""" aggregation.py -> data/ingest/ Phase 2 ingestion, LOCKED to the real HuggingFace schemas (audit 2026-07). raw source (CSV/parquet) -> grid-aligned table: node_id | date | | n_obs | centroid_lat | centroid_lon Audit-driven fixes baked in: - per-source MISSING SENTINELS (VIIRS -999, S5P 9.969e36) -> NaN before aggregating - per-source lat/lon column names (MODIS/VIIRS use lat/lon) - timestamp -> date derivation (S5P/ERA5 use time_utc) - ERA5 hourly -> daily collapse, + a separate 13:30-IST wind spec - nonneg filter (MODIS uncertainty had 67 negative rows) """ from __future__ import annotations import sys, pathlib sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1] / "grid")) import numpy as np import pandas as pd from dataclasses import dataclass, field from scipy.spatial import cKDTree from pyproj import Transformer from grid_index import GridIndex S5P_FILL_VALUE = 9.969209968386869e+36 IST_OFFSET_H = 5.5 # UTC -> IST @dataclass class SourceSpec: name: str mode: str # "containment" | "centroid" value_cols: list lat_col: str = "latitude" lon_col: str = "longitude" date_col: str | None = "date" # used if timestamp_col is None timestamp_col: str | None = None # if set, date derived from this (UTC) agg: str = "mean" # mean|median|max|min|sum|count sentinels: list = field(default_factory=list) # values -> NaN (e.g. -999) qa_col: str | None = None qa_min: float | None = None nonneg_cols: list = field(default_factory=list) # drop rows where these < 0 ist_target_h: float | None = None # select row nearest this IST hour (13.5 = 13:30) pre_daily_mean: bool = False # collapse (lat,lon,date) before centroid sample max_sample_km: float | None = None rename: dict = field(default_factory=dict) def _to_date(df: pd.DataFrame, spec: SourceSpec) -> pd.DataFrame: df = df.copy() if spec.timestamp_col is not None: ts = pd.to_datetime(df[spec.timestamp_col], utc=True, errors="coerce") ist = ts + pd.Timedelta(hours=IST_OFFSET_H) df["date"] = ist.dt.strftime("%Y-%m-%d") df["_ist_h"] = ist.dt.hour + ist.dt.minute / 60.0 # fractional IST hour else: df["date"] = pd.to_datetime(df[spec.date_col], errors="coerce").dt.strftime("%Y-%m-%d") return df.dropna(subset=["date"]) def _select_nearest_ist(df: pd.DataFrame, spec: SourceSpec) -> pd.DataFrame: """Keep, per (lat, lon, date), the single row whose IST time is nearest the target (e.g. 13:30 for S5P-aligned wind). Robust to hourly/3-hourly cadence.""" if spec.ist_target_h is None or "_ist_h" not in df: return df d = df.assign(_gap=(df["_ist_h"] - spec.ist_target_h).abs()) d = d.sort_values("_gap").drop_duplicates([spec.lat_col, spec.lon_col, "date"], keep="first") return d.drop(columns=["_gap"]) def _clean(df: pd.DataFrame, spec: SourceSpec) -> pd.DataFrame: df = df.copy() # 1. sentinels -> NaN (CRITICAL: VIIRS -999, S5P fill). Must run BEFORE dropna. sents = list(spec.sentinels) + [S5P_FILL_VALUE] for c in spec.value_cols: df[c] = df[c].replace(sents, np.nan) # 2. nonneg filter (MODIS uncertainty) for c in spec.nonneg_cols: if c in df: df = df[(df[c] >= 0) | df[c].isna()] # 3. drop rows with no valid value df = df.dropna(subset=spec.value_cols, how="all") # only drop if ALL value cols null # 4. QA gate if spec.qa_col and spec.qa_min is not None: df = df[df[spec.qa_col] >= spec.qa_min] return df def _finalize(out, gi, spec, dropped=None): out = out.rename(columns=spec.rename) out = out.merge(gi.centroids(), on="node_id", how="left").reset_index(drop=True) if dropped is not None: out.attrs["dropped_outside_grid"] = int(dropped) return out def _containment(df, gi, spec): df = df.copy() df["node_id"] = gi.assign_node_id(df[spec.lat_col].values, df[spec.lon_col].values) dropped = int(df["node_id"].isna().sum()) df = df[df["node_id"].notna()] if len(df) == 0: raise ValueError(f"[{spec.name}] nothing landed in grid") g = df.groupby(["node_id", "date"]) if spec.agg == "count": out = g.size().to_frame("count") for c in spec.value_cols: out[c] = g[c].mean() else: out = g.agg({c: spec.agg for c in spec.value_cols}) out["n_obs"] = g.size() out = out.reset_index() return _finalize(out, gi, spec, dropped) def _centroid(df, gi, spec): if spec.pre_daily_mean: # collapse hourly -> one row per (lat,lon,date) keys = [spec.lat_col, spec.lon_col, "date"] df = df.groupby(keys, as_index=False)[spec.value_cols].mean() fwd = Transformer.from_crs("EPSG:4326", gi.proj4, always_xy=True) cent = gi.centroids() cx, cy = fwd.transform(cent["centroid_lon"].values, cent["centroid_lat"].values) cxy = np.column_stack([cx, cy]) frames = [] for date, sub in df.groupby("date"): sx, sy = fwd.transform(sub[spec.lon_col].values, sub[spec.lat_col].values) tree = cKDTree(np.column_stack([sx, sy])) dist_m, idx = tree.query(cxy, k=1) rec = {"node_id": cent["node_id"].values, "date": date, "n_obs": 1} for c in spec.value_cols: v = sub[c].values[idx].astype(float) if spec.max_sample_km is not None: v[dist_m > spec.max_sample_km * 1000.0] = np.nan rec[c] = v frames.append(pd.DataFrame(rec)) if not frames: raise ValueError(f"[{spec.name}] no rows survived filtering (check ist_target_h / sentinels / qa)") return _finalize(pd.concat(frames, ignore_index=True), gi, spec) def ingest(raw, gi, spec): df = _clean(_to_date(raw, spec), spec) df = _select_nearest_ist(df, spec) if len(df) == 0: raise ValueError(f"[{spec.name}] empty after clean/filter") if spec.mode == "containment": return _containment(df, gi, spec) if spec.mode == "centroid": return _centroid(df, gi, spec) raise ValueError(f"bad mode {spec.mode!r}") # ========================================================================== # REAL SPECS — locked to the HuggingFace audit schemas. # S5P: one spec PER pollutant (each folder has a generic `value` column). # ========================================================================== SPECS = { # ---- S5P TROPOMI (per-pollutant files, col `value`, time_utc, qa_value) ---- "s5p_no2": SourceSpec("s5p_no2","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.75, rename={"value":"s5p_no2_column"}), "s5p_co": SourceSpec("s5p_co","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.5, rename={"value":"s5p_co_column"}), "s5p_so2": SourceSpec("s5p_so2","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.5, rename={"value":"s5p_so2_column"}), "s5p_ch4": SourceSpec("s5p_ch4","containment", value_cols=["value"], timestamp_col="time_utc", rename={"value":"s5p_ch4_column"}), # ch4 qa_threshold null (guide) "s5p_o3": SourceSpec("s5p_o3","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.5, rename={"value":"s5p_o3_column"}), # OUTPUT pollutant; S5P-O3 input OPTIONAL (labels from CPCB) "s5p_hcho": SourceSpec("s5p_hcho","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.5, rename={"value":"s5p_hcho_column"}), # INPUT-ONLY (blueprint Group A, biomass signal) — REQUIRED input "s5p_cloud": SourceSpec("s5p_cloud","containment", value_cols=["value"], timestamp_col="time_utc", rename={"value":"s5p_cloud_fraction"}), # QA / cloud-fraction input "s5p_aer_ai": SourceSpec("s5p_aer_ai","containment", value_cols=["value"], timestamp_col="time_utc", qa_col="qa_value", qa_min=0.80, rename={"value":"s5p_aer_ai"}), # ---- MODIS AOD (lat/lon, ~70% null, negative uncertainty) ---- "modis": SourceSpec("modis","containment", value_cols=["modis_aod"], lat_col="lat", lon_col="lon", date_col="date", nonneg_cols=["modis_aod_uncertainty"]), # ---- VIIRS AOD (lat/lon, -999 SENTINEL) ---- "viirs": SourceSpec("viirs","containment", value_cols=["viirs_aod"], lat_col="lat", lon_col="lon", date_col="date", sentinels=[-999.0]), # ---- ERA5 (hourly 0.25deg grid) -> daily-mean node features, centroid sample ---- "era5_daily": SourceSpec("era5_daily","centroid", value_cols=["era5_temperature","era5_boundary_layer_height", "era5_relative_humidity","era5_surface_pressure", "era5_cloud_cover","era5_wind_u","era5_wind_v","era5_rainfall"], # era5_solar_radiation EXCLUDED: all-NaN in data (would empty every row). Re-add if fixed. timestamp_col="time_utc", pre_daily_mean=True, max_sample_km=30), # ---- ERA5 13:30-IST wind ONLY (edge-term input; u/v kept as components) ---- "era5_wind_1330": SourceSpec("era5_wind_1330","centroid", value_cols=["era5_wind_u","era5_wind_v"], timestamp_col="time_utc", ist_target_h=13.5, max_sample_km=30, # nearest-to-13:30 from RAW hourly rename={"era5_wind_u":"u_1330ist","era5_wind_v":"v_1330ist"}), # ---- FIRMS fire -> count per cell/day (acq_date) ---- "firms": SourceSpec("firms","containment", value_cols=["frp"], lat_col="latitude", lon_col="longitude", date_col="acq_date", agg="count", rename={"count":"fire_count"}), } # DEM (Copernicus GLO-30 GeoTIFF): NOT here — raster reproject to LAEA in static_gis.py.