Package mvpa :: Package datasets :: Module miscfx

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# Module miscfx

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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.

Classes | [hide private]

| SequenceStats Simple helper to provide representation of sequence statistics

Functions | [hide private]

zscore(dataset, mean=None, std=None, perchunk=True, baselinelabels=None, pervoxel=True, targetdtype='float64') Z-Score the samples of a Dataset (in-place). | source code

aggregateFeatures(dataset, fx=N.mean) Apply a function to each row of the samples matrix of a dataset. | source code

removeInvariantFeatures(dataset) Returns a new dataset with all invariant features removed. | source code

coarsenChunks(source, nchunks=4) Change chunking of the dataset | source code

getSamplesPerChunkLabel(dataset) Returns an array with the number of samples per label in each chunk. | source code

Imports: isSequenceType, N, Dataset, datasetmethod, table2string, getBreakPoints, externals, warning, debug, detrend

Function Details | [hide private]

### zscore(dataset, mean=None, std=None, perchunk=True, baselinelabels=None, pervoxel=True, targetdtype='float64')

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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.

Decorators:
  - `@datasetmethod`

### aggregateFeatures(dataset, fx=N.mean)

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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).

Returns:
  a new Dataset object with the aggregated feature(s).
Decorators:
  - `@datasetmethod`

### removeInvariantFeatures(dataset)

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Returns a new dataset with all invariant features removed.

Decorators:
  - `@datasetmethod`

### coarsenChunks(source, nchunks=4)

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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
Decorators:
  - `@datasetmethod`

### getSamplesPerChunkLabel(dataset)

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

Array shape is (chunks x labels).

Parameters:
  - `dataset`, `Dataset` - Source dataset.
Decorators:
  - `@datasetmethod`