import numpy as np
import pandas as pd
from scipy import stats
from common import RES, hom, axes, centre, effects

Z = hom()
ag, ox, os_, ref = axes(Z.columns)
Zc = centre(Z)
E_ox, E_os = effects(Zc, ox, os_)
cd = (E_ox - E_os).abs()

sev = (Z > 2).sum() / Z.notna().sum()
sd = Z.std()
pct = sev.rank(pct=True) * 100
C = pd.DataFrame({"agent": ag, "class": np.where(ox, "oxidative", np.where(os_, "osmotic", "baseline-only")),
                  "severity_frac_z_gt2": sev.values, "severity_percentile": pct.values, "sd": sd.values}, index=Z.columns)

idx = np.where(ox | os_)[0]
rows = []
for j in idx:
    keep_ox = ox.copy()
    keep_os = os_.copy()
    keep_ox[j] = False
    keep_os[j] = False
    a, b = effects(Zc, keep_ox, keep_os)
    d = (a - b).abs()
    m = cd.notna() & d.notna()
    top = cd.rank(ascending=False) <= np.ceil(0.1 * m.sum())
    top2 = d.rank(ascending=False) <= np.ceil(0.1 * m.sum())
    rows.append({"left_out": Z.columns[j].split(":")[0], "agent": ag[j], "severity": sev.iloc[j],
                 "rho_vs_full": stats.spearmanr(cd[m], d[m]).statistic,
                 "top_decile_retained": (top & top2).sum() / top.sum()})
L = pd.DataFrame(rows)
L.to_csv(RES / "leave_one_condition_out.csv", index=False)
r = stats.spearmanr(L.severity, L.rho_vs_full)

w = 1 / sd.values
Zw = Zc * w
a_w, b_w = effects(Zw, ox, os_)
cd_w = (a_w - b_w).abs()
m = cd.notna() & cd_w.notna()
pd.DataFrame({"cd_condition_standardised": cd_w}).round(4).rename_axis("gene").to_csv(RES / "cd_condition_standardised.csv")
pd.DataFrame([
    {"statistic": "severity of omitted condition vs rho_vs_full (Spearman)", "value": r.statistic, "p": r.pvalue, "n": len(L)},
    {"statistic": "condition-standardised vs unweighted |cd| (Spearman)", "value": stats.spearmanr(cd[m], cd_w[m]).statistic,
     "p": np.nan, "n": int(m.sum())},
]).to_csv(RES / "toxicity_summary.csv", index=False)