matpy-linalg 0.2.0

A comprehensive Python library for vector and matrix operations with a clean, Pythonic API

MatPy

A comprehensive Python library for vector and matrix operations with a clean, Pythonic API. MatPy provides an intuitive interface for linear algebra operations, making it perfect for educational purposes, scientific computing, and mathematical applications.

Features

🎯 Core Components

• Vector Class: N-dimensional vectors with full operator support (2D, 3D, and beyond)
• Matrix Class: Dynamic-size matrices with comprehensive operations
• Linear System Solvers: Gaussian elimination, LU decomposition, Cramer's rule, least squares
• ODE Solvers: Systems of differential equations (homogeneous and non-homogeneous)
• Coordinate Systems: Convert between Cartesian, Polar, Spherical, and Cylindrical coordinates
• Visualization: Rich plotting capabilities with matplotlib (optional)
• Custom Error Handling: Descriptive exceptions for better debugging
• Pure Python Core: No required dependencies for core functionality

⚡ Vector Operations

• Basic Operations: +, -, *, / with full operator support
• Vector Products: Dot product, cross product (3D)
• Magnitude & Normalization: Length calculation and unit vectors
• Advanced Operations: Projection, rejection, reflection
• Geometric Functions: Angle calculation, distance, component-wise min/max
• Interpolation: Linear interpolation (lerp), clamping
• Parallel & Perpendicular Tests: Check vector relationships
• N-Dimensional Support: Works with 2D, 3D, 4D, and higher dimensions
• Full Python Protocols: Iteration, indexing, equality, hashing, etc.

🔢 Matrix Operations

• Arithmetic: Element-wise operations, matrix multiplication (@), power (**)
• Basic Operations: Transpose, trace, determinant, rank
• Advanced Operations: Inverse, adjugate, cofactor, matrix exponential
• Matrix Creation: Zeros, ones, identity, diagonal, from rows/columns
• Matrix Properties: Symmetric, orthogonal, singular, triangular, diagonal
• Hadamard Product: Element-wise multiplication
• Kronecker Product: Tensor product of matrices
• Row Operations: Row echelon form, reduced row echelon form
• Concatenation: Horizontal and vertical matrix joining

🧮 Linear Algebra Solvers

• Linear Systems: Solve Ax = b using multiple methods
  • Gaussian elimination with partial pivoting
  • LU decomposition
  • Cramer's rule
  • Least squares (for overdetermined systems)
• Differential Equations: Systems of linear ODEs
  • Homogeneous systems (dx/dt = Ax)
  • Non-homogeneous systems (dx/dt = Ax + b(t))
  • Matrix exponential computation
  • Euler method and Runge-Kutta 4

🌐 Coordinate Systems

• 2D Conversions: Cartesian ↔ Polar ↔ Complex
• 3D Conversions: Cartesian ↔ Spherical ↔ Cylindrical
• Easy API: VectorCoordinates class with intuitive methods

📊 Visualization (Optional)

• Vector Plotting: 2D and 3D vector arrows with labels
• Vector Fields: Visualize 2D vector fields
• Matrix Heatmaps: Color-coded matrix visualization
• Transformations: Before/after visualization of linear transformations
• Coordinate Systems: Side-by-side comparison of coordinate representations
• Customizable: Colors, labels, titles, and full matplotlib control

Installation

From Source

# Clone the repository
git clone https://github.com/njryan-boou/matpy.git
cd matpy

# Install core library
pip install .

# Or install in development mode
pip install -e .

# Install with visualization support
pip install .[viz]

# Install with all development tools
pip install .[dev]

From PyPI (Coming Soon)

# Core installation
pip install matpy-linalg

# With visualization support
pip install matpy-linalg[viz]

Quick Start

Vector Examples

from matpy.vector.core import Vector
from matpy.vector import ops

# Create vectors (n-dimensional)
v1 = Vector(3, 4, 5)      # 3D vector
v2 = Vector(1, 2, 3)      # 3D vector
v_2d = Vector(3, 4)       # 2D vector
v_4d = Vector(1, 2, 3, 4) # 4D vector

# Arithmetic operations
v3 = v1 + v2          # Vector addition
v4 = v1 - v2          # Vector subtraction
v5 = v1 * 2           # Scalar multiplication
v6 = v1 / 2           # Scalar division

