import csv
import os
import json
import sys
import types
import zipfile
from pathlib import Path

from apps.web import pipeline_backend as backend
from gfa_core.gene_family_scope import build_scope_id
from gfa_core.ortholog_materialize import (
    OrthologRunRecord,
    build_ortholog_run_identity,
    materialize_ortholog_final_outputs,
)


def _materialize_trusted_ppi_mapping(
    job, *, prefix, target, family, storage_key, taxon_id, query_id, subject_id,
):
    workspace = Path(job.workspace_dir)
    final_dir = workspace / "final"
    query = final_dir / f"{prefix}.final_members.pep.fa"
    reference = Path(job.input_dir) / storage_key / f"{storage_key}.pep.fasta"
    query.write_text(f">{query_id}\nMA\n", encoding="utf-8")
    pairs = workspace / "ortholog" / f"{prefix}.{target}_to_{storage_key}_pairs.txt"
    pairs.parent.mkdir(parents=True, exist_ok=True)
    pairs.write_text(f"{subject_id}\t{query_id}\t91\n", encoding="utf-8")
    scope_id = build_scope_id(family, [target], [storage_key], [], "target_plus_reference")
    identity = build_ortholog_run_identity(
        query_species=target, reference_taxon_id=taxon_id,
        reference_storage_key=storage_key, query_fasta=query,
        reference_pep=reference, scope_id=scope_id, member_set_id="",
        mapping_path=pairs,
    )
    materialize_ortholog_final_outputs(
        final_dir=final_dir, prefix=prefix,
        run_records=[OrthologRunRecord(target, storage_key, query, reference, pairs)],
        job_id=job.job_id, workspace=workspace, reference_species=storage_key,
        reference_taxon_id=taxon_id, reference_storage_key=storage_key,
        ortholog_identity=identity,
        scope_fields={
            "scope_id": scope_id, "scope_mode": "target_plus_reference",
            "target_species": [target], "reference_species": [storage_key],
            "selected_species": [target, storage_key], "family": family,
        },
    )


def test_run_cmd_logged_exports_project_root_to_pythonpath(tmp_path, monkeypatch):
    captured = {}

    class FakeProcess:
        def wait(self):
            return 0

    def fake_popen(cmd, cwd=None, stdout=None, stderr=None, text=None, env=None):
        captured["env"] = env or {}
        return FakeProcess()

    monkeypatch.setenv("PYTHONPATH", "/existing/path")
    monkeypatch.setattr(backend.subprocess, "Popen", fake_popen)

    exit_code = backend.run_cmd_logged(
        ["python", "skills/ppi/run.py"],
        tmp_path / "ppi.log",
        cwd=backend.PROJECT_ROOT,
    )

    pythonpath = captured["env"].get("PYTHONPATH", "").split(os.pathsep)
    assert exit_code == 0
    assert str(backend.PROJECT_ROOT) in pythonpath
    assert "/existing/path" in pythonpath


def _write_ppi_scope(path, target_species="Os", reference_species="At"):
    data = {
        "family": "CRR",
        "prefix": "Os.CRR",
        "scope_id": "CRR.target-Os.ref-At.target_plus_reference",
        "scope_mode": "target_plus_reference",
        "target_species": [target_species],
        "reference_species": [reference_species],
        "selected_species": [target_species, reference_species],
    }
    path.write_text(json.dumps(data), encoding="utf-8")


def _write_fake_ppi_outputs(ppi_dir, prefix="Os.CRR"):
    ppi_dir.mkdir(parents=True, exist_ok=True)
    (ppi_dir / f"{prefix}_network.tsv").write_text("protein1\tprotein2\tscore\nA\tB\t0.9\n", encoding="utf-8")
    (ppi_dir / f"{prefix}_report.md").write_text("# PPI\n", encoding="utf-8")
    (ppi_dir / f"{prefix}_url.txt").write_text("View online: https://string-db.org/cgi/network?species=39947\n", encoding="utf-8")
    (ppi_dir / f"{prefix}_id_resolution.tsv").write_text("raw_id\tnormalized_id\nOs01t0810800-01\tOs01g0810800\n", encoding="utf-8")
    (ppi_dir / f"{prefix}.manifest.json").write_text(
        json.dumps({"manifest_version": "1.0", "stage": "ppi", "status": "success", "prefix": prefix}),
        encoding="utf-8",
    )


def test_reference_bundle_id_map_preserves_loc_and_numeric_geneid(tmp_path):
    pep_path = tmp_path / "soe.pep.fasta"
    cds_path = tmp_path / "soe.cds.fasta"
    output_path = tmp_path / "soe.pep_cds_idmap.tsv"
    pep_path.write_text(
        ">XP_021867507.1 [protein_id=XP_021867507.1]\nMAAA\n",
        encoding="utf-8",
    )
    cds_path.write_text(
        ">XP_021867507.1 [gene=LOC110806180] "
        "[db_xref=GeneID:110806180] [protein_id=XP_021867507.1]\nATGGCG\n",
        encoding="utf-8",
    )

    result = backend._build_reference_bundle_id_map(
        species_code="soe",
        pep_path=pep_path,
        cds_path=cds_path,
        output_path=output_path,
    )

    with open(result["id_map_path"], encoding="utf-8", newline="") as handle:
        row = next(csv.DictReader(handle, delimiter="\t"))
    assert row["gene_id"] == "LOC110806180"
    assert row["ncbi_gene_id"] == "110806180"


def test_reference_bundle_download_extracts_ncbi_zip_and_unblocks_ppi_kaks_preflight(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test_ref_bundle",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    (workspace / "final").mkdir(parents=True)
    (workspace / "logs").mkdir(parents=True)
    input_dir.mkdir(parents=True)

    fake_zip = tmp_path / "ncbi_dataset.zip"
    with zipfile.ZipFile(fake_zip, "w") as zf:
        zf.writestr("ncbi_dataset/data/GCF_FAKE/protein.faa", ">SOE_PEP1\nMAAA\n")
        zf.writestr(
            "ncbi_dataset/data/GCF_FAKE/cds_from_genomic.fna",
            ">lcl|NC_000001.1_cds_XP_000001.1_1 [protein_id=XP_000001.1]\nATGGCG\n",
        )

