🔭 skyview-cli

中文 | English

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中文

从命令行或 Jupyter Notebook 快速查看天文巡天图像。 不用打开浏览器，不用手动输坐标，支持批量查看和系统图像查看器浏览。

安装

pip install git+https://github.com/tdccccc/skyview-cli.git

本地开发：

cd ~/Documents/code/skyview
pip install -e .

快速上手

Jupyter Notebook（推荐场景）

在 notebook 里处理数据时，发现异常源想看图：

import skyview

# ---- 单个天体 ----
skyview.show("NGC 788")
skyview.show(ra=30.28, dec=-23.50, survey="sdss", fov=2.0)

# ---- 批量查看 ----

# 方式1: 天体名列表
skyview.batch(["NGC 788", "M31", "NGC 1275", "Coma Cluster"])

# 方式2: 两个数组/Series（最常用！）
skyview.batch(df["ra"], df["dec"], fov=5)

# 方式3: tuple 包裹两个数组
skyview.batch((df["ra"], df["dec"]), survey="ls-dr9", fov=5)

# 方式4: 2D numpy 数组 / DataFrame .values
skyview.batch(df[["ra", "dec"]].values, fov=3)

# 方式5: (ra, dec) 元组列表
skyview.batch(list(zip(df["ra"], df["dec"])), fov=5, cols=4)

# 方式6: 从 CSV/FITS 文件加载
skyview.batch_from_file("catalog.fits", ra_col="RA", dec_col="DEC", name_col="NAME")

# 方式7: pandas Series / numpy array
skyview.batch(pd.Series(["NGC 788", "M31"]))

保存图像

# 保存网格到文件（不弹窗）
skyview.batch(targets, save="gallery.png", cols=6)

# 下载单张原始图像
img = skyview.fetch("NGC 788", fov=2.0)
img.save("ngc788.jpg")

名称解析

ra, dec = skyview.resolve("NGC 788")
print(f"RA={ra:.4f}, Dec={dec:.4f}")

命令行

# 查看单个天体（自动调用系统图像查看器）
skyview show NGC 788
skyview show 30.28 -23.50
skyview show "10:00:00 +02:12:00"   # 时角格式
skyview show NGC 788 -s sdss -f 3.0
skyview show NGC 788 -o ngc788.jpg  # 保存到文件
skyview show NGC 788 --viewer feh   # 指定查看器

# 批量查看（系统查看器打开，左右键切换）
skyview batch "NGC 788" "M31" "NGC 1275"
skyview batch "NGC 788" "M31" -f 5
skyview batch "NGC 788" "M31" --grid         # 强制 matplotlib 网格视图
skyview batch -F catalog.csv --ra-col RA --dec-col DEC

# 浏览模式（下载图像 + 系统查看器）
skyview browse "NGC 788" "M31" "NGC 1275" -f 5

# 名称解析
skyview resolve "NGC 788"

# 列出可用巡天
skyview surveys

# 缓存管理
skyview cache-info
skyview cache-clear

支持的巡天

名称	波段	覆盖	优先级	说明
ls-dr10	grz	Dec > -70°	100	默认，Legacy Survey DR10
ls-dr9	grz	Dec > -70°	90	Legacy Survey DR9
panstarrs	grizy	Dec > -30°	80	Pan-STARRS1
sdss	ugriz	部分天区	70	SDSS
des-dr1	grizY	-65° < Dec < 5°	60	DES DR1（南天）
unwise-neo7	W1W2	全天	20	unWISE 红外
galex	FUV/NUV	全天	10	GALEX 紫外

自动 fallback：如果指定巡天返回空白图（坐标超出覆盖范围），会自动按优先级尝试下一个巡天。

关键参数

参数	说明	默认值
fov	视场大小（角分）	1.0
survey	巡天名称	ls-dr10
size	图像像素大小	自动（由 fov 计算）
cols	批量网格列数	5
workers	并发下载线程数	8
save	保存路径（不设则显示）	—

坐标输入格式

show() 和 CLI 的 target 参数支持：

• 十进制度数："150.0 2.2" 或 150.0, 2.2
• 时角/度分秒："10:00:00 +02:12:00"
• 天体名称："NGC 788", "M31", "Coma Cluster"

依赖

• Python >= 3.9
• click, requests, matplotlib, astropy, Pillow, numpy

---

English

Quickly browse astronomical survey images from the command line or Jupyter Notebook. No browser needed, no manual coordinate entry — supports batch viewing and system image viewer browsing.

