mvpa.datasets.event.EventDataset

Class EventDataset

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

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.

Class EventDataset

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

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