settled-reach/tooling/tune-star-map-topology.py

#!/usr/bin/env python3
"""
Star Map Topology Tuner — The Settled Reach
Starting from the current map (300 nodes, 347 edges):

Current vs Target:
  dead_end      12.7% (38)   → 20%  (60)   +22
  spur_end      13.0% (39)   → 15%  (45)   +6
  through_route 29.3% (88)   → 25%  (75)   -13
  loop_member   17.7% (53)   → 15%  (45)   -8
  junction      23.3% (70)   → 18%  (54)   -16
  hub            4.0% (12)   →  7%  (21)   +9

Strategy:
  Pass A — Promote junctions→hubs: add edges to degree-4 junctions in core/inner
            to push them to degree 5+. Target: +9 hubs.
  Pass B — Increase dead_ends: remove non-bridge edges from degree-2+ nodes
            in deep_frontier/outer to reduce their degree to 1. Target: +22 dead_ends.
  Pass C — Reduce loop_members and through_routes: remove non-bridge edges
            from loop_member and through_route nodes in frontier sectors.
  Pass D — Final cleanup: reduce remaining junctions by stripping edges.

Constraints:
  - Graph must remain fully connected (checked after each pass)
  - S-001 (Gateway): degree=4 locked, gate_topology="hub", aperture_count=5
  - Max 8 apertures per node (D-095)
  - Targets are guidelines (±2-3%), not hard constraints

Standard library only. No external dependencies.
"""

from __future__ import annotations

import json
from collections import defaultdict, deque
from pathlib import Path
from typing import Optional

REPO_ROOT   = Path(__file__).parent.parent
INPUT_JSON  = REPO_ROOT / "docs" / "design" / "star-map.json"
OUTPUT_JSON = INPUT_JSON

TARGETS = {
    "dead_end":      0.20,
    "spur_end":      0.15,
    "through_route": 0.25,
    "loop_member":   0.15,
    "junction":      0.18,
    "hub":           0.07,
}

GATEWAY_ID = "S-001"


# ── Graph helpers ──────────────────────────────────────────────────────────────

def build_adjacency(edges: list) -> dict[str, set[str]]:
    adj: dict[str, set[str]] = defaultdict(set)
    for e in edges:
        a, b = e[0], e[1]
        adj[a].add(b)
        adj[b].add(a)
    return adj


def is_connected(all_nodes: list[str], adj: dict[str, set[str]]) -> bool:
    if not all_nodes:
        return True
    start = all_nodes[0]
    visited = {start}
    queue = deque([start])
    while queue:
        cur = queue.popleft()
        for nb in adj.get(cur, set()):
            if nb not in visited:
                visited.add(nb)
                queue.append(nb)
    return len(visited) == len(all_nodes)


def find_bridges(all_nodes: list[str], adj: dict[str, set[str]]) -> set[frozenset]:
    """Iterative Tarjan bridge-finding. Returns set of frozenset({u, v})."""
    n = len(all_nodes)
    idx_map = {v: i for i, v in enumerate(all_nodes)}
    disc = [-1] * n
    low  = [-1] * n
    timer = [0]
    bridges: set[frozenset] = set()

    for start_node in all_nodes:
        si = idx_map[start_node]
        if disc[si] != -1:
            continue

        nbrs_start = sorted(adj.get(start_node, set()))
        stack: list[tuple[str, Optional[str], list[str], int]] = [
            (start_node, None, nbrs_start, 0)
        ]
        disc[si] = low[si] = timer[0]
        timer[0] += 1

        while stack:
            node, par, nbr_list, ni = stack[-1]
            node_i = idx_map[node]

            if ni < len(nbr_list):
                nb = nbr_list[ni]
                stack[-1] = (node, par, nbr_list, ni + 1)
                nb_i = idx_map[nb]
                if disc[nb_i] == -1:
                    disc[nb_i] = low[nb_i] = timer[0]
                    timer[0] += 1
                    nb_nbrs = sorted(adj.get(nb, set()))
                    stack.append((nb, node, nb_nbrs, 0))
                elif nb != par:
                    low[node_i] = min(low[node_i], disc[nb_i])
            else:
                stack.pop()
                if par is not None:
                    par_i = idx_map[par]
                    low[par_i] = min(low[par_i], low[node_i])
                    if low[node_i] > disc[par_i]:
                        bridges.add(frozenset([par, node]))

    return bridges


def find_cycle_members(all_nodes: list[str], adj: dict[str, set[str]]) -> set[str]:
    bridge_set = find_bridges(all_nodes, adj)
    on_cycle: set[str] = set()
    for node in all_nodes:
        neighbors = adj.get(node, set())
        if len(neighbors) < 2:
            continue
        non_bridge_count = sum(
            1 for nb in neighbors
            if frozenset([node, nb]) not in bridge_set
        )
        if non_bridge_count > 0:
            on_cycle.add(node)
    return on_cycle


def classify_node(
    node_id: str,
    adj: dict[str, set[str]],
    cycle_members: set[str],
    is_gateway: bool,
) -> str:
    if is_gateway:
        return "hub"
    deg = len(adj.get(node_id, set()))
    if deg <= 1:
        return "dead_end"
    if deg == 2:
        if node_id in cycle_members:
            return "loop_member"
        neighbors = list(adj[node_id])
        nb_degs = [len(adj.get(nb, set())) for nb in neighbors]
        if any(d >= 3 for d in nb_degs):
            return "spur_end"
        return "through_route"
    if deg <= 4:
        return "junction"
    return "hub"


def reclassify_all(nodes: list[dict], adj: dict[str, set[str]]) -> None:
    all_ids = [n["system_id"] for n in nodes]
    cycle_members = find_cycle_members(all_ids, adj)
    for n in nodes:
        nid = n["system_id"]
        is_gw = n.get("is_gateway", False)
        topo = classify_node(nid, adj, cycle_members, is_gw)
        n["gate_topology"] = topo
        deg = len(adj.get(nid, set()))
        n["gate_connections"] = deg
        if is_gw:
            n["aperture_count"] = max(5, deg)  # +1 for dormant Sol aperture
        else:
            n["aperture_count"] = deg


def count_topology(nodes: list[dict], adj: dict[str, set[str]]) -> dict[str, int]:
    all_ids = [n["system_id"] for n in nodes]
    cycle_members = find_cycle_members(all_ids, adj)
    counts: dict[str, int] = defaultdict(int)
    for n in nodes:
        t = classify_node(n["system_id"], adj, cycle_members, n.get("is_gateway", False))
        counts[t] += 1
    return dict(counts)


def remove_edge(a: str, b: str, adj: dict[str, set[str]], edges: list) -> None:
    adj[a].discard(b)
    adj[b].discard(a)
    edges[:] = [
        e for e in edges
        if not ((e[0] == a and e[1] == b) or (e[0] == b and e[1] == a))
    ]


def add_edge(a: str, b: str, adj: dict[str, set[str]], edges: list) -> None:
    adj[a].add(b)
    adj[b].add(a)
    edges.append([a, b])


def print_distribution(counts: dict[str, int], total: int, label: str) -> None:
    print(f"\n  {label}:")
    order = ["dead_end", "spur_end", "through_route", "loop_member", "junction", "hub"]
    for t in order:
        c = counts.get(t, 0)
        pct = c / total * 100
        tgt = TARGETS.get(t, 0) * 100
        delta = pct - tgt
        flag = " <<<" if abs(delta) > 4 else ""
        print(f"    {t:<14}  {c:>4} ({pct:5.1f}%)  target={tgt:.0f}%  delta={delta:+.1f}%{flag}")


# ── Pass A: Promote junctions to hubs by adding edges ─────────────────────────

def promote_junctions_to_hubs(
    nodes: list[dict],
    edges: list,
    adj: dict[str, set[str]],
    target_hub_count: int,
) -> int:
    """
    Find degree-4 junctions in core/north_reach/east_reach/south_reach/west_reach
    inner bands, and add edges to push them to degree 5 (hub).

    Strategy: for each candidate hub-target, find another degree-3 or degree-4
    node in the same sector that is NOT already connected to it, with BFS
    distance 2-6. Prefer nodes whose degree would become 4 (stay junction)
    rather than going to 5 themselves.

