def test_d3_treemap_no_error(self):
self.install_fixtures()
lca = LCA({("a", "2"): 1}, ("method", ))
lca.lci()
lca.lcia()
ra, rp, rb = lca.reverse_dict()
CA().d3_treemap(lca.characterized_inventory, rb, ra)
def test_top_matrix_array(self):
matrix = np.array([[0, 0, 1, 0], [2, 0, 4, 0], [3, 0, 1, 1],
[0, 7, 0, 1]])
ca = CA()
elements, rows, columns = ca.top_matrix(matrix, 2, 2)
self.assertTrue(np.allclose((3, 1), rows))
self.assertTrue(np.allclose((1, 2), columns))
self.assertEqual([(3, 1, 0, 0, 7), (1, 2, 1, 1, 4)], elements)
def test_sort_array_errors(self):
ca = CA()
with self.assertRaises(ValueError):
ca.sort_array([], limit_type="foo", total=1.0)
with self.assertRaises(ValueError):
ca.sort_array([], limit=0.0, limit_type="percent", total=1.0)
with self.assertRaises(ValueError):
ca.sort_array([], limit=1.01, limit_type="percent", total=1.0)
def test_sort_array_number(self):
test_data = np.array((1.0, 2.0, 4.0, 3.0))
answer = np.array((
(4, 2),
(3, 3),
(2, 1),
))
ca = CA()
self.assertTrue(np.allclose(answer, ca.sort_array(test_data, limit=3)))
def test_sort_array_percentage(self):
test_data = np.array((1.0, 2.0, 4.0, 3.0))
answer = np.array((
(4, 2),
(3, 3),
))
ca = CA()
self.assertTrue(
np.allclose(
answer,
ca.sort_array(test_data, limit=0.3, limit_type="percent")))
def test_top_matrix_matrix(self):
matrix = sparse.lil_matrix((4, 4))
input_data = [[0, 0, 1, 0], [2, 0, 4, 0], [3, 0, 1, 1], [0, 7, 0, 1]]
for row in range(4):
for col in range(4):
if input_data[row][col]:
matrix[row, col] = input_data[row][col]
matrix = matrix.tocsr()
ca = CA()
elements, rows, columns = ca.top_matrix(matrix, 2, 2)
self.assertTrue(np.allclose((3, 1), rows))
self.assertTrue(np.allclose((1, 2), columns))
self.assertEqual([(3, 1, 0, 0, 7), (1, 2, 1, 1, 4)], elements)
def test_hinton_matrix_no_error(self):
self.install_fixtures()
lca = LCA({("a", "2"): 1}, ("method", ))
lca.lci()
lca.lcia()
CA().hinton_matrix(lca, 2, 2)