论文标题
PYNX：基于操作员的相干X射线成像的高性能计算工具包
PyNX: high performance computing toolkit for coherent X-ray imaging based on operators
论文作者
论文摘要
开源Pynx工具包[Favre-Nicolin等人（2011）ARXIV：1010.2641，Mandula等人（2016）]已扩展，为相干X射线成像数据分析和仿真提供了工具。所有计算都可以在图形处理单元（GPU）上执行，以达到高性能计算速度。这可以用于在远处或近场状态下，用于连贯的衍射成像（CDI），ptychography和波前传播。此外，所有成像操作（传播，投影，算法周期..）都可以用作简单的数学运算符，该方法可用于轻松将基本算法在量身定制的链中结合起来。计算也可以分布到多个GPU，例如用于大型PtyChophich数据集。命令行脚本也可用于在线CDI和Ptychography Analysis，来自RAW BEAM线数据集或使用连贯的X射线成像数据格式[Maia（2012）]。
The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPU) to achieve high performance computing speeds. This can be used for Coherent Diffraction Imaging (CDI), Ptychography and wavefront propagation, in the far or near field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles..) can be used in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large Ptychography datasets. Command-line scripts are also available for on-line CDI and Ptychography analysis, either from raw beamline datasets or using the Coherent X-ray Imaging data format [Maia (2012)].
