wp-math3d 0.6.10

A basic math library for spatial algebra

Math3d

A small header-only math library for vectors and matrices

Build Status

Build	Status
Ubuntu
Windows
MacOS

Yet another math library

This library is designed to be a potential replacement to various other great libraries like Eigen and glm, but with a narrow focus on 2, 3, and 4 dimensional vectors and matrices. These appear commonly when using spatial algebra in robotics, which is the main application area this library was initially developed for.

Setup

C++ setup

Clone this package into your third_party dependencies:

# Replace "third_party" with your own dependencies-folder name
git clone https://github.com/wpumacay/math3d.git third_party/math3d

There's a CMake target called math::math. Just add the source directory in your CMake workflow, and use the given target as follows:

# Add the Math3d subdirectory
add(third_party/math3d)

# Link against the exposed math::math target
target_link_library(MY_LIBRARY PRIVATE math::math)

Python setup

Use the provided setup.py file:

python setup.py install

And import the types from the math3d package:

import math3d as m3d

Usage

C++

#include <vec3_t.h>
#include <mat3_t.h>

int main()
{
    // Create a vec3-float32 and show it on the console
    ::math::Vector3f vec = { 1.0f, 2.0f, 3.0f };
    std::cout << "vec: " << vec << std::endl;

    // Create a mat3 float32, show its entries and its inverse
    auto mat = ::math::Matrix3f( 3.0f, 9.0f, 3.0f,
                                 9.0f, 0.0f, 3.0f,
                                 2.0f, 3.0f, 8.0f );

    std::cout << "mat:" << std::endl;
    std::cout << mat << std::endl;
    std::cout << "mat.inverse():" << std::endl;
    std::cout << ::math::inverse( mat ) << std::endl;

    return 0;
}

Python

import numpy as np
from math3d import Vector3f, Matrix3f

# Create a vec3-float32 and show it on the console
vec = Vector3f(np.array([1.0, 2.0, 3.0], dtype=np.float32))
print(vec)

# Create a mat3 float32, show its entries and its inverse
mat = Matrix3f(np.array([[ 3.0, 9.0, 3.0 ],
                         [ 9.0, 0.0, 3.0 ],
                         [ 2.0, 3.0, 8.0 ]], dtype=np.float32))

print(mat)
print("inverse(): \n\r{}".format(mat.inverse()))

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