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boyang2022hui-zhou-a
boyang2022
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hui-zhou-a
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FEAT: Reduce via by density (#1469)
* reduce vias by density * reduce vias by density * reduce vias by density * reduce vias by density grpc * reduce vias by density test * reduce vias by density test --------- Co-authored-by: Hui Zhou <[email protected]>
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‎src/pyedb/dotnet/database/padstack.py‎

Lines changed: 87 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1943,3 +1943,90 @@ def expand_cluster(point_idx, neighbors):
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clusters[int(label)].append(padstack_ids[i])
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return dict(clusters)
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def reduce_via_by_density(
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self, padstacks: List[int], cell_size_x: float = 1e-3, cell_size_y: float = 1e-3, delete: bool = False
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) -> tuple[List[int], List[List[List[float]]]]:
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"""
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Reduce the number of vias by density. Keep only one via which is closest to the center of the cell. The cells
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are automatically populated based on the input vias.
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Parameters
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----------
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padstacks: List[int]
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List of padstack ids to be reduced.
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cell_size_x : float
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Width of each grid cell (default is 1e-3).
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cell_size_y : float
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Height of each grid cell (default is 1e-3).
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delete: bool
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If True, delete vias that are not kept (default is False).
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Returns
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-------
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List[int]
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IDs of vias kept after reduction.
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List[List[float]]
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coordinates for grid lines (for plotting).
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"""
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to_keep = set()
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all_instances = self.instances
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positions = np.array([all_instances[_id].position for _id in padstacks])
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x_coords, y_coords = positions[:, 0], positions[:, 1]
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x_min, x_max = np.min(x_coords), np.max(x_coords)
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y_min, y_max = np.min(y_coords), np.max(y_coords)
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padstacks_array = np.array(padstacks)
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cell_map = {} # {(cell_x, cell_y): [(id1, [x1, y1]), (id2, [x2, y2), ...]}
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grid = []
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for idx, pos in enumerate(positions):
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i = int((pos[0] - x_min) // cell_size_x)
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j = int((pos[1] - y_min) // cell_size_y)
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cell_key = (i, j)
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cell_map.setdefault(cell_key, []).append((padstacks_array[idx], pos))
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for (i, j), items in cell_map.items():
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# cell center
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cell_x_min = x_min + i * cell_size_x
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cell_y_min = y_min + j * cell_size_y
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cell_x_mid = cell_x_min + 0.5 * cell_size_x
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cell_y_mid = cell_y_min + 0.5 * cell_size_y
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grid.append(
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[
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[
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cell_x_min,
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cell_x_min + cell_size_x,
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cell_x_min + cell_size_x,
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cell_x_min,
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cell_x_min,
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],
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[
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cell_y_min,
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cell_y_min,
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cell_y_min + cell_size_y,
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cell_y_min + cell_size_y,
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cell_y_min,
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],
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]
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)
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# Find closest via to cell center
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distances = [np.linalg.norm(pos - [cell_x_mid, cell_y_mid]) for _, pos in items]
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closest_idx = np.argmin(distances)
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to_keep.add(items[closest_idx][0])
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if delete:
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to_delete = set(padstacks) - to_keep
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for _id in to_delete:
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all_instances[_id].delete()
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return list(to_keep), grid

