ncpy Numerical Computing in Python.
ncpy is a compact, educational Python library that implements common numerical methods for quick prototyping and expermentations.
Built on NumPy and (optionally) SciPy, it offers easy-to-use functions for:
- Root finding
- Interpolation
- Curve fitting / Approximation
- Numerical integration
- Numerical differentiation
- Solving linear systems
- Best Approximations
✅ One package, many methods : no need to import multiple libraries
✅ Lightweight & beginner-friendly : great for teaching & learning numerical methods
✅ Educational : functions are implemented clearly for understanding algorithms
✅ Fast enough : powered by NumPy for efficiency
| Category | Methods |
|---|---|
| Root-finding | Bisection, Newton–Raphson, Secant, Fixed-point iteration |
| Interpolation | Lagrange, Newton divided differences, Linear, Cubic spline, Neville’s method |
| Approximation | Polynomial least squares, Exponential fit, Logarithmic fit |
| Integration | Trapezoidal, Simpson 1/3, Simpson 3/8, Romberg, Gaussian quadrature |
| Differentiation | Forward, Backward, Central differences, Richardson extrapolation, Numerical gradient |
| Linear Systems | Gaussian elimination, Gauss–Jordan, LU decomposition, Jacobi, Gauss–Seidel, Conjugate Gradient |
| Best Approximations | Least Squares, Gram–Schmidt Orthonormalization |
- Root finding - Newton Raphson
from ncpy import newton_raphson
f = lambda x: x**2 - 2
df = lambda x: 2*x
root = newton_raphson(f, df, x0=1.0)
print("Root:", root) # ~1.4142
- Interpolation — Lagrange
from ncpy import lagrange_interpolation
x_points = [0, 1, 2]
y_points = [1, 3, 2]
print(lagrange_interpolation(x_points, y_points, 1.5))
- Numerical Integration — Simpson's 1/3 Rule
from ncpy import simpson13
import math
area = simpson13(math.sin, 0, math.pi, n=100)
print(area) # ~2.0pip install ncpy 