Spaces:
Paused
Paused
File size: 10,570 Bytes
fb867c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 |
"""
Optimized helix caching system for web deployment.
This module provides caching for helix geometry calculations to reduce
computational overhead in web deployments where the same helix configurations
are used repeatedly.
Key Features:
- Pre-computed position caches
- Memory-efficient storage
- Thread-safe access
- Automatic cache warming
"""
import numpy as np
import threading
import hashlib
import pickle
from typing import Dict, Tuple, Optional, List, Any
from dataclasses import dataclass
import logging
from core.helix_geometry import HelixGeometry
logger = logging.getLogger(__name__)
@dataclass
class HelixCacheEntry:
"""Cache entry for helix geometry data."""
helix_params: Tuple[float, float, float, int]
positions: np.ndarray # Pre-computed positions
radii: np.ndarray # Pre-computed radii
angles: np.ndarray # Pre-computed angles
access_count: int = 0
last_accessed: float = 0.0
class HelixCache:
"""
Caching system for helix geometry calculations.
Provides fast access to pre-computed helix positions and properties
to reduce computational overhead in web deployments.
"""
def __init__(self, max_cache_size: int = 50, precompute_steps: int = 100):
"""
Initialize helix cache.
Args:
max_cache_size: Maximum number of helix configurations to cache
precompute_steps: Number of steps to precompute for each helix
"""
self.max_cache_size = max_cache_size
self.precompute_steps = precompute_steps
self._lock = threading.Lock()
self._cache: Dict[str, HelixCacheEntry] = {}
# Pre-warm cache with common configurations
self._warm_cache()
def _warm_cache(self):
"""Pre-populate cache with common helix configurations."""
common_configs = [
# ComplexityLevel.DEMO
(10.0, 0.01, 5.0, 5),
# ComplexityLevel.SIMPLE
(10.0, 0.01, 10.0, 10),
# ComplexityLevel.MEDIUM
(10.0, 0.01, 20.0, 20),
# ComplexityLevel.COMPLEX
(10.0, 0.01, 30.0, 30),
# ComplexityLevel.RESEARCH (original)
(33.0, 0.001, 33.0, 33),
]
for config in common_configs:
self._compute_and_cache(*config)
logger.info(f"Cache warmed with {len(common_configs)} common configurations")
def _make_cache_key(self, top_radius: float, bottom_radius: float,
height: float, turns: int) -> str:
"""Generate cache key from helix parameters."""
params = f"{top_radius}_{bottom_radius}_{height}_{turns}"
return hashlib.md5(params.encode()).hexdigest()
def get_helix_data(self, top_radius: float, bottom_radius: float,
height: float, turns: int) -> Optional[HelixCacheEntry]:
"""
Get cached helix data if available.
Args:
top_radius: Radius at the top of the helix
bottom_radius: Radius at the bottom of the helix
height: Total vertical height
turns: Number of complete rotations
Returns:
Cached helix data or None if not cached
"""
cache_key = self._make_cache_key(top_radius, bottom_radius, height, turns)
with self._lock:
if cache_key in self._cache:
entry = self._cache[cache_key]
entry.access_count += 1
entry.last_accessed = time.time()
# Move to end for LRU
self._cache.move_to_end(cache_key)
return entry
# Not in cache, compute and store
return self._compute_and_cache(top_radius, bottom_radius, height, turns)
def _compute_and_cache(self, top_radius: float, bottom_radius: float,
height: float, turns: int) -> HelixCacheEntry:
"""Compute helix data and add to cache."""
# Create helix geometry
helix = HelixGeometry(top_radius, bottom_radius, height, turns)
# Pre-compute positions at regular intervals
t_values = np.linspace(0, 1, self.precompute_steps)
positions = np.zeros((self.precompute_steps, 3))
radii = np.zeros(self.precompute_steps)
angles = np.zeros(self.precompute_steps)
for i, t in enumerate(t_values):
x, y, z = helix.get_position(t)
positions[i] = [x, y, z]
radii[i] = helix.get_radius(z)
angles[i] = 2 * np.pi * turns * t
# Create cache entry
entry = HelixCacheEntry(
helix_params=(top_radius, bottom_radius, height, turns),
positions=positions,
radii=radii,
angles=angles
)
# Add to cache
cache_key = self._make_cache_key(top_radius, bottom_radius, height, turns)
with self._lock:
# Evict oldest if at capacity
if len(self._cache) >= self.max_cache_size:
# Remove least recently used
oldest_key = next(iter(self._cache))
del self._cache[oldest_key]
self._cache[cache_key] = entry
logger.debug(f"Cached helix configuration: {turns} turns, {height} height")
return entry
def interpolate_position(self, entry: HelixCacheEntry, t: float) -> Tuple[float, float, float]:
"""
Interpolate position from cached data.
