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0000000000000000000000000000000000000000..2e640259769bf87c5154a1e0828e42948b71465d --- /dev/null +++ b/13659_pegase/test-00044-of-00045.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3ab99bb9acd13dd8c4902f4cf07c637e4f40752478fcf01a1eed766028de784 +size 481656050 diff --git a/PGLearn-ExtraLarge-13659_pegase.py b/PGLearn-ExtraLarge-13659_pegase.py deleted file mode 100644 index c0528a9f232a8eec148e0e9ad349c47c44f44664..0000000000000000000000000000000000000000 --- a/PGLearn-ExtraLarge-13659_pegase.py +++ /dev/null @@ -1,427 +0,0 @@ -from __future__ import annotations -from dataclasses import dataclass -from pathlib import Path -import json -import shutil - -import datasets as hfd -import h5py -import pgzip as gzip -import pyarrow as pa - -# ┌──────────────┐ -# │ Metadata │ -# └──────────────┘ - -@dataclass -class CaseSizes: - n_bus: int - n_load: int - n_gen: int - n_branch: int - -CASENAME = "13659_pegase" -SIZES = CaseSizes(n_bus=13659, n_load=5544, n_gen=4092, n_branch=20467) -NUM_TRAIN = 19532 -NUM_TEST = 4883 -NUM_INFEASIBLE = 25585 -SPLITFILES = {} - -URL = "https://huggingface.co/datasets/PGLearn/PGLearn-ExtraLarge-13659_pegase" -DESCRIPTION = """\ -The 13659_pegase PGLearn optimal power flow dataset, part of the PGLearn-ExtraLarge collection. \ -""" -VERSION = hfd.Version("1.0.0") -DEFAULT_CONFIG_DESCRIPTION="""\ -This configuration contains feasible input, primal solution, and dual solution data \ -for the ACOPF, DCOPF, and SOCOPF formulations on the {case} system. For case data, \ -download the case.json.gz file from the `script` branch of the repository. \ -https://huggingface.co/datasets/PGLearn/PGLearn-ExtraLarge-13659_pegase/blob/script/case.json.gz -""" -USE_ML4OPF_WARNING = """ -================================================================================================ - Loading PGLearn-ExtraLarge-13659_pegase through the `datasets.load_dataset` function may be slow. - - Consider using ML4OPF to directly convert to `torch.Tensor`; for more info see: - https://github.com/AI4OPT/ML4OPF?tab=readme-ov-file#manually-loading-data - - Or, use `huggingface_hub.snapshot_download` and an HDF5 reader; for more info see: - https://huggingface.co/datasets/PGLearn/PGLearn-ExtraLarge-13659_pegase#downloading-individual-files -================================================================================================ -""" -CITATION = """\ -@article{klamkinpglearn, - title={{PGLearn - An Open-Source Learning Toolkit for Optimal Power Flow}}, - author={Klamkin, Michael and Tanneau, Mathieu and Van Hentenryck, Pascal}, - year={2025}, -}\ -""" - -IS_COMPRESSED = True - -# ┌──────────────────┐ -# │ Formulations │ -# └──────────────────┘ - -def acopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(acopf_primal_features(sizes)) - if dual: features.update(acopf_dual_features(sizes)) - if meta: features.update({f"ACOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -def dcopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(dcopf_primal_features(sizes)) - if dual: features.update(dcopf_dual_features(sizes)) - if meta: features.update({f"DCOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -def socopf_features(sizes: CaseSizes, primal: bool, dual: bool, meta: bool): - features = {} - if primal: features.update(socopf_primal_features(sizes)) - if dual: features.update(socopf_dual_features(sizes)) - if meta: features.update({f"SOCOPF/{k}": v for k, v in META_FEATURES.items()}) - return features - -FORMULATIONS_TO_FEATURES = { - "ACOPF": acopf_features, - "DCOPF": dcopf_features, - "SOCOPF": socopf_features, -} - -# ┌───────────────────┐ -# │ BuilderConfig │ -# └───────────────────┘ - -class PGLearnLarge13659_pegaseConfig(hfd.BuilderConfig): - """BuilderConfig for PGLearn-ExtraLarge-13659_pegase. - By default, primal solution data, metadata, input, casejson, are included for the train and test splits. - - To modify the default configuration, pass attributes of this class to `datasets.load_dataset`: - - Attributes: - formulations (list[str]): The formulation(s) to include, e.g. ["ACOPF", "DCOPF"] - primal (bool, optional): Include