    monkeypatch.setattr(
        backend,
        "_select_ncbi_reference_assembly",
        lambda **kwargs: {
            "accession": "GCF_FAKE",
            "organism_name": "Spinacia oleracea",
            "taxon_id": "3562",
            "assembly_name": "fake",
            "assembly_level": "Chromosome",
            "annotation_name": "fake annotation",
        },
    )

    def fake_download(url, output_path, timeout_sec=300):
        output_path.parent.mkdir(parents=True, exist_ok=True)
        output_path.write_bytes(fake_zip.read_bytes())
        return output_path.stat().st_size

    monkeypatch.setattr(backend, "_download_url_to_file", fake_download)

    manifest = backend.download_reference_annotation_bundle(
        job=job,
        species_code="soe",
        species_name="Spinacia oleracea",
        taxon_id="3562",
        requested_stages=["ppi", "kaks"],
    )

    assert manifest["status"] == "success"
    assert (input_dir / "soe" / "soe.pep.fasta").exists()
    assert (input_dir / "soe" / "soe.cds.fasta").exists()
    assert (input_dir / "soe" / "soe.cds.fasta").read_text(encoding="utf-8").startswith(">XP_000001.1 ")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        target_species=["Ft"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        requested_stages=["ppi", "kaks"],
    )
    assert resolution["status"] == "needs_ortholog"
    assert resolution["selected_string_species"] == "soe"
    assert resolution["reference_species"] == "soe"
    assert resolution["reference_bundle_status"]["kaks_ready"] is True
    assert resolution["reference_bundle_status"]["missing_required"] == []


def test_ortholog_scope_uses_downloaded_reference_proteome_when_family_pep_missing(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test_ortholog_ref_bundle",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    (input_dir / "soe").mkdir(parents=True)
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    (final_dir / "Ft.SPL.identify.manifest.json").write_text(
        json.dumps({"stage": "identify", "status": "success", "prefix": "Ft.SPL"}),
        encoding="utf-8",
    )
    (final_dir / "Ft.SPL.final_members.pep.fa").write_text(">FtGene1\nMAAA\n", encoding="utf-8")
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMAAA\n", encoding="utf-8")
    scope_file = final_dir / "Ft.SPL.SPL.target-Ft.ref-soe.target_plus_reference.scope.json"
    scope_file.write_text(
        json.dumps({
            "family": "SPL",
            "prefix": "Ft.SPL",
            "scope_id": "SPL.target-Ft.ref-soe.target_plus_reference",
            "scope_mode": "target_plus_reference",
            "target_species": ["Ft"],
            "reference_species": ["Spinacia oleracea"],
            "selected_species": ["Ft", "Spinacia oleracea"],
        }),
        encoding="utf-8",
    )

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        output_dir = tmp_path / "workspace" / "ortholog"
        run_prefix = "Ft.SPL.Ft_to_soe"
        (output_dir / f"{run_prefix}_pairs.txt").write_text(
            "# Ortholog pairs identified by BLASTP\n"
            "# Format: Subject_ID\tQuery_ID\tIdentity(%)\n"
            "SoeGene1\tFtGene1\t91.00\n",
            encoding="utf-8",
        )
        (output_dir / f"{run_prefix}.blastp.txt").write_text("FtGene1\tSoeGene1\t91\n", encoding="utf-8")
        return 0

    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)

    message, manifest = backend.run_stage(
        "ortholog",
        job,
        "Ft.SPL",
        cpu=2,
        scope_file=str(scope_file),
        blast_evalue=1e-5,
    )

    assert message.startswith("✅ Ortholog analysis complete")
    assert manifest is not None
    assert str(input_dir / "soe" / "soe.pep.fasta") in captured["cmd"]


def test_ppi_auto_prefers_target_string_species_when_target_is_supported(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    input_dir.mkdir(parents=True)
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    stale_image = final_dir / "Os.CRR.ppi_network.string.png"
    stale_image.write_bytes(b"old-at-image")
    (final_dir / "Os.CRR.orthologs.txt").write_text(
        "Os01t0810800-01\tAT1G51790\t90\tOs\tAt\n",
        encoding="utf-8",
    )
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-At.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file)

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        _write_fake_ppi_outputs(workspace / "ppi")
        return 0

    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda *args, **kwargs: None)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="auto",
        ppi_score=400,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    cmd = captured["cmd"]
    assert "✅ PPI analysis completed" in message
    assert manifest and manifest.exists()
    assert cmd[cmd.index("--species") + 1] == "osa"
    assert cmd[cmd.index("--gene-ids") + 1] == str(final_dir / "Os.CRR.final_members.id")
    assert "--mapping" not in cmd
    assert "--id-map" not in cmd
    assert not stale_image.exists()


def test_ppi_direct_supported_target_forwards_exact_current_job_id_map(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    (input_dir / "osa").mkdir(parents=True)
    final_dir.mkdir(parents=True)
    (workspace / "logs").mkdir(parents=True)
    id_map_file = input_dir / "osa" / "osa.pep_cds_idmap.tsv"
    id_map_file.write_text("species_code\tprotein_id\nosa\tOs01g0810800\n", encoding="utf-8")
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-At.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file)
    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        _write_fake_ppi_outputs(workspace / "ppi")
        return 0

