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import psutil
import asyncio
import time
import logging
from collections import deque
from threading import Lock
from typing import Dict, List, Optional
from datetime import datetime
logger = logging.getLogger(__name__)
try:
import numpy as np
except Exception:
np = None
class HealthMonitor:
"""Real-time system diagnostics with quantum-aware anomaly detection"""
def __init__(self, history_size: int = 100):
self.metrics = deque(maxlen=history_size)
self.anomaly_history = deque(maxlen=50)
self.lock = Lock()
self.baseline = None
self.last_check = None
self.quantum_influence = 0.5
self.initialized = False
async def initialize(self):
"""Initialize the health monitor system"""
try:
# Get initial status to establish baseline
initial_status = await self.check_status_async()
if np is not None:
self.baseline = np.array([
initial_status["memory"],
initial_status["cpu"],
initial_status["response_time"]
])
else:
self.baseline = [
initial_status["memory"],
initial_status["cpu"],
initial_status["response_time"]
]
self.initialized = True
logger.info("Health monitor initialized successfully")
return True
except Exception as e:
logger.error(f"Health monitor initialization failed: {e}")
return False
def check_status(self, consciousness_state: Optional[Dict] = None) -> Dict:
"""Check system status with quantum consciousness integration - synchronous version"""
try:
# Get base metrics synchronously
status = {
"timestamp": datetime.now(),
"memory": psutil.virtual_memory().percent,
"cpu": psutil.cpu_percent(),
"response_time": self._measure_latency_sync(),
"quantum_coherence": consciousness_state.get("quantum_state", [0.5])[0] if consciousness_state else 0.5
}
# Calculate load score with quantum influence
quantum_factor = status["quantum_coherence"]
load_score = (
0.4 * status["memory"] +
0.4 * status["cpu"] +
0.2 * (status["response_time"] * 1000) # Convert to ms
) * (1 + (quantum_factor - 0.5)) # Quantum modification
status["load_score"] = min(100, max(0, load_score))
# Thread-safe metrics update
with self.lock:
self.metrics.append(status)
anomaly_score = self._detect_anomalies()
status["anomaly_score"] = anomaly_score
# Track anomaly if significant
if anomaly_score > 0.7:
self.anomaly_history.append({
"timestamp": status["timestamp"],
"score": anomaly_score,
"metrics": status.copy()
})
self.last_check = status["timestamp"]
return status
except Exception as e:
logger.error(f"Health check failed: {e}")
return {
"timestamp": datetime.now(),
"status": "error",
"error": str(e)
}
async def check_status_async(self, consciousness_state: Optional[Dict] = None) -> Dict:
"""Check system status with quantum consciousness integration - async version"""
try:
# Get base metrics asynchronously
status = {
"timestamp": datetime.now(),
"memory": psutil.virtual_memory().percent,
"cpu": psutil.cpu_percent(),
"response_time": await self._measure_latency(),
"quantum_coherence": consciousness_state.get("quantum_state", [0.5])[0] if consciousness_state else 0.5
}
# Calculate load score with quantum influence
quantum_factor = status["quantum_coherence"]
load_score = (
0.4 * status["memory"] +
0.4 * status["cpu"] +
0.2 * (status["response_time"] * 1000) # Convert to ms
) * (1 + (quantum_factor - 0.5)) # Quantum modification
status["load_score"] = min(100, max(0, load_score))
# Thread-safe metrics update
with self.lock:
self.metrics.append(status)
anomaly_score = self._detect_anomalies()
status["anomaly_score"] = anomaly_score
# Track anomaly if significant
if anomaly_score > 0.7:
self.anomaly_history.append({
"timestamp": status["timestamp"],
"score": anomaly_score,
"metrics": status.copy()
})
self.last_check = status["timestamp"]
return status
except Exception as e:
logger.error(f"Health check failed: {e}")
return {
"timestamp": datetime.now(),
"status": "error",
"error": str(e)
}
def _measure_latency_sync(self) -> float:
"""Measure system response latency - synchronous version"""
try:
start = time.monotonic()
time.sleep(0.1) # Simulated work
return time.monotonic() - start
except Exception as e:
logger.warning(f"Latency measurement failed: {e}")
return 0.1
async def _measure_latency(self) -> float:
"""Measure system response latency - async version"""
try:
start = time.monotonic()
await asyncio.sleep(0.1) # Simulated work
return time.monotonic() - start
except Exception as e:
logger.warning(f"Latency measurement failed: {e}")
return 0.1
def _detect_anomalies(self) -> float:
"""Detect system anomalies using statistical analysis"""
try:
if len(self.metrics) < 10:
return 0.0
# Extract recent metrics
if np is not None:
recent_data = np.array([
[m["memory"], m["cpu"], m["response_time"]]
for m in list(self.metrics)[-10:]
])
else:
recent_data = [
[m["memory"], m["cpu"], m["response_time"]]
for m in list(self.metrics)[-10:]
]
if self.baseline is None:
if np is not None:
self.baseline = np.mean(recent_data, axis=0)
else:
# Compute simple mean per column
cols = list(zip(*recent_data))
self.baseline = [sum(c)/len(c) for c in cols]
return 0.0
if np is not None:
deviations = np.abs(recent_data - self.baseline)
max_deviation = float(np.max(deviations))
# Update baseline with moving average
self.baseline = 0.9 * self.baseline + 0.1 * np.mean(recent_data, axis=0)
else:
deviations = [[abs(a - b) for a,b in zip(row, self.baseline)] for row in recent_data]
max_deviation = float(max(max(row) for row in deviations))
# Update baseline (python moving average)
cols = list(zip(*recent_data))
means = [sum(c)/len(c) for c in cols]
self.baseline = [0.9*b + 0.1*m for b,m in zip(self.baseline, means)]
# Normalize anomaly score to [0,1]
return min(1.0, max_deviation / 100.0)
except Exception as e:
logger.error(f"Anomaly detection failed: {e}")
return 0.0
def get_health_summary(self) -> Dict:
"""Get system health summary"""
try:
if not self.metrics:
return {"status": "initializing"}
recent_metrics = list(self.metrics)[-10:]
if np is not None:
avg_memory = float(np.mean([m["memory"] for m in recent_metrics]))
avg_cpu = float(np.mean([m["cpu"] for m in recent_metrics]))
avg_latency = float(np.mean([m["response_time"] for m in recent_metrics]))
else:
avg_memory = float(sum(m["memory"] for m in recent_metrics)/len(recent_metrics))
avg_cpu = float(sum(m["cpu"] for m in recent_metrics)/len(recent_metrics))
avg_latency = float(sum(m["response_time"] for m in recent_metrics)/len(recent_metrics))
return {
"status": "healthy" if avg_memory < 80 and avg_cpu < 80 else "stressed",
"avg_memory": avg_memory,
"avg_cpu": avg_cpu,
"avg_latency": avg_latency,
"recent_anomalies": len([a for a in self.anomaly_history if (datetime.now() - a["timestamp"]).seconds < 300]),
"last_check": self.last_check
}
except Exception as e:
logger.error(f"Health summary generation failed: {e}")
return {"status": "error", "error": str(e)} |