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269 lines
11 KiB
Python
269 lines
11 KiB
Python
"""
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Main connectivity-checking functionality for `snarled`
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"""
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from typing import Tuple, List, Dict, Set, Optional, Union, Sequence, Mapping
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from collections import defaultdict
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from pprint import pformat
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import logging
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import numpy
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from numpy.typing import NDArray, ArrayLike
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from pyclipper import scale_to_clipper, scale_from_clipper, PyPolyNode
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from .types import connectivity_t, layer_t, contour_t
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from .poly import poly_contains_points
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from .clipper import union_nonzero, union_evenodd, intersection_evenodd, hier2oriented
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from .tracker import NetsInfo, NetName
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from .utils import connectivity2layers
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logger = logging.getLogger(__name__)
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def trace_connectivity(
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polys: Mapping[layer_t, Sequence[ArrayLike]],
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labels: Mapping[layer_t, Sequence[Tuple[float, float, str]]],
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connectivity: Sequence[Tuple[layer_t, Optional[layer_t], layer_t]],
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clipper_scale_factor: int = int(2 ** 24),
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) -> NetsInfo:
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"""
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Analyze the electrical connectivity of the layout.
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This is the primary purpose of `snarled`.
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The resulting `NetsInfo` will contain only disjoint `nets`, and its `net_aliases` can be used to
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understand which nets are shorted (and therefore known by more than one name).
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Args:
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polys: A full description of all conducting paths in the layout. Consists of lists of polygons
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(Nx2 arrays of vertices), indexed by layer. The structure looks roughly like
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`{layer0: [poly0, poly1, ..., [(x0, y0), (x1, y1), ...]], ...}`
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labels: A list of "named points" which are used to assign names to the nets they touch.
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A collection of lists of (x, y, name) tuples, indexed *by the layer they target*.
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`{layer0: [(x0, y0, name0), (x1, y1, name1), ...], ...}`
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connectivity: A sequence of 3-tuples specifying the electrical connectivity between layers.
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Each 3-tuple looks like `(top_layer, via_layer, bottom_layer)` and indicates that
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`top_layer` and `bottom_layer` are electrically connected at any location where
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shapes are present on all three (top, via, and bottom) layers.
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`via_layer` may be `None`, in which case any overlap between shapes on `top_layer`
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and `bottom_layer` is automatically considered a short (with no third shape necessary).
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clipper_scale_factor: `pyclipper` uses 64-bit integer math, while we accept either floats or ints.
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The coordinates from `polys` are scaled by this factor to put them roughly in the middle of
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the range `pyclipper` wants; you may need to adjust this if you are already using coordinates
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with large integer values.
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Returns:
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`NetsInfo` object describing the various nets and their connectivities.
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"""
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#
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# Figure out which layers are metals vs vias, and run initial union on each layer
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#
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metal_layers, via_layers = connectivity2layers(connectivity)
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metal_polys = {layer: union_input_polys(scale_to_clipper(polys[layer], clipper_scale_factor))
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for layer in metal_layers}
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via_polys = {layer: union_input_polys(scale_to_clipper(polys[layer], clipper_scale_factor))
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for layer in via_layers}
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#
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# Check each polygon for labels, and assign it to a net (possibly anonymous).
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#
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nets_info = NetsInfo()
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merge_groups: List[List[NetName]] = []
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for layer in metal_layers:
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point_xys = []
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point_names = []
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for x, y, point_name in labels[layer]:
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point_xys.append((x, y))
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point_names.append(point_name)
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for poly in metal_polys[layer]:
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found_nets = label_poly(poly, point_xys, point_names, clipper_scale_factor)
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if found_nets:
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name = NetName(found_nets[0])
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else:
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name = NetName() # Anonymous net
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nets_info.nets[name][layer].append(poly)
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if len(found_nets) > 1:
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# Found a short
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poly = pformat(scale_from_clipper(poly.Contour, clipper_scale_factor))
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logger.warning(f'Nets {found_nets} are shorted on layer {layer} in poly:\n {poly}')
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merge_groups.append([name] + [NetName(nn) for nn in found_nets[1:]])
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#
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# Merge any nets that were shorted by having their labels on the same polygon
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#
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for group in merge_groups:
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first_net, *defunct_nets = group
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for defunct_net in defunct_nets:
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nets_info.merge(first_net, defunct_net)
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#
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# Convert to non-hierarchical polygon representation
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#
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for net in nets_info.nets.values():
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for layer in net:
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#net[layer] = union_evenodd(hier2oriented(net[layer]))
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net[layer] = hier2oriented(net[layer])
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for layer in via_polys:
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via_polys[layer] = hier2oriented(via_polys[layer])
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#
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# Figure out which nets are shorted by vias, then merge them
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#
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merge_pairs = find_merge_pairs(connectivity, nets_info.nets, via_polys)
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for net_a, net_b in merge_pairs:
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nets_info.merge(net_a, net_b)
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return nets_info
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def union_input_polys(polys: Sequence[ArrayLike]) -> List[PyPolyNode]:
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"""
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Perform a union operation on the provided sequence of polygons, and return
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a list of `PyPolyNode`s corresponding to all of the outer (i.e. non-hole)
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contours.
