.coordinates import SkyCoord
import astropy.units as u

# Create coordinate
c = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs')

# Transform to galactic
c_gal = c.galactic
print(f"l={c_gal.l.deg}, b={c_gal.b.deg}")

# Transform to alt-az (requires time and location)
from astropy.time import Time
from astropy.coordinates import EarthLocation, AltAz

observing_time = Time('2023-06-15 23:00:00', scale='utc')
observing_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)
aa_frame = AltAz(obstime=observing_time, location=observing_location, pressure=0*u.hPa)
c_altaz = c.transform_to(aa_frame)
print(f"Alt={c_altaz.alt.deg}, Az={c_altaz.az.deg}")

Reading and Analyzing FITS Files

from astropy.io import fits
import numpy as np

# Open FITS file
with fits.open('observation.fits') as hdul:
    # Display structure
    hdul.info()

    # Get image data and header
    data = hdul[1].data
    header = hdul[1].header

    # Access header values
    exptime = header['EXPTIME']
    filter_name = header['FILTER']

    # Analyze data
    mean = np.mean(data)
    median = np.median(data)
    print(f"Mean: {mean}, Median: {median}")

Cosmological Distance Calculations

from astropy.cosmology import Planck18
import astropy.units as u
import numpy as np

# Calculate distances at z=1.5
z = 1.5
d_L = Planck18.luminosity_distance(z)
d_A = Planck18.angular_diameter_distance(z)

print(f"Luminosity distance: {d_L}")
print(f"Angular diameter distance: {d_A}")

# Age of universe at that redshift
age = Planck18.age(z)
print(f"Age at z={z}: {age.to(u.Gyr)}")

# Lookback time
t_lookback = Planck18.lookback_time(z)
print(f"Lookback time: {t_lookback.to(u.Gyr)}")

Cross-Matching Catalogs

from astropy.table import Table
from astropy.coordinates import SkyCoord, match_coordinates_sky
import astropy.units as u

# Read catalogs
cat1 = Table.read('catalog1.fits')
cat2 = Table.read('catalog2.fits')

# Confirm both catalogs use ICRS and compatible reference epochs before proceeding.
# Here metadata establishes degrees for unitless columns; absence of units alone
# does not establish degrees. Existing angular column units are preserved.
# Propagate proper motion first when required and supported by the input metadata.
coords1 = SkyCoord(cat1['RA'], cat1['DEC'], unit=u.deg, frame='icrs')
coords2 = SkyCoord(cat2['RA'], cat2['DEC'], unit=u.deg, frame='icrs')

# Nearest neighbors may reuse the same catalog row; these are candidate associations.
idx, sep, _ = coords1.match_to_catalog_sky(coords2)

# Filter by separation threshold
max_sep = 1 * u.arcsec
matches = sep < max_sep

# Create matched catalogs
cat1_matched = cat1[matches]
cat2_matched = cat2[idx[matches]]
print(f"Found {len(cat1_matched)} matches")