def test_loadchornics_maintenance_ok(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path_maintenance)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.sum(maintenance_duration == 0) == 18
assert maintenance_duration[17] == 12, "incorrect duration of maintenance on powerline 17"
assert maintenance_duration[19] == 12, "incorrect duration of maintenance on powerline 19"
assert np.sum(maintenance_time == -1) == 18
assert maintenance_time[17] == 1, "incorrect time for next maintenance on powerline 17"
assert maintenance_time[19] == 276, "incorrect time for next maintenance on powerline 19"
for i in range(12):
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.sum(maintenance_duration == 0) == 18
assert int(maintenance_duration[17]) == int(12-i), "incorrect duration of maintenance on powerline 17 at iteration {}: it is {} and should be {}".format(i, maintenance_duration[17], int(12-i))
assert maintenance_duration[19] == 12, "incorrect duration of maintenance on powerline 19 at iteration {}".format(i)
assert np.sum(maintenance_time == -1) == 18
assert maintenance_time[17] == 0, "incorrect time for next maintenance on powerline 17 at iteration {}".format(i)
assert maintenance_time[19] == 275-i, "incorrect time for next maintenance on powerline 19 at iteration {}".format(i)
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.sum(maintenance_duration == 0) == 19
assert maintenance_duration[19] == 12, "incorrect duration of maintenance on powerline 19 at finish"
assert np.sum(maintenance_time == -1) == 19
assert maintenance_time[19] == 263, "incorrect time for next maintenance on powerline 19 at finish"
def test_chronicsloading(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = [18.8, 86.5, 44.5, 7.1, 10.4, 27.6, 8.1, 3.2, 5.6, 11.9, 13.6]
assert self.compare_vect(res["injection"]['load_p'], vect)
def test_chronicsloading(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = np.array([18.8, 86.5, 44.5, 7.1, 10.4, 27.6, 8.1, 3.2, 5.6, 11.9, 13.6]) # what is written on the file
backend_th = vect[self.id_chron_to_back_load] # what should be in backend
assert self.compare_vect(res["injection"]['load_p'], backend_th)
def test_loadchornics_hazard_ok(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path_hazard)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.all(hazard_duration == 0)
for i in range(12):
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.sum(hazard_duration == 0) == 19
assert hazard_duration[17] == 12-i, "error at iteration {}".format(i)
current_datetime, dict_, maintenance_time, maintenance_duration, hazard_duration, prod_v = chron_handl.next_time_step()
assert np.all(hazard_duration == 0)
def setUp(self):
self.backend = PandaPowerBackend(
detailed_infos_for_cascading_failures=True)
self.path_matpower = PATH_DATA_TEST
self.case_file = "test_case14.json"
self.backend.load_grid(self.path_matpower, self.case_file)
self.tolvect = 1e-2
self.tol_one = 1e-5
self.game_rules = GameRules()
self.action_env = HelperAction(
gridobj=self.backend, legal_action=self.game_rules.legal_action)
self.lines_flows_init = np.array([
638.28966637, 305.05042301, 17658.9674809, 26534.04334098,
10869.23856329, 4686.71726729, 15612.65903298, 300.07915572,
229.8060832, 169.97292682, 100.40192958, 265.47505664,
21193.86923911, 21216.44452327, 49701.1565287, 124.79684388,
67.59759985, 192.19424706, 666.76961936, 1113.52773632
])
# _parameters for the environment
self.env_params = Parameters()
# used for init an env too
self.chronics_handler = ChronicsHandler()
self.id_first_line_disco = 8 # due to hard overflow
self.id_2nd_line_disco = 11 # due to soft overflow
def setUp(self):
"""
The case file is a representation of the case14 as found in the ieee14 powergrid.
:return:
"""
self.tolvect = 1e-2
self.tol_one = 1e-5
self.game_rules = GameRules()
self.rewardClass = L2RPNReward
self.reward_helper = self.rewardClass()
self.obsClass = CompleteObservation
self.parameters = Parameters()
# powergrid
self.backend = PandaPowerBackend()
self.path_matpower = PATH_DATA_TEST_PP
self.case_file = "test_case14.json"
# chronics
self.path_chron = os.path.join(PATH_CHRONICS, "chronics_with_forecast")
self.chronics_handler = ChronicsHandler(chronicsClass=GridStateFromFileWithForecasts, path=self.path_chron)
self.tolvect = 1e-2
self.tol_one = 1e-5
self.id_chron_to_back_load = np.array([0, 1, 10, 2, 3, 4, 5, 6, 7, 8, 9])
# force the verbose backend
self.backend.detailed_infos_for_cascading_failures = True
self.names_chronics_to_backend = {"loads": {"2_C-10.61": 'load_1_0', "3_C151.15": 'load_2_1',
"14_C63.6": 'load_13_2', "4_C-9.47": 'load_3_3',
"5_C201.84": 'load_4_4',
"6_C-6.27": 'load_5_5', "9_C130.49": 'load_8_6',
"10_C228.66": 'load_9_7',
"11_C-138.89": 'load_10_8', "12_C-27.88": 'load_11_9',
"13_C-13.33": 'load_12_10'},
"lines": {'1_2_1': '0_1_0', '1_5_2': '0_4_1', '9_10_16': '8_9_2',
'9_14_17': '8_13_3',
'10_11_18': '9_10_4', '12_13_19': '11_12_5', '13_14_20': '12_13_6',
'2_3_3': '1_2_7', '2_4_4': '1_3_8', '2_5_5': '1_4_9',
'3_4_6': '2_3_10',
'4_5_7': '3_4_11', '6_11_11': '5_10_12', '6_12_12': '5_11_13',
'6_13_13': '5_12_14', '4_7_8': '3_6_15', '4_9_9': '3_8_16',
'5_6_10': '4_5_17',
'7_8_14': '6_7_18', '7_9_15': '6_8_19'},
"prods": {"1_G137.1": 'gen_0_4', "3_G36.31": "gen_2_1", "6_G63.29": "gen_5_2",
"2_G-56.47": "gen_1_0", "8_G40.43": "gen_7_3"},
}
# _parameters for the environment
self.env_params = Parameters()
self.env = Environment(init_grid_path=os.path.join(self.path_matpower, self.case_file),
backend=self.backend,
chronics_handler=self.chronics_handler,
parameters=self.env_params,
names_chronics_to_backend=self.names_chronics_to_backend,
rewardClass=self.rewardClass)
def test_name_invariant(self):
"""
Test that the crhonics are loaded in whatever format, but the order returned is consistent with the one
of the backend.
