SERPENTINE Tools

Tools

Here we present the tools created within the SERPENTINE project. They are available from the general SERPENTINE repository at GitHub, and can also be run on the SERPENTINE Hub server without the need to install Python locally.

此处展示SERPENTINE项目组开发的分析工具集。
所有工具均发布于GitHub平台的SERPENTINE主代码库，用户亦可直接通过SERPENTINE Hub服务器在线运行，无需本地安装Python环境。

Multi-spacecraft longitudinal configuration plotter

Solar-MACH is a hassle-free web app that displays the spatial configuration and magnetic connection of the spacecraft fleet in the inner heliosphere for a given time. With the option to freely add a reference coordinate at the Sun (e.g., flare location) and providing detailed information on coordinates and separation angles, it allows a quick start of an event analysis.

In addition to the web version, a Jupyter Notebook is provided that is offering more functionalities and ways to interact with Solar-MACH in a “pythonic” way. For example, it allows an easy implementation of an PFSS model to extend the magnetic connectivity to the photosphere, or gives an example on how to create a movie out of daily Solar-MACH constellation plots.

Open web app Solar-MACH
Obtain Jupyter Notebook from GitHub
Open Jupyter Notebook on the Hub

多航天器经度配置可视化工具（Multi-spacecraft longitudinal configuration plotter）

Solar-MACH 是一款便捷易用的网络应用程序，可动态呈现指定时刻内日球层（inner heliosphere）中多航天器编队的空间构型及其磁力连接状态。用户可自由添加日面参考坐标（如耀斑爆发位置），并获取航天器坐标与分离角的定量数据，为太阳物理事件分析提供快速切入点。

除网页版本外，项目组还提供了Jupyter Notebook交互式开发环境，支持通过Python脚本扩展Solar-MACH的功能。例如：

集成势场源表面（PFSS）模型，将磁力连接性分析延伸至光球层；
基于逐日Solar-MACH构型图生成动态演化序列的示例代码。

Data loader

The Energetic Particle Data Loaders Notebook is a collection of functions that simplifies obtaining (i.e., automatically downloading and loading into Python structures) SEP data sets measured by the current heliospheric spacecraft fleet. This is especially valuable because the data products of the newest spacecraft like Solar Orbiter or Parker Solar Probe are only released in a binary data format that needs programming skills to read just the basic data.

In addition, the Notebook provides already different examples on how to visualize these data with Python. Versed users can then easily build their own analyses based on this.

Obtain Jupyter Notebook from GitHub
Open Jupyter Notebook on the Hub

数据加载模块（Data loader）

高能粒子数据加载器交互式手册（Energetic Particle Data Loaders Notebook）
本模块集成系列函数，专用于自动化获取当前日球层航天器编队测量的太阳高能粒子（SEP）数据集，实现数据自动下载与Python结构化加载。其核心价值在于：新型航天器（如太阳轨道器Solar Orbiter、帕克太阳探测器Parker Solar Probe）的观测数据仅以二进制格式发布，需专业编程技能方可读取基础数据，而本工具显著降低了此类数据的访问门槛。

此外，手册内嵌多套基于Python的SEP数据可视化模板，资深用户可基于此快速开发定制化分析流程。

Dynamic spectrum

A dynamic spectrum plotter for different charged particle species measured by the current heliospheric spacecraft fleet and radio observations.

It offers an easy-to-use interface for selecting the mission, instrument, viewing direction, and particles species of interest, for which all needed data are automatically obtained and optionally resampled. In addition, radio observations can be plotted on top that are also downloaded automatically.

Obtain Jupyter Notebook from GitHub
Open Jupyter Notebook on the Hub

动态频谱分析（Dynamic spectrum）

本工具提供多航天器带电粒子观测与射电数据的动态频谱联合可视化功能，支持：

通过交互界面选择目标航天器、载荷设备、探测方向及粒子种类，自动获取数据并执行可选时间重采样；
叠加显示自动下载的射电波段观测数据（如射电暴事件），实现多信使数据的时空关联分析。

Onset determination

This notebook uses the established Poisson-CUSUM method to automatically derive the onset times of an SEP event.

It offers an easy-to-use interface for selecting the mission, instrument, viewing direction, and particles species of interest, for which all needed data are automatically obtained and optionally resampled. The only real input needed from the user is selecting the analysis time period and defining the pre-event background.

- Obtain Jupyter Notebook from GitHub
- Open Jupyter Notebook on the Hub

起始时间判定（Onset determination）

基于泊松累积和控制法（Poisson-CUSUM）的太阳高能粒子事件起始时间自动检测模块，具备以下功能：

交互式选择航天器、仪器、探测方向及粒子种类，自动获取数据并支持自定义时间分辨率；
用户仅需指定分析时间窗口与事件前背景区间，即可触发全自动计算流程。

Time shift analysis

The purpose of this notebook is to interactively determine the solar release time for an SEP event observed in different energy channels.

It automatically obtains particle intensities of all energy channels for a given instrument, and provides the functionality to normalize all intensities and to assume a given path length that all particles have traveled. The intensity time series are then shifted back on the time axis by the time each would have needed to travel the given path length, depending on their different energies (i.e., speeds). With the assumption that all particles have been injected at the Sun simultaneously, it should be possible to find a “true” path length for which all time series collapse on each other, which also defines the inferred injection time.