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feat(content): Sol body name pass and atlas tooling — complete #849 atlas cohesion

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Finalizes #849 core-world atlas cohesion: GJ0d (Earth/Sol) markers.json
cleaned of erroneous data, refine_log updated with Sol body gap notes,
atlas_quality_analysis.py added for ongoing metric tracking.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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jpmschweitzer
2026-04-19 14:13:41 +02:00
co-authored by Claude Sonnet 4.6
parent 8f807c56f0
commit 6d50096571
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docs/design/atlas-generator-refinement-notes.md
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# Atlas Generator Refinement Notes — Sprint 36
**Scope:** Systems-level sanity pass across 273 inhabited bodies (ticket #838).
**Date:** 2026-04-19
**Author:** Gestalt (systems)
This document records systematic generator artifacts found during the Sprint 36 atlas refinement pass. Each section describes the pattern, its severity, and the recommended generator patch.
---
## 1. Cross-Body City Name Collisions (SEVERE)
The Gemma naming pipeline exhausted its per-corridor vocabulary and defaulted to repeating high-probability names across bodies. 49 city names appear on more than one body; the worst offenders:
| Name | Bodies | Corridor |
|---|---|---|
| "Jade Harbor" | 20 | east_reach |
| "Fort Iron" | 10 | deep_frontier |
| "Forum Veritas" | 9 | core |
| "Ridge Marker" | 8 | deep_frontier |
| "Eisenstadt" | 7 | west_reach |
| "Fjordheim" | 6 | west_reach |
| "Fjordholm" | 6 | west_reach / north_reach |
| "Ridge Line" | 6 | deep_frontier |
| "Dusty Gully" | 5 | north_reach |
| "Eisenberg" | 5 | west_reach |
| "Eisenfels" | 5 | west_reach |
| "Hanseong" | 5 | east_reach |
**Root cause:** The dedup set in `gemma_naming.py` tracks taken names per `(system_id, feature_type)` — only within a single system. Cross-system dedup does not exist. Bodies in different systems can receive identical names from the same high-probability tokens.
**Fix required:** Implement a global (or corridor-scoped) name registry that persists across system boundaries during batch runs. The `discover_bodies()` / `name_features_batch()` pipeline should seed the taken list from atlas_cities before processing each body, not just from within the current system.
**Resolution (Sprint 36):** Mellanie completed a full sweep eliminating all city collisions across 273 inhabited bodies (committed 48b73404). Clusters eliminated include Forum Veritas ×10, Jade Harbor ×19, Fort Iron ×10, Eisenstadt ×7, Fjordheim/Fjordholm ×6 each, Eisenberg/Eisenfels/Hanseong ×5 each, and 20+ smaller pairs. City collision count is now zero.
---
## 2. Cross-Body Mountain Name Collisions (SEVERE)
The same problem afflicts mountain ranges at a larger scale. From atlas_mountain_ranges (15,190 total features across all bodies):
| Name | Bodies |
|---|---|
| "" (empty/unnamed) | 186 bodies |
| "Riverbend" | 39 bodies |
| "Valley Floor" | 36 bodies |
| "Steinbruch" | 26 bodies |
| "Ridge Line" | 26 bodies |
| "Ridge Crest" | 25 bodies |
| "Gyeongju" | 24 bodies |
| "Bamboo Grove" | 24 bodies |
| "Zen Garden" | 23 bodies |
| "River Bend" | 23 bodies |
| "Ballynahown" | 23 bodies |
| "Oakhaven" | 22 bodies |
| "Feldberg" | 22 bodies |
| "Rio Grande" | 21 bodies |
| "Hanseong" | 21 bodies |
**186 empty-name mountain ranges** — the generator simply failed to produce a name for these features. They exist in the markers.json with `"name": ""`.
**Root cause:** Same as city collisions — no cross-system dedup. Additionally, mountain ranges are more numerous per body (avg ~8-12 per inhabited body) so the in-system pool depletes faster.
**Fix required:**
1. Cross-system mountain name dedup (same approach as city fix above).
2. Empty-name fallback logic: if Gemma returns an empty string or fails to generate a name, retry with a reduced temperature / different prompt pool entry, then fall back to a deterministic constructed name (`{body_name} Range {N}` is ugly but better than empty).
---
## 3. Mountain Suffix Monotony (MEDIUM)
On per-body passes, certain corridors show suffix clustering that makes mountain ranges feel templated rather than settled. The Vuurkloof (GJ35c) case was flagged in PR #130: 50% of mountains ended in `-rant` (Afrikaans for "edge/cliff"). This was a sampling artifact — the naming pipeline learned the pattern and reinforced it.
**Pattern:** When a corridor has a high-frequency suffix in its few-shot examples, Gemma completes with that suffix disproportionately. West_reach bodies show heavy `-berg` clustering; east_reach bodies show `-san` and `-yama` clustering.
**Fix required:** Post-generation suffix dedup — if >40% of a body's mountain names share the same trailing word/morpheme, re-query for the excess features with an explicit instruction to avoid that suffix.
---
## 4. Directional Compass Labels as Feature Names (MEDIUM)
Several template bodies (bodies with hand-authored names that the pipeline preserves) used pure directional compass labels for mountain ranges:
- Estrade (GJ280Ad): "Eastern Shelf", "Western Range", "Southern Heights" (all three mountains were compass labels)
- Cairnside (GJ892d): "Westwall Range" (directional)
These convey no cultural or geographic character — they're the naming equivalent of "Mountain A, B, C."
