A

Skill 详情

astropy

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

来源平台:GitHub
来源标识:sickn33/antigravity-awesome-skills
源文件:原始说明
SEO 与站长 超热门 GitHub 中 风险 下载 2.02万Stars 3.68万 GitHub Copilot
来源平台GitHub
文档版本SKILL.md
热度超热门
排名信号下载 2.02万
概述 安装 文档 下载

快速判断

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

最后校验2026-05-27
来源平台GitHub
安全提示
下载副本ZIP 可用

适合任务

  • 把重复任务整理成可复用的 AI 操作流程。
  • 让 AI 在特定场景下按统一规范执行。
  • 为团队或个人工作流提供可复制的任务说明。

输入与输出

输入:任务目标、上下文材料、文件路径、约束条件或需要处理的内容。

输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 astropy 帮我处理当前任务,并说明执行前需要确认的输入。
  • 根据 astropy 的说明,给我一个安全的使用步骤清单。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容,可打开 GitHub 的 SKILL.md

在线原始地址:astropy/SKILL.md

风险边界

使用前请检查权限、外部依赖和要处理的数据类型。不要把密码、密钥、身份信息或敏感客户资料交给未经确认的 Skill。

SKILL.md 文档介绍

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

  • 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 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.
建议反馈