Source code for grid2op.Environment.Environment

# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
import os
import copy
import warnings
import numpy as np
import re

import grid2op
from grid2op.Opponent.OpponentSpace import OpponentSpace
from grid2op.dtypes import dt_float, dt_bool, dt_int
from grid2op.Action import (
    ActionSpace,
    BaseAction,
    TopologyAction,
    DontAct,
    CompleteAction,
)
from grid2op.Exceptions import *
from grid2op.Observation import CompleteObservation, ObservationSpace, BaseObservation
from grid2op.Reward import FlatReward, RewardHelper, BaseReward
from grid2op.Rules import RulesChecker, AlwaysLegal, BaseRules
from grid2op.Backend import Backend
from grid2op.Chronics import ChronicsHandler
from grid2op.VoltageControler import ControlVoltageFromFile, BaseVoltageController
from grid2op.Environment.BaseEnv import BaseEnv
from grid2op.Opponent import BaseOpponent, NeverAttackBudget
from grid2op.operator_attention import LinearAttentionBudget

from grid2op.Backend import PandaPowerBackend


[docs]class Environment(BaseEnv): """ This class is the grid2op implementation of the "Environment" entity in the RL framework. Attributes ---------- name: ``str`` The name of the environment action_space: :class:`grid2op.Action.ActionSpace` Another name for :attr:`Environment.helper_action_player` for gym compatibility. observation_space: :class:`grid2op.Observation.ObservationSpace` Another name for :attr:`Environment.helper_observation` for gym compatibility. reward_range: ``(float, float)`` The range of the reward function metadata: ``dict`` For gym compatibility, do not use spec: ``None`` For Gym compatibility, do not use viewer: ``object`` Used to display the powergrid. Currently not supported. """ REGEX_SPLIT = r"^[a-zA-Z0-9]*$" def __init__( self, init_env_path: str, init_grid_path: str, chronics_handler, backend, parameters, name="unknown", names_chronics_to_backend=None, actionClass=TopologyAction, observationClass=CompleteObservation, rewardClass=FlatReward, legalActClass=AlwaysLegal, voltagecontrolerClass=ControlVoltageFromFile, other_rewards={}, thermal_limit_a=None, with_forecast=True, epsilon_poly=1e-4, # precision of the redispatching algorithm we don't recommend to go above 1e-4 tol_poly=1e-2, # i need to compute a redispatching if the actual values are "more than tol_poly" the values they should be opponent_space_type=OpponentSpace, opponent_action_class=DontAct, opponent_class=BaseOpponent, opponent_init_budget=0.0, opponent_budget_per_ts=0.0, opponent_budget_class=NeverAttackBudget, opponent_attack_duration=0, opponent_attack_cooldown=99999, kwargs_opponent={}, attention_budget_cls=LinearAttentionBudget, kwargs_attention_budget={}, has_attention_budget=False, logger=None, kwargs_observation=None, _raw_backend_class=None, _compat_glop_version=None, _read_from_local_dir=True, # TODO runner and all here ! _is_test=False, ): BaseEnv.__init__( self, init_env_path=init_env_path, init_grid_path=init_grid_path, parameters=parameters, thermal_limit_a=thermal_limit_a, epsilon_poly=epsilon_poly, tol_poly=tol_poly, other_rewards=other_rewards, with_forecast=with_forecast, voltagecontrolerClass=voltagecontrolerClass, opponent_space_type=opponent_space_type, opponent_action_class=opponent_action_class, opponent_class=opponent_class, opponent_budget_class=opponent_budget_class, opponent_init_budget=opponent_init_budget, opponent_budget_per_ts=opponent_budget_per_ts, opponent_attack_duration=opponent_attack_duration, opponent_attack_cooldown=opponent_attack_cooldown, kwargs_opponent=kwargs_opponent, has_attention_budget=has_attention_budget, attention_budget_cls=attention_budget_cls, kwargs_attention_budget=kwargs_attention_budget, logger=logger.getChild("grid2op_Environment") if logger is not None else None, kwargs_observation=kwargs_observation, _is_test=_is_test, # is this created with "test=True" # TODO not implemented !! ) if name == "unknown": warnings.warn( 'It is NOT recommended to create an environment without "make" and EVEN LESS ' "to use an environment without a name..." ) self.name = name self._read_from_local_dir = _read_from_local_dir # for gym compatibility (initialized below) # self.action_space = None # self.observation_space = None self.reward_range = None self.viewer = None self.metadata = None self.spec = None if _raw_backend_class is None: self._raw_backend_class = type(backend) else: self._raw_backend_class = _raw_backend_class self._compat_glop_version = _compat_glop_version # for plotting self._init_backend( chronics_handler, backend, names_chronics_to_backend, actionClass, observationClass, rewardClass, legalActClass, ) self._actionClass_orig = actionClass self._observationClass_orig = observationClass def _init_backend( self, chronics_handler, backend, names_chronics_to_backend, actionClass, observationClass, rewardClass, legalActClass, ): """ INTERNAL .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\ Create a proper and valid environment. """ if isinstance(rewardClass, type): if not issubclass(rewardClass, BaseReward): raise Grid2OpException( 'Parameter "rewardClass" used to build the Environment should derived form ' 'the grid2op.BaseReward class, type provided is "{}"'.format( type(rewardClass) ) ) else: if not isinstance(rewardClass, BaseReward): raise Grid2OpException( 'Parameter "rewardClass" used to build the Environment should derived form ' 'the grid2op.BaseReward class, type provided is "{}"'.format( type(rewardClass) ) ) # backend if not isinstance(backend, Backend): raise Grid2OpException( 'Parameter "backend" used to build the Environment should derived form the ' 'grid2op.Backend class, type provided is "{}"'.format(type(backend)) ) self.backend = backend if self.backend.is_loaded: raise EnvError( "Impossible to use the same backend twice. Please create your environment with a " "new backend instance (new object)." ) if self._read_from_local_dir: # test to support pickle conveniently self.backend._PATH_ENV = self.get_path_env() # all the above should be done in this exact order, otherwise some weird behaviour might occur # this is due to the class attribute self.backend.set_env_name(self.name) self.backend.load_grid( self._init_grid_path ) # the real powergrid of the environment self.backend.load_redispacthing_data(self.get_path_env()) self.backend.load_storage_data(self.get_path_env()) exc_ = self.backend.load_grid_layout(self.get_path_env()) if exc_ is not None: warnings.warn( f"No layout have been found for you grid (or the layout provided was corrupted). You will " f'not be able to use the renderer, plot the grid etc. The error was "{exc_}"' ) self.backend.is_loaded = True # alarm set up self.load_alarm_data() # to force the initialization of the backend to the proper type self.backend.assert_grid_correct() self._handle_compat_glop_version() self._has_been_initialized() # really important to include this piece of code! and just here after the # backend has loaded everything self._line_status = np.ones(shape=self.n_line, dtype=dt_bool) self._disc_lines = np.zeros(shape=self.n_line, dtype=dt_int) - 1 if self._thermal_limit_a is None: self._thermal_limit_a = self.backend.thermal_limit_a.astype(dt_float) else: self.backend.set_thermal_limit(self._thermal_limit_a.astype(dt_float)) *_, tmp = self.backend.generators_info() # rules of the game if not isinstance(legalActClass, type): raise Grid2OpException( 'Parameter "legalActClass" used to build the Environment 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, BaseRules): raise Grid2OpException( 'Parameter "legalActClass" used to build the Environment should derived form the ' 'grid2op.BaseRules class, type provided is "{}"'.format( type(legalActClass) ) ) self._game_rules = RulesChecker(legalActClass=legalActClass) self._legalActClass = legalActClass # action helper if not isinstance(actionClass, type): raise Grid2OpException( 'Parameter "actionClass" used to build the Environment should be a type (a class) ' "and not an object (an instance of a class). " 'It is currently "{}"'.format(type(legalActClass)) ) if not issubclass(actionClass, BaseAction): raise Grid2OpException( 'Parameter "actionClass" used to build the Environment should derived form the ' 'grid2op.BaseAction class, type provided is "{}"'.format( type(actionClass) ) ) if not isinstance(observationClass, type): raise Grid2OpException( f'Parameter "observationClass" used to build the Environment should be a type (a class) ' f"and not an object (an instance of a class). " f'It is currently : {observationClass} (type "{type(observationClass)}")' ) if not issubclass(observationClass, BaseObservation): raise Grid2OpException( f'Parameter "observationClass" used to build the Environment should derived form the ' f'grid2op.BaseObservation class, type provided is "{type(observationClass)}"' ) # action affecting the grid that will be made by the agent bk_type = type( self.backend ) # be careful here: you need to initialize from the class, and not from the object self._rewardClass = rewardClass self._actionClass = actionClass.init_grid(gridobj=bk_type) self._actionClass._add_shunt_data() self._actionClass._update_value_set() self._observationClass = observationClass.init_grid(gridobj=bk_type) self._complete_action_cls = CompleteAction.init_grid(gridobj=bk_type) self._helper_action_class = ActionSpace.init_grid(gridobj=bk_type) self._action_space = self._helper_action_class( gridobj=bk_type, actionClass=actionClass, legal_action=self._game_rules.legal_action, ) # action that affect the grid made by the environment. self._helper_action_env = self._helper_action_class( gridobj=bk_type, actionClass=CompleteAction, legal_action=self._game_rules.legal_action, ) # handles input data if not isinstance(chronics_handler, ChronicsHandler): raise Grid2OpException( 'Parameter "chronics_handler" used to build the Environment should derived form the ' 'grid2op.ChronicsHandler class, type provided is "{}"'.format( type(chronics_handler) ) ) self.chronics_handler = chronics_handler self.chronics_handler.initialize( self.name_load, self.name_gen, self.name_line, self.name_sub, names_chronics_to_backend=names_chronics_to_backend, ) self._names_chronics_to_backend = names_chronics_to_backend self.delta_time_seconds = dt_float(self.chronics_handler.time_interval.seconds) # this needs to be done after the chronics handler: rewards might need information # about the chronics to work properly. self._helper_observation_class = ObservationSpace.init_grid(gridobj=bk_type) # FYI: this try to copy the backend if it fails it will modify the backend # and the environment to force the deactivation of the # forecasts self._observation_space = self._helper_observation_class( gridobj=bk_type, observationClass=observationClass, actionClass=actionClass, rewardClass=rewardClass, env=self, kwargs_observation=self._kwargs_observation, ) # test to make sure the backend is consistent with the chronics generator self.chronics_handler.check_validity(self.backend) self._reset_storage() # this should be called after the self.delta_time_seconds is set # reward function self._reward_helper = RewardHelper(self._rewardClass, logger=self.logger) self._reward_helper.initialize(self) for k, v in self.other_rewards.items(): v.initialize(self) # controller for voltage if not issubclass(self._voltagecontrolerClass, BaseVoltageController): raise Grid2OpException( 'Parameter "voltagecontrolClass" should derive from "ControlVoltageFromFile".' ) self._voltage_controler = self._voltagecontrolerClass( gridobj=bk_type, controler_backend=self.backend, actionSpace_cls=self._helper_action_class, ) # create the opponent # At least the 3 following attributes should be set before calling _create_opponent self._create_opponent() # create the attention budget self._create_attention_budget() # performs one step to load the environment properly (first action need to be taken at first time step after # first injections given) self._reset_maintenance() self._reset_redispatching() do_nothing = self._helper_action_env({}) *_, fail_to_start, info = self.step(do_nothing) if fail_to_start: raise Grid2OpException( "Impossible to initialize the powergrid, the powerflow diverge at iteration 0. " "Available information are: {}".format(info) ) # test the backend returns object of the proper size self.backend.assert_grid_correct_after_powerflow() # for gym compatibility self.reward_range = self._reward_helper.range() self.viewer = None self.viewer_fig = None self.metadata = {"render.modes": []} self.spec = None self.current_reward = self.reward_range[0] self.done = False # reset everything to be consistent self._reset_vectors_and_timings()
[docs] def max_episode_duration(self): """ Return the maximum duration (in number of steps) of the current episode. Notes ----- For possibly infinite episode, the duration is returned as `np.iinfo(np.int32).max` which corresponds to the maximum 32 bit integer (usually `2147483647`) """ tmp = dt_int(self.chronics_handler.max_episode_duration()) if tmp < 0: tmp = dt_int(np.iinfo(dt_int).max) return tmp
[docs] def set_max_iter(self, max_iter): """ Parameters ---------- max_iter: ``int`` The maximum number of iteration you can do before reaching the end of the episode. Set it to "-1" for possibly infinite episode duration. Notes ------- Maximum length of the episode can depend on the chronics used. See :attr:`Environment.chronics_handler` for more information """ self.chronics_handler.set_max_iter(max_iter)
