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BubbleML_2.py

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+# 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

dataset_dict.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dc68f752a9830c7aa2e1ce55ab1e524ba53f5ead708ffb8463abbb93c5be6c6
+size 52