# 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.


from datetime import datetime
import logging
import time
import copy
import os
import json
from typing import Optional, Tuple
import warnings
import numpy as np
from scipy.optimize import minimize
from scipy.optimize import LinearConstraint
from abc import ABC, abstractmethod
from grid2op.Action.ActionSpace import ActionSpace
from grid2op.Observation.baseObservation import BaseObservation
from grid2op.Observation.observationSpace import ObservationSpace
from grid2op.Observation.highresSimCounter import HighResSimCounter
from grid2op.Backend import Backend
from grid2op.dtypes import dt_int, dt_float, dt_bool
from grid2op.Space import GridObjects, RandomObject
from grid2op.Exceptions import *
from grid2op.Parameters import Parameters
from grid2op.Reward import BaseReward
from grid2op.Reward import RewardHelper
from grid2op.Opponent import OpponentSpace, NeverAttackBudget
from grid2op.Action import DontAct, BaseAction
from grid2op.Rules import AlwaysLegal
from grid2op.Opponent import BaseOpponent
from grid2op.operator_attention import LinearAttentionBudget
from grid2op.Action._BackendAction import _BackendAction
from grid2op.Chronics import ChronicsHandler
from grid2op.Rules import AlwaysLegal, BaseRules


# TODO put in a separate class the redispatching function

DETAILED_REDISP_ERR_MSG = (
    "\nThis is an attempt to explain why the dispatch did not succeed and caused a game over.\n"
    "To compensate the {increase} of loads and / or {decrease} of "
    "renewable energy (due to naturl causes but also through curtailment) and / or variation in the storage units, "
    "the generators should {increase} their total production of {sum_move:.2f}MW (in total).\n"
    "But, if you take into account the generator constraints ({pmax} and {max_ramp_up}) you "
    "can have at most {avail_up_sum:.2f}MW.\n"
    "Indeed at time t, generators are in state:\n\t{gen_setpoint}\ntheir ramp max is:"
    "\n\t{ramp_up}\n and pmax is:\n\t{gen_pmax}\n"
    "Wrapping up, each generator can {increase} at {maximum} of:\n\t{avail_up}\n"
    "NB: if you did not do any dispatch during this episode, it would have been possible to "
    "meet these constraints. This situation is caused by not having enough degree of freedom "
    'to "compensate" the variation of the load due to (most likely) an "over usage" of '
    "redispatching feature (some generators stuck at {pmax} as a consequence of your "
    "redispatching. They can't increase their productions to meet the {increase} in demand or "
    "{decrease} of renewables)"
)


BASE_TXT_COPYRIGHT = """# 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.

# THIS FILE HAS BEEN AUTOMATICALLY GENERATED BY "env.generate_classes()"
# WE DO NOT RECOMMEND TO ALTER IT IN ANY WAY
"""


class BaseEnv(GridObjects, RandomObject, ABC):
    """
    INTERNAL

    .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

    This class represent some usefull abstraction that is re used by :class:`Environment` and
    :class:`grid2op.Observation._Obsenv` for example.

    The documentation is showed here to document the common attributes of an "BaseEnvironment".

    Attributes
    ----------

    parameters: :class:`grid2op.Parameters.Parameters`
        The parameters of the game (to expose more control on what is being simulated)

    with_forecast: ``bool``
        Whether the chronics allow to have some kind of "forecast". See :func:`BaseEnv.activate_forceast`
        for more information

    logger:
        TO BE DONE: a way to log what is happening (**currently not implemented**)

    time_stamp: ``datetime.datetime``
        The actual time stamp of the current observation.

    nb_time_step: ``int``
        Number of time steps played in the current environment

    current_obs: :class:`grid2op.Observation.BaseObservation`
        The current observation (or None if it's not intialized)

    backend: :class:`grid2op.Backend.Backend`
        The backend used to compute the powerflows and cascading failures.

    done: ``bool``
        Whether the environment is "done". If ``True`` you need to call :func:`Environment.reset` in order
        to continue.

    current_reward: ``float``
        The last computed reward (reward of the current step)

    other_rewards: ``dict``
        Dictionary with key being the name (identifier) and value being some RewardHelper. At each time step, all the
        values will be computed by the :class:`Environment` and the information about it will be returned in the
        "reward" key of the "info" dictionnary of the :func:`Environment.step`.

    chronics_handler: :class:`grid2op.Chronics.ChronicsHandler`
        The object in charge managing the "chronics", which store the information about load and generator for example.

    reward_range: ``tuple``
        For open ai gym compatibility. It represents the range of the rewards: reward min, reward max

    _viewer:
        For open ai gym compatibility.

    viewer_fig:
        For open ai gym compatibility.

    _gen_activeprod_t:
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Should be initialized at 0. for "step" to properly recognize it's the first time step of the game

    _no_overflow_disconnection: ``bool``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Whether or not cascading failures are computed or not (TRUE = the powerlines above their thermal limits will
        not be disconnected). This is initialized based on the attribute
        :attr:`grid2op.Parameters.Parameters.NO_OVERFLOW_DISCONNECTION`.

    _timestep_overflow: ``numpy.ndarray``, dtype: int
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Number of consecutive timesteps each powerline has been on overflow.

    _nb_timestep_overflow_allowed: ``numpy.ndarray``, dtype: int
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Number of consecutive timestep each powerline can be on overflow. It is usually read from
        :attr:`grid2op.Parameters.Parameters.NB_TIMESTEP_POWERFLOW_ALLOWED`.

    _hard_overflow_threshold: ``float``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Number of timestep before an :class:`grid2op.BaseAgent.BaseAgent` can reconnet a powerline that has been
        disconnected
        by the environment due to an overflow.

    _env_dc: ``bool``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Whether the environment computes the powerflow using the DC approximation or not. It is usually read from
        :attr:`grid2op.Parameters.Parameters.ENV_DC`.

    _names_chronics_to_backend: ``dict``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Configuration file used to associated the name of the objects in the backend
        (both extremities of powerlines, load or production for
        example) with the same object in the data (:attr:`Environment.chronics_handler`). The idea is that, usually
        data generation comes from a different software that does not take into account the powergrid infrastructure.
        Hence, the same "object" can have a different name. This mapping is present to avoid the need to rename
        the "object" when providing data. A more detailed description is available at
        :func:`grid2op.ChronicsHandler.GridValue.initialize`.

    _env_modification: :class:`grid2op.Action.Action`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Representation of the actions of the environment for the modification of the powergrid.

    _rewardClass: ``type``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Type of reward used. Should be a subclass of :class:`grid2op.BaseReward.BaseReward`

    _init_grid_path: ``str``
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        The path where the description of the powergrid is located.

    _game_rules: :class:`grid2op.Rules.RulesChecker`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        The rules of the game (define which actions are legal and which are not)

    _action_space: :class:`grid2op.Action.ActionSpace`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Helper used to manipulate more easily the actions given to / provided by the :class:`grid2op.Agent.BaseAgent`
        (player)

    _helper_action_env: :class:`grid2op.Action.ActionSpace`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Helper used to manipulate more easily the actions given to / provided by the environment to the backend.

    _observation_space: :class:`grid2op.Observation.ObservationSpace`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Helper used to generate the observation that will be given to the :class:`grid2op.BaseAgent`

    _reward_helper: :class:`grid2p.BaseReward.RewardHelper`
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Helper that is called to compute the reward at each time step.

    kwargs_observation: ``dict``
        TODO

    # TODO add the units (eg MW, MWh, MW/time step,etc.) in the redispatching related attributes
    """

    ALARM_FILE_NAME = "alerts_info.json"
    ALARM_KEY = "fixed"
    ALERT_FILE_NAME = "alerts_info.json"
    ALERT_KEY = "by_line"
    
    CAN_SKIP_TS = False  # each step is exactly one time step

    def __init__(
        self,
        init_env_path: os.PathLike,
        init_grid_path: os.PathLike,
        parameters: Parameters,
        voltagecontrolerClass: type,
        thermal_limit_a: Optional[np.ndarray] = None,
        epsilon_poly: float = 1e-4,  # precision of the redispatching algorithm
        tol_poly: float = 1e-2,  # i need to compute a redispatching if the actual values are "more than tol_poly" the values they should be
        other_rewards: dict = None,
        with_forecast: bool = True,
        opponent_space_type: type = OpponentSpace,
        opponent_action_class: type = DontAct,
        opponent_class: type = BaseOpponent,
        opponent_init_budget: float = 0.0,
        opponent_budget_per_ts: float = 0.0,
        opponent_budget_class: type = NeverAttackBudget,
        opponent_attack_duration: int = 0,
        opponent_attack_cooldown: int = 99999,
        kwargs_opponent: dict = None,
        has_attention_budget: bool = False,
        attention_budget_cls: type = LinearAttentionBudget,
        kwargs_attention_budget: dict = None,
        logger: Optional[logging.Logger] = None,
        kwargs_observation: Optional[dict] = None,
        observation_bk_class=None,  # type of backend for the observation space
        observation_bk_kwargs=None,  # type of backend for the observation space
        highres_sim_counter=None,
        update_obs_after_reward=False,
        _is_test: bool = False,  # TODO not implemented !!
        _init_obs: Optional[BaseObservation] =None
    ):
        GridObjects.__init__(self)
        RandomObject.__init__(self)
        if other_rewards is None:
            other_rewards = {}
        if kwargs_attention_budget is None:
            kwargs_attention_budget = {}
        if kwargs_opponent is None:
            kwargs_opponent = {}
            
        self._is_test: bool = _is_test
        if logger is None:
            self.logger = logging.getLogger(__name__)
            self.logger.disabled = True
        else:
            self.logger: logging.Logger = logger.getChild("grid2op_BaseEnv")

        if init_grid_path is not None:
            self._init_grid_path: os.PathLike = os.path.abspath(init_grid_path)
        else:
            self._init_grid_path = None

        self._DEBUG: bool = False
        self._complete_action_cls: type = None
        self.__closed: bool = False  # by default the environment is not closed

        # specific to power system
        if not isinstance(parameters, Parameters):
            raise Grid2OpException(
                'Parameter "parameters" used to build the Environment should derived form the '
                'grid2op.Parameters class, type provided is "{}"'.format(
                    type(parameters)
                )
            )
        parameters.check_valid()  # check the provided parameters are valid
        self._parameters: Parameters = copy.deepcopy(parameters)
        self.with_forecast: bool = with_forecast
        self._forecasts = None

        # some timers
        self._time_apply_act: float = dt_float(0)
        self._time_powerflow: float = dt_float(0)
        self._time_extract_obs: float = dt_float(0)
        self._time_create_bk_act: float = dt_float(0)
        self._time_opponent: float = dt_float(0)
        self._time_redisp: float = dt_float(0)
        self._time_step: float = dt_float(0)

        # data relative to interpolation
        self._epsilon_poly: float = dt_float(epsilon_poly)
        self._tol_poly: float = dt_float(tol_poly)

        # class used for the action spaces
        self._helper_action_class: ActionSpace = None
        self._helper_observation_class: ActionSpace = None

        # and calendar data
        self.time_stamp: time.struct_time = None
        self.nb_time_step: datetime.timedelta = dt_int(0)
        self.delta_time_seconds = None  # number of seconds between two consecutive step

        # observation
        self.current_obs: Optional[BaseObservation] = None
        self._line_status: np.ndarray = None

        self._ignore_min_up_down_times: bool = self._parameters.IGNORE_MIN_UP_DOWN_TIME
        self._forbid_dispatch_off: bool = (
            not self._parameters.ALLOW_DISPATCH_GEN_SWITCH_OFF
        )

        # type of power flow to play
        # if True, then it will not disconnect lines above their thermal limits
        self._no_overflow_disconnection: bool = (
            self._parameters.NO_OVERFLOW_DISCONNECTION
        )
        self._timestep_overflow: np.ndarray = None
        self._nb_timestep_overflow_allowed: np.ndarray = None
        self._hard_overflow_threshold: float = self._parameters.HARD_OVERFLOW_THRESHOLD

        # store actions "cooldown"
        self._times_before_line_status_actionable: np.ndarray = None
        self._max_timestep_line_status_deactivated: int = (
            self._parameters.NB_TIMESTEP_COOLDOWN_LINE
        )
        self._times_before_topology_actionable: np.ndarray = None
        self._max_timestep_topology_deactivated: int = (
            self._parameters.NB_TIMESTEP_COOLDOWN_SUB
        )
        self._nb_ts_reco: int = self._parameters.NB_TIMESTEP_RECONNECTION

        # for maintenance operation
        self._time_next_maintenance: np.ndarray = None
        self._duration_next_maintenance: np.ndarray = None

        # hazard (not used outside of this class, information is given in `times_before_line_status_actionable`
        self._hazard_duration: np.ndarray = None

        self._env_dc = self._parameters.ENV_DC

        # redispatching data
        self._target_dispatch: np.ndarray = None
        self._already_modified_gen: np.ndarray = None
        self._actual_dispatch: np.ndarray = None
        self._gen_uptime: np.ndarray = None
        self._gen_downtime: np.ndarray = None
        self._gen_activeprod_t: np.ndarray = None
        self._gen_activeprod_t_redisp: np.ndarray = None

        self._thermal_limit_a: np.ndarray = thermal_limit_a
        self._disc_lines: np.ndarray = None

        # store environment modifications
        self._injection = None
        self._maintenance = None
        self._hazards = None
        self._env_modification = None

        # to use the data
        self.done = False
        self.current_reward = None
        self._helper_action_env: ActionSpace = None
        self.chronics_handler : ChronicsHandler = None
        self._game_rules = None
        self._action_space: ActionSpace = None

        self._rewardClass: type = None
        self._actionClass: type = None
        self._observationClass: type = None
        self._legalActClass: type = None
        self._observation_space: ObservationSpace = None
        self._names_chronics_to_backend: dict = None
        self._reward_helper = None

        # gym compatibility
        self.reward_range = None, None
        self._viewer = None
        self.viewer_fig = None

        # other rewards
        self.other_rewards = {}
        for k, v in other_rewards.items():
            if isinstance(v, type):
                if not issubclass(v, BaseReward):
                    raise Grid2OpException(
                        'All values of "rewards" key word argument should be classes that inherit '
                        'from "grid2op.BaseReward"'
                    )
            else:
                if not isinstance(v, BaseReward):
                    raise Grid2OpException(
                        'All values of "rewards" key word argument should be classes that inherit '
                        'from "grid2op.BaseReward"'
                    )

            if not isinstance(k, str):
                raise Grid2OpException(
                    'All keys of "rewards" should be of string type.'
                )
            self.other_rewards[k] = RewardHelper(v, self.logger)

        # opponent
        self._opponent_action_class = (
            opponent_action_class  # class of the action of the opponent
        )
        self._opponent_space_type = opponent_space_type  # type of the opponent action space
        self._opponent_class = opponent_class  # class of the opponent
        self._opponent_init_budget = dt_float(opponent_init_budget)
        self._opponent_attack_duration = dt_int(opponent_attack_duration)
        self._opponent_attack_cooldown = dt_int(opponent_attack_cooldown)
        self._opponent_budget_per_ts = dt_float(opponent_budget_per_ts)
        self._kwargs_opponent = kwargs_opponent
        self._opponent_budget_class = opponent_budget_class

        # below initialized by _create_env, above: need to be called
        self._opponent_action_space = None
        self._compute_opp_budget = None
        self._opponent = None
        self._oppSpace = None

        # voltage
        self._voltagecontrolerClass = voltagecontrolerClass
        self._voltage_controler = None

        # backend action
        self._backend_action_class = None
        self._backend_action = None

        # specific to Basic Env, do not change
        self.backend :Backend = None
        self.__is_init = False
        self.debug_dispatch = False

        # to change the parameters
        self.__new_param = None
        self.__new_forecast_param = None
        self.__new_reward_func = None

        # storage units
        # TODO storage: what to do when self.storage_Emin >0. and self.storage_loss > 0.
        # TODO and we have self._storage_current_charge - self.storage_loss < self.storage_Emin
        self._storage_current_charge = None  # the current storage charge
        self._storage_previous_charge = None  # the previous storage charge
        self._action_storage = None  # the storage action performed
        self._amount_storage = None  # total amount of storage to be dispatched
        self._amount_storage_prev = None
        self._storage_power = None
        self._storage_power_prev = None

        # curtailment
        self._limit_curtailment = None
        self._limit_curtailment_prev = None
        self._gen_before_curtailment = None
        self._sum_curtailment_mw = None
        self._sum_curtailment_mw_prev = None
        self._limited_before = 0.0  # TODO curt

        # attention budget
        self._has_attention_budget = has_attention_budget
        self._attention_budget = None
        self._attention_budget_cls = attention_budget_cls
        self._is_alarm_illegal = False
        self._is_alarm_used_in_reward = False

        # alert infos
        self._is_alert_illegal = False
        self._is_alert_used_in_reward = False

        self._kwargs_attention_budget = copy.deepcopy(kwargs_attention_budget)

        # to ensure self.get_obs() has a reproducible behaviour
        self._last_obs = None

        # to retrieve previous result (before 1.6.5 the seed of the
        # action space or observation space was not done each reset)
        self._has_just_been_seeded = False
        if kwargs_observation is not None:
            self._kwargs_observation = copy.deepcopy(kwargs_observation)
        else:
            self._kwargs_observation = {}

        if init_env_path is not None:
            self._init_env_path = os.path.abspath(init_env_path)
        else:
            self._init_env_path = None
            
        # time_dependant attributes for the "forecast env"
        if _init_obs is not None:
            self._init_obs = _init_obs.copy()
            self._init_obs._obs_env = None
        else:
            self._init_obs = None
            
        self._observation_bk_class = observation_bk_class
        self._observation_bk_kwargs = observation_bk_kwargs
        
        if highres_sim_counter is not None:
            self._highres_sim_counter = highres_sim_counter
        else:
            self._highres_sim_counter = HighResSimCounter()
            
        self._update_obs_after_reward = update_obs_after_reward
        
        # alert
        self._last_alert = None
        self._time_since_last_alert = None
        self._alert_duration= None
        self._total_number_of_alert = 0
        self._time_since_last_attack = None
        self._was_alert_used_after_attack = None
        self._attack_under_alert = None
        self._is_already_attacked = None
        
