| """
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| preprocess.py -> data\ingest\ (run: python data\ingest\preprocess.py)
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|
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| Turns the raw master into a TRAINING-READY dataset. Per month partition:
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| 1. CLIP each feature to a physical envelope (winsorises bad-pixel tails —
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| e.g. modis_aod 11 -> 5, s5p_aer_ai 151 -> 20 — without touching real highs).
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| 2. Add <source>_present missingness flags (blueprint Group A) — model must
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| know which sparse satellite inputs are present (fill ranges 21-89%).
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| 3. LEFT-JOIN CPCB labels (cpcb_labels_daily.parquet) -> 7 targets + `labeled`.
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| Output: data\ingest\train_ready\<YYYY-MM>.parquet (features + flags + targets)
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| After this, only training is left.
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| """
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| from __future__ import annotations
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| import os, sys, pathlib
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| import numpy as np, pandas as pd
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|
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| OUT = pathlib.Path(os.environ.get("ATMOS_OUT", "data/ingest"))
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| MASTER = OUT / "node_observations_daily"
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| DEST = OUT / "train_ready"; DEST.mkdir(exist_ok=True)
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|
|
|
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| CLIP = {
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| "s5p_no2_column":(-5e-4,5e-3), "s5p_so2_column":(-0.05,0.5), "s5p_co_column":(0,1.0),
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| "s5p_o3_column":(0,0.3), "s5p_hcho_column":(-0.05,0.05), "s5p_aer_ai":(-15,20),
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| "s5p_ch4_column":(1400,2200), "s5p_cloud_fraction":(0,1),
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| "modis_aod":(0,5), "viirs_aod":(0,5), "fire_count":(0,None),
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| "era5_temperature":(220,330), "era5_boundary_layer_height":(0,5000),
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| "era5_relative_humidity":(0,100), "era5_surface_pressure":(40000,105000),
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| "era5_cloud_cover":(0,1), "era5_wind_u":(-60,60), "era5_wind_v":(-60,60),
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| "era5_rainfall":(0,None), "u_1330ist":(-60,60), "v_1330ist":(-60,60),
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| }
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|
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| FLAG_COLS = ["s5p_no2_column","s5p_co_column","s5p_so2_column","s5p_o3_column",
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| "s5p_hcho_column","s5p_aer_ai","s5p_ch4_column","s5p_cloud_fraction",
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| "modis_aod","viirs_aod","fire_count"]
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| TARGETS = ["pm2_5","pm10","no2","so2","co","o3","nh3"]
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|
|
| def main():
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| cpcb_path = OUT / "cpcb_labels_daily.parquet"
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| cpcb = pd.read_parquet(cpcb_path) if cpcb_path.exists() else None
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| if cpcb is not None:
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| cpcb = cpcb[["node_id","date"] + TARGETS + ["n_stations"]]
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| print(f"CPCB labels: {len(cpcb):,} labeled node-days joined")
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| else:
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| print("!! cpcb_labels_daily.parquet not found — writing features only (run ingest_cpcb.py first)")
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|
|
| parts = sorted(MASTER.glob("*.parquet"))
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| print(f"{len(parts)} partitions -> {DEST}")
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| tot=0; labeled=0
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| for pf in parts:
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| df = pd.read_parquet(pf)
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|
|
| for col,(lo,hi) in CLIP.items():
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| if col in df:
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| df[col] = df[col].clip(lower=lo, upper=hi)
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|
|
| for col in FLAG_COLS:
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| if col in df:
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| df[col+"_present"] = df[col].notna().astype("int8")
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|
|
| if cpcb is not None:
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| df = df.merge(cpcb, on=["node_id","date"], how="left")
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| df["labeled"] = df[TARGETS].notna().any(axis=1).astype("int8")
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| labeled += int(df["labeled"].sum())
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| df.to_parquet(DEST / pf.name, index=False)
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| tot += len(df)
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| print(f" {pf.stem}: {len(df):,} rows", end="\r")
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| print(f"\ndone. {tot:,} node-days -> {DEST}")
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| if cpcb is not None:
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| print(f"labeled node-days (have >=1 CPCB target): {labeled:,} ({100*labeled/tot:.2f}%)")
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| print(" -> supervised loss uses labeled rows; SSL pretrain uses ALL rows.")
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|
|
| if __name__ == "__main__":
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| main() |
