| |
| |
| import numpy as np |
|
|
|
|
| def blocks_to_coords(blocks): |
| max_n_unit = 0 |
| coords, masks = [], [] |
| for block in blocks: |
| coords.append([unit.get_coord() for unit in block.units]) |
| max_n_unit = max(max_n_unit, len(coords[-1])) |
| masks.append([1 for _ in coords[-1]]) |
| |
| for i in range(len(coords)): |
| num_pad = max_n_unit - len(coords[i]) |
| coords[i] = coords[i] + [[0, 0, 0] for _ in range(num_pad)] |
| masks[i] = masks[i] + [0 for _ in range(num_pad)] |
| |
| return np.array(coords), np.array(masks).astype('bool') |
|
|
|
|
| def dist_matrix_from_coords(coords1, masks1, coords2, masks2): |
| dist = np.linalg.norm(coords1[:, None] - coords2[None, :], axis=-1) |
| dist = dist + np.logical_not(masks1[:, None] * masks2[None, :]) * 1e6 |
| dist = np.min(dist, axis=-1) |
| return dist |
|
|
|
|
| def blocks_interface(blocks1, blocks2, dist_th): |
| blocks_coord, blocks_mask = blocks_to_coords(blocks1 + blocks2) |
| blocks1_coord, blocks1_mask = blocks_coord[:len(blocks1)], blocks_mask[:len(blocks1)] |
| blocks2_coord, blocks2_mask = blocks_coord[len(blocks1):], blocks_mask[len(blocks1):] |
| dist = dist_matrix_from_coords(blocks1_coord, blocks1_mask, blocks2_coord, blocks2_mask) |
| |
| on_interface = dist < dist_th |
| indexes1 = np.nonzero(on_interface.sum(axis=1) > 0)[0] |
| indexes2 = np.nonzero(on_interface.sum(axis=0) > 0)[0] |
|
|
| blocks1 = [blocks1[i] for i in indexes1] |
| blocks2 = [blocks2[i] for i in indexes2] |
|
|
| return blocks1, blocks2 |
|
|
