Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.
3.1.1. fmralign.pairwise_alignment.PairwiseAlignment¶
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class
fmralign.pairwise_alignment.PairwiseAlignment(alignment_method, n_pieces=1, clustering='kmeans', n_bags=1, mask=None, smoothing_fwhm=None, standardize=None, detrend=False, target_affine=None, target_shape=None, low_pass=None, high_pass=None, t_r=None, memory=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)[source]¶ Decompose the source and target images into regions and align corresponding regions independently.
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__init__(alignment_method, n_pieces=1, clustering='kmeans', n_bags=1, mask=None, smoothing_fwhm=None, standardize=None, detrend=False, target_affine=None, target_shape=None, low_pass=None, high_pass=None, t_r=None, memory=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)[source]¶ If n_pieces > 1, decomposes the images into regions and align each source/target region independantly. If n_bags > 1, this parcellation process is applied multiple time and the resulting models are bagged.
- Parameters
alignment_method: string
Algorithm used to perform alignment between X_i and Y_i : * either ‘identity’, ‘scaled_orthogonal’, ‘ridge_cv’, ‘permutation’, ‘diagonal’ * or an instance of one of alignment classes (imported from functional_alignment.alignment_methods)
n_pieces: int, optional (default = 1)
Number of regions in which the data is parcellated for alignment. If 1 the alignment is done on full scale data. If >1, the voxels are clustered and alignment is performed on each cluster applied to X and Y.
clustering : string or 3D Niimg optional (default
‘kmeans’, ‘ward’, ‘rena’ method used for clustering of voxels based on functional signal, passed to nilearn.regions.parcellations If 3D Niimg, image used as predefined clustering, n_bags and n_pieces are then ignored.
n_bags: int, optional (default = 1)
If 1 : one estimator is fitted. If >1 number of bagged parcellations and estimators used.
mask: Niimg-like object, instance of NiftiMasker or MultiNiftiMasker, optional (default = None)
Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is given, it will be computed automatically by a MultiNiftiMasker with default parameters.
smoothing_fwhm: float, optional (default = None)
If smoothing_fwhm is not None, it gives the size in millimeters of the spatial smoothing to apply to the signal.
standardize: boolean, optional (default = None)
If standardize is True, the time-series are centered and normed: their variance is put to 1 in the time dimension.
detrend: boolean, optional (default = None)
This parameter is passed to nilearn.signal.clean. Please see the related documentation for details
target_affine: 3x3 or 4x4 matrix, optional (default = None)
This parameter is passed to nilearn.image.resample_img. Please see the related documentation for details.
target_shape: 3-tuple of integers, optional (default = None)
This parameter is passed to nilearn.image.resample_img. Please see the related documentation for details.
low_pass: None or float, optional (default = None)
This parameter is passed to nilearn.signal.clean. Please see the related documentation for details.
high_pass: None or float, optional (default = None)
This parameter is passed to nilearn.signal.clean. Please see the related documentation for details.
t_r: float, optional (default = None)
This parameter is passed to nilearn.signal.clean. Please see the related documentation for details.
memory: instance of joblib.Memory or string (default = None)
Used to cache the masking process and results of algorithms. By default, no caching is done. If a string is given, it is the path to the caching directory.
memory_level: integer, optional (default = None)
Rough estimator of the amount of memory used by caching. Higher value means more memory for caching.
n_jobs: integer, optional (default = 1)
The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on.
verbose: integer, optional (default = 0)
Indicate the level of verbosity. By default, nothing is printed.
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fit(X, Y)[source]¶ Fit data X and Y and learn transformation to map X to Y
- Parameters
X: Niimg-like object
Source data.
Y: Niimg-like object
Target data
- Returns
self
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get_params(deep=True)¶ 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)¶ 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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