nimare.meta.cbma.kernel.ALEKernel

class ALEKernel(fwhm=None, sample_size=None)

Generate ALE modeled activation images from coordinates and sample size.

Parameters

• fwhm (float, optional) – Full-width half-max for Gaussian kernel, if you want to have a constant kernel across Contrasts. Mutually exclusive with sample_size.

• sample_size (int, optional) – Sample size, used to derive FWHM for Gaussian kernel based on formulae from Eickhoff et al. (2012). This sample size overwrites the Contrast-specific sample sizes in the dataset, in order to hold kernel constant across Contrasts. Mutually exclusive with fwhm.

get_params(deep=True)

Get parameters for this estimator.

Parameters

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

Returns

params (mapping of string to any) – Parameter names mapped to their values.

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters

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

• compressed (bool, optional) – 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 (class object) – Loaded class object.

save(filename, compress=True)

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)

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.

Returns

self

transform(dataset, masker=None, return_type='image')

Generate ALE modeled activation images for each Contrast in dataset.

Parameters

• dataset (nimare.dataset.Dataset or pandas.DataFrame) – Dataset for which to make images. Can be a DataFrame if necessary.

• masker (img_like, optional) – Only used if dataset is a DataFrame.

• return_type ({‘image’, ‘array’}, optional) – Whether to return a niimg (‘image’) or a numpy array. Default is ‘image’.

Returns

imgs (list of nibabel.nifti1.Nifti1Image or numpy.ndarray) – If return_type is ‘image’, a list of modeled activation images (one for each of the Contrasts in the input dataset). If return_type is ‘array’, a 2D numpy array (C x V), where C is contrast and V is voxel.