        # general things that can be used by the reward
        self._reward_to_obs = {}
    
    @property
    def highres_sim_counter(self):
        return self._highres_sim_counter
    
    @property
    def nb_highres_called(self):
        return self._highres_sim_counter.nb_highres_called
        
    def _custom_deepcopy_for_copy(self, new_obj, dict_=None):
        if self.__closed:
            raise RuntimeError("Impossible to make a copy of a closed environment !")

        if not self.backend._can_be_copied:
            raise RuntimeError("Impossible to copy your environment: the backend "
                               "class you used cannot be copied.")
        RandomObject._custom_deepcopy_for_copy(self, new_obj)
        if dict_ is None:
            dict_ = {}

        new_obj._init_grid_path = copy.deepcopy(self._init_grid_path)
        new_obj._init_env_path = copy.deepcopy(self._init_env_path)

        new_obj._DEBUG = self._DEBUG
        new_obj._parameters = copy.deepcopy(self._parameters)
        new_obj.with_forecast = self.with_forecast
        new_obj._forecasts = copy.deepcopy(self._forecasts)

        # some timers
        new_obj._time_apply_act = self._time_apply_act
        new_obj._time_powerflow = self._time_powerflow
        new_obj._time_extract_obs = self._time_extract_obs
        new_obj._time_create_bk_act = self._time_create_bk_act
        new_obj._time_opponent = self._time_opponent
        new_obj._time_redisp = self._time_redisp
        new_obj._time_step = self._time_step

        # data relative to interpolation
        new_obj._epsilon_poly = self._epsilon_poly
        new_obj._tol_poly = self._tol_poly

        #
        new_obj._complete_action_cls = copy.deepcopy(self._complete_action_cls)

        # define logger
        new_obj.logger = copy.deepcopy(self.logger)  # TODO does that make any sense ?

        # class used for the action spaces
        new_obj._helper_action_class = self._helper_action_class  #  const
        new_obj._helper_observation_class = self._helper_observation_class

        # and calendar data
        new_obj.time_stamp = self.time_stamp
        new_obj.nb_time_step = self.nb_time_step
        new_obj.delta_time_seconds = self.delta_time_seconds

        # observation
        if self.current_obs is not None:
            new_obj.current_obs = self.current_obs.copy()

        # backend
        # backend action
        new_obj._backend_action_class = self._backend_action_class
        new_obj._backend_action = copy.deepcopy(self._backend_action)

        # specific to Basic Env, do not change
        new_obj.backend = self.backend.copy()
        if self._thermal_limit_a is not None:
            new_obj.backend.set_thermal_limit(self._thermal_limit_a)
        new_obj._thermal_limit_a = copy.deepcopy(self._thermal_limit_a)

        new_obj.__is_init = self.__is_init
        new_obj.__closed = self.__closed
        new_obj.debug_dispatch = self.debug_dispatch

        new_obj._line_status = copy.deepcopy(self._line_status)

        new_obj._ignore_min_up_down_times = self._ignore_min_up_down_times
        new_obj._forbid_dispatch_off = self._forbid_dispatch_off

        # type of power flow to play
        # if True, then it will not disconnect lines above their thermal limits
        new_obj._no_overflow_disconnection = self._no_overflow_disconnection
        new_obj._timestep_overflow = copy.deepcopy(self._timestep_overflow)
        new_obj._nb_timestep_overflow_allowed = copy.deepcopy(
            self._nb_timestep_overflow_allowed
        )
        new_obj._hard_overflow_threshold = self._hard_overflow_threshold

        # store actions "cooldown"
        new_obj._times_before_line_status_actionable = copy.deepcopy(
            self._times_before_line_status_actionable
        )
        new_obj._max_timestep_line_status_deactivated = (
            self._max_timestep_line_status_deactivated
        )
        new_obj._times_before_topology_actionable = copy.deepcopy(
            self._times_before_topology_actionable
        )
        new_obj._max_timestep_topology_deactivated = (
            self._max_timestep_topology_deactivated
        )
        new_obj._nb_ts_reco = self._nb_ts_reco

        # for maintenance operation
        new_obj._time_next_maintenance = copy.deepcopy(self._time_next_maintenance)
        new_obj._duration_next_maintenance = copy.deepcopy(
            self._duration_next_maintenance
        )

        # hazard (not used outside of this class, information is given in `times_before_line_status_actionable`
        new_obj._hazard_duration = copy.deepcopy(self._hazard_duration)

        new_obj._env_dc = self._env_dc

        # redispatching data
        new_obj._target_dispatch = copy.deepcopy(self._target_dispatch)
        new_obj._already_modified_gen = copy.deepcopy(self._already_modified_gen)
        new_obj._actual_dispatch = copy.deepcopy(self._actual_dispatch)
        new_obj._gen_uptime = copy.deepcopy(self._gen_uptime)
        new_obj._gen_downtime = copy.deepcopy(self._gen_downtime)
        new_obj._gen_activeprod_t = copy.deepcopy(self._gen_activeprod_t)
        new_obj._gen_activeprod_t_redisp = copy.deepcopy(self._gen_activeprod_t_redisp)

        new_obj._disc_lines = copy.deepcopy(self._disc_lines)

        # store environment modifications
        new_obj._injection = copy.deepcopy(self._injection)
        new_obj._maintenance = copy.deepcopy(self._maintenance)
        new_obj._hazards = copy.deepcopy(self._hazards)
        new_obj._env_modification = copy.deepcopy(self._env_modification)

        # to use the data
        new_obj.done = self.done
        new_obj.current_reward = copy.deepcopy(self.current_reward)
        new_obj.chronics_handler = copy.deepcopy(self.chronics_handler)
        new_obj._game_rules = copy.deepcopy(self._game_rules)
        new_obj._helper_action_env = self._helper_action_env.copy()
        new_obj._helper_action_env.legal_action = new_obj._game_rules.legal_action
        new_obj._action_space = self._action_space.copy()
        new_obj._action_space.legal_action = new_obj._game_rules.legal_action

        new_obj._rewardClass = self._rewardClass
        new_obj._actionClass = self._actionClass
        new_obj._observationClass = self._observationClass
        new_obj._legalActClass = self._legalActClass
        new_obj._observation_space = self._observation_space.copy(copy_backend=True)
        new_obj._observation_space._legal_action = (
            new_obj._game_rules.legal_action
        )  # TODO this does not respect SOLID principles at all !
        new_obj._kwargs_observation = copy.deepcopy(self._kwargs_observation)
        new_obj._observation_space._ptr_kwargs_observation = new_obj._kwargs_observation
        new_obj._names_chronics_to_backend = self._names_chronics_to_backend
        new_obj._reward_helper = copy.deepcopy(self._reward_helper)

        # gym compatibility
        new_obj.reward_range = copy.deepcopy(self.reward_range)
        new_obj._viewer = copy.deepcopy(self._viewer)
        new_obj.viewer_fig = copy.deepcopy(self.viewer_fig)

        # other rewards
        new_obj.other_rewards = copy.deepcopy(self.other_rewards)

        # opponent
        new_obj._opponent_space_type = self._opponent_space_type
        new_obj._opponent_action_class = self._opponent_action_class  # const
        new_obj._opponent_class = self._opponent_class  # const
        new_obj._opponent_init_budget = self._opponent_init_budget
        new_obj._opponent_attack_duration = self._opponent_attack_duration
        new_obj._opponent_attack_cooldown = self._opponent_attack_cooldown
        new_obj._opponent_budget_per_ts = self._opponent_budget_per_ts
        new_obj._kwargs_opponent = copy.deepcopy(self._kwargs_opponent)
        new_obj._opponent_budget_class = copy.deepcopy(
            self._opponent_budget_class
        )  # const
        new_obj._opponent_action_space = self._opponent_action_space  # const
        new_obj._compute_opp_budget = self._opponent_budget_class(
            self._opponent_action_space
        )

        # init the opponent
        new_obj._opponent = new_obj._opponent_class.__new__(new_obj._opponent_class)
        self._opponent._custom_deepcopy_for_copy(
            new_obj._opponent, {"partial_env": new_obj, **new_obj._kwargs_opponent}
        )

        new_obj._oppSpace = new_obj._opponent_space_type(
            compute_budget=new_obj._compute_opp_budget,
            init_budget=new_obj._opponent_init_budget,
            attack_duration=new_obj._opponent_attack_duration,
            attack_cooldown=new_obj._opponent_attack_cooldown,
            budget_per_timestep=new_obj._opponent_budget_per_ts,
            opponent=new_obj._opponent,
        )
        state_me, state_opp = self._oppSpace._get_state()
        new_obj._oppSpace._set_state(state_me)

        # voltage
        new_obj._voltagecontrolerClass = self._voltagecontrolerClass
        new_obj._voltage_controler = self._voltage_controler.copy()

        # to change the parameters
        new_obj.__new_param = copy.deepcopy(self.__new_param)
        new_obj.__new_forecast_param = copy.deepcopy(self.__new_forecast_param)
        new_obj.__new_reward_func = copy.deepcopy(self.__new_reward_func)

        # storage units
        new_obj._storage_current_charge = copy.deepcopy(self._storage_current_charge)
        new_obj._storage_previous_charge = copy.deepcopy(self._storage_previous_charge)
        new_obj._action_storage = copy.deepcopy(self._action_storage)
        new_obj._amount_storage = copy.deepcopy(self._amount_storage)
        new_obj._amount_storage_prev = copy.deepcopy(self._amount_storage_prev)
        new_obj._storage_power = copy.deepcopy(self._storage_power)
        new_obj._storage_power_prev = copy.deepcopy(self._storage_power_prev)

        # curtailment
        new_obj._limit_curtailment = copy.deepcopy(self._limit_curtailment)
        new_obj._limit_curtailment_prev = copy.deepcopy(self._limit_curtailment_prev)
        new_obj._gen_before_curtailment = copy.deepcopy(self._gen_before_curtailment)
        new_obj._sum_curtailment_mw = copy.deepcopy(self._sum_curtailment_mw)
        new_obj._sum_curtailment_mw_prev = copy.deepcopy(self._sum_curtailment_mw_prev)
        new_obj._limited_before = copy.deepcopy(self._limited_before)

        # attention budget
        new_obj._has_attention_budget = self._has_attention_budget
        new_obj._attention_budget = copy.deepcopy(self._attention_budget)
        new_obj._attention_budget_cls = self._attention_budget_cls  # const
        new_obj._is_alarm_illegal = copy.deepcopy(self._is_alarm_illegal)
        new_obj._is_alarm_used_in_reward = copy.deepcopy(self._is_alarm_used_in_reward)

        # alert 
        new_obj._is_alert_illegal = copy.deepcopy(self._is_alert_illegal)
        new_obj._is_alert_used_in_reward = copy.deepcopy(self._is_alert_used_in_reward)
        
        new_obj._kwargs_attention_budget = copy.deepcopy(self._kwargs_attention_budget)

        new_obj._last_obs = self._last_obs.copy()

        new_obj._has_just_been_seeded = self._has_just_been_seeded
        
        # extra things used by the reward to pass to the obs
        new_obj._reward_to_obs = copy.deepcopy(self._reward_to_obs)
        
        # time_dependant attributes for the "forecast env"
        if self._init_obs is None:
            new_obj._init_obs = None
        else:
            new_obj._init_obs = self._init_obs.copy()
        
        new_obj._observation_bk_class = self._observation_bk_class
        new_obj._observation_bk_kwargs = self._observation_bk_kwargs
        
        # do not forget !
        new_obj._is_test = self._is_test
        
        # do not copy it.
        new_obj._highres_sim_counter = self._highres_sim_counter        
        
        # alert
        new_obj._last_alert = copy.deepcopy(self._last_alert)
        new_obj._time_since_last_alert = copy.deepcopy(self._time_since_last_alert)
        new_obj._alert_duration = copy.deepcopy(self._alert_duration)
        new_obj._total_number_of_alert = self._total_number_of_alert
        new_obj._time_since_last_attack = copy.deepcopy(self._time_since_last_attack)
        new_obj._is_already_attacked = copy.deepcopy(self._is_already_attacked)
        new_obj._attack_under_alert = copy.deepcopy(self._attack_under_alert)
        new_obj._was_alert_used_after_attack = copy.deepcopy(self._was_alert_used_after_attack)
        
        new_obj._update_obs_after_reward = copy.deepcopy(self._update_obs_after_reward)

    def get_path_env(self):
        """
        Get the path that allows to create this environment.

        It can be used for example in :func:`grid2op.utils.EpisodeStatistics` 
        to save the information directly inside
        the environment data.

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot get its path.")
        res = self._init_env_path if self._init_env_path is not None else ""
        return res

    def _check_alarm_file_consistent(self, dict_):
        if (self.ALERT_KEY not in dict_) and (self.ALARM_KEY not in dict_):
            raise EnvError(
                f'One of {self.ALERT_KEY} or {self.ALARM_KEY} should be present in the alarm data json, for now.'
            )
    
    def _set_no_alarm(self):        
        bk_cls = type(self.backend)
        bk_cls.dim_alarms = 0
        bk_cls.alarms_area_names = []
        bk_cls.alarms_lines_area = {}
        bk_cls.alarms_area_lines = []
        
    def load_alarm_data(self):
        """
        Internal

        .. warning:: 
            /!\\\\ Only valid with "l2rpn_icaps_2021" environment /!\\\\

        Notes
        ------
        This is called when the environment class is not created, so i need to read the data of the grid from the
        backend.

        I cannot use "self.name_line" for example.

        This function update the backend INSTANCE. The backend class is then updated in the 
        :func:`BaseEnv._init_backend`
        function with a call to `self.backend.assert_grid_correct()`

        Returns
        -------

        """
        file_alarms = os.path.join(self.get_path_env(), BaseEnv.ALARM_FILE_NAME)
        if os.path.exists(file_alarms) and os.path.isfile(file_alarms):
            with open(file_alarms, mode="r", encoding="utf-8") as f:
                dict_alarm = json.load(f)
            self._check_alarm_file_consistent(dict_alarm)
            
            if self.ALARM_KEY not in dict_alarm:
                # not an alarm but an alert
                self._set_no_alarm()
                return # TODO update grid in this case !
            
            nb_areas = len(dict_alarm[self.ALARM_KEY])  # need to be remembered
            line_names = {
                el: [] for el in self.backend.name_line
            }  # need to be remembered
            area_names = sorted(dict_alarm[self.ALARM_KEY].keys())  # need to be remembered
            area_lines = [[] for _ in range(nb_areas)]  # need to be remembered
            for area_id, area_name in enumerate(area_names):
                # check that: all lines in files are in the grid
                area = dict_alarm[self.ALARM_KEY][area_name]
                for line in area:
                    if line not in line_names:
                        raise EnvError(
                            f"You provided a description of the area of the grid for the alarms, but a "
                            f'line named "{line}" is present in your file but not in the grid. Please '
                            f"check the file {file_alarms} and make sure it contains only the line named "
                            f"{sorted(self.backend.name_line)}."
                        )
                    # update the list and dictionary that remembers everything
                    line_names[line].append(area_name)
                    area_lines[area_id].append(line)

            for line, li_area in line_names.items():
                # check that all lines in the grid are in at least one area
                if not li_area:
                    raise EnvError(
                        f"Line (on the grid) named {line} is not in any areas. This is not supported at "
                        f"the moment"
                    )

            # every check pass, i update the backend class
            bk_cls = type(self.backend)
            bk_cls.tell_dim_alarm(nb_areas)
            bk_cls.alarms_area_names = copy.deepcopy(area_names)
            bk_cls.alarms_lines_area = copy.deepcopy(line_names)
            bk_cls.alarms_area_lines = copy.deepcopy(area_lines)
        else:
            self._set_no_alarm()

    def _set_no_alert(self):
        bk_cls = type(self.backend)
        bk_cls.tell_dim_alert(0)
        bk_cls.alertable_line_names = []
        bk_cls.alertable_line_ids = np.array([], dtype=dt_int)
        
    def load_alert_data(self):
        """
        Internal

        Notes
        ------
        This is called to get the alertable lines when the warning is raised "by line"

        Returns
        -------

        """
        file_alarms = os.path.join(self.get_path_env(), BaseEnv.ALERT_FILE_NAME)
        if os.path.exists(file_alarms) and os.path.isfile(file_alarms):
            with open(file_alarms, mode="r", encoding="utf-8") as f:
                dict_alert = json.load(f)
                
            self._check_alarm_file_consistent(dict_alert)
            if self.ALERT_KEY not in dict_alert:
                # not an alert but an alarm
                self._set_no_alert()
                return
            
            if dict_alert[self.ALERT_KEY] != "opponent":
                raise EnvError('You can only define alert from the opponent for now.')
            
            if "lines_attacked" in self._kwargs_opponent:
                lines_attacked = copy.deepcopy(self._kwargs_opponent["lines_attacked"])
                if isinstance(lines_attacked[0], list):
                    lines_attacked = sum(lines_attacked, start=[])
            else:
                lines_attacked = []
                warnings.warn("The kwargs \"lines_attacked\" is not present in the description of your opponent "
                              "yet you want to use alert. Know that in this case no alert will be defined...")
                    
            alertable_line_names = copy.deepcopy(lines_attacked)
            alertable_line_ids = np.empty(len(alertable_line_names), dtype=dt_int)
            for i, el in enumerate(alertable_line_names): 
                indx = np.where(self.backend.name_line == el)[0]
                if not len(indx):
                    raise Grid2OpException(f"Attacked line {el} is not found in the grid.")
                alertable_line_ids[i] = indx[0]
            nb_lines = len(alertable_line_ids)
        
            bk_cls = type(self.backend)
            bk_cls.tell_dim_alert(nb_lines)
            bk_cls.alertable_line_names = copy.deepcopy(alertable_line_names)
            bk_cls.alertable_line_ids = np.array(alertable_line_ids).astype(dt_int)
        else:
            self._set_no_alert()

    @property
    def action_space(self) -> ActionSpace:
        """this represent a view on the action space"""
        return self._action_space

    @action_space.setter
    def action_space(self, other):
        raise EnvError(
            "Impossible to modify the action space of the environment. You probably want to modify "
            "the action with which the agent is interacting. You can do that with a converter, or "
            "using the GymEnv. Please consult the documentation."
        )

    @property
    def observation_space(self) -> ObservationSpace:
        """this represent a view on the action space"""
        return self._observation_space

    @observation_space.setter
    def observation_space(self, other):
        raise EnvError(
            "Impossible to modify the observation space of the environment. You probably want to modify "
            "the observation with which the agent is interacting. You can do that with a converter, or "
            "using the GymEnv. Please consult the documentation."
        )

    def change_parameters(self, new_parameters):
        """
        Allows to change the parameters of an environment.

