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

TARGET = "YPL270W"

Z = hom()
_, ox, os_, _ = axes(Z.columns)
e_ox, e_os = effects(centre(Z), ox, os_)
cd = (e_ox - e_os).abs().dropna()

tail = (cd >= cd[TARGET]).mean()
pct = stats.percentileofscore(cd.values, cd[TARGET])

A = abc()
abc_cd = cd.reindex(A.systematic).dropna()
others = cd.drop(abc_cd.index)
mw = stats.mannwhitneyu(abc_cd.values, others.values, alternative="greater")

pd.DataFrame({"gene": A.set_index("systematic").gene.reindex(abc_cd.index).values,
              "cd": abc_cd.round(3).values}).to_csv(RES / "abc_cd.csv", index=False)
pd.DataFrame({"what": ["MDL2_cd", "reference_p90"],
              "value": [round(cd[TARGET], 3), round(np.percentile(cd.values, 90), 3)]}
             ).to_csv(RES / "mdl2_reference_markers.csv", index=False)
pd.DataFrame({"cd": cd.values}, index=cd.index).rename_axis("gene").to_csv(RES / "null_cd.csv")

pd.DataFrame([("MDL2 |cd|", cd[TARGET]), ("reference tail fraction (all analysed genes)", tail),
              ("reference percentile (all analysed genes)", pct), ("reference size", len(cd)),
              ("ABC vs other genes Mann-Whitney U", mw.statistic), ("ABC vs other genes p (one-sided)", mw.pvalue),
              ("ABC transporters", len(abc_cd)), ("other genes", len(others))],
             columns=["quantity", "value"]).to_csv(RES / "calibration_summary.csv", index=False)