ds : Dataset
The dataset that will be detrended in-place.
polyord : int or list, 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 chunks_attr 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 the length of the number of chunks.
chunks_attr : str or None
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 inspace argument).
opt_regs : list or None
Optional list of sample attribute names that should be used as
additional regressors. One example would be to regress out motion
parameters.
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.
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.
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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