from __future__ import annotations

from datetime import date

import numpy as np
import polars as pl

from countryatlas.pipeline.changes import Series, compute_changes_and_events, detect_changes, detect_events
from countryatlas.registry import indicators_by_id


def _series(ind: str, values: list[float], start: int = 2000, country: str = "CAN") -> Series:
    years = np.arange(start, start + len(values), dtype=np.int32)
    return Series(country, ind, [date(int(y), 1, 1) for y in years], years, np.array(values, dtype=float))


def test_inflation_drop_headline_and_since() -> None:
    ind = indicators_by_id()["inflation"]
    vals = [2.0, 2.1, 1.9, 2.2, 2.0, 2.3, 8.1, 5.9, 5.5, 3.4]  # 2015: 5.5 → 2016: 3.4 (−2.1 pts) hmm: last drop 2.1
    vals = [2.0, 2.1, 1.9, 2.2, 2.0, 2.3, 2.1, 2.0, 6.8, 3.4]
    out = detect_changes(_series("inflation", vals, 2016), ind)
    kinds = {r["kind"]: r for r in out}
    assert "yoy_drop" in kinds
    h = kinds["yoy_drop"]["headline"]
    assert h.startswith("Inflation fell 3.4 points to 3.4 % in 2025")
    assert "largest drop" in h
    assert 0 < kinds["yoy_drop"]["severity"] <= 1


def test_floor_blocks_small_moves() -> None:
    ind = indicators_by_id()["inflation"]  # change_floor 2 points
    vals = [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0]  # +1 pt: huge z but below the floor
    out = detect_changes(_series("inflation", vals), ind)
    assert not any(r["kind"] in ("yoy_jump", "yoy_drop") for r in out)


def test_record_high_and_relative_headline() -> None:
    ind = indicators_by_id()["gdp-per-capita"]
    vals = [1000 * 1.03**k for k in range(15)]
    vals[-1] = vals[-2] * 1.25
    out = detect_changes(_series("gdp-per-capita", vals), ind)
    kinds = {r["kind"] for r in out}
    assert "record_high" in kinds and "yoy_jump" in kinds
    jump = next(r for r in out if r["kind"] == "yoy_jump")
    assert "25.0 %" in jump["headline"] and "US$" in jump["headline"]


def test_sign_flip_and_events_history() -> None:
    ind = indicators_by_id()["gdp-growth"]
    vals = [3.0, 2.5, 3.1, 2.8, 3.0, 2.9, -4.5, 5.0, 2.0, -1.0]
    ch = detect_changes(_series("gdp-growth", vals), ind)
    assert any(r["kind"] == "sign_flip" for r in ch)
    ev = detect_events(_series("gdp-growth", vals), ind)
    flips = [r for r in ev if r["kind"] == "sign_flip"]
    assert len(flips) == 3  # 2006, 2007, 2009
    assert any(r["kind"] == "yoy_drop" and r["year"] == 2006 for r in ev)


def test_driver_over_frame() -> None:
    inds = indicators_by_id()
    rows = []
    for c in ("CAN", "FRA"):
        for k, v in enumerate([2.0, 2.1, 1.9, 2.2, 2.0, 2.3, 2.1, 2.0, 6.8, 3.4]):
            rows.append({"country_id": c, "indicator_id": "inflation", "period": date(2016 + k, 1, 1), "year": 2016 + k, "value": v})
    df = pl.DataFrame(rows)
    ch, ev = compute_changes_and_events(df, inds)
    assert ch.filter(pl.col("kind") == "yoy_drop").height == 2
    assert set(ch.columns) >= {"id", "country_id", "indicator_id", "kind", "headline", "severity", "detected_at"}
    assert ev.height >= 2