ncpy 0.2.0

A Python package for numerical computing, including root-finding, interpolation, integration, differentiation, and linear system solvers.

ncpy

ncpy — Numerical Computing in Python.

ncpy is a compact, educational Python library that implements common numerical methods for courses, assignments, and quick prototyping.
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

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Why use ncpy?

✅ 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

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✨ Features Overview

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

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Examples

• Root finding - Newton Raphson

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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)  # ~2.0

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📦 Installation

pip install ncpy

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📍 Visitors

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