NeuralBlitz What I do
- Design safe systems
- Implement safety-critical code
- Handle errors gracefully
- Prevent unsafe states
- Test for failure conditions
When to use me
When building systems where failures could cause harm.
Safety Engineering
Failure Mode Analysis
from enum import Enum
class FailureSeverity(Enum):
CATASTROPHIC = 1 # Loss of life
CRITICAL = 2 # Severe injury
MARGINAL = 3 # Minor injury
NEGLIGIBLE = 4 # No injury
class FailureModeAnalysis:
"""Analyze potential failure modes"""
def __init__(self):
self.failure_modes = []
def add_failure_mode(self, component: str, failure_mode: str,
cause: str, effect: str, severity: FailureSeverity):
"""Add failure mode to analysis"""
self.failure_modes.append({
"component": component,
"failure_mode": failure_mode,
"cause": cause,
"effect": effect,
"severity": severity.value,
"detection": "unknown",
"mitigation": "none"
})
def prioritize_mitigation(self) -> list:
"""Order failure modes by priority"""
return sorted(
self.failure_modes,
key=lambda f: f["severity"]
)
Defensive Programming
class DefensiveInputValidation:
"""Validate all inputs"""
def validate_and_convert(self, value: Any, expected_type: type,
default: Any = None) -> Any:
"""Validate and safely convert input"""
try:
if value is None:
if default is not None:
return default
raise ValueError("Required value is None")
if not isinstance(value, expected_type):
value = expected_type(value)
return self._validate_range(value)
except (ValueError, TypeError):
if default is not None:
return default
raise
def _validate_range(self, value) -> Any:
"""Validate value is within acceptable range"""
return value # Override with range checks
class SafeStateMachine:
"""Implement fail-safe state machine"""
STATES = ["initializing", "ready", "processing", "paused", "error", "stopped"]
SAFE_STATE = "error"
def __init__(self):
self.state = "initializing"
self.previous_state = None
def transition(self, new_state: str) -> bool:
"""Validate and perform state transition"""
if new_state not in self.STATES:
return False
if not self._is_valid_transition(self.state, new_state):
return False
self.previous_state = self.state
self.state = new_state
return True
def _is_valid_transition(self, from_state: str,
to_state: str) -> bool:
"""Define valid state transitions"""
valid_transitions = {
"initializing": ["ready", "error"],
"ready": ["processing", "stopped", "error"],
"processing": ["ready", "paused", "stopped", "error"],
"paused": ["ready", "stopped", "error"],
"error": ["stopped"],
"stopped": []
}
return to_state in valid_transitions.get(from_state, [])
def handle_failure(self):
"""Move to safe state on failure"""
self.previous_state = self.state
self.state = self.SAFE_STATE
Error Handling
import logging
class SafeErrorHandler:
"""Handle errors safely"""
def __init__(self):
self.logger = logging.getLogger(__name__)
def handle_exception(self, exception: Exception,
context: Dict) -> None:
"""Handle exception with appropriate action"""
error_type = type(exception).__name__
# Log the error
self.logger.error(
f"Error in {context.get('operation')}: {error_type}",
extra=context,
exc_info=True
)
# Take corrective action based on severity
if self._is_critical_error(exception):
self._trigger_safe_mode()
else:
self._attempt_recovery(exception, context)
def _is_critical_error(self, exception: Exception) -> bool:
"""Determine if error is critical"""
critical_errors = [
MemoryError,
SystemError,
KeyboardInterrupt # Don't catch Ctrl+C
]
return any(isinstance(exception, e) for e in critical_errors)
def _trigger_safe_mode(self):
"""Enter safe mode for critical errors"""
# Save state
# Stop operations gracefully
# Alert operators
pass
Resource Safety
class ResourceSafety:
"""Ensure safe resource management"""
@contextmanager
def managed_resource(self, resource, cleanup: Callable = None):
"""Safely manage resource lifecycle"""
try:
yield resource
finally:
if cleanup:
cleanup()
elif hasattr(resource, "close"):
resource.close()
def safe_allocation(self, size: int, max_size: int) -> bytes:
"""Safely allocate memory"""
if size > max_size:
raise MemoryError(f"Requested {size} exceeds max {max_size}")
return np.empty(size, dtype=np.uint8)
class ConcurrencySafety:
"""Thread-safe operations"""
def __init__(self):
self.lock = threading.RLock()
@contextmanager
def critical_section(self):
"""Protect critical section"""
with self.lock:
yield
def atomic_update(self, update_fn: Callable) -> Any:
"""Atomically update shared state"""
with self.lock:
return update_fn()