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

N_SPLITS = 1000

Z = hom()
ag, ox, os_, ref = axes(Z.columns)
Zc = centre(Z)
E_ox, E_os = effects(Zc, ox, os_)
ox_idx, os_idx = np.where(ox)[0], np.where(os_)[0]
X = Zc.values


def halves(o_a, s_a, o_b, s_b):
    a_ox, a_os = np.nanmean(X[:, o_a], axis=1), np.nanmean(X[:, s_a], axis=1)
    b_ox, b_os = np.nanmean(X[:, o_b], axis=1), np.nanmean(X[:, s_b], axis=1)
    m = ~np.isnan(a_ox + a_os + b_ox + b_os)
    r = lambda x, y: stats.spearmanr(x[m], y[m]).statistic
    return {"cd_signed": r(a_ox - a_os, b_ox - b_os), "cd_abs": r(np.abs(a_ox - a_os), np.abs(b_ox - b_os)),
            "oxidative": r(a_ox, b_ox), "osmotic": r(a_os, b_os)}


rows = []
rows.append({"split": "odd/even within axis (published)",
             **halves(ox_idx[0::2], os_idx[0::2], ox_idx[1::2], os_idx[1::2])})
rng = np.random.default_rng(SEED)
rand = []
h2o2 = set(np.where(ag == "h2o2")[0])
for _ in range(N_SPLITS):
    po, ps = rng.permutation(ox_idx), rng.permutation(os_idx)
    r = halves(po[:len(po) // 2], ps[:len(ps) // 2], po[len(po) // 2:], ps[len(ps) // 2:])
    r["h2o2_replicates_separated"] = len(set(po[:len(po) // 2]) & h2o2) == 1
    rand.append(r)
R = pd.DataFrame(rand)
R.to_csv(RES / "splithalf_random.csv", index=False)
for q, lab in ((0.5, "median"), (0.025, "2.5%"), (0.975, "97.5%")):
    rows.append({"split": f"random within-axis splits ({N_SPLITS}), {lab}",
                 **R.drop(columns="h2o2_replicates_separated").quantile(q).to_dict()})

OXA, OSA = ["h2o2", "paraquat", "4nqo"], ["nacl", "sorbitol"]
for k in (1,):
    for oa in itertools.combinations(OXA, k):
        for sa in OSA:
            ob = [a for a in OXA if a not in oa]
            sb = [a for a in OSA if a != sa]
            o_a = np.where(np.isin(ag, oa))[0]
            o_b = np.where(np.isin(ag, ob))[0]
            s_a = np.where(ag == sa)[0]
            s_b = np.where(np.isin(ag, sb))[0]
            rows.append({"split": f"agent-blocked: {'+'.join(oa)} & {sa} | {'+'.join(ob)} & {'+'.join(sb)}",
                         **halves(o_a, s_a, o_b, s_b)})

scanner = np.array([c.split(":")[9] if len(c.split(":")) > 9 else "" for c in Z.columns])
new_os = np.where(os_ & (scanner == "new scanner"))[0]
old_os = np.where(os_ & (scanner == "old scanner"))[0]
cd_full = (E_ox - E_os).abs()
for name, idx in (("osmotic restricted to 2004 batch (new scanner)", new_os),
                  ("osmotic restricted to 2001 batch (old scanner)", old_os)):
    e = np.nanmean(X[:, idx], axis=1)
    c2 = np.abs(E_ox.values - e)
    m = ~np.isnan(c2) & cd_full.notna().values
    rows.append({"split": f"{name}: |cd| vs full (n conditions {len(idx)})",
                 "cd_abs": stats.spearmanr(cd_full.values[m], c2[m]).statistic,
                 "osmotic": stats.spearmanr(E_os.values[m], e[m]).statistic})
e_new, e_old = np.nanmean(X[:, new_os], axis=1), np.nanmean(X[:, old_os], axis=1)
m = ~np.isnan(e_new + e_old)
rows.append({"split": "osmotic 2001 batch vs 2004 batch (effect agreement)",
             "osmotic": stats.spearmanr(e_new[m], e_old[m]).statistic})

out = pd.DataFrame(rows)
out.to_csv(RES / "reproducibility.csv", index=False)
pd.set_option("display.width", 220)