Installation

pip install git+https://github.com/tdccccc/skyview-cli.git

Local development:

cd ~/Documents/code/skyview
pip install -e .

Quick Start

Jupyter Notebook (Recommended)

Spot an interesting source while analyzing data? View it instantly:

import skyview

# ---- Single target ----
skyview.show("NGC 788")
skyview.show(ra=30.28, dec=-23.50, survey="sdss", fov=2.0)

# ---- Batch viewing ----

# Method 1: list of object names
skyview.batch(["NGC 788", "M31", "NGC 1275", "Coma Cluster"])

# Method 2: two arrays/Series (most common!)
skyview.batch(df["ra"], df["dec"], fov=5)

# Method 3: tuple of two arrays
skyview.batch((df["ra"], df["dec"]), survey="ls-dr9", fov=5)

# Method 4: 2D numpy array / DataFrame .values
skyview.batch(df[["ra", "dec"]].values, fov=3)

# Method 5: list of (ra, dec) tuples
skyview.batch(list(zip(df["ra"], df["dec"])), fov=5, cols=4)

# Method 6: load from CSV/FITS file
skyview.batch_from_file("catalog.fits", ra_col="RA", dec_col="DEC", name_col="NAME")

# Method 7: pandas Series / numpy array
skyview.batch(pd.Series(["NGC 788", "M31"]))

Saving Images

# Save grid to file (no popup)
skyview.batch(targets, save="gallery.png", cols=6)

# Download a single raw image
img = skyview.fetch("NGC 788", fov=2.0)
img.save("ngc788.jpg")

Name Resolution

ra, dec = skyview.resolve("NGC 788")
print(f"RA={ra:.4f}, Dec={dec:.4f}")

Command Line

# View a single target (opens system image viewer)
skyview show NGC 788
skyview show 30.28 -23.50
skyview show "10:00:00 +02:12:00"   # sexagesimal format
skyview show NGC 788 -s sdss -f 3.0
skyview show NGC 788 -o ngc788.jpg  # save to file
skyview show NGC 788 --viewer feh   # specify viewer

# Batch view (system viewer, arrow keys to navigate)
skyview batch "NGC 788" "M31" "NGC 1275"
skyview batch "NGC 788" "M31" -f 5
skyview batch "NGC 788" "M31" --grid         # force matplotlib grid
skyview batch -F catalog.csv --ra-col RA --dec-col DEC

# Browse mode (download images + system viewer)
skyview browse "NGC 788" "M31" "NGC 1275" -f 5

# Name resolution
skyview resolve "NGC 788"

# List available surveys
skyview surveys

# Cache management
skyview cache-info
skyview cache-clear

Supported Surveys

Name	Bands	Coverage	Priority	Notes
ls-dr10	grz	Dec > -70°	100	Default, Legacy Survey DR10
ls-dr9	grz	Dec > -70°	90	Legacy Survey DR9
panstarrs	grizy	Dec > -30°	80	Pan-STARRS1
sdss	ugriz	Partial	70	SDSS
des-dr1	grizY	-65° < Dec < 5°	60	DES DR1 (southern sky)
unwise-neo7	W1W2	All-sky	20	unWISE infrared
galex	FUV/NUV	All-sky	10	GALEX ultraviolet

Auto fallback: If a survey returns a blank image (coordinates outside coverage), it automatically tries the next survey by priority.

Key Parameters

Parameter	Description	Default
fov	Field of view (arcmin)	1.0
survey	Survey name	ls-dr10
size	Image pixel size	Auto (from fov)
cols	Batch grid columns	5
workers	Concurrent download threads	8
save	Save path (display if not set)	—

Coordinate Formats

The show() function and CLI target argument accept:

• Decimal degrees: "150.0 2.2" or 150.0, 2.2
• Sexagesimal: "10:00:00 +02:12:00"
• Object names: "NGC 788", "M31", "Coma Cluster"

Dependencies

• Python >= 3.9
• click, requests, matplotlib, astropy, Pillow, numpy

License

MIT