primal solution data. Defaults to True. - dual (bool, optional): Include dual solution data. Defaults to False. - meta (bool, optional): Include metadata. Defaults to True. - input (bool, optional): Include input data. Defaults to True. - casejson (bool, optional): Include case.json data. Defaults to True. - train (bool, optional): Include training samples. Defaults to True. - test (bool, optional): Include testing samples. Defaults to True. - infeasible (bool, optional): Include infeasible samples. Defaults to False. - """ - def __init__(self, - formulations: list[str], - primal: bool=True, dual: bool=False, meta: bool=True, input: bool = True, casejson: bool=True, - train: bool=True, test: bool=True, infeasible: bool=False, - compressed: bool=IS_COMPRESSED, **kwargs - ): - super(PGLearnLarge13659_pegaseConfig, self).__init__(version=VERSION, **kwargs) - - self.case = CASENAME - self.formulations = formulations - - self.primal = primal - self.dual = dual - self.meta = meta - self.input = input - self.casejson = casejson - - self.train = train - self.test = test - self.infeasible = infeasible - - self.gz_ext = ".gz" if compressed else "" - - @property - def size(self): - return SIZES - - @property - def features(self): - features = {} - if self.casejson: features.update(case_features()) - if self.input: features.update(input_features(SIZES)) - for formulation in self.formulations: - features.update(FORMULATIONS_TO_FEATURES[formulation](SIZES, self.primal, self.dual, self.meta)) - return hfd.Features(features) - - @property - def splits(self): - splits: dict[hfd.Split, dict[str, str | int]] = {} - if self.train: - splits[hfd.Split.TRAIN] = { - "name": "train", - "num_examples": NUM_TRAIN - } - if self.test: - splits[hfd.Split.TEST] = { - "name": "test", - "num_examples": NUM_TEST - } - if self.infeasible: - splits[hfd.Split("infeasible")] = { - "name": "infeasible", - "num_examples": NUM_INFEASIBLE - } - return splits - - @property - def urls(self): - urls: dict[str, None | str | list] = { - "case": None, "train": [], "test": [], "infeasible": [], - } - - if self.casejson: - urls["case"] = f"case.json" + self.gz_ext - else: - urls.pop("case") - - split_names = [] - if self.train: split_names.append("train") - if self.test: split_names.append("test") - if self.infeasible: split_names.append("infeasible") - - for split in split_names: - if self.input: urls[split].append(f"{split}/input.h5" + self.gz_ext) - for formulation in self.formulations: - if self.primal: - filename = f"{split}/{formulation}/primal.h5" + self.gz_ext - if filename in SPLITFILES: urls[split].append(SPLITFILES[filename]) - else: urls[split].append(filename) - if self.dual: - filename = f"{split}/{formulation}/dual.h5" + self.gz_ext - if filename in SPLITFILES: urls[split].append(SPLITFILES[filename]) - else: urls[split].append(filename) - if self.meta: - filename = f"{split}/{formulation}/meta.h5" + self.gz_ext - if filename in SPLITFILES: urls[split].append(SPLITFILES[filename]) - else: urls[split].append(filename) - return urls - -# ┌────────────────────┐ -# │ DatasetBuilder │ -# └────────────────────┘ - -class PGLearnLarge13659_pegase(hfd.ArrowBasedBuilder): - """DatasetBuilder for PGLearn-ExtraLarge-13659_pegase. - The main interface is `datasets.load_dataset` with `trust_remote_code=True`, e.g. - - ```python - from datasets import load_dataset - ds = load_dataset("PGLearn/PGLearn-ExtraLarge-13659_pegase", trust_remote_code=True, - # modify the default configuration by passing kwargs - formulations=["DCOPF"], - dual=False, - meta=False, - ) - ``` - """ - - DEFAULT_WRITER_BATCH_SIZE = 10000 - BUILDER_CONFIG_CLASS = PGLearnLarge13659_pegaseConfig - DEFAULT_CONFIG_NAME=CASENAME - BUILDER_CONFIGS = [ - PGLearnLarge13659_pegaseConfig( - name=CASENAME, description=DEFAULT_CONFIG_DESCRIPTION.format(case=CASENAME), - formulations=list(FORMULATIONS_TO_FEATURES.keys()), - primal=True, dual=True, meta=True, input=True, casejson=False, - train=True, test=True, infeasible=False, - ) - ] - - def _info(self): - return hfd.DatasetInfo( - features=self.config.features, splits=self.config.splits, - description=DESCRIPTION + self.config.description, - homepage=URL, citation=CITATION, - ) - - def _split_generators(self, dl_manager: hfd.DownloadManager): - hfd.logging.get_logger().warning(USE_ML4OPF_WARNING) - - filepaths = dl_manager.download_and_extract(self.config.urls) - - splits: list[hfd.SplitGenerator] = [] - if self.config.train: - splits.append(hfd.SplitGenerator( - name=hfd.Split.TRAIN, - gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["train"]), n_samples=NUM_TRAIN), - )) - if self.config.test: - splits.append(hfd.SplitGenerator( - name=hfd.Split.TEST, - gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["test"]), n_samples=NUM_TEST), - )) - if self.config.infeasible: - splits.append(hfd.SplitGenerator( - name=hfd.Split("infeasible"), - gen_kwargs=dict(case_file=filepaths.get("case", None), data_files=tuple(filepaths["infeasible"]), n_samples=NUM_INFEASIBLE), - )) - return splits - - def _generate_tables(self, case_file: str | None, data_files: tuple[hfd.utils.track.tracked_str | list[hfd.utils.track.tracked_str]], n_samples: int): - case_data: str | None = json.dumps(json.load(open_maybe_gzip_cat(case_file))) if case_file is not None else None - data: dict[str, h5py.File] = {} - for file in data_files: - v = h5py.File(open_maybe_gzip_cat(file), "r") - if isinstance(file, list): - k = "/".join(Path(file[0].get_origin()).parts[-3:-1]).split(".")[0] - else: - k = "/".join(Path(file.get_origin()).parts[-2:]).split(".")[0] - data[k] = v - for k in list(data.keys()): - if "/input" in k: data[k.split("/", 1)[1]] = data.pop(k) - - batch_size = self._writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE - for i in range(0, n_samples, batch_size): - effective_batch_size = min(batch_size, n_samples - i) - - sample_data = { - f"{dk}/{k}": - hfd.features.features.numpy_to_pyarrow_listarray(v[i:i + effective_batch_size, ...]) - for dk, d in data.items() for k, v in d.items() if f"{dk}/{k}" in self.config.features - } - - if case_data is not None: - sample_data["case/json"] = pa.array([case_data] * effective_batch_size) - - yield i, pa.Table.from_pydict(sample_data) - - for f in data.values(): - f.close() - -# ┌──────────────┐ -# │ Features │ -# └──────────────┘ - -FLOAT_TYPE = "float32" -INT_TYPE = "int64" -BOOL_TYPE = "bool" -STRING_TYPE = "string" - -def case_features(): - # FIXME: better way to share schema of case data -- need to treat jagged arrays - return { - "case/json": hfd.Value(STRING_TYPE), - } - -META_FEATURES = { - "meta/seed": hfd.Value(dtype=INT_TYPE), - "meta/formulation": hfd.Value(dtype=STRING_TYPE), - "meta/primal_objective_value": hfd.Value(dtype=FLOAT_TYPE), - "meta/dual_objective_value": hfd.Value(dtype=FLOAT_TYPE), - "meta/primal_status": hfd.Value(dtype=STRING_TYPE), - "meta/dual_status": hfd.Value(dtype=STRING_TYPE), - "meta/termination_status": hfd.Value(dtype=STRING_TYPE), - "meta/build_time": hfd.Value(dtype=FLOAT_TYPE), - "meta/extract_time": hfd.Value(dtype=FLOAT_TYPE), - "meta/solve_time": hfd.Value(dtype=FLOAT_TYPE), -} - -def input_features(sizes: CaseSizes): - return { - "input/pd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)), - "input/qd": hfd.Sequence(length=sizes.n_load, feature=hfd.Value(dtype=FLOAT_TYPE)), - "input/gen_status": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=BOOL_TYPE)), - "input/branch_status": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=BOOL_TYPE)), - "input/seed": hfd.Value(dtype=INT_TYPE), - } - -def acopf_primal_features(sizes: CaseSizes): - return { - "ACOPF/primal/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def acopf_dual_features(sizes: CaseSizes): - return { - "ACOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/vm": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/sm_fr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/sm_to": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "ACOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE), - } -def dcopf_primal_features(sizes: CaseSizes): - return { - "DCOPF/primal/va": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def dcopf_dual_features(sizes: CaseSizes): - return { - "DCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "DCOPF/dual/slack_bus": hfd.Value(dtype=FLOAT_TYPE), - } -def