Remove as_scalar from utils

Author: marvinpfoertner

src/probnum/backend/_core/__init__.py

@@ -1,4 +1,5 @@
 from probnum import backend as _backend
+from probnum.typing import ArrayType, DTypeArgType, ScalarArgType
 
 if _backend.BACKEND is _backend.Backend.NUMPY:
     from . import _numpy as _core
@@ -73,3 +74,20 @@
 # Just-in-Time Compilation
 jit = _core.jit
 jit_method = _core.jit_method
+
+
+def as_scalar(x: ScalarArgType, dtype: DTypeArgType = None) -> ArrayType:
+    """Convert a scalar into a NumPy scalar.
+
+    Parameters
+    ----------
+    x
+        Scalar value.
+    dtype
+        Data type of the scalar.
+    """
+
+    if ndim(x) != 0:
+        raise ValueError("The given input is not a scalar.")
+
+    return asarray(x, dtype=dtype)[()]

src/probnum/randprocs/kernels/_exponentiated_quadratic.py

@@ -3,7 +3,7 @@
 import functools
 from typing import Optional
 
-from probnum import backend, utils as _utils
+from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
 from ._kernel import IsotropicMixin, Kernel
@@ -46,7 +46,7 @@ class ExpQuad(Kernel, IsotropicMixin):
     """
 
     def __init__(self, input_dim: IntArgType, lengthscale: ScalarArgType = 1.0):
-        self.lengthscale = _utils.as_scalar(lengthscale)
+        self.lengthscale = backend.as_scalar(lengthscale)
         super().__init__(input_dim=input_dim)
 
     @backend.jit_method

src/probnum/randprocs/kernels/_linear.py

@@ -2,7 +2,7 @@
 
 from typing import Optional
 
-from probnum import backend, utils
+from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
 from ._kernel import Kernel
@@ -39,7 +39,7 @@ class Linear(Kernel):
     """
 
     def __init__(self, input_dim: IntArgType, constant: ScalarArgType = 0.0):
-        self.constant = utils.as_scalar(constant)
+        self.constant = backend.as_scalar(constant)
         super().__init__(input_dim=input_dim)
 
     @backend.jit_method

src/probnum/randprocs/kernels/_matern.py

@@ -2,7 +2,6 @@
 
 from typing import Optional
 
-import probnum.utils as _utils
 from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
@@ -64,7 +63,7 @@ def __init__(
         lengthscale: ScalarArgType = 1.0,
         nu: float = 1.5,
     ):
-        self.lengthscale = _utils.as_scalar(lengthscale)
+        self.lengthscale = backend.as_scalar(lengthscale)
         if not self.lengthscale > 0:
             raise ValueError(f"Lengthscale l={self.lengthscale} must be positive.")
         self.nu = float(nu)

src/probnum/randprocs/kernels/_polynomial.py

@@ -2,7 +2,7 @@
 
 from typing import Optional
 
-from probnum import backend, utils
+from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
 from ._kernel import Kernel
@@ -46,8 +46,8 @@ def __init__(
         constant: ScalarArgType = 0.0,
         exponent: IntArgType = 1.0,
     ):
-        self.constant = utils.as_scalar(constant)
-        self.exponent = utils.as_scalar(exponent)
+        self.constant = backend.as_scalar(constant)
+        self.exponent = backend.as_scalar(exponent)
         super().__init__(input_dim=input_dim)
 
     @backend.jit_method

src/probnum/randprocs/kernels/_rational_quadratic.py

@@ -2,9 +2,7 @@
 
 from typing import Optional
 
-import numpy as np
-
-from probnum import backend, utils
+from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
 from ._kernel import IsotropicMixin, Kernel
@@ -62,8 +60,8 @@ def __init__(
         lengthscale: ScalarArgType = 1.0,
         alpha: ScalarArgType = 1.0,
     ):
-        self.lengthscale = utils.as_scalar(lengthscale)
-        self.alpha = utils.as_scalar(alpha)
+        self.lengthscale = backend.as_scalar(lengthscale)
+        self.alpha = backend.as_scalar(alpha)
         if not self.alpha > 0:
             raise ValueError(f"Scale mixture alpha={self.alpha} must be positive.")
         super().__init__(input_dim=input_dim)

src/probnum/randprocs/kernels/_white_noise.py

@@ -2,7 +2,7 @@
 
 from typing import Optional
 
-from probnum import backend, utils
+from probnum import backend
 from probnum.typing import ArrayType, IntArgType, ScalarArgType
 
 from ._kernel import Kernel
@@ -25,7 +25,7 @@ class WhiteNoise(Kernel):
     """
 
     def __init__(self, input_dim: IntArgType, sigma: ScalarArgType = 1.0):
-        self.sigma = utils.as_scalar(sigma)
+        self.sigma = backend.as_scalar(sigma)
         self._sigma_sq = self.sigma ** 2
         super().__init__(input_dim=input_dim)
 

src/probnum/typing.py

@@ -59,7 +59,7 @@
 ScalarArgType = Union[int, float, complex, numbers.Number, np.number]
 """Type of a public API argument for supplying a scalar value. Values of this type
 should always be converted into :class:`np.generic` using the function
-:func:`probnum.utils.as_scalar` before further internal processing."""
+:func:`probnum.backend.as_scalar` before further internal processing."""
 
 LinearOperatorArgType = Union[
     np.ndarray,

src/probnum/utils/__init__.py

@@ -7,7 +7,6 @@
 __all__ = [
     "as_colvec",
     "atleast_1d",
-    "as_scalar",
     "as_numpy_scalar",
     "as_shape",
 ]

src/probnum/utils/argutils.py

@@ -5,16 +5,9 @@
 
 import numpy as np
 
-from probnum import backend
-from probnum.typing import (
-    ArrayType,
-    DTypeArgType,
-    ScalarArgType,
-    ShapeArgType,
-    ShapeType,
-)
+from probnum.typing import DTypeArgType, ScalarArgType, ShapeArgType, ShapeType
 
-__all__ = ["as_shape", "as_numpy_scalar", "as_scalar"]
+__all__ = ["as_shape", "as_numpy_scalar"]
 
 
 def as_shape(x: ShapeArgType, ndim: Optional[numbers.Integral] = None) -> ShapeType:
@@ -64,20 +57,3 @@ def as_numpy_scalar(x: ScalarArgType, dtype: DTypeArgType = None) -> np.generic:
         raise ValueError("The given input is not a scalar.")
 
     return np.asarray(x, dtype=dtype)[()]
-
-
-def as_scalar(x: ScalarArgType, dtype: DTypeArgType = None) -> ArrayType:
-    """Convert a scalar into a NumPy scalar.
-
-    Parameters
-    ----------
-    x
-        Scalar value.
-    dtype
-        Data type of the scalar.
-    """
-
-    if backend.ndim(x) != 0:
-        raise ValueError("The given input is not a scalar.")
-
-    return backend.asarray(x, dtype=dtype)[()]