Datasets:
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MagBridge-Battery v1.0 — minimal loader example.
Run from the bundle root:
python load_example.py
Requires: pandas, pyarrow.
Licensed under Apache-2.0 (see LICENSE-CODE).
"""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import pandas as pd
def load_bundle(base: Path) -> tuple[pd.DataFrame, dict, dict, dict]:
"""Load all shards, both split files, and the manifest.
Returns
-------
df : pd.DataFrame
Concatenated shards. One row per sample. Signal columns hold length-100
arrays.
primary_split : dict
Cell-disjoint, leakage-free split. Keys include 'train_samples',
'val_samples', 'test_samples', and 'split_guarantee'.
optimistic_split : dict
Intentionally leaky baseline. Do not use for reporting; see its
'warning' field.
manifest : dict
Provenance, hashes, bridge config.
"""
shards = sorted((base / "data").glob("shard_*.parquet"))
if not shards:
raise FileNotFoundError(f"No shards found under {base / 'data'}")
df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
primary_split = json.loads((base / "splits" / "by_cell_primary.json").read_text())
optimistic_split = json.loads((base / "splits" / "by_record_optimistic_baseline.json").read_text())
manifest = json.loads((base / "manifest.json").read_text())
return df, primary_split, optimistic_split, manifest
def apply_split(df: pd.DataFrame, split: dict) -> dict[str, pd.DataFrame]:
"""Slice df into train/val/test using a split dict."""
by_id = df.set_index("sample_id", drop=False)
out = {}
for subset in ("train", "val", "test"):
ids = split[f"{subset}_samples"]
out[subset] = by_id.loc[by_id.index.intersection(ids)].reset_index(drop=True)
return out
def stack_signals(df: pd.DataFrame, channels: list[str] | None = None) -> np.ndarray:
"""Stack signal columns into a (N, T, C) numpy array.
Default channels: the six signal channels. ``time_norm`` is omitted because
it is constant across samples (a fixed reference grid) and adds no
per-sample information.
"""
if channels is None:
channels = ["B_s1Y", "B_s1Z", "B_s2Y", "B_s2Z", "B_s1C5", "B_s2C6"]
arrays = [np.stack(df[c].values) for c in channels] # each (N, T)
return np.stack(arrays, axis=-1) # (N, T, C)
def main() -> None:
base = Path(__file__).parent.resolve()
df, primary, optimistic, manifest = load_bundle(base)
print(f"Dataset: {manifest['dataset_name']} v{manifest['dataset_version']}")
print(f"Schema: {manifest['schema_version']}")
print(f"Generated: {manifest['generated_at_utc']}")
print(f"Total samples loaded: {len(df)}")
print()
splits = apply_split(df, primary)
print("Primary (cell-disjoint) split:")
for name, sub in splits.items():
print(f" {name:5s}: {len(sub):5d} samples")
print()
print("Split guarantee:")
print(f" {primary['split_guarantee'][:120]}...")
print()
# Show that the optimistic split is shipped with a clear warning
print("Optimistic split warning (first 120 chars):")
print(f" {optimistic['warning'][:120]}...")
print(f" leakage_stats: {optimistic.get('leakage_stats', {})}")
print()
# Stack train signals into a tensor
X_train = stack_signals(splits["train"])
print(f"Train signal tensor shape: {X_train.shape} (N, T, C)")
print(f" channels = ['B_s1Y','B_s1Z','B_s2Y','B_s2Z','B_s1C5','B_s2C6']")
# Show that Regime-B has missing SOH by design
regime_b = df[df["anomaly_subtype"] == "low_voltage_regime_B"]
print()
print(f"Regime-B samples: {len(regime_b)}; SOH missing: {regime_b['soh'].isna().sum()} (expected = all)")
if __name__ == "__main__":
main()
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