Merge remote-tracking branch 'upstream/master' into blocksparse_impro…

…ved_unique_encoding_2

tensornetwork/tests/tensornetwork_symmetric_test.py

@@ -43,6 +43,28 @@ def get_random_symmetric(shape, flows, num_charges, seed=10, dtype=np.float64):
   indices = [Index(charge, flows[n]) for n in range(R)]
   return BlockSparseTensor.random(indices=indices, dtype=dtype)
 
+def get_square_matrix(shape, dtype=np.float64):
+  charge = U1Charge(np.random.randint(-5, 5, shape))
+  flows = [True, False]
+  indices = [Index(charge, flows[n]) for n in range(2)]
+  return BlockSparseTensor.random(indices=indices, dtype=dtype)
+
+
+def get_zeros(shape, dtype=np.float64):
+  R = len(shape)
+  charges = [U1Charge(np.random.randint(-5, 5, shape[n])) for n in range(R)]
+  flows = list(np.full(R, fill_value=False, dtype=np.bool))
+  indices = [Index(charges[n], flows[n]) for n in range(R)]
+  return BlockSparseTensor.zeros(indices=indices, dtype=dtype)
+
+
+def get_ones(shape, dtype=np.float64):
+  R = len(shape)
+  charges = [U1Charge(np.random.randint(-5, 5, shape[n])) for n in range(R)]
+  flows = list(np.full(R, fill_value=False, dtype=np.bool))
+  indices = [Index(charges[n], flows[n]) for n in range(R)]
+  return BlockSparseTensor.ones(indices=indices, dtype=dtype)
+
 @pytest.mark.parametrize("dtype", [np.float64, np.complex128])
 @pytest.mark.parametrize("num_charges", [1, 2, 3])
 def test_network_copy_reordered(dtype, num_charges):