    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda *args, **kwargs: None)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="osa",
        ppi_score=400,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    cmd = captured["cmd"]
    assert "✅ PPI analysis completed" in message
    assert manifest and manifest.exists()
    assert cmd[cmd.index("--id-map") + 1] == str(id_map_file)
    assert "--mapping" not in cmd


def test_infer_string_species_from_ortholog_mapping_does_not_default_to_ath(tmp_path):
    mapping = tmp_path / "Ft.unknown.orthologs.txt"
    mapping.write_text("FtGene1\tRefGene1\t90\tFt\tUnknownSpecies\n", encoding="utf-8")

    assert backend.infer_string_species_from_ortholog_mapping(mapping, "") == ""


def test_ppi_scope_with_mapping_but_unknown_reference_species_fails_instead_of_ath(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    input_dir.mkdir(parents=True)
    (final_dir / "Ft.SPL.final_members.id").write_text("FtGene1\n", encoding="utf-8")
    (final_dir / "Ft.SPL.orthologs.txt").write_text(
        "FtGene1\tRefGene1\t90\tFt\tUnknownSpecies\n",
        encoding="utf-8",
    )
    scope = {
        "family": "SPL",
        "prefix": "Ft.SPL",
        "scope_id": "SPL.target-Ft.ref-UnknownSpecies.target_plus_reference",
        "scope_mode": "target_plus_reference",
        "target_species": ["Ft"],
        "reference_species": ["UnknownSpecies"],
        "selected_species": ["Ft", "UnknownSpecies"],
    }
    scope_file = final_dir / "Ft.SPL.scope.json"
    scope_file.write_text(json.dumps(scope), encoding="utf-8")

    called = {"run": False}

    def fake_run_cmd_logged(*args, **kwargs):
        called["run"] = True
        return 0

    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Ft.SPL",
        ppi_species="auto",
        ppi_score=200,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    assert called["run"] is False
    assert "无法从 Ortholog 映射推断 STRING 参考物种" in message
    assert manifest and manifest.exists()
    data = json.loads(manifest.read_text(encoding="utf-8"))
    assert data["diagnostics"]["issue_code"] == "ppi_invalid_ortholog_mapping"
    assert data["parameters"]["ppi_species_resolution"]["decision"] == "failed_requires_ortholog"


def test_ppi_species_resolution_reports_reference_data_missing(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Ft"],
        reference_species=[],
        family="SPL",
    )

    assert resolution["status"] == "confirmation_required"
    assert resolution["issue_code"] == "ppi_reference_confirmation_required"
    assert resolution["required_next_stage"] == ""
    assert [row["selected_string_taxon_id"] for row in resolution["candidates"]] == ["3562", "3476", "3775"]


def test_os_direct_ppi_uses_complete_string_registry_without_ortholog(tmp_path):
    job = backend.JobInfo(
        job_id="job_test_os_direct_ppi",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Os.HKL",
        target_species=["Os"],
        requested_stages=["ppi"],
    )

    assert resolution["status"] == "ready"
    assert resolution["selection_mode"] == "exact_string"
    assert resolution["selected_taxon_id"] == "39947"
    assert resolution["required_next_stage"] == ""
    assert resolution["ortholog_mapping_source"] == ""


def test_ppi_species_resolution_canonicalizes_fagopyrum_tataricum_alias(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=[],
        family="SPL",
    )

    assert resolution["target_species"] == ["Ft"]
    assert resolution["target_string_supported"] is False
    assert resolution["status"] == "confirmation_required"


def test_ppi_species_resolution_recommends_near_string_species_before_arabidopsis_for_fagopyrum(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=[],
        family="SPL",
    )

    candidates = resolution["diagnostics"]["reference_recommendations"]
    assert resolution["status"] == "confirmation_required"
    assert [row["selected_string_taxon_id"] for row in candidates] == ["3562", "3476", "3775"]
    assert candidates[0]["selected_string_name"] == "Spinacia oleracea"
    assert candidates[0]["lca_taxon_id"] == "3524"
    assert candidates[0]["tree_distance"] == 10


def test_explicit_project_reference_is_not_overridden_by_downloaded_near_reference(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    (tmp_path / "workspace" / "final").mkdir(parents=True)
    (input_dir / "At").mkdir(parents=True)
    (input_dir / "soe").mkdir(parents=True)
    (input_dir / "At" / "At.pep.fasta").write_text(">AT1\nMAAA\n", encoding="utf-8")
    (input_dir / "At" / "At.cds.fasta").write_text(">AT1\nATGGCTGCTGCT\n", encoding="utf-8")
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SOE1\nMAAA\n", encoding="utf-8")
    (input_dir / "soe" / "soe.cds.fasta").write_text(">SOE1\nATGGCTGCTGCT\n", encoding="utf-8")
    (input_dir / "soe" / "soe.reference_bundle_download.manifest.json").write_text(
        json.dumps({"status": "success", "species_code": "soe"}), encoding="utf-8"
    )

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="ath",
        target_species=["Ft"],
        reference_species=["At"],
        family="SPL",
        requested_stages=["kaks"],
    )

    assert resolution["reference_species"] == "At"
    assert resolution["selected_string_species"] == "ath"
    assert resolution["reference_bundle_status"]["kaks_ready"] is True


def test_ppi_species_resolution_selected_near_string_species_still_needs_reference_data_without_pep(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )

    assert resolution["status"] == "needs_reference_data"
    assert resolution["issue_code"] == "ppi_reference_data_missing"
    assert resolution["selected_string_species"] == "soe"
    assert resolution["selected_taxon_id"] == "3562"
    assert resolution["reference_species"] == "soe"
    assert resolution["reference_data_source"] == ""


def test_ppi_species_resolution_selected_near_string_species_with_pep_needs_ortholog(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )

    assert resolution["status"] == "needs_ortholog"
    assert resolution["issue_code"] == "ppi_requires_ortholog_mapping"
    assert resolution["selected_string_species"] == "soe"
    assert resolution["selected_taxon_id"] == "3562"
    assert resolution["reference_data_source"].endswith("soe/soe.pep.fasta")
    assert resolution["reference_bundle_status"]["kaks_ready"] is True


def test_supported_target_ppi_is_ready_independently_when_kaks_lacks_reference_data(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "workspace" / "final").mkdir(parents=True)
    (tmp_path / "input" / "soe").mkdir()
    (tmp_path / "input" / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")

    ppi_resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Os.CRR",
        ppi_species="auto",
        target_species=["Os"],
        reference_species=["At"],
        family="CRR",
        requested_stages=["ppi"],
    )
    kaks_resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Os.CRR",
        ppi_species="auto",
        target_species=["Os"],
        reference_species=["Spinacia oleracea"],
        family="CRR",
        requested_stages=["kaks"],
    )

