@mytec: iter3.10 start, baseline rc ready
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@@ -277,10 +277,11 @@ class GPUService:
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lons: np.ndarray,
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terrain_cache: dict,
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) -> np.ndarray:
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"""Look up elevations from cached terrain tiles.
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"""Look up elevations from cached terrain tiles with bilinear interpolation.
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Vectorized implementation: processes per-tile (1-4 tiles) instead of
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per-point (thousands of points). Inner operations are all NumPy vectorized.
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per-point (thousands of points). Uses bilinear interpolation for
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sub-meter accuracy (vs 15m error with nearest-neighbor at 30m resolution).
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Args:
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lats, lons: Flattened arrays of coordinates
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@@ -313,16 +314,39 @@ class GPUService:
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tile_lons = lons[mask]
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size = tile.shape[0]
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# Vectorized row/col calculation
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rows = ((1 - (tile_lats - lat_int)) * (size - 1)).astype(int)
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cols = ((tile_lons - lon_int) * (size - 1)).astype(int)
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rows = np.clip(rows, 0, size - 1)
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cols = np.clip(cols, 0, size - 1)
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# Vectorized lookup - single operation for ALL points in tile
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tile_elevs = tile[rows, cols].astype(np.float64)
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tile_elevs[tile_elevs == -32768] = 0.0
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elevations[mask] = tile_elevs
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# Vectorized bilinear interpolation
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lat_frac = tile_lats - lat_int
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lon_frac = tile_lons - lon_int
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row_exact = (1.0 - lat_frac) * (size - 1)
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col_exact = lon_frac * (size - 1)
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r0 = np.clip(row_exact.astype(int), 0, size - 2)
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c0 = np.clip(col_exact.astype(int), 0, size - 2)
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r1 = r0 + 1
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c1 = c0 + 1
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dr = row_exact - r0
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dc = col_exact - c0
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# Get four corner values for all points at once
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z00 = tile[r0, c0].astype(np.float64)
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z01 = tile[r0, c1].astype(np.float64)
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z10 = tile[r1, c0].astype(np.float64)
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z11 = tile[r1, c1].astype(np.float64)
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# Bilinear interpolation (vectorized)
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result = (z00 * (1 - dr) * (1 - dc) +
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z01 * (1 - dr) * dc +
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z10 * dr * (1 - dc) +
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z11 * dr * dc)
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# Handle void values (-32768) - set to 0
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void_mask = (z00 == -32768) | (z01 == -32768) | (z10 == -32768) | (z11 == -32768)
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result[void_mask] = 0.0
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elevations[mask] = result
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return elevations
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