mvpa2.mappers.detrend.poly_detrend(ds, **kwargs)

In-place polynomial detrending.

This function behaves identical to the PolyDetrendMapper. The only difference is that the actual detrending is done in-place – potentially causing a significant reduction of the memory demands.


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