socopf_primal_features(sizes: CaseSizes): - return { - "SOCOPF/primal/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/primal/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } -def socopf_dual_features(sizes: CaseSizes): - return { - "SOCOPF/dual/kcl_p": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/kcl_q": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/w": hfd.Sequence(length=sizes.n_bus, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qg": hfd.Sequence(length=sizes.n_gen, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/ohm_qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/jabr": hfd.Array2D(shape=(sizes.n_branch, 4), dtype=FLOAT_TYPE), - "SOCOPF/dual/sm_fr": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE), - "SOCOPF/dual/sm_to": hfd.Array2D(shape=(sizes.n_branch, 3), dtype=FLOAT_TYPE), - "SOCOPF/dual/va_diff": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/wr": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/wi": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/pt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qf": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - "SOCOPF/dual/qt": hfd.Sequence(length=sizes.n_branch, feature=hfd.Value(dtype=FLOAT_TYPE)), - } - -# ┌───────────────┐ -# │ Utilities │ -# └───────────────┘ - -def open_maybe_gzip_cat(path: str | list): - if isinstance(path, list): - dest = Path(path[0]).parent.with_suffix(".h5") - if not dest.exists(): - with open(dest, "wb") as dest_f: - for piece in path: - with open(piece, "rb") as piece_f: - shutil.copyfileobj(piece_f, dest_f) - shutil.rmtree(Path(piece).parent) - path = dest.as_posix() - return gzip.open(path, "rb") if path.endswith(".gz") else open(path, "rb") diff --git a/README.md b/README.md index 4ff20f027fb05eeda082aa54282a9c1e3e1313ce..6ebe49977a488cfc47467458cfd3f51bcdf6cd9f 100644 --- a/README.md +++ b/README.md @@ -288,6 +288,14 @@ dataset_info: - name: test num_bytes: 22356219165 num_examples: 4883 - download_size: 83983465828 + download_size: 108772668920 dataset_size: 111781095820 +configs: +- config_name: 13659_pegase + data_files: + - split: train + path: 13659_pegase/train-* + - split: test + path: 13659_pegase/test-* + default: true --- diff --git a/case.json.gz b/case.json.gz deleted file mode 100644 index 26d48ea2c0ca9b555555b338bbf870b3ad31071c..0000000000000000000000000000000000000000 --- a/case.json.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0baac391ef868bdd095ebd29e58c8842b3e9ee38908425ef72b63aa26809491a -size 11384812 diff --git a/config.toml b/config.toml deleted file mode 100644 index 32d26877d2cc3768ff4cb1f8f8129648774205f7..0000000000000000000000000000000000000000 --- a/config.toml +++ /dev/null @@ -1,42 +0,0 @@ -# Name of the reference PGLib case. Must be a valid PGLib case name. -pglib_case = "pglib_opf_case13659_pegase" -floating_point_type = "Float32" - -[sampler] -# data sampler options -[sampler.load] -noise_type = "ScaledUniform" -l = 0.6 # Lower bound of base load factor -u = 1.0 # Upper bound of base load factor -sigma = 0.20 # Relative (multiplicative) noise level. - - -[OPF] - -[OPF.ACOPF] -type = "ACOPF" -solver.name = "Ipopt" -solver.attributes.tol = 1e-6 -solver.attributes.linear_solver = "ma27" - -[OPF.DCOPF] -# Formulation/solver options -type = "DCOPF" -solver.name = "HiGHS" - -[OPF.SOCOPF] -type = "SOCOPF" -solver.name = "Clarabel" -# Tight tolerances -solver.attributes.tol_gap_abs = 1e-6 -solver.attributes.tol_gap_rel = 1e-6 -solver.attributes.tol_feas = 1e-6 -solver.attributes.tol_infeas_rel = 1e-6 -solver.attributes.tol_ktratio = 1e-6 -# Reduced accuracy settings -solver.attributes.reduced_tol_gap_abs = 1e-6 -solver.attributes.reduced_tol_gap_rel = 1e-6 -solver.attributes.reduced_tol_feas = 1e-6 -solver.attributes.reduced_tol_infeas_abs = 1e-6 -solver.attributes.reduced_tol_infeas_rel = 1e-6 -solver.attributes.reduced_tol_ktratio = 1e-6 diff --git a/infeasible/ACOPF/dual.h5.gz b/infeasible/ACOPF/dual.h5.gz deleted file mode 100644 index 5ad1bc107d5100a5699e676aec3c4fa316fe3d4c..0000000000000000000000000000000000000000 --- a/infeasible/ACOPF/dual.