    assert ppi_resolution["status"] == "ready"
    assert ppi_resolution["decision"] == "ready_target_string"
    assert ppi_resolution["selected_string_species"] == "osa"
    assert ppi_resolution["ortholog_mapping_source"] == ""
    assert kaks_resolution["status"] == "needs_reference_data"
    assert kaks_resolution["issue_code"] == "kaks_reference_cds_missing"


def test_ppi_kaks_dependency_preflight_requires_reference_cds_before_ortholog(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        requested_stages=["ppi", "kaks"],
    )

    assert resolution["status"] == "needs_reference_data"
    assert resolution["issue_code"] == "kaks_reference_cds_missing"
    assert resolution["requested_stages"] == ["ppi", "kaks"]
    assert resolution["reference_bundle_status"]["missing_required"] == ["cds"]
    assert resolution["reference_bundle_status"]["kaks_ready"] is False


def test_ppi_kaks_dependency_preflight_with_pep_and_cds_needs_ortholog(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    (input_dir / "soe" / "soe.cds.fasta").write_text(">SoeGene1\nATGGCT\n", encoding="utf-8")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        requested_stages=["ppi", "kaks"],
    )

    assert resolution["status"] == "needs_ortholog"
    assert resolution["reference_bundle_status"]["kaks_ready"] is True
    assert resolution["diagnostics"]["ortholog_dependency_role"] == "producer"


def test_ppi_kaks_dependency_preflight_rejects_stale_at_mapping_when_spinacia_selected(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    (input_dir / "soe" / "soe.cds.fasta").write_text(">SoeGene1\nATGGCT\n", encoding="utf-8")
    (final_dir / "Ft.SPL.orthologs.txt").write_text(
        "FtGene1\tAT1G01010\t90\tFt\tAt\n",
        encoding="utf-8",
    )

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        requested_stages=["ppi", "kaks"],
    )

    assert resolution["status"] == "needs_ortholog"
    assert resolution["issue_code"] == "stale_ortholog_reference_conflict"
    assert resolution["mapping_reference_species"] == "At"


def test_ppi_species_resolution_selected_near_string_species_with_mapping_is_ready(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    _materialize_trusted_ppi_mapping(
        job, prefix="Ft.SPL", target="Ft", family="SPL",
        storage_key="soe", taxon_id="3562", query_id="FtGene1", subject_id="SoeGene1",
    )

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )

    assert resolution["status"] == "ready"
    assert resolution["selected_string_species"] == "soe"
    assert resolution["selected_taxon_id"] == "3562"
    assert resolution["ortholog_mapping_source"].endswith("Ft.SPL.orthologs.txt")
    assert resolution["decision"] == "ready_reference_mapping"


def test_ppi_species_resolution_can_ignore_existing_mapping_for_pre_ortholog_check(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    _materialize_trusted_ppi_mapping(
        job, prefix="Ft.SPL", target="Ft", family="SPL",
        storage_key="soe", taxon_id="3562", query_id="FtGene1", subject_id="SoeGene1",
    )

    ready = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )
    pre_ortholog = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ignore_existing_ortholog=True,
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )

    assert ready["status"] == "ready"
    assert ready["ortholog_mapping_source"].endswith("Ft.SPL.orthologs.txt")
    assert pre_ortholog["status"] == "needs_ortholog"
    assert pre_ortholog["ortholog_mapping_source"] == ""
    assert pre_ortholog["selected_string_species"] == "soe"
    assert pre_ortholog["candidates"][0]["selected_string_taxon_id"] == "3562"


def test_ppi_kaks_preflight_recovers_prior_non_ath_reference_before_config_default(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    soe_dir = input_dir / "soe"
    input_dir.mkdir()
    soe_dir.mkdir()
    final_dir.mkdir(parents=True)
    (soe_dir / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    (input_dir / "At.pep.fasta").write_text(">AT1G01010\nMA\n", encoding="utf-8")
    (final_dir / "Ft.SPL.orthologs.txt").write_text(
        "FtGene1\tAT1G01010\t90\tFt\tAt\n",
        encoding="utf-8",
    )
    soe_manifest = final_dir / "Ft.SPL.SPL.target-Ft.ref-soe.target_plus_reference.ortholog.manifest.json"
    soe_manifest.write_text(json.dumps({
        "stage": "ortholog",
        "status": "success",
        "reference_species": ["soe"],
        "scope_id": "SPL.target-Ft.ref-soe.target_plus_reference",
    }), encoding="utf-8")
    at_manifest = final_dir / "Ft.SPL.SPL.target-Ft.ref-At.target_plus_reference.ppi.manifest.json"
    at_manifest.write_text(json.dumps({
        "stage": "ppi",
        "status": "success",
        "reference_species": ["At"],
        "scope_id": "SPL.target-Ft.ref-At.target_plus_reference",
    }), encoding="utf-8")
    os.utime(soe_manifest, (1_000_000, 1_000_000))
    os.utime(at_manifest, (2_000_000, 2_000_000))

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["At"],
        requested_stages=["ppi"],
        family="SPL",
    )

    assert resolution["reference_species"] == ""
    assert resolution["selected_string_species"] == ""
    assert resolution["status"] == "confirmation_required"
    assert resolution["issue_code"] == "ppi_reference_confirmation_required"
    assert resolution["mapping_reference_species"] == ""


def test_kaks_preflight_recovers_non_ath_reference_and_prioritizes_missing_cds(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    soe_dir = input_dir / "soe"
    input_dir.mkdir()
    soe_dir.mkdir()
    final_dir.mkdir(parents=True)
    (soe_dir / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")
    (input_dir / "At.pep.fasta").write_text(">AT1G01010\nMA\n", encoding="utf-8")
    (input_dir / "At.cds.fasta").write_text(">AT1G01010\nATG\n", encoding="utf-8")
    (final_dir / "Ft.SPL.orthologs.txt").write_text(
        "FtGene1\tAT1G01010\t90\tFt\tAt\n",
        encoding="utf-8",
    )
    (final_dir / "Ft.SPL.SPL.target-Ft.ref-soe.target_plus_reference.ortholog.manifest.json").write_text(json.dumps({
        "stage": "ortholog",
        "status": "success",
        "reference_species": ["soe"],
        "scope_id": "SPL.target-Ft.ref-soe.target_plus_reference",
    }), encoding="utf-8")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Fagopyrum tataricum"],
        reference_species=["At"],
        requested_stages=["kaks"],
        family="SPL",
    )

