REF: Refactor PET model

docs/notebooks/pet_motion_estimation.ipynb

@@ -406,9 +406,9 @@
     }
    ],
    "source": [
-    "from nifreeze.model import PETModel\n",
+    "from nifreeze.model import BSplinePETModel\n",
     "\n",
-    "model = PETModel(dataset=pet_dataset, timepoints=pet_dataset.midframe, xlim=7000)"
+    "model = BSplinePETModel(dataset=pet_dataset, timepoints=pet_dataset.midframe, xlim=7000)"
    ]
   },
   {

src/nifreeze/estimator.py

@@ -40,7 +40,7 @@
 from nifreeze.data.base import BaseDataset
 from nifreeze.data.pet import PET
 from nifreeze.model.base import BaseModel, ModelFactory
-from nifreeze.model.pet import PETModel
+from nifreeze.model.pet import BSplinePETModel
 from nifreeze.registration.ants import (
     Registration,
     _prepare_registration_data,
@@ -261,8 +261,8 @@ def run(self, pet_dataset: PET, omp_nthreads: int | None = None) -> list:
                     total_duration=pet_dataset.total_duration,
                 )
 
-                # Instantiate PETModel explicitly
-                model = PETModel(
+                # Instantiate the PET model explicitly
+                model = BSplinePETModel(
                     dataset=train_dataset,
                     timepoints=train_times,
                     xlim=pet_dataset.total_duration,

src/nifreeze/model/__init__.py

@@ -33,7 +33,7 @@
     DTIModel,
     GPModel,
 )
-from nifreeze.model.pet import PETModel
+from nifreeze.model.pet import BSplinePETModel
 
 __all__ = (
     "ModelFactory",
@@ -43,5 +43,5 @@
     "DTIModel",
     "GPModel",
     "TrivialModel",
-    "PETModel",
+    "BSplinePETModel",
 )

src/nifreeze/model/pet.py

@@ -22,6 +22,7 @@
 #
 """Models for nuclear imaging."""
 
+from abc import ABC, ABCMeta, abstractmethod
 from os import cpu_count
 from typing import Union
 
@@ -39,56 +40,78 @@
 """Time frame tolerance in seconds."""
 
 
-class PETModel(BaseModel):
-    """A PET imaging realignment model based on B-Spline approximation."""
+def _exec_fit(model, data, chunk=None, **kwargs):
+    return model.fit(data, **kwargs), chunk
 
-    __slots__ = (
-        "_t",
-        "_x",
-        "_xlim",
-        "_order",
-        "_n_ctrl",
-        "_datashape",
-        "_mask",
-        "_smooth_fwhm",
-        "_thresh_pct",
-    )
+
+def _exec_predict(model, chunk=None, **kwargs):
+    """Propagate model parameters and call predict."""
+    return np.squeeze(model.predict(**kwargs)), chunk
+
+
+class BasePETModel(BaseModel, ABC):
+    """Interface and default methods for PET models."""
+
+    __metaclass__ = ABCMeta
+
+    __slots__ = {
+        "_data_mask": "A mask for the voxels that will be fitted and predicted",
+        "_x": "",
+        "_xlim": "",
+        "_smooth_fwhm": "FWHM in mm over which to smooth",
+        "_thresh_pct": "Thresholding percentile for the signal",
+        "_model_class": "Defining a model class",
+        "_modelargs": "Arguments acceptable by the underlying model",
+        "_models": "List with one or more (if parallel execution) model instances",
+    }
 
     def __init__(
         self,
         dataset: PET,
         timepoints: list | np.ndarray | None = None,
-        xlim: float | None = None,
-        n_ctrl: int | None = None,
-        order: int = 3,
+        xlim: list | np.ndarray | None = None,
         smooth_fwhm: float = 10.0,
         thresh_pct: float = 20.0,
         **kwargs,
     ):
-        """
-        Create the B-Spline interpolating matrix.
+        """Initialization.
 