# Vector operations
dot_product = v1.dot(v2)           # Dot product
cross_product = v1.cross(v2)       # Cross product (3D only)
magnitude = v1.magnitude()         # Magnitude/length
normalized = v1.normalize()        # Unit vector

# Advanced operations
angle = ops.angle_between(v1, v2)  # Angle in radians
projection = ops.projection(v1, v2)
rejection = ops.rejection(v1, v2)
reflected = ops.reflect(v1, Vector(0, 1, 0))
interpolated = ops.lerp(v1, v2, 0.5)  # 50% between v1 and v2
distance = ops.distance(v1, v2)

# Component operations
min_vec = ops.component_min(v1, v2)  # Element-wise minimum
max_vec = ops.component_max(v1, v2)  # Element-wise maximum
clamped = ops.clamp(v1, -1, 1)       # Clamp all components

# Vector tests
parallel = ops.is_parallel(v1, v2)
perpendicular = ops.is_perpendicular(v1, v2)

# Properties for 2D/3D vectors
print(v1.x, v1.y, v1.z)  # Access x, y, z components

# Python protocols
print(v1)              # (3.0, 4.0, 5.0)
print(repr(v1))        # <3.0, 4.0, 5.0>
print(len(v1))         # 3
print(v1[0])           # 3.0
for component in v1:   # Iteration
    print(component)

Matrix Examples

from matpy.matrix.core import Matrix
from matpy.matrix import ops

# Create matrices
m1 = Matrix(2, 2, [[1, 2], [3, 4]])
m2 = Matrix(2, 2, [[5, 6], [7, 8]])

# Matrix creation utilities
zeros = ops.zeros(3, 3)              # 3x3 zero matrix
ones = ops.ones(2, 4)                # 2x4 matrix of ones
identity = ops.identity(3)            # 3x3 identity matrix
diagonal = ops.diagonal([1, 2, 3])    # 3x3 diagonal matrix
from_rows = ops.from_rows([[1, 2], [3, 4]])
from_cols = ops.from_columns([[1, 3], [2, 4]])

# Arithmetic operations
m3 = m1 + m2          # Matrix addition
m4 = m1 - m2          # Matrix subtraction
m5 = m1 * 3           # Scalar multiplication
m6 = m1 @ m2          # Matrix multiplication (@ operator)
m7 = m1 ** 3          # Matrix power

# Matrix operations
transposed = m1.transpose()
determinant = m1.determinant()
inverted = m1.inverse()
trace = m1.trace()
rank = m1.rank()

# Advanced operations
adjugate = m1.adjugate()
cofactor = m1.cofactor(0, 1)
hadamard = ops.hadamard_product(m1, m2)  # Element-wise multiplication
kronecker = ops.kronecker_product(m1, m2)
rref = ops.reduced_row_echelon_form(m1)

# Matrix properties
is_square = m1.is_square()
is_symmetric = m1.is_symmetric()
is_singular = m1.is_singular()
is_invertible = m1.is_invertible()
is_orthogonal = m1.is_orthogonal()
is_diagonal = ops.is_diagonal(m1)
is_identity = ops.is_identity(m1)

# Concatenation
h_concat = ops.concatenate_horizontal(m1, m2)
v_concat = ops.concatenate_vertical(m1, m2)

# Python protocols
print(m1)              # Formatted matrix output
print(f"{m1:.2f}")     # Formatted to 2 decimal places
for value in m1:       # Iterate over all elements
    print(value)

Linear Systems

from matpy.matrix.core import Matrix
from matpy.matrix import solve

# Solve Ax = b
A = Matrix(3, 3, [[2, 1, -1], [-3, -1, 2], [-2, 1, 2]])
b = [8, -11, -3]

# Multiple solution methods
x = solve.solve_linear_system(A, b)      # Gaussian elimination
x_lu = solve.solve_lu(A, b)               # LU decomposition
x_cramer = solve.solve_cramer(A, b)       # Cramer's rule

# Least squares for overdetermined systems
A_over = Matrix(4, 2, [[1, 1], [2, 1], [3, 1], [4, 1]])
b_over = [2, 3, 5, 4]
x_ls = solve.solve_least_squares(A_over, b_over)

