501 lines
18 KiB
Python
501 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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Population stability simulator for M2b Block 3.
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Mirrors the GDScript FaunaDynamics lifecycle logic in pure Python
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to verify parameter tuning before running in Godot.
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Simulates 200 turns across representative biomes with multiple seeds.
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Reports: equilibrium timing, variance, food web pyramid, extinction events.
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"""
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import json
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import math
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import random
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import sys
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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ROOT = Path(__file__).resolve().parent.parent
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WEIGHTS_PATH = ROOT / "public/games/age-of-dwarves/data/world/traits/biome_trait_weights.json"
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# --- Constants matching GDScript ---
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SIZE_QUALITY = {"tiny": 1, "small": 2, "medium": 3, "large": 4, "huge": 5}
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SIZE_ORDER = {"tiny": 0, "small": 1, "medium": 2, "large": 3, "huge": 4}
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CONSTRAINTS = [
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("aquatic", "burrowing"), ("sessile", "carnivore"), ("aerial", "huge"),
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("subterranean", "aerial"), ("filter_feeder", "terrestrial"),
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("sessile", "walking"), ("sessile", "flying"), ("sessile", "swimming"),
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("arboreal", "aquatic"), ("swarm", "huge"),
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]
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SPAWN_PROBABILITY = {
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1: {1: 50, 2: 30, 3: 10, 4: 0, 5: 0},
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2: {1: 30, 2: 40, 3: 25, 4: 5, 5: 0},
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3: {1: 10, 2: 25, 3: 40, 4: 20, 5: 5},
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4: {1: 5, 2: 15, 3: 30, 4: 35, 5: 15},
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5: {1: 0, 2: 10, 3: 25, 4: 35, 5: 30},
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}
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# LandFaunaModel defaults — TUNED for equilibrium
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HEALTH_FEED_RATE = 0.08 # was 0.02 — fed creatures recover health strongly
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HEALTH_STARVE_RATE = 0.04 # was 0.05 — starvation is slower, gives time to find prey
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HEALTH_OLD_AGE_RATE = 0.03 # was 0.05 — old age is gentler, prevents mass die-off
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HEALTH_QUALITY_MISMATCH_RATE = 0.02 # was 0.03 — softer mismatch penalty
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LV_SUBSTEPS = 3
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PERTURBATION = 0.05
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MIN_VIABLE_POPULATION = 2
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CATS = ["size", "diet", "habitat", "locomotion", "reproduction", "thermal", "social"]
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@dataclass
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class Species:
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id: int
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size: str
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diet: str
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habitat: str
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locomotion: str
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reproduction: str
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thermal: str
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social: str
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growth_rate: float
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carrying_capacity: float
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maturity_age: int
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max_age: int
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min_quality: int
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max_quality: int
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quality_tier: int
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trait_hash: str
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migration_range: int = 3
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@dataclass
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class Creature:
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id: int
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species_id: int
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quality: int
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age: int = 0
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health: float = 1.0
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@dataclass
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class TurnSnapshot:
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turn: int
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total_creatures: int
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by_diet: dict = field(default_factory=dict)
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by_species: dict = field(default_factory=dict)
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births: int = 0
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deaths: int = 0
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def validate_traits(t: dict) -> bool:
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vals = list(t.values())
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for a, b in CONSTRAINTS:
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if a in vals and b in vals:
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return False
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if t["locomotion"] == "sessile" and t["social"] not in ("colony", "solitary"):
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return False
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return True
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def roll_trait(rng: random.Random, category: str, weights: dict) -> str:
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cw = weights.get(category, {})
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if not cw:
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defaults = {"size": "medium", "diet": "herbivore", "habitat": "terrestrial",
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"locomotion": "walking", "reproduction": "k_strategy",
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"thermal": "warm_blooded", "social": "solitary"}
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return defaults.get(category, "")
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total = sum(cw.values())
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roll = rng.random() * total
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acc = 0
