ds : Dataset
The dataset that will be detrended in-place.
space : str or None
If not None, a samples attribute of the same name is added to the
mapped dataset that stores the coordinates of each sample in the
space that is spanned by the polynomials. If an attribute of that
name is already present in the input dataset its values are interpreted
as sample coordinates in the space that should be spanned by the
polynomials.
polyord : int, optional
Order of the Legendre polynomial to remove from the data. This will
remove every polynomial up to and including the provided value. For
example, 3 will remove 0th, 1st, 2nd, and 3rd order polynomials from
the data. np.B.: The 0th polynomial is the baseline shift, the 1st
is the linear trend. If you specify a single int and the
chunks_attr parameter is not None, then this value is used for
each chunk. You can also specify a different polyord value for each
chunk by providing a list or ndarray of polyord values with the
length equal to the number of chunks. Constraints: value must be
convertible to type ‘int’. [Default: 1]
chunks_attr : None or str, optional
If None, the whole dataset is detrended at once. Otherwise, the
given samples attribute (given by its name) is used to define chunks
of the dataset that are processed individually. In that case, all
the samples within a chunk should be in contiguous order and the
chunks should be sorted in order from low to high – unless the
dataset provides information about the coordinate of each sample in
the space that should be spanned be the polynomials (see space
argument). Constraints: value must be None, or value must be a
string. [Default: None]
opt_regs : None or list(str), optional
List of sample attribute names that should be used as additional
regressors. An example use would be to regress out motion
parameters. Constraints: value must be None, or value must be
convertible to list(str). [Default: None]
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.
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
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