New to Claude Skills? Learn how to install them →

sickn33 on GitHub

Astropy

Free

Essential Python package for astronomical research and data analysis.

Get this skill

Free · Opens the source repo

What Astropy does

Astropy is a comprehensive Python package designed specifically for astronomy, providing a wide array of functionalities that are crucial for astronomical research and data analysis. It allows users to perform coordinate transformations between various celestial coordinate systems, manage physical units and quantities, and handle FITS files, which are standard in the field of astronomy. Astropy also supports cosmological calculations, precise time handling, and tabular data manipulation, making it a versatile tool for both researchers and developers in the astronomy domain.

The package excels in its ability to convert between different celestial coordinate systems, such as ICRS, Galactic, and AltAz, which is essential for accurate astronomical observations. Additionally, it provides robust support for unit conversions and calculations, ensuring that users can work with various physical quantities seamlessly. Astropy's FITS file handling capabilities enable users to read, write, and manipulate astronomical data stored in this widely used format, while its cosmological functions allow for calculations related to distances, ages, and other critical parameters in astronomy.

Astropy is particularly suited for astronomers, astrophysicists, and data scientists working with astronomical data, as it streamlines many of the complex calculations and data manipulations required in the field. Whether you are analyzing observational data, performing simulations, or developing new algorithms for astronomical research, Astropy provides the foundational tools necessary to facilitate these tasks efficiently. Its extensive documentation and community support further enhance its usability, making it a go-to package for anyone involved in astronomical research and data analysis.

When to use it

Use Astropy when performing tasks that involve astronomical data analysis, such as coordinate transformations, FITS file handling, or cosmological calculations.

When not to use it

Astropy may not be suitable for non-astronomical data analysis or for users who do not require the specific functionalities provided for astronomical research.

What you can build with it

Coordinate Transformation

Easily convert between celestial coordinate systems for accurate astronomical observations.

FITS File Analysis

Read and analyze astronomical data stored in FITS files, extracting key information efficiently.

Cosmological Calculations

Perform complex calculations related to distances, ages, and other cosmological parameters with built-in models.

How to install Astropy

View source

1. Install with the skills CLI

npx skills add sickn33/agentic-awesome-skills/astropy --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by sickn33

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

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:

  • 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.

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

# 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

  1. Always use units: Attach units to quantities to avoid errors and ensure dimensional consistency
  2. Use context managers for FITS files: Ensures proper file closing
  3. Prefer arrays over loops: Process multiple coordinates/times as arrays for better performance
  4. Check coordinate frames: Verify the frame before transformations
  5. Use appropriate cosmology: Choose the right cosmological model for your analysis
  6. Handle missing data: Use masked columns for tables with missing values
  7. Specify time scales: Be explicit about time scales (UTC, TT, TDB) for precise timing
  8. Use QTable for unit-aware tables: When table columns have units
  9. Check WCS validity: Verify WCS before using transformations
  10. Cache frequently used values: Expensive calculations (e.g., cosmological distances) can be cached

Documentation and Resources

Reference Files

For detailed information on specific modules:

  • references/units.md - Units, quantities, conversions, and equivalencies
  • references/coordinates.md - Coordinate systems, transformations, and catalog matching
  • references/cosmology.md - Cosmological models and calculations
  • references/fits.md - FITS file operations and manipulation
  • references/tables.md - Table creation, I/O, and operations
  • references/time.md - Time formats, scales, and calculations
  • references/wcs_and_other_modules.md - WCS, NDData, modeling, visualization, constants, and utilities

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Frequently asked questions about Astropy

Similar skills