:return:
"""
path = os.path.join(PATH_CHRONICS, "chronics_reorder")
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = np.array([18.8, 86.5, 44.5, 7.1, 10.4, 27.6, 8.1, 3.2, 5.6, 11.9, 13.6])
vect = vect[self.id_chron_to_back_load]
assert self.compare_vect(res["injection"]['load_p'], vect)
for i in range(287):
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = np.array([19.0, 87.9, 44.4, 7.2, 10.4, 27.5, 8.4, 3.2, 5.7, 12.2, 13.6])
vect = vect[self.id_chron_to_back_load]
assert self.compare_vect(res["injection"]['load_p'], vect)
try:
_, res = chron_handl.next_time_step() # should load the first time stamp
raise RuntimeError("This should have thrown a StopIteration exception")
except StopIteration:
pass
def _one_process_parrallel(runner, episode_this_process, process_id, path_save=None):
chronics_handler = ChronicsHandler(chronicsClass=runner.gridStateclass,
path=runner.path_chron,
**runner.gridStateclass_kwargs)
parameters = copy.deepcopy(runner.parameters)
backend = runner.backendClass()
nb_episode_this_process = len(episode_this_process)
res = [(None, None, None) for _ in range(nb_episode_this_process)]
for i, p_id in enumerate(episode_this_process):
env, agent = runner._new_env(chronics_handler=chronics_handler,
backend=backend,
parameters=parameters)
name_chron, cum_reward, nb_time_step = Runner._run_one_episode(
env, agent, runner.logger, p_id, path_save)
id_chron = chronics_handler.get_id()
max_ts = chronics_handler.max_timestep()
res[i] = (id_chron, name_chron, cum_reward, nb_time_step, max_ts)
return res
def test_chronicsloading_secondtimestep_chunksize(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path, chunk_size=1)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
_ = chron_handl.next_time_step() # should load the first time stamp
_, res, *_ = chron_handl.next_time_step() # should load the first time stamp
vect = [18.8, 85.1, 44.3, 7.1, 10.2, 27.1, 8.2, 3.2, 5.7, 11.8, 13.8]
assert self.compare_vect(res["injection"]['load_p'], vect)
def test_done(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
for i in range(288):
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = [19.0, 87.9, 44.4, 7.2, 10.4, 27.5, 8.4, 3.2, 5.7, 12.2, 13.6]
assert self.compare_vect(res["injection"]['load_p'], vect)
assert chron_handl.done()
def test_check_validity(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
backend = PandaPowerBackend()
path_matpower = PATH_DATA_TEST_PP
case_file = "test_case14.json"
backend.load_grid(path_matpower, case_file)
chron_handl.check_validity(backend)
def test_chronicsloading_secondtimestep(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
_ = chron_handl.next_time_step() # should load the first time stamp
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = np.array([18.8, 85.1, 44.3, 7.1, 10.2, 27.1, 8.2, 3.2, 5.7, 11.8, 13.8])
vect = vect[self.id_chron_to_back_load]
assert self.compare_vect(res["injection"]['load_p'], vect)
def setUp(self):
# powergrid
self.backend = PandaPowerBackend()
self.path_matpower = PATH_DATA_TEST_PP
self.case_file = "test_case14.json"
# chronics
self.path_chron = os.path.join(PATH_CHRONICS, "chronics")
self.chronics_handler = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path_chron)
self.tolvect = 1e-2
self.tol_one = 1e-5
self.id_chron_to_back_load = np.array([0, 1, 10, 2, 3, 4, 5, 6, 7, 8, 9])
# force the verbose backend
self.backend.detailed_infos_for_cascading_failures = True
self.names_chronics_to_backend = {"loads": {"2_C-10.61": 'load_1_0', "3_C151.15": 'load_2_1',
"14_C63.6": 'load_13_2', "4_C-9.47": 'load_3_3',
"5_C201.84": 'load_4_4',
"6_C-6.27": 'load_5_5', "9_C130.49": 'load_8_6',
"10_C228.66": 'load_9_7',
"11_C-138.89": 'load_10_8', "12_C-27.88": 'load_11_9',
"13_C-13.33": 'load_12_10'},
"lines": {'1_2_1': '0_1_0', '1_5_2': '0_4_1', '9_10_16': '8_9_2',
'9_14_17': '8_13_3',
'10_11_18': '9_10_4', '12_13_19': '11_12_5', '13_14_20': '12_13_6',
'2_3_3': '1_2_7', '2_4_4': '1_3_8', '2_5_5': '1_4_9',
'3_4_6': '2_3_10',
'4_5_7': '3_4_11', '6_11_11': '5_10_12', '6_12_12': '5_11_13',
'6_13_13': '5_12_14', '4_7_8': '3_6_15', '4_9_9': '3_8_16',
'5_6_10': '4_5_17',
'7_8_14': '6_7_18', '7_9_15': '6_8_19'},
"prods": {"1_G137.1": 'gen_0_4', "3_G36.31": "gen_2_1", "6_G63.29": "gen_5_2",
"2_G-56.47": "gen_1_0", "8_G40.43": "gen_7_3"},
}
# _parameters for the environment
self.env_params = Parameters()
self.env = Environment(init_grid_path=os.path.join(self.path_matpower, self.case_file),
backend=self.backend,
chronics_handler=self.chronics_handler,
parameters=self.env_params,
names_chronics_to_backend=self.names_chronics_to_backend,
actionClass=Action)
self.array_double_dispatch = np.array([0., 12.41833569, 10.89081339, 0., -23.30914908])
def test_check_validity(self):
# load a "fake" chronics with name in the correct order
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.pathfake)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
backend = PandaPowerBackend()
path_matpower = PATH_DATA_TEST_PP
case_file = "test_case14.json"
backend.load_grid(path_matpower, case_file)
chron_handl.check_validity(backend)
def test_done_chunk_size(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path, chunk_size=1)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
for i in range(288):
_, res, *_ = chron_handl.next_time_step() # should load the first time stamp
vect = np.array([19.0, 87.9, 44.4, 7.2, 10.4, 27.5, 8.4, 3.2, 5.7, 12.2, 13.6])
vect = vect[self.id_chron_to_back_load]
assert self.compare_vect(res["injection"]['load_p'], vect)
assert chron_handl.done()
def test_stopiteration(self):
chron_handl = ChronicsHandler(chronicsClass=GridStateFromFile, path=self.path)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs)
for i in range(288):
_, res = chron_handl.next_time_step() # should load the first time stamp
vect = [19.0, 87.9, 44.4, 7.2, 10.4, 27.5, 8.4, 3.2, 5.7, 12.2, 13.6]
assert self.compare_vect(res["injection"]['load_p'], vect)
try:
res = chron_handl.next_time_step() # should load the first time stamp
raise RuntimeError("This should have thrown a StopIteration exception")
except StopIteration:
pass
def test_stopiteration(self):
chron_handl = ChronicsHandler(chronicsClass=Multifolder,
path=self.path,
gridvalueClass=GridStateFromFileWithForecasts,
max_iter=self.max_iter)
chron_handl.initialize(self.order_backend_loads, self.order_backend_prods,
self.order_backend_lines, self.order_backend_subs,
self.names_chronics_to_backend)
_, res, *_ = chron_handl.next_time_step() # should load the first time stamp
for i in range(self.max_iter):
_, res, *_ = chron_handl.next_time_step() # should load the first time stamp
try:
_, res, *_ = chron_handl.next_time_step() # should load the first time stamp
raise RuntimeError("This should have thrown a StopIteration exception")
except StopIteration:
pass
def setUp(self):
"""
The case file is a representation of the case14 as found in the ieee14 powergrid.