    assert resolution["reference_species"] == "soe"
    assert resolution["selected_string_species"] == "soe"
    assert resolution["status"] == "needs_reference_data"
    assert resolution["issue_code"] == "kaks_reference_cds_missing"
    assert resolution["reference_bundle_status"]["missing_required"] == ["cds"]


def test_ppi_species_resolution_reports_needs_ortholog_when_reference_pep_exists(tmp_path):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    input_dir.mkdir()
    final_dir.mkdir(parents=True)
    (input_dir / "soe").mkdir()
    (input_dir / "soe" / "soe.pep.fasta").write_text(">SoeGene1\nMA\n", encoding="utf-8")

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        target_species=["Ft"],
        reference_species=["Spinacia oleracea"],
        family="SPL",
        ppi_reference_taxon_id="3562",
        ppi_reference_storage_key="soe",
        ppi_reference_selection_mode="tree_nearest",
        ppi_reference_user_confirmed=True,
        dependency_owner="ppi",
    )

    assert resolution["status"] == "needs_ortholog"
    assert resolution["issue_code"] == "ppi_requires_ortholog_mapping"
    assert resolution["reference_data_source"].endswith("soe/soe.pep.fasta")
    assert resolution["selected_string_species"] == "soe"


def test_ppi_reference_bundle_download_requests_only_pep(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    (tmp_path / "input").mkdir()
    (tmp_path / "input" / "soe").mkdir()
    (tmp_path / "workspace").mkdir()
    captured = {}

    monkeypatch.setattr(
        backend,
        "_select_ncbi_reference_assembly",
        lambda **kwargs: {"accession": "GCF_TEST", "organism_name": "Spinacia oleracea", "taxon_id": "3562"},
    )

    def fake_download_url(accession, file_types):
        captured["file_types"] = list(file_types)
        return "https://example.invalid/reference.zip"

    def fake_download(_url, zip_path, timeout_sec=300):
        zip_path.parent.mkdir(parents=True, exist_ok=True)
        with zipfile.ZipFile(zip_path, "w") as zf:
            zf.writestr("README.txt", "fake")
        return 4

    monkeypatch.setattr(backend, "_ncbi_dataset_download_url", fake_download_url)
    monkeypatch.setattr(backend, "_download_url_to_file", fake_download)
    monkeypatch.setattr(
        backend,
        "_extract_ncbi_reference_bundle_zip",
        lambda **kwargs: {"pep": str(tmp_path / "input" / "soe" / "soe.pep.fasta")},
    )
    monkeypatch.setattr(
        backend,
        "_build_reference_bundle_id_map",
        lambda **kwargs: {
            "id_map_path": str(tmp_path / "input" / "soe" / "soe.pep_cds_idmap.tsv"),
            "pep_records": 1,
            "cds_records": 0,
            "pep_cds_overlap": 0,
        },
    )

    result = backend.download_reference_annotation_bundle(
        job=job,
        species_code="soe",
        species_name="Spinacia oleracea",
        taxon_id="3562",
        requested_stages=["ppi"],
    )

    assert captured["file_types"] == ["pep"]
    assert result["status"] == "success"
    assert result["missing_required"] == []
    assert result["requested_file_types"] == ["pep"]


def test_ppi_execution_scope_canonicalizes_full_species_names_before_mapping_filter(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    input_dir.mkdir(parents=True)
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    ath_dir = input_dir / "ath"
    ath_dir.mkdir()
    (ath_dir / "ath.pep_cds_idmap.tsv").write_text(
        "species_code\tprotein_id\nath\tAT1G01010\n", encoding="utf-8"
    )
    (final_dir / "Ft.SPL.final_members.id").write_text("FtGene1\n", encoding="utf-8")
    (final_dir / "Ft.SPL.orthologs.txt").write_text(
        "FtGene1\tAT1G01010\t90\tFt\tAt\n",
        encoding="utf-8",
    )
    scope_file = final_dir / "Ft.SPL.full_name.scope.json"
    scope_file.write_text(json.dumps({
        "family": "SPL",
        "prefix": "Ft.SPL",
        "scope_id": "SPL.target-Fagopyrum_tataricum.ref-Arabidopsis_thaliana.target_plus_reference",
        "scope_mode": "target_plus_reference",
        "target_species": ["Fagopyrum tataricum"],
        "reference_species": ["Arabidopsis thaliana"],
    }), encoding="utf-8")

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        _write_fake_ppi_outputs(workspace / "ppi", prefix="Ft.SPL")
        return 0

    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda *args, **kwargs: None)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Ft.SPL",
        ppi_species="auto",
        ppi_score=200,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    gene_ids_file = backend.Path(captured["cmd"][captured["cmd"].index("--gene-ids") + 1])
    assert "✅ PPI analysis completed" in message
    assert manifest and manifest.exists()
    assert captured["cmd"][captured["cmd"].index("--species") + 1] == "ath"
    assert gene_ids_file.read_text(encoding="utf-8").splitlines() == ["FtGene1"]

    resolution = backend.resolve_ppi_species_resolution(
        job=job,
        prefix="Ft.SPL",
        ppi_species="auto",
        scope_file=str(scope_file),
        family="SPL",
    )
    assert resolution["status"] == "confirmation_required"
    assert resolution["issue_code"] == "ppi_reference_confirmation_required"
    assert resolution["required_next_stage"] == ""


def test_ppi_auto_prefers_curated_members_when_available(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    input_dir.mkdir(parents=True)
    curated_file = final_dir / "Target.Os.CRR.curated_members.id"
    final_file = final_dir / "Target.Os.CRR.final_members.id"
    curated_file.write_text("Os01t0810800-01\nOs09t0359500-01\n", encoding="utf-8")
    final_file.write_text(
        "Os01t0810800-01\nOs09t0359500-01\nOs05t0393100-01\n",
        encoding="utf-8",
    )
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-At.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file)