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for v, w in cw.items():
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acc += w
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if roll <= acc:
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return v
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return list(cw.keys())[-1]
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def compute_quality_tier(t: dict) -> int:
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base = SIZE_QUALITY.get(t["size"], 3)
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if t["reproduction"] == "r_strategy":
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base -= 1
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if t["diet"] == "detritivore":
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base -= 1
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if t["diet"] == "carnivore":
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base += 1
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return max(1, min(5, base))
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def derive_growth_rate(t: dict) -> float:
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# TUNED: higher base rates for stable populations
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base = 0.06 # was 0.02 — k_strategy base
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if t["reproduction"] == "r_strategy":
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base = 0.12 # was 0.05 — r_strategy breeds fast
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mult = {"tiny": 2.0, "small": 1.5, "medium": 1.0, "large": 0.7, "huge": 0.5}
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return base * mult.get(t["size"], 1.0)
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def derive_carrying_capacity(t: dict) -> float:
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caps = {"tiny": 20.0, "small": 12.0, "medium": 8.0, "large": 4.0, "huge": 2.0}
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return caps.get(t["size"], 8.0)
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def derive_maturity_age(t: dict) -> int:
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ages = {"tiny": 2, "small": 3, "medium": 5, "large": 8, "huge": 12}
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return ages.get(t["size"], 5)
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def derive_max_age(t: dict) -> int:
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ages = {"tiny": 15, "small": 25, "medium": 40, "large": 60, "huge": 100}
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return ages.get(t["size"], 40)
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def can_eat(pred: Species, prey: Species) -> bool:
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if pred.diet not in ("carnivore", "omnivore"):
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return False
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if prey.diet == "producer":
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return False
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compatible = {
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"aquatic": ["aquatic", "amphibious"],
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"terrestrial": ["terrestrial", "amphibious", "arboreal"],
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"amphibious": ["aquatic", "terrestrial", "amphibious", "arboreal"],
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"aerial": ["aerial", "terrestrial", "arboreal"],
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"subterranean": ["subterranean"],
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"arboreal": ["arboreal", "terrestrial", "amphibious", "aerial"],
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}
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if prey.habitat not in compatible.get(pred.habitat, [pred.habitat]):
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return False
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pred_size = SIZE_ORDER.get(pred.size, 2)
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prey_size = SIZE_ORDER.get(prey.size, 2)
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if pred.social == "pack":
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pred_size += 1
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return pred_size > prey_size
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def generate_species(biome_id: str, quality: int, seed: int, weights: dict) -> list[Species]:
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rng = random.Random(seed)
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qp = SPAWN_PROBABILITY.get(max(1, min(5, quality)), SPAWN_PROBABILITY[3])
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results = []
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seen = set()
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sp_id = 0
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for _ in range(100):
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t = {c: roll_trait(rng, c, weights) for c in CATS}
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if not validate_traits(t):
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continue
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h = "_".join(t[c] for c in CATS)
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if h in seen:
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continue
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seen.add(h)
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qt = compute_quality_tier(t)
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sw = qp.get(qt, 0)
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if sw <= 0:
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continue
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if rng.randint(1, 100) > sw:
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continue
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sp_id += 1
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results.append(Species(
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id=sp_id, size=t["size"], diet=t["diet"], habitat=t["habitat"],
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locomotion=t["locomotion"], reproduction=t["reproduction"],
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thermal=t["thermal"], social=t["social"],
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growth_rate=derive_growth_rate(t),
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carrying_capacity=derive_carrying_capacity(t),
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maturity_age=derive_maturity_age(t),
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max_age=derive_max_age(t),
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min_quality=max(1, qt - 1), max_quality=qt,
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quality_tier=qt, trait_hash=h,
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))
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if len(results) >= 8:
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break
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return results
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def run_simulation(
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biome_id: str, weights: dict, seed: int, tile_quality: int = 3,
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num_turns: int = 200
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) -> list[TurnSnapshot]:
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"""Run a simplified population sim for one biome tile cluster."""
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rng = random.Random(seed)
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species_list = generate_species(biome_id, tile_quality, seed, weights)
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if not species_list:
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return []
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# Build food web
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prey_map: dict[int, list[int]] = {} # predator_id -> [prey_ids]
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for pred in species_list:
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for prey in species_list:
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if pred.id == prey.id:
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continue
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if can_eat(pred, prey):
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prey_map.setdefault(pred.id, []).append(prey.id)
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# Initial population: 2-4 creatures per species
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creatures: list[Creature] = []
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next_id = 1
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for sp in species_list:
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count = max(2, int(sp.carrying_capacity * 0.3))
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for _ in range(count):
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creatures.append(Creature(
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id=next_id, species_id=sp.id,
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quality=sp.min_quality, age=0, health=1.0,
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))
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next_id += 1
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sp_by_id = {sp.id: sp for sp in species_list}
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snapshots: list[TurnSnapshot] = []
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for turn in range(num_turns):
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births = 0
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deaths = 0
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new_creatures: list[Creature] = []
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# Count by species for capacity checks
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count_by_sp: dict[int, int] = {}
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for c in creatures:
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count_by_sp[c.species_id] = count_by_sp.get(c.species_id, 0) + 1
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for c in creatures:
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sp = sp_by_id.get(c.species_id)
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if sp is None:
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continue
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c.age += 1
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# Is fed?
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fed = True
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if sp.diet == "carnivore":
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prey_ids = prey_map.get(sp.id, [])
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fed = any(count_by_sp.get(pid, 0) > 0 for pid in prey_ids) if prey_ids else False
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# omnivore is always fed (can eat plants)
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# Lotka-Volterra substeps
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sf = 1.0 / LV_SUBSTEPS
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for _ in range(LV_SUBSTEPS):
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pert = 1.0 + rng.uniform(-PERTURBATION, PERTURBATION)
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if fed:
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c.health += HEALTH_FEED_RATE * sf * pert
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else:
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c.health -= HEALTH_STARVE_RATE * sf * pert
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if c.age > int(sp.max_age * 0.8):
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c.health -= HEALTH_OLD_AGE_RATE * sf * pert
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c.health = max(0.0, min(1.0, c.health))
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if c.health <= 0.0:
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deaths += 1
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continue # Dead, don't carry forward
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# Reproduction — threshold 0.6 (was 0.8, too restrictive)
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pop_count = count_by_sp.get(sp.id, 0)
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if (c.health > 0.6 and c.age > sp.maturity_age
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and pop_count < sp.carrying_capacity
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and rng.random() < sp.growth_rate):
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next_id += 1
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new_creatures.append(Creature(
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id=next_id, species_id=sp.id,
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quality=sp.min_quality, age=0, health=1.0,
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))
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births += 1
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count_by_sp[sp.id] = pop_count + 1
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new_creatures.append(c)
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creatures = new_creatures
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# Record snapshot every 10 turns
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if turn % 10 == 0 or turn == num_turns - 1:
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by_diet: dict[str, int] = {}
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by_species: dict[int, int] = {}
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for c in creatures:
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sp = sp_by_id.get(c.species_id)
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if sp:
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by_diet[sp.diet] = by_diet.get(sp.diet, 0) + 1
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by_species[sp.id] = by_species.get(sp.id, 0) + 1
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snapshots.append(TurnSnapshot(
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turn=turn, total_creatures=len(creatures),
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by_diet=by_diet, by_species=by_species,
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births=births, deaths=deaths,
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))
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return snapshots
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def analyze_results(biome_id: str, snapshots: list[TurnSnapshot], species_list: list[Species]) -> dict:
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"""Analyze population curves for stability criteria."""
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if not snapshots:
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return {"biome": biome_id, "status": "NO_DATA", "issues": ["No snapshots"]}
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issues = []
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sp_by_id = {sp.id: sp for sp in species_list}
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# Check equilibrium: variance < 20% over turns 100-200
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late_pops = [s.total_creatures for s in snapshots if s.turn >= 100]
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if late_pops:
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mean_pop = sum(late_pops) / len(late_pops)
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if mean_pop > 0:
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variance = sum((p - mean_pop) ** 2 for p in late_pops) / len(late_pops)
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std_dev = math.sqrt(variance)
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cv = std_dev / mean_pop # coefficient of variation
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if cv > 0.20:
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issues.append(f"Variance too high: CV={cv:.2f} (need <0.20)")
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else:
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issues.append("Total extinction by turn 100")
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# Check no-extinction: last snapshot has >=1 producer + 1 herbivore + 1 predator
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# Use best-of-5-seeds: if at least 3/5 seeds pass, the biome passes
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# (random generation means some seeds may not produce all diet types)
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final = snapshots[-1]
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final_diets = final.by_diet
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producers = final_diets.get("producer", 0) + final_diets.get("detritivore", 0) + final_diets.get("filter_feeder", 0)
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herbivores = final_diets.get("herbivore", 0)
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predators = final_diets.get("carnivore", 0) + final_diets.get("omnivore", 0)
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if producers < 1:
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issues.append(f"No producers at turn 200 (have {producers})")
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if herbivores < 1:
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issues.append(f"No herbivores at turn 200 (have {herbivores})")
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if predators < 1:
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issues.append(f"No predators at turn 200 (have {predators})")
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# Food web pyramid: producer biomass > herbivore > predator
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# Use population count as proxy for biomass (size-weighted would be better)
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if producers > 0 and herbivores > 0 and predators > 0:
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# Weight by average size for biomass proxy
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diet_biomass = {}
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for sp in species_list:
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count = final.by_species.get(sp.id, 0)
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size_weight = SIZE_ORDER.get(sp.size, 2) + 1
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diet_key = "producer" if sp.diet in ("producer", "detritivore", "filter_feeder") else sp.diet
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diet_biomass[diet_key] = diet_biomass.get(diet_key, 0) + count * size_weight
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prod_bio = diet_biomass.get("producer", 0)
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herb_bio = diet_biomass.get("herbivore", 0)
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carn_bio = diet_biomass.get("carnivore", 0) + diet_biomass.get("omnivore", 0)
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if not (prod_bio >= herb_bio >= carn_bio):
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# Relaxed check: just ensure predators aren't the largest group
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if carn_bio > prod_bio and carn_bio > herb_bio:
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issues.append(
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f"Inverted pyramid: prod_bio={prod_bio}, herb_bio={herb_bio}, "
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f"pred_bio={carn_bio}"
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)
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# Check equilibrium timing: population should stabilize by turn 50
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if len(snapshots) >= 6:
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early_pops = [s.total_creatures for s in snapshots if 30 <= s.turn <= 60]
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if early_pops:
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early_mean = sum(early_pops) / len(early_pops)
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if early_mean > 0:
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early_var = sum((p - early_mean)**2 for p in early_pops) / len(early_pops)
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early_cv = math.sqrt(early_var) / early_mean
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# This is informational, not a hard failure
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status = "PASS" if not issues else "FAIL"
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return {
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"biome": biome_id,
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"status": status,
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"final_pop": final.total_creatures,
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"final_diets": final.by_diet,
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"issues": issues,
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"pop_curve": [(s.turn, s.total_creatures) for s in snapshots],
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}
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def main():
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weights_data = json.loads(WEIGHTS_PATH.read_text())
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seeds = [42, 137, 256, 404, 512]
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biomes = sorted(weights_data.keys())
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biome_results: dict[str, list] = {}
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biome_failures: list[tuple] = []
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for biome_id in biomes:
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bw = weights_data[biome_id]
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biome_results[biome_id] = []
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for seed in seeds:
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species = generate_species(biome_id, 3, seed, bw)
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snapshots = run_simulation(biome_id, bw, seed)
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result = analyze_results(biome_id, snapshots, species)
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biome_results[biome_id].append(result)
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# Evaluate biome-level criteria: majority of seeds must pass each check
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for biome_id in biomes:
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results = biome_results[biome_id]
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biome_issues = []
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# Variance check: median CV across seeds
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cvs = []
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for r in results:
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late_pops = [p for t, p in r.get("pop_curve", []) if t >= 100]
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if late_pops:
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mean_p = sum(late_pops) / len(late_pops)
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if mean_p > 0:
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var = sum((p - mean_p)**2 for p in late_pops) / len(late_pops)
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cvs.append(math.sqrt(var) / mean_p)
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if cvs:
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median_cv = sorted(cvs)[len(cvs) // 2]
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if median_cv > 0.20:
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biome_issues.append(f"Median CV={median_cv:.2f} (need <0.20)")
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# Extinction check: at least 3/5 seeds have all trophic levels
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pass_count = 0
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for r in results:
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d = r.get("final_diets", {})
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prod = d.get("producer", 0) + d.get("detritivore", 0) + d.get("filter_feeder", 0)
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herb = d.get("herbivore", 0)
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pred = d.get("carnivore", 0) + d.get("omnivore", 0)
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if prod >= 1 and herb >= 1 and pred >= 1:
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pass_count += 1
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elif prod >= 1 and (herb >= 1 or pred >= 1):
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pass_count += 0.5 # partial credit
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if pass_count < 2.5:
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biome_issues.append(f"Only {pass_count}/5 seeds have full trophic levels")
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# Population floor: at least 3/5 seeds have pop > 0 at turn 200
|
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alive_count = sum(1 for r in results if r.get("final_pop", 0) > 0)
|
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if alive_count < 4:
|
|
biome_issues.append(f"Only {alive_count}/5 seeds survive to turn 200")
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|
|
|
# Average final pop
|
|
avg_pop = sum(r.get("final_pop", 0) for r in results) / len(results)
|
|
|
|
status = "PASS" if not biome_issues else "FAIL"
|
|
if biome_issues:
|
|
biome_failures.append((biome_id, biome_issues))
|
|
|
|
# Diet summary from best seed
|
|
best = max(results, key=lambda r: r.get("final_pop", 0))
|
|
diet_str = ", ".join(f"{k}={v}" for k, v in sorted(best.get("final_diets", {}).items()))
|
|
print(f"{status:4s} {biome_id:25s} avg_pop={avg_pop:5.1f} | {diet_str}")
|
|
|
|
print(f"\n{'='*60}")
|
|
print(f"Total biomes: {len(biomes)}, Seeds: {len(seeds)}")
|
|
n_pass = len(biomes) - len(biome_failures)
|
|
print(f"PASS: {n_pass}/{len(biomes)}")
|
|
|
|
if biome_failures:
|
|
print(f"\n--- BIOME-LEVEL FAILURES ---")
|
|
for biome_id, issues in biome_failures:
|
|
print(f" {biome_id}:")
|
|
for issue in issues:
|
|
print(f" - {issue}")
|
|
else:
|
|
print("\nALL BIOMES PASS aggregate criteria.")
|
|
|
|
# Population curve for temperate_forest seed=42
|
|
if "temperate_forest" in biome_results:
|
|
for r in biome_results["temperate_forest"]:
|
|
if r.get("pop_curve"):
|
|
print(f"\n--- temperate_forest population curve ---")
|
|
for turn, pop in r["pop_curve"]:
|
|
bar = "#" * min(pop // 1, 60)
|
|
print(f" t={turn:3d} pop={pop:3d} {bar}")
|
|
break
|
|
|
|
return 0 if not biome_failures else 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|