        Notes
        ------
        This only affects the environment AFTER `env.reset()` has been called.

        This only affects the environment and NOT the forecast.

        Parameters
        ----------
        new_parameters: :class:`grid2op.Parameters.Parameters`
            The new parameters you want the environment to get.


        Examples
        ---------

        You can use this function like:

        .. code-block:: python

            import grid2op
            from grid2op.Parameters import Parameters
            env_name = "l2rpn_case14_sandbox"  # or any other name

            env = grid2op.make(env_name)
            env.parameters.NO_OVERFLOW_DISCONNECTION  # -> False

            new_param = Parameters()
            new_param.A_MEMBER = A_VALUE  # eg new_param.NO_OVERFLOW_DISCONNECTION = True
            env.change_parameters(new_param)
            obs = env.reset()
            env.parameters.NO_OVERFLOW_DISCONNECTION  # -> True

        """
        if self.__closed:
            raise EnvError(
                "This environment is closed, you cannot change its parameters."
            )
        if not isinstance(new_parameters, Parameters):
            raise EnvError(
                'The new parameters "new_parameters" should be an instance of '
                "grid2op.Parameters.Parameters. "
            )
        new_parameters.check_valid()  # check the provided parameters are valid
        self.__new_param = new_parameters

    def change_forecast_parameters(self, new_parameters):
        """
        Allows to change the parameters of a "forecast environment" that is for
        the method :func:`grid2op.Observation.BaseObservation.simulate` and 
        :func:`grid2op.Observation.BaseObservation.get_forecast_env`

        Notes
        ------
        This only affects the environment AFTER `env.reset()` has been called.

        This only affects the "forecast env" and NOT the env itself.

        Parameters
        ----------
        new_parameters: :class:`grid2op.Parameters.Parameters`
            The new parameters you want the environment to get.

        Examples
        --------
        
        This can be used like:
        
        .. code-block:: python
        
            import grid2op
            env_name = "l2rpn_case14_sandbox"  # or any other name
            env = grid2op.make(env_name)
            
            param = env.parameters
            param.NO_OVERFLOW_DISCONNECTION = True  # or any other properties of the environment
            env.change_forecast_parameters(param)
            # at this point this has no impact.
            
            obs = env.reset()
            # now, after the reset, the right parameters are used
            sim_obs, sim_reward, sim_done, sim_info = obs.simulate(env.action_space())
            # the new parameters `param` are used for this 
            # and also for
            forecasted_env = obs.get_forecast_env()
        """

        if self.__closed:
            raise EnvError(
                "This environment is closed, you cannot change its parameters (for the forecast / simulate)."
            )

        if not isinstance(new_parameters, Parameters):
            raise EnvError(
                'The new parameters "new_parameters" should be an instance of '
                "grid2op.Parameters.Parameters."
            )
        new_parameters.check_valid()  # check the provided parameters are valid
        self.__new_forecast_param = new_parameters

    def _create_attention_budget(self, **kwargs):
        if not self.__is_init:
            raise EnvError(
                "Impossible to create an attention budget with a non initialized environment!"
            )
        if self._has_attention_budget:
            if type(self).assistant_warning_type == "zonal":
                self._attention_budget = self._attention_budget_cls()
                try:
                    self._kwargs_attention_budget.update(kwargs)
                    self._attention_budget.init(
                        partial_env=self, **self._kwargs_attention_budget
                    )
                except TypeError as exc_:
                    raise EnvError(
                        "Impossible to create the attention budget with the provided argument. Please "
                        'change the content of the argument "kwargs_attention_budget".'
                    ) from exc_
            elif type(self).assistant_warning_type == "by_line":
                self._has_attention_budget = False
                
    def _create_opponent(self):
        if not self.__is_init:
            raise EnvError(
                "Impossible to create an opponent with a non initialized environment!"
            )

        if not issubclass(self._opponent_action_class, BaseAction):
            raise EnvError(
                "Impossible to make an environment with an opponent action class not derived from BaseAction"
            )
        try:
            self._opponent_init_budget = dt_float(self._opponent_init_budget)
        except Exception as e:
            raise EnvError(
                'Impossible to convert "opponent_init_budget" to a float with error {}'.format(
                    e
                )
            )
        if self._opponent_init_budget < 0.0:
            raise EnvError(
                "If you want to deactivate the opponent, please don't set its budget to a negative number."
                'Prefer the use of the DontAct action type ("opponent_action_class=DontAct" '
                "and / or set its budget to 0."
            )
        if not issubclass(self._opponent_class, BaseOpponent):
            raise EnvError(
                "Impossible to make an opponent with a type that does not inherit from BaseOpponent."
            )

        self._opponent_action_class._add_shunt_data()
        self._opponent_action_class._update_value_set()
        self._opponent_action_space = self._helper_action_class(
            gridobj=type(self.backend),
            legal_action=AlwaysLegal,
            actionClass=self._opponent_action_class,
        )

        self._compute_opp_budget = self._opponent_budget_class(
            self._opponent_action_space
        )
        self._opponent = self._opponent_class(self._opponent_action_space)
        self._oppSpace = self._opponent_space_type(
            compute_budget=self._compute_opp_budget,
            init_budget=self._opponent_init_budget,
            attack_duration=self._opponent_attack_duration,
            attack_cooldown=self._opponent_attack_cooldown,
            budget_per_timestep=self._opponent_budget_per_ts,
            opponent=self._opponent,
        )
        self._oppSpace.init_opponent(partial_env=self, **self._kwargs_opponent)
        self._oppSpace.reset()

    def _init_myclass(self):
        if self._backend_action_class is not None:
            # the class has already been initialized
            return
        # remember the original grid2op class
        type(self)._INIT_GRID_CLS = type(self)

        bk_type = type(
            self.backend
        )  # be careful here: you need to initialize from the class, and not from the object
        # create the proper environment class for this specific environment
        self.__class__ = type(self).init_grid(bk_type)

    def _has_been_initialized(self):
        # type of power flow to play
        # if True, then it will not disconnect lines above their thermal limits
        self._init_myclass()
        bk_type = type(self.backend)
        if np.min([self.n_line, self.n_gen, self.n_load, self.n_sub]) <= 0:
            raise EnvironmentError("Environment has not been initialized properly")
        self._backend_action_class = _BackendAction.init_grid(bk_type)
        self._backend_action = self._backend_action_class()

        # initialize maintenance / hazards
        self._time_next_maintenance = np.full(self.n_line, -1, dtype=dt_int)
        self._duration_next_maintenance = np.zeros(shape=(self.n_line,), dtype=dt_int)
        self._times_before_line_status_actionable = np.full(
            shape=(self.n_line,), fill_value=0, dtype=dt_int
        )

        # create the vector to the proper shape
        self._target_dispatch = np.zeros(self.n_gen, dtype=dt_float)
        self._already_modified_gen = np.zeros(self.n_gen, dtype=dt_bool)
        self._actual_dispatch = np.zeros(self.n_gen, dtype=dt_float)
        self._gen_uptime = np.zeros(self.n_gen, dtype=dt_int)
        self._gen_downtime = np.zeros(self.n_gen, dtype=dt_int)
        self._gen_activeprod_t = np.zeros(self.n_gen, dtype=dt_float)
        self._gen_activeprod_t_redisp = np.zeros(self.n_gen, dtype=dt_float)
        self._nb_timestep_overflow_allowed = np.ones(shape=self.n_line, dtype=dt_int)
        self._max_timestep_line_status_deactivated = (
            self._parameters.NB_TIMESTEP_COOLDOWN_LINE
        )

        self._times_before_line_status_actionable = np.zeros(
            shape=(self.n_line,), dtype=dt_int
        )
        self._times_before_topology_actionable = np.zeros(
            shape=(self.n_sub,), dtype=dt_int
        )
        self._nb_timestep_overflow_allowed = np.full(
            shape=(self.n_line,),
            fill_value=self._parameters.NB_TIMESTEP_OVERFLOW_ALLOWED,
            dtype=dt_int,
        )
        self._timestep_overflow = np.zeros(shape=(self.n_line,), dtype=dt_int)

        # update the parameters
        self.__new_param = self._parameters  # small hack to have it working as expected
        self._update_parameters()

        self._reset_redispatching()

        # storage
        self._storage_current_charge = np.zeros(self.n_storage, dtype=dt_float)
        self._storage_previous_charge = np.zeros(self.n_storage, dtype=dt_float)
        self._action_storage = np.zeros(self.n_storage, dtype=dt_float)
        self._storage_power = np.zeros(self.n_storage, dtype=dt_float)
        self._storage_power_prev = np.zeros(self.n_storage, dtype=dt_float)
        self._amount_storage = 0.0
        self._amount_storage_prev = 0.0

        # curtailment
        self._limit_curtailment = np.ones(
            self.n_gen, dtype=dt_float
        )  # in ratio of pmax
        self._limit_curtailment_prev = np.ones(
            self.n_gen, dtype=dt_float
        )  # in ratio of pmax
        self._gen_before_curtailment = np.zeros(self.n_gen, dtype=dt_float)  # in MW
        self._sum_curtailment_mw = dt_float(0.0)
        self._sum_curtailment_mw_prev = dt_float(0.0)
        self._reset_curtailment()

        # register this is properly initialized
        self.__is_init = True

    def _update_parameters(self):
        """update value for the new parameters"""
        self._parameters = self.__new_param
        self._ignore_min_up_down_times = self._parameters.IGNORE_MIN_UP_DOWN_TIME
        self._forbid_dispatch_off = not self._parameters.ALLOW_DISPATCH_GEN_SWITCH_OFF

        # type of power flow to play
        # if True, then it will not disconnect lines above their thermal limits
        self._no_overflow_disconnection = self._parameters.NO_OVERFLOW_DISCONNECTION
        self._hard_overflow_threshold = self._parameters.HARD_OVERFLOW_THRESHOLD

        # store actions "cooldown"
        self._max_timestep_line_status_deactivated = (
            self._parameters.NB_TIMESTEP_COOLDOWN_LINE
        )
        self._max_timestep_topology_deactivated = (
            self._parameters.NB_TIMESTEP_COOLDOWN_SUB
        )
        self._nb_ts_reco = self._parameters.NB_TIMESTEP_RECONNECTION

        self._nb_timestep_overflow_allowed[
            :
        ] = self._parameters.NB_TIMESTEP_OVERFLOW_ALLOWED

        # hard overflow part
        self._env_dc = self._parameters.ENV_DC

        self.__new_param = None

    def reset(self):
        """
        Reset the base environment (set the appropriate variables to correct initialization).
        It is (and must be) overloaded in other :class:`grid2op.Environment`
        """
        if self.__closed:
            raise EnvError("This environment is closed. You cannot use it anymore.")

        self.__is_init = True
        # current = None is an indicator that this is the first step of the environment
        # so don't change the setting of current_obs = None unless you are willing to change that
        self.current_obs = None
        self._line_status[:] = True
        if self.__new_param is not None:
            self._update_parameters()  # reset __new_param to None too
        if self.__new_forecast_param is not None:
            self._observation_space._change_parameters(self.__new_forecast_param)
            self.__new_forecast_param = None
        if self.__new_reward_func is not None:
            self._reward_helper.change_reward(self.__new_reward_func)
            self._reward_helper.initialize(self)
            self.reward_range = self._reward_helper.range()
            # change also the reward used in simulate
            self._observation_space.change_reward(self._reward_helper.template_reward)
            self.__new_reward_func = None

        self._last_obs = None

        # seeds (so that next episode does not depend on what happened in previous episode)
        if self.seed_used is not None and not self._has_just_been_seeded:
            self.seed(None, _seed_me=False)
        self._reset_storage()
        self._reset_curtailment()
        self._reset_alert()
        self._reward_to_obs = {}
        self._has_just_been_seeded = False

    def _reset_alert(self):
        self._last_alert[:] = False
        self._is_already_attacked[:] = False
        self._time_since_last_alert[:] = -1
        self._alert_duration[:] = 0
        self._total_number_of_alert = 0
        self._time_since_last_attack[:] = -1
        self._was_alert_used_after_attack[:] = 0
        self._attack_under_alert[:] = 0
        
    def _reset_storage(self):
        """reset storage capacity at the beginning of new environment if needed"""
        if self.n_storage > 0:
            tmp = self._parameters.INIT_STORAGE_CAPACITY * self.storage_Emax
            if self._parameters.ACTIVATE_STORAGE_LOSS:
                tmp += self.storage_loss * self.delta_time_seconds / 3600.0
            self._storage_previous_charge[
                :
            ] = tmp  # might not be needed, but it's not for the time it takes...
            self._storage_current_charge[:] = tmp
            self._storage_power[:] = 0.0
            self._storage_power_prev[:] = 0.0
            self._amount_storage = 0.0
            self._amount_storage_prev = 0.0
            # TODO storage: check in simulate too!

    def _reset_curtailment(self):
        self._limit_curtailment[self.gen_renewable] = 1.0
        self._limit_curtailment_prev[self.gen_renewable] = 1.0
        self._gen_before_curtailment[:] = 0.0
        self._sum_curtailment_mw = dt_float(0.0)
        self._sum_curtailment_mw_prev = dt_float(0.0)
        self._limited_before = dt_float(0.0)

    def seed(self, seed=None, _seed_me=True):
        """
        Set the seed of this :class:`Environment` for a better control and to ease reproducible experiments.

        Parameters
        ----------
        seed: ``int``
           The seed to set.

        _seed_me: ``bool``
            Whether to seed this instance or just the other things. Used internally only.

        Returns
        ---------
        seed: ``tuple``
            The seed used to set the prng (pseudo random number generator) for the environment
        seed_chron: ``tuple``
            The seed used to set the prng for the chronics_handler (if any), otherwise ``None``
        seed_obs: ``tuple``
            The seed used to set the prng for the observation space (if any), otherwise ``None``
        seed_action_space: ``tuple``
            The seed used to set the prng for the action space (if any), otherwise ``None``
        seed_env_modif: ``tuple``
            The seed used to set the prng for the modification of th environment (if any otherwise ``None``)
        seed_volt_cont: ``tuple``
            The seed used to set the prng for voltage controler (if any otherwise ``None``)
        seed_opponent: ``tuple``
            The seed used to set the prng for the opponent (if any otherwise ``None``)

        Examples
        ---------

        Seeding an environment should be done with:

        .. code-block:: python

            import grid2op
            env = grid2op.make()
            env.seed(0)
            obs = env.reset()

        As long as the environment instance (variable `env` in the above code) is not `reset` the `env.seed` has no
        real effect (but can have side effect).

        For a full control on the seed mechanism it is more than advised to reset it after it has been seeded.

        """
        if self.__closed:
            raise EnvError("This environment is closed. You cannot use it anymore.")
        
        seed_init = None
        seed_chron = None
        seed_obs = None
        seed_action_space = None
        seed_env_modif = None
        seed_volt_cont = None
        seed_opponent = None
            
        if _seed_me:
            max_int = np.iinfo(dt_int).max 
            if seed > max_int:
                raise Grid2OpException("Seed is too big. Max value is {}, provided value is {}".format(max_int, seed))

            try:
                seed = np.array(seed).astype(dt_int)
            except Exception as exc_:
                raise Grid2OpException(
                    "Impossible to seed with the seed provided. Make sure it can be converted to a"
                    "numpy 32 bits integer."
                )
            # example from gym
            # self.np_random, seed = seeding.np_random(seed)
            # inspiration from @ https://github.com/openai/gym/tree/master/gym/utils
            seed_init = seed
            super().seed(seed_init)
            
        max_seed = np.iinfo(dt_int).max  # 2**32 - 1
        if self.chronics_handler is not None:
            seed = self.space_prng.randint(max_seed)
            seed_chron = self.chronics_handler.seed(seed)
        if self._observation_space is not None:
            seed = self.space_prng.randint(max_seed)
            seed_obs = self._observation_space.seed(seed)
        if self._action_space is not None:
            seed = self.space_prng.randint(max_seed)
            seed_action_space = self._action_space.seed(seed)
        if self._helper_action_env is not None:
            seed = self.space_prng.randint(max_seed)
            seed_env_modif = self._helper_action_env.seed(seed)
        if self._voltage_controler is not None:
            seed = self.space_prng.randint(max_seed)
            seed_volt_cont = self._voltage_controler.seed(seed)
        if self._opponent is not None:
            seed = self.space_prng.randint(max_seed)
            seed_opponent = self._opponent.seed(seed)
        self._has_just_been_seeded = True
        return (
            seed_init,
            seed_chron,
            seed_obs,
            seed_action_space,
            seed_env_modif,
            seed_volt_cont,
            seed_opponent,
        )

    def deactivate_forecast(self):
        """
        This function will have the effect to deactivate the `obs.simulate`, the forecast will not be updated
        in the observation space.

        This will most likely lead to some performance increase (~10-15% faster) if you don't use the
        `obs.simulate` function.

        Notes
        ------
        If you really don't want to use the `obs.simulate` functionality, you should rather disable it at the creation
        of the environment. For example, if you use the recommended `make` function, you can pass an argument
        that will ignore the chronics even when reading it (using `GridStateFromFile` instead of
        `GridStateFromFileWithForecast` for example) this would give something like:

        .. code-block:: python

            import grid2op
            from grid2op.Chronics import GridStateFromFile
            # tell grid2op not to read the "forecast"
            env = grid2op.make("rte_case14_realistic", data_feeding_kwargs={"gridvalueClass": GridStateFromFile})

            do_nothing_action = env.action_space()

            # improve speed ups to not even try to use forecast
            env.deactivate_forecast()

            # this is normal behavior
            obs = env.reset()

            # but this will make the programm stop working
            # obs.simulate(do_nothing_action)  # DO NOT RUN IT RAISES AN ERROR

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")

        if self._observation_space is not None:
            self._observation_space.with_forecast = False
        self.with_forecast = False

    def reactivate_forecast(self):
        """
        This function will have the effect to reactivate the `obs.simulate`, the forecast will be updated
        in the observation space.

