nimare.meta.kernel
.KernelTransformer¶
-
class
KernelTransformer
[source]¶ Bases:
nimare.base.Transformer
Base class for modeled activation-generating methods in
nimare.meta.kernel
.Coordinate-based meta-analyses leverage coordinates reported in neuroimaging papers to simulate the thresholded statistical maps from the original analyses. This generally involves convolving each coordinate with a kernel (typically a Gaussian or binary sphere) that may be weighted based on some additional measure, such as statistic value or sample size.
Notes
All extra (non-ijk) parameters for a given kernel should be overrideable as parameters to __init__, so we can access them with get_params() and also apply them to datasets with missing data.
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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
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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
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