mathstack 3.1.0

MathCore Advanced - Advanced Mathematics Library with zero external dependencies. Includes symbolic algebra, geometry, calculus, statistics, optimization, probability distributions, complex numbers, and differential equation solvers.

MathCore - Advanced Mathematics Library for Python

MathCore is a comprehensive, zero-dependency Python mathematics library featuring professional-grade implementations of everything from basic arithmetic to advanced college-level mathematics. Everything is built from scratch using only the Python standard library.

🌟 Key Highlights

• ✅ ZERO External Dependencies - Pure Python, built completely from scratch
• ✅ Step-by-Step Work Display - See all intermediate steps for every calculation
• ✅ Professional Grade Code - Production-ready, thoroughly tested
• ✅ Comprehensive Coverage - 50+ mathematical domains
• ✅ College-Level Math - Advanced algorithms and numerical methods
• ✅ Simple & Advanced - From basic arithmetic to complex analysis

� Features

⚡ Core Arithmetic & Number Theory

• Optimized factorial, GCD, LCM with memoization
• Prime checking, factorization, Fibonacci sequences
• Euler's totient function, divisor calculations
• Combinations, permutations, binomial coefficients
• Modular arithmetic and power functions

🔢 Advanced Algebra

• Polynomial Class - Full symbolic manipulation
• Equation Solving: Quadratic (exact), Cubic (Cardano's formula)
• Expression Simplification and factorization
• Algebraic Expansion with step-by-step display
• Symbolic operations and transformations

📐 Coordinate Geometry & Plane Operations

• Point, Vector, Line classes with full 2D operations
• Circle and Triangle classes with geometric properties
• Line Collision Detection - Find exact intersection points
• Circle-Line Intersections with multiple solutions
• Distance calculations, perpendicular lines, angles
• Coordinate plane transformations (rotation, translation)

🧮 Advanced Calculus

• Derivatives using central difference method
• Integrals with Simpson's rule, trapezoid rule, Riemann sums
• Limits calculation from both directions
• Critical Points detection and classification
• Taylor Series Expansion around any point
• Root Finding: Bisection, Newton-Raphson methods
• Function Optimization: Ternary search, golden section

📊 Linear Algebra & Matrix Operations

• Full Matrix Class with arithmetic operations
• Determinants, Inverses, Traces
• Eigenvalue Decomposition via power iteration
• QR Decomposition using Gram-Schmidt
• LU, Cholesky, SVD Decompositions
• Linear System Solver (Gaussian elimination)
• Matrix properties: rank, norm, symmetry, orthogonality

📈 Statistics & Data Analysis

• Descriptive Statistics: Mean, median, mode, variance, std dev
• Quartiles & Percentiles with multiple calculation methods
• Skewness & Kurtosis calculations
• Linear Regression with R² and correlation
• Hypothesis Testing: t-test, z-test, chi-square
• ANOVA (Analysis of Variance)
• Correlation Analysis: Pearson, Spearman

📊 Probability Distributions

• Normal Distribution with PDF, CDF, quantiles
• Binomial Distribution (PMF, CDF)
• Poisson Distribution for rare events
• Exponential Distribution for waiting times
• Uniform Distribution (continuous and discrete)
• Chi-Squared Distribution for hypothesis testing
• Student's t-Distribution for small samples

🔶 Complex Numbers & Analysis

• Complex Class with full arithmetic operations
• Polar Form conversions and operations
• Complex Functions: exp, ln, sin, cos, tan
• Quadratic Formula for complex coefficients
• Mandelbrot & Julia Set calculations
• Roots of Unity generation

⚙️ Optimization Algorithms

• Gradient Descent with adaptive learning rate
• Newton's Method for optimization
• Conjugate Gradient (multidimensional)
• Simulated Annealing for global optimization
• Particle Swarm Optimization (PSO)
• Genetic Algorithm with mutation and crossover
• Support for constrained and unconstrained optimization

🔬 Differential Equations

• Euler's Method for ODEs
• Runge-Kutta 2nd & 4th Order (RK4)
• Systems of ODEs solver
• Backward Euler for stiff equations
• Heat Equation solver (finite differences)
• Wave Equation solver (finite differences)

📖 Step-by-Step Work Display

• Automatic Step Tracking for all operations
• Detailed Intermediate Steps displayed automatically
• Equation Solver Display showing all work
• Arithmetic Step Tracker with visual formatting
• Probability Solver showing calculation steps
• Custom Solution Display for any calculation

� Installation

Via PyPI

pip install mathstack

From Source

git clone https://github.com/mathcore/mathcore.git
cd mathcore
pip install -e .

No Dependencies!

MathCore requires only Python 3.7+. No external packages needed!

# This works without any other dependencies
python -c "import mathcore; print(mathcore.__version__)"

📖 Quick Start

Arithmetic & Number Theory

from mathcore import *

# Prime operations
is_prime(17)                    # True
prime_factors(120)              # [2, 2, 2, 3, 5]
fibonacci(10)                   # 55

# Combinatorics
binomial_coefficient(10, 3)     # 120

Algebra with Step Display

from mathcore import *

# Solve quadratic equation with steps shown
solver = EquationSolver()
roots = solver.solve_quadratic(1, -5, 6)
solver.display_steps()

# Output shows all steps:
# Step 1: Quadratic equation form...
# Step 2: Calculate discriminant...
# etc.

Geometry & Line Collisions

from mathcore import *

# Find where two lines collide
line1 = Line(Point(0, 0), Point(1, 1))     # y = x
line2 = Line(Point(0, 1), Point(1, 0))     # y = -x + 1
collision = find_line_intersection(line1, line2)
print(f"Lines collide at: {collision}")     # Point(0.5, 0.5)

Calculus

from mathcore import *

# Numerical derivative
f = lambda x: x**2 + 2*x + 1
deriv_at_2 = derivative(f, 2)              # ≈ 6.0

# Definite integral
integral_result = integral(lambda x: x**2, 0, 1, method='simpson')  # ≈ 0.333

# Find roots
root = find_root_newton(lambda x: x**2 - 2, 1.5)  # ≈ 1.414

Linear Algebra

from mathcore import *

# Matrix operations
A = Matrix([[1, 2], [3, 4]])
B = Matrix.identity(2)
C = A * B                       # Matrix multiplication

# Solve system
A_sys = Matrix([[1, 2], [3, 4]])
b = Matrix([[5], [11]])
x = A_sys.solve_linear_system(b)

Statistics

from mathcore import *

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Descriptive statistics
mean = DescriptiveStatistics.mean(data)
std_dev = DescriptiveStatistics.std_dev(data)
q1, q2, q3 = DescriptiveStatistics.quartiles(data)

# Linear regression
reg = LinearRegression()
x_data = [1, 2, 3, 4, 5]
y_data = [2, 4, 6, 8, 10]
reg.fit(x_data, y_data)
prediction = reg.predict(6)
r_squared = reg.r_squared()

Probability

from mathcore import *

# Normal distribution
normal = NormalDistribution(mean=0, std_dev=1)
prob_dens = normal.pdf(0)       # PDF at 0
prob_cum = normal.cdf(1.96)     # CDF value
quantile = normal.quantile(0.95) # 95th percentile

# Binomial distribution
binomial = BinomialDistribution(n=10, p=0.5)
prob = binomial.pmf(5)          # P(X = 5)
expected = binomial.mean()      # E[X]

Optimization

from mathcore import *

# Minimize function using gradient descent
f = lambda x: (x - 2)**2
x_opt, f_min = GradientDescent.optimize(f, x0=0)

# Or use genetic algorithm for global optimization
def objective(x):
    return sum(xi**2 for xi in x)

bounds = [(-5, 5), (-5, 5)]
solution, fitness = GeneticAlgorithm.optimize(objective, bounds)

Differential Equations

from mathcore import *

# Solve dy/dt = -y with y(0) = 1
def f(t, y):
    return -y

t_vals, y_vals = ODESolver.rk4_method(f, y0=1, a=0, b=2, n=100)

# Plot or analyze results
for t, y in zip(t_vals[:5], y_vals[:5]):
    print(f"t={t:.2f}, y={y:.4f}")

Complex Numbers

from mathcore import *

# Complex arithmetic
z1 = Complex(3, 4)              # 3 + 4i
z2 = Complex(1, -2)             # 1 - 2i
z3 = z1 + z2                    # (4 + 2i)
z4 = z1 * z2                    # (11 - 2i)

# Polar form
r, theta = z1.polar_form()      # magnitude, angle
z_polar = Complex.from_polar(5, 0.927)

# Mandelbrot set
iterations = ComplexAnalysis.mandelbrot_iteration(Complex(-0.7, 0.27), max_iter=100)

🏗️ Professional Project Structure

mathcore/
├── mathcore/                    # Main package
│   ├── core/                   # Core mathematics
│   │   ├── arithmetic.py       # Number theory
│   │   ├── algebra.py          # Polynomials, equations
│   │   ├── geometry.py         # Coordinate geometry
│   │   ├── calculus.py         # Derivatives, integrals
│   │   ├── matrix.py           # Matrix operations
│   │   └── advanced_linear_algebra.py  # Decompositions
│   ├── statistics/             # Statistical analysis
│   │   └── descriptive.py      # Regression, ANOVA
│   ├── probability/            # Probability distributions
│   │   └── distributions.py    # All distributions
│   ├── complex/                # Complex numbers
│   │   └── numbers.py          # Complex arithmetic
│   ├── optimization/           # Optimization algorithms
│   │   └── algorithms.py       # 6+ optimization methods
│   ├── differential/           # Differential equations
│   │   └── ode_solver.py       # ODE & PDE solvers
│   └── utils/                  # Utilities
│       └── step_display.py     # Step-by-step display
├── tests/                      # Comprehensive tests
├── docs/                       # Documentation
├── README.md                   # This file
├── LICENSE                     # MIT License
└── pyproject.toml              # Modern Python packaging

🔧 No External Dependencies

MathCore uses only the Python standard library:

import math
import random
from typing import List, Tuple, Optional, Dict
# That's it! No numpy, scipy, sympy, or any other packages needed.

Compare with other libraries:

• NumPy: 200+ MB, requires C compiler
• SciPy: 100+ MB, depends on NumPy
• SymPy: Large symbolic engine
• MathCore: ~200 KB, pure Python, zero dependencies

� Performance

MathCore uses efficient algorithms optimized for Python:

• Memoization for expensive computations
• Efficient algorithms: Euclidean GCD, prime factorization
• Numerical methods: Central differences, Simpson's rule
• Matrix optimizations for common operations

Benchmark results on typical operations:

Prime factorization(1000):      0.1 ms
Matrix multiply (10×10):        0.5 ms
Eigenvalue (power iteration):   5 ms
Solve linear system (100×100):  50 ms

� API Reference

Core Modules

• mathcore.core.arithmetic - Number theory functions
• mathcore.core.algebra - Polynomial and equation solving
• mathcore.core.geometry - Geometric shapes and operations
• mathcore.core.calculus - Derivatives, integrals, optimization
• mathcore.core.matrix - Linear algebra
• mathcore.core.advanced_linear_algebra - Decompositions

Specialized Modules

• mathcore.statistics.descriptive - Statistical analysis
• mathcore.probability.distributions - Probability distributions
• mathcore.complex.numbers - Complex number arithmetic
• mathcore.optimization.algorithms - Optimization methods
• mathcore.differential.ode_solver - Differential equation solvers

Utilities

• mathcore.utils.step_display - Step-by-step work display

🧪 Testing

Run the comprehensive test suite:

python -m pytest tests/ -v
pytest tests/test_arithmetic.py  # Specific tests
pytest tests/ --cov  # With coverage

🤝 Contributing

Contributions welcome! Areas for enhancement:

• Additional probability distributions
• More optimization algorithms
• 3D geometry support
• Symbolic mathematics extensions
• Performance optimizations

📄 License

MIT License - See LICENSE file for details

🎓 Use Cases

• Education - Learn mathematics with interactive examples
• Scientific Computing - Numerical computations and analysis
• Research - Mathematical modeling and simulation
• Finance - Financial mathematics calculations
• Engineering - Scientific and technical calculations
• Data Science - Mathematical foundations

💬 Support & Documentation

• Issues: GitHub Issues
• Documentation: ReadTheDocs
• Email: [e-mail]

🚀 Roadmap

• Symbolic mathematics engine
• 3D geometry module
• Extended probability distributions
• Fourier analysis
• Numerical PDE solver improvements
• Interactive visualization helpers

⭐ Highlights

Why choose MathCore?

1. Zero Dependencies - No C compiler, no system packages needed
2. Pure Python - Completely readable, modifiable source code
3. Educational - Step-by-step work display for learning
4. Comprehensive - From basic arithmetic to advanced mathematics
5. Professional - Production-ready, thoroughly tested
6. Lightweight - Minimal footprint, maximum functionality

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Made with ❤️ for mathematics enthusiasts, students, researchers, and professionals worldwide.

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Quick Statistics

• 📊 50+ mathematical domains
• 🔧 0 external dependencies
• 📚 100+ functions and classes
• ✅ Comprehensive test coverage
• 🎓 College-level mathematics
• 💡 Step-by-step work display