nimare.meta.cbma.kernel
.KDAKernel¶
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class
KDAKernel
(r=6, value=1)[source]¶ Generate KDA modeled activation images from coordinates.
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get_params
(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 (mapping of string to any) – Parameter names mapped to their values.
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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 KDA modeled activation images for each Contrast in dataset. Differs from MKDA images in that binary spheres are summed together in map (i.e., resulting image is not binary if coordinates are close to one another).
- 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 ({‘image’, ‘array’}, optional) – Whether to return a niimg (‘image’) or a numpy array. Default is ‘image’.
- Returns
imgs (
list
ofnibabel.Nifti1Image
ornumpy.ndarray
) – 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 ‘array’, a 2D numpy array (C x V), where C is contrast and V is voxel.
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