nimare.meta.kernel.ALEKernel

class ALEKernel(fwhm=None, sample_size=None, memory_limit=None)

Bases: nimare.meta.kernel.KernelTransformer

Generate ALE modeled activation images from coordinates and sample size.

By default (if neither fwhm nor sample_size is provided), the FWHM of the kernel will be determined on a study-wise basis based on the sample sizes available in the input, via the method described in 1.

Changed in version 0.0.8:

• [ENH] Add low-memory option for kernel transformers.

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.

• memory_limit (str or None, optional) – Memory limit to apply to data. If None, no memory management will be applied. Otherwise, the memory limit will be used to (1) assign memory-mapped files and (2) restrict memory during array creation to the limit. Default is None.

References

1

Eickhoff, Simon B., et al. “Activation likelihood estimation meta-analysis revisited.” Neuroimage 59.3 (2012): 2349-2361.

get_params(deep=True)

Get parameters for this estimator.

Parameters

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

Returns

params (dict) – 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 modeled activation images for each Contrast in dataset.

Parameters

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

• masker (img_like or None, optional) – Mask to apply to MA maps. Required if dataset is a DataFrame. If None (and dataset is a Dataset), the Dataset’s masker attribute will be used. Default is None.

• return_type ({‘array’, ‘image’, ‘dataset’}, optional) – Whether to return a numpy array (‘array’), a list of niimgs (‘image’), or a Dataset with MA images saved as files (‘dataset’). Default is ‘image’.

Returns

imgs ((C x V) numpy.ndarray or list of nibabel.Nifti1Image or Dataset) – If return_type is ‘array’, a 2D numpy array (C x V), where C is contrast and V is voxel. 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 ‘dataset’, a new Dataset object with modeled activation images saved to files and referenced in the Dataset.images attribute.

Variables

• filename_pattern (str) – Filename pattern for MA maps that will be saved by the transformer.

• image_type (str) – Name of the corresponding column in the Dataset.images DataFrame.