import numpy as np
import pandas as pd
from pathlib import Path

SEED = 17
DATA_PROC = Path("data/processed")
DATA_PROC.mkdir(parents=True, exist_ok=True)


def build_causal_table(transporters: list, seed: int = SEED, n: int = 6000) -> pd.DataFrame:
    rng = np.random.default_rng(seed)

    df = pd.DataFrame({
        "outcome":     rng.normal(0, 1, n),
        "ethanol_pct": rng.choice([0, 4, 6, 8, 10], n),
        "ROS":         rng.gamma(2.0, 0.7, n),
        "PDR1_reg":    rng.normal(0, 1, n),
        "YAP1_reg":    rng.normal(0, 1, n),
        "H2O2_uM":     rng.choice([0, 100, 200, 400], n),
        "NaCl_mM":     rng.choice([0, 200, 400, 800], n),
        "batch":       rng.choice(["GSE_A", "GSE_B", "GSE_C"], n),
        "accession":   rng.choice(["GSE102475", "GSE73316", "GSE40356"], n),
        "sample_id":   [f"S{i:05d}" for i in range(n)],
        "normalized":  True,
    })

    for t in transporters:
        expr = rng.normal(0, 1, n)
        if t == "ATM1":
            df["outcome"] += 0.08 * expr   
        if t == "SNQ2":
            df["outcome"] -= 0.05 * expr   
        df[f"{t}_expr"] = expr

    core = ["outcome", "ethanol_pct", "ROS", "PDR1_reg", "YAP1_reg",
            "H2O2_uM", "NaCl_mM", "batch", "accession", "sample_id", "normalized"]
    expr_cols = [f"{t}_expr" for t in transporters]
    return df[core + expr_cols]


if __name__ == "__main__":
    import sys
    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
    from scripts.compute_embeddings_protein import CANON_GENES as TRANSPORTERS

    df = build_causal_table(TRANSPORTERS, seed=SEED)
    df.to_csv(DATA_PROC / "causal_table.csv", index=False)
    print(f"✅ causal_table.csv  shape={df.shape}  NaNs={df.isna().any().any()}")