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adressing issue #76, adressing bug in data generation for the forecasts
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2024, 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 Chronix2Grid, A python package to generate "en-masse" chronics for loads and productions (thermal, renewable) | ||
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||
| import copy | ||
| import os | ||
| import json | ||
| import unittest | ||
| import warnings | ||
| import tempfile | ||
| import shutil | ||
| import numpy as np | ||
| import pandas as pd | ||
| import datetime | ||
| from packaging import version | ||
| import grid2op | ||
| from grid2op.Chronics import ChangeNothing | ||
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||
| from chronix2grid.getting_started.example.input.generation.patterns import ref_pattern_path | ||
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| class TestGrid2opUtils(unittest.TestCase): | ||
| def setUp(self) -> None: | ||
| if version.parse(grid2op.__version__) < version.parse("1.9.8"): | ||
| # a fix in grid2Op 1.9.8 : the "loads_charac.csv" was not | ||
| # part of the data shipped with the package before | ||
| self.skipTest(f"grid2op version too old {grid2op.__version__} < 1.9.8") | ||
| return super().setUp() | ||
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||
| def test_not_too_high_value_forecasts(self): | ||
| seed = 0 | ||
| with warnings.catch_warnings(): | ||
| warnings.filterwarnings("ignore") | ||
| with grid2op.make("l2rpn_idf_2023", test=True) as env: | ||
| path = env.get_path_env() | ||
| tmp_dir = tempfile.TemporaryDirectory() | ||
| new_env_path = os.path.join(tmp_dir.name, "l2rpn_idf_2023") | ||
| shutil.copytree(path, new_env_path) | ||
| shutil.rmtree(os.path.join(new_env_path, "chronics")) | ||
| # keep only the first data (not to generate everything) | ||
| with open(os.path.join(new_env_path, "scenario_params.json"), "r") as f: | ||
| scenario_params = json.load(f) | ||
| scenario_params["all_dates"] = scenario_params["all_dates"][:1] | ||
| with open(os.path.join(new_env_path, "scenario_params.json"), "w") as f: | ||
| json.dump(fp=f, obj=scenario_params) | ||
| env = grid2op.make(new_env_path, | ||
| chronics_class=ChangeNothing, | ||
| **grid2op.Opponent.get_kwargs_no_opponent()) | ||
| env.generate_data(load_weekly_pattern=None, nb_year=1, seed=seed, save_ref_curve=True) | ||
| gen_p_for_orig = pd.read_csv(os.path.join(new_env_path, | ||
| "chronics", | ||
| "2035-01-01_0", | ||
| "prod_p_forecasted.csv.bz2"), | ||
| sep=";") | ||
| assert (gen_p_for_orig.iloc[:,2] <= type(env).gen_pmax[2]).all() | ||
| tmp_dir.cleanup() | ||
|
|
||
| def test_load_weekly_pattern(self): | ||
| seed = 0 | ||
| with warnings.catch_warnings(): | ||
| warnings.filterwarnings("ignore") | ||
| with grid2op.make("l2rpn_wcci_2022", test=True) as env: | ||
| path = env.get_path_env() | ||
| tmp_dir = tempfile.TemporaryDirectory() | ||
| new_env_path = os.path.join(tmp_dir.name, "l2rpn_wcci_2022") | ||
| shutil.copytree(path, new_env_path) | ||
| shutil.rmtree(os.path.join(new_env_path, "chronics")) | ||
| # keep only the first data (not to generate everything) | ||
| with open(os.path.join(new_env_path, "scenario_params.json"), "r") as f: | ||
| scenario_params = json.load(f) | ||
| scenario_params["all_dates"] = scenario_params["all_dates"][:1] | ||
| with open(os.path.join(new_env_path, "scenario_params.json"), "w") as f: | ||
| json.dump(fp=f, obj=scenario_params) | ||
| env = grid2op.make(new_env_path, | ||
| chronics_class=ChangeNothing, | ||
| **grid2op.Opponent.get_kwargs_no_opponent()) | ||
| env.generate_data(load_weekly_pattern=None, nb_year=1, seed=seed, save_ref_curve=True) | ||
| load_weekly_pattern = pd.read_csv(os.path.join(ref_pattern_path, "load_weekly_pattern.csv"), sep=",") | ||
| load_ref_orig = np.load(os.path.join(new_env_path, "chronics", "2050-01-03_0", "load_ref.npy")) | ||
| total_demand_orig = np.sum(load_ref_orig, axis=1) | ||
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||
| # change the load weekly pattern | ||
| load_weekly_pattern2 = load_weekly_pattern[load_weekly_pattern["datetime"] >= "2018"] | ||
| load_weekly_pattern3 = load_weekly_pattern[load_weekly_pattern["datetime"] < "2018"] | ||
| load_weekly_pattern_new = pd.concat([load_weekly_pattern2, load_weekly_pattern3]) | ||
| load_weekly_pattern_new.reset_index(inplace=True) | ||
| load_weekly_pattern_new = load_weekly_pattern_new[["datetime", "test"]] | ||
| load_weekly_pattern_new["datetime"] = copy.deepcopy(load_weekly_pattern["datetime"]) | ||
| # delete original data | ||
| shutil.rmtree(os.path.join(new_env_path, "chronics")) | ||
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||
| # start a new generation | ||
| env.generate_data(load_weekly_pattern=load_weekly_pattern_new, nb_year=1, seed=seed, save_ref_curve=True) | ||
| load_ref_new = np.load(os.path.join(new_env_path, "chronics", "2050-01-03_0", "load_ref.npy")) | ||
| total_demand_new = np.sum(load_ref_new, axis=1) | ||
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| # recompute ref case to make sure it works | ||
| shutil.rmtree(os.path.join(new_env_path, "chronics")) | ||
| env.generate_data(load_weekly_pattern=None, nb_year=1, seed=seed, save_ref_curve=True) | ||
| load_ref_orig2 = np.load(os.path.join(new_env_path, "chronics", "2050-01-03_0", "load_ref.npy")) | ||
| total_demand_orig2 = np.sum(load_ref_orig2, axis=1) | ||
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| # compate the ref curves and the load data | ||
| assert np.allclose(total_demand_orig, total_demand_orig2) | ||
| assert not np.allclose(total_demand_orig, total_demand_new) | ||
| tmp_dir.cleanup() | ||
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