@property def _helper_observation(self): return self._observation_space @property def _helper_action_player(self): return self._action_space def _handle_compat_glop_version(self): if ( self._compat_glop_version is not None and self._compat_glop_version != grid2op.__version__ ): warnings.warn( 'You are using a grid2op "compatibility" environment. This means that some ' "feature will not be available. This feature is absolutely NOT recommended except to " "read back data (for example with EpisodeData) that were stored with previous " "grid2op version." ) self.backend.set_env_name(f"{self.name}_{self._compat_glop_version}") cls_bk = type(self.backend) cls_bk.glop_version = self._compat_glop_version if cls_bk.glop_version == cls_bk.BEFORE_COMPAT_VERSION: # oldest version: no storage and no curtailment available # deactivate storage # recompute the topology vector (more or less everything need to be adjusted... stor_locs = [pos for pos in cls_bk.storage_pos_topo_vect] for stor_loc in sorted(stor_locs, reverse=True): for vect in [ cls_bk.load_pos_topo_vect, cls_bk.gen_pos_topo_vect, cls_bk.line_or_pos_topo_vect, cls_bk.line_ex_pos_topo_vect, ]: vect[vect >= stor_loc] -= 1 # deals with the "sub_pos" vector for sub_id in range(cls_bk.n_sub): if np.any(cls_bk.storage_to_subid == sub_id): stor_ids = np.where(cls_bk.storage_to_subid == sub_id)[0] stor_locs = cls_bk.storage_to_sub_pos[stor_ids] for stor_loc in sorted(stor_locs, reverse=True): for vect, sub_id_me in zip( [ cls_bk.load_to_sub_pos, cls_bk.gen_to_sub_pos, cls_bk.line_or_to_sub_pos, cls_bk.line_ex_to_sub_pos, ], [ cls_bk.load_to_subid, cls_bk.gen_to_subid, cls_bk.line_or_to_subid, cls_bk.line_ex_to_subid, ], ): vect[(vect >= stor_loc) & (sub_id_me == sub_id)] -= 1 # remove storage from the number of element in the substation for sub_id in range(cls_bk.n_sub): cls_bk.sub_info[sub_id] -= np.sum(cls_bk.storage_to_subid == sub_id) # remove storage from the total number of element cls_bk.dim_topo -= cls_bk.n_storage # recompute this private member cls_bk._topo_vect_to_sub = np.repeat( np.arange(cls_bk.n_sub), repeats=cls_bk.sub_info ) self.backend._topo_vect_to_sub = np.repeat( np.arange(cls_bk.n_sub), repeats=cls_bk.sub_info ) new_grid_objects_types = cls_bk.grid_objects_types new_grid_objects_types = new_grid_objects_types[ new_grid_objects_types[:, cls_bk.STORAGE_COL] == -1, : ] cls_bk.grid_objects_types = 1 * new_grid_objects_types self.backend.grid_objects_types = 1 * new_grid_objects_types # erase all trace of storage units cls_bk.set_no_storage() Environment.deactivate_storage(self.backend) # the following line must be called BEFORE "self.backend.assert_grid_correct()" ! self.backend.storage_deact_for_backward_comaptibility() # and recomputes everything while making sure everything is consistent self.backend.assert_grid_correct() type(self.backend)._topo_vect_to_sub = np.repeat( np.arange(cls_bk.n_sub), repeats=cls_bk.sub_info ) type(self.backend).grid_objects_types = new_grid_objects_types def _voltage_control(self, agent_action, prod_v_chronics): """ INTERNAL .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\ Update the environment action "action_env" given a possibly new voltage setpoint for the generators. This function can be overide for a more complex handling of the voltages. It must update (if needed) the voltages of the environment action :attr:`BaseEnv.env_modification` Parameters ---------- agent_action: :class:`grid2op.Action.Action` The action performed by the player (or do nothing is player action were not legal or ambiguous) prod_v_chronics: ``numpy.ndarray`` or ``None`` The voltages that has been specified in the chronics """ volt_control_act = self._voltage_controler.fix_voltage( self.current_obs, agent_action, self._env_modification, prod_v_chronics ) return volt_control_act
[docs] def set_chunk_size(self, new_chunk_size): """ For an efficient data pipeline, it can be usefull to not read all part of the input data (for example for load_p, prod_p, load_q, prod_v). Grid2Op support the reading of large chronics by "chunk" of given size. Reading data in chunk can also reduce the memory footprint, useful in case of multiprocessing environment while large chronics. It is critical to set a small chunk_size in case of training machine learning algorithm (reinforcement learning agent) at the beginning when the agent performs poorly, the software might spend most of its time loading the data. **NB** this has no effect if the chronics does not support this feature. **NB** The environment need to be **reset** for this to take effect (it won't affect the chronics already loaded) Parameters ---------- new_chunk_size: ``int`` or ``None`` The new chunk size (positive integer) Examples --------- Here is an example on how to use this function .. code-block:: python import grid2op # I create an environment env = grid2op.make("rte_case5_example", test=True) env.set_chunk_size(100) env.reset() # otherwise chunk size has no effect ! # and now data will be read from the hard drive 100 time steps per 100 time steps # instead of the whole episode at once. """ if new_chunk_size is None: self.chronics_handler.set_chunk_size(new_chunk_size) return try: new_chunk_size = int(new_chunk_size) except Exception as exc_: raise Grid2OpException( "Impossible to set the chunk size. It should be convertible a integer, and not" '{}. The error was: \n"{}"'.format(new_chunk_size, exc_) ) if new_chunk_size <= 0: raise Grid2OpException( 'Impossible to read less than 1 data at a time. Please make sure "new_chunk_size"' "is a positive integer." ) self.chronics_handler.set_chunk_size(new_chunk_size)
[docs] def simulate(self, action): """ Another method to call `obs.simulate` to ensure compatibility between multi environment and regular one. Parameters ---------- action: A grid2op action Returns ------- Same return type as :func:`grid2op.Environment.BaseEnv.step` or :func:`grid2op.Observation.BaseObservation.simulate` Notes ----- Prefer using `obs.simulate` if possible, it will be faster than this function. """ return self.get_obs().simulate(action)
