mvpa2.mappers.filters.fft_resample

mvpa2.mappers.filters.fft_resample(ds, num, **kwargs)

FFT-based resampling.

Parameters:

ds : Dataset

num : int

Number of output samples. If operating on chunks, this is the number of samples per chunk.

window : str or float or tuple

Passed to scipy.signal.resample

chunks_attr : str or None

If not None, this samples attribute defines chunks that will be resampled individually.

position_attr : str

A samples attribute with positional information that is passed to scipy.signal.resample. If not None, the output dataset will also contain a sample attribute of this name, with updated positional information (this is, however, only meaningful for equally spaced samples).

attr_strategy : {‘remove’, ‘sample’, ‘resample’}

Strategy to process sample attributes during mapping. ‘remove’ will cause all sample attributes to be removed. ‘sample’ will pick orginal attribute values matching the new resampling frequency (e.g. every 10th), and ‘resample’ will also apply the actual data resampling procedure to the attributes as well (which might not be possible, e.g. for literal attributes).

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

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space : str, optional

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

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