gh-480: fix mypy failures (#490)

.pre-commit-config.yaml

@@ -71,7 +71,7 @@ repos:
       - id: mypy
         files: ^glass/
         additional_dependencies:
-          - numpy
+          - numpy>=2.2.2
   - repo: https://github.com/kynan/nbstripout
     rev: 0.8.1
     hooks:

glass/core/array.py

@@ -138,7 +138,7 @@ def trapezoid_product(
         npt.NDArray[np.float64],
     ],
     axis: int = -1,
-) -> npt.NDArray[np.float64]:
+) -> float | npt.NDArray[np.float64]:
     """
     Trapezoidal rule for a product of functions.
 
@@ -166,7 +166,7 @@ def trapezoid_product(
     y = np.interp(x, *f)
     for f_ in ff:
         y *= np.interp(x, *f_)
-    return np.trapezoid(y, x, axis=axis)  # type: ignore[return-value]
+    return np.trapezoid(y, x, axis=axis)
 
 
 def cumulative_trapezoid(

glass/lensing.py

@@ -410,8 +410,8 @@ def __init__(self, cosmo: Cosmology) -> None:
         self.cosmo = cosmo
 
         # set up initial values of variables
-        self.z2: float = 0.0
-        self.z3: float = 0.0
+        self.z2: float | npt.NDArray[np.float64] = 0.0
+        self.z3: float | npt.NDArray[np.float64] = 0.0
         self.x3: float = 0.0
         self.w3: float = 0.0
         self.r23: float = 1.0
@@ -442,7 +442,7 @@ def add_window(self, delta: npt.NDArray[np.float64], w: RadialWindow) -> None:
     def add_plane(
         self,
         delta: npt.NDArray[np.float64],
-        zsrc: float,
+        zsrc: float | npt.NDArray[np.float64],
         wlens: float = 1.0,
     ) -> None:
         """
@@ -505,7 +505,7 @@ def add_plane(
         self.kappa3 += f * delta2
 
     @property
-    def zsrc(self) -> float:
+    def zsrc(self) -> float | npt.NDArray[np.float64]:
         """The redshift of the current convergence plane."""
         return self.z3
 

glass/observations.py

@@ -125,7 +125,7 @@ def gaussian_nz(
     if norm is not None:
         nz *= norm
 
-    return nz  # type: ignore[no-any-return]
+    return nz
 
 
 def smail_nz(

glass/points.py

@@ -52,7 +52,7 @@ def effective_bias(
     z: npt.NDArray[np.float64],
     bz: npt.NDArray[np.float64],
     w: RadialWindow,
-) -> npt.NDArray[np.float64]:
+) -> float | npt.NDArray[np.double]:
     r"""
     Effective bias parameter from a redshift-dependent bias function.
 
@@ -85,7 +85,7 @@ def effective_bias(
 
     """
     norm = np.trapezoid(w.wa, w.za)
-    return trapezoid_product((z, bz), (w.za, w.wa)) / norm  # type: ignore[return-value]
+    return trapezoid_product((z, bz), (w.za, w.wa)) / norm
 
 
 def linear_bias(
@@ -107,7 +107,7 @@ def linear_bias(
         The density contrast after biasing.
 
     """
-    return b * delta  # type: ignore[return-value]
+    return b * delta
 
 
 def loglinear_bias(
@@ -413,6 +413,6 @@ def position_weights(
     densities = densities / np.sum(densities, axis=0)
     # apply bias after normalisation
     if bias is not None:
-        densities = densities * bias  # type: ignore[assignment]
+        densities = densities * bias
     # densities now contains the relative contribution with bias applied
     return densities

glass/shells.py

@@ -176,7 +176,7 @@ class RadialWindow(typing.NamedTuple):
 
     za: npt.NDArray[np.float64]
     wa: npt.NDArray[np.float64]
-    zeff: float = 0
+    zeff: float | npt.NDArray[np.float64] = 0
 
 
 def tophat_windows(
@@ -235,10 +235,10 @@ def tophat_windows(
     ws = []
     for zmin, zmax in itertools.pairwise(zbins):
         n = max(round((zmax - zmin) / dz), 2)
-        z = np.linspace(zmin, zmax, n)
-        w = wht(z)  # type: ignore[arg-type]
+        z = np.linspace(zmin, zmax, n, dtype=np.float64)
+        w = wht(z)
         zeff = np.trapezoid(w * z, z) / np.trapezoid(w, z)
-        ws.append(RadialWindow(z, w, zeff))  # type: ignore[arg-type]
+        ws.append(RadialWindow(z, w, zeff))
     return ws
 
 
@@ -410,7 +410,7 @@ def restrict(
     z_ = np.compress(np.greater(z, w.za[0]) & np.less(z, w.za[-1]), z)
     zr = np.union1d(w.za, z_)
     fr = ndinterp(zr, z, f, left=0.0, right=0.0) * ndinterp(zr, w.za, w.wa)
-    return zr, fr  # type: ignore[return-value]
+    return zr, fr
 
 
 def partition(
@@ -742,7 +742,7 @@ def _uniform_grid(
     if step is not None and num is None:
         return np.arange(start, np.nextafter(stop + step, stop), step)
     if step is None and num is not None:
-        return np.linspace(start, stop, num + 1)  # type: ignore[return-value]
+        return np.linspace(start, stop, num + 1, dtype=np.float64)
     msg = "exactly one of grid step size or number of steps must be given"
     raise ValueError(msg)