"""Shared test fixtures for Aperture tests."""
from __future__ import annotations

import numpy as np
import pytest


@pytest.fixture
def synthetic_monthly_raster(tmp_path):
    """Create a synthetic multi-band GeoTIFF with 12 monthly bands.

    Returns a factory function: call with (n_bands, shape, fill_fn) to get a path.
    fill_fn(band_idx) -> 2D numpy array.
    """
    import rasterio
    from rasterio.transform import from_bounds

    def _make(
        n_bands: int = 12,
        shape: tuple[int, int] = (100, 100),
        fill_fn=None,
        bbox: tuple[float, ...] = (37.8, 2.08, 37.88, 2.17),
    ) -> str:
        if fill_fn is None:
            fill_fn = lambda i: np.random.default_rng(i).uniform(0.2, 0.8, shape).astype(np.float32)

        path = str(tmp_path / f"synthetic_{n_bands}bands.tif")
        transform = from_bounds(*bbox, shape[1], shape[0])
        with rasterio.open(
            path, "w", driver="GTiff",
            height=shape[0], width=shape[1],
            count=n_bands, dtype="float32",
            crs="EPSG:4326", transform=transform,
            nodata=-9999.0,
        ) as dst:
            for i in range(n_bands):
                dst.write(fill_fn(i), i + 1)
        return path

    return _make


@pytest.fixture
def mock_product_result():
    """Factory for ProductResult with sensible defaults."""
    from app.models import ProductResult, StatusLevel, TrendDirection, ConfidenceLevel

    def _make(**overrides):
        defaults = dict(
            product_id="ndvi",
            headline="Test headline",
            status=StatusLevel.GREEN,
            trend=TrendDirection.STABLE,
            confidence=ConfidenceLevel.HIGH,
            map_layer_path="/tmp/fake.tif",
            chart_data={"dates": [], "values": []},
            summary="Test summary",
            methodology="Test methodology",
            limitations=["Test limitation"],
            data_source="satellite",
            anomaly_months=0,
            z_score_current=0.0,
            hotspot_pct=0.0,
            confidence_factors={},
        )
        defaults.update(overrides)
        return ProductResult(**defaults)

    return _make