def from_disk(cls, agent_path, name=str(1)):
if agent_path is None:
# TODO: proper exception
raise Grid2OpException("A path to an episode should be provided")
episode_path = os.path.abspath(os.path.join(agent_path, name))
try:
with open(os.path.join(episode_path, EpisodeData.PARAMS)) as f:
_parameters = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.META)) as f:
episode_meta = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.TIMES)) as f:
episode_times = json.load(fp=f)
with open(os.path.join(episode_path,
EpisodeData.OTHER_REWARDS)) as f:
other_rewards = json.load(fp=f)
times = np.load(
os.path.join(episode_path, EpisodeData.AG_EXEC_TIMES))["data"]
actions = np.load(os.path.join(episode_path,
EpisodeData.ACTIONS))["data"]
env_actions = np.load(
os.path.join(episode_path, EpisodeData.ENV_ACTIONS))["data"]
observations = np.load(
os.path.join(episode_path, EpisodeData.OBSERVATIONS))["data"]
disc_lines = np.load(
os.path.join(episode_path, EpisodeData.LINES_FAILURES))["data"]
rewards = np.load(os.path.join(episode_path,
EpisodeData.REWARDS))["data"]
except FileNotFoundError as ex:
raise Grid2OpException(f"EpisodeData file not found \n {str(ex)}")
observation_space = ObservationSpace.from_dict(
os.path.join(agent_path, EpisodeData.OBS_SPACE))
action_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ACTION_SPACE))
helper_action_env = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ENV_MODIF_SPACE))
return cls(actions,
env_actions,
observations,
rewards,
disc_lines,
times,
_parameters,
episode_meta,
episode_times,
observation_space,
action_space,
helper_action_env,
agent_path,
name=name,
get_dataframes=True,
other_rewards=other_rewards)
def test_json_loadable(self):
dict_ = self.helper_action.to_dict()
tmp = json.dumps(obj=dict_, indent=4, sort_keys=True)
res = ActionSpace.from_dict(json.loads(tmp))
assert np.all(res.name_gen == self.helper_action.name_gen)
assert np.all(res.name_load == self.helper_action.name_load)
assert np.all(res.name_line == self.helper_action.name_line)
assert np.all(res.sub_info == self.helper_action.sub_info)
assert np.all(res.load_to_subid == self.helper_action.load_to_subid)
assert np.all(res.gen_to_subid == self.helper_action.gen_to_subid)
assert np.all(
res.line_or_to_subid == self.helper_action.line_or_to_subid)
assert np.all(
res.line_ex_to_subid == self.helper_action.line_ex_to_subid)
assert np.all(
res.load_to_sub_pos == self.helper_action.load_to_sub_pos)
assert np.all(res.gen_to_sub_pos == self.helper_action.gen_to_sub_pos)
assert np.all(
res.line_or_to_sub_pos == self.helper_action.line_or_to_sub_pos)
assert np.all(
res.line_ex_to_sub_pos == self.helper_action.line_ex_to_sub_pos)
assert np.all(
res.load_pos_topo_vect == self.helper_action.load_pos_topo_vect)
assert np.all(
res.gen_pos_topo_vect == self.helper_action.gen_pos_topo_vect)
assert np.all(res.line_or_pos_topo_vect ==
self.helper_action.line_or_pos_topo_vect)
assert np.all(res.line_ex_pos_topo_vect ==
self.helper_action.line_ex_pos_topo_vect)
assert np.all(res.actionClass == self.helper_action.actionClass)
def test_from_dict(self):
res = ActionSpace.from_dict(self.res)
assert np.all(res.name_gen == self.helper_action.name_gen)
assert np.all(res.name_load == self.helper_action.name_load)
assert np.all(res.name_line == self.helper_action.name_line)
assert np.all(res.sub_info == self.helper_action.sub_info)
assert np.all(res.load_to_subid == self.helper_action.load_to_subid)
assert np.all(res.gen_to_subid == self.helper_action.gen_to_subid)
assert np.all(
res.line_or_to_subid == self.helper_action.line_or_to_subid)
assert np.all(
res.line_ex_to_subid == self.helper_action.line_ex_to_subid)
assert np.all(
res.load_to_sub_pos == self.helper_action.load_to_sub_pos)
assert np.all(res.gen_to_sub_pos == self.helper_action.gen_to_sub_pos)
assert np.all(
res.line_or_to_sub_pos == self.helper_action.line_or_to_sub_pos)
assert np.all(
res.line_ex_to_sub_pos == self.helper_action.line_ex_to_sub_pos)
assert np.all(
res.load_pos_topo_vect == self.helper_action.load_pos_topo_vect)
assert np.all(
res.gen_pos_topo_vect == self.helper_action.gen_pos_topo_vect)
assert np.all(res.line_or_pos_topo_vect ==
self.helper_action.line_or_pos_topo_vect)
assert np.all(res.line_ex_pos_topo_vect ==
self.helper_action.line_ex_pos_topo_vect)
# pdb.set_trace()
assert np.all(res.actionClass == self.helper_action.actionClass)
def from_disk(cls, agent_path, name="1"):
"""
This function allows you to reload an episode stored using the runner.
