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@freeman-lab freeman-lab released this 04 Aug 06:45
· 1804 commits to master since this release
v0.2.0
a24e009

This is a significant update with changes and enhancements to the API, new analyses, and bug fixes.

Major changes

  • Updated for compatibility with Spark 1.0.0, which brings with it a number of significant performance improvements
  • Reorganization of the API such that all analyses are all accessed through their respective classes and methods (e.g. ICA.fit, Stats.calc). Standalone functions use the same classes, and act as wrappers soley for non-interactive job submission (e.g. thunder-submit factorization/ica <opts>)
  • Executables included with the release for easily launching a PySpark shell, or an EC2 cluster, with Thunder dependencies and set-up handled automatically
  • Improved and expanded documentation, built with Sphinx
  • Basic functionality for colorization of results, useful for visualization, see example
  • Registered project in PyPi

New analyses and features

  • A DataSet class for easily loading simulated and real data examples
  • A decoding package and MassUnivariateClassifier class, currently supporting two mass univariate classification analyse (GaussNaiveBayes and TTest)
  • An NMF class for dense non-negative matrix factorization, a useful analysis for spatio-temporal decompositions

Bug fixes and other changes

  • Renamed sigprocessing library to timeseries
  • Replace eig with eigh for symmetric matrix
  • Use set and broadcasting to speed up filtering for subsets in Query
  • Several optimizations and bug fixes in basic saving functionality, including new pack function
  • Fixed handling of integer indices in subtoind
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