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        _write_fake_ppi_outputs(workspace / "ppi")
        return 0

    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda *args, **kwargs: None)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="auto",
        ppi_score=400,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    cmd = captured["cmd"]
    assert "✅ PPI analysis completed" in message
    assert manifest and manifest.exists()
    assert cmd[cmd.index("--gene-ids") + 1] == str(curated_file)


def test_ppi_projected_soe_uses_exact_mapping_and_same_species_id_map(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    workspace = tmp_path / "workspace"
    final_dir = workspace / "final"
    logs_dir = workspace / "logs"
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    input_dir.mkdir(parents=True)
    soe_dir = input_dir / "soe"
    soe_dir.mkdir()
    id_map_file = soe_dir / "soe.pep_cds_idmap.tsv"
    id_map_file.write_text("species_code\tprotein_id\nsoe\tSoeGene1\n", encoding="utf-8")
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    mapping_file = final_dir / "Os.CRR.orthologs.txt"
    mapping_file.write_text("Os01t0810800-01\tSoeGene1\t90\tOs\tsoe\n", encoding="utf-8")
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-soe.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file, reference_species="soe")

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        _write_fake_ppi_outputs(workspace / "ppi")
        return 0

    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda *args, **kwargs: None)

    message, manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="soe",
        ppi_score=400,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    cmd = captured["cmd"]
    assert "✅ PPI analysis completed" in message
    assert manifest and manifest.exists()
    assert cmd[cmd.index("--species") + 1] == "soe"
    assert cmd[cmd.index("--mapping") + 1] == str(mapping_file)
    assert cmd[cmd.index("--id-map") + 1] == str(id_map_file)


def test_ppi_projected_soe_rejects_only_ath_id_map(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    logs_dir = tmp_path / "workspace" / "logs"
    (input_dir / "ath").mkdir(parents=True)
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    ath_map = input_dir / "ath" / "ath.pep_cds_idmap.tsv"
    ath_map.write_text("species_code\tprotein_id\nath\tAT1G51790\n", encoding="utf-8")
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    (final_dir / "Os.CRR.orthologs.txt").write_text(
        "Os01t0810800-01\tSoeGene1\t90\tOs\tsoe\n", encoding="utf-8"
    )
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-soe.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file, reference_species="soe")
    launched = []
    monkeypatch.setattr(backend, "run_cmd_logged", lambda *args, **kwargs: launched.append(args))

    message, _manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="soe",
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    expected = input_dir / "soe" / "soe.pep_cds_idmap.tsv"
    assert launched == []
    assert str(expected) in message
    assert "species-matched ID map" in message
    assert str(ath_map) not in message


def test_ppi_projected_soe_rejects_missing_id_map(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    logs_dir = tmp_path / "workspace" / "logs"
    input_dir.mkdir(parents=True)
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    (final_dir / "Os.CRR.orthologs.txt").write_text(
        "Os01t0810800-01\tSoeGene1\t90\tOs\tsoe\n", encoding="utf-8"
    )
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-soe.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file, reference_species="soe")
    launched = []
    monkeypatch.setattr(backend, "run_cmd_logged", lambda *args, **kwargs: launched.append(args))

    message, _manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species="soe",
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    expected = input_dir / "soe" / "soe.pep_cds_idmap.tsv"
    assert launched == []
    assert message == f"❌ PPI projected analysis requires species-matched ID map: {expected}"


def test_ppi_explicit_absolute_species_rejected_before_outside_id_map_lookup(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "job" / "input"),
        workspace_dir=str(tmp_path / "job" / "workspace"),
        logs_dir=str(tmp_path / "job" / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "job" / "input"
    workspace = tmp_path / "job" / "workspace"
    final_dir = workspace / "final"
    outside_species = tmp_path / "outside" / "soe"
    outside_id_map = outside_species.with_name("soe.pep_cds_idmap.tsv")
    input_dir.mkdir(parents=True)
    final_dir.mkdir(parents=True)
    (workspace / "logs").mkdir(parents=True)
    outside_id_map.parent.mkdir(parents=True)
    outside_id_map.write_text("species_code\tprotein_id\nsoe\tSoeGene1\n", encoding="utf-8")
    (final_dir / "Os.CRR.final_members.id").write_text("Os01t0810800-01\n", encoding="utf-8")
    (final_dir / "Os.CRR.orthologs.txt").write_text(
        "Os01t0810800-01\tSoeGene1\t90\tOs\tsoe\n", encoding="utf-8"
    )
    scope_file = final_dir / "Os.CRR.CRR.target-Os.ref-soe.target_plus_reference.scope.json"
    _write_ppi_scope(scope_file, reference_species="soe")
    launched = []
    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", lambda *args, **kwargs: launched.append(args))

    requested_species = str(outside_species)
    message, _manifest = backend.run_stage(
        "ppi",
        job,
        "Os.CRR",
        ppi_species=requested_species,
        scope_file=str(scope_file),
        analysis_route="target_plus_reference",
    )

    assert launched == []
    assert "unsupported STRING species" in message
    assert requested_species in message
    assert str(outside_id_map) not in message


def test_ppi_missing_authoritative_skill_never_selects_legacy_cli(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    input_dir = tmp_path / "input"
    final_dir = tmp_path / "workspace" / "final"
    logs_dir = tmp_path / "workspace" / "logs"
    input_dir.mkdir(parents=True)
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    (final_dir / "Os.CRR.final_members.id").write_text(
        "Os01g0810800\n", encoding="utf-8"
    )

    original_exists = backend.Path.exists

    def fake_exists(path):
        if str(path) == "skills/ppi/run.py":
            return False
        if str(path) == "apps/cli/run_ppi_v2.py":
            return True
        return original_exists(path)

    launched = []

    def fail_if_launched(*args, **kwargs):
        launched.append((args, kwargs))
        raise AssertionError("no PPI implementation may launch")

    monkeypatch.setattr(backend.Path, "exists", fake_exists)
    monkeypatch.setattr(backend, "can_start_new_task", lambda *args, **kwargs: (True, "ok"))
    monkeypatch.setattr(backend, "run_cmd_logged", fail_if_launched)

