# Copyright 2026 Emile de Bruyn
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pandas as pd
import xarray as xr

from motifbench_prosite.structure_analysis import RMSDAnalysisConfig, compute_motif_stats


def _make_dummy_dataset():
    coords = np.array(
        [
            [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0]],
            [[0.0, 0.0, 0.0], [1.1, 0.0, 0.0], [2.1, 0.0, 0.0]],
        ],
        dtype=np.float32,
    )
    ds = xr.Dataset(
        {
            "motif_entry": ("instance", ["E1", "E1"]),
            "pattern": ("instance", ["G", "G"]),
            "pdb": ("instance", ["TEST", "TEST"]),
            "chain": ("instance", ["A", "A"]),
            "start": ("instance", [1, 1]),
            "end": ("instance", [3, 3]),
            "length": ("instance", [3, 3]),
            "motif_len": ("instance", [3, 3]),
            "coords": (
                ("instance", "res_idx", "xyz"),
                coords,
            ),
        },
        coords={
            "instance": [0, 1],
            "res_idx": [0, 1, 2],
            "xyz": ["x", "y", "z"],
        },
    )
    return ds


def test_compute_motif_stats_basic():
    ds = _make_dummy_dataset()
    cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=5.0, cluster_threshold=5.0)

    stats = compute_motif_stats(ds, cfg, max_workers=1)

    assert not stats.empty
    assert set(["motif_entry", "n_instances", "common_len", "median_pairwise_rmsd"]).issubset(stats.columns)
    assert stats.loc[0, "motif_entry"] == "E1"
    assert stats.loc[0, "n_instances"] == 2
    assert stats.loc[0, "common_len"] == 3
    assert stats.loc[0, "median_pairwise_rmsd"] >= 0.0


class _FakeFsCfg:
    def __init__(self, conservation_ratio=0.8):
        self.foldseek_conservation = True
        self.foldseek_conservation_ratio = conservation_ratio
        self.foldseek_conservation_workers = 1


class _FakeFsRunner:
    def __init__(self, clusters):
        # clusters: List[List[str]] of member basenames
        self._clusters = clusters
        self.cfg = type("Cfg", (), {"threads": 1})

    @property
    def enabled(self):
        return True

    def easy_cluster(self, frag_pdb_dir, out_dir, threads_override=None):
        out_dir.mkdir(parents=True, exist_ok=True)
        tsv = out_dir / "db_cluster.tsv"
        lines = []
        for idx, members in enumerate(self._clusters):
            for m in members:
                lines.append(f"{idx}\t{m}\n")
        tsv.write_text("".join(lines))
        return tsv


def test_compute_motif_stats_foldseek_positive(tmp_path):
    ds = _make_dummy_dataset()
    cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=5.0, cluster_threshold=5.0)

    # all instances in one cluster -> ratio 1.0 >= 0.8
    runner = _FakeFsRunner(clusters=[["E1_0.pdb", "E1_1.pdb"]])
    fs_cfg = _FakeFsCfg(conservation_ratio=0.8)

    stats = compute_motif_stats(
        ds,
        cfg,
        max_workers=1,
        foldseek_runner=runner,
        foldseek_cfg=fs_cfg,
        tmpdir=tmp_path,
    )

    assert "foldseek_max_cluster_ratio" in stats.columns
    assert stats.loc[0, "foldseek_max_cluster_ratio"] == 1.0
    # conserved by Foldseek
    assert stats.loc[0, "motif_entry"] == "E1"


def test_compute_motif_stats_foldseek_negative(tmp_path):
    ds = _make_dummy_dataset()
    cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=0.1, cluster_threshold=5.0)

    # split into two clusters -> ratio 0.5 < 0.8
    runner = _FakeFsRunner(clusters=[["E1_0.pdb"], ["E1_1.pdb"]])
    fs_cfg = _FakeFsCfg(conservation_ratio=0.8)

    stats = compute_motif_stats(
        ds,
        cfg,
        max_workers=1,
        foldseek_runner=runner,
        foldseek_cfg=fs_cfg,
        tmpdir=tmp_path,
    )

    assert "foldseek_max_cluster_ratio" in stats.columns
    assert stats.loc[0, "foldseek_max_cluster_ratio"] == 0.5
    # Should not be marked conserved under Foldseek ratio threshold
    assert stats.loc[0, "motif_entry"] == "E1"