skills/astropy/SKILL.md
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
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@ 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:
@ Quick Start
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')
jd = t.jd # Julian Date
# FITS files
data = fits.getdata('image.fits')
header = fits.getheader('image.fits')
# Tables
table = Table.read('catalog.fits')
# Cosmology
d_L = Planck18.luminosity_distance(z=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:
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:
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:
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:
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:
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:
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:
See: references/wcsandother_modules.md for WCS operations and transformations.
@ Additional Capabilities
The references/wcsandother_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
# Install astropy
uv pip install astropy
# With optional dependencies for full functionality
uv pip install astropy[all]
@ 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)}")
@ 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')
# Create coordinate objects
coords1 = SkyCoord(ra=cat1['RA']*u.degree, dec=cat1['DEC']*u.degree)
coords2 = SkyCoord(ra=cat2['RA']*u.degree, dec=cat2['DEC']*u.degree)
# Find matches
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")
@ Best Practices
@ Documentation and Resources
@ Reference Files
For detailed information on specific modules:
@ Limitations
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