nimare.meta.cbma.kernel.ALEKernel

class ALEKernel(fwhm=None, n=None)[source]

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 n.
  • n (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.

Methods

get_params(self[, deep]) Get parameters for this estimator.
load(filename[, compressed]) Load a pickled class instance from file.
save(self, filename[, compress]) Pickle the class instance to the provided file.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, dataset[, mask, masked]) Generate ALE modeled activation images for each Contrast in dataset.
get_params(self, deep=True)[source]

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 – Parameter names mapped to their values.
Return type:mapping of string to any
classmethod load(filename, compressed=True)[source]

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 – Loaded class object.
Return type:

class object
save(self, 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(self, **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.

Returns:
Return type:self
transform(self, dataset, mask=None, masked=False)[source]

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.
  • mask (img_like, optional) – Only used if dataset is a DataFrame.
  • masked (bool, optional) – Return an array instead of a niimg.
Returns:

imgs – A list of modeled activation images (one for each of the Contrasts in the input dataset).
Return type:

list of nibabel.Nifti1Image