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:

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=10.5u.degree, dec=41.2u.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:

# Install astropy
uv pip install astropy
With optional dependencies for full functionality

uv pip install astropy[all]

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=40u.deg, lon=-120u.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:

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