From fa55f4c463806599bccf145baf22e13ff79f9a68 Mon Sep 17 00:00:00 2001
From: Sebastian Berg <sebastian@sipsolutions.net>
Date: Wed, 10 Jul 2013 14:19:42 +0200
Subject: ENH: inv/solve work with empty inner and others empty outer array

This makes the inverse of a 0x0 array simply be 0x0 again. It
also modifies the no-empty array check in favor of a no-empty
*inner* array, since the gufuncs seem to handle the other case
fine.
---
 numpy/linalg/tests/test_linalg.py | 48 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

(limited to 'numpy/linalg/tests')

diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py
index 881311c94..7f102634e 100644
--- a/numpy/linalg/tests/test_linalg.py
+++ b/numpy/linalg/tests/test_linalg.py
@@ -204,6 +204,39 @@ class TestSolve(LinalgTestCase, LinalgGeneralizedTestCase, TestCase):
         for dtype in [single, double, csingle, cdouble]:
             yield check, dtype
 
+    def test_0_size(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        # Test system of 0x0 matrices
+        a = np.arange(8).reshape(2, 2, 2)
+        b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass)
+
+        expected = linalg.solve(a, b)[:,0:0,:]
+        result = linalg.solve(a[:,0:0,0:0], b[:,0:0,:])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        # Test errors for non-square and only b's dimension being 0
+        assert_raises(linalg.LinAlgError, linalg.solve, a[:,0:0,0:1], b)
+        assert_raises(ValueError, linalg.solve, a, b[:,0:0,:])
+
+        # Test broadcasting error
+        b = np.arange(6).reshape(1, 3, 2) # broadcasting error
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+
+        # Test zero "single equations" with 0x0 matrices.
+        b = np.arange(2).reshape(1, 2).view(ArraySubclass)
+        expected = linalg.solve(a, b)[:,0:0]
+        result = linalg.solve(a[:,0:0,0:0], b[:,0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        b = np.arange(3).reshape(1, 3)
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+        assert_raises(ValueError, linalg.solve, a[:,0:0,0:0], b)
+
 
 class TestInv(LinalgTestCase, LinalgGeneralizedTestCase, TestCase):
     def do(self, a, b):
@@ -219,6 +252,21 @@ class TestInv(LinalgTestCase, LinalgGeneralizedTestCase, TestCase):
         for dtype in [single, double, csingle, cdouble]:
             yield check, dtype
 
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0,1,1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res.shape)
+        assert_(isinstance(a, ArraySubclass))
+
+        a = np.zeros((0,0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal(a.shape, res.shape)
+
 
 class TestEigvals(LinalgTestCase, LinalgGeneralizedTestCase, TestCase):
     def do(self, a, b):
-- 
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