"""Integration: each rolled-up hour keeps the level it held (#5738).

A learned normal has to tell a two-minute backup spike from an hour spent at
that level. Both have the same peak; only the hour's 95th percentile differs,
so the rollup must keep it beside the average and the peak.
Requires STEWARD_DATABASE_URL.
"""
from __future__ import annotations

import asyncio
import os
import uuid
from datetime import datetime, timedelta, timezone

import pytest

pytestmark = pytest.mark.integration


@pytest.fixture
def app():
    if not os.environ.get("STEWARD_DATABASE_URL"):
        pytest.skip("needs Postgres")
    from steward.app import create_app
    return create_app(testing=False)


def test_a_brief_spike_raises_the_peak_and_a_held_level_raises_the_p95(app):
    from sqlalchemy import select
    from steward.core.metrics_retention import rollup_plugin_metrics
    from steward.models.metrics import PluginMetric, PluginMetricHourly

    now = datetime.now(timezone.utc)
    res = f"p95-{uuid.uuid4().hex[:8]}"
    spike_hour = (now - timedelta(days=10)).replace(minute=0, second=0, microsecond=0)
    held_hour = spike_hour + timedelta(hours=1)

    def minute(hour, m, metric, value):
        return PluginMetric(source_module="test", resource_name=res, metric_name=metric,
                            value=value, recorded_at=hour + timedelta(minutes=m))

    async def _go():
        async with app.db_sessionmaker() as s:
            async with s.begin():
                # One sample a minute. The spike hour sits at 10 and touches 95
                # for two minutes; the held hour sits at 90 bar a two-minute dip.
                s.add_all([minute(spike_hour, m, "cpu_pct", 95.0 if m in (2, 3) else 10.0)
                           for m in range(60)])
                s.add_all([minute(held_hour, m, "cpu_pct", 10.0 if m in (2, 3) else 90.0)
                           for m in range(60)])
            async with s.begin():
                await rollup_plugin_metrics(s, raw_days=7, rollup_days=90, now=now)
            rows = (await s.execute(
                select(PluginMetricHourly)
                .where(PluginMetricHourly.resource_name == res)
                .order_by(PluginMetricHourly.bucket))).scalars().all()
            return [(r.bucket, r.value_max, r.value_p95, r.value_avg) for r in rows]

    (b1, spike_max, spike_p95, _), (b2, held_max, held_p95, _) = asyncio.run(_go())
    assert (b1, b2) == (spike_hour, held_hour)
    assert spike_max == 95.0 and abs(spike_p95 - 10.0) < 0.001
    assert held_max == 90.0 and abs(held_p95 - 90.0) < 0.001