Draw random medial tire decompositions
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"""Draw medial tire decompositions of random 5-connected triangulations.
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The source graphs come from ``plantri -c5`` in graph6 format. For each sampled
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30-vertex triangulation, this script chooses a random source vertex, builds the
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BFS depth-component tire tree, recognizes every full medial tire graph in the
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decomposition, and draws both the tire tree and the realized full medial tire
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graphs.
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"""
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from __future__ import annotations
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import argparse
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import math
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import os
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import random
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import subprocess
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import sys
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from collections import defaultdict
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from dataclasses import dataclass
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from pathlib import Path
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PAPER_DIR = Path(__file__).resolve().parents[1]
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REPO_ROOT = PAPER_DIR.parents[1]
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os.environ.setdefault(
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"MPLCONFIGDIR", str(PAPER_DIR / "experiments" / ".matplotlib-cache")
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)
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os.environ.setdefault("XDG_CACHE_HOME", str(PAPER_DIR / "experiments" / ".cache"))
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from matplotlib.lines import Line2D
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import networkx as nx
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if str(PAPER_DIR) not in sys.path:
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sys.path.insert(0, str(PAPER_DIR))
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from lib.medial_tire_decomposition import ekey, medial_tire_facemodel, recognise
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from lib.full_medial_tire_generator import FullMedialTireGraph
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@dataclass(frozen=True)
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class TreadNode:
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idx: int
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depth: int
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face_indices: tuple[int, ...]
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annular: frozenset
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up: frozenset
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down: frozenset
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bites: frozenset
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tires: tuple[tuple[FullMedialTireGraph, dict], ...]
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def sample_plantri_graphs(n: int, count: int, seed: int, scan_limit: int) -> list[nx.Graph]:
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cmd = [str(REPO_ROOT / "plantri" / "plantri"), "-g", "-c5", str(n)]
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rng = random.Random(seed)
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sample: list[tuple[int, nx.Graph]] = []
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seen = 0
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with subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as proc:
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assert proc.stdout is not None
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for raw in proc.stdout:
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line = raw.strip()
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if not line or line.startswith(b">>"):
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continue
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graph = nx.from_graph6_bytes(line)
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if nx.node_connectivity(graph) < 5:
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continue
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seen += 1
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if len(sample) < count:
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sample.append((seen, graph))
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else:
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j = rng.randrange(seen)
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if j < count:
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sample[j] = (seen, graph)
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if seen >= scan_limit:
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proc.terminate()
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break
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proc.wait(timeout=10)
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if len(sample) < count:
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raise RuntimeError(f"only found {len(sample)} graphs after scanning {seen}")
