nimare.meta.kernel
.ALEKernel¶
-
class
ALEKernel
(fwhm=None, sample_size=None)[source]¶ Bases:
nimare.meta.kernel.KernelTransformer
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 withsample_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 withfwhm
.
-
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.- Returns
self
-
transform
(dataset, masker=None, return_type='image')[source]¶ Generate modeled activation images for each Contrast in dataset.
- Parameters
dataset (
nimare.dataset.Dataset
orpandas.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 ({‘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 ‘dataset’.
- Returns
imgs ((C x V)
numpy.ndarray
orlist
of) –nibabel.Nifti1Image
ornimare.dataset.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