Upload 2 files

scripts/create_viewer_tables.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""Create lightweight Parquet tables for the Hugging Face Dataset Viewer.
+
+The full multidimensional arrays remain in NPZ files. Each Parquet row
+corresponds to one NPZ sample and contains only identifiers, shapes, and
+compact summary statistics.
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+DATASETS = {
+    "global_mask": {
+        "train": "GlobalMask/train/globalmask_training_dataset.npz",
+        "test": "GlobalMask/test/globalmask_testing_dataset.npz",
+    },
+    "above": {
+        "train": "InSituMatched/above/train/InSituMatched_above_train.npz",
+        "test": "InSituMatched/above/test/InSituMatched_above_test.npz",
+    },
+    "ameriflux": {
+        "train": "InSituMatched/ameriflux/train/InSituMatched_ameriflux_train.npz",
+        "test": "InSituMatched/ameriflux/test/InSituMatched_ameriflux_test.npz",
+    },
+    "fluxnet": {
+        "train": "InSituMatched/fluxnet/train/InSituMatched_fluxnet_train.npz",
+        "test": "InSituMatched/fluxnet/test/InSituMatched_fluxnet_test.npz",
+    },
+    "icos_ww": {
+        "train": "InSituMatched/icos-ww/train/InSituMatched_icos-ww_train.npz",
+        "test": "InSituMatched/icos-ww/test/InSituMatched_icos-ww_test.npz",
+    },
+    "mix": {
+        "train": "InSituMatched/mix/train/InSituMatched_mix_train.npz",
+        "test": "InSituMatched/mix/test/InSituMatched_mix_test.npz",
+    },
+}
+
+
+def shape_text(array: np.ndarray | None) -> str:
+    if array is None:
+        return ""
+    return " × ".join(str(v) for v in array.shape[1:])
+
+
+def finite_mean(values: np.ndarray) -> float | None:
+    finite = np.isfinite(values)
+    if not finite.any():
+        return None
+    return float(values[finite].mean())
+
+
+def finite_fraction(values: np.ndarray) -> float:
+    return float(np.isfinite(values).mean())
+
+
+def build_rows(npz_path: Path, config_name: str, split: str, relative_path: str) -> list[dict[str, Any]]:
+    with np.load(npz_path, allow_pickle=False) as data:
+        arrays = {key: data[key] for key in data.files}
+
+    if "ed_simulation_x" not in arrays:
+        raise KeyError(f"{npz_path}: missing required key 'ed_simulation_x'")
+
+    sample_count = arrays["ed_simulation_x"].shape[0]
+
+    for key, value in arrays.items():
+        if value.ndim > 0 and value.shape[0] != sample_count:
+            raise ValueError(
+                f"{npz_path}: key {key!r} is not sample-first: "
+                f"first dimension {value.shape[0]} != {sample_count}"
+            )
+
+    x = arrays.get("ed_simulation_x")
+    y = arrays.get("ed_simulation_y")
+    observed = arrays.get("observed_y")
+    age = arrays.get("lidar_age_weight_fraction")
+    esa_bl = arrays.get("esa_cci_bl_fraction")
+    esa_nl = arrays.get("esa_cci_nl_fraction")
+    esa_gs = arrays.get("esa_cci_gs_fraction")
+    pft_bl = arrays.get("ed_simulation_pft_bl")
+    pft_nl = arrays.get("ed_simulation_pft_nl")
+    pft_gs = arrays.get("ed_simulation_pft_gs")
+
+    rows: list[dict[str, Any]] = []
+    for i in range(sample_count):
+        row: dict[str, Any] = {
+            "sample_index": i,
+            "split": split,
+            "subset": config_name,
+            "data_file": relative_path,
+            "ed_simulation_x_shape": shape_text(x),
+            "ed_simulation_y_shape": shape_text(y),
+            "observed_y_shape": shape_text(observed),
+        }
+
+        if age is not None:
+            row["lidar_age_weight_sum"] = float(np.sum(age[i]))
+        if esa_bl is not None:
+            row["esa_cci_bl_mean"] = finite_mean(esa_bl[i])
+        if esa_nl is not None:
+            row["esa_cci_nl_mean"] = finite_mean(esa_nl[i])
+        if esa_gs is not None:
+            row["esa_cci_gs_mean"] = finite_mean(esa_gs[i])
+        if observed is not None:
+            row["observed_y_valid_fraction"] = finite_fraction(observed[i])
+        if pft_bl is not None:
+            row["ed_simulation_pft_bl_mean"] = finite_mean(pft_bl[i])
+        if pft_nl is not None:
+            row["ed_simulation_pft_nl_mean"] = finite_mean(pft_nl[i])
+        if pft_gs is not None:
+            row["ed_simulation_pft_gs_mean"] = finite_mean(pft_gs[i])
+
+        rows.append(row)
+
+    return rows
+
+
+def output_path(root: Path, config_name: str, split: str) -> Path:
+    if config_name == "global_mask":
+        return root / "viewer" / "global_mask" / f"{split}.parquet"
+    return root / "viewer" / "insitu_matched" / config_name / f"{split}.parquet"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--root",
+        type=Path,
+        default=Path("."),
+        help="DERE repository root (default: current directory)",
+    )
+    args = parser.parse_args()
+    root = args.root.resolve()
+
+    for config_name, splits in DATASETS.items():
+        for split, relative_path in splits.items():
+            npz_path = root / relative_path
+            if not npz_path.exists():
+                raise FileNotFoundError(f"Missing data file: {npz_path}")
+
+            rows = build_rows(npz_path, config_name, split, relative_path)
+            destination = output_path(root, config_name, split)
+            destination.parent.mkdir(parents=True, exist_ok=True)
+
+            frame = pd.DataFrame(rows)
+            frame.to_parquet(destination, index=False)
+            print(f"Wrote {destination.relative_to(root)} ({len(frame)} rows)")
+
+
+if __name__ == "__main__":
+    main()

viewer/README.md

@@ -0,0 +1,21 @@
+# Dataset Viewer tables
+
+This directory contains lightweight Parquet tables used by the Hugging Face
+Dataset Viewer.
+
+Each row corresponds to one sample in an NPZ file and includes:
+
+- sample index, split, subset, and source NPZ path
+- per-sample array shapes
+- compact PFT and forest-age summaries
+- the valid-observation fraction for in-situ samples
+
+The full multidimensional scientific arrays are not duplicated or flattened
+here. They remain in the NPZ files under `GlobalMask/` and `InSituMatched/`.
+
+Regenerate the Viewer tables from the repository root with:
+
+```bash
+pip install pandas pyarrow
+python scripts/create_viewer_tables.py --root .
+```