Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
SKILL.md
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)
See:references/wcs_and_other_modules.md for WCS operations and transformations.
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==7.2.0"
# Recommended optional dependencies for plotting and common workflows
uv pip install "astropy[recommended]==7.2.0"
# Full optional dependency set for broad astronomy workflows
uv pip install "astropy[all]==7.2.0"
Astropy 7.2.0 requires Python 3.11+ and depends on NumPy, PyERFA, PyYAML, and packaging. 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')
observing_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)
aa_frame = AltAz(obstime=observing_time, location=observing_location)
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)}")
Always use units: Attach units to quantities to avoid errors and ensure dimensional consistency
Use context managers for FITS files: Ensures proper file closing
Prefer arrays over loops: Process multiple coordinates/times as arrays for better performance
Check coordinate frames: Verify the frame before transformations
Use appropriate cosmology: Choose the right cosmological model for your analysis
Handle missing data: Use masked columns for tables with missing values
Specify time scales: Be explicit about time scales (UTC, TT, TDB) for precise timing
Use QTable for unit-aware tables: When table columns have units
Check WCS validity: Verify WCS before using transformations
Cache frequently used values: Expensive calculations (e.g., cosmological distances) can be cached
Be explicit about network access: SkyCoord.from_name(), EarthLocation.of_site(refresh_cache=True), EarthLocation.of_address(), download_file(), remote FITS reads, and some IERS time/coordinate transforms can contact external services or update local caches. Avoid sending sensitive target names, addresses, URLs, or proprietary file locations to third-party services. When working with potentially sensitive targets or data locations, confirm with the user before making these network calls.
Pin for reproducibility: Use pinned versions such as astropy==7.2.0 for shared environments; update pins intentionally after reviewing release notes.
Current-Version Notes
Current stable release researched: Astropy 7.2.0 (released 2025-11-25; verified current as of 2026-06-10)
Python requirement: 3.11+
Astropy 8.0 is at release-candidate stage (8.0.0rc1, 2026-05-26). Key changes to anticipate:
The deprecated astropy.cosmology submodule shims (astropy.cosmology.flrw, .core, .funcs, .connect, .parameter) are removed — import everything directly from astropy.cosmology (e.g., from astropy.cosmology import FlatLambdaCDM, z_at_value)
astropy.constants defaults change from CODATA 2018 to CODATA 2022; pin a constants version via the astropyconst science states if reproducibility matters
NumPy 2.0 becomes the minimum supported version; the 7.2.x LTS branch retains NumPy 1.x support for six months after the 8.0 release
The built-in test runner (astropy.test(), TestRunner) is formally deprecated — invoke pytest directly
Recent 7.x deprecations to avoid in new code: passing a table index identifier as the first .loc element (t.loc["b", 2]) — use t.loc.with_index("b")[2] instead (removal planned for 9.0); astropy.utils.isiterable() — use numpy.iterable()
Recent 7.0 removals: older deprecated FITS APIs such as (Bin)Table.update, _ExtensionHDU, _NonstandardExtHDU, and the tile_size argument for CompImageHDU; CompImageHeader is deprecated. Avoid those legacy patterns in new examples.
The recommended optional extras are recommended for common plotting/scientific dependencies and all only when a broad optional feature set is needed.
