研究目的
To present a software ecosystem for processing and interactively exploring ultrafast electron scattering data, aiming to standardize and simplify analysis in this field.
研究成果
The software ecosystem provides effective tools for UES data analysis, enabling interactive exploration and high-throughput processing. Future work includes enhancing performance and expanding simulation features.
研究不足
Performance could be improved by rewriting core functionality in C; simulation capabilities for single-crystal scattering patterns are not fully implemented; relies on specific data formats and may require custom plug-ins for compatibility.
1：Experimental Design and Method Selection:
The methodology involves designing and implementing three Python packages (iris, npstreams, scikit-ued) for data processing and exploration, leveraging Python's scientific stack and open-source principles.
2：Sample Selection and Data Sources:
Uses raw UES datasets, which can be large (up to hundreds of gigabytes), and includes examples from various experiments (e.g., single-crystal and polycrystalline samples).
3：List of Experimental Equipment and Materials:
Software packages (iris, npstreams, scikit-ued), Python programming language, HDF5 for data storage, and various libraries (e.g., numpy, scipy).
4：Experimental Procedures and Operational Workflow:
Workflow includes data input via plug-ins, data reduction (alignment, normalization, averaging), storage in HDF5 format, and interactive exploration through a GUI.
5：Data Analysis Methods:
Utilizes streaming array operations for efficient processing, image analysis algorithms (e.g., masked normalized cross-correlation), and simulation routines for diffraction patterns.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容-
iris
Graphical user-interface program and library for interactive exploration of UES data
-
npstreams
Streaming array-processing library for high-throughput parallel data reduction
-
scikit-ued
Library of reusable routines and data structures for analysis of UES data
-
HDF5
Hierarchical Data Format version 5
The HDF Group
Data storage format for large datasets with compression and slicing capabilities
-
numpy
Array operations and numerical computation
-
scipy
Scientific computing and algorithms
-
scikit-image
Image processing tasks
-
scikit-learn
Machine learning tasks
-
spglib
Finding and handling crystal symmetries
-
Python
CPython interpreter version 3.6 or later
Python Software Foundation
Programming language for software development
-
登录查看剩余8件设备及参数对照表
查看全部
