Python Scientific Type

A new Python data type (ScientificNumber) that extends Python's numeric types with comprehensive scientific computing capabilities. This type seamlessly integrates mathematical, statistical, and matrix operations into a single, intuitive interface.

Why a New Type?

Unlike regular Python numbers or NumPy arrays, ScientificNumber provides:

• Unified interface for scalar and matrix operations
• Built-in scientific constants and unit conversions
• Automatic type handling between different numerical representations
• Memory management for scientific calculations
• Comprehensive mathematical operations in a single type

Features

• Basic arithmetic operations with type safety
• Matrix operations (determinant, rank, eigenvalues)
• Trigonometric functions
• Statistical functions
• Vector operations
• Unit conversions
• Physical constants
• Memory operations

Installation

pip install -r requirements.txt

Usage

from scientific_calculator import ScientificNumber
from scientific_functions import *

# Create scientific numbers
x = ScientificNumber(5)
y = ScientificNumber(3)

# Basic Operations with automatic type handling
result = BasicOperations.add(x, y)
print(result.value)  # Output: 8

# Matrix creation and operations
matrix = ScientificNumber(np.array([[1, 2], [3, 4]]))  # Create from numpy array
# Or use the utility function
matrix = MatrixOperations.create_matrix(2, 2, [1, 2, 3, 4])

# Matrix operations return ScientificNumber
determinant = MatrixOperations.determinant(matrix)
rank = MatrixOperations.rank(matrix)
eigenvalues = MatrixOperations.eigenvalues(matrix)

# Trigonometric operations with automatic unit handling
angle = ScientificNumber(45)  # degrees
rad = TrigonometricFunctions.deg_to_rad(angle)
sin_value = TrigonometricFunctions.sin(rad)

# Statistical operations on lists or arrays
data = [1, 2, 3, 4, 5]
mean = StatisticalFunctions.mean(data)
std_dev = StatisticalFunctions.std_dev(data)

Type Features

The ScientificNumber type provides:

1. Type Safety: Automatically handles type conversions and validates operations
2. Matrix Support: Seamlessly works with both scalar and matrix values
3. Operation History: Tracks calculations for debugging and analysis
4. Memory Management: Built-in memory operations for scientific calculations
5. Constants: Access to physical and mathematical constants
6. Unit Conversions: Built-in unit conversion capabilities

Classes

1. ScientificNumber: Core data type that encapsulates numerical values
2. BasicOperations: Arithmetic operations for the new type
3. MatrixOperations: Matrix manipulations preserving type safety
4. TrigonometricFunctions: Trigonometric calculations with unit handling
5. StatisticalFunctions: Statistical computations on collections
6. SpecialFunctions: Special mathematical functions
7. VectorOperations: Vector calculations
8. UnitConversion: Unit conversion utilities
9. Constants: Physical and mathematical constants
10. MemoryOperations: Memory management functions

Requirements

• Python 3.6+
• NumPy
• SciPy
• SymPy

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.