"""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 os
import sys
import tempfile
import threading

import numpy as np
import pandas as pd
import pytest

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

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-")

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")

import audit  # noqa: E402

audit.AUDIT_DIR = os.path.join(_TMP, "logs", "audit")
audit.ERROR_DIR = os.path.join(_TMP, "logs", "errors")


def make_history(years: int = 20, end: str | None = None, seed: int = 7) -> pd.DataFrame:
    """A plausible daily record (tmax/tmin/precip + doy), matching the columns
    and dtypes climate.get_history returns for an older cache."""
    end_ts = pd.Timestamp(end) if end else pd.Timestamp.today().normalize() - pd.Timedelta(days=6)
    dates = pd.date_range(end_ts - pd.DateOffset(years=years), end_ts, freq="D")
    rng = np.random.default_rng(seed)
    doy = dates.dayofyear.to_numpy()
    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)
    df = pd.DataFrame({
        "date": dates,
        "tmax": np.round(tmax, 1),
        "tmin": np.round(tmin, 1),
        "precip": np.round(precip, 2),
    })
    df["doy"] = df["date"].dt.dayofyear.astype("int16")
    return df


def make_recent(history: pd.DataFrame, future_days: int = 7, seed: int = 11) -> pd.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 = pd.Timestamp.today().normalize()
    start = pd.Timestamp(history["date"].max()) - pd.Timedelta(days=14)
    dates = pd.date_range(start, today + pd.Timedelta(days=future_days), freq="D")
    rng = np.random.default_rng(seed)
    doy = dates.dayofyear.to_numpy()
    seasonal = 55 + 30 * np.sin((doy - 100) / 366.0 * 2 * np.pi)
    tmax = seasonal + rng.normal(0, 8, len(dates))
    df = pd.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),
    })
    df["doy"] = df["date"].dt.dayofyear.astype("int16")
    return df


@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