"""Shared test setup: import path, offline guards, and synthetic weather data.

Everything here keeps the suite hermetic — no Open-Meteo, no Nominatim, no
GeoNames download, and no writes into the repo's data/ or logs/ folders.
"""
import datetime
import os
import sys
import tempfile
import threading

import numpy as np
import polars as pl
import pytest

BACKEND = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, BACKEND)

from data import places  # noqa: E402

# app.py calls places.start_loading() at import; pretend it already ran so no
# background GeoNames download starts. Tests build their own tiny index.
places._load_started = True

_TMP = tempfile.mkdtemp(prefix="thermograph-tests-")

# Keep the authoritative accounts DB out of the repo's data/ folder, and don't
# start the background notifier thread during tests. Set before db is imported.
os.environ.setdefault("THERMOGRAPH_ACCOUNTS_DB", os.path.join(_TMP, "accounts.sqlite"))
os.environ.setdefault("THERMOGRAPH_ENABLE_NOTIFIER", "0")
os.environ.setdefault("THERMOGRAPH_ENABLE_HEARTBEAT", "0")
# web/app.py (repo-split Stage 4) fails loud at import if this is unset -- tests
# never actually hit a frontend-owned path through the live proxy (that's
# frontend_ssr/tests' job), so an unreachable placeholder is fine here.
os.environ.setdefault("THERMOGRAPH_FRONTEND_BASE_INTERNAL", "http://127.0.0.1:1")
# Pin the IndexNow key so it isn't generated + written into the repo's data/ dir.
os.environ.setdefault("THERMOGRAPH_INDEXNOW_KEY", "test0000indexnow0000key000000000")

from data import store  # noqa: E402

# Keep derived-store writes out of the repo's data/ folder. Individual store
# tests re-point this per test; everything else shares one throwaway DB.
store.DB_PATH = os.path.join(_TMP, "store.sqlite")

from core import audit  # noqa: E402

audit.AUDIT_DIR = os.path.join(_TMP, "logs", "audit")
audit.ERROR_DIR = os.path.join(_TMP, "logs", "errors")
# The request-logging middleware runs under TestClient too; keep its access log
# out of the repo's logs/ so self-test traffic never pollutes the live monitor.
audit.ACCESS_DIR = os.path.join(_TMP, "logs", "access")
audit.ACTIVITY_DIR = os.path.join(_TMP, "logs", "activity")
audit.HEARTBEAT_DIR = os.path.join(_TMP, "logs", "heartbeat")


def _years_before(d: datetime.date, years: int) -> datetime.date:
    """`d` shifted back `years` years, same month/day (Feb 29 → Feb 28)."""
    try:
        return d.replace(year=d.year - years)
    except ValueError:
        return d.replace(year=d.year - years, day=28)


def _daily(start: datetime.date, end: datetime.date) -> list[datetime.date]:
    """Inclusive daily date list from `start` to `end`."""
    return [start + datetime.timedelta(days=i) for i in range((end - start).days + 1)]


def _with_doy(df: pl.DataFrame) -> pl.DataFrame:
    return df.with_columns(pl.col("date").dt.ordinal_day().cast(pl.Int16).alias("doy"))


def make_history(years: int = 20, end: str | None = None, seed: int = 7) -> pl.DataFrame:
    """A plausible daily record (tmax/tmin/precip + doy), matching the columns
    and dtypes climate.get_history returns for an older cache."""
    end_ts = (datetime.date.fromisoformat(end) if end
              else datetime.date.today() - datetime.timedelta(days=6))
    dates = _daily(_years_before(end_ts, years), end_ts)
    rng = np.random.default_rng(seed)
    doy = np.array([d.timetuple().tm_yday for d in dates])
    seasonal = 55 + 30 * np.sin((doy - 100) / 366.0 * 2 * np.pi)
    tmax = seasonal + rng.normal(0, 8, len(dates))
    tmin = tmax - 15 + rng.normal(0, 3, len(dates))
    precip = np.where(rng.random(len(dates)) < 0.3, rng.gamma(1.5, 0.2, len(dates)), 0.0)
    return _with_doy(pl.DataFrame({
        "date": dates,
        "tmax": np.round(tmax, 1),
        "tmin": np.round(tmin, 1),
        "precip": np.round(precip, 2),
    }))


def make_recent(history: pl.DataFrame, future_days: int = 7, seed: int = 11) -> pl.DataFrame:
    """A recent+forecast bundle: from a couple of weeks before the archive's end
    through `future_days` past today — the shape climate.get_recent_forecast returns."""
    today = datetime.date.today()
    start = history["date"].max() - datetime.timedelta(days=14)
    dates = _daily(start, today + datetime.timedelta(days=future_days))
    rng = np.random.default_rng(seed)
    doy = np.array([d.timetuple().tm_yday for d in dates])
    seasonal = 55 + 30 * np.sin((doy - 100) / 366.0 * 2 * np.pi)
    tmax = seasonal + rng.normal(0, 8, len(dates))
    return _with_doy(pl.DataFrame({
        "date": dates,
        "tmax": np.round(tmax, 1),
        "tmin": np.round(tmax - 15, 1),
        "precip": np.where(rng.random(len(dates)) < 0.3, 0.15, 0.0),
    }))


@pytest.fixture(scope="session")
def history():
    return make_history()


@pytest.fixture(scope="session")
def recent(history):
    return make_recent(history)


@pytest.fixture
def tmp_store(tmp_path, monkeypatch):
    """store.py against a fresh database file, isolated per test."""
    monkeypatch.setattr(store, "DB_PATH", str(tmp_path / "store.sqlite"))
    monkeypatch.setattr(store, "_local", threading.local())
    return store