        This will most likely lead to some performance decrease but you will be able to use `obs.simulate` function.

        .. warning::
            Forecast are deactivated by default (and cannot be reactivated) if the
            backend cannot be copied.
        
        .. warning::
            You need to call 'env.reset()' for this function to work properly. It is NOT recommended
            to reactivate forecasts in the middle of an episode.
            
        Notes
        ------
        You can use this function as followed:

        .. code-block:: python

            import grid2op
            from grid2op.Chronics import GridStateFromFile
            # tell grid2op not to read the "forecast"
            env = grid2op.make("rte_case14_realistic", data_feeding_kwargs={"gridvalueClass": GridStateFromFile})

            do_nothing_action = env.action_space()

            # improve speed ups to not even try to use forecast
            env.deactivate_forecast()

            # this is normal behavior
            obs = env.reset()

            # but this will make the programm stop working
            # obs.simulate(do_nothing_action)  # DO NOT RUN IT RAISES AN ERROR

            env.reactivate_forecast()
            obs = env.reset()  # you need to reset the env for this function to have any effects
            obs, reward, done, info = env.step(do_nothing_action)

            # and now forecast are available again
            simobs, sim_r, sim_d, sim_info = obs.simulate(do_nothing_action)

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        
        if not self.backend._can_be_copied:
            raise EnvError("Impossible to activate the forecasts with a "
                           "backend that cannot be copied.")
        if self._observation_space is not None:
            self._observation_space.reactivate_forecast(self)
        self.with_forecast = True

    def _init_alert_data(self):
        cls = type(self)
        self._last_alert = np.full(cls.dim_alerts,
                                   dtype=dt_bool, fill_value=False)
        self._is_already_attacked = np.full(cls.dim_alerts, dtype=dt_bool,
                                            fill_value=False)
        self._time_since_last_alert = np.full(cls.dim_alerts,
                                              dtype=dt_int, fill_value=-1)
        self._alert_duration = np.full(cls.dim_alerts,
                                       dtype=dt_int, fill_value=0)
        self._total_number_of_alert = 0
        self._time_since_last_attack = np.full(cls.dim_alerts,
                                               dtype=dt_int, fill_value=-1)
        self._was_alert_used_after_attack = np.full(cls.dim_alerts,
                                                    dtype=dt_int, fill_value=0)
        self._attack_under_alert = np.full(cls.dim_alerts,
                                           dtype=dt_int, fill_value=0)
        
    @abstractmethod
    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 /!\\\\

        This method is used for Environment specific implementation. Only use it if you know exactly what
        you are doing.

        """
        pass

    def set_thermal_limit(self, thermal_limit):
        """
        Set the thermal limit effectively.

        Parameters
        ----------
        thermal_limit: ``numpy.ndarray``
            The new thermal limit. It must be a numpy ndarray vector (or convertible to it). For each powerline it
            gives the new thermal limit.

            Alternatively, this can be a dictionary mapping the line names (keys) to its thermal limits (values). In
            that case, all thermal limits for all powerlines should be specified (this is a safety measure
            to reduce the odds of misuse).

        Examples
        ---------

        This function can be used like this:

        .. code-block:: python

            import grid2op

            # I create an environment
            env = grid2op.make("rte_case5_example", test=True)

            # i set the thermal limit of each powerline to 20000 amps
            env.set_thermal_limit([20000 for _ in range(env.n_line)])

        Notes
        -----
        As of grid2op > 1.5.0, it is possible to set the thermal limit by using a dictionary with the keys being
        the name of the powerline and the values the thermal limits.

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        if not self.__is_init:
            raise Grid2OpException(
                "Impossible to set the thermal limit to a non initialized Environment. "
                "Have you called `env.reset()` after last game over ?"
            )
        if isinstance(thermal_limit, dict):
            tmp = np.full(self.n_line, fill_value=np.NaN, dtype=dt_float)
            for key, val in thermal_limit.items():
                if key not in self.name_line:
                    raise Grid2OpException(
                        f"When setting a thermal limit with a dictionary, the keys should be line "
                        f"names. We found: {key} which is not a line name. The names of the "
                        f"powerlines are {self.name_line}"
                    )
                ind_line = np.where(self.name_line == key)[0][0]
                if np.isfinite(tmp[ind_line]):
                    raise Grid2OpException(
                        f"Humm, there is a really strange bug, some lines are set twice."
                    )
                try:
                    val_fl = float(val)
                except Exception as exc_:
                    raise Grid2OpException(
                        f"When setting thermal limit with a dictionary, the keys should be "
                        f"the values of the thermal limit (in amps) you provided something that "
                        f'cannot be converted to a float. Error was "{exc_}".'
                    )
                tmp[ind_line] = val_fl

        elif isinstance(thermal_limit, (np.ndarray, list)):
            try:
                tmp = np.array(thermal_limit).flatten().astype(dt_float)
            except Exception as exc_:
                raise Grid2OpException(
                    f"Impossible to convert the vector as input into a 1d numpy float array. "
                    f"Error was: \n {exc_}"
                )
            if tmp.shape[0] != self.n_line:
                raise Grid2OpException(
                    "Attempt to set thermal limit on {} powerlines while there are {}"
                    "on the grid".format(tmp.shape[0], self.n_line)
                )
            if np.any(~np.isfinite(tmp)):
                raise Grid2OpException(
                    "Impossible to use non finite value for thermal limits."
                )
        else:
            raise Grid2OpException(
                f"You can only set the thermal limits of the environment with a dictionary (in that "
                f"case the keys are the line names, and the values the thermal limits) or with "
                f"a numpy array that has as many components of the number of powerlines on "
                f'the grid. You provided something with type "{type(thermal_limit)}" which '
                f"is not supported."
            )
        self._thermal_limit_a[:] = tmp
        self.backend.set_thermal_limit(self._thermal_limit_a)
        self.observation_space.set_thermal_limit(self._thermal_limit_a)

    def _reset_redispatching(self):
        # redispatching
        self._target_dispatch[:] = 0.0
        self._already_modified_gen[:] = False
        self._actual_dispatch[:] = 0.0
        self._gen_uptime[:] = 0
        self._gen_downtime[:] = 0
        self._gen_activeprod_t[:] = 0.0
        self._gen_activeprod_t_redisp[:] = 0.0

    def _get_new_prod_setpoint(self, action):
        """
        NB this is overidden in _ObsEnv where the data are read from the action to set this environment
        instead
        """
        # get the modification of generator active setpoint from the action
        new_p = 1.0 * self._gen_activeprod_t
        if "prod_p" in action._dict_inj:
            tmp = action._dict_inj["prod_p"]
            indx_ok = np.isfinite(tmp)
            new_p[indx_ok] = tmp[indx_ok]

        # modification of the environment always override the modification of the agents (if any)
        # TODO have a flag there if this is the case.
        if "prod_p" in self._env_modification._dict_inj:
            # modification of the production setpoint value
            tmp = self._env_modification._dict_inj["prod_p"]
            indx_ok = np.isfinite(tmp)
            new_p[indx_ok] = tmp[indx_ok]
        return new_p

    def _get_already_modified_gen(self, action):

        redisp_act_orig = 1.0 * action._redispatch
        self._target_dispatch[self._already_modified_gen] += redisp_act_orig[self._already_modified_gen]
        first_modified = (~self._already_modified_gen) & (redisp_act_orig != 0)
        self._target_dispatch[first_modified] = (
            self._actual_dispatch[first_modified] + redisp_act_orig[first_modified]
        )
        self._already_modified_gen[redisp_act_orig != 0] = True
        return self._already_modified_gen

    def _prepare_redisp(self, action, new_p, already_modified_gen):
        # trying with an optimization method
        except_ = None
        info_ = []
        valid = True

        # get the redispatching action (if any)
        redisp_act_orig = 1.0 * action._redispatch

        if (
            np.all(redisp_act_orig == 0.0)
            and np.all(self._target_dispatch == 0.0)
            and np.all(self._actual_dispatch == 0.0)
        ):
            return valid, except_, info_
        # check that everything is consistent with pmin, pmax:
        if np.any(self._target_dispatch > self.gen_pmax - self.gen_pmin):
            # action is invalid, the target redispatching would be above pmax for at least a generator
            cond_invalid = self._target_dispatch > self.gen_pmax - self.gen_pmin
            except_ = InvalidRedispatching(
                "You cannot ask for a dispatch higher than pmax - pmin  [it would be always "
                "invalid because, even if the sepoint is pmin, this dispatch would set it "
                "to a number higher than pmax, which is impossible]. Invalid dispatch for "
                "generator(s): "
                "{}".format(np.where(cond_invalid)[0])
            )
            self._target_dispatch -= redisp_act_orig
            return valid, except_, info_
        if np.any(self._target_dispatch < self.gen_pmin - self.gen_pmax):
            # action is invalid, the target redispatching would be below pmin for at least a generator
            cond_invalid = self._target_dispatch < self.gen_pmin - self.gen_pmax
            except_ = InvalidRedispatching(
                "You cannot ask for a dispatch lower than pmin - pmax  [it would be always "
                "invalid because, even if the sepoint is pmax, this dispatch would set it "
                "to a number bellow pmin, which is impossible]. Invalid dispatch for "
                "generator(s): "
                "{}".format(np.where(cond_invalid)[0])
            )
            self._target_dispatch -= redisp_act_orig
            return valid, except_, info_

        # i can't redispatch turned off generators [turned off generators need to be turned on before redispatching]
        if np.any(redisp_act_orig[new_p == 0.0]) and self._forbid_dispatch_off:
            # action is invalid, a generator has been redispatched, but it's turned off
            except_ = InvalidRedispatching(
                "Impossible to dispatch a turned off generator"
            )
            self._target_dispatch -= redisp_act_orig
            return valid, except_, info_

        if self._forbid_dispatch_off is True:
            redisp_act_orig_cut = 1.0 * redisp_act_orig
            redisp_act_orig_cut[new_p == 0.0] = 0.0
            if np.any(redisp_act_orig_cut != redisp_act_orig):
                info_.append(
                    {
                        "INFO: redispatching cut because generator will be turned_off": np.where(
                            redisp_act_orig_cut != redisp_act_orig
                        )[
                            0
                        ]
                    }
                )
        return valid, except_, info_

    def _make_redisp(self, already_modified_gen, new_p):
        """this computes the redispaching vector, taking into account the storage units"""
        except_ = None
        valid = True
        mismatch = self._actual_dispatch - self._target_dispatch
        mismatch = np.abs(mismatch)
        if (
            np.abs(np.sum(self._actual_dispatch)) >= self._tol_poly
            or np.max(mismatch) >= self._tol_poly
            or np.abs(self._amount_storage) >= self._tol_poly
            or np.abs(self._sum_curtailment_mw) >= self._tol_poly
        ):
            except_ = self._compute_dispatch_vect(already_modified_gen, new_p)
            valid = except_ is None
        return valid, except_

    def _compute_dispatch_vect(self, already_modified_gen, new_p):        
        except_ = None
        # first i define the participating generators
        # these are the generators that will be adjusted for redispatching
        gen_participating = (
            (new_p > 0.0)
            | (self._actual_dispatch != 0.0)
            | (self._target_dispatch != self._actual_dispatch)
        )
        gen_participating[~self.gen_redispatchable] = False
        incr_in_chronics = new_p - (
            self._gen_activeprod_t_redisp - self._actual_dispatch
        )

        # check if the constraints are violated
        ## total available "juice" to go down (incl ramp and pmin / pmax)
        p_min_down = (
            self.gen_pmin[gen_participating]
            - self._gen_activeprod_t_redisp[gen_participating]
        )
        avail_down = np.maximum(p_min_down, -self.gen_max_ramp_down[gen_participating])
        ## total available "juice" to go up (incl. ramp and pmin / pmax)
        p_max_up = (
            self.gen_pmax[gen_participating]
            - self._gen_activeprod_t_redisp[gen_participating]
        )
        avail_up = np.minimum(p_max_up, self.gen_max_ramp_up[gen_participating])
        except_ = self._detect_infeasible_dispatch(
            incr_in_chronics[gen_participating], avail_down, avail_up
        )
        if except_ is not None:
            # try to force the turn on of turned off generators (if parameters allow it)
            if (
                self._parameters.IGNORE_MIN_UP_DOWN_TIME
                and self._parameters.ALLOW_DISPATCH_GEN_SWITCH_OFF
            ):
                gen_participating_tmp = self.gen_redispatchable
                p_min_down_tmp = (
                    self.gen_pmin[gen_participating_tmp]
                    - self._gen_activeprod_t_redisp[gen_participating_tmp]
                )
                avail_down_tmp = np.maximum(
                    p_min_down_tmp, -self.gen_max_ramp_down[gen_participating_tmp]
                )
                p_max_up_tmp = (
                    self.gen_pmax[gen_participating_tmp]
                    - self._gen_activeprod_t_redisp[gen_participating_tmp]
                )
                avail_up_tmp = np.minimum(
                    p_max_up_tmp, self.gen_max_ramp_up[gen_participating_tmp]
                )
                except_tmp = self._detect_infeasible_dispatch(
                    incr_in_chronics[gen_participating_tmp],
                    avail_down_tmp,
                    avail_up_tmp,
                )
                if except_tmp is None:
                    # I can "save" the situation by turning on all generators, I do it
                    # TODO logger here
                    gen_participating = gen_participating_tmp
                    except_ = None
                else:
                    return except_tmp
            else:
                return except_

        # define the objective value
        target_vals = (
            self._target_dispatch[gen_participating]
            - self._actual_dispatch[gen_participating]
        )
        already_modified_gen_me = already_modified_gen[gen_participating]
        target_vals_me = target_vals[already_modified_gen_me]
        nb_dispatchable = np.sum(gen_participating)
        tmp_zeros = np.zeros((1, nb_dispatchable), dtype=dt_float)
        coeffs = 1.0 / (
            self.gen_max_ramp_up + self.gen_max_ramp_down + self._epsilon_poly
        )
        weights = np.ones(nb_dispatchable) * coeffs[gen_participating]
        weights /= weights.sum()

        if target_vals_me.shape[0] == 0:
            # no dispatch means all dispatchable, otherwise i will never get to 0
            already_modified_gen_me[:] = True
            target_vals_me = target_vals[already_modified_gen_me]

        # for numeric stability
        # to scale the input also:
        # see https://stackoverflow.com/questions/11155721/positive-directional-derivative-for-linesearch
        scale_x = max(np.max(np.abs(self._actual_dispatch)), 1.0)
        scale_x = dt_float(scale_x)
        target_vals_me_optim = 1.0 * (target_vals_me / scale_x)
        target_vals_me_optim = target_vals_me_optim.astype(dt_float)

        # see https://stackoverflow.com/questions/11155721/positive-directional-derivative-for-linesearch
        # where they advised to scale the function
        scale_objective = max(0.5 * np.sum(np.abs(target_vals_me_optim)) ** 2, 1.0)
        scale_objective = np.round(scale_objective, decimals=4)
        scale_objective = dt_float(scale_objective)

        # add the "sum to 0"
        mat_sum_0_no_turn_on = np.ones((1, nb_dispatchable), dtype=dt_float)
        # this is where the storage is taken into account
        # storages are "load convention" this means that i need to sum the amount of production to sum of storage
        # hence the "+ self._amount_storage" below
        # self._sum_curtailment_mw is "generator convention" hence the "-" there
        const_sum_0_no_turn_on = (
            np.zeros(1, dtype=dt_float)
            + self._amount_storage
            - self._sum_curtailment_mw
        )
        # gen increase in the chronics
        new_p_th = new_p[gen_participating] + self._actual_dispatch[gen_participating]

        # minimum value available for disp
        ## first limit delta because of pmin
        p_min_const = self.gen_pmin[gen_participating] - new_p_th
        ## second limit delta because of ramps
        ramp_down_const = (
            -self.gen_max_ramp_down[gen_participating]
            - incr_in_chronics[gen_participating]
        )
        ## take max of the 2
        min_disp = np.maximum(p_min_const, ramp_down_const)
        min_disp = min_disp.astype(dt_float)

        # maximum value available for disp
        ## first limit delta because of pmin
        p_max_const = self.gen_pmax[gen_participating] - new_p_th
        ## second limit delta because of ramps
        ramp_up_const = (
            self.gen_max_ramp_up[gen_participating]
            - incr_in_chronics[gen_participating]
        )
        ## take min of the 2
        max_disp = np.minimum(p_max_const, ramp_up_const)
        max_disp = max_disp.astype(dt_float)

        # add everything into a linear constraint object
        # equality
        added = 0.5 * self._epsilon_poly
        equality_const = LinearConstraint(
            mat_sum_0_no_turn_on,  # do the sum
            (const_sum_0_no_turn_on) / scale_x,  # lower bound
            (const_sum_0_no_turn_on) / scale_x,  # upper bound
        )
        mat_pmin_max_ramps = np.eye(nb_dispatchable)
        ineq_const = LinearConstraint(
            mat_pmin_max_ramps,
            (min_disp - added) / scale_x,
            (max_disp + added) / scale_x,
        )

        # choose a good initial point (close to the solution)
        # the idea here is to chose a initial point that would be close to the
        # desired solution (split the (sum of the) dispatch to the available generators)
        x0 = np.zeros(np.sum(gen_participating))
        if np.any(self._target_dispatch != 0.) or np.any(already_modified_gen):
            gen_for_x0 = self._target_dispatch[gen_participating] != 0.
            gen_for_x0 |= already_modified_gen[gen_participating]
            x0[gen_for_x0] = (
                self._target_dispatch[gen_participating][gen_for_x0]
                - self._actual_dispatch[gen_participating][gen_for_x0]
            ) / scale_x
            # at this point x0 is made of the difference between the target and the
            # actual dispatch for all generators that have a 
            # target dispatch non 0.
            