# LU decomposition
L, U = solve.lu_decomposition(A)

# Matrix exponential
exp_A = solve.matrix_exponential(A, t=1.0)

Differential Equations

from matpy.matrix.core import Matrix
from matpy.matrix import solve

# Solve dx/dt = Ax
A = Matrix(2, 2, [[-2, 1], [1, -2]])
x0 = [1, 0]  # Initial conditions
t = 1.0      # Time

# Homogeneous system
x_t = solve.solve_linear_ode_system_homogeneous(A, x0, t)

# Non-homogeneous system: dx/dt = Ax + b(t)
def b_func(t):
    return [t, 0]

x_t_nh = solve.solve_linear_ode_system_nonhomogeneous(A, x0, b_func, t)

# Numerical methods
euler_result = solve.euler_method(A, x0, t_final=2.0, steps=100)
rk4_result = solve.runge_kutta_4(A, x0, t_final=2.0, steps=100)

Coordinate Systems

from matpy.vector.core import Vector
from matpy.vector.coordinates import VectorCoordinates
import math

# 2D Coordinate conversions
v_2d = Vector(3, 4)
coords_2d = VectorCoordinates(v_2d)

# Convert to polar
r, theta = coords_2d.to_polar()
print(f"Polar: r={r}, θ={theta}")

# Create from polar
v_from_polar = VectorCoordinates.from_polar(5, math.pi/4)

# Complex representation
z = coords_2d.to_complex()  # 3 + 4j
v_from_complex = VectorCoordinates.from_complex(3 + 4j)

# 3D Coordinate conversions
v_3d = Vector(1, 1, math.sqrt(2))
coords_3d = VectorCoordinates(v_3d)

# Spherical coordinates
r, theta, phi = coords_3d.to_spherical()
v_from_spherical = VectorCoordinates.from_spherical(2, math.pi/4, math.pi/4)

# Cylindrical coordinates
rho, phi, z = coords_3d.to_cylindrical()
v_from_cylindrical = VectorCoordinates.from_cylindrical(math.sqrt(2), math.pi/4, math.sqrt(2))

# Generic conversion
polar_coords = coords_2d.convert('polar')
spherical_coords = coords_3d.convert('spherical')

Visualization

from matpy.vector.core import Vector
from matpy.matrix.core import Matrix
from matpy.visualization import (
    plot_vectors_2d, plot_vectors_3d,
    plot_transformation_2d, plot_matrix_heatmap
)
import math

# Plot 2D vectors
v1 = Vector(2, 3)
v2 = Vector(-1, 2)
plot_vectors_2d([v1, v2], labels=['v1', 'v2'], title="My Vectors")

# Plot 3D vectors
i = Vector(1, 0, 0)
j = Vector(0, 1, 0)
k = Vector(0, 0, 1)
plot_vectors_3d([i, j, k], labels=['i', 'j', 'k'], colors=['red', 'green', 'blue'])

# Visualize transformation
angle = math.pi / 4  # 45 degrees
rotation = Matrix(2, 2, [
    [math.cos(angle), -math.sin(angle)],
    [math.sin(angle), math.cos(angle)]
])
plot_transformation_2d(rotation, title="45° Rotation")

# Matrix heatmap
m = Matrix(4, 4, [[i+j for j in range(4)] for i in range(4)])
plot_matrix_heatmap(m, title="Sample Matrix")

Running Tests

# Run all tests
python -m unittest discover -s tests -p "test_*.py"

# Run with pytest (if installed)
pytest tests/

# Run specific test file
python -m unittest tests.test_vector_core

# Run with coverage (if pytest-cov installed)
pytest --cov=matpy tests/

Examples

Check out the examples/ directory for comprehensive demonstrations:

Vector Examples

• vector_arithmatic.py - Vector arithmetic operations
• python_methods.py - Python dunder methods and protocols

Matrix Examples

• matrix_examples.py - Complete matrix operations showcase
  • Matrix creation and basic operations
  • Linear algebra operations (determinant, inverse, etc.)
  • Linear systems solving
  • Least squares fitting
  • Advanced operations (Hadamard, Kronecker products)
  • Differential equations

Visualization Examples

• visualization_examples.py - Complete visualization demonstrations
  • 2D and 3D vector plotting
  • Vector fields
  • Matrix heatmaps and grids
  • Linear transformations (rotations, scaling, shearing)
  • Coordinate system comparisons
  • Vector operations visualization

Project Structure

matpy/
├── src/
│   └── matpy/
│       ├── __init__.py
│       ├── version.py
│       ├── error.py                  # Custom exceptions
│       ├── core/                     # Core utilities
│       │   ├── __init__.py
│       │   ├── utils.py              # Utility functions (formatting, math, etc.)
│       │   └── validate.py           # Validation functions
│       ├── vector/                   # Vector implementation
│       │   ├── __init__.py
│       │   ├── core.py               # N-dimensional Vector class
│       │   ├── ops.py                # Vector operations and functions
│       │   └── coordinates.py        # Coordinate system conversions
│       ├── matrix/                   # Matrix implementation
│       │   ├── __init__.py
│       │   ├── core.py               # Matrix class
│       │   ├── ops.py                # Matrix operations and utilities
│       │   └── solve.py              # Linear systems and ODE solvers
│       └── visualization/            # Visualization tools (optional)
│           ├── __init__.py
│           ├── vector_plot.py        # Vector plotting functions
│           ├── matrix_plot.py        # Matrix visualization
│           └── coordinate_plot.py    # Coordinate system plots
├── tests/                            # Comprehensive test suite
│   ├── test_vector_core.py           # Vector class tests
│   ├── test_vector_ops.py            # Vector operations tests
│   ├── test_matrix_core.py           # Matrix class tests
│   ├── test_matrix_ops.py            # Matrix operations tests
│   └── test_matrix_solve.py          # Solver tests
├── examples/                         # Example scripts
│   ├── vector_arithmatic.py
│   ├── python_methods.py
│   ├── matrix_examples.py
│   └── visualization_examples.py
├── pyproject.toml                    # Project configuration
└── README.md                         # This file

Custom Error Handling

MatPy provides descriptive custom exceptions for better error handling:

from matpy.error import (
    MatPyError,              # Base exception
    VectorDimensionError,    # Dimension mismatch
    MatrixDimensionError,    # Matrix dimension issues
    InvalidOperationError,   # Invalid operations
    SingularMatrixError      # Singular matrix operations
)

try:
    v1 = Vector(1, 2, 3)
    v2 = Vector(1, 2)
    result = v1 + v2  # Raises VectorDimensionError
except VectorDimensionError as e:
    print(f"Error: {e}")

API Reference

Vector Class

Constructor:

• Vector(*args) - Create an n-dimensional vector
  • Vector() - Creates 3D zero vector (0, 0, 0)
  • Vector(x, y) - 2D vector
  • Vector(x, y, z) - 3D vector
  • Vector(x, y, z, w, ...) - N-dimensional vector

Properties (2D/3D):

• x, y, z - Access first three components
• components - Tuple of all components

Methods:

• dot(other) - Dot product
• cross(other) - Cross product (3D only)
• magnitude() - Calculate magnitude
• normalize() - Return unit vector

Operators:

• +, -, *, / - Arithmetic operations
• ==, != - Equality comparison
• abs() - Magnitude
• len() - Number of dimensions
• [] - Index access
• in - Membership test
• iter() - Iteration support
• bool() - True if non-zero
• round(n) - Round components

Matrix Class

Constructor:

• Matrix(rows, cols, data=None) - Create a matrix
  • If data=None, creates zero matrix
  • data should be a 2D list: [[row1], [row2], ...]

Methods:

• transpose() - Matrix transpose
• determinant() - Calculate determinant (square matrices)
• inverse() - Matrix inverse (non-singular matrices)
• adjugate() - Adjugate (adjoint) matrix
• trace() - Sum of diagonal elements
• rank() - Matrix rank
• cofactor(row, col) - Cofactor at position
• is_square() - Check if square
• is_symmetric() - Check if M = M^T
• is_singular() - Check if determinant ≈ 0
• is_invertible() - Check if non-singular
• is_orthogonal() - Check if M^T M = I

Operators:

• +, - - Matrix addition/subtraction
• * - Scalar or matrix multiplication
• / - Scalar division
• @ - Matrix multiplication (preferred)
• ** - Matrix power
• ==, !=, <, >, <=, >= - Comparisons
• -M - Negation
• abs() - Frobenius norm
• len() - Total number of elements
• [i] - Row access
• [i] = row - Row assignment
• iter() - Iteration over elements
• bool() - True if has non-zero elements
• round(n) - Round all elements

Matrix Operations Module

Creation Functions:

• zeros(rows, cols) - Zero matrix
• ones(rows, cols) - Matrix of ones
• identity(size) - Identity matrix
• diagonal(values) - Diagonal matrix
• from_rows(rows_list) - Create from row lists
• from_columns(cols_list) - Create from column lists

Advanced Operations:

• hadamard_product(m1, m2) - Element-wise multiplication
• kronecker_product(m1, m2) - Tensor product
• row_echelon_form(m) - Row echelon form
• reduced_row_echelon_form(m) - RREF
• concatenate_horizontal(m1, m2) - Join side-by-side
• concatenate_vertical(m1, m2) - Join top-to-bottom

Property Tests:

• is_diagonal(m), is_identity(m), is_upper_triangular(m), is_lower_triangular(m)

Linear Algebra Solvers

Linear Systems:

• solve_linear_system(A, b) - Gaussian elimination
• solve_lu(A, b) - Using LU decomposition
• solve_cramer(A, b) - Cramer's rule
• solve_least_squares(A, b) - Least squares solution
• lu_decomposition(A) - Returns (L, U) matrices

Differential Equations:

• solve_linear_ode_system_homogeneous(A, x0, t) - Solve dx/dt = Ax
• solve_linear_ode_system_nonhomogeneous(A, x0, b_func, t) - Solve dx/dt = Ax + b(t)
• matrix_exponential(A, t) - Compute e^(At)
• euler_method(A, x0, t_final, steps) - Numerical ODE solver
• runge_kutta_4(A, x0, t_final, steps) - RK4 numerical solver

Coordinate Systems

VectorCoordinates Class:

• 2D: to_polar(), from_polar(), to_complex(), from_complex()
• 3D: to_spherical(), from_spherical(), to_cylindrical(), from_cylindrical()
• Generic: convert(system), convert_from(system, *coords)

Visualization Functions

Vector Plotting:

• plot_vector_2d(v, ...) - Plot single 2D vector
• plot_vectors_2d(vectors, ...) - Plot multiple 2D vectors
• plot_vector_3d(v, ...) - Plot single 3D vector
• plot_vectors_3d(vectors, ...) - Plot multiple 3D vectors
• plot_vector_field_2d(func, ...) - Plot 2D vector field

Matrix Visualization:

• plot_matrix_heatmap(m, ...) - Color-coded matrix
• plot_matrix_grid(m, ...) - Bar chart representation
• plot_transformation_2d(m, ...) - Visualize 2D transformation
• plot_transformation_3d(m, ...) - Visualize 3D transformation

Coordinate Plotting:

• plot_coordinate_systems_2d(v) - Cartesian vs Polar
• plot_coordinate_systems_3d(v) - Cartesian vs Spherical vs Cylindrical

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development

# Install development dependencies
pip install -e ".[dev]"

# Install with visualization support
pip install -e ".[viz]"

# Run tests
python -m unittest discover -s tests -p "test_*.py"

# Run tests with pytest (if installed)
pytest tests/

# Format code
black src/ tests/ examples/

# Type checking
mypy src/

# Run specific test file
python -m unittest tests.test_matrix_core

Recent Updates

✅ v0.1.0 (Current)

• N-dimensional vector support (2D, 3D, 4D+)
• Complete linear systems solver suite
• ODE solver for systems of differential equations
• Coordinate system conversions (Polar, Spherical, Cylindrical, Complex)
• Comprehensive visualization module with matplotlib
• Advanced matrix operations (Hadamard, Kronecker products, RREF)
• Centralized validation and utility modules
• 300+ unit tests with comprehensive coverage

Future Enhancements

• Additional matrix decompositions (QR, SVD, Cholesky)
• Sparse matrix support
• Higher-dimensional tensors
• Complex number support for matrices
• Performance optimizations with optional NumPy backend
• Additional numerical ODE solvers
• Eigenvalue/eigenvector computation for larger matrices
• Interactive visualization widgets

License

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

Acknowledgments

• Inspired by NumPy and other linear algebra libraries
• Built with pure Python for educational purposes
• Comprehensive test coverage for reliability

Contact

Noah Ryan - [e-mail]

Project Link: https://github.com/njryan-boou/matpy