def test_tutorial_fukui_tepco(self):
nw = Network('data/test-fukui-tepco', format='fukui-tepco')
configs = nw.enumerate()
self.assertEqual(len(configs), 111)
optimal_config = nw.optimize(configs)
self.assertEqual(optimal_config, [
'switch_0003', 'switch_0007', 'switch_0295', 'switch_0297',
'switch_0301', 'switch_0304', 'switch_0308', 'switch_1056',
'switch_1060', 'switch_1064', 'switch_1065', 'switch_1067'
])
open_switches = set(nw.switches) - set(optimal_config)
self.assertEqual(
open_switches,
set(['switch_0005', 'switch_0306', 'switch_1058', 'switch_1069']))
loss, lower_bound = nw.loss(optimal_config, is_optimal=True)
self.assertAlmostEqual(loss, 72055.7, 0)
self.assertAlmostEqual(lower_bound, 69238.4, 0)
def test_tutorial(self):
nw = Network('data/test.yaml')
self.assertTrue(isinstance(nw, Network))
self.assertEqual(len(nw.nodes), 37)
self.assertEqual(nw.nodes[0],
['section_-001', 'section_0302', 'section_0303'])
self.assertEqual(len(nw.switches), 16)
self.assertEqual(len(nw.sections), 25)
self.assertEqual(len(nw.sections['section_1068']['load']), 3)
self.assertAlmostEqual(nw.sections['section_1068']['load'][0],
23.87780659 + 4.33926456j, 3)
self.assertEqual(len(nw.sections['section_1068']['impedance']), 3)
self.assertAlmostEqual(nw.sections['section_1068']['impedance'][0],
0.1539000 + 0.4512584j, 3)
self.assertFalse(nw.sections['section_1068']['substation'])
self.assertTrue(nw.graph)
self.assertEqual(nw.graph.edges,
[(1, 2), (3, 2), (2, 4), (5, 3), (4, 6), (7, 5),
(8, 6), (8, 9), (9, 7), (10, 7), (1, 11), (11, 12),
(13, 12), (10, 14), (14, 15), (13, 15)])
self.assertEqual(nw.graph.roots, set([1, 10, 13]))
self.assertEqual(len(nw.graph._switch2edge), 16)
self.assertEqual(nw.graph._switch2edge['switch_0001'], (1, 2))
self.assertEqual(len(nw.graph._edge2switch), 16)
self.assertEqual(nw.graph._edge2switch[(1, 2)], 'switch_0001')
configs = nw.enumerate()
self.assertTrue(isinstance(configs, ConfigSet))
self.assertEqual(len(configs), 111)
self.assertEqual(configs.len(), 111)
configs_w2_wo3 = configs.including('switch_0002').excluding(
'switch_0003')
self.assertEqual(len(configs_w2_wo3), 15)
configs_w2 = configs.including('switch_0002')
configs_wo3 = configs.excluding('switch_0003')
self.assertTrue(configs_w2 & configs_wo3 == configs_w2_wo3)
for config in configs_w2_wo3:
self.assertTrue(isinstance(config, list))
i = 1
sum = 0.0
for config in configs_w2_wo3.rand_iter():
sum += nw.loss(config)
if i == 5:
break
i += 1
self.assertAlmostEqual(sum / 5, 85848.1, 0)
optimal_config = nw.optimize(configs)
self.assertEqual(optimal_config, [
'switch_0001', 'switch_0002', 'switch_0003', 'switch_0005',
'switch_0006', 'switch_0008', 'switch_0009', 'switch_0010',
'switch_0011', 'switch_0013', 'switch_0014', 'switch_0016'
])
open_switches = set(nw.switches) - set(optimal_config)
self.assertEqual(
open_switches,
set(['switch_0004', 'switch_0007', 'switch_0012', 'switch_0015']))
self.assertEqual(len(nw.search_space.graph.edges()), 10)
self.assertAlmostEqual(nw.search_space.graph['38']['T']['weight'],
236.191, 3)
self.assertEqual(nw.search_space.start, '4114')
self.assertEqual(nw.search_space.end, 'T')
loss, lower_bound = nw.loss(optimal_config, is_optimal=True)
self.assertAlmostEqual(loss, 72055.7, 0)
self.assertAlmostEqual(lower_bound, 69238.4, 0)
unrestorable_cuts = nw.unrestorable_cuts(2)
self.assertEqual(len(unrestorable_cuts), 25)
self.assertEqual(unrestorable_cuts[0],
(('section_0299', 'section_0298', 'section_0300'),
('section_0006', )))