            # in this "if" block I set the other component of x0 to 
            # their "right" value
            can_adjust = (x0 == 0.0)
            if np.any(can_adjust):
                init_sum = np.sum(x0)
                denom_adjust = np.sum(1.0 / weights[can_adjust])
                if denom_adjust <= 1e-2:
                    # i don't want to divide by something too cloose to 0.
                    denom_adjust = 1.0
                x0[can_adjust] = -init_sum / (weights[can_adjust] * denom_adjust)
        else:
            # to "force" the exact reset to 0.0 for all components
            x0 -= self._actual_dispatch[gen_participating] / scale_x

        def target(actual_dispatchable):
            # define my real objective
            quad_ = (
                actual_dispatchable[already_modified_gen_me] - target_vals_me_optim
            ) ** 2
            coeffs_quads = weights[already_modified_gen_me] * quad_
            coeffs_quads_const = coeffs_quads.sum()
            coeffs_quads_const /= scale_objective  # scaling the function
            return coeffs_quads_const

        def jac(actual_dispatchable):
            res_jac = 1.0 * tmp_zeros
            res_jac[0, already_modified_gen_me] = (
                2.0
                * weights[already_modified_gen_me]
                * (actual_dispatchable[already_modified_gen_me] - target_vals_me_optim)
            )
            res_jac /= scale_objective  # scaling the function
            return res_jac

        # objective function
        def f(init):
            this_res = minimize(
                target,
                init,
                method="SLSQP",
                constraints=[equality_const, ineq_const],
                options={
                    "eps": max(self._epsilon_poly / scale_x, 1e-6),
                    "ftol": max(self._epsilon_poly / scale_x, 1e-6),
                    "disp": False,
                },
                jac=jac
                # hess=hess  # not used for SLSQP
            )
            return this_res
        res = f(x0)
        if res.success:
            self._actual_dispatch[gen_participating] += res.x * scale_x
        else:
            # check if constraints are "approximately" met
            mat_const = np.concatenate((mat_sum_0_no_turn_on, mat_pmin_max_ramps))
            downs = np.concatenate(
                (const_sum_0_no_turn_on / scale_x, (min_disp - added) / scale_x)
            )
            ups = np.concatenate(
                (const_sum_0_no_turn_on / scale_x, (max_disp + added) / scale_x)
            )
            vals = np.matmul(mat_const, res.x)
            ok_down = np.all(
                vals - downs >= -self._tol_poly
            )  # i don't violate "down" constraints
            ok_up = np.all(vals - ups <= self._tol_poly)
            if ok_up and ok_down:
                # it's ok i can tolerate "small" perturbations
                self._actual_dispatch[gen_participating] += res.x * scale_x
            else:
                # TODO try with another method here, maybe
                error_dispatch = (
                    "Redispatching automaton terminated with error (no more information available "
                    'at this point):\n"{}"'.format(res.message)
                )
                except_ = InvalidRedispatching(error_dispatch)
        return except_

    def _detect_infeasible_dispatch(self, incr_in_chronics, avail_down, avail_up):
        """This function is an attempt to give more detailed log by detecting infeasible dispatch"""
        except_ = None
        sum_move = (
            np.sum(incr_in_chronics) + self._amount_storage - self._sum_curtailment_mw
        )
        avail_down_sum = np.sum(avail_down)
        avail_up_sum = np.sum(avail_up)
        gen_setpoint = self._gen_activeprod_t_redisp[self.gen_redispatchable]
        if sum_move > avail_up_sum:
            # infeasible because too much is asked
            msg = DETAILED_REDISP_ERR_MSG.format(
                sum_move=sum_move,
                avail_up_sum=avail_up_sum,
                gen_setpoint=np.round(gen_setpoint, decimals=2),
                ramp_up=self.gen_max_ramp_up[self.gen_redispatchable],
                gen_pmax=self.gen_pmax[self.gen_redispatchable],
                avail_up=np.round(avail_up, decimals=2),
                increase="increase",
                decrease="decrease",
                maximum="maximum",
                pmax="pmax",
                max_ramp_up="max_ramp_up",
            )
            except_ = InvalidRedispatching(msg)
        elif sum_move < avail_down_sum:
            # infeasible because not enough is asked
            msg = DETAILED_REDISP_ERR_MSG.format(
                sum_move=sum_move,
                avail_up_sum=avail_down_sum,
                gen_setpoint=np.round(gen_setpoint, decimals=2),
                ramp_up=self.gen_max_ramp_down[self.gen_redispatchable],
                gen_pmax=self.gen_pmin[self.gen_redispatchable],
                avail_up=np.round(avail_up, decimals=2),
                increase="decrease",
                decrease="increase",
                maximum="minimum",
                pmax="pmin",
                max_ramp_up="max_ramp_down",
            )
            except_ = InvalidRedispatching(msg)
        return except_

    def _update_actions(self):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Retrieve the actions to perform the update of the underlying powergrid represented by
        the :class:`grid2op.Backend`in the next time step.

        A call to this function will also read the next state of :attr:`chronics_handler`, so it must be called only
        once per time step.

        Returns
        --------
        res: :class:`grid2op.Action.Action`
            The action representing the modification of the powergrid induced by the Backend.
        """
        (
            timestamp,
            tmp,
            maintenance_time,
            maintenance_duration,
            hazard_duration,
            prod_v,
        ) = self.chronics_handler.next_time_step()
        if "injection" in tmp:
            self._injection = tmp["injection"]
        else:
            self._injection = None
        if "maintenance" in tmp:
            self._maintenance = tmp["maintenance"]
        else:
            self._maintenance = None
        if "hazards" in tmp:
            self._hazards = tmp["hazards"]
        else:
            self._hazards = None
        self.time_stamp = timestamp
        self._duration_next_maintenance = maintenance_duration
        self._time_next_maintenance = maintenance_time
        self._hazard_duration = hazard_duration
        act = self._helper_action_env(
            {
                "injection": self._injection,
                "maintenance": self._maintenance,
                "hazards": self._hazards,
            }
        )
        return act, prod_v

    def _update_time_reconnection_hazards_maintenance(self):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        This supposes that :attr:`Environment.times_before_line_status_actionable` is already updated
        with the cascading failure, soft overflow and hard overflow.

        It also supposes that :func:`Environment._update_actions` has been called, so that the vectors
        :attr:`Environment.duration_next_maintenance`, :attr:`Environment._time_next_maintenance` and
        :attr:`Environment._hazard_duration` are updated with the most recent values.
        Finally the Environment supposes that this method is called before calling :func:`Environment.get_obs`
        This function integrates the hazards and maintenance in the
        :attr:`Environment.times_before_line_status_actionable` vector.

        For example, if a powerline `i` has no problem
        of overflow, but is affected by a hazard, :attr:`Environment.times_before_line_status_actionable`
        should be updated with the duration of this hazard (stored in one of the three vector mentionned in the
        above paragraph)
        For this Environment, we suppose that the maximum of the 3 values are taken into account. The reality would
        be more complicated.

        """
        first_time_maintenance = self._time_next_maintenance == 0
        self._times_before_line_status_actionable[first_time_maintenance] = np.maximum(
            self._times_before_line_status_actionable[first_time_maintenance],
            self._duration_next_maintenance[first_time_maintenance],
        )
        self._times_before_line_status_actionable[:] = np.maximum(
            self._times_before_line_status_actionable, self._hazard_duration
        )

    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

        """
        res = self._helper_action_env()
        if prod_v_chronics is not None:
            res.update({"injection": {"prod_v": prod_v_chronics}})
        return res

    def _handle_updown_times(self, gen_up_before, redisp_act):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Handles the up and down tims for the generators.
        """
        # get the generators that are not connected after the action
        except_ = None

        # computes which generator will be turned on after the action
        gen_up_after = 1.0 * self._gen_activeprod_t
        if "prod_p" in self._env_modification._dict_inj:
            tmp = self._env_modification._dict_inj["prod_p"]
            indx_ok = np.isfinite(tmp)
            gen_up_after[indx_ok] = self._env_modification._dict_inj["prod_p"][indx_ok]
        gen_up_after += redisp_act
        gen_up_after = gen_up_after > 0.0

        # update min down time, min up time etc.
        gen_disconnected_this = gen_up_before & (~gen_up_after)
        gen_connected_this_timestep = (~gen_up_before) & (gen_up_after)
        gen_still_connected = gen_up_before & gen_up_after
        gen_still_disconnected = (~gen_up_before) & (~gen_up_after)

        if (
            np.any(
                self._gen_downtime[gen_connected_this_timestep]
                < self.gen_min_downtime[gen_connected_this_timestep]
            )
            and not self._ignore_min_up_down_times
        ):
            # i reconnected a generator before the minimum time allowed
            id_gen = (
                self._gen_downtime[gen_connected_this_timestep]
                < self.gen_min_downtime[gen_connected_this_timestep]
            )
            id_gen = np.where(id_gen)[0]
            id_gen = np.where(gen_connected_this_timestep[id_gen])[0]
            except_ = GeneratorTurnedOnTooSoon(
                "Some generator has been connected too early ({})".format(id_gen)
            )
            return except_
        else:
            self._gen_downtime[gen_connected_this_timestep] = -1
            self._gen_uptime[gen_connected_this_timestep] = 1

        if (
            np.any(
                self._gen_uptime[gen_disconnected_this]
                < self.gen_min_uptime[gen_disconnected_this]
            )
            and not self._ignore_min_up_down_times
        ):
            # i disconnected a generator before the minimum time allowed
            id_gen = (
                self._gen_uptime[gen_disconnected_this]
                < self.gen_min_uptime[gen_disconnected_this]
            )
            id_gen = np.where(id_gen)[0]
            id_gen = np.where(gen_connected_this_timestep[id_gen])[0]
            except_ = GeneratorTurnedOffTooSoon(
                "Some generator has been disconnected too early ({})".format(id_gen)
            )
            return except_
        else:
            self._gen_downtime[gen_connected_this_timestep] = 0
            self._gen_uptime[gen_connected_this_timestep] = 1

        self._gen_uptime[gen_still_connected] += 1
        self._gen_downtime[gen_still_disconnected] += 1
        return except_

    def get_obs(self, _update_state=True):
        """
        Return the observations of the current environment made by the :class:`grid2op.Agent.BaseAgent`.

        .. note::
            This function is called twice when the env is reset, otherwise once per step

        Returns
        -------
        res: :class:`grid2op.Observation.BaseObservation`
            The current observation usually given to the :class:`grid2op.Agent.BaseAgent` / bot / controler.

        Examples
        ---------

        This function can be use at any moment, even if the actual observation is not present.

        .. code-block:: python

            import grid2op

            # I create an environment
            env = grid2op.make()

            obs = env.reset()

            # have a big piece of code
            obs2 = env.get_obs()

            # obs2 and obs are identical.

        """
        if self.__closed:
            raise EnvError("This environment is closed. You cannot use it anymore.")
        if not self.__is_init:
            raise EnvError(
                "This environment is not initialized. You cannot retrieve its observation. "
                "Have you called `env.reset()` after last game over ?"
            )
        if self._last_obs is None:
            self._last_obs = self._observation_space(
                env=self, _update_state=_update_state
            )
        
        return self._last_obs.copy()

    def get_thermal_limit(self):
        """
        Get the current thermal limit in amps registered for the environment.

        Examples
        ---------

        It can be used like this:

        .. code-block:: python

            import grid2op

            # I create an environment
            env = grid2op.make()

            thermal_limits = env.get_thermal_limit()

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        if not self.__is_init:
            raise EnvError(
                "This environment is not initialized. It has no thermal limits. "
                "Have you called `env.reset()` after last game over ?"
            )
        return 1.0 * self._thermal_limit_a

    def _withdraw_storage_losses(self):
        """
        empty the energy in the storage units depending on the `storage_loss`

        NB this is a loss, this is not seen grid side, so `storage_discharging_efficiency` has no impact on this
        """
        # NB this should be done AFTER the computation of self._amount_storage, because this energy is dissipated
        # in the storage units, thus NOT seen as power from the grid.
        if self._parameters.ACTIVATE_STORAGE_LOSS:
            tmp_ = self.storage_loss * self.delta_time_seconds / 3600.0
            self._storage_current_charge -= tmp_
            # charge cannot be negative, but it can be below Emin if there are some uncompensated losses
            self._storage_current_charge[:] = np.maximum(
                self._storage_current_charge, 0.0
            )

    def _aux_remove_power_too_high(self, delta_, indx_too_high):
        """
        delta_ is given in energy (and NOT power)

        handles self._storage_power in
        case we need to cut the storage action because the power would be too high
        """
        coeff_p_to_E = (
            self.delta_time_seconds / 3600.0
        )  # TODO optim this is const for all time steps
        tmp_ = 1.0 / coeff_p_to_E * delta_
        if self._parameters.ACTIVATE_STORAGE_LOSS:
            # from the storage i need to reduce of tmp_ MW (to compensate the delta_ MWh)
            # but when it's "transfer" to the grid i don't have the same amount (due to inefficiencies)
            # it's a "/" because i need more energy from the grid than what the actual charge will be
            tmp_ /= self.storage_charging_efficiency[indx_too_high]
        self._storage_power[indx_too_high] -= tmp_

    def _aux_remove_power_too_low(self, delta_, indx_too_low):
        """
        delta_ is given in energy (and NOT power)

        handles self._storage_power in
        case we need to cut the storage action because the power would be too low
        """
        coeff_p_to_E = (
            self.delta_time_seconds / 3600.0
        )  # TODO optim this is const for all time steps
        tmp_ = 1.0 / coeff_p_to_E * delta_
        if self._parameters.ACTIVATE_STORAGE_LOSS:
            # from the storage i need to increase of tmp_ MW (to compensate the delta_ MWh)
            # but when it's "transfer" to the grid i don't have the same amount (due to inefficiencies)
            # it's a "*" because i have less power on the grid than what is removed from the battery
            tmp_ *= self.storage_discharging_efficiency[indx_too_low]
        self._storage_power[indx_too_low] -= tmp_

    def _compute_storage(self, action_storage_power):
        self._storage_previous_charge[:] = self._storage_current_charge
        storage_act = np.isfinite(action_storage_power) & (action_storage_power != 0.0)
        self._action_storage[:] = 0.0
        self._storage_power[:] = 0.0
        modif = False
        coeff_p_to_E = (
            self.delta_time_seconds / 3600.0
        )  # TODO optim this is const for all time steps
        if np.any(storage_act):
            modif = True
            this_act_stor = action_storage_power[storage_act]
            eff_ = np.ones(np.sum(storage_act))
            if self._parameters.ACTIVATE_STORAGE_LOSS:
                fill_storage = (
                    this_act_stor > 0.0
                )  # index of storages that sees their charge increasing
                unfill_storage = (
                    this_act_stor < 0.0
                )  # index of storages that sees their charge decreasing
                eff_[fill_storage] *= self.storage_charging_efficiency[storage_act][
                    fill_storage
                ]
                eff_[unfill_storage] /= self.storage_discharging_efficiency[
                    storage_act
                ][unfill_storage]
            self._storage_current_charge[storage_act] += (
                this_act_stor * coeff_p_to_E * eff_
            )
            self._action_storage[storage_act] += action_storage_power[storage_act]
            self._storage_power[storage_act] = this_act_stor

        if modif:
            # indx when there is too much energy on the battery
            indx_too_high = self._storage_current_charge > self.storage_Emax
            if np.any(indx_too_high):
                delta_ = (
                    self._storage_current_charge[indx_too_high]
                    - self.storage_Emax[indx_too_high]
                )
                self._aux_remove_power_too_high(delta_, indx_too_high)
                self._storage_current_charge[indx_too_high] = self.storage_Emax[
                    indx_too_high
                ]

            # indx when there is not enough energy on the battery
            indx_too_low = self._storage_current_charge < self.storage_Emin
            if np.any(indx_too_low):
                delta_ = (
                    self._storage_current_charge[indx_too_low]
                    - self.storage_Emin[indx_too_low]
                )
                self._aux_remove_power_too_low(delta_, indx_too_low)
                self._storage_current_charge[indx_too_low] = self.storage_Emin[
                    indx_too_low
                ]

            self._storage_current_charge[:] = np.maximum(
                self._storage_current_charge, self.storage_Emin
            )
            # storage is "load convention", dispatch is "generator convention"
            # i need the generator to have the same sign as the action on the batteries
            self._amount_storage = np.sum(self._storage_power)
        else:
            # battery effect should be removed, so i multiply it by -1.
            self._amount_storage = 0.0

        tmp = self._amount_storage
        self._amount_storage -= self._amount_storage_prev
        self._amount_storage_prev = tmp

