Remove LRM, TPI, aspect, curvature, paths + add flow accumulation, directional Gabor wavelets, multi-radius ray-tracing
This commit is contained in:
Antoine Jacquin
@ -47,52 +47,8 @@ class TestSlope:
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assert np.nanmax(data) <= 90
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class TestAspect:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_aspect
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result = generate_aspect(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_aspect_values_0_360(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_aspect
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result = generate_aspect(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) <= 360
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class TestCurvature:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_curvature
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result = generate_curvature(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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# --- GPU-accelerated visualizations ---
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class TestLRM:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_lrm
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result = generate_lrm(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_lrm_has_positive_negative(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_lrm
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result = generate_lrm(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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# LRM should have both positive and negative values
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assert np.nanmax(data) > 0
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assert np.nanmin(data) < 0
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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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@ -133,15 +89,6 @@ class TestMSLRM:
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assert result.exists()
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class TestTPI:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_tpi
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result = generate_tpi(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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@ -167,14 +114,6 @@ class TestRoughness:
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assert np.nanmin(data) >= 0
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class TestAnomalies:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_anomalies
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result = generate_anomalies(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 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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@ -183,50 +122,38 @@ class TestWavelet:
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assert result.exists()
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class TestFlow:
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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
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result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
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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
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result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
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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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# log1p(x) >= 0 for x >= 0
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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 TestLocalDominance:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_local_dominance
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result = generate_local_dominance(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_dominance_values_0_1(self, synthetic_dem, tmp_output_dir):
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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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from lidar_pipeline.visualizations import generate_local_dominance
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result = generate_local_dominance(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, "Local dominance should be >= 0"
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assert np.nanmax(valid) <= 1, "Local dominance should be <= 1"
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def test_dominance_nan_mask_preserved(self, synthetic_dem, tmp_output_dir):
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"""Check that NaN zones from original DEM are preserved."""
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import rasterio
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from lidar_pipeline.visualizations import generate_local_dominance
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result = generate_local_dominance(synthetic_dem, "test", tmp_output_dir, 5.0)
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# The synthetic DEM has no NaN, so this just verifies the output is valid
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with rasterio.open(result) as src:
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data = src.read(1)
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# Shape should match input
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assert data.shape[0] > 0
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assert data.shape[1] > 0
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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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