:return:
"""
self.tolvect = 1e-2
self.tol_one = 1e-5
self.game_rules = GameRules()
# pdb.set_trace()
self.rewardClass = L2RPNReward
self.reward_helper = self.rewardClass()
self.obsClass = CompleteObservation
self.parameters = Parameters()
# powergrid
self.backend = PandaPowerBackend()
self.path_matpower = PATH_DATA_TEST_PP
self.case_file = "test_case14.json"
# chronics
self.path_chron = os.path.join(PATH_CHRONICS, "chronics_with_forecast")
self.chronics_handler = ChronicsHandler(
chronicsClass=GridStateFromFileWithForecasts, path=self.path_chron)
self.tolvect = 1e-2
self.tol_one = 1e-5
self.id_chron_to_back_load = np.array(
[0, 1, 10, 2, 3, 4, 5, 6, 7, 8, 9])
# force the verbose backend
self.backend.detailed_infos_for_cascading_failures = True
self.names_chronics_to_backend = {
"loads": {
"2_C-10.61": 'load_1_0',
"3_C151.15": 'load_2_1',
"14_C63.6": 'load_13_2',
"4_C-9.47": 'load_3_3',
"5_C201.84": 'load_4_4',
"6_C-6.27": 'load_5_5',
"9_C130.49": 'load_8_6',
"10_C228.66": 'load_9_7',
"11_C-138.89": 'load_10_8',
"12_C-27.88": 'load_11_9',
"13_C-13.33": 'load_12_10'
},
"lines": {
'1_2_1': '0_1_0',
'1_5_2': '0_4_1',
'9_10_16': '8_9_2',
'9_14_17': '8_13_3',
'10_11_18': '9_10_4',
'12_13_19': '11_12_5',
'13_14_20': '12_13_6',
'2_3_3': '1_2_7',
'2_4_4': '1_3_8',
'2_5_5': '1_4_9',
'3_4_6': '2_3_10',
'4_5_7': '3_4_11',
'6_11_11': '5_10_12',
'6_12_12': '5_11_13',
'6_13_13': '5_12_14',
'4_7_8': '3_6_15',
'4_9_9': '3_8_16',
'5_6_10': '4_5_17',
'7_8_14': '6_7_18',
'7_9_15': '6_8_19'
},
"prods": {
"1_G137.1": 'gen_0_4',
"3_G36.31": "gen_2_1",
"6_G63.29": "gen_5_2",
"2_G-56.47": "gen_1_0",
"8_G40.43": "gen_7_3"
},
}
# _parameters for the environment
self.env_params = Parameters()
self.env = Environment(
init_grid_path=os.path.join(self.path_matpower, self.case_file),
backend=self.backend,
chronics_handler=self.chronics_handler,
parameters=self.env_params,
names_chronics_to_backend=self.names_chronics_to_backend,
rewardClass=self.rewardClass)
self.dict_ = {
'name_gen':
['gen_1_0', 'gen_2_1', 'gen_5_2', 'gen_7_3', 'gen_0_4'],
'name_load': [
'load_1_0', 'load_2_1', 'load_13_2', 'load_3_3', 'load_4_4',
'load_5_5', 'load_8_6', 'load_9_7', 'load_10_8', 'load_11_9',
'load_12_10'
],
'name_line': [
'0_1_0', '0_4_1', '8_9_2', '8_13_3', '9_10_4', '11_12_5',
'12_13_6', '1_2_7', '1_3_8', '1_4_9', '2_3_10', '3_4_11',
'5_10_12', '5_11_13', '5_12_14', '3_6_15', '3_8_16', '4_5_17',
'6_7_18', '6_8_19'
],
'name_sub': [
'sub_0', 'sub_1', 'sub_10', 'sub_11', 'sub_12', 'sub_13',
'sub_2', 'sub_3', 'sub_4', 'sub_5', 'sub_6', 'sub_7', 'sub_8',
'sub_9'
],
'sub_info': [3, 6, 4, 6, 5, 6, 3, 2, 5, 3, 3, 3, 4, 3],
'load_to_subid': [1, 2, 13, 3, 4, 5, 8, 9, 10, 11, 12],
'gen_to_subid': [1, 2, 5, 7, 0],
'line_or_to_subid':
[0, 0, 8, 8, 9, 11, 12, 1, 1, 1, 2, 3, 5, 5, 5, 3, 3, 4, 6, 6],
'line_ex_to_subid': [
1, 4, 9, 13, 10, 12, 13, 2, 3, 4, 3, 4, 10, 11, 12, 6, 8, 5, 7,
8
],
'load_to_sub_pos': [5, 3, 2, 5, 4, 5, 4, 2, 2, 2, 3],
'gen_to_sub_pos': [4, 2, 4, 1, 2],
'line_or_to_sub_pos':
[0, 1, 0, 1, 1, 0, 1, 1, 2, 3, 1, 2, 0, 1, 2, 3, 4, 3, 1, 2],
'line_ex_to_sub_pos':
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 2, 1, 1, 2, 0, 2, 3, 0, 3],
'load_pos_topo_vect': [8, 12, 55, 18, 23, 29, 39, 42, 45, 48, 52],
'gen_pos_topo_vect': [7, 11, 28, 34, 2],
'line_or_pos_topo_vect': [
0, 1, 35, 36, 41, 46, 50, 4, 5, 6, 10, 15, 24, 25, 26, 16, 17,
22, 31, 32
],
'line_ex_pos_topo_vect': [
3, 19, 40, 53, 43, 49, 54, 9, 13, 20, 14, 21, 44, 47, 51, 30,
37, 27, 33, 38
],
'subtype':
'Observation.CompleteObservation'
}
self.dtypes = np.array([
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('float64'),
dtype('bool'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64'),
dtype('int64')
],
dtype=object)
self.shapes = np.array([
1, 1, 1, 1, 1, 1, 5, 5, 5, 11, 11, 11, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 56, 20, 20, 14, 20, 20, 20
])
class TestLoadingBackendPandaPower(unittest.TestCase):
def setUp(self):
# powergrid
self.backend = PandaPowerBackend()
self.path_matpower = PATH_DATA_TEST_PP
self.case_file = "test_case14.json"
# chronics
self.path_chron = os.path.join(PATH_CHRONICS, "chronics")
self.chronics_handler = ChronicsHandler(
chronicsClass=GridStateFromFile, path=self.path_chron)
self.tolvect = 1e-2
self.tol_one = 1e-5
self.id_chron_to_back_load = np.array(
[0, 1, 10, 2, 3, 4, 5, 6, 7, 8, 9])
# force the verbose backend
self.backend.detailed_infos_for_cascading_failures = True
self.names_chronics_to_backend = {
"loads": {
"2_C-10.61": 'load_1_0',
"3_C151.15": 'load_2_1',
"14_C63.6": 'load_13_2',
"4_C-9.47": 'load_3_3',
"5_C201.84": 'load_4_4',
"6_C-6.27": 'load_5_5',
"9_C130.49": 'load_8_6',
"10_C228.66": 'load_9_7',
"11_C-138.89": 'load_10_8',
"12_C-27.88": 'load_11_9',
"13_C-13.33": 'load_12_10'
},
"lines": {
'1_2_1': '0_1_0',
'1_5_2': '0_4_1',
'9_10_16': '8_9_2',
'9_14_17': '8_13_3',
'10_11_18': '9_10_4',
'12_13_19': '11_12_5',
'13_14_20': '12_13_6',
'2_3_3': '1_2_7',
'2_4_4': '1_3_8',
'2_5_5': '1_4_9',
'3_4_6': '2_3_10',
'4_5_7': '3_4_11',
'6_11_11': '5_10_12',
'6_12_12': '5_11_13',
'6_13_13': '5_12_14',
'4_7_8': '3_6_15',
'4_9_9': '3_8_16',
'5_6_10': '4_5_17',
'7_8_14': '6_7_18',
'7_9_15': '6_8_19'
},
"prods": {
"1_G137.1": 'gen_0_4',
"3_G36.31": "gen_2_1",
"6_G63.29": "gen_5_2",
"2_G-56.47": "gen_1_0",
"8_G40.43": "gen_7_3"
},
}
# _parameters for the environment
self.env_params = Parameters()
self.env = Environment(
init_grid_path=os.path.join(self.path_matpower, self.case_file),
backend=self.backend,
chronics_handler=self.chronics_handler,
parameters=self.env_params,
names_chronics_to_backend=self.names_chronics_to_backend)
def tearDown(self):
pass
def compare_vect(self, pred, true):
return np.max(np.abs(pred - true)) <= self.tolvect
def test_step_doesnt_change_action(self):
act = self.env.action_space()
act_init = copy.deepcopy(act)
res = self.env.step(act)
assert act == act_init
def test_load_env(self):
"""
Just executes the SetUp and tearDown functions.