        # dissipated energy, it's not seen on the grid, just lost in the storage unit.
        # this is why it should not be taken into account in self._amount_storage
        # and NOT absorbed by the generators either
        # NB loss in the storage unit can make it got below Emin in energy, but never below 0.
        self._withdraw_storage_losses()
        # end storage

    def _compute_max_ramp_this_step(self, new_p):
        """
        compute the total "power" i can add or remove this step that takes into account
        generators ramps and Pmin / Pmax

        new_p: array of the (temporary) new production in the chronics that should happen
        """
        # TODO
        # maximum value it can take
        th_max = np.minimum(
            self._gen_activeprod_t_redisp[self.gen_redispatchable]
            + self.gen_max_ramp_up[self.gen_redispatchable],
            self.gen_pmax[self.gen_redispatchable],
        )
        # minimum value it can take
        th_min = np.maximum(
            self._gen_activeprod_t_redisp[self.gen_redispatchable]
            - self.gen_max_ramp_down[self.gen_redispatchable],
            self.gen_pmin[self.gen_redispatchable],
        )

        max_total_up = np.sum(th_max - new_p[self.gen_redispatchable])
        max_total_down = np.sum(
            th_min - new_p[self.gen_redispatchable]
        )  # TODO is that it ?
        return max_total_down, max_total_up

    def _aux_update_curtail_env_act(self, new_p):
        if "prod_p" in self._env_modification._dict_inj:
            self._env_modification._dict_inj["prod_p"][:] = new_p
        else:
            self._env_modification._dict_inj["prod_p"] = 1.0 * new_p
            self._env_modification._modif_inj = True

    def _aux_update_curtailment_act(self, action):
        curtailment_act = 1.0 * action._curtail
        ind_curtailed_in_act = (curtailment_act != -1.0) & self.gen_renewable
        self._limit_curtailment_prev[:] = self._limit_curtailment
        self._limit_curtailment[ind_curtailed_in_act] = curtailment_act[
            ind_curtailed_in_act
        ]

    def _aux_compute_new_p_curtailment(self, new_p, curtailment_vect):
        """modifies the new_p argument !!!!"""
        gen_curtailed = (
            curtailment_vect != 1.0
        )  # curtailed either right now, or in a previous action
        max_action = self.gen_pmax[gen_curtailed] * curtailment_vect[gen_curtailed]
        new_p[gen_curtailed] = np.minimum(max_action, new_p[gen_curtailed])
        return gen_curtailed

    def _aux_handle_curtailment_without_limit(self, action, new_p):
        """modifies the new_p argument !!!! (but not the action)"""
        if self.redispatching_unit_commitment_availble and (
            action._modif_curtailment or np.any(self._limit_curtailment != 1.0)
        ):
            self._aux_update_curtailment_act(action)

            gen_curtailed = self._aux_compute_new_p_curtailment(
                new_p, self._limit_curtailment
            )

            tmp_sum_curtailment_mw = dt_float(
                np.sum(new_p[gen_curtailed])
                - np.sum(self._gen_before_curtailment[gen_curtailed])
            )

            self._sum_curtailment_mw = (
                tmp_sum_curtailment_mw - self._sum_curtailment_mw_prev
            )
            self._sum_curtailment_mw_prev = tmp_sum_curtailment_mw
            self._aux_update_curtail_env_act(new_p)
        else:
            self._sum_curtailment_mw = -self._sum_curtailment_mw_prev
            self._sum_curtailment_mw_prev = dt_float(0.0)
            gen_curtailed = self._limit_curtailment != 1.0

        return gen_curtailed

    def _aux_readjust_curtailment_after_limiting(
        self, total_curtailment, new_p_th, new_p
    ):
        self._sum_curtailment_mw += total_curtailment
        self._sum_curtailment_mw_prev += total_curtailment
        if total_curtailment > self._tol_poly:
            # in this case, the curtailment is too strong, I need to make it less strong
            curtailed = new_p_th - new_p
        else:
            # in this case, the curtailment is too low, this can happen, for example when there is a
            # "strong" curtailment but afterwards you ask to set everything to 1. (so no curtailment)
            # I cannot reuse the previous case (too_much > self._tol_poly) because the
            # curtailment is already computed there...
            new_p_with_previous_curtailment = 1.0 * new_p_th
            self._aux_compute_new_p_curtailment(
                new_p_with_previous_curtailment, self._limit_curtailment_prev
            )
            curtailed = new_p_th - new_p_with_previous_curtailment

        curt_sum = curtailed.sum()
        if abs(curt_sum) > self._tol_poly:
            curtailed[~self.gen_renewable] = 0.0
            curtailed *= total_curtailment / curt_sum
            new_p[self.gen_renewable] += curtailed[self.gen_renewable]

    def _aux_readjust_storage_after_limiting(self, total_storage):
        new_act_storage = 1.0 * self._storage_power
        sum_this_step = new_act_storage.sum()
        if abs(total_storage) < abs(sum_this_step):
            # i can modify the current action
            modif_storage = new_act_storage * total_storage / sum_this_step
        else:
            # i need to retrieve what I did in a previous action
            # because the current action is not enough (the previous actions
            # cause a problem right now)
            new_act_storage = 1.0 * self._storage_power_prev
            sum_this_step = new_act_storage.sum()
            if abs(sum_this_step) > 1e-1:
                modif_storage = new_act_storage * total_storage / sum_this_step
            else:
                # TODO: this is not cover by any test :-(
                # it happens when you do an action too strong, then a do nothing,
                # then you decrease the limit to rapidly 
                # (game over would jappen after at least one do nothing)
                
                # In this case I reset it completely or do I ? I don't really
                # know what to do !
                modif_storage = new_act_storage  # or self._storage_power ???

        # handle self._storage_power and self._storage_current_charge
        coeff_p_to_E = (
            self.delta_time_seconds / 3600.0
        )  # TODO optim this is const for all time steps
        self._storage_power -= modif_storage

        # now compute the state of charge of the storage units (with efficiencies)
        is_discharging = self._storage_power < 0.0
        is_charging = self._storage_power > 0.0
        modif_storage[is_discharging] /= type(self).storage_discharging_efficiency[
            is_discharging
        ]
        modif_storage[is_charging] *= type(self).storage_charging_efficiency[
            is_charging
        ]

        self._storage_current_charge -= coeff_p_to_E * modif_storage
        # inform the grid that the storage is reduced
        self._amount_storage -= total_storage
        self._amount_storage_prev -= total_storage

    def _aux_limit_curtail_storage_if_needed(self, new_p, new_p_th, gen_curtailed):
        gen_redisp = self.gen_redispatchable

        normal_increase = new_p - (
            self._gen_activeprod_t_redisp - self._actual_dispatch
        )
        normal_increase = normal_increase[gen_redisp]
        p_min_down = (
            self.gen_pmin[gen_redisp] - self._gen_activeprod_t_redisp[gen_redisp]
        )
        avail_down = np.maximum(p_min_down, -self.gen_max_ramp_down[gen_redisp])
        p_max_up = self.gen_pmax[gen_redisp] - self._gen_activeprod_t_redisp[gen_redisp]
        avail_up = np.minimum(p_max_up, self.gen_max_ramp_up[gen_redisp])

        sum_move = (
            np.sum(normal_increase) + self._amount_storage - self._sum_curtailment_mw
        )
        total_storage_curtail = self._amount_storage - self._sum_curtailment_mw
        update_env_act = False

        if abs(total_storage_curtail) >= self._tol_poly:
            # if there is an impact on the curtailment / storage (otherwise I cannot fix anything)
            too_much = 0.0
            if sum_move > np.sum(avail_up):
                # I need to limit curtailment (not enough ramps up available)
                too_much = dt_float(sum_move - np.sum(avail_up) + self._tol_poly)
                self._limited_before = too_much
            elif sum_move < np.sum(avail_down):
                # I need to limit storage unit (not enough ramps down available)
                too_much = dt_float(sum_move - np.sum(avail_down) - self._tol_poly)
                self._limited_before = too_much
            elif np.abs(self._limited_before) >= self._tol_poly:
                # adjust the "mess" I did before by not curtailing enough
                # max_action = self.gen_pmax[gen_curtailed] * self._limit_curtailment[gen_curtailed]
                update_env_act = True
                too_much = min(np.sum(avail_up) - self._tol_poly, self._limited_before)
                self._limited_before -= too_much
                too_much = self._limited_before

            if abs(too_much) > self._tol_poly:
                total_curtailment = (
                    -self._sum_curtailment_mw / total_storage_curtail * too_much
                )
                total_storage = (
                    self._amount_storage / total_storage_curtail * too_much
                )  # TODO !!!
                update_env_act = True
                # TODO "log" the total_curtailment and total_storage somewhere (in the info part of the step function)

                if np.sign(total_curtailment) != np.sign(total_storage):
                    # curtailment goes up, storage down, i only "limit" the one that
                    # has the same sign as too much
                    total_curtailment = (
                        too_much
                        if np.sign(total_curtailment) == np.sign(too_much)
                        else 0.0
                    )
                    total_storage = (
                        too_much if np.sign(total_storage) == np.sign(too_much) else 0.0
                    )
                    # NB i can directly assign all the "curtailment" to the maximum because in this case, too_much will
                    # necessarily be > than total_curtail (or total_storage) because the other
                    # one is of opposite sign

                # fix curtailment
                self._aux_readjust_curtailment_after_limiting(
                    total_curtailment, new_p_th, new_p
                )

                # fix storage
                self._aux_readjust_storage_after_limiting(total_storage)

            if update_env_act:
                self._aux_update_curtail_env_act(new_p)

    def _aux_handle_act_inj(self, action: BaseAction):
        for inj_key in ["load_p", "prod_p", "load_q"]:
            # modification of the injections in the action, this erases the actions in the environment
            if inj_key in action._dict_inj:
                if inj_key in self._env_modification._dict_inj:
                    this_p_load = 1.0 * self._env_modification._dict_inj[inj_key]
                    act_modif = action._dict_inj[inj_key]
                    this_p_load[np.isfinite(act_modif)] = act_modif[
                        np.isfinite(act_modif)
                    ]
                    self._env_modification._dict_inj[inj_key][:] = this_p_load
                else:
                    self._env_modification._dict_inj[inj_key] = (
                        1.0 * action._dict_inj[inj_key]
                    )
                    self._env_modification._modif_inj = True

    def _aux_handle_attack(self, action: BaseAction):
        # TODO code the opponent part here and split more the timings! here "opponent time" is
        # TODO included in time_apply_act
        lines_attacked, subs_attacked = None, None
        attack, attack_duration = self._oppSpace.attack(
            observation=self.current_obs,
            agent_action=action,
            env_action=self._env_modification,
        )

        if attack is not None:
            # the opponent choose to attack
            # i update the "cooldown" on these things
            lines_attacked, subs_attacked = attack.get_topological_impact()
            self._times_before_line_status_actionable[lines_attacked] = np.maximum(
                attack_duration,
                self._times_before_line_status_actionable[lines_attacked],
            )
            self._times_before_topology_actionable[subs_attacked] = np.maximum(
                attack_duration, self._times_before_topology_actionable[subs_attacked]
            )
            self._backend_action += attack
        return lines_attacked, subs_attacked, attack_duration

    def _aux_apply_redisp(self, action, new_p, new_p_th, gen_curtailed, except_):
        is_illegal_redisp = False
        is_done = False
        is_illegal_reco = False

        # remember generator that were "up" before the action
        gen_up_before = self._gen_activeprod_t > 0.0

        # compute the redispatching and the new productions active setpoint
        already_modified_gen = self._get_already_modified_gen(action)
        valid_disp, except_tmp, info_ = self._prepare_redisp(
            action, new_p, already_modified_gen
        )

        if except_tmp is not None:
            orig_action = action
            action = self._action_space({})
            if type(self).dim_alerts:
                action.raise_alert = orig_action.raise_alert
            is_illegal_redisp = True
            except_.append(except_tmp)

            if self.n_storage > 0:
                # TODO curtailment: cancel it here too !
                self._storage_current_charge[:] = self._storage_previous_charge
                self._amount_storage -= self._amount_storage_prev

                # dissipated energy, it's not seen on the grid, just lost in the storage unit.
                # this is why it should not be taken into account in self._amount_storage
                # and NOT absorbed by the generators either
                self._withdraw_storage_losses()
                # end storage

        # fix redispatching for curtailment storage
        if (
            self.redispatching_unit_commitment_availble
            and self._parameters.LIMIT_INFEASIBLE_CURTAILMENT_STORAGE_ACTION
        ):
            # limit the curtailment / storage in case of infeasible redispatching
            self._aux_limit_curtail_storage_if_needed(new_p, new_p_th, gen_curtailed)

        self._storage_power_prev[:] = self._storage_power
        # case where the action modifies load (TODO maybe make a different env for that...)
        self._aux_handle_act_inj(action)

        valid_disp, except_tmp = self._make_redisp(already_modified_gen, new_p)

        if not valid_disp or except_tmp is not None:
            # game over case (divergence of the scipy routine to compute redispatching)
            res_action = self._action_space({})
            if type(self).dim_alerts:
                res_action.raise_alert = action.raise_alert
            is_illegal_redisp = True
            except_.append(except_tmp)
            is_done = True
            except_.append(
                InvalidRedispatching(
                    "Game over due to infeasible redispatching state. "
                    'The routine used to compute the "next state" has diverged. '
                    "This means that there is no way to compute a physically valid generator state "
                    "(one that meets all pmin / pmax - ramp min / ramp max with the information "
                    "provided. As one of the physical constraints would be violated, this means that "
                    "a generator would be damaged in real life. This is a game over."
                )
            )
            return res_action, is_illegal_redisp, is_illegal_reco, is_done

        # check the validity of min downtime and max uptime
        except_tmp = self._handle_updown_times(gen_up_before, self._actual_dispatch)
        if except_tmp is not None:
            is_illegal_reco = True
            res_action = self._action_space({})
            if type(self).dim_alerts:
                res_action.raise_alert = action.raise_alert
            except_.append(except_tmp)
        else:
            res_action = action
        return res_action, is_illegal_redisp, is_illegal_reco, is_done

    def _aux_update_backend_action(self, action, action_storage_power, init_disp):
        # make sure the dispatching action is not implemented "as is" by the backend.
        # the environment must make sure it's a zero-sum action.
        # same kind of limit for the storage
        action._redispatch[:] = 0.0
        action._storage_power[:] = self._storage_power
        self._backend_action += action
        action._storage_power[:] = action_storage_power
        action._redispatch[:] = init_disp
        # TODO storage: check the original action, even when replaced by do nothing is not modified
        self._backend_action += self._env_modification
        self._backend_action.set_redispatch(self._actual_dispatch)

    def _update_alert_properties(self, action, lines_attacked, subs_attacked):
        # update the environment with the alert information from the
        # action (if env supports it)
        if type(self).dim_alerts == 0:
            return
        
        self._last_alert[:] = action.raise_alert
        
        self._time_since_last_alert[~self._last_alert & (self._time_since_last_alert != -1)] += 1
        self._time_since_last_alert[self._last_alert] = 0
        
        self._alert_duration[self._last_alert] += 1
        self._alert_duration[~self._last_alert] = 0
        
        self._total_number_of_alert += self._last_alert.sum()
        
        if lines_attacked is not None:
            lines_attacked_al = lines_attacked[type(self).alertable_line_ids]
            mask_first_ts_attack = lines_attacked_al & (~self._is_already_attacked)
            self._time_since_last_attack[mask_first_ts_attack] = 0
            self._time_since_last_attack[~mask_first_ts_attack & (self._time_since_last_attack != -1)] += 1
            
            # update the time already attacked
            self._is_already_attacked[lines_attacked_al] = False
            self._is_already_attacked[lines_attacked_al] = True
        else:
            self._time_since_last_attack[self._time_since_last_attack != -1] += 1
            self._is_already_attacked[:] = False
            
        mask_new_attack = self._time_since_last_attack == 0
        self._attack_under_alert[mask_new_attack] =  2 * self._last_alert[mask_new_attack] - 1
        mask_attack_too_old = self._time_since_last_attack > self._parameters.ALERT_TIME_WINDOW
        self._attack_under_alert[mask_attack_too_old] = 0
        
        # TODO more complicated (will do it in update_after_reward)
        # self._was_alert_used_after_attack[:] = XXX
        
        # TODO after alert budget will be implemented !
        # self._is_alert_illegal
    
    def _aux_register_env_converged(self, disc_lines, action, init_line_status, new_p):
        beg_res = time.perf_counter()
        self.backend.update_thermal_limit(
            self
        )  # update the thermal limit, for DLR for example
        overflow_lines = self.backend.get_line_overflow()
        # save the current topology as "last" topology (for connected powerlines)
        # and update the state of the disconnected powerline due to cascading failure
        self._backend_action.update_state(disc_lines)

        # one timestep passed, i can maybe reconnect some lines
        self._times_before_line_status_actionable[
            self._times_before_line_status_actionable > 0
        ] -= 1
        # update the vector for lines that have been disconnected
        self._times_before_line_status_actionable[disc_lines >= 0] = int(
            self._nb_ts_reco
        )
        self._update_time_reconnection_hazards_maintenance()

        # for the powerline that are on overflow, increase this time step
        self._timestep_overflow[overflow_lines] += 1

        # set to 0 the number of timestep for lines that are not on overflow
        self._timestep_overflow[~overflow_lines] = 0

        # build the topological action "cooldown"
        aff_lines, aff_subs = action.get_topological_impact(init_line_status)
        if self._max_timestep_line_status_deactivated > 0:
            # i update the cooldown only when this does not impact the line disconnected for the
            # opponent or by maintenance for example
            cond = aff_lines  # powerlines i modified
            # powerlines that are not affected by any other "forced disconnection"
            cond &= (
                self._times_before_line_status_actionable
                < self._max_timestep_line_status_deactivated
            )
            self._times_before_line_status_actionable[
                cond
            ] = self._max_timestep_line_status_deactivated
            
        if self._max_timestep_topology_deactivated > 0:
            self._times_before_topology_actionable[
                self._times_before_topology_actionable > 0
            ] -= 1
            self._times_before_topology_actionable[
                aff_subs
            ] = self._max_timestep_topology_deactivated

        # extract production active value at this time step (should be independent of action class)
        self._gen_activeprod_t[:], *_ = self.backend.generators_info()
        # problem with the gen_activeprod_t above, is that the slack bus absorbs alone all the losses
        # of the system. So basically, when it's too high (higher than the ramp) it can
        # mess up the rest of the environment
        self._gen_activeprod_t_redisp[:] = new_p + self._actual_dispatch

        # set the line status
        self._line_status[:] = copy.deepcopy(self.backend.get_line_status())

        # finally, build the observation (it's a different one at each step, we cannot reuse the same one)
        # THIS SHOULD BE DONE AFTER EVERYTHING IS INITIALIZED !
        self.current_obs = self.get_obs()
        # TODO storage: get back the result of the storage ! with the illegal action when a storage unit
        # TODO is non zero and disconnected, this should be ok.
        self._time_extract_obs += time.perf_counter() - beg_res

    def _aux_run_pf_after_state_properly_set(
        self, action, init_line_status, new_p, except_
    ):
        has_error = True
        try:
            # compute the next _grid state
            beg_pf = time.perf_counter()
            disc_lines, detailed_info, conv_ = self.backend.next_grid_state(
                env=self, is_dc=self._env_dc
            )
            self._disc_lines[:] = disc_lines
            self._time_powerflow += time.perf_counter() - beg_pf
            if conv_ is None:
                # everything went well, so i register what is needed
                self._aux_register_env_converged(
                    disc_lines, action, init_line_status, new_p
                )
                has_error = False
            else:
                except_.append(conv_)
        except Grid2OpException as exc_:
            except_.append(exc_)
            if self.logger is not None:
                self.logger.error(
                    'Impossible to compute next grid state with error "{}"'.format(exc_)
                )
        return detailed_info, has_error

    def step(self, action: BaseAction) -> Tuple[BaseObservation, float, bool, dict]:
        """
        Run one timestep of the environment's dynamics. When end of
        episode is reached, you are responsible for calling `reset()`
        to reset this environment's state.
        Accepts an action and returns a tuple (observation, reward, done, info).