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Note that while islands are "outer" contours and returned in the list, they
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also are still available through the `.Childs` property of the "hole" they
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appear in. Meanwhile, "hole" contours are only accessible through the `.Childs`
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property of their parent "outer" contour, and are not returned in the list.
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Args:
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polys: A sequence of polygons, `[[(x0, y0), (x1, y1), ...], poly1, poly2, ...]`
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Polygons may be implicitly closed.
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Returns:
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List of PyPolyNodes, representing all "outer" contours (including islands) in
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the union of `polys`.
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"""
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for poly in polys:
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if (numpy.abs(poly) % 1).any():
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logger.warning('Warning: union_polys got non-integer coordinates; all values will be truncated.')
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break
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#TODO: check if we need to reverse the order of points in some polygons
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# via sum((x2-x1)(y2+y1)) (-ve means ccw)
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poly_tree = union_nonzero(polys)
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if poly_tree is None:
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return []
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# Partially flatten the tree, reclassifying all the "outer" (non-hole) nodes as new root nodes
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unvisited_nodes = [poly_tree]
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outer_nodes = []
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while unvisited_nodes:
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node = unvisited_nodes.pop() # node will be the tree parent node (a container), or a hole
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for poly in node.Childs:
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outer_nodes.append(poly)
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for hole in poly.Childs: # type: ignore
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unvisited_nodes.append(hole)
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return outer_nodes
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def label_poly(
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poly: PyPolyNode,
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point_xys: ArrayLike,
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point_names: Sequence[str],
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clipper_scale_factor: int = int(2 ** 24),
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) -> List[str]:
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"""
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Given a `PyPolyNode` (a polygon, possibly with holes) and a sequence of named points,
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return the list of point names contained inside the polygon.
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Args:
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poly: A polygon, possibly with holes. "Islands" inside the holes (and deeper-nested
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structures) are not considered (i.e. only one non-hole contour is considered).
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point_xys: A sequence of point coordinates (Nx2, `[(x0, y0), (x1, y1), ...]`).
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point_names: A sequence of point names (same length N as point_xys)
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clipper_scale_factor: The PyPolyNode structure is from `pyclipper` and likely has
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a scale factor applied in order to use integer arithmetic. Due to precision
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limitations in `poly_contains_points`, it's prefereable to undo this scaling
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rather than asking for similarly-scaled `point_xys` coordinates.
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NOTE: This could be fixed by using `numpy.longdouble` in
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`poly_contains_points`, but the exact length of long-doubles is platform-
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dependent and so probably best avoided.
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Result:
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All the `point_names` which correspond to points inside the polygon (but not in
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its holes).
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"""
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poly_contour = scale_from_clipper(poly.Contour, clipper_scale_factor)
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inside = poly_contains_points(poly_contour, point_xys)
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for hole in poly.Childs:
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hole_contour = scale_from_clipper(hole.Contour, clipper_scale_factor)
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inside &= ~poly_contains_points(hole_contour, point_xys)
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inside_nets = sorted([net_name for net_name, ii in zip(point_names, inside) if ii])
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if inside.any():
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return inside_nets
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else:
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return []
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def find_merge_pairs(
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connectivity: connectivity_t,
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nets: Mapping[NetName, Mapping[layer_t, Sequence[contour_t]]],
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via_polys: Mapping[layer_t, Sequence[contour_t]],
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) -> Set[Tuple[NetName, NetName]]:
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"""
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Given a collection of (possibly anonymous) nets, figure out which pairs of
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nets are shorted through a via (and thus should be merged).
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Args:
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connectivity: A sequence of 3-tuples specifying the electrical connectivity between layers.
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Each 3-tuple looks like `(top_layer, via_layer, bottom_layer)` and indicates that
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`top_layer` and `bottom_layer` are electrically connected at any location where
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shapes are present on all three (top, via, and bottom) layers.
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`via_layer` may be `None`, in which case any overlap between shapes on `top_layer`
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and `bottom_layer` is automatically considered a short (with no third shape necessary).
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nets: A collection of all nets (seqences of polygons in mappings indexed by `NetName`
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and layer). See `NetsInfo.nets`.
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via_polys: A collection of all vias (in a mapping indexed by layer).
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Returns:
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A set containing pairs of `NetName`s for each pair of nets which are shorted.
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"""
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merge_pairs = set()
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for top_layer, via_layer, bot_layer in connectivity:
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if via_layer is not None:
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vias = via_polys[via_layer]
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if not vias:
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logger.warning(f'No vias on layer {via_layer}')
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continue
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for top_name in nets.keys():
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top_polys = nets[top_name][top_layer]
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if not top_polys:
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continue
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for bot_name in nets.keys():
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if bot_name == top_name:
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continue
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name_pair: Tuple[NetName, NetName] = tuple(sorted((top_name, bot_name))) #type: ignore
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if name_pair in merge_pairs:
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continue
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bot_polys = nets[bot_name][bot_layer]
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if not bot_polys:
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continue
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if via_layer is not None:
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via_top = intersection_evenodd(top_polys, vias)
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overlap = intersection_evenodd(via_top, bot_polys)
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else:
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overlap = intersection_evenodd(top_polys, bot_polys) # TODO verify there aren't any suspicious corner cases for this
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if not overlap:
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continue
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merge_pairs.add(name_pair)
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return merge_pairs
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