See the example at the definition of the class for more information on how to use it.
Parameters
----------
agent_path: ``str``
Path pass at the "runner.run" method
name: ``str``
The name of the episode you want to reload.
Returns
-------
res:
The data loaded properly in memory.
"""
if agent_path is None:
raise Grid2OpException(
"A path to an episode should be provided, please call \"from_disk\" with "
"\"agent_path other\" than None")
episode_path = os.path.abspath(os.path.join(agent_path, name))
try:
with open(os.path.join(episode_path, EpisodeData.PARAMS)) as f:
_parameters = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.META)) as f:
episode_meta = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.TIMES)) as f:
episode_times = json.load(fp=f)
with open(os.path.join(episode_path,
EpisodeData.OTHER_REWARDS)) as f:
other_rewards = json.load(fp=f)
times = np.load(
os.path.join(episode_path, EpisodeData.AG_EXEC_TIMES))["data"]
actions = np.load(os.path.join(episode_path,
EpisodeData.ACTIONS))["data"]
env_actions = np.load(
os.path.join(episode_path, EpisodeData.ENV_ACTIONS))["data"]
observations = np.load(
os.path.join(episode_path, EpisodeData.OBSERVATIONS))["data"]
disc_lines = np.load(
os.path.join(episode_path, EpisodeData.LINES_FAILURES))["data"]
attack = np.load(os.path.join(episode_path,
EpisodeData.ATTACK))["data"]
rewards = np.load(os.path.join(episode_path,
EpisodeData.REWARDS))["data"]
except FileNotFoundError as ex:
raise Grid2OpException(f"EpisodeData file not found \n {str(ex)}")
observation_space = ObservationSpace.from_dict(
os.path.join(agent_path, EpisodeData.OBS_SPACE))
action_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ACTION_SPACE))
helper_action_env = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ENV_MODIF_SPACE))
attack_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ATTACK_SPACE))
return cls(
actions=actions,
env_actions=env_actions,
observations=observations,
rewards=rewards,
disc_lines=disc_lines,
times=times,
params=_parameters,
meta=episode_meta,
episode_times=episode_times,
observation_space=observation_space,
action_space=action_space,
helper_action_env=helper_action_env,
path_save=None, # No save when reading
attack=attack,
attack_space=attack_space,
name=name,
get_dataframes=True,
other_rewards=other_rewards)
def from_disk(cls, agent_path, name=str(1)):
if agent_path is None:
raise Grid2OpException(
"A path to an episode should be provided, please call \"from_disk\" with "
"\"agent_path other\" than None")
episode_path = os.path.abspath(os.path.join(agent_path, name))
try:
with open(os.path.join(episode_path, EpisodeData.PARAMS)) as f:
_parameters = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.META)) as f:
episode_meta = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.TIMES)) as f:
episode_times = json.load(fp=f)
with open(os.path.join(episode_path,
EpisodeData.OTHER_REWARDS)) as f:
other_rewards = json.load(fp=f)
times = np.load(
os.path.join(episode_path, EpisodeData.AG_EXEC_TIMES))["data"]
actions = np.load(os.path.join(episode_path,
EpisodeData.ACTIONS))["data"]
env_actions = np.load(
os.path.join(episode_path, EpisodeData.ENV_ACTIONS))["data"]
observations = np.load(
os.path.join(episode_path, EpisodeData.OBSERVATIONS))["data"]
disc_lines = np.load(
os.path.join(episode_path, EpisodeData.LINES_FAILURES))["data"]
attack = np.load(os.path.join(episode_path,
EpisodeData.ATTACK))["data"]
rewards = np.load(os.path.join(episode_path,
EpisodeData.REWARDS))["data"]
except FileNotFoundError as ex:
raise Grid2OpException(f"EpisodeData file not found \n {str(ex)}")
observation_space = ObservationSpace.from_dict(
