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.
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
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