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datasets.miscfx

Module: datasets.miscfx

Inheritance diagram for mvpa.datasets.miscfx:

Misc function performing operations on datasets.

All the functions defined in this module must accept dataset as the first argument since they are bound to Dataset class in the trailer.

Class

SequenceStats

class mvpa.datasets.miscfx.SequenceStats(seq, order=2)

Bases: dict

Simple helper to provide representation of sequence statistics

Matlab analog: http://cfn.upenn.edu/aguirre/code/matlablib/mseq/mtest.m

WARNING: Experimental – API might change without warning! Current implementation is ugly!

Initialize SequenceStats

Parameters:

seq (list or ndarray) – Actual sequence of labels

Keywords :

order : int

Maximal order of counter-balancing check. For perfect counterbalancing all matrices should be identical

plot()

Plot correlation coefficients

Functions

mvpa.datasets.miscfx.aggregateFeatures(dataset, fx=<function mean at 0x2982c80>)

Apply a function to each row of the samples matrix of a dataset.

The functor given as fx has to honour an axis keyword argument in the way that NumPy used it (e.g. NumPy.mean, var).

Return type:a new Dataset object with the aggregated feature(s).

mvpa.datasets.miscfx.coarsenChunks(source, nchunks=4)

Change chunking of the dataset

Group chunks into groups to match desired number of chunks. Makes sense if originally there were no strong groupping into chunks or each sample was independent, thus belonged to its own chunk

Parameters:

• source (Dataset or list of chunk ids) – dataset or list of chunk ids to operate on. If Dataset, then its chunks get modified
• nchunks (int) – desired number of chunks

mvpa.datasets.miscfx.getSamplesPerChunkLabel(dataset)

Returns an array with the number of samples per label in each chunk.

Array shape is (chunks x labels).

Parameters:dataset (Dataset) – Source dataset.

mvpa.datasets.miscfx.removeInvariantFeatures(dataset)

Returns a new dataset with all invariant features removed.

mvpa.datasets.miscfx.zscore(dataset, mean=None, std=None, perchunk=True, baselinelabels=None, pervoxel=True, targetdtype='float64')

Z-Score the samples of a Dataset (in-place).

mean and std can be used to pass custom values to the z-scoring. Both may be scalars or arrays.

All computations are done in place. Data upcasting is done automatically if necessary into targetdtype

If baselinelabels provided, and mean or std aren’t provided, it would compute the corresponding measure based only on labels in baselinelabels

If perchunk is True samples within the same chunk are z-scored independent of samples from other chunks, e.i. mean and standard deviation are calculated individually.