:return:
"""
if DEBUG:
if PROFILE_CODE:
cp = cProfile.Profile()
cp.enable()
import pandapower as pp
nb_powerflow = 5000
beg_ = time.time()
for i in range(nb_powerflow):
pp.runpp(self.backend._grid)
end_ = time.time()
print("Time to compute {} powerflows: {:.2f}".format(
nb_powerflow, end_ - beg_))
if PROFILE_CODE:
cp.disable()
cp.print_stats(sort="tottime")
pass
def test_proper_injection_at_first(self):
injs_act, *_ = self.env.backend.loads_info()
# below: row as found in the file
vect = np.array(
[18.8, 86.5, 44.5, 7.1, 10.4, 27.6, 8.1, 3.2, 5.6, 11.9, 13.6])
# now it's in the "backend" order (ie properly reordered)
vect = vect[self.id_chron_to_back_load]
# and now i make sure everything is working as intentended
assert self.compare_vect(injs_act, vect)
def test_proper_voltage_modification(self):
do_nothing = self.env.helper_action_player({})
obs, reward, done, info = self.env.step(
do_nothing) # should load the first time stamp
vect = np.array([143.9, 139.1, 0.2, 13.3, 146.])
assert self.compare_vect(
obs.prod_v, vect
), "Production voltages setpoint have not changed at first time step"
obs, reward, done, info = self.env.step(
do_nothing) # should load the first time stamp
vect = np.array([145.3, 140.4, 0.2, 13.5, 147.4])
assert self.compare_vect(
obs.prod_v, vect
), "Production voltages setpoint have not changed at second time step"
def test_number_of_timesteps(self):
for i in range(287):
do_nothing = self.env.helper_action_player({})
obs, reward, done, info = self.env.step(
do_nothing) # should load the first time stamp
injs_act, *_ = self.env.backend.loads_info()
vect = np.array(
[19.0, 87.9, 44.4, 7.2, 10.4, 27.5, 8.4, 3.2, 5.7, 12.2, 13.6])
vect = vect[self.id_chron_to_back_load]
assert self.compare_vect(injs_act, vect)
def test_stop_right_time(self):
done = False
i = 0
while not done:
do_nothing = self.env.helper_action_player({})
obs, reward, done, info = self.env.step(
do_nothing) # should load the first time stamp
i += 1
assert i == 287
def test_reward(self):
done = False
i = 0
self.chronics_handler.next_chronics()
self.env = Environment(
init_grid_path=os.path.join(self.path_matpower, self.case_file),
backend=self.backend,
chronics_handler=self.chronics_handler,
parameters=self.env_params,
rewardClass=L2RPNReward,
names_chronics_to_backend=self.names_chronics_to_backend)
if PROFILE_CODE:
cp = cProfile.Profile()
cp.enable()
beg_ = time.time()
cum_reward = 0
while not done:
do_nothing = self.env.helper_action_player({})
obs, reward, done, info = self.env.step(
do_nothing) # should load the first time stamp
cum_reward += reward
i += 1
end_ = time.time()
if DEBUG:
msg_ = "\nEnv: {:.2f}s\n\t - apply act {:.2f}s\n\t - run pf: {:.2f}s\n\t - env update + observation: {:.2f}s\nTotal time: {:.2f}\nCumulative reward: {:1f}"
print(
msg_.format(
self.env._time_apply_act + self.env._time_powerflow +
self.env._time_extract_obs, self.env._time_apply_act,
self.env._time_powerflow, self.env._time_extract_obs,
end_ - beg_, cum_reward))
if PROFILE_CODE:
cp.disable()
cp.print_stats(sort="tottime")
assert i == 287, "Wrong number of timesteps"
assert np.abs(cum_reward - 5739.92911) <= self.tol_one, "Wrong reward"
def __init__(
self,
init_grid_path:
str, # full path where grid state is located, eg "./data/test_Pandapower/case14.json"
path_chron, # path where chronics of injections are stored
parameters_path=None,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AllwaysLegal,
envClass=Environment,
gridStateclass=GridStateFromFile, #type of chronics to use. For example GridStateFromFile if forecasts are not used, or GridStateFromFileWithForecasts otherwise
backendClass=PandaPowerBackend,
agentClass=DoNothingAgent, #class used to build the agent
agentInstance=None,
verbose=False,
gridStateclass_kwargs={},
):
"""
Initialize the Runner.
