mvpa2.mappers.slicing.StripBoundariesSamples

class mvpa2.mappers.slicing.StripBoundariesSamples(space, prestrip, poststrip, **kwargs)

Strip samples on boundaries defines by sample attribute values.

A sample attribute of a dataset is scanned for consecutive blocks of identical values. Every change in the value is treated as a boundary and custom number of samples is removed prior and after this boundary.

Notes

Available conditional attributes:

• calling_time+: None
• raw_results: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

space : str

name of the sample attribute that shall be used to determine the boundaries.

prestrip : int

Number of samples to be stripped prior to each boundary.

poststrip : int

Number of samples to be stripped after each boundary (this includes the boundary sample itself, i.e. the first samples with a different sample attribute value).

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods