didericis
4ceae9c68a
Rename the shared helper module to a number-resistant name. Update all 26 dependent scripts via sed. Add experiments/test_n_21_to_24.py — extends the empirical check beyond |V(G)| ≤ 20 to n_G ∈ [21, 24]. Checks per chord-apex+Kempe colouring: (1) h_φ constant on V(K_b)? (counterexample to Corollary 5.4) (2) h_φ constant on V(K_b) ∪ V(K_c)? (counterexample to Conj 5.1) (3) Deciding face exists? Writes results incrementally to test_n_21_to_24_results.jsonl (one JSON line per triangulation, plus n-level and grand summaries). Emits PROGRESS lines every 10 minutes (default) to stdout for live monitoring. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
257 lines
9.8 KiB
Python
257 lines
9.8 KiB
Python
"""Verify the structural characterization of the 30 residual
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chord-apex+Kempe colourings on which the G'-pentagon fallback
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empirically fails (= |S| = 8 AND all G'-pentagons are hit by S).
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Hypothesis: these are exactly the colourings on triangulations where
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v's cyclic neighbour-degree sequence has the form (5, 7, 7, 5, 5)
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(or cyclic shifts), and the reduction index i is chosen so that the
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two "7"s land on the flank positions (n_i, n_{i+1}) = (7, 7).
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For each of the 30 colourings, report:
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- parent triangulation order n_G,
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- cyclic degree sequence of v's 5 neighbours,
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- reduction index i,
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- p_G and # G'-pentagons hit by S,
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- the actual deciding face's length and type.
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If the hypothesis holds, all 30 are on (5, 7, 7, 5, 5)-type
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configurations.
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Run with: sage experiments/check_30_residual.py
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"""
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import os
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import sys
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import time
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from sage.all import Graph
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from sage.graphs.graph_generators import graphs
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HERE = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, HERE)
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from check_conj_final_scaled import (
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apply_reduction,
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proper_3_edge_colorings,
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matches_chord_apex_kempe,
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kempe_cycle_set,
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edge_idx,
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)
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from check_heawood_on_kempe import dual_of, vertices_of_kempe
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def is_g_prime_pentagon(f, named):
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if len(f) != 5: return False
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fset = {frozenset(e) for e in f}
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return not (named['side_0'] in fset or named['side_1'] in fset
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or named['spike'] in fset or named['merged'] in fset)
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def classify_face(f, named):
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fset = {frozenset(e) for e in f}
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has_s0 = named['side_0'] in fset
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has_s1 = named['side_1'] in fset
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has_sp = named['spike'] in fset
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has_m = named['merged'] in fset
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if has_s0 and has_sp and not has_s1 and not has_m: return 'flank-lower'
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if has_sp and has_s1 and not has_s0 and not has_m: return 'flank-upper'
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if has_s0 and has_s1 and has_m and not has_sp: return 'outer'
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if has_m and not (has_s0 or has_s1 or has_sp): return 'merged'
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if not (has_s0 or has_s1 or has_sp or has_m): return 'G-prime'
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return 'mixed'
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def test_one(D, G_parent):
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D.is_planar(set_embedding=True)
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G_parent.is_planar(set_embedding=True)
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emb_G = G_parent.get_embedding()
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examples = [] # 30 residual colourings
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# We need to map dual face → parent vertex (= the face's "dual vertex")
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# For each dual face we want to identify the parent vertex v.
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# The dual face's vertices correspond to triangular faces of G_parent
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# incident to v.
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# Build a face→parent-vertex map.
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parent_faces = G_parent.faces()
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face_to_dual_vertex_idx = {}
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# The dual vertices are indexed by face number.
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# Mapping: parent_face[fi] is a face, and dual vertex fi corresponds.
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# We need the reverse: given a degree-5 vertex v in G_parent, its
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# corresponding dual face = the face whose vertices are the 5
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# parent_face-indices that contain v.
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v_to_dual_face = {}
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for v in G_parent.vertex_iterator():
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if G_parent.degree(v) != 5: continue
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# Find the 5 parent_face-indices that contain v
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contains = []
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for fi, pf in enumerate(parent_faces):
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for e in pf:
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if v in e:
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contains.append(fi)
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break
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if len(contains) != 5: continue
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v_to_dual_face[v] = contains # 5 dual vertex indices forming F_v boundary
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for face in D.faces():
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if len(face) != 5: continue
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face_verts = [e[0] for e in face]
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# Identify which parent v this face corresponds to
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v_parent = None
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for v, dual_verts in v_to_dual_face.items():
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if set(face_verts) == set(dual_verts):
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v_parent = v
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break
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if v_parent is None: continue
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# Get the cyclic degree sequence of v_parent's neighbours
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nbrs_cyclic = emb_G[v_parent]
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cyc_degs = [G_parent.degree(u) for u in nbrs_cyclic]
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for i_red in range(5):
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res = apply_reduction(D, face, i_red, 9999)
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if res is None: continue
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H = res['H']; named = res['named']
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H.is_planar(set_embedding=True)
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edges, colorings = proper_3_edge_colorings(H)
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cand = [c for c in colorings
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if matches_chord_apex_kempe(edges, c, named)]
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v_n = 9999
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for col in cand:
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# Identify bad sub-case (ii.B)
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target = {named['side_0'], named['spike']}
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lower_flank = None
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for f in H.faces():
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if target.issubset({frozenset(e) for e in f}):
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lower_flank = f; break
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if lower_flank is None or len(lower_flank) != 5: continue
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arc_verts = [e[0] for e in lower_flank]
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if v_n not in arc_verts: continue
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k = arc_verts.index(v_n)
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cyc = arc_verts[k:] + arc_verts[:k]
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A_i = next(iter(named['side_0'] - {v_n}))
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A_ip1 = next(iter(named['spike'] - {v_n}))
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if cyc[1] == A_i and cyc[4] == A_ip1:
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P_1, P_2 = cyc[2], cyc[3]
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elif cyc[1] == A_ip1 and cyc[4] == A_i:
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P_2, P_1 = cyc[2], cyc[3]
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else: continue
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merged_idx = edge_idx(edges, named['merged'])
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c_col = col[merged_idx]
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c_0_col = col[edge_idx(edges, named['side_0'])]
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c_1_col = col[edge_idx(edges, named['side_1'])]
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e_AiP1 = edge_idx(edges, frozenset((A_i, P_1)))
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e_P1P2 = edge_idx(edges, frozenset((P_1, P_2)))
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if e_AiP1 is None or e_P1P2 is None: continue
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if col[e_AiP1] != c_1_col or col[e_P1P2] != c_0_col:
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continue
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a = c_col
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other = [x for x in range(3) if x != a]
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kc_b = kempe_cycle_set(edges, col, merged_idx, (a, other[0]))
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kc_c = kempe_cycle_set(edges, col, merged_idx, (a, other[1]))
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V_b = vertices_of_kempe(edges, kc_b)
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V_c = vertices_of_kempe(edges, kc_c)
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V_union = V_b | V_c
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S = set(H.vertices()) - V_union
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if P_1 in V_union: continue
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if len(S) != 8: continue
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# Check if hit = p_G = 8 (= all G'-pentagons hit)
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p_total = 0
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p_hit = 0
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for f in H.faces():
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if not is_g_prime_pentagon(f, named): continue
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p_total += 1
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verts = {u for (u, v) in f} | {v for (u, v) in f}
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if verts & S: p_hit += 1
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if p_total != 8 or p_hit != 8: continue
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# This is a residual case. Find the actual deciding face.
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deciding_faces = []
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for f in H.faces():
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verts = {u for (u, v) in f} | {v for (u, v) in f}
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if not verts.issubset(V_union): continue
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L = len(f)
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if L % 3 == 0: continue
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deciding_faces.append((classify_face(f, named), L))
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# Compute deg sequence around v_parent (cyclic)
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examples.append({
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'cyc_degs': cyc_degs,
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'i_red': i_red,
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'deciding_faces': deciding_faces,
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'p_total': p_total,
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'p_hit': p_hit,
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'S_size': len(S),
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})
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return examples
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def main(max_n=20, time_budget_per_n=1800):
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print("Verifying the 30 residual chord-apex+Kempe colourings.\n")
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grand_examples = []
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for n in range(12, max_n + 1):
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start = time.time()
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try:
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triangulations = list(graphs.triangulations(n, minimum_degree=5))
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except Exception as ex:
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print(f"n={n}: cannot enumerate ({ex})")
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continue
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n_count = 0
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for tri_idx, G in enumerate(triangulations):
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if time.time() - start > time_budget_per_n:
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print(f" n={n}: timeout at tri {tri_idx}")
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break
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G.is_planar(set_embedding=True)
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D = dual_of(G)
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exs = test_one(D, G)
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for ex in exs:
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ex['n_G'] = n
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ex['tri_idx'] = tri_idx
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n_count += len(exs)
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grand_examples.extend(exs)
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elapsed = time.time() - start
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print(f"n={n}: {n_count} residual colourings [{elapsed:.0f}s]")
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sys.stdout.flush()
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print()
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print("=" * 70)
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print(f"Total residual colourings: {len(grand_examples)}")
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# Count by cyclic deg sequence (canonical = min rotation)
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def canon(t):
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t = tuple(t)
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best = t
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n = len(t)
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for r in range(1, n):
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rot = t[r:] + t[:r]
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if rot < best: best = rot
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# Also reflections
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rev = t[::-1]
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for r in range(n):
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rot = rev[r:] + rev[:r]
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if rot < best: best = rot
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return best
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canon_dist = {}
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for ex in grand_examples:
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key = canon(ex['cyc_degs'])
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canon_dist[key] = canon_dist.get(key, 0) + 1
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print("\nCyclic deg sequence distribution (canonical = lex-min "
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"over rotations + reflections):")
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for k in sorted(canon_dist, key=lambda x: -canon_dist[x]):
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c = canon_dist[k]
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print(f" {k}: {c}")
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# Decision face distribution
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df_dist = {}
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for ex in grand_examples:
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for (t, L) in ex['deciding_faces']:
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df_dist[(t, L)] = df_dist.get((t, L), 0) + 1
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print("\nDeciding face (type, length) distribution:")
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for k in sorted(df_dist, key=lambda x: -df_dist[x]):
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t, L = k
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print(f" {t}, |f|={L}: {df_dist[k]}")
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# Print first 5 examples
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print("\nFirst 5 examples:")
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for ex in grand_examples[:5]:
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print(f" n={ex['n_G']}, tri#{ex['tri_idx']}, i={ex['i_red']}, "
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f"cyc_degs={ex['cyc_degs']}, "
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f"deciding={ex['deciding_faces']}")
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if __name__ == '__main__':
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main()
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