    message, manifest = backend.run_stage(
        "ppi", job, "Os.CRR", ppi_species="osa", ppi_score=400
    )

    assert "authoritative ppi skill" in message.lower()
    assert "legacy fallback disabled" in message
    assert "run_ppi_v2.py" not in message
    assert launched == []
    expected_manifest = final_dir / "Os.CRR.ppi.manifest.json"
    assert manifest == expected_manifest
    payload = json.loads(expected_manifest.read_text(encoding="utf-8"))
    assert payload["stage"] == "ppi"
    assert payload["prefix"] == "Os.CRR"
    assert payload["status"] == "failed"
    assert "authoritative ppi skill" in payload["error"].lower()
    assert "legacy fallback disabled" in payload["error"]


def test_string_network_image_falls_back_to_standard_png(tmp_path, monkeypatch):
    calls = []

    class FakeResponse:
        headers = {"content-type": "image/png"}
        status_code = 200

        def __init__(self, content, url):
            self._content = content
            self.url = url

        def raise_for_status(self):
            return None

        def iter_content(self, chunk_size=1):
            yield self._content

    def fake_post(url, data=None, timeout=None, stream=None, allow_redirects=None):
        assert allow_redirects is False
        calls.append(url)
        if "highres_image" in url:
            raise TimeoutError("highres timeout")
        return FakeResponse(b"\x89PNG\r\n\x1a\nstandard-png", url)

    monkeypatch.setitem(sys.modules, "requests", types.SimpleNamespace(post=fake_post))

    output = tmp_path / "Os.CRR.ppi_network.string.png"
    result = backend.download_string_network_image(
        "https://string-db.org/cgi/network?identifiers=Os01g0810800&species=39947",
        output,
    )

    assert result == output
    assert output.read_bytes() == b"\x89PNG\r\n\x1a\nstandard-png"
    assert calls == [
        "https://string-db.org/api/highres_image/network",
        "https://string-db.org/api/image/network",
    ]


def test_string_network_image_falls_back_to_svg(tmp_path, monkeypatch):
    calls = []

    class FakeResponse:
        status_code = 200

        def __init__(self, content_type, content, url):
            self.headers = {"content-type": content_type}
            self._content = content
            self.url = url

        def raise_for_status(self):
            return None

        def iter_content(self, chunk_size=1):
            yield self._content

    def fake_post(url, data=None, timeout=None, stream=None, allow_redirects=None):
        assert allow_redirects is False
        calls.append(url)
        if "svg" in url:
            return FakeResponse("image/svg+xml", b"<svg></svg>", url)
        raise TimeoutError("png timeout")

    monkeypatch.setitem(sys.modules, "requests", types.SimpleNamespace(post=fake_post))

    output = tmp_path / "Os.CRR.ppi_network.string.png"
    result = backend.download_string_network_image(
        "https://string-db.org/cgi/network?identifiers=Os01g0810800&species=39947",
        output,
    )

    assert result == tmp_path / "Os.CRR.ppi_network.string.svg"
    assert result.read_bytes() == b"<svg></svg>"
    assert calls == [
        "https://string-db.org/api/highres_image/network",
        "https://string-db.org/api/image/network",
        "https://string-db.org/api/svg/network",
    ]


def test_create_alphafold2_links_marks_single_protein_entries(tmp_path):
    id_resolution = tmp_path / "Os.CRR.ppi_id_resolution.tsv"
    id_resolution.write_text(
        "\t".join(["raw_id", "normalized_id", "selected_query_id", "string_id", "mapping_status", "method", "reason"])
        + "\n"
        + "\t".join(["Os01t0810800-01", "Os01g0810800", "Os01g0810800", "", "candidate", "species_rule", ""])
        + "\n",
        encoding="utf-8",
    )
    network = tmp_path / "Os.CRR.ppi_network.tsv"
    network.write_text("protein1\tprotein2\tscore\nA0A0N7KDY0\tQ60ER5_ORYSJ\t0.234\n", encoding="utf-8")
    output = tmp_path / "Os.CRR.ppi_alphafold2_links.tsv"

    result = backend.create_alphafold2_links(
        id_resolution_file=id_resolution,
        network_file=network,
        output_file=output,
    )

    assert result == output
    rows = output.read_text(encoding="utf-8").splitlines()
    assert rows[0].split("\t") == [
        "source_id",
        "lookup_id",
        "link_type",
        "alphafold_db_url",
        "structure_type",
        "note",
    ]
    assert "AlphaFold DB/Fold2 single protein" in rows[1]
    assert "complex" not in output.read_text(encoding="utf-8").lower()
    assert "https://alphafold.ebi.ac.uk/entry/A0A0N7KDY0" in output.read_text(encoding="utf-8")


def test_sync_ppi_manifest_artifacts_points_to_final_files(tmp_path):
    final_dir = tmp_path / "final"
    final_dir.mkdir()
    manifest = final_dir / "Os.CRR.ppi.manifest.json"
    for name in [
        "Os.CRR.ppi_network.tsv",
        "Os.CRR.ppi_report.md",
        "Os.CRR.ppi_id_resolution.tsv",
        "Os.CRR.ppi_url.txt",
    ]:
        (final_dir / name).write_text("x\n", encoding="utf-8")
    manifest.write_text(
        json.dumps({
            "manifest_version": "1.0",
            "stage": "ppi",
            "status": "success",
            "prefix": "Os.CRR",
            "artifacts": {
                "network": {"path": "workspace/job/ppi/Os.CRR_network.tsv", "type": "tsv"},
                "report": {"path": "workspace/job/ppi/Os.CRR_report.md", "type": "markdown"},
                "id_resolution": {"path": "workspace/job/ppi/Os.CRR_id_resolution.tsv", "type": "tsv"},
                "string_url": {"path": "workspace/job/ppi/Os.CRR_url.txt", "type": "txt"},
            },
        }),
        encoding="utf-8",
    )

    backend.sync_ppi_manifest_final_artifacts(manifest, final_dir, "Os.CRR")