Parameters
----------
init_grid_path: ``str``
Madantory, used to initialize :attr:`Runner.init_grid_path`.
path_chron: ``str``
Madantory where to look for chronics data, used to initialize :attr:`Runner.path_chron`.
parameters_path: ``str``, optional
Used to initialize :attr:`Runner.parameters_path`.
names_chronics_to_backend: ``dict``, optional
Used to initialize :attr:`Runner.names_chronics_to_backend`.
actionClass: ``type``, optional
Used to initialize :attr:`Runner.actionClass`.
observationClass: ``type``, optional
Used to initialize :attr:`Runner.observationClass`.
rewardClass: ``type``, optional
Used to initialize :attr:`Runner.rewardClass`. Default to :class:`grid2op.ConstantReward` that
*should not** be used to train or evaluate an agent, but rather as debugging purpose.
legalActClass: ``type``, optional
Used to initialize :attr:`Runner.legalActClass`.
envClass: ``type``, optional
Used to initialize :attr:`Runner.envClass`.
gridStateclass: ``type``, optional
Used to initialize :attr:`Runner.gridStateclass`.
backendClass: ``type``, optional
Used to initialize :attr:`Runner.backendClass`.
agentClass: ``type``, optional
Used to initialize :attr:`Runner.agentClass`.
agentInstance: :class:`grid2op.Agent.Agent`
Used to initialize the agent. Note that either :attr:`agentClass` or :attr:`agentInstance` is used
at the same time. If both ot them are ``None`` or both of them are "not ``None``" it throw an error.
verbose: ``bool``, optional
Used to initialize :attr:`Runner.verbose`.
"""
if not isinstance(envClass, type):
raise Grid2OpException(
"Parameter \"envClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(envClass)))
if not issubclass(envClass, Environment):
raise RuntimeError(
"Impossible to create a runner without an evnrionment derived from grid2op.Environement"
" class. Please modify \"envClass\" paramter.")
self.envClass = envClass
if not isinstance(actionClass, type):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(actionClass)))
if not issubclass(actionClass, Action):
raise RuntimeError(
"Impossible to create a runner without an action class derived from grid2op.Action. "
"Please modify \"actionClass\" paramter.")
self.actionClass = actionClass
if not isinstance(observationClass, type):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(observationClass)))
if not issubclass(observationClass, Observation):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
"grid2op.Observation. Please modify \"observationClass\" paramter."
)
self.observationClass = observationClass
if not isinstance(rewardClass, type):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(rewardClass)))
if not issubclass(rewardClass, Reward):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
"grid2op.Reward. Please modify \"rewardClass\" paramter.")
self.rewardClass = rewardClass
if not isinstance(gridStateclass, type):
raise Grid2OpException(
"Parameter \"gridStateclass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(gridStateclass)))
if not issubclass(gridStateclass, GridValue):
raise RuntimeError(
"Impossible to create a runner without an chronics class derived from "
"grid2op.GridValue. Please modify \"gridStateclass\" paramter."
)
self.gridStateclass = gridStateclass
if not isinstance(legalActClass, type):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(legalActClass)))
if not issubclass(legalActClass, LegalAction):
raise RuntimeError(
"Impossible to create a runner without a class defining legal actions derived "
"from grid2op.LegalAction. Please modify \"legalActClass\" paramter."
)
self.legalActClass = legalActClass
if not isinstance(backendClass, type):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(backendClass)))
if not issubclass(backendClass, Backend):
raise RuntimeError(
"Impossible to create a runner without a backend class derived from grid2op.GridValue. "
"Please modify \"backendClass\" paramter.")
self.backendClass = backendClass
if agentClass is not None:
if agentInstance is not None:
raise RuntimeError(
"Impossible to build the backend. Only one of AgentClass or agentInstance can be "
"used (both are not None).")
if not isinstance(agentClass, type):
raise Grid2OpException(
"Parameter \"agentClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(agentClass)))
if not issubclass(agentClass, Agent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from grid2op.Agent. "
"Please modify \"agentClass\" parameter.")
self.agentClass = agentClass
self._useclass = True
self.agent = None
elif agentInstance is not None:
if not isinstance(agentInstance, Agent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from grid2op.Agent. "
"Please modify \"agentInstance\" parameter.")
self.agentClass = None
self._useclass = False
self.agent = agentInstance
else:
raise RuntimeError(
"Impossible to build the backend. Either AgentClass or agentInstance must be provided "
"and both are None.")
self.logger = ConsoleLog(
DoNothingLog.INFO if verbose else DoNothingLog.ERROR)
# store _parameters
self.init_grid_path = init_grid_path
self.names_chronics_to_backend = names_chronics_to_backend
# game _parameters
self.parameters_path = parameters_path
self.parameters = Parameters(parameters_path)
# chronics of grid state
self.path_chron = path_chron
self.gridStateclass_kwargs = gridStateclass_kwargs
self.chronics_handler = ChronicsHandler(
chronicsClass=self.gridStateclass,
path=self.path_chron,
**self.gridStateclass_kwargs)
# the backend, used to compute powerflows
self.backend = self.backendClass()
# build the environment
self.env = None
# miscellaneous
self.verbose = verbose
class Runner(object):
"""
A runner is a utilitary tool that allows to create environment, and run simulations more easily.
This specific class as for main purpose to evaluate the performance of a trained :class:`grid2op.Agent`, rather
than to train it. Of course, it is possible to adapt it for a specific training mechanisms. Examples of such
will be made available in the future.
Attributes
----------
envClass: ``type``
The type of the environment used for the game. The class should be given, and **not** an instance (object) of
this class. The default is the :class:`grid2op.Environment`. If modified, it should derived from this class.
actionClass: ``type``
The type of action that can be performed by the agent / bot / controler. The class should be given, and
**not** an instance of this class. This type
should derived from :class:`grid2op.Action`. The default is :class:`grid2op.TopologyAction`.
observationClass: ``type``
This type represents the class that will be used to build the :class:`grid2op.Observation` visible by the
:class:`grid2op.Agent`. As :attr:`Runner.actionClass`, this should be a type, and **not** and instance (object)
of this type. This type should derived from :class:`grid2op.Observation`. The default is
:class:`grid2op.CompleteObservation`.
rewardClass: ``type``
Representes the type used to build the rewards that are given to the :class:`Agent`. As
:attr:`Runner.actionClass`, this should be a type, and **not** and instance (object) of this type.
This type should derived from :class:`grid2op.Reward`. The default is :class:`grid2op.ConstantReward` that
**should not** be used to train or evaluate an agent, but rather as debugging purpose.
gridStateclass: ``type``
This types control the mechanisms to read chronics and assign data to the powergrid. Like every "\.*Class"
attributes the type should be pass and not an intance (object) of this type. Its default is
:class:`grid2op.GridStateFromFile` and it must be a subclass of :class:`grid2op.GridValue`.
legalActClass: ``type``
This types control the mechanisms to assess if an :class:`grid2op.Action` is legal.
Like every "\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.AllwaysLegal` and it must be a subclass of :class:`grid2op.LegalAction`.
backendClass: ``type``
This types control the backend, *eg.* the software that computes the powerflows.
Like every "\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.PandaPowerBackend` and it must be a subclass of :class:`grid2op.Backend`.
agentClass: ``type``
This types control the type of Agent, *eg.* the bot / controler that will take :class:`grid2op.Action` and
avoid cascading failures.
