diffraction-scatter

Use this skill for diffraction / scattering data processing with pyFAI. It covers the full workflow: calibration (generating .poni files), azimuthal integration (1D/2D), batch processing, GIWAXS/Fiber maps, chi/azimuthal profiles, sector integration, full 2D cake maps, large HDF5/Eiger workflows, uncertainty/error-model aware integration, mask/dark/flat/polarization corrections, and installation help for Python & pyFAI. Trigger this skill whenever the user asks about any of the following tasks: - Calibration (creating or using a .poni file) - 1D/2D integration, batch integration, streaming - GIWAXS, qip/qoop, azimuthal/chi profiles, sector, cake - Error models, corrections (mask, dark, flat, polarization) - Installing or using pyFAI (Having a .poni file is a normal step in the workflow, not a prerequisite for using this skill.) --- 使用本技能处理衍射/散射数据（基于 pyFAI），覆盖完整工作流： 标定（生成 .poni 文件）、方位角积分（1D/2D）、批处理、GIWAXS/Fiber 图、 χ 积分、扇区积分、2D cake 图、大规模 HDF5/Eiger 数据处理、带误差模型的积分、 mask/dark/flat/偏振校正，以及 Python/pyFAI 安装帮助。 只要用户询问以下任一任务，即可触发本技能： - 标定（创建或使用 .poni 文件） - 1D/2D 积分、批处理、流式处理 - GIWAXS、qip/qoop、方位角/χ 剖面、扇区、cake - 误差模型、校正（mask、dark、flat、偏振） - 安装或使用 pyFAI （.poni 文件是工作流中的正常环节，并非使用本技能的前提条件。）

pyFAI Post-PONI Integrator / pyFAI PONI 后积分器

This skill is for work after the user already has a .poni geometry file. 本技能面向用户已经获得 .poni 几何文件后的后续积分工作。

It covers: 1D radial, azimuthal/chi, sector 1D, GIWAXS/Fiber 2D, full 2D cake maps, large HDF5 streaming, uncertainty propagation, and Python/pyFAI environment setup. 覆盖：1D 径向、χ/方位角、扇区 1D、GIWAXS Fiber 2D、完整 2D cake/q-χ 图、 大批量 HDF5 流式处理、误差传播、Python/pyFAI 环境安装。

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Step 0 — Confirm .poni / 先确认 .poni

EN: Confirm the user already has a .poni file. If not, guide them through calibration first (see below).

中文：先确认用户是否已有 .poni 文件。如果没有，先引导完成校准（见下方）。

What if the user does NOT have a .poni yet / 用户还没有 .poni 怎么办

A missing .poni means the user has not completed geometric calibration. Follow this sequence: 没有 .poni 意味着几何校准未完成。按以下顺序引导：

Step 1 / 第一步 — Install pyFAI / 安装 pyFAI

bash

python pyfaiskills/scripts/install_pyfai_env.py --venv .venv-pyfai

If Python itself is missing / 如果连 Python 都没有：

• Windows: Python.org installer or Miniconda
• macOS: Homebrew (brew install python) or Python.org
• Linux: apt install python3 python3-venv or mamba/conda

Step 2 / 第二步 — Learn calibration / 学习校准

• 中文视频：pyFAI 校准教程（B站-个人）
• English: pyFAI Calibration Tutorial (silx.org, 15 min，official)

Once calibration is complete and the user has a .poni file, return to the main workflow below. 校准完成后用户有了 .poni 文件，回到下面的主流程。

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Step 1 — Identify Mode / 判断积分模式

Identify the integration mode early: 优先判断积分模式：

Mode / 模式	Description / 描述	Core pyFAI call
radial1d	Standard 1D radial → I(q) / I(2θ)	integrate1d_ng
azimuthal1d	I(χ) inside a radial range / 固定 q 范围做 χ 积分	integrate_radial
sector1d	1D in an azimuthal sector / 扇区 1D	integrate1d_ng(..., azimuth_range=...)
cake2d	Full q-χ / 2θ-χ map / 完整 2D cake	integrate2d_ng
fiber2d	GIWAXS q_ip × q_oop / 纤维积分	FiberIntegrator.integrate2d_grazing_incidence

Advanced modes not yet fully scripted / 额外高级模式：

• MultiGeometry.integrate1d() — multi-detector fusion / 多探测器拼接
• sigma_clip_ng() — dynamic masking / 动态 mask
• pyFAI-integrate --no-gui --json — official JSON batch / 官方 JSON 批处理

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Step 2 — Gather Missing Info / 补齐关键信息

