Datasets:

AtmosIQ
/

atmosiq-processed

"""
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 | <value cols> | 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.

	"""
	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 \| <value cols> \| 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.