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3c25499ca146fc6f001adf8c603c7eada6fcd6c3235daef11d41879d3acee8c6 -size 20978811921 diff --git a/infeasible/ACOPF/meta.h5.gz b/infeasible/ACOPF/meta.h5.gz deleted file mode 100644 index eab830a7b8a6cdde288f32c869f06e650fe88b5d..0000000000000000000000000000000000000000 --- a/infeasible/ACOPF/meta.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:66bed0e566ed87302d2064422ad608a2047a66002f7694b42b821569b5458c65 -size 864552 diff --git a/infeasible/ACOPF/primal.h5.gz b/infeasible/ACOPF/primal.h5.gz deleted file mode 100644 index 25562174a459db2cfedb70150a9148a9b10cb509..0000000000000000000000000000000000000000 --- a/infeasible/ACOPF/primal.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:33ff85c9f638556caed1a67bc69fe4b2a6c03a9b83d357da0bd0bad8170c2bf1 -size 9441799458 diff --git a/infeasible/DCOPF/dual.h5.gz b/infeasible/DCOPF/dual.h5.gz deleted file mode 100644 index 5e5788640bf6e5e979aaac3163199b378357ab8c..0000000000000000000000000000000000000000 --- a/infeasible/DCOPF/dual.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2e1305540e86023b4900919c3cfc448c7a87a45c09e0e8fad4e23814281e2df2 -size 2200307412 diff --git a/infeasible/DCOPF/meta.h5.gz b/infeasible/DCOPF/meta.h5.gz deleted file mode 100644 index b339974cc01b83752529b8fa80296b0b13efee7e..0000000000000000000000000000000000000000 --- a/infeasible/DCOPF/meta.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0c6390ca07af3f5214e4df07ec35901a046b0fa2e552aabff90038a51124d0b3 -size 863156 diff --git a/infeasible/DCOPF/primal.h5.gz b/infeasible/DCOPF/primal.h5.gz deleted file mode 100644 index 1473824d5f0bd685c8078f46b5c71e931511d06c..0000000000000000000000000000000000000000 --- a/infeasible/DCOPF/primal.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7702dd1b5a992d44f3feb5af39f6bf75520fe8b02353fdac6e680ac5e0927621 -size 2785347118 diff --git a/infeasible/SOCOPF/dual.h5.gz b/infeasible/SOCOPF/dual.h5.gz deleted file mode 100644 index a904505a7445bc0d311e5c726da0ee3c0525ecd8..0000000000000000000000000000000000000000 --- a/infeasible/SOCOPF/dual.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9d05bdc272bdab8a5c189c8841f31b671d33b0dc7ecb4da9f2544dbe73b67a37 -size 39277309502 diff --git a/infeasible/SOCOPF/meta.h5.gz b/infeasible/SOCOPF/meta.h5.gz deleted file mode 100644 index f52f94ac818670474f8d3378ad7b796174eaba57..0000000000000000000000000000000000000000 --- a/infeasible/SOCOPF/meta.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:15d83007acddb37e9174e3c22ad9ada6d170fb386113a21d1cb17ba928201f16 -size 715114 diff --git a/infeasible/SOCOPF/primal.h5.gz b/infeasible/SOCOPF/primal.h5.gz deleted file mode 100644 index 917d3cc42e9fc4aad54b5cdb824f2c9cf372a376..0000000000000000000000000000000000000000 --- a/infeasible/SOCOPF/primal.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:393d7c1da9254222c64afce7ac0f4b0c3e709dadc3a077e5826fdd408a4d91d2 -size 11652866067 diff --git a/infeasible/input.h5.gz b/infeasible/input.h5.gz deleted file mode 100644 index c1913067331b1cc49aea4193fc30b543e3c6b186..0000000000000000000000000000000000000000 --- a/infeasible/input.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:18b99acb65a676da149f07f6221e59a8e4e4827ee5c5b692943789b3a124f413 -size 1048224063 diff --git a/test/ACOPF/dual.h5.gz b/test/ACOPF/dual.h5.gz deleted file mode 100644 index 0bac4bb97cf15cac46397be0e4ad329f49f11724..0000000000000000000000000000000000000000 --- a/test/ACOPF/dual.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:172039f0ec0603ee36ce1c98a5ef298ab9a439f42e5571629a24b3acb7b17e23 -size 4042267445 diff --git a/test/ACOPF/meta.h5.gz b/test/ACOPF/meta.h5.gz deleted file mode 100644 index e7326cf71406acb9faac017cb3dfebf3f2206b56..0000000000000000000000000000000000000000 --- a/test/ACOPF/meta.h5.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a6cba1a5a17afd6fe254212ffc02e44f0af8f773100aafb5b7194ad14a37ef54 -size 171872 diff --git a/test/ACOPF/primal.h5.gz b/test/ACOPF/primal.h5.gz deleted file mode 100644 index ecb062c5d181b9efa2d5d47de6856afbd3408103..0000000000000000000000000000000000000000 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