PyMVPA Manual

1 PyMVPA ManualRelease AuthorsAug 28, 2017 CONTENTS1 this Manual is NOT .. bit of History .. to cite PyMVPA .. publications .. and Contributors .. support ..62 .. Have .. Recommendations .. Binary Packages .. backports and inofficial Ubuntu packages .. X .. from Source .. the Sources .. it (General instructions) .. with enabled LIBSVM bindings .. build procedure .. X ..143 Getting the Impatient .. Overview ..184 Tutorial Introduction to Prerequisites .. Do I Need To Get Python Running.

2 Reading and Viewing ..20 Tutorial Introductions Into General Python Programming ..20 Scientific Computing In Python ..21 Interactive Python Shell ..21 Multivariate analysis of Neuroimaging Data .. basics and concepts ..23 For samples ..23iFor features ..24 For the entire dataset .. , resampling, feature selection .. fMRI data .. storage .. data in shape .. real data .. structure, less duplication of work ..31 Multi-session data .. preprocessing ..34 Detrending ..34 Normalization ..35 ComputingPatterns Of Activation.

3 And back again a Mapper s tale ..36 Back To NIfTI .. All Alike, Yet Different .. classifier, really .. Need To Take A Closer Look .. here and there Searchlights .. , searching, searching, .. real! .. that do more Meta Classifiers .. Model Parameters Sensitivity analysis .. s A Kind Of Magic .. For The Fish .. The Classifier .. Words .. Data analysis .. Pre-processing Is Not Event-related .. Specification .. Modeling .. Timeseries To Spatio-temporal Samples .. Plotting Example .. Searchlights.

4 Working with data .. WiP: The Earth Is Round Significance Testing .. testing ..67 Monte Carlo here I come! .. The following content is incomplete and experimental ..71If you have a clue ..71 Family-friendly .. Evaluating multi-class classifications .. Previously in part 8 .. Statistical Tools in Python .. Dataset Exploration for Confounds .. Hypothesis Testing ..78 Independent Samples .. Statistical Treatment of Sensitivities .. References ..795 (Custom) Configurations .. Tracking.

5 Output .. Messages .. Messages .. Messages .. Status Summary .. Little Helpers .. Number Generation .. at a Grasp .. Bindings ..876 Example Analyses and .. of Data Projection Methods .. Data-Exploration .. strategies and Background .. simple start .. hyperplane tutorial .. Searchlight Example .. on fMRI data .. searchlight on fMRI data .. similarity analysis (RSA) on fMRI data .. Measure .. of SVD-mapped Datasets .. testing of Classifier-based Analyses .. Nested Cross-Validation.

6 Determine the Distribution of some Variable .. Spatio-temporal analysis of event-related fMRI data .. Hyperalignment for between-subject analysis .. 125 analysis setup .. 125 Within-subject classification .. 126 Between-subject classification using anatomically aligned data .. 126 Between-subject classification with Hyperalignment(TM) .. 126 Comparing the results .. 127 Regularized Hyperalignment .. 129 Searchlight Hyperalignment .. 132 Comparing the results .. analysis of eye movement patterns.

7 133 Plotting the results .. Model Flexibility in Pictures .. Plotting of Classifier Behavior .. Topography plots .. Maps .. (f)MRI plotting .. with 3rd-party software .. scikit-learn transformers with PyMVPA .. scikit-learn classifiers with PyMVPA .. scikit-learn regressions with PyMVPA .. the MNIST handwritten digits with MDP .. 151 MDP-style classification .. 152 Doing it the PyMVPA way .. 152 Visualizing data and results .. interest and Miscellaneous .. cross-validation using a cached kernel.

8 157 BOLD-Response parameters .. 157 Searchlight accuracy distributions .. Sweep .. of the margin width in a soft-margin SVM .. SMLR to Linear SVM Classifier .. effect of different hyperparameters in GPR .. model selection: grid search for GPR .. 1677 Frequently Asked .. m a Matlab user. How hard is learning Python and PyMVPA for me? .. is sloooooow. What can I do? .. am tired of writing these endless import blocks. Any alternative? .. feel like I want to contribute something, do you mind? .. want to develop a new feature for PyMVPA .

9 How can I do it efficiently? .. Manual is quite insufficient. When will you improve it? .. import, export and storage .. file formats are understood by PyMVPA ? .. if there is no special file format for some particular datatype? .. preprocessing .. there an easy way to remove invariant features from a dataset? .. can I do block-averaging of my block-design fMRI dataset? .. analysis .. do I know which features were finally selected by a classifier doing feature selection? do I extract sensitivities from a classifier used within a cross-validation?

10 PyMVPA deal with literal class labels? .. 1728 Glossary1739 References17710 3rd Party Code .. LIBSVM .. NIPY .. PDFBook .. 19211 Development Releases .. 193 Python Module Index211 Index213ivPyMVPA Manual , Release PDF version of the Manual is available for Manual , Release is a Python module intended to ease pattern classification analysis of large datasets. It provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. Whileit is not limited to neuroimaging data it is eminently suited for such datasets.