MAINT: rename ArrayRNG to RandomGenerator

README.md

@@ -1,6 +1,6 @@
 # Omni RNG (`orng`)
 
-`orng` provides a thin facade over several Array API–compatible random number
+`orng` provides a thin wrapper over several Array API–compatible random number
 generators. It mirrors the subset of the `numpy.random.Generator` API:
 
 - `random`
@@ -39,15 +39,15 @@ You can also combine extras, e.g. `pip install "orng[numpy,torch]"`.
 ## Quick Start
 
 ```python
-from orng import ArrayRNG
+from orng import RandomGenerator
 
-rng = ArrayRNG(backend="numpy", seed=42)
+rng = RandomGenerator(backend="numpy", seed=42)
 samples = rng.normal(loc=0.0, scale=1.0, size=5)
 uniform = rng.uniform(low=-1.0, high=1.0, size=(2, 2))
 ```
 
 The backend module is imported lazily. If the requested library is missing,
-`ArrayRNG` will raise an informative `ImportError` that points to the matching
+`RandomGenerator` will raise an informative `ImportError` that points to the matching
 extra.
 
 ## Functional Backend API
@@ -143,7 +143,7 @@ through the same functional interface. JAX always uses and returns a PRNG key.
 
 ### Backend State Reference
 
-When you pass the optional `generator` argument to `ArrayRNG`, the expected
+When you pass the optional `generator` argument to `RandomGenerator`, the expected
 object depends on the backend:
 
 | Backend | Generator argument |
@@ -162,7 +162,7 @@ orng/
 ├── src/orng/
 │   ├── __init__.py      # package exports
 │   ├── _utils.py        # shared helpers (internal)
-│   ├── orng.py          # ArrayRNG facade
+│   ├── orng.py          # RandomGenerator wrapper
 │   └── backends/        # backend-specific implementations
 └── README.md
 ```

src/orng/__init__.py

@@ -10,10 +10,11 @@
     create_functional_backend,
     create_functional_backend_from_xp,
 )
-from .orng import ArrayRNG
+from .orng import ArrayRNG, RandomGenerator
 
 __all__ = [
     "ArrayRNG",
+    "RandomGenerator",
     "create_backend_from_xp",
     "create_functional_backend",
     "create_functional_backend_from_xp",

src/orng/orng.py

@@ -1,6 +1,6 @@
 """Backend-aware random number generation helpers.
 
-This module introduces :class:`ArrayRNG`, a small facade that mimics the subset
+This module introduces :class:`RandomGenerator`, a small facade that mimics the subset
 of ``numpy.random.Generator`` APIs. The class presents a uniform interface
 across NumPy, PyTorch, CuPy, and JAX.
 """
@@ -59,7 +59,7 @@ def choice(
 
 
 @dataclass
-class ArrayRNG:
+class RandomGenerator:
     """Facade exposing ``numpy.random.Generator``-style helpers across backends.
 
     Parameters
@@ -98,7 +98,7 @@ def from_xp(
         seed: int | None = None,
         generator: Any | None = None,
         device: Any | None = None,
-    ) -> "ArrayRNG":
+    ) -> "RandomGenerator":
         return cls(
             backend=infer_backend_name_from_xp(xp),
             seed=seed,
@@ -205,4 +205,18 @@ def to_functional(
         return backend, self._impl._state
 
 
-__all__ = ["ArrayRNG"]
+class ArrayRNG(RandomGenerator):
+    """Deprecated alias for :class:`RandomGenerator`."""
+
+    def __post_init__(self) -> None:
+        import warnings
+
+        warnings.warn(
+            "ArrayRNG is deprecated and will be removed in a future release. "
+            "Please use orng.Generator instead.",
+            FutureWarning,
+        )
+        super().__post_init__()
+
+
+__all__ = ["ArrayRNG", "RandomGenerator"]

tests/test_backends.py

@@ -2,7 +2,7 @@
 
 import pytest
 
-from orng import ArrayRNG
+from orng import RandomGenerator
 from orng.backends.cupy import CuPyBackend
 from orng.backends.jax import JAXBackend
 from orng.backends.numpy import NumPyBackend
@@ -240,7 +240,7 @@ def test_backend_seeding(backend_case, method_case):
 
 @pytest.fixture()
 def rng(backend_name, seed):
-    return ArrayRNG(backend_name, seed=seed)
+    return RandomGenerator(backend_name, seed=seed)
 
 
 def test_array_rng_random(rng):

tests/test_orng.py

@@ -5,7 +5,7 @@
 
 import pytest
 
-from orng import ArrayRNG, create_backend_from_xp
+from orng import RandomGenerator, create_backend_from_xp
 from orng._utils import normalize_shape, total_size
 from orng.backends import _FACTORIES
 from orng.backends import numpy as numpy_backend
@@ -76,7 +76,9 @@ def fake_factory(**kwargs):
 
     monkeypatch.setitem(_FACTORIES, "numpy", fake_factory)
 
-    rng = ArrayRNG(backend="numpy", seed=7, generator="sentinel", device="cpu")
+    rng = RandomGenerator(
+        backend="numpy", seed=7, generator="sentinel", device="cpu"
+    )
     assert len(instances) == 1
     backend = instances[0]
     assert backend.seed == 7
@@ -129,7 +131,7 @@ def fake_factory(**kwargs):
 
 def test_array_rng_rejects_unknown_backend():
     with pytest.raises(ValueError):
-        ArrayRNG(backend="unknown")
+        RandomGenerator(backend="unknown")
 
 
 def test_array_rng_from_xp_infers_backend(monkeypatch):
@@ -160,7 +162,7 @@ def choice(self, population, *, size, replace, probabilities):
         _FACTORIES, "numpy", lambda **kwargs: DummyBackend(**kwargs)
     )
 
-    rng = ArrayRNG.from_xp(np, seed=123)
+    rng = RandomGenerator.from_xp(np, seed=123)
 
     assert rng.backend == "numpy"
     assert captured_kwargs["seed"] == 123
@@ -218,7 +220,7 @@ def choice(self, population, *, size, replace, probabilities):
     )
 
     key = ("jax-key",)
-    ArrayRNG(backend="jax", generator=key)
+    RandomGenerator(backend="jax", generator=key)
 
     assert captured_kwargs["generator"] is key
 
@@ -253,7 +255,7 @@ def choice(self, state, population, *, size, replace, probabilities):
         lambda name, pure: backend,
     )
 
-    rng = ArrayRNG(backend="numpy", seed=123)
+    rng = RandomGenerator(backend="numpy", seed=123)
     functional_backend, state = rng.to_functional()
 
     assert functional_backend is backend
@@ -288,7 +290,7 @@ def choice(self, state, population, *, size, replace, probabilities):
         or DummyFunctionalBackend(),
     )
 
-    rng = ArrayRNG(backend="numpy", seed=123)
+    rng = RandomGenerator(backend="numpy", seed=123)
     rng.to_functional(pure=False)
 
     assert captured == {"name": "numpy", "pure": False}