        If the :class:`grid2op.BaseAction.BaseAction` is illegal or ambiguous, the step is performed, but the action is
        replaced with a "do nothing" action.

        Parameters
        ----------
            action: :class:`grid2op.Action.Action`
                an action provided by the agent that is applied on the underlying through the backend.

        Returns
        -------
            observation: :class:`grid2op.Observation.Observation`
                agent's observation of the current environment

            reward: ``float``
                amount of reward returned after previous action

            done: ``bool``
                whether the episode has ended, in which case further step() calls will return undefined results

            info: ``dict``
                contains auxiliary diagnostic information (helpful for debugging, and sometimes learning). It is a
                dictionary with keys:

                    - "disc_lines": a numpy array (or ``None``) saying, for each powerline if it has been disconnected
                      due to overflow (if not disconnected it will be -1, otherwise it will be a
                      positive integer: 0 meaning that is one of the cause of the cascading failure, 1 means
                      that it is disconnected just after, 2 that it's disconnected just after etc.)
                    - "is_illegal" (``bool``) whether the action given as input was illegal
                    - "is_ambiguous" (``bool``) whether the action given as input was ambiguous.
                    - "is_dispatching_illegal" (``bool``) was the action illegal due to redispatching
                    - "is_illegal_reco" (``bool``) was the action illegal due to a powerline reconnection
                    - "reason_alarm_illegal" (``None`` or ``Exception``) reason for which the alarm is illegal
                      (it's None if no alarm are raised or if the alarm feature is not used)
                    - "reason_alert_illegal" (``None`` or ``Exception``) reason for which the alert is illegal
                      (it's None if no alert are raised or if the alert feature is not used)
                    - "opponent_attack_line" (``np.ndarray``, ``bool``) for each powerline, say if the opponent
                      attacked it (``True``) or not (``False``).
                    - "opponent_attack_sub" (``np.ndarray``, ``bool``) for each substation, say if the opponent
                      attacked it (``True``) or not (``False``).
                    - "opponent_attack_duration" (``int``) the duration of the current attack (if any)
                    - "exception" (``list`` of :class:`Exceptions.Exceptions.Grid2OpException` if an exception was
                      raised  or ``[]`` if everything was fine.)
                    - "detailed_infos_for_cascading_failures" (optional, only if the backend has been create with
                      `detailed_infos_for_cascading_failures=True`) the list of the intermediate steps computed during
                      the simulation of the "cascading failures".
                    - "rewards": dictionary of all "other_rewards" provided when the env was built.

        Examples
        ---------

        As any openAI gym environment, this is used like:

        .. code-block:: python

            import grid2op
            from grid2op.Agent import RandomAgent

            # I create an environment
            env = grid2op.make()

            # define an agent here, this is an example
            agent = RandomAgent(env.action_space)

            # environment need to be "reset" before usage:
            obs = env.reset()
            reward = env.reward_range[0]
            done = False

            # now run through each steps like this
            while not done:
                action = agent.act(obs, reward, done)
                obs, reward, done, info = env.step(action)

        Notes
        -----

        If the flag `done=True` is raised (*ie* this is the end of the episode) then the observation is NOT properly
        updated and should not be used at all.

        Actually, it will be in a "game over" state (see :class:`grid2op.Observation.BaseObservation.set_game_over`).

        """
        if self.__closed:
            raise EnvError("This environment is closed. You cannot use it anymore.")

        if not self.__is_init:
            raise Grid2OpException(
                "Impossible to make a step with a non initialized backend. Have you called "
                '"env.reset()" after the last game over ?'
            )
        # I did something after calling "env.seed()" which is
        # somehow "env.step()" or "env.reset()"
        self._has_just_been_seeded =  False  
        
        has_error = True
        is_done = False
        is_illegal = False
        is_ambiguous = False
        is_illegal_redisp = False
        is_illegal_reco = False
        reason_alarm_illegal = None
        self._is_alarm_illegal = False
        self._is_alarm_used_in_reward = False
        reason_alert_illegal = None
        self._is_alert_illegal = False
        self._is_alert_used_in_reward = False
        except_ = []
        detailed_info = []
        init_disp = 1.0 * action._redispatch  # dispatching action
        init_alert = None
        if type(self).dim_alerts > 0:
            init_alert = copy.deepcopy(action._raise_alert)
            
        action_storage_power = 1.0 * action._storage_power  # battery information
        attack_duration = 0
        lines_attacked, subs_attacked = None, None
        conv_ = None
        init_line_status = copy.deepcopy(self.backend.get_line_status())
        self.nb_time_step += 1
        self._disc_lines[:] = -1

        beg_step = time.perf_counter()
        self._last_obs : Optional[BaseObservation] = None
        self._forecasts = None  # force reading the forecast from the time series
        try:
            beg_ = time.perf_counter()

            ambiguous, except_tmp = action.is_ambiguous()
            if ambiguous:
                # action is replace by do nothing
                action = self._action_space({})
                init_disp = 1.0 * action._redispatch  # dispatching action
                action_storage_power = (
                    1.0 * action._storage_power
                )  # battery information
                is_ambiguous = True
                    
                if type(self).dim_alerts > 0:
                    # keep the alert even if the rest is ambiguous (if alert is non ambiguous)
                    is_ambiguous_alert = isinstance(except_tmp, AmbiguousActionRaiseAlert)
                    if is_ambiguous_alert:
                        # reset the alert
                        init_alert = np.zeros(type(self).dim_alerts, dtype=dt_bool)
                    else:
                        action.raise_alert = init_alert
                except_.append(except_tmp)

            is_legal, reason = self._game_rules(action=action, env=self)
            if not is_legal:
                # action is replace by do nothing
                action = self._action_space({})
                init_disp = 1.0 * action._redispatch  # dispatching action
                action_storage_power = (
                    1.0 * action._storage_power
                )  # battery information
                except_.append(reason)
                if type(self).dim_alerts > 0:
                    # keep the alert even if the rest is illegal
                    action.raise_alert = init_alert
                is_illegal = True

            if self._has_attention_budget:
                if type(self).assistant_warning_type == "zonal":
                    # this feature is implemented, so i do it
                    reason_alarm_illegal = self._attention_budget.register_action(
                        self, action, is_illegal, is_ambiguous
                    )
                    self._is_alarm_illegal = reason_alarm_illegal is not None

            # get the modification of generator active setpoint from the environment
            self._env_modification, prod_v_chronics = self._update_actions()
            self._env_modification._single_act = (
                False  # because it absorbs all redispatching actions
            )
            new_p = self._get_new_prod_setpoint(action)
            new_p_th = 1.0 * new_p

            # storage unit
            if self.n_storage > 0:
                # limiting the storage units is done in `_aux_apply_redisp`
                # this only ensure the Emin / Emax and all the actions
                self._compute_storage(action_storage_power)

            # curtailment (does not attempt to "limit" the curtailment to make sure
            # it is feasible)
            self._gen_before_curtailment[self.gen_renewable] = new_p[self.gen_renewable]
            gen_curtailed = self._aux_handle_curtailment_without_limit(action, new_p)

            beg__redisp = time.perf_counter()
            if self.redispatching_unit_commitment_availble or self.n_storage > 0.0:
                # this computes the "optimal" redispatching
                # and it is also in this function that the limiting of the curtailment / storage actions
                # is perform to make the state "feasible"
                res_disp = self._aux_apply_redisp(
                    action, new_p, new_p_th, gen_curtailed, except_
                )
                action, is_illegal_redisp, is_illegal_reco, is_done = res_disp
                
            self._time_redisp += time.perf_counter() - beg__redisp
            
            if not is_done:
                self._aux_update_backend_action(action, action_storage_power, init_disp)

                # now get the new generator voltage setpoint
                voltage_control_act = self._voltage_control(action, prod_v_chronics)
                self._backend_action += voltage_control_act

                # handle the opponent here
                tick = time.perf_counter()
                lines_attacked, subs_attacked, attack_duration = self._aux_handle_attack(
                    action
                )
                tock = time.perf_counter()
                self._time_opponent += tock - tick
                self._time_create_bk_act += tock - beg_
                
                self.backend.apply_action(self._backend_action)
                self._time_apply_act += time.perf_counter() - beg_

                # now it's time to run the powerflow properly
                # and to update the time dependant properties
                self._update_alert_properties(action, lines_attacked, subs_attacked)
                detailed_info, has_error = self._aux_run_pf_after_state_properly_set(
                    action, init_line_status, new_p, except_
                )
            else:
                has_error = True

        except StopIteration:
            # episode is over
            is_done = True
            
        self._backend_action.reset()
        end_step = time.perf_counter()
        self._time_step += end_step - beg_step
        if conv_ is not None:
            except_.append(conv_)
        self.infos = {
            "disc_lines": self._disc_lines,
            "is_illegal": is_illegal,
            "is_ambiguous": is_ambiguous,
            "is_dispatching_illegal": is_illegal_redisp,
            "is_illegal_reco": is_illegal_reco,
            "reason_alarm_illegal": reason_alarm_illegal,
            "reason_alert_illegal": reason_alert_illegal,
            "opponent_attack_line": lines_attacked,
            "opponent_attack_sub": subs_attacked,
            "opponent_attack_duration": attack_duration,
            "exception": except_,
        }

        if self.backend.detailed_infos_for_cascading_failures:
            self.infos["detailed_infos_for_cascading_failures"] = detailed_info
            
        self.done = self._is_done(has_error, is_done)
        self.current_reward, other_reward = self._get_reward(
            action,
            has_error,
            self.done,  # is_done
            is_illegal or is_illegal_redisp or is_illegal_reco,
            is_ambiguous,
        )
        self.infos["rewards"] = other_reward
        if has_error and self.current_obs is not None:
            # forward to the observation if an alarm is used or not
            if hasattr(self._reward_helper.template_reward, "has_alarm_component"):
                self._is_alarm_used_in_reward = (
                    self._reward_helper.template_reward.is_alarm_used
                )
            if hasattr(self._reward_helper.template_reward, "has_alert_component"):
                self._is_alert_used_in_reward = (
                    self._reward_helper.template_reward.is_alert_used
                )
            self.current_obs = self.get_obs(_update_state=False)
            # update the observation so when it's plotted everything is "shutdown"
            self.current_obs.set_game_over(self)
            
        if self._update_obs_after_reward and self.current_obs is not None:
            # transfer some information computed in the reward into the obs (if any)
            self.current_obs.update_after_reward(self)
            
        # TODO documentation on all the possible way to be illegal now
        if self.done:
            self.__is_init = False
        return self.current_obs, self.current_reward, self.done, self.infos

    def _get_reward(self, action, has_error, is_done, is_illegal, is_ambiguous):
        res = self._reward_helper(
            action, self, has_error, is_done, is_illegal, is_ambiguous
        )
        other_rewards = {
            k: v(action, self, has_error, is_done, is_illegal, is_ambiguous)
            for k, v in self.other_rewards.items()
        }
        return res, other_rewards

    def get_reward_instance(self):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Returns the instance of the object that is used to compute the reward.
        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        
        return self._reward_helper.template_reward

    def _is_done(self, has_error, is_done):
        no_more_data = self.chronics_handler.done()
        return has_error or is_done or no_more_data

    def _reset_vectors_and_timings(self):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Maintenance are not reset, otherwise the data are not read properly (skip the first time step)
        """
        self._no_overflow_disconnection = self._parameters.NO_OVERFLOW_DISCONNECTION
        self._timestep_overflow[:] = 0
        self._nb_timestep_overflow_allowed[
            :
        ] = self._parameters.NB_TIMESTEP_OVERFLOW_ALLOWED

        self.nb_time_step = 0  # to have the first step at 0
        self._hard_overflow_threshold = self._parameters.HARD_OVERFLOW_THRESHOLD
        self._env_dc = self._parameters.ENV_DC

        self._times_before_line_status_actionable[:] = 0
        self._max_timestep_line_status_deactivated = (
            self._parameters.NB_TIMESTEP_COOLDOWN_LINE
        )

        self._times_before_topology_actionable[:] = 0
        self._max_timestep_topology_deactivated = (
            self._parameters.NB_TIMESTEP_COOLDOWN_SUB
        )

        # reset timings
        self._time_apply_act = dt_float(0.0)
        self._time_powerflow = dt_float(0.0)
        self._time_extract_obs = dt_float(0.0)
        self._time_opponent = dt_float(0.0)
        self._time_create_bk_act = dt_float(0.0)
        self._time_redisp = dt_float(0.0)
        self._time_step = dt_float(0.0)

        if self._has_attention_budget:
            self._attention_budget.reset()

        # reward and others
        self.current_reward = self.reward_range[0]
        self.done = False

    def _reset_maintenance(self):
        self._time_next_maintenance[:] = -1
        self._duration_next_maintenance[:] = 0

    def __enter__(self):
        """
        Support *with-statement* for the environment.

        Examples
        --------

        .. code-block:: python

            import grid2op
            import grid2op.BaseAgent
            with grid2op.make() as env:
                agent = grid2op.BaseAgent.DoNothingAgent(env.action_space)
                act = env.action_space()
                obs, r, done, info = env.step(act)
                act = agent.act(obs, r, info)
                obs, r, done, info = env.step(act)

        """
        return self

    def __exit__(self, *args):
        """
        Support *with-statement* for the environment.
        """
        self.close()
        # propagate exception
        return False

    def close(self):
        """close an environment: this will attempt to free as much memory as possible.
        Note that after an environment is closed, you will not be able to use anymore.

        Any attempt to use a closed environment might result in non deterministic behaviour.
        """
        if self.__closed:
            raise EnvError(
                f"This environment {id(self)} {self} is closed already, you cannot close it a second time."
            )

        # todo there might be some side effect
        if hasattr(self, "_viewer") and self._viewer is not None:
            self._viewer = None
            self.viewer_fig = None

        if hasattr(self, "backend") and self.backend is not None:
            self.backend.close()
            del self.backend
        self.backend :Backend = None

        if hasattr(self, "_observation_space") and self._observation_space is not None:
            # do not forget to close the backend of the observation (used for simulate)
            self._observation_space.close()
            self._observation_space = None

        if hasattr(self, "_voltage_controler") and self._voltage_controler is not None:
            # in case there is a backend in the voltage controler
            self._voltage_controler.close()
            self._voltage_controler = None

        if hasattr(self, "_oppSpace") and self._oppSpace is not None:
            # in case there is a backend in the opponent space
            self._oppSpace.close()
            self._oppSpace = None

        if hasattr(self, "_helper_action_env") and self._helper_action_env is not None:
            # close the action helper
            self._helper_action_env.close()
            self._helper_action_env = None

        if hasattr(self, "_action_space") and self._action_space is not None:
            # close the action space if needed
            self._action_space.close()
            self._action_space = None

        if hasattr(self, "_reward_helper") and self._reward_helper is not None:
            # close the reward if needed
            self._reward_helper.close()
            self._reward_helper = None

        if hasattr(self, "other_rewards") and self.other_rewards is not None:
            for el, reward in self.other_rewards.items():
                # close the "other rewards"
                reward.close()
            self.other_rewards = None
            
        self.backend : Backend = None
        self.__is_init = False
        self.__closed = True

        # clean all the attributes
        for attr_nm in [
            "logger",
            "_init_grid_path",
            "_DEBUG",
            "_complete_action_cls",
            "_parameters",
            "with_forecast",
            "_time_apply_act",
            "_time_powerflow",
            "_time_extract_obs",
            "_time_create_bk_act",
            "_time_opponent",
            "_time_redisp",
            "_time_step",
            "_epsilon_poly",
            "_helper_action_class",
            "_helper_observation_class",
            "time_stamp",
            "nb_time_step",
            "delta_time_seconds",
            "current_obs",
            "_line_status",
            "_ignore_min_up_down_times",
            "_forbid_dispatch_off",
            "_no_overflow_disconnection",
            "_timestep_overflow",
            "_nb_timestep_overflow_allowed",
            "_hard_overflow_threshold",
            "_times_before_line_status_actionable",
            "_max_timestep_line_status_deactivated",
            "_times_before_topology_actionable",
            "_nb_ts_reco",
            "_time_next_maintenance",
            "_duration_next_maintenance",
            "_hazard_duration",
            "_env_dc",
            "_target_dispatch",
            "_actual_dispatch",
            "_gen_uptime",
            "_gen_downtime",
            "_gen_activeprod_t",
            "_gen_activeprod_t_redisp",
            "_thermal_limit_a",
            "_disc_lines",
            "_injection",
            "_maintenance",
            "_hazards",
            "_env_modification",
            "done",
            "current_reward",
            "_helper_action_env",
            "chronics_handler",
            "_game_rules",
            "_action_space",
            "_rewardClass",
            "_actionClass",
            "_observationClass",
            "_legalActClass",
            "_observation_space",
            "_names_chronics_to_backend",
            "_reward_helper",
            "reward_range",
            "_viewer",
            "viewer_fig",
            "other_rewards",
            "_opponent_action_class",
            "_opponent_class",
            "_opponent_init_budget",
            "_opponent_attack_duration",
            "_opponent_attack_cooldown",
            "_opponent_budget_per_ts",
            "_kwargs_opponent",
            "_opponent_budget_class",
            "_opponent_action_space",
            "_compute_opp_budget",
            "_opponent",
            "_oppSpace",
            "_voltagecontrolerClass",
            "_voltage_controler",
            "_backend_action_class",
            "_backend_action",
            "backend",
            "debug_dispatch",
            # "__new_param", "__new_forecast_param", "__new_reward_func",
            "_storage_current_charge",
            "_storage_previous_charge",
            "_action_storage",
            "_amount_storage",
            "_amount_storage_prev",
            "_storage_power",
            "_storage_power_prev",
            "_limit_curtailment",
            "_limit_curtailment_prev",
            "_gen_before_curtailment",
            "_sum_curtailment_mw",
            "_sum_curtailment_mw_prev",
            "_has_attention_budget",
            "_attentiong_budget",
            "_attention_budget_cls",
            "_is_alarm_illegal",
            "_is_alarm_used_in_reward",
            "_is_alert_illegal",
            "_is_alert_used_in_reward",
            "_kwargs_attention_budget",
            "_limited_before",
        ]:
            if hasattr(self, attr_nm):
                delattr(self, attr_nm)
            setattr(self, attr_nm, None)

    def attach_layout(self, grid_layout):
        """
        Compare to the method of the base class, this one performs a check.
        This method must be called after initialization.

