| Multivariate Pattern Analysis in Python

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »

This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# datasets.meta

## Module: `datasets.meta`

Inheritance diagram for `mvpa.datasets.meta`:

Dataset container

## `MetaDataset`

class `mvpa.datasets.meta.``MetaDataset`(datasets)

  Bases: `object`

  Dataset container

  The class is useful to combine several Datasets with different origin and type and bind them together. Such a combined dataset can then by used to e.g. pass it to a classifier.

  MetaDataset does not permanently duplicate data stored in the dataset it contains. The combined samples matrix is build on demand and samples attribute access is redirected to the first dataset in the container.

  Currently operations other than samples or feature selection are not fully supported, e.g. passing a MetaDataset to detrend() will initially result in a detrended MetaDataset, but the combined and detrended samples matrix will be lost after the next call to selectSamples() or selectFeatures(), which freshly pulls samples from all datasets in the container.

  Initialize dataset instance

  Parameters: | datasets (list) –

  `applyMapper`(*args, **kwargs)

    Apply a mapper on all underlying datasets.

  `datasets`
  `getNFeatures`()

    Number of features per sample.

  `getNSamples`()

    Currently available number of samples.

  `getRandomSamples`(nperlabel)

    Return a MetaDataset with a random subset of samples.

  `mapReverse`(val)

    Perform reverse mapping

    Return : | List of results per each used mapper and the corresponding part of the provided val.

  `nfeatures`

    Number of features per sample.

  `nsamples`

    Currently available number of samples.

  `permuteLabels`(*args, **kwargs)

    Toggle label permutation.

  `rebuildSamples`()

    Update the combined samples matrix from all underlying datasets.

  `selectFeatures`(ids, sort=True)

    Do feature selection on all underlying datasets at once.

  `selectSamples`(*args, **kwargs)

    Select samples from all underlying datasets at once.

  `setSamplesDType`(dtype)

    Set the data type of the samples array.

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »