Source code for grid2op.Chronics.fromOneEpisodeData

# Copyright (c) 2023, RTE (
# 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
# 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 Union, Tuple
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.Episode import EpisodeData

TYPE_EP_DATA_INGESTED = Union[str, Path, EpisodeData, Tuple[str, str]]

[docs]class FromOneEpisodeData(GridValue): """This class allows to use the :class:`grid2op.Chronics.handlers.BaseHandler` to read back data stored in :class:`grid2op.Episode.EpisodeData` It can be used if you want to loop indefinitely through one episode. .. versionadded:: 1.9.4 TODO there will be "perfect" forecast, as original forecasts are not stored ! .. warning:: Original forecasts are not stored by the runner. This is why you cannot use the same information as available in the original "obs.simulate". However, you can still use PERFECT FORECAST if you want to by providing the extra parameters "list_perfect_forecasts=[forecast_horizon_1, forecast_horizon_2, etc.]" when you build this class. (see examples below) .. danger:: If you want the created environment to be exactly that the original environment, make sure to generate data using a "do nothing" agent. If the agent modified the injections (*eg* with redispatching, curtailment or storage) then the resulting time series will "embed" these modifications: they will NOT match the orignal implementation .. danger:: If you load an episode data with an opponent, make sure also to build your environment with :class:`grid2op.Opponent.FromEpisodeDataOpponent` and assign `opponent_attack_cooldown=1` (see example below) otherwise you might end up with different time series than what you initially had in the EpisodeData. .. note:: As this class reads from the hard drive an episode that has been played, we strongly encourage you to build this class with a complete episode (and not using an agent that games over after a few steps), for example by using the "RecoPowerlineAgent" and the `NO_OVERFLOW_DISCONNECTION` parameters (see example below) .. seealso:: :class:`grid2op.Chronics.FromMultiEpisodeData` if you want to use multiple episode data 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 = .... 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), 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 FromOneEpisodeData 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) ep_data = li_episode[0] env = grid2op.make(env_name, chronics_class=FromOneEpisodeData, data_feeding_kwargs={"ep_data": ep_data}, opponent_class=FromEpisodeDataOpponent, opponent_attack_cooldown=1, ) # ep_data can be either a tuple of 2 elements (like above) # or a full path to a saved episode # or directly an object of type EpisodeData obs = env.reset() # and now you can use "env" as any grid2op environment. If you want to include perfect forecast (unfortunately you cannot retrieve the original forecasts) you can do: .. code-block:: python # same as above env = grid2op.make(env_name, chronics_class=FromOneEpisodeData, data_feeding_kwargs={"ep_data": ep_data, "list_perfect_forecasts": (5, 10, 15)}, opponent_class=FromEpisodeDataOpponent, opponent_attack_cooldown=1, ) # it creates an environment with perfect forecasts available for the next step (5), # the step afterwards (10) and again the following one (15) .. seealso:: :class:`grid2op.Opponent.FromEpisodeDataOpponent` """ MULTI_CHRONICS = False def __init__( self, path, # can be None ! ep_data: 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 ): GridValue.__init__( self, time_interval=time_interval, max_iter=max_iter, start_datetime=start_datetime, chunk_size=chunk_size, ) self.path = path if self.path is not None: # logger: this has no impact pass if isinstance(ep_data, EpisodeData): self._episode_data = ep_data elif isinstance(ep_data, (str, Path)): try: self._episode_data = EpisodeData.from_disk(*os.path.split(ep_data)) except Exception as exc_: raise ChronicsError("Impossible to build the FromOneEpisodeData with the `ep_data` provided.") from exc_ elif isinstance(ep_data, (tuple, list)): if len(ep_data) != 2: raise ChronicsError("When you provide a tuple, or a list, FromOneEpisodeData can only be used if this list has length 2. " f"Length {len(ep_data)} found.") try: self._episode_data = EpisodeData.from_disk(*ep_data) except Exception as exc_: raise ChronicsError("Impossible to build the FromOneEpisodeData with the `ep_data` provided.") from exc_ else: raise ChronicsError("FromOneEpisodeData can only read data either directly from an EpisodeData, " "from a path pointing to one, or from a tuple") self.current_inj = None if list_perfect_forecasts is not None: self.list_perfect_forecasts = list_perfect_forecasts else: self.list_perfect_forecasts = [] self._check_list_perfect_forecasts() def _check_list_perfect_forecasts(self): if not self.list_perfect_forecasts: return self.list_perfect_forecasts = [int(el) for el in self.list_perfect_forecasts] for horizon in self.list_perfect_forecasts: tmp = horizon * 60. / self.time_interval.total_seconds() if tmp - int(tmp) != 0: raise ChronicsError(f"All forecast horizons should be multiple of self.time_interval (and given in minutes), found {horizon}") for h_id, horizon in enumerate(self.list_perfect_forecasts): if horizon * 60 != (h_id + 1) * (self.time_interval.total_seconds()): raise ChronicsError("For now all horizons should be consecutive, you cannot 'skip' a forecast: (5, 10, 15) " "is ok but (5, 15, 30) is NOT.")
[docs] def initialize( self, order_backend_loads, order_backend_prods, order_backend_lines, order_backend_subs, names_chronics_to_backend=None, ): # set the current path of the time series self.n_gen = len(order_backend_prods) self.n_load = len(order_backend_loads) self.n_line = len(order_backend_lines) self.curr_iter = 0 self.current_inj = None # TODO check if consistent, and compute the order ! # when there are no maintenance / hazards, build this only once self._no_mh_time = np.full(self.n_line, fill_value=-1, dtype=dt_int) self._no_mh_duration = np.full(self.n_line, fill_value=0, dtype=dt_int)
[docs] def load_next(self): obs = self._episode_data.observations[self.curr_iter] self.current_datetime += self.time_interval self.curr_iter += 1 res = {} # load the injection dict_inj, prod_v = self._load_injection(obs) res["injection"] = dict_inj # load maintenance res["maintenance"] = obs.time_next_maintenance == 0 maintenance_time = 1 * obs.time_next_maintenance maintenance_duration = 1 * obs.duration_next_maintenance self.current_inj = res return ( self.current_datetime, res, maintenance_time, maintenance_duration, self._no_mh_duration, prod_v, )
[docs] def max_timestep(self): if self.max_iter > 0: return min(self.max_iter, len(self._episode_data)) return len(self._episode_data)
[docs] def next_chronics(self): self.current_datetime = self.start_datetime self.curr_iter = 0
[docs] def done(self): # I am done if the part I control is "over" if self._max_iter > 0 and self.curr_iter > self._max_iter: return True if self.curr_iter > len(self._episode_data): return True return False
[docs] def check_validity(self, backend): warning_msg = ("An action modified the injection with {}, resulting data might be " "different from original data used in the generation of the initial EpisodeData.") redisp_issued = False sto_issued = False curt_issued = False for act in self._episode_data.actions: if act._modif_redispatch: if not redisp_issued: warnings.warn(warning_msg.format("redispatching")) redisp_issued = True if act._modif_storage: if not sto_issued: warnings.warn(warning_msg.format("storage")) sto_issued = True if act._modif_curtailment: if not curt_issued: warnings.warn(warning_msg.format("curtailment")) curt_issued = True return True
def _aux_forecasts(self, h_id, dict_, key, for_handler, base_handler, handlers): if for_handler is not None: tmp_ = for_handler.forecast(h_id, self.current_inj, dict_, base_handler, handlers) if tmp_ is not None: dict_[key] = dt_float(1.0) * tmp_
[docs] def forecasts(self): """Retrieve PERFECT forecast from this time series generator. .. danger:: These are **perfect forecast** and **NOT** the original forecasts. Notes ----- As in grid2op the forecast information is not stored by the runner, it is NOT POSSIBLE to retrieve the forecast informations used by the "original" env (the one that generated the EpisodeData). This class however, thanks to the `list_perfect_forecasts` kwarg you can set at building time, can generate perfect forecasts: the agent will see into the future if using these forecasts. """ if not self.list_perfect_forecasts: return [] res = [] for h_id, h in enumerate(self.list_perfect_forecasts): res_d = {} obs = self._episode_data.observations[min(self.curr_iter + h_id, len(self._episode_data) - 1)] # load the injection dict_inj, prod_v = self._load_injection(obs) dict_inj["prod_v"] = prod_v res_d["injection"] = dict_inj forecast_datetime = self.current_datetime + timedelta(minutes=h) res.append((forecast_datetime, res_d)) return res
[docs] def get_kwargs(self, dict_): dict_["ep_data"] = copy.deepcopy(self._episode_data) # dict_["list_perfect_forecasts"] = copy.deepcopy(self.list_perfect_forecasts) return dict_
[docs] def get_id(self) -> str: if self.path is not None: return self.path else: # TODO EpisodeData.path !!! return "" raise NotImplementedError()
[docs] def shuffle(self, shuffler=None): # TODO pass
[docs] def sample_next_chronics(self, probabilities=None): # TODO pass
[docs] def seed(self, seed): # nothing to do in this case, environment is purely deterministic super().seed(seed)
def _load_injection(self, obs): dict_ = {} prod_v = None tmp_ = obs.load_p if tmp_ is not None: dict_["load_p"] = dt_float(1.0) * tmp_ tmp_ = obs.load_q if tmp_ is not None: dict_["load_q"] = dt_float(1.0) * tmp_ tmp_ = obs.gen_p if tmp_ is not None: dict_["prod_p"] = dt_float(1.0) * tmp_ tmp_ = obs.gen_v if tmp_ is not None: prod_v = dt_float(1.0) * tmp_ return dict_, prod_v def _init_date_time(self): # from csv handler if os.path.exists(os.path.join(self.path, "")): with open(os.path.join(self.path, ""), "r") as f: a = try: tmp = datetime.strptime(a, "%Y-%m-%d %H:%M") except ValueError: tmp = datetime.strptime(a, "%Y-%m-%d") except Exception: raise ChronicsNotFoundError( 'Impossible to understand the content of "". Make sure ' 'it\'s composed of only one line with a datetime in the "%Y-%m-%d %H:%M"' "format." ) self.start_datetime = tmp self.current_datetime = tmp if os.path.exists(os.path.join(self.path, "")): with open(os.path.join(self.path, ""), "r") as f: a = try: tmp = datetime.strptime(a, "%H:%M") except ValueError: tmp = datetime.strptime(a, "%M") except Exception: raise ChronicsNotFoundError( 'Impossible to understand the content of "". Make sure ' 'it\'s composed of only one line with a datetime in the "%H:%M"' "format." ) self.time_interval = timedelta(hours=tmp.hour, minutes=tmp.minute)
[docs] def fast_forward(self, nb_timestep): for _ in range(nb_timestep): self.load_next() # for this class I suppose the real data AND the forecast are read each step self.forecasts()