nimare.meta.kernel
.KDAKernel
- class KDAKernel(r=10, value=1)[source]
Bases:
KernelTransformer
Generate KDA modeled activation images from coordinates.
Changed in version 0.0.13:
Remove “dataset” return_type option.
Changed in version 0.0.12:
Remove low-memory option in favor of sparse arrays for kernel transformers.
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[, masker, return_type])Generate modeled activation images for each Contrast in dataset.
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters:
- Returns:
obj – Loaded class object.
- Return type:
class object
- 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, masker=None, return_type='image')[source]
Generate modeled activation images for each Contrast in dataset.
- Parameters:
dataset (
Dataset
orpandas.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 (anddataset
is a Dataset), the Dataset’s masker attribute will be used. Default is None.return_type ({'sparse', 'array', 'image'}, optional) – Whether to return a sparse matrix (‘sparse’), a numpy array (‘array’), or a list of niimgs (‘image’). Default is ‘image’.
- Returns:
imgs – If return_type is ‘sparse’, a 4D sparse array (E x S), where E is the number of unique experiments, and the remaining 3 dimensions are equal to shape of the images. 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).
- Return type:
(C x V)
numpy.ndarray
orlist
ofnibabel.Nifti1Image
orDataset
- Variables: