mbenencase / mathcraft

A MatLab like Python package for academics, students, and enthusiasts

PyLab

Python Laboratory. Our purpose is to provide a simple toolbox, with a wide-range of features, that allows academics, STEM students and entushiasts on building their applications simple as possible.

How to Install

We've uplaoded to PyPi organization, so you can install it as follows:

pip install mathcraft

Dependencies

• Python 3.9 or above.

MathCraft is build using NumPy. We include also beartype dependency because we want to guarantee code quality and coverage across the time.

How to Use

Vector Class

The Vector class provides a NumPy array-like initialization, however it enforces the shape (N, 1) of the vector.

from mathcraft import Vector

x1 = Vector([1, 2, 3])
x2 = Vector(np.array([3, 4, 5, 1, 3, 5]))

We included a method to make simpler the computation of the vector norm.

v = Vector([1, 2, 3, 4, 5, 6])
norm = v.norm()

print(norm)

You can also return a normalized vector if you already have created one:

v = Vector([1, 2, 3, 4, 5, 6])
w = Vector([1, 2, 3, 4, 5, 6])

dotProd_vw = v.normalized().T @ w.normalized()
print(dotProd_vw)

Matrix Class

The same for our Matrix class:

from mathcraft import Matrix

A = Matrix([[2, 3], [4, 5]])
B = Matrix(np.array([[2, 3], [4, 5]]))

We included methods to make the computation of determinant and inverse/pseudo-inverse easier:

A = Matrix([[2, 3], [4, 5]])
print(A.inv())
print(A.det())
print(A.pinv())
print(A.T)

Contribute

If you want to contribute to the project, check the TODO file. Create your code and do a PR.