astropy
Skill19 starsChanged 10 months agoMITView on GitHub
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
- Installs packages
We split this skill into the rules it gives an agent
19 rules. 18 are common, 0 are distinctive, and 1 is very specific to this project.
18
Common
Most of the 12 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 14
Use astropy when tasks involve:
- Common: line 64
Create quantities by multiplying values with units
- Common: line 67
Use equivalencies for domain-specific conversions (spectral, doppler, parallax)
- Very specific: line 77
Create coordinates with SkyCoord in any frame (ICRS, Galactic, FK5, AltAz, etc.)
- Common: line 92
Use built-in cosmologies (Planck18, WMAP9, etc.)
- Common: line 93
Create custom cosmological models
- Common: line 104
Read, write, and manipulate FITS (Flexible Image Transport System) files.
- Common: line 109
Read and modify headers (keywords, comments, history)
- Common: line 112
Create new FITS files (single or multi-extension)
- Common: line 113
Use memory mapping for large files
- Common: line 123
Create tables from arrays, lists, or dictionaries
- Common: line 124
Read/write tables in multiple formats (FITS, CSV, HDF5, VOTable)
- Common: line 139
Create Time objects in various formats (ISO, JD, MJD, Unix, etc.)
- Common: line 154
Read WCS from FITS headers
- Common: line 158
Create custom WCS objects
- Common: line 302
Check coordinate frames: Verify the frame before transformations
- Common: line 304
Handle missing data: Use masked columns for tables with missing values
- Common: line 306
Use QTable for unit-aware tables: When table columns have units
- Common: line 307
Check WCS validity: Verify WCS before using transformations
“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
SkyCoordin 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
- 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
Documentation and Resources
- Official Astropy Documentation: https://docs.astropy.org/en/stable/
- Tutorials: https://learn.astropy.org/
- GitHub: https://github.com/astropy/astropy
Reference Files
For detailed information on specific modules:
references/units.md- Units, quantities, conversions, and equivalenciesreferences/coordinates.md- Coordinate systems, transformations, and catalog matchingreferences/cosmology.md- Cosmological models and calculationsreferences/fits.md- FITS file operations and manipulationreferences/tables.md- Table creation, I/O, and operationsreferences/time.md- Time formats, scales, and calculationsreferences/wcs_and_other_modules.md- WCS, NDData, modeling, visualization, constants, and utilities
Other versions12 projects use a version of this.
Word for word · 0
None found.
Edited · 11
- LeonChaoX/qinyan-academic-skills★ 91099% same
- qinyan-ai/qinyan-academic-skills★ 8099% same
- MarieLynneBlock/arcanum-artifex★ 496% same
- BrightGold70/skills★ 191% same
- garri333/Skills★ 191% same
- Luooops/skills★ 167% same
- uppifyagency/boilerplate-saas-2026★ 199% same
- yf8578/claude-skills-collection★ 191% same
- davidanthonyn/claude-skills★ 099% same
- ejoliet/claude-skills★ 082% same
1 more not listed
Wrote their own version · 1
- tondevrel/scientific-agent-skills★ 22same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository48 other files it gives its agents.
- brainstormingSkill
- causal-inference-root-causeSkill
- decision-matrixSkill
- decomposition-reconstructionSkill
- design-of-experimentsSkill
- docxSkill
- equivariant-architecture-designerSkill
- estimation-fermiSkill
- fairchemSkill
- fluidsimSkill
- latex-postersSkill
- markitdownSkill
- materials-databasesSkill
- materials-propertiesSkill
- memorySkill
- meta-prompt-engineeringSkill
- model-equivariance-auditorSkill
- openmxSkill
- pdfSkill
- planningSkill
- postmortemSkill
- pptxSkill
- pymatgen-analysisSkill
- pymatgen-coreSkill
- pymatgen-electronic-structureSkill
- pymatgen-extSkill
- pymatgen-ioSkill
- pymatgen-io-vaspSkill
- pymatgen-phononSkill
- pymatgen-symmetrySkill
- python-aseSkill
- python-jaxSkill
- python-multiobjective-optimizationSkill
- python-optimizationSkill
- python-plottingSkill
- python-regression-statisticsSkill
- pytorch-lightningSkill
- scikit-learnSkill
- skill-creatorSkill
- symmetry-discovery-questionnaireSkill
- symmetry-group-identifierSkill
- symmetry-validation-suiteSkill
- sympySkill
- systems-thinking-leverageSkill
- torch-geometricSkill
- transformersSkill
- vaspSkill
- xlsxSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

