gh-208: remove legacy random number generation

tests/core/test_algorithm.py

@@ -16,8 +16,9 @@ def test_nnls():
     from scipy.optimize import nnls as nnls_scipy
     from glass.core.algorithm import nnls as nnls_glass
 
-    a = np.random.randn(100, 20)
-    b = np.random.randn(100)
+    rng = np.random.default_rng(seed=42)
+    a = rng.standard_normal((100, 20))
+    b = rng.standard_normal((100,))
 
     x_glass = nnls_glass(a, b)
     x_scipy, _ = nnls_scipy(a, b)

tests/test_lensing.py

@@ -75,11 +75,12 @@ def test_deflect_many():
     from glass.lensing import deflect
 
     n = 1000
-    abs_alpha = np.random.uniform(0, 2 * np.pi, size=n)
-    arg_alpha = np.random.uniform(-np.pi, np.pi, size=n)
+    rng = np.random.default_rng(seed=42)
+    abs_alpha = rng.uniform(0, 2 * np.pi, size=n)
+    arg_alpha = rng.uniform(-np.pi, np.pi, size=n)
 
-    lon_ = np.degrees(np.random.uniform(-np.pi, np.pi, size=n))
-    lat_ = np.degrees(np.arcsin(np.random.uniform(-1, 1, size=n)))
+    lon_ = np.degrees(rng.uniform(-np.pi, np.pi, size=n))
+    lat_ = np.degrees(np.arcsin(rng.uniform(-1, 1, size=n)))
 
     lon, lat = deflect(lon_, lat_, abs_alpha * np.exp(1j * arg_alpha))
 
@@ -101,7 +102,8 @@ def test_multi_plane_matrix(shells, cosmo):
 
     convergence = MultiPlaneConvergence(cosmo)
 
-    deltas = np.random.rand(len(shells), 10)
+    rng = np.random.default_rng(seed=42)
+    deltas = rng.random((len(shells), 10))
     kappas = []
     for shell, delta in zip(shells, deltas):
         convergence.add_window(delta, shell)
@@ -121,8 +123,9 @@ def test_multi_plane_weights(shells, cosmo):
 
     convergence = MultiPlaneConvergence(cosmo)
 
-    deltas = np.random.rand(len(shells), 10)
-    weights = np.random.rand(len(shells), 3)
+    rng = np.random.default_rng(seed=42)
+    deltas = rng.random((len(shells), 10))
+    weights = rng.random((len(shells), 3))
     kappa = 0
     for shell, delta, weight in zip(shells, deltas, weights):
         convergence.add_window(delta, shell)

tests/test_points.py

@@ -102,8 +102,9 @@ def test_position_weights():
 
     for bshape in None, (), (100,), (100, 1):
         for cshape in (100,), (100, 50), (100, 3, 2):
-            counts = np.random.rand(*cshape)
-            bias = None if bshape is None else np.random.rand(*bshape)
+            rng = np.random.default_rng(seed=42)
+            counts = rng.random(cshape)
+            bias = None if bshape is None else rng.random(bshape)
 
             weights = position_weights(counts, bias)