Args:
entry: Cached helix data
t: Parameter value (0-1)
Returns:
Interpolated (x, y, z) position
"""
# Find surrounding cached points
scaled_t = t * (self.precompute_steps - 1)
idx_low = int(np.floor(scaled_t))
idx_high = min(idx_low + 1, self.precompute_steps - 1)
# Interpolation weight
weight = scaled_t - idx_low
# Linear interpolation
pos_low = entry.positions[idx_low]
pos_high = entry.positions[idx_high]
interpolated = pos_low * (1 - weight) + pos_high * weight
return tuple(interpolated)
def get_cached_helix(self, top_radius: float, bottom_radius: float,
height: float, turns: int) -> 'CachedHelixGeometry':
"""
Get a cached helix geometry object.
Returns a helix-like object that uses cached data for fast lookups.
"""
entry = self.get_helix_data(top_radius, bottom_radius, height, turns)
return CachedHelixGeometry(self, entry)
def get_statistics(self) -> Dict[str, Any]:
"""Get cache statistics."""
with self._lock:
total_accesses = sum(e.access_count for e in self._cache.values())
avg_accesses = total_accesses / len(self._cache) if self._cache else 0
return {
"cache_size": len(self._cache),
"max_size": self.max_cache_size,
"total_accesses": total_accesses,
"average_accesses": avg_accesses,
"precompute_steps": self.precompute_steps
}
def clear(self):
"""Clear the cache."""
with self._lock:
self._cache.clear()
logger.info("Helix cache cleared")
class CachedHelixGeometry:
"""
Helix geometry wrapper that uses cached data.
Provides the same interface as HelixGeometry but uses
pre-computed cached data for fast lookups.
"""
def __init__(self, cache: HelixCache, entry: HelixCacheEntry):
"""
Initialize cached helix geometry.
Args:
cache: Parent cache instance
entry: Cache entry with pre-computed data
"""
self.cache = cache
self.entry = entry
# Extract parameters
self.top_radius = entry.helix_params[0]
self.bottom_radius = entry.helix_params[1]
self.height = entry.helix_params[2]
self.turns = entry.helix_params[3]
def get_position(self, t: float) -> Tuple[float, float, float]:
"""
Get position at parameter t using cached data.
Args:
t: Parameter value (0-1)
Returns:
(x, y, z) position
"""
if not (0.0 <= t <= 1.0):
raise ValueError("t must be between 0 and 1")
return self.cache.interpolate_position(self.entry, t)
def get_radius(self, z: float) -> float:
"""
Get radius at height z using cached data.
Args:
z: Height value
Returns:
Radius at height z
"""
# Convert z to t parameter
t = 1.0 - (z / self.height) if self.height > 0 else 0.0
t = max(0.0, min(1.0, t))
# Interpolate from cached radii
scaled_t = t * (len(self.entry.radii) - 1)
idx_low = int(np.floor(scaled_t))
idx_high = min(idx_low + 1, len(self.entry.radii) - 1)
weight = scaled_t - idx_low
return self.entry.radii[idx_low] * (1 - weight) + self.entry.radii[idx_high] * weight
def get_velocity(self, t: float) -> Tuple[float, float, float]:
"""
Get velocity vector at parameter t.
Uses finite differences on cached data.
"""
dt = 0.01
t1 = max(0, t - dt / 2)
t2 = min(1, t + dt / 2)
pos1 = self.get_position(t1)
pos2 = self.get_position(t2)
actual_dt = t2 - t1
if actual_dt > 0:
velocity = tuple((p2 - p1) / actual_dt for p1, p2 in zip(pos1, pos2))
else:
velocity = (0.0, 0.0, 0.0)
return velocity
def calculate_arc_length(self, t1: float = 0.0, t2: float = 1.0,
steps: int = 100) -> float:
"""Calculate arc length between two parameter values."""
# Use cached positions for fast calculation
t_values = np.linspace(t1, t2, steps)
total_length = 0.0
for i in range(1, len(t_values)):
pos1 = self.get_position(t_values[i - 1])
pos2 = self.get_position(t_values[i])
segment_length = np.sqrt(sum((p2 - p1) ** 2 for p1, p2 in zip(pos1, pos2)))
total_length += segment_length
return total_length
# Global cache instance
_global_helix_cache: Optional[HelixCache] = None
def get_helix_cache() -> HelixCache:
"""Get or create global helix cache instance."""
global _global_helix_cache
if _global_helix_cache is None:
_global_helix_cache = HelixCache()
return _global_helix_cache
# Import time at the end to avoid circular import
import time |