import itertools
import numpy as np
import pandas as pd
import biotite.structure as struc
import biotite.structure.io.pdb as pdb
from scipy import stats
from common import RES, EXT, SEED, read_matrix, abc
from structure_utils import alphafold_model

AF = EXT / "alphafold"
MAX_ITER = 20
N_PERM = 10_000
A = abc().set_index("gene")
rng = np.random.default_rng(SEED)


def protein(gene):
    s = pdb.PDBFile.read(str(alphafold_model(A.uniprot[gene], AF))).get_structure(model=1)
    return s[struc.filter_amino_acids(s)]


chains = {g: protein(g) for g in A.index}
S = pd.read_csv(RES / "abc_struct_sim.csv")
rows = []
for g1, g2 in zip(S.gene1, S.gene2):
    fitted, _, fi, mi = struc.superimpose_structural_homologs(chains[g1], chains[g2], max_iterations=MAX_ITER,
                                                             reference_length="shorter")
    tm = struc.tm_score(chains[g1], fitted, fi, mi, reference_length="shorter")
    rows.append((g1, g2, tm, len(fi)))
V = pd.DataFrame(rows, columns=["gene1", "gene2", "tmscore_biotite", "superposed_pairs_biotite"]).round(4)
V = S[["gene1", "gene2", "same_subfamily", "tmscore", "paralog"]].merge(V, on=["gene1", "gene2"])
V.to_csv(RES / "abc_struct_validation.csv", index=False)


def matrix(genes, col):
    M = pd.DataFrame(np.nan, index=genes, columns=genes)
    for g1, g2, v in V[["gene1", "gene2", col]].itertuples(index=False):
        if g1 in genes and g2 in genes:
            M.loc[g1, g2] = M.loc[g2, g1] = v
    return M.values


def mantel(X, Y):
    n = len(X)
    iu = np.triu_indices(n, 1)
    obs = stats.spearmanr(X[iu], Y[iu]).statistic
    null = np.array([stats.spearmanr(X[iu], Y[np.ix_(p, p)][iu]).statistic for p in (rng.permutation(n) for _ in range(N_PERM))])
    return obs, (np.sum(np.abs(null) >= abs(obs)) + 1) / (N_PERM + 1)


genes = list(A.index)
I = pd.read_csv(RES / "abc_identity.csv", index_col=0).loc[genes, genes].values
r_id, p_id = mantel(I, matrix(genes, "tmscore_biotite"))
Z = read_matrix("hom.z_result_nm.pub")
pg = [g for g in genes if A.systematic[g] in Z.index and Z.loc[A.systematic[g]].notna().sum() >= 50]
X = Z.loc[[A.systematic[g] for g in pg]]
R = np.full((len(pg), len(pg)), np.nan)
for i, j in itertools.combinations(range(len(pg)), 2):
    m = X.iloc[i].notna() & X.iloc[j].notna()
    R[i, j] = R[j, i] = stats.pearsonr(X.iloc[i][m], X.iloc[j][m]).statistic
r_pf, p_pf = mantel(matrix(pg, "tmscore_biotite"), np.arctanh(R))
par = V[V.paralog]
summ = pd.DataFrame([
    ("pairs", len(V), np.nan),
    ("Spearman rho, this study vs Biotite", stats.spearmanr(V.tmscore, V.tmscore_biotite).statistic, np.nan),
    ("median absolute difference, paralogous pairs", float(np.median(np.abs(par.tmscore - par.tmscore_biotite))), np.nan),
    ("median absolute difference, all pairs", float(np.median(np.abs(V.tmscore - V.tmscore_biotite))), np.nan),
    ("median TM-score, same subfamily, this study", float(V[V.same_subfamily].tmscore.median()), np.nan),
    ("median TM-score, same subfamily, Biotite", float(V[V.same_subfamily].tmscore_biotite.median()), np.nan),
    ("median TM-score, different subfamilies, this study", float(V[~V.same_subfamily].tmscore.median()), np.nan),
    ("median TM-score, different subfamilies, Biotite", float(V[~V.same_subfamily].tmscore_biotite.median()), np.nan),
    ("Mantel rho, sequence identity vs Biotite TM-score", r_id, p_id),
    ("Mantel rho, Biotite TM-score vs fitness-profile similarity (Fisher z)", r_pf, p_pf),
], columns=["quantity", "value", "p_gene_label_permutation"])
summ.to_csv(RES / "abc_struct_validation_summary.csv", index=False)