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return [graph for _ordinal, graph in sample]
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def triangular_faces(g: nx.Graph):
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ok, emb = nx.check_planarity(g)
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if not ok:
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raise ValueError("not planar")
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seen = set()
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faces = []
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for u, v in list(emb.edges()):
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if (u, v) in seen:
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continue
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face = tuple(emb.traverse_face(u, v, mark_half_edges=seen))
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faces.append(face)
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return faces
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def edge_face_data(faces):
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face_edges = []
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edge_faces: dict[tuple, list[int]] = defaultdict(list)
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for i, face in enumerate(faces):
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edges = {
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ekey(face[0], face[1]),
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ekey(face[1], face[2]),
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ekey(face[2], face[0]),
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}
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face_edges.append(edges)
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for edge in edges:
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edge_faces[edge].append(i)
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return face_edges, edge_faces
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def depth_components(faces, face_edges, edge_faces, levels):
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depths = [min(levels[v] for v in face) for face in faces]
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dual_adj: dict[int, set[int]] = defaultdict(set)
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for incident in edge_faces.values():
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for a in range(len(incident)):
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for b in range(a + 1, len(incident)):
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dual_adj[incident[a]].add(incident[b])
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dual_adj[incident[b]].add(incident[a])
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comps = []
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seen = [False] * len(faces)
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for start in range(len(faces)):
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if seen[start]:
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continue
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depth = depths[start]
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stack = [start]
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comp = []
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seen[start] = True
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while stack:
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face = stack.pop()
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comp.append(face)
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for other in dual_adj[face]:
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if not seen[other] and depths[other] == depth:
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seen[other] = True
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stack.append(other)
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comps.append((depth, tuple(sorted(comp))))
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return comps, depths, dual_adj
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def tread_from_component(faces, levels, face_indices):
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tread_faces = [faces[i] for i in face_indices]
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if not tread_faces:
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return None
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depth = min(min(levels[v] for v in face) for face in tread_faces)
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annular, up, down = set(), set(), set()
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face_of_down = defaultdict(int)
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for face in tread_faces:
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for x, y in ((face[0], face[1]), (face[1], face[2]), (face[2], face[0])):
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e = ekey(x, y)
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lx, ly = levels[x], levels[y]
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if {lx, ly} == {depth, depth + 1}:
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annular.add(e)
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elif lx == ly == depth:
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up.add(e)
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elif lx == ly == depth + 1:
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down.add(e)