[docs] def set_id(self, id_): """ Set the id that will be used at the next call to :func:`Environment.reset`. **NB** this has no effect if the chronics does not support this feature. **NB** The environment need to be **reset** for this to take effect. Parameters ---------- id_: ``int`` the id of the chronics used. Examples -------- Here an example that will loop 10 times through the same chronics (always using the same injection then): .. code-block:: python import grid2op from grid2op import make from grid2op.BaseAgent import DoNothingAgent env = make("rte_case14_realistic") # create an environment agent = DoNothingAgent(env.action_space) # create an BaseAgent for i in range(10): env.set_id(0) # tell the environment you simply want to use the chronics with ID 0 obs = env.reset() # it is necessary to perform a reset reward = env.reward_range[0] done = False while not done: act = agent.act(obs, reward, done) obs, reward, done, info = env.step(act) And here you have an example on how you can loop through the scenarios in a given order: .. code-block:: python import grid2op from grid2op import make from grid2op.BaseAgent import DoNothingAgent env = make("rte_case14_realistic") # create an environment agent = DoNothingAgent(env.action_space) # create an BaseAgent scenario_order = [1,2,3,4,5,10,8,6,5,7,78, 8] for id_ in scenario_order: env.set_id(id_) # tell the environment you simply want to use the chronics with ID 0 obs = env.reset() # it is necessary to perform a reset reward = env.reward_range[0] done = False while not done: act = agent.act(obs, reward, done) obs, reward, done, info = env.step(act) """ if isinstance(id_, str): # new in grid2op 1.6.4 self.chronics_handler.tell_id(id_, previous=True) return try: id_ = int(id_) except Exception as exc_: raise EnvError( 'the "id_" parameters should be convertible to integer and not be of type {}' 'with error \n"{}"'.format(type(id_), exc_) ) self.chronics_handler.tell_id(id_ - 1)
[docs] def attach_renderer(self, graph_layout=None): """ This function will attach a renderer, necessary to use for plotting capabilities. Parameters ---------- graph_layout: ``dict`` Here for backward compatibility. Currently not used. If you want to set a specific layout call :func:`BaseEnv.attach_layout` If ``None`` this class will use the default substations layout provided when the environment was created. Otherwise it will use the data provided. Examples --------- Here is how to use the function .. code-block:: python import grid2op # create the environment env = grid2op.make() if False: # if you want to change the default layout of the powergrid # assign coordinates (0., 0.) to all substations (this is a dummy thing to do here!) layout = {sub_name: (0., 0.) for sub_name in env.name_sub} env.attach_layout(layout) # NB again, this code will make everything look super ugly !!!! Don't change the # default layout unless you have a reason to. # and if you want to use the renderer env.attach_renderer() # and now you can "render" (plot) the state of the grid obs = env.reset() done = False reward = env.reward_range[0] while not done: env.render() action = agent.act(obs, reward, done) obs, reward, done, info = env.step(action) """ # Viewer already exists: skip if self.viewer is not None: return # Do we have the dependency try: from grid2op.PlotGrid import PlotMatplot except ImportError: err_msg = ( "Cannot attach renderer: missing dependency\n" "Please install matplotlib or run pip install grid2op[optional]" ) raise Grid2OpException(err_msg) from None self.viewer = PlotMatplot(self._observation_space) self.viewer_fig = None # Set renderer modes self.metadata = {"render.modes": ["human", "silent"]}
def __str__(self): return "<{} instance named {}>".format(type(self).__name__, self.name) # TODO be closer to original gym implementation
[docs] def reset_grid(self): """ INTERNAL .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\ This is automatically called when using `env.reset` Reset the backend to a clean state by reloading the powergrid from the hard drive. This might takes some time. If the thermal has been modified, it also modify them into the new backend. """ self.backend.reset( self._init_grid_path ) # the real powergrid of the environment self.backend.assert_grid_correct() if self._thermal_limit_a is not None: self.backend.set_thermal_limit(self._thermal_limit_a.astype(dt_float)) self._backend_action = self._backend_action_class() do_nothing = self._helper_action_env({}) *_, fail_to_start, info = self.step(do_nothing) if fail_to_start: raise Grid2OpException( "Impossible to initialize the powergrid, the powerflow diverge at iteration 0. " "Available information are: {}".format(info) )
[docs] def add_text_logger(self, logger=None): """ Add a text logger to this :class:`Environment` Logging is for now an incomplete feature, really incomplete (not used) Parameters ---------- logger: The logger to use """ self.logger = logger return self
[docs] def reset(self): """ Reset the environment to a clean state. It will reload the next chronics if any. And reset the grid to a clean state. This triggers a full reloading of both the chronics (if they are stored as files) and of the powergrid, to ensure the episode is fully over. This method should be called only at the end of an episode. Examples -------- The standard "gym loop" can be done with the following code: .. code-block:: python import grid2op # create the environment env = grid2op.make() # and now you can "render" (plot) the state of the grid obs = env.reset() done = False reward = env.reward_range[0] while not done: action = agent.act(obs, reward, done) obs, reward, done, info = env.step(action) """ super().reset() self.chronics_handler.next_chronics() self.chronics_handler.initialize( self.backend.name_load, self.backend.name_gen, self.backend.name_line, self.backend.name_sub, names_chronics_to_backend=self._names_chronics_to_backend, ) self._env_modification = None self._reset_maintenance() self._reset_redispatching() self._reset_vectors_and_timings() # it need to be done BEFORE to prevent cascading failure when there has been self.reset_grid() if self.viewer_fig is not None: del self.viewer_fig self.viewer_fig = None # if True, then it will not disconnect lines above their thermal limits self._reset_vectors_and_timings() # and it needs to be done AFTER to have proper timings at tbe beginning # the attention budget is reset above # reset the opponent self._oppSpace.reset() # reset, if need, reward and other rewards self._reward_helper.reset(self) for extra_reward in self.other_rewards.values(): extra_reward.reset(self) # and reset also the "simulated env" in the observation space self._observation_space.reset(self) self._last_obs = None # force the first observation to be generated properly return self.get_obs()
