Metadata-Version: 2.4
Name: ncpy
Version: 0.2.4
Summary: A Python package for numerical computing, including root-finding, interpolation, integration, differentiation, and linear system solvers.
Home-page: https://github.com/muhammadalmaskhan/ncpy
Author: M. Almas
Author-email: mkhan@cs.qau.edu.pk
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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Dynamic: license
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# ncpy
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</p>

**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

---

## 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  

---

## ✨ 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 |
| **Best Approximations**| Least Squares, Gram–Schmidt Orthonormalization |
---
## Examples
---

- Root finding - Newton Raphson
```bash
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

```bash
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

```bash
from ncpy import simpson13
import math

area = simpson13(math.sin, 0, math.pi, n=100)
print(area)  # ~2.0
```

--- 
## 📦 Installation

```bash
pip install ncpy
```
---


 
## 📍 Visitors
<a href="https://clustrmaps.com/site/1c7ls" title="ClustrMaps"> <img src="https://www.clustrmaps.com/map_v2.png?d=GL51q-0KRQlIUM81XiBM7GahOkVrKk88t7NjiXWTcaI&cl=ffffff" /> </a> ```
