title: One-dimensional statistical parametric mapping in Python.
abstract: 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/.
authors.affiliation: Department of Bioengineering, Shinshu University, Tokida 3-15-1, Ueda, Nagano, Japan. tpataky@shinshu-u.ac.jp
authors.forename: Todd C
authors.initials: TC
authors.lastname: Pataky
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011381:Programming Languages; D012984:Software; D014584:User-Computer Interface