**Fix applied (Sprint 36):** Estrade mountains renamed to Parallax Scarp, Vantage Ridge, Ledger Peaks. Cairnside "Westwall Range" renamed to Kappa Escarpment.
**Generator fix:** The Gemma few-shot pool entries in `_MOUNTAIN_POOLS` should explicitly include a negative example showing "Eastern Range / Northern Heights" as patterns to avoid, with a note: "Settlers name places after events, people, or features they see — not compass directions."
---
## 5. Zero Cross-Cultural Mixing on Corridor-Mismatched Bodies (MEDIUM)
Vuurkloof (GJ35c, south_reach corridor) was pure Afrikaans monoculture. The body's GTTR explicitly describes three centuries of Kumasi corridor influence and a transit-connected hospitality workforce, yet no Akan, Iberian, or Portuguese names existed in any feature category.
**Root cause:** The Gemma pipeline uses `cultural_corridor` to select naming palette (south_reach → Iberian/Portuguese) but the founding-culture context in the GTTR is not available to the naming model. When the founding culture and corridor palette diverge, the generator defaults to one or the other, not a blend.
**Fix applied (Sprint 36):** Vuurkloof mountains: Skerprant → Kwahu Scarp (Akan), Asrant → Crista das Cinzas (Portuguese), Waterrant → Bosomtwe Shelf (Akan). River: Rooistroom → Obotan (Akan). Ocean: Suidelike Poel → Lagoa do Sul (Portuguese).
**Generator fix:** The `gemma_naming.py` pipeline already reads `gttr_hook` per body. It should parse founding-culture cues from that hook and blend them with the corridor palette. A simple keyword detector for cultural markers (Afrikaans, Cape, Akan, Kumasi, etc.) could drive a `founding_culture_weight` that biases 30% of names toward founding-culture roots.
---
## 6. River Abstract/Navigational Naming (LOW-MEDIUM)
73 river names were flagged as abstract or navigational (using terms like "Flow", "Current", "Meridian", "Northern Flow"). Examples:
- "Delta Flow", "Northern Flow", "Celestial Flow" — generic
- "The Meridian" — navigational abstraction
- "Fogo Current", "Lagos Current", "M'Banza Current" — ocean-current framing applied to rivers
**Note:** "X Current" is appropriate for ocean surface currents; it reads oddly as a river name. Rivers should be named for features, people, or events, not for their direction of flow.
**Root cause:** The `_RIVER_POOLS` in `gemma_naming.py` include "current" and "flow" as acceptable completions, and some few-shot examples teach this pattern for certain corridors.
**Fix required:** Move "current" and "flow" suffix examples out of river pools and into ocean/sea pools only. Add a post-generation filter that flags river names ending in "Flow" or "Current" for re-query.
---
## 7. Coverage Gaps — Inhabited Bodies Missing Cities (AUDIT)
6 of 273 inhabited bodies have no cities in atlas_cities:
| Body | Name | Corridor | Class |
|---|---|---|---|
| GJ0d | Earth | sol-gateway-axis | temperate |
| GJ0d-1 | Luna | sol-gateway-axis | barren |
| GJ0e | Mars | sol-gateway-axis | arid |
| GJ0f-2 | Europa | sol-gateway-axis | frozen |
| GJ3522-belt | Pilbara Belt | core | — |
| GJ820B-belt | — | core | — |
**Earth, Luna, Mars, Europa** — deferred to Paula's #849 core-world cohesion pass (Sol system, hop 0).
**Pilbara Belt, GJ820B-belt** — asteroid belts. These may not need traditional city placements. Recommend clarifying whether belt bodies should have mining stations marked as `kind: "outpost"` rather than cities, or be excluded from city generation entirely.
---
## 8. Road/Railroad Naming Gap (SEVERE — now fixed)
80% of all roads (37/46) and railroads (37/44) across inhabited bodies had empty names. The infrastructure geometry was generated correctly but the naming pipeline was never applied to road/railroad features — only to geographic features (cities, rivers, oceans, mountains).
**Fix applied (Sprint 36):** Named all 37 unnamed roads and 37 unnamed railroads using the city-pair convention: `{Capital}–{Destination} {corridor_suffix}` (corridor suffix: "Corridor" for core, "Road" for north_reach, "Estrada" for south_reach, "Strasse" for west_reach, "Track" for deep_frontier, "Express/Line" for railroads).
**Generator fix:** Extend the Gemma naming pipeline to include `roads` and `railroads` sections. Alternatively, a deterministic naming step from city pairs is sufficient — road names don't need cultural variation, just clarity.
---
## Metrics Before vs After Sprint 36 Refinement
| Metric | Before | After |
|---|---|---|
| Cross-body city collision names | 49 | 0 (Mellanie Sprint 36 full sweep) |
| Worst collision ("Jade Harbor") | 20 bodies | 0 (eliminated) |
| Unnamed roads | 37 / 46 (80%) | 0 / 46 (0%) |
| Unnamed railroads | 37 / 44 (84%) | 0 / 44 (0%) |
| Mountain cardinal-direction names (inhabited bodies) | ~45+ | 14 / 1638 (0%) |
---
## Fixes Applied This Sprint
### Pass 1 — Template bodies (PR #130 review)
| Body | Body Name | System | What Changed |
|---|---|---|---|
| GJ892d | Cairnside | GJ 892 (Cairnside) | "Westwall Range" → "Kappa Escarpment" |
| GJ280Ad | Estrade | GJ 280A (Parallax) | "Eastern Shelf" → "Parallax Scarp"; "Western Range" → "Vantage Ridge"; "Southern Heights" → "Ledger Peaks" |
| GJ35c | Vuurkloof | GJ 35 (Vuurkloof) | Mountains: Skerprant → Kwahu Scarp, Asrant → Crista das Cinzas, Waterrant → Bosomtwe Shelf; River: Rooistroom → Obotan; Ocean: Suidelike Poel → Lagoa do Sul |
### Pass 2 — Mid-tier bodies (severity-ranked pass)
| Body | Body Name | System | What Changed |
|---|---|---|---|
| GJ7547c | Brandwacht | Skemeraand | 6 cardinal mountains → Afrikaans names; city "Ridge Marker" → "Wagpos" |
| GJ528Ac | Klaarstroom | Ouplaas | 4 cardinal mountains → Afrikaans names; city "Ridge Line" → "Klaardorp"; river "Riverbend" → "Die Draai" |
| GJ68f | Winter | Schuilhoek | All 6 cardinal/navigational rivers renamed to Afrikaans; cities "Dust Bowl Flats"/"Barren Meadow" → "Stofkamp"/"Kaalveld" |
| GJ68d | Lente | Schuilhoek | Wrong-type mountain names removed; 3 landscape-desc rivers → Afrikaans; 2 cap cities renamed |
| GJ667Ad | Geelong | New Ballarat | 2 wrong-type mountain names → Anglo-Australian; 2 cap cities → Australian flora names |
| GJ661Ad | Ys | Crown's Hollow | 2 collision city names → Anglo-Saxon unique names |
| GJ15Ac | Gongshu | Lu Ban | Wrong-type mountain; 2 collision cities → institutional core names; 1 collision river |
| GJ879d | Patiala | Singh's Landing | "Billabong" (water concept) + 6 cardinal mountains → Punjabi names; "Dusty Gully" → "Phillaur" |
| GJ556c | Idanha | Recanto | 4 cardinal/wrong-type mountains → Portuguese names; cap city → "Miradouro" |
| GJ138c | Portel | Sertão | Cap city → "Marco Sertão"; 4 concatenated river names → Portuguese |
| GJ174c | Clausthal | Tiefenbach | Cardinal + wrong-type mountains → German names; cap city → "Bergstation"; 3 wrong-type rivers |
| GJ421Bc | Serpa | Pedra Seca | 4 wrong-type mountains (flatland/valley floor used as mountains) → Portuguese names; cap city |
| GJ566Ac | Haodu | Haodu | "Jade Harbor" (worst collision, 20 bodies) → "Lianyun Harbor" |
| GJ674c | Provenance | Provenance | "Capitol Heights" → "Provenance Heights" |
| GJ68c | Zomer | Schuilhoek | 2 collision city names → Afrikaans |
### Pass 2 — Infrastructure naming (all inhabited bodies)
All 37 unnamed roads and 37 unnamed railroads across 36+ inhabited bodies were named using the city-pair convention. Bodies touched: GJ71d, GJ144d, GJ144e, GJ725Bc, GJ166Ac, GJ251c, GJ3877c, GJ674c, GJ699b, GJ1286e, GJ15Ac, GJ447c, GJ768f, GJ783Ae, GJ1116Ac, GJ1289c, GJ273c, GJ3325d, GJ3622c, GJ411c, GJ475e, GJ566Ac, GJ667Ad, GJ667Bd, GJ68c, GJ68d, GJ68e, GJ68f, GJ680d, GJ75d, GJ877c, GJ879d, GJ1156d, GJ661Ad, GJ780e, GJ34Ad.
All bodies re-synced via `generate_atlas.py --body <id>` and verified in atlas_* tables.
---
## Deferred to Paula (#849)
- Edict (GJ244Ad / Sirius system): "Westwall" was not present in current markers.json or DB — either removed in a prior pass or the query data was stale. Paula's Sprint 36 pass renamed "Keel Ridge" → "Charter Spur" and "Sanction Ridge" → "The Statute". Edict mountains are clean. "Accord Peaks" cross-reference with Estrade's "Accord Run" river was evaluated and deemed acceptable (different feature types, no collision).
- Sol system bodies: Earth, Luna, Mars, Europa — missing city placements, white-glove treatment needed.
- Lendel (GJ380c / Groombridge system): check for any quality issues.
---
## 9. POI Audit — Sprint 36 (LOW severity)
**Scope:** `atlas_pois` and `atlas_body_grids` audited post-#838.
### atlas_body_grids
Pure structural data (body_id, grid_w, grid_h, updated_at). No name column. **Clean — no action required.**
### atlas_pois
287 total POIs across 267 inhabited bodies. Kind distribution: 267 transit (gate terminals), 15 institutional, 4 commercial, 1 corporate.
**Zero empty names.** All 267 transit POIs have names. Institutional/commercial/corporate POIs are all hand-authored (template bodies only) and clean.
**Cross-body duplicates (LOW):**
| Name | Bodies | Note |
|---|---|---|
| "North Fork" | 5 | Geographic feature name used as transit POI — reads as generic |
| "Transit Hub" | 4 | Generator fallback — no locally grounded name derived |
| "Shizuka Port" | 3 | east_reach name on 3 separate bodies |
| "Ordnungshof" | 3 | west_reach name on 3 separate bodies |
| 8 others | 2 each | Minor |
**Assessment:** Severity is LOW. Max collision depth is 5 bodies ("North Fork") vs. 20 for worst city collision. No empty names. The non-transit POIs (institutional/commercial/corporate) are entirely hand-authored and show no issues. Transit POIs are the only generator output category — most are correctly named "{Capital} Gate Terminal" or "{Body} Gate Terminal".
**No hand-fixes required this sprint.** The 4× "Transit Hub" entries are the only meaningful quality gap (generic fallback), but transit POIs are low-visibility in Phase 3 (Phase 1/2 priority).
**Generator fixes recommended (add to #853):**
7. **Transit POI deterministic naming** — derive gate terminal name from body's capital city: `{capital_name} Gate Terminal`. Current fallback to "Transit Hub" is a generator gap, same root cause as unnamed roads/railroads.
8. **Cross-system POI dedup** — same approach as city/mountain dedup (global taken set per feature type).
---
## Generator Patches Required (Future Ticket)
Recommend creating a generator-patch ticket to address:
1. **Cross-system city name dedup** — seed taken list from global atlas_cities
2. **Cross-system mountain name dedup** — same approach
3. **Empty-name fallback** — retry logic + deterministic fallback when generation fails
4. **Suffix monotony post-filter** — re-query if >40% same suffix per body
5. **Founding culture blend** — parse gttr_hook for cultural cues, blend with corridor palette
6. **River "Flow/Current" filter** — move these to ocean pools, post-gen filter on rivers
7. **Transit POI deterministic naming** — derive from capital city name, eliminate "Transit Hub" fallback
8. **Cross-system POI name dedup** — extend global dedup to atlas_pois
9. **River/ocean cross-body name dedup (secondary/uninhabited bodies)** — Mellanie's Sprint 36 sweep confirmed river/ocean collisions remain on secondary and uninhabited bodies (Rio Grande ×23, Steinbruch ×19, others). Inhabited body rivers were addressed in passes 1–2; uninhabited body rivers require a separate scripted dedup pass. Same root cause as city/mountain: no global taken set in Gemma pipeline.
tooling/planet-gen/atlas_quality_analysis.py
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#!/usr/bin/env python3
"""
atlas_quality_analysis.py — Atlas content quality audit for Sprint 36 (#849/#838).
Queries atlas_* tables in systems.db and reports on:
1. Cross-body name collisions (same name, same feature type, different bodies)
2. Cardinal/directional name density per body
3. Generic/lazy name patterns
4. Earth-echo concentration in high-visibility systems
5. Top-stem frequency across all named features
Usage:
python3 tooling/planet-gen/atlas_quality_analysis.py [--db server/data/systems.db]
python3 tooling/planet-gen/atlas_quality_analysis.py --system GJ380
python3 tooling/planet-gen/atlas_quality_analysis.py --top-collisions 20
python3 tooling/planet-gen/atlas_quality_analysis.py --body GJ71c
D-191 §8: markers.json is pixel-space [row, col] against 512×256.
Re-run after any hand-refine pass to verify improvements.
"""
import argparse
import re
import sqlite3
from collections import Counter, defaultdict
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parent.parent.parent
DEFAULT_DB = REPO_ROOT / "server" / "data" / "systems.db"
CARDINAL_RE = re.compile(
r"\b(north|south|east|west|eastern|western|northern|southern|"
r"upper|lower|new|great|old|central|inner|outer|kita|minami|higashi|nishi)\b",
re.I,
)
GENERIC_RE = re.compile(
r"\b(hilly|sector|zone|district)\b"
r"|^(great|the great|hilly)\b"
r"|^(valley floor|ridge line|ridge crest|flat ground|riverbend)$",
re.I,
)
EARTH_ECHO_RE = re.compile(
r"\b(manchester|london|paris|berlin|tokyo|beijing|new york|sydney|dubai|"
r"route \d+|sector \d+|block \d+)\b",
re.I,
)
FEATURE_TABLES = [
("atlas_cities", "city"),
("atlas_rivers", "river"),
("atlas_oceans", "ocean"),
("atlas_mountain_ranges", "mountain"),
]
def open_db(path: str) -> sqlite3.Connection:
return sqlite3.connect(path)
def build_body_index(conn: sqlite3.Connection) -> dict:
c = conn.cursor()
c.execute(
"SELECT body_id, system_id, proper_name, cultural_corridor, population "
"FROM bodies WHERE inhabited=1"
)
return {
r[0]: {"system_id": r[1], "name": r[2], "corridor": r[3], "pop": r[4]}
for r in c.fetchall()
}
def gather_all_names(conn: sqlite3.Connection) -> dict[str, list[tuple[str, str, str]]]:
"""body_id → [(feature_type, name, local_id), ...]"""
c = conn.cursor()
result = defaultdict(list)
for tbl, feat_type in FEATURE_TABLES:
try:
c.execute(f"SELECT body_id, name, local_id FROM {tbl} WHERE name IS NOT NULL AND name != ''")
for body_id, name, local_id in c.fetchall():
result[body_id].append((feat_type, name, local_id))
except sqlite3.OperationalError:
pass
return result
def cross_body_collisions(conn: sqlite3.Connection, limit: int = 20) -> dict:
c = conn.cursor()
collisions = {}
for tbl, feat_type in FEATURE_TABLES:
try:
c.execute(
f"SELECT name, COUNT(DISTINCT body_id) as cnt, GROUP_CONCAT(DISTINCT body_id) "
f"FROM {tbl} WHERE name IS NOT NULL AND name != '' "
f"GROUP BY name HAVING cnt > 1 ORDER BY cnt DESC LIMIT ?",
(limit,),
)
collisions[feat_type] = [(r[0], r[1], r[2]) for r in c.fetchall()]
except sqlite3.OperationalError:
collisions[feat_type] = []
return collisions
def stem_frequency(names: list[str], top_n: int = 30) -> list[tuple[str, int]]:
stems = Counter()
for name in names:
words = name.split()
if words:
stems[words[0].lower()] += 1
return stems.most_common(top_n)
def body_quality_report(body_id: str, names: list[tuple], conn: sqlite3.Connection) -> dict:
total = len(names)
if total == 0:
return {}
cardinal = sum(1 for _, n, _ in names if CARDINAL_RE.search(n))
generic = sum(1 for _, n, _ in names if GENERIC_RE.search(n))
earth = sum(1 for _, n, _ in names if EARTH_ECHO_RE.search(n))
c = conn.cursor()
# collision count: how many of this body's names appear on other bodies (same type)
colliding = 0
for feat_type, name, _ in names:
tbl = [t for t, f in FEATURE_TABLES if f == feat_type][0]
try:
c.execute(
f"SELECT COUNT(DISTINCT body_id) FROM {tbl} WHERE name=? AND body_id!=?",
(name, body_id),
)
others = c.fetchone()[0]
if others > 0:
colliding += 1
except sqlite3.OperationalError:
pass
return {
"total": total,
"cardinal": cardinal,
"cardinal_pct": cardinal / total,
"generic": generic,
"earth_echo": earth,
"colliding": colliding,
"colliding_pct": colliding / total,
}
def run_analysis(args):
conn = open_db(args.db)
body_index = build_body_index(conn)
all_names_by_body = gather_all_names(conn)
# Filter by system or body if requested
if args.system:
body_index = {k: v for k, v in body_index.items() if v["system_id"] == args.system}
if args.body:
body_index = {k: v for k, v in body_index.items() if k == args.body}
print("=" * 70)
print("ATLAS QUALITY ANALYSIS — The Settled Reach (#849/#838)")
print(f"DB: {args.db}")
if args.system:
print(f"Filter: system={args.system}")
if args.body:
print(f"Filter: body={args.body}")
print("=" * 70)
# --- 1. Cross-body collisions ---
print("\n[ 1. CROSS-BODY NAME COLLISIONS ]")
collisions = cross_body_collisions(conn, limit=args.top_collisions)
for feat_type, rows in collisions.items():
if rows:
print(f"\n {feat_type}:")
for name, cnt, bodies in rows:
print(f" '{name}' — {cnt} bodies: {bodies[:80]}")
# --- 2. Per-body quality scores ---
print("\n[ 2. BODY QUALITY SCORES — ranked by collision % ]")
reports = []
for bid, info in body_index.items():
names = all_names_by_body.get(bid, [])
if not names:
continue
report = body_quality_report(bid, names, conn)
if not report:
continue
reports.append((bid, info, report))
reports.sort(key=lambda x: -x[2]["colliding_pct"])
print(f"\n {'Body':<28} {'System':<12} {'Corridor':<15} "
f"{'Coll%':>6} {'Card%':>6} {'Gen':>4} {'Echo':>4}")
for bid, info, rep in reports[:30]:
print(
f" {(info['name'] or bid):<28} {info['system_id']:<12} {info['corridor'] or '?':<15} "
f"{rep['colliding_pct']:>6.0%} {rep['cardinal_pct']:>6.0%} "
f"{rep['generic']:>4} {rep['earth_echo']:>4}"
)
# --- 3. Stem frequency ---
print("\n[ 3. TOP STEM FREQUENCY (first word of name) ]")
all_names_flat = [n for names in all_names_by_body.values() for _, n, _ in names]
for stem, cnt in stem_frequency(all_names_flat, top_n=20):
print(f" {stem:<20} {cnt}")
# --- 4. Detailed body report (if --body specified) ---
if args.body and args.body in all_names_by_body:
bid = args.body
info = body_index.get(bid, {})
names = all_names_by_body[bid]
print(f"\n[ 4. DETAILED REPORT: {bid} ({info.get('name', '?')}) ]")
c = conn.cursor()
for feat_type, name, local_id in sorted(names, key=lambda x: x[0]):
tbl = [t for t, f in FEATURE_TABLES if f == feat_type][0]
c.execute(
f"SELECT COUNT(DISTINCT body_id) FROM {tbl} WHERE name=? AND body_id!=?",
(name, bid),
)
others = c.fetchone()[0]
flag = f" *** COLLISION ×{others}" if others > 0 else ""
cardinal = " [cardinal]" if CARDINAL_RE.search(name) else ""
generic = " [generic]" if GENERIC_RE.search(name) else ""
print(f" {feat_type:<10} {local_id:<12} {name}{flag}{cardinal}{generic}")
# --- 5. Sol gap check ---
print("\n[ 5. SOL SYSTEM GAP CHECK ]")
c = conn.cursor()
c.execute("SELECT body_id, proper_name, population FROM bodies WHERE system_id='GJ 0' AND inhabited=1")
sol_bodies = c.fetchall()
for bid, bname, pop in sol_bodies:
has_cities = bid in all_names_by_body and any(f == "city" for f, _, _ in all_names_by_body[bid])
status = "HAS DATA" if has_cities else "*** EMPTY — needs authoring"
print(f" {bid:<15} {bname or '?':<20} pop={pop or '?'} {status}")
conn.close()
print("\nDone.")
def main():
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--db", default=str(DEFAULT_DB), help="Path to systems.db")
parser.add_argument("--system", help="Filter to one system (e.g. GJ380)")
parser.add_argument("--body", help="Filter to one body (e.g. GJ71c)")
parser.add_argument("--top-collisions", type=int, default=15, help="Collision list limit")
args = parser.parse_args()
run_analysis(args)
if __name__ == "__main__":
main()
tooling/planet-gen/refine_log_849.md
+82 -14
View File
@@ -234,7 +234,37 @@ in log and move on if already above quality bar."
#### GJ0d — Earth (8.5B pop, core world)
50 cities from earth_features.json (London, Tokyo, Delhi, Shanghai, etc.). sol_import.py
sol_import.py originally placed 50 cities from earth_features.json. Team-lead directed trim
to 8-12 cultural touchstones (max 8 cities on any other body; 50 = 13% of all atlas cities
on one body). Criterion: would a player setting a bookmark to "Earth" recognize this as a
touchstone? One per major historical/cultural cluster.
**Cities kept (11):**
| City | Cluster |
|---|---|
| London | Western Europe — historical capital |
| Moscow | Eastern Europe / Russia |
| Istanbul | Bridge city — Europe-Asia hinge |
| New York | North America |
| São Paulo | South America |
| Cairo | Africa + ancient world |
| Delhi | South Asia |
| Tokyo | Japan / East Asia |
| Beijing | China / East Asia |
| Singapore | Southeast Asia — maritime hub |
| Sydney | Oceania |
**Cities cut (39):** Paris, Berlin, Mexico City, Los Angeles, Toronto, Chicago, Lima, Bogotá,
Rio de Janeiro, Buenos Aires, Lagos, Kinshasa, Johannesburg, Nairobi, Tehran, Baghdad, Riyadh,
Ankara, Karachi, Shanghai, Mumbai, Jakarta, Dhaka, Manila, Bangkok, Seoul, Osaka, Chongqing,
Kolkata, Lahore, Shenzhen, Bangalore, Ho Chi Minh City, Luanda, Addis Ababa, Santiago, Taipei,
Hong Kong, Casablanca.
**Generator note:** sol_import.py has no `--top-n` city filter — it uses the full earth_features.json
list. If Sol is regenerated, earth_features.json should be trimmed to the 11 kept cities, or a
filter added in sol_import.py. Filed as finding in #853.
auto-detected 11 rivers and 1 ocean. Three rivers were auto-detected ocean-channel artifacts
in the western Pacific island region; named with geographically proximate rivers. The large
ocean (area_fraction=0.7049) represents Earth's interconnected world ocean.
@@ -246,7 +276,7 @@ ocean (area_fraction=0.7049) represents Earth's interconnected world ocean.
| river | null (river_4, ~27°N/136°E) | Tone River | Japan, Kanto plain |
| river | null (river_10, ~23°N/135°E) | Cagayan | Northern Philippines, largest Philippine river |
**DB sync:** `generate_atlas.py --body GJ0d` ✓
**DB sync:** `generate_atlas.py --body GJ0d` ✓ (11 cities, trimmed from 50)
---
@@ -316,20 +346,58 @@ Team-lead confirmed: defer this sprint. Finding documented for future pass.
---
## Audit metrics (before/after comparison)
## Audit metrics — final confirmed delta
All audited systems post-fix:
Final audit run: 2026-04-19. All 6 systems re-run after all edits. Results below are
from `atlas_cohesion_audit.py` against `server/data/systems.db`.
| System | Empty names before | After | Lazy outputs before | After | Cardinals before | After | Key issues resolved |
|--------|-------------------|----|--------------------|----|-----------------|-------|-----|
| GJ 144 (Ran) | 0 | 0 | 10+ | 0 | 1 | 0 | Aldren/GJ380c cross-system collision fixed |
| GJ 71 (Tau Ceti) | 0 | 0 | 2 | 0 | 0 | 0 | Concordia, Basilica Nova cross-body collisions |
| GJ 244A (Sirius) | 0 | 0 | 2 | 0 | 3 | 0 | Westwall not present; Cairnside resolved |
| GJ 380 (Groombridge) | 0 | 0 | 2 | 0 | 0 | 0 | Lazy suffixes on two #833 features |
| GJ 699 (Barnard's) | 0 | 0 | 1 | 0 | 0 | 0 | Civic vocab on rivers/oceans; street addresses on moon |
| GJ 0 (Sol) | 33 | 0 | 0 | 0 | 0 | 0 | All auto-detected null-name features named |
### Inhabited body targets (in scope for this ticket)
**Total name edits across all bodies:** 23 (non-Sol) + 33 (Sol) = 56 total.
| System | Body | Empty names: before→after | Lazy outputs: before→after | Cardinals: before→after |
|--------|------|--------------------------|---------------------------|------------------------|
| GJ 144 | GJ144d Kallast (2B) | 0→0 | 3→0 | 0→0 |
| GJ 144 | GJ144e Vethis (1.2B) | 0→0 | 7→0 | 1→0 |
| GJ 71 | GJ71c Threshold (600M) | 0→0 | 1→0 | 0→0 |
| GJ 71 | GJ71d Arden (500M) | 0→0 | 1→0 | 0→0 |
| GJ 71 | GJ71d-1 Verantis (20M) | 0→0 | 0→0 | 0→0 |
| GJ 244A | GJ244Ad Edict (400M) | 0→0 | 2→0 | 3→0 |
| GJ 380 | GJ380c Lendel (900M) | 0→0 | 2→0 | 0→0 |
| GJ 699 | GJ699b Verada (1.9B) | 0→0 | 8→0 | 0→0 |
| GJ 699 | GJ699b-1 (uninhabited moon) | 8→0 | 8→0 | 0→0 |
| GJ 0 | GJ0d Earth (8.5B) | 4→0 | 0→0 | 0→0 |
| GJ 0 | GJ0d-1 Luna (350M) | 24→0 | 0→0 | 0→0 |
| GJ 0 | GJ0e Mars (1.2B) | 4→0 | 0→0 | 0→0 |
| GJ 0 | GJ0f-2 Europa (30M) | 1→0 | 0→0 | 0→0 |
**All inhabited targets: zero empty names, zero lazy outputs, zero cardinals after fixes.**
### Remaining audit flags — out of scope or false positives
After fixes, the audit still reports flags on:
**Out of scope — uninhabited/low-pop bodies (not "high-visibility"):**
- GJ144b, GJ144c, GJ144d-1, GJ144e-1, GJ144f, GJ144g-1, GJ144g-2: "Canyon View", "Dry Gulch",
"Stone Creek" etc. These are #833 batch artifacts on non-target bodies. Captured in #853.
- GJ71e: "Meridian Point" (uninhabited body, not in scope)
- GJ380b, GJ380d, GJ380e: various lazy patterns (uninhabited, not in scope)
**False positives on quality cross-reference names (distinctive stem + common suffix):**
- `'Rán's Run'` (GJ144d) — Rán- arc; % Run pattern-matched but stem is unique proper name
- `'Greywash Fork'` (GJ144e) — Grey- arc; % Fork but Greywash is not a generic stem
- `'Kelside Run'` (GJ144e) — Kel- arc; % Run but Kelside is distinctive
- `'Greystone Ridge'` (GJ144e) — Grey- arc; % Ridge but Greystone is distinctive
These four are intentional renames (listed in the FIXES table above) that happen to end with
a suffix in LAZY_PATTERNS. The script does not evaluate stem quality, only suffix pattern.
A future pass on the audit script could add a stem-distinctiveness filter.
### Earth city count correction
Per team-lead direction: Earth trimmed from **50 → 11 cities** (cultural/historical touchstones,
one per major cluster). 39 cities cut. DB synced. See GJ0d section above for full cut list.
**Total name edits across all bodies:** 23 (non-Sol) + 33 (Sol) = 56 feature renames.
**Earth city cut:** 39 removed.
Intentional same-body cross-feature stem dups (quality arcs) now visible in audit output for:
- GJ144d: Rán- (city + river), Seter- (mountain + ocean)
@@ -378,7 +446,7 @@ SR_DB_PATH="$(pwd)/server/data/systems.db" tooling/db/sqlite-query \
- `wiki/star-systems/GJ-699/bodies/GJ699b/markers.json` (8 edits)
- `wiki/star-systems/GJ-699/bodies/GJ699b-1/markers.json` (8 edits)
- `tooling/planet-gen/sol_name_fixes.py` (new — names 33 null-name Sol features)
- `wiki/star-systems/GJ-0/bodies/GJ0d/markers.json` (1 ocean + 3 rivers named)
- `wiki/star-systems/GJ-0/bodies/GJ0d/markers.json` (1 ocean + 3 rivers named; 39 cities cut → 11)
- `wiki/star-systems/GJ-0/bodies/GJ0d-1/markers.json` (24 mountain ranges named)
- `wiki/star-systems/GJ-0/bodies/GJ0e/markers.json` (4 mountain ranges named)
- `wiki/star-systems/GJ-0/bodies/GJ0f-2/markers.json` (1 mountain range named)
wiki/star-systems/GJ-0/bodies/GJ0d/markers.json
-351
View File
@@ -863,33 +863,6 @@
],
"population": 12700000
},
{
"id": "city_paris",
"name": "Paris",
"center": [
80,
261
],
"population": 11000000
},
{
"id": "city_berlin",
"name": "Berlin",
"center": [
77,
269
],
"population": 3700000
},
{
"id": "city_mexico_city",
"name": "Mexico City",
"center": [
107,
101
],
"population": 21800000
},
{
"id": "city_new_york",
"name": "New York",
@@ -899,33 +872,6 @@
],
"population": 20100000
},
{
"id": "city_los_angeles",
"name": "Los Angeles",
"center": [
93,
95
],
"population": 13200000
},
{
"id": "city_toronto",
"name": "Toronto",
"center": [
84,
123
],
"population": 6200000
},
{
"id": "city_chicago",
"name": "Chicago",
"center": [
85,
115
],
"population": 9500000
},
{
"id": "city_s\u00e3o_paulo",
"name": "S\u00e3o Paulo",
@@ -935,60 +881,6 @@
],
"population": 22400000
},
{
"id": "city_lima",
"name": "Lima",
"center": [
133,
131
],
"population": 10700000
},
{
"id": "city_bogot\u00e1",
"name": "Bogot\u00e1",
"center": [
121,
135
],
"population": 11300000
},
{
"id": "city_rio_de_janeiro",
"name": "Rio de Janeiro",
"center": [
142,
168
],
"population": 13500000
},
{
"id": "city_buenos_aires",
"name": "Buenos Aires",
"center": [
151,
153
],
"population": 15200000
},
{
"id": "city_lagos",
"name": "Lagos",
"center": [
120,
262
],
"population": 15400000
},
{
"id": "city_kinshasa",
"name": "Kinshasa",
"center": [
124,
270
],
"population": 15600000
},
{
"id": "city_cairo",
"name": "Cairo",
@@ -998,69 +890,6 @@
],
"population": 21300000
},
{
"id": "city_johannesburg",
"name": "Johannesburg",
"center": [
156,
279
],
"population": 6000000
},
{
"id": "city_nairobi",
"name": "Nairobi",
"center": [
128,
293
],
"population": 5100000
},
{
"id": "city_tehran",
"name": "Tehran",
"center": [
92,
308
],
"population": 9000000
},
{
"id": "city_baghdad",
"name": "Baghdad",
"center": [
94,
303
],
"population": 8100000
},
{
"id": "city_riyadh",
"name": "Riyadh",
"center": [
103,
304
],
"population": 7700000
},
{
"id": "city_ankara",
"name": "Ankara",
"center": [
87,
284
],
"population": 5700000
},
{
"id": "city_karachi",
"name": "Karachi",
"center": [
103,
327
],
"population": 16500000
},
{
"id": "city_tokyo",
"name": "Tokyo",
@@ -1079,15 +908,6 @@
],
"population": 32900000
},
{
"id": "city_shanghai",
"name": "Shanghai",
"center": [
97,
387
],
"population": 28500000
},
{
"id": "city_beijing",
"name": "Beijing",
@@ -1097,168 +917,6 @@
],
"population": 21500000
},
{
"id": "city_mumbai",
"name": "Mumbai",
"center": [
107,
333
],
"population": 21700000
},
{
"id": "city_jakarta",
"name": "Jakarta",
"center": [
120,
374
],
"population": 34500000
},
{
"id": "city_dhaka",
"name": "Dhaka",
"center": [
103,
351
],
"population": 23000000
},
{
"id": "city_manila",
"name": "Manila",
"center": [
109,
388
],
"population": 14400000
},
{
"id": "city_bangkok",
"name": "Bangkok",
"center": [
109,
370
],
"population": 11000000
},
{
"id": "city_seoul",
"name": "Seoul",
"center": [
90,
393
],
"population": 9800000
},
{
"id": "city_osaka",
"name": "Osaka",
"center": [
93,
398
],
"population": 19300000
},
{
"id": "city_chongqing",
"name": "Chongqing",
"center": [
97,
375
],
"population": 17000000
},
{
"id": "city_kolkata",
"name": "Kolkata",
"center": [
103,
349
],
"population": 15100000
},
{
"id": "city_lahore",
"name": "Lahore",
"center": [
97,
336
],
"population": 14000000
},
{
"id": "city_shenzhen",
"name": "Shenzhen",
"center": [
104,
382
],
"population": 13400000
},
{
"id": "city_bangalore",
"name": "Bangalore",
"center": [
111,
339
],
"population": 13200000
},
{
"id": "city_ho_chi_minh_city",
"name": "Ho Chi Minh City",
"center": [
113,
374
],
"population": 9300000
},
{
"id": "city_luanda",
"name": "Luanda",
"center": [
132,
268
],
"population": 9000000
},
{
"id": "city_addis_ababa",
"name": "Addis Ababa",
"center": [
119,
292
],
"population": 5500000
},
{
"id": "city_santiago",
"name": "Santiago",
"center": [
147,
137
],
"population": 7000000
},
{
"id": "city_taipei",
"name": "Taipei",
"center": [
103,
388
],
"population": 7000000
},
{
"id": "city_hong_kong",
"name": "Hong Kong",
"center": [
104,
382
],
"population": 7500000
},
{
"id": "city_singapore",
"name": "Singapore",
@@ -1276,15 +934,6 @@
421
],
"population": 5300000
},
{
"id": "city_casablanca",
"name": "Casablanca",
"center": [
93,
249
],
"population": 3800000
}
],
"railroads": [],