os.path.join(agent_path, EpisodeData.OBS_SPACE))
action_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ACTION_SPACE))
helper_action_env = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ENV_MODIF_SPACE))
attack_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ATTACK_SPACE))
return cls(
actions,
env_actions=env_actions,
observations=observations,
rewards=rewards,
disc_lines=disc_lines,
times=times,
params=_parameters,
meta=episode_meta,
episode_times=episode_times,
observation_space=observation_space,
action_space=action_space,
helper_action_env=helper_action_env,
path_save=None, # No save when reading
attack=attack,
attack_space=attack_space,
name=name,
get_dataframes=True,
other_rewards=other_rewards)
def from_disk(cls, agent_path, name="1"):
"""
This function allows you to reload an episode stored using the runner.
See the example at the definition of the class for more information on how to use it.
Parameters
----------
agent_path: ``str``
Path pass at the "runner.run" method
name: ``str``
The name of the episode you want to reload.
Returns
-------
res:
The data loaded properly in memory.
"""
if agent_path is None:
raise Grid2OpException(
"A path to an episode should be provided, please call \"from_disk\" with "
"\"agent_path other\" than None")
episode_path = os.path.abspath(os.path.join(agent_path, name))
try:
with open(os.path.join(episode_path, EpisodeData.PARAMS)) as f:
_parameters = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.META)) as f:
episode_meta = json.load(fp=f)
with open(os.path.join(episode_path, EpisodeData.TIMES)) as f:
episode_times = json.load(fp=f)
with open(os.path.join(episode_path,
EpisodeData.OTHER_REWARDS)) as f:
other_rewards = json.load(fp=f)
times = np.load(
os.path.join(episode_path, EpisodeData.AG_EXEC_TIMES))["data"]
actions = np.load(os.path.join(episode_path,
EpisodeData.ACTIONS))["data"]
env_actions = np.load(
os.path.join(episode_path, EpisodeData.ENV_ACTIONS))["data"]
observations = np.load(
os.path.join(episode_path, EpisodeData.OBSERVATIONS))["data"]
disc_lines = np.load(
os.path.join(episode_path, EpisodeData.LINES_FAILURES))["data"]
attack = np.load(os.path.join(episode_path,
EpisodeData.ATTACK))["data"]
rewards = np.load(os.path.join(episode_path,
EpisodeData.REWARDS))["data"]
except FileNotFoundError as ex:
raise Grid2OpException(f"EpisodeData file not found \n {str(ex)}")
observation_space = ObservationSpace.from_dict(
os.path.join(agent_path, EpisodeData.OBS_SPACE))
action_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ACTION_SPACE))
helper_action_env = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ENV_MODIF_SPACE))
attack_space = ActionSpace.from_dict(
os.path.join(agent_path, EpisodeData.ATTACK_SPACE))
if observation_space.glop_version != grid2op.__version__:
warnings.warn(
"You are using a \"grid2op compatibility\" feature (the data you saved "
"have been saved with a previous grid2op version). When we loaded your data, we attempted "
"to not include most recent grid2op features. This is feature is not well tested. It would "
"be wise to regenerate the data with the latest grid2Op version."
)
return cls(
actions=actions,
env_actions=env_actions,
observations=observations,
rewards=rewards,
disc_lines=disc_lines,
times=times,
params=_parameters,
meta=episode_meta,
episode_times=episode_times,
observation_space=observation_space,
action_space=action_space,
helper_action_env=helper_action_env,
path_save=None, # No save when reading
attack=attack,
attack_space=attack_space,
name=name,
get_dataframes=True,
other_rewards=other_rewards,
_init_collections=True)