astropy
Astropy is the core Python library for astronomy providing coordinate transformations, unit conversions, FITS file I/O, cosmological calculations, precise time handling, table operations, WCS transformations, and access to astronomical constants. Use this skill when implementing astronomical data analysis workflows that require celestial coordinate systems, physical quantity calculations with units, reading or writing FITS files, cosmological computations, or manipulating astronomical tabular data.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/astropy && cp -r /tmp/astropy/skills/astropy ~/.claude/skills/astropySKILL.md
# 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.
## AdditionalHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
>