nimare.diagnostics.FocusFilter

class FocusFilter(mask=None)[source]

Bases: NiMAREBase

Remove coordinates outside of the Dataset’s mask from the Dataset.

New in version 0.0.13.

Parameters:

mask (str, Nifti1Image, NiftiMasker or similar, or None, optional) – Mask(er) to use. If None, uses the masker of the Dataset provided in transform.

Notes

This filter removes any coordinates outside of the brain mask. It does not remove studies without coordinates in the brain mask, since a Dataset does not need to have coordinates for all studies (e.g., some may only have images).

Methods

get_params([deep])

Get parameters for this estimator.

load(filename[, compressed])

Load a pickled class instance from file.

save(filename[, compress])

Pickle the class instance to the provided file.

set_params(**params)

Set the parameters of this estimator.

transform(dataset)

Apply the filter to a Dataset.
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

classmethod load(filename, compressed=True)[source]

Load a pickled class instance from file.

Parameters:

  • filename (str) – Name of file containing object.

  • compressed (bool, default=True) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns:

obj – Loaded class object.

Return type:

class object

save(filename, compress=True)[source]

Pickle the class instance to the provided file.

Parameters:

  • filename (str) – File to which object will be saved.

  • compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Return type:

self

transform(dataset)[source]

Apply the filter to a Dataset.

Parameters:

dataset (Dataset) – The Dataset to filter.

Returns:

dataset – The filtered Dataset.

Return type:

Dataset