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face_of_down[e] += 1
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if len(annular) < 3:
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return None
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return {
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"tread_faces": tread_faces,
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"annular": annular,
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"up": up,
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"down": down,
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"bites": {e for e in down if face_of_down[e] == 2},
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}
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def build_tire_tree(g: nx.Graph, source: int):
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faces = triangular_faces(g)
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face_edges, edge_faces = edge_face_data(faces)
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levels = nx.single_source_shortest_path_length(g, source)
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comps, depths, dual_adj = depth_components(faces, face_edges, edge_faces, levels)
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comp_of_face = {}
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for comp_idx, (_depth, face_indices) in enumerate(comps):
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for face in face_indices:
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comp_of_face[face] = comp_idx
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nodes: list[TreadNode] = []
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comp_to_node = {}
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for comp_idx, (depth, face_indices) in enumerate(comps):
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tread = tread_from_component(faces, levels, face_indices)
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if tread is None or len(tread["up"]) < 3:
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continue
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mt = medial_tire_facemodel(tread["tread_faces"])
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tires = tuple(recognise(mt, tread))
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if not tires:
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continue
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node = TreadNode(
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idx=len(nodes),
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depth=depth,
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face_indices=face_indices,
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annular=frozenset(tread["annular"]),
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up=frozenset(tread["up"]),
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down=frozenset(tread["down"]),
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bites=frozenset(tread["bites"]),
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tires=tires,
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)
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comp_to_node[comp_idx] = node.idx
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nodes.append(node)
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tree_edges = set()
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for comp_idx, (depth, face_indices) in enumerate(comps):
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if comp_idx not in comp_to_node:
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continue
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child = comp_to_node[comp_idx]
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parent_candidates = set()
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for face in face_indices:
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for other in dual_adj[face]:
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other_comp = comp_of_face[other]
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if depths[other] == depth - 1 and other_comp in comp_to_node:
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parent_candidates.add(comp_to_node[other_comp])
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for parent in parent_candidates:
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tree_edges.add((parent, child))
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return faces, levels, nodes, sorted(tree_edges)
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def graph_layout(g: nx.Graph):
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try:
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return nx.planar_layout(g)
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except nx.NetworkXException:
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return nx.spring_layout(g, seed=0)
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def draw_base_graph(ax, g, levels, source):
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pos = graph_layout(g)
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max_level = max(levels.values())
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cmap = plt.get_cmap("viridis", max_level + 1)
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node_colors = [cmap(levels[v]) for v in g.nodes()]
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nx.draw_networkx_edges(g, pos, ax=ax, edge_color="#cbd5e1", width=0.8)
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nx.draw_networkx_nodes(
