--- name: astropy description: 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. license: BSD-3-Clause license compatibility: Requires Python 3.11+ with astropy installed (uv for package installation). Some features (object name resolution, site lookups, remote FITS reads, IERS updates) need network access. metadata: version: "1.2" skill-author: K-Dense Inc. ---

# 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

```python 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

```bash # 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

```python 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

```python 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

```python 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

```python 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 11. **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. 12. **Pin for reproducibility**: Use pinned versions such