Source code for grid2op.Chronics.handlers.csvMaintenanceHandler

# Copyright (c) 2019-2023, 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 typing import Tuple
import pandas as pd
import numpy as np

from grid2op.dtypes import dt_int, dt_float, dt_bool
from grid2op.Chronics.gridValue import GridValue
from grid2op.Chronics.handlers.csvHandler import CSVHandler

[docs]class CSVMaintenanceHandler(CSVHandler): """Reads and produce time series if given by a csv file (possibly compressed). The separator used can be specified as input. The file name should match the "array_name". If you want to use the maintenance file present in the file "my_maintenance_file.csv.gz" then you should create a CSVMaintenanceHandler with `array_name="my_maintenance_file"`. The csv should be structured as follow: - it should not have any "index" or anything, only data used by grid2op will be used - Each element powerline is represented by a `column`. - It should have a header with the name of the powerlines that should match the one in the environment. For example if "0_1_0" is the name of a powerline in your environment, then a column should be called "0_1_0". - each time step is represented as a `row` and in order. For example (removing the header), row 1 (first row) will be step 1, row 2 will be step 2 etc. - only binary data (0 or 1) should be present in the file. No "bool", no string etc. .. warning:: Use this class only for the ENVIRONMENT data ("load_p", "load_q", "prod_p" or "prod_v") and not for maintenance (in this case use :class:`CSVMaintenanceHandler`) nor for forecast (in this case use :class:`CSVForecastHandler`) This is the default way to provide data to grid2op and its used for most l2rpn environments. """ def __init__(self, array_name="maintenance", sep=";", max_iter=-1) -> None: super().__init__(array_name, sep, None, max_iter) # None corresponds to "chunk_size" which is not supported for maintenance def _init_attrs(self, array): super()._init_attrs(array) n_line = self.array.shape[1] self.maintenance_time = ( np.zeros(shape=(self.array.shape[0], n_line), dtype=dt_int) - 1 ) self.maintenance_duration = np.zeros( shape=(self.array.shape[0], n_line), dtype=dt_int ) # test that with chunk size for line_id in range(n_line): self.maintenance_time[:, line_id] = GridValue.get_maintenance_time_1d( self.array[:, line_id] ) self.maintenance_duration[ :, line_id ] = GridValue.get_maintenance_duration_1d(self.array[:, line_id]) # there are _maintenance and hazards only if the value in the file is not 0. self.array = np.abs(self.array) >= 1e-7 self.array = self.array.astype(dt_bool)
[docs] def load_next_maintenance(self) -> Tuple[np.ndarray, np.ndarray]: maint_time = 1 * self.maintenance_time[self.current_index, :] maint_duration = 1 * self.maintenance_duration[self.current_index, :] return maint_time, maint_duration
[docs] def set_chunk_size(self, chunk_size): # skip the definition in CSVHandler to jump to the level "above" return super(CSVHandler, self).set_chunk_size(chunk_size)