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_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_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',)))
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', )))
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)