mytec
defa3ad440
Major refactoring of RFCP backend: - Modular propagation models (8 models) - SharedMemoryManager for terrain data - ProcessPoolExecutor parallel processing - WebSocket progress streaming - Building filtering pipeline (351k → 15k) - 82 unit tests Performance: Standard preset 38s → 5s (7.6x speedup) Known issue: Detailed preset timeout (fix in 3.1.0)
164 lines
5.2 KiB
Python
164 lines
5.2 KiB
Python
"""
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Vectorized reflection point calculations using mirror-image method.
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"""
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import numpy as np
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from typing import Tuple, Optional
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from app.geometry.intersection import line_intersects_polygons_batch
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def calculate_reflection_points_batch(
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tx: np.ndarray, rx: np.ndarray,
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wall_starts: np.ndarray, wall_ends: np.ndarray,
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Calculate reflection points on N walls via mirror-image method.
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Args:
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tx, rx: shape (2,)
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wall_starts, wall_ends: shape (N, 2)
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Returns:
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reflection_points: (N, 2)
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valid: bool (N,)
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"""
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wall_vec = wall_ends - wall_starts
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wall_length = np.linalg.norm(wall_vec, axis=1, keepdims=True)
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wall_unit = wall_vec / np.maximum(wall_length, 1e-10)
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normals = np.stack([-wall_unit[:, 1], wall_unit[:, 0]], axis=1)
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tx_to_wall = tx - wall_starts
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tx_dist_to_wall = np.sum(tx_to_wall * normals, axis=1, keepdims=True)
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tx_mirror = tx - 2 * tx_dist_to_wall * normals
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rx_to_mirror = tx_mirror - rx
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cross_denom = (rx_to_mirror[:, 0] * wall_vec[:, 1] -
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rx_to_mirror[:, 1] * wall_vec[:, 0])
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valid_denom = np.abs(cross_denom) > 1e-10
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cross_denom_safe = np.where(valid_denom, cross_denom, 1.0)
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rx_to_start = wall_starts - rx
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t = (rx_to_start[:, 0] * rx_to_mirror[:, 1] -
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rx_to_start[:, 1] * rx_to_mirror[:, 0]) / cross_denom_safe
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reflection_points = wall_starts + t[:, np.newaxis] * wall_vec
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valid = valid_denom & (t >= 0) & (t <= 1) & (tx_dist_to_wall[:, 0] > 0)
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return reflection_points, valid
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def find_best_reflection_path(
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tx: np.ndarray, rx: np.ndarray,
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building_walls_start: np.ndarray,
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building_walls_end: np.ndarray,
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wall_to_building: np.ndarray,
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obstacle_polygons_x: np.ndarray,
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obstacle_polygons_y: np.ndarray,
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obstacle_lengths: np.ndarray,
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max_candidates: int = 50,
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max_walls: int = 100,
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max_los_checks: int = 10,
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) -> Tuple[Optional[np.ndarray], float, float]:
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"""Find best single-reflection path using vectorized ops.
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Args:
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max_walls: Only consider closest N walls for reflection candidates.
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max_los_checks: Only verify LOS for top N shortest reflection paths.
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Returns:
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best_reflection_point: (2,) or None
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best_path_length: meters
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best_reflection_loss: dB
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"""
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num_walls = len(building_walls_start)
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if num_walls == 0:
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return None, np.inf, 0.0
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# Limit walls by distance to path midpoint
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if num_walls > max_walls:
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midpoint = (tx + rx) / 2
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wall_midpoints = (building_walls_start + building_walls_end) / 2
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wall_distances = np.linalg.norm(wall_midpoints - midpoint, axis=1)
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closest = np.argpartition(wall_distances, max_walls)[:max_walls]
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building_walls_start = building_walls_start[closest]
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building_walls_end = building_walls_end[closest]
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wall_to_building = wall_to_building[closest]
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refl_points, valid = calculate_reflection_points_batch(
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tx, rx, building_walls_start, building_walls_end,
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)
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if not np.any(valid):
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return None, np.inf, 0.0
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valid_indices = np.where(valid)[0]
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valid_refl = refl_points[valid]
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tx_to_refl = np.linalg.norm(valid_refl - tx, axis=1)
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refl_to_rx = np.linalg.norm(rx - valid_refl, axis=1)
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path_lengths = tx_to_refl + refl_to_rx
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# Direct distance filter
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direct_dist = np.linalg.norm(rx - tx)
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within_range = path_lengths <= direct_dist * 2.0
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if not np.any(within_range):
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return None, np.inf, 0.0
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valid_indices = valid_indices[within_range]
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valid_refl = valid_refl[within_range]
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path_lengths = path_lengths[within_range]
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# Keep top candidates by shortest path
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if len(valid_indices) > max_candidates:
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top_idx = np.argpartition(path_lengths, max_candidates)[:max_candidates]
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valid_indices = valid_indices[top_idx]
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valid_refl = valid_refl[top_idx]
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path_lengths = path_lengths[top_idx]
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# Sort by path length for early exit
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sort_order = np.argsort(path_lengths)
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valid_refl = valid_refl[sort_order]
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path_lengths = path_lengths[sort_order]
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# Check LOS only for top N shortest candidates
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check_count = min(len(valid_refl), max_los_checks)
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best_idx = -1
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best_length = np.inf
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for i in range(check_count):
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length = path_lengths[i]
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if length >= best_length:
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continue
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refl_pt = valid_refl[i]
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intersects1, _ = line_intersects_polygons_batch(
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tx, refl_pt, obstacle_polygons_x, obstacle_polygons_y, obstacle_lengths,
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)
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if np.any(intersects1):
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continue
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intersects2, _ = line_intersects_polygons_batch(
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refl_pt, rx, obstacle_polygons_x, obstacle_polygons_y, obstacle_lengths,
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)
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if np.any(intersects2):
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continue
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best_idx = i
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best_length = length
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break # sorted by length, first valid is best
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if best_idx < 0:
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return None, np.inf, 0.0
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best_point = valid_refl[best_idx]
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# Reflection loss: 3-10 dB depending on path ratio
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path_ratio = best_length / max(direct_dist, 1.0)
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reflection_loss = 3.0 + 7.0 * min(1.0, (path_ratio - 1.0) * 2)
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return best_point, best_length, reflection_loss
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