        Parameters
        ----------
        grid_layout: ``dict``
            The layout of the grid (*i.e* the coordinates (x,y) of all substations). The keys
            should be the substation names, and the values a tuple (with two float) representing
            the coordinate of the substation.

        Examples
        ---------
        Here is an example on how to attach a layout for an environment:

        .. code-block:: python

            import grid2op

            # create the environment
            env = grid2op.make()

            # 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)

        """
        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        if isinstance(grid_layout, dict):
            pass
        elif isinstance(grid_layout, list):
            grid_layout = {k: v for k, v in zip(self.name_sub, grid_layout)}
        else:
            raise EnvError(
                "Attempt to set a layout from something different than a dictionary or a list. "
                "This is for now not supported."
            )

        if self.__is_init:
            res = {}
            for el in self.name_sub:
                if not el in grid_layout:
                    raise EnvError(
                        'The substation "{}" is not present in grid_layout while in the powergrid.'
                        "".format(el)
                    )
                tmp = grid_layout[el]
                try:
                    x, y = tmp
                    x = dt_float(x)
                    y = dt_float(y)
                    res[el] = (x, y)
                except Exception as e_:
                    raise EnvError(
                        'attach_layout: impossible to convert the value of "{}" to a pair of float '
                        'that will be used the grid layout. The error is: "{}"'
                        "".format(el, e_)
                    )
            super().attach_layout(res)
            if self._action_space is not None:
                self._action_space.attach_layout(res)
            if self._helper_action_env is not None:
                self._helper_action_env.attach_layout(res)
            if self._observation_space is not None:
                self._observation_space.attach_layout(res)
            if self._voltage_controler is not None:
                self._voltage_controler.attach_layout(res)
            if self._opponent_action_space is not None:
                self._opponent_action_space.attach_layout(res)

    def fast_forward_chronics(self, nb_timestep):
        """
        This method allows you to skip some time step at the beginning of the chronics.

        This is usefull at the beginning of the training, if you want your agent to learn on more diverse scenarios.
        Indeed, the data provided in the chronics usually starts always at the same date time (for example Jan 1st at
        00:00). This can lead to suboptimal exploration, as during this phase, only a few time steps are managed by
        the agent, so in general these few time steps will correspond to grid state around Jan 1st at 00:00.

        Parameters
        ----------
        nb_timestep: ``int``
            Number of time step to "fast forward"

        Examples
        ---------
        This can be used like this:

        .. code-block:: python

            import grid2op

            # create the environment
            env = grid2op.make()

            # skip the first 150 steps of the chronics
            env.fast_forward_chronics(150)
            done = env.is_done
            if not done:
                obs = env.get_obs()
                # do something
            else:
                # there was a "game over"
                # you need to reset the env (which will "cancel" the fast_forward)
                pass
                # do something else

        Notes
        -----
        This method can set the state of the environment in a 'game over' state (`done=True`) for example if the
        chronics last `xxx` time steps and you ask to "fast foward" more than `xxx` steps. This is why we advise to
        check the state of the environment after the call to this method if you use it (see the "Examples" paragaph)

        """

        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        if not self.__is_init:
            raise EnvError("This environment is not intialized. "
                           "Have you called `env.reset()` after last game over ?")
        nb_timestep = int(nb_timestep)

        # Go to the timestep requested minus one
        nb_timestep = max(1, nb_timestep - 1)
        self.chronics_handler.fast_forward(nb_timestep)
        self.nb_time_step += nb_timestep

        # Update the timing vectors
        min_time_line_reco = np.zeros(self.n_line, dtype=dt_int)
        min_time_topo = np.zeros(self.n_sub, dtype=dt_int)
        ff_time_line_act = self._times_before_line_status_actionable - nb_timestep
        ff_time_topo_act = self._times_before_topology_actionable - nb_timestep
        self._times_before_line_status_actionable[:] = np.maximum(
            ff_time_line_act, min_time_line_reco
        )
        self._times_before_topology_actionable[:] = np.maximum(
            ff_time_topo_act, min_time_topo
        )

        # Update to the fast forward state using a do nothing action
        self.step(self._action_space({}))

    def get_current_line_status(self):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

            prefer using :attr:`grid2op.Observation.BaseObservation.line_status`

        This method allows to retrieve the line status.
        """
        if self.current_obs is not None:
            powerline_status = self._line_status
        else:
            # at first time step, every powerline is connected
            powerline_status = np.full(self.n_line, fill_value=True, dtype=dt_bool)
        # powerline_status = self._line_status
        return powerline_status

    @property
    def parameters(self):
        """
        Return a deepcopy of the parameters used by the environment

        It is a deepcopy, so modifying it will have absolutely no effect on the environment.

        If you want to change the parameters of an environment, please use either
        :func:`grid2op.Environment.BaseEnv.change_parameters` to change the parameters of this environment or
        :func:`grid2op.Environment.BaseEnv.change_forecast_parameters` to change the parameter of the environment
        used by :func:`grid2op.Observation.BaseObservation.simulate` or 
        :func:`grid2op.Observation.BaseObservation.get_forecast_env`
        
        .. danger::
            To modify the environment parameters you need to do:
            
            .. code-block:: python
            
                params = env.parameters
                params.WHATEVER = NEW_VALUE
                env.change_parameters(params)
                env.reset()
                
            If you simply do:
            
            .. code-block:: python
            
                env.params.WHATEVER = NEW_VALUE
                
            This will have absolutely no impact.
            
        """

        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        res = copy.deepcopy(self._parameters)
        # res.read_only = True  # TODO at some point !
        return res

    @parameters.setter
    def parameters(self, value):
        raise RuntimeError(
            "Use the env.change_parameters(new_parameters) to change the parameters. "
            "NB: it will only have an effect AFTER the env is reset."
        )

    def change_reward(self, new_reward_func):
        """
        Change the reward function used for the environment.

        TODO examples !
        
        Parameters
        ----------
        new_reward_func:
            Either an object of class BaseReward, or a subclass of BaseReward: the new reward function to use

        Notes
        ------
        This only affects the environment AFTER `env.reset()` has been called.

        """

        if self.__closed:
            raise EnvError("This environment is closed, you cannot use it.")
        
        is_ok = isinstance(new_reward_func, BaseReward) or issubclass(
            new_reward_func, BaseReward
        )
        if not is_ok:
            raise EnvError(
                f"Impossible to change the reward function with type {type(new_reward_func)}. "
                f"It should be an object from a class that inherit grid2op.Reward.BaseReward "
                f"or a subclass of grid2op.Reward.BaseReward"
            )
        self.__new_reward_func = new_reward_func

    def _aux_gen_classes(self, cls, sys_path):
        if not isinstance(cls, type):
            raise RuntimeError(f"cls should be a type and not an object !: {cls}")
        if not issubclass(cls, GridObjects):
            raise RuntimeError(f"cls should inherit from GridObjects: {cls}")
        
        from pathlib import Path
        path_env = cls._PATH_ENV
        cls._PATH_ENV = str(Path(self.get_path_env()).as_posix())
        
        res = cls._get_full_cls_str()
        cls._PATH_ENV = path_env
        output_file = os.path.join(sys_path, f"{cls.__name__}_file.py")
        if not os.path.exists(output_file):
            # if the file is not already saved, i save it and add it to the __init__ file
            with open(output_file, "w", encoding="utf-8") as f:
                f.write(res)
            return f"\nfrom .{cls.__name__}_file import {cls.__name__}"
        else:
            # otherwise i do nothing
            return ""

    def generate_classes(self, _guard=None, _is_base_env__=True, sys_path=None):
        """
        Use with care, but can be incredibly useful !
        
        If you get into trouble like :

        .. code-block:: none
        
            AttributeError: Can't get attribute 'ActionSpace_l2rpn_icaps_2021_small' 
            on <module 'grid2op.Space.GridObjects' from
            /home/user/Documents/grid2op_dev/grid2op/Space/GridObjects.py'>
        
        You might want to call this function and that MIGHT solve your problem.

        This function will create a subdirectory ino the env directory, 
        that will be accessed when loading the classes
        used for the environment. 
        
        The default behaviour is to build the class on the fly which can cause some 
        issues when using `pickle` or `multiprocessing` for example.

        Examples
        --------

        Here is how to best leverage this functionality:

        First step, generated the classes once and for all.

        .. warning::
            You need to redo this step each time
            you customize the environment. This customization includes, but is not limited to:

            - change the backend type: `grid2op.make(..., backend=...)`
            - change the action class: `grid2op.make(..., action_class=...)`
            - change observation class: `grid2op.make(..., observation_class=...)`
            - change the `volagecontroler_class`
            - change the `grid_path`
            - change the `opponent_action_class`
            - etc.

        .. code-block:: python

            import grid2op
            env_name = "l2rpn_case14_sandbox"  # or any other name

            env = grid2op.make(env_name, ...)  # again: redo this step each time you customize "..."
            # for example if you change the `action_class` or the `backend` etc.

            env.generate_classes()

        Then, next time you want to use the SAME environment, you can do:

        .. code-block:: python

            import grid2op
            env_name = SAME NAME AS ABOVE
            env = grid2op.make(env_name,
                               experimental_read_from_local_dir=True,
                               SAME ENV CUSTOMIZATION AS ABOVE)

        And it should (this is experimerimental for now, and we expect feedback on the matter) solve
        the issues involving pickle.

        Again, if you customize your environment (see above for more information) you'll have to redo this step !
        """

        if self.__closed:
            return

        # create the folder
        if _guard is not None:
            raise RuntimeError("use `env.generate_classes()` with no arguments !")

        if type(self)._PATH_ENV is not None:
            raise RuntimeError(
                "This function should only be called ONCE without specifying that the classes "
                "need to be read from disk (class attribute type(self)._PATH_ENV should be None)"
            )
        import shutil

        if sys_path is None:
            if not _is_base_env__:
                raise RuntimeError("Cannot generate file from a \"sub env\" "
                                   "(eg no the top level env) if I don't know the path of "
                                   "the top level environment.")
            sys_path = os.path.join(self.get_path_env(), "_grid2op_classes")

        if _is_base_env__:
            if os.path.exists(sys_path):
                shutil.rmtree(sys_path)
            os.mkdir(sys_path)

        # initialized the "__init__" file
        _init_txt = ""
        mode = "w"
        if not _is_base_env__:
            _init_txt = BASE_TXT_COPYRIGHT + _init_txt
        else:
            # i am apppending to the __init__ file in case of obs_env
            mode = "a"

        # generate the classes
        _init_txt += self._aux_gen_classes(type(self), sys_path)
        _init_txt += self._aux_gen_classes(type(self.backend), sys_path)
        _init_txt += self._aux_gen_classes(
            self.backend._complete_action_class, sys_path
        )
        _init_txt += self._aux_gen_classes(self._backend_action_class, sys_path)
        _init_txt += self._aux_gen_classes(type(self.action_space), sys_path)
        _init_txt += self._aux_gen_classes(self._actionClass, sys_path)
        _init_txt += self._aux_gen_classes(self._complete_action_cls, sys_path)
        _init_txt += self._aux_gen_classes(type(self.observation_space), sys_path)
        _init_txt += self._aux_gen_classes(self._observationClass, sys_path)
        _init_txt += self._aux_gen_classes(
            self._opponent_action_space.subtype, sys_path
        )

        # now do the same for the obs_env
        if _is_base_env__:
            _init_txt += self._aux_gen_classes(
                self._voltage_controler.action_space.subtype, sys_path
            )

            init_grid_tmp = self._observation_space.obs_env._init_grid_path
            self._observation_space.obs_env._init_grid_path = self._init_grid_path
            self._observation_space.obs_env.generate_classes(_is_base_env__=False, sys_path=sys_path)
            self._observation_space.obs_env._init_grid_path = init_grid_tmp

        # now write the __init__ file
        _init_txt += "\n"
        with open(os.path.join(sys_path, "__init__.py"), mode, encoding="utf-8") as f:
            f.write(_init_txt)

    def __del__(self):
        """when the environment is garbage collected, free all the memory, including cross reference to itself in the observation space."""
        if hasattr(self, "_BaseEnv__closed") and not self.__closed:
            self.close()

    def _update_vector_with_timestep(self, horizon, is_overflow):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        update the value of the "time dependant" attributes, used mainly for the "_ObsEnv" (simulate) or
        the "Forecasted env" (obs.get_forecast_env())
        """
        cls = type(self)
        
        # update the cooldowns
        self._times_before_line_status_actionable[:] = np.maximum(
            self._times_before_line_status_actionable - (horizon - 1), 0
        )
        self._times_before_topology_actionable[:] = np.maximum(
            self._times_before_topology_actionable - (horizon - 1), 0
        )

        # update the maintenance
        tnm_orig = 1 * self._time_next_maintenance
        dnm_orig = 1 * self._duration_next_maintenance
        
        has_maint = self._time_next_maintenance != -1
        reconnected = np.full(cls.n_line, fill_value=False)
        maint_started = np.full(cls.n_line, fill_value=False)
        maint_over = np.full(cls.n_line, fill_value=False)
        
        maint_started[has_maint] = (tnm_orig[has_maint] <= horizon)
        maint_over[has_maint] = (tnm_orig[has_maint] + dnm_orig[has_maint] 
                                 <= horizon)
        
        reconnected[has_maint] = tnm_orig[has_maint] + dnm_orig[has_maint] == horizon
        first_ts_maintenance = tnm_orig == horizon
        still_in_maintenance = maint_started & (~maint_over) & (~first_ts_maintenance)
        
        # count down time next maintenance
        self._time_next_maintenance[:] = np.maximum(
            self._time_next_maintenance - horizon, -1
        )
        
        # powerline that are still in maintenance at this time step
        self._time_next_maintenance[still_in_maintenance] = 0
        self._duration_next_maintenance[still_in_maintenance] -= (horizon - tnm_orig[still_in_maintenance])

        # powerline that will be in maintenance at this time step
        self._time_next_maintenance[first_ts_maintenance] = 0
        
        # powerline that will be in maintenance at this time step
        self._time_next_maintenance[reconnected | maint_over] = -1
        self._duration_next_maintenance[reconnected | maint_over] = 0

        # soft overflow
        # this is tricky here because I have no model to predict the future... 
        # As i cannot do better, I simply do "if I am in overflow now, i will be later"
        self._timestep_overflow[is_overflow] += (horizon - 1)
        return still_in_maintenance, reconnected, first_ts_maintenance
    
    def _reset_to_orig_state(self, obs):
        """
        INTERNAL

        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        reset this "environment" to the state it should be
        
        update the value of the "time dependant" attributes, used mainly for the "_ObsEnv" (simulate) or
        the "Forecasted env" (obs.get_forecast_env())
        """
        self.backend.set_thermal_limit(obs._thermal_limit)
        if "opp_space_state" in obs._env_internal_params:
            self._oppSpace._set_state(obs._env_internal_params["opp_space_state"], 
                                      obs._env_internal_params["opp_state"])
        # storage unit
        self._storage_current_charge[:] = obs.storage_charge
        self._storage_previous_charge[:] = obs._env_internal_params["_storage_previous_charge"]
        self._action_storage[:] = obs.storage_power_target
        self._storage_power[:] = obs.storage_power
        self._amount_storage = obs._env_internal_params["_amount_storage"]
        self._amount_storage_prev = obs._env_internal_params["_amount_storage_prev"]

        # curtailment
        self._limit_curtailment[:] = obs.curtailment_limit
        self._gen_before_curtailment[:] = obs.gen_p_before_curtail
        self._sum_curtailment_mw = obs._env_internal_params["_sum_curtailment_mw"]
        self._sum_curtailment_mw_prev = obs._env_internal_params["_sum_curtailment_mw_prev"]

        # line status
        self._line_status[:] = obs._env_internal_params["_line_status_env"] == 1

        # attention budget
        if self._has_attention_budget:
            self._attention_budget.set_state(obs._env_internal_params["_attention_budget_state"])
            
        # cooldown
        self._times_before_line_status_actionable[
            :
        ] = obs.time_before_cooldown_line
        self._times_before_topology_actionable[
            :
        ] = obs.time_before_cooldown_sub
        
        # maintenance
        self._time_next_maintenance[:] = obs.time_next_maintenance
        self._duration_next_maintenance[:] = obs.duration_next_maintenance
        
        # redisp
        self._target_dispatch[:] = obs.target_dispatch
        self._actual_dispatch[:] = obs.actual_dispatch
        self._already_modified_gen[:] = obs._env_internal_params["_already_modified_gen"]
        self._gen_activeprod_t[:] = obs._env_internal_params["_gen_activeprod_t"]
        self._gen_activeprod_t_redisp[:] = obs._env_internal_params["_gen_activeprod_t_redisp"]
        
        # current step
        self.nb_time_step = obs.current_step
        self.delta_time_seconds = 60. * obs.delta_time
        
        # soft overflow
        self._timestep_overflow[:] = obs.timestep_overflow

    def forecasts(self):
        # ensure that the "env.chronics_handler.forecasts" is called at most once per step
        # this should NOT be called is self.deactive_forecast is true
        if not self.with_forecast:
            raise Grid2OpException("Attempt to retrieve the forecasts when they are not available.")
        
        if self._forecasts is None:
            self._forecasts = self.chronics_handler.forecasts()
        return self._forecasts

    @staticmethod
    def _check_rules_correct(legalActClass):
        if 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)
                    )
                )
        else:
            if not isinstance(legalActClass, BaseRules):
                raise Grid2OpException(
                    'Parameter "legalActClass" used to build the Environment should be an instance of the '
                    'grid2op.BaseRules class, type provided is "{}"'.format(
                        type(legalActClass)
                    )
                )