"""Tests for visualization functions.

Each test creates a small synthetic DEM and runs a visualization function,
checking that it produces a valid output file.
"""

import numpy as np
import pytest
from pathlib import Path


# --- Core terrain visualizations (no GPU required) ---

class TestHillshade:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_hillshade
        result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()
        assert result.suffix == ".tif"

    def test_output_values_valid(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_hillshade
        result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            assert data.shape[0] > 0
            assert np.nanmin(data) >= 0
            assert np.nanmax(data) <= 1


class TestSlope:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_slope
        result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_slope_values_degrees(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_slope
        result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            assert np.nanmin(data) >= 0
            assert np.nanmax(data) <= 90


class TestAspect:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_aspect
        result = generate_aspect(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_aspect_values_0_360(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_aspect
        result = generate_aspect(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            valid = data[~np.isnan(data)]
            assert np.nanmin(valid) >= 0
            assert np.nanmax(valid) <= 360


class TestCurvature:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_curvature
        result = generate_curvature(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()


# --- GPU-accelerated visualizations ---

class TestLRM:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_lrm
        result = generate_lrm(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_lrm_has_positive_negative(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_lrm
        result = generate_lrm(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            # LRM should have both positive and negative values
            assert np.nanmax(data) > 0
            assert np.nanmin(data) < 0


class TestSVF:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_svf
        result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_svf_values_0_1(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_svf
        result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            valid = data[~np.isnan(data)]
            assert np.nanmin(valid) >= 0
            assert np.nanmax(valid) <= 1


class TestOpenness:
    def test_positive_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_openness
        result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=True)
        assert result is not None
        assert result.exists()

    def test_negative_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_openness
        result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=False)
        assert result is not None
        assert result.exists()


class TestMSLRM:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_mslrm
        result = generate_mslrm(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()


class TestTPI:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_tpi
        result = generate_tpi(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()



class TestSAILORE:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_sailore
        result = generate_sailore(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()


class TestRoughness:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_roughness
        result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_roughness_non_negative(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_roughness
        result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            # Standard deviation is always >= 0
            assert np.nanmin(data) >= 0


class TestAnomalies:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_anomalies
        result = generate_anomalies(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()


class TestWavelet:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_wavelet
        result = generate_wavelet(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()


class TestFlow:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_flow
        result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()

    def test_flow_log_values(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_flow
        result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            # log1p(x) >= 0 for x >= 0
            valid = data[~np.isnan(data)]
            assert np.nanmin(valid) >= 0


class TestRRIM:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_rrim
        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()
        assert result.suffix == ".tif"

    def test_rrim_is_rgb_3band(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_rrim
        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            assert src.count == 3, f"Expected 3 bands, got {src.count}"
            assert src.dtypes[0] == 'uint8'

    def test_rrim_values_0_255(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_rrim
        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            for band in range(1, 4):
                data = src.read(band)
                assert data.min() >= 0
                assert data.max() <= 255

    def test_rrim_no_nan(self, synthetic_dem, tmp_output_dir):
        """RRIM is uint8 RGB — NaN zones are set to 0 (black)."""
        import rasterio
        from lidar_pipeline.visualizations import generate_rrim
        result = generate_rrim(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            # uint8 bands should not have NaN
            for band in range(1, 4):
                data = src.read(band)
                assert not np.isnan(data).any(), f"Band {band} has NaN values"


class TestMultiHillshade:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_multi_hillshade
        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()
        assert result.suffix == ".tif"

    def test_multi_hillshade_is_rgb_3band(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_multi_hillshade
        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            assert src.count == 3, f"Expected 3 bands, got {src.count}"
            assert src.dtypes[0] == 'uint8'

    def test_multi_hillshade_values_0_255(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_multi_hillshade
        result = generate_multi_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            for band in range(1, 4):
                data = src.read(band)
                assert data.min() >= 0
                assert data.max() <= 255


class TestLocalDominance:
    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
        from lidar_pipeline.visualizations import generate_local_dominance
        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
        assert result is not None
        assert result.exists()
        assert result.suffix == ".tif"

    def test_dominance_values_0_1(self, synthetic_dem, tmp_output_dir):
        import rasterio
        from lidar_pipeline.visualizations import generate_local_dominance
        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
        with rasterio.open(result) as src:
            data = src.read(1)
            valid = data[~np.isnan(data)]
            assert np.nanmin(valid) >= 0, "Local dominance should be >= 0"
            assert np.nanmax(valid) <= 1, "Local dominance should be <= 1"

    def test_dominance_nan_mask_preserved(self, synthetic_dem, tmp_output_dir):
        """Check that NaN zones from original DEM are preserved."""
        import rasterio
        from lidar_pipeline.visualizations import generate_local_dominance
        result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
        # The synthetic DEM has no NaN, so this just verifies the output is valid
        with rasterio.open(result) as src:
            data = src.read(1)
            # Shape should match input
            assert data.shape[0] > 0
            assert data.shape[1] > 0