Flatten one layer at a time, to slightly ease memory pressure

performance_testing
jan 2 years ago
parent 7bbed0bc28
commit 68d829ee13

@ -50,17 +50,39 @@ def read_cell(
labels_func=lambda ll: ll.layer in label_layers,
subpatterns_func=lambda ss: True,
)
cell = cell.flatten()
polys = load_polys(cell, list(poly_layers))
# load polygons
polys = defaultdict(list)
for layer in poly_layers:
shapes_hier = cell.subset(
shapes_func=lambda ss: ss.layer == layer,
subpatterns_func=lambda ss: True,
)
shapes = shapes_hier.flatten().shapes
for ss in shapes:
assert(isinstance(ss, Polygon))
if ss.repetition is None:
displacements = [(0, 0)]
else:
displacements = ss.repetition.displacements
for displacement in displacements:
polys[ss.layer].append(
ss.vertices + ss.offset + displacement
)
# load metal labels
metal_labels = defaultdict(list)
for label_layer, metal_layer in label_mapping.items():
labels = []
for ll in cell.labels:
if ll.layer != label_layer:
continue
labels_hier = cell.subset(
labels_func=lambda ll: ll.layer == label_layer,
subpatterns_func=lambda ss: True,
)
labels = labels_hier.flatten().labels
for ll in labels:
if ll.repetition is None:
displacements = [(0, 0)]
else:
@ -73,37 +95,3 @@ def read_cell(
)
return polys, metal_labels
def load_polys(
cell: Pattern,
layers: Sequence[layer_t],
) -> defaultdict[layer_t, List[NDArray[numpy.float64]]]:
"""
Given a *flat* `masque.Pattern`, extract the polygon info into the format used by `snarled`.
Args:
cell: The `Pattern` object to extract from.
layers: The layers to extract.
Returns:
`{layer0: [poly0, [(x0, y0), (x1, y1), ...], poly2, ...]}`
`polys` structure usable by `snarled.trace_connectivity`.
"""
polys = defaultdict(list)
for ss in cell.shapes:
if ss.layer not in layers:
continue
assert(isinstance(ss, Polygon))
if ss.repetition is None:
displacements = [(0, 0)]
else:
displacements = ss.repetition.displacements
for displacement in displacements:
polys[ss.layer].append(
ss.vertices + ss.offset + displacement
)
return polys

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