Before executing, confirm: 执行前确认：

• Input format / 输入格式：EDF / TIFF / HDF5 / NPY
• Output format / 输出形式：CSV / HDF5 / NPZ / PNG
• Integration unit / 积分单位 (see rules below / 见下方规则)
• Corrections needed / 需要的修正：mask / dark / flat / polarization / solid-angle
• Streaming needed / 是否流式处理（large HDF5 / Eiger）
• GIWAXS: incident_angle, sample_orientation, units, ranges

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Step 3 — Use Bundled Scripts / 优先使用脚本

Prefer the bundled scripts instead of rewriting one-off code / 优先调用脚本：

bash

# 1) Inspect .poni and detector inputs / 检查 .poni 与输入数据
python pyfaiskills/scripts/inspect_poni.py --poni geometry.poni sample.h5

# 2) Create a pyFAI-ready environment / 创建 pyFAI 环境
python pyfaiskills/scripts/install_pyfai_env.py --venv .venv-pyfai

# 3) Run streaming integration / 运行流式积分
python pyfaiskills/scripts/integrate_with_poni.py \
  --mode radial1d --poni geometry.poni -i "data/**/*.edf" -o output/radial \
  --unit q_nm^-1 --npt 2000

The runner emits JSONL progress and writes results frame-by-frame to disk. 脚本默认把进度以 JSONL 打到标准输出，逐帧写结果到磁盘。

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Unit Selection Rules / 单位选择规则

Units matter / 单位很重要 — different experiments have different conventions. 不同实验类型有不同惯例。 按以下规则处理：

1. User specified a unit explicitly / 用户明确指定 → use what the user asked for
2. User mentioned experiment type but not unit / 用户提到了实验类型：
   • WAXS / WAXD / wide-angle / 广角 → 2th_deg (2θ degrees)
   • SAXS / small-angle / 小角 → q_nm^-1 (q, nm⁻¹)
   • GIWAXS / fiber / grazing-incidence / 掠入射 → qip_nm^-1 × qoop_nm^-1(仅限该格式，无其他单位)
3. Nothing specified / 什么都没说 → ask WAXS or SAXS first / 先问是 WAXS 还是 SAXS

Common pyFAI units / 常见单位：

• q_nm^-1 — scattering vector q (nm⁻¹)
• q_A^-1 — scattering vector q (Å⁻¹)
• 2th_deg — scattering angle 2θ (degrees)
• 2th_rad — scattering angle 2θ (radians)
• r_mm — radial distance from beam center (mm)

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Good Defaults / 推荐默认参数

Mode / 模式	Command / 命令
1D radial (WAXS)	--mode radial1d --unit 2th_deg --npt 1000~4000
1D radial (SAXS)	--mode radial1d --unit q_nm^-1 --npt 1000~4000
χ profile / χ 积分	--mode azimuthal1d --azimuthal-unit chi_deg --npt 360 --npt-rad 100
Sector 1D / 扇区	--mode sector1d --azimuth-min -30 --azimuth-max 30
2D cake	--mode cake2d --unit q_nm^-1 --npt-rad 500 --npt-azim 360
GIWAXS / Fiber	--mode fiber2d --unit-ip qip_nm^-1 --unit-oop qoop_nm^-1
Throughput / 吞吐优先	--method csr
Accuracy / 精度优先	--method splitpixel

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Command Templates / 常见命令模板

Standard 1D radial / 标准 1D 径向积分

bash

python pyfaiskills/scripts/integrate_with_poni.py \
  --mode radial1d --poni geometry.poni -i sample.edf -o out/1d \
  --unit q_A^-1 --npt 2000 --method splitpixel --polarization 0.95

Azimuthal I(χ) inside a q-range / 固定 q 范围做 χ 积分

bash

python pyfaiskills/scripts/integrate_with_poni.py \
  --mode azimuthal1d --poni geometry.poni -i sample.h5 -o out/chi \
  --radial-unit q_nm^-1 --radial-min 2 --radial-max 18 --azimuthal-unit chi_deg

Sector 1D / 扇区 1D

bash

python pyfaiskills/scripts/integrate_with_poni.py \
  --mode sector1d --poni geometry.poni -i "frames/*.tif" -o out/sector \
  --unit q_nm^-1 --azimuth-min -20 --azimuth-max 20

Full 2D cake / 完整 2D cake

bash

python pyfaiskills/scripts/integrate_with_poni.py \
  --mode cake2d --poni geometry.poni -i sample.edf -o out/cake \
  --unit q_nm^-1 --npt-rad 800 --npt-azim 360