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g,
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pos,
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ax=ax,
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node_color=node_colors,
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node_size=[150 if v == source else 72 for v in g.nodes()],
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edgecolors=["#dc2626" if v == source else "#111827" for v in g.nodes()],
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linewidths=[1.8 if v == source else 0.45 for v in g.nodes()],
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)
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labels = {v: str(v) for v in g.nodes()}
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nx.draw_networkx_labels(g, pos, labels=labels, ax=ax, font_size=5)
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ax.set_title(f"G, source {source}; vertex levels 0..{max_level}", fontsize=10)
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ax.set_aspect("equal")
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ax.axis("off")
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def tree_positions(nodes: list[TreadNode], tree_edges):
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children: dict[int, list[int]] = defaultdict(list)
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has_parent = set()
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for parent, child in tree_edges:
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children[parent].append(child)
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has_parent.add(child)
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roots = [node.idx for node in nodes if node.idx not in has_parent]
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for child_list in children.values():
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child_list.sort(key=lambda idx: (nodes[idx].depth, idx))
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x_counter = 0
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pos = {}
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def place(idx, depth):
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nonlocal x_counter
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if not children[idx]:
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pos[idx] = (x_counter, -depth)
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x_counter += 1
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return pos[idx][0]
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xs = [place(child, depth + 1) for child in children[idx]]
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x = sum(xs) / len(xs)
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pos[idx] = (x, -depth)
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return x
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for root in sorted(roots, key=lambda idx: (nodes[idx].depth, idx)):
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place(root, 0)
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x_counter += 1
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return pos
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def draw_tire_tree(ax, nodes: list[TreadNode], tree_edges):
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pos = tree_positions(nodes, tree_edges)
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for parent, child in tree_edges:
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x0, y0 = pos[parent]
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x1, y1 = pos[child]
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ax.plot([x0, x1], [y0, y1], color="#374151", lw=1.0, zorder=1)
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for node in nodes:
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x, y = pos[node.idx]
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ax.text(
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x,
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y,
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f"T{node.idx}\nd={node.depth}\n{len(node.tires)} tire(s)",
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ha="center",
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va="center",
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fontsize=8,
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bbox={
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"boxstyle": "round,pad=0.32",
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"facecolor": "#fef3c7",
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"edgecolor": "#111827",
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"linewidth": 0.9,
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},
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zorder=3,
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)
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ax.set_title("Depth-component tire tree", fontsize=10)
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if pos:
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xs = [p[0] for p in pos.values()]
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ys = [p[1] for p in pos.values()]
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ax.set_xlim(min(xs) - 1.0, max(xs) + 1.0)
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ax.set_ylim(min(ys) - 0.7, max(ys) + 0.7)
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ax.axis("off")
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def vertex_xy(k: int, n: int, radius: float) -> tuple[float, float]:
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angle = math.pi / 2 - 2 * math.pi * k / n
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return radius * math.cos(angle), radius * math.sin(angle)
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def edge_midpoint_angle(i: int, n: int) -> float:
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return math.pi / 2 - 2 * math.pi * (i + 0.5) / n
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def draw_full_medial_tire(ax, graph: FullMedialTireGraph, title: str):
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n = graph.n
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ann = [vertex_xy(k, n, 1.0) for k in range(n)]
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matched = graph.bite_edges
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cyc_x = [p[0] for p in ann] + [ann[0][0]]
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cyc_y = [p[1] for p in ann] + [ann[0][1]]
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ax.plot(cyc_x, cyc_y, color="black", lw=1.3, zorder=2)
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for i, tooth in enumerate(graph.tooth_word):
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if tooth == "U":
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r, color = 1.42, "#2563eb"
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elif i not in matched:
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r, color = 0.58, "#dc2626"
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else:
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continue
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ang = edge_midpoint_angle(i, n)
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apex = (r * math.cos(ang), r * math.sin(ang))
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for corner in (ann[i], ann[(i + 1) % n]):
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ax.plot([apex[0], corner[0]], [apex[1], corner[1]], color="#9ca3af", lw=0.5)
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ax.scatter([apex[0]], [apex[1]], s=12, color=color, zorder=3)
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for i, j in sorted(graph.bites):
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corners = [ann[i], ann[(i + 1) % n], ann[j], ann[(j + 1) % n]]
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apex = (0.82 * sum(p[0] for p in corners) / 4, 0.82 * sum(p[1] for p in corners) / 4)
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for corner in corners:
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ax.plot([apex[0], corner[0]], [apex[1], corner[1]], color="#9ca3af", lw=0.5)
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ax.scatter([apex[0]], [apex[1]], s=22, color="#7f1d1d", edgecolors="black", lw=0.4)
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ax.scatter([p[0] for p in ann], [p[1] for p in ann], s=9, color="black", zorder=4)
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bites = ",".join(f"{i}{j}" for i, j in sorted(graph.bites)) or "-"
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ax.set_title(f"{title}\n{graph.tooth_word} b:{bites}", fontsize=5.8, pad=1.5)
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ax.set_xlim(-1.6, 1.6)
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ax.set_ylim(-1.6, 1.6)
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ax.set_aspect("equal")
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ax.axis("off")
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def draw_medial_tire_grid(fig, outer_spec, nodes):
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tires = []
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for node in nodes:
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for comp_idx, (graph, _bij) in enumerate(node.tires):
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||||||
|
tires.append((node.idx, comp_idx, graph))
|
||||||
|
if not tires:
|
||||||
|
ax = fig.add_subplot(outer_spec)
|
||||||
|
ax.text(0.5, 0.5, "No full medial tire graphs recognized", ha="center")
|
||||||
|
ax.axis("off")
|
||||||
|
return
|
||||||
|
cols = min(5, max(1, math.ceil(math.sqrt(len(tires)))))
|
||||||
|
rows = math.ceil(len(tires) / cols)
|
||||||
|
sub = outer_spec.subgridspec(rows, cols, wspace=0.08, hspace=0.35)
|
||||||
|
for i in range(rows * cols):
|
||||||
|
ax = fig.add_subplot(sub[i // cols, i % cols])
|
||||||
|
if i < len(tires):
|
||||||
|
node_idx, comp_idx, graph = tires[i]
|
||||||
|
draw_full_medial_tire(ax, graph, f"T{node_idx}.{comp_idx} n={graph.n}")
|
||||||
|
else:
|
||||||
|
ax.axis("off")
|
||||||
|
|
||||||
|
|
||||||
|
def write_index(path: Path, graph_idx: int, source: int, g: nx.Graph, nodes, tree_edges):
|
||||||
|
lines = [
|
||||||
|
f"# Random medial tire decomposition {graph_idx}",
|
||||||
|
"",
|
||||||
|
f"- vertices: {g.number_of_nodes()}",
|
||||||
|
f"- edges: {g.number_of_edges()}",
|
||||||
|
f"- node connectivity: {nx.node_connectivity(g)}",
|
||||||
|
f"- source vertex: {source}",
|
||||||
|
f"- tire-tree nodes: {len(nodes)}",
|
||||||
|
f"- tire-tree edges: {len(tree_edges)}",
|
||||||
|
"",
|
||||||
|
"| node | depth | faces | annular | up | down | bites | full medial tires |",
|
||||||
|
"|--:|--:|--:|--:|--:|--:|--:|--:|",
|
||||||
|
]
|
||||||
|
for node in nodes:
|
||||||
|
lines.append(
|
||||||
|
f"| T{node.idx} | {node.depth} | {len(node.face_indices)} | "
|
||||||
|
f"{len(node.annular)} | {len(node.up)} | {len(node.down)} | "
|
||||||
|
f"{len(node.bites)} | {len(node.tires)} |"
|
||||||
|
)
|
||||||