    data = json.loads(manifest.read_text(encoding="utf-8"))
    assert data["artifacts"]["network"]["path"] == "final/Os.CRR.ppi_network.tsv"
    assert data["artifacts"]["report"]["path"] == "final/Os.CRR.ppi_report.md"
    assert data["artifacts"]["id_resolution"]["path"] == "final/Os.CRR.ppi_id_resolution.tsv"
    assert data["artifacts"]["string_url"]["path"] == "final/Os.CRR.ppi_url.txt"
    assert data["artifacts"]["network"]["full_path"] == str((final_dir / "Os.CRR.ppi_network.tsv").absolute())


def test_prepare_ppi_reset_inputs_creates_isolated_diff_and_manifest(tmp_path):
    final_dir = tmp_path / "final"
    final_dir.mkdir()
    source = final_dir / "Target.Os.CRR.curated_members.id"
    source.write_text("Os01g0810800\nOs09g0359500\nOs05g0393100\n", encoding="utf-8")
    original_ppi = final_dir / "Os.CRR.ppi_network.tsv"
    original_ppi.write_text("protein1\tprotein2\tscore\nA\tB\t0.9\n", encoding="utf-8")

    result = backend.prepare_ppi_reset_inputs(
        final_dir=final_dir,
        prefix="Os.CRR",
        source_gene_id_file=source,
        submitted_gene_ids=["Os01g0810800", "Os05g0393100", "Os07g0501800"],
        instruction="remove one member and add one candidate",
        ppi_species="osa",
        ppi_score=200,
    )

    reset_dir = final_dir / "ppi_reset" / "reset_001"
    assert result["reset_id"] == "reset_001"
    assert result["reset_dir"] == reset_dir
    assert (reset_dir / "Os.CRR.ppi_reset_input.id").read_text(encoding="utf-8").splitlines() == [
        "Os01g0810800",
        "Os05g0393100",
        "Os07g0501800",
    ]
    diff_lines = (reset_dir / "Os.CRR.ppi_reset_diff.tsv").read_text(encoding="utf-8").splitlines()
    assert "Os09g0359500\tdeleted" in diff_lines
    assert "Os07g0501800\tadded" in diff_lines
    manifest = json.loads((reset_dir / "Os.CRR.ppi_reset.manifest.json").read_text(encoding="utf-8"))
    assert manifest["reset_id"] == "reset_001"
    assert manifest["source_gene_id_file"] == str(source)
    assert manifest["input_gene_count"] == 3
    assert manifest["deleted_count"] == 1
    assert manifest["added_count"] == 1
    assert manifest["parameters"]["ppi_species"] == "osa"
    assert original_ppi.read_text(encoding="utf-8").startswith("protein1\tprotein2")


def test_prepare_ppi_reset_inputs_uses_next_reset_directory(tmp_path):
    final_dir = tmp_path / "final"
    source = final_dir / "Target.Os.CRR.curated_members.id"
    source.parent.mkdir(parents=True)
    source.write_text("A\nB\n", encoding="utf-8")
    (final_dir / "ppi_reset" / "reset_001").mkdir(parents=True)

    result = backend.prepare_ppi_reset_inputs(
        final_dir=final_dir,
        prefix="Os.CRR",
        source_gene_id_file=source,
        submitted_gene_ids=["A"],
        instruction="second reset",
        ppi_species="osa",
        ppi_score=200,
    )

    assert result["reset_id"] == "reset_002"
    assert result["reset_dir"] == final_dir / "ppi_reset" / "reset_002"


def test_run_ppi_reset_analysis_writes_outputs_under_reset_dir(tmp_path, monkeypatch):
    job = backend.JobInfo(
        job_id="job_test",
        input_dir=str(tmp_path / "input"),
        workspace_dir=str(tmp_path / "workspace"),
        logs_dir=str(tmp_path / "workspace" / "logs"),
        created_at="",
    )
    final_dir = tmp_path / "workspace" / "final"
    logs_dir = tmp_path / "workspace" / "logs"
    final_dir.mkdir(parents=True)
    logs_dir.mkdir(parents=True)
    source = final_dir / "Target.Os.CRR.curated_members.id"
    source.write_text("A\nB\nC\n", encoding="utf-8")

    captured = {}

    def fake_run_cmd_logged(cmd, log_path, cwd=None):
        captured["cmd"] = cmd
        out_dir = cmd[cmd.index("--output") + 1]
        ppi_dir = backend.Path(out_dir)
        ppi_dir.mkdir(parents=True, exist_ok=True)
        _write_fake_ppi_outputs(ppi_dir, prefix="Os.CRR")
        return 0

    monkeypatch.setattr(backend, "run_cmd_logged", fake_run_cmd_logged)
    monkeypatch.setattr(backend, "download_string_network_image", lambda url, path: None)

    result = backend.run_ppi_reset_analysis(
        job=job,
        prefix="Os.CRR",
        source_gene_id_file=source,
        submitted_gene_ids=["A", "C"],
        instruction="remove B",
        ppi_species="osa",
        ppi_score=200,
    )

    reset_dir = final_dir / "ppi_reset" / "reset_001"
    assert result["success"] is True
    assert captured["cmd"][captured["cmd"].index("--gene-ids") + 1] == str(reset_dir / "Os.CRR.ppi_reset_input.id")
    assert (reset_dir / "Os.CRR.ppi_network.tsv").exists()
    assert (reset_dir / "Os.CRR.ppi_report.md").exists()
    assert (reset_dir / "Os.CRR.ppi_id_resolution.tsv").exists()
    assert (reset_dir / "Os.CRR.ppi_url.txt").exists()
    assert (reset_dir / "Os.CRR.ppi_alphafold2_links.tsv").exists()
    assert not (final_dir / "Os.CRR.ppi_network.tsv").exists()
    manifest = json.loads((reset_dir / "Os.CRR.ppi_reset.manifest.json").read_text(encoding="utf-8"))
    assert manifest["status"] == "success"
    assert manifest["artifacts"]["network"]["path"] == "final/ppi_reset/reset_001/Os.CRR.ppi_network.tsv"