Like every "\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.DoNothingAgent` and it must be a subclass of :class:`grid2op.Agent`.
logger:
A object than can be used to log information, either in a text file, or by printing them to the command prompt.
init_grid_path: ``str``
This attributes store the path where the powergrid data are located. If a relative path is given, it will be
extended as an absolute path.
names_chronics_to_backend: ``dict``
See description of :func:`grid2op.ChronicsHelper.initialize` for more information about this dictionnary
parameters_path: ``str``, optional
Where to look for the :class:`grid2op.Environment` :class:`grid2op.Parameters`. It defaults to ``None`` which
corresponds to using default values.
parameters: :class:`grid2op.Parameters`
Type of _parameters used. This is an instance (object) of type :class:`grid2op.Parameters` initialized from
:attr:`Runner.parameters_path`
path_chron: ``str``
Path indicatng where to look for temporal data.
chronics_handler: :class:`grid2op.ChronicsHandler`
Initialized from :attr:`Runner.gridStateclass` and :attr:`Runner.path_chron` it represents the input data used
to generate grid state by the :attr:`Runner.env`
backend: :class:`grid2op.Backend`
Used to compute the powerflow. This object has the type given by :attr:`Runner.backendClass`
env: :class:`grid2op.Environment`
Represents the environment which the agent / bot / control must control through action. It is initialized from
the :attr:`Runner.envClass`
agent: :class:`grid2op.Agent`
Represents the agent / bot / controler that takes action performed on a environment (the powergrid) to maximize
a certain reward.
verbose: ``bool``
If ``True`` then detailed output of each steps are written.
gridStateclass_kwargs: ``dict``
Additional keyword arguments used to build the :attr:`Runner.chronics_handler`
"""
def __init__(
self,
init_grid_path:
str, # full path where grid state is located, eg "./data/test_Pandapower/case14.json"
path_chron, # path where chronics of injections are stored
parameters_path=None,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AllwaysLegal,
envClass=Environment,
gridStateclass=GridStateFromFile, #type of chronics to use. For example GridStateFromFile if forecasts are not used, or GridStateFromFileWithForecasts otherwise
backendClass=PandaPowerBackend,
agentClass=DoNothingAgent, #class used to build the agent
agentInstance=None,
verbose=False,
gridStateclass_kwargs={},
):
"""
Initialize the Runner.
Parameters
----------
init_grid_path: ``str``
Madantory, used to initialize :attr:`Runner.init_grid_path`.
path_chron: ``str``
Madantory where to look for chronics data, used to initialize :attr:`Runner.path_chron`.
parameters_path: ``str``, optional
Used to initialize :attr:`Runner.parameters_path`.
names_chronics_to_backend: ``dict``, optional
Used to initialize :attr:`Runner.names_chronics_to_backend`.
actionClass: ``type``, optional
Used to initialize :attr:`Runner.actionClass`.
observationClass: ``type``, optional
Used to initialize :attr:`Runner.observationClass`.
rewardClass: ``type``, optional
Used to initialize :attr:`Runner.rewardClass`. Default to :class:`grid2op.ConstantReward` that
*should not** be used to train or evaluate an agent, but rather as debugging purpose.
legalActClass: ``type``, optional
Used to initialize :attr:`Runner.legalActClass`.
envClass: ``type``, optional
Used to initialize :attr:`Runner.envClass`.
gridStateclass: ``type``, optional
Used to initialize :attr:`Runner.gridStateclass`.
backendClass: ``type``, optional
Used to initialize :attr:`Runner.backendClass`.
agentClass: ``type``, optional
Used to initialize :attr:`Runner.agentClass`.
agentInstance: :class:`grid2op.Agent.Agent`
Used to initialize the agent. Note that either :attr:`agentClass` or :attr:`agentInstance` is used
at the same time. If both ot them are ``None`` or both of them are "not ``None``" it throw an error.
verbose: ``bool``, optional
Used to initialize :attr:`Runner.verbose`.
"""
if not isinstance(envClass, type):
raise Grid2OpException(
"Parameter \"envClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(envClass)))
if not issubclass(envClass, Environment):
raise RuntimeError(
"Impossible to create a runner without an evnrionment derived from grid2op.Environement"
" class. Please modify \"envClass\" paramter.")
self.envClass = envClass
if not isinstance(actionClass, type):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(actionClass)))
if not issubclass(actionClass, Action):
raise RuntimeError(
"Impossible to create a runner without an action class derived from grid2op.Action. "
"Please modify \"actionClass\" paramter.")
self.actionClass = actionClass
if not isinstance(observationClass, type):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(observationClass)))
if not issubclass(observationClass, Observation):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
"grid2op.Observation. Please modify \"observationClass\" paramter."
)
self.observationClass = observationClass
if not isinstance(rewardClass, type):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(rewardClass)))
if not issubclass(rewardClass, Reward):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
"grid2op.Reward. Please modify \"rewardClass\" paramter.")
self.rewardClass = rewardClass
if not isinstance(gridStateclass, type):
raise Grid2OpException(
"Parameter \"gridStateclass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(gridStateclass)))
if not issubclass(gridStateclass, GridValue):
raise RuntimeError(
"Impossible to create a runner without an chronics class derived from "
"grid2op.GridValue. Please modify \"gridStateclass\" paramter."
)
self.gridStateclass = gridStateclass
if not isinstance(legalActClass, type):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(legalActClass)))
if not issubclass(legalActClass, LegalAction):
raise RuntimeError(
"Impossible to create a runner without a class defining legal actions derived "
"from grid2op.LegalAction. Please modify \"legalActClass\" paramter."
)
self.legalActClass = legalActClass
if not isinstance(backendClass, type):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(backendClass)))
if not issubclass(backendClass, Backend):
raise RuntimeError(
"Impossible to create a runner without a backend class derived from grid2op.GridValue. "
"Please modify \"backendClass\" paramter.")
self.backendClass = backendClass
if agentClass is not None:
if agentInstance is not None:
raise RuntimeError(
"Impossible to build the backend. Only one of AgentClass or agentInstance can be "
"used (both are not None).")
if not isinstance(agentClass, type):
raise Grid2OpException(
"Parameter \"agentClass\" used to build the Runner should be a type (a class) and not an object "
"(an instance of a class). It is currently \"{}\"".format(
type(agentClass)))
if not issubclass(agentClass, Agent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from grid2op.Agent. "
"Please modify \"agentClass\" parameter.")
self.agentClass = agentClass
self._useclass = True
self.agent = None
elif agentInstance is not None:
if not isinstance(agentInstance, Agent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from grid2op.Agent. "
"Please modify \"agentInstance\" parameter.")
self.agentClass = None
self._useclass = False
self.agent = agentInstance
else:
raise RuntimeError(
"Impossible to build the backend. Either AgentClass or agentInstance must be provided "
"and both are None.")
self.logger = ConsoleLog(
DoNothingLog.INFO if verbose else DoNothingLog.ERROR)
# store _parameters
self.init_grid_path = init_grid_path
self.names_chronics_to_backend = names_chronics_to_backend
# game _parameters
self.parameters_path = parameters_path
self.parameters = Parameters(parameters_path)
# chronics of grid state
self.path_chron = path_chron
self.gridStateclass_kwargs = gridStateclass_kwargs
self.chronics_handler = ChronicsHandler(
chronicsClass=self.gridStateclass,
path=self.path_chron,
**self.gridStateclass_kwargs)
# the backend, used to compute powerflows
self.backend = self.backendClass()
# build the environment
self.env = None
# miscellaneous
self.verbose = verbose
def _new_env(self, chronics_handler, backend, parameters):
res = self.envClass(
init_grid_path=self.init_grid_path,
chronics_handler=chronics_handler,
backend=backend,
parameters=parameters,
names_chronics_to_backend=self.names_chronics_to_backend,
actionClass=self.actionClass,
observationClass=self.observationClass,
rewardClass=self.rewardClass,
legalActClass=self.legalActClass)
if self._useclass:
agent = self.agentClass(res.helper_action_player)
else:
agent = self.agent
return res, agent
def init_env(self):
"""
Function used to initialized the environment and the agent.
