gh-440: remove scipy as a test dependency (#462)

.github/test-constraints.txt

@@ -1,9 +1,6 @@
 numpy
 --only-binary numpy
 
-scipy
---only-binary scipy
-
 healpy
 --only-binary healpy
 

pyproject.toml

@@ -61,7 +61,6 @@ test = [
     "pytest-doctestplus",
     "pytest-mock",
     "pytest-rerunfailures",
-    "scipy",
 ]
 
 [project.urls]

tests/core/test_algorithm.py

@@ -1,33 +1,33 @@
-import importlib.util
-
 import numpy as np
 import pytest
 
-from glass.core.algorithm import nnls as nnls_glass
-
-# check if scipy is available for testing
-HAVE_SCIPY = importlib.util.find_spec("scipy") is not None
+from glass.core.algorithm import nnls
 
 
-@pytest.mark.skipif(not HAVE_SCIPY, reason="test requires SciPy")
 def test_nnls(rng: np.random.Generator) -> None:
-    from scipy.optimize import nnls as nnls_scipy
-
-    # cross-check output with scipy's nnls
+    # check output
 
-    a = rng.standard_normal((100, 20))
-    b = rng.standard_normal((100,))
+    a = np.arange(25.0).reshape(-1, 5)
+    b = np.arange(5.0)
+    y = a @ b
+    res = nnls(a, y)
+    assert np.linalg.norm((a @ res) - y) < 1e-7
 
-    x_glass = nnls_glass(a, b)
-    x_scipy, _ = nnls_scipy(a, b)
-
-    np.testing.assert_allclose(x_glass, x_scipy)
+    a = rng.uniform(low=-10, high=10, size=[50, 10])
+    b = np.abs(rng.uniform(low=-2, high=2, size=[10]))
+    b[::2] = 0
+    x = a @ b
+    res = nnls(a, x, tol=500 * np.linalg.norm(a, 1) * np.spacing(1.0))
+    np.testing.assert_allclose(res, b, rtol=0.0, atol=1e-10)
 
     # check matrix and vector's shape
 
+    a = rng.standard_normal((100, 20))
+    b = rng.standard_normal((100,))
+
     with pytest.raises(ValueError, match="input `a` is not a matrix"):
-        nnls_glass(b, a)
+        nnls(b, a)
     with pytest.raises(ValueError, match="input `b` is not a vector"):
-        nnls_glass(a, a)
+        nnls(a, a)
     with pytest.raises(ValueError, match="the shapes of `a` and `b` do not match"):
-        nnls_glass(a.T, b)
+        nnls(a.T, b)

tests/core/test_array.py

@@ -1,5 +1,3 @@
-import importlib.util
-
 import numpy as np
 import numpy.typing as npt
 import pytest
@@ -12,9 +10,6 @@
     trapezoid_product,
 )
 
-# check if scipy is available for testing
-HAVE_SCIPY = importlib.util.find_spec("scipy") is not None
-
 
 def test_broadcast_first() -> None:
     a = np.ones((2, 3, 4))
@@ -156,50 +151,47 @@ def test_trapezoid_product() -> None:
     np.testing.assert_allclose(s, 1.0)
 
 
-@pytest.mark.skipif(not HAVE_SCIPY, reason="test requires SciPy")
 def test_cumulative_trapezoid() -> None:
-    import scipy.integrate as spi
-
     # 1D f and x
 
     f = np.array([1, 2, 3, 4])
     x = np.array([0, 1, 2, 3])
 
-    # default dtype (int - not supported by scipy)
+    # default dtype (int)
 
-    glass_ct = cumulative_trapezoid(f, x)
-    np.testing.assert_allclose(glass_ct, np.array([0, 1, 4, 7]))
+    ct = cumulative_trapezoid(f, x)
+    np.testing.assert_allclose(ct, np.array([0, 1, 4, 7]))
 
     # explicit dtype (float)
 
-    glass_ct = cumulative_trapezoid(f, x, dtype=float)
-    scipy_ct = spi.cumulative_trapezoid(f, x, initial=0)
-    np.testing.assert_allclose(glass_ct, scipy_ct)
+    ct = cumulative_trapezoid(f, x, dtype=float)
+    np.testing.assert_allclose(ct, np.array([0.0, 1.5, 4.0, 7.5]))
 
     # explicit return array
 
-    result = cumulative_trapezoid(f, x, dtype=float, out=np.zeros((4,)))
-    scipy_ct = spi.cumulative_trapezoid(f, x, initial=0)
-    np.testing.assert_allclose(result, scipy_ct)
+    out = np.zeros((4,))
+    ct = cumulative_trapezoid(f, x, dtype=float, out=out)
+    np.testing.assert_equal(ct, out)
 
     # 2D f and 1D x
 
     f = np.array([[1, 4, 9, 16], [2, 3, 5, 7]])
     x = np.array([0, 1, 2.5, 4])
 
-    # default dtype (int - not supported by scipy)
+    # default dtype (int)
 
-    glass_ct = cumulative_trapezoid(f, x)
-    np.testing.assert_allclose(glass_ct, np.array([[0, 2, 12, 31], [0, 2, 8, 17]]))
+    ct = cumulative_trapezoid(f, x)
+    np.testing.assert_allclose(ct, np.array([[0, 2, 12, 31], [0, 2, 8, 17]]))
 
     # explicit dtype (float)
 
-    glass_ct = cumulative_trapezoid(f, x, dtype=float)
-    scipy_ct = spi.cumulative_trapezoid(f, x, initial=0)
-    np.testing.assert_allclose(glass_ct, scipy_ct)
+    ct = cumulative_trapezoid(f, x, dtype=float)
+    np.testing.assert_allclose(
+        ct, np.array([[0.0, 2.5, 12.25, 31.0], [0.0, 2.5, 8.5, 17.5]])
+    )
 
     # explicit return array
 
-    glass_ct = cumulative_trapezoid(f, x, dtype=float, out=np.zeros((2, 4)))
-    scipy_ct = spi.cumulative_trapezoid(f, x, initial=0)
-    np.testing.assert_allclose(glass_ct, scipy_ct)
+    out = np.zeros((2, 4))
+    ct = cumulative_trapezoid(f, x, dtype=float, out=out)
+    np.testing.assert_equal(ct, out)