160 lines
6.1 KiB
Python
160 lines
6.1 KiB
Python
"""Tests for visualization functions.
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Each test creates a small synthetic DEM and runs a visualization function,
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checking that it produces a valid output file.
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"""
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import numpy as np
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import pytest
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from pathlib import Path
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# --- Core terrain visualizations (no GPU required) ---
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class TestHillshade:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_hillshade
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result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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assert result.suffix == ".tif"
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def test_output_values_valid(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_hillshade
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result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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assert data.shape[0] > 0
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assert np.nanmin(data) >= 0
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assert np.nanmax(data) <= 1
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class TestSlope:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_slope
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result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_slope_values_degrees(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_slope
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result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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assert np.nanmin(data) >= 0
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assert np.nanmax(data) <= 90
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# --- GPU-accelerated visualizations ---
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class TestSVF:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_svf
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result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_svf_values_0_1(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_svf
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result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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valid = data[~np.isnan(data)]
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assert np.nanmin(valid) >= 0
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assert np.nanmax(valid) <= 1
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class TestOpenness:
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def test_positive_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_openness
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result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=True)
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assert result is not None
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assert result.exists()
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def test_negative_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_openness
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result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=False)
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assert result is not None
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assert result.exists()
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class TestMSLRM:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_mslrm
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result = generate_mslrm(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestSAILORE:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_sailore
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result = generate_sailore(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestRoughness:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_roughness
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result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_roughness_non_negative(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_roughness
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result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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# Standard deviation is always >= 0
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assert np.nanmin(data) >= 0
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class TestWavelet:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_wavelet
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result = generate_wavelet(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestFlowAccumulation:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_flow_accumulation
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result = generate_flow_accumulation(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_flow_log_values(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_flow_accumulation
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result = generate_flow_accumulation(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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# log10(x) >= 0 for x >= 1
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valid = data[~np.isnan(data)]
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assert np.nanmin(valid) >= 0
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class TestRayTrace:
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def test_rays_are_traced(self, synthetic_dem, tmp_output_dir):
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"""Verify _ray_trace_horizons returns expected shapes."""
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from lidar_pipeline.visualizations import _ray_trace_horizons, _prepare_dem_for_raycast
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import rasterio
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with rasterio.open(synthetic_dem) as src:
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dem_np = src.read(1)
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rows, cols = dem_np.shape
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# Create a simple filled DEM for testing
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import numpy as np
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filled = np.nan_to_num(dem_np, nan=0)
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# Test with numpy (no GPU)
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pos, neg = _ray_trace_horizons(
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filled, rows, cols, 5.0, n_dirs=4, max_dist=10, radii_m=[25, 50]
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)
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assert pos.shape == (4, 2, rows, cols)
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assert neg.shape == (4, 2, rows, cols)
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