"""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