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

THRESHOLDS = [200, 250, 300, 350, 380, 400, 418]

base = None
rows = []
for t in THRESHOLDS:
    Z = hom(min_nonmissing=t)
    ag, ox, os_, ref = axes(Z.columns)
    Zc = centre(Z)
    e_ox, e_os = effects(Zc, ox, os_)
    cd = (e_ox - e_os).abs().dropna()
    oi, si = np.where(ox)[0], np.where(os_)[0]
    a = Zc.iloc[:, oi[0::2]].mean(axis=1) - Zc.iloc[:, si[0::2]].mean(axis=1)
    b = Zc.iloc[:, oi[1::2]].mean(axis=1) - Zc.iloc[:, si[1::2]].mean(axis=1)
    m = a.notna() & b.notna()
    if t == 350:
        base = cd
    lab = sgd_labels("oxidative")
    lab = lab[lab.index.isin(e_ox.dropna().index)]
    conc = (np.sign(e_ox.loc[lab.index]) == -lab).mean()
    rows.append({"min_nonmissing": t, "genes": len(cd), "splithalf_cd_signed": stats.spearmanr(a[m], b[m]).statistic,
                 "splithalf_cd_abs": stats.spearmanr(a[m].abs(), b[m].abs()).statistic,
                 "oxidative_benchmark_n": len(lab), "oxidative_concordance": conc, "cd": cd})
for r in rows:
    top_r = set(r["cd"].sort_values(ascending=False).index[: int(np.ceil(0.1 * len(r["cd"])))])
    top_b = set(base.sort_values(ascending=False).index[: int(np.ceil(0.1 * len(base)))])
    r["top_decile_jaccard_vs_350"] = len(top_r & top_b) / len(top_r | top_b)
    del r["cd"]
out = pd.DataFrame(rows)
out.to_csv(RES / "threshold_sensitivity.csv", index=False)