Merge pull request #221 from nipreps/fix/generalize-models

FIX: Generalized model structure

docs/developers.rst

@@ -35,4 +35,5 @@ Information on specific functions, classes, and methods.
    api/eddymotion.exceptions
    api/eddymotion.math
    api/eddymotion.model
+   api/eddymotion.registration
    api/eddymotion.utils

src/eddymotion/model/__init__.py

@@ -23,21 +23,25 @@
 """Data models."""
 
 from eddymotion.model.base import (
+    AverageModel,
+    ModelFactory,
+    TrivialModel,
+)
+from eddymotion.model.dmri import (
     AverageDWModel,
     DKIModel,
     DTIModel,
     GPModel,
-    ModelFactory,
-    PETModel,
-    TrivialB0Model,
 )
+from eddymotion.model.pet import PETModel
 
 __all__ = (
     "ModelFactory",
+    "AverageModel",
     "AverageDWModel",
     "DKIModel",
     "DTIModel",
     "GPModel",
-    "TrivialB0Model",
+    "TrivialModel",
     "PETModel",
 )

src/eddymotion/model/dipy.py → src/eddymotion/model/_dipy.py

@@ -87,10 +87,11 @@
 
 from __future__ import annotations
 
+import warnings
 from sys import modules
 
 import numpy as np
-from dipy.core.gradients import GradientTable
+from dipy.core.gradients import GradientTable, gradient_table
 from dipy.reconst.base import ReconstModel
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import (
@@ -690,3 +691,21 @@ def set_params(self, **params):
         self.a = params.get("a", self.a)
         self.sigma_sq = params.get("sigma_sq", self.sigma_sq)
         return self
+
+
+def _rasb2dipy(gradient):
+    gradient = np.asanyarray(gradient)
+    if gradient.ndim == 1:
+        if gradient.size != 4:
+            raise ValueError("Missing gradient information.")
+        gradient = gradient[..., np.newaxis]
+
+    if gradient.shape[0] != 4:
+        gradient = gradient.T
+    elif gradient.shape == (4, 4):
+        print("Warning: make sure gradient information is not transposed!")
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=UserWarning)
+        retval = gradient_table(gradient[3, :], gradient[:3, :].T)
+    return retval