Merge pull request #83 from jhlegarreta/UseNumpyRng

ENH: Prefer using NumPy's random number generator

Author: oesteban

pyproject.toml

@@ -23,7 +23,7 @@ dependencies = [
     "dipy>=1.5.0",
     "joblib",
     "nipype>= 1.5.1,<2.0",
-    "nitransforms>=21.0.0,<24",
+    "nitransforms>=22.0.0,<24",
     "nireports",
     "numpy>=1.21.3",
     "nest-asyncio>=1.5.1",

src/nifreeze/model/gpr.py

@@ -25,7 +25,7 @@
 from __future__ import annotations
 
 from numbers import Integral, Real
-from typing import Callable, ClassVar, Literal, Mapping, Optional, Sequence, Union
+from typing import Callable, Literal, Mapping, Optional, Sequence, Union
 
 import numpy as np
 import numpy.typing as npt
@@ -156,14 +156,14 @@ class DiffusionGPR(GaussianProcessRegressor):
 
     optimizer: Optional[Union[StrOptions, Callable, None]] = None
 
-    _parameter_constraints: ClassVar[dict] = {
+    _parameter_constraints = {
         "kernel": [None, Kernel],
-        "alpha": [Interval(Real, 0, None, closed="left"), np.ndarray],
-        "optimizer": [StrOptions(SUPPORTED_OPTIMIZERS), callable, None],
-        "n_restarts_optimizer": [Interval(Integral, 0, None, closed="left")],
+        "alpha": [Interval(Real, 0, None, closed="left"), np.ndarray],  # type: ignore
+        "optimizer": [StrOptions(SUPPORTED_OPTIMIZERS), callable, None],  # type: ignore
+        "n_restarts_optimizer": [Interval(Integral, 0, None, closed="left")],  # type: ignore
         "copy_X_train": ["boolean"],
         "normalize_y": ["boolean"],
-        "n_targets": [Interval(Integral, 1, None, closed="left"), None],
+        "n_targets": [Interval(Integral, 1, None, closed="left"), None],  # type: ignore
         "random_state": ["random_state"],
     }
 

src/nifreeze/testing/simulations.py

@@ -27,7 +27,7 @@
 import nibabel as nb
 import numpy as np
 from dipy.core.geometry import sphere2cart
-from dipy.core.gradients import gradient_table
+from dipy.core.gradients import GradientTable, gradient_table
 from dipy.core.sphere import HemiSphere, Sphere, disperse_charges
 from dipy.sims.voxel import all_tensor_evecs, multi_tensor, single_tensor
 
@@ -148,7 +148,7 @@ def create_diffusion_encoding_gradient_dirs(
 
 def create_single_shell_gradient_table(
     hsph_dirs: int, bval_shell: float, iterations: int = 5000
-) -> gradient_table:
+) -> GradientTable:
     """
     Create a single-shell gradient table.
 
@@ -163,7 +163,7 @@ def create_single_shell_gradient_table(
 
     Returns
     -------
-    :obj:`~dipy.core.gradients.gradient_table`
+    :obj:`~dipy.core.gradients.GradientTable`
         The gradient table for the single-shell.
 
     """
@@ -182,7 +182,7 @@ def create_single_shell_gradient_table(
 
 
 def get_query_vectors(
-    gtab: gradient_table, train_mask: np.ndarray
+    gtab: GradientTable, train_mask: np.ndarray
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Get the diffusion-encoding gradient vectors for estimation from the gradient table.
@@ -193,7 +193,7 @@ def get_query_vectors(
 
     Parameters
     ----------
-    gtab : :obj:`~dipy.core.gradients.gradient_table`
+    gtab : :obj:`~dipy.core.gradients.GradientTable`
         Gradient table.
     train_mask : :obj:`~numpy.ndarray`
         Mask for selecting training vectors.

test/conftest.py

@@ -159,3 +159,9 @@ def pytest_terminal_summary(terminalreporter, exitstatus, config):
             "Warnings as errors: Activated.\n"
             f"{len(have_warnings)} warnings were raised and treated as errors.\n"
         )
+
+
+@pytest.fixture(autouse=True)
+def random_number_generator(request):
+    """Automatically set a fixed-seed random number generator for all tests."""
+    request.node.rng = np.random.default_rng(1234)

test/test_data_base.py

@@ -33,9 +33,11 @@
 
 
 @pytest.fixture
-def random_dataset() -> BaseDataset:
+def random_dataset(request) -> BaseDataset:
     """Create a BaseDataset with random data for testing."""
-    data = np.random.rand(32, 32, 32, 5).astype(np.float32)
+
+    rng = request.node.rng
+    data = rng.random((32, 32, 32, 5)).astype(np.float32)
     affine = np.eye(4, dtype=np.float32)
     return BaseDataset(dataobj=data, affine=affine)
 

test/test_data_dmri.py

@@ -29,8 +29,8 @@
 from nifreeze.data.dmri import find_shelling_scheme, load
 
 
-def _create_dwi_random_dataobj():
-    rng = np.random.default_rng(1234)
+def _create_dwi_random_dataobj(request):
+    rng = request.node.rng
 
     n_gradients = 10
     b0s = 1
@@ -134,7 +134,7 @@ def test_load(datadir, tmp_path):
     assert np.allclose(dwi_h5.gradients, dwi_from_nifti2.gradients)
 
 
-def test_equality_operator(tmp_path):
+def test_equality_operator(tmp_path, request):
     # Create some random data
     (
         dwi_dataobj,
@@ -143,7 +143,7 @@ def test_equality_operator(tmp_path):
         b0_dataobj,
         gradients,
         b0_thres,
-    ) = _create_dwi_random_dataobj()
+    ) = _create_dwi_random_dataobj(request)
 
     dwi, brainmask, b0 = _create_dwi_random_data(
         dwi_dataobj,

test/test_data_utils.py

@@ -5,8 +5,8 @@
 from nifreeze.data.utils import apply_affines
 
 
-def test_apply_affines():
-    rng = np.random.default_rng(1234)
+def test_apply_affines(request):
+    rng = request.node.rng
 
     # Create synthetic dataset
     nii_data = rng.random((10, 10, 10, 10))

test/test_model.py

@@ -36,10 +36,10 @@
 
 
 @pytest.mark.parametrize("use_mask", (False, True))
-def test_trivial_model(use_mask):
+def test_trivial_model(request, use_mask):
     """Check the implementation of the trivial B0 model."""
 
-    rng = np.random.default_rng(1234)
+    rng = request.node.rng
 
     # Should not allow initialization without an oracle
     with pytest.raises(TypeError):

test/test_splitting.py

@@ -28,7 +28,7 @@
 from nifreeze.data.splitting import lovo_split
 
 
-def test_lovo_split(datadir):
+def test_lovo_split(request, datadir):
     """
     Test the lovo_split function.
 
@@ -46,7 +46,8 @@ def test_lovo_split(datadir):
     data.gradients[:] = 0
 
     # Select a random index
-    index = np.random.randint(len(data))
+    rng = request.node.rng
+    index = rng.integers(len(data))
 
     # Set 1 in dataobj and gradients of the dwi object at this specific index
     data.dataobj[..., index] = 1