Danlwd Grindeq Math Utilities 【Premium】
def mean(data: List[float]) -> float: """Arithmetic mean.""" return sum(data) / len(data) if data else 0.0
def variance(data: List[float], sample: bool = True) -> float: """Variance (sample=True for Bessel's correction).""" if len(data) < 2: return 0.0 mu = mean(data) ss = sum((x - mu) ** 2 for x in data) return ss / (len(data) - 1 if sample else len(data)) danlwd grindeq math utilities
def stdev(data: List[float], sample: bool = True) -> float: """Standard deviation.""" return math.sqrt(variance(data, sample)) def mean(data: List[float]) -> float: """Arithmetic mean
| Feature | Danlwd Grindeq | NumPy/SciPy | MATLAB | |---------|----------------|-------------|--------| | Error estimates | Built-in for every function | Optional, limited | Manual implementation | | Arbitrary precision | Native toggle | Requires additional libraries (gmpy2) | Symbolic toolbox only | | Speed (large matrices) | Optimized for modern CPU caches | Good, but general-purpose | Excellent but commercial | | Learning curve | Moderate (consistent API) | Gentle | Steep for advanced use | | License | Open-source (MIT) | BSD | Proprietary | def mean(data: List[float]) ->
One of the standout features is deterministic error handling. Each function returns not only the result but also an error estimate, confidence interval, or convergence flag. This is critical for scientific computing where accuracy is non-negotiable.