It is called by :func:`Runner.reset`.
Returns
-------
``None``
"""
self.env, self.agent = self._new_env(self.chronics_handler,
self.backend, self.parameters)
def reset(self):
"""
Used to reset an environment. This method is called at the beginning of each new episode.
If the environment is not initialized, then it initializes it with :func:`Runner.make_env`.
Returns
-------
``None``
"""
if self.env is None:
self.init_env()
else:
self.env.reset()
def run_one_episode(self, indx=0, path_save=None):
"""
Function used to run one episode of the :attr:`Runner.agent` and see how it performs in the :attr:`Runner.env`.
Parameters
----------
indx: ``int``
The number of episode previously run
path_save: ``str``, optional
Path where to save the data. See the description of :mod:`grid2op.Runner` for the structure of the saved
file.
Returns
-------
cum_reward: ``float``
The cumulative reward obtained by the agent during this episode
time_step: ``int``
The number of timesteps that have been played before the end of the episode (because of a "game over" or
because there were no more data)
"""
self.reset()
return self._run_one_episode(self.env, self.agent, self.logger, indx,
path_save)
@staticmethod
def _run_one_episode(env, agent, logger, indx, path_save=None):
done = False
time_step = int(0)
dict_ = {}
time_act = 0.
cum_reward = 0.
# reset the environment
env.chronics_handler.tell_id(indx - 1)
# the "-1" above is because the environment will be reset. So it will increase id of 1.
obs = env.reset()
if path_save is not None:
path_save = os.path.abspath(path_save)
if not os.path.exists(path_save):
os.mkdir(path_save)
logger.info("Creating path \"{}\" to save the runner".format(
path_save))
if not os.path.exists(
os.path.join(path_save, "dict_action_space.json")):
dict_action_space = env.action_space.to_dict()
with open(os.path.join(path_save, "dict_action_space.json"),
"w",
encoding='utf8') as f:
json.dump(obj=dict_action_space,
fp=f,
indent=4,
sort_keys=True)
if not os.path.exists(
os.path.join(path_save, "dict_observation_space.json")):
dict_observation_space = env.observation_space.to_dict()
with open(os.path.join(path_save,
"dict_observation_space.json"),
"w",
encoding='utf8') as f:
json.dump(obj=dict_observation_space,
fp=f,
indent=4,
sort_keys=True)
if not os.path.exists(
os.path.join(path_save,
"dict_env_modification_space.json")):
dict_action_space = env.helper_action_env.to_dict()
with open(os.path.join(path_save,
"dict_env_modification_space.json"),
"w",
encoding='utf8') as f:
json.dump(obj=dict_action_space,
fp=f,
indent=4,
sort_keys=True)
this_path = os.path.join(
path_save,
"{}".format(os.path.split(env.chronics_handler.get_id())[-1]))
if not os.path.exists(this_path):
os.mkdir(this_path)
logger.info(
"Creating path \"{}\" to save the episode {}".format(
this_path, indx))
else:
this_path = None
if path_save is not None:
dict_ = {}
dict_["chronics_path"] = "{}".format(env.chronics_handler.get_id())
dict_["chronics_max_timestep"] = "{}".format(
env.chronics_handler.max_timestep())
dict_["grid_path"] = "{}".format(env.init_grid_path)
dict_["backend_type"] = "{}".format(type(env.backend).__name__)
dict_["env_type"] = "{}".format(type(env).__name__)
with open(os.path.join(this_path, "_parameters.json"), "w") as f:
dict_params = env.parameters.to_dict()
json.dump(obj=dict_params, fp=f, indent=4, sort_keys=True)
# compute the size and everything if it needs to be stored
nb_timestep_max = env.chronics_handler.max_timestep()
efficient_storing = nb_timestep_max > 0
nb_timestep_max = max(nb_timestep_max, 0)
if path_save is None:
# i don't store anything on drive, so i don't need to store anything on memory
nb_timestep_max = 0
times = np.full(nb_timestep_max, fill_value=np.NaN, dtype=np.float)
rewards = np.full(nb_timestep_max, fill_value=np.NaN, dtype=np.float)
actions = np.full((nb_timestep_max, env.action_space.n),
fill_value=np.NaN,
dtype=np.float)
env_actions = np.full((nb_timestep_max, env.helper_action_env.n),
fill_value=np.NaN,
dtype=np.float)
observations = np.full((nb_timestep_max + 1, env.observation_space.n),
fill_value=np.NaN,
dtype=np.float)
disc_lines = np.full((nb_timestep_max, env.backend.n_line),
fill_value=np.NaN,
dtype=np.bool)
disc_lines_templ = np.full((1, env.backend.n_line),
fill_value=False,
dtype=np.bool)
beg_ = time.time()
reward = env.reward_range[0]
done = False
if path_save is not None:
# store observation at timestep 0
if efficient_storing:
observations[time_step, :] = obs.to_vect()
else:
observations = np.concatenate((observations, obs.to_vect()))
while not done:
beg__ = time.time()
act = agent.act(obs, reward, done)
end__ = time.time()
time_act += end__ - beg__
obs, reward, done, info = env.step(
act) # should load the first time stamp
cum_reward += reward
time_step += 1
# save the results
if path_save is not None:
env_act = env.env_modification
if efficient_storing:
# efficient way of writing
times[time_step - 1] = end__ - beg__
rewards[time_step - 1] = reward
actions[time_step - 1, :] = act.to_vect()
env_actions[time_step - 1, :] = env_act.to_vect()
observations[time_step, :] = obs.to_vect()
if "disc_lines" in info:
arr = info["disc_lines"]
if arr is not None:
disc_lines[time_step - 1, :] = arr
else:
disc_lines[time_step - 1, :] = disc_lines_templ
else:
# completely inefficient way of writing
times = np.concatenate((times, (end__ - beg__, )))
rewards = np.concatenate((rewards, (reward, )))
actions = np.concatenate((actions, act.to_vect()))
env_actions = np.concatenate((actions, env_act.to_vect()))
observations = np.concatenate(
(observations, obs.to_vect()))
if "disc_lines" in info:
arr = info["disc_lines"]
if arr is not None:
disc_lines = np.concatenate((disc_lines, arr))
else:
disc_lines = np.concatenate(
(disc_lines, disc_lines_templ))
if path_save is not None:
dict_["nb_timestep_played"] = time_step
dict_["cumulative_reward"] = cum_reward
end_ = time.time()
if path_save is not None:
with open(os.path.join(this_path, "episode_meta.json"), "w") as f:
json.dump(obj=dict_, fp=f, indent=4, sort_keys=True)
np.save(os.path.join(this_path, "agent_exec_times.npy"), times)
np.save(os.path.join(this_path, "actions.npy"), actions)
np.save(os.path.join(this_path, "env_modifications.npy"),
