nifreeze.model package

Data models.

class nifreeze.model.AverageDWIModel(self, dataset: nifreeze.data.dmri.base.DWI, stat: str = 'median', atol_low: float = 100.0, atol_high: float = 100.0, detrend: bool = False, **kwargs)[source]

Bases: ExpectationModel

A trivial model that returns an average DWI volume.

Implement object initialization.

Parameters:

  • dataset (DWI) – Reference to a DWI object.

  • stat (str, optional) – Whether the summary statistic to apply is "mean" or "median".

  • atol_low (float, optional) – A lower bound for the b-value corresponding to the diffusion weighted images that will be averaged.

  • atol_low (float, optional) – An upper bound for the b-value corresponding to the diffusion weighted images that will be averaged.

  • detrend (bool, optional) – Whether the overall distribution of each diffusion weighted image will be standardized and centered around the src.nifreeze.model.base.DEFAULT_CLIP_PERCENTILE percentile.

fit_predict(index: int | None = None, *_, **kwargs) → ndarray[source]

Return the average map.

class nifreeze.model.BSplinePETModel(self, dataset: nifreeze.data.pet.base.PET, n_ctrl: int = 3, order: int = 3, **kwargs)[source]

Bases: BasePETModel

A PET imaging realignment model based on B-Spline approximation.

Create the B-Spline interpolating matrix.

Parameters:

  • n_ctrl (int, optional) – Number of internal B-Spline control points that determine the flexibility of the temporal fit. Given a hold-out index, all frames except the held-out one are used in a least-squares fit against the B-spline basis. n_ctrl sets the number of degrees of freedom of that basis. Fewer control points produce a smoother approximation; more control points allow the curve to track finer temporal detail. Boundary knots (order + 1 at each end) are added automatically to ensure proper edge behavior.

  • order (int, optional) – Order of the B-Spline approximation.

fit_predict(index: int | None = None, **kwargs) → ndarray | None[source]

Return the corrected volume using B-spline interpolation.

Predictions for times earlier than the configured start time will return the prediction for the start time.

class nifreeze.model.DKIModel(self, dataset: nifreeze.data.dmri.base.DWI, max_b: float | int | None = None, **kwargs)[source]

Bases: BaseDWIModel

A wrapper of DiffusionKurtosisModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

applicable_schemes: frozenset[str] = frozenset({'multi-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

requires_multishell: bool = True

Whether the model requires more than one non-zero shell.

class nifreeze.model.DTIModel(self, dataset: nifreeze.data.dmri.base.DWI, max_b: float | int | None = None, **kwargs)[source]

Bases: BaseDWIModel

A wrapper of dipy.reconst.dti.TensorModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

applicable_schemes: frozenset[str] = frozenset({'multi-shell', 'single-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

class nifreeze.model.ExpectationModel(self, dataset, stat='median', **kwargs)[source]

Bases: BaseModel

A trivial model that returns an expectation map (for example, average).

Initialize a new model.

fit_predict(index: int | None = None, **kwargs) → ndarray[source]

Return the expectation map.

Parameters:

index (int) – The volume index that is left-out in fitting, and then predicted.

class nifreeze.model.GPModel(self, dataset: nifreeze.data.dmri.base.DWI, max_b: float | int | None = None, **kwargs)[source]

Bases: BaseDWIModel

A wrapper of GaussianProcessModel.

The Gaussian process does not follow the DIPY Model(gtab).fit(data) convention that BaseDWIModel implements: it is constructed from a kernel_model and receives the gradient table at fit time, fitting all voxels at once (vectorized across GP targets). _fit/fit_predict are therefore overridden here rather than reusing the chunked DIPY path.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

applicable_schemes: frozenset[str] = frozenset({'multi-shell', 'single-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

fit_predict(index: int | None = None, **kwargs) → ndarray | None[source]

Fit the GP (LOVO or single-fit) and predict the held-out orientation.

single_fit_is_canary: bool = True

Whether single-fit only makes sense as a self-consistency canary (see _warn_single_fit_canary()).

class nifreeze.model.ModelFactory(self, /, *args, **kwargs)[source]

Bases: object

A factory for instantiating data models.

static init(model: str | None = None, **kwargs)[source]

Instantiate a diffusion model.

Parameters:

model (str) – Diffusion model. Options: "DTI", "DKI", "GQI", "GP", "S0", "AverageDWI". "GP" (aliases "GPR", "GaussianProcess") builds a GPModel; pass kernel_model through kwargs to select the covariance.

Returns:

model – A model object compliant with DIPY’s interface.

Return type:

ReconstModel

class nifreeze.model.TrivialModel(self, dataset, predicted=None, **kwargs)[source]

Bases: BaseModel

A trivial model that returns a given map always.

Implement object initialization.

fit_predict(*_, **kwargs) → ndarray | None[source]

Return the reference map.

Submodules