"""时序图谱测试：时间点切片、演化追加不覆盖、持久化往返。"""
from __future__ import annotations

from app.graph.tkg import TemporalKG, quarter_of, to_window
from app.models import ChangeEvent, GraphObservation, JobDefinition


def _kg() -> TemporalKG:
    kg = TemporalKG()
    kg.add_job(JobDefinition(job_id="job_T", title="测试工程师"))
    return kg


def test_quarter_and_window_conversion():
    from datetime import date
    assert quarter_of(date(2022, 2, 15)) == "2022Q1"
    assert quarter_of(date(2022, 11, 1)) == "2022Q4"
    assert to_window("2022-05-01") == "2022Q2"
    assert to_window("2022Q3") == "2022Q3"


def test_snapshot_time_slicing():
    """任意时间点切片：只看到该时刻已生效且未退出的技能。"""
    kg = _kg()
    kg.observe(GraphObservation(job_id="job_T", skill="Java", importance="required",
                                weight=1.0, window="2020Q1", source_ids=["s1"]))
    kg.observe(GraphObservation(job_id="job_T", skill="Spring Cloud", importance="required",
                                weight=1.0, window="2023Q1", source_ids=["s2"]))
    snap_2020 = kg.snapshot("job_T", "2020-06-01")
    assert {s["skill"] for s in snap_2020["skills"]} == {"Java"}
    snap_2024 = kg.snapshot("job_T", "2024-01-01")
    assert {s["skill"] for s in snap_2024["skills"]} == {"Java", "Spring Cloud"}


def test_close_interval_removes_from_snapshot():
    kg = _kg()
    kg.observe(GraphObservation(job_id="job_T", skill="Struts", importance="preferred",
                                weight=1.0, window="2019Q1", source_ids=["s1"]))
    kg.close_interval("job_T", "Struts", "2022Q1")
    assert {s["skill"] for s in kg.snapshot("job_T", "2021-01-01")["skills"]} == {"Struts"}
    assert kg.snapshot("job_T", "2023-01-01")["skills"] == []


def test_evolution_appends_not_overwrites():
    """同一技能退出后重新出现：新区间追加，旧区间保留（历史不覆盖）。"""
    kg = _kg()
    obs = GraphObservation(job_id="job_T", skill="Kafka", importance="required",
                           weight=1.0, window="2020Q1", source_ids=["s1"])
    kg.observe(obs)
    kg.close_interval("job_T", "Kafka", "2021Q1")
    kg.observe(GraphObservation(job_id="job_T", skill="Kafka", importance="required",
                                weight=0.8, window="2023Q1", source_ids=["s2"]))
    intervals = kg.g["job_T"]["Kafka"]["intervals"]
    assert intervals == [["2020Q1", "2021Q1"], ["2023Q1", None]]
    assert kg.snapshot("job_T", "2020-06-01")["skills"]     # 旧区间仍可切片
    assert kg.snapshot("job_T", "2022-01-01")["skills"] == []  # 退出期间为空
    assert kg.snapshot("job_T", "2024-01-01")["skills"]     # 重现后恢复
    # 事件流只追加
    kg.add_event(ChangeEvent(event_id="e1", job_id="job_T", skill="Kafka",
                             change_type="退出", window="2021Q1", description="x"))
    kg.add_event(ChangeEvent(event_id="e2", job_id="job_T", skill="Kafka",
                             change_type="新增", window="2023Q1", description="y"))
    assert [e.event_id for e in kg.events_of("job_T")] == ["e1", "e2"]


def test_save_load_roundtrip(tmp_path):
    kg = _kg()
    kg.observe(GraphObservation(job_id="job_T", skill="Java", importance="required",
                                weight=1.0, window="2020Q1", source_ids=["s1"]))
    kg.add_event(ChangeEvent(event_id="e1", job_id="job_T", skill="Java",
                             change_type="新增", window="2020Q1", description="d"))
    path = tmp_path / "g.json"
    kg.save(path)
    kg2 = TemporalKG.load(path)
    assert kg2.stats()["edges"] == 1
    assert kg2.jobs["job_T"].title == "测试工程师"
    assert kg2.snapshot("job_T", "2020-06-01")["skills"][0]["skill"] == "Java"
    assert kg2.events_of("job_T")[0].event_id == "e1"