env_actions)
np.save(os.path.join(this_path, "observations.npy"), observations)
np.save(
os.path.join(this_path, "disc_lines_cascading_failure.npy"),
disc_lines)
np.save(os.path.join(this_path, "rewards.npy"), rewards)
li_text = [
"Env: {:.2f}s", "\t - apply act {:.2f}s", "\t - run pf: {:.2f}s",
"\t - env update + observation: {:.2f}s", "Agent: {:.2f}s",
"Total time: {:.2f}s", "Cumulative reward: {:1f}"
]
msg_ = "\n".join(li_text)
logger.info(
msg_.format(
env._time_apply_act + env._time_powerflow +
env._time_extract_obs, env._time_apply_act,
env._time_powerflow, env._time_extract_obs, time_act,
end_ - beg_, cum_reward))
if path_save is not None:
with open(os.path.join(this_path, "episode_times.json"), "w") as f:
dict_ = {}
dict_["Env"] = {}
dict_["Env"]["total"] = float(env._time_apply_act +
env._time_powerflow +
env._time_extract_obs)
dict_["Env"]["apply_act"] = float(env._time_apply_act)
dict_["Env"]["powerflow_computation"] = float(
env._time_powerflow)
dict_["Env"]["observation_computation"] = float(
env._time_extract_obs)
dict_["Agent"] = {}
dict_["Agent"]["total"] = float(time_act)
dict_["total"] = float(end_ - beg_)
json.dump(obj=dict_, fp=f, indent=4, sort_keys=True)
return cum_reward, int(time_step)
def run_sequential(self, nb_episode, path_save=None):
"""
This method is called to see how well an agent performed on a sequence of episode.
Parameters
----------
nb_episode: ``int``
Number of episode to play.
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
Returns
-------
res: ``list``
List of tuple. Each tuple having 3 elements:
- "i" unique identifier of the episode
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.Agent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
- "max_ts" : the maximum number of time steps of the chronics
"""
res = [(None, None, None) for _ in range(nb_episode)]
for i in range(nb_episode):
cum_reward, nb_time_step = self.run_one_episode(
path_save=path_save, indx=i)
id_chron = self.chronics_handler.get_id()
max_ts = self.chronics_handler.max_timestep()
res[i] = (id_chron, cum_reward, nb_time_step, max_ts)
return res
@staticmethod
def _one_process_parrallel(runner,
episode_this_process,
process_id,
path_save=None):
chronics_handler = ChronicsHandler(chronicsClass=runner.gridStateclass,
path=runner.path_chron,
**runner.gridStateclass_kwargs)
parameters = copy.deepcopy(runner.parameters)
backend = runner.backendClass()
nb_episode_this_process = len(episode_this_process)
res = [(None, None, None) for _ in range(nb_episode_this_process)]
for i, p_id in enumerate(episode_this_process):
env, agent = runner._new_env(chronics_handler=chronics_handler,
backend=backend,
parameters=parameters)
cum_reward, nb_time_step = Runner._run_one_episode(
env, agent, runner.logger, p_id, path_save)
id_chron = chronics_handler.get_id()
max_ts = chronics_handler.max_timestep()
res[i] = (id_chron, cum_reward, nb_time_step, max_ts)
return res
def run_parrallel(self, nb_episode, nb_process=1, path_save=None):
"""
This method will run in parrallel, independantly the nb_episode over nb_process.
Note that it restarts completely the :attr:`Runner.backend` and :attr:`Runner.env` if the computation
is actually performed with more than 1 cores (nb_process > 1)
It uses the python multiprocess, and especially the :class:`multiprocess.Pool` to perform the computations.
This implies that all runs are completely independant (they happen in different process) and that the
memory consumption can be big. Tests may be recommended if the amount of RAM is low.
It has the same return type as the :func:`Runner.run_sequential`.
Parameters
----------
nb_episode: ``int``
Number of episode to simulate
nb_process: ``int``, optional
Number of process used to play the nb_episode. Default to 1.
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
Returns
-------
res: ``list``
List of tuple. Each tuple having 3 elements:
- "i" unique identifier of the episode (compared to :func:`Runner.run_sequential`, the elements of the
returned list are not necessarily sorted by this value)
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.Agent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
- "max_ts" : the maximum number of time steps of the chronics
"""
if nb_process <= 0:
raise RuntimeError(
"Runner: you need at least 1 process to run episodes")
if nb_process == 1:
warnings.warn(
"Runner.run_parrallel: number of process set to 1. Failing back into sequential mod."
)
return [self.run_sequential(nb_episode, path_save=path_save)]
else:
if self.env is not None:
self.env.close()
self.env = None
self.backend = self.backendClass()
nb_process = int(nb_process)
process_ids = [[] for i in range(nb_process)]
for i in range(nb_episode):
process_ids[i % nb_process].append(i)
res = []
with Pool(nb_process) as p:
tmp = p.starmap(Runner._one_process_parrallel,
[(self, pn, i, path_save)
for i, pn in enumerate(process_ids)])
for el in tmp:
res += el
return res
def run(self, nb_episode, nb_process=1, path_save=None):
"""
Main method of the :class:`Runner` class. It will either call :func:`Runner.run_sequential` if "nb_process" is
1 or :func:`Runner.run_parrallel` if nb_process >= 2.
Parameters
----------
nb_episode: ``int``
Number of episode to simulate
nb_process: ``int``, optional
Number of process used to play the nb_episode. Default to 1.
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
Returns
-------
res: ``list``
List of tuple. Each tuple having 3 elements:
- "i" unique identifier of the episode (compared to :func:`Runner.run_sequential`, the elements of the
returned list are not necessarily sorted by this value)
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.Agent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
"""
if nb_episode < 0:
raise RuntimeError(
"Impossible to run a negative number of scenarios.")
if nb_episode == 0:
res = []
else:
if nb_process <= 0:
raise RuntimeError(
"Impossible to run using less than 1 process.")
if nb_process == 1:
self.logger.info("Sequential runner used.")
res = self.run_sequential(nb_episode, path_save=path_save)
else:
self.logger.info("Parrallel runner used.")
res = self.run_parrallel(nb_episode,
nb_process=nb_process,
path_save=path_save)
return res