Package mvpa :: Package datasets :: Module event :: Class EventDataset

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# Class EventDataset

source code

    Event-based dataset

    This dataset type can be used to segment 'raw' data input into meaningful
    boxcar-shaped samples, by simply defining a list of events
    (see :class:`~mvpa.misc.support.Event`).

    Additionally, it can be used to add arbitrary information (as features)
    to each event-sample (extracted from the event list itself). An
    appropriate mapper is automatically constructed, that merges original
    samples and additional features into a common feature space and also
    separates them again during reverse-mapping. Otherwise, this dataset type
    is a regular dataset (in contrast to `MetaDataset`).

    The properties of an :class:`~mvpa.misc.support.Event` supported/required
    by this class are:

    `onset`
      An integer indicating the startpoint of an event as the sample
      index in the input data.

    `duration`
      How many input data samples following the onset sample should be
      considered for an event. The embedded
      :class:`~mvpa.mappers.boxcar.BoxcarMapper` will use the maximum boxlength
      (i.e., `duration`) of all defined events to create a regular-shaped data
      array.

    `label`
      The corresponding label of that event (numeric or literal).

    `chunk`
      An optional chunk id.

    `features`
      A list with an arbitrary number of features values (floats), that will
      be added to the feature vector of the corresponding sample.

Instance Methods | [hide private]

__init__(self, samples=None, events=None, mask=None, bcshape=None, dametric=None, **kwargs) If `samples` and mapper arguments are not `None` the mapper is used to forward-map the samples array and the result is passed to the Dataset constructor. | source code

Inherited from `mapped.MappedDataset`: `mapForward`, `mapReverse`, `mapSelfReverse`, `selectFeatures`

Class Variables | [hide private]

Inherited from `mapped.MappedDataset`: `O`, `__doc__`, `mapper`, `samples_original`

Method Details | [hide private]

### __init__(self, samples=None, events=None, mask=None, bcshape=None, dametric=None, **kwargs)
(Constructor)

source code

If `samples` and mapper arguments are not `None` the mapper is used to forward-map the samples array and the result is passed to the Dataset constructor.

Overrides: mapped.MappedDataset.__init__

Parameters:

samples: ndarray
  'Raw' input data from which boxcar-shaped samples will be extracted.
events: sequence of Event instances
  Both an events `onset` and `duration` are assumed to be provided as #samples. The boxlength will be determined by the maximum duration of all events.
mask: boolean array
  Only features corresponding to non-zero mask elements will be considered for the final dataset. The mask shape either has to match the shape of the generated boxcar-samples, or the shape of the 'raw' input samples. In the latter case, the mask is automatically expanded to cover the whole boxcar. If no mask is provided, a full mask will be constructed automatically.
bcshape: tuple
  Shape of the boxcar samples generated by the embedded boxcar mapper. If not provided this is determined automatically. However, this required an extra mapping step.
dametric: Metric
  Custom metric to be used by the embedded DenseArrayMapper.
**kwargs
  All additional arguments are passed to the base class.