[docs] def render(self, mode="human"): """ Render the state of the environment on the screen, using matplotlib Also returns the Matplotlib figure Examples -------- Rendering need first to define a "renderer" which can be done with the following code: .. code-block:: python import grid2op # create the environment env = grid2op.make() # if you want to use the renderer env.attach_renderer() # and now you can "render" (plot) the state of the grid obs = env.reset() done = False reward = env.reward_range[0] while not done: env.render() # this piece of code plot the grid action = agent.act(obs, reward, done) obs, reward, done, info = env.step(action) """ # Try to create a plotter instance # Does nothing if viewer exists # Raises if matplot is not installed self.attach_renderer() # Check mode is correct if mode not in self.metadata["render.modes"]: err_msg = 'Renderer mode "{}" not supported. Available modes are {}.' raise Grid2OpException(err_msg.format(mode, self.metadata["render.modes"])) # Render the current observation fig = self.viewer.plot_obs( self.current_obs, figure=self.viewer_fig, redraw=True ) # First time show for human mode if self.viewer_fig is None and mode == "human": fig.show() else: # Update the figure content fig.canvas.draw() # Store to re-use the figure self.viewer_fig = fig # Return the figure in case it needs to be saved/used return self.viewer_fig
def _custom_deepcopy_for_copy(self, new_obj): super()._custom_deepcopy_for_copy(new_obj) new_obj.name = self.name new_obj._read_from_local_dir = self._read_from_local_dir new_obj.metadata = copy.deepcopy(self.metadata) new_obj.spec = copy.deepcopy(self.spec) new_obj._raw_backend_class = self._raw_backend_class new_obj._compat_glop_version = self._compat_glop_version new_obj._actionClass_orig = self._actionClass_orig new_obj._observationClass_orig = self._observationClass_orig
[docs] def copy(self): """ Performs a deep copy of the environment Unless you have a reason to, it is not advised to make copy of an Environment. Examples -------- It should be used as follow: .. code-block:: python import grid2op env = grid2op.make() cpy_of_env = env.copy() """ # res = copy.deepcopy(self) # painfully slow... # create an empty "me" my_cls = type(self) res = my_cls.__new__(my_cls) # fill its attribute self._custom_deepcopy_for_copy(res) return res
[docs] def get_kwargs(self, with_backend=True): """ This function allows to make another Environment with the same parameters as the one that have been used to make this one. This is useful especially in cases where Environment is not pickable (for example if some non pickable c++ code are used) but you still want to make parallel processing using "MultiProcessing" module. In that case, you can send this dictionary to each child process, and have each child process make a copy of ``self`` **NB** This function should not be used to make a copy of an environment. Prefer using :func:`Environment.copy` for such purpose. Returns ------- res: ``dict`` A dictionary that helps build an environment like ``self`` (which is NOT a copy of self) but rather an instance of an environment with the same properties. Examples -------- It should be used as follow: .. code-block:: python import grid2op from grid2op.Environment import Environment env = grid2op.make() # create the environment of your choice copy_of_env = Environment(**env.get_kwargs()) # And you can use this one as you would any other environment. # NB this is not a "proper" copy. for example it will not be at the same step, it will be possible # seeded with a different seed. # use `env.copy()` to make a proper copy of an environment. """ res = {} res["init_env_path"] = self._init_env_path res["init_grid_path"] = self._init_grid_path res["chronics_handler"] = copy.deepcopy(self.chronics_handler) if with_backend: if not self.backend._can_be_copied: raise RuntimeError("Impossible to get the kwargs for this " "environment, the backend cannot be copied.") res["backend"] = self.backend.copy() res["backend"]._is_loaded = False # i can reload a copy of an environment res["parameters"] = copy.deepcopy(self._parameters) res["names_chronics_to_backend"] = copy.deepcopy( self._names_chronics_to_backend ) res["actionClass"] = self._actionClass_orig res["observationClass"] = self._observationClass_orig res["rewardClass"] = self._rewardClass res["legalActClass"] = self._legalActClass res["epsilon_poly"] = self._epsilon_poly res["tol_poly"] = self._tol_poly res["thermal_limit_a"] = self._thermal_limit_a res["voltagecontrolerClass"] = self._voltagecontrolerClass res["other_rewards"] = {k: v.rewardClass for k, v in self.other_rewards.items()} res["name"] = self.name res["_raw_backend_class"] = self._raw_backend_class res["with_forecast"] = self.with_forecast res["opponent_space_type"] = self._opponent_space_type res["opponent_action_class"] = self._opponent_action_class res["opponent_class"] = self._opponent_class res["opponent_init_budget"] = self._opponent_init_budget res["opponent_budget_per_ts"] = self._opponent_budget_per_ts res["opponent_budget_class"] = self._opponent_budget_class res["opponent_attack_duration"] = self._opponent_attack_duration res["opponent_attack_cooldown"] = self._opponent_attack_cooldown res["kwargs_opponent"] = self._kwargs_opponent res["attention_budget_cls"] = self._attention_budget_cls res["kwargs_attention_budget"] = copy.deepcopy(self._kwargs_attention_budget) res["has_attention_budget"] = self._has_attention_budget res["_read_from_local_dir"] = self._read_from_local_dir res["kwargs_observation"] = copy.deepcopy(self._kwargs_observation) res["logger"] = self.logger return res
def _chronics_folder_name(self): return "chronics"
[docs] def train_val_split( self, val_scen_id, add_for_train="train", add_for_val="val", add_for_test=None, test_scen_id=None, remove_from_name=None, deep_copy=False, ): """ This function is used as :func:`Environment.train_val_split_random`. Please refer to this the help of :func:`Environment.train_val_split_random` for more information about this function. Parameters ---------- val_scen_id: ``list`` List of the scenario names that will be placed in the validation set test_scen_id: ``list`` .. versionadded:: 2.6.5 List of the scenario names that will be placed in the test set (only used if add_for_test is not None - and mandatory in this case) add_for_train: ``str`` See :func:`Environment.train_val_split_random` for more information add_for_val: ``str`` See :func:`Environment.train_val_split_random` for more information add_for_test: ``str`` .. versionadded:: 2.6.5 See :func:`Environment.train_val_split_random` for more information remove_from_name: ``str`` See :func:`Environment.train_val_split_random` for more information deep_copy: ``bool`` .. versionadded:: 2.6.5 See :func:`Environment.train_val_split_random` for more information Returns ------- nm_train: ``str`` See :func:`Environment.train_val_split_random` for more information nm_val: ``str`` See :func:`Environment.train_val_split_random` for more information nm_test: ``str``, optionnal .. versionadded:: 2.6.5 See :func:`Environment.train_val_split_random` for more information Examples -------- A full example on a training / validation / test split with explicit specification of which chronics goes in