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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2023, 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, timedelta | ||
| import os | ||
| import numpy as np | ||
| import copy | ||
| import warnings | ||
| from typing import Optional, Union, List | ||
| from pathlib import Path | ||
|
|
||
| from grid2op.Exceptions import ( | ||
| ChronicsError, ChronicsNotFoundError | ||
| ) | ||
|
|
||
| from grid2op.Chronics.gridValue import GridValue | ||
|
|
||
| from grid2op.dtypes import dt_int, dt_float | ||
| from grid2op.Chronics.fromOneEpisodeData import TYPE_EP_DATA_INGESTED, FromOneEpisodeData | ||
|
|
||
|
|
||
| class FromMultiEpisodeData(GridValue): | ||
| """This class allows to redo some episode that have been previously run using a runner. | ||
| It is an extension of the class :class:`FromOneEpisodeData` but with multiple episodes. | ||
| .. seealso:: | ||
| :class:`grid2op.Chronics.FromOneEpisodeData`if you want to use only one episode | ||
| .. warning:: | ||
| It has the same limitation as :class:`grid2op.Chronics.FromOneEpisodeData`, including: | ||
| - forecasts are not saved so cannot be retrieved with this class. You can however | ||
| use `obs.simulate` and in this case it will lead perfect forecasts. | ||
| - to make sure you are running the exact same episode, you need to create the environment | ||
| with the :class:`grid2op.Opponent.FromEpisodeDataOpponent` opponent | ||
| Examples | ||
| --------- | ||
| You can use this class this way: | ||
| First, you generate some data by running an episode with do nothing or reco powerline agent, | ||
| preferably episode that go until the end of your time series | ||
| .. code-block:: python | ||
| import grid2op | ||
| from grid2op.Runner import Runner | ||
| from grid2op.Agent import RecoPowerlineAgent | ||
| path_agent = .... | ||
| nb_episode = ... | ||
| env_name = "l2rpn_case14_sandbox" # or any other name | ||
| env = grid2op.make(env_name, etc.) | ||
| # optional (change the parameters to allow the ) | ||
| param = env.parameters | ||
| param.NO_OVERFLOW_DISCONNECTION = True | ||
| env.change_parameters(param) | ||
| env.reset() | ||
| # end optional | ||
| runner = Runner(**env.get_params_for_runner(), | ||
| agentClass=RecoPowerlineAgent) | ||
| runner.run(nb_episode=nb_episode, | ||
| path_save=path_agent) | ||
| And then you can load it back and run the exact same environment with the same | ||
| time series, the same attacks etc. with: | ||
| .. code-block:: python | ||
| import grid2op | ||
| from grid2op.Chronics import FromMultiEpisodeData | ||
| from grid2op.Opponent import FromEpisodeDataOpponent | ||
| from grid2op.Episode import EpisodeData | ||
| path_agent = .... # same as above | ||
| env_name = .... # same as above | ||
| # path_agent is the path where data coming from a grid2op runner are stored | ||
| # NB it should come from a do nothing agent, or at least | ||
| # an agent that does not modify the injections (no redispatching, curtailment, storage) | ||
| li_episode = EpisodeData.list_episode(path_agent) | ||
| env = grid2op.make(env_name, | ||
| chronics_class=FromMultiEpisodeData, | ||
| data_feeding_kwargs={"li_ep_data": li_episode}, | ||
| opponent_class=FromEpisodeDataOpponent, | ||
| opponent_attack_cooldown=1, | ||
| ) | ||
| # li_ep_data in this case is a list of anything that is accepted by `FromOneEpisodeData` | ||
| obs = env.reset() | ||
| # and now you can use "env" as any grid2op environment. | ||
| """ | ||
| MULTI_CHRONICS = True | ||
| def __init__(self, | ||
| path, # can be None ! | ||
| li_ep_data: List[TYPE_EP_DATA_INGESTED], | ||
| time_interval=timedelta(minutes=5), | ||
| sep=";", # here for compatibility with grid2op, but not used | ||
| max_iter=-1, | ||
| start_datetime=datetime(year=2019, month=1, day=1), | ||
| chunk_size=None, | ||
| list_perfect_forecasts=None, # TODO | ||
| **kwargs, # unused | ||
| ): | ||
| super().__init__(time_interval, max_iter, start_datetime, chunk_size) | ||
| self.li_ep_data = [FromOneEpisodeData(path, | ||
| ep_data=el, | ||
| time_interval=time_interval, | ||
| max_iter=max_iter, | ||
| chunk_size=chunk_size, | ||
| list_perfect_forecasts=list_perfect_forecasts, | ||
| start_datetime=start_datetime) | ||
| for el in li_ep_data | ||
| ] | ||
| self._prev_cache_id = len(self.li_ep_data) - 1 | ||
| self.data = self.li_ep_data[self._prev_cache_id] | ||
| self._episode_data = self.data._episode_data # used by the fromEpisodeDataOpponent | ||
|
|
||
| def next_chronics(self): | ||
| self._prev_cache_id += 1 | ||
| # TODO implement the shuffling indeed. | ||
| # if self._prev_cache_id >= len(self._order): | ||
| # self.space_prng.shuffle(self._order) | ||
| self._prev_cache_id %= len(self.li_ep_data) | ||
|
|
||
| def initialize( | ||
| self, | ||
| order_backend_loads, | ||
| order_backend_prods, | ||
| order_backend_lines, | ||
| order_backend_subs, | ||
| names_chronics_to_backend=None, | ||
| ): | ||
|
|
||
| self.data = self.li_ep_data[self._prev_cache_id] | ||
| self.data.initialize( | ||
| order_backend_loads, | ||
| order_backend_prods, | ||
| order_backend_lines, | ||
| order_backend_subs, | ||
| names_chronics_to_backend=names_chronics_to_backend, | ||
| ) | ||
| self._episode_data = self.data._episode_data | ||
|
|
||
| def done(self): | ||
| return self.data.done() | ||
|
|
||
| def load_next(self): | ||
| return self.data.load_next() | ||
|
|
||
| def check_validity(self, backend): | ||
| return self.data.check_validity(backend) | ||
|
|
||
| def forecasts(self): | ||
| return self.data.forecasts() | ||
|
|
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| def tell_id(self, id_num, previous=False): | ||
| id_num = int(id_num) | ||
| if not isinstance(id_num, (int, dt_int)): | ||
| raise ChronicsError("FromMultiEpisodeData can only be used with `tell_id` being an integer " | ||
| "at the moment. Feel free to write a feature request if you want more.") | ||
|
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| self._prev_cache_id = id_num | ||
| self._prev_cache_id %= len(self.li_ep_data) | ||
|
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| if previous: | ||
| self._prev_cache_id -= 1 | ||
| self._prev_cache_id %= len(self.li_ep_data) | ||
|
|
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| def get_id(self) -> str: | ||
| return f'{self._prev_cache_id }' | ||
|
|
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| def max_timestep(self): | ||
| return self.data.max_timestep() | ||
|
|
||
| def fast_forward(self, nb_timestep): | ||
| self.data.fast_forward(nb_timestep) |
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