Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问clear审计未展示

astropyastropy 搜索

Agent Skill

astropy 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

517

周安装

22

GitHub Stars

公开资料未说明

下载量

181
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:astropy(astropy 搜索)
来源仓库:https://github.com/jackspace/claudeskillz
仓库路径:skills/astropy
安装命令:
npx skills add jackspace/claudeskillz --skill "astropy"
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add jackspace/claudeskillz --skill "astropy"

简介

提供天文学数据处理与分析相关的工具链支持。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 适用于天文科研、观测数据解析或宇宙学计算任务场景。
  • 支持常见 FITS 文件读取、坐标转换与基础统计分析。
  • 使用前需确认本地 Python 环境与依赖库版本是否匹配。
  • astropy 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.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

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
pip install astropy

# With optional dependencies for full functionality
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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

27.67%
按下载量换算50

windsurf

24.13%
按下载量换算44

OpenCode

19.29%
按下载量换算35

Codex

14.17%
按下载量换算26

Antigravity

7.93%
按下载量换算14

Gemini CLI

3.65%
按下载量换算7

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills