binding

binding ¶

ASL binding adapter for the shared fitting infrastructure.

BoundASLModel wraps the Buxton pCASL model with PLDs, M0, and labeling parameters, producing a FittableModel that the shared fitter can use without knowing about ASL-specific context.

BoundASLModel ¶

BoundASLModel(params, fixed=None, model=None)

Bases: BaseBoundModel

ASL model with PLDs and labeling parameters bound.

Wraps the Buxton pCASL model so the fitter only sees predict_array_batch(free_params) -> output.

The model always predicts with unit M0 (M0=1). Callers must normalize observed delta-M by M0 before fitting so that the fitter works on M0-normalized data. This avoids per-voxel side-channels that are incompatible with active-voxel tracking in the LM fitter.

PARAMETER	DESCRIPTION
`params`	ASL quantification parameters (PLDs, label duration, T1s, etc.). TYPE: `MultiPLDParams`
`fixed`	Parameters to fix at constant values during fitting. TYPE: `dict[str, float] \| None` DEFAULT: `None`
`model`	ASL signal model to use. If `None`, defaults to `BuxtonMultiPLDModel`. TYPE: `BaseASLModel \| None` DEFAULT: `None`

ensure_device ¶

ensure_device(xp)

Transfer PLD array to the target device.

predict_array_batch ¶

predict_array_batch(free_params_batch, xp)

Predict M0-normalized delta-M for a batch of voxels.

PARAMETER	DESCRIPTION
`free_params_batch`	Free parameter values, shape `(n_free, n_voxels)`. TYPE: `NDArray`
`xp`	Array module. TYPE: `module`

RETURNS	DESCRIPTION
`NDArray`	Predicted delta-M/M0, shape `(n_plds, n_voxels)`.

get_initial_guess_batch ¶

get_initial_guess_batch(observed_batch, xp)

Get initial parameter guesses from signal shape.

PARAMETER	DESCRIPTION
`observed_batch`	Observed data, shape `(n_plds, n_voxels)`. TYPE: `NDArray`
`xp`	Array module. TYPE: `module`

RETURNS	DESCRIPTION
`NDArray`	Initial guesses, shape `(n_free, n_voxels)`.