which scenarios is: .. code-block:: python import grid2op import os env_name = "l2rpn_case14_sandbox" # or any other... env = grid2op.make(env_name) # retrieve the names of the chronics: full_path_data = env.chronics_handler.subpaths chron_names = [os.path.split(el)[-1] for el in full_path_data] # splitting into training / test, keeping the "last" 10 chronics to the test set nm_env_train, m_env_val, nm_env_test = env.train_val_split(test_scen_id=chron_names[-10:], # last 10 in test set add_for_test="test", val_scen_id=chron_names[-20:-10], # last 20 to last 10 in val test ) env_train = grid2op.make(env_name+"_train") env_val = grid2op.make(env_name+"_val") env_test = grid2op.make(env_name+"_test") For a more simple example, with less parametrization and with random assignment (recommended), please refer to the help of :func:`Environment.train_val_split_random` **NB** read the "Notes" of this section for possible "unexpected" behaviour of the code snippet above. On Some windows based platform, if you don't have an admin account nor a "developer" account (see https://docs.python.org/3/library/os.html#os.symlink) you might need to do: .. code-block:: python import grid2op import os env_name = "l2rpn_case14_sandbox" # or any other... env = grid2op.make(env_name) # retrieve the names of the chronics: full_path_data = env.chronics_handler.subpaths chron_names = [os.path.split(el)[-1] for el in full_path_data] # splitting into training / test, keeping the "last" 10 chronics to the test set nm_env_train, m_env_val, nm_env_test = env.train_val_split(test_scen_id=chron_names[-10:], # last 10 in test set add_for_test="test", val_scen_id=chron_names[-20:-10], # last 20 to last 10 in val test deep_copy=True) .. warning:: The above code will use much more memory on your hard drive than the version using symbolic links. It will also be significantly slower ! As an "historical curiosity", this is what you needed to do in grid2op version < 1.6.5: .. code-block:: python import grid2op import os env_name = "l2rpn_case14_sandbox" # or any other... env = grid2op.make(env_name) # retrieve the names of the chronics: full_path_data = env.chronics_handler.subpaths chron_names = [os.path.split(el)[-1] for el in full_path_data] # splitting into training / test, keeping the "last" 10 chronics to the test set nm_env_trainval, nm_env_test = env.train_val_split(val_scen_id=chron_names[-10:], add_for_val="test", add_for_train="trainval") # now splitting again the training set into training and validation, keeping the last 10 chronics # of this environment for validation env_trainval = grid2op.make(nm_env_trainval) # create the "trainval" environment full_path_data = env_trainval.chronics_handler.subpaths chron_names = [os.path.split(el)[-1] for el in full_path_data] nm_env_train, nm_env_val = env_trainval.train_val_split(val_scen_id=chron_names[-10:], remove_from_name="_trainval$") # and now you can use the following code to load the environments: env_train = grid2op.make(env_name+"_train") env_val = grid2op.make(env_name+"_val") env_test = grid2op.make(env_name+"_test") Notes ------ We don't recommend you to use this function. It provides a great level of control on which scenarios goes into which dataset, which is nice, but "*with great power comes great responsibilities*". Keep in mind that scenarios might be "sorted" by having some "month" in their names. For example, the first k scenarios might be called "April_XXX" and the last k ones having names with "September_XXX". In general, we would not consider good practice to have all validation (or test) scenarios coming from the same months. Keep that in mind if you use the code snippet above. """ # define all the locations if re.match(self.REGEX_SPLIT, add_for_train) is None: raise EnvError( f"The suffixes you can use for training data (add_for_train) " f'should match the regex "{self.REGEX_SPLIT}"' ) if re.match(self.REGEX_SPLIT, add_for_val) is None: raise EnvError( f"The suffixes you can use for validation data (add_for_val)" f'should match the regex "{self.REGEX_SPLIT}"' ) if add_for_test is not None: if re.match(self.REGEX_SPLIT, add_for_test) is None: raise EnvError( f"The suffixes you can use for test data (add_for_test)" f'should match the regex "{self.REGEX_SPLIT}"' ) if add_for_test is None and test_scen_id is not None: raise EnvError(f"add_for_test is None and test_scen_id is not None.") if add_for_test is not None and test_scen_id is None: raise EnvError(f"add_for_test is not None and test_scen_id is None.") from grid2op.Chronics import MultifolderWithCache, Multifolder if not isinstance( self.chronics_handler.real_data, (MultifolderWithCache, Multifolder) ): raise EnvError( "It does not make sense to split a environment between training / validation " "if the chronics are not read from directories." ) my_path = self.get_path_env() path_train = os.path.split(my_path) my_name = path_train[1] if remove_from_name is not None: if re.match(r"^[a-zA-Z0-9\\^\\$_]*$", remove_from_name) is None: raise EnvError( "The suffixes you can remove from the name of the environment (remove_from_name)" 'should match the regex "^[a-zA-Z0-9^$_]*$"' ) my_name = re.sub(remove_from_name, "", my_name) nm_train = f"{my_name}_{add_for_train}" path_train = os.path.join(path_train[0], nm_train) path_val = os.path.split(my_path) nm_val = f"{my_name}_{add_for_val}" path_val = os.path.join(path_val[0], nm_val) nm_test = None path_test = None if add_for_test is not None: path_test = os.path.split(my_path) nm_test = f"{my_name}_{add_for_test}" path_test = os.path.join(path_test[0], nm_test) chronics_dir = self._chronics_folder_name() # create the folder if os.path.exists(path_val): raise RuntimeError( f"Impossible to create the validation environment that should have the name " f'"{nm_val}" because an environment is already named this way. If you want to ' f'continue either delete the folder "{path_val}" or name your validation environment ' f"differently " f'using the "add_for_val" keyword argument of this function.' ) if os.path.exists(path_train): raise RuntimeError( f"Impossible to create the training environment that should have the name " f'"{nm_train}" because an environment is already named this way. If you want to ' f'continue either delete the folder "{path_train}" or name your training environment ' f" differently " f'using the "add_for_train" keyword argument of this function.' ) if nm_test is not None and os.path.exists(path_test): raise RuntimeError( f"Impossible to create the test environment that should have the name " f'"{nm_test}" because an environment is already named this way. If you want to ' f'continue either delete the folder "{path_test}" or name your test environment ' f" differently " f'using the "add_for_test" keyword argument of this function.' ) os.mkdir(path_val) os.mkdir(path_train) if nm_test is not None: os.mkdir(path_test) # assign which chronics goes where chronics_path = os.path.join(my_path, chronics_dir) all_chron = sorted(os.listdir(chronics_path)) to_val = set(val_scen_id) if nm_test is not None: to_test = set(test_scen_id) if deep_copy: import shutil copy_file_fun = shutil.copy2 copy_dir_fun = shutil.copytree else: copy_file_fun = os.symlink copy_dir_fun = os.symlink # "copy" the files for el in os.listdir(my_path): tmp_path = os.path.join(my_path, el) if os.path.isfile(tmp_path): # this is a regular env file copy_file_fun(tmp_path, os.path.join(path_train, el)) copy_file_fun(tmp_path, os.path.join(path_val, el)) if nm_test is not None: copy_file_fun(tmp_path, os.path.join(path_test, el)) elif os.path.isdir(tmp_path): if el == chronics_dir: # this is the chronics folder os.mkdir(os.path.join(path_train, chronics_dir)) os.mkdir(os.path.join(path_val, chronics_dir)) if nm_test is not None: os.mkdir(os.path.join(path_test, chronics_dir)) for chron_name in all_chron: tmp_path_chron = os.path.join(tmp_path, chron_name) if chron_name in to_val: copy_dir_fun( tmp_path_chron, os.path.join(path_val, chronics_dir, chron_name), ) elif chron_name in to_test: copy_dir_fun( tmp_path_chron, os.path.join(path_test, chronics_dir, chron_name), ) else: copy_dir_fun( tmp_path_chron, os.path.join(path_train, chronics_dir, chron_name), ) if add_for_test is None: res = nm_train, nm_val else: res = nm_train, nm_val, nm_test return res
[docs] def train_val_split_random( self, pct_val=10.0, add_for_train="train", add_for_val="val", add_for_test=None, pct_test=None, remove_from_name=None, deep_copy=False, ): """ By default a grid2op environment contains multiple "scenarios" containing values for all the producers and consumers representing multiple days. In a "game like" environment, you can think of the scenarios as being different "game levels": different mazes in pacman, different levels in mario etc. We recommend to train your agent on some of these "chroncis" (aka levels) and test the performance of your agent on some others, to avoid overfitting. This function allows to easily split an environment into different part. This is most commonly used in machine learning where part of a dataset is used for training and another part is used for assessing the performance of the trained model. This function rely on "symbolic link" and will not duplicate data. New created environments will behave like regular grid2op environment and will be accessible with "make" just like any others (see the examples section for more information). This function will make the split at random. If you want more control on the which scenarios to use for training and which for validation, use the :func:`Environment.train_val_split` that allows to specify which scenarios goes in the validation environment (and the others go in the training environment). Parameters ---------- pct_val: ``float`` Percentage of chronics that will go to the validation set. For 10% of the chronics, set it to 10. and NOT to 0.1. add_for_train: ``str`` Suffix that will be added to the name of the environment for the training set. We don't recommend to modify the default value ("train") add_for_val: ``str`` Suffix that will be added to the name of the environment for the validation set. We don't recommend to modify the default value ("val") add_for_test: ``str``, (optional) .. versionadded:: 2.6.5 Suffix that will be added to the name of the environment for the test set. By default, it only splits into training and validation, so this is ignored. We recommend to assign it to "test" if you want to split into training / validation and test. If it is set, then the `pct_test` must also be set. pct_test: ``float``, (optional) .. versionadded:: 2.6.5 Percentage of chronics that will go to the test set. For 10% of the chronics, set it to 10. and NOT to 0.1. (If you set it, you need to set the `add_for_test` argument.) remove_from_name: ``str`` If you "split" an environment multiple times, this allows you to keep "short" names (for example you will be able to call `grid2op.make(env_name+"_train")` instead of `grid2op.make(env_name+"_train_train")`) deep_copy: ``bool`` .. versionadded:: 2.6.5 A function to specify to "copy" the elements of the original environment to the created one. By default it will save as much memory as possible using symbolic links (rather than performing copies). By default it does use symbolic links (`deep_copy=False`). .. note:: If set to ``True`` the new environment will take much more space on the hard drive, and the execution of this function will be much slower ! .. warning:: On windows based system, you will most likely run into issues if you don't set this parameters. Indeed, Windows does not link symbolink links (https://docs.python.org/3/library/os.html#os.symlink). In this case, you can use the ``deep_copy=True`` and it will work fine (examples in the function :func:`Environment.train_val_split`) Returns ------- nm_train: ``str`` Complete name of the "training" environment nm_val: ``str`` Complete name of the "validation" environment nm_test: ``str``, optionnal .. versionadded:: 2.6.5 Complete name of the "test" environment. It is only returned if `add_for_test` and `pct_test` are not `None`. Examples -------- This function can be used like: .. code-block:: python import grid2op env_name = "l2rpn_case14_sandbox" # or any other... env = grid2op.make(env_name) # extract 1% of the "chronics" to be used in the validation environment. The other 99% will # be used for test nm_env_train, nm_env_val = env.train_val_split_random(pct_val=1.) # and now you can use the training set only to train your agent: print(f"The name of the training environment is \\"{nm_env_train}\\"") print(f"The name of the validation environment is \\"{nm_env_val}\\"") env_train = grid2op.make(nm_env_train) And even after you close the python session, you can still use this environment for training. If you used the exact code above that will look like: .. code-block:: python import grid2op env_name_train = "l2rpn_case14_sandbox_train" # depending on the option you passed above env_train = grid2op.make(env_name_train) .. versionadded:: 2.6.5 Possibility to create a training, validation AND test set. If you have grid2op version >= 1.6.5, you can also use the following: .. code-block:: python import grid2op env_name = "l2rpn_case14_sandbox" # or any other... env = grid2op.make(env_name) # extract 1% of the "chronics" to be used in the validation environment. The other 99% will # be used for test nm_env_train, nm_env_val, nm_env_test = env.train_val_split_random(pct_val=1., pct_test=1.) # and