|
for comp_idx, (tire, _bij) in enumerate(node.tires):
|
||||||
|
bites = ",".join(f"({i},{j})" for i, j in sorted(tire.bites)) or "-"
|
||||||
|
lines.append(
|
||||||
|
f"| T{node.idx}.{comp_idx} | | | | {len(tire.up_edges)} | "
|
||||||
|
f"{len(tire.down_edges)} | {bites} | `{tire.tooth_word}` |"
|
||||||
|
)
|
||||||
|
path.write_text("\n".join(lines) + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
def draw_case(out_dir: Path, graph_idx: int, g: nx.Graph, source: int):
|
||||||
|
_faces, levels, nodes, tree_edges = build_tire_tree(g, source)
|
||||||
|
fig = plt.figure(figsize=(17, 10))
|
||||||
|
spec = fig.add_gridspec(2, 2, width_ratios=[1.15, 1.0], height_ratios=[1.0, 1.25])
|
||||||
|
ax_graph = fig.add_subplot(spec[0, 0])
|
||||||
|
ax_tree = fig.add_subplot(spec[1, 0])
|
||||||
|
draw_base_graph(ax_graph, g, levels, source)
|
||||||
|
draw_tire_tree(ax_tree, nodes, tree_edges)
|
||||||
|
draw_medial_tire_grid(fig, spec[:, 1], nodes)
|
||||||
|
fig.suptitle(
|
||||||
|
f"Random 5-connected maximal planar graph {graph_idx}: "
|
||||||
|
f"n={g.number_of_nodes()}, source={source}",
|
||||||
|
fontsize=13,
|
||||||
|
)
|
||||||
|
legend = [
|
||||||
|
Line2D([0], [0], marker="o", color="w", label="source",
|
||||||
|
markerfacecolor="#fde68a", markeredgecolor="#dc2626", markersize=8),
|
||||||
|
Line2D([0], [0], color="black", lw=1.3, label="annular cycle A(T)"),
|
||||||
|
Line2D([0], [0], marker="o", color="w", label="up tooth",
|
||||||
|
markerfacecolor="#2563eb", markersize=6),
|
||||||
|
Line2D([0], [0], marker="o", color="w", label="down tooth",
|
||||||
|
markerfacecolor="#dc2626", markersize=6),
|
||||||
|
Line2D([0], [0], marker="o", color="w", label="bite apex",
|
||||||
|
markerfacecolor="#7f1d1d", markeredgecolor="black", markersize=6),
|
||||||
|
]
|
||||||
|
fig.legend(handles=legend, loc="lower center", ncol=5, fontsize=9)
|
||||||
|
fig.subplots_adjust(left=0.03, right=0.99, top=0.92, bottom=0.08, wspace=0.08, hspace=0.16)
|
||||||
|
|
||||||
|
png = out_dir / f"random_c5_n30_medial_tire_decomposition_{graph_idx}.png"
|
||||||
|
pdf = out_dir / f"random_c5_n30_medial_tire_decomposition_{graph_idx}.pdf"
|
||||||
|
fig.savefig(png, dpi=180)
|
||||||
|
fig.savefig(pdf)
|
||||||
|
plt.close(fig)
|
||||||
|
write_index(
|
||||||
|
out_dir / f"random_c5_n30_medial_tire_decomposition_{graph_idx}.md",
|
||||||
|
graph_idx,
|
||||||
|
source,
|
||||||
|
g,
|
||||||
|
nodes,
|
||||||
|
tree_edges,
|
||||||
|
)
|
||||||
|
return png, pdf, len(nodes), sum(len(node.tires) for node in nodes)
|
||||||
|
|
||||||
|
|
||||||
|
def run(args: argparse.Namespace):
|
||||||
|
out_dir = Path(args.out_dir)
|
||||||
|
out_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
graphs = sample_plantri_graphs(args.n, args.count, args.seed, args.scan_limit)
|
||||||
|
rng = random.Random(args.seed + 101)
|
||||||
|
for i, graph in enumerate(graphs, start=1):
|
||||||
|
source = rng.choice(list(graph.nodes()))
|
||||||
|
png, pdf, node_count, tire_count = draw_case(out_dir, i, graph, source)
|
||||||
|
print(
|
||||||
|
f"case {i}: source={source}, connectivity={nx.node_connectivity(graph)}, "
|
||||||
|
f"tire nodes={node_count}, full medial tires={tire_count}"
|
||||||
|
)
|
||||||
|
print(f" wrote {png}")
|
||||||
|
print(f" wrote {pdf}")
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(description=__doc__)
|
||||||
|
parser.add_argument("--n", type=int, default=30)
|
||||||
|
parser.add_argument("--count", type=int, default=2)
|
||||||
|
parser.add_argument("--seed", type=int, default=20260615)
|
||||||
|
parser.add_argument("--scan-limit", type=int, default=500)
|
||||||
|
parser.add_argument(
|
||||||
|
"--out-dir",
|
||||||
|
default=str(PAPER_DIR / "experiments" / "random_medial_tire_decompositions"),
|
||||||
|
)
|
||||||
|
run(parser.parse_args())
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
+18
@@ -0,0 +1,18 @@
|
|||||||
|
# Random medial tire decomposition 1
|
||||||
|
|
||||||
|
- vertices: 30
|
||||||
|
- edges: 84
|
||||||
|
- node connectivity: 5
|
||||||
|
- source vertex: 9
|
||||||
|
- tire-tree nodes: 3
|
||||||
|
- tire-tree edges: 2
|
||||||
|
|
||||||
|
| node | depth | faces | annular | up | down | bites | full medial tires |
|
||||||
|
|--:|--:|--:|--:|--:|--:|--:|--:|
|
||||||
|
| T0 | 1 | 16 | 16 | 6 | 10 | 0 | 1 |
|
||||||
|
| T0.0 | | | | 6 | 10 | - | `DUDUDUDDUDDDUDDU` |
|
||||||
|
| T1 | 2 | 20 | 20 | 10 | 10 | 0 | 1 |
|
||||||
|
| T1.0 | | | | 10 | 10 | - | `UUDUUDUDDUDUDUDUUDDD` |
|
||||||
|
| T2 | 3 | 14 | 13 | 12 | 1 | 1 | 2 |
|
||||||
|
| T2.0 | | | | 6 | 2 | (1,5) | `UDUUUDUU` |
|
||||||
|
| T2.1 | | | | 5 | 0 | - | `UUUUU` |
|
||||||
BIN
Binary file not shown.
BIN
Binary file not shown.
|
After Width: | Height: | Size: 359 KiB |
+17
@@ -0,0 +1,17 @@
|
|||||||
|
# Random medial tire decomposition 2
|
||||||
|
|
||||||
|
- vertices: 30
|
||||||
|
- edges: 84
|
||||||
|
- node connectivity: 5
|
||||||
|
- source vertex: 4
|
||||||
|
- tire-tree nodes: 3
|
||||||
|
- tire-tree edges: 2
|
||||||
|
|
||||||
|
| node | depth | faces | annular | up | down | bites | full medial tires |
|
||||||
|
|--:|--:|--:|--:|--:|--:|--:|--:|
|
||||||
|
| T0 | 1 | 16 | 16 | 7 | 9 | 0 | 1 |
|
||||||
|
| T0.0 | | | | 7 | 9 | - | `DUDUDUDUDUDDUDUD` |
|
||||||
|
| T1 | 2 | 17 | 17 | 9 | 8 | 0 | 1 |
|
||||||
|
| T1.0 | | | | 9 | 8 | - | `UUUDDUDUDUDUDDUUD` |
|
||||||
|
| T2 | 3 | 14 | 14 | 8 | 5 | 1 | 1 |
|
||||||
|
| T2.0 | | | | 8 | 6 | (1,5) | `DDUUUDDUUDUDUU` |
|
||||||
BIN
Binary file not shown.
BIN
Binary file not shown.
|
After Width: | Height: | Size: 338 KiB |
Reference in New Issue
Block a user