Delete BubbleML_2.py

BubbleML_2.py

@@ -1,198 +0,0 @@
-# BubbleML_2.py
-
-import os
-import json
-import h5py
-import numpy as np
-
-import datasets
-from datasets import (
-    BuilderConfig,
-    GeneratorBasedBuilder,
-    DatasetInfo,
-    Features,
-    Array4D,
-    Sequence,
-    Value,
-    Split,
-    Version,
-)
-
-_CITATION = ""  # optional
-_DESCRIPTION = """
-BubbleML: high-fidelity single-bubble boiling simulations (FC72 & R515B).
-Pre-defined train/test splits across two directories.
-"""
-
-class BubbleMLConfig(BuilderConfig):
-    """BuilderConfig for BubbleML_2 with fixed T, H, W."""
-    def __init__(
-        self,
-        *,
-        name: str,
-        description: str,
-        data_files: dict,
-        timesteps: int,
-        height: int,
-        width: int,
-        data_dir: str = "",
-        version: Version = Version("1.0.0"),
-        **kwargs,
-    ):
-        super().__init__(name=name, version=version, description=description, **kwargs)
-        self.data_files = data_files
-        self.data_dir = data_dir
-        self.timesteps = timesteps
-        self.height = height
-        self.width = width
-
-class BubbleMLDataset(GeneratorBasedBuilder):
-    """BubbleML_2: combined single-bubble dataset."""
-    BUILDER_CONFIG_CLASS = BubbleMLConfig
-    DEFAULT_CONFIG_NAME = "single-bubble"
-    BUILDER_CONFIGS = [
-        BubbleMLConfig(
-            name="single-bubble",
-            description="Single-bubble (FC72 & R515B) train/test split",
-            data_dir="",  # local root; overridden by --data_dir
-            timesteps=5,
-            height=288,
-            width=192,
-            data_files={
-                "train": [
-                    # FC72 train
-                    "SingleBubble-Saturated-FC72-2D/Twall_90.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_91.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_92.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_94.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_96.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_98.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_99.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_100.hdf5",
-                    # R515B train
-                    "SingleBubble-Saturated-R515B-2D/Twall_13.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_14.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_15.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_17.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_19.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_21.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_22.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_23.hdf5",
-                ],
-                "test": [
-                    # FC72 test
-                    "SingleBubble-Saturated-FC72-2D/Twall_87.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_95.hdf5",
-                    "SingleBubble-Saturated-FC72-2D/Twall_103.hdf5",
-                    # R515B test
-                    "SingleBubble-Saturated-R515B-2D/Twall_10.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_18.hdf5",
-                    "SingleBubble-Saturated-R515B-2D/Twall_26.hdf5",
-                ],
-            },
-        ),
-    ]
-
-    def _info(self) -> DatasetInfo:
-        cfg = self.config
-        # C = number of fields
-        C = 4
-        T, H, W = cfg.timesteps, cfg.height, cfg.width
-        features = Features({
-            "input":  Array4D(dtype="float32", shape=(T, C, H, W)),
-            "output": Array4D(dtype="float32", shape=(T, C, H, W)),
-            "fluid_params": Sequence(Value("float32")),
-            "filename":    Value("string"),
-        })
-        return DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage="https://huggingface.co/datasets/hpcforge/BubbleML_2",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        cfg = self.config
-
-        if cfg.data_dir:
-            # Local: read files from disk
-            base_dir = cfg.data_dir
-            def resolve_local(split):
-                pairs = []
-                for rel in cfg.data_files[split]:
-                    h5p = os.path.join(base_dir, rel)
-                    jp  = h5p.replace(".hdf5", ".json")
-                    if not os.path.isfile(h5p):
-                        raise FileNotFoundError(f"{h5p} not found.")
-                    if not os.path.isfile(jp):
-                        raise FileNotFoundError(f"{jp} not found.")
-                    pairs.append((h5p, jp))
-                return pairs
-            train = resolve_local("train")
-            test  = resolve_local("test")
-        else:
-            # Remote: download from Hub
-            base_url = "https://huggingface.co/datasets/hpcforge/BubbleML_2/resolve/main/"
-            url_map = {}
-            for split in ["train","test"]:
-                for rel in cfg.data_files[split]:
-                    url_map[rel] = base_url + rel
-                    url_map[rel.replace(".hdf5", ".json")] = base_url + rel.replace(".hdf5", ".json")
-            dl = dl_manager.download(url_map)
-            train, test = [], []
-            for split in ["train","test"]:
-                for rel in cfg.data_files[split]:
-                    h5p = dl[rel]
-                    jp  = dl[rel.replace(".hdf5", ".json")]
-                    (train if split=="train" else test).append((h5p,jp))
-
-        return [
-            datasets.SplitGenerator(name=Split.TRAIN, gen_kwargs={"file_pairs": train}),
-            datasets.SplitGenerator(name=Split.TEST,  gen_kwargs={"file_pairs": test}),
-        ]
-
-    def _generate_examples(self, file_pairs):
-        """Yield sliding-window examples, yielding NumPy arrays directly."""
-        idx = 0
-        fields = ["dfun", "temperature", "velx", "vely"]
-        tw = self.config.timesteps
-        for h5_path, json_path in file_pairs:
-            with h5py.File(h5_path, "r") as h5f:
-                arrays = {k: h5f[k][...] for k in fields}  # (T, H, W)
-            T = arrays[fields[0]].shape[0]
-            max_start = T - 2*tw + 1
-
-            # metadata
-            with open(json_path, "r") as jf:
-                p = json.load(jf)
-            fluid_params = [
-                p["inv_reynolds"],
-                p["cpgas"],
-                p["mugas"],
-                p["rhogas"],
-                p["thcogas"],
-                p.get("stefan", 0.0),
-                p["prandtl"],
-                p["heater"]["nucWaitTime"],
-                p["heater"].get("wallTemp", 0.0),
-            ]
-
-            for start in range(max_start):
-                ei = start + tw
-                eo = ei + tw
-
-                inp_c_t_h_w = np.stack([arrays[k][start:ei] for k in fields], axis=0)
-                out_c_t_h_w = np.stack([arrays[k][ei:eo] for k in fields], axis=0)
-
-                # transpose (C,T,H,W) -> (T,C,H,W)
-                inp = inp_c_t_h_w.transpose(1,0,2,3).astype("float32")
-                out = out_c_t_h_w.transpose(1,0,2,3).astype("float32")
-
-                yield idx, {
-                    "input": inp,
-                    "output": out,
-                    "fluid_params": fluid_params,
-                    "filename": os.path.basename(h5_path),
-                }
-                idx += 1