    Returns number of hub promotions achieved.
    """
    node_map = {n["system_id"]: n for n in nodes}
    all_ids = [n["system_id"] for n in nodes]
    edges_set = {frozenset(e[:2]) for e in edges}

    # Count current hubs
    cycle_members = find_cycle_members(all_ids, adj)
    current_hubs = sum(
        1 for n in nodes
        if classify_node(n["system_id"], adj, cycle_members, n.get("is_gateway", False)) == "hub"
    )
    hubs_needed = target_hub_count - current_hubs
    if hubs_needed <= 0:
        print(f"  Pass A: already at {current_hubs} hubs, target={target_hub_count}. Skipping.")
        return 0

    print(f"  Pass A: promoting junctions to hubs. Need {hubs_needed} more hubs.")
    promoted = 0

    # Priority sectors for hub promotion
    priority_sectors = {"core", "north_reach", "east_reach", "south_reach", "west_reach"}
    # Priority bands: inner > mid > outer
    band_rank = {"inner": 3, "core": 3, "mid": 2, "outer": 1}

    # Find all degree-4 junctions in priority sectors (potential hub candidates)
    candidates = []
    for n in nodes:
        nid = n["system_id"]
        if nid == GATEWAY_ID:
            continue
        deg = len(adj.get(nid, set()))
        if deg != 4:
            continue
        sector = n.get("geographic_sector", "")
        if sector not in priority_sectors:
            continue
        band = n.get("geographic_band", "")
        br = band_rank.get(band, 0)
        candidates.append((nid, sector, br, deg))

    # Sort by band rank desc (prefer inner/core band nodes)
    candidates.sort(key=lambda x: -x[2])

    for (cand_id, cand_sector, cand_br, _) in candidates:
        if promoted >= hubs_needed:
            break

        # Find a suitable neighbor to connect to
        # Must be: same sector, not already connected, degree <= 4 (won't become hub itself),
        # not gateway, BFS distance 2-6
        current_deg = len(adj.get(cand_id, set()))
        if current_deg >= 5:
            # Already became a hub from a previous promotion
            continue

        best_partner = None
        best_partner_score = -999

        for n2 in nodes:
            n2id = n2["system_id"]
            if n2id == GATEWAY_ID or n2id == cand_id:
                continue
            if frozenset([cand_id, n2id]) in edges_set:
                continue
            deg2 = len(adj.get(n2id, set()))
            # Don't connect if it would push n2 over 8 apertures
            if deg2 >= 8:
                continue
            # Prefer same sector, allow adjacent sectors
            sector2 = n2.get("geographic_sector", "")
            if sector2 != cand_sector:
                continue
            band2 = n2.get("geographic_band", "")

            # Prefer connecting to a junction (deg 3-4) so it stays junction
            # rather than becoming a hub itself
            if deg2 >= 5:
                continue  # skip — would just inflate an existing hub

            # BFS distance check — avoid trivially short connections
            # (we don't want to make a multi-edge or a triangle that kills topology)
            # We do a lightweight BFS for this check
            bfs_dist = _bfs_dist(adj, cand_id, n2id)
            if bfs_dist < 2 or bfs_dist > 8:
                continue

            # Score: prefer closer BFS distance (but not adjacent), prefer same band
            br2 = band_rank.get(band2, 0)
            score = br2 * 10 - abs(bfs_dist - 3)
            if score > best_partner_score:
                best_partner_score = score
                best_partner = n2id

        if best_partner is None:
            print(f"    {cand_id}: no suitable partner found, skipping")
            continue

        # Add the edge
        add_edge(cand_id, best_partner, adj, edges)
        edges_set.add(frozenset([cand_id, best_partner]))
        promoted += 1
        new_deg = len(adj.get(cand_id, set()))
        new_deg2 = len(adj.get(best_partner, set()))
        print(f"    +edge {cand_id}(deg {new_deg}) <-> {best_partner}(deg {new_deg2})")

    assert is_connected(all_ids, adj), "ERROR: Disconnected after Pass A!"
    return promoted


def _bfs_dist(adj: dict[str, set[str]], start: str, end: str) -> int:
    if start == end:
        return 0
    visited = {start}
    queue = deque([(start, 0)])
    while queue:
        node, dist = queue.popleft()
        if dist >= 9:
            return 9999
        for nb in adj.get(node, set()):
            if nb == end:
                return dist + 1
            if nb not in visited:
                visited.add(nb)
                queue.append((nb, dist + 1))
    return 9999


# ── Pass B: Create dead_ends by removing edges ────────────────────────────────

def create_dead_ends(
    nodes: list[dict],
    edges: list,
    adj: dict[str, set[str]],
    target_dead_end_count: int,
    all_ids: list[str],
) -> int:
    """
    Remove edges to increase dead_end count toward target.

    For each edge removal, we look for a degree-2 node in deep_frontier/outer
    where one of its incident edges is NOT a bridge AND removing it leaves
    that node at degree 1 (dead_end). We do this carefully:
      - The node being demoted: must have degree 2 now (after removal: degree 1 = dead_end)
      - The removed edge must NOT be a bridge (so graph stays connected)
        OR: it IS a bridge but the other endpoint has degree >= 3 so removing it
        just splits off a dead_end leaf, which is fine (the leaf stays connected
        to the rest via its remaining edge... wait, no — a bridge removal always
        disconnects). So we must only remove non-bridges OR handle the special
        case where the node being made dead_end has degree 2 and removing ONE
        edge keeps the remaining edge still connecting it to the graph.

    Actually: for a degree-2 node, BOTH its edges are bridges (removing either
    disconnects the portion of the graph accessible only through that node's
    chain). So we need a different approach:

    For a degree-3 node: it has 3 edges. If at least one is NOT a bridge,
    we can remove it — the node drops to degree 2 (becomes spur_end/through_route).
    That's not a dead_end directly.

    Better approach: find degree-2 nodes (spur_ends or through_routes) where
    one edge IS technically "safe" to remove — meaning the other end of that
    edge has degree >= 3, so after removal the graph remains connected
    (the degree-2 node becomes a dead_end hanging off its one remaining neighbor,
    which still has degree >= 2 connecting it to the rest of the graph).

    Wait — if (A-B-C) and B has degree 2, edge A-B and B-C both connect B.
    Removing A-B: B becomes dead_end (degree 1, connected via B-C). A drops by 1.
    The graph remains connected AS LONG AS A is still connected to the rest.
    A is connected to the rest via its other edges (since A had degree >= 2 and
    we only removed one edge — A must have degree >= 2 so after removal A has
    degree >= 1). If A had degree exactly 2, it now has degree 1 — and A is now
    a dead_end too! That might be acceptable, but let's prefer A has degree >= 3
    so A stays as junction/hub.

    So the rule: pick a node B with degree 2. Find which of B's two neighbors
    (call it A) has degree >= 3. Remove edge A-B. B becomes dead_end, A stays
    junction/hub. Graph remains connected.

    This always works and never disconnects the graph.

    Returns: number of dead_ends created.
    """
    node_map = {n["system_id"]: n for n in nodes}

    # Priority sectors: deep_frontier, then outer band
    def node_priority(nid: str) -> int:
        n = node_map.get(nid, {})
        sector = n.get("geographic_sector", "")
        band = n.get("geographic_band", "")
        if sector == "deep_frontier":
            return 3
        if band == "outer":
            return 2
        if band == "mid":
            return 1
        return 0

    created = 0
    # Count current dead_ends
    cycle_members = find_cycle_members(all_ids, adj)
    current_de = sum(
        1 for n in nodes
        if classify_node(n["system_id"], adj, cycle_members, n.get("is_gateway", False)) == "dead_end"
    )
    needed = target_dead_end_count - current_de
    if needed <= 0:
        print(f"  Pass B: already at {current_de} dead_ends, target={target_dead_end_count}. Skipping.")
        return 0

    print(f"  Pass B: creating dead_ends. Need {needed} more.")

    max_iterations = needed * 8  # safety limit
    iteration = 0
    skipped = set()  # edges that would disconnect the graph

    while created < needed and iteration < max_iterations:
        iteration += 1

        # Find degree-2 nodes (not gateway) with at least one neighbor of degree >= 3
        best_node = None
        best_removable_neighbor = None
        best_score = -1

        for n in nodes:
            nid = n["system_id"]
            if nid == GATEWAY_ID:
                continue
            deg = len(adj.get(nid, set()))
            if deg != 2:
                continue

            # Check neighbors — find one with deg >= 3 to remove edge toward
            neighbors = list(adj[nid])
            # The node being demoted (nid) will keep its OTHER neighbor as its
            # sole connection. That other neighbor must have degree >= 2 (i.e.,
            # it has at least one other connection besides nid) so the remaining
            # B-C subgraph stays reachable from the rest of the graph.
            for nb in neighbors:
                nb_deg = len(adj.get(nb, set()))
                if nb_deg < 3 or nb == GATEWAY_ID:
                    continue
                if (nid, nb) in skipped or (nb, nid) in skipped:
                    continue
                # The remaining neighbor (the one we do NOT remove) must have deg >= 2
                other_neighbors = [x for x in neighbors if x != nb]
                if not other_neighbors:
                    continue
                other_nb = other_neighbors[0]
                other_deg = len(adj.get(other_nb, set()))
                if other_deg < 2:
                    # other_nb is already a dead_end; removing nb-nid would leave
                    # nid and other_nb disconnected from the rest of the graph.
                    continue
                # Safe: remove edge nid-nb. nid becomes dead_end, graph stays connected.
                score = node_priority(nid) * 10 + node_priority(nb) + nb_deg
                if score > best_score:
                    best_score = score
                    best_node = nid
                    best_removable_neighbor = nb

        if best_node is None:
            print(f"    No more suitable degree-2 nodes found after {created} dead_ends created.")
            break

        remove_edge(best_node, best_removable_neighbor, adj, edges)

        # Check connectivity after every removal — rollback if disconnected
        if not is_connected(all_ids, adj):
            # Rollback
            adj[best_node].add(best_removable_neighbor)
            adj[best_removable_neighbor].add(best_node)
            edges.append([best_node, best_removable_neighbor])
            skipped.add((best_node, best_removable_neighbor))
            continue

        created += 1
        new_deg = len(adj.get(best_node, set()))
        print(f"    -edge {best_node}(now deg {new_deg}) <- {best_removable_neighbor}")
    return created


# ── Pass C: Reduce loop_members and through_routes ────────────────────────────

def reduce_degree2_excess(
    nodes: list[dict],
    edges: list,
    adj: dict[str, set[str]],
    all_ids: list[str],
    target_lm: int,
    target_tr: int,
) -> int:
    """
    Reduce loop_member and through_route counts toward targets.

    Method: find loop_member or through_route (degree-2) nodes in frontier/outer
    sectors where one neighbor has degree >= 3. Remove the edge to that
    high-degree neighbor, converting the degree-2 node to a dead_end.

    This simultaneously reduces loop_member/through_route AND increases dead_end.
    We stop when both LM and TR are within tolerance of targets, or we've used
    our budget.

    Note: we may already be creating dead_ends in Pass B, so this pass focuses
    on reducing excess degree-2 nodes that are loop_members or through_routes.
    """
    node_map = {n["system_id"]: n for n in nodes}
    total = len(nodes)
    tolerance = int(total * 0.03)

    def node_priority(nid: str) -> int:
        n = node_map.get(nid, {})
        sector = n.get("geographic_sector", "")
        band = n.get("geographic_band", "")
        if sector == "deep_frontier":
            return 3
        if band == "outer":
            return 2
        if band == "mid":
            return 1
        return 0

    removed = 0
    max_budget = 50  # enough budget for up to 50 reductions

    for _ in range(max_budget):
        cycle_members = find_cycle_members(all_ids, adj)
        counts = {
            t: sum(1 for n in nodes if classify_node(
                n["system_id"], adj, cycle_members, n.get("is_gateway", False)
            ) == t)
            for t in ["loop_member", "through_route", "dead_end"]
        }
        lm_now = counts["loop_member"]
        tr_now = counts["through_route"]

        lm_ok = lm_now <= target_lm + tolerance
        tr_ok = tr_now <= target_tr + tolerance

        if lm_ok and tr_ok:
            print(f"    Pass C done: LM={lm_now} TR={tr_now} within tolerance.")
            break

        # Find a good candidate to demote
        best_node = None
        best_nb = None
        best_score = -1

        for n in nodes:
            nid = n["system_id"]
            if nid == GATEWAY_ID:
                continue
            deg = len(adj.get(nid, set()))
            if deg != 2:
                continue

            # Is this node a loop_member or through_route?
            topo = classify_node(nid, adj, cycle_members, n.get("is_gateway", False))
            if topo not in ("loop_member", "through_route"):
                continue

            # Check if we still need to reduce this type
            if topo == "loop_member" and lm_ok:
                continue
            if topo == "through_route" and tr_ok:
                continue

            # Find a neighbor with degree >= 3, AND the OTHER neighbor still
            # has degree >= 2 after removal (so nid-other stays connected to graph)
            neighbors_list = list(adj[nid])
            for nb in neighbors_list:
                nb_deg = len(adj.get(nb, set()))
                if nb_deg < 3 or nb == GATEWAY_ID:
                    continue
                others = [x for x in neighbors_list if x != nb]
                if not others:
                    continue
                other_deg = len(adj.get(others[0], set()))
                if other_deg < 2:
                    continue  # would strand nid with only a dead_end neighbor
                score = node_priority(nid) * 10 + nb_deg
                if score > best_score:
                    best_score = score
                    best_node = nid
                    best_nb = nb

        if best_node is None:
            print(f"    Pass C: no more candidates (LM={lm_now}, TR={tr_now}).")
            break

        remove_edge(best_node, best_nb, adj, edges)

        if not is_connected(all_ids, adj):
            # Rollback
            adj[best_node].add(best_nb)
            adj[best_nb].add(best_node)
            edges.append([best_node, best_nb])
            continue

        removed += 1
        print(f"    -edge {best_node} <- {best_nb}")
    return removed


# ── Pass D: Reduce junction count ─────────────────────────────────────────────

def reduce_junctions(
    nodes: list[dict],
    edges: list,
    adj: dict[str, set[str]],
    all_ids: list[str],
    target_junction_count: int,
) -> int:
    """
    Reduce junction count by removing edges from degree-3 junctions in
    frontier/outer sectors, demoting them to degree-2 (spur_end/through_route).

    Only removes non-bridge edges (to preserve connectivity).
    """
    node_map = {n["system_id"]: n for n in nodes}
    total = len(nodes)
    tolerance = int(total * 0.03)

    def node_priority(nid: str) -> int:
        n = node_map.get(nid, {})
        sector = n.get("geographic_sector", "")
        band = n.get("geographic_band", "")
        if sector == "deep_frontier":
            return 3
        if band == "outer":
            return 2
        if band == "mid":
            return 1
        return 0

    removed = 0
    max_budget = 50

    for _ in range(max_budget):
        cycle_members = find_cycle_members(all_ids, adj)
        counts = {
            t: sum(1 for n in nodes if classify_node(
                n["system_id"], adj, cycle_members, n.get("is_gateway", False)
            ) == t)
            for t in ["junction"]
        }
        j_now = counts["junction"]

        if j_now <= target_junction_count + tolerance:
            print(f"    Pass D done: junction={j_now} within tolerance (target={target_junction_count}).")
            break

        # Find non-bridge edges adjacent to degree-3 junctions in frontier/outer
        bridge_set = find_bridges(all_ids, adj)

        best_a = None
        best_b = None
        best_score = -1

        for e in edges:
            a, b = e[0], e[1]
            if frozenset([a, b]) in bridge_set:
                continue
            if a == GATEWAY_ID or b == GATEWAY_ID:
                continue

            da = len(adj.get(a, set()))
            db = len(adj.get(b, set()))

            # At least one endpoint should be a degree-3 junction in frontier/outer
            pa = node_priority(a)
            pb = node_priority(b)

            # Only remove if at least one is degree 3 (demotes to 2) in a priority area
            # and the other won't drop below 2 (we don't want to accidentally make more dead_ends)
            if da == 3 and pa >= 1 and db >= 3:
                score = pa * 10 + pb + db  # prefer higher priority, higher degree on the other end
                if score > best_score:
                    best_score = score
                    best_a, best_b = a, b
            elif db == 3 and pb >= 1 and da >= 3:
                score = pb * 10 + pa + da
                if score > best_score:
                    best_score = score
                    best_a, best_b = a, b

        if best_a is None:
            print(f"    Pass D: no suitable non-bridge degree-3 edges found (junction={j_now}).")
            break

        remove_edge(best_a, best_b, adj, edges)

        if not is_connected(all_ids, adj):
            adj[best_a].add(best_b)
            adj[best_b].add(best_a)
            edges.append([best_a, best_b])
            continue

        removed += 1
        da_new = len(adj.get(best_a, set()))
        db_new = len(adj.get(best_b, set()))
        print(f"    -edge {best_a}(now {da_new}) <-> {best_b}(now {db_new})")
    return removed


# ── Main ───────────────────────────────────────────────────────────────────────

def main() -> None:
    print(f"Reading {INPUT_JSON} ...")
    with open(INPUT_JSON) as f:
        data = json.load(f)

    nodes: list[dict] = data["nodes"]
    edges: list       = data["edges"]
    total = len(nodes)
    print(f"  {total} nodes, {len(edges)} edges")

    sample = edges[0]
    assert isinstance(sample, list) and len(sample) == 2, \
        f"Unexpected edge format: {sample!r}"

    adj = build_adjacency(edges)
    all_ids = [n["system_id"] for n in nodes]

    # Verify gateway constraints
    gw_deg = len(adj.get(GATEWAY_ID, set()))
    print(f"  Gateway {GATEWAY_ID}: degree={gw_deg}")
    assert gw_deg == 4, f"Gateway degree should be 4, got {gw_deg}"

    assert is_connected(all_ids, adj), "ERROR: Input graph is not connected!"
    print("  Connectivity: OK")

    before_counts = count_topology(nodes, adj)
    print_distribution(before_counts, total, "BEFORE")

    # Targets (rounded)
    target_hubs     = int(TARGETS["hub"]           * total)   # 21
    target_dead     = int(TARGETS["dead_end"]      * total)   # 60
    target_lm       = int(TARGETS["loop_member"]   * total)   # 45
    target_tr       = int(TARGETS["through_route"] * total)   # 75
    target_junction = int(TARGETS["junction"]      * total)   # 54

    print(f"\n  Targets: hubs={target_hubs} dead_ends={target_dead} "
          f"loop_member={target_lm} through_route={target_tr} junction={target_junction}")

    # ── Pass A: Promote junctions to hubs ────────────────────────────────────
    print("\n--- Pass A: Promote junctions to hubs ---")
    a_promoted = promote_junctions_to_hubs(nodes, edges, adj, target_hubs)
    reclassify_all(nodes, adj)
    after_a = count_topology(nodes, adj)
    print_distribution(after_a, total, "After Pass A")
    print(f"  Edges: {len(edges)}")

    # ── Pass B: Create dead_ends ──────────────────────────────────────────────
    print("\n--- Pass B: Create dead_ends ---")
    b_created = create_dead_ends(nodes, edges, adj, target_dead, all_ids)
    reclassify_all(nodes, adj)
    after_b = count_topology(nodes, adj)
    print_distribution(after_b, total, "After Pass B")
    print(f"  Edges: {len(edges)}")

    # ── Pass C: Reduce loop_member and through_route excess ───────────────────
    print("\n--- Pass C: Reduce loop_member / through_route excess ---")
    c_removed = reduce_degree2_excess(nodes, edges, adj, all_ids, target_lm, target_tr)
    reclassify_all(nodes, adj)
    after_c = count_topology(nodes, adj)
    print_distribution(after_c, total, "After Pass C")
    print(f"  Edges: {len(edges)}  (Pass C removed {c_removed})")

    # ── Pass D: Reduce junction count ─────────────────────────────────────────
    print("\n--- Pass D: Reduce junctions ---")
    d_removed = reduce_junctions(nodes, edges, adj, all_ids, target_junction)
    reclassify_all(nodes, adj)
    after_d = count_topology(nodes, adj)
    print_distribution(after_d, total, "After Pass D")
    print(f"  Edges: {len(edges)}  (Pass D removed {d_removed})")

    # ── Pass E: Second dead_end pass if still short ────────────────────────────
    de_now = count_topology(nodes, adj).get("dead_end", 0)
    tolerance = int(total * 0.03)  # 3% = 9 systems
    if de_now < target_dead - tolerance:
        print(f"\n--- Pass E: Second dead_end pass (have {de_now}, need {target_dead}) ---")
        e_created = create_dead_ends(nodes, edges, adj, target_dead, all_ids)
        reclassify_all(nodes, adj)
        after_e = count_topology(nodes, adj)
        print_distribution(after_e, total, "After Pass E")
        print(f"  Edges: {len(edges)}  (Pass E created {e_created} more dead_ends)")

    # ── Pass F: Second junction reduction pass if still high ───────────────────
    j_now = count_topology(nodes, adj).get("junction", 0)
    if j_now > target_junction + tolerance:
        print(f"\n--- Pass F: Second junction reduction (have {j_now}, target {target_junction}) ---")
        f_removed = reduce_junctions(nodes, edges, adj, all_ids, target_junction)
        reclassify_all(nodes, adj)
        after_f = count_topology(nodes, adj)
        print_distribution(after_f, total, "After Pass F")
        print(f"  Edges: {len(edges)}  (Pass F removed {f_removed})")

    # ── Final reclassify and aperture update ──────────────────────────────────
    reclassify_all(nodes, adj)

    # ── Verify gateway constraints ────────────────────────────────────────────
    gw_node = next(n for n in nodes if n["system_id"] == GATEWAY_ID)
    assert gw_node["gate_topology"] == "hub", "Gateway topology changed!"
    assert gw_node["aperture_count"] == 5, f"Gateway aperture_count={gw_node['aperture_count']} (should be 5)"
    assert len(adj[GATEWAY_ID]) == 4, f"Gateway degree={len(adj[GATEWAY_ID])} (should be 4)"
    print(f"\n  Gateway constraint check: OK (degree=4, aperture=5, topology=hub)")

    # ── Verify no aperture violations ────────────────────────────────────────
    violations = [
        n for n in nodes
        if n.get("aperture_count", 0) > 8
    ]
    if violations:
        print(f"\n  WARNING: {len(violations)} nodes exceed 8 apertures!")
        for v in violations:
            print(f"    {v['system_id']}: aperture={v['aperture_count']}")
    else:
        print("  Aperture max-8 constraint: OK")

    # ── Update metadata ───────────────────────────────────────────────────────
    data["_meta"]["edge_count"] = len(edges)
    data["_meta"]["tuned"] = "2026-03-13"
    data["_meta"]["tune_note"] = (
        "Topology tuning: promoted junctions to hubs, created dead_ends, "
        "reduced loop_member/through_route/junction excess. Reclassified all nodes."
    )

    # ── Write output ──────────────────────────────────────────────────────────
    print(f"\n  Writing {OUTPUT_JSON} ...")
    with open(OUTPUT_JSON, "w") as f:
        json.dump(data, f, indent=2)
    print("  Written.")

    # ── Final summary ─────────────────────────────────────────────────────────
    print("\n" + "=" * 60)
    print("FINAL TOPOLOGY SUMMARY")
    print("=" * 60)
    final = count_topology(nodes, adj)
    print_distribution(final, total, "Final distribution")
    print(f"\n  Nodes: {total}   Edges: {len(edges)}")

    print("\n  Breakdown by sector:")
    st: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
    for n in nodes:
        st[n.get("geographic_sector", "?")][n["gate_topology"]] += 1
    for sec in sorted(st.keys()):
        sd = st[sec]
        n_in = sum(sd.values())
        parts = "  ".join(f"{t}={c}" for t, c in sorted(sd.items()))
        print(f"    {sec:<15}  n={n_in:>3}  {parts}")

    print("\n  Core sector final degrees:")
    core_final = [
        (n["system_id"], len(adj.get(n["system_id"], set())), n["gate_topology"])
        for n in nodes if n.get("geographic_sector") == "core"
    ]
    core_final.sort(key=lambda x: -x[1])
    for nid, deg, topo in core_final:
        print(f"    {nid}: deg={deg}  {topo}")


if __name__ == "__main__":
    main()