One-dimensional statistical parametric mapping in Python

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 �...

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Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering Vol. 15; no. 3; pp. 295 - 301
Main Author: Pataky, Todd C.
Format: Journal Article
Language:English
Published: England Taylor & Francis Group 01.03.2012
Subjects:
Computer Simulation
Data Interpretation, Statistical
ground reaction force
Humans
kinematic trajectory analysis
Models, Biological
Models, Statistical
object-oriented programming
open-source software
probabilistic finite element modelling
Programming Languages
Software
topological statistics
User-Computer Interface
ISSN:1025-5842, 1476-8259, 1476-8259
Online Access:Get full text
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Summary: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/ .
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ISSN:1025-5842
1476-8259
1476-8259
DOI:10.1080/10255842.2010.527837