‎src/pyedb/grpc/database/padstacks.py‎

Lines changed: 87 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1853,3 +1853,90 @@ def expand_cluster(point_idx, neighbors):
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clusters[int(label)].append(padstack_ids[i])
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return dict(clusters)
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def reduce_via_by_density(
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self, padstacks: List[int], cell_size_x: float = 1e-3, cell_size_y: float = 1e-3, delete: bool = False
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) -> tuple[List[int], List[List[List[float]]]]:
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"""
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Reduce the number of vias by density. Keep only one via which is closest to the center of the cell. The cells
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are automatically populated based on the input vias.
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Parameters
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----------
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padstacks: List[int]
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List of padstack ids to be reduced.
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cell_size_x : float
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Width of each grid cell (default is 1e-3).
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cell_size_y : float
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Height of each grid cell (default is 1e-3).
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delete: bool
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If True, delete vias that are not kept (default is False).
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Returns
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-------
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List[int]
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IDs of vias kept after reduction.
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List[List[float]]
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coordinates for grid lines (for plotting).
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"""
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to_keep = set()
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all_instances = self.instances
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positions = np.array([all_instances[_id].position for _id in padstacks])
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x_coords, y_coords = positions[:, 0], positions[:, 1]
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x_min, x_max = np.min(x_coords), np.max(x_coords)
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y_min, y_max = np.min(y_coords), np.max(y_coords)
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padstacks_array = np.array(padstacks)
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cell_map = {} # {(cell_x, cell_y): [(id1, [x1, y1]), (id2, [x2, y2), ...]}
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grid = []
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for idx, pos in enumerate(positions):
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i = int((pos[0] - x_min) // cell_size_x)
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j = int((pos[1] - y_min) // cell_size_y)
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cell_key = (i, j)
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cell_map.setdefault(cell_key, []).append((padstacks_array[idx], pos))
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for (i, j), items in cell_map.items():
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# cell center
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cell_x_min = x_min + i * cell_size_x
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cell_y_min = y_min + j * cell_size_y
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cell_x_mid = cell_x_min + 0.5 * cell_size_x
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cell_y_mid = cell_y_min + 0.5 * cell_size_y
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grid.append(
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[
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[
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cell_x_min,
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cell_x_min + cell_size_x,
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cell_x_min + cell_size_x,
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cell_x_min,
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cell_x_min,
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],
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[
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cell_y_min,
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cell_y_min,
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cell_y_min + cell_size_y,
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cell_y_min + cell_size_y,
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cell_y_min,
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],
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]
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)
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# Find closest via to cell center
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distances = [np.linalg.norm(pos - [cell_x_mid, cell_y_mid]) for _, pos in items]
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closest_idx = np.argmin(distances)
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to_keep.add(items[closest_idx][0])
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if delete:
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to_delete = set(padstacks) - to_keep
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for _id in to_delete:
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all_instances[_id].delete()
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return list(to_keep), grid

‎tests/system/test_edb_padstacks.py‎

Lines changed: 21 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -561,6 +561,27 @@ def test_dbscan(self, edb_examples):
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assert len(clusters1[0]) == 20
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assert len(clusters2) == 2
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assert len(clusters2[1]) == 21
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edbapp.close(terminate_rpc_session=False)
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def test_reduce_via_by_density(self, edb_examples):
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source_path = edb_examples.example_models_path / "TEDB" / "merge_via_4layers.aedb"
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edbapp = edb_examples.load_edb(source_path)
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inst = edbapp.padstacks.instances
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all_vias = {id_: i.position for id_, i in inst.items()}
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clusters = edbapp.padstacks.dbscan(all_vias, max_distance=2e-3, min_samples=3)
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kept_2mm, grid_2mm = edbapp.padstacks.reduce_via_by_density(clusters[0], cell_size_x=2e-3, cell_size_y=2e-3)
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kept_5mm, grid_5mm = edbapp.padstacks.reduce_via_by_density(clusters[0], cell_size_x=5e-3, cell_size_y=5e-3)
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assert len(kept_2mm) == 8
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assert len(grid_2mm) == 8
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assert len(kept_5mm) == 1
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assert len(grid_5mm) == 1
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_, _ = edbapp.padstacks.reduce_via_by_density(clusters[0], cell_size_x=5e-3, cell_size_y=5e-3, delete=True)
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_, _ = edbapp.padstacks.reduce_via_by_density(clusters[1], cell_size_x=5e-3, cell_size_y=5e-3, delete=True)
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assert len(edbapp.padstacks.instances) == 2
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edbapp.close(terminate_rpc_session=False)
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def _get_padstack_polygon_data(edb, padstack_instance: EDBPadstackInstance, layer_name: str) -> PolygonData:

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