Add test

scico/test/functional/test_norm.py

@@ -0,0 +1,44 @@
+import numpy as np
+
+import pytest
+
+import scico.numpy as snp
+from scico import functional
+
+
+@pytest.mark.parametrize("axis", [0, 1, (0, 2)])
+def test_l21norm(axis):
+    x = np.ones((3, 4, 5))
+    if isinstance(axis, int):
+        l2axis = (axis,)
+    else:
+        l2axis = axis
+    l2shape = [x.shape[k] for k in l2axis]
+    l1axis = tuple(set(range(len(x))) - set(l2axis))
+    l1shape = [x.shape[k] for k in l1axis]
+
+    l21ana = np.sqrt(np.prod(l2shape)) * np.prod(l1shape)
+    F = functional.L21Norm(l2_axis=axis)
+    l21num = F(x)
+    np.testing.assert_allclose(l21ana, l21num, rtol=1e-5)
+
+    l2ana = np.sqrt(np.prod(l2shape))
+    prxana = (l2ana - 1.0) / l2ana * x
+    prxnum = F.prox(x, 1.0)
+    np.testing.assert_allclose(prxana, prxnum, rtol=1e-5)
+
+
+def test_l2norm_blockarray():
+    xa = np.random.randn(2, 3, 4)
+    xb = snp.blockarray((xa[0], xa[1]))
+
+    fa = functional.L21Norm(l2_axis=(1, 2))
+    fb = functional.L21Norm(l2_axis=None)
+
+    np.testing.assert_allclose(fa(xa), fb(xb), rtol=1e-6)
+
+    ya = fa.prox(xa)
+    yb = fb.prox(xb)
+
+    np.testing.assert_allclose(ya[0], yb[0], rtol=1e-6)
+    np.testing.assert_allclose(ya[1], yb[1], rtol=1e-6)