-        Parameters:
-        -----------
-        timepoints : :obj:`list`
+        Parameters
+        ----------
+        timepoints : :obj:`list` or :obj:`~np.ndarray`
             The timing (in sec) of each PET volume.
             E.g., ``[15.,   45.,   75.,  105.,  135.,  165.,  210.,  270.,  330.,
             420.,  540.,  750., 1050., 1350., 1650., 1950., 2250., 2550.]``
-
-        n_ctrl : :obj:`int`
-            Number of B-Spline control points. If `None`, then one control point every
-            six timepoints will be used. The less control points, the smoother is the
-            model.
-
+        xlim : .
+            .
+        smooth_fwhm : obj:`float`
+            FWHM in mm over which to smooth the signal.
+        thresh_pct : obj:`float`
+            Thresholding percentile for the signal.
         """
+
         super().__init__(dataset, **kwargs)
 
+        # Duck typing, instead of explicitly testing for PET type
+        if not hasattr(dataset, "total_duration"):
+            raise TypeError("Dataset MUST be a PET object.")
+
+        if not hasattr(dataset, "midframe"):
+            raise ValueError("Dataset MUST have a midframe.")
+
+        self._data_mask = (
+            dataset.brainmask
+            if dataset.brainmask is not None
+            else np.ones(dataset.dataobj.shape[:3], dtype=bool)
+        )
+
+        # ToDo
+        # Are timepoints and xlim features that all PET models require ??
         if timepoints is None or xlim is None:
-            raise TypeError("timepoints must be provided in initialization")
+            raise ValueError("`timepoints` and `xlim` must be specified and have a nonzero value.")
 
-        self._order = order
-        self._x = np.array(timepoints, dtype="float32")
-        self._xlim = xlim
+        self._x = np.asarray(timepoints, dtype="float32")
+        self._xlim = np.asarray(xlim)
         self._smooth_fwhm = smooth_fwhm
         self._thresh_pct = thresh_pct
 
@@ -97,62 +120,114 @@ def __init__(
         if self._x[-1] > (self._xlim - DEFAULT_TIMEFRAME_MIDPOINT_TOL):
             raise ValueError("Last frame midpoint should not be equal or greater than duration")
 
-        # Calculate index coordinates in the B-Spline grid
-        self._n_ctrl = n_ctrl or (len(timepoints) // 4) + 1
+    def _preprocess_data(self) -> np.ndarray:
+        # ToDo
+        # data, _, gtab = self._dataset[idxmask]  ### This needs the PET data model to be changed
+        data = self._dataset.dataobj
+        brainmask = self._dataset.brainmask
 
-        # B-Spline knots
-        self._t = np.arange(-3, float(self._n_ctrl) + 4, dtype="float32")
+        # Preprocess the data
+        if self._smooth_fwhm > 0:
+            smoothed_img = smooth_image(
+                nb.Nifti1Image(data, self._dataset.affine), self._smooth_fwhm
+            )
+            data = smoothed_img.get_fdata()
 
-        self._datashape = None
-        self._mask = None
+        if self._thresh_pct > 0:
+            thresh_val = np.percentile(data, self._thresh_pct)
+            data[data < thresh_val] = 0
+
+        # Convert data into V (voxels) x T (timepoints)
+        return data.reshape((-1, data.shape[-1])) if brainmask is None else data[brainmask]
 
     @property
     def is_fitted(self) -> bool:
         return self._locked_fit is not None
 
+    @abstractmethod
+    def fit_predict(self, index: int | None = None, **kwargs) -> Union[np.ndarray, None]:
+        """Predict the corrected volume."""
+        return None
+
+
+class BSplinePETModel(BasePETModel):
+    """A PET imaging realignment model based on B-Spline approximation."""
+
+    __slots__ = (
+        "_t",
+        "_order",
+        "_n_ctrl",
+    )
+
+    def __init__(
+        self,
+        dataset: PET,
+        n_ctrl: int | None = None,
+        order: int = 3,
+        **kwargs,
+    ):
+        """Create the B-Spline interpolating matrix.
+
+        Parameters
+        ----------
+        n_ctrl : :obj:`int`
+            Number of B-Spline control points. If `None`, then one control point every
+            six timepoints will be used. The less control points, the smoother is the
+            model.
+        order : :obj:`int`
+            Order of the B-Spline approximation.
+        """
+
+        super().__init__(dataset, **kwargs)
+
+        self._order = order
+
+        # Calculate index coordinates in the B-Spline grid
+        self._n_ctrl = n_ctrl or (len(self._x) // 4) + 1
+
+        # B-Spline knots
+        self._t = np.arange(-3, self._n_ctrl + 4, dtype="float32")
+
     def _fit(self, index: int | None = None, n_jobs=None, **kwargs) -> int:
         """Fit the model."""
 
