One-dimensional statistical parametric mapping in Python
One-dimensional statistical parametric mapping in Python
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DOI：
10.1080/10255842.2010.527837
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发表时间：
2012-01-01
影响因子：
1.6
通讯作者：
Pataky, Todd C.
中科院分区：
文献类型：
--
作者：
Pataky, Todd C.
统计参数映射(SPM)是一种用于检测场在光滑n维连续体中变化的拓扑方法。许多类别的生物力学数据都是平滑的，并且包含在离散的边界内，因此非常适合于SPM分析。目前的论文伴随着‘SPM1D’的发布，这是一个免费和开源的Python包，用于对一组已注册的1D曲线进行SPM分析。给出了三个应用实例：(I)运动学；(Ii)地面反力；(Iii)概率有限元模拟中的接触压力分布。除了提供各种常见统计检验(如t检验、回归和ANOVA)的高级接口外，SPM1D还通过独立的示例脚本强调SPM理论的基本概念。源代码和文档可在www.tpataky.net/spm1d/上找到。
Statistical parametric mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of 'SPM1D', a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky.net/spm1d/.