gh-163: full support of NumPy v2

.github/workflows/test.yml

@@ -22,7 +22,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.10', '3.11', '3.12']
     steps:
     - uses: actions/checkout@v4
       with:

.gitignore

@@ -6,3 +6,4 @@ __pycache__
 _build
 build
 dist
+*.Fits

examples/1-basic/photoz.ipynb

@@ -57,7 +57,7 @@
     "dndz = n_arcmin2 * glass.observations.smail_nz(z, 1.0, 2.2, 1.5)\n",
     "\n",
     "# compute the over galaxy number density on the sphere\n",
-    "ngal = np.trapz(dndz, z)"
+    "ngal = np.trapezoid(dndz, z)"
    ]
   },
   {

examples/2-advanced/cosmic_shear.ipynb

@@ -161,7 +161,7 @@
     "# localised redshift distribution with the given density\n",
     "z = np.arange(0.0, 2.0, 0.01)\n",
     "dndz = np.exp(-(z - 0.5)**2/(0.1)**2)\n",
-    "dndz *= n_arcmin2/np.trapz(dndz, z)\n",
+    "dndz *= n_arcmin2/np.trapezoid(dndz, z)\n",
     "\n",
     "# distribute dN/dz over the radial window functions\n",
     "ngal = glass.shells.partition(z, dndz, ws)"

glass/core/array.py

@@ -66,7 +66,7 @@ def trapz_product(f, *ff, axis=-1):
     y = np.interp(x, *f)
     for f_ in ff:
         y *= np.interp(x, *f_)
-    return np.trapz(y, x, axis=axis)
+    return np.trapezoid(y, x, axis=axis)
 
 
 def cumtrapz(f, x, dtype=None, out=None):

glass/lensing.py

@@ -282,7 +282,7 @@ def add_window(self, delta: np.ndarray, w: 'RadialWindow') -> None:
         '''
 
         zsrc = w.zeff
-        lens_weight = np.trapz(w.wa, w.za)/np.interp(zsrc, w.za, w.wa)
+        lens_weight = np.trapezoid(w.wa, w.za)/np.interp(zsrc, w.za, w.wa)
 
         self.add_plane(delta, zsrc, lens_weight)
 

glass/observations.py

@@ -111,7 +111,7 @@ def gaussian_nz(z: np.ndarray, mean: ArrayLike, sigma: ArrayLike, *,
     sigma = np.reshape(sigma, np.shape(sigma) + (1,)*np.ndim(z))
 
     nz = np.exp(-((z - mean)/sigma)**2/2)
-    nz /= np.trapz(nz, z, axis=-1)[..., np.newaxis]
+    nz /= np.trapezoid(nz, z, axis=-1)[..., np.newaxis]
 
     if norm is not None:
         nz *= norm
@@ -167,7 +167,7 @@ def smail_nz(z: np.ndarray, z_mode: ArrayLike, alpha: ArrayLike,
     beta = np.asanyarray(beta)[..., np.newaxis]
 
     pz = z**alpha*np.exp(-alpha/beta*(z/z_mode)**beta)
-    pz /= np.trapz(pz, z, axis=-1)[..., np.newaxis]
+    pz /= np.trapezoid(pz, z, axis=-1)[..., np.newaxis]
 
     if norm is not None:
         pz *= norm

glass/points.py

@@ -68,7 +68,7 @@ def effective_bias(z, bz, w):
         \\;.
 
     '''
-    norm = np.trapz(w.wa, w.za)
+    norm = np.trapezoid(w.wa, w.za)
     return trapz_product((z, bz), (w.za, w.wa))/norm
 
 

glass/shells.py

@@ -177,7 +177,7 @@ def tophat_windows(zbins: ArrayLike1D, dz: float = 1e-3,
         n = max(round((zmax - zmin)/dz), 2)
         z = np.linspace(zmin, zmax, n)
         w = wht(z)
-        zeff = np.trapz(w*z, z)/np.trapz(w, z)
+        zeff = np.trapezoid(w*z, z)/np.trapezoid(w, z)
         ws.append(RadialWindow(z, w, zeff))
     return ws
 
@@ -465,7 +465,7 @@ def partition_lstsq(
 
     # create the window function matrix
     a = [np.interp(zp, za, wa, left=0., right=0.) for za, wa, _ in shells]
-    a = a/np.trapz(a, zp, axis=-1)[..., None]
+    a = a/np.trapezoid(a, zp, axis=-1)[..., None]
     a = a*dz
 
     # create the target vector of distribution values
@@ -475,7 +475,7 @@ def partition_lstsq(
     # append a constraint for the integral
     mult = 1/sumtol
     a = np.concatenate([a, mult * np.ones((len(shells), 1))], axis=-1)
-    b = np.concatenate([b, mult * np.reshape(np.trapz(fz, z), (*dims, 1))], axis=-1)
+    b = np.concatenate([b, mult * np.reshape(np.trapezoid(fz, z), (*dims, 1))], axis=-1)
 
     # now a is a matrix of shape (len(shells), len(zp) + 1)
     # and b is a matrix of shape (*dims, len(zp) + 1)
@@ -522,7 +522,7 @@ def partition_nnls(
 
     # create the window function matrix
     a = [np.interp(zp, za, wa, left=0., right=0.) for za, wa, _ in shells]
-    a = a/np.trapz(a, zp, axis=-1)[..., None]
+    a = a/np.trapezoid(a, zp, axis=-1)[..., None]
     a = a*dz
 
     # create the target vector of distribution values
@@ -532,7 +532,7 @@ def partition_nnls(
     # append a constraint for the integral
     mult = 1/sumtol
     a = np.concatenate([a, mult * np.ones((len(shells), 1))], axis=-1)
-    b = np.concatenate([b, mult * np.reshape(np.trapz(fz, z), (*dims, 1))], axis=-1)
+    b = np.concatenate([b, mult * np.reshape(np.trapezoid(fz, z), (*dims, 1))], axis=-1)
 
     # now a is a matrix of shape (len(shells), len(zp) + 1)
     # and b is a matrix of shape (*dims, len(zp) + 1)
@@ -561,7 +561,7 @@ def partition_restrict(
     part = np.empty((len(shells),) + np.shape(fz)[:-1])
     for i, w in enumerate(shells):
         zr, fr = restrict(z, fz, w)
-        part[i] = np.trapz(fr, zr, axis=-1)
+        part[i] = np.trapezoid(fr, zr, axis=-1)
     return part
 
 
@@ -627,7 +627,7 @@ def combine(
         np.expand_dims(weight, -1) * np.interp(
             z,
             shell.za,
-            shell.wa / np.trapz(shell.wa, shell.za),
+            shell.wa / np.trapezoid(shell.wa, shell.za),
             left=0.,
             right=0.,
         )

pyproject.toml

@@ -11,8 +11,6 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -24,7 +22,7 @@ dependencies = [
     "gaussiancl>=2022.10.21",
     "healpix>=2022.11.1",
     "healpy>=1.15.0",
-    "numpy>=1.20.0",
+    "numpy~=2.0",
 ]
 description = "Generator for Large Scale Structure"
 dynamic = [
@@ -35,7 +33,7 @@ maintainers = [
 ]
 name = "glass"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 license.file = "LICENSE"
 
 [project.optional-dependencies]

tests/test_shells.py

@@ -73,4 +73,4 @@ def test_partition(method):
 
     assert part.shape == (len(shells), 3, 2)
 
-    assert np.allclose(part.sum(axis=0), np.trapz(fz, z))
+    assert np.allclose(part.sum(axis=0), np.trapezoid(fz, z))