Skip unsupported scipy tests on older versions

server/pypi/packages/scipy/test.py

@@ -1,8 +1,12 @@
 import unittest
 import os
+from importlib import metadata
+from unittest import skipIf
 
+scipy_version_info = tuple(map(int, metadata.version("scipy").split(".")))
 os.environ["USE_PROPACK"] = "1"
 
+
 class TestScipy(unittest.TestCase):
 
     def test_interpolate(self):
@@ -15,7 +19,10 @@ def test_interpolate(self):
             griddata(points, values, [[0, 0], [0.5, 0], [1, 0], [0.5, 0.5], [0, 1]]))
         np.testing.assert_allclose(
             [1.3, 1.7, 2.1, 2.5, np.nan],
-            griddata(points, values, [[0.3, 0], [0.3, 0.2], [0.3, 0.4], [0.3, 0.6], [0.3, 0.8]]),
+            griddata(
+                points,
+                values,
+                [[0.3, 0], [0.3, 0.2], [0.3, 0.4], [0.3, 0.6], [0.3, 0.8]]),
             equal_nan=True)
 
     def test_optimize(self):
@@ -24,17 +31,20 @@ def f(x):
             return (x - 42) ** 2
         self.assertEqual(42, round(minimize(f, [123]).x[0]))
 
+    @skipIf(scipy_version_info < (1, 8), "SciPy version too old")
     def test_csr_array(self):
         import numpy as np
         from scipy.sparse import csr_array
         indptr = np.array([0, 2, 3, 6])
         indices = np.array([0, 2, 2, 0, 1, 2])
         data = np.array([1, 2, 3, 4, 5, 6])
-        np.testing.assert_array_equal(csr_array((data, indices, indptr), shape=(3, 3)).toarray(), 
-                         [[1, 0, 2],[0, 0, 3],[4, 5, 6]])       
+        np.testing.assert_array_equal(
+            csr_array((data, indices, indptr), shape=(3, 3)).toarray(),
+            [[1, 0, 2], [0, 0, 3], [4, 5, 6]])
 
+    @skipIf(scipy_version_info < (1, 8), "SciPy version too old")
     def test_svds_propack(self):
-        import numpy as np        
+        import numpy as np
 
         from scipy.stats import ortho_group
         from scipy.sparse import csc_matrix, diags
@@ -45,15 +55,15 @@ def test_svds_propack(self):
         u = orthogonal[:, :5]         # left singular vectors
         vT = orthogonal[:, 5:].T      # right singular vectors
         A = u @ diags(s) @ vT
-        
+
         u2, s2, vT2 = svds(A, k=3, solver='propack')
         A2 = u2 @ np.diag(s2) @ vT2
         np.testing.assert_allclose(A2, A.todense(), atol=1e-3)
-        
+
         u3, s3, vT3 = svds(A, k=5, solver='propack')
         A3 = u3 @ np.diag(s3) @ vT3
-        np.testing.assert_allclose(A3, A.todense())        
+        np.testing.assert_allclose(A3, A.todense())
 
         np.testing.assert_allclose(s3, s)
         np.testing.assert_allclose(np.abs(u3), np.abs(u.toarray()))
-        np.testing.assert_allclose(np.abs(vT3), np.abs(vT.toarray()))
+        np.testing.assert_allclose(np.abs(vT3), np.abs(vT.toarray()))