GIWAXS / Fiber 2D

bash

python pyfaiskills/scripts/integrate_with_poni.py \
  --mode fiber2d --poni geometry.poni -i sample.edf -o out/fiber \
  --unit-ip qip_nm^-1 --unit-oop qoop_nm^-1 \
  --incident-angle 0.2 --sample-orientation 1

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Large-Volume / Streaming Policy / 大批量策略

When the user mentions large HDF5 stacks, tiff, Eiger data, many frames, or memory pressure: 当用户提到"海量 HDF5 / tiff /Eiger / 很多帧 / 不能一次读进内存"时：

1. Process file-by-file and frame-by-frame / 逐文件、逐帧处理
2. For HDF5, prefer explicit dataset path; otherwise /data / HDF5 优先指定 dataset path
3. For 4D Eiger, confirm channel (LowThresholdData, HighThresholdData, DiffData) / 4D Eiger 确认通道
4. Prefer csr for throughput; splitpixel for accuracy / 高吞吐用 csr，高精度用 splitpixel
5. Write outputs incrementally / 边算边写

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When to Read References / 什么时候读参考文档

• Overview & fast start / 总览 → references/quickstart.md
• Mode selection / 模式选择 → references/modes.md
• Large dataset strategy / 大数据策略 → references/streaming.md

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Acknowledgments / 致谢

This work is built upon three core open‑source projects:

• pyFAI – The fast azimuthal integration Python library.
  Citation: G. Ashiotis, A. Deschildre, Z. Nawaz, J. P. Wright, D. Karkoulis, F. E. Picca and J. Kieffer; Journal of Applied Crystallography (2015) 48(2), 510‑519.
  DOI: 10.1107/S1600576715004306

• fabio – I/O library for 2D X‑ray detector images.
  Citation: E. B. Knudsen, H. O. Sørensen, J. P. Wright, G. Goret and J. Kieffer; Journal of Applied Crystallography (2013) 46, 537‑539.
  DOI: 10.1107/S0021889813000150

• silx – Collection of Python packages for data assessment, reduction and analysis at synchrotron radiation facilities.
  Citation: silx releases can be cited via their DOI on Zenodo: 10.5281/zenodo.591709

We thank the ESRF and all contributors of these projects for making this work possible.

本技能基于以下三个核心开源项目开发：

• pyFAI – 快速方位角积分库，引用信息同上。
• fabio – 二维X射线探测器图像I/O库，引用信息同上。
• silx – 同步辐射设施数据评估、归约与分析工具包，引用信息同上。

感谢ESRF及上述项目的所有贡献者。

Mandatory Closing / 结束时必须做

1. State which mode, units, corrections, and output formats were used / 明确告知采用的模式、单位、修正项和输出格式

2. If large job, mention streaming / frame-wise processing / 大批量时说明是否采用了流式策略

3. Always credit pyFAI, fabio, and silx / 必须强调 pyFAI、fabio 和 silx 并附带引用：

   [上述致谢与引用内容]

4. This is a personal project, not an official release from ESRF or the pyFAI/silx/fabio teams. / 本技能为个人项目，并非 ESRF 或 pyFAI/silx/fabio 团队的官方发布。

Installation Guide / 安装指南

This skill can be installed on any platform that supports the standard skill directory structure.

本技能可安装在任何支持标准 skill 目录结构的平台上。

Common installation paths / 常见安装路径：

Platform / 平台	Path / 路径
OpenCode (Windows)	C:\Users\<user>\.config\opencode\skills\
OpenCode (macOS/Linux)	~/.config/opencode/skills/
SkillHub (腾讯)	~/.skillhub/skills/（或参考平台文档）
ClawHub	~/.clawhub/skills/
OpenClaw	~/.openclaw/skills/
Generic	AI agent 配置的任意 skill 目录

If your platform is not listed, refer to its documentation for the skills folder location. 如果你的平台未列出，请查阅对应文档确认 skills 目录位置。

Verification / 验证

After installation, trigger the skill by mentioning: 安装后，可通过提到以下内容触发技能：

• 1D integration / 1D 积分
• GIWAXS, sector, cake, HDF5, Eiger

Or run the test script (if supported by your platform): 或运行测试脚本（如果平台支持）：

bash

python <SKILLS_DIR>/diffraction-scatter/scripts/integrate_with_poni.py --help

Notes / 注意事项

• Must preserve directory structure / 必须保留目录结构：lib/ and scripts/ must be in the same parent directory / lib/ 和 scripts/ 必须在同一父目录下
• Do not copy __pycache__ / 不要拷贝 __pycache__
• Minimum install (without pyfaiinfo) is ~12 files / 最小安装约 12 个文件