now you can use the training set only to train your agent: print(f"The name of the training environment is \\"{nm_env_train}\\"") print(f"The name of the validation environment is \\"{nm_env_val}\\"") print(f"The name of the test environment is \\"{nm_env_test}\\"") env_train = grid2op.make(nm_env_train) .. warning:: In this case this function returns 3 elements and not 2 ! Notes ----- This function will fail if an environment already exists with one of the name that would be given to the training environment or the validation environment (or test environment). """ if re.match(self.REGEX_SPLIT, add_for_train) is None: raise EnvError( "The suffixes you can use for training data (add_for_train) " 'should match the regex "{self.REGEX_SPLIT}"' ) if re.match(self.REGEX_SPLIT, add_for_val) is None: raise EnvError( "The suffixes you can use for validation data (add_for_val)" 'should match the regex "{self.REGEX_SPLIT}"' ) if add_for_test is None and pct_test is not None: raise EnvError(f"add_for_test is None and pct_test is not None.") if add_for_test is not None and pct_test is None: raise EnvError(f"add_for_test is not None and pct_test is None.") my_path = self.get_path_env() chronics_path = os.path.join(my_path, self._chronics_folder_name()) all_chron = sorted(os.listdir(chronics_path)) all_chron = [ el for el in all_chron if os.path.isdir(os.path.join(chronics_path, el)) ] nb_init = len(all_chron) to_val = self.space_prng.choice( all_chron, size=int(nb_init * pct_val * 0.01), replace=False ) test_scen_id = None if pct_test is not None: all_chron = set(all_chron) - set(to_val) all_chron = list(all_chron) test_scen_id = self.space_prng.choice( all_chron, size=int(nb_init * pct_test * 0.01), replace=False ) return self.train_val_split( to_val, add_for_train=add_for_train, add_for_val=add_for_val, remove_from_name=remove_from_name, add_for_test=add_for_test, test_scen_id=test_scen_id, deep_copy=deep_copy, )
[docs] def get_params_for_runner(self): """ This method is used to initialize a proper :class:`grid2op.Runner.Runner` to use this specific environment. Examples -------- It should be used as followed: .. code-block:: python import grid2op from grid2op.Runner import Runner from grid2op.Agent import DoNothingAgent # for example env = grid2op.make() # create the environment of your choice # create the proper runner runner = Runner(**env.get_params_for_runner(), agentClass=DoNothingAgent) # now you can run runner.run(nb_episode=1) # run for 1 episode """ res = {} res["init_env_path"] = self._init_env_path res["init_grid_path"] = self._init_grid_path res["path_chron"] = self.chronics_handler.path res["parameters_path"] = self._parameters.to_dict() res["names_chronics_to_backend"] = self._names_chronics_to_backend res["actionClass"] = self._actionClass_orig res["observationClass"] = self._observationClass_orig res["rewardClass"] = copy.deepcopy(self._rewardClass) res["legalActClass"] = self._legalActClass res["envClass"] = Environment res["gridStateclass"] = self.chronics_handler.chronicsClass res["backendClass"] = self._raw_backend_class res["backend_kwargs"] = self.backend._my_kwargs res["verbose"] = False dict_ = copy.deepcopy(self.chronics_handler.kwargs) if "path" in dict_: # path is handled elsewhere del dict_["path"] if self.chronics_handler.max_iter is not None: res["max_iter"] = self.chronics_handler.max_iter res["gridStateclass_kwargs"] = dict_ res["thermal_limit_a"] = self._thermal_limit_a res["voltageControlerClass"] = self._voltagecontrolerClass res["other_rewards"] = {k: v.rewardClass for k, v in self.other_rewards.items()} res["grid_layout"] = self.grid_layout res["name_env"] = self.name res["opponent_space_type"] = self._opponent_space_type res["opponent_action_class"] = self._opponent_action_class res["opponent_class"] = self._opponent_class res["opponent_init_budget"] = self._opponent_init_budget res["opponent_budget_per_ts"] = self._opponent_budget_per_ts res["opponent_budget_class"] = self._opponent_budget_class res["opponent_attack_duration"] = self._opponent_attack_duration res["opponent_attack_cooldown"] = self._opponent_attack_cooldown res["opponent_kwargs"] = self._kwargs_opponent res["attention_budget_cls"] = self._attention_budget_cls res["kwargs_attention_budget"] = copy.deepcopy(self._kwargs_attention_budget) res["has_attention_budget"] = self._has_attention_budget res["_read_from_local_dir"] = self._read_from_local_dir res["logger"] = self.logger res["kwargs_observation"] = copy.deepcopy(self._kwargs_observation) res["_is_test"] = self._is_test # TODO not implemented !! return res
[docs] def generate_data(self, nb_year=1, nb_core=1, seed=None): """This function uses the chronix2grid package to generate more data that will then be available locally. You need to install it independently (see https://github.com/BDonnot/ChroniX2Grid#installation for more information) I also requires the lightsim2grid simulator. This is only available for some environment (only the environment used for wcci 2022 competition at time of writing). Generating data takes some time (around 1 - 2 minutes to generate a weekly scenario) and this why we recommend to do it "offline" and then use the generated data for training or evaluation. .. warning:: You should not start this function twice. Before starting a new run, make sure the previous one has terminated (otherwise you might erase some previously generated scenario) Examples --------- The recommended process when you want to use this function is to first generate some more data: .. code-block:: python import grid2op env = grid2op.make("l2rpn_wcci_2022") env.generate_data(nb_year=XXX) # replace XXX by the amount of data you want. If you put 1 you will have 52 different # scenarios Then, later on, you can use it as you please, transparently: .. code-block:: python import grid2op env = grid2op.make("l2rpn_wcci_2022") obs = env.reset() # obs might come from the data you have generated Parameters ---------- nb_year : int, optional the number of "year" you want to generate. Each "year" is made of 52 weeks meaning that if you ask to generate one year, you have 52 more scenarios, by default 1 nb_core : int, optional number of computer cores to use, by default 1. seed: int, optional If the same seed is given, then the same data will be generated. """ try: from chronix2grid.grid2op_utils import add_data except ImportError as exc_: raise ImportError( f"Chronix2grid package is not installed. Install it with `pip install grid2op[chronix2grid]`" f"Please visit https://github.com/bdonnot/chronix2grid#installation " f"for further install instructions." ) from exc_ add_data( env=self, seed=seed, nb_scenario=nb_year, nb_core=nb_core, with_loss=True )