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| """Virtual points: ungauged CONUS simulation outlets (HydroBASINS pour points). | |
| Level-07 sub-basin outlets fill the gaps between USGS gauges so every part of | |
| CONUS has a hindcast target. They have no observations, so a virtual-point | |
| run always uses the speed scheme: the domain is truncated at upstream USGS | |
| gauges and their observed flow is injected as the boundary condition, and the | |
| parameters are borrowed from the nearest calibrated gauge (regionalization — | |
| calibration at the point itself is impossible). | |
| Catalog: gauges/virtual_points.parquet in vincewin/CREST_data | |
| (built by scripts/prep_virtual_points.py). Ids are "V" + HYRIV_ID. | |
| """ | |
| from __future__ import annotations | |
| import threading | |
| import truststore | |
| truststore.inject_into_ssl() | |
| from huggingface_hub import hf_hub_download | |
| HF_REPO = "vincewin/CREST_data" | |
| VP_PATH = "gauges/virtual_points.parquet" | |
| _lock = threading.Lock() | |
| _df = None # pandas DataFrame indexed by vp id, or "failed" sentinel | |
| def is_virtual(gauge_id) -> bool: | |
| return str(gauge_id).upper().startswith("V") | |
| def _load(): | |
| global _df | |
| with _lock: | |
| if _df is None: | |
| try: | |
| import pandas as pd | |
| path = hf_hub_download(HF_REPO, VP_PATH, repo_type="dataset") | |
| _df = pd.read_parquet(path).set_index("vp", drop=False) | |
| except Exception: | |
| _df = "failed" # sentinel: no re-download loop per call | |
| return None if isinstance(_df, str) else _df | |
| def info(vp_id: str) -> dict | None: | |
| """Outlet dict for the pipeline — same shape as pipeline.gauge_info().""" | |
| df = _load() | |
| if df is None or vp_id not in df.index: | |
| return None | |
| r = df.loc[vp_id] | |
| return {"id": vp_id, "name": f"Ungauged point " | |
| f"({float(r.area_km2):,.0f} km² basin)", | |
| "lat": float(r.lat), "lon": float(r.lon), | |
| "area": float(r.area_km2), "virtual": True, "tz": str(r.tz)} | |
| def for_bbox(w: float, s: float, e: float, n: float, limit: int = 200) -> list[dict]: | |
| """Pin dicts for the viewport (map serving).""" | |
| df = _load() | |
| if df is None: | |
| return [] | |
| m = df[(df.lon >= w) & (df.lon <= e) & (df.lat >= s) & (df.lat <= n)] | |
| if len(m) > limit: # center-first, like the gauge pins | |
| cx, cy = (w + e) / 2, (s + n) / 2 | |
| m = m.assign(_d=(m.lat - cy) ** 2 + (m.lon - cx) ** 2).nsmallest(limit, "_d") | |
| return [{"id": str(r.vp), "name": "Ungauged point", | |
| "lat": float(r.lat), "lon": float(r.lon), | |
| "area_km2": float(r.area_km2), "tz": str(r.tz), "virtual": True} | |
| for r in m.itertuples()] | |
| def count() -> int: | |
| df = _load() | |
| return 0 if df is None else len(df) | |