+        n_jobs = n_jobs or min(cpu_count() or 1, 8)
+
         if self._locked_fit is not None:
             return n_jobs
 
         if index is not None:
             raise NotImplementedError("Fitting with held-out data is not supported")
-        timepoints = kwargs.get("timepoints", None) or self._x
-        x = np.asarray((np.array(timepoints, dtype="float32") / self._xlim) * self._n_ctrl)
-
-        data = self._dataset.dataobj
-        brainmask = self._dataset.brainmask
-
-        if self._smooth_fwhm > 0:
-            smoothed_img = smooth_image(
-                nb.Nifti1Image(data, self._dataset.affine), self._smooth_fwhm
-            )
-            data = smoothed_img.get_fdata()
 
-        if self._thresh_pct > 0:
-            thresh_val = np.percentile(data, self._thresh_pct)
-            data[data < thresh_val] = 0
+        data = self._preprocess_data()
 
-        # Convert data into V (voxels) x T (timepoints)
-        data = data.reshape((-1, data.shape[-1])) if brainmask is None else data[brainmask]
+        # ToDo
+        # Does not make sense to make timepoints be a kwarg if it is provided as a named parameter to __init__
+        timepoints = kwargs.get("timepoints", None) or self._x
+        x = np.asarray(timepoints, dtype="float32") / self._xlim * self._n_ctrl
 
         # A.shape = (T, K - 4); T= n. timepoints, K= n. knots (with padding)
         A = BSpline.design_matrix(x, self._t, k=self._order)
         AT = A.T
         ATdotA = AT @ A
 
         # Parallelize process with joblib
-        with Parallel(n_jobs=n_jobs or min(cpu_count() or 1, 8)) as executor:
+        with Parallel(n_jobs=n_jobs) as executor:
             results = executor(delayed(cg)(ATdotA, AT @ v) for v in data)
 
-        self._locked_fit = np.array([r[0] for r in results])
+        self._locked_fit = np.asarray([r[0] for r in results])
 
         return n_jobs
 
     def fit_predict(self, index: int | None = None, **kwargs) -> Union[np.ndarray, None]:
         """Return the corrected volume using B-spline interpolation."""
 
+        # ToDo
+        # Does the below apply to PET ? Martin has the return None statement
+        # if index is None:
+        #    raise RuntimeError(
+        #        f"Model {self.__class__.__name__} does not allow locking.")
+
         # Fit the BSpline basis on all data
         if self._locked_fit is None:
             self._fit(index, n_jobs=kwargs.pop("n_jobs", None), **kwargs)

test/test_integration_pet.py

@@ -84,7 +84,7 @@ def fit_predict(self, index):
                 return None
             return np.zeros(self.dataset.shape3d, dtype=np.float32)
 
-    monkeypatch.setattr("nifreeze.estimator.PETModel", DummyModel)
+    monkeypatch.setattr("nifreeze.estimator.BSplinePETModel", DummyModel)
 
     class DummyRegistration:
         def __init__(self, *args, **kwargs):

test/test_model_pet.py

@@ -25,7 +25,7 @@
 import pytest
 
 from nifreeze.data.pet import PET
-from nifreeze.model.pet import PETModel
+from nifreeze.model.pet import BSplinePETModel
 
 
 @pytest.fixture
@@ -49,9 +49,19 @@ def random_dataset(setup_random_pet_data) -> PET:
     )
 
 
+def test_pet_base_model():
+    from nifreeze.model.pet import BasePETModel
+
+    with pytest.raises(
+        TypeError,
+        match="Can't instantiate abstract class BasePETModel without an implementation for abstract method 'fit_predict'",
+    ):
+        BasePETModel(None)
+
+
 @pytest.mark.random_pet_data(5, (4, 4, 4), np.asarray([10.0, 20.0, 30.0, 40.0, 50.0]), 60.0)
 def test_petmodel_fit_predict(random_dataset):
-    model = PETModel(
+    model = BSplinePETModel(
         dataset=random_dataset,
         timepoints=random_dataset.midframe,
         xlim=random_dataset.total_duration,
@@ -72,12 +82,12 @@ def test_petmodel_fit_predict(random_dataset):
 
 @pytest.mark.random_pet_data(5, (4, 4, 4), np.asarray([10.0, 20.0, 30.0, 40.0, 50.0]), 60.0)
 def test_petmodel_invalid_init(random_dataset):
-    with pytest.raises(TypeError):
-        PETModel(dataset=random_dataset)
+    with pytest.raises(ValueError):
+        BSplinePETModel(dataset=random_dataset)
 
 
 @pytest.mark.random_pet_data(5, (4, 4, 4), np.asarray([10.0, 20.0, 30.0, 40.0, 50.0]), 60.0)
 def test_petmodel_time_check(random_dataset):
     bad_times = np.array([0, 10, 20, 30, 50], dtype=np.float32)
     with pytest.raises(ValueError):
-        PETModel(dataset=random_dataset, timepoints=bad_times, xlim=60.0)
+        BSplinePETModel(dataset=random_dataset, timepoints=bad_times, xlim=60.0)