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simulation_manager.py
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387 lines (327 loc) · 12.7 KB
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"""
Simulation Manager for the Boids Interactive Demo.
Wraps FlockOptimized, handles parameter updates, and produces frame data
for WebSocket streaming.
"""
import time
from typing import Dict, Any, Optional, List
import numpy as np
from boids import FlockOptimized, SimulationParams as FlockSimParams
from boids.metrics import (
compute_avg_distance_to_predator,
compute_min_distance_to_predator,
compute_flock_cohesion,
)
from config import SIMULATION_WIDTH, SIMULATION_HEIGHT, TARGET_FPS, DEFAULT_PARAMS
from models import SimulationParams, FrameData, FrameMetrics
class SimulationManager:
"""
Manages simulation state for a single client.
Handles:
- Flock initialization and updates
- Parameter changes (with flock recreation when needed)
- Frame data serialization
- Pause/resume functionality
- FPS tracking
"""
def __init__(
self,
params: Optional[SimulationParams] = None,
seed: Optional[int] = None
):
"""
Initialize simulation manager.
Args:
params: Initial simulation parameters (uses defaults if None)
seed: Random seed for reproducibility (random if None)
"""
self._params = params or SimulationParams()
self._seed = seed
self._flock: Optional[FlockOptimized] = None
self._frame_id: int = 0
self._paused: bool = False
self._running: bool = False
# FPS tracking
self._last_frame_time: float = time.time()
self._fps: float = TARGET_FPS
self._fps_samples: List[float] = []
# Initialize flock
self._init_flock()
def _init_flock(self) -> None:
"""Create flock from current parameters."""
if self._seed is not None:
np.random.seed(self._seed)
# Convert our params to FlockSimParams
flock_params = FlockSimParams(
width=SIMULATION_WIDTH,
height=SIMULATION_HEIGHT,
visual_range=self._params.visual_range,
protected_range=self._params.protected_range,
max_speed=self._params.max_speed,
min_speed=self._params.min_speed,
cohesion_factor=self._params.cohesion_factor,
alignment_factor=self._params.alignment_factor,
separation_strength=self._params.separation_strength,
margin=self._params.margin,
turn_factor=self._params.turn_factor,
predator_speed=self._params.predator_speed,
predator_avoidance_strength=self._params.predator_avoidance_strength,
predator_detection_range=self._params.predator_detection_range,
predator_hunting_strength=self._params.predator_hunting_strength,
)
self._flock = FlockOptimized(
num_boids=self._params.num_boids,
params=flock_params,
enable_predator=self._params.predator_enabled,
num_predators=self._params.num_predators
)
# =========================================================================
# Lifecycle
# =========================================================================
def start(self) -> None:
"""Start the simulation."""
self._running = True
self._last_frame_time = time.time()
def stop(self) -> None:
"""Stop the simulation."""
self._running = False
def pause(self) -> None:
"""Pause the simulation (frames still sent, but no updates)."""
self._paused = True
def resume(self) -> None:
"""Resume the simulation."""
self._paused = False
@property
def is_running(self) -> bool:
"""Whether simulation is running."""
return self._running
@property
def is_paused(self) -> bool:
"""Whether simulation is paused."""
return self._paused
# =========================================================================
# Simulation Update
# =========================================================================
def update(self) -> None:
"""Advance simulation by one frame (if not paused)."""
if self._paused:
return
self._flock.update()
self._frame_id += 1
# Update FPS tracking
now = time.time()
delta = now - self._last_frame_time
if delta > 0:
instant_fps = 1.0 / delta
self._fps_samples.append(instant_fps)
# Keep last 30 samples for averaging
if len(self._fps_samples) > 30:
self._fps_samples.pop(0)
self._fps = sum(self._fps_samples) / len(self._fps_samples)
self._last_frame_time = now
def reset(self) -> None:
"""Reset simulation with current parameters."""
self._frame_id = 0
self._fps_samples = []
self._init_flock()
# =========================================================================
# Parameter Management
# =========================================================================
def update_params(self, updates: Dict[str, Any]) -> None:
"""
Update simulation parameters.
Args:
updates: Dictionary of parameter updates (partial)
"""
# Check if num_boids changed (requires flock recreation)
needs_recreation = (
'num_boids' in updates and updates['num_boids'] != self._params.num_boids
)
predator_toggled = (
'predator_enabled' in updates and
updates['predator_enabled'] != self._params.predator_enabled
)
num_predators_changed = (
'num_predators' in updates and
updates['num_predators'] != self._params.num_predators
)
# Apply updates to params
current_dict = self._params.to_dict()
current_dict.update(updates)
# Validate and create new params
try:
self._params = SimulationParams(**current_dict)
except Exception:
# If validation fails, keep old params
return
if needs_recreation:
# Full recreation needed for num_boids change
self._init_flock()
elif predator_toggled:
# Toggle predator without full recreation
if self._params.predator_enabled:
self._flock.set_num_predators(self._params.num_predators)
else:
self._flock.set_num_predators(0)
elif num_predators_changed and self._params.predator_enabled:
# Update number of predators
self._flock.set_num_predators(self._params.num_predators)
else:
# Update flock params in place
self._update_flock_params()
def _update_flock_params(self) -> None:
"""Update flock parameters without recreation."""
self._flock.params.visual_range = self._params.visual_range
self._flock.params.protected_range = self._params.protected_range
self._flock.params.max_speed = self._params.max_speed
self._flock.params.min_speed = self._params.min_speed
self._flock.params.cohesion_factor = self._params.cohesion_factor
self._flock.params.alignment_factor = self._params.alignment_factor
self._flock.params.separation_strength = self._params.separation_strength
self._flock.params.margin = self._params.margin
self._flock.params.turn_factor = self._params.turn_factor
self._flock.params.predator_speed = self._params.predator_speed
self._flock.params.predator_avoidance_strength = self._params.predator_avoidance_strength
self._flock.params.predator_detection_range = self._params.predator_detection_range
self._flock.params.predator_hunting_strength = self._params.predator_hunting_strength
def get_params(self) -> SimulationParams:
"""Get current parameters."""
return self._params
def get_params_dict(self) -> Dict[str, Any]:
"""Get current parameters as dictionary."""
return self._params.to_dict()
# =========================================================================
# Frame Data
# =========================================================================
def get_frame_data(self) -> FrameData:
"""
Get current frame data for sending to client.
Returns:
FrameData with boids, predators, obstacles, and metrics
"""
# Serialize boids: [[x, y, vx, vy], ...]
boids_data = [
[b.x, b.y, b.vx, b.vy]
for b in self._flock.boids
]
# Serialize all predators with strategy info
predators_data = [
{
"x": p.x,
"y": p.y,
"vx": p.vx,
"vy": p.vy,
"strategy": p.strategy.value,
"strategy_name": p.strategy_name
}
for p in self._flock.predators
]
# First predator for backward compatibility
predator_data = None
if self._flock.predators:
p = self._flock.predators[0]
predator_data = [p.x, p.y, p.vx, p.vy]
# Serialize obstacles
obstacles_data = [
[obs.x, obs.y, obs.radius]
for obs in self._flock.obstacles
]
# Compute metrics if predator is active (uses first predator)
metrics = None
if self._flock.predator is not None:
metrics = FrameMetrics(
fps=round(self._fps, 1),
avg_distance_to_predator=round(
compute_avg_distance_to_predator(
self._flock.boids, self._flock.predator
), 1
),
min_distance_to_predator=round(
compute_min_distance_to_predator(
self._flock.boids, self._flock.predator
), 1
),
flock_cohesion=round(
compute_flock_cohesion(self._flock.boids), 1
)
)
else:
metrics = FrameMetrics(fps=round(self._fps, 1))
return FrameData(
frame_id=self._frame_id,
boids=boids_data,
predator=predator_data,
predators=predators_data,
obstacles=obstacles_data,
metrics=metrics
)
# =========================================================================
# Properties
# =========================================================================
@property
def frame_id(self) -> int:
"""Current frame number."""
return self._frame_id
@property
def num_boids(self) -> int:
"""Current number of boids."""
return len(self._flock.boids)
@property
def has_predator(self) -> bool:
"""Whether predator is active."""
return self._flock.predator is not None
@property
def fps(self) -> float:
"""Current frames per second."""
return self._fps
# =========================================================================
# Obstacle Management
# =========================================================================
def add_obstacle(self, x: float, y: float, radius: float = 30.0) -> Dict[str, Any]:
"""
Add an obstacle to the simulation.
Args:
x: X position (center)
y: Y position (center)
radius: Obstacle radius
Returns:
Dictionary with obstacle data and index
"""
obstacle = self._flock.add_obstacle(x, y, radius)
index = len(self._flock.obstacles) - 1
return {
'index': index,
'x': obstacle.x,
'y': obstacle.y,
'radius': obstacle.radius
}
def remove_obstacle(self, index: int) -> bool:
"""
Remove an obstacle by index.
Args:
index: Index of obstacle to remove
Returns:
True if removed, False if invalid index
"""
return self._flock.remove_obstacle(index)
def clear_obstacles(self) -> int:
"""
Remove all obstacles.
Returns:
Number of obstacles removed
"""
return self._flock.clear_obstacles()
def get_obstacles(self) -> List[Dict[str, Any]]:
"""
Get all obstacles.
Returns:
List of obstacle dictionaries
"""
return [
{'x': obs.x, 'y': obs.y, 'radius': obs.radius}
for obs in self._flock.obstacles
]
@property
def num_obstacles(self) -> int:
"""Current number of obstacles."""
return len(self._flock.obstacles)